From ccc4864d0077293135b93dedd8d93a2ae2307852 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 11:37:48 +0200 Subject: [PATCH 001/347] add of Hierarchical Clustering --- docs/releases/unreleased.md | 3 + river/cluster/__init__.py | 3 +- river/cluster/hcluster.py | 403 ++++++++++++++++++++++++++++++++++++ 3 files changed, 408 insertions(+), 1 deletion(-) create mode 100644 river/cluster/hcluster.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index f349aa46e6..3891042b4a 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,3 +1,6 @@ +## cluster +- Added `cluster.HierarchicalClustering`. + ## metrics - Added `metrics.MAPE`. diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 794dbbc037..6135e3f6da 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -5,5 +5,6 @@ from .k_means import KMeans from .streamkmeans import STREAMKMeans from .textclust import TextClust +from .hcluster import HierarchicalClustering -__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust"] +__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust", "HierarchicalClustering"] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py new file mode 100644 index 0000000000..c4215975e4 --- /dev/null +++ b/river/cluster/hcluster.py @@ -0,0 +1,403 @@ +from river import base, utils, datasets +import numpy as np +from typing import Callable + +# Node of a binary tree for Hierarchical Clustering +class BinaryTreeNode: + def __init__(self, key : int, data : np.array = None): + # If it's a leaf : x data, else : None + self.data = data + # Key of the node + self.key = key + # Children and parent + self.left = None + self.right = None + self.parent = None + + +def euclidean_distance(w_i, w_j): + # Euclidean distance between two nodes + return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + +class HierarchicalClustering(base.Clusterer): + """Hierarchical Clustering. + + HierarchicalClustering is a stream hierarchical clustering algorithm. + The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. + It will (begining with the whole tree T) : + + * Compare the new node to the tree T : + * if T is just a leaf : merge + * else if the nodes of T are more similar between them than with the new node : merge + * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree + * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree + + You can choose a window size to use only the recent points and not overload the tree. (default : 100) + + You can also choose a distance function to compare the nodes. (default : euclidean distance) + + Parameters + ---------- + window_size + number of data points to use + distance + distance function to use to compare the nodes + + Attributes + ---------- + n : int + number of nodes + X : dict (data point (str(data array)) : key (int)) + data points used by the algorithm with the key of the node representing them + + References + ---------- + [^1]: Menon, Aditya & Rajagopalan, Anand & Sumengen, Baris & Citovsky, Gui & Cao, Qin & Kumar, Sanjiv. (2019). Online Hierarchical Clustering Approximations. + + Examples + -------- + + The first example is with leaving the window size to 100. In the second one we put it at 2 to see how it works. + + >>> from river import cluster + >>> from river import stream + + >>> X = [[1, 1, 1], [1, 1, 0], [20, 20, 20], [20, 20, 20.1], [0 , 1, 1]] + + >>> HC = cluster.HierarchicalClustering() + + >>> for x, _ in stream.iter_array(X): + ... HC = HC.learn_one(x) + + >>> HC.X + {'[1 1 1]': 1, + '[1 1 0]': 2, + '[20 20 20]': 4, + '[20. 20. 20.1]': 6, + '[0 1 1]': 8} + + >>> HC.n + 9 + + >>> print(HC) + -> 6 + -> 7 + -> 4 + -> 5 + -> 8 + -> 9 + -> 2 + -> 3 + -> 1 + + >>> HC.get_all_clusters() + [(1, [array([1, 1, 1])]), + (2, [array([1, 1, 0])]), + (4, [array([20, 20, 20])]), + (6, [array([20. , 20. , 20.1])]), + (8, [array([0, 1, 1])]), + (3, [1, 2]), + (5, [9, 7]), + (7, [4, 6]), + (9, [3, 8])] + + >>> HC.get_clusters_by_point() + {'[1 1 1]': [1, 3, 9, 5], + '[1 1 0]': [2, 3, 9, 5], + '[20 20 20]': [4, 7, 5], + '[20. 20. 20.1]': [6, 7, 5], + '[0 1 1]': [8, 9, 5]} + + >>> HC.predict_one({0 : 20.1, 1 : 20, 2 : 20 }) + ([10, 11, 5], 9) + + >>> HC = HC.learn_one({0 : 20.1, 1 : 20, 2 : 20 }) + + >>> print(HC) + -> 6 + -> 7 + -> 4 + -> 5 + -> 10 + -> 11 + -> 8 + -> 9 + -> 2 + -> 3 + -> 1 + + + >>> HC = cluster.HierarchicalClustering(window_size=2) + + >>> for x, _ in stream.iter_array(X): + ... HC = HC.learn_one(x) + + >>> HC.X + {'[20. 20. 20.1]': 2, '[0 1 1]': 1} + + >>> HC.n + 3 + + >>> print(HC) + -> 2 + -> 3 + -> 1 + + """ + def __init__(self, window_size : int = 100, distance : Callable = euclidean_distance): + # Number of nodes + self.n = 0 + # Max number of leaves + self.w_size = window_size + # Dict : x data (str(array of size m)) -> key of the node + self.X = {} + # Dict : key -> node + self.nodes = {} + # First node of the tree + self.root = None + # Distance function + self.distance = distance + + def OTD(self, T, x): + # OTD algorithm from + if self.n == 1: + # First node in the tree + self.root = self.nodes[1] + elif T.data is not None: + # If T is a leaf, we merge the two nodes together + self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) + elif T.left is None: + # If there is no node at the left of the intermediate node, we add it there + T.left = self.nodes[self.X[np.array2string(x)]] + self.nodes[self.X[np.array2string(x)]].parent = T + elif T.right is None: + # If there is no node at the right of the intermediate node, we add it there + T.right = self.nodes[self.X[np.array2string(x)]] + self.nodes[self.X[np.array2string(x)]].parent = T + elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity(T, self.nodes[self.X[np.array2string(x)]]): + # If the nodes in T are closer between them than with the new node, we merge T and the new node + self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) + elif self.inter_subtree_similarity(T.left, self.nodes[self.X[np.array2string(x)]]) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): + # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T + self.OTD(T.right, x) + else: + # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T + self.OTD(T.left, x) + + def merge_nodes(self, T, added_node): + # Merge a new node (added node) to the tree T + # We create the node that will be the parent of T and the added node + self.n +=1 + new_node = BinaryTreeNode(self.n) + # We add T and the added node as its children + new_node.left = T + new_node.right = added_node + # The parent of the new node is the parent of T + new_node.parent = T.parent + # If T is not the root, we set the child of its parent as new node (instead of T) + if T.parent is not None: + if T.parent.left.key == T.key: + T.parent.left = new_node + else: + T.parent.right = new_node + # We add the new node as the parent of T and the added node + T.parent = new_node + added_node.parent = new_node + # We add the new node to the dict + self.nodes[self.n] = new_node + # If T was the root, the new node become the root + if self.root.key == T.key: + self.root = self.nodes[self.n] + + def learn_one(self, x): + x = utils.dict2numpy(x) + # We create the node for x and add it to the tree + if self.w_size > 0 and len(self.X.keys()) >= self.w_size: + # If we have reached the maximum of data points, we delete the oldest one and add a node with the same key as the one we deleted + oldest_key = self.X[list(self.X.keys())[0]] + oldest = self.nodes[oldest_key] + if oldest.parent.left.key == oldest_key: + oldest.parent.left = None + else: + oldest.parent.right = None + del self.nodes[oldest_key] + del self.X[list(self.X.keys())[0]] + self.X[np.array2string(x)] = oldest_key + self.nodes[oldest_key] = BinaryTreeNode(oldest_key,x) + else: + # Else we just add a node + self.n +=1 + self.X[np.array2string(x)] = self.n + self.nodes[self.n] = BinaryTreeNode(self.n,x) + # We add it to the tree + self.OTD(self.root, x) + return self + + def predict_OTD(self, x, node, clusters): + # get the list of predicted clusters for x + if node is None: + # If there is still no node in the tree + return [1], -1 + if node.data is not None: + # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) + clusters.extend([self.n+2, self.n+1]) + return clusters, node.key + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity(node, BinaryTreeNode(self.n+1,x)): + # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) + clusters.extend([self.n+2, self.n+1]) + return clusters, node.key + else: + # Else, x wouls be added in the tree, we add the key of node + clusters.extend([node.key]) + if self.inter_subtree_similarity(node.left,BinaryTreeNode(self.n+1,x)) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n+1,x)): + # If the right part of the tree is closer to x than the left part, we continue in the right part + return self.predict_OTD(x, node.right, clusters) + else: + # If the left part of the tree is closer to x than the right part, we continue in the left part + return self.predict_OTD(x, node.left, clusters) + + def predict_one(self, x): + """Predicts the clusters for a set of features `x`. + + Parameters + ---------- + x + A dictionary of features. + Returns + ------- + (list, int) + A list of clusters (from node `x` to root) and the node to which it would have been merged. + + """ + x = utils.dict2numpy(x) + # We predict to which cluster x would be if we added it in the tree + r, merged = self.predict_OTD( x, self.root, []) + r.reverse() + return r, merged + + def find_path(self, root, path, k): + # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + + if root == None: + # No path + return False + + path.append(root) + + if root.key == k: + return True + + if ((root.left != None and self.find_path(root.left, path, k)) or + (root.right != None and self.find_path(root.right, path, k))): + return True + + path.pop() + return False + + def lca(self, i, j): + # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + if self.root == None: + return -1 + + path_i = [] + path_j = [] + + if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): + return -1 + + k = 0 + while(k < len(path_i) and k < len(path_j)): + if path_i[k] != path_j[k]: + break + k += 1 + return path_i[k-1] + + def leaves(self, v): + # find all the leaves from node v + if v == None: + # No leaves + return -1 + if v.data is not None: + # If v is a leaf, returns itself + return [v] + # Else, returns leaves from its children + leavesList = [] + leavesList.extend(self.leaves(v.left)) + leavesList.extend(self.leaves(v.right)) + return leavesList + + def inter_subtree_similarity(self, A, B): + # compute the mean distance between tree A and tree B (mean of distances between nodes from tree A and tree B) + leavesA = self.leaves(A) + leavesB = self.leaves(B) + r = 0 + nb = 0 + for i, w_i in enumerate(leavesA): + for j, w_j in enumerate(leavesB): + nb +=1 + r += self.distance(w_i, w_j) + return r/nb + + def intra_subtree_similarity(self, A): + # compute mean of distances between the node from tree A + leaves = self.leaves(A) + r = 0 + nb = 0 + if len(leaves) == 1: + return 0 + for i, w_i in enumerate(leaves): + for j, w_j in enumerate(leaves): + if i < j: + nb +=1 + r += self.distance(w_i, w_j) + return r/nb + + def __str__(self): + self.printTree(self.root) + return "" + + def printTree(self, node, level=0): + # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python + if node != None: + self.printTree(node.right, level + 1) + print(' ' * 4 * level + '-> ' + str(node.key)) + self.printTree(node.left, level + 1) + + def get_parents(self,node): + # Get all the parents of the node (the clusters it belongs to) + clusters = [node.key] + if node.parent is None: + return clusters + clusters.extend(self.get_parents(node.parent)) + return clusters + + def get_clusters_by_point(self): + """Returns the list of clusters (from the data point node to the root) for all of the data points. + + Returns + ------- + {x : list} + A dict of all the data points with their clusters. + """ + # Get all the clusters each data point belong to + clusters = {} + for x in self.X.keys(): + clusters[x] = self.get_parents(self.nodes[self.X[x]]) + return clusters + + def get_all_clusters(self): + """Returns all the clusters of the tree. + + Returns + ------- + {int : list} + A dict of all the clusters with their children (or the data point for the leaves). + """ + # Get the data of each cluster + clusters = {} + for i in range(1,self.n+1): + if self.nodes[i].data is not None: + clusters[i] = [self.nodes[i].data] + else: + clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] + return sorted(clusters.items(), key = lambda x : len(x[1])) From a61677bca566adcf51d78e8ad0e917a6ac81205e Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 12:10:12 +0200 Subject: [PATCH 002/347] Fixed issues --- river/cluster/hcluster.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index c4215975e4..e3864b6634 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,10 @@ -from river import base, utils, datasets +from river import base, utils import numpy as np from typing import Callable # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key : int, data : np.array = None): + def __init__(self, key : int, data : np.typing.NDArray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -47,7 +47,7 @@ class HierarchicalClustering(base.Clusterer): ---------- n : int number of nodes - X : dict (data point (str(data array)) : key (int)) + X : dict (data point (str(ndarray)) : key (int)) data points used by the algorithm with the key of the node representing them References @@ -144,15 +144,15 @@ class HierarchicalClustering(base.Clusterer): -> 1 """ - def __init__(self, window_size : int = 100, distance : Callable = euclidean_distance): + def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = euclidean_distance): # Number of nodes self.n = 0 # Max number of leaves self.w_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X = {} + self.X : dict[np.typing.NDArray, int] = {} # Dict : key -> node - self.nodes = {} + self.nodes : dict[int, BinaryTreeNode] = {} # First node of the tree self.root = None # Distance function @@ -278,7 +278,7 @@ def predict_one(self, x): def find_path(self, root, path, k): # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ - if root == None: + if root is None: # No path return False @@ -287,8 +287,8 @@ def find_path(self, root, path, k): if root.key == k: return True - if ((root.left != None and self.find_path(root.left, path, k)) or - (root.right != None and self.find_path(root.right, path, k))): + if ((root.left is not None and self.find_path(root.left, path, k)) or + (root.right is not None and self.find_path(root.right, path, k))): return True path.pop() @@ -296,7 +296,7 @@ def find_path(self, root, path, k): def lca(self, i, j): # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ - if self.root == None: + if self.root is None: return -1 path_i = [] @@ -314,7 +314,7 @@ def lca(self, i, j): def leaves(self, v): # find all the leaves from node v - if v == None: + if v is None: # No leaves return -1 if v.data is not None: @@ -358,7 +358,7 @@ def __str__(self): def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python - if node != None: + if node is not None: self.printTree(node.right, level + 1) print(' ' * 4 * level + '-> ' + str(node.key)) self.printTree(node.left, level + 1) From c2e15dd42e1e9eb76edad24fc8598db8c484a264 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 12:21:24 +0200 Subject: [PATCH 003/347] Fix issues --- river/cluster/hcluster.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index e3864b6634..faabbe2e16 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,11 @@ from river import base, utils import numpy as np +import numpy.typing as npt from typing import Callable # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key : int, data : np.typing.NDArray = None): + def __init__(self, key : int, data : np.ndarray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -47,7 +48,7 @@ class HierarchicalClustering(base.Clusterer): ---------- n : int number of nodes - X : dict (data point (str(ndarray)) : key (int)) + X : dict (data point (str(np.ndarray)) : key (int)) data points used by the algorithm with the key of the node representing them References @@ -150,7 +151,7 @@ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, # Max number of leaves self.w_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X : dict[np.typing.NDArray, int] = {} + self.X : dict[np.ndarray, int] = {} # Dict : key -> node self.nodes : dict[int, BinaryTreeNode] = {} # First node of the tree From 3fcaca18c60b6f7e5351c348f46aed0d9c7cca75 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 12:46:06 +0200 Subject: [PATCH 004/347] Fixed issues --- river/cluster/hcluster.py | 25 ++++++++++++------------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index faabbe2e16..e6f6631ebd 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,6 +1,5 @@ from river import base, utils import numpy as np -import numpy.typing as npt from typing import Callable # Node of a binary tree for Hierarchical Clustering @@ -110,22 +109,22 @@ class HierarchicalClustering(base.Clusterer): '[0 1 1]': [8, 9, 5]} >>> HC.predict_one({0 : 20.1, 1 : 20, 2 : 20 }) - ([10, 11, 5], 9) + ([10, 11, 5], 7) >>> HC = HC.learn_one({0 : 20.1, 1 : 20, 2 : 20 }) >>> print(HC) - -> 6 - -> 7 - -> 4 - -> 5 -> 10 -> 11 - -> 8 - -> 9 - -> 2 - -> 3 - -> 1 + -> 6 + -> 7 + -> 4 + -> 5 + -> 8 + -> 9 + -> 2 + -> 3 + -> 1 >>> HC = cluster.HierarchicalClustering(window_size=2) @@ -149,7 +148,7 @@ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, # Number of nodes self.n = 0 # Max number of leaves - self.w_size = window_size + self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node self.X : dict[np.ndarray, int] = {} # Dict : key -> node @@ -213,7 +212,7 @@ def merge_nodes(self, T, added_node): def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree - if self.w_size > 0 and len(self.X.keys()) >= self.w_size: + if self.window_size > 0 and len(self.X.keys()) >= self.window_size: # If we have reached the maximum of data points, we delete the oldest one and add a node with the same key as the one we deleted oldest_key = self.X[list(self.X.keys())[0]] oldest = self.nodes[oldest_key] From d69c8e430e5a6908a1e3b35fad48e5f7c5d82d5b Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 13:01:42 +0200 Subject: [PATCH 005/347] Fix issues --- river/cluster/hcluster.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index e6f6631ebd..2dd6ee9494 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -144,7 +144,7 @@ class HierarchicalClustering(base.Clusterer): -> 1 """ - def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = euclidean_distance): + def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): # Number of nodes self.n = 0 # Max number of leaves @@ -157,6 +157,8 @@ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, self.root = None # Distance function self.distance = distance + if self.distance is None: + self.distance = euclidean_distance def OTD(self, T, x): # OTD algorithm from From c44b32e52f271f3c1ec8e160a036af425d9c6caf Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 13:39:32 +0200 Subject: [PATCH 006/347] fixed trailing spaces --- river/cluster/hcluster.py | 56 +++++++++++++++++++-------------------- 1 file changed, 27 insertions(+), 29 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 2dd6ee9494..bbeb3c265b 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -26,12 +26,12 @@ class HierarchicalClustering(base.Clusterer): The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. It will (begining with the whole tree T) : - * Compare the new node to the tree T : + * Compare the new node to the tree T : * if T is just a leaf : merge * else if the nodes of T are more similar between them than with the new node : merge * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree - + You can choose a window size to use only the recent points and not overload the tree. (default : 100) You can also choose a distance function to compare the nodes. (default : euclidean distance) @@ -39,16 +39,16 @@ class HierarchicalClustering(base.Clusterer): Parameters ---------- window_size - number of data points to use + number of data points to use. distance - distance function to use to compare the nodes - + distance function to use to compare the nodes. + Attributes ---------- n : int - number of nodes + number of nodes. X : dict (data point (str(np.ndarray)) : key (int)) - data points used by the algorithm with the key of the node representing them + data points used by the algorithm with the key of the node representing them. References ---------- @@ -68,7 +68,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[1 1 1]': 1, '[1 1 0]': 2, @@ -131,7 +131,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[20. 20. 20.1]': 2, '[0 1 1]': 1} @@ -142,7 +142,6 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 - """ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): # Number of nodes @@ -159,9 +158,9 @@ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, self.distance = distance if self.distance is None: self.distance = euclidean_distance - + def OTD(self, T, x): - # OTD algorithm from + # OTD algorithm from https://arxiv.org/pdf/1909.09667.pdf if self.n == 1: # First node in the tree self.root = self.nodes[1] @@ -185,7 +184,7 @@ def OTD(self, T, x): else: # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T self.OTD(T.left, x) - + def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node @@ -210,7 +209,7 @@ def merge_nodes(self, T, added_node): # If T was the root, the new node become the root if self.root.key == T.key: self.root = self.nodes[self.n] - + def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree @@ -234,7 +233,7 @@ def learn_one(self, x): # We add it to the tree self.OTD(self.root, x) return self - + def predict_OTD(self, x, node, clusters): # get the list of predicted clusters for x if node is None: @@ -269,7 +268,6 @@ def predict_one(self, x): ------- (list, int) A list of clusters (from node `x` to root) and the node to which it would have been merged. - """ x = utils.dict2numpy(x) # We predict to which cluster x would be if we added it in the tree @@ -283,16 +281,16 @@ def find_path(self, root, path, k): if root is None: # No path return False - + path.append(root) - + if root.key == k: return True - + if ((root.left is not None and self.find_path(root.left, path, k)) or (root.right is not None and self.find_path(root.right, path, k))): return True - + path.pop() return False @@ -300,10 +298,10 @@ def lca(self, i, j): # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if self.root is None: return -1 - + path_i = [] path_j = [] - + if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): return -1 @@ -313,7 +311,7 @@ def lca(self, i, j): break k += 1 return path_i[k-1] - + def leaves(self, v): # find all the leaves from node v if v is None: @@ -353,18 +351,18 @@ def intra_subtree_similarity(self, A): nb +=1 r += self.distance(w_i, w_j) return r/nb - + def __str__(self): self.printTree(self.root) return "" - + def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: self.printTree(node.right, level + 1) print(' ' * 4 * level + '-> ' + str(node.key)) self.printTree(node.left, level + 1) - + def get_parents(self,node): # Get all the parents of the node (the clusters it belongs to) clusters = [node.key] @@ -372,7 +370,7 @@ def get_parents(self,node): return clusters clusters.extend(self.get_parents(node.parent)) return clusters - + def get_clusters_by_point(self): """Returns the list of clusters (from the data point node to the root) for all of the data points. @@ -386,7 +384,7 @@ def get_clusters_by_point(self): for x in self.X.keys(): clusters[x] = self.get_parents(self.nodes[self.X[x]]) return clusters - + def get_all_clusters(self): """Returns all the clusters of the tree. @@ -402,4 +400,4 @@ def get_all_clusters(self): clusters[i] = [self.nodes[i].data] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] - return sorted(clusters.items(), key = lambda x : len(x[1])) + return sorted(clusters.items(), key = lambda x : len(x[1])) \ No newline at end of file From fd2d6999713d206b9b6335b998d6479253fc2c90 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 13:51:15 +0200 Subject: [PATCH 007/347] Fixed black and isort --- river/cluster/__init__.py | 12 +++++- river/cluster/hcluster.py | 86 +++++++++++++++++++++++---------------- 2 files changed, 62 insertions(+), 36 deletions(-) diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 6135e3f6da..ba4c6c6cb9 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -2,9 +2,17 @@ from .clustream import CluStream from .dbstream import DBSTREAM from .denstream import DenStream +from .hcluster import HierarchicalClustering from .k_means import KMeans from .streamkmeans import STREAMKMeans from .textclust import TextClust -from .hcluster import HierarchicalClustering -__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust", "HierarchicalClustering"] +__all__ = [ + "CluStream", + "DBSTREAM", + "DenStream", + "KMeans", + "STREAMKMeans", + "TextClust", + "HierarchicalClustering", +] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index bbeb3c265b..67b73420af 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,13 @@ -from river import base, utils -import numpy as np from typing import Callable +import numpy as np + +from river import base, utils + + # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key : int, data : np.ndarray = None): + def __init__(self, key: int, data: np.ndarray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -16,8 +19,9 @@ def __init__(self, key : int, data : np.ndarray = None): def euclidean_distance(w_i, w_j): - # Euclidean distance between two nodes - return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + # Euclidean distance between two nodes + return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. @@ -78,7 +82,7 @@ class HierarchicalClustering(base.Clusterer): >>> HC.n 9 - + >>> print(HC) -> 6 -> 7 @@ -143,15 +147,20 @@ class HierarchicalClustering(base.Clusterer): -> 3 -> 1 """ - def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): + + def __init__( + self, + window_size: int = 100, + distance: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, + ): # Number of nodes self.n = 0 # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X : dict[np.ndarray, int] = {} + self.X: dict[np.ndarray, int] = {} # Dict : key -> node - self.nodes : dict[int, BinaryTreeNode] = {} + self.nodes: dict[int, BinaryTreeNode] = {} # First node of the tree self.root = None # Distance function @@ -175,10 +184,14 @@ def OTD(self, T, x): # If there is no node at the right of the intermediate node, we add it there T.right = self.nodes[self.X[np.array2string(x)]] self.nodes[self.X[np.array2string(x)]].parent = T - elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity(T, self.nodes[self.X[np.array2string(x)]]): + elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity( + T, self.nodes[self.X[np.array2string(x)]] + ): # If the nodes in T are closer between them than with the new node, we merge T and the new node self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) - elif self.inter_subtree_similarity(T.left, self.nodes[self.X[np.array2string(x)]]) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): + elif self.inter_subtree_similarity( + T.left, self.nodes[self.X[np.array2string(x)]] + ) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T self.OTD(T.right, x) else: @@ -188,7 +201,7 @@ def OTD(self, T, x): def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node - self.n +=1 + self.n += 1 new_node = BinaryTreeNode(self.n) # We add T and the added node as its children new_node.left = T @@ -224,12 +237,12 @@ def learn_one(self, x): del self.nodes[oldest_key] del self.X[list(self.X.keys())[0]] self.X[np.array2string(x)] = oldest_key - self.nodes[oldest_key] = BinaryTreeNode(oldest_key,x) + self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: # Else we just add a node - self.n +=1 + self.n += 1 self.X[np.array2string(x)] = self.n - self.nodes[self.n] = BinaryTreeNode(self.n,x) + self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree self.OTD(self.root, x) return self @@ -241,16 +254,20 @@ def predict_OTD(self, x, node, clusters): return [1], -1 if node.data is not None: # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n+2, self.n+1]) + clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key - if self.intra_subtree_similarity(node) < self.inter_subtree_similarity(node, BinaryTreeNode(self.n+1,x)): + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( + node, BinaryTreeNode(self.n + 1, x) + ): # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n+2, self.n+1]) + clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key else: # Else, x wouls be added in the tree, we add the key of node clusters.extend([node.key]) - if self.inter_subtree_similarity(node.left,BinaryTreeNode(self.n+1,x)) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n+1,x)): + if self.inter_subtree_similarity( + node.left, BinaryTreeNode(self.n + 1, x) + ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): # If the right part of the tree is closer to x than the left part, we continue in the right part return self.predict_OTD(x, node.right, clusters) else: @@ -271,10 +288,10 @@ def predict_one(self, x): """ x = utils.dict2numpy(x) # We predict to which cluster x would be if we added it in the tree - r, merged = self.predict_OTD( x, self.root, []) + r, merged = self.predict_OTD(x, self.root, []) r.reverse() return r, merged - + def find_path(self, root, path, k): # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ @@ -287,8 +304,9 @@ def find_path(self, root, path, k): if root.key == k: return True - if ((root.left is not None and self.find_path(root.left, path, k)) or - (root.right is not None and self.find_path(root.right, path, k))): + if (root.left is not None and self.find_path(root.left, path, k)) or ( + root.right is not None and self.find_path(root.right, path, k) + ): return True path.pop() @@ -302,15 +320,15 @@ def lca(self, i, j): path_i = [] path_j = [] - if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): + if not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j): return -1 k = 0 - while(k < len(path_i) and k < len(path_j)): + while k < len(path_i) and k < len(path_j): if path_i[k] != path_j[k]: break k += 1 - return path_i[k-1] + return path_i[k - 1] def leaves(self, v): # find all the leaves from node v @@ -334,9 +352,9 @@ def inter_subtree_similarity(self, A, B): nb = 0 for i, w_i in enumerate(leavesA): for j, w_j in enumerate(leavesB): - nb +=1 + nb += 1 r += self.distance(w_i, w_j) - return r/nb + return r / nb def intra_subtree_similarity(self, A): # compute mean of distances between the node from tree A @@ -348,9 +366,9 @@ def intra_subtree_similarity(self, A): for i, w_i in enumerate(leaves): for j, w_j in enumerate(leaves): if i < j: - nb +=1 + nb += 1 r += self.distance(w_i, w_j) - return r/nb + return r / nb def __str__(self): self.printTree(self.root) @@ -360,10 +378,10 @@ def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: self.printTree(node.right, level + 1) - print(' ' * 4 * level + '-> ' + str(node.key)) + print(" " * 4 * level + "-> " + str(node.key)) self.printTree(node.left, level + 1) - def get_parents(self,node): + def get_parents(self, node): # Get all the parents of the node (the clusters it belongs to) clusters = [node.key] if node.parent is None: @@ -395,9 +413,9 @@ def get_all_clusters(self): """ # Get the data of each cluster clusters = {} - for i in range(1,self.n+1): + for i in range(1, self.n + 1): if self.nodes[i].data is not None: clusters[i] = [self.nodes[i].data] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] - return sorted(clusters.items(), key = lambda x : len(x[1])) \ No newline at end of file + return sorted(clusters.items(), key=lambda x: len(x[1])) From 6ccca75ec3d980dde86f584d8151a5905fbd3653 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 14:05:37 +0200 Subject: [PATCH 008/347] reverting to the version passing the 'build river ubuntu' --- river/cluster/__init__.py | 12 +--- river/cluster/hcluster.py | 140 +++++++++++++++++--------------------- 2 files changed, 64 insertions(+), 88 deletions(-) diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index ba4c6c6cb9..6135e3f6da 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -2,17 +2,9 @@ from .clustream import CluStream from .dbstream import DBSTREAM from .denstream import DenStream -from .hcluster import HierarchicalClustering from .k_means import KMeans from .streamkmeans import STREAMKMeans from .textclust import TextClust +from .hcluster import HierarchicalClustering -__all__ = [ - "CluStream", - "DBSTREAM", - "DenStream", - "KMeans", - "STREAMKMeans", - "TextClust", - "HierarchicalClustering", -] +__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust", "HierarchicalClustering"] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 67b73420af..2dd6ee9494 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,13 +1,10 @@ -from typing import Callable - -import numpy as np - from river import base, utils - +import numpy as np +from typing import Callable # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key: int, data: np.ndarray = None): + def __init__(self, key : int, data : np.ndarray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -19,9 +16,8 @@ def __init__(self, key: int, data: np.ndarray = None): def euclidean_distance(w_i, w_j): - # Euclidean distance between two nodes - return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) - + # Euclidean distance between two nodes + return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. @@ -30,12 +26,12 @@ class HierarchicalClustering(base.Clusterer): The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. It will (begining with the whole tree T) : - * Compare the new node to the tree T : + * Compare the new node to the tree T : * if T is just a leaf : merge * else if the nodes of T are more similar between them than with the new node : merge * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree - + You can choose a window size to use only the recent points and not overload the tree. (default : 100) You can also choose a distance function to compare the nodes. (default : euclidean distance) @@ -43,16 +39,16 @@ class HierarchicalClustering(base.Clusterer): Parameters ---------- window_size - number of data points to use. + number of data points to use distance - distance function to use to compare the nodes. - + distance function to use to compare the nodes + Attributes ---------- n : int - number of nodes. + number of nodes X : dict (data point (str(np.ndarray)) : key (int)) - data points used by the algorithm with the key of the node representing them. + data points used by the algorithm with the key of the node representing them References ---------- @@ -72,7 +68,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[1 1 1]': 1, '[1 1 0]': 2, @@ -82,7 +78,7 @@ class HierarchicalClustering(base.Clusterer): >>> HC.n 9 - + >>> print(HC) -> 6 -> 7 @@ -135,7 +131,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[20. 20. 20.1]': 2, '[0 1 1]': 1} @@ -146,30 +142,26 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 + """ - - def __init__( - self, - window_size: int = 100, - distance: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, - ): + def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): # Number of nodes self.n = 0 # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X: dict[np.ndarray, int] = {} + self.X : dict[np.ndarray, int] = {} # Dict : key -> node - self.nodes: dict[int, BinaryTreeNode] = {} + self.nodes : dict[int, BinaryTreeNode] = {} # First node of the tree self.root = None # Distance function self.distance = distance if self.distance is None: self.distance = euclidean_distance - + def OTD(self, T, x): - # OTD algorithm from https://arxiv.org/pdf/1909.09667.pdf + # OTD algorithm from if self.n == 1: # First node in the tree self.root = self.nodes[1] @@ -184,24 +176,20 @@ def OTD(self, T, x): # If there is no node at the right of the intermediate node, we add it there T.right = self.nodes[self.X[np.array2string(x)]] self.nodes[self.X[np.array2string(x)]].parent = T - elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity( - T, self.nodes[self.X[np.array2string(x)]] - ): + elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity(T, self.nodes[self.X[np.array2string(x)]]): # If the nodes in T are closer between them than with the new node, we merge T and the new node self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) - elif self.inter_subtree_similarity( - T.left, self.nodes[self.X[np.array2string(x)]] - ) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): + elif self.inter_subtree_similarity(T.left, self.nodes[self.X[np.array2string(x)]]) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T self.OTD(T.right, x) else: # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T self.OTD(T.left, x) - + def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node - self.n += 1 + self.n +=1 new_node = BinaryTreeNode(self.n) # We add T and the added node as its children new_node.left = T @@ -222,7 +210,7 @@ def merge_nodes(self, T, added_node): # If T was the root, the new node become the root if self.root.key == T.key: self.root = self.nodes[self.n] - + def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree @@ -237,16 +225,16 @@ def learn_one(self, x): del self.nodes[oldest_key] del self.X[list(self.X.keys())[0]] self.X[np.array2string(x)] = oldest_key - self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) + self.nodes[oldest_key] = BinaryTreeNode(oldest_key,x) else: # Else we just add a node - self.n += 1 + self.n +=1 self.X[np.array2string(x)] = self.n - self.nodes[self.n] = BinaryTreeNode(self.n, x) + self.nodes[self.n] = BinaryTreeNode(self.n,x) # We add it to the tree self.OTD(self.root, x) return self - + def predict_OTD(self, x, node, clusters): # get the list of predicted clusters for x if node is None: @@ -254,20 +242,16 @@ def predict_OTD(self, x, node, clusters): return [1], -1 if node.data is not None: # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n + 2, self.n + 1]) + clusters.extend([self.n+2, self.n+1]) return clusters, node.key - if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( - node, BinaryTreeNode(self.n + 1, x) - ): + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity(node, BinaryTreeNode(self.n+1,x)): # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n + 2, self.n + 1]) + clusters.extend([self.n+2, self.n+1]) return clusters, node.key else: # Else, x wouls be added in the tree, we add the key of node clusters.extend([node.key]) - if self.inter_subtree_similarity( - node.left, BinaryTreeNode(self.n + 1, x) - ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): + if self.inter_subtree_similarity(node.left,BinaryTreeNode(self.n+1,x)) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n+1,x)): # If the right part of the tree is closer to x than the left part, we continue in the right part return self.predict_OTD(x, node.right, clusters) else: @@ -285,30 +269,30 @@ def predict_one(self, x): ------- (list, int) A list of clusters (from node `x` to root) and the node to which it would have been merged. + """ x = utils.dict2numpy(x) # We predict to which cluster x would be if we added it in the tree - r, merged = self.predict_OTD(x, self.root, []) + r, merged = self.predict_OTD( x, self.root, []) r.reverse() return r, merged - + def find_path(self, root, path, k): # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if root is None: # No path return False - + path.append(root) - + if root.key == k: return True - - if (root.left is not None and self.find_path(root.left, path, k)) or ( - root.right is not None and self.find_path(root.right, path, k) - ): + + if ((root.left is not None and self.find_path(root.left, path, k)) or + (root.right is not None and self.find_path(root.right, path, k))): return True - + path.pop() return False @@ -316,20 +300,20 @@ def lca(self, i, j): # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if self.root is None: return -1 - + path_i = [] path_j = [] - - if not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j): + + if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): return -1 k = 0 - while k < len(path_i) and k < len(path_j): + while(k < len(path_i) and k < len(path_j)): if path_i[k] != path_j[k]: break k += 1 - return path_i[k - 1] - + return path_i[k-1] + def leaves(self, v): # find all the leaves from node v if v is None: @@ -352,9 +336,9 @@ def inter_subtree_similarity(self, A, B): nb = 0 for i, w_i in enumerate(leavesA): for j, w_j in enumerate(leavesB): - nb += 1 + nb +=1 r += self.distance(w_i, w_j) - return r / nb + return r/nb def intra_subtree_similarity(self, A): # compute mean of distances between the node from tree A @@ -366,29 +350,29 @@ def intra_subtree_similarity(self, A): for i, w_i in enumerate(leaves): for j, w_j in enumerate(leaves): if i < j: - nb += 1 + nb +=1 r += self.distance(w_i, w_j) - return r / nb - + return r/nb + def __str__(self): self.printTree(self.root) return "" - + def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: self.printTree(node.right, level + 1) - print(" " * 4 * level + "-> " + str(node.key)) + print(' ' * 4 * level + '-> ' + str(node.key)) self.printTree(node.left, level + 1) - - def get_parents(self, node): + + def get_parents(self,node): # Get all the parents of the node (the clusters it belongs to) clusters = [node.key] if node.parent is None: return clusters clusters.extend(self.get_parents(node.parent)) return clusters - + def get_clusters_by_point(self): """Returns the list of clusters (from the data point node to the root) for all of the data points. @@ -402,7 +386,7 @@ def get_clusters_by_point(self): for x in self.X.keys(): clusters[x] = self.get_parents(self.nodes[self.X[x]]) return clusters - + def get_all_clusters(self): """Returns all the clusters of the tree. @@ -413,9 +397,9 @@ def get_all_clusters(self): """ # Get the data of each cluster clusters = {} - for i in range(1, self.n + 1): + for i in range(1,self.n+1): if self.nodes[i].data is not None: clusters[i] = [self.nodes[i].data] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] - return sorted(clusters.items(), key=lambda x: len(x[1])) + return sorted(clusters.items(), key = lambda x : len(x[1])) From 2fd212d4b8c4a558dfccd9fd318561411e7b2431 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 14:08:33 +0200 Subject: [PATCH 009/347] fixed isort and black --- river/cluster/__init__.py | 12 +++- river/cluster/hcluster.py | 134 +++++++++++++++++++++----------------- 2 files changed, 86 insertions(+), 60 deletions(-) diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 6135e3f6da..ba4c6c6cb9 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -2,9 +2,17 @@ from .clustream import CluStream from .dbstream import DBSTREAM from .denstream import DenStream +from .hcluster import HierarchicalClustering from .k_means import KMeans from .streamkmeans import STREAMKMeans from .textclust import TextClust -from .hcluster import HierarchicalClustering -__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust", "HierarchicalClustering"] +__all__ = [ + "CluStream", + "DBSTREAM", + "DenStream", + "KMeans", + "STREAMKMeans", + "TextClust", + "HierarchicalClustering", +] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 2dd6ee9494..258be1ba80 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,13 @@ -from river import base, utils -import numpy as np from typing import Callable +import numpy as np + +from river import base, utils + + # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key : int, data : np.ndarray = None): + def __init__(self, key: int, data: np.ndarray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -16,8 +19,9 @@ def __init__(self, key : int, data : np.ndarray = None): def euclidean_distance(w_i, w_j): - # Euclidean distance between two nodes - return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + # Euclidean distance between two nodes + return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. @@ -26,12 +30,12 @@ class HierarchicalClustering(base.Clusterer): The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. It will (begining with the whole tree T) : - * Compare the new node to the tree T : + * Compare the new node to the tree T : * if T is just a leaf : merge * else if the nodes of T are more similar between them than with the new node : merge * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree - + You can choose a window size to use only the recent points and not overload the tree. (default : 100) You can also choose a distance function to compare the nodes. (default : euclidean distance) @@ -42,7 +46,7 @@ class HierarchicalClustering(base.Clusterer): number of data points to use distance distance function to use to compare the nodes - + Attributes ---------- n : int @@ -68,7 +72,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[1 1 1]': 1, '[1 1 0]': 2, @@ -78,7 +82,7 @@ class HierarchicalClustering(base.Clusterer): >>> HC.n 9 - + >>> print(HC) -> 6 -> 7 @@ -131,7 +135,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[20. 20. 20.1]': 2, '[0 1 1]': 1} @@ -142,26 +146,31 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 - + """ - def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): + + def __init__( + self, + window_size: int = 100, + distance: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, + ): # Number of nodes self.n = 0 # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X : dict[np.ndarray, int] = {} + self.X: dict[np.ndarray, int] = {} # Dict : key -> node - self.nodes : dict[int, BinaryTreeNode] = {} + self.nodes: dict[int, BinaryTreeNode] = {} # First node of the tree self.root = None # Distance function self.distance = distance if self.distance is None: self.distance = euclidean_distance - + def OTD(self, T, x): - # OTD algorithm from + # OTD algorithm from if self.n == 1: # First node in the tree self.root = self.nodes[1] @@ -176,20 +185,24 @@ def OTD(self, T, x): # If there is no node at the right of the intermediate node, we add it there T.right = self.nodes[self.X[np.array2string(x)]] self.nodes[self.X[np.array2string(x)]].parent = T - elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity(T, self.nodes[self.X[np.array2string(x)]]): + elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity( + T, self.nodes[self.X[np.array2string(x)]] + ): # If the nodes in T are closer between them than with the new node, we merge T and the new node self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) - elif self.inter_subtree_similarity(T.left, self.nodes[self.X[np.array2string(x)]]) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): + elif self.inter_subtree_similarity( + T.left, self.nodes[self.X[np.array2string(x)]] + ) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T self.OTD(T.right, x) else: # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T self.OTD(T.left, x) - + def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node - self.n +=1 + self.n += 1 new_node = BinaryTreeNode(self.n) # We add T and the added node as its children new_node.left = T @@ -210,7 +223,7 @@ def merge_nodes(self, T, added_node): # If T was the root, the new node become the root if self.root.key == T.key: self.root = self.nodes[self.n] - + def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree @@ -225,16 +238,16 @@ def learn_one(self, x): del self.nodes[oldest_key] del self.X[list(self.X.keys())[0]] self.X[np.array2string(x)] = oldest_key - self.nodes[oldest_key] = BinaryTreeNode(oldest_key,x) + self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: # Else we just add a node - self.n +=1 + self.n += 1 self.X[np.array2string(x)] = self.n - self.nodes[self.n] = BinaryTreeNode(self.n,x) + self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree self.OTD(self.root, x) return self - + def predict_OTD(self, x, node, clusters): # get the list of predicted clusters for x if node is None: @@ -242,16 +255,20 @@ def predict_OTD(self, x, node, clusters): return [1], -1 if node.data is not None: # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n+2, self.n+1]) + clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key - if self.intra_subtree_similarity(node) < self.inter_subtree_similarity(node, BinaryTreeNode(self.n+1,x)): + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( + node, BinaryTreeNode(self.n + 1, x) + ): # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n+2, self.n+1]) + clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key else: # Else, x wouls be added in the tree, we add the key of node clusters.extend([node.key]) - if self.inter_subtree_similarity(node.left,BinaryTreeNode(self.n+1,x)) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n+1,x)): + if self.inter_subtree_similarity( + node.left, BinaryTreeNode(self.n + 1, x) + ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): # If the right part of the tree is closer to x than the left part, we continue in the right part return self.predict_OTD(x, node.right, clusters) else: @@ -269,30 +286,31 @@ def predict_one(self, x): ------- (list, int) A list of clusters (from node `x` to root) and the node to which it would have been merged. - + """ x = utils.dict2numpy(x) # We predict to which cluster x would be if we added it in the tree - r, merged = self.predict_OTD( x, self.root, []) + r, merged = self.predict_OTD(x, self.root, []) r.reverse() return r, merged - + def find_path(self, root, path, k): # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if root is None: # No path return False - + path.append(root) - + if root.key == k: return True - - if ((root.left is not None and self.find_path(root.left, path, k)) or - (root.right is not None and self.find_path(root.right, path, k))): + + if (root.left is not None and self.find_path(root.left, path, k)) or ( + root.right is not None and self.find_path(root.right, path, k) + ): return True - + path.pop() return False @@ -300,20 +318,20 @@ def lca(self, i, j): # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if self.root is None: return -1 - + path_i = [] path_j = [] - - if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): + + if not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j): return -1 k = 0 - while(k < len(path_i) and k < len(path_j)): + while k < len(path_i) and k < len(path_j): if path_i[k] != path_j[k]: break k += 1 - return path_i[k-1] - + return path_i[k - 1] + def leaves(self, v): # find all the leaves from node v if v is None: @@ -336,9 +354,9 @@ def inter_subtree_similarity(self, A, B): nb = 0 for i, w_i in enumerate(leavesA): for j, w_j in enumerate(leavesB): - nb +=1 + nb += 1 r += self.distance(w_i, w_j) - return r/nb + return r / nb def intra_subtree_similarity(self, A): # compute mean of distances between the node from tree A @@ -350,29 +368,29 @@ def intra_subtree_similarity(self, A): for i, w_i in enumerate(leaves): for j, w_j in enumerate(leaves): if i < j: - nb +=1 + nb += 1 r += self.distance(w_i, w_j) - return r/nb - + return r / nb + def __str__(self): self.printTree(self.root) return "" - + def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: self.printTree(node.right, level + 1) - print(' ' * 4 * level + '-> ' + str(node.key)) + print(" " * 4 * level + "-> " + str(node.key)) self.printTree(node.left, level + 1) - - def get_parents(self,node): + + def get_parents(self, node): # Get all the parents of the node (the clusters it belongs to) clusters = [node.key] if node.parent is None: return clusters clusters.extend(self.get_parents(node.parent)) return clusters - + def get_clusters_by_point(self): """Returns the list of clusters (from the data point node to the root) for all of the data points. @@ -386,7 +404,7 @@ def get_clusters_by_point(self): for x in self.X.keys(): clusters[x] = self.get_parents(self.nodes[self.X[x]]) return clusters - + def get_all_clusters(self): """Returns all the clusters of the tree. @@ -397,9 +415,9 @@ def get_all_clusters(self): """ # Get the data of each cluster clusters = {} - for i in range(1,self.n+1): + for i in range(1, self.n + 1): if self.nodes[i].data is not None: clusters[i] = [self.nodes[i].data] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] - return sorted(clusters.items(), key = lambda x : len(x[1])) + return sorted(clusters.items(), key=lambda x: len(x[1])) From 914435d60f1e59e319dddee0304e83820b13ce67 Mon Sep 17 00:00:00 2001 From: Katia Chardon <92110501+kchardon@users.noreply.github.com> Date: Tue, 23 May 2023 22:11:39 +0200 Subject: [PATCH 010/347] Deleted the possibility to use all the data points --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 258be1ba80..02707fb93f 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -227,7 +227,7 @@ def merge_nodes(self, T, added_node): def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree - if self.window_size > 0 and len(self.X.keys()) >= self.window_size: + if len(self.X.keys()) >= self.window_size: # If we have reached the maximum of data points, we delete the oldest one and add a node with the same key as the one we deleted oldest_key = self.X[list(self.X.keys())[0]] oldest = self.nodes[oldest_key] From ddefcba38680fbb44fa10442a24f79fb8ed117e7 Mon Sep 17 00:00:00 2001 From: Katia Chardon <92110501+kchardon@users.noreply.github.com> Date: Mon, 5 Jun 2023 12:03:31 +0200 Subject: [PATCH 011/347] correction for ruff hook --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 02707fb93f..0b85506a54 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,4 +1,4 @@ -from typing import Callable +from __future__ import annotations import numpy as np From 88ad11ae8e9b980bdf43e84e954fdfef69a7fc85 Mon Sep 17 00:00:00 2001 From: Katia Chardon <92110501+kchardon@users.noreply.github.com> Date: Mon, 5 Jun 2023 14:01:22 +0200 Subject: [PATCH 012/347] correction for ruff hook --- river/cluster/hcluster.py | 1 + 1 file changed, 1 insertion(+) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0b85506a54..0ca5879dfc 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,4 +1,5 @@ from __future__ import annotations +from typing import Callable import numpy as np From 09c7413305f2bcba10eb57c85a337309c049dd3b Mon Sep 17 00:00:00 2001 From: Katia Chardon <92110501+kchardon@users.noreply.github.com> Date: Mon, 5 Jun 2023 14:14:56 +0200 Subject: [PATCH 013/347] correction for ruff hook --- river/cluster/hcluster.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0ca5879dfc..001d9fa9c7 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,5 +1,6 @@ from __future__ import annotations -from typing import Callable + +from collections.abc import Callable import numpy as np From 2444d4c127d3c64a96022f57a3707745fc742d56 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 5 Jun 2023 14:31:37 +0200 Subject: [PATCH 014/347] Update one_hot.py --- river/preprocessing/one_hot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/preprocessing/one_hot.py b/river/preprocessing/one_hot.py index bb2d87d773..212e8e15cc 100644 --- a/river/preprocessing/one_hot.py +++ b/river/preprocessing/one_hot.py @@ -217,7 +217,7 @@ def learn_many(self, X): return self def transform_many(self, X): - oh = pd.get_dummies(X, sparse=True, dtype="uint8") + oh = pd.get_dummies(X, columns=X.columns, sparse=True, dtype="uint8") if not self.drop_zeros: seen_in_the_past = {f"{col}_{val}" for col, vals in self.values.items() for val in vals} From 2bad2b5e6c8d48df7e8de28b3c6d011833597531 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 5 Jun 2023 14:38:46 +0200 Subject: [PATCH 015/347] Update test_product.py --- river/compose/test_product.py | 30 ++++++++++++++++++++++++++++-- 1 file changed, 28 insertions(+), 2 deletions(-) diff --git a/river/compose/test_product.py b/river/compose/test_product.py index 300df6b308..0a2088f48f 100644 --- a/river/compose/test_product.py +++ b/river/compose/test_product.py @@ -66,8 +66,8 @@ def test_issue_1253(): >>> import numpy as np >>> import pandas as pd - >>> from river import compose, preprocessing - >>> from sklearn import datasets + >>> from river import compat, compose, preprocessing + >>> from sklearn import datasets, linear_model >>> np.random.seed(1000) >>> X, y = datasets.make_regression(n_samples=5_000, n_features=2) @@ -86,6 +86,32 @@ def test_issue_1253(): >>> XT.sparse.to_dense().memory_usage().sum() // 1000 4455 + >>> X, y = datasets.make_regression(n_samples=6, n_features=2) + >>> X = pd.DataFrame(X) + >>> X.columns = ['feat_1','feat_2'] + >>> X['cat'] = np.random.randint(1, 3, X.shape[0]) + >>> y = pd.Series(y) + >>> group1 = compose.Select('cat') | preprocessing.OneHotEncoder() + >>> group2 = compose.Select('feat_2') | preprocessing.StandardScaler() + >>> sparsify = lambda X: X.astype({ + ... key: pd.SparseDtype(X.dtypes[key].type, fill_value=0) + ... for key in X.dtypes.keys() + ... }) + >>> model = ( + ... (group1 + group1 * group2) | + ... compose.FuncTransformer(sparsify) | + ... compat.convert_sklearn_to_river(linear_model.SGDRegressor(max_iter=3)) + ... ) + >>> _ = model.predict_many(X) + >>> model.transform_many(X) + cat_1*feat_2 cat_2*feat_2 cat_1 cat_2 + 0 -1.196841 0.000000 1 0 + 1 1.304619 0.000000 1 0 + 2 -1.294091 0.000000 1 0 + 3 0.287426 0.000000 1 0 + 4 -0.143960 0.000000 1 0 + 5 0.000000 1.042847 0 1 + """ From 1f83e047acc8cca7a5f731234a43ceefb6ec4373 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 5 Jun 2023 23:43:31 +0200 Subject: [PATCH 016/347] Update one_hot.py --- river/preprocessing/one_hot.py | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/river/preprocessing/one_hot.py b/river/preprocessing/one_hot.py index 212e8e15cc..85d9fbb5e3 100644 --- a/river/preprocessing/one_hot.py +++ b/river/preprocessing/one_hot.py @@ -21,6 +21,10 @@ class OneHotEncoder(base.MiniBatchTransformer): ---------- drop_zeros Whether or not 0s should be made explicit or not. + drop_first + Whether to get `k - 1` dummies out of `k` categorical levels by removing the first key. + This is useful in some statistical models where perfectly collinear features cause + problems. Examples -------- @@ -70,6 +74,17 @@ class OneHotEncoder(base.MiniBatchTransformer): {'c1_i': 1, 'c2_h': 1} {'c1_h': 1, 'c2_e': 1} + You can encode only `k - 1` features out of `k` by setting `drop_first` to `True`. + + >>> oh = preprocessing.OneHotEncoder(drop_first=True, drop_zeros=True) + >>> for x in X: + ... oh = oh.learn_one(x) + ... pprint(oh.transform_one(x)) + {'c2_d': 1} + {'c2_x': 1} + {'c2_h': 1} + {'c2_e': 1} + A subset of the features can be one-hot encoded by piping a `compose.Select` into the `OneHotEncoder`. @@ -173,8 +188,9 @@ class OneHotEncoder(base.MiniBatchTransformer): """ - def __init__(self, drop_zeros=False): + def __init__(self, drop_zeros=False, drop_first=False): self.drop_zeros = drop_zeros + self.drop_first = drop_first self.values = collections.defaultdict(set) def learn_one(self, x): @@ -205,6 +221,9 @@ def transform_one(self, x, y=None): else: oh[f"{i}_{xi}"] = 1 + if self.drop_first: + oh.pop(min(oh.keys())) + return oh def learn_many(self, X): @@ -225,4 +244,7 @@ def transform_many(self, X): for col in to_add: oh[col] = pd.arrays.SparseArray([0] * len(oh), dtype="uint8") + if self.drop_first: + oh.drop(columns=min(X.columns), inplace=True) + return oh From baf8fd2498928258a6b4297a956206418c48a39d Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 6 Jun 2023 00:42:57 +0200 Subject: [PATCH 017/347] Update one_hot.py --- river/preprocessing/one_hot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/preprocessing/one_hot.py b/river/preprocessing/one_hot.py index 85d9fbb5e3..c45332791a 100644 --- a/river/preprocessing/one_hot.py +++ b/river/preprocessing/one_hot.py @@ -245,6 +245,6 @@ def transform_many(self, X): oh[col] = pd.arrays.SparseArray([0] * len(oh), dtype="uint8") if self.drop_first: - oh.drop(columns=min(X.columns), inplace=True) + oh.drop(columns=min(oh.columns), inplace=True) return oh From b76a1a326c6177293366b168acb15f136e9f3ecf Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 6 Jun 2023 00:43:09 +0200 Subject: [PATCH 018/347] Update one_hot.py --- river/preprocessing/one_hot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/preprocessing/one_hot.py b/river/preprocessing/one_hot.py index c45332791a..1c296c42a1 100644 --- a/river/preprocessing/one_hot.py +++ b/river/preprocessing/one_hot.py @@ -245,6 +245,6 @@ def transform_many(self, X): oh[col] = pd.arrays.SparseArray([0] * len(oh), dtype="uint8") if self.drop_first: - oh.drop(columns=min(oh.columns), inplace=True) + oh = oh.drop(columns=min(oh.columns)) return oh From 7cd76f7a22e5a949347d6dd65efa35dd908bf120 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 6 Jun 2023 00:48:01 +0200 Subject: [PATCH 019/347] Update one_hot.py --- river/preprocessing/one_hot.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/river/preprocessing/one_hot.py b/river/preprocessing/one_hot.py index 1c296c42a1..dbe2309f57 100644 --- a/river/preprocessing/one_hot.py +++ b/river/preprocessing/one_hot.py @@ -152,8 +152,6 @@ class OneHotEncoder(base.MiniBatchTransformer): 2 i h >>> oh = preprocessing.OneHotEncoder(drop_zeros=True) - >>> oh = oh.learn_many(X) - >>> df = oh.transform_many(X) >>> df.sort_index(axis="columns") c1_a c1_i c1_u c2_d c2_h c2_x @@ -161,6 +159,14 @@ class OneHotEncoder(base.MiniBatchTransformer): 1 1 0 0 0 0 1 2 0 1 0 0 1 0 + >>> oh = preprocessing.OneHotEncoder(drop_zeros=True, drop_first=True) + >>> df = oh.transform_many(X) + >>> df.sort_index(axis="columns") + c1_i c1_u c2_d c2_h c2_x + 0 0 1 1 0 0 + 1 0 0 0 0 1 + 2 1 0 0 1 0 + Here's an example where the zeros are kept: >>> oh = preprocessing.OneHotEncoder(drop_zeros=False) From 8d760c974d6d11dcda314f85d962188034964169 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Tue, 6 Jun 2023 14:29:29 -0300 Subject: [PATCH 020/347] Add SWINN and refactor nearest neighbors (#1250) * start * typing * wip * update ruff * Update river/neighbors/ann/swinn.py Co-authored-by: Max Halford * regressor * more docs * add release notes * Update docs/releases/unreleased.md Co-authored-by: Max Halford * expose SWINN * tentative unification * rename lazyNN to LazySearch * update docs * Remove need for FunctionWrapper (#1258) * remove need for FunctionWrapper * Update base.py * fix mypy * mypy * fix tests * balance function wrapper usage --------- Co-authored-by: Max Halford --- docs/releases/unreleased.md | 9 +- river/multioutput/chain.py | 10 +- river/neighbors/__init__.py | 6 +- river/neighbors/ann/__init__.py | 5 + river/neighbors/ann/nn_vertex.py | 165 +++++++++++ river/neighbors/ann/swinn.py | 474 ++++++++++++++++++++++++++++++ river/neighbors/ann/utils.py | 10 + river/neighbors/base.py | 122 +------- river/neighbors/knn_classifier.py | 107 ++++--- river/neighbors/knn_regressor.py | 109 +++---- river/neighbors/lazy.py | 125 ++++++++ river/test_estimators.py | 2 +- 12 files changed, 910 insertions(+), 234 deletions(-) create mode 100644 river/neighbors/ann/__init__.py create mode 100644 river/neighbors/ann/nn_vertex.py create mode 100644 river/neighbors/ann/swinn.py create mode 100644 river/neighbors/ann/utils.py create mode 100644 river/neighbors/lazy.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 02d4b74ad1..c7a669fbd6 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -7,7 +7,14 @@ ## compat -- The `predict_many` method scikit-learn models wrapped with `compat.convert_sklearn_to_river` raised an exception if the model had been fitted on any data yet. Instead, default predictions will be produced, which is consistent with the rest of River. +- The `predict_many` method scikit-learn models wrapped with `compat.convert_sklearn_to_river` raised an exception if the model had not been fitted on any data yet. Instead, default predictions will be produced, which is consistent with the rest of River. + +## neighbors + +- Add `neighbors.SWINN` to power-up approximate nearest neighbor search. SWINN uses graphs to speed up nearest neighbor search in large sliding windows of data. +- Rename `neighbors.NearestNeighbors` to `neighbors.LazySearch`. +- Standardize and create base classes for generic nearest neighbor search utilities. +- The user can now select the nearest neighbor search engine to use in `neighbors.KNNClassifier` and `neighbors.KNNRegressor`. ## compose diff --git a/river/multioutput/chain.py b/river/multioutput/chain.py index 2e011c3d3f..0f8596c91a 100644 --- a/river/multioutput/chain.py +++ b/river/multioutput/chain.py @@ -2,10 +2,11 @@ import collections import copy +import functools import random from river import base, linear_model -from river.utils.math import prod +from river.utils.math import minkowski_distance, prod __all__ = [ "ClassifierChain", @@ -101,7 +102,12 @@ def _unit_test_params(cls): yield {"model": linear_model.LogisticRegression()} # binary classifier yield {"model": linear_model.SoftmaxRegression()} # multi-class classifier yield { - "model": neighbors.KNNClassifier(n_neighbors=1, window_size=5) + "model": neighbors.KNNClassifier( + n_neighbors=1, + engine=neighbors.LazySearch( + window_size=50, dist_func=functools.partial(minkowski_distance, p=2) + ), + ) } # multi-class classifier @property diff --git a/river/neighbors/__init__.py b/river/neighbors/__init__.py index d1f45ad01c..432e949ea5 100644 --- a/river/neighbors/__init__.py +++ b/river/neighbors/__init__.py @@ -6,12 +6,14 @@ """ from __future__ import annotations -from .base import NearestNeighbors +from .ann import SWINN from .knn_classifier import KNNClassifier from .knn_regressor import KNNRegressor +from .lazy import LazySearch __all__ = [ - "NearestNeighbors", + "LazySearch", "KNNClassifier", "KNNRegressor", + "SWINN", ] diff --git a/river/neighbors/ann/__init__.py b/river/neighbors/ann/__init__.py new file mode 100644 index 0000000000..d73a4c76ac --- /dev/null +++ b/river/neighbors/ann/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from .swinn import SWINN + +__all__ = ["SWINN"] diff --git a/river/neighbors/ann/nn_vertex.py b/river/neighbors/ann/nn_vertex.py new file mode 100644 index 0000000000..e1f85905c1 --- /dev/null +++ b/river/neighbors/ann/nn_vertex.py @@ -0,0 +1,165 @@ +from __future__ import annotations + +import heapq +import itertools +import math +import random + +from river import base + + +class Vertex(base.Base): + _isolated: set[Vertex] = set() + + def __init__(self, item, uuid: int) -> None: + self.item = item + self.uuid = uuid + self.edges: dict[Vertex, float] = {} + self.r_edges: dict[Vertex, float] = {} + self.flags: set[Vertex] = set() + self.worst_edge: Vertex | None = None + + def __hash__(self) -> int: + return self.uuid + + def __eq__(self, other) -> bool: + if not isinstance(other, Vertex): + raise NotImplementedError + + return self.uuid == other.uuid + + def __lt__(self, other) -> bool: + if not isinstance(other, Vertex): + raise NotImplementedError + + return self.uuid < other.uuid + + def farewell(self): + for rn in list(self.r_edges): + rn.rem_edge(self) + + for n in list(self.edges): + self.rem_edge(n) + + self.flags = None + self.worst_edge = None + + Vertex._isolated.discard(self) + + def fill(self, neighbors: list[Vertex], dists: list[float]): + for n, dist in zip(neighbors, dists): + self.edges[n] = dist + self.flags.add(n) + n.r_edges[self] = dist + + # Neighbors are ordered by distance + self.worst_edge = n + + def add_edge(self, vertex: Vertex, dist): + self.edges[vertex] = dist + self.flags.add(vertex) + vertex.r_edges[self] = dist + + if self.worst_edge is None or self.edges[self.worst_edge] < dist: + self.worst_edge = vertex + + def rem_edge(self, vertex: Vertex): + self.edges.pop(vertex) + vertex.r_edges.pop(self) + self.flags.discard(vertex) + + if self.has_neighbors(): + if vertex == self.worst_edge: + self.worst_edge = max(self.edges, key=self.edges.get) # type: ignore + else: + self.worst_edge = None + + if not self.has_rneighbors(): + Vertex._isolated.add(self) + + def push_edge(self, node: Vertex, dist: float, max_edges: int) -> int: + if self.is_neighbor(node) or node == self: + return 0 + + if len(self.edges) >= max_edges: + if self.worst_edge is None or self.edges.get(self.worst_edge, math.inf) <= dist: + return 0 + self.rem_edge(self.worst_edge) + + self.add_edge(node, dist) + + return 1 + + def is_neighbor(self, vertex): + return vertex in self.edges or vertex in self.r_edges + + def get_edge(self, vertex: Vertex): + if vertex in self.edges: + return self, vertex, self.edges[vertex] + return vertex, self, self.r_edges[vertex] + + def has_neighbors(self) -> bool: + return len(self.edges) > 0 + + def has_rneighbors(self) -> bool: + return len(self.r_edges) > 0 + + @property + def sample_flags(self): + return list(map(lambda n: n in self.flags, self.edges.keys())) + + @sample_flags.setter + def sample_flags(self, sampled): + self.flags -= set(sampled) + + def neighbors(self) -> tuple[list[Vertex], list[float]]: + res = tuple(map(list, zip(*((node, dist) for node, dist in self.edges.items())))) + return res if len(res) > 0 else ([], []) # type: ignore + + def r_neighbors(self) -> tuple[list[Vertex], list[float]]: + res = tuple(map(list, zip(*((vertex, dist) for vertex, dist in self.r_edges.items())))) + return res if len(res) > 0 else ([], []) # type: ignore + + def all_neighbors(self): + return set.union(set(self.edges.keys()), set(self.r_edges.keys())) + + def is_isolated(self): + return len(self.edges) == 0 and len(self.r_edges) == 0 + + def prune(self, prune_prob: float, prune_trigger: int, rng: random.Random): + if prune_prob == 0: + return + + total_degree = len(self.edges) + len(self.r_edges) + if total_degree <= prune_trigger: + return + + # To avoid tie in distances + counter = itertools.count() + edge_pool: list[tuple[float, int, Vertex, bool]] = [] + for n, dist in self.edges.items(): + heapq.heappush(edge_pool, (dist, next(counter), n, True)) + + for rn, dist in self.r_edges.items(): + heapq.heappush(edge_pool, (dist, next(counter), rn, False)) + + # Start with the best undirected edge + selected: list[Vertex] = [heapq.heappop(edge_pool)[2]] + while len(edge_pool) > 0: + c_dist, _, c, c_isdir = heapq.heappop(edge_pool) + discarded = False + for s in selected: + if s.is_neighbor(c) and rng.random() < prune_prob: + orig, dest, dist = s.get_edge(c) + if dist < c_dist: + if c_isdir: + self.rem_edge(c) + else: + c.rem_edge(self) + discarded = True + break + else: + orig.rem_edge(dest) + + if not discarded: + selected.append(c) diff --git a/river/neighbors/ann/swinn.py b/river/neighbors/ann/swinn.py new file mode 100644 index 0000000000..35419c8812 --- /dev/null +++ b/river/neighbors/ann/swinn.py @@ -0,0 +1,474 @@ +from __future__ import annotations + +import collections +import functools +import heapq +import itertools +import math +import operator +import random +import typing + +from river import utils +from river.neighbors.base import BaseNN, DistanceFunc, FunctionWrapper + +from .nn_vertex import Vertex + + +class SWINN(BaseNN): + """Sliding WIndow-based Nearest Neighbor (SWINN) search using Graphs. + + Extends the NNDescent algorithm[^1] to handle vertex addition and removal in a FIFO data + ingestion policy. SWINN builds and keeps a directed graph where edges connect the nearest + neighbors. Any distance metric can be used to build the graph. By using a directed graph, + the user must set the desired number of neighbors. More neighbors imply more accurate + search queries at the cost of increased running time and memory usage. Note that although + the number of directed neighbors is limited by the user, there is no direct control on the + number of reverse neighbors, i.e., the number of vertices that have an edge to a given vertex. + + The basic idea of SWINN and NNDescent is that "the neighbor of my neighbors might as well be + my neighbor". Hence, the connections are constantly revisited to improve the graph structure. + The algorithm for creating and maintaining the search graph can be described + in general lines as follows: + + * Start with a random neighborhood graph; + + * For each node in the search graph: refine the current neighborhood by checking if there + are better neighborhood options among the neighbors of the current neighbors; + + * If the total number of neighborhood changes is smaller than a given stopping criterion, then stop. + + SWINN adds strategies to remove vertices from the search graph and pruning redundant edges. SWINN is + more efficient when the selected `maxlen` is greater than 500. For small sized data windows, using + the lazy/exhaustive search, i.e., `neighbors.LazySearch` might be a better idea. + + Parameters + ---------- + graph_k + The maximum number of direct nearest neighbors each node has. + maxlen + The maximum size of the data buffer. + warm_up + How many data instances to observe before starting the search graph. + dist_func + The distance function used to compare two items. If not set, use the Minkowski distance + with `p=2`. + max_candidates + The maximum number of vertices to consider when performing local neighborhood joins. If not set + SWINN will use `min(50, max(50, self.graph_k))`. + delta + Early stop parameter for the neighborhood refinement procedure. NNDescent will stop running + if the maximum number of iterations is reached or the number of edge changes after an iteration + is smaller than or equal to `delta * graph_k * n_nodes`. In the last expression, `n_nodes` + refers to the number of graph nodes involved in the (local) neighborhood refinement. + prune_prob + The probability of removing redundant edges. Must be between `0` and `1`. If set to zero, + no edge will be pruned. When set to one, every potentially redundant edge will be dropped. + n_iters + The maximum number of NNDescent iterations to perform to refine the search index. + seed + Random seed for reproducibility. + + Notes + ----- + There is an accuracy/speed trade-off between `graph_k` and `sample_rate`. To ensure a single + connected component, and thus an effective search index, one can increase `graph_k`. The + `connectivity` method is a helper to determine whether the search index has a single connected component. + However, search accuracy might come at the cost of increased memory usage and slow processing. To alleviate + that, one can rely on decreasing the `sample_rate` to avoid exploring all the undirected edges of a node + during search queries and local graph refinements. Moreover, the edge pruning procedures also help + decreasing the computational costs. Note that, anything that limits the number of explored neighbors or + prunes edges might have a negative impact on search accuracy. + + References + ---------- + [^1]: Dong, W., Moses, C., & Li, K. (2011, March). Efficient k-nearest neighbor graph construction for + generic similarity measures. In Proceedings of the 20th international conference on World wide web (pp. 577-586). + + """ + + def __init__( + self, + graph_k: int = 20, + dist_func: DistanceFunc | FunctionWrapper | None = None, + maxlen: int = 1000, + warm_up: int = 500, + max_candidates: int = None, + delta: float = 0.0001, + prune_prob: float = 0.0, + n_iters: int = 10, + seed: int = None, + ): + self.graph_k = graph_k + if dist_func is None: + dist_func = functools.partial(utils.math.minkowski_distance, p=2) + self.dist_func = dist_func + + self.maxlen = maxlen + self.warm_up = warm_up + if max_candidates is None: + self.max_candidates = min(50, max(50, self.graph_k)) + else: + self.max_candidates = max_candidates + + self.delta = delta + self.prune_prob = prune_prob + + self.n_iters = n_iters + self.seed = seed + + self._data: collections.deque[Vertex] = collections.deque(maxlen=self.maxlen) + self._uuid = itertools.cycle(range(self.maxlen)) + self._rng = random.Random(self.seed) + self._index = False + + def __len__(self): + return len(self._data) + + def __getitem__(self, i): + return self._data[i] + + def __iter__(self): + yield from self._data + + def _init_graph(self): + """Create a random nearest neighbor graph.""" + n_nodes = len(self) + + nodes = set([i for i in range(n_nodes)]) + for nid in range(n_nodes): + nodes.remove(nid) + ns = self._rng.sample(tuple(nodes), self.graph_k) + dists = [math.inf for _ in range(self.graph_k)] + self[nid].fill([self[n] for n in ns], dists) + nodes.add(nid) + + def _fix_graph(self): + """Connect every isolated node in the graph to their nearest neighbors.""" + + for node in list(Vertex._isolated): + if not node.is_isolated(): + continue + neighbors, dists = self._search(node.item, self.graph_k) + node.fill(neighbors, dists) + + # Update class property + Vertex._isolated.clear() + + def _safe_node_removal(self): + """Remove the oldest data point from the search graph. + + Make sure nodes are accessible from any given starting point after removing the oldest + node in the search graph. New traversal paths will be added in case the removed node was + the only bridge between its neighbors. + + """ + node = self._data.popleft() + # Get previous neighborhood info + rns = node.r_neighbors()[0] + ns = node.neighbors()[0] + node.farewell() + + # Nodes whose only direct neighbor was the removed node + rns = {rn for rn in rns if not rn.has_neighbors()} + # Nodes whose only reverse neighbor was the removed node + ns = {n for n in ns if not n.has_rneighbors()} + + affected = list(rns | ns) + isolated = rns.intersection(ns) + + # First we handle the unreachable nodes + for al in isolated: + neighbors, dists = self._search(al.item, self.graph_k) + al.fill(neighbors, dists) + + rns -= isolated + ns -= isolated + ns = tuple(ns) + + # Nodes with no direct neighbors + for rn in rns: + seed = None + # Check the group of nodes without reverse neighborhood for seeds + # Thus we can join two separate groups + if len(ns) > 0: + seed = self._rng.choice(ns) + + # Use the search index to create new connections + neighbors, dists = self._search(rn.item, self.graph_k, seed=seed, exclude=rn) + rn.fill(neighbors, dists) + + self._refine(affected) + + def _refine(self, nodes: list[Vertex] = None): + """Update the nearest neighbor graph to improve the edge distances. + + Parameters + ---------- + nodes + The list of nodes for which the neighborhood refinement will be applied. + If `None`, all nodes will have their neighborhood enhanced. + """ + + if nodes is None: + nodes = [n for n in self] + + min_changes = self.delta * self.graph_k * len(nodes) + + tried = set() + for _ in range(self.n_iters): + total_changes = 0 + + new = collections.defaultdict(set) + old = collections.defaultdict(set) + + # Expand undirected neighborhood + for node in nodes: + neighbors = node.neighbors()[0] + flags = node.sample_flags + + for neigh, flag in zip(neighbors, flags): + # To avoid evaluating previous neighbors again + tried.add((node.uuid, neigh.uuid)) + if flag: + new[node].add(neigh) + new[neigh].add(node) + else: + old[node].add(neigh) + old[neigh].add(node) + + # Limits the maximum number of edges to explore and update sample flags + for node in nodes: + if len(new[node]) > self.max_candidates: + new[node] = self._rng.sample(tuple(new[node]), self.max_candidates) # type: ignore + else: + new[node] = new[node] + + if len(old[node]) > self.max_candidates: + old[node] = self._rng.sample(tuple(old[node]), self.max_candidates) # type: ignore + else: + old[node] = old[node] + + node.sample_flags = new[node] + + # Perform local joins an attempt to improve the neighborhood + for node in nodes: + # The origin of the join must have a boolean flag set to true + for n1 in new[node]: + # Consider connections between vertices whose boolean flags are both true + for n2 in new[node]: + if n1.uuid == n2.uuid or n1.is_neighbor(n2): + continue + + if (n1.uuid, n2.uuid) in tried or (n2.uuid, n1.uuid) in tried: + continue + + dist = self.dist_func(n1.item, n2.item) + total_changes += n1.push_edge(n2, dist, self.graph_k) + total_changes += n2.push_edge(n1, dist, self.graph_k) + + tried.add((n1.uuid, n2.uuid)) + + # Or one of the connections has a boolean flag set to false + for n2 in old[node]: + if n1.uuid == n2.uuid or n1.is_neighbor(n2): + continue + + if (n1.uuid, n2.uuid) in tried or (n2.uuid, n1.uuid) in tried: + continue + + dist = self.dist_func(n1.item, n2.item) + total_changes += n1.push_edge(n2, dist, self.graph_k) + total_changes += n2.push_edge(n1, dist, self.graph_k) + + tried.add((n1.uuid, n2.uuid)) + + # Stopping criterion + if total_changes <= min_changes: + break + + # Reduce the number of edges, if needed + for n in nodes: + n.prune(self.prune_prob, self.max_candidates, self._rng) + + # Ensure that no node is isolated in the graph + self._fix_graph() + + def append(self, item: typing.Any, **kwargs): + """Add a new item to the search index. + + Data is stored using the FIFO strategy. Both the data buffer and the search graph are updated. The + addition of a new item will trigger the removal of the oldest item, if the maximum size was + reached. All edges of the removed node are also dropped and safety procedures are applied to ensure + its neighbors keep accessible. The addition of a new item also trigger local neighborhood refinement + procedures, to ensure the search index is effective and the node degree constraints are met. + + Parameters + ---------- + item + Item to be added. + kwargs + Not used in this implementation. + + """ + node = Vertex(item, next(self._uuid)) + if not self._index: + self._data.append(node) + if len(self) >= self.warm_up: + self._init_graph() + self._refine() + self._index = True + return + + # A slot will be replaced, so let's update the search graph first + if len(self) == self.maxlen: + self._safe_node_removal() + + # Assign the closest neighbors to the new item + neighbors, dists = self._search(node.item, self.graph_k) + + # Add the new element to the buffer + self._data.append(node) + node.fill(neighbors, dists) + + def _linear_scan(self, item, k): + # Lazy search while the warm-up period is not finished + points = [(p.item, self.dist_func(item, p.item)) for p in self] + + if points: + return tuple(map(list, zip(*sorted(points, key=operator.itemgetter(-1))[:k]))) + + return None + + def _search(self, item, k, epsilon: float = 0.1, seed=None, exclude=None) -> tuple[list, list]: + # Limiter for the distance bound + distance_scale = 1 + epsilon + # Distance threshold for early stops + distance_bound = math.inf + + if exclude is None: + exclude = set() + else: + exclude = {exclude.uuid} + + if seed is None: + # Make sure the starting point for the search is valid + while True: + # Random seed point to start the search + seed = self[self._rng.randint(0, len(self) - 1)] + if not seed.is_isolated() and seed.uuid not in exclude: + break + + dist = self.dist_func(item, seed.item) + + # To avoid computing distances more than once for a given node + visited = {seed.uuid} + visited |= exclude + + # Search pool is a minimum heap + pool = [(dist, seed)] + + # Results are stored in a maximum heap + result = [(-dist, seed)] + + c_dist, c_n = heapq.heappop(pool) + while c_dist < distance_bound: + tns = [n for n in c_n.all_neighbors() if n.uuid not in visited] + + for n in tns: + dist = self.dist_func(item, n.item) + + if len(result) < k: + heapq.heappush(result, (-dist, n)) + heapq.heappush(pool, (dist, n)) + distance_bound = distance_scale * -result[0][0] + elif dist < -result[0][0]: + heapq.heapreplace(result, (-dist, n)) + heapq.heappush(pool, (dist, n)) + distance_bound = distance_scale * -result[0][0] + visited.add(n.uuid) + if len(pool) == 0: + break + c_dist, c_n = heapq.heappop(pool) + + result.sort(reverse=True) + neighbors, dists = map(list, zip(*((r[1], -r[0]) for r in result))) + + return neighbors, dists + + def search( + self, item: typing.Any, n_neighbors: int, epsilon: float = 0.1, **kwargs + ) -> tuple[list, list]: + """Search the underlying nearest neighbor graph given a query item. + + In case not enough samples were observed, i.e., the number of stored samples is smaller than + `warm_up`, then the search switches to a brute force strategy. + + Parameters + ---------- + item + The query item to search for nearest neighbors. + n_neighbors + The number of nearest neighbors to return. + epsilon + Distance bound to aid in avoiding local minima while traversing the search graph. Let $d_k$ + be the distance of the query item to current $k$-th nearest neighbor. At any given + moment, any point whose distance to the query item is smaller than or equal to + $(1 + \\epsilon) * d_k$ is kept as a potential path. After every addition to the heap of + candidate nodes, $d_k$ is updated. + kwargs + Not used in this implementation. + + Returns + ------- + neighbors, dists + A tuple containing the id of the neighbors in the buffer and the respective distances to them. + + """ + + if len(self) <= self.warm_up: + return self._linear_scan(item, n_neighbors) + + neighbors, dists = self._search(item, n_neighbors, epsilon) + return [n.item for n in neighbors], dists + + def connectivity(self) -> list[int]: + """Get a list with the size of each connected component in the search graph. + + This metric provides an overview of reachability in the search index by using Kruskal's + algorithm to build a forest of connected components. + + We want our search index to have a single connected component, i.e., the case where we get + a list containing a single number which is equal to `maxlen`. If that is not the case, not + every node in the search graph can be reached from any given starting point. You may want to try + increasing `graph_k` to improve connectivity. However, keep in mind the following aspects: + 1) computing this metric is a costly operation ($O(E\\log V)$), where $E$ and $V$ are, respectively, + the number of edges and vertices in the search graph; 2) often, connectivity comes at the price of + increased computational costs. Tweaking the `sample_rate` might help in such situations. The best + possible scenario is to decrease the value of `graph_k` while keeping a single connected + component. + + Returns + ------- + A list of the number of elements in each connected component of the graph. + + """ + forest = set() + trees = {n: {n} for n in self} + + edges = [((n1, n2), w) for n1 in self for n2, w in n1.edges.items()] + edges.sort(key=operator.itemgetter(1)) + + for (n1, n2), _ in edges: + if trees[n1].isdisjoint(trees[n2]): + forest.discard(frozenset(trees[n1])) + forest.discard(frozenset(trees[n2])) + + u = trees[n1] | trees[n2] + # Update the trees + for v in u: + trees[v] = u + + forest.add(frozenset(u)) + + return [len(tree) for tree in forest] diff --git a/river/neighbors/ann/utils.py b/river/neighbors/ann/utils.py new file mode 100644 index 0000000000..7d33f4f3b6 --- /dev/null +++ b/river/neighbors/ann/utils.py @@ -0,0 +1,10 @@ +from __future__ import annotations + + +def argsort(dists): + return sorted(range(len(dists)), key=dists.__getitem__) + + +def rem_duplicates(pool): + seen = set() + return [n for n in pool if not (n in seen or seen.add(n))] diff --git a/river/neighbors/base.py b/river/neighbors/base.py index ad76a32929..311c0291c4 100644 --- a/river/neighbors/base.py +++ b/river/neighbors/base.py @@ -1,9 +1,10 @@ from __future__ import annotations -import collections -import operator +import abc import typing +from river import base + class DistanceFunc(typing.Protocol): def __call__(self, a: typing.Any, b: typing.Any, **kwargs) -> float: @@ -22,7 +23,6 @@ class FunctionWrapper: ---------- distance_function The custom distance function to be wrapped. - """ def __init__(self, distance_function: DistanceFunc): @@ -33,112 +33,14 @@ def __call__(self, a, b): return self.distance_function(a[0], b[0]) -class NearestNeighbors: - """Exact nearest neighbors search data structure. - - Parameters - ---------- - window_size - Size of the sliding window use to search neighbors with. - min_distance_keep - The minimum distance (similarity) to consider adding a point to the window. - E.g., a value of 0.0 will add even exact duplicates. - distance_func - A distance function which accepts two input items to compare. - - Notes - ----- - Updates are by default stored by the FIFO (first in first out) method, - which means that when the size limit is reached, old samples are dumped to - give room for new samples. This is circular, meaning that older points - are dumped first. This also gives the implementation a temporal aspect, - because older samples are replaced with newer ones. - - The parameter `min_dinstance_keep` controls the addition of new items to the - window - items that are far enough away (> min_distance_keep) are added to - the window. Thus a value of 0 indicates that we add all points, and - increasing from 0 makes it less likely we will keep a new item. - - """ - - def __init__( - self, - window_size: int, - min_distance_keep: float, - distance_func: DistanceFunc | FunctionWrapper, - ): - self.window_size = window_size - - # A minimum distance (similarity) to determine adding to window - # The model will perform better with a more diverse window - # Since the distance function can be anything, it could be < 0 - self.min_distance_keep = min_distance_keep - - self.distance_func = distance_func - self.window: collections.deque = collections.deque(maxlen=self.window_size) - - def append(self, item: typing.Any, extra: typing.Any | None = None): - """Add a point to the window, optionally with extra metadata. - - Parameters - ---------- - item - The data intended to be provided to the distance function. It is always - the first item in the window, and typically this will be a tuple - (x,y) with features `x` and class or value `y`. - extra: - An extra set of metadata to add to the window that is not passed to - the distance function, and allows easy customization without needing - to always write a custom distance function. - - """ - self.window.append((item, *(extra or []))) - - def update( - self, - item: typing.Any, - n_neighbors: int = 1, - extra: typing.Any | None = None, - ): - """Update the window with a new point, only added if > min distance. - - If min distance is 0, we do not need to do the calculation. The item - (and extra metadata) will not be added to the window if it is too close - to an existing point. - - Parameters - ---------- - item - The data intended to be provided to the distance function. - extra - Metadata that is separate from the item that should also be added - to the window, but is not included to be passed to the distance - function. - - Returns - ------- - A boolean (true/false) to indicate if the point was added. - - """ - # If min distance is 0, we add all points - if self.min_distance_keep == 0: - self.append(item, extra=extra) - return True - - # Don't add VERY similar points to window - nearest = self.find_nearest(item, n_neighbors) - - # Distance always in the last index, (item distance) - if not nearest or nearest[0][-1] < self.min_distance_keep: - self.append(item, extra=extra) - return True - return False +class BaseNN(base.Estimator, abc.ABC): + def __init__(self, dist_func: DistanceFunc | FunctionWrapper): + self.dist_func = dist_func - def find_nearest(self, item: typing.Any, n_neighbors: int = 1): - """Find the `n_neighbors` closest points to `item`, along with their distances.""" - # Compute the distances to each point in the window - # The window is (item, , distance) - points = ((*p, self.distance_func(item, p[0])) for p in self.window) + @abc.abstractmethod + def append(self, item: typing.Any, **kwargs): + pass - # Return the k closest points (last index is distance) - return sorted(points, key=operator.itemgetter(-1))[:n_neighbors] + @abc.abstractmethod + def search(self, item: typing.Any, n_neighbors: int, **kwargs) -> tuple[list, list]: + pass diff --git a/river/neighbors/knn_classifier.py b/river/neighbors/knn_classifier.py index c13fbc37f8..8711af9f13 100644 --- a/river/neighbors/knn_classifier.py +++ b/river/neighbors/knn_classifier.py @@ -3,38 +3,32 @@ import functools from river import base, utils -from river.neighbors import NearestNeighbors -from river.neighbors.base import DistanceFunc, FunctionWrapper +from river.neighbors import SWINN + +from .base import BaseNN, FunctionWrapper class KNNClassifier(base.Classifier): """K-Nearest Neighbors (KNN) for classification. - This works by storing a buffer with the `window_size` most recent observations. - A brute-force search is used to find the `n_neighbors` nearest observations - in the buffer to make a prediction. See the NearestNeighbors parent class for more - details. + Samples are stored using a first-in, first-out strategy. The strategy to perform search + queries in the data buffer is defined by the `engine` parameter. Parameters ---------- n_neighbors The number of nearest neighbors to search for. - window_size - The maximum size of the window storing the last observed samples. - min_distance_keep - The minimum distance (similarity) to consider adding a point to the window. - E.g., a value of 0.0 will add even exact duplicates. Default is 0.05 to add - similar but not exactly the same points. + engine + The search engine used to store the instances and perform search queries. Depending + on the choose engine, search will be exact or approximate. Please, consult the + documentation of each available search engine for more details on its usage. + By default, use the `SWINN` search engine for approximate search queries. weighted Weight the contribution of each neighbor by it's inverse distance. cleanup_every This determines at which rate old classes are cleaned up. Classes that have been seen in the past but that are not present in the current window are dropped. Classes are never dropped when this is set to 0. - distance_func - An optional distance function that should accept an a=, b=, and any - custom set of kwargs. If not defined, the Minkowski distance is used with - p=2 (Euclidean distance). See the example section for more details. softmax Whether or not to use softmax normalization to normalize the neighbors contributions. Votes are divided by the total number of votes if this is `False`. @@ -48,69 +42,61 @@ class KNNClassifier(base.Classifier): Examples -------- + >>> import functools >>> from river import datasets >>> from river import evaluate >>> from river import metrics >>> from river import neighbors >>> from river import preprocessing + >>> from river import utils >>> dataset = datasets.Phishing() - >>> model = ( - ... preprocessing.StandardScaler() | - ... neighbors.KNNClassifier(window_size=50) - ... ) - - >>> evaluate.progressive_val_score(dataset, model, metrics.Accuracy()) - Accuracy: 84.55% + To select a custom distance metric which takes one or several parameter, you can wrap your + chosen distance using `functools.partial`: - When defining a custom distance function you can rely on `functools.partial` to set default - parameter values. For instance, let's use the Manhattan function instead of the default Euclidean distance: + >>> l1_dist = functools.partial(utils.math.minkowski_distance, p=1) - >>> import functools - >>> from river import utils >>> model = ( ... preprocessing.StandardScaler() | ... neighbors.KNNClassifier( - ... window_size=50, - ... distance_func=functools.partial(utils.math.minkowski_distance, p=1) + ... engine=neighbors.SWINN( + ... dist_func=l1_dist, + ... seed=42 + ... ) ... ) ... ) + >>> evaluate.progressive_val_score(dataset, model, metrics.Accuracy()) - Accuracy: 86.87% + Accuracy: 89.75% """ def __init__( self, n_neighbors: int = 5, - window_size: int = 1000, - min_distance_keep: float = 0.0, + engine: BaseNN | None = None, weighted: bool = True, cleanup_every: int = 0, - distance_func: DistanceFunc | None = None, softmax: bool = False, ): self.n_neighbors = n_neighbors - self.window_size = window_size - self.min_distance_keep = min_distance_keep - self.distance_func = ( - distance_func - if distance_func is not None - else functools.partial(utils.math.minkowski_distance, p=2) - ) + if engine is None: + engine = SWINN(dist_func=functools.partial(utils.math.minkowski_distance, p=2)) + + if not isinstance(engine.dist_func, FunctionWrapper): + engine.dist_func = FunctionWrapper(engine.dist_func) + + self.engine = engine self.weighted = weighted self.cleanup_every = cleanup_every self.classes: set[base.typing.ClfTarget] = set() self.softmax = softmax self._cleanup_counter = cleanup_every - self._nn = NearestNeighbors( - window_size=self.window_size, - min_distance_keep=min_distance_keep, - distance_func=FunctionWrapper(self.distance_func), - ) + # Create a fresh copy of the supplied search engine + self._nn: BaseNN = self.engine.clone(include_attributes=True) @property def _multiclass(self): @@ -118,7 +104,14 @@ def _multiclass(self): @classmethod def _unit_test_params(cls): - yield {"n_neighbors": 3, "window_size": 30} + from river.neighbors import LazySearch + + yield { + "n_neighbors": 3, + "engine": LazySearch( + window_size=50, dist_func=functools.partial(utils.math.minkowski_distance, p=2) + ), + } def clean_up_classes(self): """Clean up classes added to the window. @@ -132,9 +125,11 @@ def clean_up_classes(self): self.classes = {x for x in self.window if x[0][1] is not None} def learn_one(self, x, y): - # Only add the class y to known classes if we actually add the point! - if self._nn.update((x, y), n_neighbors=self.n_neighbors): - self.classes.add(y) + # Update the data buffer + self._nn.append((x, y)) + + # Update the set of known classes + self.classes.add(y) # Ensure classes known to instance reflect window self._run_class_cleanup() @@ -151,8 +146,8 @@ def _run_class_cleanup(self): return self - def predict_proba_one(self, x): - nearest = self._nn.find_nearest((x, None), n_neighbors=self.n_neighbors) + def predict_proba_one(self, x, **kwargs): + nearest = self._nn.search((x, None), n_neighbors=self.n_neighbors, **kwargs) # Default prediction for every class we know is 0. # If class_cleanup is false this can include classes not in window @@ -164,14 +159,16 @@ def predict_proba_one(self, x): default_pred = 1 / len(self.classes) if self.classes else 0.0 return {c: default_pred for c in self.classes} + neighbors, distances = nearest + # If the closest is an exact match AND has a class, return it - if nearest[0][-1] == 0 and nearest[0][0][1] is not None: + if distances[0] == 0 and neighbors[0][1] is not None: # Update the class in our prediction from 0 to 1, 100% certain! - y_pred[nearest[0][0][1]] = 1.0 + y_pred[neighbors[0][1]] = 1.0 return y_pred - for neighbor in nearest: - (x, y), distance = neighbor + for neighbor, distance in zip(neighbors, distances): + (x, y) = neighbor # Weighted votes by inverse distance if self.weighted: diff --git a/river/neighbors/knn_regressor.py b/river/neighbors/knn_regressor.py index a14f3921ed..0463047c29 100644 --- a/river/neighbors/knn_regressor.py +++ b/river/neighbors/knn_regressor.py @@ -4,35 +4,32 @@ import statistics from river import base, utils -from river.neighbors import NearestNeighbors -from river.neighbors.base import DistanceFunc, FunctionWrapper +from river.neighbors import SWINN + +from .base import BaseNN, FunctionWrapper class KNNRegressor(base.Regressor): """K-Nearest Neighbors regressor. - This non-parametric regression method keeps track of the last `window_size` - training samples. Predictions are obtained by aggregating the values of the - closest n_neighbors stored samples with respect to a query sample. + Samples are stored using a first-in, first-out strategy. The strategy to perform search + queries in the data buffer is defined by the `engine` parameter. Predictions are obtained by + aggregating the values of the closest n_neighbors stored samples with respect to a query sample. Parameters ---------- n_neighbors The number of nearest neighbors to search for. - window_size - The maximum size of the window storing the last observed samples. + engine + The search engine used to store the instances and perform search queries. Depending + on the choose engine, search will be exact or approximate. Please, consult the + documentation of each available search engine for more details on its usage. + By default, use the `SWINN` search engine for approximate search queries. aggregation_method The method to aggregate the target values of neighbors. | 'mean' | 'median' | 'weighted_mean' - min_distance_keep - The minimum distance (similarity) to consider adding a point to the window. - E.g., a value of 0.0 will add even exact duplicates. - distance_func - An optional distance function that should accept an a=, b=, and any - custom set of kwargs. If not defined, the Minkowski distance is used with - p=2 (Euclidean distance). See the example section for more details. Examples -------- @@ -44,24 +41,9 @@ class KNNRegressor(base.Regressor): >>> dataset = datasets.TrumpApproval() - >>> model = neighbors.KNNRegressor(window_size=50) - >>> evaluate.progressive_val_score(dataset, model, metrics.RMSE()) - RMSE: 1.427746 - - When defining a custom distance function you can rely on `functools.partial` to set default - parameter values. For instance, let's use the Manhattan function instead of the default Euclidean distance: - - >>> import functools - >>> from river.utils.math import minkowski_distance - >>> model = ( - ... preprocessing.StandardScaler() | - ... neighbors.KNNRegressor( - ... window_size=50, - ... distance_func=functools.partial(minkowski_distance, p=1) - ... ) - ... ) + >>> model = neighbors.KNNRegressor() >>> evaluate.progressive_val_score(dataset, model, metrics.RMSE()) - RMSE: 1.460385 + RMSE: 1.427743 """ @@ -72,29 +54,36 @@ class KNNRegressor(base.Regressor): def __init__( self, n_neighbors: int = 5, - window_size: int = 1000, + engine: BaseNN | None = None, aggregation_method: str = "mean", - min_distance_keep: float = 0.0, - distance_func: DistanceFunc | None = None, ): self.n_neighbors = n_neighbors - self.window_size = window_size - self.min_distance_keep = min_distance_keep - self.distance_func = ( - distance_func - if distance_func is not None - else functools.partial(utils.math.minkowski_distance, p=2) - ) - - self._nn = NearestNeighbors( - window_size=self.window_size, - min_distance_keep=min_distance_keep, - distance_func=FunctionWrapper(self.distance_func), - ) + + if engine is None: + engine = SWINN(dist_func=functools.partial(utils.math.minkowski_distance, p=2)) + + if not isinstance(engine.dist_func, FunctionWrapper): + engine.dist_func = FunctionWrapper(engine.dist_func) + + self.engine = engine + + # Create a fresh copy of the supplied search engine + self._nn: BaseNN = self.engine.clone(include_attributes=True) self._check_aggregation_method(aggregation_method) self.aggregation_method = aggregation_method + @classmethod + def _unit_test_params(cls): + from river.neighbors import LazySearch + + yield { + "n_neighbors": 3, + "engine": LazySearch( + window_size=50, dist_func=functools.partial(utils.math.minkowski_distance, p=2) + ), + } + def _check_aggregation_method(self, method): """Ensure validation method is known to the model. @@ -114,36 +103,30 @@ def _check_aggregation_method(self, method): ) def learn_one(self, x, y): - self._nn.update((x, y), n_neighbors=self.n_neighbors) + self._nn.append((x, y)) + return self - def predict_one(self, x): + def predict_one(self, x, **kwargs): # Find the nearest neighbors! - nearest = self._nn.find_nearest((x, None), n_neighbors=self.n_neighbors) + nearest = self._nn.search((x, None), n_neighbors=self.n_neighbors, **kwargs) if not nearest: return 0.0 - # For each in nearest, call it 'item" - # item[0] is the original item (x, y) - # item[-1] is the distance - + neighbors, distances = nearest # If the closest distance is 0 (it's the same) return it's output (y) - # BUT only if the output (y) is not None. - if nearest[0][-1] == 0 and nearest[0][0][1] is not None: - return nearest[0][0][1] + if distances[0] == 0: + return neighbors[0][1] - # Only include neighbors in the sum that are not None - neighbor_vals = [n[0][1] for n in nearest if n[0][1] is not None] + neighbor_vals = [n[1] for n in neighbors] if self.aggregation_method == self._MEDIAN: return statistics.median(neighbor_vals) - dists = [n[-1] for n in nearest if n[0][1] is not None] - sum_ = sum(1 / d for d in dists) - + sum_ = sum(1 / d for d in distances) if self.aggregation_method == self._MEAN or sum_ == 0.0: return statistics.mean(neighbor_vals) # weighted mean based on distance - return sum(y / d for y, d in zip(neighbor_vals, dists)) / sum_ + return sum(y / d for y, d in zip(neighbor_vals, distances)) / sum_ diff --git a/river/neighbors/lazy.py b/river/neighbors/lazy.py new file mode 100644 index 0000000000..f6451d3d02 --- /dev/null +++ b/river/neighbors/lazy.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +import collections +import functools +import operator +import typing + +from river import utils + +from .base import BaseNN, DistanceFunc, FunctionWrapper + + +class LazySearch(BaseNN): + """Exact nearest neighbors using a lazy search estrategy. + + Parameters + ---------- + window_size + Size of the sliding window use to search neighbors with. + min_distance_keep + The minimum distance (similarity) to consider adding a point to the window. + E.g., a value of 0.0 will add even exact duplicates. + dist_func + A distance function which accepts two input items to compare. If not set, + use the Minkowski distance with `p=2`. + + Notes + ----- + Updates are by default stored by the FIFO (first in first out) method, + which means that when the size limit is reached, old samples are dumped to + give room for new samples. This is circular, meaning that older points + are dumped first. This also gives the implementation a temporal aspect, + because older samples are replaced with newer ones. + + The parameter `min_dinstance_keep` controls the addition of new items to the + window - items that are far enough away (> min_distance_keep) are added to + the window. Thus a value of 0 indicates that we add all points, and + increasing from 0 makes it less likely we will keep a new item. + + """ + + def __init__( + self, + window_size: int = 50, + min_distance_keep: float = 0.0, + dist_func: DistanceFunc | FunctionWrapper | None = None, + ): + self.window_size = window_size + + # A minimum distance (similarity) to determine adding to window + # The model will perform better with a more diverse window + # Since the distance function can be anything, it could be < 0 + self.min_distance_keep = min_distance_keep + + if dist_func is None: + dist_func = functools.partial(utils.math.minkowski_distance, p=2) + self.dist_func = dist_func + + self.window: collections.deque = collections.deque(maxlen=self.window_size) + + def append(self, item: typing.Any, extra: typing.Any | None = None, **kwargs): + """Add a point to the window, optionally with extra metadata. + + Parameters + ---------- + item + The data intended to be provided to the distance function. It is always + the first item in the window, and typically this will be a tuple + (x,y) with features `x` and class or value `y`. + extra: + An extra set of metadata to add to the window that is not passed to + the distance function, and allows easy customization without needing + to always write a custom distance function. + + """ + self.window.append((item, *(extra or []))) + + def update( + self, + item: typing.Any, + n_neighbors: int = 1, + extra: typing.Any | None = None, + ): + """Update the window with a new point, only added if > min distance. + + If min distance is 0, we do not need to do the calculation. The item + (and extra metadata) will not be added to the window if it is too close + to an existing point. + + Parameters + ---------- + item + The data intended to be provided to the distance function. + extra + Metadata that is separate from the item that should also be added + to the window, but is not included to be passed to the distance + function. + + Returns + ------- + A boolean (true/false) to indicate if the point was added. + + """ + # If min distance is 0, we add all points + if self.min_distance_keep == 0: + self.append(item, extra=extra) + return True + + # Don't add VERY similar points to window + nearest = self.search(item, n_neighbors) + + # Distance always in the last index, (item distance) + if not nearest or nearest[0][-1] < self.min_distance_keep: + self.append(item, extra=extra) + return True + return False + + def search(self, item: typing.Any, n_neighbors: int, **kwargs): + """Find the `n_neighbors` closest points to `item`, along with their distances.""" + # Compute the distances to each point in the window + # The window is (item, , distance) + points = ((*p, self.dist_func(item, p[0])) for p in self.window) + + # Return the k closest points + return tuple(map(list, zip(*sorted(points, key=operator.itemgetter(-1))[:n_neighbors]))) diff --git a/river/test_estimators.py b/river/test_estimators.py index 87a4de6fef..ac41de1dd1 100644 --- a/river/test_estimators.py +++ b/river/test_estimators.py @@ -72,7 +72,7 @@ def iter_estimators_which_can_be_tested(): imblearn.RandomUnderSampler, imblearn.RandomSampler, model_selection.SuccessiveHalvingClassifier, - neighbors.NearestNeighbors, + neighbors.LazySearch, neural_net.MLPRegressor, preprocessing.PreviousImputer, preprocessing.OneHotEncoder, From 693d44d1fead11f0dc8d81759e50f65ac9e8bcaa Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 6 Jun 2023 23:43:48 +0200 Subject: [PATCH 021/347] Update ci.yml --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 8482c33c48..b8c89a734d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -14,7 +14,7 @@ jobs: strategy: fail-fast: false matrix: - python: [3.9, "3.10", "3.11"] + python: ["3.10", "3.11"] os: [ubuntu-latest, macos-latest] uses: ./.github/workflows/build-river.yml From afac9ab047420fd6a8a57161f8583c06709ca385 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 9 Jun 2023 00:46:36 +0200 Subject: [PATCH 022/347] Fix BanditClassifier (#1262) --- river/bandit/thompson.py | 22 ++++--- river/model_selection/bandit.py | 2 +- river/model_selection/test_bandit.py | 87 ++++++++++++++++++++++++++++ 3 files changed, 98 insertions(+), 13 deletions(-) create mode 100644 river/model_selection/test_bandit.py diff --git a/river/bandit/thompson.py b/river/bandit/thompson.py index 2920b7a261..e600bb4504 100644 --- a/river/bandit/thompson.py +++ b/river/bandit/thompson.py @@ -29,7 +29,7 @@ class ThompsonSampling(bandit.base.Policy): Parameters ---------- - dist + reward_obj A distribution to sample from. burn_in The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled @@ -51,7 +51,7 @@ class ThompsonSampling(bandit.base.Policy): >>> _ = env.reset(seed=42) >>> _ = env.action_space.seed(123) - >>> policy = bandit.ThompsonSampling(dist=proba.Beta(), seed=101) + >>> policy = bandit.ThompsonSampling(reward_obj=proba.Beta(), seed=101) >>> metric = stats.Sum() >>> while True: @@ -71,22 +71,20 @@ class ThompsonSampling(bandit.base.Policy): """ - def __init__(self, dist: proba.base.Distribution, burn_in=0, seed: int | None = None): - super().__init__(reward_obj=dist, burn_in=burn_in) + def __init__( + self, reward_obj: proba.base.Distribution = None, burn_in=0, seed: int | None = None + ): + super().__init__(reward_obj=reward_obj, burn_in=burn_in) self.seed = seed self._rng = random.Random(seed) - self._rewards.default_factory = self._clone_dist_with_seed + self._rewards.default_factory = self._clone_reward_obj_with_seed - def _clone_dist_with_seed(self): - return self.dist.clone({"seed": self._rng.randint(0, 2**32)}) - - @property - def dist(self): - return self.reward_obj + def _clone_reward_obj_with_seed(self): + return self.reward_obj.clone({"seed": self._rng.randint(0, 2**32)}) def _pull(self, arm_ids): return max(arm_ids, key=lambda arm_id: self._rewards[arm_id].sample()) @classmethod def _unit_test_params(cls): - yield {"dist": proba.Beta()} + yield {"reward_obj": proba.Beta()} diff --git a/river/model_selection/bandit.py b/river/model_selection/bandit.py index 3910aa1c5a..35758bf871 100644 --- a/river/model_selection/bandit.py +++ b/river/model_selection/bandit.py @@ -206,7 +206,7 @@ def learn_one(self, x, y): y_pred = ( model.predict_one(x) if self.metric.requires_labels else model.predict_proba_one(x) ) - self.policy.update(arm_id, y_true=y, y_pred=y_pred) + self.policy.update(arm_id, y, y_pred) model.learn_one(x, y) return self diff --git a/river/model_selection/test_bandit.py b/river/model_selection/test_bandit.py new file mode 100644 index 0000000000..135fded615 --- /dev/null +++ b/river/model_selection/test_bandit.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +import importlib +import inspect + +import pytest + +from river import ( + bandit, + datasets, + evaluate, + linear_model, + metrics, + model_selection, + optim, + preprocessing, +) + + +def test_1259(): + """ + + https://github.com/online-ml/river/issues/1259 + + >>> from river import bandit + >>> from river import datasets + >>> from river import evaluate + >>> from river import linear_model + >>> from river import metrics + >>> from river import model_selection + >>> from river import optim + >>> from river import preprocessing + + >>> models = [ + ... linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr)) + ... for lr in [0.0001, 0.001, 1e-05, 0.01] + ... ] + + >>> dataset = datasets.Phishing() + >>> model = ( + ... preprocessing.StandardScaler() | + ... model_selection.BanditClassifier( + ... models, + ... metric=metrics.Accuracy(), + ... policy=bandit.Exp3( + ... gamma=0.5, + ... seed=42 + ... ) + ... ) + ... ) + >>> metric = metrics.Accuracy() + + >>> evaluate.progressive_val_score(dataset, model, metric) + Accuracy: 87.20% + + """ + + +@pytest.mark.parametrize( + "policy", + [ + pytest.param( + policy(**params), + id=f"{policy.__name__}", + ) + for _, policy in inspect.getmembers( + importlib.import_module("river.bandit"), + lambda obj: inspect.isclass(obj) and issubclass(obj, bandit.base.Policy), + ) + for params in policy._unit_test_params() + if policy.__name__ != "ThompsonSampling" + ], +) +def test_bandit_classifier_with_each_policy(policy): + models = [ + linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr)) + for lr in [0.0001, 0.001, 1e-05, 0.01] + ] + + dataset = datasets.Phishing() + model = preprocessing.StandardScaler() | model_selection.BanditClassifier( + models, metric=metrics.Accuracy(), policy=policy + ) + metric = metrics.Accuracy() + + score = evaluate.progressive_val_score(dataset, model, metric) + assert score.get() > 0.5 From 2090e40854f5c8a5d41954e824ec6e82bb7d7105 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 9 Jun 2023 01:06:30 +0200 Subject: [PATCH 023/347] more bandit tests --- river/bandit/exp3.py | 6 +- river/model_selection/bandit.py | 2 +- river/model_selection/test_bandit.py | 118 +++++++++++++++++++++++++++ 3 files changed, 123 insertions(+), 3 deletions(-) create mode 100644 river/model_selection/test_bandit.py diff --git a/river/bandit/exp3.py b/river/bandit/exp3.py index bb8ddd8a96..7f077674cf 100644 --- a/river/bandit/exp3.py +++ b/river/bandit/exp3.py @@ -98,6 +98,8 @@ def update(self, arm_id, *reward_args, **reward_kwargs): @classmethod def _unit_test_params(cls): - yield {"gamma": 0.0} + yield {"gamma": 0} + yield {"gamma": 0.1} yield {"gamma": 0.5} - yield {"gamma": 1.0} + yield {"gamma": 0.9} + yield {"gamma": 1} diff --git a/river/model_selection/bandit.py b/river/model_selection/bandit.py index 3910aa1c5a..37d6c58f59 100644 --- a/river/model_selection/bandit.py +++ b/river/model_selection/bandit.py @@ -131,7 +131,7 @@ def learn_one(self, x, y): for arm_id in self._pick_arms(): model = self[arm_id] y_pred = model.predict_one(x) - self.policy.update(arm_id, y_true=y, y_pred=y_pred) + self.policy.update(arm_id, y, y_pred) model.learn_one(x, y) return self diff --git a/river/model_selection/test_bandit.py b/river/model_selection/test_bandit.py new file mode 100644 index 0000000000..c776cc252d --- /dev/null +++ b/river/model_selection/test_bandit.py @@ -0,0 +1,118 @@ +from __future__ import annotations + +import importlib +import inspect + +import pytest + +from river import ( + bandit, + datasets, + evaluate, + linear_model, + metrics, + model_selection, + optim, + preprocessing, +) + + +def test_1259(): + """ + + https://github.com/online-ml/river/issues/1259 + + >>> from river import bandit + >>> from river import datasets + >>> from river import evaluate + >>> from river import linear_model + >>> from river import metrics + >>> from river import model_selection + >>> from river import optim + >>> from river import preprocessing + + >>> models = [ + ... linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr)) + ... for lr in [0.0001, 0.001, 1e-05, 0.01] + ... ] + + >>> dataset = datasets.Phishing() + >>> model = ( + ... preprocessing.StandardScaler() | + ... model_selection.BanditClassifier( + ... models, + ... metric=metrics.Accuracy(), + ... policy=bandit.Exp3( + ... gamma=0.5, + ... seed=42 + ... ) + ... ) + ... ) + >>> metric = metrics.Accuracy() + + >>> evaluate.progressive_val_score(dataset, model, metric) + Accuracy: 87.20% + + """ + + +@pytest.mark.parametrize( + "policy", + [ + pytest.param( + policy(**params), + id=f"{policy.__name__}", + ) + for _, policy in inspect.getmembers( + importlib.import_module("river.bandit"), + lambda obj: inspect.isclass(obj) and issubclass(obj, bandit.base.Policy), + ) + for params in policy._unit_test_params() + if policy.__name__ != "ThompsonSampling" + ], +) +def test_bandit_classifier_with_each_policy(policy): + models = [ + linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr)) + for lr in [0.0001, 0.001, 1e-05, 0.01] + ] + + dataset = datasets.Phishing() + model = preprocessing.StandardScaler() | model_selection.BanditClassifier( + models, metric=metrics.Accuracy(), policy=policy + ) + metric = metrics.Accuracy() + + score = evaluate.progressive_val_score(dataset, model, metric) + assert score.get() > 0.5 + + +@pytest.mark.parametrize( + "policy", + [ + pytest.param( + policy(**params), + id=f"{policy.__name__}", + ) + for _, policy in inspect.getmembers( + importlib.import_module("river.bandit"), + lambda obj: inspect.isclass(obj) and issubclass(obj, bandit.base.Policy), + ) + for params in policy._unit_test_params() + if policy.__name__ not in {"ThompsonSampling", "Exp3"} + ], +) +def test_bandit_regressor_with_each_policy(policy): + models = [ + linear_model.LinearRegression(optimizer=optim.SGD(lr=lr)) + for lr in [0.0001, 0.001, 1e-05, 0.01] + ] + + dataset = datasets.TrumpApproval() + model = preprocessing.StandardScaler() | model_selection.BanditRegressor( + models, metric=metrics.MSE(), policy=policy + ) + metric = metrics.MSE() + + score = evaluate.progressive_val_score(dataset, model, metric) + assert score.get() < 300 From 3d94404e08a46d0cdae69a9ec5d89c512484e9a9 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 9 Jun 2023 01:07:40 +0200 Subject: [PATCH 024/347] more bandit tests --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b8c89a734d..1d273ba88e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -27,7 +27,7 @@ jobs: strategy: fail-fast: false matrix: - python: [3.9, "3.10", "3.11"] + python: ["3.10", "3.11"] os: [ubuntu-latest, macos-latest] uses: ./.github/workflows/unit-tests.yml From a8dbd96b22a8146221081eccb4f48f5237079647 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 9 Jun 2023 16:21:47 +0200 Subject: [PATCH 025/347] Align sklearn to River inputs (#1263) --- docs/releases/unreleased.md | 5 +++ river/cluster/denstream.py | 10 +++--- river/compat/sklearn_to_river.py | 51 ++++++++++++++++++++----------- river/compose/pipeline.py | 3 ++ river/metrics/silhouette.py | 2 +- river/neighbors/knn_classifier.py | 2 +- river/test_estimators.py | 7 +++++ river/utils/math.py | 2 +- 8 files changed, 57 insertions(+), 25 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index c7a669fbd6..06e3f96099 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -8,6 +8,7 @@ ## compat - The `predict_many` method scikit-learn models wrapped with `compat.convert_sklearn_to_river` raised an exception if the model had not been fitted on any data yet. Instead, default predictions will be produced, which is consistent with the rest of River. +- `compat.SKL2RiverRegressor` and `compat.SKL2RiverClassifier` didn't check whether features were ordered in the same way at each method call. They now store the list of feature names at the first function call, and align subsequent inputs in the same order. ## neighbors @@ -29,3 +30,7 @@ ## proba - Added a `cdf` method to `proba.Beta`. + +## utils + +- Fixed `utils.math.minkowski_distance`. diff --git a/river/cluster/denstream.py b/river/cluster/denstream.py index 0fabdf4797..c6e16bbff2 100644 --- a/river/cluster/denstream.py +++ b/river/cluster/denstream.py @@ -120,16 +120,16 @@ class DenStream(base.Clusterer): ... denstream = denstream.learn_one(x) >>> denstream.predict_one({0: -1, 1: -2}) - 1 + 0 - >>> denstream.predict_one({0:5, 1:4}) - 2 + >>> denstream.predict_one({0: 5, 1: 4}) + 1 - >>> denstream.predict_one({0:1, 1:1}) + >>> denstream.predict_one({0: 1, 1: 1}) 0 >>> denstream.n_clusters - 3 + 2 """ diff --git a/river/compat/sklearn_to_river.py b/river/compat/sklearn_to_river.py index 47c8845b81..db70f6f72e 100644 --- a/river/compat/sklearn_to_river.py +++ b/river/compat/sklearn_to_river.py @@ -5,7 +5,7 @@ import pandas as pd from sklearn import base as sklearn_base -from sklearn import exceptions +from sklearn import exceptions as sklearn_exceptions from river import base @@ -47,6 +47,23 @@ def convert_sklearn_to_river(estimator: sklearn_base.BaseEstimator, classes: lis class SKL2RiverBase: def __init__(self, estimator: sklearn_base.BaseEstimator): self.estimator = estimator + self._feature_names: list | None = None + + def _align_dict(self, x: dict) -> list: + if self._feature_names is None: + self._feature_names = list(x.keys()) + return [x[k] for k in self._feature_names] + + def _align_df(self, X: pd.DataFrame) -> pd.DataFrame: + if self._feature_names is None: + self._feature_names = list(X.columns) + return X[self._feature_names] + + def _unit_test_skips(self): # noqa + return { + "check_emerging_features", + "check_disappearing_features", + } class SKL2RiverRegressor(SKL2RiverBase, base.Regressor): @@ -86,23 +103,23 @@ class SKL2RiverRegressor(SKL2RiverBase, base.Regressor): """ def learn_one(self, x, y): - self.estimator.partial_fit(X=[list(x.values())], y=[y]) + self.estimator.partial_fit(X=[self._align_dict(x)], y=[y]) return self def learn_many(self, X, y): - self.estimator.partial_fit(X=X, y=y) + self.estimator.partial_fit(X=self._align_df(X), y=y) return self def predict_one(self, x): try: - return self.estimator.predict(X=[list(x.values())])[0] - except exceptions.NotFittedError: + return self.estimator.predict(X=[self._align_dict(x)])[0] + except sklearn_exceptions.NotFittedError: return 0 def predict_many(self, X): try: - return pd.Series(self.estimator.predict(X)) - except exceptions.NotFittedError: + return pd.Series(self.estimator.predict(self._align_df(X))) + except sklearn_exceptions.NotFittedError: return pd.Series([0] * len(X), index=X.index) @@ -158,24 +175,24 @@ def _multiclass(self): return True def learn_one(self, x, y): - self.estimator.partial_fit(X=[list(x.values())], y=[y], classes=self.classes) + self.estimator.partial_fit(X=[self._align_dict(x)], y=[y], classes=self.classes) return self def learn_many(self, X, y): - self.estimator.partial_fit(X=X, y=y, classes=self.classes) + self.estimator.partial_fit(X=self._align_df(X), y=y, classes=self.classes) return self def predict_proba_one(self, x): try: - y_pred = self.estimator.predict_proba([list(x.values())])[0] + y_pred = self.estimator.predict_proba([self._align_dict(x)])[0] return {self.classes[i]: p for i, p in enumerate(y_pred)} - except exceptions.NotFittedError: + except sklearn_exceptions.NotFittedError: return {c: 1 / len(self.classes) for c in self.classes} def predict_proba_many(self, X): try: - return pd.Series(self.estimator.predict_proba(X), columns=self.classes) - except exceptions.NotFittedError: + return pd.Series(self.estimator.predict_proba(self._align_df(X)), columns=self.classes) + except sklearn_exceptions.NotFittedError: return pd.DataFrame( [[1 / len(self.classes)] * len(self.classes)] * len(X), columns=self.classes, @@ -184,13 +201,13 @@ def predict_proba_many(self, X): def predict_one(self, x): try: - y_pred = self.estimator.predict(X=[list(x.values())])[0] + y_pred = self.estimator.predict(X=[self._align_dict(x)])[0] return y_pred - except exceptions.NotFittedError: + except sklearn_exceptions.NotFittedError: return self.classes[0] def predict_many(self, X): try: - return pd.Series(self.estimator.predict(X)) - except exceptions.NotFittedError: + return pd.Series(self.estimator.predict(self._align_df(X))) + except sklearn_exceptions.NotFittedError: return pd.Series([self.classes[0]] * len(X), index=X.index) diff --git a/river/compose/pipeline.py b/river/compose/pipeline.py index 7260e2f3bf..418fbe1206 100644 --- a/river/compose/pipeline.py +++ b/river/compose/pipeline.py @@ -801,3 +801,6 @@ def predict_proba_many(self, X: pd.DataFrame): """Call transform_many, and then predict_proba_many on the final step.""" X, last_step = self._transform_many(X=X) return last_step.predict_proba_many(X=X) + + def _unit_test_skips(self): + return self[-1]._unit_test_skips() diff --git a/river/metrics/silhouette.py b/river/metrics/silhouette.py index e3dd01cefa..9d5e44e9ae 100644 --- a/river/metrics/silhouette.py +++ b/river/metrics/silhouette.py @@ -47,7 +47,7 @@ class Silhouette(metrics.base.ClusteringMetric): ... metric = metric.update(x, y_pred, k_means.centers) >>> metric - Silhouette: 0.32145 + Silhouette: 0.568058 References ---------- diff --git a/river/neighbors/knn_classifier.py b/river/neighbors/knn_classifier.py index 8711af9f13..bc4d190373 100644 --- a/river/neighbors/knn_classifier.py +++ b/river/neighbors/knn_classifier.py @@ -109,7 +109,7 @@ def _unit_test_params(cls): yield { "n_neighbors": 3, "engine": LazySearch( - window_size=50, dist_func=functools.partial(utils.math.minkowski_distance, p=2) + window_size=30, dist_func=functools.partial(utils.math.minkowski_distance, p=2) ), } diff --git a/river/test_estimators.py b/river/test_estimators.py index ac41de1dd1..e5c3ded948 100644 --- a/river/test_estimators.py +++ b/river/test_estimators.py @@ -10,6 +10,7 @@ anomaly, base, checks, + compat, compose, facto, feature_extraction, @@ -30,6 +31,8 @@ PYTORCH_INSTALLED = True except ImportError: PYTORCH_INSTALLED = False +from sklearn import linear_model as sk_linear_model + from river.compat.river_to_sklearn import River2SKLBase from river.compat.sklearn_to_river import SKL2RiverBase @@ -124,6 +127,10 @@ def can_be_tested(estimator): | linear_model.LinearRegression() ), preprocessing.MinMaxScaler() | anomaly.HalfSpaceTrees(), + ( + preprocessing.StandardScaler() + | compat.convert_sklearn_to_river(sk_linear_model.SGDRegressor(tol=1e-10)) + ), ] for check in checks.yield_checks(estimator) if check.__name__ not in estimator._unit_test_skips() diff --git a/river/utils/math.py b/river/utils/math.py index cf4ab394e5..b208950a4f 100644 --- a/river/utils/math.py +++ b/river/utils/math.py @@ -162,7 +162,7 @@ def minkowski_distance(a: dict, b: dict, p: int): Manhattan distance. When `p=2`, this is equivalent to using the Euclidean distance. """ - return sum((abs(a.get(k, 0.0) - b.get(k, 0.0))) ** p for k in {*a.keys(), *b.keys()}) + return sum((abs(a.get(k, 0.0) - b.get(k, 0.0))) ** p for k in {*a.keys(), *b.keys()}) ** (1 / p) def softmax(y_pred: dict): From b651af919de1880dd7a0935f1a79992bab231054 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 9 Jun 2023 22:18:55 +0200 Subject: [PATCH 026/347] Allow doing stateless inference in mini-batch mode (#1264) --- docs/releases/unreleased.md | 11 +++++---- river/compose/pipeline.py | 48 +++++++++++++++++++++++++++++-------- 2 files changed, 44 insertions(+), 15 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 06e3f96099..4da67d12d6 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -10,6 +10,12 @@ - The `predict_many` method scikit-learn models wrapped with `compat.convert_sklearn_to_river` raised an exception if the model had not been fitted on any data yet. Instead, default predictions will be produced, which is consistent with the rest of River. - `compat.SKL2RiverRegressor` and `compat.SKL2RiverClassifier` didn't check whether features were ordered in the same way at each method call. They now store the list of feature names at the first function call, and align subsequent inputs in the same order. +## compose + +- `compose.TransformerProduct` will now preserve the density of sparse columns. +- Added a `transform_many` method to `compose.FuncTransformer`, allowing it to be used in mini-batch pipelines. +- The `compose.pure_inference_mode` now works with mini-batching. + ## neighbors - Add `neighbors.SWINN` to power-up approximate nearest neighbor search. SWINN uses graphs to speed up nearest neighbor search in large sliding windows of data. @@ -17,11 +23,6 @@ - Standardize and create base classes for generic nearest neighbor search utilities. - The user can now select the nearest neighbor search engine to use in `neighbors.KNNClassifier` and `neighbors.KNNRegressor`. -## compose - -- `compose.TransformerProduct` will now preserve the density of sparse columns. -- Added a `transform_many` method to `compose.FuncTransformer`, allowing it to be used in mini-batch pipelines. - ## preprocessing - Rename `sparse` parameter to `drop_zeros` in `preprocessing.OneHotEncoder`. diff --git a/river/compose/pipeline.py b/river/compose/pipeline.py index 418fbe1206..ebfd69f3e3 100644 --- a/river/compose/pipeline.py +++ b/river/compose/pipeline.py @@ -22,11 +22,11 @@ def warm_up_mode(): """A context manager for training pipelines during a warm-up phase. You don't have to worry about anything when you call `predict_one` and `learn_one` with a - pipeline during in a training loop. The methods at each step of the pipeline will be called in + pipeline during in training loop. At each step of the pipeline, the methods will be called in the correct order. However, during a warm-up phase, you might just be calling `learn_one` because you don't need - the out-of-sample predictions. In this case the unsupervised estimators in the pipeline won't + the out-of-sample predictions. In this case, the unsupervised estimators in the pipeline won't be updated, because they are usually updated when `predict_one` is called. This context manager allows you to override that behavior and make it so that unsupervised @@ -99,10 +99,10 @@ def pure_inference_mode(): """A context manager for making inferences with no side-effects. Calling `predict_one` with a pipeline will update the unsupervised steps of the pipeline. This - is the expected behavior for online machine learning. However, in some cases you might just + is the expected behavior for online machine learning. However, in some cases, you might just want to produce predictions without necessarily updating anything. - This context manager allows you to override that behavior and make it so that unsupervised + This context manager allows you to override that behavior, by making it so that unsupervised estimators are not updated when `predict_one` is called. Examples @@ -159,6 +159,33 @@ def pure_inference_mode(): We can see that the scaler did not get updated before transforming the data. + This also works when working with mini-batches. + + >>> logs = io.StringIO() + >>> sh = logging.StreamHandler(logs) + >>> sh.setLevel(logging.DEBUG) + >>> logger.addHandler(sh) + + >>> with utils.log_method_calls(class_condition): + ... for x, y in datasets.TrumpApproval().take(1): + ... _ = model.predict_many(pd.DataFrame([x])) + >>> print(logs.getvalue()) + StandardScaler.learn_many + StandardScaler.transform_many + LinearRegression.predict_many + + >>> logs = io.StringIO() + >>> sh = logging.StreamHandler(logs) + >>> sh.setLevel(logging.DEBUG) + >>> logger.addHandler(sh) + + >>> with utils.log_method_calls(class_condition), compose.pure_inference_mode(): + ... for x, y in datasets.TrumpApproval().take(1): + ... _ = model.predict_many(pd.DataFrame([x])) + >>> print(logs.getvalue()) + StandardScaler.transform_many + LinearRegression.predict_many + """ Pipeline._STATELESS = True try: @@ -764,13 +791,14 @@ def _transform_many(self, X: pd.DataFrame): steps = iter(self.steps.values()) for t in itertools.islice(steps, len(self) - 1): - if isinstance(t, union.TransformerUnion): - for sub_t in t.transformers.values(): - if not sub_t._supervised: - sub_t.learn_many(X=X) + if not self._STATELESS: + if isinstance(t, union.TransformerUnion): + for sub_t in t.transformers.values(): + if not sub_t._supervised: + sub_t.learn_many(X=X) - elif not t._supervised: - t.learn_many(X=X) + elif not t._supervised: + t.learn_many(X=X) X = t.transform_many(X=X) From 0f5b84490d78325b1ea0026e0b67b569c8699beb Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Wed, 14 Jun 2023 20:28:25 -0300 Subject: [PATCH 027/347] new tree parameters (#1267) --- docs/releases/unreleased.md | 11 +++++++-- river/drift/retrain.py | 2 +- river/forest/adaptive_random_forest.py | 20 ++++++++++++++++ river/tree/extremely_fast_decision_tree.py | 13 ++++++++++ .../hoeffding_adaptive_tree_classifier.py | 13 ++++++++++ river/tree/hoeffding_tree_classifier.py | 22 +++++++++++++---- river/tree/nodes/efdtc_nodes.py | 7 ++++++ river/tree/nodes/htc_nodes.py | 10 ++++++++ .../split_criterion/gini_split_criterion.py | 24 +++++++++++++++++++ .../hellinger_distance_criterion.py | 8 +++---- .../info_gain_split_criterion.py | 7 +++--- 11 files changed, 121 insertions(+), 16 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 4da67d12d6..d5605708f2 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -18,8 +18,8 @@ ## neighbors -- Add `neighbors.SWINN` to power-up approximate nearest neighbor search. SWINN uses graphs to speed up nearest neighbor search in large sliding windows of data. -- Rename `neighbors.NearestNeighbors` to `neighbors.LazySearch`. +- Added `neighbors.SWINN` to power-up approximate nearest neighbor search. SWINN uses graphs to speed up nearest neighbor search in large sliding windows of data. +- Renamed `neighbors.NearestNeighbors` to `neighbors.LazySearch`. - Standardize and create base classes for generic nearest neighbor search utilities. - The user can now select the nearest neighbor search engine to use in `neighbors.KNNClassifier` and `neighbors.KNNRegressor`. @@ -32,6 +32,13 @@ - Added a `cdf` method to `proba.Beta`. +## tree + +- Expose the `min_branch_fraction` parameter to avoid splits where most of the data goes to a single branch. Affects +classification trees. +- Added the `max_share_to_split` parameter to Hoeffding Tree classifiers. This parameters avoids splitting when the majority +class has most of the data. + ## utils - Fixed `utils.math.minkowski_distance`. diff --git a/river/drift/retrain.py b/river/drift/retrain.py index 1aab223de8..f2e544ff02 100644 --- a/river/drift/retrain.py +++ b/river/drift/retrain.py @@ -39,7 +39,7 @@ class DriftRetrainingClassifier(base.Wrapper, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 86.40% + Accuracy: 86.46% """ diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index 44d4decaec..3c7def6f8a 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -159,6 +159,8 @@ def __init__( nominal_attributes: list | None = None, splitter: Splitter | None = None, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 1000000, stop_mem_management: bool = False, @@ -177,6 +179,8 @@ def __init__( nominal_attributes=nominal_attributes, splitter=splitter, binary_split=binary_split, + min_branch_fraction=min_branch_fraction, + max_share_to_split=max_share_to_split, max_size=max_size, memory_estimate_period=memory_estimate_period, stop_mem_management=stop_mem_management, @@ -421,6 +425,16 @@ class ARFClassifier(BaseForest, base.Classifier): if `splitter` is `None`. binary_split [*Tree parameter*] If True, only allow binary splits. + min_branch_fraction + [*Tree parameter*] The minimum percentage of observed data required for branches + resulting from split candidates. To validate a split candidate, at least two resulting + branches must have a percentage of samples greater than `min_branch_fraction`. This + criterion prevents unnecessary splits when the majority of instances are concentrated + in a single branch. + max_share_to_split + [*Tree parameter*] Only perform a split in a leaf if the proportion of elements + in the majority class is smaller than this parameter value. This parameter avoids + performing splits when most of the data belongs to a single class. max_size [*Tree parameter*] Maximum memory (MB) consumed by the tree. memory_estimate_period @@ -484,6 +498,8 @@ def __init__( nominal_attributes: list | None = None, splitter: Splitter | None = None, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 2_000_000, stop_mem_management: bool = False, @@ -516,6 +532,8 @@ def __init__( self.nominal_attributes = nominal_attributes self.splitter = splitter self.binary_split = binary_split + self.min_branch_fraction = min_branch_fraction + self.max_share_to_split = max_share_to_split self.max_size = max_size self.memory_estimate_period = memory_estimate_period self.stop_mem_management = stop_mem_management @@ -568,6 +586,8 @@ def _new_base_model(self): splitter=self.splitter, max_depth=self.max_depth, binary_split=self.binary_split, + min_branch_fraction=self.min_branch_fraction, + max_share_to_split=self.max_share_to_split, max_size=self.max_size, memory_estimate_period=self.memory_estimate_period, stop_mem_management=self.stop_mem_management, diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index 567cc79e65..a7da6fb07b 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -59,6 +59,15 @@ class ExtremelyFastDecisionTreeClassifier(HoeffdingTreeClassifier): By default, `tree.splitter.GaussianSplitter` is used if `splitter` is `None`. binary_split If True, only allow binary splits. + min_branch_fraction + The minimum percentage of observed data required for branches resulting from split + candidates. To validate a split candidate, at least two resulting branches must have + a percentage of samples greater than `min_branch_fraction`. This criterion prevents + unnecessary splits when the majority of instances are concentrated in a single branch. + max_share_to_split + Only perform a split in a leaf if the proportion of elements in the majority class is + smaller than this parameter value. This parameter avoids performing splits when most + of the data belongs to a single class. max_size The max size of the tree, in Megabytes (MB). memory_estimate_period @@ -122,6 +131,8 @@ def __init__( nominal_attributes: list | None = None, splitter: Splitter | None = None, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 1000000, stop_mem_management: bool = False, @@ -139,6 +150,8 @@ def __init__( nominal_attributes=nominal_attributes, splitter=splitter, binary_split=binary_split, + min_branch_fraction=min_branch_fraction, + max_share_to_split=max_share_to_split, max_size=max_size, memory_estimate_period=memory_estimate_period, stop_mem_management=stop_mem_management, diff --git a/river/tree/hoeffding_adaptive_tree_classifier.py b/river/tree/hoeffding_adaptive_tree_classifier.py index 208616de95..98c3b06df2 100644 --- a/river/tree/hoeffding_adaptive_tree_classifier.py +++ b/river/tree/hoeffding_adaptive_tree_classifier.py @@ -67,6 +67,15 @@ class HoeffdingAdaptiveTreeClassifier(HoeffdingTreeClassifier): than their main subtree counterparts. binary_split If True, only allow binary splits. + min_branch_fraction + The minimum percentage of observed data required for branches resulting from split + candidates. To validate a split candidate, at least two resulting branches must have + a percentage of samples greater than `min_branch_fraction`. This criterion prevents + unnecessary splits when the majority of instances are concentrated in a single branch. + max_share_to_split + Only perform a split in a leaf if the proportion of elements in the majority class is + smaller than this parameter value. This parameter avoids performing splits when most + of the data belongs to a single class. max_size The max size of the tree, in Megabytes (MB). memory_estimate_period @@ -139,6 +148,8 @@ def __init__( drift_detector: base.DriftDetector | None = None, switch_significance: float = 0.05, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 1000000, stop_mem_management: bool = False, @@ -157,6 +168,8 @@ def __init__( nominal_attributes=nominal_attributes, splitter=splitter, binary_split=binary_split, + min_branch_fraction=min_branch_fraction, + max_share_to_split=max_share_to_split, max_size=max_size, memory_estimate_period=memory_estimate_period, stop_mem_management=stop_mem_management, diff --git a/river/tree/hoeffding_tree_classifier.py b/river/tree/hoeffding_tree_classifier.py index ab4f179ba6..91d09f79c2 100755 --- a/river/tree/hoeffding_tree_classifier.py +++ b/river/tree/hoeffding_tree_classifier.py @@ -48,6 +48,15 @@ class HoeffdingTreeClassifier(HoeffdingTree, base.Classifier): By default, `tree.splitter.GaussianSplitter` is used if `splitter` is `None`. binary_split If True, only allow binary splits. + min_branch_fraction + The minimum percentage of observed data required for branches resulting from split + candidates. To validate a split candidate, at least two resulting branches must have + a percentage of samples greater than `min_branch_fraction`. This criterion prevents + unnecessary splits when the majority of instances are concentrated in a single branch. + max_share_to_split + Only perform a split in a leaf if the proportion of elements in the majority class is + smaller than this parameter value. This parameter avoids performing splits when most + of the data belongs to a single class. max_size The max size of the tree, in Megabytes (MB). memory_estimate_period @@ -129,6 +138,8 @@ def __init__( nominal_attributes: list | None = None, splitter: Splitter | None = None, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 1000000, stop_mem_management: bool = False, @@ -159,6 +170,9 @@ def __init__( raise ValueError("The chosen splitter cannot be used in classification tasks.") self.splitter = splitter # type: ignore + self.min_branch_fraction = min_branch_fraction + self.max_share_to_split = max_share_to_split + # To keep track of the observed classes self.classes: set = set() @@ -207,13 +221,13 @@ def _new_leaf(self, initial_stats=None, parent=None): def _new_split_criterion(self): if self._split_criterion == self._GINI_SPLIT: - split_criterion = GiniSplitCriterion() + split_criterion = GiniSplitCriterion(self.min_branch_fraction) elif self._split_criterion == self._INFO_GAIN_SPLIT: - split_criterion = InfoGainSplitCriterion() + split_criterion = InfoGainSplitCriterion(self.min_branch_fraction) elif self._split_criterion == self._HELLINGER_SPLIT: - split_criterion = HellingerDistanceCriterion() + split_criterion = HellingerDistanceCriterion(self.min_branch_fraction) else: - split_criterion = InfoGainSplitCriterion() + split_criterion = InfoGainSplitCriterion(self.min_branch_fraction) return split_criterion diff --git a/river/tree/nodes/efdtc_nodes.py b/river/tree/nodes/efdtc_nodes.py index 46acc12d5c..34c870da13 100644 --- a/river/tree/nodes/efdtc_nodes.py +++ b/river/tree/nodes/efdtc_nodes.py @@ -3,6 +3,7 @@ import copy import numbers +from river.tree.utils import BranchFactory from river.utils.norm import normalize_values_in_dict from ..splitter.nominal_splitter_classif import NominalSplitterClassif @@ -56,6 +57,12 @@ def best_split_suggestions(self, criterion, tree): ------- The list of split candidates. """ + maj_class = max(self.stats.values()) + # Only perform split attempts when the majority class does not dominate + # the amount of observed instances + if maj_class and maj_class / self.total_weight > tree.max_share_to_split: + return [BranchFactory()] + best_suggestions = [] pre_split_dist = self.stats diff --git a/river/tree/nodes/htc_nodes.py b/river/tree/nodes/htc_nodes.py index ceb7d03eee..6063af90e3 100644 --- a/river/tree/nodes/htc_nodes.py +++ b/river/tree/nodes/htc_nodes.py @@ -1,5 +1,6 @@ from __future__ import annotations +from river.tree.utils import BranchFactory from river.utils.norm import normalize_values_in_dict from ..splitter.nominal_splitter_classif import NominalSplitterClassif @@ -50,6 +51,15 @@ def total_weight(self): """ return sum(self.stats.values()) if self.stats else 0 + def best_split_suggestions(self, criterion, tree) -> list[BranchFactory]: + maj_class = max(self.stats.values()) + # Only perform split attempts when the majority class does not dominate + # the amount of observed instances + if maj_class and maj_class / self.total_weight > tree.max_share_to_split: + return [BranchFactory()] + + return super().best_split_suggestions(criterion, tree) + def calculate_promise(self): """Calculate how likely a node is going to be split. diff --git a/river/tree/split_criterion/gini_split_criterion.py b/river/tree/split_criterion/gini_split_criterion.py index fc6ef58a85..9b4c6e3187 100644 --- a/river/tree/split_criterion/gini_split_criterion.py +++ b/river/tree/split_criterion/gini_split_criterion.py @@ -1,12 +1,21 @@ from __future__ import annotations +import math + from .base import SplitCriterion class GiniSplitCriterion(SplitCriterion): """Gini Impurity split criterion.""" + def __init__(self, min_branch_fraction): + super().__init__() + self.min_branch_fraction = min_branch_fraction + def merit_of_split(self, pre_split_dist, post_split_dist): + if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_fraction) < 2: + return -math.inf + total_weight = 0.0 dist_weights = [0.0] * len(post_split_dist) for i in range(len(post_split_dist)): @@ -31,3 +40,18 @@ def compute_gini(dist, dist_sum_of_weights): @staticmethod def range_of_merit(pre_split_dist): return 1.0 + + @staticmethod + def num_subsets_greater_than_frac(distributions, min_frac): + total_weight = 0.0 + dist_sums = [0.0] * len(distributions) + for i in range(len(dist_sums)): + dist_sums[i] = sum(distributions[i].values()) + total_weight += dist_sums[i] + num_greater = 0 + + if total_weight > 0: + for d in dist_sums: + if (d / total_weight) > min_frac: + num_greater += 1 + return num_greater diff --git a/river/tree/split_criterion/hellinger_distance_criterion.py b/river/tree/split_criterion/hellinger_distance_criterion.py index fb27edbc8d..5ad379b6a9 100644 --- a/river/tree/split_criterion/hellinger_distance_criterion.py +++ b/river/tree/split_criterion/hellinger_distance_criterion.py @@ -19,14 +19,12 @@ class HellingerDistanceCriterion(SplitCriterion): and Knowledge Discovery 24, no. 1 (2012): 136-158. """ - def __init__(self, min_branch_frac_option=0.01): + def __init__(self, min_branch_fraction): super().__init__() - self.min_branch_frac_option = min_branch_frac_option - self.lowest_entropy = None - self.best_idx = 0 + self.min_branch_fraction = min_branch_fraction def merit_of_split(self, pre_split_dist, post_split_dist): - if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_frac_option) < 2: + if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_fraction) < 2: return -math.inf return self.compute_hellinger(post_split_dist) diff --git a/river/tree/split_criterion/info_gain_split_criterion.py b/river/tree/split_criterion/info_gain_split_criterion.py index 1123b1b06a..0863e6ac84 100644 --- a/river/tree/split_criterion/info_gain_split_criterion.py +++ b/river/tree/split_criterion/info_gain_split_criterion.py @@ -18,13 +18,12 @@ class InfoGainSplitCriterion(SplitCriterion): """ - def __init__(self, min_branch_frac_option=0.01): + def __init__(self, min_branch_fraction): super().__init__() - # Minimum fraction of weight required down at least two branches. - self.min_branch_frac_option = min_branch_frac_option + self.min_branch_fraction = min_branch_fraction def merit_of_split(self, pre_split_dist, post_split_dist): - if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_frac_option) < 2: + if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_fraction) < 2: return -math.inf return self.compute_entropy(pre_split_dist) - self.compute_entropy(post_split_dist) From 0af58db5d6a3ef97ef3a25bafd70652d9c299688 Mon Sep 17 00:00:00 2001 From: Benjamin Matschke <38161508+bmatschke@users.noreply.github.com> Date: Fri, 23 Jun 2023 09:22:26 +0300 Subject: [PATCH 028/347] in DBSTREAM: recluster when needed (#1213) Co-authored-by: Max Halford --- river/cluster/dbstream.py | 113 +++++++++++++++++++++++--------------- 1 file changed, 70 insertions(+), 43 deletions(-) diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 26560abb59..93b9853b00 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -126,7 +126,7 @@ class DBSTREAM(base.Clusterer): >>> dbstream.predict_one({0: 5, 1: 2}) 1 - >>> dbstream.n_clusters + >>> dbstream._n_clusters 2 """ @@ -139,31 +139,33 @@ def __init__( minimum_weight: float = 1.0, ): super().__init__() - self.time_stamp = 0 + self._time_stamp = 0 self.clustering_threshold = clustering_threshold self.fading_factor = fading_factor self.cleanup_interval = cleanup_interval - self.minimum_weight = minimum_weight self.intersection_factor = intersection_factor + self.minimum_weight = minimum_weight - self.n_clusters = 0 - self.clusters: dict[int, DBSTREAMMicroCluster] = {} - self.centers: dict = {} - self.micro_clusters: dict[int, DBSTREAMMicroCluster] = {} + self._n_clusters: int = 0 + self._clusters: typing.Dict[int, "DBSTREAMMicroCluster"] = {} + self._centers: typing.Dict = {} + self._micro_clusters: typing.Dict[int, "DBSTREAMMicroCluster"] = {} self.s: dict[int, dict[int, float]] = {} self.s_t: dict[int, dict[int, float]] = {} + self.clustering_is_up_to_date = False + @staticmethod def _distance(point_a, point_b): return math.sqrt(utils.math.minkowski_distance(point_a, point_b, 2)) def _find_fixed_radius_nn(self, x): fixed_radius_nn = {} - for i in self.micro_clusters.keys(): - if self._distance(self.micro_clusters[i].center, x) < self.clustering_threshold: - fixed_radius_nn[i] = self.micro_clusters[i] + for i in self._micro_clusters.keys(): + if self._distance(self._micro_clusters[i].center, x) < self.clustering_threshold: + fixed_radius_nn[i] = self._micro_clusters[i] return fixed_radius_nn def _gaussian_neighborhood(self, point_a, point_b): @@ -178,32 +180,33 @@ def _update(self, x): if len(neighbor_clusters) < 1: # create new micro cluster - self.micro_clusters[len(self.micro_clusters)] = DBSTREAMMicroCluster( - x=x, last_update=self.time_stamp, weight=1 + self._micro_clusters[len(self._micro_clusters)] = DBSTREAMMicroCluster( + x=x, last_update=self._time_stamp, weight=1 ) else: # update existing micro clusters current_centers = {} for i in neighbor_clusters.keys(): - current_centers[i] = self.micro_clusters[i].center - self.micro_clusters[i].weight = ( - self.micro_clusters[i].weight + current_centers[i] = self._micro_clusters[i].center + self._micro_clusters[i].weight = ( + self._micro_clusters[i].weight * 2 ** ( - -self.fading_factor * (self.time_stamp - self.micro_clusters[i].last_update) + -self.fading_factor + * (self._time_stamp - self._micro_clusters[i].last_update) ) + 1 ) # Update the center (i) with overlapping keys (j) - self.micro_clusters[i].center = { - j: self.micro_clusters[i].center[j] - + self._gaussian_neighborhood(x, self.micro_clusters[i].center) - * (x[j] - self.micro_clusters[i].center[j]) - for j in self.micro_clusters[i].center.keys() + self._micro_clusters[i].center = { + j: self._micro_clusters[i].center[j] + + self._gaussian_neighborhood(x, self._micro_clusters[i].center) + * (x[j] - self._micro_clusters[i].center[j]) + for j in self._micro_clusters[i].center.keys() if j in x } - self.micro_clusters[i].last_update = self.time_stamp + self._micro_clusters[i].last_update = self._time_stamp # update shared density for j in neighbor_clusters.keys(): @@ -211,10 +214,10 @@ def _update(self, x): try: self.s[i][j] = ( self.s[i][j] - * 2 ** (-self.fading_factor * (self.time_stamp - self.s_t[i][j])) + * 2 ** (-self.fading_factor * (self._time_stamp - self.s_t[i][j])) + 1 ) - self.s_t[i][j] = self.time_stamp + self.s_t[i][j] = self._time_stamp except KeyError: try: self.s[i][j] = 0 @@ -229,26 +232,26 @@ def _update(self, x): if j > i: if ( self._distance( - self.micro_clusters[i].center, - self.micro_clusters[j].center, + self._micro_clusters[i].center, + self._micro_clusters[j].center, ) < self.clustering_threshold ): # revert centers of mc_i and mc_j to previous positions - self.micro_clusters[i].center = current_centers[i] - self.micro_clusters[j].center = current_centers[j] + self._micro_clusters[i].center = current_centers[i] + self._micro_clusters[j].center = current_centers[j] - self.time_stamp += 1 + self._time_stamp += 1 def _cleanup(self): # Algorithm 2 of Michael Hahsler and Matthew Bolanos: Cleanup process to remove # inactive clusters and shared density entries from memory weight_weak = 2 ** (-self.fading_factor * self.cleanup_interval) - micro_clusters = copy.deepcopy(self.micro_clusters) - for i, micro_cluster_i in self.micro_clusters.items(): + micro_clusters = copy.deepcopy(self._micro_clusters) + for i, micro_cluster_i in self._micro_clusters.items(): try: - value = 2 ** (self.fading_factor * (self.time_stamp - micro_cluster_i.last_update)) + value = 2 ** (self.fading_factor * (self._time_stamp - micro_cluster_i.last_update)) except OverflowError: continue @@ -256,12 +259,12 @@ def _cleanup(self): micro_clusters.pop(i) # Update microclusters - self.micro_clusters = micro_clusters + self._micro_clusters = micro_clusters for i in self.s.keys(): for j in self.s[i].keys(): try: - value = 2 ** (self.fading_factor * (self.time_stamp - self.s_t[i][j])) + value = 2 ** (self.fading_factor * (self._time_stamp - self.s_t[i][j])) except OverflowError: continue @@ -276,11 +279,11 @@ def _generate_weighted_adjacency_matrix(self): for i in list(self.s.keys()): for j in list(self.s[i].keys()): if ( - self.micro_clusters[i].weight >= self.minimum_weight - and self.micro_clusters[j].weight >= self.minimum_weight + self._micro_clusters[i].weight >= self.minimum_weight + and self._micro_clusters[j].weight >= self.minimum_weight ): value = self.s[i][j] / ( - (self.micro_clusters[i].weight + self.micro_clusters[j].weight) / 2 + (self._micro_clusters[i].weight + self._micro_clusters[j].weight) / 2 ) if value > self.intersection_factor: try: @@ -296,7 +299,7 @@ def _generate_labels(self, weighted_adjacency_list): # the DBSCAN algorithm proposed by Ester et al. for alpha-connected micro clusters # initiate labels of micro clusters to None - labels = {i: None for i in self.micro_clusters.keys()} + labels = {i: None for i in self._micro_clusters.keys()} # cluster counter; in this algorithm, cluster labels starts with 0 count = -1 @@ -340,7 +343,7 @@ def _generate_clusters_from_labels(self, cluster_labels): mcs_with_label_i = {} for index, label in cluster_labels.items(): if label == i: - mcs_with_label_i[j] = self.micro_clusters[index] + mcs_with_label_i[j] = self._micro_clusters[index] j += 1 # generate a final macro-cluster from clusters with the same label using the @@ -358,21 +361,26 @@ def _generate_clusters_from_labels(self, cluster_labels): def _recluster(self): # Algorithm 3 of Michael Hahsler and Matthew Bolanos: Reclustering # using shared density graph + if self.clustering_is_up_to_date: + return + weighted_adjacency_list = self._generate_weighted_adjacency_matrix() labels = self._generate_labels(weighted_adjacency_list) # We can only update given we have labels (possibly not on first pass) if labels: - self.n_clusters, self.clusters = self._generate_clusters_from_labels(labels) - self.centers = {i: self.clusters[i].center for i in self.clusters.keys()} + self._n_clusters, self._clusters = self._generate_clusters_from_labels(labels) + self._centers = {i: self._clusters[i].center for i in self._clusters.keys()} def learn_one(self, x, sample_weight=None): self._update(x) - if self.time_stamp % self.cleanup_interval == 0: + if self._time_stamp % self.cleanup_interval == 0: self._cleanup() + self.clustering_is_up_to_date = False + return self def predict_one(self, x, sample_weight=None): @@ -384,13 +392,32 @@ def predict_one(self, x, sample_weight=None): # exists macro-clusters closest_cluster_index = 0 - for i, center_i in self.centers.items(): + for i, center_i in self._centers.items(): distance = self._distance(center_i, x) if distance < min_distance: min_distance = distance closest_cluster_index = i return closest_cluster_index + @property + def n_clusters(self) -> int: + self._recluster() + return self._n_clusters + + @property + def clusters(self) -> typing.Dict[int, "DBSTREAMMicroCluster"]: + self._recluster() + return self._clusters + + @property + def centers(self) -> typing.Dict: + self._recluster() + return self._centers + + @property + def micro_clusters(self) -> typing.Dict[int, "DBSTREAMMicroCluster"]: + return self._micro_clusters + class DBSTREAMMicroCluster(metaclass=ABCMeta): """DBStream Micro-cluster class""" From 7fdf10728f0ae5ca992a65fca074c7b2f471338e Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 23 Jun 2023 08:26:06 +0200 Subject: [PATCH 029/347] tidy up dbstream --- docs/releases/unreleased.md | 8 ++++++-- river/cluster/dbstream.py | 22 ++++++++++++---------- 2 files changed, 18 insertions(+), 12 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index d5605708f2..9cf45acb08 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -5,6 +5,10 @@ - Added `bandit.BayesUCB`. - Added `bandit.evaluate_offline`, for evaluating bandits on historical (logged) data. +## cluster + +- `DBStream` will now only recluster on demand, rather than at every call to `learn_one`. + ## compat - The `predict_many` method scikit-learn models wrapped with `compat.convert_sklearn_to_river` raised an exception if the model had not been fitted on any data yet. Instead, default predictions will be produced, which is consistent with the rest of River. @@ -35,9 +39,9 @@ ## tree - Expose the `min_branch_fraction` parameter to avoid splits where most of the data goes to a single branch. Affects -classification trees. + classification trees. - Added the `max_share_to_split` parameter to Hoeffding Tree classifiers. This parameters avoids splitting when the majority -class has most of the data. + class has most of the data. ## utils diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 93b9853b00..7127254597 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -75,7 +75,6 @@ class DBSTREAM(base.Clusterer): relative to the area cover by micro clusters. This parameter is used to determine whether a micro cluster or a shared density is weak. - Attributes ---------- n_clusters @@ -111,11 +110,13 @@ class DBSTREAM(base.Clusterer): ... [4, 1.5], [4, 2.25], [4, 2.5], [4, 3], [4, 3.25], [4, 3.5] ... ] - >>> dbstream = cluster.DBSTREAM(clustering_threshold = 1.5, - ... fading_factor = 0.05, - ... cleanup_interval = 4, - ... intersection_factor = 0.5, - ... minimum_weight = 1) + >>> dbstream = cluster.DBSTREAM( + ... clustering_threshold=1.5, + ... fading_factor=0.05, + ... cleanup_interval=4, + ... intersection_factor=0.5, + ... minimum_weight=1 + ... ) >>> for x, _ in stream.iter_array(X): ... dbstream = dbstream.learn_one(x) @@ -128,6 +129,7 @@ class DBSTREAM(base.Clusterer): >>> dbstream._n_clusters 2 + """ def __init__( @@ -148,9 +150,9 @@ def __init__( self.minimum_weight = minimum_weight self._n_clusters: int = 0 - self._clusters: typing.Dict[int, "DBSTREAMMicroCluster"] = {} + self._clusters: typing.Dict[int, DBSTREAMMicroCluster] = {} self._centers: typing.Dict = {} - self._micro_clusters: typing.Dict[int, "DBSTREAMMicroCluster"] = {} + self._micro_clusters: typing.Dict[int, DBSTREAMMicroCluster] = {} self.s: dict[int, dict[int, float]] = {} self.s_t: dict[int, dict[int, float]] = {} @@ -405,7 +407,7 @@ def n_clusters(self) -> int: return self._n_clusters @property - def clusters(self) -> typing.Dict[int, "DBSTREAMMicroCluster"]: + def clusters(self) -> typing.Dict[int, DBSTREAMMicroCluster]: self._recluster() return self._clusters @@ -415,7 +417,7 @@ def centers(self) -> typing.Dict: return self._centers @property - def micro_clusters(self) -> typing.Dict[int, "DBSTREAMMicroCluster"]: + def micro_clusters(self) -> typing.Dict[int, DBSTREAMMicroCluster]: return self._micro_clusters From 90d0dd8c1ece347894a0c814edabfd8926fb13ae Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 23 Jun 2023 08:26:25 +0200 Subject: [PATCH 030/347] Update dbstream.py --- river/cluster/dbstream.py | 1 + 1 file changed, 1 insertion(+) diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 7127254597..684ae7c29c 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -3,6 +3,7 @@ import collections import copy import math +import typing from abc import ABCMeta from river import base, utils From ca29443c33cc26b2fb4f04bb6b6e607b24a9292e Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 23 Jun 2023 08:26:42 +0200 Subject: [PATCH 031/347] apply ruff --- river/cluster/dbstream.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 684ae7c29c..1fb380be5f 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -3,7 +3,6 @@ import collections import copy import math -import typing from abc import ABCMeta from river import base, utils @@ -151,9 +150,9 @@ def __init__( self.minimum_weight = minimum_weight self._n_clusters: int = 0 - self._clusters: typing.Dict[int, DBSTREAMMicroCluster] = {} - self._centers: typing.Dict = {} - self._micro_clusters: typing.Dict[int, DBSTREAMMicroCluster] = {} + self._clusters: dict[int, DBSTREAMMicroCluster] = {} + self._centers: dict = {} + self._micro_clusters: dict[int, DBSTREAMMicroCluster] = {} self.s: dict[int, dict[int, float]] = {} self.s_t: dict[int, dict[int, float]] = {} @@ -408,17 +407,17 @@ def n_clusters(self) -> int: return self._n_clusters @property - def clusters(self) -> typing.Dict[int, DBSTREAMMicroCluster]: + def clusters(self) -> dict[int, DBSTREAMMicroCluster]: self._recluster() return self._clusters @property - def centers(self) -> typing.Dict: + def centers(self) -> dict: self._recluster() return self._centers @property - def micro_clusters(self) -> typing.Dict[int, DBSTREAMMicroCluster]: + def micro_clusters(self) -> dict[int, DBSTREAMMicroCluster]: return self._micro_clusters From 636a9d75f0589b4c8e2166a78fa0c67ccd6ed3b6 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 23 Jun 2023 08:43:42 +0200 Subject: [PATCH 032/347] Update base.py --- river/datasets/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/datasets/base.py b/river/datasets/base.py index 63e234f0ba..53a5ba2f88 100644 --- a/river/datasets/base.py +++ b/river/datasets/base.py @@ -276,7 +276,7 @@ def download(self, force=False, verbose=True): try: n_bytes = int(meta["Content-Length"]) msg = f"Downloading {self.url} ({utils.pretty.humanize_bytes(n_bytes)})" - except KeyError: + except (KeyError, TypeError): msg = f"Downloading {self.url}" print(msg) From eb426dbfd2ae0f25622b7c867f618154ace3c6ed Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 26 Jun 2023 19:05:46 +0200 Subject: [PATCH 033/347] 0.18 --- CONTRIBUTING.md | 4 +- .../binary-classification.ipynb | 101 ++- .../concept-drift-detection.ipynb | 34 +- .../multiclass-classification.ipynb | 161 ++-- .../getting-started/regression.ipynb | 111 ++- docs/recipes/active-learning.ipynb | 34 +- docs/recipes/bandits-101.ipynb | 158 ++-- docs/recipes/cloning-and-mutating.ipynb | 237 ++--- docs/recipes/feature-extraction.ipynb | 2 +- docs/recipes/hyperparameter-tuning.ipynb | 2 +- docs/recipes/mini-batching.ipynb | 56 +- docs/recipes/model-evaluation.ipynb | 2 +- docs/recipes/on-hoeffding-trees.ipynb | 608 ++++++------- docs/recipes/pipelines.ipynb | 372 ++++---- docs/recipes/rolling-computations.ipynb | 826 ++---------------- docs/releases/{unreleased.md => 0.18.0.md} | 2 +- river/__version__.py | 2 +- 17 files changed, 1041 insertions(+), 1671 deletions(-) rename docs/releases/{unreleased.md => 0.18.0.md} (98%) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 310318ea09..75ee838599 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -43,7 +43,6 @@ You need a `Rust` compiler you can install it by following this [link](https://w Then, navigate to your cloned fork and install River and the required dependencies in [development mode](https://stackoverflow.com/questions/19048732/python-setup-py-develop-vs-install): - ```sh pip install -e ".[dev]" ``` @@ -66,12 +65,15 @@ You're now ready to make some changes. We strongly recommend that you to check o - Create and open a Jupyter notebook at the root of the directory. - Add the following in the code cell: + ```py %load_ext autoreload %autoreload 2 ``` + - The previous code will automatically reimport River for you whenever you make changes. - For instance, if a change is made to `linear_model.LinearRegression`, then rerunning the following code doesn't require rebooting the notebook: + ```py from river import linear_model diff --git a/docs/introduction/getting-started/binary-classification.ipynb b/docs/introduction/getting-started/binary-classification.ipynb index d58014261a..f4c8485f99 100644 --- a/docs/introduction/getting-started/binary-classification.ipynb +++ b/docs/introduction/getting-started/binary-classification.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:13.491348Z", - "iopub.status.busy": "2023-05-05T10:11:13.491157Z", - "iopub.status.idle": "2023-05-05T10:11:14.135580Z", - "shell.execute_reply": "2023-05-05T10:11:14.135257Z" + "iopub.execute_input": "2023-06-26T15:07:14.897961Z", + "iopub.status.busy": "2023-06-26T15:07:14.897697Z", + "iopub.status.idle": "2023-06-26T15:07:15.526269Z", + "shell.execute_reply": "2023-06-26T15:07:15.525980Z" } }, "outputs": [ @@ -67,10 +67,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.150653Z", - "iopub.status.busy": "2023-05-05T10:11:14.150484Z", - "iopub.status.idle": "2023-05-05T10:11:14.166766Z", - "shell.execute_reply": "2023-05-05T10:11:14.166473Z" + "iopub.execute_input": "2023-06-26T15:07:15.539054Z", + "iopub.status.busy": "2023-06-26T15:07:15.538918Z", + "iopub.status.idle": "2023-06-26T15:07:15.557006Z", + "shell.execute_reply": "2023-06-26T15:07:15.556748Z" } }, "outputs": [], @@ -91,10 +91,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.168845Z", - "iopub.status.busy": "2023-05-05T10:11:14.168695Z", - "iopub.status.idle": "2023-05-05T10:11:14.181753Z", - "shell.execute_reply": "2023-05-05T10:11:14.181370Z" + "iopub.execute_input": "2023-06-26T15:07:15.558598Z", + "iopub.status.busy": "2023-06-26T15:07:15.558510Z", + "iopub.status.idle": "2023-06-26T15:07:15.572706Z", + "shell.execute_reply": "2023-06-26T15:07:15.572396Z" }, "tags": [] }, @@ -128,10 +128,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.183763Z", - "iopub.status.busy": "2023-05-05T10:11:14.183634Z", - "iopub.status.idle": "2023-05-05T10:11:14.195635Z", - "shell.execute_reply": "2023-05-05T10:11:14.195263Z" + "iopub.execute_input": "2023-06-26T15:07:15.574423Z", + "iopub.status.busy": "2023-06-26T15:07:15.574288Z", + "iopub.status.idle": "2023-06-26T15:07:15.588592Z", + "shell.execute_reply": "2023-06-26T15:07:15.588310Z" } }, "outputs": [ @@ -162,10 +162,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.197651Z", - "iopub.status.busy": "2023-05-05T10:11:14.197498Z", - "iopub.status.idle": "2023-05-05T10:11:14.222280Z", - "shell.execute_reply": "2023-05-05T10:11:14.221984Z" + "iopub.execute_input": "2023-06-26T15:07:15.590305Z", + "iopub.status.busy": "2023-06-26T15:07:15.590186Z", + "iopub.status.idle": "2023-06-26T15:07:15.617959Z", + "shell.execute_reply": "2023-06-26T15:07:15.617699Z" } }, "outputs": [ @@ -201,10 +201,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.224123Z", - "iopub.status.busy": "2023-05-05T10:11:14.223995Z", - "iopub.status.idle": "2023-05-05T10:11:14.236012Z", - "shell.execute_reply": "2023-05-05T10:11:14.235664Z" + "iopub.execute_input": "2023-06-26T15:07:15.619511Z", + "iopub.status.busy": "2023-06-26T15:07:15.619403Z", + "iopub.status.idle": "2023-06-26T15:07:15.632831Z", + "shell.execute_reply": "2023-06-26T15:07:15.632554Z" } }, "outputs": [], @@ -224,10 +224,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.238006Z", - "iopub.status.busy": "2023-05-05T10:11:14.237847Z", - "iopub.status.idle": "2023-05-05T10:11:14.251868Z", - "shell.execute_reply": "2023-05-05T10:11:14.251490Z" + "iopub.execute_input": "2023-06-26T15:07:15.634423Z", + "iopub.status.busy": "2023-06-26T15:07:15.634337Z", + "iopub.status.idle": "2023-06-26T15:07:15.647906Z", + "shell.execute_reply": "2023-06-26T15:07:15.647649Z" } }, "outputs": [ @@ -258,10 +258,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.253862Z", - "iopub.status.busy": "2023-05-05T10:11:14.253717Z", - "iopub.status.idle": "2023-05-05T10:11:14.266336Z", - "shell.execute_reply": "2023-05-05T10:11:14.265953Z" + "iopub.execute_input": "2023-06-26T15:07:15.649382Z", + "iopub.status.busy": "2023-06-26T15:07:15.649298Z", + "iopub.status.idle": "2023-06-26T15:07:15.662866Z", + "shell.execute_reply": "2023-06-26T15:07:15.662615Z" } }, "outputs": [ @@ -292,10 +292,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.268352Z", - "iopub.status.busy": "2023-05-05T10:11:14.268210Z", - "iopub.status.idle": "2023-05-05T10:11:14.315175Z", - "shell.execute_reply": "2023-05-05T10:11:14.314795Z" + "iopub.execute_input": "2023-06-26T15:07:15.664436Z", + "iopub.status.busy": "2023-06-26T15:07:15.664350Z", + "iopub.status.idle": "2023-06-26T15:07:15.708866Z", + "shell.execute_reply": "2023-06-26T15:07:15.708602Z" }, "tags": [] }, @@ -338,10 +338,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.317346Z", - "iopub.status.busy": "2023-05-05T10:11:14.317194Z", - "iopub.status.idle": "2023-05-05T10:11:14.365770Z", - "shell.execute_reply": "2023-05-05T10:11:14.365345Z" + "iopub.execute_input": "2023-06-26T15:07:15.710419Z", + "iopub.status.busy": "2023-06-26T15:07:15.710315Z", + "iopub.status.idle": "2023-06-26T15:07:15.755660Z", + "shell.execute_reply": "2023-06-26T15:07:15.755417Z" } }, "outputs": [ @@ -377,10 +377,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.367819Z", - "iopub.status.busy": "2023-05-05T10:11:14.367651Z", - "iopub.status.idle": "2023-05-05T10:11:14.389332Z", - "shell.execute_reply": "2023-05-05T10:11:14.389052Z" + "iopub.execute_input": "2023-06-26T15:07:15.757334Z", + "iopub.status.busy": "2023-06-26T15:07:15.757229Z", + "iopub.status.idle": "2023-06-26T15:07:15.779551Z", + "shell.execute_reply": "2023-06-26T15:07:15.779263Z" }, "tags": [] }, @@ -415,6 +415,7 @@ " padding: 1em;\n", " border-style: solid;\n", " background: white;\n", + " max-width: max-content;\n", "}\n", "\n", ".river-pipeline {\n", @@ -486,6 +487,10 @@ " background-color: white !important;\n", "}\n", "\n", + ".river-wrapper > .river-details {\n", + " margin-bottom: 1em;\n", + "}\n", + "\n", ".river-estimator-name {\n", " display: inline;\n", " margin: 0;\n", @@ -554,10 +559,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.391330Z", - "iopub.status.busy": "2023-05-05T10:11:14.391065Z", - "iopub.status.idle": "2023-05-05T10:11:14.456114Z", - "shell.execute_reply": "2023-05-05T10:11:14.455726Z" + "iopub.execute_input": "2023-06-26T15:07:15.781043Z", + "iopub.status.busy": "2023-06-26T15:07:15.780931Z", + "iopub.status.idle": "2023-06-26T15:07:15.842108Z", + "shell.execute_reply": "2023-06-26T15:07:15.841859Z" }, "tags": [] }, diff --git a/docs/introduction/getting-started/concept-drift-detection.ipynb b/docs/introduction/getting-started/concept-drift-detection.ipynb index f70483d842..972a55389e 100644 --- a/docs/introduction/getting-started/concept-drift-detection.ipynb +++ b/docs/introduction/getting-started/concept-drift-detection.ipynb @@ -45,10 +45,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2022-05-28T14:07:39.576810Z", - "iopub.status.busy": "2022-05-28T14:07:39.576050Z", - "iopub.status.idle": "2022-05-28T14:07:40.450245Z", - "shell.execute_reply": "2022-05-28T14:07:40.450673Z" + "iopub.execute_input": "2023-06-26T15:07:17.338546Z", + "iopub.status.busy": "2023-06-26T15:07:17.338126Z", + "iopub.status.idle": "2023-06-26T15:07:17.728332Z", + "shell.execute_reply": "2023-06-26T15:07:17.727982Z" }, "jupyter": { "outputs_hidden": false @@ -61,14 +61,12 @@ "outputs": [ { "data": { - "image/png": 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yRESWi8iViQ8VamgGVyqsUJXZyV9v5rt9lQvu3P76At6avyVFUVXybkFIRSt/RTO9A5omRKSN9Tm6WkRWicjPrO1NRORTEdlg/W5sbRcR+bP1ubxcRPqk9xmoVIuYEUUkG5gIDAe6ATeKSDe/Yo8A7xpjegMjgZcSHag7lmQcVSnnifS/4N2/bIzhsSmrGPHiV5wuc68JP2ftXh7+MDGzCkZj9lqvcSwpyK0Oyt8ApcD9xphuwEDgbuuzdhww2xjTGZht3Qf3Z3Jn62c08HLqQ1bpZKdK2x8oNMZsNMaUAO8AI/zKGOAM63YjYGfiQqyUHWkNTaWqiVB94eH+Q4qKS+n88CfsPZq69d332DhXNF/M73h9ATNW7a44dqSmcc/LFG0/++YEzbgYDWPMLmPMYuv2MWAN0Ar35+1kq9hk4HvW7RHAG8ZtHpAjIi1TG7VKJzszsbUCvOcd3Q4M8CszHpgpIvcA9YFLgx1IREbj/qZIbm4uLpcrqmAPHUzdB48KLtq/WSYpKipybPye2HfsLAZgw4YNuEo2B5TbsMU98cj2HTtwufYDgQvwTJn9VcXtm/48g7UHy3ihoF7Q88b7eg14ZnbIx5YtXw7AwQMHbZ9n9trjzF67l045WRQeLqdTThaPDKwbtKzL5WL7MXeLw8kTx4OeI9i2BbtLmbi0uOJ+Ot43IpIH9AbmA7nGmF3WQ7uBXOt2sM/mVsAuVLWQqKlUbwReN8b8XkQGAf8Uke7GmHLvQsaYScAkgH79+pmCgoKoTjJt/zKW7dueoJBVLKL9m2USl8vl2Pg9sc8+vBK2bqFz584UDM4LKLf5q02wZjWtWrWioKA7YK18N2NaRZneffrA/K8B+HpnKRDk7zp9avDtdln7h5Ofnw+LF9KkaRMKCvpHddzCw+UVv8PFvnrnUfjqCxo2aEBBwdCgZfwtmLEW+K7ifoMGDVL6vhGRBsD7wH3GmKPerQfGGCMiUQ0fiLfiFNTZj1fejuF4YxqMiT+GKPg/5/vzSxN2rHSyk8B3AG287re2tnm7AxgGYIz5RkTqAM2AhF64/dj/ncu7CzWBq+orUktwpszEZsep0+4knMyIP1zi/rxYu/uY7X3SubiKiNTEnbzfMsZ8YG3eIyItjTG7rCZyz+eqnc/muCtOQY336kW9MXA540jumXxP/DFEYcV1vnOhjxoX+QtmKJtvLogzmsSx0we+AOgsIu1FpBbuQWpT/MpsBS4BEJFzgDpAwidjrl+72q69ohRgf4KWVI/2jsWLn7mX1A32paO0rJwyvwUDop345bfT17Jhb1HYMlf8YS7XvPilz7Z0fQcS9wvxD2CNMeYFr4emALdat28FPvLa/iNrNPpA4IhXU7uqBiJmRGNMqYiMBWYA2cCrxphVIvIEsNAYMwW4H3hFRH6O+/NilEnFNEtKVSOHT5Twz3nhL/2yk3yue/lr2+dcueMI3Vs1sl0+GkdPhl4opNPDn9C3XWPeHzO4Ylu0nygvu76LOPB13Z7Amnka2zCGAD8EVojIUmvbQ8AE4F0RuQPYAtxgPTYNuBIoBE4At6U0WpV2tqq0xphpuN8s3tse9bq9GvebTymVJEu2HbZd1sRR7562orIS98B/ljH9vqFhSld67atNtG5cj8u65UYuTOXgujlrg/e0+S+5G+oZXfbC53x87/nUrpEd8hx2TZr7Hat2Ho1qn0QxxnxJ6O8PlwQpb4C7kxqUymiOmxnllnN0bV5VPdmpGQYrE01j2JYDx/npW4ttl/f2+P9WRzVlaaiwQl16Fup5bNhbxNYDJ6I6RyjPTFvre626UhnMcQm8aV3nDNJxgv/cNSjdISibIg1QO3bqNPuKSnzuj5+yilOl5WH28uUZWOaxdvcxfj9zXdCyq3ceJW/cVAoj9DOHEqqVINSXgPIwybikzP5zVKqqcFwCV4mlc+M4R6Q/1UXPu/jz7A2Au+b5lzmFvP71Zt6evzXiscPV0v8ypzDo9inL3PM1eSZWiVaohHzoREnQ7eG6BUrLdMiNqn40gdtQp2bVfZnOqFMz3SEomyINUNtf5Jv4PEnt6WlrIh5737FiW+fwlm39W5SHqxqHEe0413DFS8vD18AHd2wa9vFnbbxGSmWaqpuZEuSGfq2ZEWIQzwOXd0lxNInXObdhukNQNkVzffJb87fy6lebbJc/bk1JGukMf5q1gcVb3YPLPMv7/v7T9bbP4y30dLCVUeywuY53pBb0SF9U/zZ3o63zKJVJHJfA4704rVHd6Gqc1/RsRbum9YM+lntGnfiCSQE7rQc59bQW7gTeteNEX6V50fMuW+X+MGs933/JfRlaVoj+lxfnbLB1rFDPwPt5Dpkwp+J2tCPKvW3YG3kil5kxdgUolS7OS+Bx7v/EiHOjKh+qSfG1UefFGUlw3z4UcLVIXHLqRh61r93gzhDs73S8uJRjp05z9V++SMw5bLahr9h+hKwQZZ+faa9GHiwh3/z3eWwJMaJ8/7HgfeN2fLcv8uIko/+5KObjK5UOjkvg8RrQPnxfmL9QH2eDO0V3HLtaJLhWX6uG75+4Xq3Aa2VDfRCrDOP1ZxIRNu4r4tzHZjD2X0tYuSMx1y7bfSv84G9fx706YLC+868KD4QsP/R3n8V1Po9Tp8OvYKaUU1SrBP6Dvq1p0bB2dDtZn1Ejz2vj84GVneKkZ/d0/p+p/k3oq58YFvOxVXqt3OE757RnmtDP1yd81uKIskUy6otfNKE89OGKyIWUcgDHJfBQ3WCdWjSIuO+E63qE7LcLxTOgZsJ1PfjumSsrttupfXz408ERy9iPo9K3D4duZm/gN1/8b67uFvHYus66Mzwzba3P/WT81eweM0sk7ksQ0zXZ8tIoZrRTKpM5L4HHsa9/ompoY3GUUN/s7fQVnn1mckZ4hxtR+8qP+vncb+7V4vDPO3yXbBzUwd0N8OYdA/jJ0A4Bx7LzpUilhzEmKTVgu33gIvG33Pj3gb+7YFuIkkqpYByXwBPhrEbufuYVj18Rsaz/h+Tk2/tz/2X2Lh/zv+xn7ZPDeGxQbH3c3jXrOjUD+7EBerfNYUAH3775Zg3cCfzeSzpzQefmPo+9PXog4L6U7MErzwk43pt3DIgpVpUaWQn+733JFXzCFo+dXpd0ZWdJ3DVo7y7we95ewq/eXx7zsezEsmzbYV6YuY4DRbEPhlMqkzh2fc7h3c/kk5XBL/tY/9RwujzyCQC1a2RRXFruU/t+/6eDWRakGa1OzayKqSTzWzVixY4jAbWMC7s058IuzQP2BRjR6yw+Wrqz4r7/vnVqZlMjxnbHNk3qRVxkoUOzwBpzswa1Wfbo5TSsU/mn/ujuIUGnnnx/zOCKlar+NLIXZzbK/MvkqisRSfja389NX8cX6/cHfays3DDY65Iu/9asvSHmL7frf8t2Ri4UpxETv0r6OZRKJcfVwO186ff+bFn31HCe/0FPZv68cjKWlo3qMqx7SwBqZlcW9u8/hvB9gv4foH8a2Ttk2ddvc192Vj/GS679P6t/EaQVwDMz1ponhtE/r0nF9kb1avr0/fdsk8N5Xo979G3XuOL2iF6tYgtUpcSJkrKk9IF/szH4KHD/tbn93/v9n5kd9bniua5bKWUzgYvIMBFZJyKFIjIuyON/EJGl1s96ETmc8EgtnhkTww1G8/9wub5vazo2D96fWzdEc7TnEOEqOd6TaTxzbX7IYwAUnN0CgCZ17H9nmjJ2CCPPa2O7vKdWVLdWNv+8sz+Lf3OZ7X2Vs/x2+lo27498bXOizFzt29qViHGPmr+Vik/EbCIi2cBEYDjQDbhRRHyGNhtjfm6M6WWM6QX8BfggCbECUGLVBOp5Jd65v7zIN+Yojtco4ixkkY/2/T6tuGlA2yB7Bt/3N1d346KzgzfDA7wzeiBv3TmAHq1zuKbnWUDoD7t7Lu5U0Vfd7axGFdtr18imSX1derUq2xDjKmCxGPuvJT73y8qhJIpVzoKJZ81ypZS9Gnh/oNAYs9EYUwK8A4wIU/5G4O1EBBdM24bukC/tllu5rWk9nzKemq8n+YXzrzsHcs/FnQK2e6ZcrZUd+iWK1AcZ6uE7zm/Pa7f1D/4gMLBDU4Z0ahb22N7O79yMqfeezy1BvkQolQz7i4pjngPdQ2vgDjS+kftHZQQ7g9haAd7Xd2wHgg5PFpF2QHtgTojHRwOjAXJzc3G5XNHECkCz7JP87dL61N63lpu71qJ9ThYul4vjx93TL/Zqns3nn3/OXy6uR90ah22do6u4P0ma1izFM4TnB61P0Dq7Fvs3LMZVGDwTr91xGoA9u/cEPc/nn39ecdvzeFFRUdiY/B9bfcA9a9SxY0U+ZTZtco+k3bxlCy7XLvf54vs8DRtHpO1OEOm1z2RFRUX4twbt3Jn8gV/J5N+vHoqdv9mSJUs4sSV4d1i8nPy+UVVbokehjwTeM8YEnavQGDMJmATQr18/U1BQEPUJXC4Xnv289663yAXHj/O7W4bEtMJWw3b76Nk6h55PzATgmisu4poI+xQt3wkrltCxXSsKCroDMKvbMca9v4KN+49zcUEBzJzmjtWK2Tt+pk8F3E3mIyfN8ynnUatwPyyYT/0GDeDY0YoyDdsf5MPCb7j5kr4M7JDAaV2tmPxjBKiTHRifk/i89g5z+8QZQKnPttwzW8I25147bbcCHuy96K937970b98kYrlYNGjQwLHvG1W12UngOwDvkVStrW3BjATujjeodBga4tKwcIZ3b8mvh53kR4PaVWzr1KIh742Jbga2sAnYqnR5BsydYV0O1rddE7575sqUzaJ2+5D2vD3f/vKUKrHmbCsN2LZwy6E0RJJ6B4+XRBzPccPfvuGju4foyHZVrdhJ4AuAziLSHnfiHgnc5F9IRLoCjYFvEhphBsvOEsYUdEzIsV66uQ+tcuoGPuD3eeTd756M5P3+mEG0aBj8+m/9bMwshSkcxJZOfZ78lPVPDY9YTq/zVtVNxARujCkVkbHADCAbeNUYs0pEngAWGmOmWEVHAu+YRC9U7HD/uLUfpTb6+q7Mb5mCaCLr2y7w+nDQBU9Ueq3ZlZjV1pSqSmz1gRtjpgHT/LY96nd/fOLCil2mJZpLzsmNXCgKqX5+3Vqewd5j8c2ypVS8Dp2oHtOfisirwNXAXmNMd2vbeODHgGfZuYesz2RE5EHgDqAMuNcYMyPlQau0cexUqlXBX2/pyxl1M/tPMO1nFwDw1Mer0xyJqs5GvbYgbedOcaPi68CLwBt+2/9gjHnee4M1H8dI4FzgLGCWiHQJNYhYVT2Om0o1lJ5tcgBoUDvGuUrTYFj3MxncMcL13hWD2Ny/ayR6BYsoaN+Iqo5S+b43xswFDtosPgJ3t2WxMWYTUIh73g5VTWR29S8Kz1ybz22D21e5BTjOauQe2HZVj5ZccroF1/ZOzxzlmdY1oVSqZMionrEi8iNgIXC/MeYQ7jk65nmV2W5tU9VElUngdWpmk9+66s0QlNesPl/++iLOalQ37PzvqZAZn2NKpVYGvO9fBp7EHcqTwO+B2+3unIgJtCqc/XjgthiON6bBmNhjiIH/c74/P/CyzFiPlU5VJoFXZa0b14tcKMkSvXSlUk6R7gRujNnjuS0irwAfW3dtzdGRiAm0KowPMov2Os9jR2wf5p7J98QeQwxWXLfC5/6ocbFP9rP55oI4o0mcKtMHrlIg3Z9kSqVBupvQRcT7GtNrgZXW7SnASBGpbc3T0Rn4NtXxqfTRGriyRevfqrpKZf4WkbdxzxLdTES2A48BBSLSywplM/ATAGs+jneB1bjn2b1bR6BXL5rAlW1aAVfVUSpr4MaYG4Ns/keY8k8DTycvIpXJtAld2aNVcFVN6RdXlam0Bu5n1i+GsmFP9ZhjOlr6Qaaqo+mbThN5JnalUk9r4H46tWjI8AyZlzyTiFbBVTX10Xen0x2CUkFpAlf2aRVcKaUyhiZwZYteBp4+u4/oYjJKqUCawJVtWgFPj+qy7rdSKjq2EriIDBORdSJSKCLjQpS5QURWi8gqEflXYsNU6aYV8PSpka2vvlIqUMRR6CKSDUwELsM9Wf4CEZlijFntVaYz8CAwxBhzSERaJCtgpaqbGmmeA18plZns1MD7A4XGmI3GmBLgHdzL2Hn7MTDRWiEHY8zexIap0k37wNOnRrb2dCmlAtn5ZGgFbPO6H2zJui5AFxH5SkTmiciwRAWoMof2gaeH1sCVUsEkaiKXGrgn0i/AvSLOXBHJN8Yc9i6UiGXtioqKMmo5t2g5Nf6tW0owxjgydg+nvvZbjur01unmxPeNqvrsJHA7S9ZtB+YbY04Dm0RkPe6EvsC7UCKWtXO5XMS1HF6aOTX+RSXrYGOhI2P3cOprv3rnUfj6i3SHUa058X2TDvmT89MdQrVipwl9AdBZRNqLSC1gJO5l7Lz9F3ftGxFphrtJfWPiwlSq+lq09VC6Q1BKZaCICdwYUwqMBWYAa4B3rWXsnhCRa6xiM4ADIrIa+Az4pTHmQLKCVqmnvbDp85v/roxcSClV7djqAzfGTAOm+W171Ou2AX5h/agqSgexKaVU5tDrU5Q9eh2ZUkplFE3gSimllANpAle2aP1bKaUyiyZwFRX3cAellFLppglc2aJd4EoplVk0gauoaAVcKaUygyZwZYtoL7hSSmUUTeAqKloBVyp5RORVEdkrIiu9tjURkU9FZIP1u7G1XUTkzyJSKCLLRaRP+iJX6ZCoxUxUFad94EqlxOvAi8AbXtvGAbONMRNEZJx1/9fAcNxrTnQGBgAvW79VEuWNmxqwbfOEq9IQidbAVZR0FLpSyWOMmQsc9Ns8Aphs3Z4MfM9r+xvGbR6QIyItUxKoygiawJUtWgFXKm1yjTG7rNu7gVzrditgm1e57dY2VU1oE7qKita/lUofY4wRkaj+DUVkNDAaIDc3N761zc9+PPRjLhdjGoyJ/dhJ5P+c788vTerxU0UTuLJF+8BVVfXc9T341XvL0x1GOHtEpKUxZpfVRL7X2r4DaONVrrW1zYcxZhIwCaBfv34mrrXNx48I/diNR7hn8j2xHzuJVly3wuf+qCD92PHYfHNBQo9nlyZwFRXtAldVTf1aGf8xOAW4FZhg/f7Ia/tYEXkH9+C1I15N7cpLsIFnVUHGv3NVZhCtgqsqKjuDRgKJyNtAAdBMRLYDj+FO3O+KyB3AFuAGq/g04EqgEDgB3JbygFVa2XrrisgwEVlnXW84Lsjjo0Rkn4gstX7uTHyoKhMY7QVXSfankb1Ser4erXNSer5wjDE3GmNaGmNqGmNaG2P+YYw5YIy5xBjT2RhzqTHmoFXWGGPuNsZ0NMbkG2MWpjt+lVoRa+Aikg1MBC7DPcpxgYhMMcas9iv6b2PM2CTEqJSqRs7La2KrXKucuuw4fDLu87VsVCfuYyiVDnaa0PsDhcaYjQBWf8sIwD+Bq2pA+8BVVaPdQ1Vfw3MCGo6DOrZmQpIjSSw7CTzYtYbBZvu5TkSGAuuBnxtjtvkXSMTlDEVFRWkbsp8ITo1/06YSAObOnUutbGd+4Dn1ta9uFn87z1a54uJTCTmfnfeEvm9UJkrUILb/AW8bY4pF5Ce4Zwu62L9QIi5ncLlcxHUZRJo5Nf7VFML6dQwdOpQ6NbPTHU5MnPraM71qjqAN5erLL2LsnMjP+cGre3D/f5bFfb6CgoKIr7Ej3zeqyrMziC3itYbWIIti6+7fgb6JCU9lCl2NrPoZ1KFp2s49anBexDJX5uusoap6s5PAFwCdRaS9iNQCRuK+/rCC3/y71wBrEheiyiTaB1599GjTiI/vOT8t5+7UokHEMt5d133a5kQsn64FJ5RKlogJ3BhTCowFZuBOzO8aY1aJyBMico1V7F4RWSUiy4B7gVHJClilh47zUZlsWPcz0x2CUilnqw/cGDMN96QB3tse9br9IPBgYkNTmUivA3eGGllCaXl6/lY9Wjdi+fYjKT1nMluG+uU6c8yHqvoyaA4ilcm0Au4s3i0m44Z3DXj8mWvzk3bu//50SMjHlvzmsqScM1n5+7JuudzVs3aSju4g4xu5f1RG0QSuolLV+8CNMby3aDsnS8rSFsPRU6c5eLwkYce7aUBbn/vDzj2TG/u3CVE6fllZob/uNa5fy9YxQnXZNKwTvNHQ877sbaMvPBqN69WkRpjno1Q6aQJXtlSXPvBvNh7ggf8s44mP0zdPUb+nZtHnyU/jOka4qwZ+Oexs25OXRPrCVrdmdsSkObBD+JnVomkNePvHA4Nu93Tt9G9vbxY3u7KqyxtfOZImcBWVKl4B53ixu+a992hiJgmJRUlpua1yl3RtEdPxOzaPPMLbLhF3Evc3+/4LKy4Fy2ta3+exvu0a+9wPNq5iSMdmAPx7dPCE7Tl3xTGsQyT6ckfN3yqTaQJXtng+GE2C29B3Hj7JH2etDzju3qOnOF5cGtMxv910kGenVV7JOH3lLgr3HrO1r+fz2hPNK3M3snDzwZjiiNYVf5jLU1HU/MMllw7N64d+MIF+OLBd0O0dmzfg7DMbBn2sTeO6PvfPalQ3oExes/psnnAVA6K8Fj3xCVczuMpcmsBVws1YtZvbX19Qcb+0rJy8cVP5xbtLA8qOeXMRf5y1gQ17iwB37XP6yl30f2Y21770VUznv+Fv3/C3uRsr7t/15mIufWGurX09CcDzheLpaWu4/q/fxBRHtNbtOcbfv9yUkGO9cEOvuI/RreUZ1Igwbe7ooR2iPq538/3mCVdRv3b8E0J6/l6JTrdaA1eZTBO4ssXzQTZv40H+NGsDZX6XKB0oKmbbwRMU7i3iJ/9cxJy1eyse6/TwJwB8sNhnAj8+W7uXZX6XG73w6XruenMxAOv3FIWM59ip0zz60UpOlpSx71gxD36wnG0HTwTEFY1Dx0s4edrdhG6A3Ueia0b/x5eb+N+ynVHtc7KkjLxxU31aDBKhQRxJsVHdmsy+/0JG9GpF1zMb8mCQUeweTRvU5t5LOlOvVuyXWkXTqtOmcb2K297N5R2sboFQE8C8+5NBQbf/567g2z10/FoUdJR6yiVqLnRVTfz4DfeSw+e0bMjl51ZOnjHo2TmUlNnru/W4zauW7vmc3Glzeci/zCnkjW+28MY3W+jVJoel2w7z9rfbGFPQMeQ+J04bXOv20qhuTXq3bRzweG+vgWNff3eAgc/ODiizdNth5qzdS+cWDbi6R0v2Hism9wz3cpRPWs3f/9fzLDbtP872Qye4oHPzsM9jf5F7BmLvFoNY/fWWPjw1dQ3bD0W3xOZl3XL5dPUen22efnIR4ScXduTZT9aG3H9gh6asfmIYeeOSO2d74dPDqZEdvM5xZX5LPrp7CD1aN+IX7wbOjx5qcFukpUt1CmGVyTSBq5jM33SQ3DPq0LNNDkDQ5L3t4ImQay2/OW+Lz/19RcUVzeiRHDt1mkleCW/ptsMVt2es3F1xe/rK3RSXVl4O5tp+mndnu780tMqpy8Sb+9DLit+f/0Cy02XlnDpdxvcmVjbrr951lJdd33F1j5Y8/4OeFduNMVz0vAuAFeMvp2GdmgD8/N9LEXE3bxcVl1IrOyuuFgN/w7q35OkgNfksES7o3IwvNuynTs3ABPjjCzoEJPBYPPZ/3QIGqNlh9xUIlbw9eob4W9pRr1Y2J4JcOqhN6CqTaQJXtvhfdvSPLzfxjy838cWvLqJVTuAgJIALnvssYNuJklLq1arBI/9d6bP9plfmBz1GSWk5p0rL6DF+Jq/ddh4Xnd2C304PXRvcuP94xe273lzk89i7605X3N5x+CR3Tl7AuOHncH3f1iGP53HjpHks3HLIZ9vn6/YB8PHyXXy8fFfF9q+/O1BxO3/8TFwPFNCqcV0+XOLuQvjd9T3p/tiMiOeMh8Hw7UOX8O3mgzSoXYPbhuTxxYb9SV2g5LYh7W2V88+JsY6LTGRynXN/QUCLy9Auzbn7ok6sWbw/cSdSKoG0D1zF5YLnPmPJtkORC1q6PTojqsFpXR75hB7jZwJw22sLKCktZ+0ueyPKI9lfVMIDNpej9E/eAFkh/nv8v2AUPO/i1+8tr7jf8aFp/ruEFO2o/8qrBaDFGXW4usdZUZ+jS274y8wGd0zfKmWxsLOy2ZlBWoreuL1/RfeIUplIa+DKlnCVnetejm6U9pKth2OO46dvLQ6aTOMRa9/tyh1Hg24PNg/4B0t2BCkZXt64qTSuVzPoY6FmB4u3VjqmoCN3DQ09jgDgXz8emND+7mTPrz/+mnOTenyl0kVr4MpRZq2Jv6/WSQ6dOB2w7Y7z2zP3VxeF3S/WlPjLy8+mUYgvDQAv3tQ7xiOHEWsTeojtl57Tgq4hrkFXqirRBK5s8VxepdKvb7vGnBVi3EE8FfDz8hqHncccqGiSP6flGbGfKMkDw/5+63lMv29ock+iVAawlcBFZJiIrBORQhEZF6bcdSJiRKRf4kJUmWDljtQuD6ni49+vbacrPZpLpj4YM5iFj1wabVgAXN8n8qBBpVRkERO4iGQDE4HhQDfgRhHpFqRcQ+BnQPDhxMrRgg3yUZnH7iIl8apbK5tmDcIvs3lhF/c18P6roQ3u1MznfqzN/Xaea/8I13kH89adA7jjfHsj6lNJRDaLyAoRWSoiC61tTUTkUxHZYP2O/jo+5Vh2auD9gUJjzEZjTAnwDjAiSLkngd8C6VsFQiVNsAUrVLqFTmD+STFYrrv0nNgWQ7HrrJy6bJ5wFT1a58R1nEeuOifkpYrhbHh6OG+HWQwllCGdmvGbqwPqKJniImNML2OMp5VzHDDbGNMZmG3dV9WEnVHorYBtXve3AwO8C4hIH6CNMWaqiPwy1IFEZDQwGiA3NxeXyxV1wEVFRTHtlymcGv+2rYlbn1rFZ9WqVdQ7sI79+32/K7tcLk6eOAHA/Pnfsq1B5ffz5XvdC8McOHiw4v13U1vDTW3rsXTpUgAOHzkc8b0Z73vXf3+Xy8Wq/WVhy3QCnh6YFbD9888r78caV7D9/Ldl+P/sCKDAuj0ZcAG/TlcwKrXivoxMRLKAF4BRkcoaYyYBkwD69etnCgoKoj6fy+Uilv0yhVPjX3x6PWzckO4wqrXh3c/kk5W7OffccynIb8mbWxbAvso55wsKCqi30AUnjtO/f3+fecHL1+6BxQtp2qQJBQX9fY5bf+MB+HYeOY1yKCgIMTf49KkV54iJ//5e97M37IOF3/o8D1vHurAAZkyLLq7pvpe/+ewX4jlm0P+sAWaKiAH+Zn2e5hpjPLMI7QZy/XdKRMWJsx+3VWxMrVrRHzuDlOXHtgJiur7g2UngO4A2XvdbW9s8GgLdAZfVJ3UmMEVErjHGLExUoCq9dFGHzJHgFV0dp1HdmpTH+CJc1aMlU71mzXOY840xO0SkBfCpiPjMGGSMMVZyx2973BUnxgfrNQ10T/u2kQtlsGNrJsS03+abCxIbiE12+sAXAJ1FpL2I1AJGAlM8Dxpjjhhjmhlj8owxecA8QJN3FXNLiHWfVYaJ4YuW3VTYMUlrjEebixc9cilLfnNZTOf6zVWVfdtv3jEgTMnMY4zZYf3eC3yIe3zSHhFpCWD93hv6CKqqiZjAjTGlwFhgBrAGeNcYs0pEnhCRa5IdoMoMzRrU5vVh9dk84ap0h1Khe6s4rkV2kJrZwrC8yslV7A00982K8a6qNe3eC3h/zOC4jhGKd6RN6kdugq2RnUWN7KyYZp3zXqTm/M7NwpTMLCJS37rSBxGpD1wOrMRdmbrVKnYr8FF6IlTpYOs6cGPMNGNMF2NMR2PM09a2R40xU4KULdDat0qERnVDzwgG0LB2+Me93R5koY0ercOvXzyoQ1Nyz6i8VKpNk+hHQifC/+45n5Fd7fUtenKaf632/M7NuHlAW579fo+Q+4TT7awzyKkXe//mqHNrce5Zwb9weV/h8N+fDon5HHYEW43NIXKBL0VkGfAtMNUYMx2YAFwmIhuAS637qppw7LtZZbb1Tw3H9UBBxHKhlhsFd23pweFdQz4erB+0g1cz7wWdm1Uk7mYNA5NPqITi8fbogcx/qHKykmn3XkDD2jV8BofZESrx253us2n9UNdbB6beUNdG18zO4ulr84Nez5+KLvWCNjWZeu8FQR87L68xz1ybz4rxl9O2ab2kxtHCoYuTWJfx9rR+zvWqSB0wxlxijOlsjLnUGHMw3bGq1NEErhKmp1eNtlaNLPKa1eeuC4MvjHHzgLZ8+/AlzP3VRax+4oqK7f+5q3IU9KnSMn4SYn+A3m0bs3nCVbw/ZhAPXN4FgIEdmnJ5t1zrHJX99qEW/wjlrTsD+0cb1qnJisevYNYvLgy53/DuZwZsC9V8PaB9k4BJTvy9cENPmjcMP2HK9X1bM/GmPj7bnDTOTUS4aUDbinXTo9lPqepME7iK2qxfDOWvt/Rl3VPDKrZtnnAV7ZoGDnLyrn1e3LUFr992HgsevpQnR3SnRcM61MzOol6tyoshzstrQr927smk6tcKfZHEjPuGViTtvu2aVMwKVlZm+NGgPAB6t83hml7uubuvODcwsXoq8Od7zQzWvpm7n39IJ3v9o/MfusSn1v/iTX2oX8t30hvv7w4/tAYD/uySzjxwxdk8/b3uPmVfvtk3EUdK3gCXd8vlqh4tgaRPM66UCiJv3FSfn1TR5URV1Dq1aEinFoHNv1f3aMmUZTt9tnnXPm8e0JaCs4PP/uV9ec/DV53DtS99TTurOfXa3q0o3FvECq/52M/2a372HPfmgW3p0TqnYrBd7hl1Km4/MqAOBUMG8Mv3lrFk6+GKBO7djB9tpS73jDp0at6AjfuO86eRvcjOEqbfN5R1u4/RuH5Nrnv5G0SE/949hBMlpQzu2Iwn/ZL29PsuYNgfvwBgeH7LiOe0M2o7pqusNPurMPIdfolYVaQJXCXM5UFquVf3bMlDH64AoDxMUpl4Ux8m3uS+3b1VIy49pwX3XequYf/h//UCwq/bfWajOhFHyHdqnE2nFg3ItrK0Zx1q77iC5bDJt/fn6EnfZT17tcnh+r6+i3LUruFu0GrTpB5tmtSjtKyc4d3PZExBx7DTiXY907cv/rnre/Cr95aHfS4APxzUjllr9tCzTeWx42pVdlK7u1JKm9BVcp1Rpyajh3YAICfMOtPeamZn8fdbz6N7q/CjxGOVZWW53m3dTfVX92hJC6up+vFrugeUv7BLc/6v51k+2/5795Ag18b7Zs8a2Vm8fEvfqOcCv6FfG59m/VAu7NKczROuItehA7OUUvHRGrhKugcuP5vBHZtyXgwrQ3l74/b+/OydJYy9uHNcx8myvra2a1qvotZe27q8qG2T6EdBn31mQ2au3kOLMyL3V4cy7d4L2H30ZMz7+zOxVKcd2oR+6yCdZEhVT5rAVVx6tsmh+LTvYhS92+b43K9VIytk33c0hnZpzpJHL4/7OFflt2TexoM+ybpHqxy2HTxJnVrRN0r97JLODO3SnD5tY1/JsdtZZ9DN67K21o3dg/+iHZn9xIjujJ+yirwgAwqroo3PXBlft4FSDqYJXMXlo7t9J96Y/9AlnBFl0km1Wwa247q+rX1Gvz//g57ccUF7WjSMvjm6RnZW3K0L/h77v3O5sEtzenn1b9sxsENTpt83NKp9erXJoXfbHB7N3CU0Q8rSSfpVNaYJXCWUE/pjRcQneQPUrZUdVw060erWyrY1Ij0R6tTM5sMkz4Dm77VR59G0gbNXrlIq3TSBK6VS7qKu8XepqBQZn5zBpCp+OgpdKVWt2JkcRykn0Bq4Ug6hg7US48tfX1Tt11RXwTU8Z1zEMrGuGZ4MmsCVUtVK7RrZkQsp5QC2mtBFZJiIrBORQhEJ+IoiIneJyAoRWSoiX4qI84azKqWUUg4SMYGLSDYwERgOdANuDJKg/2WMyTfG9AKeA15IdKBKKaWUqmSnBt4fKLTWoy0B3gFGeBcwxhz1ulsfnVVZKaWUSio7feCtgG1e97cDAYsli8jdwC+AWsDFwQ4kIqOB0QC5ubm4XK4ow4WioqKY9ssUTo7fybGDs+MvKiqi9GgxAJvWr8J1cF2aI4qOE177G7rUpLiMgDidELuqnhI2iM0YMxGYKCI3AY8AtwYpMwmYBNCvXz9TUFAQ9XlcLhex7JcpnBy/k2MHZ8fvcrn4850XMGPVbq7peRbisCHpTnjtQ4XnhNiTQq//znh2mtB3AG287re2toXyDvC9OGJSSgVRp2Y2I3q1clzyVkolh50a+AKgs4i0x524RwI3eRcQkc7GmA3W3auADSillFLVUN64qQHbPCsfJlLEBG6MKRWRscAMIBt41RizSkSeABYaY6YAY0XkUuA0cIggzedKKaUyT/7k/OAPtG+b2kBU1Gz1gRtjpgHT/LY96nX7ZwmOSymllMo4dmZrg9TM2KZzoSullINFmmhLVV2awJVSyqFsTrSlqiidC10ppZyrYqItABHxTLS1Oq1RqYCm9vzJgY0jK25dEdc50pbAFy1atF9EtsSwazNgf6LjSSEnx+/k2MHZ8Ts5dnB2/LHG3i7RgQQRcaIt7wm0gCIRSfcsQE5+LySUjBIIfD1sv2/SlsCNMc1j2U9EFhpj+iU6nlRxcvxOjh2cHb+TYwdnx+/k2MF3Aq1M4PTXM9HieT20D1wppZwr2om2VBWiCVwppZyrYqItEamFe6KtKWmOSaWIEwexZUxTUIycHL+TYwdnx+/k2MHZ8Wds7KEm2kpzWJFk7OuZJjG/HmKMrvyplFJKOY02oSullFIOpAlcKaWUciBHJXAnTBkoIptFZIWILBWRhda2JiLyqYhssH43traLiPzZej7LRaRPGuJ9VUT2ishKr21Rxysit1rlN4hIShazCRH7eBHZYb3+S0XkSq/HHrRiXyciV3htT/n7SkTaiMhnIrJaRFaJyM+s7U557UPFn/Gvv4jUEZFvRWSZFfvj1vb2IjLfiuPf1qAwRKS2db/Qejwv0nOq7iL9TUVklIjs83qf3JmOOFMl2GeV3+Ox5QJjjCN+cA/Q+A7oANQClgHd0h1XkDg3A838tj0HjLNujwN+a92+EvgEEGAgMD8N8Q4F+gArY40XaAJstH43tm43TlPs44EHgpTtZr1nagPtrfdSdrreV0BLoI91uyGw3orRKa99qPgz/vW3XsMG1u2awHzrNX0XGGlt/yswxrr9U+Cv1u2RwL/DPadkv/aZ/mPnbwqMAl5Md6wpfE0CPqv8Ho8pFzipBl4xZaAxpgTwTBnoBCOAydbtycD3vLa/YdzmATki0jKVgRlj5gIH/TZHG+8VwKfGmIPGmEPAp8CwNMUeygjgHWNMsTFmE1CI+z2VlveVMWaXMWaxdfsYsAb3rFpOee1DxR9Kxrz+1mtYZN2taf0Y4GLgPWu7/2vv+Zu8B1wiIhLmOVV3Tv6sTgobn1Ux5QInJfBgUwaG+8BIFwPMFJFF4p7CECDXGLPLur0byLVuZ+pzijbeTHseY61mqFc9TdBkcOxWk2xv3DVBx732fvGDA15/EckWkaXAXtxfer4DDhtjSoPEURGj9fgRoGm6YncAu6/Lddb75D0RaRPk8eokpveSkxK4U5xvjOmDe3Wgu0VkqPeDxt1e4phr95wWL/Ay0BHoBewCfp/WaCIQkQbA+8B9xpij3o854bUPEr8jXn9jTJkxphfumcv6A13TG1G18z8gzxjTA/cXqMkRyqsgnJTAHTFloDFmh/V7L/Ah7g+HPZ7mEOv3Xqt4pj6naOPNmOdhjNljfTiXA69Q2aSZcbGLSE3cye8tY8wH1mbHvPbB4nfS6w9gjDkMfAYMwt1s6ZncyjuOihitxxsBB8ig932Gifi6GGMOGGOKrbt/B/qmKLZMFdN7yUkJPOOnDBSR+iLS0HMbuBxYiTtOz+jgW4GPrNtTgB9ZIxAHAke8mk/TKdp4ZwCXi0hjq8n0cmtbyvn1G12L+/UHd+wjrRHF7YHOwLek6X1l9aH+A1hjjHnB6yFHvPah4nfC6y8izUUkx7pdF7gMdx/+Z8D1VjH/197zN7kemGO1joR6TtVdxL+p3/vkGtyvf3UWWy5I9ui7RP7gHqm3Hnd/1cPpjidIfB1wj7hcBqzyxIi7v2w2sAGYBTSxtgsw0Xo+K4B+aYj5bdxNnadx97vcEUu8wO24B/EUArelMfZ/WrEtt/4pWnqVf9iKfR0wPJ3vK+B83M3jy4Gl1s+VDnrtQ8Wf8a8/0ANYYsW4EnjU2t4BdwIuBP4D1La217HuF1qPd4j0nKr7T7C/KfAEcI11+1ncn5HLcH9x6prumJP8egT7rLoLuMt6PKZcoFOpKqWUUg7kpCZ0pZRSSlk0gSullFIOpAlcKaWUciBN4EoppZQDaQJXSimlHEgTuFJKKeVAmsCVUkopB/r/3tSwUuyuQI8AAAAASUVORK5CYII=", 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", 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", 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gUNvPi/nqediRmYOCWzzUZ+bAk3WoNtk2sr+T7CRv3759b7+ydc2tYsVxKivb86RvZ4G7be3H0bTd6YqO5yRLf/x4IWruqnZsUV5fVVWJzMxMSBgo/kbasF0PguZmPHTv9Z49eyDp8GPMERl3XzgeXeVgvWtDdn3funXL7J1yyhqAFd/lYtsl2X32SVybji1smazcjhw+gjvnDK+O5eq+4GKGIsKNiE0R1s4C4YDZRyf45JNPMH/+fISFhYHH46Fv376YO3euxuYHgHlnBhGLxcjJycHEiRMhFApxp6kV3+SXYWp0EHr7aA6Qv/yjFMAFlWVJSUkQCvhY/VsJPtun2tFixIgRGBjQGW8e1d5LNDk5GX9dvoN/nf5L8V7uxfxsAMDoUaMxpGcXxfIz2RfwW3mp7HUNH7d8wjF7ZC/wiirwbclJjftRlnnHDwcuV6O41RM/LVSfhUS+DZ8vAKSyWlbfbt1wQdQVgOxcW6U88HoNxaTBAVh+8ncAYrVja1N3V4xFf/2ueD9p0iS1ER9uHyoDSs8p9ivPV2RkJL4tKVJJq7ze398fB1pdkXmqAq8m9sd9fp0wIsRHLQ8drwcVje3NOxITE4FOnfQ6L3uktRyciN7lYKVrQ359d/ftjuTkGLMdp/BKNV78z1GVZfre17ZIXm6xsbEY2Uf9c0ATru8LLmYoIoS0MyiI9fX1hUAgUExPJ1dZWalxqrru3btjx44daG5uxu3btxEUFIRFixahT58+Go9jiZlB5Pt6Y0chfjtbhc1HrqJwSYLG9B9mX1BbJhQKIRTw1QJYAHBxEcBFj7wKhUIIBAKV9+r7clFZzuOrNmVeufsc+nT3hMi1PY2ucjpwUdYxpKi8Xmta5ToLHo+Pf3foFf2PbSdxOToY1Y3tj8nY9sc2JJlLh4qdlZnnsXLqYJVlfKVzVd6vPP/KTpa3j9TA5/PxQ4FsNIVlv5wFAFx+bzLe2X0G3h6uSHmgH1rbpFjx6xl0quMhme3aEnYoTycI7sw++46d0FkOVr42eDyeWf9OBdfUgy1HuC4ELgKjzoOr+8IRypAQW2JQxy5XV1fExMQgNzdXsUwqlSI3NxdxcdrnFHZzc0OPHj3Q1taG//3vf3j44YeNyzHHDl+SBUM1TYa3VdLWr4lhzDftLFuHqh8KrsJV0P7nlGgbOsFI+k4wUFHbjDHv78VnebIRAi7ebEDk8mys23dRNWGHLH57SL0Tmaaz2FFYrrastEPHuI4u32rEV3+U4sM9xQCA7w5fwda/rmF9MY23SYgz4NHwBIQ4FINHJ0hLS8NXX32FTZs24ezZs1i4cCEaGxsxd+5cAMDs2bNVOn4dPnwY27dvx6VLl/DHH38gKSkJUqkUr732GndnYQoTPtMYMBp76DNQn+yAjTHBJtsWkg4TH7RJ9RtzVhflEQA0BbGbOwSfa347j2t37uKDLFmwuPinItQ1t+G9X88p8vp53kUcu3pHbV9cYctqc1t7O2zZEGKGz4NNiC0w94x1jhrq6fOZTAixHwa3iZ0+fTpu3ryJJUuWoKKiAlFRUcjKylJ09iorK1N5BNzc3IzFixfj0qVL6Ny5M5KTk/Htt9/C29ubs5MwlJQBbta3IMhHaPKHtam1rQOXZGFqVJCJuZCdk/K5PL4uH/5ebvhq9jCT963L4h2qbVPbOgTm8skS5H4suIr3s9g7AnZkbPmy/ThQroUxVy05IYQQQizDqI5dqampSE1NZV2Xl5en8v7+++/HmTPmGZDfWCuPC1B9aB9+fC5O49Sx/d/8Fa0SKS68MwlCAXuF9Ws/nsTbHdpwqtAjUGptk+L7o9fUN9UQZZ26VosDF26pLZdKGfxZ0r785LVaALW6M2AAfR/FdRx1TPktwzC4UGncLGMRy/bonTb3HPvkG4p8wPy1WcQxXLvTBD9PN7i6GDXBIbEh1JyAEMfidJ/KN2qbUd0i+yB7P+scalmmNT106TZa700BO2v9YY2D6u8sLNc4M5SpEwF0JJUy+O7wFUz59wGcYRnIX8ow+GK/egczrvOhj45BufIPBSljWPCo3Cyivln7ED+GnGvOmQp8nndRd0Li1I6UVmPM+7/jsXUHrZ0VQgghHZh9iC1bI6uhlPnrMnubTOUOQocuVeOrPzTPU36t5q7GdaaEjx3jse3Hr+PNn4rYEwOQaDiYoTHsP/57HNc1nJOxNZfKNbNSAzK053QF3s3Ur9kBoLu8E9e0T+v73OZjeu+XOK/vj14FoPq5QayvtU1KNeOEEOcLYvUJojJP3VB5f0rbF5im4BGmtbvsuOmpazVa0/9Vys1c6j+fUO/1byqekUHs/31bYNBxXvvxpEHpCSH2p7iiHolr9uOZ0aFYMiXc2tkhxGJCFu22dhZsjtMFsfr4o0ObU201kLs7BLwKJgSwJ67W4OOc83odX+6umH0GNC4bE9xuaDVqO75ycwJuBk0ghJiRpr4CtmD1vc/GDX+WUhDrTJbdm/BnGT0VMYS2wPfye5MtmBPzcLrnMcYMn2rs57kxw7kUXa/Fw2v/xP7zN9v3wxj/pcJlm9iGFsOnnWyTSFWDWD3zY422vIQQ/bW0SZD2fSF+McPTG0II0YfTBbG6giO29cb2aDUmDjt06bbasmYNtaxc01U2xsTR/zt2DXVKneekLDN3sblZT2O4EmLLvjtUhu3HruOF/x63dlYIIU7K+YJYXevZEhhZE9vUanjwyXb8pT+fNi4DMKw5ga6g25hieP1/p1CvVIN7rqJeS2qgqbUNzWIJDUlOiI271WB/PzRtuJUEIcQITtcmVlug9uymo+jZ1V1tuTGfewwYPPnVISO2VHeuoh4j+3QzaltDaoN1Peq/fLvJqDwoe3xdPv4+rKfG9eFLZGPBjh/Q3eRjEULMh35oEkKszQmDWM0fvb+drWRdfvCi+iN+3ccBqox4JK6pHa0x7VEBzR2+2Pxy0jJt29gmd+gor/imzjSEEKILta8nxHE5XXMCYzp2VTca1yvfGBerGlmX/1igO/BjE7k8W++0L287YdQxCCHEUqhJACFEzumCWGNGDDDGf/4oNWq7bfcGVyeE2B+plHHomr+dhddRdF02xJEDnyYAqsElxB44YXMCyxwnn2WUAUKI42qTSJG4Zj+CvN3x7bxYa2eHcwdLbuHFrYUAZONLWqpCwFTGfObnnKnEaz+ewMePRXCfIeJUIjYZfg2dmnPKDDlxTE5YE0s6Clm0GylbaBpWQkxxurwOF282qk2WYg8uVNbj5xPlWmsfdY0sYg/0bYkw/5ujuNMkxjPf0OciIbbMCWtiKYxls/ukhpnHCCE2h+sZtSau3g8A8BS54IEwP073TQgh5uJ8NbEUwxJCzMCSHY7M9WP81L32rnqdix1+ltphlgkhWjhdEGvM6ATE8TW1GjeEGSHOyl4+Su0ln4QQwzldEPvNoSvWzgKxQT8dp/nfiWmMnZ7aqGNZYZwpWxnaypR82MgpEEI44nRB7NU7d62dBWKDqLaGEPsN8q7cbsQzG//CX5errZ0VQogFOV0QK7CV6gRiU6xRs0UciyNdQno1ibWhDgbPf3cMe89V4fF1+WrrbCmfhBBuOV0QS8EKYUNXBeESBU6WdbW6Sa909PlPiGNxwiDW2jkgtohPFwZhYexVYa8xrCG3gS2do75ZoR8XhDgWo4LYtWvXIiQkBG5uboiNjcWRI0e0pl+zZg0GDBgAd3d3BAcH4+WXX0Zzc7NRGTYVhSqEDcWwhI2xIQ/bdvvP38Qnv12AVGmIlGaxBA99+gdW/HLGyCNZjvItUlXXbFvtyLVkxqbyyYGMjAwMHz4cnp6e8PPzw9SpU1FcXKySprm5GSkpKejWrRs6d+6MadOmobKyUiVNWVkZJk+eDA8PD/j5+eHVV19FWxuN0kLsi8FB7LZt25CWloalS5fi2LFjiIyMRGJiIqqqqljTb9myBYsWLcLSpUtx9uxZrF+/Htu2bcMbb7xhcuaNQTVuhA2fLgvCIbYav9kbjmD1b+eRWdQ+sUhWUQWKrtdhw5+llsyeTmyP3c/cqFO8nv9tgSWzo5NUzxpWR2hOsG/fPqSkpODQoUPIycmBWCxGQkICGhsbFWlefvll/PLLL/jhhx+wb98+lJeX49FHH1Wsl0gkmDx5MlpbW3Hw4EFs2rQJGzduxJIlS6xxSoQYzeAgdtWqVZg/fz7mzp2L8PBwrFu3Dh4eHtiwYQNr+oMHD2L06NF48sknERISgoSEBDzxxBM6a2/Nhe90DSgIIZagHB9pC6l+LarA+gOlqGsWo82OBq7+/ug1xesTV2s42299s9igx/xsQ5nZTymaLisrC08//TQGDRqEyMhIbNy4EWVlZSgokP2wqK2txfr167Fq1So8+OCDiImJwddff42DBw/i0KFDAIDs7GycOXMGmzdvRlRUFCZNmoSVK1di7dq1aG1ttebpEWIQg6adbW1tRUFBAdLT0xXL+Hw+4uPjkZ+v3isUAEaNGoXNmzfjyJEjGDFiBC5duoTMzEzMmjVL43FaWlrQ0tKieF9XJ6sBEIvFEIvFhmRZjSXHciT2QyKRqF9bYjGEipdiwMRrz5bJz93U+8vedSwHqVSqtu7eG7VrQ/lRbGurGHBh/8W8++QN7D55A8evVGPsfd3Y968LIzXL32rL4TJMHuwPiUSiM61yGmPzkn/pNmZ/XYCZI4KxbMpAAEBxRT26dnKFn6eIdRu2v4lyENwxL61t7enb2tq05rWptQ2ny+vVlnNV1ub4m9XWymZZ8/HxAQAUFBRALBYjPj5ekSYsLAy9evVCfn4+Ro4cifz8fERERMDf31+RJjExEQsXLsTp06cRHR3NeT4JMQeDgthbt25BIpGoXPgA4O/vj3PnzrFu8+STT+LWrVsYM2YMGIZBW1sbnnvuOa3NCTIyMrB8+XK15dnZ2fDw8DAky2rErQJQy1jS0enTZ5B567TKMkFzMx6693rPnj2QuLlZPmMWlpOTY+0s2AR5OVy7xof8gVVmZqZiPdu1cb0RkH+kZmVlscSwqh+3uWduoEvTdQACAMDu3Zn48hwfYimQEi7V0E5bto+qqpsq+TGdbL/ltc1IWLMff+slVeRLk9LSy2ArG0OsPiX7PP7uyFX0bi3FrjI+TlbL9vlJHHv7zBsV6n+Ttrb2z/WOeZHFsLLzO3QoHze1ND9eUyRAab16wXN1XzQ16TeKgr6kUileeukljB49GoMHDwYAVFRUwNXVFd7e3ipp/f39UVFRoUjD9j0uX8fGnJVLWvE7fO5ydCwR2H8k2QJN5SkScPvMwdqVFlwc36Ag1hh5eXl499138dlnnyE2NhYlJSV48cUXsXLlSrz11lus26SnpyMtLU3xvq6uDsHBwUhISICXl5dJ+XmnKA91YnpcQlQNGhSO5BG9VRcqtTFLTEwEOnWycK4sRywWIycnBxMnToRQKNS9gYPqWA77thfhyE3ZbG7JycntCVmujbM36vHBSdkTqcSkJIg6RLEv5mervBcKhRgSOQDfXZT9eLo/PgEvHdoLAIgafT96eLur5U++j+7duyM5OcbEs2XPm1jKw4CwMODyBa3bhISGYF9FGYAOZWOAr68dBhpkNYnvFqp+HWnaZ1bdCRTerlRJ8+pfv8mjVbXtWtuk+Ofh3wAAI0fGYXhIV4356fg3kuPqvpAHflxJSUlBUVERDhw4wOl+2ZizckmryC9V33P04+0tb/b4wxZo+lH4wQjLHMdSuPhRZ1AQ6+vrC4FAoNbLsbKyEgEBAazbvPXWW5g1axaeffZZAEBERAQaGxuxYMECvPnmm+CzNFIViUQQidR/JQmFQpM/SEb37YafCm/oTkicikAgUL+2lN4LhUKV946Ki3vMEcjLQfnzSaVcWK4NF5f2j1MXFxcIhdprMnk8HgQC1W1Ut9f8d+Dz+Wb9OwkE2vMuz4OcsXnR1tFK0z7ZjqvcZKDjdgyvfZ2uctWWFy7Km8u/WWpqKnbt2oX9+/ejZ8+eiuUBAQFobW1FTU2NSm2s8vd0QECAWr8U+fe6pu9yc1YuaZXRU/V9+jX2dAaK2xLHyX7MIf9J9uaZg5ft4fQ4RcsSOd2fobj4UWdQEOvq6oqYmBjk5uZi6tSpAGSPM3Jzc5Gamsq6TVNTk1qgKv+AtMaYfa8nDaAglqih4SMJG0MaHpna8V35ErR2L3r9Zuwy/ThG7cPAomEcrNsXwzB44YUX8NNPPyEvLw+hoaEq62NiYiAUCpGbm4tp06YBAIqLi1FWVoa4OFngFhcXh3feeQdVVVXw8/MDIGsy4eXlhfDwcNbjmrNySStph+E4OTpWC1p0J7ISTeXZIuH2c8HaFRZcHN/g5gRpaWmYM2cOhg0bhhEjRmDNmjVobGzE3LlzAQCzZ89Gjx49kJGRAQCYMmUKVq1ahejoaEVzgrfeegtTpkzR69c+13w8qJaJEKIfo8eJday4yWYdvHgLI0J89E5fe1eMv3+Rj8eG9sTfhwebMWfmk5KSgi1btmDnzp3w9PRUtGHt0qUL3N3d0aVLF8ybNw9paWnw8fGBl5cXXnjhBcTFxWHkyJEAgISEBISHh2PWrFn44IMPUFFRgcWLFyMlJYU1UCXEVhkcxE6fPh03b97EkiVLUFFRgaioKGRlZSkahZeVlanUvC5evBg8Hg+LFy/G9evX0b17d0yZMgXvvPMOd2dhAGvXcBBC7INEyuB42R2906sOsaU7iq29K0Zji3GDy99t1T16gC0or7mLrX9dxVOxveDnpd4x0tSP4ye/Oox/PNhP7/T/yr2AU9drcaS02m6D2M8//xwAMH78eJXlX3/9NZ5++mkAwOrVq8Hn8zFt2jS0tLQgMTERn332mSKtQCDArl27sHDhQsTFxaFTp06YM2cOVqxYYanTIIQTRnXsSk1N1dh8IC8vT/UALi5YunQpli5dasyhCCHEKj7cU4yLNxt1J2Shb03s0p/bR8RQ3kZXbHfkcjVqm8ToYuNPlmatP4yLNxux//xN7EgZrbaeixrrbw9d0bpe+Rh1zfY/hJw+zfDc3Nywdu1arF27VmOa3r17W71jDyGmoqH/CSGExbp9Fw1Kz+UY1B1rKBtb2lBwpVpl2eHS20bv/+cT5Vidc15jQKRPDanytsc01FjLfwQUGjE5wms/nsBbO4p058PgPavbcfw6fjrOTYchQojlmH2ILVv0j0Ft+Ndppzx1Qoie+DzA2Am1uG4SO+PLQzh1vZaz/f3jv8cBAKP7+WJEqP5tSpUpn+PTG47gJMc9neUzhC2aFIZOIs2f1zVN+teuXq1WH9KnqbUNL20rNDh/hBDrc8qa2L5mHA2EWMfKqYNN2r6Lu20/liWWZ0r7eaNGXlFpTqB6bLYAtvRWo8rQUppIpQyu3WEfj/FWAzc9tFtY8nGugpsxUcUS3eeoL7YfJS1i7vZPCLEspwxiiWPxFLkgMdxfd0ItunV25Sg3xBlVN7aioaW9RlAeK7296wze+OmUXvswdCiojF/PYcaX7ONJKnv5+0KMef937Cy8rrZOYmxVsx7e/5V9FkdDtXYIYrnumkt9fQmxXxTE2qilU9jH6rOUx2J66k5kK3jcP74lRN/Ypqm1DUNX5mDa5+0BJcMALW0S/OdAKbYcLkN5zV2Djq1vQHusrEZnmp2FshnH/r23RG2dVFObWD2OrauymauRYNokdHcTQthREGujXNUnXrcovh3VTvB5PBqXk3BO3xjs+h2WAJVRDfLKWNpiarPjeLlB6fXBFrBqCmL1YalJBEwNYjWdYu1dWc05lx3yCCGWRUGsjRrg72nV4/Pt6Bkbjwd0djOtox4FwaQjU4KbFokEN+vb25vO+PKQzm2Ur8F956uMPrYmbC0HOGxuqkav2lw90oil5slk5PJsbD92jfv2CYQQi6Eg1sYkDQrAG8lhGKZlFhqfTq6YHdfbrPmwt0khOotcsGBcH2tngxAAwISP92HsB79b/Lg5Zyrxxk+n0NKmPhlC6a1G3O7QkcukmlilTdn20vEjpNaAUQSUSc3Ybvf1/500274JIeZHQayNmRXXGwvG9dWaZtx9vrjPzDW1lm5O8MCA7mrL+vl11mtb+ZfpmH6+XGaJODsT7oH6ZsNn4lIO1Q5dqlY87jbE/G+OYsvhMmw+VMa6/oOsYpX3jS1taoEtAI56O6nuI3JFto4U5qGt2QPDAAcu3LJALggh5kBBrI1h+1CfNlS1k9WI0G5m//Bna06QOMi0EQC0YWv29vdh+nUuM2o4I0J00PceM9fV923+ZaO3rahl70jWMTBe/ssZxLz9m9HH0aSyrtmkWl5TGDLiQpuUwSYTypkQYl0UxHLowTA/vWsPNWL55lR+NDi4hxemW2DOb7aa2HVPxZitwxlbIGroU0RTKo8s1UmF2A9N15NYIsXd1vZ7cv43f3FyvI73gClP0b/6oxTZpyvUlut7jyz75azONMrZUx6v9vCl24h9Nxd7z2lv18swjFEzeelyoMSwmtUjpdW6ExFCbBIFsRxiGAab58WatA+2ziTNSoNxTxvaEwILPOtnaxPL4/EgEpjnkmGrtRFqONbQXt4q7yn8JOagqWPXgx/nYejKHMX7qrpWTo73f98WcLIfuQUc768jTRWt3xy6otf2P58wbgQGXe31xR0mXqAHNYQ4LgpiOTQsxAcBXdxM2gfb57NI2P5nstSoARqPY6bDs3VAdhOqX56fzxyKjEeHsO6DhsohXGrT0Cv+arVhY77q6+iVO2bZr636U88aU1Nj0Nkbjpi4B0KIraIglkXaxP4Gb7Py4UGYP9b03vHKYdia6VGIDPbGm8kDFcuMqYQ15nw0HcdcYSJbTay7UKC2bFJEIDxc1ZcTwiWplIHYyoPsm+Ne4/Y3sGnlY4kf5AzDoMDJfhwQ4kwoiGXx/HjtowOwmRUXwkl7Ub5S9Dg1ugd2poxGkLd7ewIjPvjnj+2DYB933QmVaGqyYEhThsieXZCf/qBeadke+amct9aNZf/pKpoELVPT0iNHouyuWH2IKqLK1HtG32H8TDkO3deEODYKYlkYOkZqdIc2miYd28T1bNxdBQY/atdUBoYEsS4CPgK76A5Ee/l4sNbEKi8aFOSlcSrccf1lw3OFB3ppPU5sn24680II4LjtrLlscsMWINY3i/HH+Zt6bW9PswISQmwTBbEsDP1sHd/fT+O6ZVPCDTu2joOH+nYyaH+a9tvbxwMA8OG0wQblw5Ag9u2p7PvuaGfKaEjYglilUGL3P8bio8cj1dK8FH8f3n00AgDQtZMrjrw5AaeWJbAeh4biIvakmWXCAlvS8Ydna5sUczYcQZ2WMXKV70F9mxOYMnII3fGEODanD2LDAtQnDej42fpodA+VdN09RXrte+x9vnh6dKjOdI+r1DCyf7Cvnh6JVxMHYFRfbmoTN82NwSdxbZgcEcC6XtPXi8CAWmp9hhvbmTIaXTu5sg4nFBbAXrPqptRW9rn7+6KLu1Dx3s/TDZ5uQrbN4OrCx56XxunMEyG28Htn7e8X8W6m7qGu5I6XWbft5/gPf8exshqtaZTL1Vw1sV8fLMX+8zfx8L8PYGfhdfMchBBiE5w+iB3Ss4vaso6P0ldNj8L6p4cr3s8eqX3K1x0po/FIdA98+Jh6zaHc5CGBitczdewPAB6J7omUB/rpbOrwZGwv1uUdv5R1fUl3PEzSIFmwyzfgm0eflPKa3UFB6gGrTydXHFz0IAqXTFRZ3t1ThH882A+vJg5QCWiVbXpmhMr7Yb274vGYYAR4mTZ6BCGW9OX+S8g9W6kz3eFLt/HIZwd175DDwLHjR0h5bbNB2xjbJlbXVn+W3MbsDUdw4lot0r4/odcxCCH2yemDWH3biCmnmh0Xgh0po+HvJauRTRqsWpsZFeyN1dOjFMNtiVg6fC2bMoh136bUThxdHI+VD7M/wu/46K97Z1et++pYLvLvGxdDgth7G80bo7k2Wv5IcdGkMCxk6VAX5O0Obw/1vKYlDEDKA/007vf+/qrT2P64cBTcaVQDYofmbTqqM83+C/q1Q9198ga2H7tmapYAGFdbbUxzAkII0cTpg1hj8PiyQHXfqw8gP/1BDGBpkqCsB0sve+XPb9XXxn+w+3bW3MxB+QvnxJIEiO7VYGoKSjtmQ/5euSZ2RIiP1vzIk/ppaX4hr4n1chPi9aQwfHyv3esnM6K07ttYNDMXMZYtD9VkSEDIVe2kMfeS8hb6/h62haYdhBDbZFQQu3btWoSEhMDNzQ2xsbE4ckTzYNLjx48Hj8dT+zd58mSjM80lF4HxQaObUKBX73s2PJXXPNbl+goL8MR79zo3adpeuQaki0d7m1Eej4cx/Xz1PpabS3tt5tqZQ/GPCfeptElVxhaQR/bsomiaAKh/kU2L6Ynzb0/Cw1E99M4TIdxTj5ymfX4QtxtarJAX7W43tODTvSXWzoZeVNrE0vAEhBATGRzEbtu2DWlpaVi6dCmOHTuGyMhIJCYmoqqKfZ7s7du348aNG4p/RUVFEAgEePzxx03OPBc6Dpz/1kOGjSbABU21svrKemkcZoyQtYXV9MWgbR72h+61z/VWDm475vHeklXTIxHYxQ3vT4tAd08R0ib21388VwA7U8fg309GK96zjXbAxXi7mmhqPkK1PUQfFXW6231a2qLtp6xzYGOaE8CyoxMQQhybwdHCqlWrMH/+fMydOxfh4eFYt24dPDw8sGHDBtb0Pj4+CAgIUPzLycmBh4eHzQSxyrNCDfD31Np+01gPKXXiktPUbMBcU6eyjcMq9/iwYPxn9jBkv6zUc1+tPYHsv7AAL+SnT8D04ewdyPThIuAjfqAfRoT6IKSbcUOGGUtgQs07cR729KPm5LUajeve3nXGbMfV9pmiiT2VKyHE9hkUxLa2tqKgoADx8fHtO+DzER8fj/z8fL32sX79esyYMQOdOlk2eOkoro+sPec0peGtND0WB4BuSh2hPDT0iNfkhQn34YtZMSo1jCrNCZTeuLvq9ycxtMZW23eHgM9DfLg//Dw199zXdjhdWWE79n/mDMf3/xdn8UeKnUUuFj0ecSy2GIRp++H7nwOlZjuuMUXx+7n2J3b61rDaYpkTM1vWpf0fIVoY9I1+69YtSCQS+PurTt/p7++Pc+fO6dz+yJEjKCoqwvr167Wma2lpQUtLe9uzuro6AIBYLIZYLDYky2rk23/55BCIGR483YT41/QhWLe/FO9ODWfdv1gsBh/AoUXjwecBjFQCsdSwgcgf7N8NAV4ilFXfle2zrf04bW1tSIvvh9uNrejd1U2vc2xTGghdV3qxWAypUnsC5XLUtC3T4fwYKaMxraYaGXl6iUT/vJqLzjJqa1NPIxZDqHgpBqyUd0vQdT04C/n5t2ooh7Y2zQP5W491orw2idTgbYqu1yA+TNYGX6rn9m0d7k2p1PDjmoqr+8LZ7y9CuGbRaqn169cjIiICI0aM0JouIyMDy5cvV1uenZ0NDw8PTvKyN/c3lff/FwKcPpyH04olsqIZ3FWKzMxMTo45vSewoVGAKb2k+C3nN8Ux/jxwAL07Ab0BZGZe0mtfRRU8ALIa4Y75G9yVj6I77TW6mZmZaG4RQF5nqpw+Jyenw55lebpw4QK8hHzUiWXb3LhRjsxM9qF56uva961MfpxSLXk1r/bLW/W46pd9QUEB6ktUgwFBczMeuvd6z549kLg5/hiz6teD87nVDGRs2wf5NavswIEDsPDHpk7Nzc3gdABYPZWXl8PQFmklF0qQ2XIeAHDxCl+v7Q8cOIArSvOmlJfrtx2XuLovmpqaONkPIUTGoE9jX19fCAQCVFaqDr5dWVmJgAD2mZ/kGhsbsXXrVqxYsULncdLT05GWlqZ4X1dXh+DgYCQkJMDLi30WJ32JxWLk5ORg4sSJEAo1Nx94MT8bABA9IATJyWEmHVPZgnv/194V442jvwMAxo4dyzpzmDZdL93GD6UFAIDk5GSVdUlJDCrrW7DlyFWMva8bRoT4YNmJ39F4r/Y3OTlZYznIz7t///74eF4oBi2XBftBQUFITh7CmpfPS/OBpnq15fJ8PSiWoHzzcYy9zxfJY0IMOk9TyM9FOS/KyyeEdUfBlRrU3BUjJiYGYztOH9zYqHiZmJgIWLkJjDnpe184OrFYjPAVv2tcP2bMGHx06pAFc6Sbh7s7alot3+Hs+G3DA8l+9/VD8gTZ+M6n9pzH3vLLOreJGj4SsaHtw/n91ngSx25XGHxsU3B1X8ifKhJCuGFQEOvq6oqYmBjk5uZi6tSpAGSPdnJzc5Gamqp12x9++AEtLS146qmndB5HJBJBJFIfW1QoFHL2BavvvgR8vlm+1IVKT5VcXFwMPsbY/v5495EI9PfvzLptL5ErFiW3j7SgPDqBcnpN5eAiEKCTe/vfQCTUnEdNndTk6YVCIbYsiNN+QmbgKuCjVSJFz67urHnv7CZEd09X1NwVw8VFoJ6mQznBCYI7Lu8xR/TzSd2zZ1maPjNl2YoNf17Bq0kDAcj6U+jjqQ1H8UpCf0T09Ianm4ve23GJq/uC7i1CuGXwc7G0tDTMmTMHw4YNw4gRI7BmzRo0NjZi7ty5AIDZs2ejR48eyMjIUNlu/fr1mDp1Krp168ZNzu2dicNq8Xg8jVPMsjG0J7E8S0seCsfmw1fwauIAnWltzY6U0Vj7ewn+mdCfdb3yED/UeYToY8Of5uso5QzuiiXIKqpQm+VQl4+yzytePxwVxHW2CCF2yuAgdvr06bh58yaWLFmCiooKREVFISsrS9HZq6ysTO2XcnFxMQ4cOIDs7Gy2XTollbFhLRAGThvaExsPXkZM7656pZfn75kxoXjGDMOOWUJ4kBfWzhyqcT0Plil7Qki75zYX4PJ7k1UmYCGEEGMY1UMhNTVVY/OBvLw8tWUDBgygDywrWzQpDLGhPhil5+xchkx/a69/WVOm+CWEmIa+EgghprJ84yICQPM4sebiJhRgUkSg1rFwnUWne7O0PRDW3co5IcQ5SaSMWcewdXT79+/HlClTEBQUBB6Phx07dqisf/rpp9Wmek9KSlJJU11djZkzZ8LLywve3t6YN28eGhoaLHgWhJiOglgbYEv1gcPuNTf4W6TjtjvLe/UBfDtvBCZHtM+kRpVChFjO5H/9Ye0s2LXGxkZERkZi7dq1GtMkJSWpTPn+3//+V2X9zJkzcfr0aeTk5GDXrl3Yv38/FixYoGFvhNgm2xrw0InY6qPsbf8Xh8bWNni56V9ja5tnoll3TxG6e8pqYW30z0CIQztXoT4kn77olgUmTZqESZMmaU0jEok0Dn159uxZZGVl4a+//sKwYcMAAJ9++imSk5Px0UcfISjIcSsxiGOhmlgdwoNMG5dWE0s3J9CXgM8zKIAlhBBie/Ly8uDn54cBAwZg4cKFuH37tmJdfn4+vL29FQEsAMTHx4PP5+Pw4cPWyC4hRqGaWA12/2MMjl6+g8eG9rTA0WwoijVCkLcbztyQDeK96ZkReO7bArw3LcLKuSKEEOeUlJSERx99FKGhobh48SLeeOMNTJo0Cfn5+RAIBKioqICfn+rkLi4uLvDx8UFFBftEEuacDl4NX8vsiBwdSwT1sehthabyFAm4bfhm7WmQuTg+BbEaDArqgkFBXcy2f56J48TaknceiQCfV4Q5o0Iwup8vTi9PBJ9vXydFPaUJsQ8/nyi3dhZs3owZMxSvIyIiMGTIEPTt2xd5eXmYMGGCUfu0xHTwCpFfal7H0dTlb3m/xcl+zEHT9OwfjLDMcSyFi2mYKYi1Ekcan9Tfyw1fzm5/LGVPAaz95JQQAqjOPkj006dPH/j6+qKkpAQTJkxAQEAAqqqqVNK0tbWhurpaYztac04HryZDyxPQ9GucHCJui+VnkdRX/pP5rMsHL9vD6XGKliVyuj9DcTENMwWxNoACKUIIIeZy7do13L59G4GBshFZ4uLiUFNTg4KCAsTExAAA9u7dC6lUitjYWNZ9WGI6eAWplqmUOTpWC1p0J7ISTeXZIuE2WrD2NMhcHJ+CWCtRbU5AYSwhhBD9NDQ0oKSkRPG+tLQUhYWF8PHxgY+PD5YvX45p06YhICAAFy9exGuvvYZ+/fohMVFW8zZw4EAkJSVh/vz5WLduHcRiMVJTUzFjxgzbH5lgmVIzv2W11ssHsQk0OoENoBDW+hgaKZYQYieOHj2K6OhoREdHAwDS0tIQHR2NJUuWQCAQ4OTJk/jb3/6G/v37Y968eYiJicEff/yhUpP63XffISwsDBMmTEBycjLGjBmDL7/U0haVEBtENbE2gCpirYgKnxBiZ8aPH691Kvc9e3S3nfTx8cGWLVu4zBYhFkc1sYQQQgghxO5QEGslyj+iHWmkArtFrQkIIYQQu0JBrJUIlIah8nSjVh3WQj8fCCGEEPtE0ZOVuLrw8eWsGLRKpOjaydXa2SGEEMIi+xoPydbOBCGEFQWxVpQwiH1QaUIIIbZh91UB1lg7E4QQVtScgBBQk1hCCCHE3lAQS5wajbBFCCGE2CcKYgkh5J7LtxutnQVCCCF6oiCWEELu2X6s3NpZIIQQoicKYgkBtM5+Q5yHi4DalxBCiL0wKohdu3YtQkJC4ObmhtjYWBw5ckRr+pqaGqSkpCAwMBAikQj9+/dHZmamURkmhEvUJpYoE/Dpdz0hhNgLg4fY2rZtG9LS0rBu3TrExsZizZo1SExMRHFxMfz8/NTSt7a2YuLEifDz88OPP/6IHj164MqVK/D29uYi/4QQwhkXPv2qIYQQe2FwELtq1SrMnz8fc+fOBQCsW7cOu3fvxoYNG7Bo0SK19Bs2bEB1dTUOHjwIoVAIAAgJCTEt14QQYgbUnIAQQuyHQc/OWltbUVBQgPj4+PYd8PmIj49Hfn4+6zY///wz4uLikJKSAn9/fwwePBjvvvsuJBKJaTknhEPUIpYAVBNLCCH2xKCa2Fu3bkEikcDf319lub+/P86dO8e6zaVLl7B3717MnDkTmZmZKCkpwfPPPw+xWIylS5eybtPS0oKWlhbF+7q6OgCAWCyGWCw2JMtq5Nubuh97R+Vwz73ota1Nol4WYjGEipdiwIHLiq4HGR518CMsuLovnP3+IoRrZp92ViqVws/PD19++SUEAgFiYmJw/fp1fPjhhxqD2IyMDCxfvlxteXZ2Njw8PDjJV05ODif7sXfOXg51dQIAPBw/fhytl1UDGEFzMx6693rPnj2QuLlZPH+W5uzXw7kKHgCBtbNBbAxX90VTUxMn+yGEyBgUxPr6+kIgEKCyslJleWVlJQICAli3CQwMhFAohEDQ/sUwcOBAVFRUoLW1Fa6urmrbpKenIy0tTfG+rq4OwcHBSEhIgJeXlyFZViMWi5GTk4OJEycq2ug6IyoHmS8v5wON9YiOjkZ8eIdruLF94PvExESgUycL585y6HqQuZ1/GSg9b+1sEBvD1X0hf6pICOGGQUGsq6srYmJikJubi6lTpwKQ1bTm5uYiNTWVdZvRo0djy5YtkEql4N8bvub8+fMIDAxkDWABQCQSQSQSqS0XCoWcfcFyuS975uzlwLs3xpZAIFAvB6X3QqFQ5b2jcvbrwcWFamGJOq7uC2e+t8wlYlOEtbNArMjgQRHT0tLw1VdfYdOmTTh79iwWLlyIxsZGxWgFs2fPRnp6uiL9woULUV1djRdffBHnz5/H7t278e677yIlJYW7syDESDROLFF2/GqttbNACCFETwa3iZ0+fTpu3ryJJUuWoKKiAlFRUcjKylJ09iorK1PUuAJAcHAw9uzZg5dffhlDhgxBjx498OKLL+L111/n7iwIIYQDPx2naWcJIcReGNWxKzU1VWPzgby8PLVlcXFxOHTokDGHIsQiqE86IYQQYl9ojkXi1Kg1ASGEEGKfKIglhBBCCCF2h4JYQgghhBBidyiIJQQAQzM1EUIIIXaFglji3KhRLCGEEGKXKIglhBBCCCF2h4JYQgghhBBidyiIJQSggWIJIYQQO0NBLHFqPGoUSwghhNglCmIJIYQQQojdoSCWEEIIIYTYHQpiCQE1iSWEEELsDQWxxKnxqEksIcTO7N+/H1OmTEFQUBB4PB527Nihsp5hGCxZsgSBgYFwd3dHfHw8Lly4oJKmuroaM2fOhJeXF7y9vTFv3jw0NDRY8CwIMZ2LtTNACCGEEP01NjYiMjISzzzzDB599FG19R988AH+9a9/YdOmTQgNDcVbb72FxMREnDlzBm5ubgCAmTNn4saNG8jJyYFYLMbcuXOxYMECbNmyxdKnQ6wkZNFujesuvzfZgjkxHgWxhBBCiB2ZNGkSJk2axLqOYRisWbMGixcvxsMPPwwA+Oabb+Dv748dO3ZgxowZOHv2LLKysvDXX39h2LBhAIBPP/0UycnJ+OijjxAUFGSxcyHEFBTEEgKAoUaxhBAHUFpaioqKCsTHxyuWdenSBbGxscjPz8eMGTOQn58Pb29vRQALAPHx8eDz+Th8+DAeeeQRtf22tLSgpaVF8b6urg4AIBaLIRaLuT0Jvpt+6cRiiCDi9tg2QFN5igSW+6Li/G9qpmNQEEucGjWJJYQ4koqKCgCAv7+/ynJ/f3/FuoqKCvj5+amsd3FxgY+PjyJNRxkZGVi+fLna8uzsbHh4eHCR9XaRX+qXLjMTb3m/xe2xbUBmZibr8g9GWD8PXGpqajJ5HxTEEkIIIUSr9PR0pKWlKd7X1dUhODgYCQkJ8PLy4vZgGT31zNQ1xG2J4/bYNiD/yXzW5YOX7bFYHoqWJZr9GPLafFNQEEsIAIYG2SLEaWS/PA4Jq/dbOxtmERAQAACorKxEYGCgYnllZSWioqIUaaqqqlS2a2trQ3V1tWL7jkQiEUQi9Uf3QqEQQqGQo9zfI23WL51QiBa06E5nZzSVZ4vEcs8OOf+bmukYFMQSp8ajMbYIcTp8B77tQ0NDERAQgNzcXEXQWldXh8OHD2PhwoUAgLi4ONTU1KCgoAAxMTEAgL1790IqlSI2NtZaWSf3aBs1gKiiIJYQQoiTse8otqGhASUlJYr3paWlKCwshI+PD3r16oWXXnoJb7/9Nu677z7FEFtBQUGYOnUqAGDgwIFISkrC/PnzsW7dOojFYqSmpmLGjBk0MgGxK0ZNdrB27VqEhITAzc0NsbGxOHLkiMa0GzduBI/HU/knH6eOEEIIsTR7r4k9evQooqOjER0dDQBIS0tDdHQ0lixZAgB47bXX8MILL2DBggUYPnw4GhoakJWVpfLd+9133yEsLAwTJkxAcnIyxowZgy+/1LNDFSE2wuCa2G3btiEtLQ3r1q1DbGws1qxZg8TERBQXF6v1dpTz8vJCcXGx4j09wiW2hobYIoRbbyYPxDuZZ62dDVbdOtn3sEzjx48Ho+VDi8fjYcWKFVixYoXGND4+PjSxAbF7BtfErlq1CvPnz8fcuXMRHh6OdevWwcPDAxs2bNC4DY/HQ0BAgOJfx6E/CLEW+jlFiHnMH9fHqO3G9e/OcU7UdfEQYnAPjnvUE0IszqAgtrW1FQUFBSqDKPP5fMTHxyM/n31ICEDWfqd3794IDg7Gww8/jNOnTxufY0IIIQ7LU2SZrhqxod0schxCiPkY9Glx69YtSCQS1kGUz507x7rNgAEDsGHDBgwZMgS1tbX46KOPMGrUKJw+fRo9e7KPBWfOmUHk21tiNgpbRuUgI38kJ5FI1MtCLIZQ8VIMOHBZ0fXgnLp3dsXNhlaz7NvYa0kqlXKcE3Visdig43B1X9D9RfThOXCRwdvUn33PDDmxfWb/yRsXF4e4uPbBiEeNGoWBAwfiiy++wMqVK1m3scTMIDk5OZzsx945ezncqREA4KHwxAngWqHKOkFzMx6693rPnj2QOEGHRGe/HpxtwBZZZYF5GtXIZvwxvDwrKm7AyD7HesvMzETpZb7ex+HqvuBihiJCSDuDPmF8fX0hEAhQWVmpsryyslLjAMkdCYVCREdHqwwP0pE5ZwYRi8XIycnBxIkTLTKYr62icpDZdO0wSutrERk5BMlDeqiubGxUvExMTAQ6dbJw7iyHrgeZF/OzrZ0Fi3Jzc0Od2DyDxScnJyOzthB7zlTpTqwktFdPHL9dbpY8ySUnJ6Pw12Lk3biiV3qu7gsuZigihLQzKIh1dXVFTEwMcnNzFePNSaVS5ObmIjU1Va99SCQSnDp1CsnJyRrTWGJmELPMMmKHnL0c+PfG2hEIXNTLQem9UChUee+onP16INwRCoX48O9R2LPMsB8Gr08aiO3HzRvECoVC8Hj61/ZydV/QvUUItwx+1pOWloY5c+Zg2LBhGDFiBNasWYPGxkbMnTsXADB79mz06NEDGRkZAIAVK1Zg5MiR6NevH2pqavDhhx/iypUrePbZZ7k9E0IIcRLeHkLUNJnevtLcox16uRketPl7OX6zHUIINwwOYqdPn46bN29iyZIlqKioQFRUFLKyshSdvcrKysDnt//CvXPnDubPn4+Kigp07doVMTExOHjwIMLDw7k7C0JMpG3MRUJszZ+vP4htf13Fil1nrJ0Vm9PdU4RPZkThya8Oa01Hw5UTYv+M6sWQmpqqsflAXl6eyvvVq1dj9erVxhyGEEIIi04iFwR5u1vseLGhPjhcWq13+pVTB3Ny3KAubvhu/kg88FGe3tssnjwQMb27alz/t0iaVpUQR2HeLqCEEEJsFk/PkQkCuxj2iN/fk5sZsaZEBcGnk6tB2yQO0t7JWD7lLD18IcT+URBLCCEcMDTYMp3jR2H6BtmGbOf4pUaI86AglhBCOGBsE8vkCP2GJzQHHg/4bOZQnenko3hYgzXbro7t1w0vD26zXgaczbIusn+E6ImCWOLUeNS7g1jAn4se1Lhu7ZO6g0hzSRocgOSIQL3Snl2RZObcsDPmDtXnttYnzYY5MQjxNCIDhBCLoCCWEGKSmqZW5J6tRJvE/NOFckkq5fbBsra99dDQCWvXC2OM/iHFRZvOl+L7653W3VWgd1pDshbqq3kSER5P9Yfm/43rY8CetaM2sYTYPwpiCQF9oZni71/kY96mo1i376K1s6K3nYXXEbk8G39cuGnVfAzuYZlHp68mDsCOlNFqy0Uupn8FBJg4rutvafdrXNcxvH9qZG+99knPVwhxDhTEEqdGX3amO1/ZAAD4+YR5Z1ni0otbC1Hf0oY5G47ovc3kIYFIeaAvZ3l4YkQvk7Y3pAKXz+MhpJuH2nL5j7fHY3qqrVs6VL+2oG5CPrq4657UoIu7ENG9vNWWC3S0t6V7lBCiCQWxhBBO2GNttiEtCgQ8HrzdNY9AYOiEGeGBlmtsqSvgfffRCOxIGY3Oovahw31YRska1bcb6/Ydz1357RvJYQCAT5+IxnfPxmrNxwB/9TJRzjs1YSeEKKMglhArOVZ2B39d1n8AeS7sOlmOHcevqywrr7mLq3eaTN63chizs/A6TlytMXmfXDpw4RYe+/wgSqrqjdre1gIoQ2LmAQHsATNz768mFPARFewNXYMQsDU/eDDMX2sb2AXj+qL47SSM699d55BZHUdB4PGM+3FEHTYJcQ4UxBIC48aOlEoZFF6tQUubRG3dvvM3seuk5sfrrW1SPPrZQTy+Lh/1zWK19ddr7mLq2j/xC4eP6JvFEqRuOY6XthWipqkVACCRMhj13l48uOoAWtRPwyDy2riCK9V4cWshHl77p6lZ5tRT6w/j6JU7WLj5mFHbGxIWrXx4kB47tFygNV6PAFIbTdt+8NgQvJY0QOf2IhdZpzBjTlmqFMXyKTglhCihIJY4NVO+E7/64xKmrv0TKd+pBkUFV6oxZ8MRpG45jhu1d1m3bVXqyV/f3N72sKapFSlbjuH+D35H4dUavPDf48ZnsIM2pWfnTa2yiFWslI8G9VjaIPJY48K9NrK2gGEYVNY1qyy73dhq1L4Mqd2bFRdi1DG0eexeu9VnRocCALw99J9cgauaSeX9+HZ2xd+HBcNNKGD5Fcj+s1BXNjo2S+gYPOs7dJa2ZL181NsGE0LsEwWxhACouSvGsbI7+CDrHBpb2Du0tEmkqKqXBUQlVfX49+8lAIDfzlYp0jSLJZj2eb7i/e0G9YDp93NVSFy9n/UY72edw+6TN1QCTn3caWzFqz+cwJF789s3trThhf8ex6+nbrAOfcVF81WJlIFEKZ/yV42tplXp7jpZjmc3/YXauyZG1QDe+KkIse/m4seCa4plxoZzurYT8M37cZryQD/8/sp4vPXQQADAyD4+Bg051dnNRaXNK2D4o3pD2/121DEojQz21rmNi1K5ymt0NXl76mCdaRaO565zHrEymhjB6bnoTkKI41v2y1nF68aWNix/eLBamulfHkLBlTt466FwrNx1hnU/DR0CYLZgdO7Gv1TeK9culdc0wxgrd5/B9mPX8UPBNbzwYD/weDz8cqIcv5woB48HfDVrGEaydMpRjkl2XuFjlp7Hk0oZJK3Zr5J3hmHw770X8FH2eY3bXbzZgM4iF3Tr5AoXAXvQl7pFVvv8ae4FLH4oHIDsB4Sm9Nr890gZAOCVH04olhldKclrb0OqbFjvrvAN8MGV24241dBi5M71OjxClMZU5fF4SE8eiC/2X9JrewGfh4K34lFe04wHPsoDwN0UrPruR7nJ66i+3bDh6eEq69lqjN1dBVg8eSAkUkbn1L66huBaMK4PPFzpa48QR0E1sYR0cKyshnV5wZU7AKAxgAXUa+skHYLY1jbzTAiw68QNxetP95bganV7Ry2GAZ795qhaXgDVoOxENR/1zWIkrdmPkEW78f3Rqyi91ajS5EDudmMrLlQ1KIbXAoDLt5u0BrA361sw4eN9iH03F+FL9yCr6AbOVdRh2c+ncZsl+Ku+99j/es1dRCzLxpKdRWpppFIGFyrrDawhNC6K1dQudFFyGFZPjzL4kf3IUB/W5UcXxxucN32JXATwUWqG4GLgdLK6hsOS0/TnUC6jtIn9ZU0RtJAnf3ZsH/zf/frXoFLTWUKcAwWxxKmxfdddud2IpTuLcKFS/17sJVX1aJNIcVes+ihduVNKbZMYQ5bvYTleE349dQPfH71q1Jfvt/mXVdrYAmANPO8otQX9Yt9FHL1cjYc+PaCS5vN9pThXITvv1348iQc+ykPa9ydgirutElTVNauUZ2ubFM9tPoakNX9g48HLiHn7N+wsvK7ShGD7vVEUvtp/CXfFEnyTfwUVtao11St3n8HE1fuxOkcWPEulDMpuNymOyyVZW0v9/0D/d7/qo/7d/xijsq/7WIaTAgDfzixjW3Goi4cQnz4Rjc9nDtUZRHakqWOVvj8ibCm2DPWltrGE2Dt6rkJIB3XNbdiUfwXf7yuGvJFBU6v2gd/jV+1HWICnIgCUU6793HOmAs1i9eByxpeHFK/ZxslU3tfz3xXAt7MI7zwSoVj+1s7Taml/O1uptmz8vUfIAPBN/hV8k39FLc1XBy6rLfvlRDmmDe2BHt7uGgMvbQYuyQIAvPBgP63pXtxaqHNfIzNysX7OMEwY6A8A+PpPWX7/tbcEaQkDsOTnImw+VIbhIV3x1+U7rPswtpZO12Yd178c3x8361qQMEiW10FB7e337vPrbFwmODIlMsio7TTVxOpbD67S/IRtPxwNNqypVlx56RMjemHf+VuKe6WfX2eUVLU/WRihoaacEGI7KIglRA8fZJ3TmaZjAAsA3+Zfwba/ruL1pDB8nF2scx93mtQ7gh0vu4N1+y4iokcX7Dkt+8JNjgjE6H6+GvfDFiyb4umvZe14L783GQB721A2ymPSfrq3xODjltfcxa6TN1SWzdt0FIOCvPDV7GEqy4+V3cHmQ7I2sJoCWMCEjl0GbugmFGDV9CjWdeaeGMLDVaAYgYJLHcdx1UTT6SkHl/qUgSFF7uVm2NeZi4CPfyb0VwSxysc6ujgePh6ukEj0m7WMEGIdFMQSp6bvl/K2v64BrobPEb/7lCwA+6nDBAOaVNWrtw195LODAKAIYAHgn9+fQNZLY83akYhNU2sb/iy5jT7dO+lODOClbYUmHW/8h3lqTSUA4HR5HV7/30mVZY/eKyddOpZxs1gCkQsfx8o0B76AYU0JzKGrjk5Nyr7/vzi1piJc0NSG1pignKtaVwDo3c0D6+cM05lO3yO6CwXg83mQcP87gBDCIQpiiVNjm4HIHlTUNWPh5mPIv3TbosdN/uQPXL5t+uxe+mILYOX+uHCLk2OEvZWFqGBvFOqYYcyQIJJLD0cFYc6oEHRxF+qVfkKYHwb3MM/QQwIOe0zpFVDqebz3pw1BPz/TpvGlzmCE2B/7/AYnhCOuRgzbZCssHcACsGgAa0naAlg+D4gf6IeUB6wzvugbyQMxtFdXvdJ6ubkgPXmg2fKid3MCPSJULptU6Bt/dkynnAflmnYKaAmxD1QTS5yaroHRCflbZBDWzIjWmc4cgc+a6VHw99KvGQuPBxQuSdA70DSGxuYERow4a8w2AHDkjQm4XnMXkT290eeNTKP2wSaubzcUGzAiCSHE+uy3GooQDly945g1i8QxuAkN+4hWDmDlQ3zNGB7MWX5UA2TDOmmpMbJjl5+XG6J7deUkWFf+4TFjBHflRAixDKOC2LVr1yIkJARubm6IjY3FkSNH9Npu69at4PF4mDp1qjGHJYRzx6/WWjsLhHCiYyD5WmIYfkkdg7enqs8+Z6wHBvgpH1FzXvSIUM08QAO7DnGvcplx2d6XEGIZBgex27ZtQ1paGpYuXYpjx44hMjISiYmJqKqq0rrd5cuX8corr2Ds2LFGZ5YQrvl00q+zDCGA9uDMlNELEsL9jd5WEwGfh4ieXQyarndIT2/FtmziB/qxLjeqIpbDKNZFoGfZazlmF4/2zwKhHbeVB4Bly5aBx+Op/AsLC1Osb25uRkpKCrp164bOnTtj2rRpqKxUH1uaEFtn8J26atUqzJ8/H3PnzkV4eDjWrVsHDw8PbNiwQeM2EokEM2fOxPLly9GnTx+N6QixtN4+NGsPIXKrpkfi6VEh+CUljnW96iQCOmYu0EGf2lpdlaNPjeyFsff5IjpYv45v2vbvKRJi+/Oj8HPqaLsPYgFg0KBBuHHjhuLfgQPtQ669/PLL+OWXX/DDDz9g3759KC8vx6OPPmrF3BJiHIM6drW2tqKgoADp6emKZXw+H/Hx8cjPz9e43YoVK+Dn54d58+bhjz/+0HmclpYWtLS0j+VYV1cHABCLxRCLxZo204t8e1P3Y++oHGTM2AeGOAiplFHcJ1Kp+pBfkrY2iMViSJn2dbruK4ZhVNI8HBmA7DOVGODfWWW5uE1i0D1q6v3c1U2ANyf1h1gsxgWl5VJGqrZv5XPoGJC2acn3AwN8UVLViOieXuppOpSLVKp+XGVLJ8tqFyWSNr3GdJV02J9Y3Kb0WoyIwM6K12z/m8qSn7cuLi4ICAhQW15bW4v169djy5YtePDBBwEAX3/9NQYOHIhDhw5h5MiRFssjIaYyKIi9desWJBIJ/P1VH335+/vj3Dn2GY0OHDiA9evXo7CwUO/jZGRkYPny5WrLs7Oz4eHBTc1ZTk4OJ/uxd85eDnU1fFD/RtKRj4hBdYvsF8718uvIzLwKADhbzgOgOqLFwYP5KD3vhjvVAshrJzMzNfWal33kNjQ0qKRhGOC1IUB3t5p7y2Xpjh07BukVXTWW7R/jmo9rmuvXriEzs0zleC2tLYrjSaXt5w4Ax48fB+8qe74f7gowXYHc7CylpbJ91tXXq5z/hfPnkXlX90x37NS/3kovXUJmZvvMcdcb29Pt2bMHrhoGK+Hqc7KpyXIdSS9cuICgoCC4ubkhLi4OGRkZ6NWrFwoKCiAWixEfH69IGxYWhl69eiE/P19jEGvOyiUFvuETyogg4ubYdq5VwG0rc0v84OLiGGYdYqu+vh6zZs3CV199BV9fzVNkdpSeno60tDTF+7q6OgQHByMhIQFeXl4m5UksFiMnJwcTJ06EUOi87SGpHGS2VvyFC3XaZ2oizufHhaPw4BrZ06UeQT2QnBwBALjx52XsvHJeJe2oUXEY2C8Qm2/8hYv1smspOTmZdb8v5mcDADp37ozk5NEajy9PN3ToUCQN0t5eVp5W23ENJf98kOvRsyeSkwerHM9NJEJy8ngAwCtHciCRtH+JRkdHIzlCvRZQE/k+vTw9kZw8SvH+vv79kWzk+LzK5SLXp08fJCf2V7w/V1GPD07K/s5JSYlwE6pGsVx/TsoDP3OLjY3Fxo0bMWDAANy4cQPLly/H2LFjUVRUhIqKCri6usLb21tlG39/f1RUVGjcpyUqlxD5pcGbvMXNke3fCG6nlzPXD2JlXPyoMyiI9fX1hUAgUGsAXllZyfrY4uLFi7h8+TKmTJmiWCZ/HOfi4oLi4mL07av+ASUSiSASqf+6EgqFnAVcXO7Lnjl7OQjtdMYuYl7dPN0Vr/l8nuIeEfDVq+r4AhcIhUKV9qK67ikej6fXfeciEBh0f5rrXubz+Cz71nwOfAPz3b5L1X0aev66uApV9ycQtH8Fyj4L2atiufqctNRn7aRJkxSvhwwZgtjYWPTu3Rvff/893N3dtWypmTkrlxQyehq8SVxvGhoNAOrPL+N0f0XLEjndHxsuftQZFMS6uroiJiYGubm5imGypFIpcnNzkZqaqpY+LCwMp06dUlm2ePFi1NfX45NPPkFwMF18xLr4NKwOYWHKSAPOyNSRBnw7u+JWQyvGD2Af/cAYsaE+OFxarbJs/ljn7Fjs7e2N/v37o6SkBBMnTkRraytqampUamM1VUbJWaJyCdJmgzdpQYvuRE6gRcLtZ5YlfnBxcQyDq6HS0tLw1VdfYdOmTTh79iwWLlyIxsZGzJ07FwAwe/ZsRccvNzc3DB48WOWft7c3PD09MXjwYLi6WmcuckLkJoQZ9qUZG+pjppwQrk2NCjJ6W4kBUZn8d5A5wl6rjKXKQtdvPVPzueuFsfjgsSF4Kf4+1eOasM+tC1Tbdg4K8oK3h3N+5zQ0NODixYsIDAxETEwMhEIhcnNzFeuLi4tRVlaGuDj2USkIsVUGt4mdPn06bt68iSVLlqCiogJRUVHIyspSdPYqKysDn0+PaIl9+HtMD5SeO4V6z144db0O5yq0TzupafxMYnt6mTB8WidNPXxYcDneqTFSHuiLtb9fxFsPhVs3I0oYAwsloIsb/j6M2ydzvA6R97fzYjndvy175ZVXMGXKFPTu3Rvl5eVYunQpBAIBnnjiCXTp0gXz5s1DWloafHx84OXlhRdeeAFxcXE0MgGxO0Z17EpNTWVtPgAAeXl5WrfduHGjMYckxCz4fB4iuzFITh4EoVCIkEW7taa35dYH3T1FuFlPj9bkehoRxPbr3gkRnerUAiBb9krCAMwaGYKALob37DYXkQ22NffppF4Lq/xntqM/uU7Xrl3DE088gdu3b6N79+4YM2YMDh06hO7duwMAVq9eDT6fj2nTpqGlpQWJiYn47LPPrJxrQgxne580hNgwS7eh9enkig8fG2LRY5rCt7Nhw908P964nuf6KKlqMHibX/8xGuMDzVe1+mriAADgdCpYHo9ntgDW31P295w0WPtIA8o1r/f37474gdzMQGbu283atejmsnXrVpSXl6OlpQXXrl3D1q1bVTpRu7m5Ye3ataiurkZjYyO2b9+utT0sIbaKglhCDNCxhu7PRbLBwoX6TnupB3+v9kCQB8DTTf/G74Y0dxjdr5vK+6f6sQ/RommqUTZpE/vj1LIEnFuZBA+lR/I/PMfe1q6fX2e9963s66eHa10/ItQHLWJuh5zhQsoD/XDhnUmI7dNNd2Ib8Os/RmFnymg8qKPt+OLJsqYM88aEYtMzIwya6pYQQoxFnzSEGKBjW78e3u64/N5knH97Ep4eFaLXPrw9hPjjtQfgwufhiRG91NZ37EE9MdxfZd/39+/Out/mVgk2a2j398CA7vjHhPZOM0mDAvDds6rt37po6PMiNaC2KtS3EzzdhHATClRquYaH+CDvlfHo798Zq/4eadS+lY0f0F4GS6eotwVd/rdBaJWoz66lzf/dz95zXVcWDX10bsiUptauKfR0EyIy2Ftn84pnxoTiwOsPYPHkgRbKmX4ie3bRul6lOQGNSEGI3aEglhADaOp8wuPxkJ4cpnP7zH+MxZ+vP4hgHw+cWZGEjEcjVNZvXTASCeGqj/UEfB6W/W2Q4r2mWt/wIC/E9e2Gy+9Nxsa5w5H3ynilffCRNrF9kHd3lo5Lmr7CpXpGUh8+NgRxfTXXMIb4dkL2y/fj0aHtY0Fq6wC0aJLm8lQOqthqnwV8Hsbdxx7ssxnVtxvSJxkXgMlrkx2pTaUxenb14Lwtscn707G9tX8kEEJMQ0EsIUo+mDYE3Tq5YuuCkfhkepTKusx/jMVDQwI1bityYe/RPvY+X3z42BD8b2EcwoO80Ekk60/pylKDN7JPN4iE7cvbWKoqOwZty6aE4+lRIVitlN/xA/wQ4ttJaT+qtZL9/T0BANG9vAEAET28wNNQ59gxCwvGqdZY7nphDP56Mx6PG9G7nAHwn9nD1GpBv3lmBJ67X7/2sh0DHd/Orgjp1glJgwMwpp/qTIED/D0RFuCptg8vLU02dIVR9tQJzOkYMVQaIcR+mHXaWULszd+HB+PxYT1lgYl/e2eZXt3cER5k2Kw0/fw64/v/i2PtFa2NcueoziL1W9S1Q7Dc27cTnh4dqnWf8kfeO1NGI6/4Jp4ZEwIA+GzmUHx3qAzThwVh/+97Fen/tzAO0z6XTccpVYpi+/t3xhvJA/Hl/kuKZYN7aH9kqw3DMIgP90d8uD++2CfbZ1SwN8bdazIxMdwfOWcqFYPhs4kO9sZ3z8ai9q4YCeH+kDLtPxC+mBWDQUv3KNLyeMB/5gzDmPd/NzrPhBDi6DSN1HP5vckWzol2FMQS0gFbzVq3Tob1ugdkwZ8+Aeya6VF4aVsh3n1E1rRAU+esp0b2wpHSakyOCMQvJ8rb86tl3x9MG4L1B0oVHW8ig70RGeytWB/YxR2vJA6AWCxGZyGwN20MvDzcVAJp5eYEhrRh7ezmgrs6Olex7W/a0B6K158+EY3jZTUYHtIVu07ewEvbChXr/njtAZTX3NUaRHcSueDH5+Lw2Lp8xbKeXTma570Dc7SpZGxmugN1VHNJuBYRqt5HgBBtqDkBIQbq4a157vFxSp2u9G1LOjW6B86tTMKTse0f4P94sB8AWQclubenRiD75ftZ27Nq8vfhwdjz8jgE6zlmanBXD7VhspTPw5BB7NfPGYa+3Tth/ZxhauvkIxeM0tKGFgDchALE9e0GFwFf0Zkr6l4QHuzjoVcv/2Eh6rOsvZk8EN0MrCEHgKG9uxq8jaOyRHtSSwbKFJMTYn+oJpYQA7loGU5r49PD0eeNTACG1Vq6CVUD07SEAVhwf1/W5gSWnjRMuTmtl7v+w30N6emN3H+OZ1135M143Gls1Tu4BgBvD1ecXZFk0kD68lr2+eP64NmxoQhNzzRo+5jeXbHl2Vj0EjHAaqOzQQghhANUE0sIh/h8Hobe6yz1WExP7Yl1YAtgAaBLh0DS3FPhShkGX80ehqhgb3z8eKTKujlxvY3aZ2eRi0EBrJy7qwB8I85XXkYjQtprUvXtkNXxt8iofr5GzQbmaCxRS0rDXhFCtKGaWEI49s28WBwvu4M4Mw1oH9GjC54f3xef5V3E0F7eZjuOnJRhMDHcHxPD1WdhMnSGLn109+R+9qnf0u5HVlEFZhsZdOvD2dqIWqI5QccJOQxlSBZplAlC7A8FsYToITywfVimTq7ab5vOIheMNWCMUkPxeDy8lhSG15J0j0vLhUAtbYCNnayAzbqnhuL41RoksATLpgr17YSFZpziFgAmRQTi4MXb6NlVc3kZylnHMT321kSdnfYIIYSCWEL08NLEAYrXn8yIQsqWY3hxQn8tW9i/756NxXeHr7DOiPW3yCBkFVVgxgjDx4bVJGlwIJIGax6H1xzG3ueLPy7c4qSGduaIXujt44EhOmaJMkTHpiO2xJwVlz6dXA0ems5UVA9LiP2hIJYQPSi3T73P3xPZL99vxdxYxuh+vhjdYbIAuU9mREEsYVgnbLAnG+eOwM36FgR0Mb0JA5/PUxmdwhQfPDYE527UY+x97OVvC0K7ddKdiBBCzIiCWEKIwXg8Hlxd7L/uSsDncRLAck3T9Ma2YEfKaGw4UKp1WmBCCLEECmIJIYToLSrYG/96Itra2eAc9esixP5QEEsIIYQQ61l2rx05zdhFDGTfDdoIIYQQDR6Nlk1hPCjIy8o5IYSYA9XEEkIIcUiz40IQFuil11BdNE4ssWeeAxcZvE392ffMkBPLoiCWEEKIQ+LzeRhp5slACCHWQ80JCCGEEEKI3TEqiF27di1CQkLg5uaG2NhYHDlyRGPa7du3Y9iwYfD29kanTp0QFRWFb7/91ugME0IIIYQQYnAQu23bNqSlpWHp0qU4duwYIiMjkZiYiKqqKtb0Pj4+ePPNN5Gfn4+TJ09i7ty5mDt3Lvbs2WNy5gkhhBBCiHMyOIhdtWoV5s+fj7lz5yI8PBzr1q2Dh4cHNmzYwJp+/PjxeOSRRzBw4ED07dsXL774IoYMGYIDBw6YnHlCCCGEEOKcDOrY1draioKCAqSnpyuW8fl8xMfHIz8/X+f2DMNg7969KC4uxvvvv68xXUtLC1paWhTv6+rqAABisRhisdiQLKuRb2/qfuwdlYOM1nIQiyFUTufAZUXXgwxbOUilUvVycfBrw1muh7a2NsVrtnPluhwcvTwJsTSDgthbt25BIpHA399fZbm/vz/OnTuncbva2lr06NEDLS0tEAgE+OyzzzBx4kSN6TMyMrB8+XK15dnZ2fDw8DAkyxrl5ORwsh97R+Ugw1YOguZmPHTv9Z49eyBxs73pSblG14OMrBxkH4/l5eXIzLymst5Zrg1Hvx5aJID875yZmakxHVfl0NTUxMl+CCEyFhliy9PTE4WFhWhoaEBubi7S0tLQp08fjB8/njV9eno60tLSFO/r6uoQHByMhIQEeHmZNmi1WCxGTk4OJk6cCKFQqHsDB0XlIKO1HBobFS8TExOBTp0snDvLoetBRrkcprUU43/HyrH076MxMNBTNaGDXxvOdD08GC+GUMCDh6v61yHX5SB/qkgI4YZBQayvry8EAgEqKytVlldWViIgIEDjdnw+H/369QMAREVF4ezZs8jIyNAYxIpEIohEIrXlQqGQsw9ULvdlz6gcZFjLQem9UChUee+o6HqQEQqF+OjxKLw9dQjcXQVsCVTSOuq14QzXg68e58dVOTh6WRpkme4JKAjRxaCOXa6uroiJiUFubq5imVQqRW5uLuLi4vTej1QqVWnzSgghtobH47EHsIQQQmyCwc0J0tLSMGfOHAwbNgwjRozAmjVr0NjYiLlz5wIAZs+ejR49eiAjIwOArH3rsGHD0LdvX7S0tCAzMxPffvstPv/8c27PhBBCCCGEOA2Dg9jp06fj5s2bWLJkCSoqKhAVFYWsrCxFZ6+ysjLw+e0VvI2NjXj++edx7do1uLu7IywsDJs3b8b06dO5OwtCCCGE2IyITRHaE4T2skxGCKdCFu3WuO7ye5MtmBMZozp2paamIjU1lXVdXl6eyvu3334bb7/9tjGHIYQQQgghhJVFRicghBBCCCG2w3PgIqO2qz/7Hsc5MZ7BM3YRQgghxDGsXbsWISEhcHNzQ2xsLI4cOWLtLBGiNwpiCSGEECe0bds2pKWlYenSpTh27BgiIyORmJiIqqoqa2eNEL1QEEsIIYQ4oVWrVmH+/PmYO3cuwsPDsW7dOnh4eGDDhg3WzhoherGLNrEMwwDgZrYTsViMpqYm1NXVOfXA01QOMlrLQWlWJtTVARKJZTNnQXQ9yOhdDg5+bdD1IMN1Oci/w+TfadbU2tqKgoICpKenK5bx+XzEx8cjPz9fLX1LS4vK+O61tbUAgOrqaojFYrX0LnftIrwgRugasph1efQ69uUA8Nvjv6ktq6+vB2Da/WAXV5n8RIODg62cE+K0goKsnQNiq+jaIAaqr69Hly7WnbHq1q1bkEgkiuEx5fz9/XHu3Dm19BkZGVi+fLna8tDQULPlkTgO34W+GteZcj/YRRAbFBSEq1evwtPTEzwez6R91dXVITg4GFevXoWXlxdHObQ/VA4yVA4yVA4yVA4yVA4yXJcDwzCor69HkB3+8ElPT0daWprivVQqRXV1Nbp162by97Kl0HVtPWxlz8X9YBdBLJ/PR8+ePTndp5eXF13EoHKQo3KQoXKQoXKQoXKQ4bIcrF0DK+fr6wuBQIDKykqV5ZWVlQgICFBLLxKJIBKJVJZ5e3ubM4tmQ9e19XQse1PvB+rYRQghhDgZV1dXxMTEIDc3V7FMKpUiNzcXcXFxVswZIfqzi5pYQgghhHArLS0Nc+bMwbBhwzBixAisWbMGjY2NmDt3rrWzRohenC6IFYlEWLp0qdpjEWdD5SBD5SBD5SBD5SBD5SDj6OUwffp03Lx5E0uWLEFFRQWioqKQlZWl1tnLUTj639OWmavseYwtjPVBCCGEEEKIAahNLCGEEEIIsTsUxBJCCCGEELtDQSwhhBBCCLE7FMQSQgghhBC743RB7Nq1axESEgI3NzfExsbiyJEj1s4SZ5YtWwYej6fyLywsTLG+ubkZKSkp6NatGzp37oxp06apDXRdVlaGyZMnw8PDA35+fnj11VfR1tZm6VMxyP79+zFlyhQEBQWBx+Nhx44dKusZhsGSJUsQGBgId3d3xMfH48KFCyppqqurMXPmTHh5ecHb2xvz5s1DQ0ODSpqTJ09i7NixcHNzQ3BwMD744ANzn5pBdJXD008/rXZ9JCUlqaRxhHLIyMjA8OHD4enpCT8/P0ydOhXFxcUqabi6F/Ly8jB06FCIRCL069cPGzduNPfp6U2fchg/frzaNfHcc8+ppLHncvj8888xZMgQxQDrcXFx+PXXXxXrneE6cDaGfMdv3LhR7fp3c3OzYG4dh67vHzac3DeME9m6dSvj6urKbNiwgTl9+jQzf/58xtvbm6msrLR21jixdOlSZtCgQcyNGzcU/27evKlY/9xzzzHBwcFMbm4uc/ToUWbkyJHMqFGjFOvb2tqYwYMHM/Hx8czx48eZzMxMxtfXl0lPT7fG6egtMzOTefPNN5nt27czAJiffvpJZf17773HdOnShdmxYwdz4sQJ5m9/+xsTGhrK3L17V5EmKSmJiYyMZA4dOsT88ccfTL9+/ZgnnnhCsb62tpbx9/dnZs6cyRQVFTH//e9/GXd3d+aLL76w1GnqpKsc5syZwyQlJalcH9XV1SppHKEcEhMTma+//popKipiCgsLmeTkZKZXr15MQ0ODIg0X98KlS5cYDw8PJi0tjTlz5gzz6aefMgKBgMnKyrLo+WqiTzncf//9zPz581WuidraWsV6ey+Hn3/+mdm9ezdz/vx5pri4mHnjjTcYoVDIFBUVMQzjHNeBMzH0O/7rr79mvLy8VK7/iooKC+faMej6/umIq/vGqYLYESNGMCkpKYr3EomECQoKYjIyMqyYK+4sXbqUiYyMZF1XU1PDCIVC5ocfflAsO3v2LAOAyc/PZxhGdhHy+XyVm/jzzz9nvLy8mJaWFrPmnSsdbx6pVMoEBAQwH374oWJZTU0NIxKJmP/+978MwzDMmTNnGADMX3/9pUjz66+/Mjwej7l+/TrDMAzz2WefMV27dlUph9dff50ZMGCAmc/IOJqC2IcffljjNo5YDgzDMFVVVQwAZt++fQzDcHcvvPbaa8ygQYNUjjV9+nQmMTHR3KdklI7lwDCyIPbFF1/UuI0jlkPXrl2Z//znP057HTgyQ7/jv/76a6ZLly4Wyp3z0CeI5eq+cZrmBK2trSgoKEB8fLxiGZ/PR3x8PPLz862YM25duHABQUFB6NOnD2bOnImysjIAQEFBAcRiscr5h4WFoVevXorzz8/PR0REhMpA14mJiairq8Pp06cteyIcKS0tRUVFhcp5d+nSBbGxsSrn7e3tjWHDhinSxMfHg8/n4/Dhw4o048aNg6urqyJNYmIiiouLcefOHQudjeny8vLg5+eHAQMGYOHChbh9+7ZinaOWQ21tLQDAx8cHAHf3Qn5+vso+5Gls9fOkYznIfffdd/D19cXgwYORnp6OpqYmxTpHKgeJRIKtW7eisbERcXFxTnsdOCpjv+MbGhrQu3dvBAcH4+GHH7bb7zp7w9V94zRB7K1btyCRSNRmIvH390dFRYWVcsWt2NhYbNy4EVlZWfj8889RWlqKsWPHor6+HhUVFXB1dYW3t7fKNsrnX1FRwVo+8nX2SJ5vbX/3iooK+Pn5qax3cXGBj4+PQ5VNUlISvvnmG+Tm5uL999/Hvn37MGnSJEgkEgCOWQ5SqRQvvfQSRo8ejcGDBwMAZ/eCpjR1dXW4e/euOU7HaGzlAABPPvkkNm/ejN9//x3p6en49ttv8dRTTynWO0I5nDp1Cp07d4ZIJMJzzz2Hn376CeHh4U55HTgyY77jBwwYgA0bNmDnzp3YvHkzpFIpRo0ahWvXrlkiy06Nq/vG6aaddWSTJk1SvB4yZAhiY2PRu3dvfP/993B3d7dizogtmDFjhuJ1REQEhgwZgr59+yIvLw8TJkywYs7MJyUlBUVFRThw4IC1s2JVmsphwYIFitcREREIDAzEhAkTcPHiRfTt29fS2TSLAQMGoLCwELW1tfjxxx8xZ84c7Nu3z9rZIjYgLi4OcXFxivejRo3CwIED8cUXX2DlypVWzBnRl9PUxPr6+kIgEKj1PK2srERAQICVcmVe3t7e6N+/P0pKShAQEIDW1lbU1NSopFE+/4CAANbyka+zR/J8a/u7BwQEoKqqSmV9W1sbqqurHbps+vTpA19fX5SUlABwvHJITU3Frl278Pvvv6Nnz56K5VzdC5rSeHl52dSPRk3lwCY2NhYAVK4Jey8HV1dX9OvXDzExMcjIyEBkZCQ++eQTp7sOHB0X3/FCoRDR0dGK65+YD1f3jdMEsa6uroiJiUFubq5imVQqRW5ursovMUfS0NCAixcvIjAwEDExMRAKhSrnX1xcjLKyMsX5x8XF4dSpUyqBTE5ODry8vBAeHm7x/HMhNDQUAQEBKuddV1eHw4cPq5x3TU0NCgoKFGn27t0LqVSq+FKPi4vD/v37IRaLFWlycnIwYMAAdO3a1UJnw61r167h9u3bCAwMBOA45cAwDFJTU/HTTz9h7969CA0NVVnP1b0QFxensg95Glv5PNFVDmwKCwsBQOWasPdy6EgqlaKlpcVprgNnwcV3vEQiwalTpxTXPzEfzu4bw/qc2betW7cyIpGI2bhxI3PmzBlmwYIFjLe3t8MMqfHPf/6TycvLY0pLS5k///yTiY+PZ3x9fZmqqiqGYWTDyfTq1YvZu3cvc/ToUSYuLo6Ji4tTbC8fTiYhIYEpLCxksrKymO7du9v8EFv19fXM8ePHmePHjzMAmFWrVjHHjx9nrly5wjCMbIgtb29vZufOnczJkyeZhx9+mHWIrejoaObw4cPMgQMHmPvuu09laKmamhrG39+fmTVrFlNUVMRs3bqV8fDwsKmhpbSVQ319PfPKK68w+fn5TGlpKfPbb78xQ4cOZe677z6mublZsQ9HKIeFCxcyXbp0YfLy8lSGzmlqalKk4eJekA8R8+qrrzJnz55l1q5da1NDK+kqh5KSEmbFihXM0aNHmdLSUmbnzp1Mnz59mHHjxin2Ye/lsGjRImbfvn1MaWkpc/LkSWbRokUMj8djsrOzGYZxjuvAmej6jp81axazaNEiRfrly5cze/bsYS5evMgUFBQwM2bMYNzc3JjTp09b6xTslq7v4UWLFjGzZs1SpOfqvnGqIJZhGObTTz9levXqxbi6ujIjRoxgDh06ZO0scWb69OlMYGAg4+rqyvTo0YOZPn06U1JSolh/9+5d5vnnn2e6du3KeHh4MI888ghz48YNlX1cvnyZmTRpEuPu7s74+voy//znPxmxWGzpUzHI77//zgBQ+zdnzhyGYWTDbL311luMv78/IxKJmAkTJjDFxcUq+7h9+zbzxBNPMJ07d2a8vLyYuXPnMvX19SppTpw4wYwZM4YRiURMjx49mPfee89Sp6gXbeXQ1NTEJCQkMN27d2eEQiHTu3dvZv78+Wo/4ByhHNjKAADz9ddfK9JwdS/8/vvvTFRUFOPq6sr06dNH5RjWpqscysrKmHHjxjE+Pj6MSCRi+vXrx7z66qsq48QyjH2XwzPPPMP07t2bcXV1Zbp3785MmDBBEcAyjHNcB85G23f8/fffr/heYBiGeemllxRp/f39meTkZObYsWNWyLX90/U9PGfOHOb+++9X28bU+4bHMAxjWN0tIYQQQggh1uU0bWIJIYQQQojjoCCWEEIIIYTYHQpiCSGEEEKI3aEglhBCCCGE2B0KYgkhhBBCiN2hIJYQQgghhNgdCmIJIYQQQojdoSCWEEIIIYTYHQpiCSGEEEKI3aEglhBCCCGE2B0KYgkhhBBCiN2hIJYQQgghhNid/wcW35eaF0pU/wAAAABJRU5ErkJggg==", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -211,7 +207,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.8" }, "vscode": { "interpreter": { diff --git a/docs/introduction/getting-started/multiclass-classification.ipynb b/docs/introduction/getting-started/multiclass-classification.ipynb index c8ab0d7f83..a520ec9ff2 100644 --- a/docs/introduction/getting-started/multiclass-classification.ipynb +++ b/docs/introduction/getting-started/multiclass-classification.ipynb @@ -19,7 +19,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:19.761621Z", + "iopub.status.busy": "2023-06-26T15:07:19.761079Z", + "iopub.status.idle": "2023-06-26T15:07:20.208391Z", + "shell.execute_reply": "2023-06-26T15:07:20.208026Z" + } + }, "outputs": [ { "data": { @@ -29,12 +36,13 @@ "This dataset contains features that describe image segments into 7 classes: brickface, sky,\n", "foliage, cement, window, path, and grass.\n", "\n", - " Name ImageSegments \n", - " Task Multi-class classification \n", - " Samples 2,310 \n", - "Features 18 \n", - " Sparse False \n", - " Path /Users/max.halford/projects/river/river/datasets/segment.csv.zip" + " Name ImageSegments \n", + " Task Multi-class classification \n", + " Samples 2,310 \n", + "Features 18 \n", + " Classes 7 \n", + " Sparse False \n", + " Path /Users/max/projects/online-ml/river/river/datasets/segment.csv.zip" ] }, "execution_count": 1, @@ -59,7 +67,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:20.210187Z", + "iopub.status.busy": "2023-06-26T15:07:20.210095Z", + "iopub.status.idle": "2023-06-26T15:07:20.238888Z", + "shell.execute_reply": "2023-06-26T15:07:20.238604Z" + } + }, "outputs": [], "source": [ "for x, y in dataset:\n", @@ -76,7 +91,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:20.240678Z", + "iopub.status.busy": "2023-06-26T15:07:20.240559Z", + "iopub.status.idle": "2023-06-26T15:07:20.254621Z", + "shell.execute_reply": "2023-06-26T15:07:20.254365Z" + } + }, "outputs": [ { "data": { @@ -114,7 +136,14 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:20.256203Z", + "iopub.status.busy": "2023-06-26T15:07:20.256086Z", + "iopub.status.idle": "2023-06-26T15:07:20.270987Z", + "shell.execute_reply": "2023-06-26T15:07:20.270725Z" + } + }, "outputs": [ { "data": { @@ -140,8 +169,15 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:20.272660Z", + "iopub.status.busy": "2023-06-26T15:07:20.272548Z", + "iopub.status.idle": "2023-06-26T15:07:20.310677Z", + "shell.execute_reply": "2023-06-26T15:07:20.310316Z" + } + }, "outputs": [ { "data": { @@ -149,7 +185,7 @@ "{}" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -172,8 +208,15 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:20.312313Z", + "iopub.status.busy": "2023-06-26T15:07:20.312201Z", + "iopub.status.idle": "2023-06-26T15:07:20.327301Z", + "shell.execute_reply": "2023-06-26T15:07:20.327059Z" + } + }, "outputs": [ { "name": "stdout", @@ -196,8 +239,15 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:20.329031Z", + "iopub.status.busy": "2023-06-26T15:07:20.328914Z", + "iopub.status.idle": "2023-06-26T15:07:20.344407Z", + "shell.execute_reply": "2023-06-26T15:07:20.344096Z" + } + }, "outputs": [ { "data": { @@ -205,7 +255,7 @@ "{'path': 1.0}" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -226,30 +276,37 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:20.346153Z", + "iopub.status.busy": "2023-06-26T15:07:20.346037Z", + "iopub.status.idle": "2023-06-26T15:07:21.008346Z", + "shell.execute_reply": "2023-06-26T15:07:21.008071Z" + } + }, "outputs": [ { "data": { "text/plain": [ " Precision Recall F1 Support \n", " \n", - "brickface 77.13% 84.85% 80.81% 33 \n", - " cement 78.92% 83.94% 81.35% 33 \n", - " foliage 67.00% 20.30% 31.16% 33 \n", - " grass 100.00% 96.97% 98.46% 33 \n", - " path 90.39% 91.49% 90.94% 329 \n", - " sky 99.08% 98.18% 98.63% 33 \n", - " window 43.50% 67.88% 53.02% 33 \n", + "brickface 77.13% 84.85% 80.81% 330 \n", + " cement 78.92% 83.94% 81.35% 330 \n", + " foliage 65.69% 20.30% 31.02% 330 \n", + " grass 100.00% 96.97% 98.46% 330 \n", + " path 90.63% 91.19% 90.91% 329 \n", + " sky 99.08% 98.18% 98.63% 330 \n", + " window 43.50% 67.88% 53.02% 330 \n", " \n", - " Macro 79.43% 77.66% 76.34% \n", - " Micro 77.65% 77.65% 77.65% \n", - " Weighted 79.43% 77.65% 76.33% \n", + " Macro 79.28% 77.62% 76.31% \n", + " Micro 77.61% 77.61% 77.61% \n", + " Weighted 79.27% 77.61% 76.31% \n", "\n", - " 77.65% accuracy " + " 77.61% accuracy " ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -279,30 +336,37 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:21.010194Z", + "iopub.status.busy": "2023-06-26T15:07:21.010071Z", + "iopub.status.idle": "2023-06-26T15:07:21.708092Z", + "shell.execute_reply": "2023-06-26T15:07:21.707803Z" + } + }, "outputs": [ { "data": { "text/plain": [ " Precision Recall F1 Support \n", " \n", - "brickface 77.13% 84.85% 80.81% 33 \n", - " cement 78.92% 83.94% 81.35% 33 \n", - " foliage 67.00% 20.30% 31.16% 33 \n", - " grass 100.00% 96.97% 98.46% 33 \n", - " path 90.39% 91.49% 90.94% 329 \n", - " sky 99.08% 98.18% 98.63% 33 \n", - " window 43.50% 67.88% 53.02% 33 \n", + "brickface 77.13% 84.85% 80.81% 330 \n", + " cement 78.92% 83.94% 81.35% 330 \n", + " foliage 65.69% 20.30% 31.02% 330 \n", + " grass 100.00% 96.97% 98.46% 330 \n", + " path 90.63% 91.19% 90.91% 329 \n", + " sky 99.08% 98.18% 98.63% 330 \n", + " window 43.50% 67.88% 53.02% 330 \n", " \n", - " Macro 79.43% 77.66% 76.34% \n", - " Micro 77.65% 77.65% 77.65% \n", - " Weighted 79.43% 77.65% 76.33% \n", + " Macro 79.28% 77.62% 76.31% \n", + " Micro 77.61% 77.61% 77.61% \n", + " Weighted 79.27% 77.61% 76.31% \n", "\n", - " 77.65% accuracy " + " 77.61% accuracy " ] }, - "execution_count": 15, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -343,9 +407,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "orig_nbformat": 4 + "version": "3.10.8" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/docs/introduction/getting-started/regression.ipynb b/docs/introduction/getting-started/regression.ipynb index ae5d4abc06..22cd371912 100644 --- a/docs/introduction/getting-started/regression.ipynb +++ b/docs/introduction/getting-started/regression.ipynb @@ -17,7 +17,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:23.240481Z", + "iopub.status.busy": "2023-06-26T15:07:23.239957Z", + "iopub.status.idle": "2023-06-26T15:07:23.678784Z", + "shell.execute_reply": "2023-06-26T15:07:23.678465Z" + } + }, "outputs": [ { "data": { @@ -29,12 +36,12 @@ "5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of\n", "this task is to see if we can reproduce FiveThirtyEight's model.\n", "\n", - " Name TrumpApproval \n", - " Task Regression \n", - " Samples 1,001 \n", - "Features 6 \n", - " Sparse False \n", - " Path /Users/max.halford/projects/river/river/datasets/trump_approval.csv.gz" + " Name TrumpApproval \n", + " Task Regression \n", + " Samples 1,001 \n", + "Features 6 \n", + " Sparse False \n", + " Path /Users/max/projects/online-ml/river/river/datasets/trump_approval.csv.gz" ] }, "execution_count": 1, @@ -59,7 +66,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:23.680823Z", + "iopub.status.busy": "2023-06-26T15:07:23.680689Z", + "iopub.status.idle": "2023-06-26T15:07:23.698617Z", + "shell.execute_reply": "2023-06-26T15:07:23.698332Z" + } + }, "outputs": [], "source": [ "for x, y in dataset:\n", @@ -76,7 +90,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:23.700265Z", + "iopub.status.busy": "2023-06-26T15:07:23.700176Z", + "iopub.status.idle": "2023-06-26T15:07:23.713896Z", + "shell.execute_reply": "2023-06-26T15:07:23.713640Z" + } + }, "outputs": [ { "data": { @@ -108,8 +129,15 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:23.715485Z", + "iopub.status.busy": "2023-06-26T15:07:23.715398Z", + "iopub.status.idle": "2023-06-26T15:07:23.733966Z", + "shell.execute_reply": "2023-06-26T15:07:23.733700Z" + } + }, "outputs": [ { "data": { @@ -117,7 +145,7 @@ "0.0" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -140,8 +168,15 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:23.735455Z", + "iopub.status.busy": "2023-06-26T15:07:23.735349Z", + "iopub.status.idle": "2023-06-26T15:07:23.749504Z", + "shell.execute_reply": "2023-06-26T15:07:23.749190Z" + } + }, "outputs": [], "source": [ "model = model.learn_one(x, y)" @@ -156,8 +191,15 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:23.751249Z", + "iopub.status.busy": "2023-06-26T15:07:23.751162Z", + "iopub.status.idle": "2023-06-26T15:07:23.764551Z", + "shell.execute_reply": "2023-06-26T15:07:23.764312Z" + } + }, "outputs": [ { "data": { @@ -165,7 +207,7 @@ "43.75505" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -183,16 +225,23 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:23.766918Z", + "iopub.status.busy": "2023-06-26T15:07:23.766771Z", + "iopub.status.idle": "2023-06-26T15:07:26.016586Z", + "shell.execute_reply": "2023-06-26T15:07:26.016320Z" + } + }, "outputs": [ { "data": { "text/plain": [ - "MAE: 0.31039" + "MAE: 0.310353" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -221,16 +270,23 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:26.018529Z", + "iopub.status.busy": "2023-06-26T15:07:26.018286Z", + "iopub.status.idle": "2023-06-26T15:07:28.285287Z", + "shell.execute_reply": "2023-06-26T15:07:28.285024Z" + } + }, "outputs": [ { "data": { "text/plain": [ - "MAE: 0.31039" + "MAE: 0.310353" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -271,9 +327,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "orig_nbformat": 4 + "version": "3.10.8" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/docs/recipes/active-learning.ipynb b/docs/recipes/active-learning.ipynb index 5bab814088..8680317387 100644 --- a/docs/recipes/active-learning.ipynb +++ b/docs/recipes/active-learning.ipynb @@ -51,7 +51,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:30.639390Z", + "iopub.status.busy": "2023-06-26T15:07:30.639006Z", + "iopub.status.idle": "2023-06-26T15:07:31.532486Z", + "shell.execute_reply": "2023-06-26T15:07:31.532176Z" + } + }, "outputs": [ { "data": { @@ -100,8 +107,15 @@ }, { "cell_type": "code", - "execution_count": 71, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:31.544616Z", + "iopub.status.busy": "2023-06-26T15:07:31.544474Z", + "iopub.status.idle": "2023-06-26T15:07:31.560157Z", + "shell.execute_reply": "2023-06-26T15:07:31.559886Z" + } + }, "outputs": [ { "name": "stdout", @@ -125,8 +139,15 @@ }, { "cell_type": "code", - "execution_count": 72, - "metadata": {}, + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-06-26T15:07:31.561854Z", + "iopub.status.busy": "2023-06-26T15:07:31.561733Z", + "iopub.status.idle": "2023-06-26T15:07:32.079589Z", + "shell.execute_reply": "2023-06-26T15:07:32.079300Z" + } + }, "outputs": [ { "name": "stdout", @@ -146,7 +167,7 @@ "Accuracy: 86.60%" ] }, - "execution_count": 72, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -223,7 +244,6 @@ "pygments_lexer": "ipython3", "version": "3.10.8" }, - "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "14b46bd212fa4dd89e3980db6ba7efbb9fe535833e1e483b914b71733e0a56d2" diff --git a/docs/recipes/bandits-101.ipynb b/docs/recipes/bandits-101.ipynb index 0ba41e3084..f9b1ea46ee 100644 --- a/docs/recipes/bandits-101.ipynb +++ b/docs/recipes/bandits-101.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:01:56.055080Z", - "iopub.status.busy": "2022-10-26T11:01:56.054056Z", - "iopub.status.idle": "2022-10-26T11:01:56.177551Z", - "shell.execute_reply": "2022-10-26T11:01:56.177957Z" + "iopub.execute_input": "2023-06-26T15:07:33.629358Z", + "iopub.status.busy": "2023-06-26T15:07:33.628723Z", + "iopub.status.idle": "2023-06-26T15:07:33.698515Z", + "shell.execute_reply": "2023-06-26T15:07:33.698182Z" } }, "outputs": [], @@ -48,13 +48,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:01:56.183214Z", - "iopub.status.busy": "2022-10-26T11:01:56.182610Z", - "iopub.status.idle": "2022-10-26T11:01:56.562107Z", - "shell.execute_reply": "2022-10-26T11:01:56.562518Z" + "iopub.execute_input": "2023-06-26T15:07:33.700564Z", + "iopub.status.busy": "2023-06-26T15:07:33.700430Z", + "iopub.status.idle": "2023-06-26T15:07:33.998325Z", + "shell.execute_reply": "2023-06-26T15:07:33.997361Z" } }, "outputs": [], @@ -94,13 +94,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:01:56.566620Z", - "iopub.status.busy": "2022-10-26T11:01:56.565967Z", - "iopub.status.idle": "2022-10-26T11:03:32.171796Z", - "shell.execute_reply": "2022-10-26T11:03:32.172475Z" + "iopub.execute_input": "2023-06-26T15:07:34.003974Z", + "iopub.status.busy": "2023-06-26T15:07:34.003640Z", + "iopub.status.idle": "2023-06-26T15:08:15.294826Z", + "shell.execute_reply": "2023-06-26T15:08:15.294349Z" } }, "outputs": [ @@ -108,9 +108,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/6000000 [00:00episode\n", " step\n", " policy_idx\n", - " action\n", + " arm\n", " reward\n", " reward_stat\n", " \n", @@ -148,18 +148,18 @@ " 441\n", " 632\n", " 0\n", - " 4\n", - " 1.215703\n", - " 1.798879\n", + " 2\n", + " 2.080471\n", + " 2.007966\n", " \n", " \n", " 3566176\n", " 1188\n", " 725\n", " 1\n", - " 8\n", - " -0.427939\n", - " 0.757612\n", + " 0\n", + " 1.386654\n", + " 1.694128\n", " \n", " \n", " 1109043\n", @@ -167,41 +167,41 @@ " 681\n", " 0\n", " 7\n", - " 0.256075\n", - " 0.908808\n", + " 1.620536\n", + " 1.508527\n", " \n", " \n", " 4286042\n", " 1428\n", " 680\n", " 2\n", - " 2\n", - " 2.794259\n", - " 1.435460\n", + " 3\n", + " 2.540870\n", + " 1.542765\n", " \n", " \n", " 5395174\n", " 1798\n", " 391\n", " 1\n", - " 1\n", - " -0.206970\n", - " 0.709420\n", + " 8\n", + " 2.210705\n", + " 0.575857\n", " \n", " \n", "\n", "" ], "text/plain": [ - " episode step policy_idx action reward reward_stat\n", - "1324896 441 632 0 4 1.215703 1.798879\n", - "3566176 1188 725 1 8 -0.427939 0.757612\n", - "1109043 369 681 0 7 0.256075 0.908808\n", - "4286042 1428 680 2 2 2.794259 1.435460\n", - "5395174 1798 391 1 1 -0.206970 0.709420" + " episode step policy_idx arm reward reward_stat\n", + "1324896 441 632 0 2 2.080471 2.007966\n", + "3566176 1188 725 1 0 1.386654 1.694128\n", + "1109043 369 681 0 7 1.620536 1.508527\n", + "4286042 1428 680 2 3 2.540870 1.542765\n", + "5395174 1798 391 1 8 2.210705 0.575857" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -227,13 +227,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:03:32.178419Z", - "iopub.status.busy": "2022-10-26T11:03:32.177746Z", - "iopub.status.idle": "2022-10-26T11:03:34.135656Z", - "shell.execute_reply": "2022-10-26T11:03:34.136110Z" + "iopub.execute_input": "2023-06-26T15:08:15.296808Z", + "iopub.status.busy": "2023-06-26T15:08:15.296607Z", + "iopub.status.idle": "2023-06-26T15:08:16.075316Z", + "shell.execute_reply": "2023-06-26T15:08:16.069989Z" } }, "outputs": [ @@ -243,13 +243,13 @@ "" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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somBoy1g8zNfhUnIOzt/LFpjGC/VG/HNdmJdj/zXrckGOhBgp0Bng6yV+fKblaLD7irgWim1mV350iu17BSCItrClaqiv3X18bAdNvmXGwgmbAVTK18KWb/bcQMMYs7hK4fmjAOIlDNvMphYBdyMtFwv/viW5j4+3Uia5LGKPTWeT8M2zzRDoo5QUkYDQylfgYAluwHcHcC1V7AcihTMhotEb4a2Uiyx+tuRozCHU41edEmzPyNehaqh94WD7Wdrjvk3E1D3e12e7pFbekBghHikMRhPkMqbMLQ6uoLQz831nrbVsd3G6mZhRgKspuTCyLPI0BjxV5DMhBf/h4Wx2BACXknLQ55t/UCnQG9vf6IgAbyXWnb6Pt34/i5ggHxyc0kXyuI1n7mPzuWTMGdqUMxNbYFlW8AC3nT37eMm5v8etPIkdbzwu8ivQG1lJEzUATFh1WrTNtq3UkllytvlJyreoFOqN3HY+e66kYayEH4E9+A9lqUGtyawdqBQotjQMWXhYtDwlhZTAEex3EM0VHegtiIgoTdp/tteldrbG8eupuZi24QJaVA12eJzlc33mR/ESU7bEkoiUJcEZG07fd2rVzNcaUKg34sgtq9Ox7T3nqhBxhcwCHSoF+kgKTz4Z+TrsuyoWsxn5OsFyV3FJlXD2tZBTqC/2ZKU0IDFCPDJoDUZ0n7MfVULUWDmmfCKttp5PRri/Ci3jQkT7pBw1S4Mp687h4A2rr0GHWmGI4K27aw1GDF98DEdvZ6BJZesMPqtQj71X03AjNQ9jOlSTFGx9vvkHgHmg3nQmCS+0qYq9RTN121mRhcSMAm4tev7eG3i3V11uH8uyeGHxUUF/lx++ixfbV+Nm6N5KqxixPMBtZ7M6g8mtmjZ/nk3Cbyfu4ZtnmqJqqK/kjHLCqtP4ePNlgfCZuPqMZIZYR0IkLUcsXvjr+FJixMRKf56uCBHABTEiMTBzfWPZEkW0lAYXebVQWJblQnGPOokoUsoZFOqMkvl5pPK3FCfSZNKaM/ByMsO/n1UosH4BwKdbr7h9LVfJzNejUqCPU8tIZr5eMo9LZoEO19NKnvlYyufKQraHxcij4aFHEADOJGQhIaOACzUsa24+yMMrv5zC0wsOS+5XyGXYfjHFrgc7ADBOFmoe5mkxZOFhrDludermD+yA0GwMAOtP3ece6mfvWUMBswv1GLX0OD7ectnpQx+wOuc5s6b0nGvN6XD3odDxT2swifoLAI9/sQ/Di5ZL+GJE7SVHdoFeMgTV2ayQz5FbGTibmIV3i6xQeoP0scnZGmTyHEx3XpJO++2IixJFxviWCXvm/pKQXagHy7J2Q1kd+UusPJIgCCuNDvS227Y8uJzs+iCpM7B46w/p7LCl+TnbWu/a1RTWGkrJ1ogiqBYfuI2yIjnbLFylLD2NKweiZ4NIAEBGgU4y78jDfJ3LQtcRJ+7Yf26UxX3uDiRGiEcGvrndkRn6Qa5WMsJh5ZG76Dbnb8n8IGcSs/DMj4dxgRfnLxXRwX8QMDCHCE5cfcZ+plUnxpOPNl/GsdsZeHftebttbGdLWXYeCvz3nCKxFCHum3kWyl/Xt5iAj956iB/33xQtwVxKyuEe0vezCrH7sthkbGH/tQfYdiEZPkrrY6RAZ0STD3aIfBF0BpNkojNnHLmVAZZlXc6oWRykBAHfMlFWYuSvc8l48vtDzhtL8HFR6LRSzkDFE4OeYPP5JOeNith1ORWbz0knfSsssnC1qR7C5UkpKf7eClQP98VjNcIE27/f5zxBWWlisdZJLVWqveTchOFOer6kU21KtkbyPhx/Zq1oW90of7v9cBSV5GkxQss0xCMDP5xSazDnrcjR6HHk5kM8XiccKoUceVoDl/baNqeDJVHRUz8cxq7Jj6NmhB+378nvD8LEmus6nJneA4BQR1gcPvnmb/5DITNfh7BimDD5yaXOJmZJ+pjYLmnYS9HMj3xwxVclu0CHYYuPCoTBR5svw8SyGL3MXKvjky1C0/SdhwV4549zeKN7LTz1wyGHa8wAMG7lKfRtVEm0PdemGuqphEy7YYnOqDZ1S7GOc5UrRTP7vo0rcQMl/8Hs7DMoDjsvpWLDGdcHcXt4K+QOnTDLg/l7b5bq+ZRyGXo0iMKvx0qWIiLQR4njr7WEMjhYlJCO7ytSloT5qZCep8UX268iM18naR1UK+UIkpu321pngtVKZBbocT+zEPlasZhv+kCcdbi4/nY5JaxgXFLIMkI8MvB9zix+AONWnMTYFScxd5f5R3eHFzbIn2XYmjZtq5ZadvPTWn/Fy9iZWaDHD/tu4iqvJgY/26e9NfqziVnI1ZjDAC39YVkWtx7kwWhiBVaJgfMPcvkk+NhaDOxFzvOXI04nZGHCqlMOLSRf7rgmslDcSMvjhIg91p66h/af7XV5EHbFf6G4QqQ8sFhdWseFoG9js7Dif2+2IaelQb6DUGl3UClliJJwpHXGtL71SuX6ZYGXXIbHa4cX69jmVYK4v5/MvIibj7WD7sYNxFcPxcdPNET1MOeRSK4mynOF5+KrcH8vOnBbFH0EALJL52FcvEDy+CpFflm7r6Rh7SlxttSY/HS0Szon2Db7yUaIC1UjJsi9+8LTlhESI0SpcikpB33m/YO9Eh7hzuCv4VoGuEM3zZaFtSfFP0T+8kJqrvBH7mxu8EFRBlALs/68iM+2XcGg+VaxsIPnf2DJi2Ebsqo1mDB04RF0+Hwveny9HyzLYtWxBHT56m98+Ncll0qUiywjdg7ih4cuO3QHf51Lxuu/iiNRypviLL/Y0rVuRLGPPT+zB+Y/17zEffBTKeAjseRhG5brKrV4lrmyQqWQ47OnXMuiWjvS2h9vF5d2utWL5P529ToA4K9SoHfDKJfb81HKZWgaG8S9ntC5psvHPtO6Ct7qURuvd6mJp7f9BADI/P13AMDz8VXRpkaoo8PhJZch0EfpfqftwA8hByDpxK1IvIPAAul7rEa4Y/HEAJh2bDkGRlifGVHLvsP62FQMNLhX7d6ZU3VZQ2KEKFVeXnkCl5JzMGrpcbeP5fsF2IaGBvgooTWYsz5a4CfiEoW9OTBVmkwslhwUmkP3SOSG4PPMj0dw8m4Gnv3piGjfpeQcPMjV4nZ6Pvp+cwDvrTcvFy07dMfhOS1Y3muBzoDsQj022ymktkPCOfMYzyGtNApuFQfbHB3uEhPk4/LgaEu4vwr+3kr0bVwJXdwQNNP61uOsIBb8vaXFiBT8QdqCbTTpohEtXe5PcVEpZKgbFQBfL+f9XjS8FRrGBGDOkCYuZ/OtFmZt17luBO582hfrX33M6XFeChk+faqxS9ewRSFnEBXojen96mNa33oC64Iznm5eGRO61MLkHnUgK0obJvOxvoeMPMcVlzdOaCfy62gisRTiCuH6PMSFOv+cfQxaBOjEGWMBIOruVbRMlY7yic21Pg+qyK3vK/OXX5Aycxa6rv4ajdJvYvilrS711zYBXnlDYoQoVdJzi39D89dTbU3/gT5KzNlxDQt5Tmf8tfJMG4dWe1JEKWeQKfGjc6Xq5YRVp52uNRfHpK/Rm9Nr9/vmAJrM2iEZmuoMrcGIzl/tc/u44uAoO6i7vNenHn4b17bYYmT7pI7c3/Uq2Xfcs6VhTKDIyuXvrYS30rVHopSVwDY/i1SYJD9UuzSwOK/6e4tn83yBUjnYB1VC1fjrtQ54snlldKwVhkndatk9b3y1EJyY1g1qXmI37e7dyNqwwaXvKldrsGtheKtHbfzzTmdc+bCX5H5LWO7o9tUwpkN1RAf5oHakH5ROQu0rB/lwTvAmrXWJUeZjXa5oHOv4868TqEDuNaH46HtbOtrOGT9s/xSF773rtJ09MeKnUqDdyq9EyzAWvv77W+7vp9Q5qGbMwaAb1sg4tUGLzw/8gGeuuVZlOifPBaf4MoTECFGqSMXIuwo/s6etZSTQR4nfbZZqCnltbFW9JRqnQGcQ5A9QyGRIk8gy6UpyJalkWqWB1mDEpjNJkvU0AAhM1vaOP3Y7A3dLIfTPGSG+XgIBUFJe6lgdMUE+aFzMQZofthysdhyBsXF8O+5vb6VcJED9vRUuDbQ1I/wkhYatILC1VrSKC8aql9o4Pb87WNJpSGUv/efdLvh5dGuMblcNv9pcl2EYTOpWG5WKwoL7NqqEXZMf5/aH+asQ5qcSCLb096YiecpUeGmk71M+luWIRcNbQi5j0OOuNcnZhC61EBuihrdSjqeaixP+KRjxb3Hj+PZ2E/Zx74mXQN2YYZ00MArr9zC6nf36K8PaVEXyzJnIuSTMXBuodS/52cCb/+D7PV/C16CB/MA+p+3VBi0CtcLP9Ph73XBiWjeEaHPRMvUK/PTiyD+lyToB89XlY2nSZrx8YZOoHQNg7PmN3OtgTQ7CCzIRly10oM4iMUL8l3Cl0JY9+CGuGr1JYPnw91aIzKf8ZRpb5yuLg+CCfTex4G+rt79CznCltPmUZeioMzR6k928IU81r4wpvetK7rPww76bGLb4WFl0TUSruGAEl0LYZf8m0dj7Vifu9fPxVTCpWy2MfCzO4XHv9qqLpaNaoUOtMNEAG+REjPiqrIOSSiFzKkY+HNgAL3es7uSdWLG1jNhGNcx+sjF8VQqsHtvGrSUlAPhzQntM7l5btN0Snm6boPbs9B4I8fXC47XDMb1/fbvLMptf74DxnWvgta41BdFnqiKVw/+IlEbz71HFin2Enm5RGcPbWuv/WHyAutWPxKUPeuKN079j1uHFWHlvo+C4Dwc1EC15FWzcgJQPPhRs8/GSiyru2sL/tA3p1ig2U6EGrF4P7Y0bUGRnQmWnzIPaS468v/fDyyh8lqh4r5ulXUNVo2PLZZXcVFTLMeeJkYFF81RxLRs+oYXZCORZRiZ2rYWggmzcbtoEABCmycGa/V9g4bAWeL51LNdOwXtemgoKYMqxpi2w5Ymb/3B/hxVmY/mOj/HD3jn4c+M7eOaqOToxm5ZpiP8Srix32EMnWKYxCkLZvOQykRgp0BnxwZ+X8PyiI0i3WQsu1BmRpzVgOa9YGGB2jpOyjHgSc6n0LLv7nSUtK06yJmeDvj0sX8Hv49oW63gAaFE1GN8+2wzVeJENCrkMk7rVxpCWsQ6OBAY0jUbnOhFY8WI82to4I0YHWQcrW5O+l1wmWHIw1wkR3qt+KgXC/Kyf9YAmMeheX+wbYg8Vb4nncwl/Cctg36Z6KBrGuGcJqh3lh1YSWYIt0WH8ysWDW1RGoNo1J8wQXy+83bMuV8XWgqUWED/MXF5kefCRWC4Z3KIyPhjYEKfe747pfepg0pEVSP/R7ECqKrJMtE69jMqs0Hqn9lLgo0ENBdvkOg0yV60Ca3Az1JTXV0Oa1Z8iff58XGnUGLf69cf19h3wcm3pKBO1lwKMXI7hl7dBZbA+T7xM1n4Mvr4XC/6cxb1mWPEkRm4SirUPDy9y2O0wTTa8jTr0uHsUveuFY1K3WrjzzDOA0XoepYxBjzphGPXdG5h5eDFmH1jA+cQAgObsORizzUvEXlWriq7BRyu33hsK1oQ6meYQ6gIHJQjKAxIjRKniTIvsupSKd/84J1qGAWyXaUyCNgYTK1pKKdAZsOTgbRy88VBg/QDMSyoNZ2wXhPIC5qJuUtcuS/ge9U+3qIzvnxdGfqw8ctfuEk18tRDUCPeD2oGDom1OD1cY0DQaK15sjQ8HNnDrOEsRulZxIYgMsC5VNKkciOPvdcOsAc7P91xr+w6J1XnRA1LOf478BtpWD8ULbapgWt96ooJfCjkjKGynkDEi4Rzgo8TApjGY2b8+fn2pDQLVSsllG3sRDvx6KIOaxQAAZ1mxDaWNDXYcdmm7xKOUydCmeghGtYuTFDr8hIHv4CZM+eb7SXf3LjRX3E9zbqkxI/VzVkH8+7EIvRBfLzyVexWKfTvxYM4cUTtGLv48bf10lEWDefL0GYLthvR0DMg2WxlapVwWn5vXW/19+8m9BmxeKLk9NsQHrE6HiMIsLNvxCbddYTLgp52fYtrRZVxeD0WRQHmS56PBtbexHMnA4n9N7EdWRXjLwKjVeOP075jXKRLZa9fCkGzjxK5UImfLFuiTkhCfehlN028IdhecOAFDmtkJXxnrWNDrZUILnsXyU1jOz0VbSIwQxcZiqcgu1AtKwlvo+PleXEkROnSOWX4Ca04k4od94kRJfMuH1mAUhIzqjCbRTFYqg6oFqZh8wGwZcadGSmnAH7yei6+CPjZJwm6l50talL4c3ARPt6gMuYzBgXcdr5fbMmdIE9E2voVF7SVHh1rhXB4DPt3rR2JIS+nifXw9qOKtxc97phnC/VUC59b4auKZ/JTedR0WBvRWyrF1Ygdsfr09mkj4yjiqOcIwDD4a1AhjOlQXCU6FjBEU9LP1GTn6v65QymXwVsoxsl01zurCF4GxIT54snkMPhokHeJaK8LqQGsRTe/2qot9b3XCi+2FvgpPNIvBuMdr2H0vtreDrKh45Iz+DTCklXiw4Quh9PffQ+pnnwMAbvbshduDnoAh03l5ewDYNqkDZnWqjN5MWlE/xPelwiCeQfM/W7bQQUE3ie9P/7ewOJ+iSIxkr1tnrsKcY36G3Ht9Il7euwhLd3yCWUcWi84T62+d8TsSI0gX1qT537HlGHjzH/SrGQRTntk/xNtotZ6aGBkq56ejXfIFbilo3r55eOn8Joy6uBnTjyzFWydX2b8egGfD9VxU0JTjK/D1399w+yIDvKEIN2eINTx4gLQvvxKfwGRCwWlxGL+6dWvRNnuWkXHnNkBhNGDimd8F21VGsxXInerIZQGJEaJY3EjLQ6OZ2zHrz4toMmsH4j/eJfIXScgowJu/SdehsJTcPnA9HW/9fhbZhXqhGNGbBD8O27L1gDmbqLvczyrEVzuuOW9YivBLvvurXEt67K2U4ekWlbkZb4ivF1o6qYjK58nmlfFaF2F+hnCe06VFSLSTyLvQvX4k2tUME20HrJYR8zmsjw+LWT+AF0FRSaJmiqNU1RbqVQpAg+hADGwqTj7laolz28H886ebwEshwxdPN8aHgxoi3F8lELeRAdL+CPxBNr5aKOYMaYpwiWiimhF+eLd3Xex8oyP2v92Z8xeRyRjEhfmK/EcUchmm9K4rCge24MyHyeL4aXH8ldmcP3f7dph01qUGQ6prNXvqRgWg9aRnkPj8C9BcvSqdgE9i+URgueP1hdXrBc8FRiZH4fnzuD1kKApOmJPvPXhjkuBcCt4yx+0BA3GtdTzyjxxF4alTkIFFVEEGGADhBVaB1S7pHD5qY72XdQ7EiOGhsNZSh6RzGHd+I262bsVt4/uJ2DqXAkD1nGQ8eXM/5GDRNuUiOidahUK+Qmz1ejD/e8zs3wCrLi3H4/fPom5mAiafXI3/HVsOdXAAFGHmJG+G9HQYs7JExxszMpD162rR9uBhL4i2qeqIfYsAYOCtA1i7+T00SRdOBC0+MoUezuRLYoRwieN3MrDw75vcjHPOzqvQ6E1YevAOALPDqFQmznwJiwkAJBVVPH1h8VH8cfIepq47J/AZuZ9VKJjd2is/b4ttQSwpimuOdBZaaA/LwK5SyBBZNEC/1UP8wOAnyZIq7ilzUhbdgmUWbjtohvlbLSMW07hCLsP/+ggdZFmWxYAm0ZJZOvlLZV4SYqQKz1EyOsgHI4qcGsd2rI4PBjZwK7Nm5zoR2DShnSAKxtVKypZMnJ8/3RjnZ/ZAr6IEXINbxmJYG3OfXPFv8rEpAshnzpAm8FcpsGpMPHZNfhwhvl6oFemPKi7klrDQuHIQ93fv6lah5qxvHwxsgA8HNsCi4eZcJlVsHFRlAQHcUg0AZK5Zg7wD4uy/jig4cUIydw2rE1tG7C0jXm3ZCrf68Mo2yGVIfGksNOfO4e4Lw3CjZ08wACae/o1rwhcj2uvmZZHk6dNF5zbxRM+0Y8sRmveQa6+/bz/VPqvRwLfo/o/OeyDZhgEw5+9v8MnBhQjSOY+m4ftv5CvFwlZ7+TL0x44gLN1qse2eeAIdks5BHhQMRZj5GaFPEucY8qph34KmCAoSbVNVsx8x5GUSP/u8i8SIxsOVoKk2DWGXtFwNwv1UYBgGg4sq287eegUXZ/WUrFYrtVRj75l6L6sQCbxQ1C3nUwQP5nm7rwtqUyRluRZ2FupbNiWwVQoZrn7UG2k5Gjy36ChupLke7te6WggOTekCo4lFQFH4pyW8kZ8mvVKQD64XnVcqRFrupObEjP714adS4MmiWXPXepGYvvEitz+E99l485ZYnouvij/PJuN8URFBljUveYx4LE5kfeIPkvzZuEWMRPNSUMtlDGYOaIDxXWo6jYSQgmEYNK4cBJOJRZvqIVB7KZyWhreweEQrPMzXomaEfUuM7YD/cMlSZG/ciCrLlkIRbLZC8S0jttE6TzavjEFNY1wWiVJ8+2wzvPPOQjx5aQf2pHUEIlxLFOarUmBY2zhorlxB0uzlmPJ4N+huF6DHrhUAALm/v0CMZP26Glm/rka9K5fBGgxInjEDiogIREyciMKLF5H64UeIeHMy1K2s1gFTQQE02eIsnqzeyTINz/GS1Wqhu211sGZkcsHMX3/X/BvnWx9sfS4AwCixzMTaPIPujXsFABDz7TeCZZqgoUPhXa8uUmZaHU8/3/4Ffq3TDSMubxOdl/HxAVtYiHqZxauNozZIP6sSRr8ouV0eEsI53xaePiXYF/7mZGivXoPupnT9H1mA2BFaUclqUVRWqQJ9guP3YVmm0bKetU2QZYSQZOOZ+2j98W40mbUD2y4Iy5kfvf1QMsHplLXi5Dz8Bz7/b53BhI5fCNeK+flAAAiiXvg1YxxhL/LkBxunUXc5+r+uAICIAG9M6SUOteWHRb7aSTyTiQ7yEYVX2i4NqHmzcKmZsTOrQI1wPwxuGcvlnIgJ8hFky/TmWTL4kR9+KgX+fK296HxSSyJ8kcS/ByxLNvx8F/HVQsEwTLGECB+ZjMHqsW2xZGQrl4uABft6ORQigPgzTvv8c2ivXsXDoigQY24u50wJAEESSbzcFSImG3+K2BA1ph/4EQ0z7gD5zvN32JI6+1Nkr18P7RsT8O6v09GsyMHS1jJigWVZ5B86hOy16/DwhwXI2bYdiS+PQ+GZM7g7+kXoU62ZiNlCDXL37hGdo+DYMczrUw3DG4fhi46R+OH55gL/IUc+I3n79klu9zZal5T4kSwWTLni3z9r5164//pEmIr8TOqcPIFKs2aCUQnvweo5yXjv+ApUtrGM+DRpAu96Qotg8AvipRAp/ndsObomnECvO0edN+YhDw7ifEZyd+4S7GMUSnhVkXZIjZk3D/IgoRhRt24NZXQl+LRsAXWbNqj2x+8IfPJJYZs2bRDy4mjE/fEHYhctgrePeZKil4lz75QnJEYIST4umhHnaAwYt/KkYJ/OYJJMsrRLotw8f704vwzWJGuE+6IZrzgWPzSTj9qJr4ajMUWlkAlmxX7e4nPxnVSlMrxKEWQTfuksYZyUQyh/acdX4j3yE6Zl5Fv7xbeM2OKoF/yHFV8Y8C0We9/qhAUvNEf7WtJ+J48KHYr6F2DzfbJaLQwZGbjWqjXuDB7CbQ/2LVnNkoKTJ3G1VWskTZkqub+Og5l4/rFjuNGzJ/L++UewnYuUsbl3ZGq1tBgpKED+IWtG0fuTJsGYXpTOX6/Hjcetic9MBQXod+sQAOCxpPPc9gdz56L22Kfw7Acj0XDyMHSvpEThxYu42bcfMn/9FaYC95PvqXhiJExjP18GHxNjZ/gq+ixkgYGQ+Rb9Ll3RjEol4tasBqMUfs/eDV2LOOuQdA5vnVoNFS8U2LuJc0uXMioK8kA7od4sC2WsNfpMHhQEdevW8O/eDQE9ewiOi/ttDWIXLgAjk6HqihWoumwp5AEBiP7kY8jDeL9FoxGRb78Nn4YN4Ne+HWotskYXlXekIR8SI4QkjjJRag0mkcOcPfgDbIFECeySEu6vwpNFYZSAcCmCj9LJDJaffZKPjAHWviKsxSElCjryfCFcdbKsE+mP/rwKoQHeSs5/48vB4miYlyQScPEFiG3SLcAsGCoXhZG+0MbqZS81o+9cJxzeShl6NbBf4IzvM8K35PCFSbUwX/RqKIwYchV9UhI0ly4V61h3eb1rLXw0qCG2TOwg3CGTIf/AAQCA9upV9GoQhcrBPuhRX/y5mHQ6aK5ec5jsL//IEWguX0bh2XOAwYDsDRugT0kRteuecAIfP9FQMsNtwvAR0N9NQOJLY7ltrNHIWQDEHTNJihFjdjYMFvHhhIxly1A5Px1r/3oP0479bLed9to13Hnqaehu3kTa13PxYN43dtvaw5tnDQkrdE2M2C7T2GJZanMVeYA51wo//Dj6i88R2K8fQl8aA3V8vEvnCX3pJajq1UPIi6Ph105scbRFGRMDWUCA9E6WhVesNfosZMRwVF3+Myp/a04FL/P2RvikSQgd9zJ8GjfmUt/bWhArf2P9TozZws9XHWwVNAUedGIlnxFCEkcFww7ffIj1px2EzvHgW/2kfEpKip9KIRiQQ20sI4OaRmPEY3GCuje21Aj3RfVw6TwA3z3XXJSgimEYTO9XHx/8ZR40n2kVi6EtY+GnUmDV0QRM6FwTyw/flTqd6DzfPtsMreKCsezQHbzRvRYqB6sxpGWsZDZRlUKO1tVCcIyXrZUvQOx9Z1sndkBqjgY1I/yx6qV4+KukZ/hLRraC1mASCNHn46vgl6PWGTvfMiJliSkpN7qYl8Nq7t4FZUyMk9Ylw1spFwg0DoYBeAPSDy80h9FoApuWCkQLI3yS3n0XuVu3IWbOVwjo00d0Kt29+0gYOQqA2TxuwZidA2WUUNzIwOLZplHQXHQuxliWxe2nnhZZRLj9er2kGHkwfz5y/vrL6fn5qA2OkwTyl3bsiiMnyHnJw1wVIyaJCZGqXj1oL5utunKeGGEUzq1aihDzJENVty7yD5ktQoH9+wMAIt58EwBwua7YqVvUh9q1EfHmZHMfNRrI/PxgzM7Gw4VWC4RXtWqcL42ycmVJPxwAAGsSWEYYH3GkTti4l532Sd28Gfe3IkqYyE/hHwAvox46uRKavAKghMuqxYUsI4QkjgqGrT7uemlqfs0YqciaepUCsGRkS8kwTlfwVSkEmTVtl2kmdKmJZlWCJf0tBjSJRrd6kfhxuP3qqvYcJvnRPZ8+1RgKuQwDm8ZgzcttEWEnTNQew9vGYc+bnVA52OxT4iit+WdPNRbk8hDmzpDuq7+3kvOfeKxGGBrZqQPDMIzIIvbhwIZYPro1r411Hz+9emmjuV68SqmlAgMwcp7QMhiQs2Y1bnTpiodLlwma5m41O0A++PY70WmMeXlI/XQ297rgiLXis6kgn2vD52qTprj73HMYKLMO8EOvCgudsSwLVqeD1kEys/yDB3F/0hui7dlr19k9prgkv/deic/Br/8S1VA6NNWWDvfNaQPisq0RKLELfuD+5ls4/Lt1hap2bfj37Gn3fL7tzVaM8PGvIvj551F15QrXOm+DKc/q3yLz9kbo6FGIsAlfrvaHNdeHMjIScn+rf1Pl778XtFVEWK2uplz36uTwqbL8Z/h17owom8gkma+aC+/Ny3S/SGdpQZaR/yhnErOw6UwS3uheC4V6I34/cQ9DWsbitxOJ2H05FStejHc4s1UVs4qqLXoji0X/3EKIr5dkLoc3utVCl7qROHjjocTRzlF7KQT5LmyjaSyDq9K2eAfMdVaGtY1zeH4fOyGLroYalzbVwnxx9H9d8eZvZxHur8Kdh9bZr8qBH0hxkckYdKwdjk+eaIQvd1zFF09bl4/4ItBVMn75BYxCieChQ8CyrMCc7Gipw6TR4M4zz8K7YQNEf/SR29d1B4ZhBMXVTIWFePiTOaV32mefIXTUSNExutu3kff33/Dt2BEMwyB33z4uukMKU34BCs+cwZ1nnpXcP2b9l+gcGIOaWfe4NOwWkt5+B3n7xZk/PYZUHLqbBOny8eX+76A2aCFvas2PI/P3l3ReBYCxF/5EncwExBdlY1W3aQNlpHXWb+AVy5N5e6P6po1gWRZX6tUXnCf0pZdQcOIEwl41f18yX19EvT9N8ppecXHQ3bnDvVZGR0OfJAwjZryc126S+fqi2vp1YJRKMEqlYJlGHhSIoCFDkLtnDwKfekrwG+F8YIqBb+vW8JVIksYwDLxNBuQBKMwhMUKUMoPmm3MKGE0mnEnMwtl72ThwPR2Hb5kH/Z/+uYVJ3YQzkBN3MqCQy9A0NshuManiYAkPfVciCsWyzOCs/oqFJ5vFoEaEH77YfrXoeLlgtm67TGNZupCyjChsrB5LR7bCxNWn8elTjXEtNRd3HxageRXpdednW1fBT//ctlu75L0+9fDxlsuY/aR0ts6SwDAM5gxtCgB4afkJbrtXKX5ntjwXXwVD6gdDn5AANiYADMPAz03LiCEjA6kfmoWEunkzJLw4BiEjRyJ0tHkZg9XaXw7I3bkL2itXoL1ypUzESPZfm60vGBlYfhGywkIowsNhKPLzMOl0kEkMOIkvj4N3/fqI+eYbh0IEMFtG+GZ7WxSsCXWypC2Q7i6zeASFAuGvvy6ZEt4eDTLuAAAYL6tYiJzyLpLf4wkDuZyr2eJt1KFnwnEAgH/vXoi0cQw2PhRPcETRWDIZt6TiCrE/LkTmL6vgVS0OhafPIGT0KNweOAgAEPP1HOQfPYqAfv1cOhc/aodvGZH7+6PSB7MQNWM6Z92JmTsXuTu2I3joENF5SgNLiv/8nOJbXkoKLdP8x7mSkouz98xrsBYhAgBzd10XLKHcSMvF0wsOY9D8gzCaWIc+I8Xl1gPxjW6xzvAzdz7roHZJVKA3mvDykfiqFAJnWlsnTs4yIiFGbFNdd64bgbMzeqBPo0qY1K02vh7a1K5lJNRPhZPTukk6mgJmZ9PT73d3+F5KA741oSzFCAAkPPc87jz1NLee3qGW/QRmhvR0JM+aheyN1iqt/JDPlI8+hiEtDWmff27dzxMjtoOGMdM6y7W1oLB6PYx2Zs+syQTWaETh+QswZGaCZVk8+P57PFy0CIXnrKHoSW+9xf2dsWwZWK31t2HKLxAUbRPVDeGhuXQJN7t1s7uff04pnw53Yby8oIgunrNwaSIVDRI6coRk29CXxjg8F6NQovL38xE5bZogLNUrLg51Tp9CrYMHoKov9N2o/PXXUEaaKwWHT3wdABA1U1jbxgLfd6fqcvuOuVJ4VamCyKlTEPzMM4j+7FN416mD4OeeRcioUQjo3RuVZs6ETOU415GULxTfMsL4mJdr+ctMAb16ImbOnBJZRhzxdSM51lRJR5O6juvalCVkGfmP4yhMs+kHOzGwaTTmPdMMG89YTY15WoPDaBpbFDJGVDdGit9PiuvFWMJk+eGVneqECxKejXwsDssO3QFgTrTFFwh+KoWg+ivDMHi7Zx3OcmIRVVIrAFLZJV3NYwGILSu2BLto7SkJfIdSqXDr0sSS3TJ35074tWuHjrXDsWh4S9SKFDv/Zq1bzyXa8u/eHTK1WvAdSCWxMmmsYoS1WQYz8szHbEEBmKKHMqvX4+7IUdBcuoTqGzfAq4pV/LE6HW498ST0yclgCwogDw1FzJw5SP/mW8G5654X58fJ3bHD2q/CAkF/b/bsBWXlyvCqbj/TpTOS33sPjJNByxUYlQqV587FnSFD3TrOv3s3hIwcibvPm3NoqGrXhvZa8cskqFu3Ru7OndYNBoN5uUImEy3jRLz5JsJfew2a69eRu3MnHi4QWogYpRL+Xay1mHxatEDhyZOImjkTMi8vyEJDUWXxYlxvK4xysxA6bhyCBg/mspraEjv/O2Ss/AWBA/pDWankQs7WB8Meke+9hwfffouYb+aJ9slUKoSOHQtTfj68Kpet47YUrV+UXi4sT0iM/NdxohE2nknCl4ObIKfQ6s2drzUICm85w1elQHbR8fUqBeBysmOP+hrhvrj5wDwrDCyyiPjzxIivjS/CzAENEOLrhQ2n72NkuzhB2nmtwYTYEDVWj22D4CLHz/Gda+L5+CqQyRguhFWq1ocrAupRp7zeAn/ZwhJ1AADd7CxT8df5Tfn5ZjGi1wm2ia6htWauZHXCJRu+GDDm5XMzxPxjx1B40pwH52aPnoiZNw8BPXuY9x05IshcaXz4EJkrV4quW3jmjGibnlfLhS0sFJn89ffuQX9PuhijS5hMjgvKuQjj5QWfxo2hjI2FPtE1x/LKC36Af6dOgnwgXlWrSooRv86dkbd3r2i7LXYrxdoR94yXF3waNIB3nTqA0YSHP/1k3WeT56PKjwuhu3cf3ryaK4rgYHhVrQrdXXHUGsMwdoUIYPa7CHt5rN39ZUXIsBcQ/MLzdic8EZPFDscVCRIj/3FyNHZCxnjUem8rWsdZB5h8rQF6N0a5bJ6QCfBWwNdLjnydeQ3SRykX1YJpEhuEke2qQas3IqyoeJu/t/UBpJbwRXi9ay283rWWaHvVojogbaoLa9LYRqRUChSHxJUk26CpsBDaGzfg3bChW9aU0kaqqioffVIS8o8dQ2DfvqKHvDsYHlhzU9jNicDvF2+gNebmQREeDpZXuI0/EOqTkpCzYwfULa2pyFmtFqaCAsjU5u+XH6HAFzK2IuH+xInwv3wJDMNI5ivJ3bVLtE1qQOMXg0t65137oZcexmhx0nThHlS3bAlZYCDnxGj5bAGzpUQeHIys334THOPMciDz9QWjUsG/cycoK1VC6scfCxvw+hU65kX4NG8h3K1QIGT4MIdiRObrKxAiFliJgn2POp58VjzqkM/If5wrKa55Rx+7Y12Tz9MaYHAjWqR5lSC0KKoo+3ybqljLS0F+cVZPUchpmJ8Kw9pUxZgO1iReATwxYmsZkeKfdzrjs6caoY+LybVCfL2w+fX22PtWJ25bSSwjCS+OwZ3BQ5C9dm2xz+EqWes3IHvzZsl9zsTIrX79kTxlKjKWFy9M0YIhjWcpsBmYTYWFyPztN+jTePkmCq1i41ZR/g2+GOFbOpKnvY+0Tz/DvQkTuG1pc77G1dbxyFzzGy7XrYfsjZus5863+h4ZJbz/DampSJs7F3n7/ha/EYnPSy9R0ZY/0NlGS3iSkJEjhRss78dmjKuxXVxzperKFYid/51AhER/8TmCBj+NgD59UOmDWfBu2JDbV3P/32B8rBFwfp07A4DAQTPinXdQ+9BBqFu1QohEBdlKH30IAAh7/TVEvPUW/Lt0FrWxjT5xVTQ/qgKRKB5kGSFE5GuNDpOE2fL10KYI81PheloemlQOBMMwWPliPHxVcshkDOpXCsCphCwA5tLn4x4X127hL9OoveSQMY6XIGJD1Bga4p5zaINooZMdv6ibuxSeMhe0yvr9DwQ9/XSxz+MMQ3o6kqeaowQCuncXPbidaBHOApF34B+EvjjaYVtjXh6yVq+Gf8+e8LIxu/OdS1mNcAnlwbxvkLFsGQAgbu0f8GnQQLQEYcmNIYXFIdbAy0pqKXmfMkPshMi3jPAtJhZSP/0MudvEg7E9pKIuTHmeiypwRODAAcj45RfAZiC2LVypiJRePhOdr39/LrEXIAwdVUZEAAarVTP6i8+Rf+Ag/B7vyEX0sAbHgiBo0CD4dewoWNqzRSRGFK4NSyRG/luQZYQQkac1wOBi7oCZ/eujaqgvfFUKNI0N4syQ7WuFoVlRWOzXQ5tiUNNobJ3YAV8NaSIZxsuv96KQM2WS3dPC8tGt8XrXWujXqBSiEMrY7MrPlWCSCH11danJFfNw2udfIO3Lr3D7yadE+/gPfmN2NrTXr3OZN/k+BXeeehoFp07BVCAUI5mrVpXIQZKP/n4SdEWVSKUsI7m7d4u2OcLwMEO0jV9Z1oIsIAChLmS7dIZ/r17FPlZVowZqHzwg3mGTR0fmXbwsmn4di1LjF0VyyIODuH1yPz8E9OoJmY8PAgb0hzwkRJR1VhFujrBSVramMHckRIDiW0ZCx5ir4Ab06e1Se+LRhiwjhIjLyTnYd/WB03ZjO1bHs/HOrRNVQ30x95lmDtv4qxTo0ygKWr0JUQHe8PVSIFdTNmvCHWuHC2rJlIgSiBHtjRswFWrg06ih3TYsz7fCVFgoyEcASFtGbJOJAUD+ocMwZGZy9Tp09+5Bd/sO/DpYa2cUHDtmvk6R86nhwQOkfPQx/Lt0FoiRzJUrOUfQOidPiFJUJ739jmAwAsDlFykNLBk/o2bNQsaSJeIGdmbMivBwGB6I7+vc7dtF26SKvcl8fBD26qui6A938W33GAqOHpWMKLJHlWVLoW7RAoxSCblUUq1SEsUhI0ZA5usL36Lw15Bhw1B47jz8e3QXtIv+7DNzxIyts+mypUj//geEjX/V5WsycjmCnhmKrNVrzK9dFCMho0ZB3Tpe0p+E+PdBlpEKhlS+DVvm7ZZOxd23sdCS8L8+9Uot6yfDMPj++RZYXFQmnl9ttrwx5eej8Px5hxlBSwrLsrjVrz/uDB4Mg4NBycCboUslBbOt9Js2bx6ut2sv6efAr0B7s1t3JBZlnjSkpyNn61bB7JplWdwdOQq527cj6d0pSJ72vmT/bnTrLpqFG9LTBQ6sZQV/CcenaVOn7SPeedvta/CXLWTe3pLJziy4Eqrr07y5eVlE7t7vxrdNG8eDtET0W5Uli926BmBeIgl+5hl4xcWZT+vri9jv5yNo0CBhO4aR7I+qRg3EfPUlVNXFRR0dYan9Apiz7brUV5kMPo0aupTxlHj0ITHyH8TRIOqodLwzXu1k9fWoX8l5REVJ+PiJhnimVSzWvyqdS6AsuTNsGO4MHoKczVucNy7mjJQvLKRm6xb4ywVSA7ytA+vDHxbAmJGB9B8WiNpKhaIWnj2LO0Ofwf03JkN36xa3/Vp8G0FYrN3+ZWaCsREjrFYLtlBsWShLfB97DMEviB0o+cgkiozx8aoh9mXi/5YsFqCQF0dDVUsc2SUPtS5HhL8hDtP0790Lcat+gczbG1H/myraz6fyd9863G+7LCO1DOf72GOI/XEh5EFBqPz9fMfn8zB8i5+lgBxRsSAx8i+HZVmR+HDkfOptJ6OoM7zkMkGK+HnPNC3WeVwl1E+FT59qzPmdlCfaS+b09dmbNjppiWKLEb6wcOTPYeKV+5ayjETaqbDJGo2S221J++JL6O+LKzC7U31VKuOkMcu1yqsWlJUrI+63NW4dY0FRqRICnxjE+RDYg/G2ihGpWb1fhw4IHjZMsC148GDub4sFKPLtt1H9z02CdoxSifAJr3GvQ0YMR43t21D76BFUWboEgU8/haj3rdalgD59UP2vP63H+/gggOdI6t+tGyLesloLRO9FZA2Qvof8OnZErcOHBEnEHnXkLoSOE/893BYj+/fvR//+/REdHQ2GYbBhwwaH7detW4fu3bsjPDwcAQEBaNu2LbZLrNES7sOyLJ796QieXnAYJhOLY7cz8MmWy3jnj7N2j3FUjVdqCcdSBfedXnUESzLuZGgtL0xaLefYWBq4Una8uPB9QUShsgUFuPPsc3jw3XzBMo2UZWTWwAboUjcCS0e1EmzPXrfObvRKaZP3tziE1pG1R4oa27bCp3FjRM1wLZulBe/69VFr7x54xcZCGRWFOqdOSlotwDCQqa1ipMrSJfCzGaDlgQHwqlqVe+3fo4drDqsMg9onT8D3sbbWTSoVvKpWhTwwEL5t2yL6o49EjpyqmtaCcGxhIRiJYo72EDmoOhC0/5bcFlVXrUJAnz4Ir+DJvyoqbjuw5ufno0mTJhg9ejSe5NUNsMf+/fvRvXt3fPLJJwgKCsLSpUvRv39/HD16FM2aOXZqJKQxmVizcycDHLlljgRIzdVgyMLDTo915OPRNDYIx+8I/Re+GNwEgT5KNK0chAd51pl5aRbSKy3uPvc8NBcvourKFVC3bFni89kLMczZ4sLyjRP46+Imm1DZrHXrUXj6NApPnxZst1hGtDduIOXjj+HTsCHCJ0zAkpFCIWIh+0/pgmpllSxK5u8PeUCApKXFGZbP2uKr4Iy4339H5upfETZWmElTplaj8vzvcLOHTal4uVwwgDMqlUgE+nboCN3dO9zrgD59IA8K4l7b9WWQycxpyqOiUPmH7yFT+xZPANj4kTjyEan83bdIfOVVRE6ZwvXh3466eTOom9OYUFFxW4z07t0bvXu7Hko1d+5cwetPPvkEGzduxJ9//klipBj8djwR76w119JYyhuEDC7mBXFUAK9BdKBIjKiVcq5yLb8gXVnXQSkOmosXAZgH4bIUI/cn88znxfwY+KGvotTn2VnSxxQWovDcOa4OScHhIzAVFNotd26JOuGT/edf8Ov0ePE67YSoGTOQ/89+ZBdDjFjgR+EE9OkjEn4yX1+Ev/4afBo1hE+jj20PByBdwp2RyYTLNF5CMRL5v6nwadgAplzr8pTMVygq+D41wpNb2/h3Fif1coSgBL3NbyrwqaeRtW49/Dp3Eh2nbtkStY8esVpTSqnMPEF4inKX0yaTCbm5uQhxEHuu1WqRk5Mj+EeYsQgRAPi8qBgcADy/6KhLxyt4SzH1bJxQ40LVts0F/if8irhlmQekpJSWdz2jVCB740Zk2qTIFrRxQ42wRiPuvfY6Hnz7nSBDqe2M214yJ1ajERVEy/zlF+7vghMnnPYh6e23cbNbd6ft7BE4cIDdfTIfb8iDXPfxUdUWh2QqY2Kgql0bXjVqQOYnLsAXt/pXhIyQrgZrQTKqRS4XLNPIVF6CZayQ4cPNzYKt/bcM6l5FkSFe1aQL45VkGaTyD99DVbcuKn//PRiZcKIg9/NF9Y0bEDFpkvR1edYQfuKyuDWri90fgvAU5S5GvvzyS+Tl5WHIkCF228yePRuBgYHcv1h7RZgqOHla66CVkOE4eqFZlSCBJQUAutaNELyWyxi82qmGwHdEy8vA6OMlx8bx7bBxfLsy8xnJXPMbcvfsKdE5ZKrSCvVjkPTuFKRMnwE9LzuosInrA1H+wYPI3bkT6fPnI2G4dUC9N+4VaHmzblYnLUZ0dyRqqMAsXrLWrsPdF4ZJ7rfFmO2ecykfxssLPnYsmoyXlyBJliMUkZGI/vILeDdogMo/fG89h0yGauvXofqG9ZKizJUcFIxS2jIi8LNQKCTPLxQjZnFeZcliBD7xBKI/+1T6giVYIvGuUwfVN6yHf5fOCHrKvOzt06SJ2+cJGTEcsT8uRO2jRwS+KATxb6FcxciqVaswa9Ys/Pbbb4iIiLDbburUqcjOzub+JbpYjfK/TnK20IExz0lSsFkDGnB/P9EsBp3rRmB6v/oAzIXn+FbhUF8v9G8Sjbd61MHFWdYMkbYJPpvEBqFJGeUA0d66hZQZM3Dv1fFuH8uPKHq4aHGp+EWwrDULrT45GYA5FNYd9CkpuNG9B+48+xwSx9p3hrzVpy8SX3kVmkuXUHhO+hrp338vuT1z1SrJJZmygFEqEfP1HFHUCVCUlI3nYwEAEW+/LbJUKaKiEPvD9/CuXRvV1v4hWtpg5HIwSqXAmZTb54LVS+YlIVjkcjC8miyMTIaAokyoXjWtIb18MWJZg1NGRSF69ifwrltXcMqwV82JvaIkUtYXB58mTVBzz25UXbHc7WMZuRx+HTtCHhjovDFBPIKUm6199erVGDNmDH7//Xd069bNYVuVSgWVCwmEKhJ5WgPGrTgp2JavtR++2aVuBIa1qYoZm8x+FBZ/j2ZVgnHlw17wVsoxd5c1PfeR/3WFUm7Wpl4yBqPbVcPpxEw8XlqZSl3AkG6tDCuVRdQRrM1SR87WrQLTtcvn4YsYnhKzWBPuDH1GeICTPqZ9NQf6xESXyrvn7d3rUrl2W0paBM8tGBmUUVGIeu9/yD90SJCLRO7nJ4qiCX1xNEKGvYArjc2z/fA3JyPspZdculTIyBEwZmXBq1o1LsGZS9k5Jdr4tm0LmZcXQkaMgKmgAMroaISMGA6vanECS4/Mywv+PXvC8OABVDXFeUf4hL02AcHPPwdFaKjDdu6gjI4utXMRxL+JchEjv/76K0aPHo3Vq1ejb9++5XHJfzWrjibg0E3zwKz2kmN42zj0+1Zcj0LnoLLu4hEtBYM53+HUssTCd0i1CBEL0/vXL17nSwC/v6xe75bvh21hM0Oae6Gl3Hl4kS2WYmAAYExPBytVr0dCjOhT0/Bw4QIEP/ss9KUYamwPZXR0sSJYioMgPwpveSL4+eehbtsWXtWrc6nfLUnI+N+jrV+EI2Te3oic8i50d+5Yj3fhnuDfR0GDn4YiKgohzz8PAIicOsXaTqGQzL9Red5cl/rHMEypChGCqMi4LUby8vJw48YN7vXt27dx5swZhISEoEqVKpg6dSru37+P5cvNpsZVq1ZhxIgRmDdvHuLj45FStPbu4+ODQDIpSvK/9ecFr387Ic6c6Qxbq4JU9MsjFxHDFyNaLWBboVavR/6RIzCkPYAiMhJ+7dtx+4w2YsTWHyB5+gwYc3MQ89VXgkGUNRjMJvyia2svX5LsWvK09+2mRDfpdDCkpUEZEwOGYZA8dQryDx1G9p9/lTiBU+DAAfCKi8ODed/YbaN1IVMqAMiDgiQLwNmj+p+bcKu/0GFVULOF931FvPsOGIaBMioKdS9egO7uXclZvu0yjru465zsVa06QkePKtE1CYIoe9wWIydOnEBn3hrv5MmTAQAjRozAsmXLkJycjATebPDHH3+EwWDA+PHjMX681RfA0p4oPbrVi0CorwrPtBY7/ErlBWnmwfovUvAtD6xWC9gUhUtfsBDp861pretevsSJCFNevqAtf7nFpNEgqygiRjtuHLzr1DFv12pxq28/eFWvhtiFC1F45gzuDhvuVp8Ljh7FjU6dYczIQOTUKdAnJSH/kDnfiyk3VzJrqjsEP/ssNJcvO2xjzBBXnZXCq1o1Ue4SeyirVoGXhCOkqSBforVw+YSRy0W1SaJmzkD+kaMIHOD+0hlfwLhaRK247QmC8Axui5FOnTo5rH1iKzD27dvn7iUqNCUpzhYbosaM/g0E24a3rYoTdzLRs0GUqP1jNcPw47AWqBEhDqEsLqxOh7xDh6Bu2QpyP/fyHfCtGVKDeKZtunCDgfMPsJ3xW8I2WaMRWWusxxnS0oAiMXJ7wEDo792D/t49ZK9da9fy4QyLGEidLY62KEkW1Jr79kIZFSUoesd4e4v8Y2xh1GpBhlcLMh/XyspHzZyJgH79wDAMwsaPh/baVeTu3AVAaBnhh9A68+8JfuYZBD/zjMM29pAHBSF24QIwSqXd3C/28K5bp1jXJAiifHl0k0VUUAp0rtUUkcJPIvfHBwPtl6cHgB4SIqUkpM2bh4zFS+Dbrh2qLF7k1rH8gduk1XHbCk6eNCcxs/GRMWl1YHNyYMzJgTHjofBcRcImc9WvApGgS0iA9vp13B48RDCoF1eIlCXKKPN3w0/WJQ8OhqEossceMh8fwFcN4wOrQzCUSvh17cpZbVS1akF7Xbo6s8xXzQnJ8NcmAAAu160HQChGbNOblyV+j7uXqC1u7R/Q3b4DdSvp7LQEQTxakBjxMAU6Axb8fQut40JwJSUHHWoVP3pF7eX5rzPrV3PCpfyDB+22yT92DLrbdxA81JxrpvDsWSirVAGrtYoRS1bSu6NHo/DESYS+/LJoOYLVanCzZy+Y8vIQxCtoBljFSPZGYbG7h4sXcw6WnoLvuxH26itI//4HAIC6dWuo41sj/dvvEDnNmlWV4aXwF9UkkYBRKlFj21aYCgthys2FMjoaxtxcGDOt2XUdlbuXKrLn16kT8vbtQ8hzz1nfxyPsvOnToAF8GjRw3pAgiEcCz49eFZy5u67jx/3WhFfNqjie9TrCV+X54nX8gazw3Dl4N2wocBg15uVzCb/UzZvBkJ6OhFGj4VWtGsJefcV6Ho0GJo0GhSfM4cwPFy4UXcuYk8NF0WT9/rtgnyXDqW2CL0NS8T/f0oLvG+Pbti0nRhilEmGvvorAgQOhjImxtmnTBupWreBdv75LCeEYhQIyb2+zcCnKm6EIDoYiOBiR06ZBHhyEjCVLufaxCxdAHhqGO08/bd4gEaUV88086G7ehIqXa8OrWpxb75sgCMIeJEY8xK5Lqfh61zUkZQkTmZ1OyCr2OUN8SyvzqHuwRiOS35sG7/r1BWLkzpChiHx/GhdWCQA5vLLpxtxcZK1fDwDQ3b4tWqYxOikDcGew/Sy+prx8mHS6EmUbLQ2UMTHisFujEXFr/4A+8Z5wGYFhwDAMvHj1WQBzBIklEVbOzh1Or+nIryLkBfN3kbd7D1fLR7QEworFiMzLC9716gnPNWwYCs+chV/Hjk77RBAE4QgSIx5izHLndUTcJdyv+IniWJZF8pQpkPn6Imq6e2Xc8/btQ/aGDcjesEGUdyNz+QqhGNlsLXzGarUwZmZxr008p1VWp3XqqGmbW4RP7vbtuLpnD2Cnzkt5wRqNwmJoAGSBAdLLCK6EWjvILWOBUTr/WUe+9z8wSoVweUsmA0wm+LRo4bwfMC8Zxc7/zqW2BEEQjvj3153+F5GWo8HA+Qfx2/GSpbefYSchWbi/VYywBgN091zPT6JPSED2xk3IXPWr2aKQm4uHS5ZyadDtkbNtG+6Nn2DdYBMNZOt/YHhodTQ1aTQCPwa+ZYTVamEqdCxGnFJGQiR03MsIGT3a7v7wSRO5v1mDAdU2Cf1WKn/zreRxDOP85xj9xedgvL0RNXMGKn8nPI8ljFXdpq3T8yhCQxH92WeC6sa1Dh5A9c1/QWWnIBxBEERZQZaRcmTOzms4m5iFs4lZJTpPoxjpZHF8MXJvwmvI27cPled/B/+uXZ2ekx8lwWo0SP30M2SvW4eM5ctRa585RblUivaMnx3X0WBNQjFiyrfmqchetx6aCxck+5D+/Q8IHTvWab/Lm1qHD0EeFISMn38WbJf5+fEsNbzPSK+H3M8Pvu3bQ5eQgOp//QmZTeIurxo1oLt5EwH9+jm9vm/r1qhz4jgYhUIUBl5t00bk7tiJkGEvFOu9WfxKCIIgyhuyjJQjGn3xw3b5eClkXLG6mCBr2Cc/tDevKL9LxjLhoGkPfppvU6EG+YcOAQAMRRlzUz76GFfq1cfDxUu4dizLcn4HdrENx+WJkdydO4VNM6xWEs2lS7hvp3S6LapaJa9SGvbaBOeNYB6wGYZBQO8+ghooSn5laZ7PhSX5WuxPP6LG1i0iIQIAVVeuQJWlSxDQz7VSCRafEIZhBJYNVbVqCHt5LGS8gnAEQRD/BkiMlAN6owlXUnK4mjAlxUshw4oXW2NG//pY9VI8fni+OVaPbeNyYbkH383HvTfeEER18B09WU0hGJVw0MxcuRIAkPbFF9BcugR9aipu9urlNKkX3zLCsqwwnbgNlmu4S9CQocU6jk/4eMeVgn1atECN7du418rICNQ5cph7rW7RArELF8C/Z09BRVtLiDHDMGDk0t+/IjgYvm3bulUYkDt/CZLkEQRBPCrQMk05MH3jBfx6LBFeEinZi4NCxiDAW4lR7cxr+1VD3ct0mv6d2emw4Jln4RvfGgBg4kWumDQayLzsO8Nqrl1DwZGj0N91oQicgSdGCgsBqWJzJYBRKiHzK2EGWReyesb9IhZKMl9fRH/1JTQXLiL8jUmQeXmJIlMEVYDLAhIjBEH8ByDLSDnw6zGzw6rOUPyBuE11a7ZLFwIqrLAsCs+fx91Ro5CzfQcSX7VaACyJxQDAmG0VI2xhoSAplq0TqvHhQ0ElVYfwxIeUVcS/Rw/XzmMHmVoNGc+KE/rSmBKdT4qYuV/b3RfYty8i331HcvkFQKmLLxEkRgiC+A9AlpEyxmQq+WAxsGk05g5timpTzWGxgT6uF/9iweLOkKEAy6Lg8BHhTl70htHGMsJ4W8XItfg2gsPSvvjS9esXDcbG7GzoU1NF+wMH9Idv+3ZImT7D5XPyMWZnC4STunU8Hv5kTkMfNXMGZL5+0Fy6BENqCnK2bHX7/LVPHIdcwvKiMWhwM/sm6ofUl1xeUdWqCe31G/B9zHlkS4kgMUIQxH8AEiNlTEZB8QulAcDqsW3Qpro57faPw1ogT2tAVKBrBc847AxYBceOQREeBu86dQQ5O0yFhYJlGkf5PJxe2mhEwotj7KaHl4eEwL9bN+T/8w9XjM1dGJX185AHWSON5AEBCOjTB4H9i6JUZHLk/PWXRCfNn48lNTujVHK+HlJCBADG7RqHk6kn8WG7DzGo5iDR/tiffkLWH2sR/EzJ/VkcQmKEIIj/ALRMU8ZkFbif60LOS34VwQvX7dEgCk82ryx1iH0cjFUPf/wRtwcOMjuW8qJcWI3GYe0Sty5fUOCwTo2l2JosUDpcWdQ+XFi7x7t+fUHGUTnvPPwCcwDg06ypw3OHTZiAaps24tLE3k77cTLVnKb+92u/S+5XRkUhfMJ4KMLCnJ6rJLCOvmCCIAgJVl5aiZmHZj5SDvAkRsqYrGJYRhQyBitfjMfXQ5ugengJnTNduNmMGRkCfw5TocatGbdvxw6IW/0rfJo0Qdjrr7nVPUVkpPn/oCCnbaM/+xQ19/+NyP/9j9sWu3gR+IpLzjuPbUSQrZDhKHqvjEwG79q1MVOxGQt6y/DL1JbS7XkwcD8ChiAIwpN8dvwzrL2+FkdTjnq6KxwkRsqYzGJYRrzkMrSvFYYnmrlpBZGAb/Gwh+7uXYEYKTh+nMtT4goylQo+TZsibs1qhL30klv9k/mYrRfyYKuD7mdPC2/LwEGDEPHOOwgcOBAMw0Dm78/tkwcFCYTT2nvWdPOWc1vw79YNgQMHWo8tslpEvDlZ0I5lGOxpKkNWZdesNZ5E3bSZp7vwnyFXl4tLDy95uhtEOcKyLP659w8eFDzwdFc8Qr7O+fhQXpAYKWNcsYwMb1sV0/pai5AVJwSYZVnk7NwJfVGSMgvaq1edHqu7myDMjFpUvM5VFOER3N+MUgm/Xj3tN5bLUfmH7xE0ZAji/viD26yMrsT9/dCfgaKS9XX0p7MROnoU9zqgZw+o6tdD8LBhZudRngPph8c/RlbX5lC3agWfJk0El2ZkMlT6dDb3Ovy111B9yxa7qd0VMucuVZ62jIRPfB3hkyah+mYJXxjCLZ7Y+ASG/jUUR5KPOG9cATGxJhhNxUvcePnhZXx+/HNkaz1buNKWbXe24dXdr6L/hv6e7opHeJSWeUmMlAJJWYVYf/oeDBIxt858Rt7rUw8fDGyIMR2qc9uciRFTfj7uvT4R2X9aB6Dcbdtw/7XXcfvJp9zsPZA8dSoKjh1z+zgLigirGDmafBQj6/7DvZaHhnJ/q+rUQd3Tp+DfuTMqfTALPg2theKU0dHc34VeAOT2PwOZWo3q69Yh6j3zco26eXOoatXE0TpmYbB3eANUXbFcMskYP/LFVFAAVfVqdpONyRnnSeqKk6jMXU6mnsSZtDOS+2RqNcLGvQxVjRpl3g8A0BtLVu+HZVmH69S77u7CsgvLSnSN4pJakMr1gRDCsiye+esZDP5rcLEEyZC/hmDFpRX48oTrkXgl5adzP6H/+v5IL0y32+bve38DAPL1j46FoKzh//5IjPzH6DV3P95YcxbLD99FcnYh5uy4irQcc5G39Dytw2Nf6lhdtI0vRvQpKchavwHGrCzuJkpfsBC5O3Yg6e23kbNjB3K2beOWVYwZGcjds6eU3plj1PHxYNRqBD31JLdt0t5JSPPR4bVxctT6Zz9qHzyAWocOIuy1CYhd8AMYO/k4FFFWS4jWiRixhfHyQrVNm/DVk2bx4KPwcdhe3dYcqhzQu5fDdnKZtBi5nX3beu0SWEZmH52Nd/5+x+HgnK3NxshtIzFs6zAYTO4nULuWeQ0Lzy5EoaHQeWMnfHH8C7Rb3U7w/t3BxJrwwpYXMGr7KGRoMvDqrlex++5uQZs39r2Br05+hYvpTsoMlCGW7/RRcu5zlVOppzBm+xjcyLxRqufN0eXgcsZlXM+87nBwd8bVDOeW2tLim9Pf4E7OHay6vMpuGxNbxnmAPEymJhOn004LthlY63PkUbrHSYyUAjka85e779oDvLDoKL7ZcwNT150HANzLcjwIsCYTCo4fhzE3Fy2rmouUPdu6Crf/7gvDkDx1Kq61aYvUjz4GABSeP8/tv//6RNyf9AZMWuty0L1XHac2d4egIUMgt1M8rcriRah94B+BY6iRNc+aUoMZbrsiJATh48dDyVt6sUUREQ5j/Rq4FQlkqwFWJr41r2ZcxRMbn8DCswuRmCOsfMxX+Fcyrjh8T1UWL0btE8ehjIpy2E4pU8LEmkQPrGFbh9k5wnUMJgNWXVmFrXe24naO/cE9KS+J+7s4guKpTU/huzPfoecfPZGYW7Jq0csvLUehodDhw90RyfnJOJd+DidTT+LTo5/in/v/YNK+Sdx+/oPxoeah6PgbmTdKNBC6CsMwKDQUYuDGgZh5aKbDthtvbMSgDYNE96MUt7Ju4YmNT2DLrS2ifaU1KIzYNgJHU47i7f1vAwCuZ17H2QdnS3xerdE6qZK5UF3aHq4sfbrL/DPzsebKGrv77U0qAPc+d72pbKqAlyX91vfD8K3DcfC+NaKRb9004dERYyRGShGFjMHNB2Zz37HbGQCA+5mOB5Ds9etxd9hwXGvVGnOC7mNht2gMurQLugRzqnX9vXtc28xffgFgdji1xZBeNg5Y3m1ao/bhQ6iydIloH6NQiIqyFXemwTAMcr6Ziqmj5GBlDLxfN1fs5dd5eXf/u7iRdQPfnfkOfdb3ERzPN7MeSjqEiw/tz6wZmcxu/hA+ckaOEVtH4Ok/nxZYJUpj3ZvfX0cPxCxtFvd3SawbmdpM9F/veF388sPL6PFHD/x580/RPv77D1QVz7GX/z4TcsWlBBxZfhJzEvHEpifQb73zysYlhQGDvQl7cTv7NtZeXwvAfF/fz7svajvt4DTczL6JWUdmOT3v16e+xo2sG3j3n3cF2/cl7kPHNR2x/97+Uuk/ADwofACWZfHkpifxwpYXkKHJKNH5CvRWB/eSDMrOhEyWJgvPb3kev1751aXzXc+8jgVnF+Cjox8JtvN/K35K+791y+TJEQfuH8CgDYPQfEVzLDy7ULDvaPJRbLuzzc6RZY/RZHT4XMjRmZNZWpajAKFl5BFapSExUpowWg33d4CPEuN/OYUziVkOj3nwzbfc3/mzpqPm3PeROW8ubvboCUNmpqj9neeehyE5WbS98MRJl/pY2NO9jKArr68GAPi2bYsqy60VgMPsFJbj/7gXnV8kuRa74OwCvLLrFZH/gY7Vgy3ywWDbtUCtQwcR+b+p3P5MrfDzcCQQTqSccPi+XMHIGnHmwRlcz7yOG1nSZu/i+ozwPxdHD0S+JUBj0NhtZ0Fv1AusKXycPXinH5qO5Pxk/O/A/0T7kvOt95y/l79ovy25uly8tvs1vLnvTU6E8K8v9QAtNFq32Q5aB5IOACiftX2GYURr6b9e+RW91vbCt6e/lTzmaPJRgfVACns+SK/teQ1Z2iyM3y38Ta2/vh691/bGraxbDs+bmJuIbG027uZYJylqhRoao/V+GfLnEHx/5nuH57Gw4tIKvP3324LfV4HBPTGiN+klRbYzP6yfL/2Mcw/O4ZOjn0juN5gMeH3P6/jx3I8AhL97vi9LeoH1d+Pomq5YRl7Z9QpuZt8EAHx35jvBvjE7xuDtv98ukdVx081NePvvtwWCzxEsyyI5LxmHkw6j7/q+aPdrO6cTJP5yMv+5a2SN+PHcjzh0/1DxOl+KkBgpRXKvWwes+1mF2HxeLBpsMdikSNfduMn9nfbpZ6L2hadOlaCHwJSq9h1VdzVloLf53R5PN19Pb9Qjh7UOFupWrbi/72TfwRt738D66+sFlpF5p+bhqxNfCc6nNWox/8x8HLh/ACfTzALq0sNLyNHlQGfUCdpZEqKl5KeAZVnRYMpvn63LtrvP8trZYAEIBQ7/QWX5sR9POS5oX1yfEf6gKiUyDicdxr3ce3hQaLV4uWIZGbNjDHqu7WnX4dWC1qjlwhn1Rj2uZ1536Jz6sNC6bOKKKFp3fR323duHHXd3cJ8d/zvhD24WCvXW92frJMkXZa58j47Ym7AXi84vwlcnvuLOxf+uGTCCAczEmrDxxkYAwI/nfrQ7gI3dMVbwOiEnAa/teQ1rrqxBQk4CQryt4eu5ulyn/Zx+aDru5d3DrMNmq4vld8DnYvpF9FnXB2/se0NgNVIr1IJrpBak4oezP0he50TKCfT4owf2Je4DAHx+/HNsu7MNO+/u5NrwB0rb35YtObocdP+9O978+03RPmeWEf69lacTZ34+lHQIexP34tvT3+KL419g1HZrlB3/vkgrTLOe02j/fi2JzwhflLm7fHgr+xYm75uMqxlX8d6B97DtzjYsvrDYpWM/PfYpeqztgbE7x+J+3n3oTXocTj7s8Bj+585/xu26uwvfnv4WL+962a3+lwUkRkqInhdBk2hyvWYMALRJvuBwf/bGjcXqkyMKvIGTNaUH0JuVGLw7Sg7lc9aIHGPRHfLs5mcxdp915saovKAxaLD++nr039AfuxJ2Yfqh6aIf9+Ek4Y/knb/f4f6WM3IcTzmOoX8NxZjtY6AzWR9yloHxyxNfovsf3bHkwhKR+uc/fPgzIdt9LMti6F9D0Xttb6cDGX8/fz01rcD8cBu9XRgG7K5l5HjKcdzIvIE/rlnDmm37dCbtDMbuHIve63rjSJI1zNQVMXIqzSweLcsL9hj852B0+b0L7mTfwet7X8eTm57kZn9S8B+8UkLCFr54+eO6+b3yB7GUfGsI+tbbW9FnXR+Bo12GJgMnUk5w9xN/mcF2IF9zZQ0GbBiAeafmSQ6ULMtizZU1uPzwMu7m3MXre1/HvFPzsOziMs4nZM7JOYJj+N+rxqCBUm79bdsb3CyfPQCsvbYWfdf3xb7Effjo6Efou76vIFsv//Ox7astOqMOv139Dd3/6M5ZBADgt6u/4ZnNzwAQi2S1Ui05mFswmAzcb2zcrnFIzk/Ga3uECQsfFDxApiYTB+8fFIhnZ5aR7Xe246HmISdm+MJSyn9j5qGZeGLjE0gvTIeX3Org/sa+N0Rt+edafmm5YB//d8QX8RaBcyv7FpZfXO7QZ+J02ml8cPgD7v60J7x0Rp1AoPEnJfn6fGy/s92hpWPcznHYeXcnnv7zaW7b+QfnJdva3hOrroh9tiyfy4OCBw6dv40mI365/Av3+k7OHbttyxuqTVNCcjVWlZmilF5LDynMRoaPdd+gG/tRK+se4lPKP8GSRgn82EuGQYdNaBhQB7F7rc6eOgVwL5yBodtgYJV5MJMV/Q6uZl5FDO9uSdSl4ulfhju9nmB9EkBintWcWWgoxF+3zOHJlzMuCx4SOpMOBpOBe+DMPTVXdG7+w4c/uFmOt1BgKOCWWa5lXEOj8EYAgCPJRzBxz0QMrTsUk1uYE59dz7zOHbf51mbu7/8d+B8i1ZFO368t6YXpeHXXq2gf0x7XM69j3719oja2loZDSVaTKX/G44oIsGBv9s6yLBiG4SJiHOVX0Jv0+DvxbzSNaCr4bg4nHUZibiK6VumKvtX7IjU/FWN2jMGdnDsYUnsI3mr1FrdWDZgtZGMajbErBN/ZbxaoFqdLwGwVAID327yP5hHNBQ/YPF0ewnzMCevOPzjP+QssOr8IWdoszGhrLrqoN+oxYtsInE+3PuQ/aS80//916y9MbD4Ryy4u47Ztvb0VzSKsyeQKDAWC72jZhWV4pekrku/l1yu/4tm6z2Lm4ZmS+y3k6/NhYk04cP+AYPvv137HU7WeEgzaFx5ewIWH5onLd2e+w8tNXobBZMCHRz60e34fhY/gO7CgM+qw8+5OTPlnivm99Fom+F74fisFhgIM+WsIUvJT0LFyR267MzHCv/d+PPejIFTaYnHK0GRwliKLcP7k6Ce4lnmNa3sk+Qh+v/Y7fjz3I37o+gNqBteESm6/TIW9ycnCcwtx8eFF7rMuMBRgXJNxAMSWkQVnF+BQ0iHsv7cfuwbvEj1XALOQ7LOuD2oF1+K2XXp4CTvu7kBSXhKOJB9Bvj4ffav3xacdPpXsK3/Z04KtdRcwP4M+PvIx5nSegzaV2oj2WzCyRphYEwZsGIA8fR56xvVEi8gW3H6LuN6XuA9LLy7lthcnQq+sIDFSTAp0Bqw+loi6lazr56wdE2SgLl8gRlRGPbrcK9lyS3HRKQGtF4OlPeTof+Qa+HEh8qLfpVZhghdvmyUcz8Cb1Ky65XjmbcH2Zueb4j85+onAHG67TONsHVRn1OGvW38hMTdRclnGAv88lhlTri4XL+0wZ4tdemEpXm3yKrbc3oIZh+xXD7Z1PATMM6KvTnyFDE0GPmz3ocgMPf/MfFzOuIzLGZftntd2pm3PL4I/IKYXpuNQ0iH0jOsp+YDeeHMjPmwnHqy+Of0NJjafaLcvfN7++23sTjCH3kaorblkLO9n592d6BzbGR8d/YibYf127TeoFCqR9cLEmoq1vCI14PLPbRuJ9Me1Pzgxsjtxt0CIAOblI1tsQ5Ufah7iRKrV50hj0AgsM9+f/R6tK7XG58c/F53rk6OfSG63JU+fh7XX1+KDwx8Itn945EN8duwzu2LHgrOlEqVMKbkUlK/PFyR1G7ltpGA/32+lwFDADcZ8kWLv2hahy19OtfWxYcDg92u/44PDH+Ctlm9hWH3rE2hPwh7RUqzl85l5eCbqBNfBb9d+k7y2bb/4lhEAAtF34P4Bu2LEMhFILUiFzqhDUr7Y/6rTb50AQBClNPvYbFG7zbc2IzEnEb2r9cYL9V+w228LttZdAJxonLx3Mg49Z9+vQ2vU4njKceTpzdaw7Xe2Y/ud7dz+FZdWoGuVriInbP7nbWJNJYqUKikkRorJ7C1XsOKIOKpFCoXN+rdRImy1rDhTjYHcBDAs8CAQnIMoAJypzmDYXmtby3ym0KTlxIjMBM6UyPcn2f/gMBDofInCdu2fP/Da/jD4+3RGndNlCY1Rg6n/mB1cQ71DhfuKBm6dUYd5p+Zx2y3Ofq/veV3QvtUvreAMqXVhFiw3qx5QYwDiK8VDY9Bgd8JutI9pb9eZlM/PF39Gu+h2UCvV+OvWX3YjCQoNhTiddhqXHl7Cp8fMM64rGVfwTqt3JNs3Xt5YtG3R+UUuiZF8fT4nRADrMpUtObocJOQII2M23tiIBqENBNt+v/p7qYVG8gfZLE2WaD/Lsrj48KJoiRCAQGRYsB24AGE+jAJDATI1Qudp20GcjyuzzTx9Hjbd2CS5T2cS3rNSOPssr2RckYxY2n9vPzbc2OC0f4Bw4sBn7qm5eLfVu2gcbr2/bmXfwvCtwzGywUiHeX4OJh3EwSRzmOmXJ77EkgvWKD1HDtbJ+clOQ5T5zw9H6d35y1eOHFi7/N4FYxqOcXhNZ5xLP4fz6eddEiMPNQ/tCgJnQj5Xl4ujyY7rzLy882XRs4J/Xo1BA7VSbXtYuUFipJjsviQO87OH0ubhZCyG+jxYj0G7y449v3UKwMvmOfjJM/Y9yRMjGEweI8cPGf2QfOoAjtTNAmB+CFnsODLeJfmWEb2Ld47IMuJAYPBzS+iNeqeOkq/stM4ebfNSWGYIi84vwpbb1rwOt7Jv4d1/3hWs75cE/lr9wnMLEa4Ox8AN5vo37WKce7kD5hlWj7U90Ciskchsz6fQUIjhW4VLYxuub+AeMD+d+8ntPtujzSr7JmE+yfnJosFHb9Jzn78F29DLkpCrzzU77hn1+PrU16L9f1z/Q2RxcIRUyC7/vs3QZLgUAuoO+fp80WfkKnNOzsGvlx2Hvj7UPOQEK59pB6e5fJ1NN6XF0rkH5/D8ludxbvg5zvz/4eEPka3NxrxT89C9aneXr+FqyLE9Mcxn1qFZGN9sPB6LfkxSYFrI1ZvFbKYmkxNGgNgima3Ndtmp1BEsWBToC6BWqs21cO7/I9nOyBqRpc3ilq/4QskiUKSW3gCzGLmQ7tgHUWvUiu45/udaaCj0qBghB9Zionnouvd0l0ThwGdwkITHHruaOrdC/NxVhiXd3ftK74UzkE15Fdumd4XWy3wNjVGDy60jkRQMnK5hvS7L64LRRb9N22x/jsQIfzYzad8kPLHpCYfn5nvM26I1mBW/JULAwrrr6wQJgEqT4ynHOSECAAfvH3SY74RPtjbboRABxMsJgPnByrIsttzagm9Of+PStWydcEvC8K3DxWLEqHcpWqS4XMu8hl5re6H/hv6SVojvTn8ncZR9pJxJ+ZYHe46FJSFXl1vsz2jphaUOI0RKC2c+Si/ueBFPb3oaGoNGYIXhR+GUJ+fSz+HlnS+jQF/gMLolV5eLW1m3REsrI7aOELXl5/kpCZblxJWXV4pCuPlY+n0m7YzAsiljZGBZFoM3DZY8LkebI7Le2eIt93bo1FwaWZpLAomR4uLiRGlCtUL0unsUjR9Yw35733G/bLPGy/no/9Af2NbS/a80R5cjuEkLDYXYP6opJr0sh05pvW6umsH+Bgz+bsggT+2aGuE/1PUmvcNQOlcHblc4kHQAY7aPceir8W/DNnrAwoYbGyT9WcoL26UPA2sQ5YRxBt8nxRn8iBIp7M22h9eXdrhOzU8VbePPQF0Vee7w6bFPuVo4/1aOpxzH1cyrOPPgjEuWi/Li46Mfi/rjr7T69lmy6269vVXQ5mpm2aWqtwjer0+KLXkh3iGcM2x6QTpuZt0UZXmWM3KcTjst6cMCmAWWxephu2RtQa1UOxTAnhYjtExTbISDsYw1wSSx/FJDkQM5a8JnBxfACAZ5Xj4I1DmecejlgNJG7Oh431SmLxAs4eOY66JAsOXTY5/CW+FtPY8u1+wvIBG2+t0A96w6/Jmrs5u9uDVP7F33aIr7ou/fyBcnvii3a/WM6ylwjLOHu1lqdz29CwbWgH7r+tl94DpibOOxuJtz12HfagbVlNwuFeEkNSMO9Q7Ft12+xXNbnnO7f2WFr9LX40XeVl9Z7dHr22LJWwQA0+KnoVNsJ/xz/x8uX4snsPRHytcn3CccId4huI7rSNek4+dLP4vaMAwj6e9kYesdq7CK8YuRLKcgZ+QOlwY9LUbIMlJMCniDNwAEaaQVZ6jB+lCWg3UqRICiqrVFHKzH4OOhMoGPxryBMrw9WiwKcmz8xgwufrtnH5wVOD9JhdE6wlvu7XD/0gtL8fnxzz1+sz8quFqf468n/sKohqOctrM3EyoLvGTShQ5LCsMwUMqUgugKV2kf0x4v1HvBrtiw4OflvASABakB3kfhg0hf98O7yxJHqc4d8Vj0Y6XWB76j86OAJYzfS+aFIXWGINI30q0okeKE8Dtj6j9T7YrGEO8QLlQ9vTBdclKWo8sRLTnbI9ovWnK7jJE5XHby9POZxIgb/HJpJX65tBKbDx2HRiEMpwzSWhVnszSruc9H434dEy0vd9qO5jKcrS4UI3oFg7uR4mypuUW+R9uamy0aKzuLv95wn3DRNlvcjT1vFdUKT9c2R9y83+Z90f45J+dgxaUV6P6H2LFtXJNx2DRokyAm3l0mNJ1Q7GM9gasDetWAqpjcYrJkiK7gfPKyEQhSKOVKtItpJ7nvi45foG/1viU6v70HqT0i1ZH4odsPCPYOxuOVH3fY1lfp63Z/Wka25P5WK9XlKvyk4Of7AMwhqO/Fv+fWOTYN2oSF3Rfiz0HiGkSPAqUllMLV4ZyDrauZktcPWI8YvxjBtsp+lUulP/acwkN8QhCmtooR2xwk1QPNld1tw9QBYHrb6aJt/PwnfB4WPsSxZPsZuD1tYSMx4iI5mmz4vvYxmj/5MSZuEJuRQ7RWy0j3hBOonpeIVzrEQf2z+1lUWQbY34DB9UrAtaLfBX+ZxiJCZg8Rfn0FRfpoaXcZ3hwjx5ZW4h/gB+1cjzIAgCBVkNM2EeoIvN3ybWwcuJETJa7SJbYLqgVWK/ZsRMEoEK52LrBKi7GNx2LzE5tdFhRRvlH4vKMw78QrTV7BB499gKF1hmJNP/vVRi3wv4Mq/lVE+/mJosoapUyJT9p/glebvira563wRoBXQInOzxcjg2oOsttucY/F6Fi5I+Z3nc9tqxdaD9Pi7UeLOLMiDKgxQLSN7zvio/BxWAHWVVz5TdljUvNJ6FNNWCTymbrPoFpgNZeOr+RbiWsb428ddO1ZlVyNjLF1YvZV+mLtANdyEdmilFlnY+sGiPPCuAp/4uWKZcRf6Y+awTUxssFIwfb+NRwXmHS1D/YI8Q5BmLdZjNzMEmZBbhfdDm2j7dcTaxwmDt8fWmeo5PPUwBoEySBtceYAW9aQGHGR7LRE1CmKADRImNmDeGLEx6DF/F3zMHjBFMgK7Hu9J9i5T02M2TfjvZEKGOVmQRHkb21siWq5ECfD82/Jsacxg186yTgfD1bGIDGckfT5qBZYDTUCazh8r3zGNx2P5hHNHbaJUEdArVSjelB1yBgZXqjnPKbegiWUrLjmbx+lj2CZyNWEXnzkjBx/D/3beUOYrTBVAqpgxmP2k6MNrm31eNcZdajkW4l7PaPtDAxvMBxP1HoC09pME9Qr4TOivtWzn/8gtZhzi0NcQFyxj7VgZI0I8Q7BK01eEbwvwGyh4YsRR9kyAev7io+K57bxzymVr6JFZAvM7zofrSu1xvyu81EnpI5g/9C6Q7FxoHgCIGNkAsvIj91/FGW0rB1cG2+2ENZTia9k7ZulP5+0/wRP1XoKtvAH9Ah1hOQAOKX1FOwful9SVLpCuE+4ZNIx25n/681eF7X5udfPWNXXmkqcP+hLWZUahTVy+Z6x/TwMJgNqB9eWbLtvyD5sGrTJ7nNCKVNiZZ+VmNd5HmoF13K6/Mbn177WkGf+slyrKOd5hCztO8V2wup+Vj+YhmENXb6+LXGBcU7b8Jdp+NmXAaBNpTYia9zC7gvRPqY9predLin+A1WB2DRoE7Y/5dy3i4+j6MTygMSIi+RkmDMRXg6uKrnfn+cLYskjouUVzpNi9hDpWRYrYVF8Lf5Nyf16JYMFfeXY2Na1r9JH4eNWuFqQKgg/9/5ZcvnFgq3pmD8Y284M2lRqw6Vet5wfADczcJdcXa7A+bZRWCPJdlNaT3F4HnuigE/NoJqc2dc2mRt/4Az1sT489Ca9YLbfKKyRYJCynSVvfXIrfurxk0BU2Xuou0vVAOl7Vwp/pb+k1Ya/rqxWCHMS5OnyBBV9W0e1Fh3/YsMXMa/zPJx84SROPH8C8zrPw5zO1rowgapATGw+EeObjpc0rb8X/57ofrNFSsTYipEYvxiRWGgR2QLB3sHc68eiH8NLjV4Snat/jf6Y+dhMwbaXG7+MsY2thfJ+6/cbjjx3BLsHW/0pagbVxPP1ngfDMC5VPrbliZpPIMg7CM/VEzvQ2n5WUlal5pHNRWJ206BN+LbLt2ga0VTU/pc+v4i22aNBWAPBJMfy+5D6DkN9QlEtsBrebf2u5D2plCnRJLwJulTpAgD4tMOniI+Kx0uNXhItoVjY8sQWnBt+TpBsj+97Ee0XjW1PbcOmQdK5UyxtALP/UoPQBpjTaQ4+eOwDdKzcEWMbjxUkeXPExOYT8WP3H7H9qe1oGt7UaftY/1i7kwwWrGBf0/CmeCz6MfzQ7QcMrj3Y7n2kVqoR7ReNHlV72L1utK9wSdRRorjywG0xsn//fvTv3x/R0dFgGAYbNmxwesy+ffvQvHlzqFQq1KxZE8uWLStGVz0Dy7LYm7AXd++bw04nP/6aZLsEf+vM3jaq5tfHpT/mhwEMtrZgkOULvPS6VZhIpTYL9LUOlqlB0n19s8WbTh0e1Qq13R8Vf33cgsXZ8unaT4usDlue2ILFPRaLZg7Vg6pjftf5+LnXz9gzZA9n/m4a3hQ/9fgJoxqOwpp+a7C051IEqszp1Sz/A5AcACyMbzpeJDj4PhP8mULdkLp4p9U7WNVnlaDWSHHYN2SfYHDuVa0X2laymk/55mxfhXXQ0xv1goeJrX8HX0i1j2mPyv6V0aZSG0FhtijfKGwYuAF7h+x1ycmM/xl89bi1arKrVpVBNQdh3cB1kiKIX/yL/14UMgXaRLcRPBwH1RwkCqcNUAWgS5Uu8JJ7QSlXokuVLqLZ3ZhGYzCuyTjJRGOu+MdItZFBKEYYhhH4RjUMbYi6IXUFIcYjG4wUiEVHvlQTmk0Q3MM+Ch/4KHwQoY7AK03Myfn+F/8/bn+sf6zoHE3Cm9g9f4h3CN5saZ6QSM3ybQs2uuqsWy2wGjrFdpKc/TMMI8gTZJlkRPlGidrGR8VjRZ8V3GvLcfxyD0/UfEJUGyjQS1zPi3/vA0CdkDpY1HMRXm/+Olb0XiE5MYoNiAXDMGAYBr3jegMARjUQPgtj/GIcWnpsP9fuVbvjiVrmXEevNXvNZXEW7RuNttFtEe0XjTGNxuDVJq9iauupgjZ8q1xcQJxDMcKf3LSMEj6jfZW+6FS5E1pHmS2Ftn5AUs+9x6Ifw1ePf4Vf+v6Ct1ta60F5Woy4Hdqbn5+PJk2aYPTo0XjyySedtr99+zb69u2LcePG4ZdffsHu3bsxZswYVKpUCT179ixWp8uTnRc3IHvye8hVA45WZfvdPoRUdQju+UegcbrVInK+KoOrEmL+w2fMAmVpDzmWdWcFadqlLCN+Sj+MfEMOhRFccjJbov2iMbLhSCy9YC2E1KZSG0EtCpVcheltpyMuMA4PCx8KsiyOaDACBpMBZx6c4bZZxIiMkYlmurEBsYgNED9UAaG15N3W76J+aH30jLN+3/VD6wva8x/k7WLa4Y9rf3C5Ko49fww52hwk5CagZWRL+Cp9Bc5c/NkX/zyvNnkVnat0BiBdjtwCKyn/hPAfCIB5sPmxx49IyU/Buuvr0Ld6XzxR8wlsv7MdT9V+Cl+dNIsAvUkPGSPD5x0/R2p+qsO1fUdOdjWCzLNOV+q7+Cp9oVaqkV6Qzplz/7n3D4bWGcoVJasXUs9uDpa2ldpKDjiAMBEW37Jw4JkD8FX6CsRSlypd0L1qd4xqOAqdfzN/D/zByRmjGo7C3oS9AvMxf2nBHlJiRC6TC8RIuE+4IMzy135m8z5fjAR4BYBhGMT4xeB+3n2ngpafy4K/RPVq01cxssFIQXbLwbUHY9udbdxrhUzh0G9q1+BdLr13C84i3GwJ8wnDjqd2wMAaMOfEHG4CwbcATo2fiidrPYl6IfXQdEVTAGYxM6z+MLt+W2Mbj8X3Z7/HgBoDJP3VRjcajUl7Jwm2OXqf4epwDKkzBCq5ym422Y/bf4ynaz8t6RTPMAzax7QXJBic2HwijiQdEYkXKeZ1noePjnzEZXh9pckr+OHsD4I2/IR0aqWaqzPET7L25eNfov3q9gDMwpQv+gbVHMSl7GdZVmANshVvDMPg267C+j98htYdCpVChfmn53PhvoWGQvSIM1tMhjcYjhi/GEzaN8lhorjywG0x0rt3b/Tu3dvl9gsWLEC1atXw1Vfmh3O9evVw4MABfP311/8KMZLz42I0THA8WL194hfEp1xCi9Sr0CqU8NNbb8YrseD8Piz83FWG89WsD3KLEMlWA4EFwIWaStjaR1RyFQq8ze0G1x4sKEduQWo29EO3H6A1avH71d8R5B0EhmEQ5hOGyS0mC9KHj2k0Bh0rd0TDsIbYk7CHK1DGfzDwH/KOnAttCfAKwPP1nnfYxnZWyQ9/tcwyLX4l/Nn3mn5rBMtO/Fk2v79+Xn4Y23is04RZtjQIbSC5/m4hyjeKc+asGlBVNHOxzO57V3P+m7Gd3UrhimVEJVdh7YC10Jv0UCvVGFx7MDerfb/N+whSBWHR+UVc+65VuqJ6YHX8dN58P9hWWhZcn1evhP/5WpZselTtgZ8v/ow+1fpw3yF/1udOZEKMXwx2Dd6FW9m3MGjjIADFFyMMGMgYGQ48cwAGkwHeCm9BJWILfDFi6f+CbguQlJeENtHS0RAKxtyuXmg91AuphxCfEJGjq22a7daVWmNF7xVcciuDySAQap91+EyQyM4dIQKI7yVXIlQq+Zn9db7ubE3MxbcMKWVKkQXl43Yfc1WwpRjbeCzaxbRDvZB6kvu7xHbBz71+xohtVh8pV0LfB9QYYFeMKOVKtK4kXiK08E3nb/DO/newK2EXvun8DTpX6YwxjVyrQdOlShd0qdIFCTkJOPPgDPpX74+GYQ2hkCnw8s6XAdh3lJYxMi7pY6AqENPip0GtVHNp4ptHNEe+Ph/T207nxEit4FqCpSx3SwgoZUoMrj0YPeN6ot2v5ig428RnIT5mq7tU1eDypMyTnh0+fBjdunUTbOvZsycmTZpk9xitVgut1joDzMmRzsdfHninm798FsCMNi9KtokqyAADQMkaodQbOVEBAFm+DPwV/gCyuPYKO9lb/zdCjpbXWZxqHQiw5vb1Q+tjQI0BAnN+n2p9cD79PK5kXBEcz5+ZcdeSKaCQKTCy4UjRPr5jmGUJJswnDD3jenJihD9b50eQuOOk6gp8xa9WqB0+kPg1G+qG1BXkSOE/9G0f4Pb8SSwPj5ltZ2Lm4Zmo4l+FS2/Nd2Rzh8eiH8OhpEPoX925J77FSiEVzWFLv+r9ONEQrAqWzHTaNKIpvORekoPykDpDAAB7E/dylpG5necCgFWM8JYj6obUFdxnQd5B3N/8h65l8AtXh2PH0ztE113QbQEupF/g/ABchWEYwb3g0jKNRKSTxYrDF73NIpvhwsMLAhHgp/RDtG80cvW53CAQFxjn0BHRcm6FTOFSdJQFWz8NGa+AZp/qfZCcn2w3549SphRYdqSE7MAaA7E7YTfGNx1f7IiQ5+s9j0sPL6Fr1a6C7Vue2ILEvESHQgQwW6Qc+VowDIO6IXUF21wRXgzDIFIdWawstkq5El93/hpGk7HY0VFVAqqgSoDZCdliBV7eezmOJR+ze4/bZp8eWnco9zfDMFjWaxn39+q+q3E54zI6xHQQfLeWMF934U/SbLMdW5697iYqLG3KXIykpKQgMlJofoyMjEROTg4KCwvh4yN2Nps9ezZmzfJctjw+8kKz53qe0gfHo6TVvVEp9Ak5UYtB17PmAbNl096oHl4HgNVJzyJGlvRcgj9v/on1N9YDAB4EMdjaikGoSo5J9Sfhft59vN/mfTAMw5XyBsyWgg4xHURixJ2kToDZa3xK6ymiJRO+8OHPkvlruc4iJdwlQGX9sXgrvB2KEb4vg4yRcf33UfgIlg5sc1Y8XvlxPFv3Wa4qbq+4XriccRkftTMXcXuq9lPoEdcDKy+txPdnvy/R+/m84+f4+97f6Fqlq9O2i3suxo2sGy45u41rMg71QutxS2YWUy8A/N7/d/x18y+MbTLW3uEc77R6Byq5SmDhah/THidTT6JLrPVh+nOvn5Gvz8e1zGtYfmm5wO/BHSfMdjHt7OYncQZfELs6UNkiFdkyoekEhHiHoFsV62SJYRj8+cSfnPXEEa82eRXfn/1ekOvBFeuWoK9guGVCmY0LnyPhJRIjEkt8H7X/CB+wH5SoLLxaqRZYSizYW6J9od4LWHl5pUtLHhZs36etz0hZURph2nyaRTRzuJQX5hOG9MJ0u/cw/95pENYADcKszrh/9P8DJ1JPCJa63WVRj0VYemGpKC+N5dmbq8stkUArKY9kOvipU6di8mRrxEVOTg5iY6V9E8oaucb8g89X2i+LbZwwDLnybPj/z1z2Oya8BgCz34hvi1ZQ3BZadpRGFmv6rUH90PpoGdkSefo8XM+8jjs5d8zXZOR4sZHQCsOPEFDIFHi5ycvcTNaC7eDgbH2eYRjJ5RP+zJI/S+Zvd/agdhe+cvdT+jkUIw3CGuDrTl9zToCBqkDsG7KP+4yW9FyCHG0OKvsLlwQYhsGU1lM4MdK6Umt88bgwlbq/lz8G1xmMNVfXoE91YT4HdwhUBbpk6bBc01UHWy+5l8BZtnFYY5xLPwfAbMWwnWXaI9g7WBQR8n3X76Ez6QRC02JGDleHi8REcSJCioOto2xxGN1QXBxQrVRLmuftWZVsGddkHIbUGSLyJ3KHJ2s9ibXX16JBaAORaHiy1pPYeGMjHo8Vh90+X+95/HT+J7SLNn8n9vyNSiJEisObLd9E3+p9Xb4PAfN32juuN5fS3LLs5YwvHv8CE/dMxNut3nbe+BFgWptpWH5xueh35wp1QuqIwtjdJb5SvCBU3YLFWsiCRZ4+T2A9LE/KXIxERUUhNVVoSktNTUVAQICkVQQAVCoVVKrSnXkXF6XGPBjnKe0PvtWDFPCO5t0obZrh/PWbuBYDjKreHik+whDf1iHNuNk8wzCY02kOWJblqjRKKVNbS4RKrhKttVrM5pYf9kuN7UelOIKv0PmmRf5A4O4atjO85F5Y0XsF9CY9/Lz8MKPtDIzcNtJu3pBuVYVLf/wBwVFOAf7D2VLZ15YwnzDsGbKn3B/knoZhGLcsXlIRIWVBlG8UXqj3AnwUPm5nm53UfBLiK8WLrH+lAcMwJRIigNlCVS+kHrpU6YLvzgirDfsqffHHgD8kj3ul6StoEdmCW+px1yJTVihkimLl5fikwyecGLFX6NCWZhHN8PfQvx+Z9+6MrlW6umQpLW+UMiVX4yhLm/XfFSNt27bFli1bBNt27tyJtm3tZ5V7lJAZzSZUKcvI8m0fwteggbLXNPiFRcEytEUHxMK4ZBFa+0Ujxi8GSf5JGDVJjqVzzeszOj/xA5//g5IaBPmDhMWK0DyyOd5p9Q4+P/65oM0H7T7A4DqDSxzOCghnv3zrjFQuh5LCX0NvEdkCJ144UerLQa5S0YRIcXim7jM4++As2se0d964hLzbunhViWP9Y0uUtKqsUSvVnO+AO/ecUiZMy8/37fm6k3hZ5VGHP9Fxxw/k3yJEHnUCvQKRr8/3qN+I22IkLy8PN25YZ/q3b9/GmTNnEBISgipVqmDq1Km4f/8+li83lzsfN24cvvvuO7zzzjsYPXo09uzZg99++w2bN28uvXdRhljWc/MlLCNqgwZqgxYGRgZ//zBOjCi0BsTHWL3XDSYD8n0YfPmkDPFXWeh7On44SpmiGYbBpx0+Ra4ul/N6B4Bn6z6LpLwkVPavzP0wvRXeLmUcdMQn7T/B9czrgnBetVKNbzp/A4ZhRNEBZUFZCZGXG7+MfYn73IoIIsSo5CrM6TTHeUMP8FmHz3A+/bzIgvYoM7LBSPxx7Q/J7K7OeKvlW/BT+qFfjX6CxF//RhyVuSfKht7VeiNPn1ficg4lwW0xcuLECXTu3Jl7bfHtGDFiBJYtW4bk5GQkJCRw+6tVq4bNmzfjjTfewLx581C5cmUsWrToXxHWCwAwmZcppCwjSl4MfoAqAJYobV11oeOkJbzzWB0ZjtUBXvRyPAOyV89AqgiZQqYo9qzREfa87y15O/7NTGg2AROa/buK6xHu0ad6nxL5/HiCqgFVceKFE8WqjByoCiyT50B5MrX1VMw7NQ9vtXzL012pcExqMcnTXXBfjHTq1EkQWmmLVHbVTp064fTp0+5e6pHAZJRhfuMnkOIrXhuWF4mRgBp1oJKr8PIEOQIKgDfChVEtraJaIdY/Fom5iQAgmVkSAL7t8i2WXFiCDx5zr5gdQRD/DTy1LPko8Fy95zC0zlCPRXMQnuWRjKZ5lNga8zj+qi4dknjv15nIyU3HkMpms2imP4NMf4jMpCq5Cn8O+pPLWmgvrXSn2E7oFNup1PpOEATxb4KESMWFxIgDWJbFDQfFxXo1GyJ4vX/ofmRrsyVTafN/ZI5qXBCEO7iSyp4gCOJRh0IGHKAxaJDtRi6FYO9gh5kaLZn6LFkwCaKkOFoyJQiC+LdAlhEHbL22CXleQsdVhhUWtXOHb7t8i1xdrsfiuAmCIAjiUYQsI3bI0mRh17IPAJvMhkqm+DNRGSMjIUKUKpaKoANrDPRwTwiCIIoPWUYkKNAXYOHfn+O1P00Y1sNGjCgY6Mjlg3hE6Fi5I/YO2YtQ75JlAiUIgvAkZBmRYPml5dh1aRMAQKMQxvzXD6WMf8SjRZhPGGWiJAjiXw1ZRiS4nnkdSiPAAiiwybzaJorBsC5NUTeqfIqEEQRBEMR/HRIjElT2r4ybekAj94LJpl5E5Wr1MKBJtJ0jCYIgCIJwF1qmkUB5Lw0f/2wUWUUAwNvb/VTNBEEQBEHYh8SIBOFHb0IGIF8hFiNecvrICIIgCKI0oZFVAlZrrr9rm2MEAJQkRgiCIAiiVKGRVQqtDgCQpfIT7fJS0EdGEARBEKUJjawSMBYx4kVihCAIgiDKGhpZJZBp9ACA7CLLSPVwX24fiRGCIAiCKF1oZJVApjWLEcsyTb1KAdw+cmAlCIIgiNKFRlYJ5DqzGEnxNafYbhBtFSM6o8kjfSIIgiCI/yokRiSQa40wMjJcCK0GAGhfM4zbF+ar8lS3CIIgCOI/CWVgtWHjjY2ARoMPW49CgdIHCpMBDaIDsXF8O6TlalElVO3pLhIEQRDEfwoSIzZMOzgNb3hF4WilBgAAg0wBuYxBk9ggz3aMIAiCIP6j0DKNDXIji3tetTzdDYIgCIKoMJAYseHtzUroZeQXQhAEQRDlBYkRG5pf1EAno9UrgiAIgigvSIxIoJMrPd0FgiAIgqgwkBjhYTAZkOsN6GQkRgiCIAiivCAxwkNr1EJhAnRy6zINpX8nCIIgiLKFRloeBfoCqPSAnuczsmJ0aw/2iCAIgiD++5AY4aHV5kPGAtoin5FPnmiE+OqhHu4VQRAEQfy3ITHCQ5OXjQyVP05F1AEAqGiJhiAIgiDKHBpteWjzczGm27vQKrwAAColfTwEQRAEUdbQaMtDk5+NQqU391qlkHuwNwRBEARRMSAxwuNkwmHBa2+yjBAEQRBEmUOjLY/E9JuC12QZIQiCIIiyh8QID5lGK3hNDqwEQRAEUfbQaMsjOCFP8NrHiywjBEEQBFHWkBjhEXZXI3gdE+TjoZ4QBEEQRMWhWGJk/vz5iIuLg7e3N+Lj43Hs2DGH7efOnYs6derAx8cHsbGxeOONN6DRaBwe4xG0wo/DV0XVewmCIAiirHFbjKxZswaTJ0/GjBkzcOrUKTRp0gQ9e/ZEWlqaZPtVq1ZhypQpmDFjBi5fvozFixdjzZo1+N///lfizpc2OpD4IAiCIIjyxm0xMmfOHLz00ksYNWoU6tevjwULFkCtVmPJkiWS7Q8dOoR27drhueeeQ1xcHHr06IFnn33WqTXFExhMVjGydFQrD/aEIAiCICoObokRnU6HkydPolu3btYTyGTo1q0bDh8+LHnMY489hpMnT3Li49atW9iyZQv69Olj9zparRY5OTmCf+WBgTWLkRgfEzrXiSiXaxIEQRBERcetdYn09HQYjUZERkYKtkdGRuLKlSuSxzz33HNIT09H+/btwbIsDAYDxo0b53CZZvbs2Zg1a5Y7XSsVdIwKAOCjYMr92gRBEARRUSnzaJp9+/bhk08+wffff49Tp05h3bp12Lx5Mz788EO7x0ydOhXZ2dncv8TExLLuJgBAB3NNGj8vCjIiCIIgiPLCLctIWFgY5HI5UlNTBdtTU1MRFRUlecz777+PYcOGYcyYMQCARo0aIT8/H2PHjsV7770HmUw88KtUKqhUKne6VipoZeZr+qsovwhBEARBlBdumQC8vLzQokUL7N69m9tmMpmwe/dutG3bVvKYgoICkeCQy82DPcuy7va3zDCxJmhk5rwifhTSSxAEQRDlhtuj7uTJkzFixAi0bNkSrVu3xty5c5Gfn49Ro0YBAIYPH46YmBjMnj0bANC/f3/MmTMHzZo1Q3x8PG7cuIH3338f/fv350TJo4DBZIBGbq7YG+Dj5eHeEARBEETFwW0xMnToUDx48ADTp09HSkoKmjZtim3btnFOrQkJCQJLyLRp08AwDKZNm4b79+8jPDwc/fv3x8cff1x676IU0Bl1nBgJVJf/EhFBEARBVFQY9lFaK7FDTk4OAgMDkZ2djYCAgDK5RkZhBt5+8VvsrtIS73SsjFf7NCmT6xAEQRBERcHV8ZvCRorQaQtQoDRbRvzV3h7uDUEQBEFUHEiMFJGUmQCN3Owr4ks+IwRBEARRbpAYKWLpmR+hl5ldaLxUJEYIgiAIorwgMVJEfn4WJ0a8vSi0lyAIgiDKCxIjRQQl5UJfFGrspaCPhSAIgiDKCxp1YU6+1m1rinWZhsQIQRAEQZQbNOoCyNfnw7fARGKEIAiCIDwAjboANEYNNF6wihE5fSwEQRAEUV7QqAtAa9QiKM8qRlRkGSEIgiCIcoNGXQBaTT4CCgG9jBxYCYIgCKK8oVEXgC4nGwCgl5PPCEEQBEGUNzTqAtAX5sMEBgbyGSEIgiCIcoeyewHQa/IhL1qiAcgyQhAEQRDlCYkRAHpNAUwy60dBYoQgCIIgyg8SIwAMhYVCMULLNARBEARRbpAYAWDQFoCxpIKXy8AwjId7RBAEQRAVBxIjAIyaQrCUfZUgCIIgPAKJEQAGjQY6hQoA4K2UO2lNEARBEERpQmYAACZtIdK9AwEAUYEqD/eGIAiCICoWJEYAGLQapPsEAQCiAnw82xmCIAiCqGCQGAFg0mrxwMdsGYkO8vZwbwiCIAiiYkFiBACr1SJVHQIAqBRIlhGCIAiCKE9IjABgdTrcCowGANSN8vdwbwiCIAiiYkFiBIBBY8A9v3AAQL1KAR7uDUEQBEFULEiMACjUMzAV1aaJDKBoGoIgCIIoT0iMADDpjAAAOWuk7KsEQRAEUc6QGAFgMJgAAAqG9XBPCIIgCKLiQWIEgNFg/l8Bk2c7QhAEQRAVEBIjAIx6swiRMyRGCIIgCKK8ITECgDWa/URomYYgCIIgyh8SIwAMZv9VKOnTIAiCIIhyh4ZfAKYiMUKWEYIgCIIof0iMgLdMI6ewXoIgCIIob0iMADCaisQIfRoEQRAEUe7Q8AvAVCRGlGQZIQiCIIhyh8QIANZk/hhomYYgCIIgyh8SIwCMrLkujReF0xAEQRBEuVOs0Xf+/PmIi4uDt7c34uPjcezYMYfts7KyMH78eFSqVAkqlQq1a9fGli1bitXhsoBlzR+DUi73cE8IgiAIouKhcPeANWvWYPLkyViwYAHi4+Mxd+5c9OzZE1evXkVERISovU6nQ/fu3REREYE//vgDMTExuHv3LoKCgkqj/6WCySJGlCRGCIIgCKK8cVuMzJkzBy+99BJGjRoFAFiwYAE2b96MJUuWYMqUKaL2S5YsQUZGBg4dOgSlUgkAiIuLc3gNrVYLrVbLvc7JyXG3m25hKlqmUZEYIQiCIIhyx61lGp1Oh5MnT6Jbt27WE8hk6NatGw4fPix5zKZNm9C2bVuMHz8ekZGRaNiwIT755BMYjUa715k9ezYCAwO5f7Gxse50020MMrMmUypIjBAEQRBEeeOWGElPT4fRaERkZKRge2RkJFJSUiSPuXXrFv744w8YjUZs2bIF77//Pr766it89NFHdq8zdepUZGdnc/8SExPd6aZbsCYTjIzFgZXECEEQBEGUN24v07iLyWRCREQEfvzxR8jlcrRo0QL379/HF198gRkzZkgeo1KpoFKpyrprZoxG6OTm5SNvZZl/HARBEARB2ODW6BsWFga5XI7U1FTB9tTUVERFRUkeU6lSJSiVSsh5kSr16tVDSkoKdDodvLy8itHt0oM1GKCRm/vg5+3ZvhAEQRBERcStZRovLy+0aNECu3fv5raZTCbs3r0bbdu2lTymXbt2uHHjBkwmE7ft2rVrqFSpkseFCACwRiMKFWYrjK+30sO9IQiCIIiKh9t5RiZPnoyffvoJP//8My5fvoxXXnkF+fn5XHTN8OHDMXXqVK79K6+8goyMDEycOBHXrl3D5s2b8cknn2D8+PGl9y5KgFGng0ZRZBnxKaelIYIgCIIgONx2khg6dCgePHiA6dOnIyUlBU2bNsW2bds4p9aEhATIZFaNExsbi+3bt+ONN95A48aNERMTg4kTJ+Ldd98tvXdRAgwGLQrlZhHi501ihCAIgiDKG4ZlWdbTnXBGTk4OAgMDkZ2djYCAgFI9d/b9O3h25l+4FFoN3zzbCAOaVCnV8xMEQRBERcXV8bvCF2Mx6DTcMk0AWUYIgiAIotwhMWLQccs0/uTAShAEQRDlDokRvZazjPhQnhGCIAiCKHcqvBjRazVcnhFfFWVgJQiCIIjypsKLEYNBC21RBlYV1aYhCIIgiHKnwosRnU4Lk8wsQpRyxsO9IQiCIIiKB4kRnY77W6mo8B8HQRAEQZQ7FX701er03N9KWYX/OAiCIAii3Knwo6+GbxmhZRqCIAiCKHcqvBjR6Q0AAIY1QS4jMUIQBEEQ5U2FFyNardkyImdNYBgSIwRBEARR3lR4MaI3mC0jctbo4Z4QBEEQRMWkwosRncEsQuSsycM9IQiCIIiKCYmRIp8RBVlGCIIgCMIjVHgxordYRkCWEYIgCILwBCRG9JZlGrKMEARBEIQnIDFClhGCIAiC8CgkRkwsAEBBYoQgCIIgPEKFFyMGvVmEkGWEIAiCIDxDhRcjehOJEYIgCILwJBVejBgMZhFCyzQEQRAE4RlIjBSJETnDergnBEEQBFExqfBixFgU0SsHiRGCIAiC8AQVXowYjGYRIpeRGCEIgiAIT1DhxYjRIkZomYYgCIIgPAKJkSK/VUWF/yQIgiAIwjNU+CGYLUp6JmM83BGCIAiCqKBUeDFSlGYEclIjBEEQBOERKrwYMZosDqwkRgiCIAjCE1R4MWIymUWIXE5ihCAIgiA8QYUXI2xREA1ZRgiCIAjCM1R4McL5jMgr/EdBEARBEB6hwo/AJliWaSr8R0EQBEEQHqHCj8Bskc+IQlbhPwqCIAiC8AgVfgRm2SIxQlnPCIIgCMIjVPgR2LJMo5DLPdwTgiAIgqiYFEuMzJ8/H3FxcfD29kZ8fDyOHTvm0nGrV68GwzAYNGhQcS5bJpg4ywiJEYIgCILwBG6LkTVr1mDy5MmYMWMGTp06hSZNmqBnz55IS0tzeNydO3fw1ltvoUOHDsXubFnAWiwjCoWHe0IQBEEQFRO3xcicOXPw0ksvYdSoUahfvz4WLFgAtVqNJUuW2D3GaDTi+eefx6xZs1C9enWn19BqtcjJyRH8KytMrPkjIMsIQRAEQXgGt8SITqfDyZMn0a1bN+sJZDJ069YNhw8ftnvcBx98gIiICLz44osuXWf27NkIDAzk/sXGxrrTTbdgiz4COTmwEgRBEIRHcGsETk9Ph9FoRGRkpGB7ZGQkUlJSJI85cOAAFi9ejJ9++snl60ydOhXZ2dncv8TERHe66RbGoo+AHFgJgiAIwjOUqaNEbm4uhg0bhp9++glhYWEuH6dSqaBSqcqwZ1ZYhvKMEARBEIQncUuMhIWFQS6XIzU1VbA9NTUVUVFRovY3b97EnTt30L9/f26bqSj/ukKhwNWrV1GjRo3i9LvUMBVZRpSUgZUgCIIgPIJbI7CXlxdatGiB3bt3c9tMJhN2796Ntm3bitrXrVsX58+fx5kzZ7h/AwYMQOfOnXHmzJky9QVxFRNDPiMEQRAE4UncXqaZPHkyRowYgZYtW6J169aYO3cu8vPzMWrUKADA8OHDERMTg9mzZ8Pb2xsNGzYUHB8UFAQAou2ewsiYfUUUMvIZIQiCIAhP4LYYGTp0KB48eIDp06cjJSUFTZs2xbZt2zin1oSEBMj+Rf4XlIGVIAiCIDxLsRxYJ0yYgAkTJkju27dvn8Njly1bVpxLlhmmIsuIkpZpCIIgCMIjVPgR2MhQaC9BEARBeJIKL0YsDqxKSgdPEARBEB6hQosRlmU5MaKgZRqCIAiC8AgVegRmYRUjXnKyjBAEQRCEJ6jQYsTIGjmfETkt0xAEQRCER6jYYsRk5FlGyIGVIAiCIDxBxRYjRgNnGVEqyTJCEARBEJ6gQosRk8HAZWBVKpQe7g1BEARBVEwqtDnAaNJzlhEvsowQBFFBMBqN0Ov1nu4G8R9AqVRCXgpuDhV6BDYZDDDKLFV7K/RHQRBEBYBlWaSkpCArK8vTXSH+QwQFBSEqKgoMwxT7HBV6BDaa9NakZ2QZIQjiP45FiERERECtVpdo8CAIlmVRUFCAtLQ0AEClSpWKfa4KPQIbDVYHVgWF9hIE8R/GaDRyQiQ0NNTT3SH+I/j4+AAA0tLSEBERUewlm4rtwGoy8sRIhf4oCIL4j2PxEVGr1R7uCfFfw3JPlcQPqUKPwCajgZcOnvKMEATx34eWZojSpjTuqQotRoxGPefASmKEIAiCIDxDhRYjLC/pmVxGswWCIIiKzLJlyxAUFMS9njlzJpo2beqx/lQkKrQY0Rv0YC3LNLIK/VEQBEEQNrz11lvYvXu3p7tRIajQISQGvYH7mywjBEEQBB8/Pz/4+fl5uhsVggptDtAZSIwQBEH8V+jUqRMmTJiACRMmIDAwEGFhYXj//ffBsiwAIDMzE8OHD0dwcDDUajV69+6N69ev2z2f1DLNkiVL0KBBA6hUKlSqVAkTJkwAAIwePRr9+vUTtNXr9YiIiMDixYtL943+B6nQYsSgN3J/K0iMEARB/Ov5+eefoVAocOzYMcybNw9z5szBokWLAAAjR47EiRMnsGnTJhw+fBgsy6JPnz4uh6T+8MMPGD9+PMaOHYvz589j06ZNqFmzJgBgzJgx2LZtG5KTk7n2f/31FwoKCjB06NDSf6P/MSr0Mo3eZBUjZBkhCIL49xMbG4uvv/4aDMOgTp06OH/+PL7++mt06tQJmzZtwsGDB/HYY48BAH755RfExsZiw4YNGDx4sNNzf/TRR3jzzTcxceJEblurVq0AAI899hjq1KmDFStW4J133gEALF26FIMHD6alHheo4JYR3jINxd4TBEH862nTpo0g70Xbtm1x/fp1XLp0CQqFAvHx8dy+0NBQ1KlTB5cvX3Z63rS0NCQlJaFr165224wZMwZLly4FAKSmpmLr1q0YPXp0Cd5NxaFCixG9wWwZYVgTZGQZIQiCIOxgSXvuiOHDh+PWrVs4fPgwVq5ciWrVqqFDhw7l0Lt/PxVajBiKHFjlrMnDPSEIgiBKg6NHjwpeHzlyBLVq1UL9+vVhMBgE+x8+/H97dx8XVZ3vAfwzPAwzCAwC8SgPs8nVVHxECFNzX8wVXU3Q3W56vRtSWrZy1SUfoBQ3E3Hpqqxek6zEddXV271K5VXLSFYrBEVRyQ0tcfGaA5HBgDzK/O4fyqmJB3kYOOB83q/XvGrO+Z1zvufLyPny+/3Ome9RWFiIIUOGPHC/jo6OCAgIaPNWX1dXV0RFRSE9PR27du1CTExM50/Ewlj2nJHGe0UIixEioodDcXEx4uLi8OKLL+LcuXPYunUrNm7ciMDAQERGRmLBggV466234OjoiPj4ePj4+CAyMrJd+/7DH/6AhQsXwt3dHVOnTkVlZSU+//xz/Pu//7vUZv78+Zg+fToaGxsRHR3dXaf50LHoYuRez4gdrFiMEBE9FJ599lnU1NQgJCQE1tbWWLJkCV544QUA9yaULlmyBNOnT0d9fT0mTpyII0eOwNbWtl37jo6ORm1tLTZv3oxly5bBzc0Nv/nNb0za6HQ6eHl5YejQofD29jb7+T2sFKLpBuxezGAwQKPRoKKiAk5OTmbb78EP9yPuc0f0a6jGlxsfPJOaiKivqq2tRVFREbRaLVQqldzhdItJkyZh5MiRSE1NlS2Gqqoq+Pj4ID09HbNmzZItjp7U1mervddvi+4ZaWy8N4GVwzRERNQVRqMRZWVl2LhxI5ydnTFjxgy5Q+pTLLoYaZozwmEaIiLqiuLiYmi1WgwYMAC7du2CjY1FX147zKKz1cgJrERED42srCzZjh0QEIA+MOuh17LwW3vv94yAxQgREZFcLLsYuT9nhMM0RERE8rHsYsR4r0vNCuxaIyIikotFFyOCc0aIiIhkZ9HFyI89IyxGiIiI5GLZxQhv7SUiIpJdp4qRbdu2ISAgACqVCqGhocjNzW217dtvv40JEyagf//+6N+/P3Q6XZvte5Lxfs+INXtGiIioi77//nu4u7vj+vXrcofSqoCAgA49oXb27NnYuHFj9wV0X4eLkQMHDiAuLg5r1qzBuXPnMGLECERERKC0tLTF9llZWZgzZw5OnDiB7Oxs+Pr6YvLkybh582aXg++qxvvFiIITWImI6D4hBBITE+Hl5QW1Wg2dToerV68+cLukpCRERkYiICCg+4PsIatWrUJSUhIqKiq69TgdLkY2bdqEBQsWICYmBkOGDEFaWhrs7e2xc+fOFtvv3bsXv/vd7zBy5EgMHjwY77zzDoxGY5tfw1xXVweDwWDy6g5NxYgVH1RDRET3paSkYMuWLUhLS0NOTg769euHiIgI1NbWtrpNdXU13n33XTz//PNdOnZjYyOMxt7TWz9s2DA8+uij2LNnT7cep0PFSH19PfLy8qDT6X7cgZUVdDodsrOz27WP6upqNDQ0wMXFpdU2ycnJ0Gg00svX17cjYbabsZG39hKR5RJCoLqhWpZXR55WmpubiwkTJsDR0RH9+vVDUFAQzpw50205SU1NxapVqxAZGYnhw4dj9+7d+Pbbb5GRkdHqdkeOHIGdnR0ef/xxk+UffPABAgMDoVKp8Mtf/hJ//vOfoVAoUF5eDgDYtWsXnJ2d8cEHH2DIkCGws7NDcXEx6urqsGzZMvj4+KBfv34IDQ1t9oTZzz77DBMmTIBarYavry8WL16MO3fuSOtLS0vx1FNPQa1WQ6vVYu/evSbbP/fcc5g+fbrJsoaGBri7u+Pdd9+Vlj311FPYv39/B7LYcR16HHxZWRkaGxvh4eFhstzDwwNfffVVu/axcuVKeHt7mxQ0P5eQkIC4uDjpvcFg6JaCpOkfA4dpiMgS1dytQei+UFmOnfOvObC3tW9X29mzZ2PcuHHYsWMHVCoVrl271uw69FMLFy584F/yVVVVLS4vKiqCXq83uUZpNBqEhoYiOzsbs2fPbnG7U6dOYcyYMc329Zvf/AZLlizB/Pnzcf78eSxbtqzZttXV1fjjH/+Id955B66urnB3d0dsbCwuX76M/fv3w9vbG4cOHcKUKVNw6dIlBAYG4ptvvsGUKVOwbt067Ny5E9999x1iY2MRGxuL9PR0AMC8efPw7bff4sSJE7C1tcXixYtNplTMnz8fEydOxK1bt+Dl5QUAOHz4MKqrq/HMM89I7UJCQpCUlIS6ujrY2dm1mdfO6tHvptmwYQP279+PrKysNr/C2s7OrttO+KeMnDNCRNTr3b17F35+fhg4cCBsbW2h1WrbbL927doWL/rtodfrAaDFP7qb1rXkH//4B7y9vU2WvfXWWxg0aBDeeOMNAMCgQYNQUFCApKQkk3YNDQ148803MWLECAD3vnQvPT0dxcXF0j6XLVuGY8eOIT09HevXr0dycjLmzp2LpUuXAgACAwOxZcsWPPnkk9i+fTuKi4tx9OhR5ObmYuzYsQCAd999F4899ph03HHjxmHQoEH4y1/+ghUrVgAA0tPT8fTTT8PBwUFq5+3tjfr6euj1evj7+7cvkR3UoWLEzc0N1tbWKCkpMVleUlICT0/PNrf9j//4D2zYsAGffPIJhg8f3vFIu4FRcJiGiCyX2kaNnH/Nke3Y7XXw4EHMnDkTKSkpUKlUuHnzJjQaTavt3d3d4e7ubo4w262mpqbZH9mFhYVSIdAkJCSk2bZKpdLkunjp0iU0Njbin/7pn0za1dXVwdXVFQBw4cIFXLx40WToRQgBo9GIoqIiXLlyBTY2Nia9NYMHD4azs7PJPufPn48dO3ZgxYoVKCkpwdGjR/Hpp5+atFGr7/2sqqurH5SGTutQMaJUKjFmzBhkZmYiKioKAKTJqLGxsa1ul5KSgqSkJHz00UcIDg7uUsDmZLz/nBEo5I2DiEgOCoWi3UMlckpISMDYsWMRHx8PFxcXODo6ttm+K8M0TX9Yl5SUSEMXTe9HjhzZ6v7c3Nzwww8/tHnM1qjVaigUP16IqqqqYG1tjby8PFhbW5u0beqxqKqqwosvvojFixc325+fnx+uXLnSrmM/++yziI+PR3Z2Nr744gtotVpMmDDBpM3t27cBAI888kiHzqsjOjxMExcXh+joaAQHByMkJASpqam4c+cOYmJiANw7MR8fHyQnJwMA/vjHPyIxMRH79u1DQECA1M3l4OBg0g0kB6O498NnzwgRUe9UVlaGTz75BPn5+dIwxoN0ZZhGq9XC09MTmZmZUvFhMBiQk5ODl156qdXtRo0a1awAGjRoEI4cOWKyrD0Tb0eNGoXGxkaUlpY2KwyajB49GpcvX8bAgQNbXD948GDcvXsXeXl5Uu9MYWGhNHG2iaurK6KiopCeno7s7GzpWv5TBQUFGDBgANzc3B4Ye6eJTti6davw8/MTSqVShISEiNOnT0vrnnzySREdHS299/f3FwCavdasWdPu41VUVAgAoqKiojPhtiph/RvCf+VhMSN2s1n3S0TU29TU1IjLly+LmpoauUPpMF9fXzFjxgxx9uxZcf36dXHixAnx0UcfddvxNmzYIJydncX7778vLl68KCIjI4VWq20zdxcvXhQ2Njbi9u3b0rJr164JW1tbsWLFClFYWCgOHDggBgwYIACI8vJyIYQQ6enpQqPRNNvf3LlzRUBAgPif//kfce3aNZGTkyPWr18vDh8+LIQQ4sKFC0KtVotFixaJ8+fPiytXroiMjAyxaNEiaR9TpkwRo0aNEqdPnxZnz54V48ePF2q1WmzevNnkWB9//LFQKpXC2tpa3Lx5s1ks0dHR4rnnnmv13Nv6bLX3+t2pYqSndVcxsnLdvWIkMnaTWfdLRNTb9OVipKCgQEyfPl24uroKpVIpBg4cKHbv3t1txzMajWL16tXCw8ND2NnZifDwcFFYWPjA7UJCQkRaWprJsvfff18MHDhQ2NnZiUmTJont27cLANLPobVipL6+XiQmJoqAgABha2srvLy8xMyZM8XFixelNrm5ueKf//mfhYODg+jXr58YPny4SEpKktbfunVLTJs2TdjZ2Qk/Pz+xe/du4e/v36wYMRqNwt/fX/zqV79qFkdNTY3QaDQiOzu71fM2RzGiEKL3P/HLYDBAo9GgoqICTk5OZtvvynVv4EDVEIyuvoKDW35vtv0SEfU2tbW1KCoqglarbfNuRuq8//3f/8Xy5ctRUFAAK6uWH+OVlJSEtLQ03Lhxo4eja11VVRV8fHyQnp6OWbNmmazbvn07Dh06hI8//rjV7dv6bLX3+t2jt/b2NkZp/mqvr8eIiKiXmzZtGq5evYqbN29Kz8Z68803MXbsWLi6uuLzzz/HG2+80eYNHz3JaDSirKwMGzduhLOzM2bMmNGsja2tLbZu3drtsVh0MSKkh54RERF1XdNzP5pcvXoV69atw+3bt+Hn54eXX34ZCQkJ8gT3M8XFxdBqtRgwYAB27doFG5vmJcH8+fN7JBaLLkbuPw0eCgV7RoiIyPw2b96MzZs3yx1GiwICAjr0WP7u1OEvynuYiKYvypM5DiIiIktm0ddhwZ4RIiIi2Vl0MfLj4+CJiIhILhZ9HTY29YzIGwYREZFFs+hi5MdhGnnjICIismQWXYxIzxlhMUJERCQbiy5GmvCL8oiIiORj0cWIkcM0RET0M0IIJCYmwsvLC2q1GjqdDlevXn3gdtu2bUNAQABUKhVCQ0ORm5trsn7Hjh2YNGkSnJycoFAomn2DriVjMQLAisUIERHdl5KSgi1btiAtLQ05OTno168fIiIiUFtb2+o2Bw4cQFxcHNasWYNz585hxIgRiIiIQGlpqdSmuroaU6ZMwSuvvNITp9GnWHQxIliMEJEFE0LAWF0ty6sjT/7Mzc3FhAkT4OjoiH79+iEoKAhnzpzptpykpqZi1apViIyMxPDhw7F79258++23yMjIaHW7TZs2YcGCBYiJicGQIUOQlpYGe3t77Ny5U2qzdOlSxMfH4/HHH++W2Psyi34cvPRvgcUIEVkgUVODwtFjZDn2oHN5UNjbt6vt7NmzMW7cOOzYsQMqlQrXrl2Dh4dHq+0XLlyIPXv2tLnPqqqqFpcXFRVBr9dDp9NJyzQaDUJDQ5GdnY3Zs2c326a+vh55eXkm3zljZWUFnU6H7OzsB50ewcKLkfs308CaT2AlIuq17t69Cz8/PwwcOBC2trbQarVttl+7di2WLVvWqWPp9XoAaFbseHh4SOt+rqysDI2NjS1u89VXX3UqDktj0cWIkG7tZdcIEVkehVqNQefyZDt2ex08eBAzZ85ESkoKVCoVbt68CY1G02p7d3d3uLu7myNM6iGWXYzcH59hKUJElkihULR7qEROCQkJGDt2LOLj4+Hi4gJHR8c223dlmMbT0xMAUFJSAi8vL2l5SUkJRo4c2eI2bm5usLa2RklJicnykpISaX/UNssuRjiBlYioVysrK8Mnn3yC/Px8jBgxol3bdGWYRqvVwtPTE5mZmVLxYTAYkJOTg5deeqnFbZRKJcaMGYPMzExERUUBAIxGIzIzMxEbG9upOCyNRRcjTXNGFBZ9TxERUe/l5uYGX19fJCYmIjExEW5ubigqKkJ9fT0mT57c4jZdGaZRKBRYunQp1q1bh8DAQGi1WqxevRre3t5SoQEA4eHhmDlzplRsxMXFITo6GsHBwQgJCUFqairu3LmDmJgYaRu9Xg+9Xo+vv/4aAHDp0iU4OjrCz88PLi4unYr3YWHRxYgQ97pErDhnhIio1zp69Cji4+MRERGByspK+Pn5ITExsduOt2LFCty5cwcvvPACysvLMX78eBw7dgwqlUpq880336CsrEx6/8wzz+C7775DYmIi9Ho9Ro4ciWPHjplMak1LS8Nrr70mvZ84cSIAID09HfPmzeu28+kLFKIjN3vLxGAwQKPRoKKiAk5OTmbb75wlKchWD0WU7d+R+nrnuvSIiPqC2tpaFBUVQavVmlxUibqqrc9We6/fFj1AIT1mhB0jREREsrHoYsQIDtMQERHJzaKLkR/njMgcCBERkQWz7GLk/n8VrEaIiIhkY9HFCIdpiIiI5GfRxYj00DOLzgIREZG8LPoyLD0Onj0jREREsrHsYqRpAivnjBAREcnGsouR+/+1tugsEBERycuiL8McpiEiIpKfRRcj0t00nMFKRET3CSGQmJgILy8vqNVq6HQ6XL16tc1tTp48iaeeegre3t5QKBTIyMjomWAfEhZ9FRbgQ8+IiMhUSkoKtmzZgrS0NOTk5KBfv36IiIhAbW1tq9vcuXMHI0aMwLZt23ow0oeHRX9rL3tGiMiSCSFQ09Aoy7HVttbtHiLPzc3Fyy+/jPz8fBiNRvziF7/Azp07MXbsWLPHJYRAamoqVq1ahcjISADA7t274eHhgYyMDMyePbvF7aZOnYqpU6eaPR5LYdHFCJ/ASkSWrKahEUMSP5Ll2JfXRsBe2b5L0OzZszFu3Djs2LEDKpUK165dg4eHR6vtFy5ciD179rS5z6qqqhaXFxUVQa/XQ6fTScs0Gg1CQ0ORnZ3dajFCXdOpLoFt27YhICAAKpUKoaGhyM3NbbP9e++9h8GDB0OlUiEoKAhHjhzpVLDm1jRMY2PNYoSIqLe6e/cu/Pz8MHDgQGi1WoSHh8PPz6/V9mvXrkV+fn6br9bo9XoAaFbseHh4SOvI/DrcM3LgwAHExcUhLS0NoaGhSE1NRUREBAoLC+Hu7t6s/RdffIE5c+YgOTkZ06dPx759+xAVFYVz585h2LBhZjmJzhL3azErBYdpiMjyqG2tcXlthGzHbq+DBw9i5syZSElJgUqlws2bN6HRaFpt7+7u3uL1iHqvDl+FN23ahAULFiAmJgZDhgxBWloa7O3tsXPnzhbb/+lPf8KUKVOwfPlyPPbYY3j99dcxevRo/Od//meXg++qpmEaPvSMiCyRQqGAvdJGlldHHqmQkJCAsWPH4osvvkB+fj4cHR3bbL9w4UI4ODi0+WqNp6cnAKCkpMRkeUlJibSOzK9DPSP19fXIy8tDQkKCtMzKygo6nQ7Z2dktbpOdnY24uDiTZREREW3e9lRXV4e6ujrpvcFg6EiY7Vbf3xFoAOx9BnTL/omIqGvKysrwySefID8/HyNGjGjXNmvXrsWyZcs6dTytVgtPT09kZmZi5MiRAO5dg3JycvDSSy91ap/0YB0qRsrKytDY2NjiWNpXX33V4jZ6vb7DY2/Jycl47bXXOhJap3gNGIiSG+V4dODobj8WERF1nJubG3x9fZGYmIjExES4ubmhqKgI9fX1mDx5covbdGWYRqFQYOnSpVi3bh0CAwOh1WqxevVqeHt7IyoqSmoXHh6OmTNnIjY2FsC9CbFff/21tL6oqAj5+flwcXFpc34L3dMr76ZJSEgw6U0xGAzw9fU1+3EOvBhm9n0SEZF5HT16FPHx8YiIiEBlZSX8/PyQmJjYbcdbsWIF7ty5gxdeeAHl5eUYP348jh07BpVKJbX55ptvUFZWJr0/e/YsfvnLX0rvm65h0dHR2LVrV7fF+rDoUDHi5uYGa2vrDo2leXp6dnjszc7ODnZ2dh0JjYiIHlJDhw7Fhx9+2GPHUygUWLt2LdauXdtqm+vXr5u8nzRpEoQQLTemB+rQBFalUokxY8YgMzNTWmY0GpGZmYmwsJZ7GcLCwkzaA8Dx48dbbU9ERESWpcPDNHFxcYiOjkZwcDBCQkKQmpqKO3fuICYmBgDw7LPPwsfHB8nJyQCAJUuW4Mknn8TGjRsxbdo07N+/H2fPnsWOHTvMeyZERETUJ3W4GHnmmWfw3XffITExEXq9HiNHjsSxY8ekSarFxcUmj1cfN24c9u3bh1WrVuGVV15BYGAgMjIyZH/GCBEREfUOCtEHBrkMBgM0Gg0qKirg5OQkdzhERH1ObW0tioqKoNVqTSZiEnVVW5+t9l6/+ehRIiIL0gf+/qQ+xhyfKRYjREQWwNbWFgBQXV0tcyT0sGn6TDV9xjqjVz5nhIiIzMva2hrOzs4oLS0FANjb23fokexEPyeEQHV1NUpLS+Hs7Axr6/Z/39DPsRghIrIQTc93aipIiMzB2dm5y9/bw2KEiMhCKBQKeHl5wd3dHQ0NDXKHQw8BW1vbLvWINGExQkRkYaytrc1yASEyF05gJSIiIlmxGCEiIiJZsRghIiIiWfWJOSNND1QxGAwyR0JERETt1XTdftCD0fpEMVJZWQkA8PX1lTkSIiIi6qjKykpoNJpW1/eJ76YxGo349ttv4ejoaNaH9BgMBvj6+uLGjRv8zptuxlz3DOa5ZzDPPYN57jndlWshBCorK+Ht7W3yJbo/1yd6RqysrDBgwIBu27+TkxM/6D2Eue4ZzHPPYJ57BvPcc7oj1231iDThBFYiIiKSFYsRIiIikpVFFyN2dnZYs2YN7Ozs5A7locdc9wzmuWcwzz2Dee45cue6T0xgJSIiooeXRfeMEBERkfxYjBAREZGsWIwQERGRrFiMEBERkawsuhjZtm0bAgICoFKpEBoaitzcXLlD6jOSk5MxduxYODo6wt3dHVFRUSgsLDRpU1tbi0WLFsHV1RUODg749a9/jZKSEpM2xcXFmDZtGuzt7eHu7o7ly5fj7t27PXkqfcqGDRugUCiwdOlSaRnzbD43b97Ev/3bv8HV1RVqtRpBQUE4e/astF4IgcTERHh5eUGtVkOn0+Hq1asm+7h9+zbmzp0LJycnODs74/nnn0dVVVVPn0qv1djYiNWrV0Or1UKtVuPRRx/F66+/bvLdJcxz55w8eRJPPfUUvL29oVAokJGRYbLeXHm9ePEiJkyYAJVKBV9fX6SkpHQ9eGGh9u/fL5RKpdi5c6f48ssvxYIFC4Szs7MoKSmRO7Q+ISIiQqSnp4uCggKRn58vfvWrXwk/Pz9RVVUltVm4cKHw9fUVmZmZ4uzZs+Lxxx8X48aNk9bfvXtXDBs2TOh0OnH+/Hlx5MgR4ebmJhISEuQ4pV4vNzdXBAQEiOHDh4slS5ZIy5ln87h9+7bw9/cX8+bNEzk5OeLatWvio48+El9//bXUZsOGDUKj0YiMjAxx4cIFMWPGDKHVakVNTY3UZsqUKWLEiBHi9OnT4tSpU2LgwIFizpw5cpxSr5SUlCRcXV3F4cOHRVFRkXjvvfeEg4OD+NOf/iS1YZ4758iRI+LVV18VBw8eFADEoUOHTNabI68VFRXCw8NDzJ07VxQUFIi//vWvQq1Wi7feeqtLsVtsMRISEiIWLVokvW9sbBTe3t4iOTlZxqj6rtLSUgFA/O1vfxNCCFFeXi5sbW3Fe++9J7X5+9//LgCI7OxsIcS9fzhWVlZCr9dLbbZv3y6cnJxEXV1dz55AL1dZWSkCAwPF8ePHxZNPPikVI8yz+axcuVKMHz++1fVGo1F4enqKN954Q1pWXl4u7OzsxF//+lchhBCXL18WAMSZM2ekNkePHhUKhULcvHmz+4LvQ6ZNmyaee+45k2WzZs0Sc+fOFUIwz+by82LEXHl98803Rf/+/U1+d6xcuVIMGjSoS/Fa5DBNfX098vLyoNPppGVWVlbQ6XTIzs6WMbK+q6KiAgDg4uICAMjLy0NDQ4NJjgcPHgw/Pz8px9nZ2QgKCoKHh4fUJiIiAgaDAV9++WUPRt/7LVq0CNOmTTPJJ8A8m9MHH3yA4OBgPP3003B3d8eoUaPw9ttvS+uLioqg1+tNcq3RaBAaGmqSa2dnZwQHB0ttdDodrKyskJOT03Mn04uNGzcOmZmZuHLlCgDgwoUL+OyzzzB16lQAzHN3MVdes7OzMXHiRCiVSqlNREQECgsL8cMPP3Q6vj7xRXnmVlZWhsbGRpNfzgDg4eGBr776Sqao+i6j0YilS5fiiSeewLBhwwAAer0eSqUSzs7OJm09PDyg1+ulNi39DJrW0T379+/HuXPncObMmWbrmGfzuXbtGrZv3464uDi88sorOHPmDBYvXgylUono6GgpVy3l8qe5dnd3N1lvY2MDFxcX5vq++Ph4GAwGDB48GNbW1mhsbERSUhLmzp0LAMxzNzFXXvV6PbRabbN9NK3r379/p+KzyGKEzGvRokUoKCjAZ599JncoD50bN25gyZIlOH78OFQqldzhPNSMRiOCg4Oxfv16AMCoUaNQUFCAtLQ0REdHyxzdw+O//uu/sHfvXuzbtw9Dhw5Ffn4+li5dCm9vb+bZglnkMI2bmxusra2b3XFQUlICT09PmaLqm2JjY3H48GGcOHECAwYMkJZ7enqivr4e5eXlJu1/mmNPT88WfwZN6+jeMExpaSlGjx4NGxsb2NjY4G9/+xu2bNkCGxsbeHh4MM9m4uXlhSFDhpgse+yxx1BcXAzgx1y19XvD09MTpaWlJuvv3r2L27dvM9f3LV++HPHx8Zg9ezaCgoLw29/+Fr///e+RnJwMgHnuLubKa3f9PrHIYkSpVGLMmDHIzMyUlhmNRmRmZiIsLEzGyPoOIQRiY2Nx6NAhfPrpp8267caMGQNbW1uTHBcWFqK4uFjKcVhYGC5dumTy4T9+/DicnJyaXRQsVXh4OC5duoT8/HzpFRwcjLlz50r/zzybxxNPPNHs9vQrV67A398fAKDVauHp6WmSa4PBgJycHJNcl5eXIy8vT2rz6aefwmg0IjQ0tAfOoverrq6GlZXppcfa2hpGoxEA89xdzJXXsLAwnDx5Eg0NDVKb48ePY9CgQZ0eogFg2bf22tnZiV27donLly+LF154QTg7O5vccUCte+mll4RGoxFZWVni1q1b0qu6ulpqs3DhQuHn5yc+/fRTcfbsWREWFibCwsKk9U23nE6ePFnk5+eLY8eOiUceeYS3nD7AT++mEYJ5Npfc3FxhY2MjkpKSxNWrV8XevXuFvb292LNnj9Rmw4YNwtnZWbz//vvi4sWLIjIyssVbI0eNGiVycnLEZ599JgIDAy3+ltOfio6OFj4+PtKtvQcPHhRubm5ixYoVUhvmuXMqKyvF+fPnxfnz5wUAsWnTJnH+/Hnxj3/8QwhhnryWl5cLDw8P8dvf/lYUFBSI/fv3C3t7e97a2xVbt24Vfn5+QqlUipCQEHH69Gm5Q+ozALT4Sk9Pl9rU1NSI3/3ud6J///7C3t5ezJw5U9y6dctkP9evXxdTp04VarVauLm5iZdfflk0NDT08Nn0LT8vRphn8/nwww/FsGHDhJ2dnRg8eLDYsWOHyXqj0ShWr14tPDw8hJ2dnQgPDxeFhYUmbb7//nsxZ84c4eDgIJycnERMTIyorKzsydPo1QwGg1iyZInw8/MTKpVK/OIXvxCvvvqqya2izHPnnDhxosXfy9HR0UII8+X1woULYvz48cLOzk74+PiIDRs2dDl2hRA/eewdERERUQ+zyDkjRERE1HuwGCEiIiJZsRghIiIiWbEYISIiIlmxGCEiIiJZsRghIiIiWbEYISIiIlmxGCEiIiJZsRghIiIiWbEYIaJuN2/ePERFRckdBhH1UixGiIiISFYsRojIbP77v/8bQUFBUKvVcHV1hU6nw/Lly/HnP/8Z77//PhQKBRQKBbKysgAAN27cwL/8y7/A2dkZLi4uiIyMxPXr16X9NfWovPbaa3jkkUfg5OSEhQsXor6+Xp4TJKJuYSN3AET0cLh16xbmzJmDlJQUzJw5E5WVlTh16hSeffZZFBcXw2AwID09HQDg4uKChoYGREREICwsDKdOnYKNjQ3WrVuHKVOm4OLFi1AqlQCAzMxMqFQqZGVl4fr164iJiYGrqyuSkpLkPF0iMiMWI0RkFrdu3cLdu3cxa9Ys+Pv7AwCCgoIAAGq1GnV1dfD09JTa79mzB0ajEe+88w4UCgUAID09Hc7OzsjKysLkyZMBAEqlEjt37oS9vT2GDh2KtWvXYvny5Xj99ddhZcXOXaKHAf8lE5FZjBgxAuHh4QgKCsLTTz+Nt99+Gz/88EOr7S9cuICvv/4ajo6OcHBwgIODA1xcXFBbW4tvvvnGZL/29vbS+7CwMFRVVeHGjRvdej5E1HPYM0JEZmFtbY3jx4/jiy++wMcff4ytW7fi1VdfRU5OTovtq6qqMGbMGOzdu7fZukceeaS7wyWiXoTFCBGZjUKhwBNPPIEnnngCiYmJ8Pf3x6FDh6BUKtHY2GjSdvTo0Thw4ADc3d3h5OTU6j4vXLiAmpoaqNVqAMDp06fh4OAAX1/fbj0XIuo5HKYhIrPIycnB+vXrcfbsWRQXF+PgwYP47rvv8NhjjyEgIAAXL15EYWEhysrK0NDQgLlz58LNzQ2RkZE4deoUioqKkJWVhcWLF+P//u//pP3W19fj+eefx+XLl3HkyBGsWbMGsbGxnC9C9BBhzwgRmYWTkxNOnjyJ1NRUGAwG+Pv7Y+PGjZg6dSqCg4ORlZWF4OBgVFVV4cSJE5g0aRJOnjyJlStXYtasWaisrISPjw/Cw8NNekrCw8MRGBiIiRMnoq6uDnPmzMEf/vAH+U6UiMxOIYQQcgdBRNSSefPmoby8HBkZGXKHQkTdiP2cREREJCsWI0RERCQrDtMQERGRrNgzQkRERLJiMUJERESyYjFCREREsmIxQkRERLJiMUJERESyYjFCREREsmIxQkRERLJiMUJERESy+n+w1lpPRahGXAAAAABJRU5ErkJggg==", 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" ] @@ -303,13 +303,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:03:34.143839Z", - "iopub.status.busy": "2022-10-26T11:03:34.143054Z", - "iopub.status.idle": "2022-10-26T11:05:04.817389Z", - "shell.execute_reply": "2022-10-26T11:05:04.817907Z" + "iopub.execute_input": "2023-06-26T15:08:16.087582Z", + "iopub.status.busy": "2023-06-26T15:08:16.087413Z", + "iopub.status.idle": "2023-06-26T15:08:53.967501Z", + "shell.execute_reply": "2023-06-26T15:08:53.967197Z" } }, "outputs": [ @@ -317,9 +317,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/6000000 [00:00" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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-       "Higgs dataset.\n",
-       "\n",
-       "The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22)\n",
-       "are kinematic properties measured by the particle detectors in the accelerator. The last seven\n",
-       "features are functions of the first 21 features; these are high-level features derived by\n",
-       "physicists to help discriminate between the two classes.\n",
-       "\n",
-       "      Name  Higgs                                                                       \n",
-       "      Task  Binary classification                                                       \n",
-       "   Samples  11,000,000                                                                  \n",
-       "  Features  28                                                                          \n",
-       "    Sparse  False                                                                       \n",
-       "      Path  /Users/max.halford/river_data/Higgs/HIGGS.csv.gz                            \n",
-       "       URL  https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\n",
-       "      Size  2.62 GB                                                                     \n",
-       "Downloaded  True                                                                        \n",
-       "
\n" - ], - "text/plain": [ - "\n", - "Higgs dataset.\n", - "\n", - "The data has been produced using Monte Carlo simulations. The first \u001b[1;36m21\u001b[0m features \u001b[1m(\u001b[0mcolumns \u001b[1;36m2\u001b[0m-\u001b[1;36m22\u001b[0m\u001b[1m)\u001b[0m\n", - "are kinematic properties measured by the particle detectors in the accelerator. The last seven\n", - "features are functions of the first \u001b[1;36m21\u001b[0m features; these are high-level features derived by\n", - "physicists to help discriminate between the two classes.\n", - "\n", - " Name Higgs \n", - " Task Binary classification \n", - " Samples \u001b[1;36m11\u001b[0m,\u001b[1;36m000\u001b[0m,\u001b[1;36m000\u001b[0m \n", - " Features \u001b[1;36m28\u001b[0m \n", - " Sparse \u001b[3;91mFalse\u001b[0m \n", - " Path \u001b[35m/Users/max.halford/river_data/Higgs/\u001b[0m\u001b[95mHIGGS.csv.gz\u001b[0m \n", - " URL \u001b[4;94mhttps://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\u001b[0m\n", - " Size \u001b[1;36m2.62\u001b[0m GB \n", - "Downloaded \u001b[3;92mTrue\u001b[0m \n" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -127,6 +85,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -166,6 +125,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -177,6 +137,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -511,6 +472,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -536,7 +498,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.8" }, "vscode": { "interpreter": { diff --git a/docs/recipes/model-evaluation.ipynb b/docs/recipes/model-evaluation.ipynb index 57222cd363..fdec5e2a62 100644 --- a/docs/recipes/model-evaluation.ipynb +++ b/docs/recipes/model-evaluation.ipynb @@ -26,7 +26,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/docs/recipes/on-hoeffding-trees.ipynb b/docs/recipes/on-hoeffding-trees.ipynb index 10bc1fab4b..0bb789c4d2 100644 --- a/docs/recipes/on-hoeffding-trees.ipynb +++ b/docs/recipes/on-hoeffding-trees.ipynb @@ -42,10 +42,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:24:46.945181Z", - "iopub.status.busy": "2022-10-26T11:24:46.944051Z", - "iopub.status.idle": "2022-10-26T11:24:49.005760Z", - "shell.execute_reply": "2022-10-26T11:24:49.006242Z" + "iopub.execute_input": "2023-06-26T17:03:29.661157Z", + "iopub.status.busy": "2023-06-26T17:03:29.660672Z", + "iopub.status.idle": "2023-06-26T17:03:30.293247Z", + "shell.execute_reply": "2023-06-26T17:03:30.292936Z" }, "tags": [] }, @@ -101,46 +101,32 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:24:49.012623Z", - "iopub.status.busy": "2022-10-26T11:24:49.011705Z", - "iopub.status.idle": "2022-10-26T11:24:49.062760Z", - "shell.execute_reply": "2022-10-26T11:24:49.062194Z" + "iopub.execute_input": "2023-06-26T17:03:30.295322Z", + "iopub.status.busy": "2023-06-26T17:03:30.295158Z", + "iopub.status.idle": "2023-06-26T17:03:30.314403Z", + "shell.execute_reply": "2023-06-26T17:03:30.313991Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
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-       "Phishing websites.\n",
-       "\n",
-       "This dataset contains features from web pages that are classified as phishing or not.\n",
-       "\n",
-       "    Name  Phishing                                                        \n",
-       "    Task  Binary classification                                           \n",
-       " Samples  1,250                                                           \n",
-       "Features  9                                                               \n",
-       "  Sparse  False                                                           \n",
-       "    Path  /Users/max.halford/projects/river/river/datasets/phishing.csv.gz\n",
-       "
\n" - ], "text/plain": [ - "\n", "Phishing websites.\n", "\n", "This dataset contains features from web pages that are classified as phishing or not.\n", "\n", - " Name Phishing \n", - " Task Binary classification \n", - " Samples \u001b[1;36m1\u001b[0m,\u001b[1;36m250\u001b[0m \n", - "Features \u001b[1;36m9\u001b[0m \n", - " Sparse \u001b[3;91mFalse\u001b[0m \n", - " Path \u001b[35m/Users/max.halford/projects/river/river/datasets/\u001b[0m\u001b[95mphishing.csv.gz\u001b[0m\n" + " Name Phishing \n", + " Task Binary classification \n", + " Samples 1,250 \n", + "Features 9 \n", + " Sparse False \n", + " Path /Users/max/projects/online-ml/river/river/datasets/phishing.csv.gz" ] }, + "execution_count": 2, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -160,10 +146,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:24:49.067834Z", - "iopub.status.busy": "2022-10-26T11:24:49.066753Z", - "iopub.status.idle": "2022-10-26T11:24:49.306295Z", - "shell.execute_reply": "2022-10-26T11:24:49.307072Z" + "iopub.execute_input": "2023-06-26T17:03:30.335520Z", + "iopub.status.busy": "2023-06-26T17:03:30.335361Z", + "iopub.status.idle": "2023-06-26T17:03:30.414614Z", + "shell.execute_reply": "2023-06-26T17:03:30.414339Z" }, "tags": [] }, @@ -172,60 +158,161 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 174 ms, sys: 5.16 ms, total: 180 ms\n", - "Wall time: 192 ms\n" + "CPU times: user 58.4 ms, sys: 1.29 ms, total: 59.7 ms\n", + "Wall time: 60.7 ms\n" ] }, { "data": { "text/html": [ - "
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empty_server_form_handler ≤ 0.5454545454545454\n",
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oyy+/LDQ+JSVFt9xyi6no6cYbb9SNN95YYHxZ98bq1avLz8/P1s7OzlZCQoJL5nLXfl+SNQEA4G68Vi9+noqwJrgWheYATM6cOSPDMGxtb29vRURElGiM2rVrm9qJiYlOyQ0AgCvdwoUL9csvv5iOjR49usg++ffZOnXqlGjO/Pv26dOnHZonf7/SzFXYzwjuWhMAAM70/vvv67fffpMk+fj46OOPP5bFYinzuGXdF2vUqCEvLy9b22q1KikpyS7OnfcD3LUmAACcKf+nfHl4eKh79+52bxgvSGZmpl599VX1799f58+fLzSOfd8e+z4AwN26du2qDz74wPaaPjc3V6NHj1bHjh01ZcoUzZkzRz/99JP++9//6qGHHlL9+vX1448/2vr36dNH3377baHjl3VvlKRatWoVOeYl+e+Nl/Veu6v2e8nxNQEA4G68VrdXEdcE1/IqPgTA1SQtLc3UDggIKPEvxgMDA4scEwAA2EtOTtZ9991nOjZ48GB17NixyH7599n8+3Bx8sfn5OQoKytLvr6+Tp2noD6F/YzgrjUBAOAsCQkJGj9+vK397LPPKi4uziljl3VftFgs8vf3NxW1FbQHu/N+gLvWBACAs1itVrsC8Ycffli///67pIt708CBA9W/f3/VqVNH6enp+v333/X111/r+PHjtj5Lly7V6NGj9f333xc4D/u+PfZ9AEB5eOCBB9S4cWM9/PDD2rFjh6SLn26S/xNOLhcbG6unnnpK99xzj+mJ6Pm56157Zmam8vLyyjSXu/b7kswFAIC78VrdXkVcE1yLJ5oDMMn/zfjyj6hylL+/f5FjAgAAM6vVqttvv11Hjx61HQsJCdH7779fbN+y7t359+2CxnTGPAXN5egLW1etCQAAZ7n33nuVnp4uSYqLi9O4ceOcNra79uDKtNeXZC4AAJwhNTXV9AQuSdqyZYskKSwsTKtWrdL8+fN1//33a+DAgbr11ls1ZcoU7d69WyNHjjT1mz17tr766qsC52HfL9tcAAA403XXXaeNGzfqiSeekKenZ5Gx9erV0xNPPKGRI0cWWWQuld9+X5q52O8BAOC1elnm4meEKweF5gBMLly4YGr7+PiUeIz8TwvNzMwsU04AAFzpnnzySS1atMh07D//+Y/q1q1bbN+y7t0FPeW7oL3bnT8juGtNAAA4w2effaalS5dKuvgUj48//rhU+2Rh3LUHV6a9viRzAQDgDIX9EtPT01MLFixQt27dCjwfFBSkr7/+Wn379jUdf+WVV+wK1yX2/bLOBQCAM/373/9W/fr19eabb9o9GTy/w4cPa+zYsYqOjtbUqVOLjC2v/b40c7HfAwDAa/WyzMXPCFcOCs0BmOR/51B2dnaJx8jKyipyTAAA8D/vv/++3n77bdOxp556SrfeeqtD/cu6d+fftwsa0xnzFDRXYT8juGtNAACU1YkTJ/TEE0/Y2nfffXehhWal5a49uDLt9SWZCwAAZyhsn7n77rvVqVOnIvt6eHjoX//6l+npprt379aqVauKnYd9v2RzAQDgDDk5Obrlllv0wAMP6MSJE5KkatWq6YUXXtCGDRt09uxZZWdn6/jx45o/f76GDBkii8UiSUpOTtaYMWP05JNPFjp+ee33pZmL/R4AAF6rl2Uufka4clBoDsAkKCjI1C7onc7Fyf/OofxjAgCAi6ZNm6ZHH33UdGz06NGaMmWKw2OUde8u6B2/Be3d7vwZwV1rAgCgrP7+978rJSVFkhQZGanXX3/d6XO4aw+uTHt9SeYCAMAZCttn7rnnHof6x8bGqnfv3qZjBRWas++XbS4AAJzhgQce0Pfff29rd+zYUTt27NDEiRPVoUMHhYaGytvbWzVr1tSNN96o2bNna+7cuaaipzfffFOff/55geOX135fmrnY7wEA4LV6WebiZ4QrB4XmAEzyfzPOyMgo8CM8i5Kenl7kmAAAQPrxxx915513mvbZoUOH6tNPP7U9/cQR+ffZ/PtwcfLHe3l5Ffgu4LLOU1AfR1/YumpNAACUxcyZMzVnzhxb+7333lNoaKjT5ynrvmgYRqlu+LryfoC71gQAgLP4+/vL09PTdCw4OFht2rRxeIxrr73W1N60aZNdDPu+PfZ9AIA7rVy5Up999pmtHRERoR9//FGRkZFF9rvpppv04Ycfmo49+eSTDj0UxVX32gv6+aWs99pdtd+XZC4AANyN1+r2KuKa4FoUmgMwCQ8PNxW35eTkKDExsURjHDt2zNSOiIhwSm4AAFwpVqxYoWHDhik3N9d2rE+fPvr222/tbvwWJ/8+e/To0RL1z79vV69e3aF58vcrzVyF/YzgrjUBAFAWl38M9oABAzR8+HCXzFPWffHUqVOmnzk8PDwUHh5uF+fO+wHuWhMAAM6Uf/9q0KCBPDwc/zVb48aNTe2C9ln2fXvs+wAAd3r//fdN7UcffdTh+8ujR49Wo0aNbO2kpCTNnj3bLq6se6MkHT9+vMgxL8mfe1nvtbtqv5ccXxMAAO7Ga3V7FXFNcC0KzQGY+Pv7q169eqZjhw8fLtEY+ePj4uLKnBcAAFeK9evX66abbjJ9LFTnzp01Z84c+fj4lHi8/L+odtW+HRsbKy8vL1s7MzNTp0+fdslc7loTAABlkZKSYvvzggULZLFYiv3q2bOnaYxDhw7Zxfzxxx+mGGfvi1FRUQV+0oc77we4a00AADhTkyZNTO0qVaqUqH/++LNnz9rFsO8XPw/7PgDAVQzD0PLly03HbrzxRof7e3h4aMCAAaZjv/zyi11cWffGxMRE0+8XfHx8FBsbW2Csu+61u3NNAAC4G6/Vi5+nIqwJrkWhOQA7+b8h79y5s0T94+PjixwPAICr1datW9WvXz+lpaXZjrVp00YLFy5UYGBgqcZ0177t7e2t+vXrl3qurKwsHThwwKG5+FkEAID/cee+6K652OsBAJVR06ZNTe2srKwS9b+8eEqSAgIC7GLY90s/DwAAZXX27FmlpqaajsXExJRojPzxBX0yaP69bP/+/crOznZ4jvx7Y/369U0PiSlqLnft965cEwAA7sZr9dLP4+654DoUmgOw07p1a1N73bp1Dvc9ceKEDh48aGt7e3vb3YAHAOBqtHv3bvXp08f0xLImTZpo8eLFCgkJKfW4+fftjRs3mj6mqjhr164tcryizpXkZ4TNmzebfglfs2bNQj/Wyp1rAgCgomvWrJm8vb1t7YMHD+rEiRMO93fXXl+S+wHuXBMAAM7Stm1bU/vUqVMl6p//o6HDwsLsYtj37bHvAwDcpaA3kZW02PnyPU+S8vLy7GIiIyMVGRlpmnfz5s0Oz+Gu/T43N1cbNmxwaC53rgkAAHfjtbq9irgmuBaF5gDsDBw40NReunSpDMNwqO/PP/9savfs2VNBQUFOyw0AgMro0KFD6t27t+mXyjExMVqyZImqV69eprHj4uJMTxpPT093+MVZenq6fv31V1vbYrHY/RxwufznlixZ4nCe+WOL+shRd64JAIDSmjdvnpYsWVKirzfffNM0Ro0aNexiGjRoYIoJDg5W9+7dTccc3YMNw9DSpUtNx4rag911P8CdawIAwFkGDBggD4///VotISFBycnJDvfPX2yV/2OqJfb9/Nj3AQDuVNCbwI4fP16iMfI/wbyw+/8DBgwwtV11rz3/POvWrVN6erpD86xdu1YZGRm2dqNGjdSoUSOH53LVmgAAcDdeq5tV1DXBtSg0B2Cnc+fOCg8Pt7UPHDiglStXOtT3s88+M7UHDRrkzNQAAKh0Tpw4oV69euno0aO2Y7Vr19ayZctUu3Ztp8xx0003mdr59+PCTJ8+XWlpabZ2+/btVatWrULj+/fvb3qCy8qVK3XgwIFi5zEMQ1988YXpWHE/I7hrTQAAlNa1116r3r17l+irXbt2pjH8/PzsYgq6SVrafXHFihVKSEiwtWvUqKFOnToVGu/O+wHuWhMAAM4SERGhLl26mI7Nnj3bob65ubmaM2eO6ViPHj0KjGXf/x/2fQCAO/n4+KhmzZqmY8uXLy/RGMuWLTO1L3+gyuXy742ff/65QwVX+/fv16pVq2xtb29v9e/fv9D4unXrqk2bNrZ2WlqaZsyYUew8Utn3e1etCQCA8sBr9f+pyGuC61BoDsCOh4eHRo8ebTo2ceLEYl8ILlu2TKtXr7a1g4ODNXz4cFekCABApZCcnKw+ffpo//79tmPVq1fXkiVLFBMT47R5/va3v8lisdja3333neLj44vsc+HCBU2ZMsV0bMyYMUX2qVatmgYPHmxrG4ahCRMmFJvf1KlTTR9pFRUVpd69exfZx11rAgCgMhgxYoQCAwNt7V9++aXYX3YbhqGJEyeajt11112mJ7Hm5877Ae5aEwAAznTfffeZ2m+88YaysrKK7ffJJ5/o5MmTtnaVKlV0/fXXFxjLvn8R+z4AoDz06tXL1H733XeVm5vrUN9Vq1aZPm2zoPEuuf7661WnTh1b++DBg/r888+LnWPChAmmvfrmm29WSEhIkX3y3yOfMmWKLly4UGSf+Ph4TZ8+3dYu6OeG/Ny5JgAA3I3X6hdV9DXBhQwAKMDp06eNoKAgQ5Lt69VXXy00/ujRo0Z0dLQpfvz48W7MGACAiuXcuXNGhw4dTHtjaGio8fvvv7tkvltvvdU0V4cOHYzU1NQCY61Wq3HfffeZ4mNjY43s7Oxi59mxY4fh4eFh6jtt2rQi40NDQ03xn376aYVaEwAA7rJixQrTXhUVFeVw36efftrUNyYmxjh27Fih8ZMnTzbFh4SEGElJScXO4877Ae5aEwAAzpKXl2e0aNHCtB/deeedRl5eXqF9fvvtN7u99ZlnnilyHvZ99n0AQPn46aefTPuPJOOee+4pcq83DMPYu3evUatWLVO/hg0bGrm5uYX2+de//mWKr1q1qrFjx45C47/55htTvKenp7F79+5i15SVlWXUq1fP1Pf+++83rFZrgfGpqalG+/btTfG33357sfO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DxxqaLm2muK0QQngpyWrfjf+7vAf46hhjbZ2Xaw5zH13n+Ueajrvuv5ckSZIkSZIkSZIk7QYxRq7NVDh1NQmRn746w6mxZJsczzJbWe+vlFtv/0Cee44kYfLnHR7m3qPDHB4uGiqXJEmSpG2qm4PmTzXs9wIvAf5iE9d7CdDXcPzsJq7VciGEzwTeARQbmj8E/LMY40ZWX3+86fj4Os9v7v+JDdQgSZIkSZIkSZIkSVqD8aYgeXOgfLrc+SD5vFw2cOv+fm4fHeCO0UHuPZKEykcHC4bKJUmSJGkH6eag+T8A48D8uv7fw+aC5t/dsD8N/O0mrtVSIYT7gD8BGte3/0fgFTHG6Q1etjkY/tx1nn/3KteTJEmSJEmSJEmSJK1BjJGrMxXOXpvl7LXZhVXIG4Plk6Vqp8u8TjGX4fbRAW4/MMAdBwc5cWCAOw4OcHxvH7lsptPlSZIkSZK2WNcGzWOM9RDCHwNfnTY9EEJ4Q4zxZ9Z7rRDCfwBeCsS06c9ijJUWlbopIYQ7gfcAexqaHwO+KMY4volLf6zp+DNCCD0xxrU+nfjsVa4nSZIkSZIkSZIkSQJmylXOXitxbnw2DZOXOHttlnPjyfbs+CylSr3TZd7QYLGHO0YHFlYovz3dPzLSSybjCuWSJEmStFt1bdA89cPAVwEhff1kCGEP8P0xxlW/FyyEkAXeRLIaekyvUQd+cKsKXo8Qws0kq7SPNjR/CviCGOOlzVw7xviJEMJTwIm0qR94MfCBNdTVD3xW4+WAd22mHkmSJEmSJEmSJEnajiq1OhcmSouh8YUQebo/Psu1ma5Y52xV+wcK3D7avyRMfsfoAAcGC4RgoFySJEmStFRXB81jjI+GEH4F+DckYecM8F+Arw4h/ALwJzHGTzSfF0K4C3gF8C3AbSQB85i+3hJj/HibbuGGQgg3AX8JHG1oPgN8fozxTIuGeQfwhobj17OGoDlJuH+g4fgfYoxnW1STJEmSJEmSJEmSJHWFej1yZbq8EBo/N56sQn7m2izn0lD5xckS9bj6tbpFMZfh6J4+ju7p5cSBxTD57aMDjPTlO12eJEmSJGkb6eqgeerbgecBn8viquQngJ8CfiqEMA1cBqZIwtH7SVbvJu1Lw3nvA/5tuwq/kRDCXuA9LK42DnCJZCXzT7VwqP8JfAeL/x3+eQjhx2KMj61QWxH47qbmt7SwJkmSJEmSJEmSJEnacjGmIfKGAHnymk3aJma5MD5HuVbvdKnrku/JcHSklyN7ejm2NwmUH0uD5Uf39LF/IO/q5JIkSZKkluj6oHmMsRpC+BLgrcBrSELjsBieHmDp6ttLTm/o+3+B18cYq1tT6dqEEAaBPyUJz8+7BnzhSgHwjYgxPhxC+F2SFcoB8sCvhxBeHmOcWKa2ALwZuKOh+WmSwLokSZIkSZIkSZIkdYUYI2PT5YXw+PnxWc6Olzh3bbahrbTtQuQAuWzg8EhjeHwxUH50Tx8HBgpkMgbJJUmSJElbr+uD5gBpKPq1IYRvBr4HOD7/1gqnhfT1DPBDMcZuCUu/A/iMprafBvaHEF6+zmt9NMZ4dZU+/xX4EqAvPf4M4AMhhO+IMb5vvlMI4TnAjwKvbTr/u2OMlXXWJUmSJEmSJEmSJEkbUqtHLk3OcWGixPmJUrJNg+Nnx2c5nwbJ56rbL0QO0JMJHBouLgTJG0Pkx/b2MjpYJGuQXJIkSZLUBbZF0HxejPGXQwi/RhKG/kLgc4HbWHofVeBJ4IMkK4f/UYyxm54wPLBM2w9s8FovBd63UocY45MhhNcDb2NxFfjnA+8NIVwCTgKjwNGG9+f9jxjj2zdYmyRJkiRJkiRJkiQtMVmqpMHxuYUQ+XyQfD5YfmlyjvpKS451uf0DeQ6P9HLTcJHDI70cHu5NjkeKHBnpZf9AwSC5JEmSJGlb2FZBc4AYYw14e/oCIIQwCAwCkzHGyU7V1q1ijL8dQgjAW4DehrcOpK/l/BTwnVtdmyRJkiRJkiRJkqTtr1Krc2kyCY9fnF+BfGJuSYj8wkSJ6XKt06VuykChh8MjRW5Kw+OHh4vcNNLL4ZEih4d7OTRcpJjLdrpMSZIkSZJaYtsFzZeThssNmK8gxvhbIYS/B34Q+Aogd4OuHwD+W4zx/W0rTpIkSZIkSZIkSdK2EGPk9NVZHjozzoOnr/HQ6XGevDjFpak54jZehRwgn81wcLiwsAL5fKD8SLoa+eGRXoaKN/o1qyRJkiRJO8+OCJpvJzHGjn0HWozxaeBrQwjfAnwOcAfJSvAl4CTwNzHGM52qT5IkSZIkSZIkSVJ3uThR4sHT4zx0+lqyPTPO2HS502WtWy4bODiUrDp+00iRQ8OLK5DPb/f158lkOvbrXEmSJEmSuo5B810oxjgB/HGn65AkSZIkSZIkSZLUPa5Ol/n4mXE+fupasj19jQsTc50ua1U9mTREPlLk0HAvh4eL3DSc7qeh8v39BUPkkiRJkiSt07YNmocQ9gJ3A3uBYSAD/FmM8UJHC5MkSZIkSZIkSZKkLjdZqvDQmXEeOj3Ox0+P8/Ez1zg1Ntvpsq6T78lwcKjAoaFiGibv5VAaKr9puJebhovsHzBELkmSJEnSVthWQfMQwijwbcCXAXct0+ULgOuC5iGE1wHH0sOzMcZf27IiJUmSJEmSJEmSJKmLzJZrPHpunAdPjfPQmXEePH2Npy9Nd7os9vbnOThU5NBQgUPDxXS/yMHhIgcHk5XI9/TlCMEQuSRJkiRJnbBtguYhhP8M/ACQB5Z7khBXOH0AeFPapxZCeKcrn0uSJEmSJEmSJEnaCSq1Opcm5zg/UeLiRIkLE3NcmChxfrzEo+cm+OTFKWr1lX6d2lr5ngyHGkLjh4YKSYi8IUw+OlSg0JNtW02SJEmSJGn9uj5oHkLIAm8HXk0SMG9+AhJZPnje6C3ADwJDQBb4GuBnWlupJEmSJEmSJEmSJLVOvR65Ml3mwkQpfSUB8ouTyf758WT/ynSZ2L4c+YJb9/dz39Fh7j0yzInRgYVw+YirkEuSJEmStCN0fdAc+HngNen+fKj8H4E/B06m768oxjgTQngn8LVp0yswaC5JkiRJkiRJkiSpQ8ZnK9cFyJuPL03OUW3jSuQrOTLSy31Hh7nv6Aj3HR3mniPDDPfmOl2WJEmSJEnaQl0dNA8hfA7wTSyuYn4Z+Fcxxj9p6PPzXL/K+XL+kCRoHoDPDiHkY4zl1lYsSZIkSZIkSZIkSUtdnprjoTPjPHR6fGF7fqLU6bJu6MBggecfHebeIyPcdyxZsXz/QKHTZUmSJEmSpDbr6qA58APpNgATwEtijJ/Y4LX+vmG/ANwJPLSJ2iRJkiRJkiRJkiRpiSvNofIz45wb795Q+UhfjnuPDPP8oyPcezTZHhwqEELodGmSJEmSJKnDujZoHkLYA3wui6uV/9AmQubEGE+HEK4Ce9KmuzBoLkmSJEmSJEmSJGmDxqbLaaj82kK4/GwXh8oHCj3cc2SI+46OcN/RYe47MsKxvb2GyiVJkiRJ0rK6NmgOfA6QTfdrwK+14JoXWQyaj7bgepIkSZIkSZIkSZJ2gavzofKG1crPXJvtdFnXKfRkODhU5OBQgYNDRQ4NFXnu4SRcftv+fjIZQ+WSJEmSJGltujlofjjdRuDpGOO1FlxzvGF/sAXXkyRJkiRJkiRJkrTDXJtJQuUfPz3Ow+m206HybCYwOlhgdKjIwcECh4aLHBwqMjpYSIPlSah8qLfHFcolSZIkSVJLdHPQfG/D/liLrllo2K+06JqSJEmSJEmSJEmStonZco3zEyXOj5c4PzHL+fE5zo/PLrSdGy9xcXKurTXtH8gzOri4CvniKzkeHSqwr79A1tXIJUmSJElSG3Vz0HwrVh8fbdi/3KJrSpIkSZIkSZIkSeqwGCMTs1XOT5Q4Nz6bBslL122vzXRuPaqbhovcc2SYe48Mc+/RYe4YHWB0sEi+J9OxmiRJkiRJkm6km4Pml9JtAG4OIWRijPWNXiyEcAy4qaHp7GaKkyRJkiRJkiRJktQe1VqdK9NlLkwkK47Pb8+PLw2Sz1ZqnS51waGhxVD5fUeHuefIMAcGC6ufKEmSJEmS1CW6OWj+YMN+H/DZwAc3cb2vaNivAX+3iWtJkiRJkiRJkiRJ2qQYI1dnKpwfL3FhssTFiRIXJua4kG4vTiYB8stTc9Rjp6u9sdHBwkKYfH47OljsdFmSJEmSJEmb0rVB8xjjEyGETwG3pE3/gQ0GzUMIQ8AbgPnHTx+JMU5uukhJkiRJkiRJkiRJ14kxMjlXvS44nmxLC8eXJuco1zb8pcYdcWCwwH1HFkPl9x4ZZnTIULkkSZIkSdp5ujZonvoN4I3p/peGEL4+xvjr67lACCGbXudI2hSBX2hdiZIkSZIkSZIkSdLuE2Pk3HiJh8+M8/DZCZ65PM2FiRIXJ+c4P15itlLrdImbtn9gcaXye9Ng+UFD5ZIkSZIkaZfo9qD5TwHfAhwAAvBrIYRR4KdjjKs+mQoh3AX8MvA5LK5m/gTwtq0pV5IkSZIkSZIkSdp56vXIybEZHj47ziNnJ3j4TLIdmy53urRNGe7NcWioyKHhIjcNFzk4lGwPDRe569AQB4cKhBA6XaYkSZIkSVJHdHXQPMY4HUL4RuAPgAyQBX4M+NYQwm8BH027BpIg+YtCCHuB24GXpa+QvgBmga+JMUYkSZIkSZIkSZIkXadWjzx9aYqHz47z8JkkVP7o2Qkm56qdLm3NQoADAwUODRc5lIbHDy4Jk/dyaKhIbz7b6VIlSZIkSZK6VlcHzQFijO8KIfxb4BfSpgDcDHxXU9dAEkJvbpsPlVeA18UY/3GrapUkSZIkSZIkSZK2k3K1zhMXJnlkPlR+dpzHzk1QqtQ7XdoN5bMZDg4XuGmotyk8vrg9MFggl810ulRJkiRJkqRtreuD5gAxxl8JITwF/CZwiMXweGOQfP544bT0FYALwFfEGP+6DeVKkiRJkiRJkiRJXadcgwfPTPDMI2MLofInLkxSqXXHlwFnAuxPVyEfHSxycKjAwaFkOzqUrEx+cKjISG+OTCasfkFJkiRJkiRtyrYImgPEGP8yhHA38C3AtwGH07du9BQpAFeBNwM/G2Oc2PIiJUmSJEmSJEmSpC4wV63x2LlJ/u6Jc/zlJzOcmg5cmIX44Qc7Us/e/jyjg4vB8UNDRUbT4Ph8oHxff54eVyGXJEmSJEnqGtsmaA4QYxwHfiyE8BPA84HPBe4G9gEjwAxwGfgU8F7gwzHGameqlSRJkiRJkiRJkrZejJFnr8zwsVPXFl6Pnp2gXKunPbY+vH18bx/POzzEzfv6l65EPlhkdKhAoSe75TVIkiRJkiSptbZV0HxejLEO/GP6kiRJkiRJkiRJknaNq9NlPnb6Gh87mYTKHzx9jWszlbaMHQKcODDA8w4Pcc/hYZ53ZIjn3TTMcF+uLeNLkiRJkiSpfbZl0FySJEmSJEmSJEnaDeaqNR45O8GDDauVP3tlpi1j92QCdxwc5J7DQ9xzZJh7jgxx16Eh+gv+ilGSJEmSJGk38CmQJEmSJEmSJEmS1AVijHzq8nSySnkaKn/03ASVWtzysfM9Ge6+aWghVP68w0M85+AgxVx2y8eWJEmSJElSdzJoLkmSJEmSJEmSJLVRjJHpco2LEyWeuTLNx05e42Onx3nw1DXGZytbPn5/PstzDw/xvMPDCyuVnzgwQC6b2fKxJUmSJEmStH3suKB5COF+4A3A5wD7gavAR4FfiTG+s5O1SZIkSZIkSZIkaeeq1upcmS5zaXKOi5MlLk3Opftz1+3PVmptqamYy3CkWOP4QOT4QOTLXnY/99x8kEwmtGV8SZIkSZIkbV9dHTQPIXwh8F8bmr4xxvjECv2/A/gpIKQvgEPAK4BXhBB+C/i6GGN9ayqWJEmSJEmSJEnSThJjZGquumxYvDFQfnlqjivTZWLsXK0hwHNGB3nBsRGef2yEFxwb4VBf5IPvf99Cn1v29RkylyRJkiRJ0pp0ddAc+AaSlckj8NAqIfOXA/+dxYB582O8AHw1MAl8S+tLlSRJkiRJkiRJ0nY0Warw7JUZnr0ywzNXpnnm8jTPXpnh3MQslybnKFW6cw2jg0MFXnBshBcc28Pzjw1z39ERBgpLf/03MTHRoeokSZIkSZK03XV70PxlDfu/vUrfnyYJk0cWVzS/AAwBvQ3t3xRC+M0Y44daX64kSZIkSZIkSZK60USpwjOXp3nmygzPzm+vTPPMlWkuT5U7Xd6q+vJZ7j0yzAuOj/Bp6YrlNw33drosSZIkSZIk7WBdGzQPIdwG7G9o+pMV+r4UuIfFVcw/AnxNjPGpEEIO+DbgpxrefwNg0FySJEmSJEmSJGkHuTZTXgyQX05XJ7+SrE4+Nt39YfJ5mQDPOTiYrlY+wguOj3DH6CDZTFj9ZEmSJEmSJKlFujZoDjynYb8CPLxC33+ebgNQBr48xngKIMZYAX4mhHAC+Na03ytDCL0xxtkW1yxJkiRJkiRJkqQtVKrUeOzcBM9cmeZTl+dXJU+212YqnS5vQ24aLvKCdJXyFxwb4d4jw/QXuvnXeJIkSZIkSdoNuvkJ1c3pNgLPxhhrK/T9QhZXK3/HfMi8yc+yGDQvAM8H/q4VhUqSJEmSJEmSJGnrzFVrfPCJy7zz42f5i0cvMF1e6ddG3SGbCewfyDM6WOTAYIEDAwVGhwocGCwwOlhI25L3evPZTpcrSZIkSZIkXaebg+aDDfvjN+oUQjhOEkqfD5r/4XL9YoyfDCFcAg6kTXdh0FySJEmSJEmSJKkrVWp1PvTUFd714Fn+9JHzTJaqnS4JgMFCDweGkuB4EhpPg+QNAfLRwQJ7+vJkMqHT5UqSJEmSJEkb1s1B80LD/krLUrw43QaSsPlfrdD3FItB8z0bL02SJEmSJEmSJEmtVqtH/v5TV3jXx8/xpw+fZ2y63Nbx89kMx/b2csu+fm7e18/N+/o4OLQYKN8/4OrjkiRJkiRJ2j26OWg+3bA/vEK/lzTsPx1jPL9C38ankX0bqkqSJEmSJEmSJEktU69H/vHUVd754Dne/dA5Lk3Obel4hZ4MN+/r4+Z9/dySbm/dn4TKbxruJesq5JIkSZIkSRLQ3UHzK+k2ALeEEHpijMt9J+IXpdsIfGCVa4407M9srjxJkiRJkiRJkiRtRIyRh86M866Pn+NdD57l7Hippdcv5jLcsq8/WZl8f1+6QnmyPTRUJGOYXJIkSZIkSVpVNwfNH27YLwAvB/60sUMI4bOAW0hC5gDvW+Wahxr2xzZXniRJkiRJkiRJktYqxsjjFyZ554NnedfHz/Hslc2tCdSXzyZh8v3pquTzYfL9/YwOFgjBMLkkSZIkSZK0Gd0cNH8IuEqyCnkAfiiE8N4Y4xxACCEL/GDaNwBV4M9vdLEQwnGWrmj+dOtLliRJkiRJkiRJUqOnLk3xrgfP8c6Pn+XJi1ObutZgsYcvet4hvuT5h3nxiX3kspkWVSlJkiRJkiSpWdcGzWOM1RDCbwHfSrJi+acB/y+E8FagAnwl8Jksrmb+ZzHGCytc8sVNx4+2tmJJkiRJkiRJkiQBnBqb4Z0fP8s7HzzHY+cmNnWtvnyWL3juQV5132E+7zn7KfRkW1SlJEmSJEmSpJV0bdA89YPA1wJD6fHdwI819QlAHfiBVa71ZQ37T8YYr7SkQkmSJEmSJEmSpF0sxsilqTmeOD/FQ2fG+dNHzvPgqWubumahJ8PL7hrlVfcd5mV3jdKbN1wuSZIkSZIktVtXB81jjBdCCF8JvAMosLh6+byQbv9bjPEjN7pOCGEE+OKG89/X2kolSZIkSZIkSZJ2vum5Kk9cmOTx85N84nyyffzCJGPT5U1fO5cNfN4dB/iS5x/m5c89yEChq3+NJUmSJEmSJO14Xf+ELsb4nhDCpwM/AXwBS2v+JPCmGONvrXKZfwP0zV8SeGfLC5UkSZIkSZIkSdohqrU6z1yZXgiTz29Pjs20dJxsJvDiE/v4kvsO80XPO8RwX66l15ckSZIkSZK0cV0fNAeIMT4CvDKEMATcCvQCZ2OMJ9d4iWeBNzQcv6fFJUqSJEmSJEmSJG07MUbOT5QWVydPQ+VPXZyiXKtvyZghwGfeupdX3XeYL77nEPsGClsyjiRJkiRJkqTN2RZB83kxxgngwQ2ct9qK55IkSZIkSZIkSTvaRKnCEw2rkyeh8gkmStW2jP/C4yO86r7DvPK+mzg4VGzLmJIkSZIkSZI2blsFzSVJkiRJkiRJknRj9Xrk7PgsT12a5qmLUzx9eYqnLk7z1KUpLk7Otb2ee44M8SVpuPzonr62jy9JkiRJkiRp4wyaS5IkSZIkSZIkbTPTc1U+dTkJkD91Kdk+fWmaT12eolSpd6yu/QN57jw0yD+5dR+vev5hbt3f37FaJEmSJEmSJG2OQXNJkiRJkiRJkqQuFGPk/ERpYUXypxtC5efGSx2trTeX5TmHBrnr4CB3Hlp87R8odLQuSZIkSZIkSa1j0FySJEmSJEmSJKlDYoxMzFY5c22Wpy9PLYbKLycrlM+Uax2tL5sJ3Lq/PwmSp6Hyuw4NcmxPH5lM6GhtkiRJkiRJkrZWR4LmIYTfAb43xvhUJ8ZvFkK4HfjhGONXdboWSZIkSZIkSZK0fcUYmShVuTI1x9h0mSvTZa5MlRmbnuPyVJmx6eR1OX1/bLpMtR47XTYANw0Xec7BJEg+v0L5iQMDFHPZTpcmSZIkSZIkqQM6taL5VwCvCSH8OvCTMcZPdqKIEMIdwHcCX4eru0uSJEmSJEmSpCYxRibnqgth8StTSXi8OSx+OX1/bLpMpdYdwfEbGSz2LFmd/M5DQ9x5cJDhvlynS5MkSZIkSZLURToZru4BXg98Qwjhj4BfijG+px0DhxC+APhW4EuAkL66+6mvJEmSJEmSJEnaEtVanTPXZnn2ygzPXpnm2SszPHNlhpNj05wcm6FUqXe6xA05NFTkxGg/t+0f4MSBfm47MMCJ0QEODxcJIXS6PEmSJEmSJEldrlNB87cArwMyJCHv15CscP4s8H+A348x/mMrBwwhvBB4LfA1wM3zzem2ntYkSZIkSZIkSZJ2oFKlxsmxmSVh8mfHkv0zV2ep1rfnejT5ngy37e/nxIEBbjuQbE8cGODWA/0MFPwyV0mSJEmSJEkb15EnjDHGfx1C+GXgzcCLWQx83wJ8L/C9IYQzwHuB9wP/ADwWY6ys5fohhDzwXOBFwOcBLwWOzL/d1P1vgO+IMX50o/cjSZIkSZIkSZI6b6JU4eSVGZ6ZD5IvbGc4P1HqdHmbsn+gwIkD/ZwYHUiC5aMD3H5ggMMjvWQzrk4uSZIkSZIkqfU6tpRFjPEfgM8JIbwC+AHghQ1vB+Ao8C/SF0AtXfH8NHAOmAJm075FYBC4KT3vOJBtuh5A43IkHwX+W4zxT1p4W5IkSZIkSZIkqcXq9chEqcLlqTJj02XGpue4PFXm4uQcJ69M88yVGU6OzTA2Xe50qZuSywZu3tefBMoPDHDbgQFOHOjntgMDDPfmOl2eJEmSJEmSpF2m49+ZGGP8Y+CPQwgvB/498MUkwfD5UPh8SLwHOAHctsolm5ftiOlrvv3dwJtjjH+5ydIlSZIkSZIkSdIG1OuR8dkKV6aT4PiVqbll98emy1yeKnN1pkytHle/cBcq5jLs6y+wbyDP3v58036efQNJ297+PDcNF+nJZjpdsiRJkiRJkiQBXRA0nxdj/AvgL0IIx4GvBb4auKexy1ovlW5Dw/YR4G3A/4kxnmxBuZIkSZIkSZIkKVWt1bk6U+HqTBIOvzpdvkGIPGnfzsHxQk8mDYgn4fAkKN5wnO7Ph8j78l3zqxhJkiRJkiRJWpeue7qZBsF/FPjRNHT+RcDnAi8C7gRWW8qjDjwOfBT4IPBnhsslSZIkSZIkSVqbej0yWaoy1hAaH5suMzazuL8QKJ+pMDZdZny20umyW2Z0sMDN+/q4eV8/N+/t4+b9/Rzd08v+dCXyvnyWEJq/XFWSJEmSJEmSdp6uC5o3SgPiv5q+CCHkgePAMWAY6Eu7zgDXgFPAqRhjue3FSpIkSZIkSZLUper1yKWpOU6OzXDm6myyqngaHh+bWgyRX51JwuPbdbXxtcgEOLKnl5v39qeB8j6O7+3nlv19HN/b5wrkkiRJkiRJkpTaVk9L0wD5k+lLkiRJkiRJkiSlpuaqnBqb4dTYDCcbtifHZjh9dZa5ar3TJbZNPpvh2N5ebtnXz/F9fUu2R0Z6yfes9uWpkiRJkiRJkqRtFTSXJEmSJEmSJGm3qtUj58ZnF0Lkp8ZmF4Lkp8ZmuDK9e77ssy+fZW9/nn39eQ6P9HLzvsXVyW/e18+hoSLZTOh0mZIkSZIkSZK0rRk0lyRJkiRJkiSpS4zPVpasRL4YKp/hzLVZKrXY6RK3xEJwfKDAvv58up9P9wsN+3n29RfozWc7XbIkSZIkSZIk7XgGzSVJkiRJkiRJarOJUoVPXpjk8fNTPH5+gscvTPLEhSnGdsiq5AOFHkb6cgvB8X39efamYfF9/YXF/fT9Ys7guCRJkiRJkiR1G4PmkiRJkiRJkiRtkblqjScvTvFEQ6j8iQtTnLk22+nS1iyfzbC3P8+eNDC+pz/P3r5csu3Ps6evadufo9BjcFySJEmSJEmStjuD5pIkSZIkSZIkbVKtHjk5NpOsTn4+CZZ/4vwEz1yZoVaPnS5vicFCDweHi+ztz7O3Lw2O9+fY05dn38D1wfG+fJYQQqfLliRJkiRJkiS1mUFzSZIkSZIkSZLWKMbIhYk5Hr8wuRAqf/zCBJ+8MMVctd7p8gDIZgJHRno5vrePY3t7Oba3j+MNr+HenMFxSZIkSZIkSdKqDJpLkiRJkiRJkrSMcrXOExcmefjMOA+fHeeJ81M8fmGS8dlKp0tjT18uDZIvBsjn928aLtKTzXS6REmSJEmSJEnSNmfQXJIkSZIkSZK0682Hyh86M85DZ8Z5+Mw4nzg3SbnWmVXK89kMR/f2cmzP9UHyY3t7GSzmOlKXJEmSJEmSJGn3MGguSZIkSZIkSdpVGkPlHz+dhMofP9+ZUPlAoYfnHBzgzkND3HlwgOccHOTWA/0cHCySyYS21yNJkiRJkiRJ0jyD5pIkSZIkSZKkHatcrfP4+aUrlXciVJ7PZjgxOsBdhwZ5zsFB7jyUhMsPDxcJwUC5JEmSJEmSJKn7GDSXJEmSJEmSJO0Ic9UaT5yfWhIq/8T5CSq12LYaQoBb9vU3rFKehMpv2ddPTzbTtjokSZIkSZIkSdosg+aSJEmSJEmSpG1jaq7KxYkSFybmuDhZ4tLkHE9dSsLlj5+fbGuo/NBQkeccGuTOhlD57aMD9OazbatBkiRJkiRJkqStYtBckiRJkiRJktRRMUYmSlUuTS4GyC9OzC3uT85xcSLZzpRrba8v35Ph7puGuOfwEHcdGuTOQ0M85+AAI335ttciSZIkSZIkSVK7GDSXJEmSJEmSJG2JGCPXZipcSIPjFycXQ+QLYfJ0O1etd7pcIAmVP/emIe49Msy9R4a558gwdxwcIJfNdLo0SZIkSZIkSZLayqC5JEmSJEmSJGnNavXI1Zkyl6fmuDyZbqfmuDzVuJ+8d2V6jkotdrrkGyqkK5UbKpckSZIkSZIk6XoGzSVJkiRJkiRpl6vU6lxJg+KXpuYW9i9PNofIy4xNz1Hv3uz4DS0JlR9NguW3jxoqlyRJkiRJkiTpRgyaS5IkSZIkSdIOFmPk8lSZk2MznByb5uSVWU6OzXDm2sxCgPzaTKXTZbZUoSfDcw8PLaxSfu+RYe4YHaDHULkkSZIkSZIkSWu2LYPmIYQAfBpwN7AXGAYywG/EGJ/pYGmSJEmSJEmS1HZz1RqnryYB8pNXZtJQ+eL+bKXW6RK3xFCxh9GhIgeHCpw4MMA9R4a57+gwtx8wVC5JkiRJkiRJ0mZtq6B5COH5wH8EXg0MLNPlr4FnljnvO4G70sOTMcY3bVGJkiRJkiRJktRyMUbGpsvXBcjnX+cnSsTY6SpbZ09fjtHBIqNDhYZtgYNDRUYHF9uKuWynS5UkSZIkSZIkacfaFkHzEEIe+Bng38w3LdNtpV+jnAd+LO0TQwhvdeVzSZIkSZIkSd1keq7K2WuznLk2y6mxxiB5cjw1V+10iZu2fyDPgcFiGhovLBsmPzBYoNBjgFySJEmSJEmSpE7r+qB5CKEP+EvgfpKAeXOgPLJ88LzR24CfBA6kx18L/HALy5QkSZIkSZKkG6rXIxcn5zhzbZazDa8z10oLbeOzlU6XuSF7+nLsGyiwfyDP/oEC+weSsPi+/vQ4DZXvHyiQy2Y6Xa4kSZIkSZIkSVqjrg+aA78FfCaLAfMI/CHw58Ap4F2rXSDGWA0h/AHwTWnTF2PQXJIkSZIkSVKLzJSrC8Hxs9dmOXN1dmF18rPjs5wfL1GprfSljN0jmwns7c+zrz/PgcFCGh5fDJHvS/cPDBbY2583PC5JkiRJkiRJ0g7V1UHzEMKXAF/CYsj8k8CXxRgfbugD169yvpx3kgTNA3B/gXr5ygABAABJREFUCKE3xjjb2oolSZIkSZIk7SSVWp2rM2WuTlcYmy5zeWqOc+OznG1YifzMtVmuzWyP1cj781mO7+vn+N5eju/t49Bw75IQ+f6BPHv68mQyq32JpCRJkiRJkiRJ2um6OmgOfF+6DcB54IEY47kNXusjDftZ4G7g/22iNkmSJEmSJEnbSL0eGZ+tMDZT5up0mbHpMldnyoxNV7g6U+bK1Pzx4nayVO102esSAhwaKnJ8b9/ia9/i/t7+/PziHZIkSZIkSZIkSSvq2qB5COEg8CIWVyv/vk2EzIkxXgwhXAIOpE13YtBckiRJkiRJ2pbK1Trjs5X0VWZ8tsLVNDDeGBS/Op0Ey8emy1ybKVNfy3cjdrneXJbje/s4trePm/ctDZQfGemlmMt2ukRJkiRJkiRJkrQDdG3QHPhskpXMASrAb7fgmpdZDJrvb8H1JEmSJEmSJG1QrR6ZLCVh8WszlYXg+LXZChOzFa7NlK97b/41U651uvwtEwKMDhYWw+R7+zm+r3fh+MBAwVXJJUmSJEmSJEnSluvmoPmhdBuBJ2OM0y245kTD/kALridJkiRJkiSpSb0eOTs+y1OXpnny4hTPXJ5mbKbM+Mx8kDzZn5yrEnfACuPr1ZvLcnikyJE9fRwZKXJ4uJfDI8nryEgvh4aL5HsynS5TkiRJkiRJkiTtct0cNB9u2J+4Ya/16W/Yn23RNSVJkiRJkqRdqVSp8cyVaZ66mATKn7qUvJ6+NM1sZeeuOL6a0cHCQmj88EixYT/ZjvTlXJFckiRJkiRJkiR1vW4Oml9t2B++Ya/1OdSwf6VF15QkSZIkSZJ2tKvT5YUQeRIon+apS1OcGpuhvstWJB8s9HBgsMCRPYvh8cNpoHx+NfJCT7bTZUqSJEmSJEmSJG1aNwfNL6TbANwaQsjHGMsbvVgI4Q5gf0PTqc0UJ0mSJEmSJO0k9Rg5NTbDk5emeGp+dfKLSaD8yvSGH8t1tUJPhn39efb059nbn2dPX8N2IM/evjx7+nPs7U/2R/ry5HsynS5bkiRJkiRJkiSpLbo5aP4PDft54GXAn27iel/bsF8G/m4T15IkSZIkSZK2nWqtzrnxEqeuzvDJs2P8zckMl0pwcTbwnR/5EHPVeqdL3LB8NsNQb46988Hw5uD4fKC8IUTem3flcUmSJEmSJEmSpBvp2qB5jPFUCOFR4O606bvYYNA8hHAT8O+A+S/y/esYY2nzVUqSJEmSJEndI8bIpck5Tl2d4dTYLKfGZhb3r85wbrxErR4bzmhcnbvzIfMQYLg3x3BvjpHeHEPz+33zbfnk/fS48b3eXJYQQqdvQZIkSZIkSZIkacfo2qB56leBnyEJiH9eCOG/xBh/eD0XCCEMAv8X2JM2ReDNrSxSkiRJkiRJapfxmUoaHl8aIj81NsPpq7Ndtyr5waECJw4McGxPHyN9SXi8OTg+3z5Y6CGTMSwuSZIkSZIkSZLUDbo9aP4LwL8HbgYC8AMhhMPA98YYx1c7OYTwRSSh8uewuJr5R2KM796aciVJkiRJkqTNuzhR4pFzEzx7eZpTV2c53RAonyxVO13edXoygZv39XH76AAnDiSv20cHuO1AP4PFXKfLkyRJkiRJkiRJ0gZ0ddA8xlgJIXw18FdAkSRs/m+ArwshvBP4aNo1kATJXxlCeCFwO/Ay4ETDewEYA766rTchSZIkSZIkreDiRImHzozz0JlxHj4zzsdPj3Nxcq7TZS1rsNDDifkw+Wj/QqD8+N4+ctlMp8uTJEmSJEmSJElSC3V10Bwgxvj3IYR/Dvw2SdgcoB/4qvQ1LwDf0XQMiyHzceDLY4yf2tKCJUmSJEmSpBtoDJU/dDrZdmOo/KbhYsPq5P2cGB3g9gMDHBgsEEJY/QKSJEmSJEmSJEna9ro+aA4QY3xnCOF+krD580jC4wtvN+w3hstjQ9sjwJfFGJ/Y6lolSZIkSZIk6P5Q+UhvD4OZCvuKkdEiPPCiu7n3+AFuPdDPQGFbPDaUJEmSJEmSJEnSFto2vzGKMT4SQngB8BXAvwfuZzFY3my+/WHgJ4H/E2Osb3mRkiRJkiRJ2pXmQ+UfPz3Ow2e6I1Ten89ybG8fR/f0cWxvb7Ld08uxvX0c29tHfW6G9773vQv9X3rPKENDQx2sWJIkSZIkSZIkSd1k2wTNAWKMNZJVzX87hLAX+BzgbmAfMALMAJeBTwHvjTGe7VCpkiRJkiRJ2mFq9ci1mTJXpsucvDLDQ2c6GyrPZzMc2dPL0fnweBooT7Z97OnLEcKN1mmAie5ZXF2SJEmSJEmSJEldaFsFzRvFGMeAd6QvSZIkSZIkad1myzUuT81xeWqOK1NlrkzPcXmqvOT4ylSZy1NlxqbnqMf21ndoqMjxfdeHyI/t7eXgYJFM5sZBckmSJEmSJEmSJGkztm3QXJIkSZIkSWpWq0euziwGxZcEyCcXg+TzAfKZcq3TJS84MtLLPUeGuPfIMPccGebeI8PsGyh0uixJkiRJkiRJkiTtUgbNJUmSJEmS1JVKlRpXZ8pcna5wbabM1ZkKYzNlrk0n+0nb4v7YdJmJUrXTZa+JoXJJkiRJkiRJkiR1O4PmkiRJkiRJ2lL1emSyVOXabBIKvzq9NCC+NCy+2Faq1DtdekvMh8rvOzrCPUeGuefwkKFySZIkSZIkSZIkdT2D5pIkSZIkSVqTUqXGxGyFa7MVrqXB8PHZCuPp8fjCe+Ul/SZKFWLsdPXtcWSkl3uPDHPv0WFD5ZIkSZIkSZIkSdrWtlXQPISQBT4d+DTgODAE9AJhnZeKMcbXt7g8SZIkSZKkrhZjpFSpM1GqMFmqMD5bZbJUYaJUZWIhMF5eCI5fm60wvhAg3zkrjLdKc6j83iPD7O3Pd7osSZIkSZIkSZIkqSW2RdA8hDAM/FfgXwIHNns5IAIGzSVJkiRJ0rZSq0emSlUmSpU0LJ4ExCdKaWB8ITievrekLdlWartkafENyGcz7BvIs28gz/6BAvv6C+xPj/f1F9g/WGBff/Le3v48+Z5Mp0uWJEmSJEmSJEmStkzXB81DCJ8F/AFJwLxx5XJ/KypJkiRJknaUiVKFpy9N8/SlqWR7eYpnLs9wbabMZKnK5Fy10yVuOyN9Ofb159k3kIbG+wtJiHwgn4bIF48HCz2EsN4vzpMkSZIkSZIkSZJ2pq4OmocQngf8GTCQNkUWw+b+1k+SJEmSJG071Vqd01dnefpyEiZ/6tIUT12a5ulL01yemut0eV1tsNjDnr48e/pyjPTl2dufZ6Qvt6RtT1/SdmCwwJ4+Vx2XJEmSJEmSJEmSNqqrg+bAL5CEzOdXLw/AnwN/BDwEXAFmOlOaJEmSJEnSjV2dLvP05cUQ+dOXpnj68jTPXpmmUtvdX9TWkwkNAfHFsPhIf+66IPn8/khvjp6soXFJkiRJkiRJkiSpXbo2aB5COAF8LourmF8FvizG+L5O1iVJkiRJkjSvXK1zcmz6ujD505emuDpT6XR5W643l2W4N8dIX47h3tzC/khffsnxcG+Okd6kbaQ/x2ChhxD8sjpJkiRJkiRJkiSpm3Vt0Bz47HQbSMLm32zIXJIkSZIktVqMkelyjfHZChOzFcYbXhPLtI3PVpgoVRmfrTA2XaZW396rk/dkAkO9OQaLPYz05hhOVw9fEhJPg+MjfbmF94Z6cxRz2U6XL0mSJEmSJEmSJGmLdHPQ/FDD/lXg9zpViCRJkiRJ2j6uTpf55MUpLk/NLQmMXxcgT8PiE7MVqts4LN6XzzJY7GGomFsIjA8V021vbsn+/HvDvT0MFpP3irmMq4tLkiRJkiRJkiRJuk43B81r6TYCT8cYt+9vfCVJkiRJUsvNlmt88uIknzg/yRPnJ3n8QrJ/aXKu06VtWjGX4bb9A9x2oJ/bDgxwZKSYrCJezCUB8TQoPljsIZfNdLpcSZIkSZIkSZIkSTtQNwfNTzbsFztWhSRJkiRJ6qhqrc4zV2Z4/Pwkj5+f4PELkzx+fpJnx2bY7n+WfmSkNwmT708C5fPB8puGimQyrjIuSZIkSZIkSZIkqXO6OWj+IZLVzANwSwihJ8ZY7XBNkiRJkiRpi8QYOTdeWgiSP3E+WaH8yUtTlKv1Tpe3YQOFnuvD5PsHuHV/P735bKfLkyRJkiRJkiRJkqRldW3QPMZ4JoTwF8AXAP3AK4B3dLYqSZIkSZLUCuMzlTRQPsEnzk/yRBounyhtz78xzwQ4uqdvIUSerEzez+0HBjgwWCAEVyeXJEmSJEmSJEmStL10bdA89V3AAyR1/ngI4S9jjNOdLUmSJEmSJDWq1OqMz1a4NlNhfLacbpPja7MVxmfKXGs4vjBe4vxEqdNlr6gvn2WomGO4N3kN9Tbu9yzsD/fmOLa3j5v39VHocXVySZIkSZIkSZIkSTtHVwfNY4wfCyF8K/ArwHOAd4UQvirGeLHDpUmSJEmStOOUq3WuziRB8WtpOHx8psK1NDx+3XEaKJ+a685VyPvzWUb68ktC4c1B8aHlguTFHPmeTKfLlyRJkiRJkiRJkqSO6uqgOUCM8S0hhBngV4GXAI+EEH4e+D3g4Rhj7GiBkiRJkiRtM1NzVZ66OMWTF6d48lKyferiFM+OzVCrb7+P2cVchjtGB7nz0CB3HRrkOQeT7YHBAiGETpcnSZIkSZIkSZIkSdtS1wfNAWKMvxVC+HvgncDdwPelr0oIYQxY7/dtxxjjiRaXKUmSJElSV7kyNccn5wPlF6d4Kg2Vnxtf78fo7pAJcOv+fu48NMidB4eS7aFBju/tI5sxUC5JkiRJkiRJkiRJrbQtguYhhM8Gfhq4C4jA/G+P88ChDVxy+y3PJkmSJEnSMur1yNnx2evC5E9enOLqTKXT5W3YTcPFNFA+uBAoP3FggGIu2+nSJEmSJEmSJEmSJGlX6PqgeQjh24CfATIkAfPI5oLiLnEmSZIkSdp2KrU6z16ZXgiRP3lxiicvTfHUxWlmK7VOl7dhQ8Ue7jqUrE7+nEOD3HVokOccHGS4N9fp0iRJkiRJkiRJkiRpV+vqoHkI4ZXAz7I0YD4fFC8D48BMZ6qTJEmSJKk1Zss1zk+UOD9e4sJE6br9C+MlLk7OUa1vjy/oymczjPTlkldvnuG+HCO96XFfnqHeHMf29HLXoSEODhUIwb8JlyRJkiRJkiRJkqRu09VBc+DNLIbMA3AR+GngHcATMcZ650qTJEmSJGll9Xrk8vQcF8bnFkPjaYi8cX+iVO10qcvqz2cZ6csz3JtjeCEonmO4N5+GyJuO02B5MZcxPC5JkiRJkiRJkiRJ21zXBs1DCP8EOEESMgf4OPD5McYrnatKkiRJkrSbxRgpVepMlipMlKpMlipMlqqMTZe35SrkxVyGEwcGuH10gNvT7a0H+tnXX2C4N0e+J9PpEiVJkiRJkiRJkiRJHdK1QXPghel2fkXzbzRkLkmSJEnaqBgjs5Uak2lAPAmKL4bFF7dVJpZpm9/v5uD4jQz35rhjNA2Ujw5wIg2WHxnpJZNx5XFJkiRJkiRJkiRJ0vW6OWg+3LB/Jsb4Dx2rRJIkSZLUMfV6ZLpcZWquyvRcEvqemqsyNb9t3k+PJxv6b+eQ+HocGipeFya/fXSA/QN5QjBQLkmSJEmSJEmSJElau24Oml9KtxE428lCJEmSJEmbV6tHLkyUOH11ltNXZ7g4ObcQEE/C4xWm52pMzlWZKlXSYHmNqblqp0vvKpkAx/f2LYTJ7xgdTPYP9DNYzHW6PEmSJEmSJEmSJEnSDtHNQfNTDftDHatCkiRJkrQmtXrk4uRikPz02Cynrs6kx7OcvTa741cUb4WhYg+HhoscHCpyaKi4ZP/o3l5u2ddPMZftdJmSJEmSJEmSJEmSpB2um4Pmfw1MAQPAiRDCUIxxosM1SZIkSdKuVa9HLk7OpeHxJEh++uosp6/NLATJKzWD5DfSkwkcHCoyOlTg0FAaHh8uLtk/OFSgL9/NH9UlSZIkSZIkSZIkSbtF1/72OsY4HUJ4O/A6kjq/Dvj/OluVJEmSJO0sMUbmqnWm5qpMlapMzVWZLFW5MFFKwuTpauSnr85wxiD5sjIBhnpzjA4Wll2FfH5/X3+eTCZ0ulxJkiRJkiRJkiRJktaka4Pmqe8DvhTYC3x/COE9McbHO1yTJEmSJHXcXLXGVKnK9FyNybkK03M1puYqTKZtU3MVpubm+yQB8vnXdBomn9+v1ndveDwTYLCYY7DYs7AdatgfXLJ//ftDxRx9+SwhGCCXJEmSJEmSJEmSJO0sXR00jzGeDSF8KfBuYA/w3hDC18cY39Ph0iRJkiRpXer1yHR5PgS+NPw9vXBcW2ifnqsyXV5sa2yfmqvu+pXFQ4CBQs/iq9hz42ND4pIkSZIkSZIkSZIkrVtXB81DCMeBM8BXAr8C3AL8aQjhQ8DbgY8Cl4DSeq8dYzzZukolSZIk7TS1NBg+kwbDZ8pJwHtmrpYGwJvfawqEN4XKZ8q1Tt9SVxks9HBkTy8jfTkGCknwe6DQQ3+hZ2H/uuOGdgPikiRJkiRJkiRJkiRtra4OmgPPAI3L9EUgAC9OXxsV6f57lyRJkrSCaq3OXLVOqVJjrlpPXzVKlTpzDW2lSo1SpcZspbYkHD4f/m4Mis+UF4PipUq907e4rQ0Weji6t4+je3rT19L94d5cp0uUJEmSJEmSJEmSJEkr2C5h68Bi4Dw2tEmSJEnaZqq1Os+OzfDkxSmeujTFpcm5JBxeTcPh8/sNbaWm4PhctU6tHlcfTFvGILkkSZIkSZIkSZIkSTvbdgmag8FySZIkaVuZKVd5+tL0QqD8yYvJ65kr01RqhsS73WChhyPLBMiP7unl2J4+hnp7CMGPaZIkSZIkSZIkSZIk7VTdHjT/9U4XIEmSJGllY9PlhRB5Y6D8zLXZTpe26+R7MgwWehgo9tCfT7YDheTVX+hhsNiwn24b+wwUexjI99BfyNKTzXT6diRJkiRJkiRJkiRJUgd1ddA8xvi6TtcgSZIkCeoxcvrqTEOgfJqnLk7x5KUpxqbLnS5vW+vPZ5PAdxr87i9kG/bT9vzS9iXh8IZz8z2GwyVJkiRJkiRJkiRJUmt0ddBckiRJUuvEGCnX6pTKdWYqVWbKNWbLNWYrtXS/urB/dWKah09luFSCC7OB7/qHD1Gq1Dt9C12hN5ddCITPB8D7G8LgS0PjPQyk/a4Lj6ftmUzo9C1JkiRJkiRJkiRJkiRdx6C5JEmS1MVijEzNVbk6XeHK9BxXZ8qMTVe4NlOmlIbCrwuMV6rMzrenbaVyjZlKjVo9rmP0xtWxt1/IPJcNFHqyFHoyFHoy9BV6FlYP78snAfC+NPTdl8+m28YAefpeIbvQpy/fQ9ZguCRJkiRJkiRJkiRJ2gUMmkuSJEltVKrU0rD44uvqdJmxmUq6LTM2VV7oc3WmTKW2nnD49hECHNvTx637+xko9lDoyVDMzQfDsxRzmYWg+EJ7LkOxJ0thlffy2Qw92czqRUiSJEmSJEmSJEmSJGlZBs0lSZKkNarVIzPlZLXw6XKNmXJ1YUXxmbl0v1JjPF11vDEsfiUNj8+Ua52+jbbL92S4bX8/J0YHuP3AALePJq9b9/dTzGU7XZ4kSZIkSZIkSZIkSZKWYdBckiRJO1aMkcm5KmNTyUrh12bKTM/V0qD4fEg82c6Hx2fLVabnksD4Qng87TNXrXf6lrraULGH20cHONEQJr99dICje/rIZkKny5MkSZIkSZIkSZIkSdI6GDSXJEnStlGq1BZWCZ9/XZ0uMzZTYWx6jqvTlcX3ZpL3qvXY6bJ3nENDxTRQ3p9s00D5gYECIRgolyRJkiRJkiRJkiRJ2gk6EjQPIfxVU1OMMX7+Gvq1yrLjSZIkaWtVa3VmKzVmKzVK5TozlSqz5eR4Yra6JER+NQ2LNx5Pl2udvoUdqycT6M1n6ctn6cv3kM9AeXaKgZ7IaC888MK7uPfmA5w40M9gMdfpciVJkiRJkiRJkiRJkrTFOrWi+QPA/NKSoWF/pX6tstJ4kiRJu1K9HilVa5QqSRC8tPCqM5cGw2crtYVQ+MK2UqNUrjGTHpfStply0qfUdF6l5o9hm5XLBvb05dnTl6e/kKU3n6U315MGxLMUc9mF/d58D725+f3skv2+9L35cHkum1kyzsTEBO9973sXjl9630GGhobafbuSJEmSJEmSJEmSJEnqkE4FzSVJkrRGMUbmqnUmS1UmSxWm5qpMlapMlKrMlKtLwuFzlRqlan0hKD5bWdyfq9TTMHmt6Zw65Vq907e5K4UAI7059vTn2duXZ09/nn39+SXHe/tz7OnLs6+/wJ7+HAOFHkIInS5dkiRJkiRJkiRJkiRJO1wng+bhBvsr9VOLhBBOAPcDR4E8cBX4BPChGGOpk7VJkrSTlKv1hXB4EhSvJkHxucrCcdJWYWr+OA2ST6ZtU3NVVwLvcj2ZQG8+S38+WVl8oNjD3iVh8eS1p29+PwmPj/TlyWb8cVeSJEmSJEmSJEmSJEndpyNB8xhjppX9tHYhhNcA3we88AZdpkIIbwW+P8Z4uV11SZK01Wr1yLnxWU6OzXBpco5KLVKp1anU6pSrdSq1SHX+uOG95NXYd3G/WouUm/qVq3Wq9eSa0+Ua5aorhXeTXDYwUOihLw2EJ690v9BDXy5LX2Fpe3++JwmRF7L05nrob3q/N58ln824yrgkSZIkSZIkSZIkSZJ2lE6uaE4I4b+luxH4zRjjMx0sZ0cLIRSAtwBfu0rXAeDbgK8KIXx5jPEDW16cJEktEGPk2kyFk2MznLo6k2zHZjmVHp+5Oku17qrgO81IX27pquHp/r7++bZk5fB9/QX29OcYKPQYCJckSZIkSZIkSZIkSZLWoKNBc+BNJCFzgL8BnulYJTtYCCED/A7w6qa3asBJYBy4FRhueO8A8CchhJfHGP+2LYVKkrSKUqXG6YYQebJNjk9fnWVqrtrpErUJvblsEhafD4n35djbX0jC4mmIvPH9kd4cPVm/AEeSJEmSJEmSJEmSJEnaCp0OmgMEFsPm2hr/metD5r8E/GCM8SwshNFfDbwZOJ726QN+N4RwT4xxvE21SpJ2sVo9cmGitBAgT1YjXwyUX5yc63SJu1Yxl6Ev30NvLksxl6E3n6U3l6U330NvLpPuZynmsvSl7xXTtt60rZjLLvSbf78vn57XkyWTcaVxSZIkSZIkSZIkSZIkqVt0Q9DckPkWCiHsA/5LU/P3xBh/rLEhxlgH/iCE8GHgr4Fb0reOAv8BeOMWlypJ6rAYI5VapFKrU67WKTds59vmqnXmKnVKlVqyX023lRql9L35tsU+je8vbVu4RiVpK9fqnf7PsK0MFHroLyyGtgu5LMWezEKgu5hL9pP3MhR75sPei+3FXCY9bzFAPt9v/vxCT4YQDIFLkiRJkiRJkiRJkiRJu0k3BM21tb4TGGw4/gDw4zfqHGM8E0L4RuAvGprfEEL4uRjjlS2qUZJ2pBgjs5Ua5WqdSi1SrdeppkHuaj1STdsqtUg1bavU6kvbF96/cd9KvU6l2hQQT/cX2tL9ufm2hffjklC52qM3l2Ww2MNAsYfBQg+DxRwDhZ7r24o9C+3JK+k3UOxhIN/jCuCSJEmSJEmSJEmSJEmStoxB8x0shJABXtfU/KYY44qryMcY/zKE8EHgc9OmQeArgV9sfZWS1L3q9ch0ucpEqcrEbCV5pfuTpcX9iVKFidkqk3PJNjlO3q/V/eKObjTcm6M/nyXXkyGXTV75bCCXzdCTbvNpe9InkF/uvWyGXE8gl0n6zF9vvm9fPstAIZcEyOeD5IUeerKZTv8nkCRJkiRJkiRJkiRJkqQVGTTf2V4MHGg4fhp43xrPfQuLQXOA12DQXFIHxBgXV/auJqt3z68KnrwWVwhvbKsueW/x3Ep1vm+kVKkthMQnSml4vCEoPjlXZeU/zVG3KvRkOLa3j2N7ejm+ty/Z39vHsT19HNvby2Ax1+kSJUmSJEmSJEmSJEmSJKmrGTTf2V7ZdPye1VYzb+zbdPxACKE/xjjdgrokdaEYIzPlGpOlKlNzFSZL1XS/ylQpCV9PzVWZKdeo1iK1ehLYrqWh7cbjpdt62n+Z9vnj2vX95wPlVVcE1zJCgJuGihzd25cEyff0cXxfb7Ld28f+gQKZTOh0mZIkSZIkSZIkSZIkSZK0bRk039le0HT8obWeGGM8G0J4BrglbcoDzwU+0orCJK1fjJF6hGq9Tr2ebGtpULtWj9TSoPhUGg6fLFUWguKN28k0MD5Zqi70nShVmJ6rYqZb3WS4N8exvb0LQfL5VcmP7+3j8EiRQk+20yVKkiRJkiRJkiRJkiRJ0o5l0Hxnu7vp+NF1nv8oi0Hz+esZNFdXq9cjM5UaM3PVhdW3p+eqTJerTM/VmClXmZpL3y9XmZmrUa7WqcdIhGQbF0Pd823Exffm+8am43psamvq2xgIn1/Bu9bY3vCq1iP1+RW+G/pI3SAToJjLUujJUOjJUshlKKbb+bZiLn2vJ5O2Zxe3PWm/XJZiup1vK+ayDBR6OLa3j+HeXKdvVZIkSZIkSZIkSZIkSZJ2LYPmO1QIoRc43tR8ap2Xae5/58Yrktbm2SvTPHNlZmlQvFxNwuJzSWi8uW0hPF5O3pO2qxAgl8nQkw30ZAK57Px+hlw20JPNLGlf6JvNkM8G8j0ZctkM+WxmYb8w39az2JbvWeyfz2bJLewv129xm+tZvJ4kSZIkSZIkSZIkSZIkaWfrpqD5fw8hXG3TWDHG+PltGqtT9gOh4bgCXFznNc40HY9uqiJpDd724ZP88vuf7nQZ0g0VcxmGijmGenMMFXvSbY7Bhv2h3p6FtsFijmIuCWevFh7PZsLqBUiSJEmSJEmSJEmSJEmS1AbdEjQPwH1tHCu2aaxOGmg6nokxrve+p1e55rqFEEaBA+s87UTjwdTUFBMTE5stRRs0PT294vFm5aIrkmvzejIhWfF7PsTdfNywYvhgsYehYg8DhSyDhZ40HN7DYCGbbpceb3w175i+6ouHVahXoUzykqRustVzviRJ6jzne0mSdgfnfEmSdgfnfEmSdj7n+91pamqqo+N3S9BcrdccCi9t4Bqzq1xzI74VeONmLvDhD3+Y8+fPt6AUtcKHP/zhll7vzLkAZFt6TW1cIFLMQm8PFLLQm4VCNpINkAlct82E5H+9TOPxwn5ceszi+43nN563+IpkM8l+T8P7PZnFc3sarhM2ujB4JPmXL/3Xr5S+LrXgv6UkbXetnvMlSVL3cb6XJGl3cM6XJGl3cM6XJGnnc77fHU6ePNnR8bspaL7RWKCWV2w63shiuXNNx70brEVas8JGF4vWEoFIIQvFhldvT1xyXMxGij1NfZra8plNhLYlSZIkSZIkSZIkSZIkSdK21S1B8wj8FPBopwvZQZpXMM9v4BqFVa4ptVxhixYz7wlJ8LqQTcLshXRl7kIWcpnkL11CSLfpOZl0p7F9SZ8V2jNL+kQC16/YnblBWzZdkXthu6RfXPHcnkyy6ng+u1i/JEmSJEmSJEmSJEmSJEnSenVL0Bzgz2KMf9XpInaQqabj5hXO16J5BfPma27ELwBvX+c5J4A/mj+4//77ufvuu1tQijZienp6yVdu3H///fT397fs+tknx/jfTz5CX6GH/nyWvlyGvnx2yas/n6U3n6Uvl6W/kGyb32887s1lyGVdKl2SpPXY6jlfkiR1nvO9JEm7g3O+JEm7g3O+JEk7n/P97vTYY491dPxuCpqrtZpD4X0hhBBjjOu4RvO/QJsOmscYLwIX13NOCEuXZR4YGGBoaGizpahF+vv7W/q/xys/bZBXftrN1/3vLkmSOqvVc74kSeo+zveSJO0OzvmSJO0OzvmSJO18zve7w8DAQEfHd4nfnesy0BgqzwGj67zGkabjdQXEpY0IIRgylyRJkiRJkiRJkiRJkiRJ6jCD5jtUjHEWONnUfHydl2nu/4mNVyRJkiRJkiRJkiRJkiRJkiRpuzBovrM1B8Ofu87z717lepIkSZIkSZIkSZIkSZIkSZJ2IIPmO9vHmo5fvNYTQwg3Abc0NFWARzdfkiRJkiRJkiRJkiRJkiRJkqRuZ9B8Z3tX0/HLQwhhjed+YdPxe2OMUy2oSZIkSZIkSZIkSZIkSZIkSVKXM2i+s30IuNxwfBvwwBrPfX3T8R+1oiBJkiRJkiRJkiRJkiRJkiRJ3c+g+Q4WY6wDb21qfuNqq5qHED4f+NyGpkngd1tbnSRJkiRJkiRJkiRJkiRJkqRu1Q1B85C+tDV+HJhqOH4J8F036hxCOAL8WlPzz8YYLy/XX5IkSZIkSZIkSZIkSZIkSdLO09Ph8W9t2D/fsSp2sBjj5RDCjwA/0tD8oyGE48APxRjPAoQQMsCXAj8LHG/oexb47+2q9wbyjQdPPvlkp+oQMDU1xcmTJxeOH3vsMQYGBjpYkSRJ2grO+ZIk7XzO95Ik7Q7O+ZIk7Q7O+ZIk7XzO97vTMpnZ/HL9tkqIMbZzPHVAGiL/I+BVTW/VgGeBcZLQ/0jT+7PAF8QY/2ara1xJCOFLSeqXJEmSJEmSJEmSJEmSJEmSdqtXxxjf0a7BMu0aSJ0TY6wDXwH8dtNbWeA24NO4PmR+BXhFp0PmkiRJkiRJkiRJkiRJkiRJktrPoPkuEWMsxRi/Gvhy4GMrdJ0GfgF4bozxfW0oTZIkSZIkSZIkSZIkSZIkSVKXCTHGTtegDggh3A58JnAEyAPXgMeAv4kxljpY2nVCCMPASxqaTgHlDpUjOAH8UcPxq4GnOlSLJEnaOs75kiTtfM73kiTtDs75kiTtDs75kiTtfM73u1MeONZw/P4Y43i7Bu9p10DqLjHGJ4EnO13HWqT/h3hHp+tQIoTQ3PRUjPGRTtQiSZK2jnO+JEk7n/O9JEm7g3O+JEm7g3O+JEk7n/P9rvaPnRo406mBJUmSJEmSJEmSJEmSJEmSJEndyaC5JEmSJEmSJEmSJEmSJEmSJGkJg+aSJEmSJEmSJEmSJEmSJEmSpCUMmkuSJEmSJEmSJEmSJEmSJEmSljBoLkmSJEmSJEmSJEmSJEmSJElawqC5JEmSJEmSJEmSJEmSJEmSJGkJg+aSJEmSJEmSJEmSJEmSJEmSpCUMmkuSJEmSJEmSJEmSJEmSJEmSljBoLkmSJEmSJEmSJEmSJEmSJElawqC5JEmSJEmSJEmSJEmSJEmSJGkJg+aSJEmSJEmSJEmSJEmSJEmSpCV6Ol2ApG3nEvD9TceSJGnncc6XJGnnc76XJGl3cM6XJGl3cM6XJGnnc75X24UYY6drkCRJkiRJkiRJkiRJkiRJkiR1kUynC5AkSZIkSZIkSZIkSZIkSZIkdReD5pIkSZIkSZIkSZIkSZIkSZKkJQyaS5IkSZIkSZIkSZIkSZIkSZKWMGguSZIkSZIkSZIkSZIkSZIkSVrCoLkkSZIkSZIkSZIkSZIkSZIkaQmD5pIkSZIkSZIkSZIkSZIkSZKkJQyaS5IkSZIkSZIkSZIkSZIkSZKWMGguSZIkSZIkSZIkSZIkSZIkSVrCoLkkSZIkSZIkSZIkSZIkSZIkaQmD5pIkSZIkSZIkSZIkSZIkSZKkJQyaS5IkSZIkSZIkSZIkSZIkSZKWMGguSZIkSZIkSZIkSZIkSZIkSVrCoLkkSZIkSZIkSZIkSZIkSZIkaYmeThcgaXsJIZwA7geOAnngKvAJ4EMxxlIna5MkqZuFEAJwC3AvyTw6AsyRzKWfBD7S6rk0hDAIfDbwHGAImAWeJZm3z7Z4rOcBLwJuArLAFeBh4O9jjNUWjtO2e5IkqduFEIrAi4G7gD1AGThNMv8+3eKx2vI8oJ33JElSq4UQ7gSeTzJf9pF8Zr0APAE8GGOc28S1nfclSeqQEEIB+DTgbpI5qxeYAC4C/w94MsYYWzBOD/CZwD3APqAGnAM+GmN8ZLPXbxrrCPBZwM0s3s8TwF/HGKdaOE7b7kmSpHbzs/qmxzKHuE2EFvysK2kXCCG8Bvg+4IU36DIFvBX4/hjj5TaVJUlSVwsh7AFeA/xT4GXA/hW6V4B3A2+OMb5/k+PeCvwA8JUkH8iaReD9wBtjjB/YxDgBeB3wXSTB7+VcAX4R+LEY4/QmxmrLPUmStFVCCL8F/POm5mdjjLds4FoHgDcC/wrov0G3jwI/GGP8o/Vev2ms19CG5wHtvCdJklop/YPofwd8I3DrCl3LwIeB/xtj/Nl1XN95X5KkDgkhvAh4A/DlQGGFrmeAtwA/G2Mc28A4A8B3A98C7L1Bt8eBHwfeuplQewjhJcCbgAdu0KUM/A7w32KMz2xinLbdkyRJ89I/pLqf5I+c7gc+HRhs6LKhZ/LLjONn9U0wh7j9GDSXtKL0r7PfAnztGk+5BHy5AS9J0m4XQvh5kl8yLxeKXs1vAP8uxjixgXG/EvhfJCunrSYCPwF8z3of4oYQRoDfBb5gjac8DXzpRlYoadc9SZK0VUIIXwK8Y5m31v1QO4TwAPB2Vv4Dtka/AfzrGGN5neO07XlAu+5JkqRWCyG8Cvg14OA6TrsQYzy0xus/gPO+874kqe1CCBngR4D/DGTWceoF4F/FGP90HWPdC/wRK//BWqM/A74qxji+jrrmF475cZJ7Wotp4OtjjL+3nnHSsdpyT5IkAYQQPhv4jyTh8sOrdN900NzP6sA2uCe1lkFzSTeUfoD+feDVTW/VgJPAOMmHw+Gm92eAl8cY/3bLi5QkqUuFEP4BeNEyb81/LeQFIEfytZTNcykkq5x9/nq+ojKE8BXAb3P9g+9LwClgFDgChKb33xxjfMM6xukF3kfyV+CNysAzwBxwG9f/pfMl4MUxxifXMVZb7kmSpK0SQhgGHiGZr5qt66F2COFzgD8n+UrrRteAT5F8jeUxINv0/u+TPIxd04PAdj4PaNc9SZLUaiGENwD/nes/j5aAs8BlkvntJpb+onZNQXPn/SWc9yVJbRVC+FWShWSazQBPAbPAPpLn4M0/C5SB18QY/2QN49wJ/DXXh7qmSBZv6QVuIfldQqO/BV4WYyytNkbDWP8D+Lam5gicJnnefnyZOmrAV8QY/2Ad47TtniRJAgghfAfwM2vsvqmguZ/Vl+jae1LrrecvLyXtPv+Z6/9x/yXgeIzxthjjp5F8zdVrSf7Bn9cH/G76y3RJkpR8CPsF4JXAnhjjsRjjp8cYn0/yMPqlwAebzrmf5Oug1iSEcIJk1e/Gn/EfJHkwOxpjfFGM8RhwN8kHuEbfEUJ47Tru56dZGjKvAz8IHIox3hljvI/kZ4TXAVcb+h0g+Rmh+UNoN9yTJElb5SdZDJlPb/QiIYQ9JF9d3fiw91ngNcDeGOMLY4y3kvyy9pebTn8tyVd9r1Vbnge0+Z4kSWqZEMLrST4bNwbL/gT4YmAkxngixviZMcb7YowHSH4W+JfA75GEz1a7vvP+Us77kqS2CSF8OdeHzB8leb4/nM7vnxljvJ3kW03eyNL5PQ/8ejr3rTROD9evHDoGfD3J3Pj8GONzgEPAD5M8h5/3WSTf7LnWe/pKrg+Z/x5wZ4zxePqs/QDwcuDjDX2y6b3cssZx2nZPkiSt0ZoXdVuNn9W3xz1pa7iiuaRlhRD2kfxV0mBD8/fEGH/sBv2PkPxl8i0NzT8QY3zjlhUpSVIXS1c03wf8EPC2GOPsKv2zJGH0b2p662UxxveuYby3AV/d0PQRkr/snVimbyD50NY41lPAXTHG6irj3AU8zNK/WP6aGONv3aD/80h+RhhpaP6GGOP/Wmmc9Ny23JMkSVsl/brJvyIJodWB72bpL03XvHpKCOFHgO9paPoU8DkxxrM36P+9JL+0nTcO3BpjvLpc/4bz2vY8oF33JElSK4UQbgceAoppUwX4+ht9Ll7m/D1rmI+d9533JUkdEkJ4CLinoekfgAdijDf84/EQwsuAPwN6Gpq/N8b4oyuc800sDWxdJZkbH71B/68B/k9DUxV4bozxkzcaIz0vDzzO0vn7l4BvXW4F0jTE9RfApzc0/0aM8etXGic9ty33JElSo4YVzSeBj5L8TvnD6fZWoPF37Rte0dzP6tvjnrQ1DJpLWlYI4ceB72xo+gDJB+gb/qMRQvh8kg+d8yZJJpMrW1OlJEndK4TwSuA9McZVVyprOCcL/B1LH+C+Lcb4tauc9zySVUbmV/4uAy+IMT62wjnF9Jw7Gpq/Kcb4q6uM9TvAVzY0/WaM8etWOef1wK81ND0L3BFjrKxwTtvuSZKkrRBC6CUJoZ1Im34W+EM28FA7hHCA5OulBxqaXx5j/MsVzgnA+4DPa2j+kRjjf1llrLY8D2jnPUmS1EohhL8i+WayeV8ZY3x7C6/vvI/zviSpM0IIt5EsYNLo/hjjR9Zw7i8B39zQ9LcxxhffoG8eeBI41tD8+hjj/1xljN8E/kVD01p+f/AtJIvczPskcF+MsbTCOc8F/pFkdXaAGnBPjPETK5zTtnuSJKlR+i3ZBeATMcZ603sP0IKguZ/VF87p6nvS1sms3kXSbhNCyACva2p+00r/uAOkE80HG5oGWRpEkyRp14gxvns9IfP0nBrXfzXkF63h1G9g6c/2v71SIDsdqwQ0/4Vw89eBLpF+ddZrGy8DvGkN9f0vknD5vJtJvoJzJW25J0mSttAPshgyPwn8101c65+z9GHvB1Z62AuQfob//qbmb0gfBC+rzc8D2nJPkiS1Ugjh1SwNmb+9lSHzlPM+zvuSpI65s+n49FpC5qnfazq+fYW+X8TSQPYzJM/RV/Mmkufy874iXYF8Jc3PyH90pZA5QLoC+e80NGW5/ueGZu28J0mSFsQYn4oxPtocMm8xP6uzLe5JW8SguaTlvBg40HD8NMlfI63FW5qOX9OCeiRJ2k0+2HS8L4TQt8o5X9p03Dwf38jvAI1f9/kZIYTDK/R/JUu/+vN9McanVxsk/VDf/ED5Nauc1q57kiSp5UIInwF8R0PTv40xTm3ikq9uOl7rvPhekq+jnHcI+Ccr9G/n84B23ZMkSa30TU3Hzb9cbQXn/UXO+5KkdtvbdHxqHeeebDoeWaFv89z4v1YLW0ESpAPe39CUA15xo/4hhKPACxuapoDfXW2cVPN83Vxzs7bckyRJHeJn9UXdfE/aIgbNJS3nlU3H71nLh8D5vk3HD4QQ+ltQkyRJu8XVZdpuuHpHCOFOlq6MMg18aC0DxRib+wau/zmgUfN7f76WcVLNPyO86kYd23xPkiS1VAghR/LwM5s2vT3G+K5NXG+ApV9DCWucg9PP8n/R1HzDOZg2PQ9o8z1JktQSIYQjLP3WsY/FGB9p8RjO+w2c9yVJHTDedNy7jnOb+15eoW9bnrUvM87fpM/Q1+JvgJmG4ztDCHesY6ytuidJktrKz+pLdes9aWsZNJe0nBc0Ha8p2AUQYzxL8jVY8/LAczdfkiRJu8aRZdq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eQ22rrbNa219btVqrMmSjraNqh7Vurata9h4uloAgkISEsEJCIIEwkpCQec/vD0BRQu4BMm7C+/l45JHkns/5ns8lITe5930+h7v6ROBSzXS3wtIKnlu+i/e+PoBZ89XMAQhv7sWMMYncGBN0uXdDREREREREriI2m8FtvdoyKP7M1brmbLV2svbFtG3uzcyxjWMwhkh9atBnJ03TXAmsNAxjFPA40Oe8zeeekQrjTFh8zGUc4vxntczzblsHPGOa5uLLWFNERERERERERETkspVVVvGvL/bxj8/3UF7l+DKt5xw8cWaK7LDEYKaMSiA4wLMOu7TmwLFiJs1LYc3ufEv1PSOaMXtcUqOe1hzg7cbT45O4uVsoE+emsCuv5gm9OSdP88t3nOvr1lidLCnnmaXpfLT5wqn5hwpK+dV7WxgY15qpo+MJa+bdAB1a9/W+Y0ycm8zeo8XVbt9y4ARbDpxg+sKdjOocws96htMpLADD0KWam7KMvCImzEnmmwMnLNX3jmzBzHGJdGjlWyvHd3e18bubohjVOYTJ81P5KuNojfXF5VVMX7STOd8eZPa4JDqFBQJnTkBalpLL1IWp5BWWOTyuq83g/usjefAm575yh4iIiIiIiDinlr4evPCTLtzaPZxJ8y7+fMvFuNoMfnV9JA8OiGpUgzFE6otTjMEwTXMhsNAwjG7Ab4GbAf9zm88rtfoMqnne+3P7FAGfAq+YprnlyjoWERERERERERERuXQb9h5j4rxk9l3iix3nW5qSy5rd+Tw6OJo7e1c/RbaulVfaeW3NPl5evdvh1FuAAC83JgyP5dbu4dgaoN+60COiOYsevI7X1+znpdUZlFbU/O9w7uv2x8HR3NFAX7fGyjRN5m3LYeaiNIdT81el5bFuTz4PD4zi3uva4+Ziq6curTlRXM7sJWl8suWgpfpTZZV8uCmLDzdlERvsx896hjO2ayiB3u513KnUp9PlVfzts938+6t9VNodj/5u7uPOpBFxjOsaWicnH7Rr4cM79/Q8c0WGRTs5WlRzWDwlp5Ax/1jHnde24/Zr2vHs8nRWpR2xdKyubQN5enwSscH+jotFREREREREatC7QwuWPnT9JT1v2b3dmcEYMcGNdzCGSF1ziqD5OaZpbgV+aRjG/wEjgJHAYCCkuvIffW5U8/EhYAWwGFhkmqbjsQkiIiIiIiIiIiI1KKusYu+RYtq18MbHw6meXhMndvxsuPRTi+FSR06VVTJt4U7mbM1h1rjE76bI1octB44zYY7jSd7njOsaysQRcbT09ajjzuqfm4uN39zYgZGd2jB5fgpf7Kp5+u+pskqmLtzJnG9zmD0uicTQgHrqtPHan1/MpHnJrNtzzPI+pyuqeHpp+nf/P3pENK/DDq0xTZP/bc1h9pI0jjsIy19Mem4RUxfuZPbSdIYlBvPTnuFc275Fkzl542r1ZcZRJs9LIet4iaX6n/QI48lhcTTzqduTDQzDYFTnEK6PbsVzy3fxn40HMGvIwJsmvLPhAO9sOGBpfT8PV/40LJaf92qr72ERERERERGpNe6uNn7bvyOjOoUweX4KX17kal1+nq48MSyW23rq71IRR5zylbCzgfA5Z98wDCMS6A50AtoD4UAAcO7alyXASSAbyAR2AFtM09xXn32LiIiIiIiIiEjTVVpxZtrou+sPUFRWiWHAgNjW3NM3gj4dWtTJRFFp/EzT5NMtB5m9JI0TJRUO691cDH59Qwc6tPJl5uI08k/VPDsjOaeAsf9Yx529I3h0cDR+nm611foFCk5X8Odl6XywMctSfbsW3swcm0i/qFZ11pOzCG/uzVt392RJci5TF6Y6nP6742ABo/++lrv7tOeRwdH46qSVC5RVVvHql/v4++d7KLcwfao6u/KKuOVfG/hpj3CeGBZb58Hci9l79BQT5ybz9b7jtbJeeaWd+dsOMX/bIdq18OYnPcK5tXsYQf6etbK+1I8jRaXMWJTGwu2HLNV3DPJl1thErolsUced/VCAlxszxiZyc/cwJsxJZufhwitec0RSG6aMitf3rIiIiIiIiNSZti28efuenixNyWXawlTyCr9/vm5kpzY8NSqeID/9XSpiRaN49vpsYHwf8ElD9yIiIiIiIiIiIlef6qaNmiasSstjVVoeUUG+3NkngvFdQzXlXL6z58iZcOnG/dbCpb0imjN7fCIdg85cprV/bBDPLk/n/Y1ZNU6RtZvw9vpMliQfZsqoBIYnBdfqiQ+mabJox2GmLdzpMPgO4Go7E5b/3U0d8XRzqbU+nJ1hGIzo1IZ+0S15bvku3vu65um/dhPeXLefpSmHmTo6gSEJwfXXrJP7et8xJsxNZt/R4lpZ7+NvslmxM5cJw+O4pXtYvZ0YVFpRxStf7OWVL/ZSXuU4LO/harN0SefzHThWwrPLd/HCygz6xwTxs57h3BjTClcX2+W2LXXMbjf5YFMWf16WTlFppcN6d1cbD97UkV9d3wF314b7unYJD2TB7/ry9vpMXliZQUl51SWvERroxYyxCdwU27oOOhQRERERERH5IcMwGJ7Uhptig9iceZwDx0q4rmNLIlr6NHRrIo2KXvUSERERERERERG5iCNFpcxclMYCB9NGdx85xeR5KfxlWTq3dg/nzt7t9GT1Vay0oop/frGXV77YQ0VVDUnjswK93Zgw7EwA9vzLtAZ4uTFzbBI3dwtjwtwU0hxMkT1SVMZvP9jKjTGtmDEmkfDm3jXWW5F9vIRJ8y5+idkf696uGU+PTyK6td8VH7ux8vd0Y/qYRMZ3C+PJOckOv26HC0p54L0tDIpvzbTRCYQEetVTp87neHE5s5ek8emWg5bq27Xw5v9u7MD7G7PYcbCgxtoTJRU89ukOPtlykFljE4mq4+/R9XvzmTQ3hX351sLy47uGMmFEHCeKy/l4czb/23rQ0lUQzqmym9+d/BTk58GtPcL4SY9w2rXQY5EzSc8t5Mk5yXybddJSfb+olswYk+g0v1O4uti4r18kw5PaMHVBKit25lnaz8Vm8Mvr2vPwwCi83fXSpIiIiIiIiNQvTzcX+kW1ol9UQ3ci0jjp2RwREREREREREZEfudRpo+cUlVby5rr9vLV+PzdGt+KuPhFcH9XqB+FhadrW7cln0rwU9lsNl3YLZeLwOFr4ely0pmvbZiy8hCmyX+w6yqC/fsmDA6K4v18kbpcx2biiys4ba/fz4qoMSiscT1j283TlyWFx/KxnuL7fz+oSHvjd1+35FRmcrqj567ZyZx7r9uTzyKBo7u4TcVVNpDZNk0+3HGT2kjRL4Wo3lzNT83/b/8zU/Fu6h/P+xgM8u2wXRWU1/8zetP84w15aw6+uj+T3N0Xh5V67U/ePnSpj1pI05mzNsVTfvqUPM8cm0rdjSwBa+nowaWQ8jw2NYdXOI3y0OYu1e/JrnI7/Y0eKyvjH53v5x+d76dOhBT/r1ZbB8a2vqisMOJuS8kpeWr2b19fsp8ru+IvZ0tedySPjGd05pN4m8F+KkEAv/n1nD1buzGPqglRyTp6+aG3nsABmj08iISSgHjsUEREREREREZHaoqC5iIiIiIiIiIjIedJzC5kwJ5mtFqeNVsc04fNdR/l811EiW/pwR+923NI9DD9Pt9prVJxK/qkyZi1OY+631sKlkWfDpX3OhksdOTdFdlhSG6ZZmCJbWmHnL8t2Me/bHGaNS6JnRHNLxwH4NusET85JJj23yFL9qM4hTB4ZR5Cfp+VjXC3O/7pNmZ/CqrQjNdaXlFcxc/GZkPJLP+tS51O3ncGeI6eYODeZjfuPW6rvFdGc2eMT6Rj0/b+Ni83gzt4RDE0IZsbiNBY6uApFpd3kn1/sZcH2Q0wfk8BNsa2v6D7AmbD8J98cZPbSNE5aCMu7u9j49Y0d+L8bO1QbAPdwdWFEpzaM6NSG7OMlfLLlIJ98k83hgtJL6mv93mOs33uMQG83xnUN5ac9w4kN9r+kNeTKfJ5+hEnzUmoMY5/vtl5teWJoLAHezv87w6D41vTp0IKXVu/mjbU/DNH7erjyx8HR3NE7AhedgCQiIiIiIiIi0mgZ5qWMwRBpAIZhJAAp5z5PSUkhISGhATu6uhUWFvL5559/93n//v3x99cLEyIiIk2NHvNFRORqdKnTRi+Vj7sLt3QP484+EXRo5Vvr618qPd7XDrvd5JMt2cxekk7BaWvh0t/c2IHfXCRcatXKnXlMmZ/CIYuh05/2COfJ4bEEertftKawtILnlu/iva8PWJqeHN7cixljErkxJshq21c10zRZnnpm+m9uoeOvm7+nK2/c3fOSThJoTEorqvjnF3v51xd7Ka9yPDU/0NuNCcPiuKV7mMOp+V9lHGXy/BQOHCux1MvQhGCmjI6nTYCXpfof23OkiAlzU9hkMSx/TfvmzBqXRMegS3ssqLKbfLX7KB9vymZVWh6Vl/lY1Tk8kAeuj2RYYrBTTstuKvIKS5m2MJUlybmW6mNa+zFrXCI96uD/fH085qfnFvL2ukwOHCshMdSfe69rf9n/p0REROTy6O98ERGRpk+P91en1NRUEhMTz78p0TTN1Po6viaai4iIiIiIiIjIVe/z9CNMnp/CwRPWpo3e2j2MwtIKVu7Mw2rOr7i8inc2HOCdDQfoF9WSu/tE0D8myGFgUpzX7rwiJsxNZnPmCUv110aeCZfWxokG56bIvrgqgzfXZTo8OeLjb7JZmZbHxOFxjO8W+oNwqWmaLEvJZerCVPIKyxwe28VmcH+/SB4aEIWX++WH5a82hmEwNDGY66Ja8vyKXbyzPrPGnx+FpZX84vWN/OP2bgyMv/KJ285k3Z58Js1LYX9+saX68d1CmTg8jha+Hpbqr49uxfKHr+eVL/byioUg+7LUXNbsPsofBkVzd58IXF1slo5TWlHF3z/bw6tf7aWiyvGDQTNvNyYMPxOWv5yAt4vNoH9MEP1jgjhaVMacrQf5eHM2+yz+O56zPfsk//f+Vq6NbM600YnEBDf9yfn1qcpu8v7GA/xl2S5OlVU6rPd0s/HQgGju69ceN4vfe84oNtifZ27u1NBtiIiIiIiIiIhILVPQXERERERERERErlp5haVMX7iTxcmHLdVHt/Zl9rik76aNHjxRwn++zuKjzVmcLHE8zfqcNbvzWbM7n7bNvbmzdztu7RFOgJfbZd0HqX+XEy6dNCL+goD3lfLxcGXiiHjGdQ1jwtxktmWfrLH+eHE5j36ynU+3HGTmuEQ6tPIl5+RpnpqXwur0I5aO2SU8kKfHJxHXRlNyLpevhytTRiVwc7cwnpyTTHJOwUVryyrtPPCfLTw9Pomf9Aivxy7rRv6pMmYtTmPutzmW6iNb+jBzbCJ9Ora85GN5urnwh0HRjOkSwuT5Kazbc6zG+uLyKmYuTuN/W3OYNS6Rbm2b1Vi/ZvdRJs2zPjX9lu5hTBgeR3Ofi19V4FK08vPggRs68KvrI9mceYKPNmexeMdhyiodT4c/5+t9xxn+8hru7N2OhwdG63GoFqQeKmDCnGS2H7z4/+vz3RDdihljEmnbwruOOxMREREREREREbk8CpqLiIiIiIiIiMhV59y00WeX7aLI4rTRBwdEcd91kbi7fj9tNKyZN08Mi+XhgVHM35bD2+sPkHa40HIfWcdLmLk4jedXZDC+Wyh39YkgurUmyzqzrzKOMnm+9XDprd3DeLIWw6XViQ/xZ85v+vDBpiz+vCydotKav6c37DvGsBfXMLJTG5al5lJSXuXwGH4ervxpaAy3X9MOF03hrxWJoQHM+21f3tuQyXMrMi46+bjKbvKnT3dwvLicB66PrNWTFeqL3W7yyZZsZi9Jp+C045Ny3F1s/ObGDvzmxg54ul3Z1PzIVr7855fXsGD7IWYs2kn+qfIa69MOF3LzK+u5rVdbHh8SS4D3D8PXR4vKmLl4J/O3HbJ4fB9mjU2id4cWl30famIYBr3aN6dX++ZMGZXAgu2H+HhzFik51h6Lquwmb63LZMG2Qzw+NJZbuofpShuXobiskr+uzOCt9Y6vMAFnThSYMiqeEUltGuX/aRERERERERERuXooaC4iIiIiIiIiIleVS502en10K2Y6mDbq6ebCT3u25Sc9wtmceYJ31meyLDXXUtgM4HRFFe9vzOL9jVn06dCCu/pEMDCutQK9TuRSw6UdWvkwa1wS10bWTbj0x2w2g19c247BCa2ZuSiNBdtr7rO8ys4ci1OlRyS14alR8bT296yNVuU8LjaDu/u2Z2hiGx7/3w6+zDh60dpnlqaTX1TGhOFxjSoIvDuviAlzk9mcecJS/bWRzZk1LokOrXxrrQfDMBjTJZQbo4N4dkU672/Mwqzhx7Npwgcbs1iRmsvEEXGM7RKKacJHm7N5ZmkahQ5O5gBwd7Xx2xs78usbI/FwvbKwvFUBXm7ccW077ri2HSk5BXy8OZt523IcnnwCcKy4nD/9bwfvb8pi2ugEuoQH1n3DTcSqnXk8NT+FQwWlDmsNA35xTTv+OCRGE+RFRERERERERKRRUNBcRERERERERESuCsVllby4KoM311mfNvrUyHhGdrI+bfT8ybKHC07z/tdZfLgpi2PFNU/QPd/6vcdYv/cYoYFe3NG7Hbf1bHvBRF2pP3a7yYebs/jz0nTL4dLf9+/Ir26ov3Dp+YL8PHn5tq7c0j3skiavVyc00IvpYxIYENe6FjuU6gQHePLGXT14ck4yn2w5eNG619fu51hxOX+5pRNuLraL1jmD0ooq/vbZbv791T4qqhz/zG3m7cakEfGM7xZaZxOeA7zdmDk2iZu7hTFxbgo7HVyBIv9UOX/4eDv/3XyQ8io7Ww5YC8v36dCCmWMTiazFsPylSgwNIDE0gAnD41iacpiPNmezaf9xh/ttzz7J2H+s46c9wnlsaAwtfT3qodvG6XDBaaYuSGV5ap6l+thgP2aPT6Jb22Z13JmIiIiIiIiIiEjtUdBcRERERERERESavEudNvrza9ry2JDYK5o22ibAiz8OieF3N3Vk0Y7DvLM+k+Qca1PUAXJOnuaZpen88/M9PD4sltt6tm1UU4ybgvTcQibMSWZr1klL9dd1bMmMsYm0b+lTt41ZcH10K5Y/fD3//HwPr3y511LQ9xwXm8G9fSN4eGA0Ph56Crm+uLrY+MstnWjh68G/vtx70bq53+ZwvLicV37RDW935/z6fJVxlEnzUsg6bu1Eh1u7h/Hk8Dia+7jXcWdndG3bjAW/68s7Gw7wwopdFJdX1Vi/Yd8xS+s293Fn0og4xnWtu7D8pfJyd2F8tzDGdwsj7XAh0xfutHR/Pv4mmyUph3lkUDR3XNsOVyc/saE+VdlN3lmfyfMWvncAvNxc+MOgKO7p297pTxARERERERERERH5Med8FlpERERERERERKQWOMO0UU83F27pHsbN3ULZmnWSd9ZnsiT5MJUWpqoDFJZWMnFuCv/bcpBZ45KIa+Nfa71J9U6XV/HS6t28vmafpa9TCx93Jo+MZ0yXEKcJl8KZ771HBscwuksoE+cms9HCNONOYQHMHpdEYmhAPXQoP2YYBk8Mi6WlrzszF6ddtO7LjKPc/tpG3rq7J83qKZxtxZGiUmYuSmPB9kOW6ju08mHWuCSujWxRx51dyNXFxi+va8/wpGCmL9zJ0pTcK1rvZz3DeWJYLIHezvP1+LG4Nv58cP81LE4+zKzFaRx2cPJVUWkl0xbu5KNN2UwdnUDvDvX/dXI2yQcLmDA32fKJYzfFBjF9TAJhzbzruDMREREREREREZG6oaC5iIiIiIiIiIg0Oc44bdQwDLq3a0b3ds2YNCKO9zdm8f7GLPJPlVnaf2vWSUb+bS33XdeehwZGOe0k48bu811HmDwvhYMnTluqv61XOI8Pde5waccgXz761bX8b2sOs5ekcby4/IIaH3cXHhsSwx29I3DR5PwGd1+/SFr4uvPYJzsuerLDtuyT3PrqBt69txchgV713OEP2e0mH27O4s9L0yksrXRY7+5q4/f9O/KrGyLxcHWphw4vrk2AF6/8ojufpx9h8nzr//fP6Rjky+xxSfRq37yOOqxdhmEwslMIN8UG8c/P9/Lvr/ZRXmWvcZ9deUXc9trXjOzUhokj4mgT0LDfbw3hVFklz6/YxTvrM7Fynlhrfw+mjkpgaGKwU52AJCIiIiIiIiIicqn0apSIiIiIiIiIiDQpyQcLeHLuDlJyCi3V3xQbxLTRCYQ3r79po0H+nvxhUDS/7d+RJcmHeXt9JtuyTzrcr8pu8upX+1i04zDTxyQwIK513Td7lcgrLGX6wp0sTj5sqT4qyJfZ45PoGdF4wqW3dA9jQGwQzyxN55Mt2d+FJYcktGbq6ISrMjzqzMZ1DSPQ253/+89WTldUf8LMniOnuPmV9bx7by+iWvvVc4dnpOcWMmFOMluzTlqq79uxBTPHJtG+pU/dNnaJ+scGsTLyBv7++W7+/dU+KqpqThN7uNp4cEAU9/eLxN21bk5Qqkve7q78cUgMt3QPY8ainaxOP+Jwn0U7DrM67Qi/u6kj9/Vr3+AnCdQH0zRZnprH1AWp5BbWPAEewDDgrt4RPDo4Gj9Pt3roUEREREREREREpG4paC4iIiIiIiIiIk1CY5w26u5qY2zXUMZ2DWV79kneWZ/Joh2HHU6XzTl5ml++840CwrWgym7ywcYD/GXZLorKHE9ibuzh0mY+7vz5lk48PCiKXblFtGvh43SBX/le/5gg3r//Gu59ezMnSyqqrTlcUMqtr27gjbt60r1ds3rr7XR5FS+t3s3ra/ZddOr6+Vr4uDN5ZDxjuoQ47YRnL3cXHhsSy9guoUycl8Km/cerresX1ZIZYxKJaAL/dyJa+vDG3T35PP0I0xamknmspMb60xVVPLt8F598k81To+K5KbbpnvCUc/I0U+ansiotz1J9Qog/s8cl0Tk8sG4bExERERERERERqUcKmouIiIiIiIiISKPWVKaNdg4P5IWfduHJ4XG8tyGTV7/aR1llzYHz5al5rN2dzyODY7irdztcXRpf8LkhpR4qYMLcFLZbmCYPcH10K2aMSaBdi8YfLm0T4KUTFBqJbm2b8emve3PHG5s4XFD9z7iTJRX8/PWveeXn3ekfG1TnPX2+6wiT56Vw8MRpS/W39Qrn8aGxBHq713FntSOqtR8f/+pa/rc1h2eWppF/qhyAID8PJo6IY3Rn5w3LX67+sUH06diCN9bu52+r91x0iv45mcdKuPftb7gpNoinRsY3idD9OZVVdt5en8kLKzMoKa/53wHA292FRwZFc3efCD0Oi4iIiIiIiIhIk6OguYiIiIiIiIiINFpnpo2msCrtiKX6+Db+zB6fRBcnnjbays+DRwbHcHP3MCbPT+WrjKM11heXVzFj0U7mbD2oSaoWFZdV8uKqDN5cl0mVhUnMLX09eGpUPKM6tWly4VJpHDoG+fG/3/Thzjc3sefIqWprSivs3PfuNzx7SyfGdwurkz6OFJYybdFOFu84bKk+KsiX2eOT6BnRvE76qUuGYXBL9zDGdQ1lW/ZJbAZ0CgvExdZ0fwZ4uLrwfzd2ZFzXUGYvSWfh9kMO9/ks/Qhrd+dz//Xt+W3/jni7N+6XnbZln2TCnGR2Hi60VD8wrjXTxiQQGqgTd0REREREREREpGlq3M/4iYiIiIiIiIjIVelqmDbaroUP79zTk0U7DjN90U6OFpXVWJ96qJCx/1zHHde2449DYvB3omntzmTVzjymLEgl56TjScyGAbf3asufhsYS4KV/T2lYIYFefPJAb+59ZzPfZp2stqbKbvLIf7dz7FQ5918fWWvHrrKbfLDxAH9ZtouiskqH9R6uNh4cEMX9/SJxd20cP3MvxsVm0L1ds4Zuo161CfDib7d15fZebZm2MJX03KIa68ur7Pzj873M2ZrDhOFxjGyEJ+UUllbw/PJdvPv1AUzH5x8R7O/JtDEJDEkIrvvmREREREREREREGpBTB80Nw9gAvAp8bJqmtWtwioiIiIiIiIhIk3ap00YHxbdm6ujGOW3UMAxGdQ7hhphWPLd8F+85CMCZJry74QDLUnJ5alQ8I5IaX9ivrhwuOM20BTtZlpprqT422I/Z45Po1vbqCpiKc2vm4877913D/72/lS92XfxqB7OWpJF/qownhsVe8c+AnYcKmTA3mW3ZJy3VXx/dihljEmjXwueKjisNr3eHFiz6/XX85+sDvLAyg8LSmk8yOFxQyu8//Jb3Nx5g2uhEYoL96qnTy2eaJktTcpm6IJUjDk7oArAZcHef9jwyOBpfD6d+iU1ERERERERERKRWOPuzYNcAvYAXDcP4AHjdNM2tDdyTiIiIiIiIiIg0gMLSCkth63PaBHgydXTTmDbq7+nG9DGJjO8WZilkf6SojN998C2fRB9kxphE2rbwrqdOnU+V3eTdDZk8t3wXxRam33u62fjDwGjuva49bo1k+r1cXbzdXXntzh48/ukO5nybc9G6V7/ax7Hicp4Zn3RZV3IoKa/kxVW7eWPtfqrsjn/otvT14KlR8YxqhNOs5eJcXWzc3bc9ozqH8OzyXXz8TbbDx+Cv9x1n+Mtr+MU1bXnghg6EOOmJXtnHS3hqfgqf13DSxvmSQgN4enwSiaEBddyZiIiIiIiIiIiI83D2oPk5/sADwAOGYWwD/g18YJpmzddrFBERERERERGRRu9ypo3e1SeCRwfHNLlpo13CA1nwu768vT6TF1ZmUOIgOP1lxlEG/fVLHhwQxf39InF3vbqC0yk5BUyYm8yOgwWW6vvHtGL6mETCm1+9wXxpHNxcbDx3a2da+Lrz2pr9F637dMtBThSX8/fbu+Hl7mJ5/dVpeTw1P5Wck9YuNPrza9ryp6GxBHi5WT6GNC4tfD145uZO3NarLU8tSGW7gwn3VXaTdzYc4D8bsxiaGMy9fdvTrW2gU5yEUFFl5821+/nrqgxKK+wO633cXXh0cAx39YnAxdbw/YuIiIiIiIiIiNSnxvBKmwGY533cFfgn8JxhGP/lzJTzDQ3VnIiIiIiIiIiI1B1NG72Qq4uN+/pFMjypDdMWprI8Na/G+rJKO88u38W8b3OYNS6JXu2b11OnDedUWSUvrMjg7fX7sTCImSA/D6aOTmBYYrBThCBFrLDZDCaOiKelrwdPL02/aN3q9CPc8cZG3rirJwHeNQfBcwtKmbYwlaUpuZZ6iA32Y9a4JLq3a3ZJvUvj1Tk8kLm/6cOnWw/yl2Xp5J8qr7G+ym6yeMdhFu84TOfwQO7tG8HwpDYNdsWILQdOMHFuMum51uYYDU0IZsroeNoEOOdUdhERERERERERkbrm7EHzzsCvgJ8DgWdvMzkTOPcB7gbuNgwjjTNTzt8zTfNE/bcpIiIiIiIiIladKC4n0NtNYU6p0eVMG31sSAx39L56po2GBHrx6h09WLUzjykLHE8e3n3kFD95dQO3dg9jwvA4rM82blyWp56Zfn+4oNRhrWHAnde249EhMfh7ahKzNE4P3NCB5j7uPDEnmaqLnFnxzYET3Prqet699xqCAzwv2F5lN3lvQybPrcjgVFmlw2N6utl4eGA0v7yufYMFhqXh2GwGP+kRzpCEYF5atZt3NmRe9HvvfNuzT/LQR9uYvSSNO3tHcFuvtjT3ca+HjqHgdAV/WZbOB5uyMC2cgBQa6MW00QkMjG9d982JiIiIiIiIiIg4MacOmpummQz83jCMx4BbgV8C15/bfPa9AcQDfwWeMQxjDvCaaZpf1ne/IiIiIiIiIlK9KrvJBxsP8Na6TPblFxPk58GkkfGM7hzS0K2JE9K00UszML41fTq24KVVu3l97X6HYb9PthxkVVoej9zUHj/zTNi6Kcg5eZop81NZlVbzhPdz4tv4M3t8El3CA+u2MZF6cGuPcJp5u/PbD7ZSVln9yTkZeae4+ZX1vHNvLzoG+X53e0pOARPmJrPjYIGlY/WPacX0MYmEN/euld6l8QrwcuOpUfH8tGc4UxeksmHfMUv75RWW8ezyXby8ejfju4VyT9/2RLf2q5MeTdNk0Y7DTFu4k/xTZQ7rXWwGv7yuPQ8NiMLHw6lfQhMREREREREREakXjeJZMtM0S4H3gPcMw4gG7gfuAILOlXAmcO4J3AbcZhjGHuA14B3TNK1dW1lEREREREREal3qoQImzE1he/bJ7247UlTGQx99i5ebC4M0KVLO0rTRy+ft7sqTw+MY2zWUiXOT2Zp1ssb6EyUVTF6UQUd/F25tX0VwI86LVlbZeXt9Ji+szKCkvMphvbe7C48MiubuPhG4ahKzNCED41vz/n3XcO/bmyksrX4qec7J09z6r/W8dU8vooJ8eWFlBm+t24+FYdQE+XkwdXQCwxKDdVUS+YGYYD8+uP8aliTnMmvxTg5ZuKIEQFmlnQ83ZfPhpmz6RbXk3r7tuSG6FbZaujJJ1rESJs1P4asMay8RdQ4PZPa4RBJCAmrl+CIiIiIiIiIiIk1Bowian880zQzgMcMwngTGAvcBAzkTND9/ynkU8GdgpmEYC4DXTdNcUf8di4iIiIiIiFydissqeXFVBm+uy6x2wrJpwsS5yVwT2Rx/T7cG6FCchaaN1p64Nv58+us+fLQ5m2eWpl00bHrOnkKDv+xwYUCISe+KKvzrqc/asj37JE/OSWbn4UJL9QPjWjNtTAKhgVfn9Htp+npENOeTX/fhzjc3kldY/c/TEyUV3P7a1wR4uXHYQiDYMOCOa9vxxyExeryWizIMgxGd2nBTbBCfbsnmrfWZ7DtabHn/NbvzWbM7n8iWPtzdN4Kbu4Vd9mN8eaWd19bs4+XVuy864f98fh6u/GloDLdf0w6XWgq5i4iIiIiIiIiINBWN9pU40zQrgU+BTw3DaMuZwPndQNi5Es4Ezt2Bm4GbDcPIAl4H3jJN81C9Ny0iIiIiIiJylVidlsdT81PJOXm6xrojRWX8eWk6s8Yl1VNn4mw0bbT22WwGt1/TlkHxrZm1eCfzttX8NFiVabAix2DHq1t4cngcozuHOP204sLSCp5fvot3vz5gafp9sL8n08YkMCQhuO6bE2lgMcF+/O83fbjzzU0XDfqWlFdZugJAXBt/nh6fRJfwwFruUpoqL3cX7ugdwc+vaceXu4/y5tr9rNmdb3n/ffnFPDU/lWeX7+K2Xm25s3c7wppZv+zGN5nHmTA3mYy8U5bqR3Rqw1Mj42nt72n5GCIiIiIiIiIiIleTRhs0P59pmlnAU4ZhTAWGAfcDw/n+/p17ZawdMB2YYhjGUuA1YIlpmo5HWoiIiIiIiIg0kMoqO+m5RYQEetHcx72h26lRbkEpUxeksiw11/I+72/MYmzXUHpGNK/DzsTZaNpo3Wvl58GLP+vKLd3DmTw/hf35NU+WzS0s46GPtvHO+kwmj4yna9tm9dSpdaZpsjQll2kLUy86rfl8NgPu7tOeRwZH46vp93IVCWvmzae/7sM9b21i+8GCS97fy82FRwdHc3efCFxdbHXQoTR1NptB/5gg+scEsTuviLfWZzJn60FKK6y9HFNUWsm/v9rH62v2MTQxmHv6tqdHu2YXPRHqZEk5f16Wzoebsi2tH9bMixljE+kfE2T5PomIiIiIiIiIiFyNmtSrK2cD44uBxYZhBHNmwvl9QCRnJpzDmdC5KzDy7FuOYRj/Av5tmqb1sRqNwNng/ZQrWOId0zTvrp1uRERERERE5FJVVtl5e30m//xiL8eLywHoGdGMu/u0Z0hCa6cKflXZTd7dkMnzKzI4VVZ5yfs/8b8dLHmoHx6uLnXQnTgbTRutX9dFtWTpQ/145Yu9vPLFXsqrag75bc06ybh/rmdc11D+NDSGNgFe9dRpzbKPlzBlQSqfpR+xVJ8UGsDT45NIDNX0e7k6Nfdx54P7r+XX/9lySROlB8YFMW1MIqGBzvF/Xxq/qNZ+zB6XxGODY/hwcxbvrj9AbmGppX3tJixJzmVJci5JoQHce10EI5JCcHc983uwaZrM33aIGYt2cuzs78s1cbUZ3NcvkocGROHlrt87RUREREREREREHGlSQfPzmaaZaxjGVmA7Z4Lm320672MDCANmAE8ahvE3YIZpmjVf11tERERERESkjm3PPsmEucmkHir8we2bM0+wOfMEIQGe3NE7gp/1DKdZA085T8kpYMLcZHZcxsTUc/YeLeYfn+/lkUHRtdiZOJvLmjY6JpH+sZo2eqU83Vz4w6BoRncJYfK8FNbvPeZwn7nf5rA05TC/vqEDD1zfocECeRVVdt5cu58XV+3mdEWVw3ofdxf+OCSGO3tHaPq9XPV8PFx5466e/PGT7SzYfqjG2mB/T6aOTmBIQuuLTo0WuRLNfNz5vxs7cn+/SJam5PLG2v1szz5pef/knAL+8PF2Zi9J585r29EvuhXPLd/F2j3WTqTo1jaQ2eOTiA32v8x7ICIiIiIiIiIicvVpckFzwzBCgHvPvrU7e/OPw+XnM8/e5gM8DvzEMIzRpmnurOteRURERERERH6sqLSC55bv4t2vD2CaF687VFDKn5el8+KqDMZ1DeXuvhH1Hpo5VVbJCysyeHv9fuw19HpOkJ8Ht/YI4x+f7612+ytf7GFkpzZEt/ar5U6loZmmyYLtZ6aN5p/StNGG1KGVL+/fdw1zv81hxqKdnCipqLG+tMLOi6t28/HmbB4fGsvoziHY6jG8vTXrBBPmJJOeW2SpfmhCMFNGxzvNFHYRZ+DuauPFn3ahuY87b6/PvGC7zYC7+kTw6OAYfD2a3EsG4oTcXGyM7hzC6M4hbM06wZtr97M0JZcqK79QAkeLynh+ZQbPr8ywVO/v6crjw2K5rWfben0MExERERERERERaQqaxLPGhmHYgBHA/cBQwIXvA+XmeR9nAq8BbwFxZ+vHAR7n1UUCqwzDSDJN0/Fop8blj5yZ8G5VzSNuREREREREpNaYpsnSlFymLUwlr7DM8n5llXY+2pzNR5uz6R3Zgnv6RjAgrnWdT/FdnprL1AWpHC4odVhrGHDnte14dEgM/p5u5Jw4zbxtF/7JWVFl8uScZD55oLdCQE1IZn4xk+alaNqoEzEMg/HdwugZ6sXED9ezNtfAfsFshh86XFDKwx9v4+31mTw1Kp5ubZvVaY8Fpyt4dnk672/MqvGkm3NCA72YNjqBgfGt67QvkcbKZjOYMiqe0EAvnluxi7JKOwCdwgKYNTaJpLCABu5Qrlbd2jaj2+3NOHTyNO9uOMCHm7IoOF3zSVCXYkyXECaNiKeVn0etrSkiIiIiIiIiInI1adRBc8MwIoD7gLuBNudu5kxo/Fxw3A4sAl4Flpvmdy9N5QKfG4bRAvjD2TfPs/u0Pvv5pPq4H/Voi2maXzR0EyIiIiIiIvJD2cdLmLIglc/Sj1zROhv2HWPDvmOEN/firt4R3NojnAAvt1rq8oxDJ08zZUEqK3fmWaqPb+PP7PFJdAkP/O62ySPj+TLjaLWTlLccOMH7Gw9wR++IWupYGkpZZRX//nIff/t8D+VnA4018fN05QlNG61XAV5u3NzeTt/WMP+AjZ0nbQ732ZZ9kvH/XM+YLiE8PjSWkMDanRxumiaLdhxm+qKdHC1yfNKNi83g3r4RPDwwGh9NYhapkWEY3H99JKM6h5B6qICWvh50CgvAMPQzVxpeSKAXTwyL5cEBHZmzNYe31u1n79Hiy16vbXNvZo5N5ProVrXYpYiIiIiIiIiIyNWn0b36YhiGK2emkN8P3MSZYPj508s5+3kO8DrwmmmaF53MfXZq+STDMN4H1gP+Z/cfRdMLmouIiIiIiIgTqaiy8+ba/by4ajenK6pqbd3s46eZuTiN51dkcHP3UO7u056OQb5XtGZllZ2312fywsoMSsod9+rt7sIjg6K5u08Eri4/DK+28PVg8sh4Hvlv9Rfd+vOyXQyMb02bgNoNsEr92bjvGBPmJlsOiI3uHMKkkXEE+XnWcWdSnWBveCDOjlvbJF74/AB7jpxyuM/8bYdYnprLr67vwK9viMTb/cqfZsw6VsLk+Sl8mXHUUn3n8EBmj0skIUSTmEUuRXCAJ8EB+nkrzsnb3ZVfXNuO23u1Zc2efN5cu9/y4wKAm4vBA9d34Hc3dcTTzaUOOxUREREREREREbk6NJqguWEY0ZwJl98JtDx3Mz+cXm4CyzgzvXyRaZqOx2WdZZpmmmEYfwcmnr0pspZaFxEREREREbnA1qwTTJiTTHpukaX6IQmt8fd0Y/72Q5amQwOcrqjiP19n8Z+vs+gX1ZJ7+7bnhuhWlzwtenv2SSbMTSb1UKGl+oFxQUwbk0hoDZOOx3UNZe63OazZnX/BtlNllTw1P5V/39FdU1YbmRPF5cxeksYnWw5aqte0UedyXYfmDO7Ulg82ZfHCygxOVnPVgfOVVth5efVuPt6cxeNDYxnbJfSyptFXVNl5bc0+Xlq1mzIr0+89XPnT0Bhuv6YdLpp+LyLSJNlsBjdEt+KG6FbsOVLEW+sy+d/Wg5RWXPxxomdEM2aPSyKqtV89dioiIiIiIiIiItK0OXXQ3DAMD+BWzgTMrzt389n3508vzwXe5Mz08gNXcMit533sfQXriIiIiIiIiFSr4HQFzy5P5/2NWZim4/rQQC+mjU5gYHxrAJ4YFstHm7N5d0MmeYVllo+7Znc+a3bnE9HCm7v6RHBL9zD8PN1q3KeotILnlu/i3a8PWOo12N+TqaMTGJLQ2mFA3DAMZo1NYvCLX1YbGFq5M49lKbkMS2rj+MDS4EzTZM7WHGYtSeN4cbnDelebwQM3RPL7m6I0bdTJuLrYuLN3BGM6h/LS6t28uyGTSnvNPwDyCst45L/beWd9Jk+Niqd7u+aWj/dN5nEmzE0mI8/xFHWAEUlteGpUPK39NY1ZRORq0THIj1njknhsSAwfbjrze/DhgtLvtgd4uTFheCy3dg+/rBOeRERERERERERE5OKcOmjOmQC5/9mPfzy9HGA18C9gvmmalbVwvHOj2Sy8fC4iIiIiIiJinWmaLNpxmOmLdnK0yHFA3MVmcG/fCB4eGI2Px/d/vrfw9eC3/Tvyq+sjWZaSy1vr9rM166TlPjKPlTBt4U6eX5HBLd3DuLtPBBEtfS7odVlKLlMXploKs9sMuKtPBI8OjsHXw/pTDW1bePPIoGhmL0mvdvtTC1Lp07ElAV41B+KlYe09eopJc1PYsO+YpfqeEc2YNS6JaE0bdWoB3m48NSqen1/bltmL01idfsThPtsPFnDzKxsY2akNTwyLJazZxec4FJRU8MyydD7clGWpn9BAL2aOTaR/bJDl+yAiIk1LoLc7v7mxA/f1a89XGUdJySmkTaAnQ+KDCfDW74siIiIiIiIiIiJ1wdmD5gH8MPRtAPnA28C/TdPcU0fHPRdqFxEREREREbliWcdKmDQ/ha8yjlqq7xweyOxxiSSEBFy0xs3FxqjOIYzqHML27JO8vT6TRTsOUVFl7c/ZU2WVvL0+k3c2ZNI/Joh7+kZwXceWHDxxmikLUvnMQqgUICk0gNnjkkgKu3ivNbm3b3sWbD9ESk7hBduOFpXxzNJ0nh6fdFlrS90qrajilS/28soXeymvunAq/Y9p2mjj1KGVL2/c3ZM1u48yY9FOS5PHF+04zMqdedzfL5Lf3NjhByfLmKbJgu2HmLFoJ/mnHE+/d7EZ3NevPQ8NiMLb3dmfyhQRkfrg5mJjQFxrBsS1buhWREREREREREREmrzG8OrMuVce13Bmevn/TNN0/CrU5VkLtK+jtZ2CYRgeQCTQAqgAjgGHTNMsadDGREREREREmqCKKjuvrdnHS6t2U1bpOIjr5+HKY0Nj+Pk17XC5hCBu5/BA/vrTLjw5PJb3v87i/Y1Z5J9yPIkcwDThs/QjfJZ+hMhWPhw+WcrpiiqH+/m4u/DHITHc2Tviknr9MVcXG8+M78SYf6yjyn5hSP7DTVmM7RLCNZEtLvsYUvvW781n0twU9uUXW6of3zWUCSPiaOnrUcedSV3pF9WKJQ/246PN2bywMoPjxTU/PVdWaefvn+/h42+y+dOQGG7uFkbW8RImz09hze58S8fs2jaQ2eOSiGvj77hYREREREREREREREREap2zB80LgHeAV03TTKvrg5mmWQYcqOvjNKB/cCZk7vmj2ysNw9gCLAX+aZqmtRF7l8EwjCCg1SXu1uH8T06dOkVh4YWT7qR+FBcX1/i5iIiINA16zBe5ct9mFzB96R725ls7r3dwbEv+NCiSID8Pik8VXdYxPYFfXhPMHd2DWJGez/ubc0g97Hj68Dn7jlr7vz4gugWPD+5AsP/l93q+tn4Gv+gZwjsbc6rd/qdPt/Ppfd3wcLVd8bHkyhwvLuf5z/azMNnaxPu2zTyZNLQj17ZvBvYyCgutnQAh9eNyHu9Hxzfjxsju/HtdFh9sPkRlNSeInO9oURmPfbqDf32xh+wTpym3cNUFPw8XHu7fnpu7BmMz0PNAIiIiV0h/44uIiFwd9JgvIiLS9Onx/up06pT113vrgmGa1i6p3RAMw/A0TbO0oftorAzDmApMucTdTgPPAdNM03Q8wq5+evqBl19+mbZt29ZOQyIiIiIiIrWsuAIWZtnYcMRaKLq5h8kt7e0kNKv9v89NEzJPwZeHbWw/ZmDn8iePAzRzP9NrYvPa77W8Cp7Z7sKxsup7HBxqZ0Rbx1PhpW6YJmw8ajD/gI2SSsffRy6GycBQk0Ghdtx0fkCTdeQ0LDhgI/lE7X2Ru7WwMy7Cjr97rS0pIiIiIiIiIiIiIiLSaGVlZfHggw+ef1OiaZqp9XV8p51obhhGV+AOw/juxcsPTNP8pgFbulp4AZOBfoZhjDJNs2FPhRAREREREWkkTBO25BvMzbRxykIQ14ZJ/xCToWF23F3qpifDgPZ+0N7PzskyWJdnY12eQbGF/n7c6w1tTIaF2/Goo17dXeAnkXZeSav+AKsOGXRtCSHedXN8ubjcEvjvPhf2Fln7vunob/KTyCpae9VxY9Lggrzgvlg7uwpM5mbaOFxy+SeztPAwuTXSTlyg8w7FEBERERERERERERERudo4bdAcuAF4GDCBKuDpBu2m8TKBDcBiYBOQBhwH7EALoBswEriLM1dZP+dG4CPDMMbUxWRzERERERGRpuToafjvfhsZBdam+kb4ngnihvrUcWPnCfSAEW3tDAqFrccMvjpsI8dCKLTd2V7D6qHX2ECTnq3sbD564b+j3TT4eK8LDyVWYbuywexiUXkVrMyxsfqQQZXp+B/dx9VkTDs7vVqZGPoaXVViAkz+1KmKr48YLM6ydrLNOTbDZECIyeDQujvpRkRERERERERERERERC6PYZrOOSXIMIwngNlnP800TTOyIftpjAzD6APkm6aZYaE2DPgI6PujTQ+apvm3WuwpCGh1ibt1AOaf++Trr78mLi6utlqSS1RcXMymTZu++7xXr174+NRjOkZERETqhR7zRawpr7Tz9tcH+fe6LMqrHP997efhwsP923Nz12BsDZzENU2TLdmFfLA5h88yjmH/Ufu+Hi48eEMEt3Zrg0s9JrtPlFQw9tVvOHG6strtTw7uwG09Quqtn6vVhv0nmLlsD9knSi3Vj+nUmkduak8zb7c67kxqU1083heVVvL6+mz+szmHCgc/F7uG+TN5WEc6ttLvGCIiInVJf+OLiIhcHfSYLyIi0vTp8f7qlJaWxrXXXnv+TYmmaabW1/GdeaJ57tn3JnCkIRtprEzTXH8JtQcNwxgIfAb0Pm/TJMMw3jBNs6SWejrCJX49jR+FL3x9ffH396+NdqQW+Pj46OshIiJyFdBjvsiFNu47xoS5yew9WmypfnTnECaNjCPIz9NxcT25KSCAmxLDOXiihPe+PsCajHwKTlcwMC6I/+vfkdb+9d+rvz9MGZ3Iwx9vq3b7y19kMqpbO0ICveq3savE0aIyZi7eyfxthyzVR7byYdbYJHp3aFHHnUl9qI3He39/mDK2OXf368jTS9JZlpp7YY2nKxOGx/GTHuHYdIkCERGReqe/8UVERK4OeswXERFp+vR4f3Xw9fVt0OM7c9D8/Fc0mzdYF1cR0zRLDcO4E0jj+++NIGAwMK+h+hIREREREXEmJ4rLmb0kjU+2HLRU37a5NzPGJnJD9KVe3Kn+hDXz5slhcTw5rKE7OWNMlxDmfJvDVxlHL9hWXF7FU/NTeO3OHhecmCyXz243+WhzNs8sTaOwtPpp8udzd7Xx2xs78usbI/FwdamHDqWxadfCh3/d0Z0Ne4/x8urdbDlwAm8PF4YlBvPIoBha+Xk0dIsiIiIiIiIiIiIiIiLigDMHzdcBJYA30N4wjOamaR5v4J6aPNM09xiGsQAYf97NCpqLiIiIiMhVzzRN5mzNYdaSNI4Xlzusd7UZPHBDJL+/KQpPNwVxL4VhGMwam8jgv37F6YqqC7avSjvCkuRcRnRq0wDd/VB5pR13V1tDt3FFduUWMWFuMlsOnLBU36dDC2aOTSSyVcNOT5DGoXeHFvTu0ILSiircXGy4aIK5iIiIiIiIiIiIiIhIo+G0QXPTNIvPBp5/BtiAO4CXGrarq8Zqfhg0j2moRkRERERERJzB3qOnmDQ3hQ37jlmq79GuGbPHJxHd2q+OO2u6wpt78+jgaGYuTqt2+5QFqVzXsSUB3m713BlUVtl57+sDfLw5m91HTtE5LIC7+7ZnVKc2jWrK+unyKl7+bDevfbWPSrvpsL6FjzuTRsYxtktoo7qf4hx0wo2IiIiIiIiIiIiIiEjj47RB87NmcCbw7AZMMQxjiWmauxu4p6tB9o8+d97ru4uIiIiIiNSh0ooqXvliL698sZfyKrvD+gAvN54cFstPeoRj09TeK3Z3nwjmbztEck7BBdvyT5Xx9NI0nrm5U732tOPgSZ6ck0zqocLvbtuadZKtWd/y4cYspo1JaBQnGHyx6wiT56eQffy0pfqf9QzniWGxBHq713FnIiIiIiIiIiIiIiIiIuIsnDpobppmmmEYvwTeAQKBLwzDuMs0zVUN21mTV/Gjz+t/PJyIiIiIiEgDW783n0lzU9iXX2ypflzXUCaOiKOlr0cdd3b1cHWx8czNSYz++zqqqpm4/dHmbMZ2DeXayBZ13ktRaQXPr8jg3Q2ZXGz494Z9xxj+0hru6RvBQwOj8fVwvqddjhSWMn3RThbtOGypPirIl1njkujVvnkddyYiIiIiIiIiIiIiIiIizsb5XvH8EdM03zcM4zjwJtAGWG4YxlrgQ2ATsB8oNE2zqgHbbGqCf/T50QbpQkREREREpAEcO1XGrCVpzNmaY6k+ooU3M8cmcV1Uyzru7OqUEBLA/f0i+deXe6vd/uScZJY+1A9PN5c6Ob5pmixLyWXqwlTyCssc1lfaTV5bs5/52w4xcUQcozuHYBgNP93ebjd5f1MWf1maTlFZpcN6D1cbDw6I4v5+kbi72uqhQxERERERERERERERERFxNk4dNDcM48fhcRMwgOvOvp1feylLm6ZpOvV9b2DX/ejz7AbpQkREREREpB6Zpskn3xxk9tI0Tpb8+EJPF3JzMfjNDR34v/4d6yzkLGc8PDCKpSmHOXCs5IJt+/OL+dtnu3lsSGytH/fgiRKmzE9ldfqRS973SFEZD320jQ82ZjF9TCIxwX613p9VOw8VMmFuMtuyT1qq7xfVkpljE2nXwqduGxMRERERERERERERERERp+bsYevz0+Pmj943/DiwJsgwjEDg5h/dvLoBWhEREREREak3e44UMWFuCpv2H7dU36t9c2aPS6RjUMOFh68mnm4uzB6XxM9f31jt9le/3MfITiHEtfGvleNVVNl5a91+/rpyN6crruwCahv3H2f4y2u4u08EDw+Mws/TrVZ6tKKkvJIXV+3mjbX7qbKbDutb+noweaTzTGEXERERERERERERERERkYbl7EFz+H6KeW28wnluLbm454DA8z4vB5Y2TCsiIiIiIiJ1q7Siin98vod/fbmXiirHQdxm3m5MGB7HLd3DFMStZ307tuSW7mF8uuXgBdsq7SZPzElmzm/64GK7sq/L1qwTTJiTTHpukaX6ts29yTp+4aT181XZTd5Yu58F2w8xcXgcY7rUfZB7dVoeT81PJefkaUv1t1/TlseHxBLgXX9BeBERERERERERERERERFxbs4eNP+K7yeYyyUwDOMJYKVpmlss1rsCfwZ++aNN/zJN83Bt9yciIiIiItLQ1u7OZ9K8ZDKP1RwSPueW7mFMGB5Hcx/3Ou5MLmbi8Dg+Tz/CseLyC7Ztzz7Juxsyuadv+8tau7C0gmeX7eI/Gw9gWngmIjTQi2mjExgQF8SiHYeZuXgneYVlNe5ztKiMhz/exgebspg+JoHY4NqZwH6+3IJSpi1MZWlKrqX6mNZ+zB6fSPd2zWu9FxERERERERERERERERFp3Jw6aG6a5o0N3UMjNhR42jCM9cB/gdVAummalecXGYYRAAwH/gR0+dEae4Hpdd+qiIiIiIhI/ck/VcbMRTuZt+2QpfrIlj7MHJdInw4t67gzcaSZjztPjYrnoY+2Vbv92eW7GJwQTGigl+U1TdNkcfJhpi3cydGimoPiAC42g3v7RvDwwGh8PM48rTKqcwj9Y4P42+rdvLF2P5X2mpPqm/YfZ8TLa7mrdwQPD4rC3/PKp4hX2U3e25DJcysyOFVW6bDe083GwwOj+eV17XFzsV3x8UVERERERERERERERESk6XHqoLnUij5n3wDKDMM4CBQAVUALIAKo7hXlXGCYaZrH6qNJERERERGRuma3m3z8TTZPL0mjsNRxENfdxcZv+3fk1zdG4uHqUg8dihWjO4cw99scvth19IJtJeVVTJ6Xwht39cAwDIdrZR8vYdK8FL7MuHCt6nQOD2T2uEQSQgIu2Obr4cqTw+O4tUcYT81PZf3emv+crrKbvLluPwu2H2LC8FjGdQ211HN1UnIKmDA3mR0HCyzV3xjTihljEglv7n1ZxxMRERERERERERERERGRq4OC5lcXD6CDhbolwD2maR6p435ERERERETqxa7cIibOTeabAycs1ffp0IKZYxOJbOVbx53JpTIMg5ljExn8168oKa+6YPtn6UdYtOMwozqHXHSNiio7r63Zx8urd1NaYXd4TD8PVx4bGsPPr2mHi63mMHjHID/ev+8aFicfZuaiNHILS2uszz9VxiP/3c6Hm7KYPiaRuDb+Dvs5p7iskhdWZvDWuv04GKIOQCs/D6aOSmB4UvBlh9pFRERERERERERERERE5OqhoHnTNQtIA/oBsYCj8XungKXA303T/KqOexMREREREakXp8urePmz3bz21T4qLSRxm/u4M2lE3BVNl5a6F9bMm0cHxzBj0c5qt09bmEq/qJYEertfsG3LgeNMmJPCrrwiS8cakdSGp0bF09rf03J/hmEwslMI/WOC+Ntne3h9jePvv82ZJxj5t7XccW07/jAomgAvtxrrV6TmMmVBKocLag6yn+kH7ri2HX8cEoO/Z83rioiIiIiIiIiIiIiIiIico6B5E2Wa5kpgJYBhGN5APBABtAF8ARtwEjgB7ASSTdO8cBSciIiIiIhII/VlxlEmzUsm+/hpS/U/7RHOE8NiaeZzYThZnM/dfSJYsC2H7QcLLtiWf6qc2UvS+Mstnb+7raCkgmeWpfPhpixL64c182LGmET6xwZddo8+Hq48MSyWW7qHMXVBKmv35NdYX2U3eXt9Jot2HOLJYXGM73bhCQ+HTp5m6oJUVuzMs9RDXBt/Zo9LpGvbZpd9P0RERERERERERERERETk6tSog+aGYbgCrYFmgB9QxJngdJ5pmpUN2ZszMU2zBPjm7JuIiIiIiEiTdqSwlOmLdrJox2FL9R2DfJk9Lole7ZvXcWdSm1xsBk+P78Tov6+tdlr4f785yNiuofSObMGC7YeYsWgn+afKLa17f79IHhoQhZe7o4uDWdMxyJf3ftmLJcm5zFy80+EU8vxT5Tz6yXY+3JTF9DGJxIf4U1ll550NB3hhxS6Kyx2fJ+7l5sIjg6K5p28Eri62WrkfIiIiIiIiIiIiIiIiInJ1aXRBc8MwOgD3AdcDXQGPasrKDMPYCnwJvG6a5v56bFFEREREREQagN1u8v6mLP6yLJ2iUsfnHru72njwpo786voOuLsqiNsYxYf486vrI/nnF3ur3f7knGTaNvdmze6aJ4mf07VtILPHJRHXxr822wTAMAxGdGrDjTGt+Pvne3h9zT4qqi4MyJ/vmwMnGPm3NfysV1t2HDxJSk6hpWMNiA1i2pgEwpp510brIiIiIiIiIiIiIiIiInKVajRBc8MwgoF/AqOBc9eNNi5S7gn0Pvv2uGEY84DfmaaZW9d9ioiIiIiISP1LO1zIhLnJfJt10lJ9v6iWzBiTSERLn7ptTOrcgwOiWJJ8mMxjJRdsO3CshAPV3P5jfp6uPDEsltt6tsVmu9hTDbXDx8OVx4fGckv3MKYuSHUYgreb8MHGLEtrB/t7MnV0PEMSgjGMur0fIiIiIiIiIiIiIiIiItL0NYqguWEYg4APgOZ8Hy43z77BDwPn548DM86+jQNuMAzjNtM0V9VxuyIiIiIiIlJPSsoreWnVbl5fu58qe83ToQFa+rozeWQ8ozuHKIjbRHi6uTB7fBK3v7bxsvYf3TmESSPjCPLzrOXOatahlS/v3tuLpSm5zFy0k0MFpZe9lmHAXb0jeHRwNH6ebrXYpYiIiIiIiIiIiIiIiIhczZw+aG4YRl9gHuB19iaT7wPklUA6kA8UAz5ASyAGcPtRfQtgnmEYg0zT3FBf/YuIiIiIiDQmx4vLWZ2Wh7urjR4RzQkN9HK8UwP5LD2PyfNSyTl52lL9bb3a8sTQWAK8FcRtavp0aMlPeoTx328OWt6nbXNvZoxN5IboVnXYWc0Mw2B4UhtujGnF3z/bw2tr9lFR5fiEifMlhvoze1wSncIC66ZJEREREYicgQIAAIR4SURBVBEREREREREREblqOXXQ3DAMH+ATzoTMzwXGAT4C3gS+Mk2zvJr93IF+wC+Bn/L9lHNv4BPDMKJN03R87WwREREREZGrRFllFa9+uY+/f76H8ko7ADYDBsa15q4+EfTp0MJpJoDnFZYybWEqS5JzLdXHtPZj1rhEekQ0r+POpCFNGB7HZ+lHyD91wdMEP+BqM3jghkh+f1MUnm4u9dRdzbzdXfnT0Fhu7h7G1AWprNmd73AfH3cXHhkcw1292+HqYquHLkVERERERERERERERETkauPUQXPgT0Aw34fMc4BbTdP8uqadzobPVwOrDcN4kTNh9bCzm9sAjwHT6qhnERERERGRRuXrfceYODeZvUeLf3C73YQVO/NYsTOPjkG+3NW7HeO7heHj0TB/SlbZTf7z9QGeXb6LU2WVDus93Ww8NCCa+/q1x01B3CYv0NudKaMS+P2H3160pmdEM2aNSyK6tV89dmZdh1a+vHtvL5an5jJ94U4OFZRWWzc4vjVTRycQ4sRXHBARERERERERERERERGRxs/Zg+b38n3I/BjQ1zTNrEtZwDTNTYZh9AO+AVqcXes+FDQXEREREZGr3InicmYvSeOTLQcd1u45corJ81P5y7Jd3Nw9jDt7tyOylW89dHlGSk4BE+cms/1ggaX6G6JbMWNMIm1beNdxZ+JMRnZqw7LUXBbvOPyD2wO83JgwPJZbu4djsznHZP6LMQyDoYltuD66Ff/4fA+vfbWf8qozVxkICfBkyugEhiQEN3CXIiIiIiIiIiIiIiIiInI1cNqguWEYnYBQzgTNTeDxSw2Zn2OaZpZhGE8Cr529KcQwjE6mae6onW5FREREREQaD9M0+d/WHGYvSeN4cfkl7VtUVsnb6zN5e30m10e34q7e7bgxJgiXOgrvFpdV8teVGby5bj9203F9Kz8PpoyKZ0RSGwzDuQPFUvsMw+DFn3YhyM+DZSm5VNlNhiUG8/sBUbT09Wjo9i6Jt7srjw2J5dc3dGDjvuM083GnS3hgnf1fExERERERERERERERERH5MacNmgMJZ98bwGngoytc70PgZeDcdaUTAAXNRURERETkqrL36Ckmzk3m633Hr3itrzKO8lXGUdo29+aOa9vxkx7hBHi71UKXZ6zcmceU+SkcKih1WGsY8Itr2vHHITEEeNVeD9L4uLnYmDIqgSmjEjBNs9GfcODn6cbA+NYN3YaIiIiIiIiIiIiIiIiIXIWcOWgedPa9Cew3TbPkShYzTbPEMIz9QPyP1hcREREREWnySiuqeOWLvbzyxV7Kq+y1unbW8RJmLUnj+ZW7GNc1lDt7RxDXxv+y1zt08jRTF6SyYmeepfrYYD+eHp9E17bNLvuY0jQ19pC5iIiIiIiIiIiIiIiIiEhDcuagued5H5+upTXPH4PXuK6ZLSIiIiIicpnW78ln4rwU9ucXW6of3zWUkZ3b8PHmbFbuzMNuWjtOaYWdDzdl8+GmbHq1b87dfSIYFN8aNxebpf2r7CbvrM/k+RW7KC6vcljv5ebCHwZFcU/f9paPISIiIiIiIiIiIiIiIiIiItY4c9D86Nn3BtC2ltYMP+/j/FpaU0RERERExCkdO1XGrMVpzPk2x1J9+5Y+zBqbSJ+OLQG4KbY1OSdP85+vD/DRpixOlFRYPvam/cfZtP84wf6e/OLatvysV1ta+l78fN/kgwU8OXcHKTmFlta/KTaI6WMSCGvmbbknERERERERERERERERERERsc6Zg+bZ533c0jCMa0zT3Hi5ixmGcQ3Q6rybsi67MxERERERESdmt5t8siWbp5emc9JCONzdxcZvbuzAb27sgKebyw+2hQZ68fjQWB4aEMXC7Yd4Z0Om5TA4QG5hKc+tyODl1XsY2akNd/aJoEt44Hfbi0oreH5FBu9uyLQ0Ob21vwdTRyUwNDEYwzAs9yEiIiIiIiIiIiIiIiIiIiKXxpmD5muBUuDcyLtngP5XsN7T531cdnZ9ERERERGRJmV3XhET56awKfO4pfpr2jdn1rgkOgb51ljn6ebCrT3CuaV7GFuzTvLuhkyWJB+mospCOhwor7Iz59sc5nybQ+ewAO7qE4GnmwvTF+4kt7DU4f6GAXf1juDRwdH4ebpZOqaIiIiIiIiIiIiIiIiIiIhcPqcNmpumedowjBXA6LM3XW8YxhvA/aZp2q2uY5wZcfcv4EbgXAJiuWmajpMMIiIiIiIijURpRRV//2wPr36111L4u5m3GxNHxHNzt9BLmgxuGAbd2zWje7tmTBwRx4cbs3l/4wGOFJVZXmP7wQIe+e92y/WJof7MHpdEp7BAy/uIiIiIiIiIiIiIiIiIiIjIlXHaoPlZU4CRgHH27W6gi2EYfzRN83NHOxuGcSPwLNCNMyFzA6gCnqqbdkVEREREROrfVxlHmTw/hQPHSizV39o9jCeHx9Hcx/2Kjhvk58lDA6P4zY0dWJ6ayzvrM/nmwIkrWvN83u4uPDo4hrt6t8PVxVZr64qIiIiIiIiIiIiIiIiIiIhjTh00N01zu2EYzwOP8X1QvCuwyjCMLOBzYAeQDxQDPkALoDNnJpi3O7uUcXZ/E3jeNM3kerwbIiIiIiIideJoURkzF+9k/rZDluojW/kwa2wSvTu0qNU+3F1tjOocwqjOIaTkFPDehgPM25ZDWaXli1FdYHB8a6aOTiAk0KsWOxURERERERERERERERERERGrnDpoDmCa5uOGYQQDd3AmKA5nguPtgLtq2PXctd/PBcwN4F3TNJ+oq15FRERERETqg91u8tHmbJ5ZmkZhaaXDendXG7/r35EHbojEw9WlTntLDA3gz7d04olhsfz3m2ze+/oAB0+ctrx/mwBPpo1OYHBCcB12KSIiIiIiIiIiIiIiIiIiIo44fdAcwDTNuwzD+AZ4BvDi+8A5fB8o/8Eu/DCUfhr4k2ma/6jTRkVEREREROpYem4hE+emsOXACUv1fTu2YObYJNq39Knjzn6omY87D9zQgfv6RfJZ+hHeWZ/J2j35F623GXBP3/b8YVA0vh6N4k9VERERERERERERERERERGRJq3RvHpvmubfDMOYA/wWuBcIqqH8XPg8D3gDeMU0zZw6blFERERERKTOnC6v4uXPdvPaV/uotJsO61v4uDNpZBxju4RiGNWdn1s/XGwGg+JbMyi+NXuOFPHuhgP8b8tBisurvqvpFBbA7HFJJIYGNFifIiIiIiIiIiIiIiIiIiIi8kONJmgOcDYsPgGYYBhGLHAN0A5oBvgCp4ATwAHga9M0dzVUryIiIiIiIrXl811HmDwvhYMnTluq/1nPcJ4YFkugt3sdd3ZpOgb5MX1MIo8NieHzXUfJOlZMt7bNuCayBS62hgvDi4iIiIiIiIiIiIiIiIiIyIUaVdD8fKZppgPpDd2HiIiIiIhIXTlSWMq0RTtZvOOwpfqoIF9mj0+iZ0TzOu7syvh5ujG6c0hDtyEiIiIiIiIiIiIiIiIiIiI1aLRBcxERERERkaaqym7ywcYD/GXZLorKKh3We7jaeHBAFPf3i8Td1VYPHYqIiIiIiIiIiIiIiIiIiEhTp6C5iIiIiIiIE9l5qJAJc5PZln3SUn2/qJbMHJtIuxY+dduYiIiIiIiIiIiIiIiIiIiIXFUUNBcREREREXECJeWVvLhqN2+s3U+V3XRY39LXg6dGxTOqUxsMw6iHDkVERERERERERERERERERORqoqC5iIiIiDS4o0VlFJZWENnSp1EHZk+XV7FhXz6GYXBt+xZ4ubs0dEuXLflQEUuybfi5mSQ2cxx6liuzOi2Pp+anknPytKX6269py+NDYgnwdqvjzkRERERERERERERERERERORq1aiC5oZhuAA9gK5AW8Af8AIuNY1kmqb5y1puT0REREQuUUpOAdMX7mRT5nEAwpt78cfBMYzuHNKoAueVVXbeXp/JX1dmUFxeBYCvhyuPDo7mzt4RuNgaz33JOXmaKfNTWZWWB9gAWHbQJLFbMT38/Ru2uSYot6CUaQtTWZqSa6k+prUfs8cn0r1d8zruTERERERERERERERERERERK52jSJobhhGADAJuANodaXLASagoLmIiIhIAzlVVskLKzJ4e/1+7OcNy84+fpqHPtrGJ98cZMbYRNq39Gm4Ji3acfAkT85JJvVQ4Q9uP1VWybSFO/nf1oPMGptE5/DAhmnQonNh+RdWZlByNix/zqkKg0fnpLH8D0F4ujXeKe3OpMpu8t6GTJ5bkcGpskqH9Z5uNh4eGM0vr2uPm4utHjoUERERERERERERERERERGRq53TB80Nw+gNzOVMwPz8UZBm9XuIiIiIiDNbnprL1AWpHC4ovWjN2j35DPnrV/zmxg785sYOThluLiqt4PkVGby7IfMHYfkfS8kpZOw/13HHte3445AY/D3d6q9Ji7ZnnwnL7zxceNGaA8dP8+qX+3hoYFQ9dtY0peQUMGFuMjsOFliqvzGmFTPGJBLe3LuOOxMRERERERERERERERERERH5nlMHzQ3DSACWA75nbzL5PmxuVLuTiIiIiDilQydPM2VBKit35lmqL6+y89Lq3czflsOMsYn0i7rSC9vUDtM0WZaSy9SFqeQVllncB97dcIClKblMHhnPqE5tMIyG/3W2sLSC55fv4t2vD2BaOI3zH1/sYUyXECIawaR5Z1RcVskLKzN4a93+Gk9OOKeVnwdTRyUwPCnYKb5fRERERERERERERERERERE5Ori1EFz4J+cCZmfi2EYwApgPpAMHANKGqY1EREREbGissrO2+szeWFlBiXlVZe8f+axEu54YxOjO4cwaWQcQX6eddClNdnHS5iyIJXP0o9c1v5Hi8p48MNv+eSbbKaPSaR9AwW2TdNkacqZyfJHiqyF5QHKK+08tSCVd+7pqeDzJVqRmssUB5P8zzEMnHoCvoiIiIiIiIiIiIiIiIiIiFwdnDZobhhGB6Af308xPwHcbJrmFw3Zl4iIiIhYtz37JBPmJpN6qPCK11qw/RCfpx/hsaEx/PyadrjY6i/oXFFl5611+/nryt2crrj0sPyPrdmdz5AXv+K3N3bk1zdG4uHqUgtdWpN9vISn5qfw+a6jl7X/VxlHWZKcy4hObWq5s6bp0MnTTF2QygqLk/zj2vjz9PgkuoQH1m1jIiIiIiIiIiIiIiIiIiIiIg44bdAc6Hv2vcGZsPkDCpmLiIiINA5FpRU8t3wX7359ANN0XB/s70loMy+2HDhR87pllTw1P5VPtxxk9rgkEkMDaqnji9uadYIJc5JJzy2yVD84vjWGActTaw4Wl1fa+euqDOZvy2HG2ET6dmxZG+1eVEWVnTfX7uevqzIorbA7rHe1GVTaq//iTV+Uyg0xrfD1cOY/JxpWZZWddzYc4IUVuyi2MMnfy82FRwZFc0/fCFxdbPXQoYiIiIiIiIiIiIiIiIiIiEjNnDkZEnzexyeA/zVUIyIiIiJijWmaLEvJZerCVPIKyxzW2wy4u097HhkcjY+7y5kA+ZI0TpRU1LjfjoMFjP77Wu7sHcGjg6Px83SrrbvwnYLTFTy7PJ33N2ZZCsuHBHgybUwig+JbA7BqZx5TFqSSc/J0jfvtyy/m569vZEyXECaOiCPIz7M22v+BLQdOMHGu9bD8sMRg/nBjW3719kYyT104OT6vsIy/rsxg8sj42m61Sdhx8Mwk/5Qca5P8B8YFMXV0AmHNvOu4MxERERERERERERERERERERHrnDlofm7snwnsM00r8R4RERERaSjZx0uYsiCVz9KPWKpPCg3g6fE/nEp+a49wBsa15pml6Xz8TXaN+9tNeHt9JkuSDzNlVALDk4IxjAtD0ZfKNE0W7TjM9EU7OVpkLSx/b9/2/GFQND7nTfgeGN+aPh1b8PLqPby+Zt9Fp4OfM3/bIT5LP8KfhsZye6+2uNiu/L4UnK7gL8vS+WCTtbB8aKAX08ckMCCuNYWFhfwksorndrhg58Je3l6fyc3dwogP8b/iPpuKotIKnl+RwbsbMnHw5QbOTPKfOjqBIQmta+V7V0RERERERERERERERERERKQ2OXPQPOu8j2t/rKOIiIiI1IqKKjtvrt3Pi6t2c7qiymG9j7sLfxwSw529I6oNUzfzcefPt3Tilh5hTJybTEbeqRrXO1JUxm8/2MoN0a2YPiaBdi18Lvu+ZB8vYdK8FL7MOGqpvnNYALPG/TAsfz5vd1eeGBbLuK6hTJqXzObMEzWuV1RayeR5KXy65SCzxiZedF1HTNNk4Y7DTF+4k/xTjsPyLjaDX17XnocHRuHt/v2fCKE+cH0bky8OX/h1qrKbTJqXzKe/7oOtFkLxjZlpmixPzWXKAuuT/OtyGr+IiIiIiIiIiIiIiIiIiIhIbXDmoPl6zkwzN4AIwzBcTdOsbOCeREREROQ8W7NOMGFOMum5RZbqhyYEM2V0PG0CvBzW9oxozuIH+/HG2v28uCqD0gp7jfVfZhxl8F+/4vc3deT+6yPxcHWx1BOcCcu/tmYfL6/e7fA4AL4erjw2JIZfXNvO0uTxmGA/Pv5Vbz7dcpDZS9M4WVJRY/327JOM/vta7u7TnkcGR+PrYf3X9qxjJUyan8JXFsPyXcIDmT0u6aKTyYeF2/n2mEFB+YX3c2vWST7+JpvberW13F9Tc/BECVPmp7La4iT/xFB/Zo9LolNYYN02JiIiIiIiIiIiIiIiIiIiInKFnDZobppmjmEYq4BBgA8wHFjQsF2JiIiICEDB6QqeXZ7O+xuzME3H9aGBXkwbncDA+NaXdBw3Fxu/vqEDI5LaMHWB4zBvWaWd51ZkMPfbHGaOTaJ3hxYOj7HlwHEmzElhV561sPzwpGCeGplAcMClXXTHZjP4Sc9wBsa35pmlafz3m4M11ttNeHPdfhYnH2LKqASGJQZjGBcPtZdXfh+WL6t0HJb383DlT0NjuP2amsPyni4wPsLOWxnVB/efWZrO4PjWtPD1cHjMpqSyys5b6zJ5YWWG5Un+jw6O4c7e7XB1sdVDhyIiIiIiIiIiIiIiIiIiIiJXxmmD5mc9DtzImT7/bBjGatM0ixu2JREREZGrl2maLNpxmOmLdnK0qMxhvYvN4JfXteehAVH4XMJU7h8Lb+7N63f1YMXOPKYuSOVwQWmN9XuPFnPba18zvmsoE0bE0bKaEHRBSQXPLEvnw01ZlnoIDfRixtgEboq9tLD8jzX3cecvt3Tmlu7hTJybzO4jp2qszyss4//e38qNMa2YPjqRti28L6jZnHmciXOTycirea1zRnRqw5SR8QT5WwvLd25u0jeyGev2nbhgW8HpCp5Zms6zt3a2tFZT8G3WCSbMTSHtcKGl+sHxrZk6OoGQQMeT/EVERERERERERERERERERESchVMHzU3T3GYYxv8B/waigUWGYfzUNE1r16UXERERkVqTfbyESfNS+DLjqKX6zuGBzB6XSEJIQK0c3zAMhiQEc13Hlry4KoM312VSZa95nPqcb3NYnX6Ex4fG8rOe4dhsBqZpsmD7IWYs2kn+qXKHx3WxGdx3XXseGhiFt3vt/frcq31zFj/YjzfW7uel1RmUVtQ8hfyLXUcZ9NcveXBAFPf3i8Td1cbJknKeWZrOR5uzLR0zrJkXM8Ym0j8m6JJ6NQyYMKQD41/bWu209E+2HOQnPcPpGdH8ktZtbApLK3h22S7+s/GApUn+IQGeTB2dwOCE4LpvTkRERERERERERERERERERKSWOXXQHMA0zTcMwygBXgNuAFINw/gH8D8gxTStRDxERERE5HJVVNl5bc0+Xl6922EYGsDPw5U/DY3h9mva4WIzar0fHw9XJo6IZ3y3MCbOTWZr1ska6wtOVzBhbjKfbMnm9zd15K11mazZnW/pWF3CA5k9Lon4EP9a6PxC7q42fnNjB0Z2asOUBal8ll7z+ZRllXaeXb6Lud/mcEv3MF77ah/Hih2H5V1tBvf1i+ShAVF4ubtcVq/hzbz4bf+OvLAyo9rtk+amsOjB63BzsV3W+s7MNE0WJx9m2kJrk/xtBtzbtz1/GBR9RZP8RURERERERERERERERERERBpSo0g9mKb5oWEYG4GFQBww+exbhWEYx4HSS1/S7FDLbYqIiIg0OVsOHGfCnBR25RVZqh/RqQ1PjYyntb9nHXcGcW38+fTXffj4m2yeWZpOwemKGuu/zTrJvW9/Y2ltP09XHh8ay+292mKrg7D8j4U39+aNu3qwPDWPaQtTOVxQ86+3e46c4pml6ZbW7t6uGbPGJRIbfOVh+QduiGTutznszy++YNuuvCLeXLufB25oWr9mZx8vYfL8FL7YZW2Sf6ewAGaPSyIxtHYm+YuIiIiIiIiIiIiIiIiIiIg0lEYRNDcMoy/wAhALmMC5tI87cDnXodcUdBEREZEaFJRU8MyydD7clGWpPqyZFzPGJtI/JqiOO/shm83gtl5tGRTfmtlL0pizNeeK1xx5NiwfVA9h+fMZhsHQxGCui2rJiyszeGt9JlX2y/+11d/TlSeGxfGznuG1Fpb3cHVhxphEfvHGxmq3v7hqNyM7hxAa6FUrx2tIFVV2Xl+zn5dWZ1ia5O/r4cpjQ2L4xbV1M8lfREREREREREREREREREREpL45fdDcMIzfAX8FbJwJmJtcWVBcqQ8RERGRizBNkwXbDzFj0U7yT5U7rHe1GdzXL5KHBkTh5e5SDx1Wr6WvBy/8pAu3dA9j0rwU9h29cOK2I+HNvZgxJpEb6zks/2O+Hq5MGhnPuG6hTJybwrbsk5e8xpguIUwaEU8rP49a7++6qJaM6hzCwu2HLth2uqKK6QtTefWOHrV+3Pp0qZP8hycF89TIBIID6vfkBBEREREREREREREREREREZG65NRBc8MwRgAv8cOA+bmgeDlQAJQ0THciIiIiTUtmfjGT56ewZne+pfpubQOZPT6J2GD/Ou7Muj4dWrL0oX689tU+/vbZHsoqHU+idrUZ/Or6SH5/U8OG5X8sISSAOb/pw4ebs/jz0nQKSysd7tOuhTczxiRyfXSrOu1t8og4vkg/QlHZhT0tT83js/Q8boptXac91IVLneQfGujFjLEJjfK+ioiIiIiIiIiIiIiIiIiIiDji1EFz4EW+D5kbwBHgBWABkGGapuPkkIiIiIjUqLzSzr+/2svLn+2h3EIw29/TlceHxXJbz7bYbM53sRgPVxd+d1MUozqH8NT8VL7MOHrR2u7tmjF7XBIxwX712KF1NpvBz69px+D4YJ5eksacb3OqrXNzMXjg+g787qaOeLrVfVg+yN+TRwdHM3Xhzmq3PzU/ld6RLZ0quF+TS53k72IzuO+69jw0MApvd2f/k0pERERERERERERERERERETk8jhtKsIwjGuBDpwJmQPsAAaYpnms4boSERERaVo27jvGxHkp7DlyylL96M4hTBoZR5CfZx13duXatfDh7Xt6sjQll2kLU8krLPtum7+nK08Oj+OnPcKdMiz/Y638PHjhp124pXsYk+ansO9o8XfbekU0Z9a4RKJa129Y/hfXtuOTLQdJPVR4wbaDJ07z989389iQ2Hrt6XIcOFbMpHnWJ/l3CQ9k9rgk4kOcZ5K/iIiIiIiIiIiIiIiIiIiISF1w2qA50O3s+3MTze9TyFxERKTpKqusYtP+47jabHRtG1gvU5mvZieKy3l6aRr//eagpfq2zb2ZMTaRG6Jb1XFntcswDIYntaFfVEtWpObxzYHjJIYGMDQhmBa+Hg3d3iXr07Elyx++nq0HTpB6qJBe7ZuTEOKPYdR/WN7VxcascUmM++c6TPPC7f/+ah/juobSMcg5p8Wfm+T/t8/2UGZhkr+fpyuPD43l9l7OOclfREREREREREREREREREREpLY5c9A84LyPc0zT/KbBOhEREZE6Y7ebfLg5i78s20XB6QoAQgO9+M9919C+pU8Dd9f0mKbJnK05zFqSxvHicof1bi4GD1zfgd/d1LFRh//9PN24uXsYN3cPa+hWrpibi41rIltwTWSLhm6FLuGB3N6rLe9vzLpgW0WVyeR5qXxw/zUNEoSvyab9x5kwN9nyJP9RnUOY3Egm+YuIiIiIiIiIiIiIiIiIiIjUFmcOmh89+94EDjVkIyIiIlI30nMLmTAnma1ZJ39we87J09z15iZW/OH6Rh1udjb7jp5i0rwU1u+1dpGYnhHNmDUuiejWzjmRWpzDn4bEsjw1l/xTF564sGHfMeZvO8TYrqEN0NmFThSX88zSdD7+JttSfXhzL2aOTWp0k/xFREREREREREREREREREREaoMzB83PT3/4N1gXIiIiUutOl1fx0urdvL5mH5V2s9qarOMl/OPzPTw6OKaeu2t6yiqreOWLvfzz872UV9kd1gd4uTFheCy3dg/HZnOuSdTifAK83ZgwPI5H/ru92u0zF++kf2wQAV5u9dzZ90zTZO63OcxcbG2Sv6vN4FfXR/L7m6LwctfJLiIiIiIiIiIiIiIiIiIiInJ1cuag+VrgFOALdDAMw980zcIG7klERESu0Oe7jjB5XgoHT5x2WPuvL/cytmsoHVr51kNnTdP6vflMmpvCvvxiS/XjuoYycUQcLX096rgzaUrGdQ3l483ZbNx//IJt+afKeW75LmaMTWyAzi59kn+Pdmcm+ccEa5K/iIiIiIiIiIiIiIiIiIiIXN1sDd3AxZimWQx8cvZTV+DOBmxHRERErtCRwlJ++8FW7nlrs6WQOUBFlcnkeSmYZvVTz+XijheX8+h/t3P7axsthcwjWnjzn19ew19/2kUhc7lkhmEwc2wirheZgP+fjQfYnn2yXnsqq6zixVUZDH1xjaWQub+nK0+PT+K/D/RWyFxEREREREREREREREREREQEJw6anzUZOJcKmWYYRkxDNiMiIiKXrspu8t6GTAY8/yWLdxy+5P3X7z3Ggu2H6qCzpsk0Tf77TTY3Pf8F/9t60GG9m4vBgwOiWPbw9VwX1bIeOpSmKqq1H/dfH1ntNtOESfNSqLLXz0kjG/YeY9hLa3hx1W7Kq+wO68d2CWH1ozdyW6+22C4SlhcRERERERERERERERERERG52rg2dAM1MU3zkGEYo4HFQDPgc8Mw7jJNc2UDtyYiIiIWpB4qYMLclCueZDxzcRr9Y4Pw93SrncaaqD1HipgwN4VN+49bqu/VvjmzxyXRMci3jjuTq8WDN0WxYNshck5eeNWC5JwC3t94gDt7R9TZ8Y8XlzNrcZqlkyzgzCT/mWOTdJKFiIiIiIiIiIiIiIiIiIiISDWcOmhuGEZbIAf4CfBvIAJYZhjGeuATYAtwFCi91LVN08yqvU5FRETkfMVllby4KoM312VammDc0teDp0bF858NB9iUeWFI+mhRGc8v38W0MYl10W6jV1pRxT8+38O/vtxLRZXjf+9m3m5MGB7HLd3DMAxNb5ba4+XuwtTRCdz/7jfVbn922S6GJgYT5OdZq8c1TZNPthzk6SVpnCipcFjv5mLwmxs68H/9O+Lp5lKrvYiIiIiIiIiIiIiIiIiIiIg0FU4dNAcygfPTUiZgAH3Ovl0uE+e/7yIiIo3S6rQ8npqfWu1E4x8zDLi9V1v+NDSWAC83YoP9GP7SGiqrCae/9/UBbukeTlJYQF203Wit3Z3PpHnJZB4rsVR/S/cwJgyPo7mPex13JlerQfGtGRjXmlVpeRdsKyqrZNbiNF76WddaO96eI6eYODeZjZc0yT+RjkF+tdaDiIiIiIiIiIiIiIiIiIiISFPUWMLWBt8Hzs3zbhMREREnkVtQytQFqSxLzbVUHxvsx+zxSXRr2+y726Jb+/HLfu159ct9F9TbTZg0L5k5/9cXF5t+Dcg/VcbMRTuZt+2QpfrIVj7MGptE7w4t6rgzEZg6Op51e/I5XVF1wbb52w7xkx7h9O3Y8oqOUVpRxT8/38MrFif5B56d5H+rJvmLiIiIiIiIiIiIiIiIiIiIWNJYguagYLmIiIhTqrKbvLshk+dXZHCqrNJhvaebjT8MjObe69rj5mK7YPtDA6JYtP1wtRPRtx8s4INNWdxxbbta6b0xsttNPv4mm6eXpFFY6vjf293Fxm/7d+TXN0bi4epSDx2KQFgzbx4cEMWfl6VXu33yvBSWPtzvsr8n1+3JZ9K8FPbnF1uqv7lbGBOGx9LC1+OyjiciIiIiIiIiIiIiIiIiIiJyNXL2oPk7Dd2AiIiIXFxKTgET5iaz42CBpfr+Ma2YPiaR8ObeF63xdndlyqh4fvXelmq3/2VZOkMTgmnld/UFRnflFjFxbjLfHDhhqb5PhxbMHJtIZCvfOu5M5EK/vK49c7YeZPeRUxds25dfzGtf7eN3N0Vd0pr5p8qYtTiNud/mWKqPbOnDzHGJ9OlwZdPTRURERERERERERERERERERK5GTh00N03znobuQURERC50qqySF1Zk8Pb6/dhNx/VBfh5MHZ3AsMRgDMPxRUoGxbdmQGwQq9OPXLCtqLSSp5ek8cJPu1xG543T6fIq/vbZbv791T4qLfyDN/dxZ9KIOMZ1DbX07y1SF9xdbcwYm8jP/v11tdv/9tkeRncOpW2Li594co7dbvLfb7J5emk6BacrHB/bxcb/9e/Ab27soEn+IiIiIiIiIiIiIiIiIiIiIpfJqYPmIiIi4nyWp+YydUEqhwtKHdYaBtx5bTseHRKDv6eb5WMYhsHU0Qms25tPaYX9gu1zvs3h1h7h9O7Q4pJ6b4y+2HWEyfNTyD5+2lL9T3uE88SwWJr5uNdxZyKOXRvZgvHdQpmz9cIJ5GWVdqYsSOHNu3vWeEJERt6ZSf6bM61N8u8d2YKZ4xLpoEn+IiIiIiIiIiIiIiIiIiIiIldEQXMRERGx5NDJ00xZkMrKnXmW6uPb+DN7fBJdwgMv63jhzb35/U1RPLt8V7XbJ89PYcmD/XB3tV3W+s7uSGEp0xftZNGOw5bqOwb5MntcEr3aN6/jzkQuzYThcaxOO1LtJPLPdx1leWouQxPbXLCttOLMJP9Xv7Q+yX/i8DjGd9MkfxEREREREREREREREREREZHaoKC5iIiI1Kiyys7b6zN5YWUGJeVVDuu93V14ZFA0d/eJwNXlykLg9/eLZM7Wg+w9WnzBtj1HTvHamn38tn/HKzqGs7HbTT7YlMWfl6VTVFrpsN7d1cbv+3fkgRs6NNnQvTRuLX09+NPQGCbOTal2+7SFO+kX1Qofj+//NPky4yiT56WQdbzE0jF+0iOMJ4fFaZK/iIiIiIiIiIiIiIiIiIiISC1S0FxEREQuanv2SSbMTSb1UKGl+oFxrZk2JoHQQK9aOb67q40ZYxO5/bWN1W7/22e7Gd05hPDm3rVyvIaWdriQCXOT+TbrpKX6flEtmTEmkYiWPnXbmMgVuq1nW/77zUG2Z5+8YNvhglJeXJXBxBHxHCkqZeaiNBZsP2Rp3Q6tfJg9LolrIlvUcsciIiIiIiIiIiIiIiIiIiIi0miD5oZhNAfigOZAAGADlpummdegjYmIiDQBRaUVPLd8F+9+fQDTdFwf7O/JtDEJDEkIrvVe+nRoybiuocz9NueCbaUVdqYtTOX1u3rW+nHrU0l5JS+t3s3ra/ZTZXf8D97S153JI+MZ3TkEwzDqoUORK2OzGcwam8jov6+lum/xN9dl4uXuylvr9l/SJP9f3RCJh6tLHXQsIiIiIiIiIiIiIiIiIiIiIo0qaG4YRhDwO+BmILaakkHABUFzwzDuAcLPfnrINM3X66xJERGRRsw0TZal5DJ1YSp5hWUO620G3NUngkcHx+DrUXe/VkwYHsfqtDwKqwmgrko7worUXAbXQci9PnyefoRJ81LIOXnaUv1tvdryxNBYArzd6rgzkdqVGBrAnb0jeHt95gXbquwmL6/ebWmd6zq2ZOZYTfIXERERERERERERERERERERqWuNJmhuGMZjwHTAHahudGdN4z99galna6oMw1ioyeciIiI/lH28hCkLUvks/Yil+qTQAGaPSyIpLKCOO4NWfh48NjSWyfNSqt0+beFOrotqibd7o/nVhrzCUqYtTGVJcq6l+pjWfswal0iPiOZ13JlI3Xl0cDRLkg9zpMjxiSw/1tLXnUkj4hnTRZP8RUREREREREREREREREREROqDraEbcMQwDBfDMOYAzwAe1ZTUFDA/5w2gkDMBdRfg9trrUEREpHGrqLLz6pd7GfzXryyFzH3cXZgyKp55v+1bLyHzc27v1ZZOFzlezsnTvLx6T731ciWq7CbvbshkwPNfWgqZe7rZeHxoLIsevE4hc2n0/DzdmDwy/pL3u61XOKseuYGxXUMVMhcRERERERERERERERERERGpJ41h7Oc/gLFnPzY5Exb/FlgBZJ3dXiPTNEsMw1gI/PzsTcOBv9Z6pyIiIo3M1qwTTJiTTHpukaX6IQmtmTo6gTYBXnXc2YVcbAazxiYx5h9rsVdzmtnra/Yxvlso0a396r03q1IPFTBhTjLbDxZYqr8huhUzxiTStoV3HXcmUn9GdmrDx5uzWbsn32FtdGtfZo9L0kkWIiIiIiIiIiIiIiIiIiIiIg3AqYPmhmFcB/yK76eW5wN3m6a59Lyaf2Btqvk8zgTNDaCvYRjupmmW127HIiIijUPB6QqeXZ7O+xuzMC08ioYGejFtdAID41vXfXM1SAoL4I5r2/HOhgMXbKu0m0yal8LHv7rW6SYeF5dV8uKqDN5cl0lVdSn5H2nl58GUUfGMSGrjdPdF5EoZhsH0MQkMfXEN5VX2ams8XG08OCCK+/tF4u7q9BdhEhEREREREREREREREREREWmSnDpoDkw/+94ACoEbTNNMv8y1Np73sQcQAyRfQW8iIiKNjmmaLNpxmOmLdnK0qMxhvYvN4N6+ETw8MBofD+f4teHRITEsTs4l/9SF/W/af5w5W3O4uXtYA3RWvVU783hqfgqHCkod1hoG/OKadvxxSAwBXm710J1Iw4hs5cuDAzry3IqMC7ZdH92KmZrkLyIiIiIiIiIiIiIiIiIiItLgnCMxVg3DMJoB/fh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- }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" @@ -793,31 +851,21 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:25:24.678552Z", - "iopub.status.busy": "2022-10-26T11:25:24.677145Z", - "iopub.status.idle": "2022-10-26T11:26:03.562172Z", - "shell.execute_reply": "2022-10-26T11:26:03.562703Z" + "iopub.execute_input": "2023-06-26T17:03:45.400911Z", + "iopub.status.busy": "2023-06-26T17:03:45.400811Z", + "iopub.status.idle": "2023-06-26T17:03:58.283322Z", + "shell.execute_reply": "2023-06-26T17:03:58.283008Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
<Figure size 3000x1500 with 3 Axes>\n",
-       "
\n" - ], + "image/png": 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8jDfeeMOIiorKiElOTjZmzJhhFC9e3CK2efPmRlpaWr47JwAActOjjz5qMerYf/ur7IzSEhUVlWXkl6pVqxqLFi2ymDUkLCzMeOyxx7L0jRMnTnS6LXc9E3DnOQEA4CpTp07N0ifdddddxh9//GEkJSVliT979qzx008/GQMGDDAqV67s8Pj0+fT5AIC8NX/+/Cx9UYMGDYxly5YZqampFrGXLl0y3nnnnSwzqZYuXdrimbk1qampRuPGjS32K1GihPHDDz8YKSkpGXGRkZHGa6+9lmUU96eeesrpc5o7d67VdxDHjh2ziFu5cqXRpEkTi7giRYoYoaGhTrXjznMCAOC6zz77zObI20FBQTf8TD4z7tcLxjnB9ShOB+AyERERRpEiRSz+kf/www9txoeHhxvVqlWziH/zzTfdmDEAAPlLy5YtjWrVqhlTp041EhISHManpaUZo0aNynIzlrkQ25b77rvPYr/WrVsbMTExVmPNZnOWtmrWrJnlobo1hw8fNjw9PS32nTVrls34AwcOGMHBwRbx06dPz1fnBABAblq9erVhMpkM6doHXRMmTLjhB+GvvPKKxb7Vq1e3eGib2fvvv28RX6xYMYcvxg3Dvc8E3HVOAAC4yvHjxw0/P7+Mvsjb29vufXFmzvRb9Pn0+QCAvNWoUSOLfqhVq1ZGXFyc3X1WrVpleHl5Wez3wQcf2N3nu+++s4gvXry4cfDgQZvxv/zyi0W8l5dXluJya5KTk7P04aNHj7YoOPuv6Ohoo1WrVhbxDz74oMN23HlOAAD81/Xi9CJFihhdunQxxo0bZ8yfP984deqUsXr1apcVp3O/XjDOCa5HcToAl3nxxRct/nG/9dZbbd6cXrdy5UqLfYoUKWJERES4KWMAAPKXpUuXGsnJydnaJy0tLcsD36FDhzrc78CBAxaji/j4+BiHDh2yu09iYqJRu3Zti7amTJnisK1BgwZZ7DNs2DCH+2QeUa5q1aoWI6Tk9TkBAJBbEhISjJo1a2b0S08//fQNPwi/dOlSlhFeVq5caXcfs9ls3HrrrRb7vPrqqw7bctczAXeeEwAArtK1a1eLfmjevHkuPT59fs7OCQCAnDp58qRF/yPJ2LZtm1P7Zh5NtF27djZjk5OTjcqVK1vET5s2zWEbDzzwQLbfIUyePNlin9q1axuJiYl29zl48KDFaPCenp7G4cOH7e7jznMCAOC/Tpw4YRw8eNBIT0/Pss1VxencrxeMc0Lu8BAAuIDZbNaMGTMs1r399tsymUx29+vWrZs6deqUsRwbG6t58+blSo4AAOR3vXr1ko+PT7b28fT01Isvvmix7q+//nK43/Tp02U2mzOWhwwZovr169vdx8/PTy+//LLFuqlTp9rd58qVK1qwYEHGsslk0ttvv+0wv4ceekhVq1bNWD59+rRWrlxpdx93nRMAALnpjTfe0MmTJyVJVapU0XvvvXfDx5ozZ47i4uIylm+99VZ169bN7j4mk0lvvfWWxbrp06fLMAyb+7jzmYC7zgkAAFdZvHixVq9enbE8cOBADRw40KVt0OdfQ58PAMgrR48etViuVKmSWrdu7dS+AwYMsFg+ceKEzdi//vpLYWFhGcvVqlXTQw895LCNzP31/PnzFRMTY3efzM/JX3nlFfn5+dndp0GDBho8eHDGcnp6epZrh8zceU4AAPxXzZo11aBBA3l45F4JLffr1+T3c0LuoDgdgEts2rRJly9fzliuUaOGunTp4tS+I0eOtFhetGiRCzMDAKDw++/NlSRFRkYqISHB7j5LliyxWM7cH9syePBgBQYGZixv375d586dsxm/bNkypaWlZSx36dJFNWrUcNiOh4dHlgfQjq4R3HVOAADklu3bt+vzzz/PWP76668VFBR0w8dbvHixxbKzfWPXrl1VvXr1jOULFy5oy5YtNuPd+UzAXecEAICrTJkyxWI588tYV6DP/xd9PgAgL0RFRVksV65c2el9q1SpYrEcHR1tMzZz//jQQw85LNCSrhXfde7cOWM5NTVVy5cvtxkfHh6uXbt2ZSwHBQVp0KBBDtuRsvbZmXPOzF3nBABAXuB+/V/5+ZyQOyhOB+ASy5Yts1ju0aOHUzeN12P/a82aNYqPj3dZbgAAFHbFixfPss7eCCFHjx61GH0lMDBQ7du3d6qtzLGGYWS5DvivzNtuv/12p9qRsl4jLF261GasO88JAIDckJqaqpEjRyo9PV3StVFVe/fufcPHi4uL07p16yzWOdsPm0wmde/e3WKdvX7YXc8E3HlOAAC4wtmzZy1mN2vWrJkaNmzo0jbo8y3R5wMA8kKxYsUslhMTE53eN3NsqVKlbMa663l75nY6dOhgMcCLPR06dFBAQEDG8tGjR3X8+HGn28qtcwIAwN24X7eUX88JuYfidAAusWfPHotlZ4vBJKlChQqqVq1axnJKSooOHTrkoswAACj8zp49m2VdyZIlbcZn7rfbtGkjLy8vp9vr0KGD3ePZ25ada4SWLVvK19c3Y/ncuXMWX0fbayc3zwkAgNzw4Ycfav/+/ZKk4OBgTZo0KUfHO3jwoFJTUzOWq1evrnLlyjm9v7v6++w8E3DnOQEA4Ap//vlnxodn0rVRwlyNPj8r+nwAgLs1a9bMYvnw4cNOF0Bt27bNYrlNmzZW4y5evKgLFy5kLPv6+qpFixZO5+iuPt/LyyvLOdhqy53nBACAu3G/nlV+PCfkHorTAbjE4cOHLZYbNGiQrf0zx2c+HgAAsG39+vUWy1WrVpWPj4/NeHf126mpqRajmWe3LV9fX9WsWdOptrgWAQAUZIcOHdL777+fsfzRRx9l64GuNe7sG93VFv09AKCg2b59u8Vy06ZNM/7/7t27NXbsWDVt2lTFixdXQECAqlWrph49euiTTz6x+iG6NfT5N94OAACuUqlSJYuCqeTkZKc+Ok9OTtbnn39usW7kyJFWYzP3Z7Vq1bL7HiCzzP3jiRMnlJaW5lRb7urzc/OcAABwN+7Xb7wdd7eF3EFxOoAcS0xM1JkzZyzWVa5cOVvHyBx/9OjRHOcFAMDNYvr06RbLPXv2tBufuZ/NrX47JCTE4kGwv7+/3SlJc9KWu84JAABXM5vNGjlypFJSUiRJnTp10qOPPprj47q6bzx9+rSSkpKyxLnzmYC7zgkAAFfJXJxeo0YNxcXFaeTIkWrRooW+/PJL7du3T9HR0UpMTNTp06e1cuVKjRs3TrVr19arr75qMSKZNfT5jtuhzwcAuMNHH30kD49/S3DefPNN/fDDDzbjo6Ojde+991oUSt199926++67rcbntH8sXbq0/Pz8MpZTUlIUGhqaK225q8/PzjkBAOBu3K87bic/nBNyD8XpAHIsIiJChmFkLHt7e6tMmTLZOkbFihUtli9duuSS3AAAKOyWL1+udevWWawbMWKE3X0y97OVKlXKVpuZ++3Lly871U7m/W6kLVvXCO46JwAAXG3SpEnasmWLJMnHx0dTpkyRyWTK8XFz2jeWLVtWXl5eGctms1mRkZFZ4tz5TMBd5wQAgKtknk3Mw8NDt956a5aPzK1JTEzUhx9+qJ49eyo2NtZmHH1+VvT5AIC80LFjR3311VcZ9/RpaWkaMWKE2rRpo/Hjx2vhwoX6888/9fPPP+upp55SzZo1tXTp0oz9e/ToodmzZ9s8fk77R0mqUKGC3WNel/n5eE6ft+dWny85f04AALgb9+tZ5cdzQu7xchwCAPbFxcVZLAcEBGT7RXpgYKDdYwIAgKyioqL02GOPWazr27ev2rRpY3e/zP1s5n7YkczxqampSk5Olq+vr0vbsbaPrWsEd50TAACuFBoaqtdffz1j+ZVXXlG9evVccuyc9o0mk0n+/v4WxXDW+mF3PhNw1zkBAOAKZrM5S1H52LFjtXv3bknX+qXevXurZ8+eqlSpkuLj47V792799NNPOnfuXMY+K1eu1IgRI/Tbb79ZbYc+Pyv6fABAXnn88cdVt25djR07VgcPHpR0bSaVzLOp/FeNGjX04osv6tFHH7UYeT0zdz1vT0xMVHp6eo7aclefn522AABwN+7Xs8qP54Tcw8jpAHIs8z/e/506y1n+/v52jwkAACyZzWY98MADCg8Pz1hXrFgxTZo0yeG+Oe27M/fb1o7pinasteXsjXBunRMAAK40atQoxcfHS5Lq1aunV1991WXHdlc/XJD6++y0BQBATsXExFiM8iVJu3btkiSVLFlSa9eu1ZIlSzR69Gj17t1bgwcP1vjx43X06FENHTrUYr8FCxboxx9/tNoOfX7O2gIAwNVuu+02bd++XS+88II8PT3txlapUkUvvPCChg4darcwXcq7Pv9G2qLPBwCA+/WctMU1QuFAcTqAHEtKSrJY9vHxyfYxMo9KmpiYmKOcAAAo7MaNG6c//vjDYt13332nypUrO9w3p323tdHErfXd7rxGcNc5AQDgKtOmTdPKlSslXRstZMqUKTfUV9rirn64IPX32WkLAICcsvXS09PTU8uWLVOnTp2sbg8KCtJPP/2k22+/3WL9Bx98kKXYXaLPz2lbAAC42rfffquaNWvqk08+yTICeWZnzpzRmDFjVK1aNU2fPt1ubF71+TfSFn0+AADcr+ekLa4RCgeK0wHkWOavk1JSUrJ9jOTkZLvHBAAA/5o0aZI+/fRTi3UvvviiBg8e7NT+Oe27M/fb1o7pinastWXrGsFd5wQAgCucP39eL7zwQsbyI488YrNA7Ua5qx8uSP19dtoCACCnbPUxjzzyiNq2bWt3Xw8PD33zzTcWI6gePXpUa9euddgOfX722gIAwFVSU1N177336vHHH9f58+clSSVKlNCbb76pbdu26cqVK0pJSdG5c+e0ZMkS9evXTyaTSZIUFRWlkSNHaty4cTaPn1d9/o20RZ8PAAD36zlpi2uEwoHidAA5FhQUZLFs7WtqRzJ/nZT5mAAA4JpZs2bpmWeesVg3YsQIjR8/3ulj5LTvtvZVsbW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PTRzY1O7MBJJ09GKs+n69UfvDHQ/AAgAAAACwLTktXV+uOq7bP1untccuO4z39fLQ8z3qaPnYTmrjYPar3HJno3Ia0rqyze1vLDqoM5EJbswIAADAvfJFcfqBAwdUr149/e9//9Phw4dlNpszissjIiI0Z84ctW3bVl999VXGPlu3btU999yj6OhoGYYhk8lk88cwDK1YsUK9e/fOw7MEAAAAAAD2GIahCX8d1Xdr7RemS9KvO8OVmm678Bg3r4W7w/Xk7N1KTbc9PfIdDctqyoMt5e/j6cbM8kbH2qXUo0FZq9si4lL01T8n3JwRHJm3PUwjZmxTbLLt4uwifl764aE2urdlJavbO9Qqpff6NrK5/9noRI36caeSUm3PLOBul2KTNHTqVn3n4OMkTw+TXu9VX98Na5lnsx6YTCaN7VZbnw9uJh9P24/YL8cma9B3m/X3wQtuzA4AAAAACo9NJyJ01xfrNXHFMbuDEFzXpW5prXi2s57qVls+XnlbEvXm3Q1Uo1Sg1W1xyWl6Zu5upfF8EwAAFFJ5Xpx+9uxZ3XbbbTp79qzNInNJSk9P1zPPPKP169fLbDZr2LBhSklJydhubdR0w7j2EvJ6gfqaNWv0xRdf5Nm5AgAAAAAA6wzD0Md/HdU3a046FX85Nlmrj1zK5axQ0MzaekbPzdurdLPtwvS+zSro66Et5OtV+AvTr3utZ32bBbTTN4YqNCLezRnBGsMw9MlfR/Xib/uUZud3uGKwvxY83l7ta5Wye7whbarosVtr2Ny+Jyxaz8/bK7Odttxl+6ko9Z60QdtCo+zGlS3qqzmjbtEjnWpYjBafV/o2r6ifH2mr4ADbRfKJqel67OedmrYhNON5NQAAAADAvsuxyXpmzm4NnbpVIZcdP7coV9RP39zfQjNGtFaVkgFuyNCxAB8vfTGkuc2ZzXadidaXDBoAAAAKqTwvTn/66acVERFhMcq5tR+TySSz2ay3335bv/76q06cOJER3759e82ZM0dnzpxRUlKSYmNjdfDgQU2cOFEVK1bM2N8wDL3//vtKS7M/JSwAAAAAAHAfwzA08e9jmuxkYfp1c7eH5VJGKIimbQjVqwv3y17t55DWlTVxUDN52RnpuDCqVipQD3esbnVbarqh95cdcnNGyCw5LV1Pz9mjr1bbfyndtFIxLXyivWqXLeLUcV+6s55utzFyviQt239en644lq1cXckwDE1dH6IhU7boUmyy3dgOtUpq2dhOal0tb6Zkt6VN9RJaOKaDqtkpfjAM6d2lh/T5yuNuzAwAAAAACp50s6GfNp/SbRPXaNGecw7jPT1MGtmxulY+31l3NS6fLz5k/q/GlYrp+dvr2tz+5T/HteOU/Q+1AQAACqI8fRMXEhKiBQsWWBSlP/zww9q2bZvi4uIUHx+vHTt2aNSoUZKujYC+bt06ff/99xnHePrpp7VhwwYNGjRIlSpVko+PjwIDA1W/fn09++yz2rt3r9q0aZMRHxkZqcWLF7v9XAEAAAAAcKetIZF6e8lBvbX4gPaERed1OjYZhqHPVhxzWJBpzeqjl3QhJikXskJB89U/x/XuUvsF1g91qKYP+zeWp43Rqgq7J2+rpdJFfK1uW3n4ktYeu+zmjHDdlfgUDZu6TUv22n/pfnuDspozqp3KFPFz+tgeHiZ9PqSZGlUsajPmq9Un9OvOcKeP6SpxyWl6ctZuvbfssN3ZDiRp7G219OPDbVUqyPrvcF6rXipQC8d0UOtqxe3GfbHquBbvOeumrAAAAACgYNkfHqP+kzfqjcUHFZvkeNDJ5lWC9fuTHfVG7wYK8vVyQ4Y35rFba6hdjZJWt5kN6ek5e3Q1KdXNWQEAAOSuPC1OnzNnjiRljGz+wQcfaOrUqWrVqpUCAgLk7++vFi1a6Ntvv9WECRNkGIbMZrP++ecfSVKTJk306aef2m2jePHimjNnjry8vDK+kLy+PwAAAAAAhc21UciPavCULZq56ZR+2Hxafb/eqNcW7ldCSv6bSezzlcc1yc70tZ4eJj12aw2r28yG9Nsu9xdUIv8wDEMT/jyiT/62P/LzE11r6s3eDfLd6FnuFOTrpRfvsD1S17tLDyk13ezGjCBJpyPjNeCbTdrmYJS0hztU1zcPtJS/j2e22wjw8dK04a1VrqjtovZXFuzT1pDIbB/7Rh2/GKs+X23Qsv3n7cYFB3hrxkOt9dztdfP9hyXFA3308yNt1adZBbtx437dl68/GgMAAAAAd7ualKq3lxxUn683aG94jMP4Yv7e+rB/Y/02ur0aVLD9MXZ+4eFh0qeDm6qYv7fV7WejE/XmogNuzgoAACB35Wlx+rZt2zL+f82aNfXSSy/ZjH3uuedUt+61F2jXi9lHjx7t1EvFatWqqXfv3jL+f17nnTt35jBzAAAAAADyH8Mw9MnfR/WllWLvX7aeUc8v1mv3mSt5kJl1n688pi9WHbe53dPDpElDmuvlu+qpRulAqzFzt4fJ7GDEXRROZrOhd34/pMlrTtqNG3dHXY27o95NXZh+3YAWldSkUjGr205citPPW067OaOb287TV9Rv8iaFRMTbjPEwSW/f3UBv3t0gR8XZZYv6adqIVgqwUdyemm7osZ93KtROLq7y+95z6vP1Rp28bL+tJpWKaelTHdW1bplcz8lVfL089fngZhrbrbbNmJQ0s0b9uIOZPwAAAADc9AzD0JK959Rt4lrN3HRKzjziG9Cikv55vrPua1NFHvn8I+b/Kl/MX+P7N7a5fdGec1q0m5m2AABA4ZGnxekHDlz78s9kMql///52XxKaTCb169cvo8Bckjp27Oh0W506dZJ07eI2PJxR1QAAAAAAhcu1EdOP6evVtgt1T0Um6N5vN+vTFcfyfITkSauO6/OV9gvTPx/cTL2alJfJZNKQ1pWtxp2JStCWUPeN9ov8Id1s6NWF+zVz0ym7cW/2bqAnutZyT1IFgIeHSW/d3dDm9s9WHFNUfIobM7p5Ldt3Xvd9v8Xun7e/t6e+G9ZKIzpUd0mbDSsU06QhzWXrEWx0Qqoenrld0Qm58zuQkmbWO78f1FOzdyshJd1u7NC2VTTvsXaqVDwgV3LJTSaTSc/1qKNPBja1+Wd9KTZZo37aoUQHfw4AAAAA8qfUdLN2no7SgbMxef6MraAKjYjXsGnbNHb2bl2OTXYYX7tMkOaOukUTBzVVySBfN2Toenc1Lq/Braw/45Sk1xcdUFhUghszAgAAyD15WpweFfXvdLVNmjRxGN+oUSOL5apVqzrdVrVq1TL+f0yM42mAAAAAAAAoSD5beVxfrc46Ynpm6WZDk1Yd173fbNLJy3FuyCyrL1cd16crjtnc7mGSPhvcTHc3rZCxrn+LSvKyMRrS3O1hLs8R+VdqulnPzdujOXb+u5tM0of9G+vhjq4p6i1MWlYtrr7NKljddjUpTZ+uOOrmjG4uhmHou7Un9cSsXUpJs13AULqIr+Y91k49GpR1afvdG5TV670a2NweGhGv0T/vtJvbjbgQk6T7vt+iGRtP2Y3z9fLQJwOb6oN+jeXnbX2U94Li3paV9PKd9Wxu3xceo3G/7rUYjAUAAABA/paabtYPm07plg9WacA3m9X7yw26+8sNFBRnQ1Jquj5dcUx3fLZOG05EOIz38/bQS3fW07KxndS2Rkk3ZJi73ry7gaqXsj5DZFxymp6Zu0dpfPAAAAAKgTwtTo+Njc0YLb1EiRIO4zPHBAUFOd3Wf2MTErgxyA7DMLRz505NmzZNH374oT788ENNnz5dO3fu5OUJAAAAAOQDn604pkmrbI9Cbs3e8Bj1mrReP20+5dZ7u69Xn9BEJwrT72lqWTxbKshX3etbL9L848AFxSSkujRP5E/Jael64pddWrznnM0YTw+TPhvUTPe1qeLGzAqWl++qL38bhb+ztp7R4fNX3ZzRzSEt3azXFx3Qh38csRtXp2yQFo5pr8aViuVKHg93qKYHbrH992NLSJReW7jfZX3DppMR6v3leu08fcVuXNWSAVo4poPubVnJJe3mB6NuraH+LSra3L5033l99Y/jD8sAAAAA5C3DMPTXwQu6/bN1emvJQUX+ZxasIxdiNWLGNsUnp+VhhgXDumOXdefn6zRp1XGlOFGA3b1+Wa18rrMe71JTPl55Wt7kMoG+Xvp8cDObg3DsPH3FqQFIAAAA8rs8vXpLT/932lIfHx+H8d7e3jfclsnWHKp54O2335bJZLrhnxEjRrglz9TUVH3yySeqXLmyWrVqpUceeUSvvvqqXn31VY0cOVKtWrVSlSpVNHHiRKWmUgQAAAAAAHnh85XH9EU2C9OvS0o1643FBzVixnZduprk4syymrzmhD7+y/aozB4m6dNBzdSnmfVCvsFtrE97m5Jm1qI9Z12SI/KvxJR0PfrjTv196KLNGG9Pk74e2kJ9m9suBoVUrpifnuha0+o2syH97/dDDEjgYnHJaXrkxx36ZesZu3EdapXU/NHtVal4QK7lYjKZ9PbdDdWpdimbMfN3huubtSdz1I5hGPpmzUk9MHWrIuJS7MZ2r19WS57sqAYViuaozfzGZDLpg36N1aJKsM2YiSuO6c8D592XFAAAAIBs2X3migZ9t1mP/bRToRHxVmNOXo536Ue+hc3Fq0l6YtYuPTh9m05FOh5MsmKwv6YMa6mpw1vl6v1xXmlaOVjP3V7H5vZJq45r5+koN2YEAADgeoXj00K4XFhYmNq2batx48bp7FnbL/jDw8P1wgsvqF27dnbjAAAAAACuN2nVcX2+0nZhuodJdosPr1t77LJu/3yd/tife8Vx36w5qQl/2i5MN5mkiYOa2i0qvrV2aZUv5md12+xtZ3gBWIjFJadpxIxtWnfsss0YXy8Pff9gK93ZqJwbMyu4HulUQ5WK+1vdtjkkUn8dvODmjAqvCzFJGvjtZq05avv3V5IGtqykGSPaqJj/jQ/Q4SwvTw99fX8L1S5je2bKCX8e1fIb7BeuJqVq1E879dGfR2S280+zh0l68c66mjKspVvOOy/4eXvq22EtVcFG/yVJz87dqwNnY9yYFQAAAABHwqIS9NTs3eo3eZO2n7I/E5QkLdpzTnO2h7khs4LDMAzN2xGm7p+u1bJ9ju8vvTxMeqxzDa147lbd3rBwP9957NaauqVGCavbzIb0zNw9ik1ikEgAAFBwUZyOLC5duqSuXbtq9+7dFuv9/f3VsGFD1a9fX35+li9Tdu7cqa5duyoiIsKdqQIAAADATeurf47r0xXHbG73MEmfDW6mn0a21ddDWyg4wH7RX3RCqh7/ZZeem7dHV1384uO7tSf10Z9HbG43maRP7m2qfs0r2T2Op4dJA1tajzlyIVb7KewrlGISUvXA1K3aGmp7xKgAH0/NfKiNutQt48bMCjY/b0+91rO+ze3vLTuspNR0m9vhnEPnrqrv1xt1+PxVu3Ev3F5HE+5t4tZpyov6eWv6iNYqFWR7Rstn5+7RnrDobB338PmruufLDVphZ5YDSSoZ6KOfRrbVmC615GFjOvPCokwRP30/vJX8vT2tbk9MTdeoH3foUmzuz2ICAAAAwL6YhFS9v+yQuk1cq9/3nsvWvm8tOahD5+zf/90szsckasSM7Xrx132KTUpzGN+mWgktG9tJr9xVXwE+Xm7IMG95epj06aBmNj/UDotK1JuLD7o5KwAAANcp/Fd0BcAnn3yipk2bOh1foUKFXMxGGjFihE6e/HfaXj8/P40fP16PPvqoAgKuTZkUHx+vKVOm6NVXX1VS0rWXJsePH9fDDz+sJUuW5Gp+AAAAAHCz+3r1CX3yt+PC9D7Nro1C3qtJebWqVlwvzN+r9cftf1S8YNdZbQ2J0qeDmqptjZI5zvX7dSH68A/7hekf39tUA2wUnWc2sFVlfbn6hKwNkj53e5iaVAq+wUyRH0XGJWvYtG06ZKewt4ifl354uI1aVCnuxswKhzsbldMtNUpoS0jWwv/wK4matiFUT3StlQeZFQ5rjl7SE7/sUnyK7SJ/H08PTbi3id1ZI3JT5RIBmvJgKw2ZskUpaeYs25PTzHrkhx1a/GQHVQy2PtL+fy3YFa5XF+5XUmrWY/1XiyrB+vr+FipfzPExC4uGFYrps8FNNfrnXVa3n4tJ0mM/7dTsR2+Rn40idgAAAAC5JzktXT9tPq0v/zmhmMQbG7ghJc2sJ2bt0pInO6iIX+GcHcoRwzA0f0e43l16SLHJjovSSwT66JW76unelpVkMhXuD5czqxDsrw/6NdYTs6zfJy7cfVZd6pbOeMYLAABQkFCcng+0bNlSXbp0yes0JEl///23/vjjj4xlb29v/fXXX7r11lst4gIDA/Xss8+qRYsW6tGjh1JTr92c/f7771q9erW6du3q1rwBAAAA4GYxec0JffzXUZvbPUzSxEFNs7y0KFvUTz8+3EY/bTmt95cdVrKVIsTrzkYnasj3WzSqUw09d3sd+XrdWJHc1PUhen/5YZvbTSZpwoAmutfJwnTpWiFlx1qlrBbZL9lzTq/1ujlGV7oZXLyapPunbtWJS3E2Y0oE+ujHh9uoUcVibsys8DCZTHqzd0P1/nK9zFY++Ph69QkNaFFJ5Yr5Zd0Iu2ZtPaM3Fh9QurU/2P9XzN9b3w1rqVtc8CFQTrSoUlwTBzbVU7N3W90eEZeskTO3a/7odjaLK5LT0vW/3w/pl61nHLY3on01vdqzvltHic8v7mxUXs/3qKOJNmY+2X0mWq8u3K+JA5vedEUZAAAAQF4xDEPL91/QR38e0ZmoBKf2MZkkT5NJaVbu+UIj4vXKgv368r7mN911/bnoRL28YL/WHbvsVPyQ1pX10p31VDzQ9oxehV2vJuW15mglzd8ZbnX76wsPqEWV4qpcIsDNmQEAAORMvnkDcLNdlOdXb7zxhsXyyy+/nKUw/b86d+6sl156yWLd66+/niu5AQAAAMDN7tu1JzXhT9uF6SaT9MnApurX3Hqxt8lk0oPtqmnZ2E5qUsl+Ma9hSN+tC1GfrzbqyIXsT0c8bUOo3ltmuzBdkj7q30QDW1XO9rEH2dgnNjlNy/dfyPbxkP+cj0nUkClb7Bamlyniq7mjbqEwPYcaVCiq+9pUsbotISVdE/60PfMBsjKbDY3/44heXbjfbmF6lRIBWjCmfZ4Xpl93d9MKer5HHZvbj1yI1VOzdystPeuHTWejEzXo280OC9P9vT31xZBmevuehjdlYfp1T95WS3c3tT0z5oJdZzVlXYgbMwIAAABuXjtPR6n/N5v0xKxdThemd6pdSsvHdtKbdzewGbN033n97MTHu4WFYRias+2Mbv9snVOF6fXKFdFvj7fT+AFNburC9OvevqehqpW0Xnwem5ymZ+fusfuMAQAAID/KF28BDMNQ9+7d5enpaffn9ttvt9jHUXzmfSmAt2///v3atm1bxnJgYKDGjRvncL8XX3xRgYGBGcubNm3S4cP2CxAAAAAAANkzZd1Jjf/DdpGoySR9fG9T9W/heBTyWmWC9Nvj7TX2tlrycHCrfORCrO75cqO+Xxcis5MvQWZsDNW7Sw/ZjfloQGMNap39wnRJur1hWQUHWB+9d+72m+fFX2F1LvpaYXpoRLzNmIrB/pr3WDvVLlvEjZkVXs/1qKOiftZnHFiw+6x2nbni5owKpqTUdD01Z7e+XXvSblzzKsFaOKa9apYOclNmznnytlrq39z2VOFrjl7O8tHRumOX1XvSeu0Nj7F77BqlA7X4yQ5MRa5rH4p9fG8Tux+Jjf/ziFYdvujGrAAAAICby6mIeD3+804N+Gazdp+JdmqfeuWK6IeH2+inkW1Vv3xRDbulqno1Lm8z/t3fD+nAWfv3SoXB2ehEPTh9m15esF9xyWl2Y308PfTinXW19KmOalm1hJsyzP8Cfb30xZDm8rLxoHbH6Sv648B5N2cFAACQM/miOF26VmzuzM+N7GNtX2S1ePFii+VBgwapSBHHL3mLFCmigQMHWqxbtGiRK1MDAAAAgJva9+tC9MFy+4XpEwY00b0tHRemX+ft6aHnbq+r+aPbq6qNkXmuS0k36/3lhzV06hadjU60GztzY6je+d1+YfqH/RtrcGvrIzU7w9fLU/1tjA6//dQVnbxse7Rt5G/hVxI0eMpmnY60PVpZtZIBmje6naqVCrQZg+wpGeSrZ7rbHjX7nd8POf1xys0qKj5F90/dqmX77L8svqtROc1+9BaVDPJ1U2bOM5lM+nBAY7WpZrtAYOamU/ph0ymZzYYmrTqu4TO26UpCqt3j9mxcTouf6KA6fEySwc/bU1OGtVKZItZ/DwxDGjt7t45eiHVzZgAAAEDhdiU+Re/8flA9PlurPw44N/temSK+mjCgiZaN7aTOdUpnrDeZTBo/oLHNEa9T0s0a88suXU2yf89UUBmGoVlbz+iOz9Zp/fEIh/FNKwdr2diOGtOllrw8802pUr7RtHKwnrUzo9nve8+5MRsAAICcyxdXfCaTyS0/sG/ZsmUWy/8dqd6RHj16WCwvXbrUJTkBAAAAwM1u6voQvb/c9uxUJpP00YAmGtjqxkYhb1m1uJaP7aT72jguFt8SEqU7P1unhbvDrX4E/uPmU3rbQWH6B/0aO9WWI4PtjLo+b3tYjo8P9wuLStDg77YoLMr2BxC1ywRp3mPtVDHY342Z3RyGtauqmqWtF/zvDYvWwt1n3ZxRwREaEa/+kzdq52n7I8yPurWGvh7aQn7enm7KLPt8vTz17bCWdj9aeuf3gxr43WZ9uuKY7I0H4ulh0uu96uvroS1UxM/6bBc3s3LF/DTlwVby9bL+iD4+JV2P/LhdUfEpbs4MAAAAKHySUtP13dqTuvXj1Zqx8ZRS0x1/gB3g46nnetTRmnFdNKh1ZXlaGdW6iJ+3vhraQj42ruvPRCXopV/3FbrBFMOvJGjYtG16daETo6V7eejlu+rpt9HMgOfI6M411ba69Q/Gt4VGFbrfIwAAULjleXF6dkc/z+kPrDMMQ/v27bNY1759e6f379Chg8Xy3r17+fMGAAAAgByatiFU7y2zXZguSR/1b6JBN1iYfl2gr5c+7N9Y04a3UqkgH7uxsclpenbuXj05a7eiE/4tmPtp8ym9ufig3X3f69tIQ9vmvDBdkuqWK6JmlYOtbvttV7hS080uaQfucToyXkOm2B+Zv375opr7WDuVKernxsxuHt6eHnrz7oY2t3/05xGHL5xvRttPRanf5I06ZWe0fw+T9G7fRnq1Z3152JiiOz8pEeij6SNaq6ifl9XtZkMOC/FLF/HV7Edv0SOdajBoiB3NKgdrwr1NbG4Pi0rU4z/vVEoafRoAAABwI8xmQ4v3nFW3iWv14R9HFJvk+L7WwyTd16aK1ozrorHdaivAx/q90XWNKhbTm70b2Nz+x4EL+mHTqeymni+ZzYZ+3nJad3y2ThtOOB4tvVnlYC0f21GjO9dktHQneHqYNLZbbavbriSk6vglZosEAAAFh/2r6FwWGhqal83nK8nJyQoJCVFkZKS8vb1VsmRJVahQQQEB9qdWd5XTp08rIeHfl2iBgYGqUsX5goGqVasqICAg4xjx8fEKCwvL1jEAAAAAAP+asTFU7y61Pwr5+P6NNcjOCOLZ1a1+Wf31zK16ecF+rTh00W7ssv3ntf1UlD4Z2FRnohL0hoPC9Hf7NtIDt1R1Wa6SNKR1Ze0Ji86yPiIuRasOX9Kdjcq5tD3kjtCIeA39fovOxyTZjGlUsah+HtlWwQH2P55AznSuU1rd6pXRqiOXsmy7FJusyatP6MU76+VBZvnTkr3n9MK8vUqx8zFMgI+nvh7aQl3rlXFjZjlXs3SQvh3WUg9O26Y0c/YGoGhTrYS+GtqcD0mc1KdZRR2/GKevVp+wun1raJTeWnJAH/RrTKE/AAAAkA1bQiL1wfLD2hce4/Q+t9Uro5fvqqc62Rzh+/62VbQ1NEq/7z1ndfv7yw+reZXiampjoIWCICwqQS/9tk+bTkY6jPXx8tALt9fRyI41rI44D9uaVwmWl4fJ6r341tCobP9uAgAA5JU8LU6vWtW1L6ULqieeeEIhISFKSrJ8Cevl5aWWLVvqrrvu0pgxY1S6dOlcy+Ho0aMWy5UrZ7+4oXLlyhbHOXr0KMXpAAAAAHADfth0Su/8br8w/YN+jTWkjevvuUoG+WrKsJaavyNc7/x+UPEp6TZjL8Um68Hp2xwe8399GmqYiwvTJal30wr639JDSrCS49ztZyhOLwBOXo7T0O+36OLVZJsxTSoV008Pt1WxAG83Znbzeq1Xfa07ftnqFOdT14dqSOsqCr7J/1MYhqHJa07q47+O2o0rW9RX04a3VqOKxdyUmWu1r1lK7/drpJd+2+/0PqNuraFxd9SVNyPiZctzPero2MVY/W3jw7DZ28JUp2wRPdShupszAwAAAAqek5fjNP6PIw4HXvivBuWL6rVe9dWhVqkbatNkMunD/o118GyMQiLis2xPTTf0xKxdWvZUpwL3fMNsNvTL1tP68I8jVp/BZdaiSrAm3NtUtcoEuSG7wifAx0uNKxXT7jPRWbZtC43KlWesAAAAuYG3BPnAoUOHshSmS1JaWpq2bt2qt99+W1WrVtWbb76p9HTHF/s34tIlyxGxKlWqlO1jVKxY0e4xAQAAAACO/bj5lN5aYn8U8vf7NdLQtrn3MbDJZNKg1pX1x9O3qlXV4jk61jv3NNSD7aq5JrFMgny91LtJeavb1h67rPMxibnSLlzjxKU4DZlivzC9aeVg/TSSwnR3qlE6yGYBbEq6We8vt//hTGGXmm7WKwv2OyxMr1euiBaO6VBgC9OvG9y6ikZ3rukwLsjXS9/c30Kv9qxPYfoN8PAw6bPBzVSvnO0R8N5dekjrjl12Y1YAAABAwRKfnKb3lh7S7Z+tc7owvXwxP306qKmWPtXxhgvTrwvy9dLX97eQr5f1e6LwK4l64de9MozszU6Vl85EJmjo1C16Y/FBh4Xpvl4eer1Xfc0f3Z7C9BxqU72E1fXbQiML1O8PAAC4ueXpyOlwXmJiot59912tX79ev//+u4KCXHsxHxcXZ7EcGBiY7WNk3ifzMW/UpUuXdPly9l68nDhhOQ1uXFycrl696pJ8cGPi4+PtLgMAgIKP/h7IuTk7z+mDv07ajXn9zlq6u35xt9zjBHtL39/XUDO2hGvyutNWp5O156UeNdSvUYlczfXuBiU1b0d4lvVmQ/pl40mN6siMXvnRycvxemTWfkXGp9qMaVKxiCYPrC9TaqKupvKhgTsNb1VWv+4IU1RC1v8+fx28qF51LAtob5Y+Py45TS8sOKxNodF249rXKK5P+tVTkEeqrl61/TteUIxuX17HL0Rr1VHrU7fXLBWgTwfUV/WSATx/y6HPB9TTfTP26IqVv3tmQ3ril536eUQzVS8ZkAfZAbjZcI8PILckpqZrS2i0/L091LhCEQX6UrKAnFtzPFIf/HVSF+x8AP9fgT6eGtm+sh5oXUF+3p6Ki4t1SR4VA6WXb6+pd5Yft7p9xaGLmrzqiIa1qWh1e16w1uebDUNzd57XZ6tDlZRqdniMZpWK6n+9aqtayQDFu+jP8mbWuKy/1fUXrybr0JlLqlzc+nYAAGzhHv/m5Kr63RvFnV4eMZlMateunXr16qU2bdqofv36KlGihDw8PBQZGaldu3Zp6dKl+uGHHyxGVV+zZo2GDBmixYsXy9PT02X5ZP5F9PPzy/Yx/P0tL4Bd9cs9efJkvfPOOzk6xrZt23ThwgWX5APX2LZtW16nAAAAchn9PZA9Gy6YND/U/n3evdXTVTrmiFavPuKmrK6pKenZRtKPxz11MdHk1D79qqWrQtwxrV59LFdzMwypnL+nLljJa9bWU6qZclIezqUMNzmXIH190FNxabb/w1QvYuj+Cle0Y/N6N2aG/7q9nElzQqz/m/Thn8c1rqnk+f//CW+GPv9KsvTdEU+dT7D/D0q7MmbdW/qytm8qXCNc31lMOhHkqdNxluffspRZg2tc1al9W3Uqb1IrdB6sLn11yFPpRtbftdjkdD3yww491zhdATzZB+BmN0N/DyB3mY1rzz6WhnkoOf3atU4pX0Oj6qfLRh0m4FB0svTbKQ/ti3JuBicPGepQ1tAdldNUJPmENm844XinbCpuSK1KeWhHhPWcJq46qbQLx1TN9sRJeWr5um2adcJTJ2MdP1DzNhnqVcWszuWjFLpvq0LdkN/NICFNMslThrL+N/j57626pQyjpwMAcoZ7/JvDmTNn8rR95ljNA7fffruOHDmijRs36tVXX1X37t1VsWJF+fv7y9fXVxUqVFDv3r317bff6vjx4+rQoYPF/suWLdPkyZNdmtN/C+AlycfHJ9vH8PX1tVhOTGRUMwAAAABwxqaLzhWmdyqXdy8eKgVKLzROV+dyjkdL6ls1XV3KuydXk0m6pYz1nKKSTToeQ2V6fnI2XvrKQWF6zSKGRtdPlx+Fl3mqbRlDlQKt/z0+n2jSpos3z9+t8Hjp0/2OC9PvrpKuwTXM8iyET1x9PKWnGqarWwWzqgQaqlnE0Ig66RpWyyxf142fAUk1ikqDa9juay8nmTTjmIfSqUUAAAAFyIUE6YsDnvrtlGdGYbokRSSb9N1hTyWl5WFyKJDMhrT2vEkf7PF0ujC9cXGzXm6WrntrmFXEO/dyM5mkQTXMKutv/aLdbJg085in7Ewmlyeu/5l+tNe5wvTqRQy92DRdXSsYDAzhYgFeUgUbE2advMofNgAAKBgK4auS/K99+/aqU6eOU7GVKlXSypUr1a5dO4v17733nhISElyWU+aR0lNSUrJ9jORkyymybmT0dQAAAAC42Wy6aNJcG6MTXzegWt4Wpl/n4yn1r27WmPrpKuZjPZ++Va+9lHKn1qUNeZqst7n5Ei9s8ovw+GujAcfbKUyvVdSsx+qny49i1zznYZL6V0u3uX15mEe+e5GeGw5eMemLA566mmr799bLZGh47XR1r2jIVIj/yfH2kO6patbzTdI1tlG6mpcs3Oebl9qWMXRbedsF6sdiPLToFI/24T5m49psNQAAZFeaWfojzKQJ+zx1Ks76xWNksknzQ7m2sSfdfO3PEteExV37gHjBKU8lmx3flFQJNPRUwzQ9Us/stlH6fT2lEXXS5e1h/SLqSopJv5z0kDmfXGNdTrw2mMCCU55KcfBn6u1hqF+1dI1tmK4yzHqQa2oWtf7LQXE6AAAoKBiDqgDw8/PTjz/+qPr16yst7dpn45cuXdLff/+tvn37uqSNoKAgi+XMI6k7I/NI6ZmPeaPGjBmjgQMHZmufEydOWPzZtGnTRvXr13dJPrgx8fHxFlOCtGnTRoGBgXmYEQAAcDX6eyD7Fuy5oLmbj9uNebF7DT3QpqKbMnJOV0lDE1P10coQLT9wSemGFOTrqRe61VD/ZuXyJKe1cYf195GILOsPRHuqedv2Cg7IxSGx4NCh87F6Y/YBJaTZHg6vTdVi+nJQQ/l7U5meX3SVdMw4oj8PXc6yLSHNpD/DPTSgurnQ9vlzdp7T1C0n7RYLBPt76Yt7G6h55WLuSww3hVvNhsbOP6j1J69Y3b7ugoc6N6ujgS3Kuzkz3Ezik9M0ef0Z/bbngsxmQ11ql9Ard9RSca6rbgrc4wPIqX1nr+qtZcd1MsLxgGs7Ijw0oEN99WpYxg2ZFRxxyWn6dv0ZLd5/UVcT09SsUlG93bO2qpeyMaRyIRefnKav153WrAPnnCrqLhPko2e7VdddDUrLI4++rA2qfFFvLD1mddvBKx46E1BTD91Syc1Z/SvNbOiHTac0eWuYUp0o9G9Ruaj+16uOqpSgKj23pZWP0LoFh7Osj0w2qX7L9ipX1DcPsgIAFFTc49+cDh/Oei3hTnlanP7www/nSbsmk0nTpk3Lk7ZvVK1atXTPPfdowYIFGetyszg9Pj4+28fIvI+ritPLlCmjMmVy9iAiKChIRYsWdUk+cI3AwED+mwAAUMjR3wP2zdsepnf+sF+Y/kbvBhrZsbqbMsqeokWlL+8vqTdik3TparIqlwhQMf+8K1S6v30Nq8XpqemGVp64qofz6Z/jzWBvWLRGzT6gq3bmae9Uu5SmDGslfx8K0/ObN+5prDXH1ygpNeswfRsumNS+bOHr881mQ+P/PKIp60LsxlUrGaAZD7VR9VI8xEfumDystfpP3qTjl+Ksbv/w75NqULmU2tUs6ebMcDMIjYjXqJ92W/z+/Xk4Qicik/Tb6PYqRoH6Taew9fcAck98cpo++fuoZm46la2ZNz7486Q61q2gyiVuzsLrzP45clGvLTyg8zH/Diq3O/yqhv+8T3NHtVPdckXyMDv3++vgBb295KDFn4ctJpM0vF01PX97HRXxy9trlmEdi2rv+QT9ujPc6vZJa06pQ51yalWthFvzMgxDq49e0gfLj+jEpThJ9gvT/b099dKddfVgu2ry8GDkbnfo3MBXslKcLkmHI1JUp1JpN2cEAChMuMe/ObiqfvdG5Wlx+syZM2Vy8xeqhmEUyOJ0SerWrZtFcfrRo0ddduzMxd/h4dZvjuw5e/as3WMCAAAAAK6ZtyNMLy3YZ/cl7eu96ufbwvT/KlPET2WK+OV1GupYq5QqBvvrbHRilm1zt4fpoQ7V3P4MAtKuM1c0fNo2xSbbLky/tU5pTRnWUn6MmJ4vVQz21+jONfX5yqwf05hl0sJTHrovOxUn+VxSarqenbtHfxy4YDeuVdXimvJgK5UI9HFTZrgZFfHz1tThrdTn642KTkjNsj3NbOjxX3Zq8RMdVLUkH0nAdVYfvaSxs3cr1sqHZScuxemxn3foh4fbyNeLvhsAYGndsct6ZcF+q/fmjsQmp+npObs177F28vL0yIXsCobIuGS98/shLdl7zur26IRUDZu2VfNHt7sprgHPRSfqrSUHteLQRafiG1Yoqg/6NVbTysG5m1g2vNunkfaFR+vYxawfnaabDT05a7eWP93JbfeXB87G6P1lh7U5JNKp+LbVS2jCvU1uit+3/KRUkK9qlg7UyctZB5bcFhqlPs3y10ybAAAAmeWLuzrDMDJ+YFvlypUtli9fzjql8o2qW7euxXJYWFi2j5F5n3r16uUoJwAAAAAojH7dGa6XfrNfmP5az/p6pFMN9yVVCHh6mDSwlfVpkI9ejNXe8Bg3Z4Sdp6P0oIPC9K51KUwvCB67taYqFLP+EcrRGA/N3XXezRnljoi4ZN33/RaHhem9m5TXz4+0pTAdblG1ZKAm399CXjZGJ4xOSNXIH3YoNilr8TqQXYZhaPKaE3p45narhenXbQmJ0ku/7uOdDgAgw5X4FD03b48enL7thgrTr9t1Jlpf/nPChZkVHIZhaMGucHX/dK3NwvTrLsUm64FpW3XBiVHEC6q0dLOmbQhV90/XOlWYHuDjqdd71dfiJzrkq8J0SfL38dTk+1sowMZscReuJunZuXtkNufutdW56EQ9N3ePen+5wanCdH9vT/2vT0PNfvQWCtPzSJvq1mfJ2hYa5eZMAAAAsi9fFKebTKaM0cv+W6ieGz8Fmbe35ZRTqamue+FQtWpV+fv7ZyzHx8fr9OnTTu9/+vRpJSQkZCwHBgZmKaYHAAAAgJvdbzvDNe7XvXYL01+5q54evZXC9BsxsFVl2Rocfe72M+5N5ia3/dS1wvQ4O4Xp3euX0bcUphcI/j6eeqVnfZvbP/jrpOZtz/5AB/nJiUtx6jd5o3afibYb93iXmpo0pDm/t3Cr9jVL6Z0+DW1uP3EpTmNn71Z6LhezoHBLSEnTk7N3a8KfR+1eq163aM85Tfz7WO4nBgDI1wzD0O97z6n7p2u1YNdZh/HBAd6aOLCpxt1R12bMl/8c145TN1fhZfiVBI2YsV3PzdurK1ZmzLEmLCpRw6ZtVVR8Si5n5377wqPVd/JGvbv0kBJS0h3Gd69fViue66xHOtXIt6Pu1ypTRO/3a2Rz+9pjl/XN2pO50vbVpFR99OcRdf1kjRbsdvz3VJJuqVFCfz1zqx5sV00eNj6URe5rW72E1fXHL8UpMi7ZzdkAAABkj1deJyBdu2n19PTUbbfdpiFDhqhECesXWDe7CxcsR20qXbq0y45tMpnUpEkTbd26NWPdpk2bVLVqVaf237hxo8VykyZNmC4dAAAAAP5j4e5wveCgMP3lu+rpsc413ZdUIVMx2F+dapfWumNZZxpbsuecXu/VQIG++eJRSKG2NSRSD83cbvcFco8GZfX10Bby8cqfL42RVe8m5fXj5lPafuqK1e0vLdgnTw+TBrS0PoNBfrY1JFKjftqpmETbRSCeHia917eR7mtTxY2ZAf+6v21VHbsQqx82Wx9QZPXRy/rozyN61c6HJIAtYVEJevTHHTpyITZb+321+oQqFffXEP5tBIBcZTYbWrz3rObvCFea2VCD8kXVo0FZtaleQt55WIh7PiZRbyw6oJWHLzkV37tJeb11d0OVLuKrdLOhdccua6uV0X/NhvT0nD3645lOKurnbeVIhUe62dCPm0/p47+OOlWEndnxS3EaPn2bZj3aVkUKwZ9VbFKqJv59TD9uPiVnvrssV9RP7/RpqDsalsv95FygX/NK2hoSpTk2Pu6e+PdRtapaXG1rWB8tO7tS082ave2MPl953OmPGHw8DL3Qo5Ye6VyXovR8oI2N4nRJ2n7qiu5sVDB+9wEAwM0pX7yRNZlMMpvNWrlypdatW6devXpp+PDh6tmzpzw9GYXoug0bNlgsu3pk8t69e1sUp69YsUL33XefU/uuWLHCYvnuu+92aW4AAAAAUJAt2n1Wz8+zX5j+4p11NZrC9Bwb0rqy1eL0+JR0Ldt/XoNaMctXbtp0MkIjZ+5QYqrtl+p3NiynL4c2z9MiCmSfyWTSW3c31D1fbbBaJGAY0rhf98rL06Q+zSq6P8EbtGj3Wb346z6lpJttxgT5eunr+1uocx3XDRQB3Ig3ejfQycvx2nAiwur2KetCVLtMkAbS1yEbNp6I0BOzdinayVFaM3tt0QGVK+anLnXLuDgzAIB0rQD8hfl7tfFEZMa6baFRmrnplIr6ealrvTLq0aCsOtcp7bbiZLPZ0KxtZzT+jyN2Z8u6rlxRP73Xt5G6Nyibsc7Tw6TPBjfTXV+st/qR6Nnoa4XvXwxp7tLc85PjF2P14m/7HM7e5Mj+szEa+cMO/fhwmwI7w5NhGPrr4AW9teSgLl51PBq0h0ka3r6anr+9roIK2CAEb9/TUHvCoq1+FGg2pKdm79bypzupVJDvDbdhGIZWHLqo8X8cUUhEvNP7tSxlVu8qZg1oWYHC9HyiQrC/KhX3V/iVxCzbtoVGUZwOAMi2dEPypJuHm+TpW8AHH3xQgYGBMgxDxv+/oU9OTtbChQvVt29fVahQQc8++6x27dqVl2nmC9HR0frtt98s1nXr1s2lbdxzzz0Wy/Pnz1dcXJzD/WJjYzV//nyLdX369HFpbgAAAABQUC3ec1bPzdtjd8SncXfU1ZgutdyXVCHWvX5ZlQj0sbptro2RqeAaG09E6OGZ2+0WpvdqXJ7C9AKsUcVierN3A5vbzYb07Nw9WrbvvBuzujGGYWjSquN6Zu4eu4Xp5Yv5af7odhSmI1/w8vTQ10NbqHqpQJsxry08oEPnrroxKxRUhmFo2oZQPTh9m8PC9EGtKsnfRrFbutnQE7/s0sFzMbmRJgDc1BbvOas7PltnUZj+X1eT0rR4zzk9OWu3Wry7QsOmbdVPm0/pfEzWIkZXOXk5TkOmbNHriw44VZh+f9sq+vu5Wy0K06+rEOyvD/s3trnv4j3ntHB3eI7yzY9S0sz6fOUx9Zy03qnC9NplgvTTyDZqXLGYzZhtoVF6/OedSkmzfW+TX52NTtSjP+7Q6J93OVWY3qhiUS1+oqPeurthgStMlyQ/b09Nvr+FAn2sX1tdik3Ws3P3KN2ZoeOt2BMWrcHfbdGon3Y6XZjeukoxPd84TQ/WNqvEjdfEI5fYGj192ynrfQMAALYcvmLSR3s9FeX4kgtwiTx9Ezhz5kxduHBBM2fO1G233Zax/nqx+uXLlzVp0iS1bt1ajRs31sSJE3XhwoU8zDjvvPDCC4qOjs5Y9vHx0V133eXSNpo0aaLWrVtnLMfFxWnChAkO95swYYLi4/+9sbnlllvUoIHtF5UAAAAAcLNYsvecnp1rvzD9hdvr6ImuFKa7io+Xh/o3tz5q887TV3TiUtaRqZBz645d1sMztysp1faL8LubVtAXQ5pRmF7AjehQXc90rWZzu9mQxs7ZrT8P5N9neEmp6Xp6zh59uuKY3bgG5Ytq4ZgOql++qJsyAxwrFuCt7x9spSJ+1gtxUtLNemPxAZlvsJgFN4ek1HQ9P2+v3l16yG7hk5+3h768r7km3NtUk+5rLlsDaManpOvhmdt1Ljr3iiEB4GYSnZCip2bv1tNz9uhqkuMCcElKTTe0/niE3lh8UO0+/Ed3f7lBk1Yd16FzVzMGicuJ1HSzvl59Qnd9sV7bTkU5jK9RKlBzR92i9/s1VlE7I7r3bFxeg1pVsrn9jUUHdSYy4YZyzo92nbmi3l+u1+crjys13f5/F29Pk57uVltLx3ZUp9ql9cPDbVSrTJDN+NVHL+u5eTde1OxuaelmTV0foh6frtXKw5ccxgf6eOrN3g20aEwHNa5ku1C/IKhROkgfDmhic/v64xH6evWJbB0zLCpBY2fvVt+vNzr1d1SSapYO1NQHW2nq/Y1VxfavFvJYWxvF6YfOXdXVpBub/QgAcPPZGHJFU4966GKiSV8d9NS5mKS8Tgk3gTx/GxgQEKAHH3xQK1eu1KlTp/Tuu++qTp06FjGGYejQoUN68cUXVblyZd11112aO3eukpML3mcc48eP186dO52OT0tL0/PPP69p06ZZrB89erTKly9vd1+TyWTxs2bNGoft/e9//8uS77p162zGr127Vh999JHFuvfee89hOwAAAABQ2P2+95yembPbbmH6cz3q6MnbarsvqZvE4NaVbW5j9HTXW3P0kh75cYeS7YzQ1rdZBX02qKm8KEwvFB5uV1m9KtseIT/dbOip2bu06vBFN2blnEuxSRoyZYuW7D1nN65r3dKaN7qdyhXzc1NmgPNqlQnS10Nb2CwU3nn6in7dVfhGGXWVdLOhA2djdDY60SXFegXNuehEDfx2sxbsPms3rlJxfy14vIPublpBktSjQVm9fU9Dm/EXrybroRnbKZApJFLSzLoYm6zweCkySboJ/6oAeWb98cu68/P1+t3B9aoj+8/G6NMV10bn7jRhtd5eclCbTkQo1c6sQbbsC4/WPV9t1Md/HXU4Mrenh0lPdK2p5U93UtsaJZ06/lt3N7Q5M0xccpqenrtbaTeQd34Sn5ymd34/qAHfbNKxi45nLm9eJVjLxnbSsz3qyNfr2gjbJQJ99PPItqpU3N/mfkv3ndfri/bn+2ucPWHXfqfeW3ZYCSm27y2vu6NhWa18vrMe7li90DxXuKdpBd3ftorN7Z+vPKZNJyMcHicmIVUfLD+sbhPXOrzPva5UkI/e69tIfz1zbVYDk8nGjQXyhTbVrf9bajau3fsBAODIumOX9fT8g0ozrvX5kckmPfzzPoVFFZ6PQJE/mYx8emeyZcsWzZw5U/PmzcsYMdxkMskwjIyL4yJFimjQoEEaNmyYOnXqlIfZOq9Lly5au3at2rdvr0GDBqlbt26qV6+evLwsR7qJiYnR8uXLNWHCBO3Zs8diW82aNbV161aVLGn/hj7zTcTq1avVpUsXhznecccd+vvvvzOW/fz8NH78eD366KMKCAiQJMXHx+v777/XK6+8oqSkf7+k6dmzp5YtW+awjdx28OBBNWrUKGP5wIEDatjQ9oNz5L6rV69q9erVGctdu3ZV0aKMPAYAQGFCfw/8a9m+8xo7Z7fd0aqe7V5HT3enMD23DPhmk9UXNCUCfbTllW7y8cqbl5kbT0Ro95krKhHoqwEtK2a8ZC6o/jlyUaN/2qUUO4UC/VtU1Mf3NpWnrSpKFDjX+/w/wjz0Z7jtv0s+nh6a8mBLdalbxo3Z2XbgbIwe/XGHzjsYFeaBW6ro7bsbFpqiBxReMzaG6p3fD1ndViLQR/8831nBAT5uzip/+/PABb237JDCr1wb4btXk/L6sL/9EV0Lk60hkRrzyy5FxqfYjWtfs6S+GtpCJQKz/v68v+yQvl8fanPfDrVKasaINnl2rQXr0s2GriSkKDIuRZFxyYqM/8//Xv//cf/+/8wjNQf7GJoyrIXa1K6QR2cAFH6JKen66M8jmrnpVK62U9TPS13rlVGPBmXVuU5pFbHTByampOuzlcc0dX2I3Q/vr2tcsZg+GtBEDSpk/3ng3rBoDfhmk9JsNDT2tlp67va62T5ufrD22GW9umC/zjoxw0iAj6fG3VFXD7arZvMe+nRkvO79drMux9oeyG/UrTX0yl318l3R8dWkVE3866h+3HLaqQ+fyhfz0zv3NNTtDcvlfnJ5ICk1XQO+2aSD565a3V4qyFfLn+6oMkWyfjSdkmbWz1tOa9I/xxWd4NzHgX7eHnq0Uw091rmmgnz/rU/huX7+ZhiG2nywyurf+ce71NRLd9bLg6wAAAXFhuMRGvnDdquDC1UM9tecUbeocomAPMgM7pDXNbT5tjj9upSUFC1atEg//vij/vrrL6WnX/tyNnOherVq1TR8+HANGzZM1atXz8uU7bpenP5fvr6+qlSpkooVKyZPT09FRkbq1KlTMpuz/qNQrlw5rVu3TrVrOy5guNHi9IsXL6pdu3YKDbV8wOzv768aNWrIMAyFhIRYFKVL14rmN2/erNKlSztsI7fl9V8sZMVNLQAAhR/9PXDN8v3n9dRs+4XpT3errWd71LG5HTk3b3uYXvxtn9Vtk+9voZ6N7c9G5mpJqel6fv5eLdt3PmNdtZIBmvFQG5sjxOV3Kw5d1Jhfdtqdinxgy0oaP6AJhemFzPU+3zCkpWEeWnnWToG6l4emDW+lTrXz9nnRnwcu6Nm5e5SYan9Uvtd61tcjnarnuyIOwBrDMDR8xnatO3bZ6vYHbqmi9/o2dnNW+VNaulmf/H1M3649mWVb+5ol9cPDbeRdiD9IMQxDP285rXd+P2Sz6O+6kR2r65W76tn8QMdsNvTk7F1avv+CzWMMaFFJnwxswr+luSw2KVWXYpMzFZynKDI+OdP/puhKQkqORz8P8vXUX892VsVg2yP2Argx+8Kj9ezcPTp5Od5hbPVSgbp0NUnxTow27Yi3p0ntapZSjwZl1b1+GZUv9u/f700nIvTKwv06Hel4NEU/bw8916OOHu6Qs1GtJ685oQl/HrW6zcMkzRnVTm2ql7jh47vblfgUvbv0kMPZSq67tU5pvd+3kVPFQUcvxGrQd5sVk2i7KHncHXX1RNdaTueb2/4+eEFvLD6gi1dtF9Vf52GSHupQXc/2qGNRRF0YnYqIV+8vNyguOc3q9nY1SurnR9pmPFcxDEN/HLigj/484tTfT0kyma5dnz1/ex2Lv+fX8Vw//3ti1i6LZ4rXtaxaXL893j4PMgIAFAQbT0To4ZnWC9OvK8gfgcKxvK6hzffF6f918eJF/fzzz/rxxx+1f/9+Sf8WqV///5LUoUMHDR8+XAMHDsx3F83WitOd1bNnT82YMUNlyjg32tSNFqdL0unTp9WnTx/t3bvXqfhmzZppyZIlqlzZ9tTp7pTXf7GQFTe1AAAUfvT3gPTH/vN60kFh+tjbaunZHnUo1sll8clpavP+Sqsv7G+tU1o/PtzGbbkkpKTpsZ92av3xrNMxlyvqp7mP3aKqJQtWgfpfBy/oyVm77BamD2ldWR/0aywPCtMLnf/2+YYh7Taq6oettgsufL08NOOh1mpfs5S7UsxgGIYmrzmpj/+yXuRynb+3pz4b3FR3NnLvhytAToVGxOuOz9ZZncHCZJIWP9FBTSoFuz+xfCQyLllj5+zWxhORNmOGtq2i9/s2KpTXZ8lp6Xpz0UHN3RFmN87Hy0Pj+zdW/xaVHB4zKTVd90/danWWmuv4GDP3HLsYq9cXHdC20Ci3t925TmnNfKh1ofy7AuSFtHSzvllzUl+sOu7w4yFPD5Oe7lZbY7rUVLphaPPJSK04dFErD190qtjXGY0rFlOPBmV19kqiw37juvY1S+rD/o1dck+bbjb0wNSt2hxivc+uGOyv5U93UjH//D3jiWEY+n3feb2z5KDD2UokKTjAW2/2bqB+zStm69/XPWHRuv/7LXY/VPhfn4Z6sF01p4+ZGy7HJuvtJQe1bH/WwlprmlQqpg/6NVajisVyObP8Y/n+8xrzyy6b28d2q63netTRztNX9P6yQ9p1JtrpY3esVUqv9qxvd0YDnuvnfz9uPqU3Fx/Mst7b06R9b90hf5+CPTMjAMD1Np2I0MM/bFdSqu3C9KFtq+i9Po14h1OI5XUNbYEaCqRs2bJ6/vnntXfvXu3atUtjx45VqVL/vtQyDEOGYWjjxo0aNWqUKlSokGV077z22muvafTo0WrYsKE8PR1fIAYFBWngwIFau3atli1b5nRhek5VrVpV27Zt00cffaQKFWxP01ihQgVNmDBBW7duzTeF6QAAAACQF/48cMHhiOlPUZjuNoG+XrqnmfX72fXHLzs1nbYrxCalavj0bVYL0yXpwtUk3Tdli8KinBvtKj9YvOesxvxivzB9aNsqFKbfJEwm6bnbquuhDtVsxiSnmTVy5g63F9ElpabruXl7HRamlyvqp/mj21GYjgKpeqlAPda5htVthiG9seiA3WuTwm5vWLTu/nKD3cJ0SZq19YxmbjrlnqTc6OLVJA2ZssVhgWH5Yn76dXQ7pwrTJcnP21PfP9jK7uwvX6w6rvlOFjbCeb/vPac+X23Mk8J0SVp77LIWOjkCMAD7QiPiNfC7zZq44pjDwvQapQO1cEx7je1WW16eHvL18lSXumX0fr/G2vxyNy1+ooOeuq2W6pUrkqOc9p+N0acrjjlVmF7Ez0sfDWisXx5p67KPrT09TPp0cFObxednoxP1+qIDys9j752PSdQjP+zQ2Nm7nSpMv7tpBa18rrP6t6iU7WdFzSoH6/vhreTjZbvk483FB7Vwd3i2jusqhmFo/o4wdf90rVOF6UG+Xnr77gZaOKbDTVWYLkk9G5fX8HZVbW7/8p/jenD6Ng34ZpPThel1yxbRzIda66eRbewWpqNgsDVrRGq6od1htj8YBQDcnDafjHRYmH5vs3IUpiPXFaiR061JS0vT8uXL9cMPP2jZsmVKTb02dZVhGDKZTIqNjVVAgOOpr/JCQkKCDh06pFOnTun8+fOKi4uT2WxWcHCwihcvrgYNGqhx48ZOFbHnJrPZrJ07d2rv3r26dOmSJKlMmTJq1qyZWrRoIQ+P/PeNQ15/9YGs+OIaAIDCj/4eN7O/Dl7QE7/ssvtC+YmuNfXC7XUpTHejPWHR6vv1RqvbnuleW890z93RPKMTUjR8+jbtDY9xGFupuL/mPtZOFYOzTq+cn8zbHqaXFuyTvadJw26pqv/1acjveiFmrc8vUqSI3lpyUD9uPm1zv0AfT/04so1aVrX+UtWVLscm67Gfdjh8cd+0UjF9/2ArlSnql+s5AbklMSVdPT5bq/Ar1j+8er9fI93f1naxS2E1Z9sZvbn4oNVR5a3xMEnTR7RWl7ruGSAmt+08fUWjf96py7H2R9NtXa24Jt/fUqWL+Ga7jdOR8eo3eZOibBTfeXmYNPOhNupY2/0zZxQ2qelmjf/jiKZtCM3rVBQc4K0Vz3a+od8ZANfeYf8fe/cdFsXVRgH8LEvvUqRXuyK92hNN7CX23luiJqY306tRY42x966xR429S7FhxwKooKKIFOmw8/1h8NOwMwsIuwuc3/P4JO69zLzgsrszc+a9ayLv4MedV5GdL97xusjQJu74tF39EnfFvfM4C/uuJmH/lSRExqdUyE1q7b3s8V2XRhX2GXrPpfsYu0q8i/S0Xj7oEVCyG6rURaEQsDryDibvvoanuQUq5ztYGOLHbl5o3cDulfe9/0oSxqw6I/pvLdeRYe4Af7RtZP/K+yqpO4+z8MWWizh+U/mN+v/VrpE9vunSEA4W2n1OpCLlFhSi17xTuFCCc0hSbM0M8NGbddEzwAXyEobNeF5f+ykUAvx+2Ie07PxiY+o4x0lERJVHeOxjDFsaJXmsEVZTgT9HtIClRfW6IbA60nSGVldte6ogurq66NKlCxo2bAh7e3v8+eeflebip7GxMQIDAxEYGKjpUiTp6OggKCgIQUFBmi6FiIiIiIhI6+wtQTD97VYMpmuCj7MF6tmZISYpo9jYxtMJmPB6nRJfqCutRxm5GLQ4AtceFN+3MglPstFvQTjWjwnV2ouxy07E4dsdVyTnDG3ijm86N+RzvRqSyWT4tnMj5BcKWBt5R+mczLxCDFkShVUjQ+DrYllhtVy5l45RK06rXCGhk7cDpvbygaEel7+mys1IX45vOjfCqBWnlY7/ticG7b0cYGWir+bKNCMnvxDfbr+MdVGl69qtEIAJa85h8ztNUMfu1TrPatq6yDv4atslyVVOgGc3lH3VqaFkx1UpbtYmWDQkEP0WhCO3oPhNAAUKAWNXncHGsWFo4MCAU1k9ysjF+DVnEaGhbun/lZqVj2+3X8YfA/w1XQpRpfMwIwefbrqAQzGPVM61MzfAlJ4+aFHXtlT7cLU2xohmHhjRzAOpWXk4FPMQ+64k4UjMI2TmqQ7DS7E1M8APXb3QzqtiQ87tvBzQN8hF9L38622XEOheo9w6tr+qW4+e4vO/LiIyvmSv0wNDXfFpu/owM1TeIb602jS0w++9fTBx/XmlN5IXKgRMWHMOS4cFoWntir1hrFAhYOmJOEzbe71EN184WRrhuy6N0Kbhq4f0KzsDXTnm9PNHx9nHkJGj+gaH/zLWl2N0C0+Mau4JE4NKHwOi/9DRkSHI3Qr7ryYVG9PUijpERKR9IkoQTA+tqUBvTwV0eA2H1KBSfypNS0vDunXrsGLFCoSHhwMAL34SERERERGR2uy7koRxa6SD6WNb1sInbRlM1wSZTIY+QS74fmfxQHViajZO3Ewu9YX+kriflo0BiyIQ+yizVF93JyXr34B6GOy0rIvzn4dvYfKea5JzRjTzwKSODfhcr8Z0dGT4qZsXCgoV2HhG+dLxT3MLMGhxBNaOCq2Qpdr3XUnCe+vOIUtF8Ob9NnXxbuvafL5SlfFGQzu0rl8TB649LDaWlp2PybuvYXJPbw1Upl6Jqdl4e9WZMneczMgtwPDlUdg2rlmlDPPnFSjww84rWBkuvooFAOjJZfihqxf6Bru+8j79XWtgZl8/vL36jNJA3NPcAgxbGoWt45rC3kK7Pt9UBmfvPMHbq84gKV26A35pmRvqwsbUANam+rAy0Ye1qQFsTP7//9am+khNf4oJGy6hUCj+Xvn3xfvofOlBhQdUK5vsvELcfPgUnrYmDAZSMXsu3cfnmy/iSVbxrrf/1cnbAT9284Kl8au9F1ka6+MtP2e85eeM3IJCnLr1GPuuJGH/1aRSv670DXLB5x0awMKofALVqnzduSEi41IQm1z8uDozrxDvrTuPjWPDoCfX3Arf+YUKLDgai5kHbiBPyU1a/+Vpa4LJPbwR5F7+K0l19XVCRk4BJm29pHQ8r1CBUStOY9XIEPi71ij3/QPAtQfp+HTThRKtHqcjA4Y19cAHb9Tl6+ULXK2NMaWnD8auOlPir9GRAX2CXPB+m7pcEayKC/FQHk4/e+cJ8goUZb7hlIiIqobIuBQMWyYdTA+xVaCPpwIV1LOJqJhK90m/sLAQu3fvxooVK7Bz507k5j47cBYEATKZDMK/Zz+dnJwwcOBAGBryAzgRERERERGVvwNXk/DO6jOSHSnHtPDEp+0YTNekt/yc8Ovua8grLH6heH3U3XIPp99NyUL/ReG4myLdsVlM/ONnAfV1Y0JR00zz5zQEQcDv+65j9sGbkvPGtPDEZ+3r87lO0NGR4dce3ihUCNh8LlHpnIycAgxY9Cyg3tCxfDrpCoKA+UdjMXnPNaXhyCKGejqY1ssXHb0dymW/RNrk2y6NcPxmstIO1utP30XvIBcEuFVMGEkbHL+RjAlrz6oM/TVwMEcnbwdM+SdG6fjdlGyMXXkGq0aGVKqAx6OMXIxbfVZl19aaZgb4c2BAuT4X2nnZ46uODZXeEAgAD9JzMGxZFDaMCS23LrFVnSAIWBVxB9/vuKyyAz4AmOjLYW1qACsTfdiY6sPaxABWpvqwNtGHzb+PW5s++/8axvolem6np+vjDScF9iQoX2Hkq22XEOZpDQtj/pumZedj+r7rWBd1Bzn5Csh1ZPi8fX2MaObBz8eE9Jx8fLf9Cv46q/zmzReZGerix25e6OrrVO51GOjK0apeTbSqVxM/dvPCxcQ07LuShH1XkiRX/HKzNsYv3RujSa2K7bj9X8b6upjZ1w/d/zyh9HXw/N1UzDpwAx++WU+tdRU5czsFX2y+pHSltv/S1ZFhbMtaGP967QpdtWlgqBsycgpEbyzPyivE0CWRWD+mfFc0yS0oxJyDN/Hn4VuSzRuK1Lc3w689vCt0Na3KrJ2XPYY1dcfSE/Eq57aqZ4vP2zdAPfvKveoPlUywh/IbW3LyFbiYmFalj/WIiEhaVHwKhi6NlGzY0tXbDq2MExlMJ7WqNOH0c+fOYcWKFVi7di0ePXq21Nl/A+lGRkZ46623MGTIELRu3ZonfIiIiIiIiKhCHLyWhLdXnZUMioxq7sGwrhaoYaKPNxvZYeeF+8XG9l55gMdPc2FtalAu+7r16CkGLIzAg/Qc0TnONYwwb2AAvtxyUbSbWGxyJvovjMC60aGwKafaykIQBPz491UsPh4nOY8dqOm/5DoyTOnlg3yFgB3R95TOScvOx8B/O6i/6oX03IJCfLH5ksrAj525ARYODoS3s+Ur7Y9IW7lYGWPca7Xx+77rSse/2noJ28c3ha4GO4xWBEEQ8OeRW5j6TwxU5aG6+znhp7caw0hfjvTsfMw/Gqt0XmR8Cr7cchG/9fSuFO9vFxJSMWblGdxPE/8MAgC+LpaYPyigQlZoGd7MA3efZIkGqa7eT8c7q89iydAgjXa5rQxy8gvx5RbV72s6MuDDN+theFMPGOlXTNjxDScB0Y8F3M8u/nvwKCMXP+26gt96+lTIvisDQRCw5Vwift51DclP/9+FulDx7HO0vq4OBoe5a65A0rjw2Mf4cEM0ElNV37zctLY1pvT0gaOlUYXXJZPJ4O1sCW9nS3z4Zj3cTcl6HlSPjE9BoUKAib4cA8PcMLF13Qp7jVGlsbMFPnyzHn7drTxs/cehm2hW2wYhntZqqyktKx+/7rmGtZF3SjTf29kCv3b3LrebclV5u1UtpOfk48/Dt5SOp+cUYNDiSGwaGwZ3G5NX3t/p+BR8+tcF3CrBynH6ch1MeL02xrSsValuANSEz9s3wNk7qYi+m6p0vIGDOb7s0ADN6qj3phHSrEaO5jDWlysNHkbGpTCcTkRUTZ2OT8HQJdLB9O7+TvjqTQ8cPaK8mQxRRdHqT/3379/HlClT4O3tjcDAQMyaNQsPHz6EIAjPA+kA0LJlSyxZsgQPHjzAypUr0aZNm0pxwpiIiIiIiIgqn0PXHmLsyrNKO3EXGdnMA190aMBjUy3RN8hV6eP5hc/CJOXh6v109Jl/SjKY7mljgg1jwuDlZIEVw0Pg5SR+cfrmw2dB98dPS7fUenlRKAR8ufWSymD6lx0a4L02dfhcp2LkOjJM7+2D9l72onNSMvMwYFE4bj5U3W1QTPLTXAxYGKEywNfYyQLbxjVjMJ2qvNEtPOFmbax07Mr9dKyOKFmQqrLIyMnH2FVn8Nse6WC6ro4M33dthGm9fZ4H7D5pVx9tGtiJfs3GMwlYeEx5eF2bbD6bgJ7zTqkMpvcOdMb6MaEVEkwvMqljQ7RtJP4zPXYjGV9uufjS9R162d2ULPT486TK97UaxnpYPjwY416rXaGhUV0doF/tQsig/N9sw+kEHLvxqML2r82uPUhHn/nh+GBD9EvB9Bf9sPMKzt15oubKSBvkFhTi511X0W9huMpgur6uDr7u1BArh4eoJZiujIuVMYY388Da0aGI/uZN7P+gJaImtcHn7RtoLJheZHRzTzSppTx8rhCA99efR5qKVVPKgyAI2HY+Ea1/P1yiYLqhng6+7NAAm99uorZgepFP2tbDgBDl50GAf4+hFkXgXglumhDzNLcAX2+7hF7zT5UomB7oVgO73muGCa3rMJheAvq6Ovijvx88/nMDgb25Iab28sHOCc0YTK+GdOU6ogH0yLjHaq6GiIi0wZnbKRiyJBKZUsF0PydM6ekDOVumkwZo3Sf/nJwcrFmzBu3atYOrqys+++wzXL58udjJyjp16uD7779HbGwsDh06hKFDh8LU1FRDVRMREREREVF1cDjmIcasPCMZTB/e1ANfdmQwXZs0qWUN5xrKL/Kvj7r7ygGp6Lup6LsgHMlP80Tn1LMzw/oxYc/DBhbGelg1IkRyKe2YpAwMWBSBJ5ni260IBYUKfLQxGmtUBBh/6OaFUS081VQVVUa6ch3M6ueHNxqKBxWTn+ah38IIxD56WurtX3uQjq5zTuD0benQV4fG9tgwJgz2FhUXyCTSFoZ6cnzXpZHo+NS9MXiUoZkbn8rbzYcZ6PrHCfxzOUlyXk0zA6wfE4rBYe4vfT6T68gwo68v6kus3vDL7mvYf0V6+5pSUKjADzuv4IMN0cgrEP9sqqsjww9dG2FyD28Y6FZswFCuI8OMPn7wdbEUnbPhdALmHLxZoXVUVkeuP0Kn2cdx+V665LzGThbYMaEZmtexVUtdbqbAaw7in5c/33wRmbkFaqlFGzzNLcCPO6+g46zjiIxPkZybXyhg3OqzSFHz53nSrKv3n31GXXA0FqoONRs5muPvCc0wvJkHdLQkLGJqoIvaNU1hrK8dC7Dr6Mjwe29fWBrrKR2/l5aDL7ZW7I1Ptx9nYvCSSLy37rzkcX+RprWt8c/EFhjVwlMjK9bIZDL80NULXX0dReckpmZj4OKy3RB/6NpDvPn7Eaw4dVvlc9xEX47vuzbChjFhqF3z1VbMqm6caxhj6ztNMaljAwwIccW0Xj449FEr9AxwZrisGgvxsFL6+On4JyhUtYwUERFVKWduP8GQJVGSwfRuvo6Y0ovBdNIcrQmnHzlyBCNGjICdnR0GDRqEffv2obDw2S9P0cGkhYUFRo8ejRMnTiAmJgaTJk2Cm5ubJssmIiIiIiKiauLI9UcYrSKYPqypO77qxGC6ttHRkaF3oIvSsRsPn+LsndQybzsqPgUDFkUgLVu8U1tjJwusGx0KWzODlx63NNbH6pEhkqG4aw8yMHBxhFo6wQFAXoECE9aew2aJjvI6MmBqLx8MCuU5GVJNT66DOf398Hr9mqJzHmXkov/CCNx+rLrjXpEDV5PQY+5JlZ0o321dB3P6+Wu84yOROrWqVxPtGilftSAjpwC/7Lqq5orK366L99F1zgnEqujUGexuhZ3vNkOAm/IQh6mBLhYNCYSNqb7ScUEA3lt3DlfvS4eF1e1RRi4GLo5QucKJtcmzzxqD/hPMr0hG+nIsHhIo2sEfAKbtu47NKjqDVycKhYDZB25g6NJIyc+UwLMO+BvHhsG5hvjPtyK0d1HAtYbym7wSnmRj6t4YtdajCYIgYHv0PbSedhiLjseVOAB2Ly0HE9efZ2CsGihUCJh/5Ba6zjmBaw+kVwbSkQHjX6uNLe80RR07BnZVsbcwxOQe3qLjf1+4j01nyv99Ja9AgT8O3cSb04/i2I1klfPNDXXxW09vrBoRAjdrE5XzK5KOjgxTe/mgTQPx47DYR89C9+k5JTvf8PhpLt5bdw7DlkXhnooVWwDgtXq22PtBSwwOc9eamy8qGwtjPYxs7omf3mqMHgHOPK4lBHsoX0kiI7dA645ZiIio4py98wRDlkTiqcSN4l19HTGtty+D6aRRGg2n37hxA1999RU8PDzw+uuvY9myZcjIeHawXhRIl8vl6NChA9avX4/79+9j3rx5CAsL02TZREREREREVM0cvf4Io1acluxKObSJO77u1JDBdC3VM8AZYufg1kepXpJbmWM3HmHQ4gjJE4CBbjWwelQIapgoD71Zmehj1cgQ1Kkpvhrc5XvpGLREOgBfHnLyCzFm5WnsvvRAdI6ujgyz+/mjZ4BzhdZCVYuBrhxzB/ijRV3xDq8P0nPQb0E47qZkSW5LEAQsPBqLkStOS3aF0dd91rX9gzfqMghB1dJXnRvCSE95eGXzuURExFbOZd8LChX4ZddVvLP6rORrAPBsNZvVo0JQ00x61QTnGsaYPygQ+rrKL5dk5hVi5PLTWtNx/nR8CjrOOobwWOmOzUXdtUM8lQdYKpK1qQGWDg0S7XILAJ/+dQEnb6oO+lV1adn5GL3yNKbtuy7ZfVZfroOf32qMyT28YSjyu12R9OXANx3qiI4vOxmPM7eln5OV2c2Hz1Y0enftOSSll/614Oj1R5h98EYFVEba4m5KFvotCMcvu69J3tAOAK5Wxtg4Ngwfta0n+t5DxbVtZI9+wa6i499sv4z45JLf7KpK1L/vt1P+iUGuxLmgIl18HLH/w5boHeiiNeeFnt0o7I8wic8Cl++lY8SyKGRLfK4SBAFbzyWize9HsO38PZX7tTLRx8y+vlgyNAhOlspXsSOisvF2thB974iMq7qfxYiI6P/O303FkMXSwfTOPo6Yxo7ppAU0esRbr149/Pzzz7h9+zYEQXgeSBcEAT4+Ppg2bRoSEhKwc+dO9OrVCwYGBiq2SERERERERFS+jt1QHUwfEuaGbzozmK7NHC2N0FIkGLvzwn3JE3nK7L+ShBHLTiMnX/x50ay2DVaMCIa5oXgwCwBsTA2welQIPG3FO6tdSEjDkCWRyChhR7PSyswtwPBlUTgU80h0jr6uDuYPCkBHb4cKqYGqNkM9ORYMCkDT2uLBiHtpOei3MFy0G3pegQKfbLqAn3ZdlQzw2ZoZYMOYMHTxEV/Gnqiqc7I0woTWtUXHv952GfkqwnPaJvlpLgYtjsT8o7GS84z05JjZ1xdfd24IPXnJLoEEuNXAbxIdWRNTszFm5Wnk5EsH4iuSIAhYcjwOfReE46GKoPxbfk7YODYMjhoMhHnammLRYPHQf36hgDGrzuB6knR34aos5kEGus45jv1XH0rOc7AwxIaxYegf4qrR440gN0v0D1EeDBUE4JNNFzT6O1IRsvIK8Ovua2g/8xhO3nq1m3pmHriBI9fFP2tT5SQIAjaevov2M48hMl51KLBfsAt2v9dcdEUPkvZVpwaix81ZeYV4b925V/58k5qVh8/+uoBe807hxsOnKue7WhljxfBgzOrnp/KGOE0w1JNj4ZBA+DhbiM6Jin+CsavOKD3vlfAkC8OWRWHi+vN4UoIV3br5OmL/By3R1deJ58iIKoChnhy+LpZKxxhOJyKq+qLvpmLQ4ghkSFzP6uTtgOm9faBbwnNiRBVJK56FMpkMMpkM9vb2+OCDDxAdHY1z587h/fffR82a4ktNEREREREREVWk4zeSMXL5ackuWYNC3fBtl0a86FYJ9AlSHqbJyivEzmjV3b+K7Lxw79mFW4mL3q3r18SiIYEw1tct0TZrmhli7ahQeNiIB9TP303F0KVRpQ7Sq5KWnY/BSyIlAzdGenIsHRqE1g3synXfVL0Y6smxaHAQQjzEwzgJT7LRb0E4HvxnmfiUzDwMXBSBjWcSJPfRyNEc28c3Fb1YS1SdjGzmiVoiAa6YpAwsPxmv3oJewfm7qeg8+zhOqej47m5tjC3jmqCrr1Op99HNzwnjXxMP9J+9k4rPN1983mRHnZ7mFmD82nP4fucVFCjE9y/XkeGrTg3xe28fjXTX/q9AdytM7+0rOp6RU4ChSyKRlJ4jOqeq2h59D93+OIH4x9IrhjSpZY2dE5ppzfvaZ+3rw95cefjy1qNMzDl4U80VVQxBELD74n20mXYE847cQn6h6t/7prWf/VvVtzcT2SYwcd050ZvwqPJ5/DQXY1edwcebLqg8RrMx1cfiIYH4pbs3TAxKdoxIxRnr62JWXz/oyZWff4lOSMOM/dfLtG1BELDlXAJaTzuCdVF3Vc7X1ZFh3Gu1sPf9FpIrRGkDUwNdLBsWjLp24iu2Hbn+CO+vP4/Cfz9nKBQClp2Iw5vTj+KwxE3sRRwtDLF0WBBm9PWDlcjKcURUPsTOqUTGp2jkWIWIiNTjQkIqBi6OQEaO+LFHx8YOmNHHl8F00hpacfQrCALkcjlsbGxw8OBBHDx4sEL3J5PJcObMmQrdBxEREREREVVuJ24mY8TyKMlg+sBQV3zflcH0yqJ1g5qwMdVH8tO8YmProu6ir8QS4UU2nr6LT/+6AIlcGDo2dsD0Pr6lXqLdztwQa0aFoM/8cNxJUR5UOnP7CYYvjcKy4UElDr5LScnMw+AlEbiUmC46x8xAF0uGBSHInd396NUZ6cuxZGgQhi6NRFT8E6Vz7qRkod/CcKwfHYqa5oa4npSBEcujcDdFOszVrpE9fu/jUy6/G0RVgb6uDr7v6oUBiyKUjk/fdx2dvB1hb6F9XT6LCIKAtZF38e32y5I3hQFAmwY1Ma23LyyMpFcskfLBG3Vx8+FT7Ln8QOn4lnOJqF3TFOMkQuzl7ebDDIxddRY3VXRvtTTWwx/9/dG0to2aKiuZjt4OuJfaAD/tuqp0/F5aDoYvi8KGMWHVIrCZX6jAL7uuYcmJOJVzx7T0xMdv1tOqi8rmhnr4ubsXhi87rXR83pFbaN/YHo0cxTv0aru45Ex8s/0yjpawy7mduQEmdWyITt4OkMlk+HNgALrMPq60k92TrHyMW30WG8aElfpYgbTLwWtJ+GTTRSQ/lV7JAgDebGiHX7o3hrUpVwgvD15OFvi4bT38vOua0vG5h2+heR1bhHqKr9j0X3HJmZi09SJO3CzZCgmBbjXwc/fGqGun/GYUbVTDRB8rR4Sg17xToucb/r54HyYGcoxs7onP/rqAs3dSVW5XJgOGhLnjo7b1YFoN3seJtEGwSDg9JTMPtx49Re2alee1iYiISuZiQhoGLpIOpndobI8ZfRlMJ+2iNUcICoUCly9fBoAKu5tPJpNBEASGBoiIiIiIiEjSyVuqg+n9Q1zxfRcvHmNWInpyHfTwd8b8o7HFxs7fTUXMgwzUE+l0CAArT8Xjq22XJffRw98Zk3s0LvMJQAcLI6wdHYo+808h4YnyIG5kfAqGL4vC0qHBMNIve1fUh+k5GLg4AteTxMNulsZ6WDE8GN7OlmXeD9F/mRjoYumwYAxaHIFzIoGHuORM9FsYjnGv1cbX2y6r7EY5/rXa+OCNutDR4Wsy0Yua1rZBJ28H7Lxwv9hYZl4hftp1FbP7+WmgMtVy8gvx9bZL2HBaesUEmQz48I26eKdV7Vd+DdDRkeH3Pj5ImJ8leuPWlH9iUMvWBO28HF5pXyWx88I9fLLpArLyCiXnNXaywNwB/nCxMq7wmspiZHMP3H2ShRWnbisdv3wvHePWnMWiwYFV+iLqw4wcjF9zDpFxKZLzTPTlmNrLB+0bV/xzrCxer2+Hrr6O2Ha++MpDBQoBn/51AVvfaVrp/i2z8wox9/BNzD8Sq/JmGODZSgXDm7rjvTZ1XwpketiYYEovb4xddVbp152/m4qf/r6C77p6lVvtpD6ZuQX48e+rWBt5R+VcUwNdfN25IXoFOPO8QTkb2cwTR68n4/jN5GJjggC8v/489rzXAhbG0jes5RYUYv6RWMw5dBN5Eud/ipgb6uLzDg3QJ9ClUh532JkbYvXIEPScdxJJ6cpvrNhwOgEbzySgJHGN2jVNMblHYwS48UZ2InXyd60BuY7s+UoHL4qIS2E4nYioirmUmIaBiyOQLhFMb+9lj5l9/aBXyY7DqerTimfkfw/IZTJZhfwhIiIiIiIiUuXUrccYviwKOfniFyb7Bbvix65elfJiZHXXO8hFdGy9xNLd84/cUhlMHxjqiik9vV85iONkaYS1o0LhZGkkOic8NgUjV0QhJ186rCYmMTUbveefkgym25gaYN3oUAbTqUKYGuhi+fBgeDuLd1a99SgTH2yIlgym6+vqYEYfX3zUth5fk4lETOrYECYiNzPtiL6HE0qCXZqW8CQLveadUhlMtzTWw7JhwRj/ep1yew0w1tfFwsGBqGkm3uH2/fXRuJSYVi77Uya/UIHvdlzG+DXnVAbT+wW7YuPYMK0NpgPPrvl807kR2jSoKTrncMwjfLXtcoU1L9K0M7dT0Hn2cZXBdE9bE2wb31Rrg+lFvuncCFYm+krHLiWmY+Ex1Z3htcn+K0l4Y/oRzD54s0TB9GAPK+x6tzm+7NhQaafgdl4OGNXcQ/Trl5+6je3RxcP9pN3O3H6CDrOOlSiYHuReA7vfa47egS68Rl0BdHRkmNbbBzVEwuf303Lw+ZYLku8pEbGP0WHmMfy+73qJgundfB1x4MNW6BfsWqmPO1ysjLFqRIjozw6AymC6nlyGd1vXwd/vNmMwnUgDTAx04eWk/FyKqs+aRERUuVxKTMOARRFIy84XndO2kR1m9WMwnbSTxp+VgiCo9Q8RERERERGRmPBY1cH0vkEu+Kkbg+mVVS1bUwS511A6tvlcAnILXg6ACYKA6fuu45fdypcMLzKquQd+KMcbFlysjLF2VCgcLAxF55y4+RijV54pdUA9PjkTveedQvxj5Ut5A4CDhSE2jAlFfXvzUm2bqDTMDfWwcngIGjmW7XlmY6qPtaNC0c3PqZwrI6pa7C0M8f4bdUXHv952qUShLHU5duMROs8+josqwt+NHM2xY3wztKxrW+41OFgYYeHgQBjoKr+Ekp1fiJHLTyMpPafc9/0gLQf9FoRj6Yl4yXkGujqY0tMbv3RvDEO9sq+koi5yHRlm9fOTvClpbeQd/HnklhqrqniCIGDFqXj0XRAu2qW2SLtG9tg2rmml6HZpZaKPb7s0Eh2fvv86Yh+J3wSpLe6mZGHEsiiMXHFadNWiF9mYGmB6Hx+sHx0queISAHzSrr7ocQcAfPbXBdxIyih1zaR++YUKTNsbg17zTuK2xDEU8Cy0+2m7+lg3WrtvGqoK7MwN8VtPH9HxXRcfYKOSm9yeZObhk03R6LMgHLceZarcj5u1MVaOCMaMvn6wlbhxrTKpY2eG5cODld5co4qPiyV2TmiOD96oCwNd7f/8QVRVhXgovzEkIjaFuSgioiri8r1nHdOlgulvNrTD7H7+DKaT1ir9EUc5iourXJ0TiIiIiIiIqOqKiH2MYUujkC0R9O0T6IKf32rMYHol1yfIFVHxT4o9npqVj31XktDJ2xHAszDRL7uvYcHRWMntvde6Dia2qVPuHfFcrY2xZlQo+i44JRpmOnr9Ed5edQbzBgWU6MLwjaQMDFgUgYcZ4uEoVytjrB4ZwjAFqYWFsR5WjQhBv4XhuPag5AGtBg7mWDQkUHKFASL6vyFN3LHxdAJilAQhbz3KxOLjcXi7VS0NVPZ/giBg7uFbmLY3BkpWqH9JzwBn/NjNq0JD2T4ulpjW2wfj15xTOv4gPQejVpzG+tFhMBLpTF9aJ28l492155D8NE9ynquVMf4c6I9GjuJBb21krK+LxUOC8NbcE6JB4N/2xMDJ0ghdfSv/jUfZeYX4cstFbD6XKDlPRwZ83LY+xrb0rFQdljt7O2D7+XvYfzWp2FhegQKf/nUB60eHaeWxU05+IeYficXcwzeRW4Kbc3RkwOAwd3zwZl2YG4p3G36RnlwHc/r7o+Os40h+Wvyzd1ZeId5efRbbxjWFSRkCoqQeNx9m4P310SpvmAKAenZmmN7HFw3LeOMlld4bDe0wIMQVqyOUd7P/dsdlBHlYwcPGBIIgYPPZRPy06ypSMqXfZ4FnNxqMbVkL416rXSluAistb2dLLBoSiCFLIkv0OmikJ8dHbethaBN3yLXwdZ2ougl2t1J6vvJBeg4SnmTznB4RUSV35V46BiyKQGqWeDD9jYZ2mNPfH/oijRWItIFGz3a4ublpcvdEREREREREAJ4teTpsmXQwvVeAM37pzmB6VdChsT2+234ZGbkFxcbWR91FJ29HKBQCvt5+CavCpZds/7x9fYxpWXFhPg8bk38D6uF4JBIoPxTzCONWn8XcAQGSJyIvJaZh8JJIyQvxtWxNsHpkKOwlOrYTlbcaJvpYPfJZQP16kuouq20a2GFmX18GuYhKQU+ug++7NkKfBeFKx2cduIEuvo4au+HjSWYePt50QWnI9UV6chm+6dwIA0Jc1RLi7eTtiFsPMzF9/3Wl4xcS0vDRpmjM6ef3SvUIgoD5R2Px255rKoP5bRrUxLTevrAwKllAVtvYmhlg2bBg9PjzpGj3rw83RENHJkNnH0c1V1d+7jzOwphVZ3D1frrkPCsTfczu54emtW3UVFn5kclk+LGbFyJiHyv9XB0V/wSrI25jUJi7+ouTcCjmIb7dflllB+wiAW418ENXrzIFju3MDTG7nx8GLApX+rt98+FTfLb5Imb19a1UNyZUBwqFgOWn4vHr7msqg7syGTCymQc+fLNelQwxa7tJHRsiIi4FNx8WP47IyivEe+vOYWovH3yz7TJOxT4u0TaD3a3w01teqGOn/StZvIpQT2v8OdAfo1ecQYHEB5DmdWzw81uNGXYl0iJB7laQyQBlTdIj4lL4+0pEVIldvZ+OAYvCJYPpbRrUxB8MplMlwGcoERERERERVWtR8SkYujQSWXniwfSeAc6Y3MObwfQqwlhfF519lYedjt1IRnxyJj7edEFlMP2Hro0qNJhepJatKdaOCoGNqb7onP1XH2LC2rPIL1QenDh75wn6LwyXDKY3cDDH+jFhDKaTRlibGmD1yFDUsjWRnDe2ZS0sGBTAYDpRGYR4WqO7n/Ju1Nn5hfhhxxU1V/TMqVuP0X7mMZXBdHtzQ6wfE4aBoW5qDXG+27q2ZEj67wv3MWP/jTJvPz0nH2NWnsGvu6WD6c+6a9fDgkGBlTaYXqR2TVMsGBQAfZFlpwsUAt5ddw5rI6U/i2mrQzEP0XnOcZXBdG9nC+yY0KxSBtOL2FsY4suODUTHf919DQlPShYCr2iJqdkYs/I0hi2NKlEw3cpEH7/19MbGMWGv1Ak7rJY1PmpbT3R8R/Q9rDh1u8zbp/J3Py0bg5dE4rsdV1QG050sjbBmZCi+7NiQwXQNMdKXY2ZfX9H3lAsJaXhz+tESBdMtjPTwWw9vrBsdWuWD6UVer2+H6X18oeyjlYWRHqb28sGK4cEMuhJpGQtjPdQTeZ2KjCvZjThERKR9rj141jH9iUQwvXX9mvhjAIPpVDnwWUpERERERETV1un4FAxdIh1M7+HPYHpV1DfIRXSs1/xT+Otsgui4jgyY0tNbrV0ga9c0w+qRobAyEQ+o/3M5CRPXnUfBfwLqp249xqBFEUjPKd7RsoiviyXWjQqFjalBudVMVFq2ZgZYMyoUHjbFA+r6ch1M6+WDz9rX5+sx0Sv4vEMDmInc3LHn8gMcjnmotlryCxWY+k8M+i8Kx4P0HMm5IR5W2DGhGfxda6ipuv+TyWSY0tMbPi6WonNmHriB7dH3Sr3tq/fT0WX2cey9Ih3MtzbRx8oRIRj3Wu0q8xoY4mmNqb19RMcFAfh880UsOHpLjVW9GoVCwKwDNzB8WZRoV/gifYNcsGFMmMZWKyhPfYJcEOZprXQsM68QX265BEFZW081KShU4M/Dt9Bm2hH8c1n6dw141gV7YKgrDn7YEr0DXcrld25si1po06Cm6PiPf1/B2TtPXnk/9Oq2R99D2+lHcfxmssq53f2dsHtic4TVUv78J/Vp5GiBT9qJ3wRSEt39nHDgw5boHVQ+v/eVSWcfR/w5wP/5+QZdHRm6+Tpi/wct0TPAmSs7EGmpEA8rpY9HxqWouRIiKg8KhYANUXcxYe05fL75Ai4mpGm6JFKzmAcZ6L8wQrLB0Ov1a2LuQH8Y6PLGWKocGE4nIiIiIiKiaunM7RQMWRKJTIlgenc/J/zW0xvyanZhsjpo7GSBBg7KOyA+ysgV/TpdHRlm9vVDr0DxcHtFqWdvhtUjQ2BpLN4t9e+L9/H+hujnAfXDMQ8xdKn08zzYwwqrRobAQmK7ROpiZ26ItaNC0aKu7fPH6tmZYc2oEPQIcNZgZURVg62ZAT58s67o+DfbLyMnX/w9o7zcTclCn/mnMOfQTaVL0b9oZDMPrB4ZAlszzd1AZagnx8JBAXCQWF3k443ROH83tcTb3HIuAW/NPYF4FR2c/VwtsfPdyt1dW0wXH0d82q6+5Jyfd13D1H9iNBpuLomsvAKMX3sWv++7Lvmc1pfr4NfujfFrD+8q02VZJpPh1x6NYain/JLjkeuPsOVcopqreubq/XR0m3sCk/dcQ3YJXtt8nC2wbVxT/NitMSyNxW8KLS0dHRmm9fKFi5XymxHyCwWMX31WMoRAFSstKx8T1p7Du2vPSd7UCwCWxnqYO8Afv/f2hbkhj6G0xfCmHmhep/Tvle7Wxlg9MgS/9/Gt1jdrt/NywIlPX8fe91sg8ss2mNHXT6OfvYhItWAP5TdHxT/OQpKKm3+JSLvkFhRi9Moz+OSvC9gRfQ9rI++ix7yTCC/Byi9UNVxPylC58u1r9WzxJ4PpVMkwnE5ERERERETVztk7TzBkSZRkYPctPydM6eXDYHoVJZPJ0CewdEFXfbkO5g0MQGcfxwqqSrUGDuZYNSIE5obKu94CwI7oe/h40wXsvngfo1acllyKvnkdGywfFgxTkS66RJpgb2GI5cOCcOTjVtgzsTl2v9ccge7KO4IRUekNDHVDQ5EbtG4/zsKCo7EVuv8d0ffQYeYxnL2TKjnPWF+O2f38MKlTQ+jKNX8po6a5IRYODoSRSKA4t0CBUStO415qtuR2cgsKMWnrRby/Pho5+eLv0QAwtIk71o8Og4NF5e+uLWZsS0+Mau4hOWfOoZv4ZvtlKBTaGVC/l5qNXvNOYdfFB5LzHC0MsXFsGPoGu6qpMvVxszbBR2+Kdy3+fucVyRtAy1tegQIz9l9HlznHcSkxXeV8S2M9/PxWY2x5pym8nS0rpCYLYz38OSBAdOn1e2k5eG/dORRqyfP8bkoWTt5KVuu/m6Ycv5GMtjOOYkcJVsBoVc8Weye2QIfGDmqojErj2U0gPpKrjb1ITy7Du63rYM/EFlXyBrCyMNKXo66dWYl/hkSkWUEe4qtKsXs6UeWRV6DAuNXnsP9qUrHHP9wQjWyJa1jVWX6hAivDb6PHnyfR9Y8T+OPQTeRJXAfRZjf+DaY/lgimt6xriz8HBjCYTpWO5s/oEhEREREREanRuTtPMGRxJJ7mindD6+rriKkMpld53fycRMMh/2Wop4PFQwPRpqFdBVelmpeTBVaNDIGZREB9y7lEvL36LPILxcMtbzS0w6IhgTDS5wlN0j4ymQxu1iaob28OHb4WE5UrXbkOfujmJTr+x6GbuJsi3c27LLLyCvDJpmhMWHsOGRKfwwCgrp0pto5rqtEbwpTxcrLA9D6+ouOPMnIxcvlpZOUp//4SU7PRe344VoXfkdyPkZ4cM/v64tsujUr8WaWykslk+KJDA3zwhnhHfwBYceo2PtwYjfxC7brYfOb2E3SZcwKX70kHoJvWtsaOCc3g42KpnsI0YFhTD/iKfH+pWfn4dvtltdRxKTENXeYcx4z9NyQ/CxfpG+SCgx+2Qv8Q1wr/zOHlZIHvuzQSHT92IxmzDtyo0BpUiX30FEOXRqL5b4fQf2EEmv92EIuPx2n96gVlkZNfiG+3X8bAxRF4oKLDrJGeHD9288LSoUGoaS6+igZpVk1zQ/zWw1vlvGAPK+x+rzk+eKNulVnFgoiqn5pmhvC0MVE6xnA6UeWQX6jAu2uLB9OLJKZm488jt9RclVObcTMAAOgSSURBVPY7e+cJOs8+jq+2XsKZ208QfTcVU/6JwdClkZUuzH/z4VP0WxiB5KfiwfQWdW0xf1AAP7dSpaSRs5qXL6vnBFRpaGNNRERERERElUVccibO3E5BboF2n/g5fzcVgxdHSgaiuvg4YhqD6dWCpbE+2nvZq5xnaqCLFcND0LyOrRqqKhlvZ0usHBECszJ2PO/i44i5A7gEJBFRdRXgVgN9Al2UjuUWKPDdjvI9X34pMQ2dZh3HhtMJKucOCnXD9vHNUNfOrFxrKC/tvOzxcVvxDtFX7qfj/fXni3X5Pnr9ETrNOobou6mS2/e0McHWcU3R1depPMqtFGSyZ91rv+ncUHLelnOJeHvVWeTka8cxx6YzCei3IBzJT6U7S7/dqhaWDwuGtamBmirTDLmODL/19IaeXPlx1N8X72PPJenu8q8it6AQU/+JQdc/TuDagwyV8xs5mmPzO03waw9vtXYJ7hPkgp4B4is4zTp4A4djHqqtniKZuQWYvOca2s44isMxj54/npOvwA87r2Dq3pgqFVC/mJCGjrOOYdnJeJVzfVws8fe7zTAw1A0yGc8TaLs2De0wKNRN6ZilsR6m9PTG+tGhqF1TOz9nEBGVRrCH8lXmGE4n0n4FhQpMXHceey5LHyPNO3ILdx6XfwOByigtOx+Ttl5Ejz9PKj3mO3nrMUYsj6o0AfVbj56i30LpcwrN69hgAYPpVIlpJJzu6+uLYcOG4fbt25rY/Utu376NIUOGwNfXV9OlEBERERERVTpp2fkYu/IMXpt6GD3+PIXQnw9g5al4rbxoHX03FYMWR0gG0zv7OOL33j7QlVftDpX0f32ClAfzilgY6WH1yBDRiz2a5OtiiWXDg2FSys7nfQJdML2PL/T4PCciqtY+aVcPFkZ6Ssf2X32I/VeUd+4qDYVCwKJjsXhr7gnEJmdKzrU01sP8QQH4oZuX1l90e6dVLXT3Ew+P/3M5CdP2xQB49jOYfeAGhiyNxJOsfMnttveyx7bxTVHPvnoG5oY19cDUXj6Qukd0/9UkDF8WJbkKUkUrVAj4eddVfLQxGnkSndyN9eX4c4A/Pm1Xv9ocX9S1M8P41+qIjn+17RLSVPwelMX5u6noPPs45hy6iUKF9LGomaEuvu/aCNvHN4O/a41yr0UVmUyGH7p6ob7I77kgABPXn0diarZa6hEEATui76H1tCP48/At0W7zfxy6hZ93XdXKY/3SKChUYM7BG3hr7gnceiT9viTXkeGDN+rir7Fh8LQ1VVOFVB4mdWqAbr7/X31FJgO6+zvhwAct0SvQhTcZEFGVIXa+MiYpA08yxbvwEpFmFSoEfLAhGn9fvK9ybl6BAj/8fUUNVWmvomOWNr8fwarwO5A6JKksAfW45Ez0XxiORxniwfRmtW2wcHCg1p8jI5KikbNhhYWFWLFiBerUqYNBgwbhwoULaq8hOjoaAwcORN26dbFq1SooFNq1FCQREREREZG2S8vOx+DFES91dniSlY+vtl3G4CWRuJ+mnovpJXEhIRUDF0cgI0c8xNLR2wHTGUyvdkI9rOFmbax0zMZUH+tGh8LHxVK9RZVCgFsNLBseDOMSBtSHNnHHL90bc2UAIiKCtakBPmkn3gH82x2XX+liXvLTXAxfHoUf/74qGnYsEuJhhd3vNUfbRqpXNNEGMpkMv/RojAA38WDrH4duYfnJeIxYHoVp+65LXjyV68jwZYcGmDvAH2aGym8YqC56Bjhj7oAA6Et8Jj956zEGLopAapb6AzfpOfkYuTwKC47GSs5zsjTCX283QfvGDmqqTHu83aqWaPD6UUYuftpVfsGKnPxC/LLrKrrPPYHrSU9Vzm/ToCb2f9ASg8PcNfp52EhfjnkDA0RXQUrNysc7q89W+MpkMQ8y0G9hOCasPYcH6Tkq5y88Fodvt18utjJEZRGfnIne809h6t7rKFDxPXjammDz203wbus6PEdQCRnoyjG9jy92v9cccwf4I+Lz1vi9t2+VX8GCiKofqWYaUfHsnk6kjQoVAj7eGI3t0fdK/DX7riThyPVHqidWQXceZ2HI0ihMWHtOMsj9Im0PqN9+nIl+C8KRlC7+/TStbc1gOlUJGjma1tfXhyAIKCgowJo1a+Dn54emTZti+fLlyMyUvkv9VWRmZmLp0qVo2rQp/P39sXbtWuTn50MQBBgY8GCUiIiIiIiopNJz8jF4SSSiE9KUjh+7kYw3px/FlnMJGu+sdjEhDQMXqQimN3bAzD6+vOhcDenoyPB5+wbFHrc3N8S60WFo4GCugapKJ8jdCkuGBsFQT/r5+3arWvimc0PoMJhORET/6hvkCh9nC6VjCU+yMffwzTJt9+j1R2g34xgOx0hfPJXryPDRm3WxZlQoHCyMyrQvTTHQlWP+oAA4WYrX/c32yzik4mdga2aANSNDMKqFJzu5/qudlz2WDA2CkcRF2PN3U9FnfjgeliBQW17ikzPRfe5Jlf+mQe41sG1800rxObIi6OvqYHIPb9EO+BtOJ+DYjVcPVpy5nYIOs45h/tFYqMpKWxrrYUYfXywcHAg7c8NX3nd5cLcxwZRePqLj0XdT8dPfVytk3+k5+fh+xxV0mHUM4bGlC64tP3UbX269WKkC6oIgYE3EHXSYdQxn76SqnD8kzA1/T2iu1Tcpk2oymQwNHMzRobEDamrJ7z0RUXlzrmEsejwSGcdwOpG2USgEfPbXBWw+l1jqr/1u+2XkFVSfxrt5BQr8cegm3ph+BEfLEMw/eesxhi/TvoD6ncdZ6LcgXPLm4DBPaywaHASjUq6YS6SNNHLV/fLly+jUqROAZycEBEFAeHg4hg8fDltbW3Tt2hVLlixBXFzcK+8rLi4OS5YsQZcuXWBra4uRI0ciPDz8+X4BoHPnzrh06dIr74uIiIiIiKg6SM/Jx+DFkYi+myo5LyOnAO+vj8bbq87i8dOSdTQob5cS0zBwcQTSJYLp7b3sMaMvg+nVWTsve/ze2wd17UzhaGGIHv7O2Da+KWrXrDxLt4d6WmPxkCAY6Cp/Hn/0Zl182q4+Q29ERPQSuY4MP3Tzgtjbw/wjsYhLLnlDmbwCBX7edRWDl0QiWcXnPydLI2wYE4bxr9eptCt62JgaYPHQQJiU8YJhsLsV/p7QDCGe1uVcWeXXrI4NVo0Mgbmh8s7SABCTlIFe80/hbkpWhddz8mYyuv5xAjcfSnfn7hPogtUjQ2FTzbvz+rhYYlRzT9HxzzdfRGau+DGalOy8Qny/4wp6zjuF2EeqX5/aNrLD3vdboJufk9Z9Fm7nZY/RLcR/TitO3ca286UProhRKARsOpOA16cexpITcSgsY8B8beRdfLzpQpm/Xp0eZuRgxPLT+GLLRWSpCKbYmRtgxfBgfNfVi0EQIiKqNMS6p0eyczqRVlEoBHy59SI2nkkQnWNqoItatiZKx2KTM7H0xKvnKCuDqPgUdJp9DFP+iUHuKwTyT8VqV0D9bkoW+i0Mx7008WB6qKcVFg8N5PEIVRniZ/UqUK1atbB9+3bs27cPkyZNQlRUFARBgEwmQ05ODnbu3ImdO3cCAJycnBAYGAgvLy/Uq1cPzs7OcHBwgKmpKYyMjCAIAnJycpCRkYH79+8jISEBMTExuHjxIs6ePYvExP+ftCnaR1EoPSgoCD/99BPatGmjiR8DERERERFRpZORk48hSyJxXkUw/UV7Lj/A6dsp+PmtxnizkX3FFfcflxLTMGBRBNKy80XntGtkj1n9/KDHYHq1193fGd39nTVdxitpWtsGi4YE4r1155GSmQfgWefKz9vXx7CmHhqujoiItJW3syX6B7tidcSdYmN5hQp8s/0ylg8LUhnqjEvOxLtrz+FiovKVdV7UydsBP73VGBZGemWuW1vUtzfHrH5+GLniNEqzYNCo5h74pF19fg6VEOBWA+tGh2HwkggkP81TOuf24yz0nHcSq0aEoI6dWYXUsfJUPL7dcUUyiKsjAyZ1bIhhTd21LgCtKRPb1MU/lx8g/nHxmwcSnmRjyj8x+LZLo1JtMzz2MT796wJuK9nmf1mZ6OP7ro3QsbGDVv+bfNK2Hs7fSRUNkH3210U0dDB/5ef3pcQ0fL3tUok6hwOAtYk+0rLzUSDyvP/rbAIKFApM6+WjtTd6H7n+CB+sP4/HmcpfP17U0dsBP3XzgqWxvhoqIyIiKj/BHlbYoqQL86XENDzNLYCpgUZiYUT0AkEQ8PX2S1gbeVd0jom+HMuHB0FHJsNbc08qnTPrwA1083PSmtWgyltqVh5+3X0N66LEf04vcrI0wrjXauP3fddFGyQUBdQXDw2Esb7mXg8TnjwLpiemZovOCf53hVxN1klU3jT6bH7jjTfwxhtvYMeOHZg8eTJOnvz/i2tRgDwhIQGJiYnYtm1bqbf/4tLxRSeeBEFA06ZN8dlnn6Fjx46v+B0QERERERFVH09zCzBkSSTOlfBi9ouSn+Zh9Moz6OHvjG+6NIS5YcUGkS7fe9YxXSqY3raRHWb3ZzCdqpbmdWxx6MNW2B6diLxCAW0b2cG5hrGmyyIiIi33cdt62H3pwfObm1509Poj7Ln0AO0bOyj9WkEQsPlsIr7adkllV1ojPTm+69IIvQKdtTosWlqtG9jhi/YN8NOuqyrnmujLMaWXDzqI/DzpZQ0dzbFxbBMMXBQhehE3KT0XveefwvLhwfB2tiy3fecXKvDt9stKb9x4kZmhLub090fLurbltu+qwEhfjl97eKPvgnCl48tPxaOzjwMC3JR3+3xRZm4BJu+5hhWnbpdo3528HfBdl0awrgQd7HXlOpjT3w8dZh1XGqjIzi/E2FVnsG18szKFy55k5mHq3hisibxTohto5DoyDG3ijvfa1EFEbArGrT6LvELl3Qq3nb+HvAIFZvb1g77ICk6aUFCowO/7rmPu4Vsq55oZ6uLHbl7o4uNYpd6XiIio+hDrnK4QgDO3n/AzKpGGCYKA73Zcwapw8eNKIz05lg4Lfn5s1CvAWWmH9cy8Qvyy6ypm9PWrsHo1QRAEbD2fiB93Xi3RjaVyHRlGNvfAe63rwFhfF8EeNdB3QYRkQH3EstMaC6jfS81Gv4XhSHgiHkwPdKuBpcMYTKeqRyvOFHTu3BnHjx/H6dOnMWzYMJiZ/f/u/xdPBAiCUKI/yr7WzMwMw4cPR1RUFI4dO8ZgOhERERERUSkUBdOluqzJdVRfyP3rbALaTT+KEzeTy7G6l125l44BiyKQmiUeTH+joR1m9/NnMJ2qJAtjPQwKc8eIZh4MphMRUYlYGuvjs/b1Rce/33kFWXkFxR7PyMnHxPXn8eHGaJXB9EaO5tj5bjP0DnKpkgHAkc090CfQRXJOnZqm2D6hGYPppeRhY4KNY8PgKbK8OQA8ycpH/4URCI99XC77TMnMw8BFESqD6Z42Jtg6rilDPyJCPa0xIMRV6ZggAJ9suoCcfOnXjhM3k9F2xtESBdNtTA0wb2AA5vT3rxTB9CI1zQ0xu58fxA6pbz3KxGd/XXjpGqgqhQoBqyNu47Vph7E6omTB9DBPa+x+rzm+6vTshvI3GtphweAAyeD57ksP8M7qM8gtkP53VJd7qdnouyC8RMH0prWt8c/EFujq61Ql35eIiKh68LQxgY2p8pU/IuPK57MxEZWNIAj48e+rWHYyXnSOoZ4OlgwNeulGk0/a1YeZyI2pW8/fQ2Sc8lWXKqO45EwMXByB99dHlyiY7udqiZ0TmuHz9g2eB7lr1zTDutEhsJE4BiwKqCs7t1WR7qc9C6bfTREPpvu7WmLZ8GCYcKULqoK06iq8v78/Fi9ejKSkJGzatAlDhw6Fg4NDsdA58Cx4/uKfFxXNd3BwwNChQ7Fx40YkJSVh0aJFCAgIUOe3REREREREVOk9zS3A0CWROHP7iegcJ0sjHPigJT56sy50VYTU76XlYMCiCHyz7RKyVYSYSuvq/XQMWBQuGUxv08AOf/T316rObkRERESa1tPfGf6ulkrH7qflYNaBmy89du7OE3ScdRzbzt9Tue0RzTyw+Z0mqGVrWh6laiWZTIYfunmJdi7s4uOIreOaVumfQUVytDTCxjFhaORoLjqn6Ibag9eSXmlf15My0PWP44hQccG/eR0bbHmH/6aqfNa+PhwslC87f+tRJuYcvKl0LCMnH59vvogBiyIkO8wVecvPCfveb4F2XvavVK+mhNWyxsdtxW8S2nnhPpZLhFpedOb2E3T94zi+3HJJ8ti4iIOFIeb098OaUSGoa2f20lirejWxdGgQDPXEj5/3X32I0SvOqLzRoKIdvJaEDrOO4bTEuQsA0NfVwdedGmLl8BA4WhqpqToiIqKKIZPJRI9BqlKAlaiyEQQBv+6+hsXH40TnGOjqYNHgIITVsn7pcVszA0x8o67o132z/TIKFSW/cVUb5RYUYtaBG2g74yhO3FR9I42ZoS5+essLf41tggYOxc8LlDSgPnxZlNoC6knpOei/MAK3H2eJzvFxeRZML8sqWUSVgVY+sw0MDNC9e3d0794dABAbG4szZ87gwoULiIuLw927d5GWloasrGe/vMbGxrC0tISLiwvc3d3h7e2NgIAAeHp6avLbICIiIiIiqvQycwswfGmU5MVdRwtDrBsdChcrY4x/vQ5a1auJDzdEIyYpQ3Lby0/dxrEbyZja2wf+rjVeudZrD551TH8iGUyvibkDGEwnIiIi+i8dnWfh6s6zj0PZNc5Fx2LRM8AJnjammHf0Fn7fex0FKi6GWpvoY2pvH7xWr2YFVa1d9HV1sGBQAD7cEI0D1x4CAMwNdfHhm/UwOMyNnXlfkbWpAdaODsWIZVGIild+fJJboMDoFWfwex9fdPFxLPU+9l9JwnvrziFTxU20w5t64IsO9aHLlZhUMjPUw09veWH4stNKx+cduYX2je3RyNHi+WNHrj/C539dwL20HJXbtzM3wM9vNUbrBnblVrOmjG3piTO3n2D/VeU3WPy06yq8XSxFj58fZeRi8p5r2HQmoUT705PLMKq5J8a9VluyS1/T2jZYPiwYw5dFif5uHLn+CCOWR2Hh4EC1L0WfX6jAb3uuYeEx8eBPkUaO5pjRxxd1/hPCJyIiqsyC3a2w6+KDYo9H301DTn4hDPXkGqiKqPoSBAFT98Zg/tFY0Tn6ch0sGByIZnVslI4PDnPD+qg7uJ70tNjY1fvpWBNxG4PC3MurZLUKj32ML7ZcROyjzBLN7+LjiEmdGqCmmfKbnosUBdT7LohA8tNckX2nYPiyKCwZGlShxy0P03PQb0E44pLFv0dvZwusGB4Mc0O9CquDSNO0Mpz+X56envD09ESvXr00XQoREREREVG1kZlbgGHLohAZL95hxcHCEOtGh8HFyvj5Y15OFtg+oSmm77uBBUdvKQ03FYlNzkTPP0/i7Va18F7rumUOjcc8yED/hRFIkVj2r3X9mviDwXQiIiIiUY0cLTA4zF3pktMFCgGfb74IfV2dEnW1al7HBtN6+6i8eFjVWBrrY9GQQFy9n4Hkp7kIdK+h9qBmVWZuqIcVw0MwdtUZHLn+SOmcAoWA99adw9OcAvQPcS3RdgVBwLwjsfjtn2sQJI5f9OQy/NjNC32CSrZdeub1+nbo5uuIrUpWWihQCPj0rwvY+k5TZOYV4sedV7CxhOHqXgHOmNSpISyMqsbFfJlMhmm9fdB59nHcSSneXS+/UMC41Wexc0IzWL/QEbCgUIEVp25j+r7ryMgtWRfAlnVt8U3nhvAsYef/EE9rrBgRgqFLIkX3ceLmYwxdEoUlw4LU1vnvbkoWJqw9h/N3U1XOHdXcAx+3rc9zAkREVOUEe1grfTyvUIHzd1MR6ql8nIgqxoz9N/DHoVui43pyGeYPCkDLurYSc3TwbZdG6L8wQun41L3X0dHbEVYm+q9cr7qkZObh511XS3wzrauVMX7s5oUWEj+n/3oWUA9F3wXhGguoP8zIQb+F4YiVCKZ7OZlj5fCQKnMsSySGR99ERERERERUTFZeAYYvi5Jc+tPh347prtbGxcYMdOX4rH19bBgTBjcl4y9SCMAfh26h6x8ncPV+eqlrvZ6Ugf4LwyWD6a/Vs8Xcgf4w0GWXGCIiIiIpH7xZV3QZ5Kj4JyqD6XpyGb7oUB/LhwVXu2B6EZlMhoaO5mhR15bB9ApgpC/HwsGB6OjtIDpHEIAvtlzEvCPigYAiOfmF+GBDNCbvkQ6mW5noY/XIUAbTy+jrzo1gLRKcuJSYjo83XcCb04+UKJjuaGGIZcOCMKWXT5W7mG9hpCe52tf9tBxMXH8ehf/eBX7q1mN0nHUc3++8UqJguouVERYODsSyYUElDqYXCXCrgVUjQ2BuKP66FhmfgsGLI5CeI76iWXn55/IDdJx1TGUw3dJYD4uHBOLLjg0ZTCcioiqpnr2Z6Puz1Pl9Iip/sw/cwMwDN0THdXVkmDsgAK/VV73CXJNaNujYWPlxb1p2PqbujSlzneokCAI2nr6L1tMOlyiYrieXYfxrtbH3/RalCqYXqV3TFOtGh4qe2wL+H1DPyivZzb0llfw0FwMWRuCWRFf4hg7mWDUiBBbGVetYlkgZHoETERERERHRS7LzCjF8WRQiJE5c25sbYu2oULhZm0huK9DdCrvebY6BoaoDHFfvp6PLnOOYe/jm8wvtqtz4N5j+WCKY3qqeLf4cGMBgOhEREVEJmBvq4cuO9cv0te7Wxvjr7SYY3aIWdHRk5VwZ0f/p6+pgVl8/9A1ykZz36+5r+G3PNQgiqfOH6TnosyAcW84lSm6nvr0Zto1rimAPqzLXXN1Zmejj2y6NRMe3nEtEUrryznYv6h/iin/eb4FW9VSHOSorLycL/NBV/Gd17EYyfvz7CiasPYd+C8MRk5ShcpsGujp4v01d7Hu/Jd5oaAeZrGyv0T4ullg7OhQ1JIIUZ++kYuCiCKRmiR+nv4rcgkJ8u/0yxqw8g/Qc6TBJoFsN7Hq3OVo3sKuQWoiIiLSBXEeGIHfln1MZTidSn7mHb2Lavuui43IdGeb098MbDUv+2fSLjg1gqKc83rk28g4uJqSVuk51uvnwKfouCMfHmy7gSZbqG1iD3J99fv+obT0Y6pX9mp4mAuqPn+ai/8Jw3Hj4VHROAwdzrB4ZAkvjytPxnuhVMJxOREREREREzxUF08NjxU9a25kbYO3oULjbSAfTi5gY6OLHbo2xYngw7M2lu2fmFwr4bU8Mes07iTiJJe8A4ObDDPRbGIHkp+IXvFvWtcW8gQGvdBKLiIiIqLrp5uuEkFKGcHv4O2Pnu83h7WxZMUUR/YdcR4ZfujfGqOYekvPmHr6Fr7ZdguI/N8BeSEhFlzknEK2i6/IbDe3w19tN4GIlvSIUqdbJ26FUQYwXOdcwwuqRIfj5rcYwM6z6Heb6BLmiV4Cz6PjSE/HYEX2vRNtq28gO+z9oiffa1CmXY+NGjhZYNzoMNqbigYoLCWnotzACj5+qvuGgNG4/zkTPP09h2cl4lXPfaVULa0eHwtHSqFxrICIi0kZiN1Geuf0E+YUKNVdDVP0sPBqL3/aIdzKX68gwq68f2nmJrwCmjJOlEca/VlvpmCAA32wvfqyrDXLyC/H73hi0n3lUshFWEQsjPUzu0RjrR4ehjp1ZudRQFFC3NZMOqA9b+uoB9ZTMPAxYFIHrSeLB9Pr2Zlg9MgQ1RFYUI6qKGE4nIiIiIiIiAM+C6SOWR+FU7GPROTXNDLB2VCg8ShhMf1GLurb4Z2ILvOXnpHLu2Tup6DDzGFaeilfa5fBZt4UIJEtc6G5exwbzBzGYTkRERFRaMpkMP3TzgrwE3c9NDXQxs68vpvX2gamB8qXkiSqKTCbDFx0a4KM360rOWxV+Bx9sOP88mLMj+h56zTuFB+k5kl83/rXamD8wACZ8bpcLmUyGH7t5wcywdD/PIWFu+GdiCzStbVNBlWmnH7p5oYGDeZm/3tPGBMuHB2P+oMByv7minr0Z1o0OQ02JoMfV++notzAcDzOkf89K6u8L99Fp1nFcTJTuDmltoo/lw4PxSbv60JPzUjgREVUPYuH07PxCXFLx3klEr2bJ8Tj8tOuq6LiODPi9tw86epcumF5kZHNPuIp8nj97J1XlSmDqdiEhFe1nHsOsgzeRX6g6ON/dzwkHPmyJPkGu5b4KX+2aplg7SjqgHhH3agH1J/8G0689EF/Rqq6dKVaPDIEVg+lUzfCInIiIiIiIiJCTX4hRK07j5C0VwfTRofC0NS3zfiyM9TC9jy/+HOCv8iRMdn4hvtp2GYOXROJ+Wvbzx289eop+C8NVBtMXDg5kMJ2IiIiojOramWF4U3fJOT4ultj1bnN09VV98yFRRZHJZBj/eh1816WR5Lyt5+/h7VVnMOWfa5iw9hxyC8Q7SBro6mBWPz981LZeuV8cr+7szA0xqWODEs11szbG+tGh+K6rV7W8QcBQT44/B/jDrJTfu7G+HJ+1r489E1ugZV3bCqruWdBjw5gwOFqIr5B2Pekp+i4Ix4O0sgfUc/ILMWnrRYxbcxYZudKBkRAPK+x6r3mFft9ERETayMvJAkYi58IjS9C1mIjKZsWpeHy/84rouEwGTO3l80rnTQz15Pi6U0PR8V92X0NGTn6Zt1+eIuNS0HdBuMqVkQHAw8YEa0aG4Pc+vrAxFQ+Pv6qKDKinZuVh4OIIXL2fLrn/1SNDYV2B3yORtmI4nYiIiIiIqJorCqYfv5ksOsf232B6rVcIpr+ofWMH/DOxRYmWdD92IxlvTj+KzWcTngXTF4TjUYZ4ML1ZbQbTiYiIiMrDe23qwkFJ6FAmA95pVQubxobB1bp8u/ESldWQJu74vbePZMf//Vcf4o9DtyS3Y2dugI1jw9DFx7G8S6R/9Q50QZNa1qLjMhkwopkH9rzXAiGe4vOqA3cbE0zt7VPi+V18HHHww1YY27IW9HUr/jKwu40J1o8Jg3MNI9E5sY8y0WfBKSSmZovOEf/ap3hr7kmsCr8jOU8mA95tXQerR4bAzlw8LE9ERFRV6cl1EOBWQ+kYw+lEFWN1xG18ve2y5JzJPbzR3d/5lffVukFNvFZP+Q2YyU9zMXP/jVfex6uKjEvB0KWRyMorlJynL9fBe63rYPd7zdFETatjlTSgPrQUAfW07HwMWhyJy/fEg+m1bE2wZlSI5H6JqjKG04mIiIiIiKqxomD6sRviwXQbUwOsHVV+wfQitmYGWDAoAFN6eqvsBJeRU4APNkSjw8xjeCgRTG9a25rBdCIiIqJyYmqgi1UjQ17qiutqZYxVI0LwSbv60JPzEgNpl+7+zpg7wB/6ZXxu+rhYYvv4ZvB2tizfwuglMpkMv3b3hplh8eNAT1sTbBobhq86NYSRPo/rAKBtI3uMaeEpOaeenRnWjgrFrH5+sJfoZF4RXKyMsWFMGNwlbla6/TgLveedwp3HWSXe7tZzieg0+7hkF0Lg2TmLVSNC8MEbdaHL9yUiIqrGgj2slD4eGZ+CQoWg5mqIqrb1UXfw5ZZLknN+6d4YvQNdymV/MpkMX3duJHqsu+xkPG4+zCiXfZVFROzjEgXTQz2tsHtic7z/Rl21X8crSUA9soQB9fScfAxeHIGLiWmiczxtTLB2VChqmvHmWaq+eIRORERERERUTeXkF2LMyjMqgun6WDc6BLVrlm8wvYhMJkOvQBfseb+FZOe8IrkFCtGxJrWssWhwEAMMREREROWolq0pDn7UClveaYJNY8Nw+KNWaKqmzlZEZdG2kT2WDguCcSmPC7r5OmL96FB2XVYTV2tjbBwbhvr2ZgCAmmYGmPB6bex6tzkC3JQHq6qzj9vWQ5iSLvJmBrr4pnND/P1uM4SV4Ji6ojhaGmH9mDDUsjURnZOYmo0+C04hLjlTclvZeYX4dNMFTFx/XmW4pWlta+x6rxnfl4iIiCAeTs/IKUDMA82FVomqmk1nEvDZ5ouSc37o5oV+wa7lul8PGxOMaO6hdKxAIeDb7VcgCOq/ESUi9jGGLYuS/Oxew1gP03r5VEgjrNKoXdMU60aXLKCemas8oJ6Rk48hSyIRnSAeTHe3NsaaUaGoyfMLVM0xnE5ERERERFQN5eQXYuyqMzhy/ZHoHBtTfawdFYraNc0qvB4nSyOsGhGCbzs3hKFe6Q9VwzytsXgIg+lEREREFcFQTw4/1xoIdLeCjo5M0+UQqdS0tg1WjQyBuZLO3P8lkwGftKuH6X18uQKTmtW3N8eeiS1w6bu2CP+8NT58sx7/DUToynWwdFgQ+oe4wsZUHzamBhgc5oaDH7XCsKYeWtEx3M7cEOtGh6Genfg5hPtpOeg9/xRuJCkPyN1IykDXP45j/em7kvvSkQEfvlEXK4aHsBMhERHRv3xdLEW7KkfGPVZzNURV09Zzifh4UzSkMuDfdm6IQaFuFbL/8a/Vhr1I4Pn4zWTsufSgQvYrJjz28b+dxsWD6e0a2ePgh63QI8AZMpnmzynVsi1ZQH3YsuIB9ae5BRi6NArn7qSKfq2btTHWjg5V+4pWRNpI82cqiIiIiIiISK1yCwrx9qozOBwjHky3NtHHmlGhqCNxUbm86ejIMLSpB3a92xy+LpYl/roQDyssHhrIYDoRERERET3n71oDG8aGSV5wNtGXY+GgQLzTqrZWXCSvrkwNdHnjSwkY6snx81uNEfVlG0R92Rrfd/WSfH5rgq2ZAdaODkVDB3PROY8yctF3QTiu3k9/6fGNp++iy5wTuJ70VHIfduYGWDMqFBNa14GczxsiIqLnDPXk8HGxUDoWGZ+i5mqIqp4d0ffwwYbzksH0SR0bYGhT5d3Ny4OJgS6+6NhAdPzHv68iW8XqQ+Xl1K3HGLY0Ctn54vvr6uuIOf39UMNEXy01lVRRQL1mKQLqmbkFGLY0EmduPxH9GhcrI6wdFQoHC6Nyr5moMmI4nYiIiIiIqBp5Fkw/i0MSwXSrf4PpddUYTH+Rp60pNo0Nw8dt60FPLn2hOdjDCkuHBcFYX3VHRCIiIiIiql7q25tj45gwOFkWvzDsYmWEze80RZuGdhqojKjsZDKZVt9MYWXybBU2H2fl4TgAeJyZh34Lw3EpMQ2ZuQX4YMN5fLzpgmSwBQBa1rXFrnebI9TTurzLJiIiqhKCPayUPh4ZlwJBKlFLRJJ2XbyPievPQyHxa/R5+/oY2dyzwmvp7O2AEJHf9cTUbPx55FaF13Dq1mMMXyYdTO/m64jfe/tqxSpPytSyNcXaEgbUH2XkYtiyKETFiwfTnSyfBdMdlZx/IKqutPO3n4iIiIiIiMpdbkEhxq0+i4PXHorOeRZMD0E9e80E04voynUw7rXa2DquqeiS4MHuVlg6lMF0IiIiIiIS525jgs3vNEEnbwcAgK6ODL0CnLFtXDONH/cQVVUWxnpYOTIEAW41ROekZuWj38JwdJ5zHJvPJkpuT64jw6ft6mPp0CBYm2pXt3giIiJtEuyh/Aau5Kd5iE3OVHM1RFXDnkv38e7acyiUSKZ/3LYexrSspZZ6ZDIZvu3SCGKLCM07cgt3HmdV2P5P3krGsGWRksH0t/ycMK23r9avdFTSgHrz3w4iMk58BQonSyOsGx0K5xrGFVEmUaXFcDoREREREVE1kFegwLjV57D/qngwvYaxHlaPDEF9e/Hlt9WtkaMFtk9oirEta0Ff9/+HsB0bO2DpsCCYGDCYTkRERERE0uzMDTGnvz9u/tQel75riym9fGClZcuKE1U15oZ6WDE8WLSrIwBk5BQg9pF0UM7RwhAbxoTi7Va1oKPl4RYiIiJNC3CrIRpYlQpWElFxadn5+OyvCxi76iwKJILp77epi3Gv1VZjZUADB3MMCnVTOpZXoMAPf1+pkP2evJmM4cuikJOvEJ3T3c8JU3v5aH0wvUhJAupS36+DhSHWjgqFixWD6UT/xXA6ERERERFRFZdXoMC4NWex/2qS6BxLYz2sHhmKBg7aE0wvYqArx2ft6yPyi9ZYNiwIeyY2xx8D/BlMJyIiIiKiUtGV68BQT67pMoiqDRMDXSwbFoxmtW3K9PVtGtTE3+82R4CbeMCdiIiI/s/UQBdeThZKxxhOJyq5PZfuo83vR7Au6q7kvAmv18Z7beqoqaqXffBGPdGbrvddScKR64/KdX8nbiZj+HIVwXR/J0ypRMH0IiUJqCtjb/4smO5qzWA6kTIMpxMREREREVVh+YUKTFh7FvuuqAqmh6Cho/YF019kaayPVvVqalVndyIiIiIiIiISZ6Qvx6IhgWhVz7bEX6OrI8Okjg2wcHAganCVAyIiolIJdld+U1dE7GMIgnj3ZyICktJzMGblaYxddRaPMnIl577dqhY+eKOumiorzsJYD5+0rSc6/t32y8grEA+Sl8aJm8kYUZJges/KF0wvUsvWFOtKEVCvaWaAtaND4W5jUsGVEVVeDKcTERERERFVUfmFCkxYcw7/XBYPplsY6WHViBA0clTeTYWIiIiIiIiI6FUY6skxf1AA3mhop3Kucw0jbHq7CUY294RMVjmDLURERJoU7KE8nH4vLQcJT7LVXA1R5aBQCFgbeQdtfj8ieU2tyOgWnvikbT2Nf17tHegCb2fl1/dikzOx5ETcK+/jxM1kDF8mHUzv4e9cqYPpRTxLGFC3/TeY7sFgOpEkhtOJiIiIiIiqoPxCBd5bdw57Lj8QnWNuqIvVI0NEl/kkIiIiIiIiIioPBrpyzB3gj46NHUTntG1kh7/fbQ5fF0v1FUZERFTFBIl0TgeAyLgUNVZCVDnEPnqKfgvD8fnmi8jIKVA5/+1WtfB5+/oaD6YDgI6ODN91aSQ6PvvADSSl55R5+8dvPAum50p0YO8Z4IzfenpX+mB6EVUBdRtTA6wdFYJatqZqroyo8tHqcHpYWBiWLVuG7GzeuUdERERERFRS+YUKTFx3HrsuqgqmhzKYTkRERERERERqoSfXwcy+vujh7/zS4/pyHXzXpRHmDQyAhZGehqojIiKqGmqY6KOenZnSMYbTif4vv1CBPw7dRLuZxxBRgt8NVytjrB4Zgk/baUcwvYifaw30CnBWOpaZV4hfdl0t03aP3XiEEculg+m9ApwxuUfVCaYXKQqoO1gYvvS4jakB1owKQe2ayl9jiehlupouQEpERAQiIyMxceJE9O/fHyNHjoS/v7+myyIiIiIiItJaBYUKTFx/Hn9fvC86x8xQF6tGhqCxyFJ/REREREREREQVQVeug2m9fdDJ2wHHbiTDWF+OPkEucLEy1nRpREREVUawhxVikjKKPR4Zz3A6EQBcSEjFp39dxNX76Srn6siAUc09MbFNXRjpy9VQXel90q4+9lx6gIzc4p3ft56/h/4hbgj2EF9V4b+OXn+EUStOlyiYrlPFgulFPG1Nsff9FlhwNBZxyZlwrmGMt1vWgoUxb6YlKimtDqcXSU9Px/z58zF//nz4+vpi9OjR6N+/P8zMeBcKERERERFRkYJCBd7fEI2/L6gIpo8IgbezpfoKIyIiIiIiIiJ6wWv1a+K1+jU1XQYREVGVFOxhhZXht4s9HpeciYfpOahpbqjkq4iqvuy8Qkzffx2LjsVCIaie39DBHJN7eGt9sydbMwNMfKMufth5Ren4N9svY+eEZiXqcH70+iOMXHEaeRLB9N6Bzvi1e9UNphcxM9TDh2/W03QZRJWWjqYLUEUQhOdLYQiCgHPnzuGdd96Bo6MjRowYgVOnTmm4QiIiIiIiIs0rKFTggw3R2BF9T3SOmYEuVo4IgY+LpfoKIyIiIiIiIiIiIiIitZHqkMzu6VRdHb+RjLYzjmLBUdXBdANdHXzarj62jW+q9cH0IoPD3FDXzlTp2NX76VgTUfyGlf86UoJgep9Al2oRTCeiV6fV4fTo6GiMGzcOFhYWEIRn7woymQyCICAzMxPLli1Ds2bN4OXlhVmzZuHJkycarpiIiIiIiEj9ChUCPtwYje0SwXRTA12sGBEMXwbTiYiIiIiIiIiIiIiqLDtzQ7hbGysdi4xjOJ2ql9SsPHy8MRoDF0fgTkqWyvmhnlbYM7EF3m5VC3pyrY5WvkRProNvuzQSHZ+69zpSMvNExw/HPMQoFcH0vkEu+KV7YwbTiahEtPoVtHHjxpg9ezbu3buH5cuXo0WLFi+F1IFn3dSvXLmC999/H05OThg4cCCOHDmiybKJiIiIiIjUplAh4KON0dh2XnUw3c+1hhorIyIiIiIiIiIiIiIiTRDrns5wOlUXgiBg54V7aPP7EWw8k6ByvpmhLn7t3hhrR4XCw8ZEDRWWvya1bNCxsYPSsbTsfEzdG6N07HDMQ4xeeUYymN4v2AU/v8VgOhGVnFaH04sYGhpi0KBBOHz4MK5du4YPP/wQtra2xbqp5+TkYO3atXj99ddRr149TJ06FY8ePdJw9URERERERBWjUCHg443R2HIuUXSOib4cy4cHw5/BdCIiIiIiIiIiIiKiaiHYw1rp49ceZCA1S7x7MlFVcD8tG6NWnMb4NeeQ/FT18729lz0OfNASfYNdnzfMray+6NgAhnrKI6FrI+/gYkLaS4+VLJjuip+6MZhORKVTKcLpL6pbty6mTJmChIQEbNiwAW+++ebzN4UXu6nfuHEDn376KVxcXNC7d2/s3btXk2UTERERERGVq0KFgI83RWNzCYLpAW4MphMRERERERERERERVRchIp3TASAq/okaKyFSH4VCwMrw23jj96PYf/Whyvk1zQwwb2AA/hwYgJrmhmqosOI5WRph/Gu1lY4JAvDN9ktQKJ41BD4U8xCjV0gH0/uHuOKnbl4MphNRqVW6cHoRXV1d9OzZE3v27EFsbCwmTZoEJyenYt3U8/Ly8Ndff6F9+/bw8PDATz/9hHv3xJe7JyIiIqLKIS07H4mp2cjJL9R0Ka8kO68QqyNuY8HRW8XuVK9MBEHArssPMeOSHFMuyHHwngy5Eicy6NUoFAI+/esCNp8VD6Yb68uxbHgwAt3FT0ATEREREREREREREVHV41zDCA4WysO2kXGP1VwNUcW7+fAp+iw4ha+2XsLT3AKV8/sFu2LfBy3RzsteDdWp18jmnnC1MlY6dvZOKracS8Shaw8xZsUZ5BWKX88dEOKKH7symE5EZVNpw+kvcnV1xffff4/4+Hjs2LEDXbp0gVwufz4uCAIEQcDt27fx9ddfw93dHV27dsXOnTuhUDAwQ0RERFSZFCoEzNx/A8E/7UfTXw+i5ZRD+PvCfU2XVSaX76Whze9H8OWWS/h51zV0+eM4ftx55fnd6pWFQiFg0tZL+GxbDOIyZEjIlGHbbTmm7o/VdGlVUlEwfdOZBNE5xvpyLBsWjCAG04mIiIiIiIiIiIiIqh2ZTIZgke7pkXEpaq6GqOLkFSgw+8ANdJh5rESrAnjYmGDd6FD80r0xLIz01FCh+hnqyfF1p4ai4z/8fQVjVqoOpv/AYDoRvYIqEU4voqOjg44dO2Lr1q24c+cOfv75Z3h6egJ49qGrqJt6QUEBdu7cia5du8Ld3R0///wzkpOTNVw9EREREalSqBDw8aZoTN9//XlX7qT0XIxbcxYrw29ruLrSuXwvDQMWRSAxNfv5Y4IALDoeh0nbLlWagLogCPh6+yWsjrhTbGz92fu4kZShgaqqLoVCwOebL2KjRDDdSE+OpUODRE86ExERERERERERERFR1Sd2neDSvfQSdZYm0nbn76aiy5zjmLbvumTQGgDkOjK806oWdr/XHKGe1mqqUHNaN6iJ1+rZKh1LzcqX/HkNDGUwnYheXZUKp7/I3t4e/v7+8PHxeenxopA68CxIk5CQgK+++goeHh744osvkJ2drWxzRERERKRhhf92i958NlHp+FdbL2HFqXj1FlVGV+6lY8CiCKRm5SsdXxNxB19u1f6AuiAI+HrbZawKLx5MLyIVoqbSUSgEfLHlItafvis6x0hPjqXDghBSDU6qERERERERERERERGRuBCRcHqhQsDZ26o7TBNpK4VCwOwDN9B97glce6C6UVZjJwvsGN8Mn7SrD0M9uRoq1DyZTIavOzeCvrx08VAG04movFS5cPq9e/fw448/wtPTE+3bt8fWrVufh9GBZwGaoj8AnndTz8zMxOTJk9G4cWNcuXJFU+UTERERkRIKhYDP/rqATSqCzl9vu4zlJ+PVU1QZXb2fjgGLwkWD6UXWRt7BF1suam1AXRAEfLv9ssqO9ZvPJiJfRacCUk2hEPDl1otYFyUeTDfU08GSoUHVotsDERERERERERERERFJq2VrCisTfaVjkXEpaq6GqHw8fpqLIUsjMW3fdai6jGqop4NJHRtgyztN0NDRXD0FahEPGxOMaO5R4vmDQt3wQ1evl7KWRERlVSXC6QqFAjt27ECXLl3g7u6Ob775BvHx8c8D6EVhdDc3N/z000+4d+8eDhw4gL59+0Jf/9mHsKKQemxsLNq0aYPHjx9r8lsiIiIion8pFAI+33yxxB24v9l+GUtPxFVwVWVz7cGzjulPVATTi6yLuovPNl/QuoC6IAj4bscVLD8lHUwHgOSnuTgS80gNVVVdCoWASdsuYW2kimD6kCCE1WIwnYiIiIiIiIiIiIiInmWhgt2Vd09nOJ0qo6j4FHScdRzHbiSrnNustg32TmyJkc09oVvK7uFVyfjXasPe3FDlvMFhbvi+ayMG04mo3FTqV974+HhMmjQJrq6u6NatG/7++28UFBQAwPMXSh0dHXTt2hW7du3CrVu38Pnnn8Pe3h6vvfYa1qxZg4SEBHzxxRcwNDR8/jVJSUmYPn26xr4vIiIiInpGoRDwxZaLWH9aPJSrzHc7rmDxce0KqMc8yED/hRFIycwr1ddtOJ2AT//SnoC6IAj4fucVLCtFh/oNpfz3o/8TBAFfb7+ENRF3ROcY6Opg8ZAgNKlto8bKiIiIiIiIiIiIiIhI2wV7KA+nn7+bipz8wgrd98P0HCw9EYevtl7CX2cSUKgl17qo8lEoBPx5+Bb6LgjHg/QcybkWRnqY0tMbK0cEw9XaWE0Vai8TA1180bGB5JwhYW74rguD6URUvnQ1XUBpFRQUYMuWLVi4cCEOHjz4vCs68P9AuiAIcHJywsiRIzFq1Cg4OjqKbs/a2ho//vgjBgwYgCZNmiA9PR2CIGDHjh348ccf1fI9EREREVFxCoWAL7dexLqosgWbf9h5BYIgYGRzz3KurPSuJ2Wg/8LwUgfTi2w8kwABwOQe3pDraO6kgCAI+GHnVSw9EV+qrzt47SGSn+bCxtSgYgqrogRBwNfbLmNVuOpgelMG04mIiIiIiIiIiIiI6D/Ewul5hQpE301FiGf5r8ialVeABUdjMf9ILLL/DcCvDL+N3Zce4M+B/tCrxl2sqfSeZObhgw3ncagEKzV38nbAN50bwdaM1yRf1NnbAavDbyNCyYoJQ5u445vODRlMJ6JyV2ne7a9fv46PP/4YTk5O6Nu3Lw4cOACFQgHg/6F0mUyG9u3bY+vWrbh9+za++eYbyWD6ixo0aIDx48c/D7rHxsZWzDdCRERERCopFAImbbuEtZHiwXQDXR0MCnWT3M6Pf1/FomOa/Vx3499g+mOJYHrLurZYPCQQBrriH883nUnAx5uiNdZVQhAE/PT3VSw5Id6RXuyURYFCwNZziRVTWBUlCAK+3X4ZK8Nvi84x0NXBoiGBaFaHwXQiIiIiIiIiIiIiIiqugYM5zAyV9y6NVBJUfRWFCgEbTt/Fa1MPY8b+G8+D6UX2X03C7/uul+s+qWo7c/sJOs46pjKYbmqgiz/6+2NOf38G05WQyWSY3scXTpZGLz0+pqUng+lEVGG0unN6bm4uNm7ciIULF+L48eMAoLRLur29PYYPH45Ro0bBzU06oCTF39//+f9nZWW9QuVEREREVFaCIODr7ZewJkK6W/SSoc+6RdeuaYpvtl8Wnfvj31ehEASMblGrIsqVdPNhBvotjEDyU/Fgeou6tpg/KACGenIsGRqEEcujkJOvUDp389lECAIwtZePWjuoC4KAX3Zfw6Lj4sF0PbkM07o3wHfbL+NxbvHaNp1JwIhmHjy5UQKCIOC7HVew/JR4MF1fVwcLBweieR1bNVZGRERERERERERERESViVxHhiB3Kxy89rDYWGR8+YXTT9xMxo9/X8XV++mS8+YduYWmtWzYeIckCYKAxcfj8OvuayhQ0biroYM55g7wh7uNiZqqq5wcLY2we2Jz7L54Hxk5BQhyt4KPi6WmyyKiKkyrw+n29vZIT3/2oUUQBMhkMshksucB9datW2Ps2LHo2rUrdHVf/VsxNzcHAAZmiIiIiDREEAR8ve0yVoVLB9MXD3kWTAeAIU3cIZMBX28TD6j/vOsaFAIwtqX6Auo3Hz5F3wURSH6aKzqneR0bLPg3mA4ATWvbYMmQIAyXCKhvOZcIQRAwrbevWgLqgiDg193XsOCoeAd6PbkMfw4IQLCzEXbbKrA7QV5szrUHGbiUmI7GzhYVWW6lJwgCvt95BctOxovOKQqmt6jLYDoREREREREREREREUkL9lAeTj9z+wnyCxXQk4uv7KvKzYdP8cuuqzigZPvKCALw/obz2PNec1ibssM1FZeWlY+PNkVj35UklXP7h7ji604Nn19rJWnmhnroE+Sq6TKIqJoo+6cLNUhLS3vp74IgwNraGh999BFiYmKwb98+9OjRo1yC6f/dDxERERGplyAI+Hb7ZawMV90t+r/dFAaHueOHbl6S2/919zXMPXyzXGpV5dajp+i3MFxlMH3h4MBiJ0ua1LbBkqFBMNQT/6i+9fw9fLDhPAoKlQfYy4sgCJi8JwbzJYLpujoy/NHfH20a2gEAgmsKkEH55+mNZ+5WSJ1VhSAI+GHnVSw9ES86R1+ugwWDAtCSwXQiIiIiIiIiIiIiIiqBYA8rpY9n5RXi8j3pTudiHj/NxVdbL6HtjKMlDqYXeZSRi483XWA+i4qJvpuKjrOPqQymm+jLMbOvL35+qzGD6UREWkqrw+nAs4CGIAho3rw5Vq9ejYSEBPz222+oXbt2ue+rWbNmiIuLQ1xcHGJjxQM4RERERFS+BEHAdzuuYPkp1cF0sW7Rg0Ld8NNb0gH13/bE4I9DFRtQj330FP0WhONRhngwvVlt5cH0Ik1q2WDp0GAYSZxM2Xb+Ht7fEF1hAXVBEDDlnxjMO3JLdI6ujgx/DPDHm43snz9mZQDUsVB+MnHruUTk5BeWe61VgSAI+Onvq1hyIk50jr5cB/MHBaBVvZpqrIyIiIiIiIiIiIiIiCozL0cL0WtOkXGPS7WtnPxC/Hn4FlpNOYyV4bdRqChbwPzgtYeSq8hS9SIIApaeiEPPeSeR8CRbcm59ezNsn9AMXX2d1FQdERGVhVaH0y0sLPDuu+/i8uXLOHLkCPr16wd9ff0K25+BgQHc3Nye/yEiIiKiiicIAr7feUXyBFRJu0UPCHHDz281lpwz5Z8YzDl4oyylqhSXnIl+C8PxUCKY3qSWtWQwvUhYLWssGxYEY33xeTui72Hi+vLvoC4IAqbujcHcw9LB9Dn9/dH2hWB6kRBb5Sci03MKSrQEX3UjCAJ+3nUVi46LB9P15DLMG+SP1+ozmE5ERERERERERERERCWnr6sDfzdLpWORcSkl2oYgCNgefQ+tpx3B5D3XkJFboPJr9OQy+Lgo3y8A/LLrGi7fSyvR/qnqSs/Jxzurz+K7HVeQXyh9s0OfQBdseacpatmaqqk6IiIqK11NFyDl/v37MDQ01HQZRERERFRBBEHADzuvYumJeNE5pe0W3T/EFToy4LPNF0XnTN17HQoBeLd1ndKWLCo+ORP9FoQjKV08mB7maY3FQ4JgJBE4f1GIpzWWDQvG0KWRyMpT3nF854X7EARgRl9f6Mlf/d5TQRDw+77r+OOQeDBdriPD7H5+aOdVPJgOAN5WAozkArILZcXGNp5JQGcfx1eus6oQBAG/7r6GhcdUBNMHBuD1+nZqrIyIiIiIiIiIiIiIiKqKYHdrnLhZvEt6ZFwKFAoBOjrFr+kUOXM7BT/svIrzd1NLvL8Oje3xabv6sDM3xFtzT+Lq/fRic/IKFXh37TnsmNAMxvpaHWGjCnIpMQ3j1pzF7cdZkvOM9OT4sZsXegQ4q6kyIiJ6VVr7zn7u3DmsXLny+d/79++PwMBADVZEREREROVJEAT89PdVLDkhHsrVl+uUqVt032BXyP4NqAsiN9j/vu86FIKAiW3qlmrbysQnZ6LvgnA8SM8RnRPqaYXFQwNLHEwvEuxhheXDgzF0SSQyRQLqf1+8DwECZvb1e+WA+vT9NzD74E3R8aJgevvGDqJz9OWAn42Ak0nFT2Qeu/EI99Oy4WBh9Ep1VgWCIGDynhjMPxorOkdPLsOfAwLQugGD6UREREREREREREREVDbBHlZKH0/PKUBMUgYaOJgXG7v9OBOT91zDrosPSrwfHxdLfNWxAQLd/7+/2f180Wn2ceTkF18J+NajTHy/4wp+7eFd4n1Q5ScIAlZH3MH3O64gT8UK0bVrmuLPAf6oY2empuqIiKg8vHprxQpy5MgRzJgxAzNnzsScOXPg5uam6ZKIiIiIqJwIgoBfdl/DouPS3aL/HOhf5m7RfYJcMbm7N2TijR4wY/8NTN93vUzbL3L7cSb6LZQOpgd7WGHJ0KAyd30Icn8WUDeRCLbvuvgAE9acQ76KEzhSZuy/jlkHboiOy3VkmNXXDx0kgulFQmyV1yEIwOaziWWusaoQBAFT/onBvCPiHep1dWT4o78/2jRkMJ2IiIiIiIiIiIiIiMrOz9USenLlF80i41Je+ntaVj5+3HkFbX4/UuJgupOlEWb29cWWt5u8FEwHgNo1zfBt50aiX7su6i7+vnC/RPuhyu9pbgHeXXcek7ZeUhlM7+7nhO3jmzKYTkRUCWltOD0n51m4RxAEODs7w9bWVsMVEREREVF5EAQBv+6+hgVq6BbdO8gFv/WQDqjPPHADv++7DkGsxbqEO4+z0G9BOO6nSQTT3a2w9BWC6UUC3a2wYkQwTA3Et7Pn8gOMX3MWeQWlD6jP3H8DM/aLB9N1ZMCMPr7o6K06mA4AbqZALRtjpWMbT98t08+7qhAEAVP3xmDuYRXB9AH+eLORvRorIyIiIiIiIiIiIiKiqshQTw4fZ0ulY0Xh9LwCBZYcj0PLqYew6Hgc8gtVX8sxM9DFp+3q48CHLdHV1wk6OsovyvUJckGHxuLXPD7bfAEJT7JUfyNUqV29n44us49jR/Q9yXkGujqY3KMxpvX2eeVrrEREpBlaG063t3/2gUQmk6FmzZoaroaIiIiIyoMgCJi8JwbzVQTTy7NbdK9AF0zt6SMZUJ9VhoD63ZQs9FsYjnsSwfQg9xpYOiwIJhKB8tIIcHvWQV0qoP7P5aRSB9RnH7iB6fvFO8jryIAZff3Q2cexxNuUyYCu3sr/DeMfZyEq/kmJt1WVCIKAaXuv449D0sH0Of390ZbBdCIiIiIiIiIiIiIiKifBHlZKH4+IS8E/lx+g7Yyj+H7nFaRm5avcllxHhkGhbjj8cSu83aoWDPXEV/8FnuW/fnnLG06WRkrHM3IK8N668yh4hRWCSXsJgoB1kXfQ7Y8TiE3OlJzraWOCreOaok+QK2RSF3iJiEiraW043dHx/8GXlJQUiZlEREREVBkIgoAp/8Rg3hHVodzy7hbdI8AZ03pJB9RnH7yJqXtjShRQv5uShb4LwpGYmi06J9CtBpYOCy63YHqRALcaWDEiGGYS2917JQnvrC5ZQH3OwRuYtk86mD69jy+6lCKYXqSTV03IRTpkbDx9t9Tbqwqm77+BOYduio7LdWSY3c8P7bwYTCciIiIiIiIiIiIiovIjFk5PfpqLMSvPIE5FaLhI6/o18c/E5vihmxesTQ1KvH8LYz3M7OsLkUtHOHP7CWYdEF/llyqnrLwCfLghGp9tvohcFdcuO/s4YvuEZmjgYK6m6oiIqKJobTi9adOmMDY2hiAIiIuLY0CdiIiIqBITBAFT98Zg7mHNdYvu7u+M33v7iJ7wAoA/Dt3ClH+kA+oJT551TJcKpge41cAyFR3OX4W/aw2sHBkCM0Px7e+/moR3Vp9BbkGh6Jw/Dt3E1L3SwfTfe/uiq69Tmeq0MdXHa/VslY79ffE+MnMLyrTdymrG/uuSJ1WLguntGzuosSoiIiIiIiIiIiIiIqoOAtxqSF4nU6WBgzlWjwzB4qFBqF3TrEzbCHS3wnut64qOzzl0E+Gxj8taImmZ60kZ6DLnBDafS5Scp6+rgx+7eWFWX98Ku75KRETqpbXhdBMTE3Tp0gUAoFAosHLlSg1XRERERERlIQgCft93HX8cEg+mq6tb9Ft+zpjeR7wjAwDMPXwLk/coD6gnpmaj74JwJDwRD6b7u1pi2bCgCj9x4utiiVUjVAXUH+LtVWeVBtT/PPwsiC9GJgOm9fZBN7+yBdOL9AxwUfp4Vl4hdl28/0rbrkxm7r+BGfulg+kz+/qiA4PpRERERERERERERERUAcwM9dDI0aLUX1fTzAC/9fTGzgnN0LS2zSvXMf712qJd3BUC8P7683iSmffK+yHN2nQmAV3nnMDNh08l57lZG2Pz200wMNQNMqllsImIqFLR2nA6AHz11VfQ19cHAHz33Xe4cYNLtxARERFVNtP338DsgzdFx9XdLbqrr5PKgPq8I7fw6+5rLwXU76Vmo++CU5LBdD9XSywfHgwzQ73yLFmUj4slVo8MgblEQP3gtYcYu/IMcvL/H1Cfd+QWJu+5Jvo1MhkwtacP3vJzfuUaX69fE9Ym+krHNp5OeOXtVwazDtzA9P3SHepn9PFFJ29HNVZFRERERERERERERETVjVgoXBkjPTkmtqmDwx+3Qu9AF8hfpe36C+Q6Mszo4wsLI+XX0+6n5eDTvy5IrnRM2istOx8fbDiPjzZGIztffIVnAOjQ2B47JjSDl1Ppb5ogIiLtptXh9AYNGmDx4sWQyWRITU1Fq1atsH//fk2XRUREREQlNGP/dcw6IN0telZfP7V3i+7q64SZff0kT6LNPxqLX/4NqN/7t2P63RTxYLqvi3qD6UW8nS2xemSo6Ak8ADgU8whjVz0LqC84+ix4L0YmA6b09EGPgFcPpgPPluET674eGZ+C+OTMctmPtppz8AZ+36cimN7XD519GEwnIiIiIiIiIiIiIqKKVZJwukwG9ApwxuGPW2Fim7ow1i//1YIdLY0wuYe36PjeK0lYHXGn3PdLFev4jWS0m3EUm88mSs7Tk8vwbeeG+KO/P8zVfG2ViIjUQ6vD6QAwYMAA7NixA3Z2drh//z7atm2Lli1bYt68eTh79iyePHmCwkLpu6yIiIiISP1m7r+BGfulg+kz+viio7d6g+lFOvs4YmZfX8mA+oKjsfhy6yX0WxiOOylZovN8XCyxYkSwxk6eNHa2wOqRIZIB9cMxj9Bx1jH8vEs6mD65hzd6llMwvUivQPHtbTpTdbun/3HoJqbulQ6mT+/jiy4MphMRERERERERERERkRqEeFjBUE88Lta0tjV2TmiGKb18YGduWKG1tPOyx4AQV9HxH3ZeQcyDjAqtgcpHdl4hvtl2CQMXR+B+Wo7kXOcaRtg0tgmGNvWATFY+3fiJiEj7lP+tbeVILpe/9HeZTAZBEHD8+HEcP368zNuVyWQoKCh41fKIiIiISMTsAzcwfb/qUK6mu0V38naEDDK8u+4cChXKlwZco6Irg7ezBVYM11wwvYiX07OA+sDFEUjNylc659Yj6S7lk7t7o3egS7nXVt/eHI2dLHAxMa3Y2F9nE/D+G3XLbSlIbTH38E1M+SdGdFxHBvze2xddfZV3lSciIiIiIiIiIiIiIipvlsb6GNXcE7MP3nzp8do1TfFFh/p4rV5NtQaGv+rUEFHxKbie9LTYWG6BAhPWnsX28c1gqCdX8tWkDc7eeYIPN0QjrgSrJb/R0A5Te/rAwpjd0omIqjqt7pwuCMLzP0WKPgC9OFaWP0RERERUMeYcvIFp+ypPt+iO3g6Y088PumUIRzd2ssDK4dIdy9WpKKBuWYYTOr92b4zeQeUfTC8i1j39floOTtxMrrD9asK8I7fw2x7xYLpMBkzr7YNufgymExERERERERERERGRen3wRl1M7tEYrerZ4rV6tvithzf2vNccr9e3U3sna0M9OWb384eBrvII2/Wkp/jp76tqrYlKJq9AgSn/XEPPP0+qDKbr6sgwqWMDLBgUwGA6EVE1odXhdEA8jP4q2yIiIiKiijH38E1M3SsdTNfGbtHtGztgTv/SBdS9nMyxakSI1p1AaeRogTUjQ1GjFHX90r0x+gaLL5tYHrr4OEJf5MTihtN3K3Tf6rTg6C38uvua6LhMBkzt6YO3/JSH9YmIiIiIiIiIiIiIiCqSTCZDnyBXLBsWjKXDgtE7yAW6cs1FyOrZm2FSxwai4yvDb+Ofyw/UWBGpcvV+Orr+cQJ/HLoFkcWpn3O3NsaGsWEY2dyT2T0iompEV9MFSGnRogXflIiIiIgqiT8PV+5u0e28HDCnvwzj15xFgYqzKI0ctTOYXqShoznWjArFgEURSMnMk5z781uN0a+Cg+nAs2Ui32xoh50X7hcb23slCWlZ+Vr78yyphUdj8fMu6WD6lJ4+6BHAYDoREREREREREREREVGRgaFuOHojGfuuJCkd//SvC/B2toCDhZGaK6MXFSoELDgai9/3xSC/UHVz2cFhbvisfX0Y62t1RJGIiCqAVr/yHz58WNMlEBEREVEJzDtyC5P3VP5u0e287DF3gD/GrTkrekKloYM5Vo8MgaWxvpqrK50GDuZYMyoE/ReKB9R/7OaF/iEVH0wv0ivQRWk4Pa9Age3RiRgU5q62WsrbomOx+GmX+LKSMhkwuYc3ejKYTkRERERERERERERE9BKZTIbfenijfcIxPEjPKTaempWPievOY82oUMhLsRIylZ/45Ex8uDEaZ24/UTnX3twQU3p5o3kdWzVURkRE2khza7IQERERUZWw4Ogt/Lq76nSLfrORPeYOCICevPiJrQaVJJhepL69OdaOCoW1SfF6f+jaCAND3dRaT7PaNnCwMFQ6tvFMglprKU+Lj8fhx7/Fg+kAMLm7N3oHuqipIiIiIiIiIiIiIiIiosqlhok+pvfxhUwkex4Rl4K5h26qtyiCIAhYeSoe7WceK1EwvbufE/55vwWD6URE1RzD6URERERUZouOxeLnXdLB9MrYLfqNhnZYOjQYrlbGzx/r0Ngeq0eGoIaSoLc2q2dvhp3vNkN3Pyd42pog0K0G1owK0UiXcrmODD38lT8XLiSk4dqDdDVX9OqWnojDDzuvSM75tXtj9A5iMJ2IiIiIiIiIiIiIiEhKWC1rjH+ttuj4jAM3cOZ2ihorqt7up2Vj8JJIfLXtMrLzCyXnWpnoY95Af/zexxcWRnpqqpCIiLSVrqYLqO4EQUB8fDwuXryIhIQEpKamwsDAADVq1ECdOnUQFBQEQ0Pl3SWJiIiINGnRsdgq3S26WR0b7PugBWIfZcJITw53GxNNl1RmDhZG+L2Pr6bLAAD0DHDGHJGuFhtPJ+CrTg3VXFHZLT8Zj+92SAfTf36rMfoGu6qpIiIiIiIiIiIiIiIiosrtvdZ1cOJmMs7eSS02VqgQ8O7a89j1XnMGoCuQIAjYej4RX2+7jIycApXz32hoh5/fagxbMwM1VEdERJUBw+ka8OTJE2zduhV79uzBwYMHkZycLDpXT08PHTt2xMSJE9GyZUu11Ofu7o7bt2+X+esPHTqEVq1alV9BREREpHWWHI9THUzvUfm7RRvoytHAwVzTZVQp7jYmCHa3QmR88a4WW88l4rP29aEn1/4Fnlacisc32y9LzvnpLS/0D2EwnYiIiIiIiIiIiIiIqKR05TqY2dcPHWYdUxqMTkzNxhebL2JOfz/IZDINVFi1PX6ai0lbL2H3pQcq55oZ6OKbLo3Qw9+J/xZERPQS7U99SCgoKEBiYiIuXbqEU6dO4dKlS0hMTERBgeo7tjRl3LhxsLe3x/Dhw7FhwwbJYDoA5OfnY+vWrWjVqhWGDBmC9PR0NVVKREREpNzSE3H4fqd0t+hfujdGnyCGckm5noHOSh9/nJmHg9ceqrma0lt5Kh5fb5MOpv/YzQsDQtzUVBEREREREREREREREVHV4WJljJ/faiw6/vfF+9hw+q4aK6oe9l1JQtsZR0sUTG9Syxp73m+BngHODKYTEVExla5z+q1bt7Bo0SIcPXoU586dQ25ubrE5BgYG8Pf3R8uWLTFy5Eh4eHhooFLlIiIikJeXV+xxuVwOBwcH2NnZIT8/H7dv30ZaWtpLc1asWIFr167hwIEDMDU1VVfJRERERM8tPxmP73ZIB9N/fqsx+gUzmE7iOjZ2wLfbLyMrr7DY2MbTCWjbyF4DVZXMqvDb+EpFMP2Hro0wMJTBdCIiIiIiIiIiIiIiorLq7OOI4zeSsV4khP7t9isIcLNC7ZrMUL2qjJx8fL/jCjaeSVA510BXB5+3r4/BYe7Q0WEonYiIlKs04fQHDx7gnXfewfbt2yEIAgA8/+9/5eTk4NSpUzh16hQmT56Mbt26Yc6cObC3166Qi6WlJfr374+OHTuiefPmMDMzez5WWFiIY8eO4euvv8axY8eePx4ZGYmhQ4di06ZNaqnRzs4Oq1atKtXX+Pj4VFA1RERELzt35wnmHr6FtOx8tKxri6FN3GFiUGk+3lQ6K07F45vtqrtF9w9hMJ2kmRjoomNjB6UnuA7FPMTDjBzUNDPUQGXS1kTcwaStlyTnfNelEQaFuaunICIiIiIiIiIiIiIioirsmy4NEXU7BbGPMouNZecX4t2157BlXBMY6Mo1UF3VcPJWMj7eeAGJqdkq5/q4WOL33j6oZcsbAoiISFqlSG/t27cP/fv3R0pKyvNAukwme74kyIsh9ReXCREEAYIgYMuWLThy5AjWrl2LNm3aqLd4Jdzd3TFp0iT0798fRkZGSufI5XK0atUKhw4dwjvvvIMFCxY8H/vrr79w6NAhvPbaaxVeq6GhoVb8zIiIiP5rTcQdfLHl4vO/R8alIPpuKuYPCuCyYRVgZfhtfK2qW3Q3L3aLphLrFeiiNJxeqBCw9VwiRreopYGqxK2NfPk1R5lvOzfEkCbu6imIiIiIiIiIiIiIiIioijPW18Wsvn7oPvck8goVxcav3E/H5N0x+LpzQw1UV7ll5xVi8p5rWHYyXuVcXR0ZJrapg7Eta0FXrlPxxRERUaWn9e8WJ06cQLdu3fD48WMIgvBSIF0ul6NRo0Zo1aoVOnbsiFatWqFRo0bQ1dV9KcQuCAIeP36Mbt264dSpU5r8dvDdd98hJiYGI0aMEA2mv0gul2Pu3LkIDAx86fFFixZVVIlERERaTywkuvdKEo5cf6SBiqq2VeG38ZWKbtHfd22EQQymUykEudeAu7Wx0rGNpxNEV0nShPVRd/D5Zulg+tedGmJoUw81VURERERERERERERERFQ9eDlZ4LP29UXHl5yIw8FrSWqsqPI7fzcVHWcfK1EwvZ6dGbaOa4rxr9dhMJ2IiEpMq98xMjMz0atXL2RnZ7/UAbVv377Yu3cvnj59igsXLuDgwYPYsWMHDh48iAsXLiAjIwP79u1D3759Afy/y3pWVhZ69eqFrKwsTX1L6NixI/T19Uv1NXK5HJ988slLj/3zzz/lWRYREVGlsS5SOiS68XTxTsxUdmsi7mCSimD6d10aYXCYu3oKoipDJpOhZ4Cz0rEbD58iOiFNzRUptyHqLj5TEUyf1LEBhjdjMJ2IiIiIiIiIiIiIiKgiDGvqjtfq2YqOf7TxAh6m56ixosopJ78Q0/bGoMefJxH7KFNyrkwGjGnpie0TmsLLyUJNFRIRUVWh1eH03377DQ8ePHje/dzJyQknTpzAmjVr0KZNG9GQt76+Plq3bo01a9bg1KlTcHb+f+jl/v37mDJlirq+hXLTvHnzl/7++PFjjYbsiYiINKEkIdF9V5KQmpWnpoqqtnUiHepf9E3nhhjSxF09BVGV093fGS/cg/qSjafvqrcYJTacvotPN1+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l2OrSSacC1+3r4ZaD/dx5dIA7Dw9w44FesulUq0uTJEmSJEm7VLsFzT8HTAB91eMfZ3NB8x+r2S8An9pEX5IkSZIkSZIkSZLWaHGl8bFCEggfKyyGxYtcKKw8XgyOj80UqbQ+M76q7lyamw70ccvBPm4+0MctB/u57ooeOrPpVpcmSZIkSZK0pK2C5jHGSgjhL4Bvqza9MoTwphjjr663rxDCvwe+Alh8K+qvY4ylBpUqSZIkSZIkSZIk7SpzpfJSKHwxPD46Pc/oTCkJjdeFyccKJYrlSqvL3rThnhw3H+yvCZX3ceWePOlUaHVpkiRJkiRJl9VWQfOqnwG+FQjVxy+FEAaBn4oxlle7OISQBt5Cshp6rPZRAd62VQVLkiRJkiRJkiRJO934TJFnRgo8M1Lg2ZECT48UODE6w4VqeHymuOpHdTvelXu6ueVgskL5Yqh8b28HIRgqlyRJkiRJO0/bBc1jjA+HEH4T+NckQfEU8J+AbwshvAP4yxjjo/XXhRBuBF4NfD9wDUnAPFYf74wxfrFJt7CqkLwTdRVwG3AYGADmgTHgCeCzMca5Bo/ZC7wUuB7oA2aB54BPxhhPNXIsSZIkSZIkSZIkbU+F+YUkSH6hwDPnCzxzYTlYPjaze34cOJsOXLevtxoq7+Pmg/3cdKCX3s5sq0uTJEmSJElqmLYLmlf9EHAL8HKWVyU/Bvwy8MshhAIwAkwDPcAwkK9eu7icwOJ1Hwb+TbMKv5TqquyvBf4J8JUkNV9KKYTwAeDtMcaPbHLcq4G3Aq8Hchc5JYYQPgK8Ocb40c2MJUmSJEmSJEmSpNabXyhz/MIMT1cD5M/UPM5Nzbe6vC2XCjDYnWOgO8tQPsdAd46h7hyD+RzX7M1zy8E+rtvXSy6TanWpkiRJkiRJW6otg+YxxoUQwtcB7yIJZ8fqU4sh8p7q46KX15z7f4E3xhgXtqbStQkh/Hfge7h40PtisiT3/doQwu8B/zbGOLmBcV8P/A7QfbnTgFcCHw4h/CLw4zHGeJnzJUmSJEmSJEmS1GKVCKPzcG42cOqzJzk9fXwpTH5yfJZ2+bQnmw4MdOcY7M4y2J1LHvnkeClEns9Wz0kC5b2dGVKpsHrnkiRJkiRJba4tg+YA1WD1N4UQvg/4ceDo4lOXuSxUH88CPx1j/F9bWuTafSkXD5mXgdPAWZJw+ZVAf9053wncGEL4qhjj9FoHDCG8DvgDoH4phvPACWAfcIjl8H4AfhToAN601nEkSZIkSZIkSZK09c5PzXPfiXHuPT7GZ58Z4f4TaYqV6sc8jz7d2uLWKJMKDOYXVxdPguKD3bkVgfHFMHnSlqWnI0MIhsYlSZIkSZI2om2D5otijP8zhPDbwDcB/xh4OXANK+99AXgS+BjwV8B7Y4yVZte6RuPAu4EPAB+LMU4tPhFCSJPc31ur20X3kKzu/i1rGSCEcIxkJfPakPn9wJtijB+qOe8G4GdJ/m0X/XAI4WMxxv+39luSJEmSJEmSJElSoxQXKjxyepIvHB/j3uPj3HtijBOjs3VntT58PdCdrYbGF8PhWYbyHUuB8aF8riZYnqOv09C4JEmSJElSM7V90BwgxlgG3lN9ABBC6AV6ganasPY29izw08C7Y4z17wQCS/f54RDCVwDvAL635ulvDiF8RW1Q/DLeBuRrjj8LvKq6SnzteI+FEL4F+I26sX4xhPC+GOPCGsaSJEmSJEmSJEnSBsUYOT0xlwTKj49x74lxHjg5QXGhdWsqdWXTXDWc55rhPFcNd3NwoIs91TD5np5k29+VJZOu/2FdSZIkSZIkbSe7Imh+MdVw+U4ImAO8GfhgjLG4lpNjjOUQwg8AdwMvrnnqe4DLBs1DCLcA31rTVAS+qz5kXjNWDCH8O+ArgOuqzceANwC/tZZ6JUmSJEmSJEmStDZzpTIPnJzg3uNjfOG5ZLXys5PzTa8jl05xdE83V+3Jc83ePFftyXP1cPK4oq/DlcclSZIkSZLawK4Nmu8kMcYPbOCacgjhF4E/rmn+mjVc+t1A7fIRfxhjfGSVseZCCD8PvLOm+XswaC5JkiRJkiRJkrRhMUaeuzDDvSfGqiuWj/PI6UkWKrEp46cCHB7sXgqQXz2cX1qp/OBAF+mUYXJJkiRJkqR2ZtC8vX2s7nhPCKE7xjhzmWu+vu74nRc964X+CPh1IF89/pIQwsEY46k1Xi9JkiRJkiRJkrSrzJXKjM0UGS0sP8YKRS4Uijx8apJ7T4wzWljTD95uyhW9Oa7Z28vVe/NcvWc5UH50qJtcJrV6B5IkSZIkSWpLuypoHkIYAm4ChoB+kpW7/zrGeLalhW2dsYu09QMXDZqHEG4Arq1pKgCfXMtAMcZCCOGTwFcvdge8Blc1lyRJkiRJkiRJu0ClEpmcK3GhJixeux2dWRkkHysUKRTLTa/z0EAXtx3I0zV7lqt6Ige64Z+86uX09fU1vRZJkiRJkiRtb20fNA8h7AN+EPhm4MaLnPLVwAuC5iGENwBHqoenYoy/vWVFbp1DF2m7cJnz76w7/kyMcWEd432C5aD5xfqTJEmSJEmSJEna9sqVyMRsifGZImMzyXZ8psRYdTs6UxciLxQZmylSia2ufKWubJrbD/dz19FB7jo6wF1HBtjX18nk5CQf+tCZVpcnSZIkSZKkba6tg+YhhP8IvBXIkaywXe9yb/f1AG+pnlMOIbx/B658/vK64+dijJf7fcWb6o4fXud49efX9ydJkiRJkiRJktQ0MUam5xcYnyktBcXHZopMzJYYKywGx4uMz5ZWBMon50rEbRYaX4trhvPceXQgCZYfGeDG/b1k0qlWlyVJkiRJkqQdqi2D5iGENPAe4BtIAub1bwVGLh48r/VO4G1AH5AGvh341cZWuuW+u+74L1Y5/4a64xPrHK/+/Pr+JEmSJEmSJEmSGibGyPNjs3zx+Qm+eHKcZ84XqmHy5dD4wnZbZrxBejsz3HlkYGm18jsPDzCYz7W6LEmSJEmSJLWRtgyaA/8deG11fzFUfi/wN8Dx6vOXFWOcCSG8H/iOatOr2UFB8xDCq4F/VNf8rlUu21d3/Pw6hz1Zd7x3nddfVAhh3wb6OlZ7MD09zeTkZCPK0QYUCoXLHkuSpJ3LeV6SpPblPC9J2o7OTxd56PQUD52a4sHT0zx8eoqx2YVWl7XlAnDt3m5uP9TH7Yd6uf1gL1cPd5MKNesqleeYnJxbU3/O85IktTfnekmS2pfz/O4zPT3d0vFD3Im/+3cZIYSXAR9leRXzEeBfxhj/suacSs3zXx1j/PtL9PXNJCujA8wBAzHG4pYU3kAhhCHgfuBwTfOfxRi/cZXrPg3cU9P0jTHGP1vnuBfqmjtjjPNr7eMS/b4FePNm+vj1X/91jh49upkuJEmSJEmSJElSExVKcKIQOD4Nx6cDxwuBieJqP1i7cwUi3RnIZ6AnC73ZyOF85KpeOJqPdLbr8lGSJEmSJEm6pOPHj/NDP/RDtU23xhgfatb47fiW1Fur2wBMAq+IMT66wb4+XbPfAdwAPLCJ2rZcCCEF/G9WhswngB+6+BUr9NQdr23Zi2Wzl+hzU0FzSZIkSZIkSZLU3ubK8Pw0HC+EJFQ+Hbgwv7ND5dkQyWeT0HhPprqfgZ7s8n4+G6tt0J2B1M6+ZUmSJEmSJLWZtgqahxAGgZezvFr5T28iZE6M8fkQwhgwWG26kW0eNAd+CfjaurbvizGeWMO1nXXH6129/WKB8q519iFJkiRJkiRJktpYqQInC8urlJ+YDpydhcj2TVmnQySfYWnF8e5MTALkWcgv7teFyHPpVlctSZIkSZIkbU5bBc2BlwGLb9uVgd9uQJ/nWA6a72tAf1smhPBDwL+va/7FGOMfrbGL+hXMc+ssoWMNfW7EO4D3rPOaY8B7Fw/uuecebrrppgaUoo0oFAp85jOfWTq+5557yOfzLaxIkiQ1ivO8JEnty3lektQIhfkFnhyZ4YlzBR4+Pc2Dp6d48vwMC5W4+sVbIAD9XRn6u7L0d2UY6MrS15lhYHG/uh3oytBX3Q50ZenKpghh+wbh18t5XpKk9uZcL0lS+3Ke330eeeSRlo7fbkHzg9VtBJ6OMY43oM+Jmv3eBvS3JUII3w68va75XcCPraOb6brj+hXOV3Ox1cvr+1y3GOM5ksD/mtW/2dvT00NfX99mS1GD5PN5//eQJKlNOc9LktS+nOclSZezUK7wzEiBR89M8diZqWR7dpITo7NNq+Ga4Ty3He7nyqFuBrpzDOazDHTlGOjOMtidbPs6s6RS7RMYbxTneUmS2ptzvSRJ7ct5vv319PS0dPx2C5oP1eyPNqjP2lW6Sw3qs6FCCP8U+F1Y8ZuS/w/4nhjjepYEqQ+Fr/drLvXnL8QYG7GiuSRJkiRJkiRJ2gZijJyZnFsKlC+Gyp86N02xXGlaHYcGurj9cD+3Hx7gjsP93HKon/6ubNPGlyRJkiRJknaDdguab8Xq4/tq9kca1GfDhBC+AngPK/+3/CDwbTHG8jq7q181/PA6rz9Ud3x+nddLkiRJkiRJkqRtYmquxONnp1auUn5mionZ5q7LM9zTwR3VUPnth/u57XA/wz0dq18oSZIkSZIkaVPaLWi+GGwOwJUhhFSMccPLZ4QQjgAHappObaa4RgshfCnwPqCzpvmTwDfGGIsb6PKxuuOj67y+/vxHN1CDJEmSJEmSJElqolK5wtPnCzx6ZnLFKuUnx2ebXktfZ4Y7jgxw26HqauVH+tnf10kIYfWLJUmSJEmSJDVUuwXN76/Z7wZeCnxsE/29rma/DPzDJvpqqBDC7cBfAj01zfcCr44xFjbYbX0w/OZ1Xn/TKv1JkiRJkiRJkqQWmi2WeeTMJA+dnODBk5M8dHqCx89MUyxveN2eDevOpbn1YP/SKuV3HB7gyj3dhsolSZIkSZKkbaKtguYxxsdDCM8AV1Wb/j0bDJqHEPqANwGx2vTZGOPUpotsgBDCDcAHgcGa5keAr4kxTmyi6/vqjr8khJCJMS6s8fqXrtKfJEmSJEmSJElqkonZEg+fmuShUxM8dGqSB09O8NT5aSpx9WsbLZ9Lc/3+3qVg+R1HBji2t4d0ylC5JEmSJEmStF21VdC86veAN1f3vz6E8F0xxt9dTwchhHS1n0PVpgi8o3ElblwI4Urgb4F9Nc3PAF8dYzy/mb5jjI+GEJ4CjlWb8sBLgI+uoa488OW13QF/vpl6JEmSJEmSJEnS2oxMz/PgySRQ/tCpZLXy46MzTa8jnQpcPZznxv293Li/lxv293Hj/l4ODXSRMlQuSZIkSZIk7SjtGDT/ZeD7gb1AAH47hLAP+JUYY3m1i0MINwL/E3gZy6uZPw68e2vKXbsQwgHg74DDNc0nga+KMZ5s0DDvI1nJfdEbWUPQHPhWoKfm+HMxxlMNqkmSJEmSJEmSJAExRk5NzPHQyQkePDXJQ9Vw+ZnJuabXsr+vkxuWAuXJ49p9PXRk0k2vRZIkSZIkSVLjtV3QPMZYCCF8D/CnQApIAz8P/EAI4Q+Az1dPDSRB8heFEIaAa4GvrD5C9QEwC3x7jLEFPyS5rFrjB1lebRzgPMlK5s80cKj/Bfwwy/f/z0IIPx9jfOQytXUCP1bX/M4G1iRJkiRJkiRJ0q4SY2SuVOH0xCwPnZrkwVMTPHxqkgdPTjA2U2pqLT0dmaUg+Y37e7nhimR/oDvX1DokSZIkSZIkNVfbBc0BYox/HkL4N8A7qk0BuBL40bpTA0kIvb5tMVReAt4QY7x3q2pdixBCL/BXwC01zePAP75cAHwjYowPhhD+mGSFcoAc8LshhFfFGCcvUlsA3g5cV9P8NElgXZIkSZIkSZKkXS3GyGypzPhMibGZIhMzJcZnS4zPlBifTY7HZorV41L1+SJjMyWKC5Wm1ppJBY7t7VkZKt/fy6GBLpKPAyRJkiRJkiTtJm0ZNAeIMf5mCOEp4PeB/SyHx2uD5IvHS5dVHwE4C7wuxvjxJpS7mvcBX1LX9ivAcAjhVevs6/MxxrFVzvnPwNcB3dXjLwE+GkL44RjjhxdPCiFcD/wc8E111/9YjLG5y6lIkiRJkiRJktQEMUbGZko8MzLNmYl5xmerIfG6sPjYTHFpv1hubmB8LfK5NLcc7Ofmg33ceqifmw/0ce2+HnKZVKtLkyRJkiRJkrRNtG3QHCDG+HchhJuA7wd+EDhYfepSy24EYIxkhe5fu9gK3i3yyou0vXWDfX0F8OHLnRBjfDKE8Ebg3Sz/W90BfCiEcB44DuwDDvPCf8v/FmN8zwZrkyRJkiRJkiRpW5ieX+DZkQLP1DyeHinw7EiBidmdtdbKYHeWWw72c8uhPm492M8tB/u4ak+eVMpVyiVJkiRJkiRdWlsHzQFijBPAz4cQfpEkLP1y4CZgDzAAzAAjwDPAh4DPxBgXWlPt9hFj/MOQ/A7mO4Gumqf2Vh8X88vAj2x1bZIkSZIkSZIkNcL8QpkTozM8fX5loPyZkQLnpuZbXd6GXNHXkYTJDyWB8lsP9XOwv5PkLX9JkiRJkiRJWru2D5ovijFWgHurD61BjPEPQgifBt4GvA7IXuLUjwL/Jcb4kaYVJ0mSJEmSJEnSGpQrkVPjs0urkS+uTP7MyDQnx2apxFZXuHFHh7q59VBfslr5wWS7t7ej1WVJkiRJkiRJahO7Jmi+k8UYW7bMSIzxaeA7QgjfD7wMuA7oBeaA48AnYownW1WfJEmSJEmSJGl3KpUrjM0UGZ8pMVooMj5TZLRQYmymyGihyInRGZ4ZKfDchRmK5Uqry92UVIBr9vZwa3WF8lsO9nPzwT76uy61PowkSZIkSZIkbZ5Bc61JjHES+ItW1yFJkiRJkiRJaj/zC+WlwPjYTJGxamB8rFBkbKa6X3tcKDI1v9DqsjestyPDQD7LQFeOge4s/V1ZBrqXjwe6cwwstnVnOTjQRXfOj3QkSZIkSZIkNZfvSkqSJEmSJEmSpIYoLlQYny0yOVtifCZ5TMyWGJ8tMTFTZGK2tBQcr12JvFAst7r0DenOpRnu6agJi+cY7M4y0JWlf0VYvBog78rS15Ulm061unRJkiRJkiRJWpVBcyCEcA/wJuBlwDAwBnwe+M0Y4/tbWZskSZIkSZIkSc0UY2R6fmEpJD4xWxsYLzIxs/J4fKaUBMtnS8zs0MD45eQyKa4c6ubq4TxX781zzXCeq/Yk+3t7OgghtLpESZIkSZIkSdoSbRc0DyH8Y+A/1zR9T4zx8cuc/8PALwOh+gDYD7waeHUI4Q+A74wxVramYkmSJEmSJEmSWuPU+CxfOD7GF54b594TYxy/MMP4bIlyJba6tKZKBTg8WA2T1z0ODnSRThkmlyRJkiRJkrT7tF3QHPhukpXJI/DAKiHzVwH/leWAef075wH4NmAK+P7GlypJkiRJkiRJUnPML5R58OQk9x4fWwqXn5mca3VZTXVFX0ddkLyHq4fzHBnqoiOTbnV5kiRJkiRJkrSttGPQ/Ctr9v9wlXN/hSRMHlle0fws0Ad01bR/bwjh92OMn2x8uZIkSZIkSZIkNd7piVm+8Nx4Eio/PsZDJycpltv3xzvzuTSD+RyD3TkG8zmGe3JcvSfP1XvzXLUnCZbnO9rxYxFJkiRJkiRJ2hpt9Y5qCOEaYLim6S8vc+5XALeyvIr5Z4FvjzE+FULIAj8I/HLN828CDJpLkiRJkiRJkrad+YUyD52a5AvPjXHv8SRcfnpi565W3tuZWQqMD3ZnGerOMdCdYyifrW5zDHRnGaoGywe6s65ILkmSJEmSJEkN1lZBc+D6mv0S8OBlzv1n1W0AisC3xBhPAMQYS8CvhhCOAT9QPe81IYSuGONsg2uWJEmSJEmSJGldzk7O8YXnxqqrlY/zwMkJigvbb7XyXCbFQFeWge4sA105+rqy9HdlVwTGB7uzNaHyJDSeTadaXbokSZIkSZIk7XrtFjS/srqNwHMxxvJlzv3HLK9W/r7FkHmdX2M5aN4B3AH8QyMKlSRJkiRJkiRpLWaLZR49M7m0Uvm9x8c5Od7cNVH6OjP0V8PiA91Z+rqyKwLk/V3Z6vPZFed1Zl1lXJIkSZIkSZJ2qnYLmvfW7E9c6qQQwlGSUPpi0PzPLnZejPGJEMJ5YG+16UYMmkuSJEmSJEmStkCMkdMTczxyepJHz0zx8OlJHjk9ybMjBSpx9es345rhPHcdHeSuowMcHuxioDsJjw90JaHydCpsbQGSJEmSJEmSpG2n3YLmHTX7l1vN/CXVbSAJm//9Zc49wXLQfHDjpUmSJEmSJEmSlJgrlXny3PRSmHwxXD4+U9rysfO5NHccGeDuo4PcfeUAdx0ZZDCf2/JxJUmSJEmSJEk7S7sFzQs1+/2XOe8VNftPxxjPXObcYs1+94aqkiRJkiRJkiTtSjFGzk/NVwPlU0uh8qdHCpS3epnyqquH89x1tBosPzrIDft7XaFckiRJkiRJkrSqdguaX6huA3BVCCETY1y4yHlfU91G4KOr9DlQsz+zufIkSZIkSZIkSe2quFDhyXPTS2HyR85M8ujpKS4Uiqtf3CDduTR3HB7g7iuTYPldRwcZcrVySZIkSZIkSdIGtFvQ/MGa/Q7gVcBf1Z4QQvhy4CqSkDnAh1fpc3/N/ujmypMkSZIkSZIktYO5UpmHTk1y/4lxHjw5wcOnJ3nq/DSlcnNWKV901Z7uJFB+5SB3Hx3ghit6yaRTTa1BkiRJkiRJktSe2i1o/gAwRrIKeQB+OoTwoRjjPEAIIQ28rXpuABaAv7lUZyGEo6xc0fzpxpcsSZIkSZIkSdrOFsoVnjg3zRefH+e+ExN88flxHjszxUKluaHy7lya2w71c/eVg9XVygcY7uloag2SJEmSJEmSpN2jrYLmMcaFEMIfAD9AsmL5XcAXQgjvAkrA64EvZXk187+OMZ69TJcvqTt+uLEVS5IkSZIkSZK2kxgjJ0Znue/5cb54Ypz7nx/nwZOTzJbKTa3j0EAXNx3o5aYDfUuPK4e6SaVCU+uQJEmSJEmSJO1ebRU0r3ob8B1AX/X4JuDn684JQAV46yp9fXPN/pMxxgsNqVCSJEmSJEmStC2cn5rni8+Pc/+Jce5/PlmtfGym1LTxOzIpbtjfy437a0Ll+/vo7842rQZJkiRJkiRJki6m7YLmMcazIYTXA+8DOlhevXzR4nIv/yXG+NlL9RNCGAC+tub6Dze2UkmSJEmSJElSM03PL/DA8xPc//x4NVw+wcnx2aaNf0Vfx4oVym8+0MtVe/Jk0qmm1SBJkiRJkiRJ0lq1XdAcIMb4wRDCi4FfBL6alff5BPCWGOMfrNLNvwa6F7sE3t/wQiVJkiRJkiRJDVWpREZnioxMzzMyVeSZkWnuO5GsVP7k+Wli/dIkWyCbDly3b3GF8uWVyofyua0fXJIkSZIkSZKkBmnLoDlAjPEh4DUhhD7gaqALOBVjPL7GLp4D3lRz/MEGlyhJkiRJkiRJWoNyJTJWEx4/Pz3HyFRyfH56nvNT84xMJ8ejhSLlShPS5FX5XJpbD/Vz++F+bj6YBMqP7e0h6yrlkiRJkiRJkqQdrm2D5otijJPA/Ru4brUVzyVJkiRJkiRJG1QbHk+C4vPL4fGpJEC+GB6/MD1PE7Pjl5RNB2460Mcdhwe4/XA/dx4Z4Jq9PaRTodWlSZIkSZIkSZLUcG0fNJckSZIkSZIkbb1KJTIxW+JCociF6sriI4Uio9NFLhTmV7RfmC4yNlPcFuHxSwkBrhnOc8eRAe48MsDthwe46UAvHZl0q0uTJEmSJEmSJKkpDJpLkiRJkiRJkl4gxsjk7EJNSDwJjCfB8eLKQHk1OF7ezsnxVRzs7+T2wwPccWSAOw73c+vhfvo6s60uS5IkSZIkSZKkljFoLkmSJEmSJEni+IUZPv7kCJ94coR7j49xfnqeUnnnBscvp78ruxQov+PwALcf6Wdfb2ery5IkSZIkSZIkaVsxaC5JkiRJkiRJu9Boocgnn0qC5R9/coQTo7OtLmlLDHZnOba3hzuODHD74X7uPDLA0aFuQgitLk2SJEmSJEmSpG1txwTNQwh/BPxEjPGpVtcCEEK4FviZGOO3troWSZIkSZIkSVrNbLHMZ58dXQqWP3RqstUlbdhgd5a9vR0M9yw/kuMcw70d7K0eD+VzZNOpVpcrSZIkSZIkSdKOtGOC5sDrgNeGEH4X+KUY4xOtKCKEcB3wI8B3srP+/SRJkiRJkiTtIgvlCg+cnOATT47wiScv8PnnxiiWK60u65Lqw+PL+4bHJUmSJEmSJElqhZ0WlM4AbwS+O4TwXuA3YowfbMbAIYSvBn4A+DogVB+xGWNLkiRJkiRJ0mpijDw9UkhWLH9ihE89fYGpuYWW1ZPPpdnTkwTDh3tyDOVz7OnpYE8+x56eHEP5ZN/wuCRJkiRJkiRJ29NOCpq/E3gDkCIJeb+WZIXz54D/A/y/GOO9jRwwhHA38E3AtwNXLjZXt5VqTZIkSZIkSZLUEuem5vjkkxf4+JMjfOLJEU5PzG3ZWN259Iqw+FA1ML4nn2NPvoOhnhzD1e2efI7ObHrLapEkSZIkSZIkSVtvxwTNY4z/KoTwP4G3Ay9hOfB9FfATwE+EEE4CHwI+AnwOeCTGWFpL/yGEHHAz8CLgHwFfARxafLru9E8APxxj/PxG70eSJEmSJEmS1mOuVGZkep7Hz07x8Scu8IknR3js7FRDxwgBbjnYx0uvHebFVw6xr7ejGibvoCtncFySJEmSJEmSpN1kxwTNAWKMnwNeFkJ4NfBW4O6apwNwGPjn1QdAubri+fPAaWAamK2e2wn0Ageq1x0F0nX9AcSats8D/yXG+JcNvC1JkiRJkiRJu1BxocLYTJGR6XkuTBcZLVT3C0VGp4tcKMwzUm2/MD1PoVjekjqODnXz0muHedm1w3z5sT0M5XNbMo4kSZIkSZIkSdpZdlTQfFGM8S+AvwghvAr4d8DXkgTDF0PhiyHxDHAMuGaVLutXLI/Vx2L7B4C3xxj/bpOlS5IkSZIkSWpT5UpkbKYmMD6dBMRHC0VGCsv7F6aT5yfnFlpS52B3lpdUg+UvPTbM0T3dLalDkiRJkiRJkiRtbzsyaL4oxvi3wN+GEI4C3wF8G3Br7Slr7aq6DTXbh4B3A/8nxni8AeVKkiRJkiRJ2iFK5WS18fGZEqOFIuMzRUYLJcZmiowViozNlJK2mnMm50rEtb4j2USd2RT3XL2Hlx7bw0uvHebmA32kUvVrb0iSJEmSJEmSJK20o4Pmi6pB8J8Dfq4aOv8a4OXAi4AbgNQqXVSAx4DPAx8D/tpwuSRJkiRJktQe5kplxmdWhsRHZ4qMF5aD4rXPjRWKTM23ZrXxRkgFuP3wQLJi+bXD3H3lAB2ZdKvLkiRJkiRJkiRJO0xbBM1rVQPiv1V9EELIAUeBI0A/sPg7sDPAOHACOBFjLDa9WEmSJEmSJElrEmNken6B8ZkSE7Ol5e1ssaYt2R+fLTExs/zc/EKl1eVvuWv25peC5V92zR76u7KtLkmSJEmSJEmSJO1wbRc0r1cNkD9ZfUiSJEmSJEnaJgrzCzxxbprnLhSSgHg1HD5RDYuPzxRrQuMlypXY6pK3jb29Hbz02B5eWg2XHxzoanVJkiRJkiRJkiSpzbR90FySJEmSJElSay0Gyp84O8UT56Z5/OwUT5yd5uT4bKtL2zZCgMHuHHvyOfb05NjT05Hs5zuS43y1rbrf35UlhNDqsiVJkiRJkiRJUhszaC5JkiRJkiSpIRYD5Y+fneJJA+X0dWaWA+MrwuO1ofFkO9idI50yOC5JkiRJkiRJkrYPg+aSJEmSJEmS1mU3BsrzuTQD3TmG8jkGurMM5ZNw+GB3jsF8NnmuO3luuKeDoXyOXCbV6rIlSZIkSZIkSZI2zKC5JEmSJEmSpIuaL8OZGTg9G/jC3z3Nc+PFtgiU93ZmqgHxHIPd2WpAPMdQNTC+GB4frAmWd2TSrS5bkiRJkiRJkiSpqQyaS5IkSZIkSQJgrlTm88+N8amnLvCxx8/ywMk0FULy5FMnW1tcnRCgrzNLf1eWge7FbY6BSxwPdGfp60rC49m0K41LkiRJkiRJkiStxqC5JEmSJEmStEsVFyrc//w4n3zyAp96eoQvPDdOsVypOSM0pY6ubJqDA50rwuH93VkGunJLIfH64HhvZ5Z0qjn1SZIkSZIkSZIk7UYGzSVJkiRJkqRdYqFc4aFTk3zyqQt88qkRPvfsGLOlctPG78qmue6KHq7d18P1V/Ry/RU9XLevl0MDXaQMjUuSJEmSJEmSJG0rBs0lSZIkSZKkNlWpRB49M8Wnnr7Ap54a4dNPjzI1v7Dl4xoolyRJkiRJkiRJ2vkMmkuSJEmSJEltIsbIU+cLfOqpkWq4/AJjM6UtG89AuSRJkiRJkiRJUvsyaC5JkiRJkiTtYCdGZ/jkUyN88qkkWH5uar7hY+RSkeuu6OWGA/0GyiVJkiRJkiRJknYJg+aSJEmSJEnSDjFXKvPYmSkeODnB/SfG+dTTF3h+bLbh4/R3ZXnxkT4Giue4tj+yvwu+6ivv3Nlc3wABAABJREFUoq+vr+FjSZIkSZIkSZIkaXsyaC5JkiRJkiRtQ4uh8i+enODB5yd44OQEj5+dYqESGz5WPpfmnquHeMmxYb782B5uOtBHYXqKD33obMPHkiRJkiRJkiRJ0s5g0FySJEmSJElqsblSmUerK5VvdagcoCOT4sVXDfKSY8N82TV7uP1wP9l0akvGkiRJkiRJkiRJ0s5k0FySJEmSJElqomaHygGy6cCdRwb48mPDvOTYHu48MkBnNr1l40mSJEmSJEmSJGnnM2guSZIkSZIkbZH6UPkXT07wxBaHygFSAW47PMCXX7OHlxzbw4uvGqQ751uBkiRJkiRJkiRJWjs/XZIkSZIkSZI2qLhQYWR6nvNT1Ud1//mxGR44OdmUUPmimw708ZJje/jya/ZwzzVD9HVmmzKuJEmSJEmSJEmS2pNBc0mSJEmSJKlGjJGJ2dJSePxcXYj83NTc0nNjM6WW1NiZTXHzgT5uPzzAPVcP8WXX7GEon2tJLZIkSZIkSZIkSWpPuyZoHkIIwF3ATcAQ0A+kgN+LMT7bwtIkSZIkSZLUBDPFBS5MF5cC4ytC5ItB8sk5RqaLFMuVVpe7pCub5uaDfdx2qJ9bD/Vz26F+ju3Nk0mnWl2aJEmSJEmSJEmS2ljbB81DCHcA/wH4BqDnIqd8HHj2Itf9CHBj9fB4jPEtW1SiJEmSJEmSNqC4UGG0UORCYZ4L08vbkekiF6bnuVCo3RaZLZVbXfKq6kPltx/u59jeHtKp0OrSJEmSJEmSJEmStMu0bdA8hJADfhX414tNFzktXqaLM8DPV8+JIYR3ufK5JEmSJEnS1qlUIuOzJS5Mzydh8aUAeTUwviJMPs/k3EKrS96UrmyaWw72La1SfpuhckmSJEmSJEmSJG0jbRk0DyF0A38H3EMSMK8PlEcuHjyv9W7gl4C91ePvAH6mgWVKkiRJkiS1rRgjM8Uyo4Vi8pgpMra4XygyNlPdFkqMVvfHZ4pULrcswA5mqFySJEmSJEmSJEk7TVsGzYE/AL6U5YB5BP4M+BvgBPDnq3UQY1wIIfwp8L3Vpq/FoLkkSZIkSdqliguVmnB4ssp47fHoTInRwjyjhVL1uEhxodLqspsqmw7s7elgb2/yODLUnYTKD/VzjaFySZIkSZIkSZIk7TBtFzQPIXwd8HUsh8yfAL45xvhgzTnwwlXOL+b9JEHzANwTQuiKMc42tmJJkiRJkqTtZaFc4fGz09x3Ypz7Toxx34lxnjg3TWzT1cZXM9CdZW9PB/v6OlYEyff1di7t7+3pYKA7u/i+kyRJkiRJkiRJkrTjtV3QHPjJ6jYAZ4BXxhhPb7Cvz9bsp4GbgC9sojZJkiRJkqRt5+zkHPceH+PeE+Pcd3ycB05OMFMst7qsLZXLpNhXExJ/QXC8t4N9vR3s6cnRkUm3ulxJkiRJkiRJkiSp6doqaB5CuAJ4Ecurlf/kJkLmxBjPhRDOA3urTTdg0FySJEmSJO1gs8UyD5yc4N7jY9UVy8c5PTHX6rI2LQQY6s6xpyfHnnwHQz05hvM59vR0LLUN9yTHQ/kcfZ0ZVx+XJEmSJEmSJEmSLqOtgubAS0lWMgcoAX/YgD5HWA6aDzegP0mSJEmSpKaoVCJPj0xz7/HxpdXKHzs7RbkSV794G+jtyCQh8Z4O9lRD48M9uaX9PTVB8sHuHOmUwXFJkiRJkiRJkiSpUdotaL6/uo3AkzHGQgP6nKzZ72lAf5IkSZIkSVviwvT80irl9x4f5/7nx5maW2h1WQCkU4HB7hxD+Wx1u/xYPB7M5xjqzjHcmxx3ZNKtLluSJEmSJEmSJEnatdotaN5fsz95ybPWJ1+zP9ugPiVJkiRJkjYsxsj5qXkePTPFY2emeODkBPedGOf46EzTaujrzFw6LF4NjNfu93ZmSLniuCRJkiRJkiRJkrRjtFvQfKxmv/+SZ63P/pr9Cw3qU5IkSZIkaU2m5ko8fnaKR89M8fiZZPvY2SnGZ0pbPnYuneLmg33ceWSAu44OcMP+XvbkOxjozpJNp7Z8fEmSJEmSJEmSJEmt025B87PVbQCuDiHkYozFjXYWQrgOGK5pOrGZ4iRJkiRJki6luFDhqfPTS6Hyx6qPk+PN+4G1o0Pd3HV0gDuPJI+bD/bRkUk3bXxJkiRJkiRJkiRJ20e7Bc0/V7OfA74S+KtN9PcdNftF4B820ZckSZIkSRKVSuTk+Gw1TD7JY2eneezMJE+fL7BQiU2ro7czk6xUfmSAO48OcMfhAfb0dDRtfEmSJEmSJEmSJEnbW1sFzWOMJ0IIDwM3VZt+lA0GzUMIB4B/Cyx+wvvxGOPc5quUJEmSJEm7wWyxzNhMkWdHCjx2Nlmd/NEzUzxxdopCsdzUWtKpwI37e5dWKr/r6CDXDOdJpUJT65AkSZIkSZIkSZK0c7RV0Lzqt4BfJQmI/6MQwn+KMf7MejoIIfQC/xcYrDZF4O2NLFKSJEmSJO0M8wtlxmdKjM+UGJspVveLjM8mxxM1z03MLp8zv1BpWc0H+ju56+hANVg+yG2H+unKpVtWjyRJkiRJkiRJkqSdpx2D5u8A/h1wJRCAt4YQDgI/EWOcWO3iEMLXkITKr2d5NfPPxhg/sDXlSpIkSZKkZooxcnpijsfOTHF+ep6JxQD5bDVAPlNibKbExEyRsZkSs6Xmrj6+Xvt6O7hhfy83H+zjriOD3HV0gCv6OltdliRJkiRJkiRJkqQdru2C5jHGUgjh24C/BzpJwub/GvjOEML7gc9XTw0kQfLXhBDuBq4FvhI4VvNcAEaBb2vqTUiSJEmSpIaIMXJ8dIYHT07y4KkJHjw5wUOnJhktFFtd2rr1dGS4/ooebtjfx437e7n+il5u2N/LUD7X6tIkSZIkSZIkSZIktaG2C5oDxBg/HUL4Z8AfkoTNAfLAt1YfiwLww3XHsBwynwC+Jcb4zJYWLEmSJEmSNq1ciTwzUuChaqB8MVw+NbfQ6tLWJZsOHNvbww3VMPmN+5NA+aGBLkIIq3cgSZIkSZIkSZIkSQ3QlkFzgBjj+0MI95CEzW8hCY8vPV2zXxsujzVtDwHfHGN8fKtrlSRJkiRJ67NQrvDk+ekkTH5ygodOJSuVzxTLrS5tXY4MdXFDdWXyG/b3ccMVvVw9nCeXSbW6NEmSJEmSJEmSJEm7XNsGzQFijA+FEO4EXgf8O+AeloPl9RbbHwR+Cfg/McbKlhcpSZIkSZIua36hzBNnp3nw5AQPnJzgwVOTPHp6kvmFnfOyfSifWwqU37i/l+urq5X3dLT1WzOSJEmSJEmSJEmSdrC2/zQzxlgmWdX8D0MIQ8DLgJuAPcAAMAOMAM8AH4oxnmpRqZIkSZIk7VrzC2VGC0UuTBc5Pz3P86MzyWrlpyZ4/OwUpXJcvZMm6c6lGezO0d+VZTCfZaArx0B3loHu7HJ792JbjqF8jsHuLCFc6rvvkiRJkiRJkiRJkrT9tH3QvFaMcRR4X/UhSZIkSZK2SIyRybkFRqbnuTBd5ML0PCPT84xMF7lQmGdkKtkuBsun5hZaUucVfR1LofDacPhANSzev6I9S39Xlo5MuiW1SpIkSZIkSZIkSVIz7aqguSRJkiRJ2pypuRLPXZhZDpAXkvD4iuNqiHw7rUIeAlwznOfWQ/3cerCfWw71ccuBfvq7s60uTZIkSZIkSZIkSZK2JYPmkiRJkiTpskam5/mrB8/wgS+e5tPPXKCyffLjF5VOBa7b18MtB/u59VAftx7q5+YDfeQ7fBtEkiRJkiRJkiRJktbKT1glSZIkSdILLIbL/+KB0/zD09s3XJ5NB27Y31tdpbyf2w71c+P+Xjqz6VaXJkmSJEmSJEmSJEk7mkFzSZIkSZIEbP9weUcmxU0H+rj1UB+3HernloP9XH9FL7lMqtWlSZIkSZIkSZIkSVLbafugeQghDbwYuAs4CvQBXUBYZ1cxxvjGBpcnSZIkSVJLXZie568eOsMHvrh9wuXpVGAon+OqPd3ceqifWw/2c+uhfo7tzZNJGyqXJEmSJEmSJEmSpGZo26B5CKEf+M/AvwD2brY7IAIGzSVJkiRJO95iuPwvHjjNp55qTrg8n0uzp6eD4Z7c0na4p4M9+cXj5ecGurKkUuv9frgkSZIkSZIkSZIkqZHaMmgeQvhy4E9JAua1n0xvg3XZJEmSJElqvkaHy1MBhvLVsHhPjj35jqX9pRB5NUg+3NNBVy7dmBuRJEmSJEmSJEmSJDVF2wXNQwi3AH8N9FSbIsthc5dDkyRJkiTtGhem5/nrh87ygQdO8Q9Pj1LeZLr8muE8r77tAK++7QA37O8l7arjkiRJkiRJkiRJktS22i5oDryDJGS++Ol5AP4GeC/wAHABmGlNaZIkSZIkba3FcPlfPHCaTz19YdPh8quH87ymGi6/6UAvIRgulyRJkiRJkiRJkqTdoK2C5iGEY8DLWV7FfAz45hjjh1tZlyRJkiRJjbRQrnBuap7TE7OcGp9b2j5+dopPP7P5lcsNl0uSJEmSJEmSJEmS2ipoDry0ug0kYfPvM2QuSZIkSdpJKpXIyPQ8pybmOFMbJJ+Y4/T4LKcn5jg7Occms+QvcPVwnlfftp/X3HbQcLkkSZIkSZIkSZIkqe2C5vtr9seAP2lVIZIkSZIk1YsxMjZT4lQ1MF67Ivnp8TlOTcxydnKOUrnBKfJLuGpPN6+5PVm5/OYDfYbLJUmSJEmSJEmSJElL2i1oXq5uI/B0jLE5n8xLkiRJkna1+YUyI9NFzk/NMzI1z8j0fLI/Pb/Ufn56ntMTs8yVKi2t1XC5JEmSJEmSJEmSJGkt2i1ofrxmv7NlVUiSJEmSdrzF8PjlguOL7VNzC60u97Ku2tPNq29LwuW3HDRcLkmSJEmSJEmSJElaXbsFzT9Jspp5AK4KIWRijNv7035JkiRJ0pYrVyJTcyXGZ0qMz5aYmC0xPlOsbkuMFopJcHwxQD41z+Q2D4+v5so93bzGcLkkSZIkSZIkSZIkaYPaKmgeYzwZQvhb4KuBPPBq4H2trUqSJEmS1CjFhQoTsyUmZotJaHymGhqfLTExU2R8tiZMXnM8OVcixlZXvzW6c2kO9HdycKCLA/2dXLknzyuu32u4XJIkSZIkSZIkSZK0KW0VNK/6UeCVJPf2CyGEv4sxFlpbkiRJkiRpLUYLRZ44O8WT56d58tw0T50vMDI1v7QCeaFYbnWJTZXLpDjQ35kEyfu7ODDQyYH+Lg4ubvu76OvKGCiXJEmSJEmSJEmSJDVc2wXNY4z3hRB+APhN4Hrgz0MI3xpjPNfi0iRJkiRJQIyRM5NzPHlumifOTieh8up2tFBsdXlNk0kFrujrXAqNHxiohslrVicfyucMkUuSJEmSJEmSJEmSWqLtguYAMcZ3hhBmgN8CXgE8FEL478CfAA/G2K4/mC5JkiRJ20e5EjkxOsOT55IQ+WKo/Klz00zPL7S6vC2TTQeGezoY7ulgb28Hwz25peP9i6uTD3Qx3NNBOmWIXJIkSZIkSZIkSZK0PbVl0BwgxvgHIYRPA+8HbgJ+svoohRBGgbn1dxmPNbhMSZIkSdrxigsVnr1QSILkS6HyKZ4eKVBcqLS6vIbIpgN78iuD48l+B8O9Hezt6WBvb9Le35V1FXJJkiRJkiRJkiRJ0o7XtkHzEMJLgV8BbgQisPgpfw7Yv4EuXQVdkiRJ0q42PlPkqfMFnj4/zdMjBZ6qhsqfuzBDubKzXjKlU4H+riwDXVn6u5PtYmi8diXyvdV9w+OSJEmSJEmSJEmSpN2mLYPmIYQfBH4VSJEEzCObC4qbJpAkSZK0K5TKFY6PzvD0YqD8fIGnqsHy0UKx1eW9QC6TYrA7y0BXjv7u7FJ4fKA7y0B3LjmuPj+w+Hx3lp6OjMFxSZIkSZIkSZIkSZIuo+2C5iGE1wC/xsqA+WJ6oAhMADOtqU6SJEmSWi/GyGihyNMjtWHyZP/46AwL22B18v6uLNfu6+G6fT1cuSfPUD5LfzUsXhsc78ymW12qJEmSJEmSJEmSJEltqe2C5sDbWQ6ZB+Ac8CvA+4DHY4yV1pUmSZIkSc1TXKjw3IVqiHxkenmV8pEC4zOlVpcHwL7eDq7d17MUKj+2r4fr9vUy3JNzxXFJkiRJkiRJkiRJklqorYLmIYQvA46RhMwBvgh8VYzxQuuqkiRJkqSNiTEyUywzPb/A1NwC0/MLTM8tMD1fqjteYKpmf3pugXNTc5wYm6W8DVYnBzg82MV1S4HyXo5V9/u7sq0uTZIkSZIkSZIkSZIkXURbBc2Bu6vbxRXNv8eQuSRJkqRmKS5UmC2VmS2WmS2VmSku1OyXmatuC/OXDolPzZWYnkvaCvMLbJOc+JpkUoEr93QvhckXVyo/treHrly61eVJkiRJkiRJkiRJkqR1aLegeX/N/skY4+daVokkSZKkbWWhnITA50oV5krlFYHwuepjMRA+WyyvCIi/MCy+sCI4vtjXwk5KhW9CPpfmmr09HNub55q9PUsrlV+5J08uk2p1eZIkSZIkSZIkSZIkqQHaLWh+vrqNwKlWFiJJkiSpMc5PzfPI6UmeH5tlprjA/EJlKfg9WyozVywzt1Cuaau8oG2uVKZU3h0h8EYJAQ4PdnHNcLIi+TV781yzN8+xvT3s6+0ghNDqEiVJkiRJkiRJkiRJ0hZqt6D5iZr9vpZVIUmSJGndSuUKT58v8MjpSR45PcnDpyd55PQUI9PzrS6trfV2ZpLVyYfzHNvXwzXDySrlV+7ppjObbnV5kiRJkiRJkiRJkiSpRdotaP5xYBroAY6FEPpijJMtrkmSJElSnfGZ4lKQfDFY/sTZaYrlSqtLa0upAEeHurlm73KQ/NjeZDvck3N1ckmSJEmSJEmSJEmS9AJtFTSPMRZCCO8B3kByb98J/P9aW5UkSZK0e5UrkWcvLK9SvhgsPz0x1+rSdqwQoKcjQ29Hhp7ODD0dGXo6s8lxTVtvZ4bDg90c25vn6J5uOjKuTi5JkiRJkiRJkiRJktaurYLmVT8JfD0wBPxUCOGDMcbHWlyTJEmS1PbmFuALJyY4PjnKI6cnefj0FI+dmWSu5CrlqQDduQxduTRd2TTduTS9NSHxxWB4T01YvLcjQ29ndkVwvKcjQ1c2TSrlCuSSJEmSJEmSJEmSJGlrtV3QPMZ4KoTw9cAHgEHgQyGE74oxfrDFpUmSJEltYWK2xFPnp3nq3DSPnBzlM4+mOD0TuDAf4LNfbHV5G5JLp+jMpujMpleEwZP9JCDevfhc/X71/K5cpmZ/ZR+5dIoQDIdLkiRJkiRJkiRJkqSdo+2C5iGEo8BJ4PXAbwJXAX8VQvgk8B7g88B5YG69fccYjzeuUkmSJGn7qlQipyZmeep8gafOTSfB8vPTPHW+wPmp+bqzU02paW9vB8M9HXQtBsKzaToXg93ZNJ3Z1Iq2pXOqwe/OTGopAN5Z155JN+ceJEmSJEmSJEmSJEmSdoq2C5oDzwKx5jgCAXhJ9bFRkfb895IkSdIuNlcq8+yFAk+em+apc4WlQPnT5wvMlsotqSmTCly7r4ebDvRx04Febj7Qz40Hehnu6WhJPZIkSZIkSZIkSZIkSbtROwenA8uB81jTJkmSJO06o4UiT52frgbKl1cnPzE2Q4yrX79VBruz1UB531Kw/Np9PXRk0q0rSpIkSZIkSZIkSZIkSW0dNAeD5ZIkSWpzM8UFRqaKnJ+e5/zUPCPTNY+pIuem5nhmpMDYTKmldaYCXD2cXwqU31zdXtHXQQj+2S5JkiRJkiRJkiRJkrTdtGPQ/HdbXYAkSZK0UTFGCsUyIzWh8fPTxeUQ+VJ7kZHpeWaK5VaX/AK9nRlu2p+sTr4YLL9hfy+dWVcplyRJkiRJkiRJkiRJ2inaLmgeY3xDq2uQJEnS7lWpRGZLZQrFBWaLZQrzZWaKC8wUl7eFYpmZ+QWm5xeWAuO1q5HPlSqtvo016c6luWqoi66FSa7oihzshm/+qi/jxiN7XaVckiRJkiRJkiRJkiRph2u7oLkkSZK0EcWFChcKSeD7/NQ84zOlFcHw2eLCUkA8CY3XhMlrQuWzpe23wvhmXdHXwbG9PdVHnmP7erh2Xw/7+zqZmpriQx/60NK5hwY6DZlLkiRJkiRJkiRJkiS1AYPmkiRJalvlSuRCYZ6RqSLna1YNXwyTL+1PJ8Hy3SyTClw1nE+C5Iuh8n1JsLy3M9vq8iRJkiRJkiRJkiRJktRkBs0lSZK0o8QYGZ8prQiJr9hOzTMyXeT81DyjhXkqsdUVby+9nRmu3dezYoXya/f1cGSom2w61eryJEmSJEmSJEmSJEmStE0YNJckSdKWiTEyV6pQKC4wM19OtsUFpufLzMwvUCiWmSkuUJgvU5hfqDuvzPR8cv5SW3VrePziOjIphns6GO7tYG9PjuGeDvb2dnBFXyfH9vZw7b4ehntyhBBaXaokSZIkSZIkSZIkSZK2OYPmkiRJWrNyJXJhep6zk/OcnZzj7NQc5ybnOTc1x9nJZDXx+sC4ofDN6cqmGe5NQuOLj709uWqYPAmVJ+05ejoyhsglSZIkSZIkSZIkSZLUEAbNJUmSRKUSuVAocnZybik0nuzPc25y+Xhket7g+AalU4HuXJruXJp8LkNXdTuYzy6HyGtWIl9cjTzf4Z/skiRJkiRJkiRJkiRJar4dk1oJIfx9XVOMMX7VGs5rlIuOJ0mStF1UKpFiucJcqcz8QoX5UoX5her+QpnJuYWl0PhimHzx+Pz0PGUT5Euy6cBQPke+I1MTCk/TncskQfGO5bauXCZ5riNDdzZNd0dyXvJcEibv7kiTS6dcbVySJEmSJEmSJEmSJEk7xo4JmgOvBBbTT6Fm/3LnNcrlxpMkSbvcQrlCsVyhtBCZL5cpLlQolSPFhUryKC9vS7XHdc8tBsPnVgTEK8yXysxVt0ttC+XlIHkpaSuWK63+p9jWUgGG8skq4Xt7OxjuySX71ZXDl7a9HfR3ZQ2FS5IkSZIkSZIkSZIkaVfbSUFzSZKkLRVj5NzUPI+dmUoeZ6d4dqTATLFMqbwcCi+Vq8Hu6r4LgbfWQHd2KSQ+XBMW39vTwXBNgHwonyOdMjwuSZIkSZIkSZIkSZIkrcVOC5qHS+xf7jxJkqQXmJgp8djZJEz+eDVU/tiZKSZmS60ubVdIpwL5XJp8RyZ55NJ05zLkOxa31bbqNjmv+lwuQ19Xhr29HezJd5DLpFp9O5IkSZIkSZIkSZIkSVLb2TFB8xjjmhJEaz1PkiTtDnOlMk+cnU4C5WenePRMEiw/MznX6tJ2vEwqsK+3g319nVzR18EVfZ3s6+2gvzu3FBzv6cjQ3ZEmn8vQnUsvHefSKULwu4GSJEmSJEmSJEmSJEnSdrVjguaLQgj/pbobgd+PMT7bwnIkSdI2sVCu8OyFAo+dmeaxM5PVYPk0z14oEGOrq9tZ0qnA3p4OruhbDpHv6+1cPq7uD3bnSKUMi0uSJEmSJEmSJEmSJEntaMcFzYG3kITMAT4BPNuySnaZEMIx4B7gMJADxoBHgU/GGF0WVpLUEDFG5hcqzBbLzC2UmS2WmS2VmStVmCuVV7Sfn57n8TNTPHZ2mqfOTVMsV1pd/raWCjDck6w8fkVfB3t7l1ciXw6TdzKUz5E2QC5JkiRJkiRJkiRJkiTtajsxaA4QWA6ba4uFEF4L/CRw9yVOmQ4hvAv4qRjjSLPqkiRtD6VyhbGZIqOF5cdYocjU/AJzxTJz1dB4EhZPHovB8dniyrbF9naVSQVymRTZdIpcJkWubtuZTdGRSdORSdFR3V/RlknRkU2v3Gaqz2dTdFa3S23Vfjqr5+bSKUIwQC5JkiRJkiRJkiRJkiRpdTs1aG7IvAlCCB3AO4HvWOXUHuAHgW8NIXxLjPGjW16cJGlLxBiZml9grFDkQjUwvrgdnSkyOl1kbKamrVBkcm6h1WVviX29Hdywv5cbrujlir7OJBD+gpB4IJdOV9vDyvB4/bnpFClXCZckSZIkSZIkSZIkSZK0Q+zUoLm2WAghBfwR8A11T5WB48AEcDXQX/PcXuAvQwivijF+qimFStIuEGNkoRIplSuUysl2obx4vNxWKleS8xYqlKrbhUqFYjmyUHNucaHCxGwpWX18ZjkwPlpIQuSl8u76PldvZ4Yb9/dy/RW9S9vrr+hlMJ9rdWmSJEmSJEmSJEmSJEmS1DIGzXUp/5EXhsx/A3hbjPEULIXRvwF4O3C0ek438MchhFtjjBNNqlWStp0YIzPFMhOzJSbnSkzMlJiYXX5Mzi0wOVvXNltiam6BUrlCsRomX6hUdl3we6t0ZFJLIfIb9vdUg+V9XNHXQQiuNC5JkiRJkiRJkiRJkiRJtQya6wVCCHuA/1TX/OMxxp+vbYgxVoA/DSF8Bvg4cFX1qcPAvwfevMWlStKmXW618GLd/lw1OL4yMF5iYnZhRVh8MUC+UDEg3grpVODq4Tw3LIXKk8fRoW7SKQPlkiRJkiRJkiRJkiRJkrQWBs11MT8C9NYcfxT4hUudHGM8GUL4HuBva5rfFEL49RjjhS2qUdI2NlNcYLRQZLRQZHymxEIlCWyXK0moe3lbWT4uX6J9xfMXaa+evxgSrw2ML4bGizX79c+7WvjO05VN05VL05lJ0ZVLc9WePNfv710Klh/bl6cjk251mZIkSZIkSZIkSZIkSZK0oxk01wohhBTwhrrmt8QYL5vEjDH+XQjhY8DLq029wOuB/9H4KiU1U7kSGZ8pLgXHx2aKXCgUGSvUbWeKjE4XGZ0pMleqtLpstVBvZ4ahfI7+rmxNKLy6zabpzKaS9mz1OLe4v9zeUd0m16SWzu3IpAjBVcklSZIkSZIkSZIkSZIkaasZNFe9lwB7a46fBj68xmvfyXLQHOC1GDSXtoVKJTJTKjNTXGC2WGam+piaKy0FyJdC5NMrw+TjsyUu/1UTtbNsOjCUzzHYnWNPT7Idytc9unMM5nPsyecY6M6Ry6RaXbYkSZIkSZIkSZIkSZIkaZMMmqvea+qOP7jaaua159YdvzKEkI8xFhpQl7RtxRipRKjESCVGYoRYewzEysrjxfPqt7XXzS9UmCmWq8HwBWZLZQrzNWHx0vJzM0vh8ZVB8uSaBeYXXGF8t8mmA9l0ikwqkMukyKRSZDOBbCpFJh3o6cgwlO9gKJ9dConXhsn35DsYzGfp6ci4grgkSZIkSZIkSZIkSZIk7UI7PWj+X0MIY00aK8YYv6pJY7XSnXXHn1zrhTHGUyGEZ4Grqk054Gbgs40oTGqESiVSKC4wNbf4KDE1v7w/Xds+t1B9rrR0/vR88ihXlkPl0lp1ZdP0dWXo78ouPfq6svR1rjzuyKSWg+Lp5f2VwfHltmw6kEmnyKWTEHkmFQyHS5IkSZIkSZIkSZIkSZI2ZScHzQNwexPH2i1x0pvqjh9e5/UPsxw0X+zPoLm21PvuP8VYoVgXDl8OiE/X7hcXDIdrU3o7Mkk4vCtLf31ovDNLf/elAuQZOjLpVpcvSZIkSZIkSZIkSZIkSdKa7OSguRoshNAFHK1rPrHOburPv2HjFUlr87Y/f5jzU/OtLkOXkArQkUmTSQXS1dW206lAJpWqbpPjdCqQSQfSqVTNObXb1EX6SNrTKcikUuSqK4HX72czKXI1+9nF1cAX9zMXXy18aT+TrBaeTSc1S5IkSZIkSZIkSZIkSZLU7nZ60Ny0X2MNs/LftAScW2cfJ+uO922qIiCEsA/Yu87LjtUeTE9PMzk5udlStEGFQuGyx5vVnU01tD9dXncuzWB3lsGuDAPd2ep+sh3ozjLUnWWgK5O0d2fp7cyQCjvpP9ex+qgkhxWgCMVkI0mqs9XzvCRJah3neUmS2pfzvCRJ7c25XpKk9uU8v/tMT0+3dPydHDSPwC8DD7e6kDbSU3c8E2OM6+yj/r9a9X1uxA8Ab95MB5/5zGc4c+ZMA0pRI3zmM59paH9xPo3fO9mYFJHuLPRkoCcLPZlIvnqcz0Z6spDPQE82Vtsgm1oALrKC/Hz1MQbjJI9nmngvkqTtodHzvCRJ2j6c5yVJal/O85IktTfnekmS2pfzfPs7fvx4S8ffyUFzgL+OMf59q4toI/Wh8LkN9DG7Sp9Sw3VmIu0cNA9EcinIpVnadqQgl47V7QbbU5BNwY5abFySJEmSJEmSJEmSJEmSJDXFTg+aq7E6646LG+ijfpnjrg3WIq1ZV3pz16dDpDOd9NOZgc50rNmvtqcjXdXjjjRkUkm0PQAhxJp9Xrhf00aAVE0bQKpmP1Sfh2QMw+CSJEmSJEmSJEmSJEmSJKkVDJqrVv0K5rkN9NGxSp8b8Q7gPeu85hjw3sWDe+65h5tuuqkBpWgjCoXCip/ouOeee8jn8w3r//OVp5l4YpSezgy9HWl6OjL0XGJ7sec7MimCSW5JkjZkq+d5SZLUOs7zkiS1L+d5SZLam3O9JEnty3l+93nkkUdaOr5Bc9WarjuuX+F8LepXMK/vc91ijOeAc+u5pj403NPTQ19f32ZLUYPk8/mG/u/x1m+8s2F9SZKkzWn0PC9JkrYP53lJktqX87wkSe3NuV6SpPblPN/+enp6Wjp+qqWja7upD4V3h/Uv81z/1ZhNB80lSZIkSZIkSZIkSZIkSZIkNZdBc9UaAWLNcRbYt84+DtUdr2slckmSJEmSJEmSJEmSJEmSJEmtZ9BcS2KMs8Dxuuaj6+ym/vxHN16RJEmSJEmSJEmSJEmSJEmSpFYwaK569cHwm9d5/U2r9CdJkiRJkiRJkiRJkiRJkiRpmzNornr31R2/ZK0XhhAOAFfVNJWAhzdfkiRJkiRJkiRJkiRJkiRJkqRm2qlB81B9qPH+vO74VSGEtf5b/+O64w/FGKcbUJMkSZIkSZIkSZIkSZIkSZKkJsq0uoANuLpm/0zLqmhfnwRGgOHq8TXAK4EPreHaN9Ydv7dxZUmSJEmSJEmSJEmSJEmSJElqlh23onmM8bmax3yr62k3McYK8K665jevtqp5COGrgJfXNE0Bf9zY6iRJkiRJkiRJkiRJkiRJkiQ1w44LmqspfgGYrjl+BfCjlzo5hHAI+O265l+LMY5sQW2SJEmSJEmSJEmSJEmSJEmStlim1QVo+4kxjoQQfhb42ZrmnwshHAV+OsZ4CiCEkAK+Hvg14GjNuaeA/9qsei8hV3vw5JNPtqoOAdPT0xw/fnzp+JFHHqGnp6eFFUmSpEZxnpckqX05z0uS1L6c5yVJam/O9ZIktS/n+d3nIvnX3MXO2yohxtjM8bRDVEPk7wX+ad1TZeA5YAK4Ghioe34W+OoY4ye2usbLCSF8PUn9kiRJkiRJkiRJkiRJkiRJUjv4hhjj+5o1WKpZA2lniTFWgNcBf1j3VBq4BriLF4bMLwCvbnXIXJIkSZIkSZIkSZIkSZIkSdLmGDTXJcUY52KM3wZ8C3DfZU4tAO8Abo4xfrgJpUmSJEmSJEmSJEmSJEmSJEnaQiHG2OoatEOEEK4FvhQ4BOSAceAR4BMxxrkWlvYCIYR+4BU1TSeAYovKERwD3ltz/A3AUy2qRZIkNZbzvCRJ7ct5XpKk9uU8L0lSe3OulySpfTnP7z454EjN8UdijBPNGjzTrIG088UYnwSebHUda1H9P9H7Wl2HEiGE+qanYowPtaIWSZLUWM7zkiS1L+d5SZLal/O8JEntzblekqT25Ty/a93bqoFTrRpYkiRJkiRJkiRJkiRJkiRJkrQ9GTSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCgbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCgbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStEKm1QVI2hXOAz9VdyxJktqD87wkSe3LeV6SpPblPC9JUntzrpckqX05z6upQoyx1TVIkiRJkiRJkiRJkiRJkiRJkraRVKsLkCRJkiRJkiRJkiRJkiRJkiRtLwbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCgbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCgbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCplWFyCp/YUQjgH3AIeBHDAGPAp8MsY418raJEnabkIIAbgKuI1k7hwA5knmzyeAzzZ6/gwh9AIvBa4H+oBZ4DmSufpUg8e6BXgRcABIAxeAB4FPxxgXGjhO0+5JkqTtKoTQCbwEuBEYBIrA8yTz7tMNHqspr/2beU+SJDVCCOEG4A6SObKb5PXpWeBx4P4Y4/wm+naulySpyUIIHcBdwE0kc1UXMAmcA74APBljjA0YJwN8KXArsAcoA6eBz8cYH9ps/3VjHQK+HLiS5ft5HPh4jHG6geM07Z4kSWoWX5tveixzhTtAaMDft5J0USGE1wI/Cdx9iVOmgXcBPxVjHGlSWZIkbTshhEHgtcA/Ab4SGL7M6SXgA8DbY4wf2eS4VwNvBV5P8qKtXgQ+Arw5xvjRTYwTgDcAP0oS/L6YC8D/AH4+xljYxFhNuSdJkhothPAHwD+ra34uxnjVBvraC7wZ+JdA/hKnfR54W4zxvevtv26s19KE1/7NvCdJkjar+uXnfwt8D3D1ZU4tAp8B/m+M8dfW0b9zvSRJTRZCeBHwJuBbgI7LnHoSeCfwazHG0Q2M0wP8GPD9wNAlTnsM+AXgXZsJtYcQXgG8BXjlJU4pAn8E/JcY47ObGKdp9yRJUvULVPeQfLnpHuDFQG/NKRt63/0i4/jafBPMFe4sBs0lNVz1W9zvBL5jjZecB77FsJckaTcKIfx3kg+eLxaKXs3vAf82xji5gXFfD/wOyWpqq4nALwI/vt43eEMIA8AfA1+9xkueBr5+I6uXNOueJElqtBDC1wHvu8hT637DO4TwSuA9XP6La7V+D/hXMcbiOsdp2mv/Zt2TJEmNEEL4p8BvA1es47KzMcb9a+z/lTjXO9dLkpomhJACfhb4j0BqHZeeBf5ljPGv1jHWbcB7ufwX1Wr9NfCtMcaJddS1uDjML5Dc01oUgO+KMf7JesapjtWUe5Ik7W4hhJcC/4EkXH5wldM3HTT3tTmwA+5JjWPQXFJDVV9o/z/gG+qeKgPHgQmSF5H9dc/PAK+KMX5qy4uUJGkbCSF8DnjRRZ5a/MnIs0CW5Ccr6+dPSFY++6r1/HxlCOF1wB/ywjfFzwMngH3AISDUPf/2GOOb1jFOF/Bhkm+K1yoCzwLzwDW88NvQ54GXxBifXMdYTbknSZIaLYTQDzxEMk/VW9cb3iGElwF/Q/Iz17XGgWdIfuLyCJCue/7/kbxRu6Y3Cpv52r9Z9yRJUiOEEN4E/Fde+NpzDjgFjJDMaQdY+cHtmoLmzvUrONdLkpoihPBbJIvF1JsBngJmgT0k73XX/w1QBF4bY/zLNYxzA/BxXhjumiZZoKULuIrk84JanwK+MsY4t9oYNWP9N+AH65oj8DzJe+pHL1JHGXhdjPFP1zFO0+5JkrS7hRB+GPjVNZ6+qaC5r81X2Lb3pMZaz7ctJWkt/iMvnAx+AzgaY7wmxngXyc9hfRPJBLGoG/jj6gfskiTtVuPAO4DXAIMxxiMxxhfHGO8geaP6K4CP1V1zD8lPRq1JCOEYyarfta8F7id503ZfjPFFMcYjwE0kL/Jq/XAI4ZvWcT+/wsqQeQV4G7A/xnhDjPF2kr8L3gCM1Zy3l+TvgvoXqtvhniRJarRfYjlkXthoJyGEQZKfs659I/g54LXAUIzx7hjj1SQf4P7Pusu/ieTnv9eqKa/9m3xPkiRtSgjhjSSvg2sDZn8JfC0wEGM8FmP80hjj7THGvSTz/78A/oQkhLZa/871KznXS5K2XAjhW3hhyPxhkvfw+6vz+pfGGK8l+TWTN7NyXs8Bv1ud8y43ToYXriA6CnwXyZx4R4zxemA/8DMk77Uv+nKSX+9c6z29nheGzP8EuCHGeLT6fvpe4FXAF2vOSVfv5ao1jtO0e5IkaRVrXrBtNb423xn3pMZzRXNJDRNC2EPyLabemuYfjzH+/CXOP0TyDearaprfGmN885YVKUnSNlNd0XwP8NPAu2OMs6ucnyYJo39v3VNfGWP80BrGezfwbTVNnyX59u/kRc4NJC/sasd6Crgxxriwyjg3Ag+y8lvN3x5j/INLnH8Lyd8FAzXN3x1j/J3LjVO9tin3JElSo1V/ivLvSQJpFeDHWPlB6ppXVgkh/Czw4zVNzwAvizGeusT5P0HyQe6iCeDqGOPYxc6vua5pr/2bdU+SJG1WCOFa4AGgs9pUAr7rUq+BL3L94BrmYOd653pJUpOFEB4Abq1p+hzwyhjjJb8oHkL4SuCvgUxN80/EGH/uMtd8LyuDW2Mkc+LDlzj/24H/U9O0ANwcY3ziUmNUr8sBj7Fy3v4N4AcuthJpNcz1t8CLa5p/L8b4XZcbp3ptU+5JkiRYsaL5FPB5ks+LP1PdXg3Ufo6+4RXNfW2+M+5JjWfQXFLDhBB+AfiRmqaPkrzQvuR/aEIIX0Xy4nTRFMnkc2FrqpQkaXsJIbwG+GCMcdXVy2quSQP/wMo3d98dY/yOVa67hWQFksWVv4vAnTHGRy5zTWf1mutqmr83xvhbq4z1R8Dra5p+P8b4natc80bgt2uangOuizGWLnNN0+5JkqRGCiF0kQTSjlWbfg34MzbwhncIYS/JT0731DS/Ksb4d5e5JgAfBv5RTfPPxhj/0ypjNeW1fzPvSZKkzQoh/D3Jr5Aten2M8T0N7N+5Hud6SVJzhRCuIVmkpNY9McbPruHa3wC+r6bpUzHGl1zi3BzwJHCkpvmNMcb/tcoYvw/885qmtXxG8P0kC9ksegK4PcY4d5lrbgbuJVmdHaAM3BpjfPQy1zTtniRJgqVfwO4AHo0xVuqeeyUNCJr72nzpmm19T9oaqdVPkaTVhRBSwBvqmt9yuckAoDoxfaymqZeVoTRJktpajPED6wmZV68p88KfjfyaNVz63ax8DfCHlwtkV8eaA+q/RVz/U6ErVH9e65tquwHesob6fockXL7oSpKf57ycptyTJElb4G0sh8yPA/95E339M1a+EfzRy70RDFB9vf5Tdc3fXX2T+KKa/Nq/KfckSdJmhRC+gZUh8/c0MmRe5VyPc70kqeluqDt+fi0h86o/qTu+9jLnfg0rA9nPkrxXvpq3kLz3vuh11RXIL6f+ffCfu1zIHKC6Avkf1TSleeHfC/WaeU+SJBFjfCrG+HB9yLzBfG3OjrgnbQGD5pIa5SXA3prjp0m+vbQW76w7fm0D6pEkqd19rO54Twihe5Vrvr7uuH4OvpQ/Amp/CvRLQggHL3P+a1j5s6AfjjE+vdog1Rf+9W82v3aVy5p1T5IkNUwI4UuAH65p+jcxxulNdPkNdcdrnQ8/RPJTlYv2A192mfOb+dq/WfckSdJmfW/dcf2HrY3gXL/MuV6S1CxDdccn1nHt8brjgcucWz8n/s5qoStIAnXAR2qassCrL3V+COEwcHdN0zTwx6uNU1U/T9fXXK8p9yRJUpP52nzZdr4nbQGD5pIa5TV1xx9cy4vFxXPrjl8ZQsg3oCZJktrZ2EXaLrmyRwjhBlaumlIAPrmWgWKM9ecGXjj316p/7m/WMk5V/d8F//RSJzb5niRJaogQQpbkjdF0tek9McY/30R/Paz8iUpY49xbfd3+t3XNl5x7adJr/ybfkyRJGxZCOMTKXxi7L8b4UIPHcK6v4VwvSWqiibrjrnVcW3/uyGXObcr76RcZ5xPV98nX4hPATM3xDSGE69Yx1lbdkyRJTeFr85W26z1p6xg0l9Qod9YdrynkBRBjPEXyc1mLcsDNmy9JkqS2dugibRcuc/6ddcefiTEurGO8T6zS3+WeW/PfBcDngfma44MhhL2XOLd+nK28J0mSGuXHgduq++PAD22yv1tIVvha9EyM8cw6rm/KHL/O1/7NvCdJkjbjn7D85TFIVvRqNOf6F3KulyQ1w311xzetI9R0T93xZy52UgjhCpIVQBfNA19Y4xjQvHl+gRfew0XHavI9SZLULL42f6HteE/aIgbNJTXKTXXHD6/z+vrz6/uTJEkrvbzu+LkYY/Ey5zdlrq6u0nptXfOax4oxzgNPrWWsi7T794ckaVsLIdwM/Keaph9d5xu3F9PM+bBZYznHS5J2ii+pO75/cSeEcFcI4ddDCPeHEMZCCDMhhGdDCB8MIfx/1dXQ18K5fuPjSJK0YTHG51kZgupgDV8WDyF0AD9c1/zOS5xeP4c9ucr7/PXq58RrQwiZNY7VrHl+K+9JkqRm8bX5xsdp9ljaAgbNJW1aCKELOFrXfGKd3dSff8PGK5IkaVf47rrjv1jl/Pq5davm6muA2jd9Z2OMl/tZ0M2M1ax7kiRp00IIKZIPlnPVpo8Bv9WArhs9H14ZQuisP6nJr/2bck+SJDVAfdD86RBCTwjhnSQrd/5b4HZgAOgCrgReBfwS8EQI4WerX9i+HOf61cdxrpckbZUfBSo1x28NIXzXpU4OIQwA/5eV4af3xxjff4lLNjUnxhjPA3M1TTng6q0Y6yLnb8k8v857kiSpWXxtvvo42+GetEUMmktqhGEg1ByXgHPr7ONk3fG+TVUkSVIbCyG8GvhHdc3vWuWy+rn1+XUOWz9X713jOPXXbWSsS/1d0Kx7kiSpEX4I+LLqfhH43hhjbEC/m50PzwILNccpYM9Fzmvma/9m3ZMkSZtV/4teFeCjvPDL4RfTBfw48BchhN7LnOdc/0LO9ZKkpogxfhz4QWDx9XsGeFcI4TMhhB8LIXxjCOGfhBD+eQjhv5H8Wuc/renig8C3XWaIzc6JAKdW6XNR/fvfm30/favmeVj7PUmS1Cy+Nn+h7XhP2iL+vIykRuipO57ZwIflhVX6lCRJQAhhCPifdc1/FmP8zCqX1s+t9XPvaurPz4YQOmKM8w0e52LXXOrvgmbdkyRJmxJCuBr46Zqmn4sxPtqg7jc1H8YYYwhhFqgNuF1s7m3ma/9m3ZMkSRtW/bWS+oD4rwN3Vfcj8Ockv0D2PJCvPvcvgIM117yK5Mvj33yJoZzr6zjXS5KaKcb4P0IIj5HM87dUm7+EF/6ySa2ngV8EfivGWLnMeU15P726kmh6k2M163379YwlSVKz+Nq8zja9J20RVzSX1Aj1//Geu+hZlze7Sp+SJO161Q+x/zdwuKZ5gmSF1NVsdr6un6sv1mcjxrnYWGt98btV9yRJ0mb9Jkm4DOBR4Gcb2Hez5t6dNMevZyxJkjaqn5UrcgHcXd1eAF4RY/z6GONvxBj/PMb4RzHGHyP5eed31133TSGE77zEOM71mxtLkqRNizH+PUmw/JeB8iqnH6+e9+5VQubQunl+I2M5z0uSdjNfm298LP82aAMGzSU1QmfdcXEDfdSvHNq1wVokSWpnvwR8bV3b98UYT6zh2s3O1xdb5fti83Uz/y5o1j1JkrRhIYQ3kqxUCsnKpt8bY9zI/HgpzZp7d9Icv56xJEnaqEt9qFkGXhNj/NjFnowxTpOsav43dU/9RAihPrgOzvWbHUuSpE0LIfxr4Cng/+OFK4PXOwq8A3g2hPDdq5zbqnl+I2M5z0uSdjNfm298LP82aAMGzSU1Qv03jXIb6KNjlT4lSdrVQgg/BPz7uuZfjDH+0Rq72Ox8XT9XX6zPRoxzsbEu9XdBs+5JkqQNCSEcIFnFbNFvXyp0tgnNmnt30hy/nrEkSdqoS80tvx1j/PTlLqyubvr9QO0qpzcAr1jDOM716xtLkqQNCyFkQwj/F/gfwIFq8yjwVuAeYJBkHjsIfD3wpyRfMgcYAt4ZQvilywzRqnl+I2M5z0uSdjNfm298LP82aAMGzSU1wnTd8cW+Eb2a+m8a1fcpSdKuFUL4duDtdc3vAn5sHd1sdr6+2LeCLzZfN/PvgmbdkyRJG/XfgYHq/hngR7ZgjGbNvTtpjl/PWJIkbdSl5pbfWsvFMf7/2bvrOKuq9Y/j3zXN0N3d3R02BlgIIogKiIGBdb3q76rXe+1uQhFBQBEDUFFs6e7u7pzus35/zOCdGc4+Z/LMmZnP+/Wal5y91t772U7setaz7B5Jv2Va7C7RnHN97vYFAEBujJN0U7rPKyS1tNb+21q70lp7zlqbZK09aq393lo7QNINypj89A9jzAiH7RfUeT4n++I8DwAozrg3z/m+uDYoAkg0B5AXMv/xDneY4tOTkl62CQBAsWSM6S9piqT059ZvJY2y1lr3a7mV+dya+dzrTeb+ydZadyOFc7sfd+tk9eY3v44JAIBsM8YMknRjukUPWWvP5cOucnU+TLt/z8nD4Py89/fVMQEAkGPW2jhJKZkWR0lam43NzM/0uZObPpzrM+FcDwDwBWPMxZLuTLfohKT+1tpjntaz1n4n6f5Mi183xmSl8Em+PE93uG7J7fP0/Hpun519AQDgK9ybZ+Knx4R8QqI5gLxwSv+bAkySgiVVyeY2amb6fCJXEQEAUAQYYy6R9JWkoHSLf5U0xFqb+aGwN5nPrbWyuX7mc/XJLO4n83o52ZfTdYGvjgkAgJxIPzX2XGvtzHzaT27Ph1WV8VrDpdT7/Mx8ee/vq2MCACC3Mp+zdllrXdlYf3umz+7OrZzrL8S5HgDgC2MyfX7HWpvVZ8iTJe1I97mipAFu+uX2nChJNbxs87zMsef2eXp+neelrB8TAAC+wr35hfzxmJBPSDQHkGtpI6APZFpcJ5ubydx/W84jAgCg8DPGdJX0nTJOHbVE0o3W2sQcbDLzy+v8OlfvkZSc7nMJY0zlfNqXr44JAICcKJfu3/2MMdbbl6Q/M22jrpt+7TL1yevz4X53M3z4+N7fJ8cEAEAe2Jrpc2Q218/cv7ybPpzrve+Hcz0AIE+lVdm8NNPi77O6ftrAs7mZFvdx0zVX50RjTBVlfIeQqNRn9O746nm6L48JAABf4d7c+3784ZiQT0g0B5BXMv8Bb5HN9Zt72R4AAMWGMaaNpJ8klUq3eK2ka6y1MTncrE/O1dbaJEm7c7ovY0yopAZZ2Zeb5Vx/AACKI1+eD321L87xAIDCYkumz6HZXD8s0+dYN3041+d8PwAA5FR5SWUzLdubzW1k7u9u9s/M57CGxpiQbOwj8zlxt7U22W3PgjvP5+cxAQDgK9yb53w/vt4X8gGJ5gDyyrpMn3tkdUVjTHVJ9dItStKFD+gBACgWjDFNJf2qjFXMtkq60lobkYtNr8v0ubMxJshdRwc9vWzPU1uWrwskdVTGF/NHrbVOU19l3k9+HhMAAP5qs1Lvo8+rl3afnVU+Ocdn897fl8cEAEBurMn0uWo21888VfRpN30411+Icz0AIL+5GzyW3WTnpEyfAzN3sNYek3Qs0347ZmMfvjrPB0nqkpV9+fiYAADwFe7NL+SPx4R8QqI5gLzyQ6bPl6dNKZYVfTN9/tNaG50HMQEAUKgYY+pK+k0ZXzTvlXSFtfZkbrZtrd2mjJXGSyqLN3DGmJKSuqffnC4896eXue2KrOzHoa/jdKQ+PiYAALLreqWe17Lz9Y9M2zjups+u9B2stVGSFmRaL0vn3rT79sszLfY0FbhP7v19fEwAAOTGXEmudJ/rG2MqZGP9zElXmaet5lyfCed6AICPuBv8VSOb28hcwdzpGf/cTJ/z5Xm6m/30SHtOnhU9JYWn+7zDWrsjG/vKr2MCAMAnuDfPyF+PCfmHRHMAeWWJpFPpPjeQdHEW170z0+c5eREQAACFSdpI3N8l1Uq3+LCky6y1h/NoN99l+pz5HOxksKRS6T6vstYe8dD/R2Ws7nKxMaaBt52k3UwOz7TY23WBr44JAIBssdbOt9b+lp0vSaszbSbeTT93D1Bzej68RFL9dJ+PS1ruob8v7/19dUwAAORY2gxcizMtHpCVddMqg96YafFfDt051/8P53oAQL6z1iZKOppp8aXZ3MxlmT7vdtvrwnPiiKwkXhljGkq6KN2iJKU+m3fLWntQ0tp0i0pJutnbftLk9jyfL8cEAICPcW/+P/58TMgHJJoDyBPWWpekyZkW/9vbDaMx5jJJvdMtipI0M2+jAwDAv6VVO/tVUsN0i08qtZL53jz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- }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" @@ -858,31 +906,21 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:26:03.569294Z", - "iopub.status.busy": "2022-10-26T11:26:03.568507Z", - "iopub.status.idle": "2022-10-26T11:26:25.165695Z", - "shell.execute_reply": "2022-10-26T11:26:25.166233Z" + "iopub.execute_input": "2023-06-26T17:03:58.285215Z", + "iopub.status.busy": "2023-06-26T17:03:58.285118Z", + "iopub.status.idle": "2023-06-26T17:04:06.413734Z", + "shell.execute_reply": "2023-06-26T17:04:06.413443Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
<Figure size 3000x1500 with 3 Axes>\n",
-       "
\n" - ], + "image/png": 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OPvjgao+zOXPmxNChQ2POnDm7lG1H9pbHPQAAAMCuKtj5EgAAAAAAsmXixIlRWVmZdmzw4MG1fp/Ro0fHX//619T4ueeeS9ule1sffPBBxnLw4sWLo7i4OHr37p3xvI8XvUePHv2J85544onx9NNPR0TEL3/5y3jqqadSc3fdddd2y+5Nmzbd6bWvv/76VJH9S1/6Ulx11VVx8MEHp+ZLS0vjnnvuie985zuxYcOGiPiwGHzllVfG3/72t0/8Me1Ifn5+nHTSSXHaaafFcccdFz179qxW3N64cWM88sgjcd1116UKlyUlJfHFL34xXnvttYw7ud99992xZcuWWL58eZx77rmp4yeccEJcccUV280zYMCAWvrI0n31q1+NdevWRVFRUfz4xz+OsWPHRlFRUWp+2rRpcdFFF8Vbb70VERFVVVXx3e9+N+6777646KKLIplMRp8+feL666+PT33qU6mdiSsrK+P++++PSy65JNatWxcREStXroyf/vSn8etf/3qHmf7whz9U2yl5xIgRcf3118fQoUNTn9dNmzbFfffdF1dddVXqxRdbtmyJL37xi/HWW2/Fvvvuu93rz507NzVu3759/OxnP4vPf/7z1QroyWQy5s+fH88880w88MADqX8D27ryyitTX8tzzz03li9fHhEfvgDkrrvu2u7Hub18teG0006Ld999t9ouyk2aNIkjjzwyTjvttLj44oujZcuWdZbhk5o5c2Z84QtfiC1btqSOjRgxIq666qoYPXp0WoF/48aNcd9998U111yT+rxPmzYtLr/88vjTn/5U7dqdO3eO448/Pp544omIiJg/f35Mnjw5RowYUaNsS5YsSStl9+vXL4YMGZJxbUlJSZx++ulpZfT9998/vv/978eZZ56Z9lirqKiIZ555Jq6++up47bXXIuLDfy9f+MIX4uWXX47GjRvXKN+O1PbjHgAAAGBPocgNAAAAQM5VVFbFB+sz715J7ercukkU5Ne/P9T3n//8p1rhr6a2t/NpQ1RRURE//elP0471798/DjrooFq/17Y7ckdEPPvss3HRRRdlXLttIbtt27bRokWLWLx4ceq8mha5P37PXdG5c+fo3LlzRES1cuqwYcOiR48en/ja8+fPj0QiEbfeemvGz0Hjxo3jwgsvjN69e8fIkSNTuzQ/+OCDsXLlymjfvv0nvncmgwcPjnnz5sV+++23w3UtW7aML33pS3HGGWfEGWecEY8//nhERLzxxhvxxBNPxMknn1ztnGHDhkXEh7uwb6tz584xZsyY2vkAdsHChQujY8eO8dxzz8WBBx5YbX7IkCHx3HPPxYABA2LFihURETF9+vT49Kc/HSUlJTFixIj417/+Va0QnJ+fH2eddVa0b98+7XF35513xvXXXx+NGjXKmOe9996Lq666Ku3Y2LFj44477qhWjG/RokVcdNFFMWbMmBg+fHgsXbo0IiLWrFkTl112WTz00EMZ7/GPf/wj9X7jxo3jhRdeiH79+mVcm0gkolevXtGrV6+45JJLYtasWWk7OkdEHHjgganP3bYl4yZNmuTkaxoRqeL9x23dujUmTZoUkyZNiuuuuy5++tOfxje+8Y0sp9u+ioqKOPvss9NK3Ndee21cc801GV8Y0bJly7j44ovjlFNOiVGjRqWKyrfcckt89atfjcMOO6zaOWPHjk0VuSM+3GG7pkXuO++8M22X+B3txv0///M/aS/A+fSnPx333HNPxt2uCwoK4qSTTopRo0bFOeecEw8++GBEfPi95Oabb47LL7+8Rvl2pLYf9wAAAAB7CkVuAAAAAHLug/VbY8Qvns91jL3Ci1eOim5tm+U6RjXf+973ch0h5zZt2hQXXXRRvPTSS2nHr7vuujq5X4cOHWLAgAExc+bMiKheut7WtnOjRo2KVq1axR133BERHxa5L7nkkp2e16NHj9h///1rI3qd+Na3vrXdIvtHRowYEWeeeWZqF+7y8vJ49tln4+yzz67VLLv6eWrSpEnceeed0bNnz1i/fn1ERNxxxx0Zi9z10bhx4zKWuD/Srl27+O53vxvf//73U8fefffdaNu2bfz973/f4a7Oo0aNihNOOCG1g/vKlStj+vTpMXTo0Izrb7755rQS7yGHHBJ//etfM5Z4P9KjR4/4xz/+EcOGDYtkMhkREY888kjMnTs3DjjggGrrP9o9/aN82yuzZrIra+u79evXxze/+c2YMmVK3HnnnVFQkPtfWf3zn/+Mt99+OzW+5JJL4oc//OFOz+vSpUvcf//9ceihh6aK1r/+9a8z7oj+mc98Jlq3bp36t/rPf/4z/vCHP6SV8Ldn253i8/Ly0nbV39aSJUvS/uLCwIED4x//+MdOd9Zu3Lhx3HXXXTFgwIDUiz1+97vfxbe+9a3dLlJ73AMAAABkVv+23gEAAAAA2AM988wz1d4ef/zxmDBhQnzjG9+IHj16pArCH/n2t78dn//85+ss07Y7Fa9YsSKtwLitbQvZo0ePjtGjR6fGEydOTJVXt7Vo0aKYP39+xnvVN02bNo1rrrmmRmu/8IUvpI1fe+21uoi0y9q2bZtW3J46dWoO09Tc0UcfHSeddNJO15122mnVjl122WXRqVOnnZ57+umnp41ff/31jOuSyWTcdtttacd+9atf1ahgPHTo0LTHRjKZTCvSbmvbovj2dgZviAoKCmLMmDHxm9/8JiZNmhTLli2LrVu3RklJSSxatCj++c9/xhe/+MVqn8/77rsvvvnNb+Yodbobb7wx9X6zZs3i5z//eY3PPfjgg9Meaw8//HBUVlZWW9ekSZM466yzUuP169dvd/f2bb3yyisxe/bs1Hj06NHRtWvXjGv/+Mc/pv2Vi1/+8pc7LXF/pFmzZmk7cC9atCimT59eo3N3ZE993AMAAADsLkVuAAAAAIAsOP7446u9fepTn4qxY8fGH//4x1i9enVqbZs2beKPf/xjWqmwLmxbyI74cHftj5s/f35qZ9aIDwvZ25ayV65cGTNmzKh23sev9fF71SdjxoyJffbZp0ZrDz300LTxkiVL6iDRJ7PtTt5Lly6NlStX5jBNzZx55pk1WtenT58oLCxMO1bTFzkcfPDBaePFixdnXDdr1qxYsWJFaty9e/ddetx++ctfThu/8MILGdftu+++aWu2l6ch+dKXvhQLFiyIp59+Oi6//PI45phjomPHjtG4ceNo2rRpdO/ePc4444y4++6744033qi2A/stt9wSjz76aI7Sf2j16tUxbdq01PjUU0+NNm3a7NI1TjjhhNT7mzZt2u6LBsaOHZs2Hj9+/E6vve1u3Jmusa3HH3889X6nTp1izJgxO73+trb9OCIiXnzxxV06P5M98XEPAAAAUBsUuQEAAACAnHv++ecjmUx+orf99tsv1/Fr1eGHHx6zZs2Kyy67rM7vdeyxx0Z+fn5qvO3O2x/ZtpDdpUuX6Nu3b3Tu3Dn69+9f4/Mi6veO3IcffniN13bo0CFtvH79+tqOk2bdunVx2223xYUXXhiDBg2Kzp07R/PmzSORSFR7+/juwatWrarTbLVh8ODBNVqXn58frVu3To0bNWoUBx10UI3O/XhJf8OGDRnXvfLKK2njUaNGRSKRqNE9IiKOOeaYtN2mX3/99SgrK6u27vjjj0+9v379+hg1alT84x//iPLy8hrfq745/vjjt7s79McNGDAgJk2aFL179047fvXVV2fc3T9bJk+enHb/Xfm+8JHu3bunjd99992M64YNGxYHHHBAavz000/HsmXLtnvdsrKyuO+++1Ljli1bxmc/+9mMa9euXZv21xUGDRoUeXm79uvAmn4cu2JPfNwDAAAA1AZFbgAAAACAemT69OkxatSorOxWWlRUFIcddlhqPGnSpKisrExbs21Je9vdibd9P9NO3s8//3zq/f79+0enTp1qJXNd+Hg5e0eaN2+eNt6yZUttx4mIiM2bN8eVV14ZnTp1iosuuijGjRsXr7/+eixbtixKSkpqdI1169bVSbba1L59+xqvbdasWer9tm3bpr0IoabnRWz/a7Zo0aK08cCBA2ucLSKicePG0a9fv9S4tLQ0li9fXm3dFVdckZZp/vz5cdZZZ0WHDh3i7LPPjltuuSVmzJgRVVVVu3T/hqRdu3bxl7/8Je3YW2+9FW+++WaOElUvK1955ZUZXzCxo7dPfepTaddYs2bNdu93/vnnp96vrKyMu+++e7trH3vssbRrnXnmmdUe1x+ZPXt2WiH98ccf3+WP4+Pf53b0cdSUxz0AAABAZorcAAAAAABZ8PGdxCsrK2PNmjXx4osvxje+8Y0oLCxMrX3nnXdizJgxWSnibrtT9vr162P69Olp89sWsrctb2973gsvvBAVFRWp8bvvvhsffPBBxrX1UZMmTT7xuXWxg/CqVati6NCh8ctf/jJKS0s/8XV259xs+aSf+7r4mq1duzZt3K5du12+9sfP+fg1IyIOOOCA+Oc//xmtWrVKO75u3br429/+Fpdeemkccsgh0a5duzjjjDPi3nvvrbMXDOTSyJEjY9CgQWnHnnrqqRyliVi9enWtX3NHO/afd955aTu+T5gwYbtrx48fnzYeO3bsdtdm++OoKY97AAAAgMwKdr4EAAAAAOpW59ZN4sUrR+U6xl6hc+tPXn6kduXl5UWbNm1i+PDhMXz48DjvvPPixBNPTJW3586dG1/96lfj73//e53mGD16dPziF79IjZ977rk48sgjIyLi7bffTttReNsi98iRIyMvLy+qqqpi48aN8eqrr8bQoUMjovoO3duex86deeaZ8dZbb6Ud69atW4waNSoOPPDA6Nq1a7Ro0SKaNm0aeXn/3a9lwoQJceedd2Y77h5j06ZNaeOP70pcEx8/Z+PGjRnXnXzyyTFz5sz4yU9+Evfcc0+1e0d8WAJ/4IEH4oEHHoj27dvHNddcE9/4xjfSyr8N3ejRo+O1115LjWfPnp2zLHXxwpkd7S693377xciRI1M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\n" - }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" @@ -949,31 +987,21 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:26:25.174881Z", - "iopub.status.busy": "2022-10-26T11:26:25.173304Z", - "iopub.status.idle": "2022-10-26T11:27:28.373180Z", - "shell.execute_reply": "2022-10-26T11:27:28.373721Z" + "iopub.execute_input": "2023-06-26T17:04:06.416076Z", + "iopub.status.busy": "2023-06-26T17:04:06.415951Z", + "iopub.status.idle": "2023-06-26T17:04:29.992825Z", + "shell.execute_reply": "2023-06-26T17:04:29.992389Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
<Figure size 3000x1500 with 3 Axes>\n",
-       "
\n" - ], + "image/png": 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- }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" @@ -1023,31 +1051,21 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:27:28.378822Z", - "iopub.status.busy": "2022-10-26T11:27:28.378261Z", - "iopub.status.idle": "2022-10-26T11:28:28.621256Z", - "shell.execute_reply": "2022-10-26T11:28:28.621813Z" + "iopub.execute_input": "2023-06-26T17:04:29.995236Z", + "iopub.status.busy": "2023-06-26T17:04:29.995077Z", + "iopub.status.idle": "2023-06-26T17:04:50.701160Z", + "shell.execute_reply": "2023-06-26T17:04:50.700801Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
<Figure size 3000x1500 with 3 Axes>\n",
-       "
\n" - ], + "image/png": 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- }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" @@ -1107,7 +1125,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/docs/recipes/pipelines.ipynb b/docs/recipes/pipelines.ipynb index ff7965fdfb..ce8eb4dc08 100644 --- a/docs/recipes/pipelines.ipynb +++ b/docs/recipes/pipelines.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.404078Z", - "iopub.status.busy": "2022-10-26T11:28:32.403407Z", - "iopub.status.idle": "2022-10-26T11:28:32.658166Z", - "shell.execute_reply": "2022-10-26T11:28:32.657437Z" + "iopub.execute_input": "2023-06-26T17:02:56.508261Z", + "iopub.status.busy": "2023-06-26T17:02:56.508069Z", + "iopub.status.idle": "2023-06-26T17:02:56.631267Z", + "shell.execute_reply": "2023-06-26T17:02:56.630163Z" }, "tags": [] }, @@ -54,10 +54,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.666984Z", - "iopub.status.busy": "2022-10-26T11:28:32.666054Z", - "iopub.status.idle": "2022-10-26T11:28:32.694462Z", - "shell.execute_reply": "2022-10-26T11:28:32.694987Z" + "iopub.execute_input": "2023-06-26T17:02:56.637636Z", + "iopub.status.busy": "2023-06-26T17:02:56.637235Z", + "iopub.status.idle": "2023-06-26T17:02:56.671036Z", + "shell.execute_reply": "2023-06-26T17:02:56.669047Z" }, "tags": [] }, @@ -82,10 +82,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.700650Z", - "iopub.status.busy": "2022-10-26T11:28:32.699995Z", - "iopub.status.idle": "2022-10-26T11:28:32.727495Z", - "shell.execute_reply": "2022-10-26T11:28:32.726548Z" + "iopub.execute_input": "2023-06-26T17:02:56.682809Z", + "iopub.status.busy": "2023-06-26T17:02:56.682035Z", + "iopub.status.idle": "2023-06-26T17:02:56.706464Z", + "shell.execute_reply": "2023-06-26T17:02:56.704627Z" }, "tags": [] }, @@ -108,10 +108,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.734230Z", - "iopub.status.busy": "2022-10-26T11:28:32.733546Z", - "iopub.status.idle": "2022-10-26T11:28:32.768994Z", - "shell.execute_reply": "2022-10-26T11:28:32.768518Z" + "iopub.execute_input": "2023-06-26T17:02:56.732648Z", + "iopub.status.busy": "2023-06-26T17:02:56.727813Z", + "iopub.status.idle": "2023-06-26T17:02:56.786018Z", + "shell.execute_reply": "2023-06-26T17:02:56.780690Z" }, "tags": [] }, @@ -119,18 +119,16 @@ { "data": { "text/html": [ - "
StandardScaler
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StandardScaler
StandardScaler (\n", " with_std=True\n", ")\n", - "\n", - "
PolynomialExtender
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PolynomialExtender
PolynomialExtender (\n", " degree=2\n", " interaction_only=False\n", " include_bias=False\n", " bias_name=\"bias\"\n", ")\n", - "\n", - "
LinearRegression
(\n", + "
LinearRegression
LinearRegression (\n", " optimizer=SGD (\n", " lr=Constant (\n", " learning_rate=0.01\n", @@ -146,19 +144,19 @@ " clip_gradient=1e+12\n", " initializer=Zeros ()\n", ")\n", - "\n", "
" + ], + "text/plain": [ + "Pipeline (\n", + " StandardScaler (\n", + " with_std=True\n", + " ),\n", + " PolynomialExtender (\n", + " degree=2\n", + " interaction_only=False\n", + " include_bias=False\n", + " bias_name=\"bias\"\n", + " ),\n", + " LinearRegression (\n", + " optimizer=SGD (\n", + " lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " )\n", + " loss=Squared ()\n", + " l2=0.\n", + " l1=0.\n", + " intercept_init=0.\n", + " intercept_lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " clip_gradient=1e+12\n", + " initializer=Zeros ()\n", + " )\n", + ")" ] }, "execution_count": 4, @@ -262,48 +297,29 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.773807Z", - "iopub.status.busy": "2022-10-26T11:28:32.772796Z", - "iopub.status.idle": "2022-10-26T11:28:33.624482Z", - "shell.execute_reply": "2022-10-26T11:28:33.625033Z" + "iopub.execute_input": "2023-06-26T17:02:56.807840Z", + "iopub.status.busy": "2023-06-26T17:02:56.806993Z", + "iopub.status.idle": "2023-06-26T17:02:57.142845Z", + "shell.execute_reply": "2023-06-26T17:02:57.142564Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
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to docs/releases/0.18.0.md index 9cf45acb08..65a1dd4b68 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/0.18.0.md @@ -1,4 +1,4 @@ -# Unreleased +# 0.17.0 - 2023-06-26 ## bandit diff --git a/river/__version__.py b/river/__version__.py index 29add72136..d243b01263 100644 --- a/river/__version__.py +++ b/river/__version__.py @@ -1,5 +1,5 @@ from __future__ import annotations -VERSION = (0, 17, 0) +VERSION = (0, 18, 0) __version__ = ".".join(map(str, VERSION)) # noqa: F401 From dd9d92de2d8e0e9822af9a1781b78cd4b24011ac Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 26 Jun 2023 23:31:28 +0200 Subject: [PATCH 034/347] Update release-docs.yml --- .github/workflows/release-docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 156c3baaf0..fdb4a1430d 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -18,7 +18,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v4 with: - python-version: "3.10" + python-version: "3.9" - run: curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show if: matrix.os == 'ubuntu-latest' From 9b4e57715ef295d894dcf9c8a8af905f18b38ba5 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 27 Jun 2023 09:06:42 +0200 Subject: [PATCH 035/347] fix notebooks --- docs/examples/content-personalization.ipynb | 221 +++++++++++--------- docs/recipes/mini-batching.ipynb | 12 +- 2 files changed, 120 insertions(+), 113 deletions(-) diff --git a/docs/examples/content-personalization.ipynb b/docs/examples/content-personalization.ipynb index 7d85819855..c1670942e3 100644 --- a/docs/examples/content-personalization.ipynb +++ b/docs/examples/content-personalization.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -15,6 +17,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -25,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:09.647679Z", @@ -41,7 +44,7 @@ "1" ] }, - "execution_count": 18, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -73,6 +76,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -81,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:09.661875Z", @@ -136,6 +140,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -148,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:09.859123Z", @@ -159,17 +164,14 @@ }, "outputs": [ { - "ename": "TypeError", - "evalue": "string indices must be integers", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[15], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mriver\u001b[39;00m \u001b[39mimport\u001b[39;00m reco\n\u001b[1;32m 3\u001b[0m model \u001b[39m=\u001b[39m reco\u001b[39m.\u001b[39mRandomNormal(seed\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m simulate(\u001b[39m5_000\u001b[39;49m, get_reward, model, seed\u001b[39m=\u001b[39;49m\u001b[39m42\u001b[39;49m)\n", - "Cell \u001b[0;32mIn[14], line 34\u001b[0m, in \u001b[0;36msimulate\u001b[0;34m(n, reward_func, model, seed)\u001b[0m\n\u001b[1;32m 31\u001b[0m item \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mrank(user, items\u001b[39m=\u001b[39mitems, x\u001b[39m=\u001b[39mcontext)[\u001b[39m0\u001b[39m]\n\u001b[1;32m 33\u001b[0m \u001b[39m# Measure the reward\u001b[39;00m\n\u001b[0;32m---> 34\u001b[0m clicked \u001b[39m=\u001b[39m reward_func(user, item)\n\u001b[1;32m 35\u001b[0m n_clicks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m clicked\n\u001b[1;32m 36\u001b[0m ctr\u001b[39m.\u001b[39mappend(n_clicks \u001b[39m/\u001b[39m (i \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m))\n", - "Cell \u001b[0;32mIn[13], line 6\u001b[0m, in \u001b[0;36mget_reward\u001b[0;34m(context, item)\u001b[0m\n\u001b[1;32m 3\u001b[0m USER_LIKED_ARTICLE \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m 4\u001b[0m USER_DISLIKED_ARTICLE \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m----> 6\u001b[0m \u001b[39mif\u001b[39;00m context[\u001b[39m'\u001b[39;49m\u001b[39muser\u001b[39;49m\u001b[39m'\u001b[39;49m] \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mTom\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m 7\u001b[0m \u001b[39mif\u001b[39;00m context[\u001b[39m'\u001b[39m\u001b[39mtime_of_day\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mmorning\u001b[39m\u001b[39m'\u001b[39m \u001b[39mand\u001b[39;00m item \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mpolitics\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m 8\u001b[0m \u001b[39mreturn\u001b[39;00m USER_LIKED_ARTICLE\n", - "\u001b[0;31mTypeError\u001b[0m: string indices must be integers" - ] + "data": { + "image/png": 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", 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" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -224,6 +230,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -244,34 +251,20 @@ "outputs": [ { "data": { - "text/html": [ - "
\n",
-       "defaultdict(Zeros (), {\n",
-       "    'music': -0.013333333333333336,\n",
-       "    'camping': -0.01,\n",
-       "    'health': -0.0078000000000000005,\n",
-       "    'sports': -0.0063106666666666675,\n",
-       "    'food': -0.0052320723809523816,\n",
-       "    'finance': -0.00441314521904762,\n",
-       "    'politics': 0.01650162758658716\n",
-       "})\n",
-       "
\n" - ], "text/plain": [ - "\n", - "\u001b[1;35mdefaultdict\u001b[0m\u001b[1m(\u001b[0mZeros \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m{\u001b[0m\n", - " \u001b[32m'music'\u001b[0m: \u001b[1;36m-0.013333333333333336\u001b[0m,\n", - " \u001b[32m'camping'\u001b[0m: \u001b[1;36m-0.01\u001b[0m,\n", - " \u001b[32m'health'\u001b[0m: \u001b[1;36m-0.0078000000000000005\u001b[0m,\n", - " \u001b[32m'sports'\u001b[0m: \u001b[1;36m-0.0063106666666666675\u001b[0m,\n", - " \u001b[32m'food'\u001b[0m: \u001b[1;36m-0.0052320723809523816\u001b[0m,\n", - " \u001b[32m'finance'\u001b[0m: \u001b[1;36m-0.00441314521904762\u001b[0m,\n", - " \u001b[32m'politics'\u001b[0m: \u001b[1;36m0.01650162758658716\u001b[0m\n", - "\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n" + "defaultdict(Zeros (),\n", + " {'politics': 0.0705754422774087,\n", + " 'music': -0.037183485013932496,\n", + " 'camping': -0.03648626640209432,\n", + " 'sports': -0.039708144989247955,\n", + " 'health': -0.03873860066029714,\n", + " 'food': -0.03668470146816211,\n", + " 'finance': -0.038730297969974126})" ] }, + "execution_count": 5, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -279,6 +272,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -287,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.359874Z", @@ -299,7 +293,10 @@ "outputs": [ { "data": { - "image/png": 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" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -308,7 +305,7 @@ "source": [ "simulate(\n", " 5_000,\n", - " reward_func=lambda user, item: (\n", + " reward_func=lambda user, item, context: (\n", " item in {'music', 'politics'} if user == \"Tom\" else\n", " item in {'food', 'sports'}\n", " ),\n", @@ -318,6 +315,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -326,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.461719Z", @@ -338,7 +336,10 @@ "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -350,6 +351,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -361,6 +363,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -368,6 +371,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -376,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.612840Z", @@ -403,6 +407,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -411,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.634015Z", @@ -447,6 +452,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -455,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.653296Z", @@ -467,16 +473,13 @@ "outputs": [ { "data": { - "text/html": [ - "
False\n",
-       "
\n" - ], "text/plain": [ - "\u001b[3;91mFalse\u001b[0m\n" + "False" ] }, + "execution_count": 12, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -485,6 +488,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -493,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.673154Z", @@ -505,7 +509,10 @@ "outputs": [ { "data": { - "image/png": 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" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -516,6 +523,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -526,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.819127Z", @@ -538,7 +546,10 @@ "outputs": [ { "data": { - "image/png": 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" + "image/png": 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+ "text/plain": [ + "
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 preferencepreference
itemcampingfinancefoodhealthmusicpoliticssportscampingfinancefoodhealthmusicpoliticssports
user
Anna@afternoon0.0000000.000000-0.030868-0.0195620.000000-0.0033850.000000Anna@afternoon-0.0181050.1097510.0355630.0692220.1688851.000000-0.059041
Anna@morning0.0000000.000000-0.008942-0.050784-0.0531850.000000-0.035998Anna@morning-0.0502960.0000000.000000-0.011807-0.007567-0.038986-0.045754
Tom@afternoon0.124429-0.0320770.1370960.0893321.0000000.174578-0.179105Tom@afternoon0.0606440.070561-0.026601-0.0743281.0000000.164659-0.229550
Tom@morning-0.000000-0.024397-0.017108-0.016825-0.118900-0.014694-0.000848Tom@morning-0.0285620.090628-0.0004070.0611630.0653131.000000-0.050107
\n" + ], + "text/plain": [ + "" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/recipes/mini-batching.ipynb b/docs/recipes/mini-batching.ipynb index a973e8b6e2..f463ee3335 100644 --- a/docs/recipes/mini-batching.ipynb +++ b/docs/recipes/mini-batching.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-10-26T11:05:45.359145Z", @@ -66,15 +66,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\n" - ] - } - ], + "outputs": [], "source": [ "from river import datasets\n", "\n", From 6fa6445feaecb42ac34b3a2024a03606179b9a5c Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 27 Jun 2023 09:26:51 +0200 Subject: [PATCH 036/347] Update content-personalization.ipynb --- docs/examples/content-personalization.ipynb | 150 +++++++++----------- 1 file changed, 71 insertions(+), 79 deletions(-) diff --git a/docs/examples/content-personalization.ipynb b/docs/examples/content-personalization.ipynb index c1670942e3..101c0846a9 100644 --- a/docs/examples/content-personalization.ipynb +++ b/docs/examples/content-personalization.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:09.647679Z", @@ -44,7 +44,7 @@ "1" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:09.661875Z", @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:09.859123Z", @@ -165,7 +165,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -203,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.234151Z", @@ -215,7 +215,7 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -239,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.337936Z", @@ -253,16 +253,16 @@ "data": { "text/plain": [ "defaultdict(Zeros (),\n", - " {'politics': 0.0705754422774087,\n", - " 'music': -0.037183485013932496,\n", - " 'camping': -0.03648626640209432,\n", - " 'sports': -0.039708144989247955,\n", - " 'health': -0.03873860066029714,\n", - " 'food': -0.03668470146816211,\n", - " 'finance': -0.038730297969974126})" + " {'camping': 0.0,\n", + " 'politics': 0.0,\n", + " 'finance': 0.0,\n", + " 'music': 0.0,\n", + " 'food': 0.0,\n", + " 'health': 0.0,\n", + " 'sports': 0.0})" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -293,7 +293,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -336,7 +336,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -416,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.634015Z", @@ -461,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.653296Z", @@ -477,7 +477,7 @@ "False" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.673154Z", @@ -509,7 +509,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -534,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.819127Z", @@ -546,7 +546,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -580,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2023-01-29T19:27:10.971908Z", @@ -590,37 +590,29 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/8q/xqx16rw14bl7rqyybl7zk8sh0000gn/T/ipykernel_52697/1851263114.py:13: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.\n", - " .pivot('user', 'item')\n" - ] - }, { "data": { "text/html": [ "\n", - "\n", + "
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 preferencepreference
itemcampingfinancefoodhealthmusicpoliticssportscampingfinancefoodhealthmusicpoliticssports
user
Anna@afternoon-0.0181050.1097510.0355630.0692220.1688851.000000-0.059041Anna@afternoon0.046338-0.045078-0.1134830.010867-0.0931691.0000000.024078
Anna@morning-0.0502960.0000000.000000-0.011807-0.007567-0.038986-0.045754Anna@morning0.160048-0.1123900.0763000.081779-0.1349040.0671141.000000
Tom@afternoon0.0606440.070561-0.026601-0.0743281.0000000.164659-0.229550Tom@afternoon0.0000000.000000-0.027919-0.021783-0.035434-0.029329-0.021462
Tom@morning-0.0285620.090628-0.0004070.0611630.0653131.000000-0.050107Tom@morning0.027675-0.036132-0.096759-0.020550-0.1188371.0000000.023644
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -699,7 +691,7 @@ " for user in model.u_latents\n", " for item in model.i_latents\n", " )\n", - " .pivot('user', 'item')\n", + " .pivot(index='user', columns='item')\n", " .style.highlight_max(color='lightgreen', axis='columns')\n", ")" ] From 0dfeb8a65306b7399579eddbdd2adaebdf57ccbc Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 27 Jun 2023 10:30:16 +0200 Subject: [PATCH 037/347] Update __main__.py --- docs/parse/__main__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/parse/__main__.py b/docs/parse/__main__.py index d4021ba01e..6615af5d87 100644 --- a/docs/parse/__main__.py +++ b/docs/parse/__main__.py @@ -62,7 +62,7 @@ def snake_to_kebab(text: str) -> str: return text.replace("_", "-") -def find_method_docstring(klass, method: str) -> str | None: +def find_method_docstring(klass, method: str) -> str: """Look through a class' ancestors for the first non-empty method docstring. Since Python 3.5, inspect.getdoc is supposed to do exactly this. However, it doesn't seem to @@ -95,7 +95,7 @@ def find_method_docstring(klass, method: str) -> str | None: return doc -def find_method_signature(klass, method: str) -> inspect.Signature | None: +def find_method_signature(klass, method: str) -> inspect.Signature: """Look through a class' ancestors and fill out the methods signature. A class method has a signature. But it might now always be complete. When a parameter is not From 241e6f26cbd3f08f8b33d9f09b38e9c1270770a3 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 27 Jun 2023 11:21:35 +0200 Subject: [PATCH 038/347] Update 0.18.0.md --- docs/releases/0.18.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/releases/0.18.0.md b/docs/releases/0.18.0.md index 65a1dd4b68..d73b3c3cf3 100644 --- a/docs/releases/0.18.0.md +++ b/docs/releases/0.18.0.md @@ -1,4 +1,4 @@ -# 0.17.0 - 2023-06-26 +# 0.18.0 - 2023-06-26 ## bandit From b1792bbad00d9fc54a487ef38a237aeccde43d5d Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 3 Jul 2023 12:28:41 +0200 Subject: [PATCH 039/347] Fix ARFClassifier issue with classification metrics (#1273) --- docs/releases/unreleased.md | 5 +++++ river/forest/adaptive_random_forest.py | 7 ++++++- river/forest/test_amf.py | 18 ++++++++++++++++++ 3 files changed, 29 insertions(+), 1 deletion(-) create mode 100644 docs/releases/unreleased.md create mode 100644 river/forest/test_amf.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md new file mode 100644 index 0000000000..bb14128433 --- /dev/null +++ b/docs/releases/unreleased.md @@ -0,0 +1,5 @@ +# Unreleased + +## forest + +- Fixed issue with `forest.ARFClassifier` which couldn't be passed a `CrossEntropy` metric. diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index 3c7def6f8a..975aa5dad7 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -72,7 +72,12 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs): for model in self: # Get prediction for instance - y_pred = model.predict_one(x) + y_pred = ( + model.predict_proba_one(x) + if isinstance(model.metric, metrics.base.ClassificationMetric) + and not model.metric.requires_labels + else model.predict_one(x) + ) # Update performance evaluator model.metric.update(y_true=y, y_pred=y_pred) diff --git a/river/forest/test_amf.py b/river/forest/test_amf.py new file mode 100644 index 0000000000..177009228c --- /dev/null +++ b/river/forest/test_amf.py @@ -0,0 +1,18 @@ +from __future__ import annotations + + +def test_issue_1272(): + """ + + https://github.com/online-ml/river/issues/1272 + + >>> import river + >>> from river import forest, metrics + + >>> model = forest.ARFClassifier(metric=metrics.CrossEntropy()) + >>> model = model.learn_one({"x": 1}, True) + + >>> model = forest.ARFClassifier() + >>> model = model.learn_one({"x": 1}, True) + + """ From d789a1573e5d3f581939785062cda4127fc8f258 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 3 Jul 2023 22:32:52 +0200 Subject: [PATCH 040/347] Make unsupervised parts stateful during learn_one (#1274) --- README.md | 2 +- docs/faq/index.md | 2 +- docs/releases/unreleased.md | 7 ++ river/active/entropy.py | 4 +- river/base/base.py | 2 +- river/compat/sklearn_to_river.py | 4 +- river/compat/test_sklearn.py | 4 +- river/compose/__init__.py | 4 +- river/compose/pipeline.py | 115 +++--------------- river/compose/target_transform.py | 2 +- river/compose/test_.py | 20 +-- river/conf/jackknife.py | 4 +- river/ensemble/bagging.py | 8 +- river/ensemble/boosting.py | 2 +- river/ensemble/ewa.py | 8 +- river/ensemble/voting.py | 2 +- river/evaluate/progressive_validation.py | 32 ++--- river/forest/adaptive_random_forest.py | 2 +- river/imblearn/chebyshev.py | 8 +- river/imblearn/hard_sampling.py | 16 +-- river/imblearn/random.py | 6 +- river/linear_model/alma.py | 2 +- river/linear_model/lin_reg.py | 2 +- river/model_selection/bandit.py | 4 +- river/model_selection/greedy.py | 2 +- river/model_selection/sh.py | 12 +- river/multiclass/occ.py | 2 +- river/multioutput/chain.py | 6 +- river/neighbors/knn_classifier.py | 2 +- river/neural_net/mlp.py | 20 +-- river/optim/ada_bound.py | 2 +- river/optim/ada_max.py | 2 +- river/optim/adam.py | 2 +- river/optim/ams_grad.py | 2 +- river/optim/average.py | 2 +- river/optim/ftrl.py | 2 +- river/optim/momentum.py | 2 +- river/optim/nesterov.py | 2 +- river/preprocessing/random_projection.py | 4 +- river/preprocessing/scale_target.py | 6 +- river/rules/amrules.py | 2 +- river/time_series/snarimax.py | 48 ++++---- .../tree/hoeffding_adaptive_tree_regressor.py | 3 +- river/tree/hoeffding_tree_regressor.py | 3 +- 44 files changed, 158 insertions(+), 230 deletions(-) diff --git a/README.md b/README.md index 80e94208a3..87e780453c 100644 --- a/README.md +++ b/README.md @@ -95,7 +95,7 @@ Now let's run the model on the dataset in a streaming fashion. We sequentially i ... model = model.learn_one(x, y) # make the model learn >>> metric -Accuracy: 89.20% +Accuracy: 89.28% ``` diff --git a/docs/faq/index.md b/docs/faq/index.md index 9c6a08f2b9..7218ed5149 100644 --- a/docs/faq/index.md +++ b/docs/faq/index.md @@ -29,7 +29,7 @@ Python encourages a coding style called [EAFP](https://docs.python.org/2/glossar Reinforcement learning works in an online manner because of the nature of the task. Reinforcement learning can be therefore be seen as a subcase of online machine learning. However, we prefer not to support it because there are already many existing opensource libraries dedicated to it. -## What are the differences between scikit-learn's online learning algorithm which have a partial_fit method and their equivalents in river? +## What are the differences between scikit-learn's online learning algorithm which have a partial_fit method and their equivalents in River? The algorithms from `sklearn` that support incremental learning are mostly meant for mini-batch learning. In a pure streaming context where the observations arrive one by one, then River is much faster than `sklearn`. This is mostly because `sklearn` incurs a lot of overhead by performing data checks. Also, sklearn assumes that you're always using the same number of features. This is not the case with River because it use dictionaries which allows you to drop and add features as you wish. diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index bb14128433..8853747d26 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,5 +1,12 @@ # Unreleased +Calling `learn_one` in a pipeline will now update each part of the pipeline in turn. Before the unsupervised parts of the pipeline were updated during `predict_one`. This is more intuitive for new users. The old behavior, which yields better results, can be restored by calling `learn_one` with the new `compose.pure_inference_mode` context manager. + +## compose + +- Removed the `compose.warm_up_mode` context manager. +- Removed the `compose.pure_inference_mode` context manager. + ## forest - Fixed issue with `forest.ARFClassifier` which couldn't be passed a `CrossEntropy` metric. diff --git a/river/active/entropy.py b/river/active/entropy.py index 214edb16aa..e40663472a 100644 --- a/river/active/entropy.py +++ b/river/active/entropy.py @@ -56,10 +56,10 @@ class EntropySampler(ActiveLearningClassifier): Accuracy: 86.60% >>> dataset.n_samples, n_samples_used - (5574, 1922) + (5574, 1921) >>> print(f"{n_samples_used / dataset.n_samples:.2%}") - 34.48% + 34.46% """ diff --git a/river/base/base.py b/river/base/base.py index e5607b725a..af760f58c2 100644 --- a/river/base/base.py +++ b/river/base/base.py @@ -456,9 +456,9 @@ def log_method_calls( ... model = model.learn_one(x) >>> print(logs.getvalue()) - MinMaxScaler.learn_one MinMaxScaler.transform_one HalfSpaceTrees.score_one + MinMaxScaler.learn_one MinMaxScaler.transform_one HalfSpaceTrees.learn_one diff --git a/river/compat/sklearn_to_river.py b/river/compat/sklearn_to_river.py index db70f6f72e..61506012b2 100644 --- a/river/compat/sklearn_to_river.py +++ b/river/compat/sklearn_to_river.py @@ -98,7 +98,7 @@ class SKL2RiverRegressor(SKL2RiverBase, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 84.519485 + MAE: 84.501421 """ @@ -162,7 +162,7 @@ class SKL2RiverClassifier(SKL2RiverBase, base.Classifier): >>> metric = metrics.LogLoss() >>> evaluate.progressive_val_score(dataset, model, metric) - LogLoss: 0.199554 + LogLoss: 0.198029 """ diff --git a/river/compat/test_sklearn.py b/river/compat/test_sklearn.py index 50f8de4c48..dd2a4256e4 100644 --- a/river/compat/test_sklearn.py +++ b/river/compat/test_sklearn.py @@ -63,12 +63,12 @@ def test_not_fitted_still_works_regression(estimator): for n_classes in [2, 3] for estimator in [ compat.convert_sklearn_to_river( - sk_linear_model.SGDClassifier(loss="log"), classes=list(range(n_classes)) + sk_linear_model.SGDClassifier(loss="log_loss"), classes=list(range(n_classes)) ), ( preprocessing.StandardScaler() | compat.convert_sklearn_to_river( - sk_linear_model.SGDClassifier(loss="log"), classes=list(range(n_classes)) + sk_linear_model.SGDClassifier(loss="log_loss"), classes=list(range(n_classes)) ) ), ] diff --git a/river/compose/__init__.py b/river/compose/__init__.py index fc44117d0c..2c2b6b5c93 100644 --- a/river/compose/__init__.py +++ b/river/compose/__init__.py @@ -8,7 +8,7 @@ from .func import FuncTransformer from .grouper import Grouper -from .pipeline import Pipeline, pure_inference_mode, warm_up_mode +from .pipeline import Pipeline, learn_during_predict from .product import TransformerProduct from .renamer import Prefixer, Renamer, Suffixer from .select import Discard, Select, SelectType @@ -29,5 +29,5 @@ "TargetTransformRegressor", "TransformerProduct", "TransformerUnion", - "warm_up_mode", + "learn_during_predict", ] diff --git a/river/compose/pipeline.py b/river/compose/pipeline.py index ebfd69f3e3..cf4def0080 100644 --- a/river/compose/pipeline.py +++ b/river/compose/pipeline.py @@ -18,84 +18,7 @@ @contextlib.contextmanager -def warm_up_mode(): - """A context manager for training pipelines during a warm-up phase. - - You don't have to worry about anything when you call `predict_one` and `learn_one` with a - pipeline during in training loop. At each step of the pipeline, the methods will be called in - the correct order. - - However, during a warm-up phase, you might just be calling `learn_one` because you don't need - the out-of-sample predictions. In this case, the unsupervised estimators in the pipeline won't - be updated, because they are usually updated when `predict_one` is called. - - This context manager allows you to override that behavior and make it so that unsupervised - estimators are updated when `learn_one` is called. - - Examples - -------- - - Let's first see what methods are called if we just call `learn_one`. - - >>> import io - >>> import logging - >>> from river import anomaly - >>> from river import compose - >>> from river import datasets - >>> from river import preprocessing - >>> from river import utils - - >>> model = compose.Pipeline( - ... preprocessing.MinMaxScaler(), - ... anomaly.HalfSpaceTrees() - ... ) - - >>> class_condition = lambda x: x.__class__.__name__ in ('MinMaxScaler', 'HalfSpaceTrees') - - >>> logger = logging.getLogger() - >>> logger.setLevel(logging.DEBUG) - - >>> logs = io.StringIO() - >>> sh = logging.StreamHandler(logs) - >>> sh.setLevel(logging.DEBUG) - >>> logger.addHandler(sh) - - >>> with utils.log_method_calls(class_condition): - ... for x, y in datasets.CreditCard().take(1): - ... model = model.learn_one(x) - - >>> print(logs.getvalue()) - MinMaxScaler.transform_one - HalfSpaceTrees.learn_one - - Now let's use the context manager and see what methods get called. - - >>> logs = io.StringIO() - >>> sh = logging.StreamHandler(logs) - >>> sh.setLevel(logging.DEBUG) - >>> logger.addHandler(sh) - - >>> with utils.log_method_calls(class_condition), compose.warm_up_mode(): - ... for x, y in datasets.CreditCard().take(1): - ... model = model.learn_one(x) - - >>> print(logs.getvalue()) - MinMaxScaler.learn_one - MinMaxScaler.transform_one - HalfSpaceTrees.learn_one - - We can see that the scaler got updated before transforming the data. - - """ - Pipeline._WARM_UP = True - try: - yield - finally: - Pipeline._WARM_UP = False - - -@contextlib.contextmanager -def pure_inference_mode(): +def learn_during_predict(): """A context manager for making inferences with no side-effects. Calling `predict_one` with a pipeline will update the unsupervised steps of the pipeline. This @@ -138,7 +61,6 @@ def pure_inference_mode(): ... _ = model.predict_one(x) >>> print(logs.getvalue()) - StandardScaler.learn_one StandardScaler.transform_one LinearRegression.predict_one @@ -149,11 +71,12 @@ def pure_inference_mode(): >>> sh.setLevel(logging.DEBUG) >>> logger.addHandler(sh) - >>> with utils.log_method_calls(class_condition), compose.pure_inference_mode(): + >>> with utils.log_method_calls(class_condition), compose.learn_during_predict(): ... for x, y in datasets.TrumpApproval().take(1): ... _ = model.predict_one(x) >>> print(logs.getvalue()) + StandardScaler.learn_one StandardScaler.transform_one LinearRegression.predict_one @@ -170,7 +93,6 @@ def pure_inference_mode(): ... for x, y in datasets.TrumpApproval().take(1): ... _ = model.predict_many(pd.DataFrame([x])) >>> print(logs.getvalue()) - StandardScaler.learn_many StandardScaler.transform_many LinearRegression.predict_many @@ -179,19 +101,20 @@ def pure_inference_mode(): >>> sh.setLevel(logging.DEBUG) >>> logger.addHandler(sh) - >>> with utils.log_method_calls(class_condition), compose.pure_inference_mode(): + >>> with utils.log_method_calls(class_condition), compose.learn_during_predict(): ... for x, y in datasets.TrumpApproval().take(1): ... _ = model.predict_many(pd.DataFrame([x])) >>> print(logs.getvalue()) + StandardScaler.learn_many StandardScaler.transform_many LinearRegression.predict_many """ - Pipeline._STATELESS = True + Pipeline._LEARN_UNSUPERVISED_DURING_PREDICT = True try: yield finally: - Pipeline._STATELESS = False + Pipeline._LEARN_UNSUPERVISED_DURING_PREDICT = False class Pipeline(base.Estimator): @@ -331,25 +254,24 @@ class Pipeline(base.Estimator): -------------------- 1.0 TFIDF | Prefixer -------------------- - tfidf_comment: 0.47606 (float) - tfidf_positive: 0.87942 (float) + tfidf_comment: 0.43017 (float) + tfidf_positive: 0.90275 (float) 1.1 BagOfWords | Prefixer ------------------------- count_comment: 1 (int) count_positive: 1 (int) count_comment: 1 (int) count_positive: 1 (int) - tfidf_comment: 0.50854 (float) - tfidf_positive: 0.86104 (float) + tfidf_comment: 0.43017 (float) + tfidf_positive: 0.90275 (float) 2. MultinomialNB ---------------- - False: 0.19313 - True: 0.80687 + False: 0.19221 + True: 0.80779 """ - _WARM_UP = False - _STATELESS = False + _LEARN_UNSUPERVISED_DURING_PREDICT = False def __init__(self, *steps): self.steps = collections.OrderedDict() @@ -524,7 +446,7 @@ def learn_one(self, x: dict, y=None, **params): break continue - if self._WARM_UP: + if not self._LEARN_UNSUPERVISED_DURING_PREDICT: if isinstance(t, union.TransformerUnion): for sub_t in t.transformers.values(): if not sub_t._supervised: @@ -568,12 +490,11 @@ def _transform_one(self, x: dict): if utils.inspect.ischildobject(obj=t, class_name="AnomalyFilter"): continue - if not self._STATELESS: + if self._LEARN_UNSUPERVISED_DURING_PREDICT: if isinstance(t, union.TransformerUnion): for sub_t in t.transformers.values(): if not sub_t._supervised: sub_t.learn_one(x) - elif not t._supervised: t.learn_one(x) @@ -752,7 +673,7 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series | None = None, **params): # Loop over the first n - 1 steps, which should all be transformers for t in itertools.islice(steps, len(self) - 1): - if self._WARM_UP: + if not self._LEARN_UNSUPERVISED_DURING_PREDICT: if isinstance(t, union.TransformerUnion): for sub_t in t.transformers.values(): if not sub_t._supervised: @@ -791,7 +712,7 @@ def _transform_many(self, X: pd.DataFrame): steps = iter(self.steps.values()) for t in itertools.islice(steps, len(self) - 1): - if not self._STATELESS: + if self._LEARN_UNSUPERVISED_DURING_PREDICT: if isinstance(t, union.TransformerUnion): for sub_t in t.transformers.values(): if not sub_t._supervised: diff --git a/river/compose/target_transform.py b/river/compose/target_transform.py index 6e665701bc..4d1804ac25 100644 --- a/river/compose/target_transform.py +++ b/river/compose/target_transform.py @@ -42,7 +42,7 @@ class TargetTransformRegressor(base.Wrapper, base.Regressor): >>> metric = metrics.MSE() >>> evaluate.progressive_val_score(dataset, model, metric) - MSE: 8.759624 + MSE: 10.999752 """ diff --git a/river/compose/test_.py b/river/compose/test_.py index 694f7dcf9c..5453121186 100644 --- a/river/compose/test_.py +++ b/river/compose/test_.py @@ -55,7 +55,7 @@ def b(x): assert str(pipeline) == "a + b" -def test_learn_one_warm_up_mode(): +def test_learn_one_with_learn_during_predict(): pipeline = compose.Pipeline( ("scale", preprocessing.StandardScaler()), ("log_reg", linear_model.LogisticRegression()), @@ -65,17 +65,17 @@ def test_learn_one_warm_up_mode(): for x, y in dataset: counts_pre = dict(pipeline["scale"].counts) - with compose.warm_up_mode(): + with compose.learn_during_predict(): pipeline.learn_one(x, y) - counts_post = dict(pipeline["scale"].counts) - pipeline.learn_one(x, y) counts_no_learn = dict(pipeline["scale"].counts) + pipeline.learn_one(x, y) + counts_post = dict(pipeline["scale"].counts) assert counts_pre != counts_post - assert counts_post == counts_no_learn + assert counts_pre == counts_no_learn -def test_learn_many_warm_up_mode(): +def test_learn_many_with_learn_during_predict(): pipeline = compose.Pipeline( ("scale", preprocessing.StandardScaler()), ("log_reg", linear_model.LogisticRegression()), @@ -88,14 +88,14 @@ def test_learn_many_warm_up_mode(): y = pd.Series([bool(y % 2) for _, y in dataset][i : i + 5]) counts_pre = dict(pipeline["scale"].counts) - with compose.warm_up_mode(): + with compose.learn_during_predict(): pipeline.learn_many(X, y) - counts_post = dict(pipeline["scale"].counts) - pipeline.learn_many(X, y) counts_no_learn = dict(pipeline["scale"].counts) + pipeline.learn_many(X, y) + counts_post = dict(pipeline["scale"].counts) assert counts_pre != counts_post - assert counts_post == counts_no_learn + assert counts_pre == counts_no_learn def test_list_of_funcs(): diff --git a/river/conf/jackknife.py b/river/conf/jackknife.py index 2e2056ad34..7f64b5e2eb 100644 --- a/river/conf/jackknife.py +++ b/river/conf/jackknife.py @@ -64,12 +64,12 @@ class RegressionJackknife(base.Wrapper, base.Regressor): specified a confidence level of 90%, so we expect the validity to be around 90%. >>> validity - Mean: 0.903097 + Mean: 0.939061 The interval's efficiency is the average width of the intervals. >>> efficiency - Mean: 3.593173 + Mean: 4.078361 Lowering the confidence lowering will mechanically improve the efficiency. diff --git a/river/ensemble/bagging.py b/river/ensemble/bagging.py index 160171ad9c..9b11e0b43e 100644 --- a/river/ensemble/bagging.py +++ b/river/ensemble/bagging.py @@ -68,7 +68,7 @@ class BaggingClassifier(BaseBagging, base.Classifier): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.83% + F1: 87.65% >>> print(model) BaggingClassifier(StandardScaler | LogisticRegression) @@ -143,7 +143,7 @@ class BaggingRegressor(BaseBagging, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.68886 + MAE: 0.677586 References ---------- @@ -204,7 +204,7 @@ class ADWINBaggingClassifier(BaggingClassifier): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.83% + F1: 87.65% References ---------- @@ -310,7 +310,7 @@ class LeveragingBaggingClassifier(BaggingClassifier): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 88.73% + F1: 88.55% """ diff --git a/river/ensemble/boosting.py b/river/ensemble/boosting.py index 4712bd8284..40a13f8153 100644 --- a/river/ensemble/boosting.py +++ b/river/ensemble/boosting.py @@ -151,7 +151,7 @@ class ADWINBoostingClassifier(AdaBoostClassifier): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.41% + F1: 87.61% References ---------- diff --git a/river/ensemble/ewa.py b/river/ensemble/ewa.py index a4e3a282e5..7a15a95738 100644 --- a/river/ensemble/ewa.py +++ b/river/ensemble/ewa.py @@ -52,9 +52,9 @@ class EWARegressor(base.Ensemble, base.Regressor): ... ) ... ... print(optimizer, evaluate.progressive_val_score(dataset, model, metric)) - SGD MAE: 0.555971 - RMSProp MAE: 0.528284 - AdaGrad MAE: 0.481461 + SGD MAE: 0.558735 + RMSProp MAE: 0.522449 + AdaGrad MAE: 0.477289 >>> dataset = datasets.TrumpApproval() >>> metric = metrics.MAE() @@ -70,7 +70,7 @@ class EWARegressor(base.Ensemble, base.Regressor): ... ) >>> evaluate.progressive_val_score(dataset, hedge, metric) - MAE: 0.494832 + MAE: 0.496298 References ---------- diff --git a/river/ensemble/voting.py b/river/ensemble/voting.py index ffe3e5b108..1ee81730ec 100644 --- a/river/ensemble/voting.py +++ b/river/ensemble/voting.py @@ -47,7 +47,7 @@ class VotingClassifier(base.Classifier, base.Ensemble): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.14% + F1: 86.94% """ diff --git a/river/evaluate/progressive_validation.py b/river/evaluate/progressive_validation.py index bf70069349..9aa1714276 100644 --- a/river/evaluate/progressive_validation.py +++ b/river/evaluate/progressive_validation.py @@ -172,13 +172,13 @@ def iter_progressive_val_score( >>> for step in steps: ... print(step) - {'ROCAUC': ROCAUC: 89.80%, 'Step': 200} - {'ROCAUC': ROCAUC: 92.09%, 'Step': 400} - {'ROCAUC': ROCAUC: 93.13%, 'Step': 600} - {'ROCAUC': ROCAUC: 93.99%, 'Step': 800} - {'ROCAUC': ROCAUC: 94.74%, 'Step': 1000} - {'ROCAUC': ROCAUC: 95.03%, 'Step': 1200} - {'ROCAUC': ROCAUC: 95.04%, 'Step': 1250} + {'ROCAUC': ROCAUC: 90.20%, 'Step': 200} + {'ROCAUC': ROCAUC: 92.25%, 'Step': 400} + {'ROCAUC': ROCAUC: 93.23%, 'Step': 600} + {'ROCAUC': ROCAUC: 94.05%, 'Step': 800} + {'ROCAUC': ROCAUC: 94.79%, 'Step': 1000} + {'ROCAUC': ROCAUC: 95.07%, 'Step': 1200} + {'ROCAUC': ROCAUC: 95.07%, 'Step': 1250} References ---------- @@ -287,14 +287,14 @@ def progressive_val_score( ... metric=metrics.ROCAUC(), ... print_every=200 ... ) - [200] ROCAUC: 89.80% - [400] ROCAUC: 92.09% - [600] ROCAUC: 93.13% - [800] ROCAUC: 93.99% - [1,000] ROCAUC: 94.74% - [1,200] ROCAUC: 95.03% - [1,250] ROCAUC: 95.04% - ROCAUC: 95.04% + [200] ROCAUC: 90.20% + [400] ROCAUC: 92.25% + [600] ROCAUC: 93.23% + [800] ROCAUC: 94.05% + [1,000] ROCAUC: 94.79% + [1,200] ROCAUC: 95.07% + [1,250] ROCAUC: 95.07% + ROCAUC: 95.07% We haven't specified a delay, therefore this is strictly equivalent to the following piece of code: @@ -312,7 +312,7 @@ def progressive_val_score( ... model = model.learn_one(x, y) >>> metric - ROCAUC: 95.04% + ROCAUC: 95.07% When `print_every` is specified, the current state is printed at regular intervals. Under the hood, Python's `print` method is being used. You can pass extra keyword arguments to diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index 975aa5dad7..413ba539e4 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -742,7 +742,7 @@ class ARFRegressor(BaseForest, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.800649 + MAE: 0.800378 """ diff --git a/river/imblearn/chebyshev.py b/river/imblearn/chebyshev.py index cb7e370a9c..4db150341d 100644 --- a/river/imblearn/chebyshev.py +++ b/river/imblearn/chebyshev.py @@ -164,10 +164,10 @@ class ChebyshevOverSampler(base.Wrapper, base.Regressor): ... metrics.MAE(), ... print_every=500 ... ) - [500] MAE: 1.682627 - [1,000] MAE: 1.761306 - [1,001] MAE: 1.759576 - MAE: 1.759576 + [500] MAE: 1.673902 + [1,000] MAE: 1.743046 + [1,001] MAE: 1.741335 + MAE: 1.741335 References ---------- diff --git a/river/imblearn/hard_sampling.py b/river/imblearn/hard_sampling.py index 994e9d014a..ed1036cde6 100644 --- a/river/imblearn/hard_sampling.py +++ b/river/imblearn/hard_sampling.py @@ -122,10 +122,10 @@ class HardSamplingRegressor(HardSampling, base.Regressor): ... metrics.MAE(), ... print_every=500 ... ) - [500] MAE: 2.292501 - [1,000] MAE: 1.395797 - [1,001] MAE: 1.394693 - MAE: 1.394693 + [500] MAE: 2.274021 + [1,000] MAE: 1.392399 + [1,001] MAE: 1.391246 + MAE: 1.391246 """ @@ -203,10 +203,10 @@ class HardSamplingClassifier(HardSampling, base.Classifier): ... metric=metrics.ROCAUC(), ... print_every=500, ... ) - [500] ROCAUC: 92.71% - [1,000] ROCAUC: 94.75% - [1,250] ROCAUC: 95.05% - ROCAUC: 95.05% + [500] ROCAUC: 92.78% + [1,000] ROCAUC: 94.76% + [1,250] ROCAUC: 95.06% + ROCAUC: 95.06% """ diff --git a/river/imblearn/random.py b/river/imblearn/random.py index 469d3d1465..dd4ddec930 100644 --- a/river/imblearn/random.py +++ b/river/imblearn/random.py @@ -66,7 +66,7 @@ class percentages. The values must sum up to 1. >>> metric = metrics.LogLoss() >>> evaluate.progressive_val_score(dataset, model, metric) - LogLoss: 0.0728 + LogLoss: 0.0336... References ---------- @@ -146,7 +146,7 @@ class percentages. The values must sum up to 1. >>> metric = metrics.LogLoss() >>> evaluate.progressive_val_score(dataset, model, metric) - LogLoss: 0.054338 + LogLoss: 0.0457... """ @@ -224,7 +224,7 @@ class percentages. The values must sum up to 1. If set to `None`, then the obser >>> metric = metrics.LogLoss() >>> evaluate.progressive_val_score(dataset, model, metric) - LogLoss: 0.131988 + LogLoss: 0.09... """ diff --git a/river/linear_model/alma.py b/river/linear_model/alma.py index 260547cd9a..c46e5caf73 100644 --- a/river/linear_model/alma.py +++ b/river/linear_model/alma.py @@ -44,7 +44,7 @@ class ALMAClassifier(base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 82.64% + Accuracy: 82.56% References ---------- diff --git a/river/linear_model/lin_reg.py b/river/linear_model/lin_reg.py index c152c7694c..8c33cb7079 100644 --- a/river/linear_model/lin_reg.py +++ b/river/linear_model/lin_reg.py @@ -62,7 +62,7 @@ class LinearRegression(linear_model.base.GLM, base.MiniBatchRegressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.555971 + MAE: 0.558735 >>> model['LinearRegression'].intercept 35.617670 diff --git a/river/model_selection/bandit.py b/river/model_selection/bandit.py index 2105421169..89ccaed63d 100644 --- a/river/model_selection/bandit.py +++ b/river/model_selection/bandit.py @@ -88,7 +88,7 @@ class BanditRegressor(BanditModelSection, model_selection.base.ModelSelectionReg >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 3.13815 + MAE: 3.134089 Here's another example using the UCB policy. The latter is more sensitive to the target scale, and usually works better when the target is rescaled. @@ -114,7 +114,7 @@ class BanditRegressor(BanditModelSection, model_selection.base.ModelSelectionReg >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.875457 + MAE: 0.875333 """ diff --git a/river/model_selection/greedy.py b/river/model_selection/greedy.py index 8f2deb873e..ae006b0859 100644 --- a/river/model_selection/greedy.py +++ b/river/model_selection/greedy.py @@ -45,7 +45,7 @@ class GreedyRegressor(ModelSelectionRegressor): ... ) >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 1.35 + MAE: 1.319678 """ diff --git a/river/model_selection/sh.py b/river/model_selection/sh.py index c63a73f80e..5ef28d99c2 100644 --- a/river/model_selection/sh.py +++ b/river/model_selection/sh.py @@ -198,11 +198,11 @@ class SuccessiveHalvingRegressor(SuccessiveHalving, ModelSelectionRegressor): ... model=sh, ... metric=metrics.MAE() ... ) - [1] 5 removed 5 left 50 iterations budget used: 500 budget left: 1500 best MAE: 4.540491 - [2] 2 removed 3 left 100 iterations budget used: 1000 budget left: 1000 best MAE: 2.458765 - [3] 1 removed 2 left 166 iterations budget used: 1498 budget left: 502 best MAE: 1.583751 - [4] 1 removed 1 left 250 iterations budget used: 1998 budget left: 2 best MAE: 1.147296 - MAE: 0.488387 + [1] 5 removed 5 left 50 iterations budget used: 500 budget left: 1500 best MAE: 4.419643 + [2] 2 removed 3 left 100 iterations budget used: 1000 budget left: 1000 best MAE: 2.392266 + [3] 1 removed 2 left 166 iterations budget used: 1498 budget left: 502 best MAE: 1.541383 + [4] 1 removed 1 left 250 iterations budget used: 1998 budget left: 2 best MAE: 1.112122 + MAE: 0.490688 We can now view the best model. @@ -359,7 +359,7 @@ class SuccessiveHalvingClassifier(SuccessiveHalving, ModelSelectionClassifier): [2] 2 removed 3 left 100 iterations budget used: 1000 budget left: 1000 best Accuracy: 84.00% [3] 1 removed 2 left 166 iterations budget used: 1498 budget left: 502 best Accuracy: 86.14% [4] 1 removed 1 left 250 iterations budget used: 1998 budget left: 2 best Accuracy: 84.80% - ROCAUC: 95.29% + ROCAUC: 95.22% We can now view the best model. diff --git a/river/multiclass/occ.py b/river/multiclass/occ.py index f17bce7179..e3ada07d56 100644 --- a/river/multiclass/occ.py +++ b/river/multiclass/occ.py @@ -74,7 +74,7 @@ class is converted to a code of 0s and 1s. The length of the code is called the >>> metric = metrics.MacroF1() >>> evaluate.progressive_val_score(dataset, model, metric) - MacroF1: 79.32% + MacroF1: 79.58% References ---------- diff --git a/river/multioutput/chain.py b/river/multioutput/chain.py index 0f8596c91a..13b381818f 100644 --- a/river/multioutput/chain.py +++ b/river/multioutput/chain.py @@ -84,7 +84,7 @@ class ClassifierChain(BaseChain, base.MultiLabelClassifier): ... model = model.learn_one(x, y) >>> metric - MicroAverage(Jaccard): 41.95% + MicroAverage(Jaccard): 41.81% References ---------- @@ -105,7 +105,7 @@ def _unit_test_params(cls): "model": neighbors.KNNClassifier( n_neighbors=1, engine=neighbors.LazySearch( - window_size=50, dist_func=functools.partial(minkowski_distance, p=2) + window_size=10, dist_func=functools.partial(minkowski_distance, p=2) ), ) } # multi-class classifier @@ -212,7 +212,7 @@ class RegressorChain(BaseChain, base.MultiTargetRegressor): >>> metric = metrics.multioutput.MicroAverage(metrics.MAE()) >>> evaluate.progressive_val_score(dataset, model, metric) - MicroAverage(MAE): 12.649592 + MicroAverage(MAE): 12.733525 """ diff --git a/river/neighbors/knn_classifier.py b/river/neighbors/knn_classifier.py index bc4d190373..eb3f9931a2 100644 --- a/river/neighbors/knn_classifier.py +++ b/river/neighbors/knn_classifier.py @@ -68,7 +68,7 @@ class KNNClassifier(base.Classifier): ... ) >>> evaluate.progressive_val_score(dataset, model, metrics.Accuracy()) - Accuracy: 89.75% + Accuracy: 89.67% """ diff --git a/river/neural_net/mlp.py b/river/neural_net/mlp.py index 2a54b5f995..9bdb6d7d6a 100644 --- a/river/neural_net/mlp.py +++ b/river/neural_net/mlp.py @@ -236,7 +236,7 @@ class MLPRegressor(base.Regressor, MLP): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 1.589827 + MAE: 1.580578 You can also use this to process mini-batches of data. @@ -265,15 +265,15 @@ class MLPRegressor(base.Regressor, MLP): >>> model.predict_many(xb) five_thirty_eight - 992 39.361609 - 993 46.398536 - 994 42.094086 - 995 40.195802 - 996 40.782954 - 997 40.839678 - 998 40.896403 - 999 48.362659 - 1000 42.021849 + 992 39.405231 + 993 46.447481 + 994 42.121865 + 995 40.251148 + 996 40.836378 + 997 40.893153 + 998 40.949927 + 999 48.416504 + 1000 42.077830 """ diff --git a/river/optim/ada_bound.py b/river/optim/ada_bound.py index 707c1c86a7..66ecec4b33 100644 --- a/river/optim/ada_bound.py +++ b/river/optim/ada_bound.py @@ -45,7 +45,7 @@ class AdaBound(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.90% + F1: 88.06% References ---------- diff --git a/river/optim/ada_max.py b/river/optim/ada_max.py index 921fe65ed1..95b03c6ebd 100644 --- a/river/optim/ada_max.py +++ b/river/optim/ada_max.py @@ -41,7 +41,7 @@ class AdaMax(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.53% + F1: 87.61% References ---------- diff --git a/river/optim/adam.py b/river/optim/adam.py index e581027aed..f78afe05ec 100644 --- a/river/optim/adam.py +++ b/river/optim/adam.py @@ -43,7 +43,7 @@ class Adam(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 86.50% + F1: 86.52% References ---------- diff --git a/river/optim/ams_grad.py b/river/optim/ams_grad.py index 9b1524290a..4d800c9cff 100644 --- a/river/optim/ams_grad.py +++ b/river/optim/ams_grad.py @@ -44,7 +44,7 @@ class AMSGrad(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 86.57% + F1: 86.60% References ---------- diff --git a/river/optim/average.py b/river/optim/average.py index c688cdb01f..0bd516808b 100644 --- a/river/optim/average.py +++ b/river/optim/average.py @@ -41,7 +41,7 @@ class Averager(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.89% + F1: 87.97% References ---------- diff --git a/river/optim/ftrl.py b/river/optim/ftrl.py index 5915c868cb..0583c02d12 100644 --- a/river/optim/ftrl.py +++ b/river/optim/ftrl.py @@ -43,7 +43,7 @@ class FTRLProximal(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.66% + F1: 87.56% References ---------- diff --git a/river/optim/momentum.py b/river/optim/momentum.py index b26f1c46a6..86f90a4e8d 100644 --- a/river/optim/momentum.py +++ b/river/optim/momentum.py @@ -34,7 +34,7 @@ class Momentum(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 84.16% + F1: 84.09% """ diff --git a/river/optim/nesterov.py b/river/optim/nesterov.py index ec4e39f0cd..0b930564b1 100644 --- a/river/optim/nesterov.py +++ b/river/optim/nesterov.py @@ -34,7 +34,7 @@ class NesterovMomentum(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 84.29% + F1: 84.22% """ diff --git a/river/preprocessing/random_projection.py b/river/preprocessing/random_projection.py index 8db4647045..13e2152f04 100644 --- a/river/preprocessing/random_projection.py +++ b/river/preprocessing/random_projection.py @@ -50,7 +50,7 @@ class GaussianRandomProjector(base.Transformer): ... linear_model.LinearRegression() ... ) >>> evaluate.progressive_val_score(dataset, model, metrics.MAE()) - MAE: 0.860464 + MAE: 0.933502 References ---------- @@ -126,7 +126,7 @@ class SparseRandomProjector(base.Transformer): ... linear_model.LinearRegression() ... ) >>> evaluate.progressive_val_score(dataset, model, metrics.MAE()) - MAE: 1.296503 + MAE: 1.292572 References ---------- diff --git a/river/preprocessing/scale_target.py b/river/preprocessing/scale_target.py index 03da7452c4..a558d4d07e 100644 --- a/river/preprocessing/scale_target.py +++ b/river/preprocessing/scale_target.py @@ -47,9 +47,7 @@ class TargetStandardScaler(compose.TargetTransformRegressor): >>> metric = metrics.MSE() >>> evaluate.progressive_val_score(dataset, model, metric) - MSE: 2.003724 - - + MSE: 2.005999 """ @@ -94,7 +92,7 @@ class TargetMinMaxScaler(compose.TargetTransformRegressor): >>> metric = metrics.MSE() >>> evaluate.progressive_val_score(dataset, model, metric) - MSE: 2.01689 + MSE: 2.018905 """ diff --git a/river/rules/amrules.py b/river/rules/amrules.py index fba7c1df01..3ec132f3e5 100644 --- a/river/rules/amrules.py +++ b/river/rules/amrules.py @@ -259,7 +259,7 @@ class AMRules(base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 1.117705 + MAE: 1.119553 References ---------- diff --git a/river/time_series/snarimax.py b/river/time_series/snarimax.py index e32a28f9c9..e543737a11 100644 --- a/river/time_series/snarimax.py +++ b/river/time_series/snarimax.py @@ -186,18 +186,18 @@ class SNARIMAX(time_series.base.Forecaster): >>> forecast = model.forecast(horizon=horizon) >>> for x, y_pred in zip(future, forecast): ... print(x['month'], f'{y_pred:.3f}') - 1961-01-01 491.988 - 1961-02-01 447.593 - 1961-03-01 481.405 - 1961-04-01 566.278 - 1961-05-01 551.561 - 1961-06-01 605.414 - 1961-07-01 711.140 - 1961-08-01 668.204 - 1961-09-01 570.517 - 1961-10-01 549.589 - 1961-11-01 466.344 - 1961-12-01 506.945 + 1961-01-01 494.542 + 1961-02-01 450.825 + 1961-03-01 484.972 + 1961-04-01 576.401 + 1961-05-01 559.489 + 1961-06-01 612.251 + 1961-07-01 722.410 + 1961-08-01 674.604 + 1961-09-01 575.716 + 1961-10-01 562.808 + 1961-11-01 477.049 + 1961-12-01 515.191 Classic ARIMA models learn solely on the time series values. You can also include features built at each step. @@ -249,18 +249,18 @@ class SNARIMAX(time_series.base.Forecaster): >>> forecast = model.forecast(horizon=horizon) >>> for x, y_pred in zip(future, forecast): ... print(x['month'], f'{y_pred:.3f}') - 1961-01-01 446.874 - 1961-02-01 423.998 - 1961-03-01 439.957 - 1961-04-01 457.958 - 1961-05-01 457.303 - 1961-06-01 496.554 - 1961-07-01 553.798 - 1961-08-01 551.388 - 1961-09-01 479.620 - 1961-10-01 440.613 - 1961-11-01 409.914 - 1961-12-01 433.774 + 1961-01-01 444.821 + 1961-02-01 432.612 + 1961-03-01 457.739 + 1961-04-01 465.544 + 1961-05-01 476.575 + 1961-06-01 516.255 + 1961-07-01 565.405 + 1961-08-01 572.470 + 1961-09-01 512.645 + 1961-10-01 475.919 + 1961-11-01 438.033 + 1961-12-01 456.892 References ---------- diff --git a/river/tree/hoeffding_adaptive_tree_regressor.py b/river/tree/hoeffding_adaptive_tree_regressor.py index a885efcc0d..eeab272d0b 100644 --- a/river/tree/hoeffding_adaptive_tree_regressor.py +++ b/river/tree/hoeffding_adaptive_tree_regressor.py @@ -139,7 +139,8 @@ class HoeffdingAdaptiveTreeRegressor(HoeffdingTreeRegressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.809874 + MAE: 0.823026 + """ def __init__( diff --git a/river/tree/hoeffding_tree_regressor.py b/river/tree/hoeffding_tree_regressor.py index 903e191aa1..f5a873f7a0 100644 --- a/river/tree/hoeffding_tree_regressor.py +++ b/river/tree/hoeffding_tree_regressor.py @@ -98,7 +98,8 @@ class HoeffdingTreeRegressor(HoeffdingTree, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.781781 + MAE: 0.793345 + """ _TARGET_MEAN = "mean" From 237ece6b20734c6a2d5aec3ef2110896e4510f71 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 4 Jul 2023 14:41:37 +0700 Subject: [PATCH 041/347] [DBSTREAM clusterer] Use copy.deepcopy to copy selected micro-clusters to set of macro-clusters --- river/cluster/dbstream.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 1fb380be5f..18bef4e5d7 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -350,7 +350,7 @@ def _generate_clusters_from_labels(self, cluster_labels): # generate a final macro-cluster from clusters with the same label using the # merge function of DBStreamMicroCluster - macro_cluster = mcs_with_label_i[0] + macro_cluster = copy.deepcopy(mcs_with_label_i[0]) for m in range(1, len(mcs_with_label_i)): macro_cluster.merge(mcs_with_label_i[m]) From 7b245cce8cfe39190163176c81c415ccfdbf1973 Mon Sep 17 00:00:00 2001 From: Tafadzwa Demba Date: Tue, 4 Jul 2023 15:50:31 +0100 Subject: [PATCH 042/347] Update cloning-and-mutating.ipynb (#1276) --- docs/recipes/cloning-and-mutating.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/recipes/cloning-and-mutating.ipynb b/docs/recipes/cloning-and-mutating.ipynb index a366714493..96356bfcf0 100644 --- a/docs/recipes/cloning-and-mutating.ipynb +++ b/docs/recipes/cloning-and-mutating.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Sometimes you might want to reset a model, or edit (what we call mutate) its attributes. This can be useful in an online environment. Indeed, if you detect a drift, then you might want to mutate a model's attributes. Or if you see that a performance's model is plummeting, then you might to reset it to its \"factory settings\".\n", + "Sometimes you might want to reset a model, or edit (what we call mutate) its attributes. This can be useful in an online environment. Indeed, if you detect a drift, then you might want to mutate a model's attributes. Or if you see that a model's performance is plummeting, then you might to reset it to its \"factory settings\".\n", "\n", "Anyway, this is not to convince you, but rather to say that a model's attributes don't have be to set in stone throughout its lifetime. In particular, if you're developping your own model, then you might want to have good tools to do this. This is what this recipe is about." ] From add980bf02e30998326a3b0240bc54c88f34b563 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Wed, 5 Jul 2023 00:48:57 +0200 Subject: [PATCH 043/347] Handle case where last step of pipeline is unsupervised (#1277) --- docs/releases/unreleased.md | 1 + river/compose/pipeline.py | 76 ++++++++++++++++++----------------- river/compose/test_product.py | 12 +++--- river/preprocessing/lda.py | 6 +-- 4 files changed, 50 insertions(+), 45 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 8853747d26..258b9a2cf1 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -6,6 +6,7 @@ Calling `learn_one` in a pipeline will now update each part of the pipeline in t - Removed the `compose.warm_up_mode` context manager. - Removed the `compose.pure_inference_mode` context manager. +- The last step of a pipeline will be correctly updated if it is unsupervised, which wasn't the case before. ## forest diff --git a/river/compose/pipeline.py b/river/compose/pipeline.py index cf4def0080..89e4803150 100644 --- a/river/compose/pipeline.py +++ b/river/compose/pipeline.py @@ -426,53 +426,55 @@ def learn_one(self, x: dict, y=None, **params): """ - steps = iter(self.steps.values()) - is_anomaly = False - # Loop over the first n - 1 steps, which should all be transformers - for t in itertools.islice(steps, len(self) - 1): + for step in self.steps.values(): # There might be an anomaly filter in the pipeline. Its purpose is to prevent anomalous # data from being learned by the subsequent parts of the pipeline. - if utils.inspect.ischildobject(obj=t, class_name="AnomalyFilter"): - if t._supervised: - t.learn_one(x, y) - score = t.score_one(x, y) + if utils.inspect.ischildobject(obj=step, class_name="AnomalyFilter"): + if step._supervised: + step.learn_one(x, y) + score = step.score_one(x, y) else: - t.learn_one(x) - score = t.score_one(x) + step.learn_one(x) + score = step.score_one(x) # Skip the next parts of the pipeline if the score is classified as anomalous - if t.classify(score): - is_anomaly = True + if step.classify(score): break continue - if not self._LEARN_UNSUPERVISED_DURING_PREDICT: - if isinstance(t, union.TransformerUnion): - for sub_t in t.transformers.values(): - if not sub_t._supervised: - sub_t.learn_one(x) - elif not t._supervised: - t.learn_one(x) - x_pre = x - x = t.transform_one(x) + if isinstance(step, base.Transformer): + # In case of _LEARN_UNSUPERVISED_DURING_PREDICT, then the unsupervised transformers + # are updated before transforming. + if not self._LEARN_UNSUPERVISED_DURING_PREDICT: + if isinstance(step, union.TransformerUnion): + for sub_step in step.transformers.values(): + if not sub_step._supervised: + sub_step.learn_one(x) + elif not step._supervised: + step.learn_one(x) + # Transform the data + x = step.transform_one(x) # The supervised transformers have to be updated. # Note that this is done after transforming in order to avoid target leakage. - if isinstance(t, union.TransformerUnion): - for sub_t in t.transformers.values(): - if sub_t._supervised: - sub_t.learn_one(x_pre, y) - - elif t._supervised: - t.learn_one(x_pre, y) - - if not is_anomaly: - last_step = next(steps) - if last_step._supervised: - last_step.learn_one(x=x, y=y, **params) + if isinstance(step, base.Transformer): + if isinstance(step, union.TransformerUnion): + for sub_step in step.transformers.values(): + if sub_step._supervised: + sub_step.learn_one(x=x_pre, y=y) + # Here the step is a supervised transformer, such as a TargetAgg. It's important + # to pass the original features to the transformer, not the transformed ones. + elif step._supervised: + step.learn_one(x=x_pre, y=y) # type: ignore + # Here the step is not a transformer, and it's supervised, such as a LinearRegression. + # This is usually the last step of the pipeline. + elif step._supervised: + step.learn_one(x=x, y=y) + # Here the step is not a transformer, and it's unsupervised, such as a KMeans. This + # is also usually the last step of the pipeline. else: - last_step.learn_one(x, **params) + step.learn_one(x=x) return self @@ -490,6 +492,8 @@ def _transform_one(self, x: dict): if utils.inspect.ischildobject(obj=t, class_name="AnomalyFilter"): continue + # In case of _LEARN_UNSUPERVISED_DURING_PREDICT, then the unsupervised transformers + # are updated during the inference phase. if self._LEARN_UNSUPERVISED_DURING_PREDICT: if isinstance(t, union.TransformerUnion): for sub_t in t.transformers.values(): @@ -513,7 +517,7 @@ def transform_one(self, x: dict, **params): """ x, last_step = self._transform_one(x) if isinstance(last_step, base.Transformer): - if not last_step._supervised: + if not last_step._supervised and self._LEARN_UNSUPERVISED_DURING_PREDICT: last_step.learn_one(x) return last_step.transform_one(x, **params) return x @@ -736,7 +740,7 @@ def transform_many(self, X: pd.DataFrame): """ X, last_step = self._transform_many(X=X) if isinstance(last_step, base.MiniBatchTransformer): - if not last_step._supervised: + if not last_step._supervised and not self._LEARN_UNSUPERVISED_DURING_PREDICT: last_step.learn_many(X) return last_step.transform_many(X) return X diff --git a/river/compose/test_product.py b/river/compose/test_product.py index 0a2088f48f..83c007b1ff 100644 --- a/river/compose/test_product.py +++ b/river/compose/test_product.py @@ -49,12 +49,12 @@ def test_issue_1243(): >>> model = model.learn_many(X) >>> for x in X.to_dict('records'): ... print(model.transform_one(x)) - {'z*x': 0.697074..., 'x': 0.697074..., 'z': 1} - {'z*x': -1.351716..., 'x': -1.351716..., 'z': 1} - {'z*x': -0.430886..., 'x': -0.430886..., 'z': 1} - {'z*x': -0.580868..., 'x': -0.580868..., 'z': 1} - {'z*x': 1.228463..., 'x': 1.228463..., 'z': 1} - {'z*x': 0.924721..., 'x': 0.924721..., 'z': 1} + {'z*x': 0.785..., 'x': 0.785..., 'z': 1} + {'z*x': -1.511..., 'x': -1.511..., 'z': 1} + {'z*x': -0.576..., 'x': -0.576..., 'z': 1} + {'z*x': -0.770..., 'x': -0.770..., 'z': 1} + {'z*x': 1.148..., 'x': 1.148..., 'z': 1} + {'z*x': 0.924..., 'x': 0.924..., 'z': 1} """ diff --git a/river/preprocessing/lda.py b/river/preprocessing/lda.py index fdb5ac0ecd..d3f6908868 100644 --- a/river/preprocessing/lda.py +++ b/river/preprocessing/lda.py @@ -99,10 +99,10 @@ class LDA(base.Transformer): ... topics = lda.transform_one(x) ... print(topics) {0: 0.5, 1: 2.5} + {0: 2.499..., 1: 1.5} + {0: 0.5, 1: 3.5} + {0: 0.5, 1: 2.5} {0: 1.5, 1: 2.5} - {0: 3.5, 1: 0.5} - {0: 1.5, 1: 1.5} - {0: 2.5, 1: 1.5} References ---------- From 63823f865386e6e9283ff451f8327c9bafa88b55 Mon Sep 17 00:00:00 2001 From: Alexey C <54956904+ColdTeapot273K@users.noreply.github.com> Date: Wed, 5 Jul 2023 03:50:21 +0500 Subject: [PATCH 044/347] [WIP] Feature/ online ordinal encoder (#1271) --- river/preprocessing/ordinal.py | 91 ++++++++++++++++++++++++++++++++++ 1 file changed, 91 insertions(+) create mode 100644 river/preprocessing/ordinal.py diff --git a/river/preprocessing/ordinal.py b/river/preprocessing/ordinal.py new file mode 100644 index 0000000000..82ddc1921f --- /dev/null +++ b/river/preprocessing/ordinal.py @@ -0,0 +1,91 @@ +import collections +import math +import numbers + +import numpy as np +import pandas as pd + +from river import base + + +class OrdinalEncoder(base.MiniBatchTransformer): + # INFO: makes sense to add None initially? so it'll be easier to spot + def __init__(self, handle_unknown="use_reserved_value", unknown_value=1, encoded_missing_value=0): + # self.categories = categories + # self.dtype = dtype + self.handle_unknown = handle_unknown + self.unknown_value = unknown_value + self.encoded_missing_value = encoded_missing_value + self.categories_ = collections.defaultdict(dict) + # self.categories_ = collections.defaultdict(collections.defaultdict) + + def learn_one(self, x): + for i, xi in x.items(): + if self.handle_unknown == "use_reserved_value": + # if self.categories_[i].get(xi, None) is None: + if xi not in self.categories_[i]: + if xi is None: + self.categories_[i][xi] = self.encoded_missing_value + else: + self.categories_[i][xi] = 2 + len(self.categories_[i]) + # self.abs_max[i].update(xi) + + return self + + def transform_one(self, x): + # return {i: self.categories_[i][xi] for i, xi in x.items()} + return {i: self.categories_[i].get(xi, self.unknown_value) for i, xi in x.items()} + + def learn_many(self, X): + for col in X.columns: + # self.values[col].update(X[col].unique()) + known_uniqs = np.array( + list(self.categories_[col].keys()) + ) # INFO: convert to numpy array for np.isin to work properly, otherwise unexpected behavior + current_uniqs = X[col].unique() + + # if None in known_uniqs: + # current_uniqs.dropna(inplace=True) + # elif + upd_encoding = dict() + + # current_uniqs = pd.DataFrame(X)['country'].dropna().unique() + + # new_unique_mask = np.isin(current_uniqs, known_uniqs, assume_unique=True, invert=True) + new_unique_mask = np.isin(current_uniqs, known_uniqs, assume_unique=True, invert=True) + + new_uniqs = current_uniqs[new_unique_mask] + print(f"{known_uniqs=}") + print(f"{current_uniqs=}") + print(f"{new_uniqs=}") + + # INFO: record and mask the first encounters of None or equivalents in the batch + if None in new_uniqs: + print("None detected") + + upd_encoding.update({None: self.encoded_missing_value}) + none_mask = np.isin(new_uniqs, [None], assume_unique=True, invert=True) + new_uniqs = new_uniqs[none_mask] + + # INFO: process first encounters of other unique values except None or equivalents + upd_encoding.update( + { + k: v + 2 + len(known_uniqs) + # for k, v in zip(current_uniqs, range(0, current_uniqs.shape[0])) + for v, k in enumerate(new_uniqs) + # if (k not in known_uniqs) and (k is not None) + } + ) + + print(upd_encoding) + + self.categories_[col].update(upd_encoding) + + return self + + def transform_many(self, X): + X_encoded = X.copy() + for col in X_encoded.columns: + X_encoded[col] = X_encoded[col].transform(lambda x: self.categories_[col].get(x, self.unknown_value)) + + return X_encoded From 10a20287249cfd41c566b24b5887f449ee23131d Mon Sep 17 00:00:00 2001 From: Max Halford Date: Wed, 5 Jul 2023 01:41:14 +0200 Subject: [PATCH 045/347] implement predict_many (#1278) --- river/linear_model/bayesian_lin_reg.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/river/linear_model/bayesian_lin_reg.py b/river/linear_model/bayesian_lin_reg.py index 31b40f8cdc..4ac02e164c 100644 --- a/river/linear_model/bayesian_lin_reg.py +++ b/river/linear_model/bayesian_lin_reg.py @@ -1,6 +1,7 @@ from __future__ import annotations import numpy as np +import pandas as pd from river import base, proba, utils @@ -133,8 +134,12 @@ def predict_one(self, x, with_dist=False): return y_pred_mean x_arr = np.array(list(x.values())) - _, _, ss_inv_arr = self._get_arrays(x.keys(), m=False, ss=False) + *_, ss_inv_arr = self._get_arrays(x.keys(), m=False, ss=False) # Bishop equation 3.59 y_pred_var = 1 / self.beta + x_arr @ ss_inv_arr @ x_arr.T return proba.Gaussian._from_state(n=1, m=y_pred_mean, sig=y_pred_var**0.5, ddof=0) + + def predict_many(self, X): + m, *_ = self._get_arrays(X.columns, m=True, ss=False, ss_inv=False) + return pd.Series(X.values @ m, index=X.index) From 244e906f2647d6463c792990cc33e8abfc227c9c Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 10 Jul 2023 06:46:55 +0200 Subject: [PATCH 046/347] remove getting started dead links --- .../binary-classification.ipynb | 18 +++++++++++------- .../multiclass-classification.ipynb | 16 +++++++++------- .../getting-started/regression.ipynb | 16 +++++++++------- 3 files changed, 29 insertions(+), 21 deletions(-) diff --git a/docs/introduction/getting-started/binary-classification.ipynb b/docs/introduction/getting-started/binary-classification.ipynb index f4c8485f99..c6a3c89990 100644 --- a/docs/introduction/getting-started/binary-classification.ipynb +++ b/docs/introduction/getting-started/binary-classification.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -56,6 +58,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -80,6 +83,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -151,6 +155,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -188,6 +193,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -213,6 +219,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -247,6 +254,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -281,6 +289,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -327,6 +336,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -366,6 +376,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -582,13 +593,6 @@ "metric = metrics.ROCAUC()\n", "evaluate.progressive_val_score(dataset, model, metric)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That concludes the getting started introduction to binary classification! You can now move on to the [next steps](/introduction/next-steps)." - ] } ], "metadata": { diff --git a/docs/introduction/getting-started/multiclass-classification.ipynb b/docs/introduction/getting-started/multiclass-classification.ipynb index a520ec9ff2..e2d94d1415 100644 --- a/docs/introduction/getting-started/multiclass-classification.ipynb +++ b/docs/introduction/getting-started/multiclass-classification.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -58,6 +60,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -82,6 +85,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -161,6 +165,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -198,6 +203,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -231,6 +237,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -266,6 +273,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -328,6 +336,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -379,13 +388,6 @@ "\n", "evaluate.progressive_val_score(dataset, model, metric)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That concludes the getting started introduction to multiclass classification! You can now move on to the [next steps](/introduction/next-steps)." - ] } ], "metadata": { diff --git a/docs/introduction/getting-started/regression.ipynb b/docs/introduction/getting-started/regression.ipynb index 22cd371912..b4c721320b 100644 --- a/docs/introduction/getting-started/regression.ipynb +++ b/docs/introduction/getting-started/regression.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -57,6 +59,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -81,6 +84,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -121,6 +125,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -158,6 +163,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -183,6 +189,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -217,6 +224,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -262,6 +270,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -299,13 +308,6 @@ "\n", "evaluate.progressive_val_score(dataset, model, metric)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That concludes the getting started introduction to regression! You can now move on to the [next steps](/introduction/next-steps)." - ] } ], "metadata": { From 815bb9159f934bf4f5fc3fb24d19041ba8e89e63 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 10 Jul 2023 07:11:44 +0200 Subject: [PATCH 047/347] Implement ordinal encoder (#1279) --- docs/releases/unreleased.md | 8 ++ river/preprocessing/__init__.py | 2 + river/preprocessing/ordinal.py | 191 ++++++++++++++++++++------------ 3 files changed, 128 insertions(+), 73 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 258b9a2cf1..73a0382824 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -8,6 +8,14 @@ Calling `learn_one` in a pipeline will now update each part of the pipeline in t - Removed the `compose.pure_inference_mode` context manager. - The last step of a pipeline will be correctly updated if it is unsupervised, which wasn't the case before. +## linear_model + +- Added a `predict_many` method to `linear_model.BayesianLinearRegression`. + ## forest - Fixed issue with `forest.ARFClassifier` which couldn't be passed a `CrossEntropy` metric. + +## preprocessing + +- Added `preprocessing.OrdinalEncoder`, to map string features to integers. diff --git a/river/preprocessing/__init__.py b/river/preprocessing/__init__.py index 5a09a978b5..458def0676 100644 --- a/river/preprocessing/__init__.py +++ b/river/preprocessing/__init__.py @@ -12,6 +12,7 @@ from .impute import PreviousImputer, StatImputer from .lda import LDA from .one_hot import OneHotEncoder +from .ordinal import OrdinalEncoder from .pred_clipper import PredClipper from .random_projection import GaussianRandomProjector, SparseRandomProjector from .scale import ( @@ -35,6 +36,7 @@ "MinMaxScaler", "Normalizer", "OneHotEncoder", + "OrdinalEncoder", "PredClipper", "PreviousImputer", "RobustScaler", diff --git a/river/preprocessing/ordinal.py b/river/preprocessing/ordinal.py index 82ddc1921f..199e6522ba 100644 --- a/river/preprocessing/ordinal.py +++ b/river/preprocessing/ordinal.py @@ -1,6 +1,8 @@ +from __future__ import annotations + import collections -import math -import numbers +import functools +import itertools import numpy as np import pandas as pd @@ -8,84 +10,127 @@ from river import base +def make_counter(skip): + return (i for i in itertools.count() if i not in skip) + + class OrdinalEncoder(base.MiniBatchTransformer): - # INFO: makes sense to add None initially? so it'll be easier to spot - def __init__(self, handle_unknown="use_reserved_value", unknown_value=1, encoded_missing_value=0): - # self.categories = categories - # self.dtype = dtype - self.handle_unknown = handle_unknown + """Ordinal encoder. + + This transformer maps each feature to integers. It can useful when a feature has string values + (i.e. categorical variables). + + Parameters + ---------- + unknown_value + The value to use for unknown categories seen during `transform_one`. Unknown categories + will be mapped to an integer once they are seen during `learn_one`. This value can be set + to `None` in order to categories to `None` if they've never been seen before. + none_value + The value to encode `None` with. + + Attributes + ---------- + categories + A dict of dicts. The outer dict maps each feature to its inner dict. The inner dict maps + each category to its code. + + Examples + -------- + + >>> from river import preprocessing + + >>> X = [ + ... {"country": "France", "place": "Taco Bell"}, + ... {"country": None, "place": None}, + ... {"country": "Sweden", "place": "Burger King"}, + ... {"country": "France", "place": "Burger King"}, + ... {"country": "Russia", "place": "Starbucks"}, + ... {"country": "Russia", "place": "Starbucks"}, + ... {"country": "Sweden", "place": "Taco Bell"}, + ... {"country": None, "place": None}, + ... ] + + >>> encoder = preprocessing.OrdinalEncoder() + >>> for x in X: + ... print(encoder.transform_one(x)) + ... encoder = encoder.learn_one(x) + {'country': 0, 'place': 0} + {'country': -1, 'place': -1} + {'country': 0, 'place': 0} + {'country': 1, 'place': 2} + {'country': 0, 'place': 0} + {'country': 3, 'place': 3} + {'country': 2, 'place': 1} + {'country': -1, 'place': -1} + + >>> xb1 = pd.DataFrame(X[0:4], index=[0, 1, 2, 3]) + >>> xb2 = pd.DataFrame(X[4:8], index=[4, 5, 6, 7]) + + >>> encoder = preprocessing.OrdinalEncoder() + >>> encoder.transform_many(xb1) + country place + 0 0 0 + 1 -1 -1 + 2 0 0 + 3 0 0 + + >>> encoder = encoder.learn_many(xb1) + >>> encoder.transform_many(xb2) + country place + 4 0 0 + 5 0 0 + 6 2 1 + 7 -1 -1 + + """ + + def __init__( + self, + unknown_value: int | None = 0, + none_value: int = -1, + ): self.unknown_value = unknown_value - self.encoded_missing_value = encoded_missing_value - self.categories_ = collections.defaultdict(dict) - # self.categories_ = collections.defaultdict(collections.defaultdict) + self.none_value = none_value - def learn_one(self, x): - for i, xi in x.items(): - if self.handle_unknown == "use_reserved_value": - # if self.categories_[i].get(xi, None) is None: - if xi not in self.categories_[i]: - if xi is None: - self.categories_[i][xi] = self.encoded_missing_value - else: - self.categories_[i][xi] = 2 + len(self.categories_[i]) - # self.abs_max[i].update(xi) + # We're going to have one auto-incrementing counter per feature. This counter will generate + # the category codes for each feature. + self._counters: collections.defaultdict = collections.defaultdict( + functools.partial(make_counter, {unknown_value, none_value}) + ) - return self + # We're going to store the categories in a dict of dicts. The outer dict will map each + # feature to its inner dict. The inner dict will map each category to its code. + self.categories: collections.defaultdict = collections.defaultdict(dict) def transform_one(self, x): - # return {i: self.categories_[i][xi] for i, xi in x.items()} - return {i: self.categories_[i].get(xi, self.unknown_value) for i, xi in x.items()} - - def learn_many(self, X): - for col in X.columns: - # self.values[col].update(X[col].unique()) - known_uniqs = np.array( - list(self.categories_[col].keys()) - ) # INFO: convert to numpy array for np.isin to work properly, otherwise unexpected behavior - current_uniqs = X[col].unique() - - # if None in known_uniqs: - # current_uniqs.dropna(inplace=True) - # elif - upd_encoding = dict() - - # current_uniqs = pd.DataFrame(X)['country'].dropna().unique() - - # new_unique_mask = np.isin(current_uniqs, known_uniqs, assume_unique=True, invert=True) - new_unique_mask = np.isin(current_uniqs, known_uniqs, assume_unique=True, invert=True) - - new_uniqs = current_uniqs[new_unique_mask] - print(f"{known_uniqs=}") - print(f"{current_uniqs=}") - print(f"{new_uniqs=}") - - # INFO: record and mask the first encounters of None or equivalents in the batch - if None in new_uniqs: - print("None detected") - - upd_encoding.update({None: self.encoded_missing_value}) - none_mask = np.isin(new_uniqs, [None], assume_unique=True, invert=True) - new_uniqs = new_uniqs[none_mask] - - # INFO: process first encounters of other unique values except None or equivalents - upd_encoding.update( - { - k: v + 2 + len(known_uniqs) - # for k, v in zip(current_uniqs, range(0, current_uniqs.shape[0])) - for v, k in enumerate(new_uniqs) - # if (k not in known_uniqs) and (k is not None) - } - ) - - print(upd_encoding) - - self.categories_[col].update(upd_encoding) + return { + i: self.none_value if xi is None else self.categories[i].get(xi, self.unknown_value) + for i, xi in x.items() + } + def learn_one(self, x): + for i, xi in x.items(): + if xi is not None and xi not in self.categories[i]: + self.categories[i][xi] = next(self._counters[i]) return self def transform_many(self, X): - X_encoded = X.copy() - for col in X_encoded.columns: - X_encoded[col] = X_encoded[col].transform(lambda x: self.categories_[col].get(x, self.unknown_value)) - - return X_encoded + return pd.DataFrame( + { + i: pd.Series( + X[i] + .map({**self.categories[i], None: self.none_value}) + .fillna(self.unknown_value), + dtype=np.int64, + ) + for i in X.columns + } + ) + + def learn_many(self, X, y=None): + for i in X.columns: + for xi in X[i].dropna().unique(): + if xi not in self.categories[i]: + self.categories[i][xi] = next(self._counters[i]) + return self From 6ff310168f265ef11b90caf8852cba612f3e2973 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 10 Jul 2023 07:15:05 +0200 Subject: [PATCH 048/347] fix learn_during_predict docstring --- river/compose/pipeline.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/river/compose/pipeline.py b/river/compose/pipeline.py index 89e4803150..5ff21d9cae 100644 --- a/river/compose/pipeline.py +++ b/river/compose/pipeline.py @@ -19,14 +19,15 @@ @contextlib.contextmanager def learn_during_predict(): - """A context manager for making inferences with no side-effects. + """A context manager for fitting unsupervised steps during prediction. - Calling `predict_one` with a pipeline will update the unsupervised steps of the pipeline. This - is the expected behavior for online machine learning. However, in some cases, you might just - want to produce predictions without necessarily updating anything. + Usually, unsupervised parts of a pipeline are updated during `learn_one`. However, in the case + of online learning, it is possible to update them before, during the prediction step. This + context manager allows you to do so. - This context manager allows you to override that behavior, by making it so that unsupervised - estimators are not updated when `predict_one` is called. + This usually brings a slight performance improvement. But it is not done by default because it + is not intuitive and is more difficult to test. It also means that you have to call + `predict_one` before `learn_one` in order for the whole pipeline to be updated. Examples -------- From 9d020696ac4e7f603c501a4e2a44f1c3f3152d90 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Tue, 11 Jul 2023 10:42:08 -0300 Subject: [PATCH 049/347] Fix bug in AMFClassifier and finish AMFRegressor (#1166) (#1281) * AMFRegressor (#1166) * AMF Classifier & Mondrian Tree Classifier implementation * [Pull request Update] - Adding a "mondrian" folder in the "tree" folder for better file structure - Using "random.choices" instead of the "sample_discrete" functions in "utils.py", and removing "sample_discrete" from the "utils.py" * [Pull Request] - Removing the "__repr__" method of AMF - Removing the @setter and @getter - Removing the "loss" parameter of the classifiers since only the "log-loss" is being used in the end * Updating docstring * [Pull request] - Making `learn_one` and `predict_proba_one` accepting all kinds of supported labels for `y` as input - `predict_proba_one` outputs a dictionary of scores with matching labels * [Fix] Reability Co-authored-by: Saulo Martiello Mastelini * [Fix] Language Co-authored-by: Saulo Martiello Mastelini * [Fix] Language Co-authored-by: Saulo Martiello Mastelini * [Fix] math package implementation usage Co-authored-by: Saulo Martiello Mastelini * [Pull request] - Leaving `__all__` in alphabetical order for the classifiers - Removing type parameters in the description of `log_2_sum` of math utils - Replacing java-like getters and setters by python-like properties and setter * - Adding support for random state (seed) - Replacing Overflow from infinity to maximum possible float (so it makes computations still possible) * [Ignoring testing environment] * Fixing style & typos Co-authored-by: Saulo Martiello Mastelini * [Pull request] - Fixing import order in __init__ file of ensemble - Using LaTeX formulation in AMFClassifier description - Making all nodes related methods private (it shouldn't be used outside) - Docstring syntax update and fixes - Importing river.base instead of typing module for better readability - Adding a short description to the MondrianTreeClassifier - Renaming MondrianTreeLeaf into MondrianLeaf - Reordering functions in MondrianTreeClassifier for better readability * Pre-commit clean up * Pre-commit clean up * [MyPy issue] - Trying to fix the left-right issue uppercast (that shouldn't be a problem normally, but mypy keeps being unhappy) - Fixing assignment issue to the parent during upward procedure - Fixing type assignment to the root branch of the tree - Fixing arg-type for list of intensities - Fixing arg-type issue with current samples proceeding - Fixing dirichlet arg-type issue - Fixing some typing issues - Removing call-overload as int in the memories features range list - Correcting output of predict function * Fixing MyPy issues (detyping) * suggestions and style issues fix * addingnecessary files, classes and methods for regressor * minor import modifications * minor list to typing.List and dict to typing.Dict modifs * minor modifs to pass tests * minor changes * changing names * Fixing predict function to support the "model not trained" situation instead of raising an exception * more style suggestions * testing * regressor fix * fixing docstring * [Pull request Update] - Fixing some TODOs from Mastelini suggestions - Factorizing a bit of code from nodes that should be shared with regressor - Removing branch structure as of now for future changes * Removing all "array-like" structure for full dict support * Pre-commit hookups fixes * regressor fix * Delete tests.py * [Pull request] - Adding suggestions from Mastelini on keys usage - Removing useless initialization of scores in the MondrianTreeClassifier * bug fix * fix conflicts * refactored, but has bugs * remove mypy skip * tests * tests * cleanup * better, but not fixed * minor fix * [Fixes] - Fixing scoring bug (no propagation of counts) - Removing unused parameters in docs - Replacing type union of Python 3.10 in 3.9 annotations - Adding little description for MondrianBranch * Pre-commit hookups fixes * fix some tests * Reworking intensities * fix remaining tests and remove duplicated method call * [Pull request] - Adding examples for AMF & Mondrian Tree Classifiers - Reordering __init__ in alphabetical order - Cleaning the comments - Adding string representation for nodes * Hiding MondrianTree from users visibility * Fixing import on Mondrian Tree example Co-authored-by: Saulo Martiello Mastelini * tests * merge fix * merge fix * docstring fixes --------- Co-authored-by: AlexandreChaussard Co-authored-by: Alexandre Chaussard <78101027+AlexandreChaussard@users.noreply.github.com> Co-authored-by: Saulo Martiello Mastelini Co-authored-by: Kenza Ben jelloun Co-authored-by: Saulo Martiello Mastelini * fix exponential sampling * lint * mypy * AMF Reg * documentation * expose exponential sampling * fix mypy * release notes * Update docs/releases/unreleased.md Co-authored-by: Max Halford * Update river/forest/aggregated_mondrian_forest.py Co-authored-by: Max Halford * Update river/utils/random.py Co-authored-by: Max Halford * fix docstrings * add missing import --------- Co-authored-by: kenzabenjelloun <74252706+kenzabenjelloun@users.noreply.github.com> Co-authored-by: AlexandreChaussard Co-authored-by: Alexandre Chaussard <78101027+AlexandreChaussard@users.noreply.github.com> Co-authored-by: Kenza Ben jelloun Co-authored-by: Max Halford --- docs/releases/unreleased.md | 8 +- river/forest/__init__.py | 3 +- river/forest/aggregated_mondrian_forest.py | 122 ++++- river/tree/mondrian/__init__.py | 5 +- river/tree/mondrian/mondrian_tree.py | 9 +- .../tree/mondrian/mondrian_tree_classifier.py | 23 +- river/tree/mondrian/mondrian_tree_nodes.py | 171 ++++++- .../tree/mondrian/mondrian_tree_regressor.py | 428 ++++++++++++++++++ river/utils/random.py | 23 +- 9 files changed, 766 insertions(+), 26 deletions(-) create mode 100644 river/tree/mondrian/mondrian_tree_regressor.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 73a0382824..1c2d3b9ffa 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,6 +1,6 @@ # Unreleased -Calling `learn_one` in a pipeline will now update each part of the pipeline in turn. Before the unsupervised parts of the pipeline were updated during `predict_one`. This is more intuitive for new users. The old behavior, which yields better results, can be restored by calling `learn_one` with the new `compose.pure_inference_mode` context manager. +Calling `learn_one` in a pipeline will now update each part of the pipeline in turn. Before the unsupervised parts of the pipeline were updated during `predict_one`. This is more intuitive for new users. The old behavior, which yields better results, can be restored by calling `learn_one` with the new `compose.learn_during_predict` context manager. ## compose @@ -15,7 +15,13 @@ Calling `learn_one` in a pipeline will now update each part of the pipeline in t ## forest - Fixed issue with `forest.ARFClassifier` which couldn't be passed a `CrossEntropy` metric. +- Fixed a bug in `forest.AMFClassifier` which slightly improves predictive accurary. +- Added `forest.AMFRegressor`. ## preprocessing - Added `preprocessing.OrdinalEncoder`, to map string features to integers. + +## utils + +- Added `utils.random.exponential` to retrieve random samples following an exponential distribution. diff --git a/river/forest/__init__.py b/river/forest/__init__.py index c73ebd50ae..f9210f0c2d 100644 --- a/river/forest/__init__.py +++ b/river/forest/__init__.py @@ -2,12 +2,13 @@ from __future__ import annotations from .adaptive_random_forest import ARFClassifier, ARFRegressor -from .aggregated_mondrian_forest import AMFClassifier +from .aggregated_mondrian_forest import AMFClassifier, AMFRegressor from .online_extra_trees import OXTRegressor __all__ = [ "ARFClassifier", "ARFRegressor", "AMFClassifier", + "AMFRegressor", "OXTRegressor", ] diff --git a/river/forest/aggregated_mondrian_forest.py b/river/forest/aggregated_mondrian_forest.py index 08b7f1f62b..c2a392eacb 100644 --- a/river/forest/aggregated_mondrian_forest.py +++ b/river/forest/aggregated_mondrian_forest.py @@ -4,7 +4,7 @@ import random from river import base -from river.tree.mondrian import MondrianTreeClassifier +from river.tree.mondrian import MondrianTreeClassifier, MondrianTreeRegressor class AMFLearner(base.Ensemble, abc.ABC): @@ -71,7 +71,7 @@ def _min_number_of_models(self): class AMFClassifier(AMFLearner, base.Classifier): """Aggregated Mondrian Forest classifier for online learning. - This implementation is truly online, in the sense that a single pass is performed, and that + This implementation is truly online[^1], in the sense that a single pass is performed, and that predictions can be produced anytime. Each node in a tree predicts according to the distribution of the labels @@ -139,11 +139,12 @@ class AMFClassifier(AMFLearner, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 84.97% + Accuracy: 85.37% References ---------- - J. Mourtada, S. Gaiffas and E. Scornet, *AMF: Aggregated Mondrian Forests for Online Learning*, arXiv:1906.10529, 2019. + [^1]: Mourtada, J., Gaïffas, S., & Scornet, E. (2021). AMF: Aggregated Mondrian forests for online + learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(3), 505-533. """ @@ -217,3 +218,116 @@ def predict_proba_one(self, x): @property def _multiclass(self): return True + + +class AMFRegressor(AMFLearner, base.Regressor): + """Aggregated Mondrian Forest regressor for online learning. + + This algorithm is truly online, in the sense that a single pass is performed, and that + predictions can be produced anytime. + + Each node in a tree predicts according to the average of the labels it contains. + The prediction for a sample is computed as the aggregated predictions of all the subtrees + along the path leading to the leaf node containing the sample. The aggregation weights are + exponential weights with learning rate `step` using a squared loss when `use_aggregation` + is `True`. + + This computation is performed exactly thanks to a context tree weighting algorithm. + More details can be found in the original paper[^1]. + + The final predictions are the average of the predictions of each of the + ``n_estimators`` trees in the forest. + + Parameters + ---------- + n_estimators + The number of trees in the forest. + step + Step-size for the aggregation weights. + use_aggregation + Controls if aggregation is used in the trees. It is highly recommended to + leave it as `True`. + seed + Random seed for reproducibility. + + Examples + -------- + + >>> from river import datasets + >>> from river import evaluate + >>> from river import forest + >>> from river import metrics + + >>> dataset = datasets.TrumpApproval() + >>> model = forest.AMFRegressor(seed=42) + >>> metric = metrics.MAE() + + >>> evaluate.progressive_val_score(dataset, model, metric) + MAE: 0.268533 + + References + ---------- + [^1]: Mourtada, J., Gaïffas, S., & Scornet, E. (2021). AMF: Aggregated Mondrian forests for online + learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(3), 505-533. + + """ + + def __init__( + self, + n_estimators: int = 10, + step: float = 1.0, + use_aggregation: bool = True, + seed: int = None, + ): + super().__init__( + n_estimators=n_estimators, + step=step, + loss="least-squares", + use_aggregation=use_aggregation, + seed=seed, + ) + + self.iteration = 0 + + def _initialize_trees(self): + """Initialize the forest.""" + + self.data: list[MondrianTreeRegressor] = [] + for _ in range(self.n_estimators): + # We don't want to have the same stochastic scheme for each tree, or it'll break the randomness + # Hence we introduce a new seed for each, that is derived of the given seed by a deterministic process + seed = self._rng.randint(0, 9999999) + + tree = MondrianTreeRegressor( + self.step, + self.use_aggregation, + self.iteration, + seed, + ) + self.data.append(tree) + + def learn_one(self, x, y): + # Checking if the forest has been created + if not self._is_initialized: + self._initialize_trees() + + # we fit all the trees using the new sample + for tree in self: + tree.learn_one(x, y) + + self.iteration += 1 + + return self + + def predict_one(self, x): + # Checking that the model has been trained once at least + if not self._is_initialized: + return None + + prediction = 0 + for tree in self: + tree.use_aggregation = self.use_aggregation + prediction += tree.predict_one(x) + prediction = prediction / self.n_estimators + + return prediction diff --git a/river/tree/mondrian/__init__.py b/river/tree/mondrian/__init__.py index c009792d3b..15b596ba75 100644 --- a/river/tree/mondrian/__init__.py +++ b/river/tree/mondrian/__init__.py @@ -3,12 +3,13 @@ implementations for the Mondrian trees. Note that this module is not exposed in the tree module, and is instead used by the -AMFClassifier class in the ensemble module. +AMFClassifier and AMFRegressor classes in the ensemble module. """ from __future__ import annotations from .mondrian_tree import MondrianTree from .mondrian_tree_classifier import MondrianTreeClassifier +from .mondrian_tree_regressor import MondrianTreeRegressor -__all__ = ["MondrianTree", "MondrianTreeClassifier"] +__all__ = ["MondrianTree", "MondrianTreeClassifier", "MondrianTreeRegressor"] diff --git a/river/tree/mondrian/mondrian_tree.py b/river/tree/mondrian/mondrian_tree.py index 6894bd5b95..f9b664ba8e 100644 --- a/river/tree/mondrian/mondrian_tree.py +++ b/river/tree/mondrian/mondrian_tree.py @@ -16,16 +16,15 @@ class MondrianTree(abc.ABC): step Step parameter of the tree. loss - Loss to minimize for each node of the tree - Pick between: "log", ... + Loss to minimize for each node of the tree. At the moment it is a placeholder. + In the future, different optimization metrics might become available. use_aggregation Whether or not the tree should it use aggregation. - split_pure - Whether or not the tree should split pure leaves when training. iteration Number of iterations to run when training. seed Random seed for reproducibility. + """ def __init__( @@ -33,7 +32,6 @@ def __init__( step: float = 0.1, loss: str = "log", use_aggregation: bool = True, - split_pure: bool = False, iteration: int = 0, seed: int | None = None, ): @@ -41,7 +39,6 @@ def __init__( self.step = step self.loss = loss self.use_aggregation = use_aggregation - self.split_pure = split_pure self.iteration = iteration # Controls the randomness in the tree diff --git a/river/tree/mondrian/mondrian_tree_classifier.py b/river/tree/mondrian/mondrian_tree_classifier.py index d2302733be..51e96a7dd2 100644 --- a/river/tree/mondrian/mondrian_tree_classifier.py +++ b/river/tree/mondrian/mondrian_tree_classifier.py @@ -1,7 +1,6 @@ from __future__ import annotations import math -import sys from river import base, utils from river.tree.mondrian.mondrian_tree import MondrianTree @@ -54,7 +53,7 @@ class MondrianTreeClassifier(MondrianTree, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 57.52% + Accuracy: 58.52% References ---------- @@ -76,11 +75,12 @@ def __init__( step=step, loss="log", use_aggregation=use_aggregation, - split_pure=split_pure, iteration=iteration, seed=seed, ) + self.dirichlet = dirichlet + self.split_pure = split_pure # Training attributes # The previously observed classes set @@ -107,6 +107,7 @@ def _score(self, node: MondrianNodeClassifier) -> float: ---------- node Node to evaluate the score. + """ return node.score(self._y, self.dirichlet, len(self._classes)) @@ -118,6 +119,7 @@ def _predict(self, node: MondrianNodeClassifier) -> dict[base.typing.ClfTarget, ---------- node Node to make predictions. + """ return node.predict(self.dirichlet, self._classes, len(self._classes)) @@ -129,6 +131,7 @@ def _loss(self, node: MondrianNodeClassifier) -> float: ---------- node Node to evaluate the loss. + """ return node.loss(self._y, self.dirichlet, len(self._classes)) @@ -140,6 +143,7 @@ def _update_weight(self, node: MondrianNodeClassifier) -> float: ---------- node Node to update the weight. + """ return node.update_weight( @@ -154,6 +158,7 @@ def _update_count(self, node: MondrianNodeClassifier): ---------- node Target node. + """ node.update_count(self._y) @@ -169,6 +174,7 @@ def _update_downwards( Target node. do_weight_update Whether we should update the weights or not. + """ return node.update_downwards( @@ -193,6 +199,7 @@ def _compute_split_time( ---------- node Target node. + """ # Don't split if the node is pure: all labels are equal to the one of y_t @@ -202,11 +209,7 @@ def _compute_split_time( # If x_t extends the current range of the node if extensions_sum > 0: # Sample an exponential with intensity = extensions_sum - # try catch to handle the Overflow situation in the exponential - try: - T = math.exp(1 / extensions_sum) - except OverflowError: - T = sys.float_info.max # we get the largest possible output instead + T = utils.random.exponential(1 / extensions_sum, rng=self._rng) time = node.time # Splitting time of the node (if splitting occurs) @@ -246,6 +249,7 @@ def _split( Feature of the node. is_right_extension Should we extend the tree in the right or left direction. + """ new_depth = node.depth + 1 @@ -420,6 +424,7 @@ def _go_upwards(self, leaf: MondrianLeafClassifier): ---------- leaf Leaf to start from when going upward. + """ current_node = leaf @@ -460,10 +465,12 @@ def predict_proba_one(self, x): ---------- x Feature vector. + """ # If the tree hasn't seen any sample, then it should return # the default empty dict + if not self._is_initialized: return {} diff --git a/river/tree/mondrian/mondrian_tree_nodes.py b/river/tree/mondrian/mondrian_tree_nodes.py index f262515175..d1c3a5cd37 100644 --- a/river/tree/mondrian/mondrian_tree_nodes.py +++ b/river/tree/mondrian/mondrian_tree_nodes.py @@ -3,7 +3,7 @@ import collections import math -from river import base +from river import base, stats from river.tree.base import Branch, Leaf from river.utils.math import log_sum_2_exp @@ -19,6 +19,7 @@ class MondrianLeaf(Leaf): Split time of the node for Mondrian process. depth Depth of the leaf. + """ def __init__(self, parent, time, depth): @@ -81,6 +82,7 @@ def update_depth(self, depth): ---------- depth Depth of the node. + """ self.depth = depth @@ -112,6 +114,7 @@ def range(self, feature) -> tuple[float, float]: ---------- feature Feature for which you want to know the range. + """ return ( @@ -126,6 +129,7 @@ def range_extension(self, x) -> tuple[float, dict[base.typing.ClfTarget, float]] ---------- x Sample to deal with. + """ extensions: dict[base.typing.ClfTarget, float] = {} @@ -176,6 +180,7 @@ def score(self, y: base.typing.ClfTarget, dirichlet: float, n_classes: int) -> f Notes ----- This uses Jeffreys prior with Dirichlet parameter for smoothing. + """ count = self.counts[y] @@ -197,6 +202,7 @@ def predict( The set of classes seen so far n_classes The total number of classes of the problem. + """ scores = {} @@ -215,6 +221,7 @@ def loss(self, y: base.typing.ClfTarget, dirichlet: float, n_classes: int) -> fl Dirichlet parameter of the problem. n_classes The total number of classes of the problem. + """ sc = self.score(y, dirichlet, n_classes) @@ -242,6 +249,7 @@ def update_weight( Step parameter of the tree. n_classes The total number of classes of the problem. + """ loss_t = self.loss(y, dirichlet, n_classes) @@ -257,6 +265,7 @@ def update_count(self, y): ---------- y Class of a given sample. + """ self.counts[y] += 1 @@ -269,6 +278,7 @@ def is_dirac(self, y: base.typing.ClfTarget) -> bool: ---------- y Class of a given sample. + """ return self.n_samples == self.counts[y] @@ -288,9 +298,9 @@ def update_downwards( Parameters ---------- x - Sample to proceed (as a list). + Sample to proceed. y - Class of the sample x_t. + Class of the sample x. dirichlet Dirichlet parameter of the tree. use_aggregation @@ -301,6 +311,7 @@ def update_downwards( Should we update the weights of the node as well. n_classes The total number of classes of the problem. + """ # Updating the range of the feature values known by the node @@ -339,6 +350,7 @@ class MondrianLeafClassifier(MondrianNodeClassifier, MondrianLeaf): Split time of the node. depth The depth of the leaf. + """ def __init__(self, parent, time, depth): @@ -362,6 +374,159 @@ class MondrianBranchClassifier(MondrianNodeClassifier, MondrianBranch): Acceptation threshold of the branch. *children Children nodes of the branch. + + """ + + def __init__(self, parent, time, depth, feature, threshold, *children): + super().__init__(parent, time, depth, feature, threshold, *children) + + +class MondrianNodeRegressor(MondrianNode): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + self.n_samples = 0 + self.mean = stats.Mean() + + def replant(self, leaf: MondrianNodeRegressor, copy_all: bool = False): + """Transfer information from a leaf to a new branch.""" + self.weight = leaf.weight # type: ignore + self.log_weight_tree = leaf.log_weight_tree # type: ignore + self.mean = leaf.mean + + if copy_all: + self.memory_range_min = leaf.memory_range_min + self.memory_range_max = leaf.memory_range_max + self.n_samples = leaf.n_samples + + def predict(self) -> base.typing.RegTarget: + """Return the prediction of the node.""" + return self.mean.get() + + def loss(self, sample_value: base.typing.RegTarget) -> float: + """Compute the loss of the node. + + Parameters + ---------- + sample_value + A given value. + + """ + + r = self.predict() - sample_value # type: ignore + return r * r / 2 + + def update_weight( + self, + sample_value: base.typing.RegTarget, + use_aggregation: bool, + step: float, + ) -> float: + """Update the weight of the node given a label and the method used. + + Parameters + ---------- + sample_value + Label of a given sample. + use_aggregation + Whether to use aggregation or not during computation (given by the tree). + step + Step parameter of the tree. + + """ + + loss_t = self.loss(sample_value) + if use_aggregation: + self.weight -= step * loss_t + return loss_t + + def update_downwards( + self, + x, + sample_value: base.typing.RegTarget, + use_aggregation: bool, + step: float, + do_update_weight: bool, + ): + """Update the node when running a downward procedure updating the tree. + + Parameters + ---------- + x + Sample to proceed (as a list). + sample_value + Label of the sample x. + use_aggregation + Should it use the aggregation or not + step + Step of the tree. + do_update_weight + Should we update the weights of the node as well. + + """ + + # Updating the range of the feature values known by the node + # If it is the first sample, we copy the features vector into the min and max range + if self.n_samples == 0: + for feature in x: + x_f = x[feature] + self.memory_range_min[feature] = x_f + self.memory_range_max[feature] = x_f + # Otherwise, we update the range + else: + for feature in x: + x_f = x[feature] + if x_f < self.memory_range_min[feature]: + self.memory_range_min[feature] = x_f + if x_f > self.memory_range_max[feature]: + self.memory_range_max[feature] = x_f + + # One more sample in the node + self.n_samples += 1 + + if do_update_weight: + self.update_weight(sample_value, use_aggregation, step) + + # Update the mean of the labels in the node online + self.mean.update(sample_value) + + +class MondrianLeafRegressor(MondrianNodeRegressor, MondrianLeaf): + """Mondrian Tree Regressor leaf node. + + Parameters + ---------- + parent + Parent node. + time + Split time of the node. + depth + The depth of the leaf. + + """ + + def __init__(self, parent, time, depth): + super().__init__(parent, time, depth) + + +class MondrianBranchRegressor(MondrianNodeRegressor, MondrianBranch): + """Mondrian Tree Regressor branch node. + + Parameters + ---------- + parent + Parent node of the branch. + time + Split time characterizing the branch. + depth + Depth of the branch in the tree. + feature + Feature of the branch. + threshold + Acceptation threshold of the branch. + *children + Children nodes of the branch. + """ def __init__(self, parent, time, depth, feature, threshold, *children): diff --git a/river/tree/mondrian/mondrian_tree_regressor.py b/river/tree/mondrian/mondrian_tree_regressor.py new file mode 100644 index 0000000000..c83d30cbbd --- /dev/null +++ b/river/tree/mondrian/mondrian_tree_regressor.py @@ -0,0 +1,428 @@ +from __future__ import annotations + +import math + +from river import base, utils +from river.tree.mondrian.mondrian_tree import MondrianTree +from river.tree.mondrian.mondrian_tree_nodes import ( + MondrianBranchRegressor, + MondrianLeafRegressor, + MondrianNodeRegressor, +) + + +class MondrianTreeRegressor(MondrianTree, base.Regressor): + """Mondrian Tree Regressor. + + Parameters + ---------- + step + Step of the tree. + use_aggregation + Whether to use aggregation weighting techniques or not. + iteration + Number iterations to do during training. + seed + Random seed for reproducibility. + + Notes + ----- + The Mondrian Tree Regressor is a type of decision tree that bases splitting decisions over a + Mondrian process. + + References + ---------- + [^1]: Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh. Mondrian Forests: Efficient Online Random Forests. + arXiv:1406.2673, pages 2-4. + + """ + + def __init__( + self, + step: float = 0.1, + use_aggregation: bool = True, + iteration: int = 0, + seed: int = None, + ): + super().__init__( + step=step, + loss="least-squares", + use_aggregation=use_aggregation, + iteration=iteration, + seed=seed, + ) + # Controls the randomness in the tree + self.seed = seed + + # The current sample being proceeded + self._x: dict[base.typing.FeatureName, int | float] + # The current label index being proceeded + self._y: base.typing.RegTarget + + # Initialization of the root of the tree + # It's the root so it doesn't have any parent (hence None) + self._root = MondrianLeafRegressor(None, 0.0, 0) + + def _is_initialized(self): + """Check if the tree has learnt at least one sample""" + return self.iteration != 0 + + def _predict(self, node: MondrianNodeRegressor) -> base.typing.RegTarget: + """Compute the prediction. + + Parameters + ---------- + node + Node to make predictions. + + """ + + return node.predict() # type: ignore + + def _loss(self, node: MondrianNodeRegressor) -> float: + """Compute the loss for the given node regarding the current label. + + Parameters + ---------- + node + Node to evaluate the loss. + + """ + + return node.loss(self._y) + + def _update_weight(self, node: MondrianNodeRegressor) -> float: + """Update the weight of the node regarding the current label with the tree parameters. + + Parameters + ---------- + node + Node to update the weight. + + """ + + return node.update_weight(self._y, self.use_aggregation, self.step) + + def _update_downwards(self, node: MondrianNodeRegressor, do_update_weight): + """Update the node when running a downward procedure updating the tree. + + Parameters + ---------- + node + Target node. + do_update_weight + Whether we should update the weights or not. + + """ + + return node.update_downwards( + self._x, self._y, self.use_aggregation, self.step, do_update_weight + ) + + def _compute_split_time( + self, + node: MondrianLeafRegressor | MondrianBranchRegressor, + extensions_sum: float, + ) -> float: + """Computes the split time of the given node. + + Parameters + ---------- + node + Target node. + + """ + + if extensions_sum > 0: + # Sample an exponential with intensity = extensions_sum + T = utils.random.exponential(1 / extensions_sum, rng=self._rng) + + time = node.time + # Splitting time of the node (if splitting occurs) + split_time = time + T + # If the node is a leaf we must split it + if isinstance(node, MondrianLeafRegressor): + return split_time + # Otherwise we apply Mondrian process dark magic :) + # 1. We get the creation time of the childs (left and right is the same) + left, _ = node.children + child_time = left.time + # 2. We check if splitting time occurs before child creation time + if split_time < child_time: + return split_time + + return 0.0 + + def _split( + self, + node: MondrianLeafRegressor | MondrianBranchRegressor, + split_time: float, + threshold: float, + feature: base.typing.FeatureName, + is_right_extension: bool, + ) -> MondrianBranchRegressor: + """Split the given node and attributes the split time, threshold, etc... to the node. + + Parameters + ---------- + node + Target node. + split_time + Split time of the node in the Mondrian process. + threshold + Threshold of acceptance of the node. + feature + Feature index of the node. + is_right_extension + Should we extend the tree in the right or left direction. + + """ + + new_depth = node.depth + 1 + + # To calm down mypy + left: MondrianLeafRegressor | MondrianBranchRegressor + right: MondrianLeafRegressor | MondrianBranchRegressor + + # The node is already a branch: we create a new branch above it and move the existing + # node one level down the tree + if isinstance(node, MondrianBranchRegressor): + old_left, old_right = node.children + if is_right_extension: + left = MondrianBranchRegressor( + node, split_time, new_depth, node.feature, node.threshold + ) + right = MondrianLeafRegressor(node, split_time, new_depth) + left.replant(node) + + old_left.parent = left + old_right.parent = left + + left.children = (old_left, old_right) + else: + right = MondrianBranchRegressor( + node, split_time, new_depth, node.feature, node.threshold + ) + left = MondrianLeafRegressor(node, split_time, new_depth) + right.replant(node) + + old_left.parent = right + old_right.parent = right + + right.children = (old_left, old_right) + + # Update the level of the modified nodes + new_depth += 1 + old_left.update_depth(new_depth) + old_right.update_depth(new_depth) + + # Update split info + node.feature = feature + node.threshold = threshold + node.children = (left, right) + + return node + + # We promote the leaf to a branch + branch = MondrianBranchRegressor(node.parent, node.time, node.depth, feature, threshold) + left = MondrianLeafRegressor(branch, split_time, new_depth) + right = MondrianLeafRegressor(branch, split_time, new_depth) + branch.children = (left, right) + + # Copy properties from the previous leaf + branch.replant(node, True) + + if is_right_extension: + left.replant(node) + else: + right.replant(node) + + # To avoid leaving garbage behind + del node + + return branch + + def _go_downwards(self): + """Update the tree (downward procedure).""" + + # We update the nodes along the path which leads to the leaf containing the current + # sample. For each node on the path, we consider the possibility of splitting it, + # following the Mondrian process definition. + + # We start at the root + current_node = self._root + + if self.iteration == 0: + # If it's the first iteration, we just put the current sample in the range of root + self._update_downwards(current_node, False) + return current_node + else: + # Path from the parent to the current node + branch_no = None + while True: + # Computing the extensions to get the intensities + extensions_sum, extensions = current_node.range_extension(self._x) + + # If it's not the first iteration (otherwise the current node + # is root with no range), we consider the possibility of a split + split_time = self._compute_split_time(current_node, extensions_sum) + + if split_time > 0: + # We split the current node: because the current node is a + # leaf, or because we add a new node along the path + + # We normalize the range extensions to get probabilities + intensities = utils.norm.normalize_values_in_dict(extensions, inplace=False) + + # Sample the feature at random with a probability + # proportional to the range extensions + + candidates = sorted(list(self._x.keys())) + feature = self._rng.choices( + candidates, [intensities[c] for c in candidates], k=1 + )[0] + + x_f = self._x[feature] + + # Is it a right extension of the node ? + range_min, range_max = current_node.range(feature) + is_right_extension = x_f > range_max + if is_right_extension: + threshold = self._rng.uniform(range_max, x_f) + else: + threshold = self._rng.uniform(x_f, range_min) + + was_leaf = isinstance(current_node, MondrianLeafRegressor) + + # We split the current node + current_node = self._split( + current_node, + split_time, + threshold, + feature, + is_right_extension, + ) + + # The root node has become a branch + if current_node.parent is None: + self._root = current_node + # Update path from the previous parent to the recently updated node + elif was_leaf: + parent = current_node.parent + if branch_no == 0: + parent.children = (current_node, parent.children[1]) + else: + parent.children = (parent.children[0], current_node) + + # Update the current node + self._update_downwards(current_node, True) + + left, right = current_node.children + + # Now, get the next node + if is_right_extension: + current_node = right + else: + current_node = left + + # This is the leaf containing the sample point (we've just + # splitted the current node with the data point) + leaf = current_node + self._update_downwards(leaf, False) + return leaf + else: + # There is no split, so we just update the node and go to the next one + self._update_downwards(current_node, True) + if isinstance(current_node, MondrianLeafRegressor): + return current_node + else: + # Save the path direction to keep the tree consistent + try: + branch_no = current_node.branch_no(self._x) + current_node = current_node.children[branch_no] + except KeyError: # Missing split feature + branch_no, current_node = current_node.most_common_path() + + def _go_upwards(self, leaf: MondrianLeafRegressor): + """Update the tree (upwards procedure). + + Parameters + ---------- + leaf + Leaf to start from when going upward. + + """ + + current_node = leaf + + if self.iteration >= 1: + while True: + current_node.update_weight_tree() + if current_node.parent is None: + # We arrived at the root + break + # Note that the root node is updated as well + # We go up to the root in the tree + current_node = current_node.parent + + def learn_one(self, x, y): + # Setting current sample + self._x = x + self._y = y + + # Learning step + leaf = self._go_downwards() + if self.use_aggregation: + self._go_upwards(leaf) + + # Incrementing iteration + self.iteration += 1 + return self + + def predict_one(self, x): + """Predict the label of the samples. + + Parameters + ---------- + x + Feature vector. + + """ + + # If the tree hasn't seen any sample, then it should return + # the default empty dict + if not self._is_initialized: + return + + leaf = ( + self._root.traverse(x, until_leaf=True) + if isinstance(self._root, MondrianBranchRegressor) + else self._root + ) + + if not self.use_aggregation: + return self._predict(leaf) + + current = leaf + prediction = 0.0 + + while True: + # This test is useless ? + if isinstance(current, MondrianLeafRegressor): + prediction = self._predict(current) + else: + weight = current.weight + log_weight_tree = current.log_weight_tree + w = math.exp(weight - log_weight_tree) + # Get the predictions of the current node + pred_new = self._predict(current) + prediction = 0.5 * w * pred_new + (1 - 0.5 * w) * prediction + + # Root must be updated as well + if current.parent is None: + break + + # And now we go up + current = current.parent + + return prediction diff --git a/river/utils/random.py b/river/utils/random.py index ce99b8f5c6..346eaa9591 100644 --- a/river/utils/random.py +++ b/river/utils/random.py @@ -3,7 +3,7 @@ import math import random -__all__ = ["poisson"] +__all__ = ["poisson", "exponential"] def poisson(rate: float, rng=random) -> int: @@ -29,3 +29,24 @@ def poisson(rate: float, rng=random) -> int: p *= rng.random() return k - 1 + + +def exponential(rate: float = 1.0, rng=random) -> float: + """Sample a random value from a Poisson distribution. + + Parameters + ---------- + rate + rng + + References + ---------- + [^1]: [Wikipedia article](https://www.wikiwand.com/en/Exponential_distribution#Random_variate_generation) + + """ + + u = rng.random() + + # Retrive the λ value from the rate (β): β = 1 / λ + lmbda = 1.0 / rate + return -math.log(1 - u) / lmbda From b503b8c278860239e4b7160950da88d5abadc0f7 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 14 Jul 2023 07:30:56 +0200 Subject: [PATCH 050/347] by can be None in Agg and TargetAgg --- docs/releases/unreleased.md | 4 ++++ river/feature_extraction/agg.py | 4 ++-- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 1c2d3b9ffa..943712c58e 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -8,6 +8,10 @@ Calling `learn_one` in a pipeline will now update each part of the pipeline in t - Removed the `compose.pure_inference_mode` context manager. - The last step of a pipeline will be correctly updated if it is unsupervised, which wasn't the case before. +## feature_extraction + +- The `by` parameter of `Agg` and `TargetAgg` can now be set to `None` in order to compute aggregates on the whole data. + ## linear_model - Added a `predict_many` method to `linear_model.BayesianLinearRegression`. diff --git a/river/feature_extraction/agg.py b/river/feature_extraction/agg.py index a059ba0afe..de8681c98e 100644 --- a/river/feature_extraction/agg.py +++ b/river/feature_extraction/agg.py @@ -169,8 +169,8 @@ class Agg(base.Transformer): def __init__( self, on: str, - by: str | list[str] | None, how: stats.base.Univariate | utils.Rolling | utils.TimeRolling, + by: str | list[str] | None = None, ): self.on = on self.by = (by if isinstance(by, list) else [by]) if by is not None else by @@ -338,8 +338,8 @@ class TargetAgg(base.SupervisedTransformer, Agg): def __init__( self, - by: str | list[str] | None, how: stats.base.Univariate | utils.Rolling | utils.TimeRolling, + by: str | list[str] | None = None, target_name="y", ): super().__init__(on=target_name, by=by, how=how) From a2a3bb4b0102542d7c57f30d4e974b7889cef29f Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 14 Jul 2023 08:55:59 +0200 Subject: [PATCH 051/347] revert --- docs/releases/unreleased.md | 4 ---- river/feature_extraction/agg.py | 4 ++-- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 943712c58e..1c2d3b9ffa 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -8,10 +8,6 @@ Calling `learn_one` in a pipeline will now update each part of the pipeline in t - Removed the `compose.pure_inference_mode` context manager. - The last step of a pipeline will be correctly updated if it is unsupervised, which wasn't the case before. -## feature_extraction - -- The `by` parameter of `Agg` and `TargetAgg` can now be set to `None` in order to compute aggregates on the whole data. - ## linear_model - Added a `predict_many` method to `linear_model.BayesianLinearRegression`. diff --git a/river/feature_extraction/agg.py b/river/feature_extraction/agg.py index de8681c98e..a059ba0afe 100644 --- a/river/feature_extraction/agg.py +++ b/river/feature_extraction/agg.py @@ -169,8 +169,8 @@ class Agg(base.Transformer): def __init__( self, on: str, + by: str | list[str] | None, how: stats.base.Univariate | utils.Rolling | utils.TimeRolling, - by: str | list[str] | None = None, ): self.on = on self.by = (by if isinstance(by, list) else [by]) if by is not None else by @@ -338,8 +338,8 @@ class TargetAgg(base.SupervisedTransformer, Agg): def __init__( self, + by: str | list[str] | None, how: stats.base.Univariate | utils.Rolling | utils.TimeRolling, - by: str | list[str] | None = None, target_name="y", ): super().__init__(on=target_name, by=by, how=how) From 42918d77df1b009837cbff6104f2cb8255bcf8ae Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 18 Jul 2023 09:03:38 +0200 Subject: [PATCH 052/347] nit --- .../concept-drift-detection_1_0.png | Bin 16028 -> 29315 bytes .../concept-drift-detection_3_1.png | Bin 16157 -> 29647 bytes .../on-hoeffding-trees_12_0.svg | 70 +++++++++--------- .../on-hoeffding-trees_21_0.png | Bin 178051 -> 190653 bytes .../on-hoeffding-trees_23_0.png | Bin 177270 -> 190161 bytes .../on-hoeffding-trees_25_0.png | Bin 189462 -> 207243 bytes .../on-hoeffding-trees_27_0.png | Bin 195511 -> 211985 bytes docs/releases/0.7.1.md | 4 + docs/releases/0.7.2.md | 5 -- mkdocs.yml | 1 + 10 files changed, 40 insertions(+), 40 deletions(-) delete mode 100644 docs/releases/0.7.2.md diff --git 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['clouds', 'humidity', 'pressure', 'temperature', 'wind']
(\n", + "
['clouds', [...]
Select (\n", " clouds\n", " humidity\n", " pressure\n", " temperature\n", " wind\n", ")\n", - "\n", - "
y_mean_by_station_and_hour
(\n", + "
y_mean_by_station_and_hour
TargetAgg (\n", " by=['station', 'hour']\n", " how=Mean ()\n", " target_name=\"y\"\n", ")\n", - "\n", - "
y_ewm_0.5_by_station
(\n", + "
y_ewm_0.5_by_station
TargetAgg (\n", " by=['station']\n", " how=EWMean (\n", " fading_factor=0.5\n", " )\n", " target_name=\"y\"\n", ")\n", - "\n", - "
StandardScaler
(\n", + "
StandardScaler
StandardScaler (\n", " with_std=True\n", ")\n", - "\n", - "
LinearRegression
(\n", + "
LinearRegression
LinearRegression (\n", " optimizer=SGD (\n", " lr=Constant (\n", " learning_rate=0.01\n", @@ -166,19 +162,19 @@ " clip_gradient=1e+12\n", " initializer=Zeros ()\n", ")\n", - "\n", "
" + ], + "text/plain": [ + "Pipeline (\n", + " FuncTransformer (\n", + " func=\"add_time_features\"\n", + " ),\n", + " TransformerUnion (\n", + " Select (\n", + " clouds\n", + " humidity\n", + " pressure\n", + " temperature\n", + " wind\n", + " ),\n", + " TargetAgg (\n", + " by=['station', 'hour']\n", + " how=Mean ()\n", + " target_name=\"y\"\n", + " ),\n", + " TargetAgg (\n", + " by=['station']\n", + " how=EWMean (\n", + " fading_factor=0.5\n", + " )\n", + " target_name=\"y\"\n", + " )\n", + " ),\n", + " StandardScaler (\n", + " with_std=True\n", + " ),\n", + " LinearRegression (\n", + " optimizer=SGD (\n", + " lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " )\n", + " loss=Squared ()\n", + " l2=0.\n", + " l1=0.\n", + " intercept_init=0.\n", + " intercept_lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " clip_gradient=1e+12\n", + " initializer=Zeros ()\n", + " )\n", + ")" ] }, "execution_count": 2, @@ -280,10 +331,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:21.558294Z", - "iopub.status.busy": "2023-01-29T19:27:21.558145Z", - "iopub.status.idle": "2023-01-29T19:27:21.571901Z", - "shell.execute_reply": "2023-01-29T19:27:21.571426Z" + "iopub.execute_input": "2023-09-02T00:49:41.388613Z", + "iopub.status.busy": "2023-09-02T00:49:41.388503Z", + "iopub.status.idle": "2023-09-02T00:49:41.403489Z", + "shell.execute_reply": "2023-09-02T00:49:41.403226Z" }, "tags": [] }, @@ -349,22 +400,22 @@ "pressure: 0.04916 (float)\n", "temperature: -0.51938 (float)\n", "wind: -0.69426 (float)\n", - "y_ewm_0.5_by_station: 0.19214 (float)\n", - "y_mean_by_station_and_hour: -0.26013 (float)\n", + "y_ewm_0.5_by_station: 0.19640 (float)\n", + "y_mean_by_station_and_hour: -0.27110 (float)\n", "\n", "4. LinearRegression\n", "-------------------\n", - "Name Value Weight Contribution \n", - " Intercept 1.00000 9.22316 9.22316 \n", - " y_ewm_0.5_by_station 0.19214 9.26418 1.78000 \n", - " humidity 1.16366 1.01252 1.17823 \n", - " temperature -0.51938 -0.42112 0.21872 \n", - " wind -0.69426 -0.04088 0.02838 \n", - " pressure 0.04916 0.18137 0.00892 \n", - "y_mean_by_station_and_hour -0.26013 0.19801 -0.05151 \n", - " clouds 1.54778 -0.32697 -0.50608 \n", + "Name Value Weight Contribution \n", + "Intercept 1.00000 9.19960 9.19960 \n", + "y_ewm_0.5_by_station 0.19640 9.19349 1.80562 \n", + "humidity 1.16366 1.01680 1.18320 \n", + "temperature -0.51938 -0.41575 0.21593 \n", + " wind -0.69426 -0.03810 0.02645 \n", + "pressure 0.04916 0.18321 0.00901 \n", + "y_mean_by_station_and_hour -0.27110 0.19553 -0.05301 \n", + " clouds 1.54778 -0.32838 -0.50827 \n", "\n", - "Prediction: 11.87982\n" + "Prediction: 11.87854\n" ] } ], @@ -396,7 +447,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/docs/examples/imbalanced-learning.ipynb b/docs/examples/imbalanced-learning.ipynb index 305898a4bd..815b19b31c 100644 --- a/docs/examples/imbalanced-learning.ipynb +++ b/docs/examples/imbalanced-learning.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:22.871223Z", - "iopub.status.busy": "2023-01-29T19:27:22.871035Z", - "iopub.status.idle": "2023-01-29T19:27:25.447782Z", - "shell.execute_reply": "2023-01-29T19:27:25.447185Z" + "iopub.execute_input": "2023-09-02T00:49:42.902654Z", + "iopub.status.busy": "2023-09-02T00:49:42.902258Z", + "iopub.status.idle": "2023-09-02T00:49:45.426420Z", + "shell.execute_reply": "2023-09-02T00:49:45.425895Z" }, "tags": [] }, @@ -69,26 +69,23 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:25.450558Z", - "iopub.status.busy": "2023-01-29T19:27:25.450384Z", - "iopub.status.idle": "2023-01-29T19:27:43.258204Z", - "shell.execute_reply": "2023-01-29T19:27:43.257676Z" + "iopub.execute_input": "2023-09-02T00:49:45.428695Z", + "iopub.status.busy": "2023-09-02T00:49:45.428513Z", + "iopub.status.idle": "2023-09-02T00:50:06.626723Z", + "shell.execute_reply": "2023-09-02T00:50:06.626362Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
ROCAUC: 89.11%\n",
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ROCAUC: 91.43%\n",
-       "
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ROCAUC: 91.31%\n",
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ROCAUC: 94.75%\n",
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\n" - ], "text/plain": [ - "ROCAUC: \u001b[1;36m94.75\u001b[0m%\n" + "ROCAUC: 94.75%" ] }, + "execution_count": 5, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -283,10 +271,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:28:35.471457Z", - "iopub.status.busy": "2023-01-29T19:28:35.471367Z", - "iopub.status.idle": "2023-01-29T19:28:35.488980Z", - "shell.execute_reply": "2023-01-29T19:28:35.488434Z" + "iopub.execute_input": "2023-09-02T00:51:07.331679Z", + "iopub.status.busy": "2023-09-02T00:51:07.331568Z", + "iopub.status.idle": "2023-09-02T00:51:07.352762Z", + "shell.execute_reply": "2023-09-02T00:51:07.352509Z" }, "tags": [] }, @@ -294,11 +282,10 @@ { "data": { "text/html": [ - "
StandardScaler
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StandardScaler
StandardScaler (\n", " with_std=True\n", ")\n", - "\n", - "
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RandomUnderSampler
RandomUnderSampler (\n", " classifier=LogisticRegression (\n", " optimizer=SGD (\n", " lr=Constant (\n", @@ -321,8 +308,7 @@ " desired_dist={0: 0.8, 1: 0.2}\n", " seed=42\n", ")\n", - "\n", - "
LogisticRegression
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LogisticRegression
LogisticRegression (\n", " optimizer=SGD (\n", " lr=Constant (\n", " learning_rate=0.01\n", @@ -341,19 +327,19 @@ " clip_gradient=1e+12\n", " initializer=Zeros ()\n", ")\n", - "\n", "
" + ], + "text/plain": [ + "Pipeline (\n", + " StandardScaler (\n", + " with_std=True\n", + " ),\n", + " RandomUnderSampler (\n", + " classifier=LogisticRegression (\n", + " optimizer=SGD (\n", + " lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " )\n", + " loss=Log (\n", + " weight_pos=1.\n", + " weight_neg=1.\n", + " )\n", + " l2=0.\n", + " l1=0.\n", + " intercept_init=0.\n", + " intercept_lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " clip_gradient=1e+12\n", + " initializer=Zeros ()\n", + " )\n", + " desired_dist={0: 0.8, 1: 0.2}\n", + " seed=42\n", + " )\n", + ")" ] }, "execution_count": 6, @@ -462,26 +486,23 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:28:35.492048Z", - "iopub.status.busy": "2023-01-29T19:28:35.491933Z", - "iopub.status.idle": "2023-01-29T19:28:58.578400Z", - "shell.execute_reply": "2023-01-29T19:28:58.577023Z" + "iopub.execute_input": "2023-09-02T00:51:07.354551Z", + "iopub.status.busy": "2023-09-02T00:51:07.354436Z", + "iopub.status.idle": "2023-09-02T00:51:29.235692Z", + "shell.execute_reply": "2023-09-02T00:51:29.235410Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
ROCAUC: 91.71%\n",
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ROCAUC: 94.71%\n",
-       "
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ROCAUC: 96.52%\n",
-       "
\n" - ], "text/plain": [ - "ROCAUC: \u001b[1;36m96.52\u001b[0m%\n" + "ROCAUC: 96.52%" ] }, + "execution_count": 9, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -621,7 +636,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb index 566a81283b..a127737eb7 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb @@ -92,10 +92,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:47.626421Z", - "iopub.status.busy": "2021-09-02T16:04:47.625342Z", - "iopub.status.idle": "2021-09-02T16:04:49.382871Z", - "shell.execute_reply": "2021-09-02T16:04:49.383355Z" + "iopub.execute_input": "2023-09-02T09:23:57.769553Z", + "iopub.status.busy": "2023-09-02T09:23:57.768668Z", + "iopub.status.idle": "2023-09-02T09:23:59.110942Z", + "shell.execute_reply": "2023-09-02T09:23:59.110580Z" } }, "outputs": [ @@ -103,9 +103,11 @@ "name": "stdout", "output_type": "stream", "text": [ + "Downloading https://maxhalford.github.io/files/datasets/ml_100k.zip (1.83 MB)\n", + "Uncompressing into /Users/max/river_data/MovieLens100K\n", "x = {\n", - " \"user\": 259,\n", - " \"item\": 255,\n", + " \"user\": \"259\",\n", + " \"item\": \"255\",\n", " \"timestamp\": 874731910000000000,\n", " \"title\": \"My Best Friend's Wedding (1997)\",\n", " \"release_date\": 866764800000000000,\n", @@ -142,10 +144,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:49.388868Z", - "iopub.status.busy": "2021-09-02T16:04:49.388279Z", - "iopub.status.idle": "2021-09-02T16:04:49.389859Z", - "shell.execute_reply": "2021-09-02T16:04:49.390339Z" + "iopub.execute_input": "2023-09-02T09:23:59.113085Z", + "iopub.status.busy": "2023-09-02T09:23:59.112881Z", + "iopub.status.idle": "2023-09-02T09:23:59.144637Z", + "shell.execute_reply": "2023-09-02T09:23:59.144346Z" } }, "outputs": [], @@ -178,10 +180,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:49.394825Z", - "iopub.status.busy": "2021-09-02T16:04:49.394246Z", - "iopub.status.idle": "2021-09-02T16:04:50.550082Z", - "shell.execute_reply": "2021-09-02T16:04:50.550612Z" + "iopub.execute_input": "2023-09-02T09:23:59.146456Z", + "iopub.status.busy": "2023-09-02T09:23:59.146348Z", + "iopub.status.idle": "2023-09-02T09:24:00.520498Z", + "shell.execute_reply": "2023-09-02T09:24:00.520052Z" } }, "outputs": [ @@ -190,9 +192,9 @@ "output_type": "stream", "text": [ "[25,000] MAE: 0.934259, RMSE: 1.124469 – 00:00:00 – 514 B\n", - "[50,000] MAE: 0.923893, RMSE: 1.105 – 00:00:01 – 514 B\n", + "[50,000] MAE: 0.923893, RMSE: 1.105 – 00:00:00 – 514 B\n", "[75,000] MAE: 0.937359, RMSE: 1.123696 – 00:00:01 – 514 B\n", - "[100,000] MAE: 0.942162, RMSE: 1.125783 – 00:00:02 – 514 B\n" + "[100,000] MAE: 0.942162, RMSE: 1.125783 – 00:00:01 – 514 B\n" ] } ], @@ -238,13 +240,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:50.555652Z", - "iopub.status.busy": "2021-09-02T16:04:50.554908Z", - "iopub.status.idle": "2021-09-02T16:04:54.278018Z", - "shell.execute_reply": "2021-09-02T16:04:54.278496Z" + "iopub.execute_input": "2023-09-02T09:24:00.522525Z", + "iopub.status.busy": "2023-09-02T09:24:00.522377Z", + "iopub.status.idle": "2023-09-02T09:24:02.249242Z", + "shell.execute_reply": "2023-09-02T09:24:02.248988Z" } }, "outputs": [ @@ -252,10 +254,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761844, RMSE: 0.960972 – 0:00:00.864336 – 132.26 KB\n", - "[50,000] MAE: 0.753292, RMSE: 0.951223 – 0:00:01.737809 – 191.78 KB\n", - "[75,000] MAE: 0.754177, RMSE: 0.953376 – 0:00:02.598330 – 225.88 KB\n", - "[100,000] MAE: 0.754651, RMSE: 0.954148 – 0:00:03.464756 – 240.29 KB\n" + "[25,000] MAE: 0.761844, RMSE: 0.960972 – 00:00:00 – 173.6 KB\n", + "[50,000] MAE: 0.753292, RMSE: 0.951223 – 00:00:00 – 242.23 KB\n", + "[75,000] MAE: 0.754177, RMSE: 0.953376 – 00:00:01 – 286.04 KB\n", + "[100,000] MAE: 0.754651, RMSE: 0.954148 – 00:00:01 – 309.64 KB\n" ] } ], @@ -312,10 +314,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:54.283903Z", - "iopub.status.busy": "2021-09-02T16:04:54.283307Z", - "iopub.status.idle": "2021-09-02T16:05:01.252564Z", - "shell.execute_reply": "2021-09-02T16:05:01.253150Z" + "iopub.execute_input": "2023-09-02T09:24:02.250998Z", + "iopub.status.busy": "2023-09-02T09:24:02.250911Z", + "iopub.status.idle": "2023-09-02T09:24:04.707646Z", + "shell.execute_reply": "2023-09-02T09:24:04.707360Z" } }, "outputs": [ @@ -323,10 +325,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 1.070136, RMSE: 1.397014 – 0:00:01.705144 – 938.07 KB\n", - "[50,000] MAE: 0.99174, RMSE: 1.290666 – 0:00:03.466905 – 1.13 MB\n", - "[75,000] MAE: 0.961072, RMSE: 1.250842 – 0:00:05.205363 – 1.33 MB\n", - "[100,000] MAE: 0.944883, RMSE: 1.227688 – 0:00:06.934770 – 1.5 MB\n" + "[25,000] MAE: 1.070136, RMSE: 1.397014 – 00:00:00 – 570.35 KB\n", + "[50,000] MAE: 0.99174, RMSE: 1.290666 – 00:00:01 – 716 KB\n", + "[75,000] MAE: 0.961072, RMSE: 1.250842 – 00:00:01 – 844.09 KB\n", + "[100,000] MAE: 0.944883, RMSE: 1.227688 – 00:00:02 – 945.19 KB\n" ] } ], @@ -380,10 +382,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:01.259368Z", - "iopub.status.busy": "2021-09-02T16:05:01.258783Z", - "iopub.status.idle": "2021-09-02T16:05:08.962142Z", - "shell.execute_reply": "2021-09-02T16:05:08.962611Z" + "iopub.execute_input": "2023-09-02T09:24:04.709466Z", + "iopub.status.busy": "2023-09-02T09:24:04.709346Z", + "iopub.status.idle": "2023-09-02T09:24:07.491578Z", + "shell.execute_reply": "2023-09-02T09:24:07.491259Z" } }, "outputs": [ @@ -391,10 +393,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761818, RMSE: 0.961057 – 0:00:01.917323 – 1.01 MB\n", - "[50,000] MAE: 0.751667, RMSE: 0.949443 – 0:00:03.825794 – 1.28 MB\n", - "[75,000] MAE: 0.749653, RMSE: 0.948723 – 0:00:05.737369 – 1.51 MB\n", - "[100,000] MAE: 0.748559, RMSE: 0.947854 – 0:00:07.666314 – 1.69 MB\n" + "[25,000] MAE: 0.761818, RMSE: 0.961057 – 00:00:00 – 669.27 KB\n", + "[50,000] MAE: 0.751667, RMSE: 0.949443 – 00:00:01 – 869.85 KB\n", + "[75,000] MAE: 0.749653, RMSE: 0.948723 – 00:00:02 – 1 MB\n", + "[100,000] MAE: 0.748559, RMSE: 0.947854 – 00:00:02 – 1.11 MB\n" ] } ], @@ -453,7 +455,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb index 0e5e5db67a..9eb28d3aa6 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb @@ -81,10 +81,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:11.275107Z", - "iopub.status.busy": "2021-09-02T16:05:11.274331Z", - "iopub.status.idle": "2021-09-02T16:05:12.222291Z", - "shell.execute_reply": "2021-09-02T16:05:12.222707Z" + "iopub.execute_input": "2023-09-02T09:24:08.860210Z", + "iopub.status.busy": "2023-09-02T09:24:08.859842Z", + "iopub.status.idle": "2023-09-02T09:24:09.275194Z", + "shell.execute_reply": "2023-09-02T09:24:09.274737Z" } }, "outputs": [], @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:12.230617Z", - "iopub.status.busy": "2021-09-02T16:05:12.229997Z", - "iopub.status.idle": "2021-09-02T16:05:29.301168Z", - "shell.execute_reply": "2021-09-02T16:05:29.301749Z" + "iopub.execute_input": "2023-09-02T09:24:09.277314Z", + "iopub.status.busy": "2023-09-02T09:24:09.277147Z", + "iopub.status.idle": "2023-09-02T09:24:15.610729Z", + "shell.execute_reply": "2023-09-02T09:24:15.610317Z" } }, "outputs": [ @@ -122,12 +122,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading https://maxhalford.github.io/files/datasets/ml_100k.zip (1.83 MB)\n", - "Uncompressing into /Users/max/river_data/MovieLens100K\n", - "[25,000] MAE: 0.761778, RMSE: 0.960803 – 00:00:02 – 818.86 KB\n", - "[50,000] MAE: 0.751986, RMSE: 0.949941 – 00:00:04 – 948.77 KB\n", - "[75,000] MAE: 0.750044, RMSE: 0.948911 – 00:00:05 – 1.07 MB\n", - "[100,000] MAE: 0.748609, RMSE: 0.947994 – 00:00:07 – 1.19 MB\n" + "[25,000] MAE: 0.761761, RMSE: 0.960662 – 00:00:01 – 818.86 KB\n", + "[50,000] MAE: 0.751922, RMSE: 0.949783 – 00:00:03 – 948.77 KB\n", + "[75,000] MAE: 0.749822, RMSE: 0.948634 – 00:00:04 – 1.07 MB\n", + "[100,000] MAE: 0.748393, RMSE: 0.94776 – 00:00:06 – 1.19 MB\n" ] } ], @@ -191,10 +189,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:29.306371Z", - "iopub.status.busy": "2021-09-02T16:05:29.305547Z", - "iopub.status.idle": "2021-09-02T16:05:29.308166Z", - "shell.execute_reply": "2021-09-02T16:05:29.308976Z" + "iopub.execute_input": "2023-09-02T09:24:15.612862Z", + "iopub.status.busy": "2023-09-02T09:24:15.612710Z", + "iopub.status.idle": "2023-09-02T09:24:15.630272Z", + "shell.execute_reply": "2023-09-02T09:24:15.629805Z" } }, "outputs": [ @@ -247,10 +245,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:29.313500Z", - "iopub.status.busy": "2021-09-02T16:05:29.312843Z", - "iopub.status.idle": "2021-09-02T16:05:29.314659Z", - "shell.execute_reply": "2021-09-02T16:05:29.315075Z" + "iopub.execute_input": "2023-09-02T09:24:15.632338Z", + "iopub.status.busy": "2023-09-02T09:24:15.632212Z", + "iopub.status.idle": "2023-09-02T09:24:15.648735Z", + "shell.execute_reply": "2023-09-02T09:24:15.648479Z" } }, "outputs": [], @@ -274,10 +272,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:29.319543Z", - "iopub.status.busy": "2021-09-02T16:05:29.318952Z", - "iopub.status.idle": "2021-09-02T16:05:29.320561Z", - "shell.execute_reply": "2021-09-02T16:05:29.320961Z" + "iopub.execute_input": "2023-09-02T09:24:15.650350Z", + "iopub.status.busy": "2023-09-02T09:24:15.650257Z", + "iopub.status.idle": "2023-09-02T09:24:15.664513Z", + "shell.execute_reply": "2023-09-02T09:24:15.664250Z" } }, "outputs": [], @@ -305,10 +303,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:29.327666Z", - "iopub.status.busy": "2021-09-02T16:05:29.327057Z", - "iopub.status.idle": "2021-09-02T16:06:11.277539Z", - "shell.execute_reply": "2021-09-02T16:06:11.278025Z" + "iopub.execute_input": "2023-09-02T09:24:15.666206Z", + "iopub.status.busy": "2023-09-02T09:24:15.666117Z", + "iopub.status.idle": "2023-09-02T09:24:33.502271Z", + "shell.execute_reply": "2023-09-02T09:24:33.501964Z" } }, "outputs": [ @@ -316,10 +314,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.759838, RMSE: 0.961281 – 00:00:04 – 935.54 KB\n", - "[50,000] MAE: 0.751307, RMSE: 0.951391 – 00:00:09 – 1.06 MB\n", - "[75,000] MAE: 0.750361, RMSE: 0.951393 – 00:00:14 – 1.22 MB\n", - "[100,000] MAE: 0.749994, RMSE: 0.951435 – 00:00:20 – 1.37 MB\n" + "[25,000] MAE: 0.760059, RMSE: 0.961415 – 00:00:04 – 935.54 KB\n", + "[50,000] MAE: 0.751429, RMSE: 0.951504 – 00:00:08 – 1.06 MB\n", + "[75,000] MAE: 0.750568, RMSE: 0.951592 – 00:00:13 – 1.22 MB\n", + "[100,000] MAE: 0.75018, RMSE: 0.951622 – 00:00:17 – 1.37 MB\n" ] } ], @@ -388,10 +386,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:06:11.285040Z", - "iopub.status.busy": "2021-09-02T16:06:11.284442Z", - "iopub.status.idle": "2021-09-02T16:09:36.626866Z", - "shell.execute_reply": "2021-09-02T16:09:36.627349Z" + "iopub.execute_input": "2023-09-02T09:24:33.504144Z", + "iopub.status.busy": "2023-09-02T09:24:33.504021Z", + "iopub.status.idle": "2023-09-02T09:25:38.175942Z", + "shell.execute_reply": "2023-09-02T09:25:38.175657Z" } }, "outputs": [ @@ -399,10 +397,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761297, RMSE: 0.962054 – 0:00:51.632190 – 2.61 MB\n", - "[50,000] MAE: 0.751865, RMSE: 0.951499 – 0:01:42.890329 – 3.08 MB\n", - "[75,000] MAE: 0.750853, RMSE: 0.951526 – 0:02:34.207244 – 3.6 MB\n", - "[100,000] MAE: 0.750607, RMSE: 0.951982 – 0:03:25.248686 – 4.07 MB\n" + "[25,000] MAE: 0.761379, RMSE: 0.96214 – 00:00:16 – 1.73 MB\n", + "[50,000] MAE: 0.751998, RMSE: 0.951589 – 00:00:32 – 2.03 MB\n", + "[75,000] MAE: 0.750994, RMSE: 0.951616 – 00:00:48 – 2.36 MB\n", + "[100,000] MAE: 0.750849, RMSE: 0.952142 – 00:01:04 – 2.66 MB\n" ] } ], @@ -473,10 +471,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:09:36.635563Z", - "iopub.status.busy": "2021-09-02T16:09:36.634918Z", - "iopub.status.idle": "2021-09-02T16:10:39.510781Z", - "shell.execute_reply": "2021-09-02T16:10:39.511270Z" + "iopub.execute_input": "2023-09-02T09:25:38.177709Z", + "iopub.status.busy": "2023-09-02T09:25:38.177604Z", + "iopub.status.idle": "2023-09-02T09:26:04.802711Z", + "shell.execute_reply": "2023-09-02T09:26:04.802410Z" } }, "outputs": [ @@ -484,10 +482,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.757718, RMSE: 0.958158 – 0:00:15.781740 – 3.04 MB\n", - "[50,000] MAE: 0.749502, RMSE: 0.948065 – 0:00:31.431484 – 3.59 MB\n", - "[75,000] MAE: 0.749275, RMSE: 0.948918 – 0:00:47.079510 – 4.19 MB\n", - "[100,000] MAE: 0.749542, RMSE: 0.949769 – 0:01:02.776969 – 4.75 MB\n" + "[25,000] MAE: 0.758339, RMSE: 0.959047 – 00:00:06 – 2.16 MB\n", + "[50,000] MAE: 0.749833, RMSE: 0.948531 – 00:00:13 – 2.54 MB\n", + "[75,000] MAE: 0.749631, RMSE: 0.949418 – 00:00:19 – 2.96 MB\n", + "[100,000] MAE: 0.749776, RMSE: 0.950131 – 00:00:26 – 3.35 MB\n" ] } ], @@ -553,10 +551,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:10:39.519856Z", - "iopub.status.busy": "2021-09-02T16:10:39.519214Z", - "iopub.status.idle": "2021-09-02T16:12:05.385426Z", - "shell.execute_reply": "2021-09-02T16:12:05.386017Z" + "iopub.execute_input": "2023-09-02T09:26:04.804485Z", + "iopub.status.busy": "2023-09-02T09:26:04.804363Z", + "iopub.status.idle": "2023-09-02T09:26:39.079247Z", + "shell.execute_reply": "2023-09-02T09:26:39.078802Z" } }, "outputs": [ @@ -564,10 +562,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761539, RMSE: 0.962241 – 0:00:20.963815 – 1.18 MB\n", - "[50,000] MAE: 0.754089, RMSE: 0.953181 – 0:00:42.057991 – 1.38 MB\n", - "[75,000] MAE: 0.754806, RMSE: 0.954979 – 0:01:04.051777 – 1.6 MB\n", - "[100,000] MAE: 0.755404, RMSE: 0.95604 – 0:01:25.823651 – 1.79 MB\n" + "[25,000] MAE: 0.761435, RMSE: 0.962211 – 00:00:08 – 834.1 KB\n", + "[50,000] MAE: 0.754063, RMSE: 0.953248 – 00:00:17 – 964.01 KB\n", + "[75,000] MAE: 0.754729, RMSE: 0.95507 – 00:00:25 – 1.08 MB\n", + "[100,000] MAE: 0.755697, RMSE: 0.956542 – 00:00:34 – 1.21 MB\n" ] } ], diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb index be82de7caa..d2be37bc56 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb @@ -31,7 +31,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/docs/examples/quantile-regression-uncertainty.ipynb b/docs/examples/quantile-regression-uncertainty.ipynb index 4ca53a2757..1bfa244ca9 100644 --- a/docs/examples/quantile-regression-uncertainty.ipynb +++ b/docs/examples/quantile-regression-uncertainty.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:29:30.584608Z", - "iopub.status.busy": "2023-01-29T19:29:30.584421Z", - "iopub.status.idle": "2023-01-29T19:29:30.805677Z", - "shell.execute_reply": "2023-01-29T19:29:30.805298Z" + "iopub.execute_input": "2023-09-02T00:52:07.634046Z", + "iopub.status.busy": "2023-09-02T00:52:07.633637Z", + "iopub.status.idle": "2023-09-02T00:52:07.828667Z", + "shell.execute_reply": "2023-09-02T00:52:07.828369Z" } }, "outputs": [], @@ -39,17 +39,20 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:29:30.808090Z", - "iopub.status.busy": "2023-01-29T19:29:30.807933Z", - "iopub.status.idle": "2023-01-29T19:29:31.647342Z", - "shell.execute_reply": "2023-01-29T19:29:31.646891Z" + "iopub.execute_input": "2023-09-02T00:52:07.830510Z", + "iopub.status.busy": "2023-09-02T00:52:07.830367Z", + "iopub.status.idle": "2023-09-02T00:52:08.513721Z", + "shell.execute_reply": "2023-09-02T00:52:08.512874Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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\n" 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", 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" + ] }, "metadata": {}, "output_type": "display_data" @@ -157,7 +160,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/docs/examples/sentence-classification.ipynb b/docs/examples/sentence-classification.ipynb index b5710f8f9a..0c7af11fe4 100644 --- a/docs/examples/sentence-classification.ipynb +++ b/docs/examples/sentence-classification.ipynb @@ -19,54 +19,36 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T10:54:17.554992Z", - "iopub.status.busy": "2022-10-26T10:54:17.553914Z", - "iopub.status.idle": "2022-10-26T10:54:18.187632Z", - "shell.execute_reply": "2022-10-26T10:54:18.188333Z" + "iopub.execute_input": "2023-09-02T00:52:10.006055Z", + "iopub.status.busy": "2023-09-02T00:52:10.005732Z", + "iopub.status.idle": "2023-09-02T00:52:10.388324Z", + "shell.execute_reply": "2023-09-02T00:52:10.388002Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
\n",
-       "SMS Spam Collection dataset.\n",
-       "\n",
-       "The data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent\n",
-       "13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.\n",
-       "\n",
-       "      Name  SMSSpam                                                                          \n",
-       "      Task  Binary classification                                                            \n",
-       "   Samples  5,574                                                                            \n",
-       "  Features  1                                                                                \n",
-       "    Sparse  False                                                                            \n",
-       "      Path  /Users/max.halford/river_data/SMSSpam/SMSSpamCollection                          \n",
-       "       URL  https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\n",
-       "      Size  466.71 KB                                                                        \n",
-       "Downloaded  True                                                                             \n",
-       "
\n" - ], "text/plain": [ - "\n", "SMS Spam Collection dataset.\n", "\n", - "The data contains \u001b[1;36m5\u001b[0m,\u001b[1;36m574\u001b[0m items and \u001b[1;36m1\u001b[0m feature \u001b[1m(\u001b[0mi.e. SMS body\u001b[1m)\u001b[0m. Spam messages represent\n", - "\u001b[1;36m13.4\u001b[0m% of the dataset. The goal is to predict whether an SMS is a spam or not.\n", - "\n", - " Name SMSSpam \n", - " Task Binary classification \n", - " Samples \u001b[1;36m5\u001b[0m,\u001b[1;36m574\u001b[0m \n", - " Features \u001b[1;36m1\u001b[0m \n", - " Sparse \u001b[3;91mFalse\u001b[0m \n", - " Path \u001b[35m/Users/max.halford/river_data/SMSSpam/\u001b[0m\u001b[95mSMSSpamCollection\u001b[0m \n", - " URL \u001b[4;94mhttps://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\u001b[0m\n", - " Size \u001b[1;36m466.71\u001b[0m KB \n", - "Downloaded \u001b[3;92mTrue\u001b[0m \n" + "The data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent\n", + "13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.\n", + "\n", + " Name SMSSpam \n", + " Task Binary classification \n", + " Samples 5,574 \n", + " Features 1 \n", + " Sparse False \n", + " Path /Users/max/river_data/SMSSpam/SMSSpamCollection \n", + " URL https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\n", + " Size 466.71 KB \n", + "Downloaded True " ] }, + "execution_count": 1, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -80,10 +62,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T10:54:18.193109Z", - "iopub.status.busy": "2022-10-26T10:54:18.192345Z", - "iopub.status.idle": "2022-10-26T10:54:18.222366Z", - "shell.execute_reply": "2022-10-26T10:54:18.222796Z" + "iopub.execute_input": "2023-09-02T00:52:10.390078Z", + "iopub.status.busy": "2023-09-02T00:52:10.389940Z", + "iopub.status.idle": "2023-09-02T00:52:10.405704Z", + "shell.execute_reply": "2023-09-02T00:52:10.405433Z" }, "tags": [] }, @@ -93,21 +75,9 @@ "output_type": "stream", "text": [ "{'body': 'Go until jurong point, crazy.. Available only in bugis n great world '\n", - " 'la e buffet... Cine there got amore wat...\\n'}\n" + " 'la e buffet... Cine there got amore wat...\\n'}\n", + "Spam: False\n" ] - }, - { - "data": { - "text/html": [ - "
Spam: False\n",
-       "
\n" - ], - "text/plain": [ - "Spam: \u001b[3;91mFalse\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -133,26 +103,23 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T10:54:18.227903Z", - "iopub.status.busy": "2022-10-26T10:54:18.227159Z", - "iopub.status.idle": "2022-10-26T10:55:05.710003Z", - "shell.execute_reply": "2022-10-26T10:55:05.710491Z" + "iopub.execute_input": "2023-09-02T00:52:10.407372Z", + "iopub.status.busy": "2023-09-02T00:52:10.407258Z", + "iopub.status.idle": "2023-09-02T01:59:03.297216Z", + "shell.execute_reply": "2023-09-02T01:59:03.296855Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
ROCAUC: 93.00%\n",
-       "
\n" - ], "text/plain": [ - "ROCAUC: \u001b[1;36m93.00\u001b[0m%\n" + "ROCAUC: 93.00%" ] }, + "execution_count": 3, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -195,32 +162,25 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T10:55:05.714445Z", - "iopub.status.busy": "2022-10-26T10:55:05.713771Z", - "iopub.status.idle": "2022-10-26T10:55:05.741641Z", - "shell.execute_reply": "2022-10-26T10:55:05.742035Z" + "iopub.execute_input": "2023-09-02T01:59:03.299143Z", + "iopub.status.busy": "2023-09-02T01:59:03.299031Z", + "iopub.status.idle": "2023-09-02T01:59:03.319929Z", + "shell.execute_reply": "2023-09-02T01:59:03.319675Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
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-       "        False   True  \n",
-       "False   4,809     17  \n",
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y_mean_by_store_id
(\n", + "
y_mean_by_store_id
TargetAgg (\n", " by=['store_id']\n", " how=Rolling (\n", " obj=Mean ()\n", @@ -615,8 +548,7 @@ " )\n", " target_name=\"y\"\n", ")\n", - "\n", - "
y_mean_by_store_id
(\n", + "
y_mean_by_store_id
TargetAgg (\n", " by=['store_id']\n", " how=Rolling (\n", " obj=Mean ()\n", @@ -624,8 +556,7 @@ " )\n", " target_name=\"y\"\n", ")\n", - "\n", - "
y_mean_by_store_id
(\n", + "
y_mean_by_store_id
TargetAgg (\n", " by=['store_id']\n", " how=Rolling (\n", " obj=Mean ()\n", @@ -633,8 +564,7 @@ " )\n", " target_name=\"y\"\n", ")\n", - "\n", - "
~['area_name', 'date', 'genre_name', 'latitude', 'longitude', 'store_id']
(\n", + "
~['area_name', [...]
Discard (\n", " area_name\n", " date\n", " genre_name\n", @@ -642,12 +572,10 @@ " longitude\n", " store_id\n", ")\n", - "\n", - "
StandardScaler
(\n", + "
StandardScaler
StandardScaler (\n", " with_std=True\n", ")\n", - "\n", - "
LinearRegression
(\n", + "
LinearRegression
LinearRegression (\n", " optimizer=SGD (\n", " lr=Constant (\n", " learning_rate=0.01\n", @@ -663,19 +591,19 @@ " clip_gradient=1e+12\n", " initializer=Zeros ()\n", ")\n", - "\n", "
" + ], + "text/plain": [ + "Pipeline (\n", + " TransformerUnion (\n", + " FuncTransformer (\n", + " func=\"get_date_features\"\n", + " ),\n", + " TargetAgg (\n", + " by=['store_id']\n", + " how=Rolling (\n", + " obj=Mean ()\n", + " window_size=7\n", + " )\n", + " target_name=\"y\"\n", + " ),\n", + " TargetAgg (\n", + " by=['store_id']\n", + " how=Rolling (\n", + " obj=Mean ()\n", + " window_size=14\n", + " )\n", + " target_name=\"y\"\n", + " ),\n", + " TargetAgg (\n", + " by=['store_id']\n", + " how=Rolling (\n", + " obj=Mean ()\n", + " window_size=21\n", + " )\n", + " target_name=\"y\"\n", + " )\n", + " ),\n", + " Discard (\n", + " area_name\n", + " date\n", + " genre_name\n", + " latitude\n", + " longitude\n", + " store_id\n", + " ),\n", + " StandardScaler (\n", + " with_std=True\n", + " ),\n", + " LinearRegression (\n", + " optimizer=SGD (\n", + " lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " )\n", + " loss=Squared ()\n", + " l2=0.\n", + " l1=0.\n", + " intercept_init=0.\n", + " intercept_lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " clip_gradient=1e+12\n", + " initializer=Zeros ()\n", + " )\n", + ")" ] }, "execution_count": 11, @@ -782,7 +778,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/docs/introduction/getting-started/binary-classification.ipynb b/docs/introduction/getting-started/binary-classification.ipynb index c6a3c89990..30df2f0f20 100644 --- a/docs/introduction/getting-started/binary-classification.ipynb +++ b/docs/introduction/getting-started/binary-classification.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:14.897961Z", - "iopub.status.busy": "2023-06-26T15:07:14.897697Z", - "iopub.status.idle": "2023-06-26T15:07:15.526269Z", - "shell.execute_reply": "2023-06-26T15:07:15.525980Z" + "iopub.execute_input": "2023-09-02T00:45:17.434889Z", + "iopub.status.busy": "2023-09-02T00:45:17.434128Z", + "iopub.status.idle": "2023-09-02T00:45:18.027890Z", + "shell.execute_reply": "2023-09-02T00:45:18.027583Z" } }, "outputs": [ @@ -70,10 +70,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.539054Z", - "iopub.status.busy": "2023-06-26T15:07:15.538918Z", - "iopub.status.idle": "2023-06-26T15:07:15.557006Z", - "shell.execute_reply": "2023-06-26T15:07:15.556748Z" + "iopub.execute_input": "2023-09-02T00:45:18.041516Z", + "iopub.status.busy": "2023-09-02T00:45:18.041318Z", + "iopub.status.idle": "2023-09-02T00:45:18.059626Z", + "shell.execute_reply": "2023-09-02T00:45:18.059374Z" } }, "outputs": [], @@ -95,10 +95,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.558598Z", - "iopub.status.busy": "2023-06-26T15:07:15.558510Z", - "iopub.status.idle": "2023-06-26T15:07:15.572706Z", - "shell.execute_reply": "2023-06-26T15:07:15.572396Z" + "iopub.execute_input": "2023-09-02T00:45:18.061201Z", + "iopub.status.busy": "2023-09-02T00:45:18.061119Z", + "iopub.status.idle": "2023-09-02T00:45:18.075142Z", + "shell.execute_reply": "2023-09-02T00:45:18.074885Z" }, "tags": [] }, @@ -132,10 +132,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.574423Z", - "iopub.status.busy": "2023-06-26T15:07:15.574288Z", - "iopub.status.idle": "2023-06-26T15:07:15.588592Z", - "shell.execute_reply": "2023-06-26T15:07:15.588310Z" + "iopub.execute_input": "2023-09-02T00:45:18.076772Z", + "iopub.status.busy": "2023-09-02T00:45:18.076664Z", + "iopub.status.idle": "2023-09-02T00:45:18.089842Z", + "shell.execute_reply": "2023-09-02T00:45:18.089591Z" } }, "outputs": [ @@ -167,10 +167,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.590305Z", - "iopub.status.busy": "2023-06-26T15:07:15.590186Z", - "iopub.status.idle": "2023-06-26T15:07:15.617959Z", - "shell.execute_reply": "2023-06-26T15:07:15.617699Z" + "iopub.execute_input": "2023-09-02T00:45:18.091471Z", + "iopub.status.busy": "2023-09-02T00:45:18.091386Z", + "iopub.status.idle": "2023-09-02T00:45:18.160160Z", + "shell.execute_reply": "2023-09-02T00:45:18.159890Z" } }, "outputs": [ @@ -207,10 +207,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.619511Z", - "iopub.status.busy": "2023-06-26T15:07:15.619403Z", - "iopub.status.idle": "2023-06-26T15:07:15.632831Z", - "shell.execute_reply": "2023-06-26T15:07:15.632554Z" + "iopub.execute_input": "2023-09-02T00:45:18.161731Z", + "iopub.status.busy": "2023-09-02T00:45:18.161621Z", + "iopub.status.idle": "2023-09-02T00:45:18.177328Z", + "shell.execute_reply": "2023-09-02T00:45:18.177058Z" } }, "outputs": [], @@ -231,10 +231,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.634423Z", - "iopub.status.busy": "2023-06-26T15:07:15.634337Z", - "iopub.status.idle": "2023-06-26T15:07:15.647906Z", - "shell.execute_reply": "2023-06-26T15:07:15.647649Z" + "iopub.execute_input": "2023-09-02T00:45:18.179063Z", + "iopub.status.busy": "2023-09-02T00:45:18.178977Z", + "iopub.status.idle": "2023-09-02T00:45:18.193209Z", + "shell.execute_reply": "2023-09-02T00:45:18.192956Z" } }, "outputs": [ @@ -266,10 +266,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.649382Z", - "iopub.status.busy": "2023-06-26T15:07:15.649298Z", - "iopub.status.idle": "2023-06-26T15:07:15.662866Z", - "shell.execute_reply": "2023-06-26T15:07:15.662615Z" + "iopub.execute_input": "2023-09-02T00:45:18.194798Z", + "iopub.status.busy": "2023-09-02T00:45:18.194697Z", + "iopub.status.idle": "2023-09-02T00:45:18.208875Z", + "shell.execute_reply": "2023-09-02T00:45:18.208617Z" } }, "outputs": [ @@ -301,10 +301,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.664436Z", - "iopub.status.busy": "2023-06-26T15:07:15.664350Z", - "iopub.status.idle": "2023-06-26T15:07:15.708866Z", - "shell.execute_reply": "2023-06-26T15:07:15.708602Z" + "iopub.execute_input": "2023-09-02T00:45:18.210425Z", + "iopub.status.busy": "2023-09-02T00:45:18.210338Z", + "iopub.status.idle": "2023-09-02T00:45:18.254722Z", + "shell.execute_reply": "2023-09-02T00:45:18.254440Z" }, "tags": [] }, @@ -348,10 +348,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.710419Z", - "iopub.status.busy": "2023-06-26T15:07:15.710315Z", - "iopub.status.idle": "2023-06-26T15:07:15.755660Z", - "shell.execute_reply": "2023-06-26T15:07:15.755417Z" + "iopub.execute_input": "2023-09-02T00:45:18.256289Z", + "iopub.status.busy": "2023-09-02T00:45:18.256179Z", + "iopub.status.idle": "2023-09-02T00:45:18.302491Z", + "shell.execute_reply": "2023-09-02T00:45:18.302238Z" } }, "outputs": [ @@ -388,10 +388,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.757334Z", - "iopub.status.busy": "2023-06-26T15:07:15.757229Z", - "iopub.status.idle": "2023-06-26T15:07:15.779551Z", - "shell.execute_reply": "2023-06-26T15:07:15.779263Z" + "iopub.execute_input": "2023-09-02T00:45:18.304245Z", + "iopub.status.busy": "2023-09-02T00:45:18.304129Z", + "iopub.status.idle": "2023-09-02T00:45:18.326675Z", + "shell.execute_reply": "2023-09-02T00:45:18.326410Z" }, "tags": [] }, @@ -570,10 +570,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:15.781043Z", - "iopub.status.busy": "2023-06-26T15:07:15.780931Z", - "iopub.status.idle": "2023-06-26T15:07:15.842108Z", - "shell.execute_reply": "2023-06-26T15:07:15.841859Z" + "iopub.execute_input": "2023-09-02T00:45:18.328153Z", + "iopub.status.busy": "2023-09-02T00:45:18.328049Z", + "iopub.status.idle": "2023-09-02T00:45:18.391428Z", + "shell.execute_reply": "2023-09-02T00:45:18.391165Z" }, "tags": [] }, @@ -581,7 +581,7 @@ { "data": { "text/plain": [ - "ROCAUC: 95.04%" + "ROCAUC: 95.07%" ] }, "execution_count": 12, diff --git a/docs/introduction/getting-started/concept-drift-detection.ipynb b/docs/introduction/getting-started/concept-drift-detection.ipynb index 972a55389e..6cdab5c912 100644 --- a/docs/introduction/getting-started/concept-drift-detection.ipynb +++ b/docs/introduction/getting-started/concept-drift-detection.ipynb @@ -45,10 +45,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2023-06-26T15:07:17.338546Z", - "iopub.status.busy": "2023-06-26T15:07:17.338126Z", - "iopub.status.idle": "2023-06-26T15:07:17.728332Z", - "shell.execute_reply": "2023-06-26T15:07:17.727982Z" + "iopub.execute_input": "2023-09-02T00:45:19.877606Z", + "iopub.status.busy": "2023-09-02T00:45:19.877215Z", + "iopub.status.idle": "2023-09-02T00:45:20.252739Z", + "shell.execute_reply": "2023-09-02T00:45:20.252378Z" }, "jupyter": { "outputs_hidden": false @@ -122,10 +122,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2023-06-26T15:07:17.730231Z", - "iopub.status.busy": "2023-06-26T15:07:17.730078Z", - "iopub.status.idle": "2023-06-26T15:07:18.202924Z", - "shell.execute_reply": "2023-06-26T15:07:18.202632Z" + "iopub.execute_input": "2023-09-02T00:45:20.254722Z", + "iopub.status.busy": "2023-09-02T00:45:20.254540Z", + "iopub.status.idle": "2023-09-02T00:45:20.885226Z", + "shell.execute_reply": "2023-09-02T00:45:20.882399Z" }, "jupyter": { "outputs_hidden": false diff --git a/docs/introduction/getting-started/multiclass-classification.ipynb b/docs/introduction/getting-started/multiclass-classification.ipynb index e2d94d1415..54910c5956 100644 --- a/docs/introduction/getting-started/multiclass-classification.ipynb +++ b/docs/introduction/getting-started/multiclass-classification.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:19.761621Z", - "iopub.status.busy": "2023-06-26T15:07:19.761079Z", - "iopub.status.idle": "2023-06-26T15:07:20.208391Z", - "shell.execute_reply": "2023-06-26T15:07:20.208026Z" + "iopub.execute_input": "2023-09-02T00:45:22.384019Z", + "iopub.status.busy": "2023-09-02T00:45:22.383719Z", + "iopub.status.idle": "2023-09-02T00:45:22.764865Z", + "shell.execute_reply": "2023-09-02T00:45:22.764514Z" } }, "outputs": [ @@ -72,10 +72,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:20.210187Z", - "iopub.status.busy": "2023-06-26T15:07:20.210095Z", - "iopub.status.idle": "2023-06-26T15:07:20.238888Z", - "shell.execute_reply": "2023-06-26T15:07:20.238604Z" + "iopub.execute_input": "2023-09-02T00:45:22.766683Z", + "iopub.status.busy": "2023-09-02T00:45:22.766546Z", + "iopub.status.idle": "2023-09-02T00:45:22.795125Z", + "shell.execute_reply": "2023-09-02T00:45:22.794865Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:20.240678Z", - "iopub.status.busy": "2023-06-26T15:07:20.240559Z", - "iopub.status.idle": "2023-06-26T15:07:20.254621Z", - "shell.execute_reply": "2023-06-26T15:07:20.254365Z" + "iopub.execute_input": "2023-09-02T00:45:22.796768Z", + "iopub.status.busy": "2023-09-02T00:45:22.796675Z", + "iopub.status.idle": "2023-09-02T00:45:22.810815Z", + "shell.execute_reply": "2023-09-02T00:45:22.810551Z" } }, "outputs": [ @@ -142,10 +142,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:20.256203Z", - "iopub.status.busy": "2023-06-26T15:07:20.256086Z", - "iopub.status.idle": "2023-06-26T15:07:20.270987Z", - "shell.execute_reply": "2023-06-26T15:07:20.270725Z" + "iopub.execute_input": "2023-09-02T00:45:22.812271Z", + "iopub.status.busy": "2023-09-02T00:45:22.812168Z", + "iopub.status.idle": "2023-09-02T00:45:22.825425Z", + "shell.execute_reply": "2023-09-02T00:45:22.825199Z" } }, "outputs": [ @@ -177,10 +177,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:20.272660Z", - "iopub.status.busy": "2023-06-26T15:07:20.272548Z", - "iopub.status.idle": "2023-06-26T15:07:20.310677Z", - "shell.execute_reply": "2023-06-26T15:07:20.310316Z" + "iopub.execute_input": "2023-09-02T00:45:22.826975Z", + "iopub.status.busy": "2023-09-02T00:45:22.826886Z", + "iopub.status.idle": "2023-09-02T00:45:22.899444Z", + "shell.execute_reply": "2023-09-02T00:45:22.899168Z" } }, "outputs": [ @@ -217,10 +217,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:20.312313Z", - "iopub.status.busy": "2023-06-26T15:07:20.312201Z", - "iopub.status.idle": "2023-06-26T15:07:20.327301Z", - "shell.execute_reply": "2023-06-26T15:07:20.327059Z" + "iopub.execute_input": "2023-09-02T00:45:22.901156Z", + "iopub.status.busy": "2023-09-02T00:45:22.900959Z", + "iopub.status.idle": "2023-09-02T00:45:22.917152Z", + "shell.execute_reply": "2023-09-02T00:45:22.916895Z" } }, "outputs": [ @@ -249,10 +249,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:20.329031Z", - "iopub.status.busy": "2023-06-26T15:07:20.328914Z", - "iopub.status.idle": "2023-06-26T15:07:20.344407Z", - "shell.execute_reply": "2023-06-26T15:07:20.344096Z" + "iopub.execute_input": "2023-09-02T00:45:22.918722Z", + "iopub.status.busy": "2023-09-02T00:45:22.918636Z", + "iopub.status.idle": "2023-09-02T00:45:22.935428Z", + "shell.execute_reply": "2023-09-02T00:45:22.935168Z" } }, "outputs": [ @@ -287,31 +287,31 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:20.346153Z", - "iopub.status.busy": "2023-06-26T15:07:20.346037Z", - "iopub.status.idle": "2023-06-26T15:07:21.008346Z", - "shell.execute_reply": "2023-06-26T15:07:21.008071Z" + "iopub.execute_input": "2023-09-02T00:45:22.937156Z", + "iopub.status.busy": "2023-09-02T00:45:22.937040Z", + "iopub.status.idle": "2023-09-02T00:45:23.575411Z", + "shell.execute_reply": "2023-09-02T00:45:23.575063Z" } }, "outputs": [ { "data": { "text/plain": [ - " Precision Recall F1 Support \n", - " \n", - "brickface 77.13% 84.85% 80.81% 330 \n", - " cement 78.92% 83.94% 81.35% 330 \n", - " foliage 65.69% 20.30% 31.02% 330 \n", - " grass 100.00% 96.97% 98.46% 330 \n", - " path 90.63% 91.19% 90.91% 329 \n", - " sky 99.08% 98.18% 98.63% 330 \n", - " window 43.50% 67.88% 53.02% 330 \n", - " \n", - " Macro 79.28% 77.62% 76.31% \n", - " Micro 77.61% 77.61% 77.61% \n", - " Weighted 79.27% 77.61% 76.31% \n", + " Precision Recall F1 Support \n", + " \n", + " brickface 77.13% 84.85% 80.81% 330 \n", + " cement 78.92% 83.94% 81.35% 330 \n", + " foliage 65.69% 20.30% 31.02% 330 \n", + " grass 100.00% 96.97% 98.46% 330 \n", + " path 90.63% 91.19% 90.91% 329 \n", + " sky 99.08% 98.18% 98.63% 330 \n", + " window 43.50% 67.88% 53.02% 330 \n", + " \n", + " Macro 79.28% 77.62% 76.31% \n", + " Micro 77.61% 77.61% 77.61% \n", + " Weighted 79.27% 77.61% 76.31% \n", "\n", - " 77.61% accuracy " + " 77.61% accuracy " ] }, "execution_count": 8, @@ -348,31 +348,31 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:21.010194Z", - "iopub.status.busy": "2023-06-26T15:07:21.010071Z", - "iopub.status.idle": "2023-06-26T15:07:21.708092Z", - "shell.execute_reply": "2023-06-26T15:07:21.707803Z" + "iopub.execute_input": "2023-09-02T00:45:23.577203Z", + "iopub.status.busy": "2023-09-02T00:45:23.577088Z", + "iopub.status.idle": "2023-09-02T00:45:24.242152Z", + "shell.execute_reply": "2023-09-02T00:45:24.241854Z" } }, "outputs": [ { "data": { "text/plain": [ - " Precision Recall F1 Support \n", - " \n", - "brickface 77.13% 84.85% 80.81% 330 \n", - " cement 78.92% 83.94% 81.35% 330 \n", - " foliage 65.69% 20.30% 31.02% 330 \n", - " grass 100.00% 96.97% 98.46% 330 \n", - " path 90.63% 91.19% 90.91% 329 \n", - " sky 99.08% 98.18% 98.63% 330 \n", - " window 43.50% 67.88% 53.02% 330 \n", - " \n", - " Macro 79.28% 77.62% 76.31% \n", - " Micro 77.61% 77.61% 77.61% \n", - " Weighted 79.27% 77.61% 76.31% \n", + " Precision Recall F1 Support \n", + " \n", + " brickface 77.13% 84.85% 80.81% 330 \n", + " cement 78.92% 83.94% 81.35% 330 \n", + " foliage 65.69% 20.30% 31.02% 330 \n", + " grass 100.00% 96.97% 98.46% 330 \n", + " path 90.63% 91.19% 90.91% 329 \n", + " sky 99.08% 98.18% 98.63% 330 \n", + " window 43.50% 67.88% 53.02% 330 \n", + " \n", + " Macro 79.28% 77.62% 76.31% \n", + " Micro 77.61% 77.61% 77.61% \n", + " Weighted 79.27% 77.61% 76.31% \n", "\n", - " 77.61% accuracy " + " 77.61% accuracy " ] }, "execution_count": 9, diff --git a/docs/introduction/getting-started/regression.ipynb b/docs/introduction/getting-started/regression.ipynb index b4c721320b..4c32dd4c2e 100644 --- a/docs/introduction/getting-started/regression.ipynb +++ b/docs/introduction/getting-started/regression.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:23.240481Z", - "iopub.status.busy": "2023-06-26T15:07:23.239957Z", - "iopub.status.idle": "2023-06-26T15:07:23.678784Z", - "shell.execute_reply": "2023-06-26T15:07:23.678465Z" + "iopub.execute_input": "2023-09-02T00:45:25.707518Z", + "iopub.status.busy": "2023-09-02T00:45:25.707204Z", + "iopub.status.idle": "2023-09-02T00:45:26.094554Z", + "shell.execute_reply": "2023-09-02T00:45:26.094173Z" } }, "outputs": [ @@ -71,10 +71,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:23.680823Z", - "iopub.status.busy": "2023-06-26T15:07:23.680689Z", - "iopub.status.idle": "2023-06-26T15:07:23.698617Z", - "shell.execute_reply": "2023-06-26T15:07:23.698332Z" + "iopub.execute_input": "2023-09-02T00:45:26.096271Z", + "iopub.status.busy": "2023-09-02T00:45:26.096140Z", + "iopub.status.idle": "2023-09-02T00:45:26.113530Z", + "shell.execute_reply": "2023-09-02T00:45:26.113248Z" } }, "outputs": [], @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:23.700265Z", - "iopub.status.busy": "2023-06-26T15:07:23.700176Z", - "iopub.status.idle": "2023-06-26T15:07:23.713896Z", - "shell.execute_reply": "2023-06-26T15:07:23.713640Z" + "iopub.execute_input": "2023-09-02T00:45:26.115160Z", + "iopub.status.busy": "2023-09-02T00:45:26.115076Z", + "iopub.status.idle": "2023-09-02T00:45:26.128786Z", + "shell.execute_reply": "2023-09-02T00:45:26.128528Z" } }, "outputs": [ @@ -137,10 +137,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:23.715485Z", - "iopub.status.busy": "2023-06-26T15:07:23.715398Z", - "iopub.status.idle": "2023-06-26T15:07:23.733966Z", - "shell.execute_reply": "2023-06-26T15:07:23.733700Z" + "iopub.execute_input": "2023-09-02T00:45:26.130304Z", + "iopub.status.busy": "2023-09-02T00:45:26.130222Z", + "iopub.status.idle": "2023-09-02T00:45:26.148212Z", + "shell.execute_reply": "2023-09-02T00:45:26.147967Z" } }, "outputs": [ @@ -177,10 +177,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:23.735455Z", - "iopub.status.busy": "2023-06-26T15:07:23.735349Z", - "iopub.status.idle": "2023-06-26T15:07:23.749504Z", - "shell.execute_reply": "2023-06-26T15:07:23.749190Z" + "iopub.execute_input": "2023-09-02T00:45:26.149704Z", + "iopub.status.busy": "2023-09-02T00:45:26.149608Z", + "iopub.status.idle": "2023-09-02T00:45:26.162699Z", + "shell.execute_reply": "2023-09-02T00:45:26.162432Z" } }, "outputs": [], @@ -201,10 +201,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:23.751249Z", - "iopub.status.busy": "2023-06-26T15:07:23.751162Z", - "iopub.status.idle": "2023-06-26T15:07:23.764551Z", - "shell.execute_reply": "2023-06-26T15:07:23.764312Z" + "iopub.execute_input": "2023-09-02T00:45:26.164248Z", + "iopub.status.busy": "2023-09-02T00:45:26.164161Z", + "iopub.status.idle": "2023-09-02T00:45:26.177605Z", + "shell.execute_reply": "2023-09-02T00:45:26.177371Z" } }, "outputs": [ @@ -236,10 +236,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:23.766918Z", - "iopub.status.busy": "2023-06-26T15:07:23.766771Z", - "iopub.status.idle": "2023-06-26T15:07:26.016586Z", - "shell.execute_reply": "2023-06-26T15:07:26.016320Z" + "iopub.execute_input": "2023-09-02T00:45:26.179257Z", + "iopub.status.busy": "2023-09-02T00:45:26.179157Z", + "iopub.status.idle": "2023-09-02T00:45:28.368646Z", + "shell.execute_reply": "2023-09-02T00:45:28.368368Z" } }, "outputs": [ @@ -282,10 +282,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:26.018529Z", - "iopub.status.busy": "2023-06-26T15:07:26.018286Z", - "iopub.status.idle": "2023-06-26T15:07:28.285287Z", - "shell.execute_reply": "2023-06-26T15:07:28.285024Z" + "iopub.execute_input": "2023-09-02T00:45:28.370301Z", + "iopub.status.busy": "2023-09-02T00:45:28.370188Z", + "iopub.status.idle": "2023-09-02T00:45:30.543139Z", + "shell.execute_reply": "2023-09-02T00:45:30.542840Z" } }, "outputs": [ diff --git a/docs/recipes/active-learning.ipynb b/docs/recipes/active-learning.ipynb index 8680317387..d79aff4ece 100644 --- a/docs/recipes/active-learning.ipynb +++ b/docs/recipes/active-learning.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:30.639390Z", - "iopub.status.busy": "2023-06-26T15:07:30.639006Z", - "iopub.status.idle": "2023-06-26T15:07:31.532486Z", - "shell.execute_reply": "2023-06-26T15:07:31.532176Z" + "iopub.execute_input": "2023-09-02T00:45:32.751403Z", + "iopub.status.busy": "2023-09-02T00:45:32.751058Z", + "iopub.status.idle": "2023-09-02T00:45:33.602726Z", + "shell.execute_reply": "2023-09-02T00:45:33.602405Z" } }, "outputs": [ @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:31.544616Z", - "iopub.status.busy": "2023-06-26T15:07:31.544474Z", - "iopub.status.idle": "2023-06-26T15:07:31.560157Z", - "shell.execute_reply": "2023-06-26T15:07:31.559886Z" + "iopub.execute_input": "2023-09-02T00:45:33.614078Z", + "iopub.status.busy": "2023-09-02T00:45:33.613917Z", + "iopub.status.idle": "2023-09-02T00:45:33.630138Z", + "shell.execute_reply": "2023-09-02T00:45:33.629896Z" } }, "outputs": [ @@ -121,7 +121,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1922 / 5574 = 34.48%\n" + "1921 / 5574 = 34.46%\n" ] } ], @@ -142,10 +142,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:31.561854Z", - "iopub.status.busy": "2023-06-26T15:07:31.561733Z", - "iopub.status.idle": "2023-06-26T15:07:32.079589Z", - "shell.execute_reply": "2023-06-26T15:07:32.079300Z" + "iopub.execute_input": "2023-09-02T00:45:33.631781Z", + "iopub.status.busy": "2023-09-02T00:45:33.631672Z", + "iopub.status.idle": "2023-09-02T00:45:34.092919Z", + "shell.execute_reply": "2023-09-02T00:45:34.092647Z" } }, "outputs": [ @@ -156,9 +156,9 @@ "[1,000] Accuracy: 86.32% – 661 samples used\n", "[2,000] Accuracy: 86.44% – 1,057 samples used\n", "[3,000] Accuracy: 86.52% – 1,339 samples used\n", - "[4,000] Accuracy: 86.62% – 1,569 samples used\n", - "[5,000] Accuracy: 86.57% – 1,791 samples used\n", - "[5,574] Accuracy: 86.60% – 1,922 samples used\n" + "[4,000] Accuracy: 86.62% – 1,568 samples used\n", + "[5,000] Accuracy: 86.57% – 1,790 samples used\n", + "[5,574] Accuracy: 86.60% – 1,921 samples used\n" ] }, { diff --git a/docs/recipes/bandits-101.ipynb b/docs/recipes/bandits-101.ipynb index f9b1ea46ee..0ba4c6debd 100644 --- a/docs/recipes/bandits-101.ipynb +++ b/docs/recipes/bandits-101.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:33.629358Z", - "iopub.status.busy": "2023-06-26T15:07:33.628723Z", - "iopub.status.idle": "2023-06-26T15:07:33.698515Z", - "shell.execute_reply": "2023-06-26T15:07:33.698182Z" + "iopub.execute_input": "2023-09-02T00:45:35.575425Z", + "iopub.status.busy": "2023-09-02T00:45:35.575172Z", + "iopub.status.idle": "2023-09-02T00:45:35.634396Z", + "shell.execute_reply": "2023-09-02T00:45:35.634109Z" } }, "outputs": [], @@ -51,10 +51,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:33.700564Z", - "iopub.status.busy": "2023-06-26T15:07:33.700430Z", - "iopub.status.idle": "2023-06-26T15:07:33.998325Z", - "shell.execute_reply": "2023-06-26T15:07:33.997361Z" + "iopub.execute_input": "2023-09-02T00:45:35.636137Z", + "iopub.status.busy": "2023-09-02T00:45:35.636026Z", + "iopub.status.idle": "2023-09-02T00:45:36.107306Z", + "shell.execute_reply": "2023-09-02T00:45:36.106935Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:07:34.003974Z", - "iopub.status.busy": "2023-06-26T15:07:34.003640Z", - "iopub.status.idle": "2023-06-26T15:08:15.294826Z", - "shell.execute_reply": "2023-06-26T15:08:15.294349Z" + "iopub.execute_input": "2023-09-02T00:45:36.109239Z", + "iopub.status.busy": "2023-09-02T00:45:36.109080Z", + "iopub.status.idle": "2023-09-02T00:46:16.897601Z", + "shell.execute_reply": "2023-09-02T00:46:16.897128Z" } }, "outputs": [ @@ -108,9 +108,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/6000000 [00:00441\n", " 632\n", " 0\n", - " 2\n", - " 2.080471\n", - " 2.007966\n", + " 4\n", + " -0.036801\n", + " 0.457068\n", " \n", " \n", " 3566176\n", " 1188\n", " 725\n", " 1\n", - " 0\n", - " 1.386654\n", - " 1.694128\n", + " 5\n", + " 1.837321\n", + " 2.220956\n", " \n", " \n", " 1109043\n", " 369\n", " 681\n", " 0\n", - " 7\n", - " 1.620536\n", - " 1.508527\n", + " 6\n", + " 0.616991\n", + " 1.324610\n", " \n", " \n", " 4286042\n", @@ -176,17 +176,17 @@ " 680\n", " 2\n", " 3\n", - " 2.540870\n", - " 1.542765\n", + " 0.236458\n", + " 0.570999\n", " \n", " \n", " 5395174\n", " 1798\n", " 391\n", " 1\n", - " 8\n", - " 2.210705\n", - " 0.575857\n", + " 1\n", + " -0.851223\n", + " 0.446835\n", " \n", " \n", "\n", @@ -194,11 +194,11 @@ ], "text/plain": [ " episode step policy_idx arm reward reward_stat\n", - "1324896 441 632 0 2 2.080471 2.007966\n", - "3566176 1188 725 1 0 1.386654 1.694128\n", - "1109043 369 681 0 7 1.620536 1.508527\n", - "4286042 1428 680 2 3 2.540870 1.542765\n", - "5395174 1798 391 1 8 2.210705 0.575857" + "1324896 441 632 0 4 -0.036801 0.457068\n", + "3566176 1188 725 1 5 1.837321 2.220956\n", + "1109043 369 681 0 6 0.616991 1.324610\n", + "4286042 1428 680 2 3 0.236458 0.570999\n", + "5395174 1798 391 1 1 -0.851223 0.446835" ] }, "execution_count": 3, @@ -230,10 +230,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T15:08:15.296808Z", - "iopub.status.busy": "2023-06-26T15:08:15.296607Z", - "iopub.status.idle": "2023-06-26T15:08:16.075316Z", - "shell.execute_reply": "2023-06-26T15:08:16.069989Z" + "iopub.execute_input": "2023-09-02T00:46:16.899601Z", + "iopub.status.busy": "2023-09-02T00:46:16.899491Z", + "iopub.status.idle": "2023-09-02T00:46:17.665862Z", + "shell.execute_reply": "2023-09-02T00:46:17.665055Z" } }, "outputs": [ @@ -249,7 +249,7 @@ }, { "data": { - "image/png": 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", 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"execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:03:29.661157Z", - "iopub.status.busy": "2023-06-26T17:03:29.660672Z", - "iopub.status.idle": "2023-06-26T17:03:30.293247Z", - "shell.execute_reply": "2023-06-26T17:03:30.292936Z" + "iopub.execute_input": "2023-09-02T00:47:25.579132Z", + "iopub.status.busy": "2023-09-02T00:47:25.577642Z", + "iopub.status.idle": "2023-09-02T00:47:26.259239Z", + "shell.execute_reply": "2023-09-02T00:47:26.258767Z" }, "tags": [] }, @@ -101,10 +101,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:03:30.295322Z", - "iopub.status.busy": "2023-06-26T17:03:30.295158Z", - "iopub.status.idle": "2023-06-26T17:03:30.314403Z", - "shell.execute_reply": "2023-06-26T17:03:30.313991Z" + "iopub.execute_input": "2023-09-02T00:47:26.261286Z", + "iopub.status.busy": "2023-09-02T00:47:26.261109Z", + "iopub.status.idle": "2023-09-02T00:47:26.279789Z", + "shell.execute_reply": 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+ "iopub.status.busy": "2023-09-02T00:47:26.662130Z", + "iopub.status.idle": "2023-09-02T00:47:26.680747Z", + "shell.execute_reply": "2023-09-02T00:47:26.680428Z" }, "tags": [] }, @@ -794,10 +794,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:03:30.727761Z", - "iopub.status.busy": "2023-06-26T17:03:30.727585Z", - "iopub.status.idle": "2023-06-26T17:03:45.399145Z", - "shell.execute_reply": "2023-06-26T17:03:45.398796Z" + "iopub.execute_input": "2023-09-02T00:47:26.682371Z", + "iopub.status.busy": "2023-09-02T00:47:26.682280Z", + "iopub.status.idle": "2023-09-02T00:47:39.546460Z", + "shell.execute_reply": "2023-09-02T00:47:39.546168Z" }, "scrolled": true, "tags": [] @@ -805,7 +805,7 @@ "outputs": [ { "data": { - "image/png": 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oyy+/LDQ+JSVFt9xyi6no6cYbb9SNN95YYHxZ98bq1avLz8/P1s7OzlZCQoJL5nLXfl+SNQEA4G68Vi9+noqwJrgWheYATM6cOSPDMGxtb29vRURElGiM2rVrm9qJiYlOyQ0AgCvdwoUL9csvv5iOjR49usg++ffZOnXqlGjO/Pv26dOnHZonf7/SzFXYzwjuWhMAAM70/vvv67fffpMk+fj46OOPP5bFYinzuGXdF2vUqCEvLy9b22q1KikpyS7OnfcD3LUmAACcKf+nfHl4eKh79+52bxgvSGZmpl599VX1799f58+fLzSOfd8e+z4AwN26du2qDz74wPaaPjc3V6NHj1bHjh01ZcoUzZkzRz/99JP++9//6qGHHlL9+vX1448/2vr36dNH3377baHjl3VvlKRatWoVOeYl+e+Nl/Veu6v2e8nxNQEA4G68VrdXEdcE1/IqPgTA1SQtLc3UDggIKPEvxgMDA4scEwAA2EtOTtZ9991nOjZ48GB17NixyH7599n8+3Bx8sfn5OQoKytLvr6+Tp2noD6F/YzgrjUBAOAsCQkJGj9+vK397LPPKi4uziljl3VftFgs8vf3NxW1FbQHu/N+gLvWBACAs1itVrsC8Ycffli///67pIt708CBA9W/f3/VqVNH6enp+v333/X111/r+PHjtj5Lly7V6NGj9f333xc4D/u+PfZ9AEB5eOCBB9S4cWM9/PDD2rFjh6SLn26S/xNOLhcbG6unnnpK99xzj+mJ6Pm56157Zmam8vLyyjSXu/b7kswFAIC78VrdXkVcE1yLJ5oDMMn/zfjyj6hylL+/f5FjAgAAM6vVqttvv11Hjx61HQsJCdH7779fbN+y7t359+2CxnTGPAXN5egLW1etCQAAZ7n33nuVnp4uSYqLi9O4ceOcNra79uDKtNeXZC4AAJwhNTXV9AQuSdqyZYskKSwsTKtWrdL8+fN1//33a+DAgbr11ls1ZcoU7d69WyNHjjT1mz17tr766qsC52HfL9tcAAA403XXXaeNGzfqiSeekKenZ5Gx9erV0xNPPKGRI0cWWWQuld9+X5q52O8BAOC1elnm4meEKweF5gBMLly4YGr7+PiUeIz8TwvNzMwsU04AAFzpnnzySS1atMh07D//+Y/q1q1bbN+y7t0FPeW7oL3bnT8juGtNAAA4w2effaalS5dKuvgUj48//rhU+2Rh3LUHV6a9viRzAQDgDIX9EtPT01MLFixQt27dCjwfFBSkr7/+Wn379jUdf+WVV+wK1yX2/bLOBQCAM/373/9W/fr19eabb9o9GTy/w4cPa+zYsYqOjtbUqVOLjC2v/b40c7HfAwDAa/WyzMXPCFcOCs0BmOR/51B2dnaJx8jKyipyTAAA8D/vv/++3n77bdOxp556SrfeeqtD/cu6d+fftwsa0xnzFDRXYT8juGtNAACU1YkTJ/TEE0/Y2nfffXehhWal5a49uDLt9SWZCwAAZyhsn7n77rvVqVOnIvt6eHjoX//6l+npprt379aqVauKnYd9v2RzAQDgDDk5Obrlllv0wAMP6MSJE5KkatWq6YUXXtCGDRt09uxZZWdn6/jx45o/f76GDBkii8UiSUpOTtaYMWP05JNPFjp+ee33pZmL/R4AAF6rl2Uufka4clBoDsAkKCjI1C7onc7Fyf/OofxjAgCAi6ZNm6ZHH33UdGz06NGaMmWKw2OUde8u6B2/Be3d7vwZwV1rAgCgrP7+978rJSVFkhQZGanXX3/d6XO4aw+uTHt9SeYCAMAZCttn7rnnHof6x8bGqnfv3qZjBRWas++XbS4AAJzhgQce0Pfff29rd+zYUTt27NDEiRPVoUMHhYaGytvbWzVr1tSNN96o2bNna+7cuaaipzfffFOff/55geOX135fmrnY7wEA4LV6WebiZ4QrB4XmAEzyfzPOyMgo8CM8i5Kenl7kmAAAQPrxxx915513mvbZoUOH6tNPP7U9/cQR+ffZ/PtwcfLHe3l5Ffgu4LLOU1AfR1/YumpNAACUxcyZMzVnzhxb+7333lNoaKjT5ynrvmgYRqlu+LryfoC71gQAgLP4+/vL09PTdCw4OFht2rRxeIxrr73W1N60aZNdDPu+PfZ9AIA7rVy5Up999pmtHRERoR9//FGRkZFF9rvpppv04Ycfmo49+eSTDj0UxVX32gv6+aWs99pdtd+XZC4AANyN1+r2KuKa4FoUmgMwCQ8PNxW35eTkKDExsURjHDt2zNSOiIhwSm4AAFwpVqxYoWHDhik3N9d2rE+fPvr222/tbvwWJ/8+e/To0RL1z79vV69e3aF58vcrzVyF/YzgrjUBAFAWl38M9oABAzR8+HCXzFPWffHUqVOmnzk8PDwUHh5uF+fO+wHuWhMAAM6Uf/9q0KCBPDwc/zVb48aNTe2C9ln2fXvs+wAAd3r//fdN7UcffdTh+8ujR49Wo0aNbO2kpCTNnj3bLq6se6MkHT9+vMgxL8mfe1nvtbtqv5ccXxMAAO7Ga3V7FXFNcC0KzQGY+Pv7q169eqZjhw8fLtEY+ePj4uLKnBcAAFeK9evX66abbjJ9LFTnzp01Z84c+fj4lHi8/L+odtW+HRsbKy8vL1s7MzNTp0+fdslc7loTAABlkZKSYvvzggULZLFYiv3q2bOnaYxDhw7Zxfzxxx+mGGfvi1FRUQV+0oc77we4a00AADhTkyZNTO0qVaqUqH/++LNnz9rFsO8XPw/7PgDAVQzD0PLly03HbrzxRof7e3h4aMCAAaZjv/zyi11cWffGxMRE0+8XfHx8FBsbW2Csu+61u3NNAAC4G6/Vi5+nIqwJrkWhOQA7+b8h79y5s0T94+PjixwPAICr1datW9WvXz+lpaXZjrVp00YLFy5UYGBgqcZ0177t7e2t+vXrl3qurKwsHThwwKG5+FkEAID/cee+6K652OsBAJVR06ZNTe2srKwS9b+8eEqSAgIC7GLY90s/DwAAZXX27FmlpqaajsXExJRojPzxBX0yaP69bP/+/crOznZ4jvx7Y/369U0PiSlqLnft965cEwAA7sZr9dLP4+654DoUmgOw07p1a1N73bp1Dvc9ceKEDh48aGt7e3vb3YAHAOBqtHv3bvXp08f0xLImTZpo8eLFCgkJKfW4+fftjRs3mj6mqjhr164tcryizpXkZ4TNmzebfglfs2bNQj/Wyp1rAgCgomvWrJm8vb1t7YMHD+rEiRMO93fXXl+S+wHuXBMAAM7Stm1bU/vUqVMl6p//o6HDwsLsYtj37bHvAwDcpaA3kZW02PnyPU+S8vLy7GIiIyMVGRlpmnfz5s0Oz+Gu/T43N1cbNmxwaC53rgkAAHfjtbq9irgmuBaF5gDsDBw40NReunSpDMNwqO/PP/9savfs2VNBQUFOyw0AgMro0KFD6t27t+mXyjExMVqyZImqV69eprHj4uJMTxpPT093+MVZenq6fv31V1vbYrHY/RxwufznlixZ4nCe+WOL+shRd64JAIDSmjdvnpYsWVKirzfffNM0Ro0aNexiGjRoYIoJDg5W9+7dTccc3YMNw9DSpUtNx4rag911P8CdawIAwFkGDBggD4///VotISFBycnJDvfPX2yV/2OqJfb9/Nj3AQDuVNCbwI4fP16iMfI/wbyw+/8DBgwwtV11rz3/POvWrVN6erpD86xdu1YZGRm2dqNGjdSoUSOH53LVmgAAcDdeq5tV1DXBtSg0B2Cnc+fOCg8Pt7UPHDiglStXOtT3s88+M7UHDRrkzNQAAKh0Tpw4oV69euno0aO2Y7Vr19ayZctUu3Ztp8xx0003mdr59+PCTJ8+XWlpabZ2+/btVatWrULj+/fvb3qCy8qVK3XgwIFi5zEMQ1988YXpWHE/I7hrTQAAlNa1116r3r17l+irXbt2pjH8/PzsYgq6SVrafXHFihVKSEiwtWvUqKFOnToVGu/O+wHuWhMAAM4SERGhLl26mI7Nnj3bob65ubmaM2eO6ViPHj0KjGXf/x/2fQCAO/n4+KhmzZqmY8uXLy/RGMuWLTO1L3+gyuXy742ff/65QwVX+/fv16pVq2xtb29v9e/fv9D4unXrqk2bNrZ2WlqaZsyYUew8Utn3e1etCQCA8sBr9f+pyGuC61BoDsCOh4eHRo8ebTo2ceLEYl8ILlu2TKtXr7a1g4ODNXz4cFekCABApZCcnKw+ffpo//79tmPVq1fXkiVLFBMT47R5/va3v8lisdja3333neLj44vsc+HCBU2ZMsV0bMyYMUX2qVatmgYPHmxrG4ahCRMmFJvf1KlTTR9pFRUVpd69exfZx11rAgCgMhgxYoQCAwNt7V9++aXYX3YbhqGJEyeajt11112mJ7Hm5877Ae5aEwAAznTfffeZ2m+88YaysrKK7ffJJ5/o5MmTtnaVKlV0/fXXFxjLvn8R+z4AoDz06tXL1H733XeVm5vrUN9Vq1aZPm2zoPEuuf7661WnTh1b++DBg/r888+LnWPChAmmvfrmm29WSEhIkX3y3yOfMmWKLly4UGSf+Ph4TZ8+3dYu6OeG/Ny5JgAA3I3X6hdV9DXBhQwAKMDp06eNoKAgQ5Lt69VXXy00/ujRo0Z0dLQpfvz48W7MGACAiuXcuXNGhw4dTHtjaGio8fvvv7tkvltvvdU0V4cOHYzU1NQCY61Wq3HfffeZ4mNjY43s7Oxi59mxY4fh4eFh6jtt2rQi40NDQ03xn376aYVaEwAA7rJixQrTXhUVFeVw36efftrUNyYmxjh27Fih8ZMnTzbFh4SEGElJScXO4877Ae5aEwAAzpKXl2e0aNHCtB/deeedRl5eXqF9fvvtN7u99ZlnnilyHvZ99n0AQPn46aefTPuPJOOee+4pcq83DMPYu3evUatWLVO/hg0bGrm5uYX2+de//mWKr1q1qrFjx45C47/55htTvKenp7F79+5i15SVlWXUq1fP1Pf+++83rFZrgfGpqalG+/btTfG33357sfO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" ] @@ -851,17 +851,17 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:03:45.400911Z", - "iopub.status.busy": "2023-06-26T17:03:45.400811Z", - "iopub.status.idle": "2023-06-26T17:03:58.283322Z", - "shell.execute_reply": "2023-06-26T17:03:58.283008Z" + "iopub.execute_input": "2023-09-02T00:47:39.548276Z", + "iopub.status.busy": "2023-09-02T00:47:39.548181Z", + "iopub.status.idle": "2023-09-02T00:47:50.829755Z", + "shell.execute_reply": "2023-09-02T00:47:50.829454Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -906,17 +906,17 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:03:58.285215Z", - "iopub.status.busy": "2023-06-26T17:03:58.285118Z", - "iopub.status.idle": "2023-06-26T17:04:06.413734Z", - "shell.execute_reply": "2023-06-26T17:04:06.413443Z" + "iopub.execute_input": "2023-09-02T00:47:50.832249Z", + "iopub.status.busy": "2023-09-02T00:47:50.831992Z", + "iopub.status.idle": "2023-09-02T00:47:58.646407Z", + "shell.execute_reply": "2023-09-02T00:47:58.646096Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -987,17 +987,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:04:06.416076Z", - "iopub.status.busy": "2023-06-26T17:04:06.415951Z", - "iopub.status.idle": "2023-06-26T17:04:29.992825Z", - "shell.execute_reply": "2023-06-26T17:04:29.992389Z" + "iopub.execute_input": "2023-09-02T00:47:58.648463Z", + "iopub.status.busy": "2023-09-02T00:47:58.648373Z", + "iopub.status.idle": "2023-09-02T00:48:21.211763Z", + "shell.execute_reply": "2023-09-02T00:48:21.211450Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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jHtb6c+yufi7eXWu61PF7AgCgO/Ceu63eeE8wX/zxTwGAo6uuro7YTk1N7fQvudPS0o45JgAAJ6PS0lLdfPPNEfsuvfRSTZ069ZjXtV5HW6+zx9P6/KamJjU0NCgpKcnUOu1dc7TXANG6JwAAOqu4uFj33XdfePunP/2pTj31VFPG7ur6Z7FYlJKSEhFEa2+tjeb7+mjdEwAAHRUKhdqEtu+44w6tW7dOUsvac+GFF+qCCy7Q0KFDVVNTo3Xr1umvf/2r9uzZE75myZIlmjt3rl5//fV267Cut8W6DgDoLrfeeqtGjRqlO+64Q5s2bZLU8k0frb/t40gFBQW65557dOONN0Z0Dm8tWp+L19XVKRgMdqlWtNb0ztQCAKA78J67rd54TzAfnb8BdEnr/2Af+ZVOHZWSknLMMQEAONmEQiFdc801KikpCe/LysrS/Pnzj3ttV9fm1utye2OaUae9Wh19c9td9wQAQGfddNNNqqmpkSSdeuqp+tnPfmba2NFaa2NpTe9MLQAAOqKioiKiw5UkrV27VpJks9n04Ycf6p///KduueUWXXjhhbrqqqv0yCOPaOvWrZo9e3bEdW+88YYWLFjQbh3W9a7VAgCgs77yla9o9erVuuuuuxQXF3fMc4cPH6677rpLs2fPPmbwW+q5Nf1EarGmAwBOFrznPvFavA6IbYS/AXRJfX19xHZiYmKnx2jddbOurq5LcwIAINbdfffd+ve//x2x709/+pOGDRt23Gu7uja31w27vbU5mq8BonVPAAB0xrPPPqslS5ZIaumi8dRTT53Qeng00VprY2lN70wtAAA64mi/kIyLi9Nbb72ls846q93j6enp+utf/6qvfe1rEfv/7//+r02YXGJd72otAAA6649//KMcDocee+yxNh20W9u5c6duu+025efn67nnnjvmuT21pp9ILdZ0AMDJgvfcJ16L1wGxjfA3gC5p/cRPY2Njp8doaGg45pgAAJxM5s+fr9/85jcR++655x5dddVVHbq+q2tz63W5vTHNqNNeraO9BojWPQEA0FF79+7VXXfdFd6+4YYbjhoOO1HRWmtjaU3vTC0AADriaOvIDTfcoGnTph3zWqvVqieffDKiQ+jWrVv14YcfHrcO63rnagEA0FFNTU26/PLLdeutt2rv3r2SpNzcXD3wwANatWqVysrK1NjYqD179uif//ynvvWtb8lisUiSSktLdf311+vuu+8+6vg9taafSC3WdADAyYL33Cdei9cBsY3wN4AuSU9Pj9hu7ynk42n9xE/rMQEAOFm8+OKLuvPOOyP2zZ07V4888kiHx+jq2tzek7jtrc3RfA0QrXsCAKCjvv/976u8vFySNHDgQP3qV78yvUa01tpYWtM7UwsAgI442jpy4403duj6goICnX/++RH72gt/s653rRYAAB1166236vXXXw9vT506VZs2bdJDDz2kKVOmKDs7WwkJCRo0aJAuuugivfHGG3rzzTcjQkqPPfaY/vznP7c7fk+t6SdSizUdAHCy4D33idfidUBsI/wNoEta/we7tra23a+1PJaamppjjgkAwMlg0aJFmjNnTsQ6etlll+mZZ54Jdx7piNbraOt19nhanx8fH9/u07ldrdPeNR19c9td9wQAQEe89tprWrhwYXj7t7/9rbKzs02v09X1zzCME/pwtzvf10frngAA6KiUlBTFxcVF7MvIyNDEiRM7PMY555wTsb1mzZo257Cut8W6DgAw2wcffKBnn302vN2/f38tWrRIAwcOPOZ1F198sf7whz9E7Lv77rs71FSkuz4Xb+81Slc/F++uNb0ztQAA6A68526rN94TzEf4G0CX2O32iEBaU1OTDhw40Kkxdu/eHbHdv39/U+YGAECsWLp0qa644go1NzeH982cOVMvvfRSmw94j6f1OlpSUtKp61uvy/369etQndbXnUito70GiNY9AQDQEUd+/fOsWbN05ZVXdkudrq5/+/fvj3htYbVaZbfb25wXzff10bonAAA6o/X65HQ6ZbV2/Ndno0aNithubx1lXW+LdR0AYLb58+dHbN95550d/ix47ty5OuWUU8LbgUBAb7zxRpvzurr+SdKePXuOOeZhrefe1c/Fu2tNlzp+TwAAdAfec7fVG+8J5iP8DaBLUlJSNHz48Ih9O3fu7NQYrc8/9dRTuzwvAABixcqVK3XxxRdHfIVSYWGhFi5cqMTExE6P1/qXzt21LhcUFCg+Pj68XVdXp4MHD3ZLrWjdEwAAHVFeXh7++1tvvSWLxXLcP+edd17EGDt27Ghzzvr16yPOMXv9y8vLa/ebL6L5vj5a9wQAQGeMHj06YjszM7NT17c+v6ysrM05rOvHr8O6DgDoCsMw9P7770fsu+iiizp8vdVq1axZsyL2ffTRR23O6+r6d+DAgYjfBSQmJqqgoKDdc6P1uXg07wkAgO7Ae+7j1+kN9wTzEf4G0GWt/6O9efPmTl2/ZcuWY44HAEBftWHDBn3zm99UdXV1eN/EiRP19ttvKy0t7YTGjNa6nJCQIIfDccK1GhoaVFRU1KFavNYAAJyMorn+RasWazoAoDcaM2ZMxHZDQ0Onrj8y7CRJqampbc5hXT/xOgAAdERZWZkqKioi9o0YMaJTY7Q+v71vu2y9Xvl8PjU2Nna4Ruv1z+FwRDRZOVataK3p3XlPAAB0B95zn3idaNeCuQh/A+iyCRMmRGx7PJ4OX7t3715t3749vJ2QkNDmw3YAAPqirVu3aubMmREdwUaPHq3//Oc/ysrKOuFxW6/Lq1evjvhKp+Nxu93HHO9YxzrzGuCTTz6J+IX6oEGDjvoVUNG8JwAAeovTTjtNCQkJ4e3t27dr7969Hb4+Wmt6Z97XR/OeAADoqEmTJkVs79+/v1PXt/46ZJvN1uYc1vW2WNcBAGZq7+GtzgaQj1zXJCkYDLY5Z+DAgRo4cGBE3U8++aTDNaK1pjc3N2vVqlUdqhXNewIAoDvwnrut3nhPMB/hbwBdduGFF0ZsL1myRIZhdOjad999N2L7vPPOU3p6umlzAwCgN9qxY4fOP//8iF8QjxgxQosXL1a/fv26NPapp54a0ZG7pqamw2/QampqtHz58vC2xWJps84fqfWxxYsXd3ierc891ldwRvOeAAA4nn/84x9avHhxp/489thjEWMMGDCgzTlOpzPinIyMDJ199tkR+zq61hqGoSVLlkTsO9ZaG6339dG8JwAAOmrWrFmyWr/8dVlxcbFKS0s7fH3rcFTrr2aWWNdbY10HAJitvYev9uzZ06kxWnf6Ptpn9bNmzYrY7q7PxVvX8Xg8qqmp6VAdt9ut2tra8PYpp5yiU045pcO1uuueAADoDrznjtRb7wnmI/wNoMsKCwtlt9vD20VFRfrggw86dO2zzz4bsX3JJZeYOTUAAHqdvXv36qtf/apKSkrC+4YMGaL33ntPQ4YMMaXGxRdfHLHder09mldeeUXV1dXh7TPOOEODBw8+6vkXXHBBRPeUDz74QEVFRcetYxiGnn/++Yh9x3sNEK17AgDgeM455xydf/75nfozefLkiDGSk5PbnNPeB6Inuv4tXbpUxcXF4e0BAwZo2rRpRz0/mu/ro3VPAAB0VP/+/eVyuSL2vfHGGx26trm5WQsXLozYd+6557Z7Luv6l1jXAQBmS0xM1KBBgyL2vf/++50a47333ovYPrIhyZFar39//vOfOxSQ8vl8+vDDD8PbCQkJuuCCC456/rBhwzRx4sTwdnV1tV599dXj1pG6vqZ31z0BANBdeM/9pd58TzAX4W8AXWa1WjV37tyIfQ899NBx3xC+9957+vjjj8PbGRkZuvLKK7tjigAA9AqlpaWaOXOmfD5feF+/fv20ePFijRgxwrQ61113nSwWS3j75Zdf1pYtW455TX19vR555JGIfddff/0xr8nNzdWll14a3jYMQw8++OBx5/fcc89FfP1TXl6ezj///GNeE617AgCgN7n66quVlpYW3v7oo4+O+8trwzD00EMPRez73ve+F9HRtLVovq+P1j0BANAZN998c8T2r3/9azU0NBz3uqefflr79u0Lb2dmZurrX/96u+eyrrdgXQcAdJevfvWrEduPP/64mpubO3Tthx9+GPENku2Nd9jXv/51DR06NLy9fft2/fnPfz5ujQcffDBiPf6v//ovZWVlHfOa1p9nP/LII6qvrz/mNVu2bNErr7wS3m7vtUFr0bwnAAC6A++5W/T2e4LJDAAwwcGDB4309HRDUvjPww8/fNTzS0pKjPz8/Ijz77vvvijOGACA6KqsrDSmTJkSsfZlZ2cb69at65Z6V111VUStKVOmGBUVFe2eGwqFjJtvvjni/IKCAqOxsfG4dTZt2mRYrdaIa1988cVjnp+dnR1x/jPPPNOr7gkAALMtXbo0Yk3Ky8vr8LU/+clPIq4dMWKEsXv37qOe/8tf/jLi/KysLCMQCBy3TjTf10frngAA6KhgMGiMHTs2Yr2ZM2eOEQwGj3rNihUr2qyd99577zHrsK6zrgMAus8777wTscZIMm688cZjrueGYRjbtm0zBg8eHHHdyJEjjebm5qNe8+STT0acn5OTY2zatOmo57/wwgsR58fFxRlbt2497j01NDQYw4cPj7j2lltuMUKhULvnV1RUGGeccUbE+ddcc81x60TzngAAOFJXPjtvjffcsXFPMA/hbwCm+b//+782b6hvvfXWiEUnGAwaCxcubPMmdfDgwUZZWVnPTR4AgG527rnntlknf/GLXxiLFy/u9J/S0tLj1tu2bZuRmpoaUW/8+PHG0qVLI87bunWrcdlll7WZ26uvvtrhe7vpppsirrVarcb9998fMc/Gxkbjz3/+s5GTkxNx7rhx44ympqYO1YnmPQEAYKaufIAdCASMgQMHtrn+H//4R8Qve3ft2tXmwSdJxq9+9asO14rW+/po3hMAAB21ZMkSw2KxRKw5559/vrFmzZqI88rLy43/9//+X5t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" ] @@ -1051,17 +1051,17 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:04:29.995236Z", - "iopub.status.busy": "2023-06-26T17:04:29.995077Z", - "iopub.status.idle": "2023-06-26T17:04:50.701160Z", - "shell.execute_reply": "2023-06-26T17:04:50.700801Z" + "iopub.execute_input": "2023-09-02T00:48:21.213893Z", + "iopub.status.busy": "2023-09-02T00:48:21.213730Z", + "iopub.status.idle": "2023-09-02T00:48:40.890890Z", + "shell.execute_reply": "2023-09-02T00:48:40.890553Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] diff --git a/docs/recipes/pipelines.ipynb b/docs/recipes/pipelines.ipynb index ce8eb4dc08..9a7a431866 100644 --- a/docs/recipes/pipelines.ipynb +++ b/docs/recipes/pipelines.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:56.508261Z", - "iopub.status.busy": "2023-06-26T17:02:56.508069Z", - "iopub.status.idle": "2023-06-26T17:02:56.631267Z", - "shell.execute_reply": "2023-06-26T17:02:56.630163Z" + "iopub.execute_input": "2023-09-02T00:48:42.421626Z", + "iopub.status.busy": "2023-09-02T00:48:42.420909Z", + "iopub.status.idle": "2023-09-02T00:48:42.759593Z", + "shell.execute_reply": "2023-09-02T00:48:42.759147Z" }, "tags": [] }, @@ -54,10 +54,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:56.637636Z", - "iopub.status.busy": "2023-06-26T17:02:56.637235Z", - "iopub.status.idle": "2023-06-26T17:02:56.671036Z", - "shell.execute_reply": "2023-06-26T17:02:56.669047Z" + "iopub.execute_input": "2023-09-02T00:48:42.761738Z", + "iopub.status.busy": "2023-09-02T00:48:42.761619Z", + "iopub.status.idle": "2023-09-02T00:48:42.775627Z", + "shell.execute_reply": "2023-09-02T00:48:42.775366Z" }, "tags": [] }, @@ -82,10 +82,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:56.682809Z", - "iopub.status.busy": "2023-06-26T17:02:56.682035Z", - "iopub.status.idle": "2023-06-26T17:02:56.706464Z", - "shell.execute_reply": "2023-06-26T17:02:56.704627Z" + "iopub.execute_input": "2023-09-02T00:48:42.777613Z", + "iopub.status.busy": "2023-09-02T00:48:42.777493Z", + "iopub.status.idle": "2023-09-02T00:48:42.790303Z", + "shell.execute_reply": "2023-09-02T00:48:42.790024Z" }, "tags": [] }, @@ -108,10 +108,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:56.732648Z", - "iopub.status.busy": "2023-06-26T17:02:56.727813Z", - "iopub.status.idle": "2023-06-26T17:02:56.786018Z", - "shell.execute_reply": "2023-06-26T17:02:56.780690Z" + "iopub.execute_input": "2023-09-02T00:48:42.792053Z", + "iopub.status.busy": "2023-09-02T00:48:42.791972Z", + "iopub.status.idle": "2023-09-02T00:48:42.809051Z", + "shell.execute_reply": "2023-09-02T00:48:42.808598Z" }, "tags": [] }, @@ -297,10 +297,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:56.807840Z", - "iopub.status.busy": "2023-06-26T17:02:56.806993Z", - "iopub.status.idle": "2023-06-26T17:02:57.142845Z", - "shell.execute_reply": "2023-06-26T17:02:57.142564Z" + "iopub.execute_input": "2023-09-02T00:48:42.810930Z", + "iopub.status.busy": "2023-09-02T00:48:42.810799Z", + "iopub.status.idle": "2023-09-02T00:48:42.967393Z", + "shell.execute_reply": "2023-09-02T00:48:42.967087Z" }, "tags": [] }, @@ -342,10 +342,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:57.144500Z", - "iopub.status.busy": "2023-06-26T17:02:57.144391Z", - "iopub.status.idle": "2023-06-26T17:02:57.160073Z", - "shell.execute_reply": "2023-06-26T17:02:57.159813Z" + "iopub.execute_input": "2023-09-02T00:48:42.969138Z", + "iopub.status.busy": "2023-09-02T00:48:42.968997Z", + "iopub.status.idle": "2023-09-02T00:48:42.986924Z", + "shell.execute_reply": "2023-09-02T00:48:42.986634Z" }, "tags": [] }, @@ -377,10 +377,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:57.161866Z", - "iopub.status.busy": "2023-06-26T17:02:57.161736Z", - "iopub.status.idle": "2023-06-26T17:02:57.176750Z", - "shell.execute_reply": "2023-06-26T17:02:57.176473Z" + "iopub.execute_input": "2023-09-02T00:48:42.988739Z", + "iopub.status.busy": "2023-09-02T00:48:42.988622Z", + "iopub.status.idle": "2023-09-02T00:48:43.006141Z", + "shell.execute_reply": "2023-09-02T00:48:43.005784Z" }, "tags": [] }, @@ -389,12 +389,12 @@ "data": { "text/plain": [ "defaultdict(float,\n", - " {'ordinal_date': 736389.0,\n", - " 'gallup': 43.843213,\n", - " 'ipsos': 46.19925042857143,\n", - " 'morning_consult': 48.318749,\n", - " 'rasmussen': 44.104692,\n", - " 'you_gov': 43.636914000000004})" + " {'ordinal_date': 0.0,\n", + " 'gallup': 0.0,\n", + " 'ipsos': 0.0,\n", + " 'morning_consult': 0.0,\n", + " 'rasmussen': 0.0,\n", + " 'you_gov': 0.0})" ] }, "execution_count": 7, @@ -420,10 +420,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:57.178442Z", - "iopub.status.busy": "2023-06-26T17:02:57.178329Z", - "iopub.status.idle": "2023-06-26T17:02:57.195031Z", - "shell.execute_reply": "2023-06-26T17:02:57.194763Z" + "iopub.execute_input": "2023-09-02T00:48:43.007822Z", + "iopub.status.busy": "2023-09-02T00:48:43.007708Z", + "iopub.status.idle": "2023-09-02T00:48:43.025846Z", + "shell.execute_reply": "2023-09-02T00:48:43.025585Z" }, "tags": [] }, @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:57.196667Z", - "iopub.status.busy": "2023-06-26T17:02:57.196553Z", - "iopub.status.idle": "2023-06-26T17:02:57.215049Z", - "shell.execute_reply": "2023-06-26T17:02:57.214779Z" + "iopub.execute_input": "2023-09-02T00:48:43.027465Z", + "iopub.status.busy": "2023-09-02T00:48:43.027354Z", + "iopub.status.idle": "2023-09-02T00:48:43.044487Z", + "shell.execute_reply": "2023-09-02T00:48:43.044207Z" }, "tags": [] }, @@ -686,10 +686,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-06-26T17:02:57.216750Z", - "iopub.status.busy": "2023-06-26T17:02:57.216641Z", - "iopub.status.idle": "2023-06-26T17:02:57.231804Z", - "shell.execute_reply": "2023-06-26T17:02:57.231544Z" + "iopub.execute_input": "2023-09-02T00:48:43.046037Z", + "iopub.status.busy": "2023-09-02T00:48:43.045956Z", + "iopub.status.idle": "2023-09-02T00:48:43.062148Z", + "shell.execute_reply": "2023-09-02T00:48:43.061800Z" }, "tags": [] }, diff --git a/docs/recipes/reading-data.ipynb b/docs/recipes/reading-data.ipynb index 9ea6d628fd..9b49a16165 100644 --- a/docs/recipes/reading-data.ipynb +++ b/docs/recipes/reading-data.ipynb @@ -25,54 +25,36 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:36.098680Z", - "iopub.status.busy": "2022-10-26T11:28:36.097765Z", - "iopub.status.idle": "2022-10-26T11:28:36.706759Z", - "shell.execute_reply": "2022-10-26T11:28:36.707258Z" + "iopub.execute_input": "2023-09-02T00:48:44.738314Z", + "iopub.status.busy": "2023-09-02T00:48:44.737893Z", + "iopub.status.idle": "2023-09-02T00:48:45.178807Z", + "shell.execute_reply": "2023-09-02T00:48:45.178378Z" }, "tags": [] }, "outputs": [ { "data": { - 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-       "Bike sharing station information from the city of Toulouse.\n",
-       "\n",
-       "The goal is to predict the number of bikes in 5 different bike stations from the city of\n",
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-       "\n",
-       "      Name  Bikes                                                         \n",
-       "      Task  Regression                                                    \n",
-       "   Samples  182,470                                                       \n",
-       "  Features  8                                                             \n",
-       "    Sparse  False                                                         \n",
-       "      Path  /Users/max.halford/river_data/Bikes/toulouse_bikes.csv        \n",
-       "       URL  https://maxhalford.github.io/files/datasets/toulouse_bikes.zip\n",
-       "      Size  12.52 MB                                                      \n",
-       "Downloaded  True                                                          \n",
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Before the unsupervised parts of the pipeline were updated during `predict_one`. This is more intuitive for new users. The old behavior, which yields better results, can be restored by calling `learn_one` with the new `compose.learn_during_predict` context manager. diff --git a/river/__version__.py b/river/__version__.py index d243b01263..dc93f05d71 100644 --- a/river/__version__.py +++ b/river/__version__.py @@ -1,5 +1,5 @@ from __future__ import annotations -VERSION = (0, 18, 0) +VERSION = (0, 19, 0) __version__ = ".".join(map(str, VERSION)) # noqa: F401 From ead66035c7e79d8c88810ad27a06075de97673d8 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 10:50:26 +0700 Subject: [PATCH 089/347] Modify Eucliean distance calculation using np.linalg.norm for better computational speed. --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 001d9fa9c7..0c09fcd2a2 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -22,7 +22,7 @@ def __init__(self, key: int, data: np.ndarray = None): def euclidean_distance(w_i, w_j): # Euclidean distance between two nodes - return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + return np.linalg.norm(w_i.data - w_j.data) class HierarchicalClustering(base.Clusterer): From 7b92fca5a121de618abded2cd4049cf49246b211 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 11:28:17 +0700 Subject: [PATCH 090/347] Refactor elements related to the distance function. --- river/cluster/hcluster.py | 8 +++----- river/linear_model/test_glm.py | 5 +---- 2 files changed, 4 insertions(+), 9 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0c09fcd2a2..624a432307 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -46,7 +46,7 @@ class HierarchicalClustering(base.Clusterer): ---------- window_size number of data points to use - distance + distance_func distance function to use to compare the nodes Attributes @@ -154,7 +154,7 @@ class HierarchicalClustering(base.Clusterer): def __init__( self, window_size: int = 100, - distance: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, + distance_func: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, ): # Number of nodes self.n = 0 @@ -167,9 +167,7 @@ def __init__( # First node of the tree self.root = None # Distance function - self.distance = distance - if self.distance is None: - self.distance = euclidean_distance + self.distance = distance_func if distance_func is not None else euclidean_distance def OTD(self, T, x): # OTD algorithm from diff --git a/river/linear_model/test_glm.py b/river/linear_model/test_glm.py index 2d95f2ce73..64a3ed3c37 100644 --- a/river/linear_model/test_glm.py +++ b/river/linear_model/test_glm.py @@ -420,10 +420,7 @@ def test_lin_reg_sklearn_l1_non_regression(): def test_log_reg_sklearn_l1_non_regression(): """Checks that the river L1 implementation results are no worse than sklearn L1.""" - ( - X, - y, - ) = make_classification( + (X, y,) = make_classification( n_samples=1000, n_features=20, n_informative=4, From 62c94195bdf3c228c59af178c9b442bb4662c466 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 11:38:25 +0700 Subject: [PATCH 091/347] Remove data types of attributes --- river/cluster/hcluster.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 624a432307..5e6b284762 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -51,9 +51,9 @@ class HierarchicalClustering(base.Clusterer): Attributes ---------- - n : int + n number of nodes - X : dict (data point (str(np.ndarray)) : key (int)) + X data points used by the algorithm with the key of the node representing them References From 43f3722cf693a03bf64e2b89235dce2a21b30d49 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:10:22 +0700 Subject: [PATCH 092/347] Refactor inter subtree similarity function. --- river/cluster/hcluster.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 5e6b284762..e8e506e333 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -346,14 +346,14 @@ def leaves(self, v): leavesList.extend(self.leaves(v.right)) return leavesList - def inter_subtree_similarity(self, A, B): - # compute the mean distance between tree A and tree B (mean of distances between nodes from tree A and tree B) - leavesA = self.leaves(A) - leavesB = self.leaves(B) + def inter_subtree_similarity(self, tree_a, tree_b): + # compute the mean distance (mean of distances) between two trees + leaves_a = self.leaves(tree_a) + leaves_b = self.leaves(tree_b) r = 0 nb = 0 - for i, w_i in enumerate(leavesA): - for j, w_j in enumerate(leavesB): + for i, w_i in enumerate(leaves_a): + for j, w_j in enumerate(leaves_b): nb += 1 r += self.distance(w_i, w_j) return r / nb From d03de5c43f90e2cae6605f63384b8c92128346a6 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:10:40 +0700 Subject: [PATCH 093/347] Refactor intra subtree similarity function --- river/cluster/hcluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index e8e506e333..427a5a435b 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -358,9 +358,9 @@ def inter_subtree_similarity(self, tree_a, tree_b): r += self.distance(w_i, w_j) return r / nb - def intra_subtree_similarity(self, A): - # compute mean of distances between the node from tree A - leaves = self.leaves(A) + def intra_subtree_similarity(self, tree): + # compute mean of distances between the nodes from a certain tree + leaves = self.leaves(tree) r = 0 nb = 0 if len(leaves) == 1: From 41b368c41cba8e970eb0e06e76bd5b23617e7d2c Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:13:19 +0700 Subject: [PATCH 094/347] Refactor leave finding function. --- river/cluster/hcluster.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 427a5a435b..c426b8912a 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -341,10 +341,10 @@ def leaves(self, v): # If v is a leaf, returns itself return [v] # Else, returns leaves from its children - leavesList = [] - leavesList.extend(self.leaves(v.left)) - leavesList.extend(self.leaves(v.right)) - return leavesList + leave_list = [] + leave_list.extend(self.leaves(v.left)) + leave_list.extend(self.leaves(v.right)) + return leave_list def inter_subtree_similarity(self, tree_a, tree_b): # compute the mean distance (mean of distances) between two trees From 0a98f0b5a4eb00ec077229d5e16cfdd0065db6f8 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:40:30 +0700 Subject: [PATCH 095/347] Refactor online top down (OTD) clustering function. --- river/cluster/hcluster.py | 39 ++++++++++++++++++++------------------- 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index c426b8912a..8ab1d32ad1 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -169,35 +169,36 @@ def __init__( # Distance function self.distance = distance_func if distance_func is not None else euclidean_distance - def OTD(self, T, x): - # OTD algorithm from + def otd_clustering(self, tree, x): + # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. + # The algorithm performs highly efficient online updates and provably approximates Moseley-Wang revenue. if self.n == 1: # First node in the tree self.root = self.nodes[1] - elif T.data is not None: + elif tree.data is not None: # If T is a leaf, we merge the two nodes together - self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) - elif T.left is None: + self.merge_nodes(tree, self.nodes[self.X[np.array2string(x)]]) + elif tree.left is None: # If there is no node at the left of the intermediate node, we add it there - T.left = self.nodes[self.X[np.array2string(x)]] - self.nodes[self.X[np.array2string(x)]].parent = T - elif T.right is None: + tree.left = self.nodes[self.X[np.array2string(x)]] + self.nodes[self.X[np.array2string(x)]].parent = tree + elif tree.right is None: # If there is no node at the right of the intermediate node, we add it there - T.right = self.nodes[self.X[np.array2string(x)]] - self.nodes[self.X[np.array2string(x)]].parent = T - elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity( - T, self.nodes[self.X[np.array2string(x)]] + tree.right = self.nodes[self.X[np.array2string(x)]] + self.nodes[self.X[np.array2string(x)]].parent = tree + elif self.intra_subtree_similarity(tree) < self.inter_subtree_similarity( + tree, self.nodes[self.X[np.array2string(x)]] ): # If the nodes in T are closer between them than with the new node, we merge T and the new node - self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) + self.merge_nodes(tree, self.nodes[self.X[np.array2string(x)]]) elif self.inter_subtree_similarity( - T.left, self.nodes[self.X[np.array2string(x)]] - ) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): - # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T - self.OTD(T.right, x) + tree.left, self.nodes[self.X[np.array2string(x)]] + ) > self.inter_subtree_similarity(tree.right, self.nodes[self.X[np.array2string(x)]]): + # Continue to search where to merge the new node in the right part of T + self.otd_clustering(tree.right, x) else: - # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T - self.OTD(T.left, x) + # Continue to search where to merge the new node in the left part of T + self.otd_clustering(tree.left, x) def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T From edf3b3d126ea42f90bde0073617e18048bf36400 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:58:45 +0700 Subject: [PATCH 096/347] Refactor. --- river/cluster/hcluster.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 8ab1d32ad1..4b8d89a1f1 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -172,28 +172,29 @@ def __init__( def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. # The algorithm performs highly efficient online updates and provably approximates Moseley-Wang revenue. + x_string = np.array2string(x) if self.n == 1: # First node in the tree self.root = self.nodes[1] elif tree.data is not None: # If T is a leaf, we merge the two nodes together - self.merge_nodes(tree, self.nodes[self.X[np.array2string(x)]]) + self.merge_nodes(tree, self.nodes[self.X[x_string]]) elif tree.left is None: # If there is no node at the left of the intermediate node, we add it there - tree.left = self.nodes[self.X[np.array2string(x)]] - self.nodes[self.X[np.array2string(x)]].parent = tree + tree.left = self.nodes[self.X[x_string]] + self.nodes[self.X[x_string]].parent = tree elif tree.right is None: # If there is no node at the right of the intermediate node, we add it there - tree.right = self.nodes[self.X[np.array2string(x)]] - self.nodes[self.X[np.array2string(x)]].parent = tree + tree.right = self.nodes[self.X[x_string]] + self.nodes[self.X[x_string]].parent = tree elif self.intra_subtree_similarity(tree) < self.inter_subtree_similarity( - tree, self.nodes[self.X[np.array2string(x)]] + tree, self.nodes[self.X[x_string]] ): # If the nodes in T are closer between them than with the new node, we merge T and the new node - self.merge_nodes(tree, self.nodes[self.X[np.array2string(x)]]) + self.merge_nodes(tree, self.nodes[self.X[x_string]]) elif self.inter_subtree_similarity( - tree.left, self.nodes[self.X[np.array2string(x)]] - ) > self.inter_subtree_similarity(tree.right, self.nodes[self.X[np.array2string(x)]]): + tree.left, self.nodes[self.X[x_string]] + ) > self.inter_subtree_similarity(tree.right, self.nodes[self.X[x_string]]): # Continue to search where to merge the new node in the right part of T self.otd_clustering(tree.right, x) else: From 13f1ea7ed17d417190f118536ef5233bb36fce06 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:59:04 +0700 Subject: [PATCH 097/347] Cheng function name OTD to otd_clustering. --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 4b8d89a1f1..0589c914a7 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -247,7 +247,7 @@ def learn_one(self, x): self.X[np.array2string(x)] = self.n self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree - self.OTD(self.root, x) + self.otd_clustering(self.root, x) return self def predict_OTD(self, x, node, clusters): From 11f6cf98492226dce63f79b484195ef2a5046e7d Mon Sep 17 00:00:00 2001 From: Pietro Tanure Onnis <95176103+pietro-tanure@users.noreply.github.com> Date: Mon, 11 Sep 2023 09:59:51 +0200 Subject: [PATCH 098/347] ilof pietro (#1317) * Merge branch 'ilof-pietro' of https://github.com/pietro-tanure/river into ilof-pietro * Update ilof_notebook.ipynb * Modify name IncrementalLOF in __init__ file of anomaly module * Refactor code after precommit run * Remove window_score in score_one function of Incremental LOF (since score_one already assumes that one sample is taken into account at a certain time point). * Remove learn_many and refactor Docstring test * Remove import pandas since unused. * Refactor * Change output of `score_one` to only return one single number, not list (to comply with the implementation of other incremental anomaly detection algorithms) * Refactor Docstring test * Refactor from IncrementalLOF to LocalOutlierFactor * Refactor * Refactor X_batch to x_batch to align with PEP8. * Verbose re-wording. * Refactor X_score to x_scores * Remove type of returned output in DocString * Make check_equal static method and refactor related variable names according to PEP8 * Refactor capital X variable to x_list to represent the stored list of observations. * Refactor code with black * Remove description for unnecessary variables. * Re-wording for variable description. * Refactor. * Add learn_many to learn multiple instances at a time and update Docstring test to take into account the newly implemented function. * Remove description of unnecessary variables. * Spelling correction. * Modify variable names by removing capital letters to align with PEP8. * Refactor calc_local_reach_dist and cal_lof and change these functions to static methods. * Change expand_objects to static method * Refactor. * Change define_sets to static method. * Change calc_reach_dist_newpoints to static method. * Change calc_reach_dist_otherpoints to static method and refactor. * Refactor. * Refactor calc_reach_dist_new_points * Refactor calc_reach_dist_other_points. * Remove import * Refactor and add description in docstring regarding expected performance of ILOF. * Refactor. * Remove one unnecessary variable of calc_reach_dist_other_points. * Modify docstring description of the algorithm * Add comments to justify the returning results of score_one function. * Add tests for the newly implemented iLOF algorithm. * Remove iLOF notebook (since the content has been covered by the test file within the same module). --------- Co-authored-by: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> --- river/anomaly/__init__.py | 2 + river/anomaly/ilof.py | 523 +++++++++++++++++++++++++++++++++ river/anomaly/test_ilof.py | 73 +++++ river/linear_model/test_glm.py | 5 +- 4 files changed, 599 insertions(+), 4 deletions(-) create mode 100644 river/anomaly/ilof.py create mode 100644 river/anomaly/test_ilof.py diff --git a/river/anomaly/__init__.py b/river/anomaly/__init__.py index 932023896e..74882f55c6 100644 --- a/river/anomaly/__init__.py +++ b/river/anomaly/__init__.py @@ -17,6 +17,7 @@ from .filter import QuantileFilter, ThresholdFilter from .gaussian import GaussianScorer from .hst import HalfSpaceTrees +from .ilof import LocalOutlierFactor from .svm import OneClassSVM __all__ = [ @@ -27,4 +28,5 @@ "OneClassSVM", "QuantileFilter", "ThresholdFilter", + "LocalOutlierFactor", ] diff --git a/river/anomaly/ilof.py b/river/anomaly/ilof.py new file mode 100644 index 0000000000..e66104c4e5 --- /dev/null +++ b/river/anomaly/ilof.py @@ -0,0 +1,523 @@ +from __future__ import annotations + +import functools + +import pandas as pd + +from river import anomaly, utils +from river.neighbors.base import DistanceFunc + + +class LocalOutlierFactor(anomaly.base.AnomalyDetector): + """Incremental Local Outlier Factor (Incremental LOF). + + The Incremental Local Outlier Factor (ILOF) is an online version of the Local Outlier Factor (LOF), proposed by + Pokrajac et al. (2017), and is used to identify outliers based on density of local neighbors. + + The algorithm take into account the following elements: + - `NewPoints`: new points; + - `kNN(p)`: the k-nearest neighboors of `p` (the k-closest points to `p`); + - `RkNN(p)`: the reverse-k-nearest neighboors of `p` (points that have `p` as one of their neighboors); + - `set_upd_lrd`: Set of points that need to have the local reachability distance updated; + - `set_upd_lof`: Set of points that need to have the local outlier factor updated. + + This current implementation within `River`, based on the original one in the paper, follows the following steps: + 1) Insert new data points (`NewPoints`) and calculate its distance to existing points; + 2) Update the nreaest neighboors and reverse nearest neighboors of all the points; + 3) Define sets of affected points that required updates; + 4) Calculate the reachability-distance from new point to neighboors (`NewPoints` -> `kNN(NewPoints)`) + and from rev-neighboors to new point (`RkNN(NewPoints)` -> `NewPoints`); + 5) Update the reachability-distance for affected points: `RkNN(RkNN(NewPoints))` -> `RkNN(NewPoints)` + 6) Update local reachability distance of affected points: `lrd(set_upd_lrd)`; + 7) Update local outlier factor: `lof(set_upd_lof)`. + + The incremental LOF algorithm is expected to provide equivalent detection performance as the iterated static + LOF algroithm (applied after insertion of each data record), while requiring significantly less computational time. + Moreover, the insertion of a new data point as well as deletion of an old data point influence only a limited number + of their closest neighbors, which means that the number of updates per such insertion/deletion does not depend + on the total number of instances learned/in the data set. + + Parameters + ---------- + n_neighbors + The number of nearest neighbors to use for density estimation. + distance_func + Distance function to be used. By default, the Euclidean distance is used. + verbose + Whether to print warning/messages + + Attributes + ---------- + x_list + A list of stored observations. + x_batch + A buffer to hold incoming observations until it's time to update the model. + x_scores + A buffer to hold incoming observations until it's time to score them. + dist_dict + A dictionary to hold distances between observations. + neighborhoods + A dictionary to hold neighborhoods for each observation. + rev_neighborhoods + A dictionary to hold reverse neighborhoods for each observation. + k_dist + A dictionary to hold k-distances for each observation. + reach_dist + A dictionary to hold reachability distances for each observation. + lof + A dictionary to hold Local Outlier Factors for each observation. + local_reach + A dictionary to hold local reachability distances for each observation. + + Example + ---------- + + >>> from river import anomaly + >>> from river import datasets + >>> import pandas as pd + + >>> cc_df = pd.DataFrame(datasets.CreditCard()) + + >>> k = 20 # Define number of nearest neighbors + >>> incremental_lof = anomaly.LocalOutlierFactor(k, verbose=False) + + >>> for x, _ in datasets.CreditCard().take(200): + ... incremental_lof.learn_one(x) + + >>> incremental_lof.learn_many(cc_df[201:401]) + + >>> ilof_scores = [] + >>> for x in cc_df[0][401:406]: + ... ilof_scores.append(incremental_lof.score_one(x)) + + >>> [round(ilof_score, 3) for ilof_score in ilof_scores] + [1.802, 1.937, 1.567, 1.181, 1.28] + + References + ---------- + David Pokrajac, Aleksandar Lazarevic, and Longin Jan Latecki (2007). Incremental Local Outlier Detection for Data + Streams. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007). 504-515. + DOI: 10.1109/CIDM.2007.368917. + """ + + def __init__( + self, + n_neighbors: int = 10, + verbose=True, + distance_func: DistanceFunc = None, + ): + self.n_neighbors = n_neighbors + self.x_list: list = [] + self.x_batch: list = [] + self.x_scores: list = [] + self.dist_dict: dict = {} + self.neighborhoods: dict = {} + self.rev_neighborhoods: dict = {} + self.k_dist: dict = {} + self.reach_dist: dict = {} + self.lof: dict = {} + self.local_reach: dict = {} + self.verbose = verbose + self.distance = ( + distance_func + if distance_func is not None + else functools.partial(utils.math.minkowski_distance, p=2) + ) + + def learn_many(self, x: pd.DataFrame): + """ + Update the model with multiple incoming observations simultaneously. + This function assumes that the observations are stored in the first column of the dataset. + + Parameters + ---------- + x + A Pandas DataFrame including multiple instances to be learned at the same time + """ + x = x[0].tolist() + self.learn(x) + + def learn_one(self, x: dict): + """ + Update the model with one incoming observation + + Parameters + ---------- + x + A dictionary of feature values. + """ + self.x_batch.append(x) + if len(self.x_list) or len(self.x_batch) > 1: + self.learn(self.x_batch) + self.x_batch = [] + + def learn(self, x_batch: list): + x_batch, equal = self.check_equal(x_batch, self.x_list) + if equal != 0 and self.verbose: + print("At least one sample is equal to previously observed instances.") + + if len(x_batch) == 0: + if self.verbose: + print("No new data was added.") + else: + # Increase size of objects to accommodate new data + ( + nm, + self.x_list, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.reach_dist, + self.dist_dict, + self.local_reach, + self.lof, + ) = self.expand_objects( + x_batch, + self.x_list, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.reach_dist, + self.dist_dict, + self.local_reach, + self.lof, + ) + + # Calculate neighborhoods, reverse neighborhoods, k-distances and distances between neighbors + ( + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.dist_dict, + ) = self.initial_calculations( + self.x_list, + nm, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.dist_dict, + ) + + # Define sets of particles + ( + set_new_points, + set_neighbors, + set_rev_neighbors, + set_upd_lrd, + set_upd_lof, + ) = self.define_sets(nm, self.neighborhoods, self.rev_neighborhoods) + + # Calculate new reachability distance of all affected points + self.reach_dist = self.calc_reach_dist_new_points( + set_new_points, + self.neighborhoods, + self.rev_neighborhoods, + self.reach_dist, + self.dist_dict, + self.k_dist, + ) + self.reach_dist = self.calc_reach_dist_other_points( + set_rev_neighbors, + self.rev_neighborhoods, + self.reach_dist, + self.dist_dict, + self.k_dist, + ) + + # Calculate new local reachability distance of all affected points + self.local_reach = self.calc_local_reach_dist( + set_upd_lrd, self.neighborhoods, self.reach_dist, self.local_reach + ) + + # Calculate new Local Outlier Factor of all affected points + self.lof = self.calc_lof(set_upd_lof, self.neighborhoods, self.local_reach, self.lof) + + def score_one(self, x: dict): + """ + Score a new incoming observation based on model constructed previously. + Perform same calculations as 'learn_one' function but doesn't add the new calculations to the attributes + Data samples that are equal to samples stored by the model are not considered. + + Parameters + ---------- + x + A dictionary of feature values. + + Returns + ------- + lof : list + List of LOF calculated for incoming data + """ + + self.x_scores.append(x) + + self.x_scores, equal = self.check_equal(self.x_scores, self.x_list) + if equal != 0 and self.verbose: + print("The new observation is the same to one of the previously observed instances.") + + if len(self.x_scores) == 0: + if self.verbose: + print("No new data was added.") + else: + x_list_copy = self.x_list.copy() + ( + nm, + x_list_copy, + neighborhoods, + rev_neighborhoods, + k_dist, + reach_dist, + dist_dict, + local_reach, + lof, + ) = self.expand_objects( + self.x_scores, + x_list_copy, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.reach_dist, + self.dist_dict, + self.local_reach, + self.lof, + ) + + neighborhoods, rev_neighborhoods, k_dist, dist_dict = self.initial_calculations( + x_list_copy, nm, neighborhoods, rev_neighborhoods, k_dist, dist_dict + ) + ( + set_new_points, + set_neighbors, + set_rev_neighbors, + set_upd_lrd, + set_upd_lof, + ) = self.define_sets(nm, neighborhoods, rev_neighborhoods) + reach_dist = self.calc_reach_dist_new_points( + set_new_points, neighborhoods, rev_neighborhoods, reach_dist, dist_dict, k_dist + ) + reach_dist = self.calc_reach_dist_other_points( + set_rev_neighbors, + rev_neighborhoods, + reach_dist, + dist_dict, + k_dist, + ) + local_reach = self.calc_local_reach_dist( + set_upd_lrd, neighborhoods, reach_dist, local_reach + ) + lof = self.calc_lof(set_upd_lof, neighborhoods, local_reach, lof) + self.x_scores = [] + + # Use nm[0] as index since upon this configuration nm[1] is expected to be 1. + return lof[nm[0]] + + def initial_calculations( + self, + x_list: list, + nm: tuple, + neighborhoods: dict, + rev_neighborhoods: dict, + k_distances: dict, + dist_dict: dict, + ): + """ + Perform initial calculations on the incoming data before applying the Incremental LOF algorithm. + Taking the new data, it updates the neighborhoods, reverse neighborhoods, k-distances and distances between particles. + + Parameters + ---------- + x_list + A list of stored observations. + nm + A tuple representing the current size of the dataset. + neighborhoods + A dictionary of particle neighborhoods. + rev_neighborhoods + A dictionary of reverse particle neighborhoods. + k_distances + A dictionary to hold k-distances for each observation. + dist_dict + A dictionary of dictionaries storing distances between particles + + Returns + ------- + neighborhoods + Updated dictionary of particle neighborhoods + rev_neighborhoods + Updated dictionary of reverse particle neighborhoods + k_distances + Updated dictionary to hold k-distances for each observation + dist_dict + Updated dictionary of dictionaries storing distances between particles + """ + + n = nm[0] + m = nm[1] + k = self.n_neighbors + + # Calculate distances all particles considering new and old ones + new_distances = [ + [i, j, self.distance(x_list[i], x_list[j])] + for i in range(n + m) + for j in range(i) + if i >= n + ] + # Add new distances to distance dictionary + for i in range(len(new_distances)): + dist_dict[new_distances[i][0]][new_distances[i][1]] = new_distances[i][2] + dist_dict[new_distances[i][1]][new_distances[i][0]] = new_distances[i][2] + + # Calculate new k-dist for each particle + for i, inner_dict in enumerate(dist_dict.values()): + k_distances[i] = sorted(inner_dict.values())[min(k, len(inner_dict.values())) - 1] + + # Only keep particles that are neighbors in distance dictionary + dist_dict = { + k: {k2: v2 for k2, v2 in v.items() if v2 <= k_distances[k]} + for k, v in dist_dict.items() + } + + # Define new neighborhoods for particles + for key, value in dist_dict.items(): + neighborhoods[key] = [index for index in value] + + # Define new reverse neighborhoods for particles + for particle_id, neighbor_ids in neighborhoods.items(): + for neighbor_id in neighbor_ids: + rev_neighborhoods[neighbor_id].append(particle_id) + + return neighborhoods, rev_neighborhoods, k_distances, dist_dict + + @staticmethod + def check_equal(x_list: list, y_list: list): + """ + Check if new list of observations (x_list) has any data sample that is equal to any previous data recorded (y_list). + """ + result = [x for x in x_list if not any(x == y for y in y_list)] + return result, len(x_list) - len(result) + + @staticmethod + def expand_objects( + new_particles: list, + x_list: list, + neighborhoods: dict, + rev_neighborhoods: dict, + k_dist: dict, + reach_dist: dict, + dist_dict: dict, + local_reach: dict, + lof: dict, + ): + """ + Expand size of dictionaries and lists to take into account new data points. + """ + n = len(x_list) + m = len(new_particles) + x_list.extend(new_particles) + neighborhoods.update({i: [] for i in range(n + m)}) + rev_neighborhoods.update({i: [] for i in range(n + m)}) + k_dist.update({i: float("inf") for i in range(n + m)}) + reach_dist.update({i + n: {} for i in range(m)}) + dist_dict.update({i + n: {} for i in range(m)}) + local_reach.update({i + n: [] for i in range(m)}) + lof.update({i + n: [] for i in range(m)}) + return ( + (n, m), + x_list, + neighborhoods, + rev_neighborhoods, + k_dist, + reach_dist, + dist_dict, + local_reach, + lof, + ) + + @staticmethod + def define_sets(nm, neighborhoods: dict, rev_neighborhoods: dict): + """ + Define sets of points for the incremental LOF algorithm. + """ + # Define set of new points from batch + set_new_points = set(range(nm[0], nm[0] + nm[1])) + set_neighbors: set = set() + set_rev_neighbors: set = set() + + # Define neighbors and reverse neighbors of new data points + for i in set_new_points: + set_neighbors = set(set_neighbors) | set(neighborhoods[i]) + set_rev_neighbors = set(set_rev_neighbors) | set(rev_neighborhoods[i]) + + # Define points that need to update their local reachability distance because of new data points + set_upd_lrd = set_rev_neighbors + for j in set_rev_neighbors: + set_upd_lrd = set_upd_lrd | set(rev_neighborhoods[j]) + set_upd_lrd = set_upd_lrd | set_new_points + + # Define points that need to update their lof because of new data points + set_upd_lof = set_upd_lrd + for m in set_upd_lrd: + set_upd_lof = set_upd_lof | set(rev_neighborhoods[m]) + set_upd_lof = set_upd_lof + + return set_new_points, set_neighbors, set_rev_neighbors, set_upd_lrd, set_upd_lof + + @staticmethod + def calc_reach_dist_new_points( + set_index: set, + neighborhoods: dict, + rev_neighborhoods: dict, + reach_dist: dict, + dist_dict: dict, + k_dist: dict, + ): + """ + Calculate reachability distance from new points to neighbors and from neighbors to new points. + """ + for c in set_index: + for j in set(neighborhoods[c]): + reach_dist[c][j] = max(dist_dict[c][j], k_dist[j]) + for j in set(rev_neighborhoods[c]): + reach_dist[j][c] = max(dist_dict[j][c], k_dist[c]) + return reach_dist + + @staticmethod + def calc_reach_dist_other_points( + set_index: set, + rev_neighborhoods: dict, + reach_dist: dict, + dist_dict: dict, + k_dist: dict, + ): + """ + Calculate reachability distance from reverse neighbors of reverse neighbors ( RkNN(RkNN(NewPoints)) ) + to reverse neighbors ( RkNN(NewPoints) ). These values change due to the insertion of new points. + """ + for j in set_index: + for i in set(rev_neighborhoods[j]): + reach_dist[i][j] = max(dist_dict[i][j], k_dist[j]) + return reach_dist + + @staticmethod + def calc_local_reach_dist( + set_index: set, neighborhoods: dict, reach_dist: dict, local_reach_dist: dict + ): + """ + Calculate local reachability distance of affected points. + """ + for i in set_index: + local_reach_dist[i] = len(neighborhoods[i]) / sum( + [reach_dist[i][j] for j in neighborhoods[i]] + ) + return local_reach_dist + + @staticmethod + def calc_lof(set_index: set, neighborhoods: dict, local_reach: dict, lof: dict): + """ + Calculate local outlier factor (LOF) of affected points. + """ + for i in set_index: + lof[i] = sum([local_reach[j] for j in neighborhoods[i]]) / ( + len(neighborhoods[i]) * local_reach[i] + ) + return lof diff --git a/river/anomaly/test_ilof.py b/river/anomaly/test_ilof.py new file mode 100644 index 0000000000..23fb7375e1 --- /dev/null +++ b/river/anomaly/test_ilof.py @@ -0,0 +1,73 @@ +import numpy as np +import pandas as pd + +from river import anomaly, datasets +from river.utils import dict2numpy +from sklearn import neighbors + +np.random.seed(42) + + +def test_incremental_lof_scores(): + """ + Test that the incremental LOF algorithm returns similar LOF scores for each observation + compared with the original static LOF algorithm implemented in scikit-learn. + """ + norm_dist = 0.5 * np.random.rand(100, 2) + x_inliers = np.concatenate((norm_dist - 2, norm_dist, norm_dist + 2), axis=0) + x_outliers = np.concatenate( + ( + np.random.uniform(low=-4, high=4, size=(20, 2)), + np.random.uniform(low=-10, high=-5, size=(10, 2)), + np.random.uniform(low=5, high=10, size=(10, 2)), + ), + axis=0, + ) + x_train = np.concatenate((x_inliers, x_outliers), axis=0) + x_train_dict = [{f"feature_{i + 1}": elem[i] for i in range(2)} for elem in x_train] + ground_truth = np.ones(len(x_train), dtype=int) + ground_truth[-len(x_outliers) :] = -1 + df_train = pd.DataFrame({"observations": x_train_dict, "ground_truth": ground_truth}) + x_pred = np.random.uniform(low=-5, high=5, size=(30, 2)) + x_pred_dict = [{f"feature_{i + 1}": elem[i] for i in range(2)} for elem in x_pred] + incremental_lof = anomaly.LocalOutlierFactor(n_neighbors=20, verbose=False) + + for x in df_train["observations"]: + incremental_lof.learn_one(x) + + ilof_scores_train = np.array([ilof_score for ilof_score in incremental_lof.lof.values()]) + + ilof_scores_pred = [] + for x in x_pred_dict: + ilof_scores_pred.append(incremental_lof.score_one(x)) + + lof_sklearn = neighbors.LocalOutlierFactor(n_neighbors=20) + lof_sklearn.fit_predict(x_train) + lof_sklearn_scores_train = -lof_sklearn.negative_outlier_factor_ + + assert np.allclose(ilof_scores_train, lof_sklearn_scores_train, rtol=1e-08, atol=1e-08) + + +def test_batch_lof_scores(): + """ + Test that the incremental LOF algorithm returns similar LOF scores for each batch + with `learn_many` compared with the original static LOF algorithm implemented in scikit-learn, + under different batch sizes. + """ + cc_df = pd.DataFrame(datasets.CreditCard()) + cc_df_np = [dict2numpy(i) for i in cc_df[0].to_dict().values()] + + batch_sizes = [20, 50, 100] + + for batch_size in batch_sizes: + ilof_river_batch = anomaly.LocalOutlierFactor(n_neighbors=20, verbose=False) + ilof_river_batch.learn_many(cc_df[0:batch_size]) + ilof_scores_river_batch = np.array([v for v in ilof_river_batch.lof.values()]) + + lof_sklearn_batch = neighbors.LocalOutlierFactor(n_neighbors=20) + lof_sklearn_batch.fit_predict(cc_df_np[0:batch_size]) + lof_scores_sklearn_batch = -lof_sklearn_batch.negative_outlier_factor_ + + assert np.allclose( + ilof_scores_river_batch, lof_scores_sklearn_batch, rtol=1e-02, atol=1e-02 + ) diff --git a/river/linear_model/test_glm.py b/river/linear_model/test_glm.py index 2d95f2ce73..64a3ed3c37 100644 --- a/river/linear_model/test_glm.py +++ b/river/linear_model/test_glm.py @@ -420,10 +420,7 @@ def test_lin_reg_sklearn_l1_non_regression(): def test_log_reg_sklearn_l1_non_regression(): """Checks that the river L1 implementation results are no worse than sklearn L1.""" - ( - X, - y, - ) = make_classification( + (X, y,) = make_classification( n_samples=1000, n_features=20, n_informative=4, From 274f05602be2f9bbc3ee43e7f34098d1e083ac32 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 11 Sep 2023 10:18:15 +0200 Subject: [PATCH 099/347] ruff --- docs/releases/0.19.0.md | 2 +- docs/releases/unreleased.md | 5 +++++ river/anomaly/__init__.py | 2 +- river/anomaly/{ilof.py => lof.py} | 0 river/anomaly/test_ilof.py | 4 +++- river/linear_model/test_glm.py | 5 ++++- 6 files changed, 14 insertions(+), 4 deletions(-) create mode 100644 docs/releases/unreleased.md rename river/anomaly/{ilof.py => lof.py} (100%) diff --git a/docs/releases/0.19.0.md b/docs/releases/0.19.0.md index 54847e3698..0c41605df4 100644 --- a/docs/releases/0.19.0.md +++ b/docs/releases/0.19.0.md @@ -6,7 +6,7 @@ Calling `learn_one` in a pipeline will now update each part of the pipeline in t - Added a `bandit.datasets` submodule, which is meant to contain contextual bandit datasets. - Added `bandit.base.ContextualPolicy`. -- Added `bandit.datasets.NewsDataset`. +- Added `bandit.datasets.NewsArticles`. - Added `bandit.LinUCBDisjoint`, which is River's first contextual bandit policy. - Added `bandit.RandomPolicy`. diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md new file mode 100644 index 0000000000..1b1690e26e --- /dev/null +++ b/docs/releases/unreleased.md @@ -0,0 +1,5 @@ +# Unreleased + +## anomaly + +- Added `anomaly.LocalOutlierFactor`, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation. diff --git a/river/anomaly/__init__.py b/river/anomaly/__init__.py index 74882f55c6..ac0cd08269 100644 --- a/river/anomaly/__init__.py +++ b/river/anomaly/__init__.py @@ -17,7 +17,7 @@ from .filter import QuantileFilter, ThresholdFilter from .gaussian import GaussianScorer from .hst import HalfSpaceTrees -from .ilof import LocalOutlierFactor +from .lof import LocalOutlierFactor from .svm import OneClassSVM __all__ = [ diff --git a/river/anomaly/ilof.py b/river/anomaly/lof.py similarity index 100% rename from river/anomaly/ilof.py rename to river/anomaly/lof.py diff --git a/river/anomaly/test_ilof.py b/river/anomaly/test_ilof.py index 23fb7375e1..b3855fba79 100644 --- a/river/anomaly/test_ilof.py +++ b/river/anomaly/test_ilof.py @@ -1,9 +1,11 @@ +from __future__ import annotations + import numpy as np import pandas as pd +from sklearn import neighbors from river import anomaly, datasets from river.utils import dict2numpy -from sklearn import neighbors np.random.seed(42) diff --git a/river/linear_model/test_glm.py b/river/linear_model/test_glm.py index 64a3ed3c37..2d95f2ce73 100644 --- a/river/linear_model/test_glm.py +++ b/river/linear_model/test_glm.py @@ -420,7 +420,10 @@ def test_lin_reg_sklearn_l1_non_regression(): def test_log_reg_sklearn_l1_non_regression(): """Checks that the river L1 implementation results are no worse than sklearn L1.""" - (X, y,) = make_classification( + ( + X, + y, + ) = make_classification( n_samples=1000, n_features=20, n_informative=4, From 72456130842b128e9e13656dc75de463f4fe8517 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 11 Sep 2023 12:19:27 +0200 Subject: [PATCH 100/347] tidy up lof --- river/anomaly/lof.py | 589 +++++++++++++++++-------------------- river/anomaly/test_ilof.py | 4 +- river/test_estimators.py | 1 + 3 files changed, 274 insertions(+), 320 deletions(-) diff --git a/river/anomaly/lof.py b/river/anomaly/lof.py index e66104c4e5..e523a23121 100644 --- a/river/anomaly/lof.py +++ b/river/anomaly/lof.py @@ -8,6 +8,140 @@ from river.neighbors.base import DistanceFunc +def check_equal(x_list: list, y_list: list): + """ + Check if new list of observations (x_list) has any data sample that is equal to any previous data recorded (y_list). + """ + result = [x for x in x_list if not any(x == y for y in y_list)] + return result, len(x_list) - len(result) + + +def expand_objects( + new_particles: list, + x_list: list, + neighborhoods: dict, + rev_neighborhoods: dict, + k_dist: dict, + reach_dist: dict, + dist_dict: dict, + local_reach: dict, + lof: dict, +): + """ + Expand size of dictionaries and lists to take into account new data points. + """ + n = len(x_list) + m = len(new_particles) + x_list.extend(new_particles) + neighborhoods.update({i: [] for i in range(n + m)}) + rev_neighborhoods.update({i: [] for i in range(n + m)}) + k_dist.update({i: float("inf") for i in range(n + m)}) + reach_dist.update({i + n: {} for i in range(m)}) + dist_dict.update({i + n: {} for i in range(m)}) + local_reach.update({i + n: [] for i in range(m)}) + lof.update({i + n: [] for i in range(m)}) + return ( + (n, m), + x_list, + neighborhoods, + rev_neighborhoods, + k_dist, + reach_dist, + dist_dict, + local_reach, + lof, + ) + + +def define_sets(nm, neighborhoods: dict, rev_neighborhoods: dict): + """ + Define sets of points for the incremental LOF algorithm. + """ + # Define set of new points from batch + set_new_points = set(range(nm[0], nm[0] + nm[1])) + set_neighbors: set = set() + set_rev_neighbors: set = set() + + # Define neighbors and reverse neighbors of new data points + for i in set_new_points: + set_neighbors = set(set_neighbors) | set(neighborhoods[i]) + set_rev_neighbors = set(set_rev_neighbors) | set(rev_neighborhoods[i]) + + # Define points that need to update their local reachability distance because of new data points + set_upd_lrd = set_rev_neighbors + for j in set_rev_neighbors: + set_upd_lrd = set_upd_lrd | set(rev_neighborhoods[j]) + set_upd_lrd = set_upd_lrd | set_new_points + + # Define points that need to update their lof because of new data points + set_upd_lof = set_upd_lrd + for m in set_upd_lrd: + set_upd_lof = set_upd_lof | set(rev_neighborhoods[m]) + set_upd_lof = set_upd_lof + + return set_new_points, set_neighbors, set_rev_neighbors, set_upd_lrd, set_upd_lof + + +def calc_reach_dist_new_points( + set_index: set, + neighborhoods: dict, + rev_neighborhoods: dict, + reach_dist: dict, + dist_dict: dict, + k_dist: dict, +): + """ + Calculate reachability distance from new points to neighbors and from neighbors to new points. + """ + for c in set_index: + for j in set(neighborhoods[c]): + reach_dist[c][j] = max(dist_dict[c][j], k_dist[j]) + for j in set(rev_neighborhoods[c]): + reach_dist[j][c] = max(dist_dict[j][c], k_dist[c]) + return reach_dist + + +def calc_reach_dist_other_points( + set_index: set, + rev_neighborhoods: dict, + reach_dist: dict, + dist_dict: dict, + k_dist: dict, +): + """ + Calculate reachability distance from reverse neighbors of reverse neighbors ( RkNN(RkNN(NewPoints)) ) + to reverse neighbors ( RkNN(NewPoints) ). These values change due to the insertion of new points. + """ + for j in set_index: + for i in set(rev_neighborhoods[j]): + reach_dist[i][j] = max(dist_dict[i][j], k_dist[j]) + return reach_dist + + +def calc_local_reach_dist( + set_index: set, neighborhoods: dict, reach_dist: dict, local_reach_dist: dict +): + """ + Calculate local reachability distance of affected points. + """ + for i in set_index: + local_reach_dist[i] = len(neighborhoods[i]) / sum( + [reach_dist[i][j] for j in neighborhoods[i]] + ) + return local_reach_dist + + +def calc_lof(set_index: set, neighborhoods: dict, local_reach: dict, lof: dict): + """ + Calculate local outlier factor (LOF) of affected points. + """ + for i in set_index: + lof[i] = sum([local_reach[j] for j in neighborhoods[i]]) / ( + len(neighborhoods[i]) * local_reach[i] + ) + return lof + + class LocalOutlierFactor(anomaly.base.AnomalyDetector): """Incremental Local Outlier Factor (Incremental LOF). @@ -43,8 +177,6 @@ class LocalOutlierFactor(anomaly.base.AnomalyDetector): The number of nearest neighbors to use for density estimation. distance_func Distance function to be used. By default, the Euclidean distance is used. - verbose - Whether to print warning/messages Attributes ---------- @@ -69,28 +201,27 @@ class LocalOutlierFactor(anomaly.base.AnomalyDetector): local_reach A dictionary to hold local reachability distances for each observation. - Example - ---------- + Examples + -------- + >>> import pandas as pd >>> from river import anomaly >>> from river import datasets - >>> import pandas as pd >>> cc_df = pd.DataFrame(datasets.CreditCard()) - >>> k = 20 # Define number of nearest neighbors - >>> incremental_lof = anomaly.LocalOutlierFactor(k, verbose=False) + >>> lof = anomaly.LocalOutlierFactor(n_neighbors=20) >>> for x, _ in datasets.CreditCard().take(200): - ... incremental_lof.learn_one(x) + ... lof.learn_one(x) - >>> incremental_lof.learn_many(cc_df[201:401]) + >>> lof.learn_many(cc_df[201:401]) - >>> ilof_scores = [] + >>> scores = [] >>> for x in cc_df[0][401:406]: - ... ilof_scores.append(incremental_lof.score_one(x)) + ... scores.append(lof.score_one(x)) - >>> [round(ilof_score, 3) for ilof_score in ilof_scores] + >>> [round(score, 3) for score in scores] [1.802, 1.937, 1.567, 1.181, 1.28] References @@ -98,12 +229,12 @@ class LocalOutlierFactor(anomaly.base.AnomalyDetector): David Pokrajac, Aleksandar Lazarevic, and Longin Jan Latecki (2007). Incremental Local Outlier Detection for Data Streams. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007). 504-515. DOI: 10.1109/CIDM.2007.368917. + """ def __init__( self, n_neighbors: int = 10, - verbose=True, distance_func: DistanceFunc = None, ): self.n_neighbors = n_neighbors @@ -117,7 +248,7 @@ def __init__( self.reach_dist: dict = {} self.lof: dict = {} self.local_reach: dict = {} - self.verbose = verbose + self.distance_func = distance_func self.distance = ( distance_func if distance_func is not None @@ -125,193 +256,148 @@ def __init__( ) def learn_many(self, x: pd.DataFrame): - """ - Update the model with multiple incoming observations simultaneously. - This function assumes that the observations are stored in the first column of the dataset. - - Parameters - ---------- - x - A Pandas DataFrame including multiple instances to be learned at the same time - """ x = x[0].tolist() self.learn(x) def learn_one(self, x: dict): - """ - Update the model with one incoming observation - - Parameters - ---------- - x - A dictionary of feature values. - """ self.x_batch.append(x) if len(self.x_list) or len(self.x_batch) > 1: self.learn(self.x_batch) self.x_batch = [] def learn(self, x_batch: list): - x_batch, equal = self.check_equal(x_batch, self.x_list) - if equal != 0 and self.verbose: - print("At least one sample is equal to previously observed instances.") - - if len(x_batch) == 0: - if self.verbose: - print("No new data was added.") - else: - # Increase size of objects to accommodate new data - ( - nm, - self.x_list, - self.neighborhoods, - self.rev_neighborhoods, - self.k_dist, - self.reach_dist, - self.dist_dict, - self.local_reach, - self.lof, - ) = self.expand_objects( - x_batch, - self.x_list, - self.neighborhoods, - self.rev_neighborhoods, - self.k_dist, - self.reach_dist, - self.dist_dict, - self.local_reach, - self.lof, - ) - - # Calculate neighborhoods, reverse neighborhoods, k-distances and distances between neighbors - ( - self.neighborhoods, - self.rev_neighborhoods, - self.k_dist, - self.dist_dict, - ) = self.initial_calculations( - self.x_list, - nm, - self.neighborhoods, - self.rev_neighborhoods, - self.k_dist, - self.dist_dict, - ) - - # Define sets of particles - ( - set_new_points, - set_neighbors, - set_rev_neighbors, - set_upd_lrd, - set_upd_lof, - ) = self.define_sets(nm, self.neighborhoods, self.rev_neighborhoods) - - # Calculate new reachability distance of all affected points - self.reach_dist = self.calc_reach_dist_new_points( - set_new_points, - self.neighborhoods, - self.rev_neighborhoods, - self.reach_dist, - self.dist_dict, - self.k_dist, - ) - self.reach_dist = self.calc_reach_dist_other_points( - set_rev_neighbors, - self.rev_neighborhoods, - self.reach_dist, - self.dist_dict, - self.k_dist, - ) - - # Calculate new local reachability distance of all affected points - self.local_reach = self.calc_local_reach_dist( - set_upd_lrd, self.neighborhoods, self.reach_dist, self.local_reach - ) - - # Calculate new Local Outlier Factor of all affected points - self.lof = self.calc_lof(set_upd_lof, self.neighborhoods, self.local_reach, self.lof) + x_batch, equal = check_equal(x_batch, self.x_list) + + # Increase size of objects to accommodate new data + ( + nm, + self.x_list, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.reach_dist, + self.dist_dict, + self.local_reach, + self.lof, + ) = expand_objects( + x_batch, + self.x_list, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.reach_dist, + self.dist_dict, + self.local_reach, + self.lof, + ) - def score_one(self, x: dict): - """ - Score a new incoming observation based on model constructed previously. - Perform same calculations as 'learn_one' function but doesn't add the new calculations to the attributes - Data samples that are equal to samples stored by the model are not considered. + # Calculate neighborhoods, reverse neighborhoods, k-distances and distances between neighbors + ( + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.dist_dict, + ) = self._initial_calculations( + self.x_list, + nm, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.dist_dict, + ) - Parameters - ---------- - x - A dictionary of feature values. + # Define sets of particles + ( + set_new_points, + set_neighbors, + set_rev_neighbors, + set_upd_lrd, + set_upd_lof, + ) = define_sets(nm, self.neighborhoods, self.rev_neighborhoods) + + # Calculate new reachability distance of all affected points + self.reach_dist = calc_reach_dist_new_points( + set_new_points, + self.neighborhoods, + self.rev_neighborhoods, + self.reach_dist, + self.dist_dict, + self.k_dist, + ) + self.reach_dist = calc_reach_dist_other_points( + set_rev_neighbors, + self.rev_neighborhoods, + self.reach_dist, + self.dist_dict, + self.k_dist, + ) - Returns - ------- - lof : list - List of LOF calculated for incoming data - """ + # Calculate new local reachability distance of all affected points + self.local_reach = calc_local_reach_dist( + set_upd_lrd, self.neighborhoods, self.reach_dist, self.local_reach + ) - self.x_scores.append(x) + # Calculate new Local Outlier Factor of all affected points + self.lof = calc_lof(set_upd_lof, self.neighborhoods, self.local_reach, self.lof) - self.x_scores, equal = self.check_equal(self.x_scores, self.x_list) - if equal != 0 and self.verbose: - print("The new observation is the same to one of the previously observed instances.") + def score_one(self, x: dict): + self.x_scores.append(x) + self.x_scores, equal = check_equal(self.x_scores, self.x_list) if len(self.x_scores) == 0: - if self.verbose: - print("No new data was added.") - else: - x_list_copy = self.x_list.copy() - ( - nm, - x_list_copy, - neighborhoods, - rev_neighborhoods, - k_dist, - reach_dist, - dist_dict, - local_reach, - lof, - ) = self.expand_objects( - self.x_scores, - x_list_copy, - self.neighborhoods, - self.rev_neighborhoods, - self.k_dist, - self.reach_dist, - self.dist_dict, - self.local_reach, - self.lof, - ) - - neighborhoods, rev_neighborhoods, k_dist, dist_dict = self.initial_calculations( - x_list_copy, nm, neighborhoods, rev_neighborhoods, k_dist, dist_dict - ) - ( - set_new_points, - set_neighbors, - set_rev_neighbors, - set_upd_lrd, - set_upd_lof, - ) = self.define_sets(nm, neighborhoods, rev_neighborhoods) - reach_dist = self.calc_reach_dist_new_points( - set_new_points, neighborhoods, rev_neighborhoods, reach_dist, dist_dict, k_dist - ) - reach_dist = self.calc_reach_dist_other_points( - set_rev_neighbors, - rev_neighborhoods, - reach_dist, - dist_dict, - k_dist, - ) - local_reach = self.calc_local_reach_dist( - set_upd_lrd, neighborhoods, reach_dist, local_reach - ) - lof = self.calc_lof(set_upd_lof, neighborhoods, local_reach, lof) - self.x_scores = [] - - # Use nm[0] as index since upon this configuration nm[1] is expected to be 1. - return lof[nm[0]] - - def initial_calculations( + return None + + x_list_copy = self.x_list.copy() + ( + nm, + x_list_copy, + neighborhoods, + rev_neighborhoods, + k_dist, + reach_dist, + dist_dict, + local_reach, + lof, + ) = expand_objects( + self.x_scores, + x_list_copy, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.reach_dist, + self.dist_dict, + self.local_reach, + self.lof, + ) + + neighborhoods, rev_neighborhoods, k_dist, dist_dict = self._initial_calculations( + x_list_copy, nm, neighborhoods, rev_neighborhoods, k_dist, dist_dict + ) + ( + set_new_points, + set_neighbors, + set_rev_neighbors, + set_upd_lrd, + set_upd_lof, + ) = define_sets(nm, neighborhoods, rev_neighborhoods) + reach_dist = calc_reach_dist_new_points( + set_new_points, neighborhoods, rev_neighborhoods, reach_dist, dist_dict, k_dist + ) + reach_dist = calc_reach_dist_other_points( + set_rev_neighbors, + rev_neighborhoods, + reach_dist, + dist_dict, + k_dist, + ) + local_reach = calc_local_reach_dist(set_upd_lrd, neighborhoods, reach_dist, local_reach) + lof = calc_lof(set_upd_lof, neighborhoods, local_reach, lof) + self.x_scores = [] + + # Use nm[0] as index since upon this configuration nm[1] is expected to be 1. + return lof[nm[0]] + + def _initial_calculations( self, x_list: list, nm: tuple, @@ -349,6 +435,7 @@ def initial_calculations( Updated dictionary to hold k-distances for each observation dist_dict Updated dictionary of dictionaries storing distances between particles + """ n = nm[0] @@ -387,137 +474,3 @@ def initial_calculations( rev_neighborhoods[neighbor_id].append(particle_id) return neighborhoods, rev_neighborhoods, k_distances, dist_dict - - @staticmethod - def check_equal(x_list: list, y_list: list): - """ - Check if new list of observations (x_list) has any data sample that is equal to any previous data recorded (y_list). - """ - result = [x for x in x_list if not any(x == y for y in y_list)] - return result, len(x_list) - len(result) - - @staticmethod - def expand_objects( - new_particles: list, - x_list: list, - neighborhoods: dict, - rev_neighborhoods: dict, - k_dist: dict, - reach_dist: dict, - dist_dict: dict, - local_reach: dict, - lof: dict, - ): - """ - Expand size of dictionaries and lists to take into account new data points. - """ - n = len(x_list) - m = len(new_particles) - x_list.extend(new_particles) - neighborhoods.update({i: [] for i in range(n + m)}) - rev_neighborhoods.update({i: [] for i in range(n + m)}) - k_dist.update({i: float("inf") for i in range(n + m)}) - reach_dist.update({i + n: {} for i in range(m)}) - dist_dict.update({i + n: {} for i in range(m)}) - local_reach.update({i + n: [] for i in range(m)}) - lof.update({i + n: [] for i in range(m)}) - return ( - (n, m), - x_list, - neighborhoods, - rev_neighborhoods, - k_dist, - reach_dist, - dist_dict, - local_reach, - lof, - ) - - @staticmethod - def define_sets(nm, neighborhoods: dict, rev_neighborhoods: dict): - """ - Define sets of points for the incremental LOF algorithm. - """ - # Define set of new points from batch - set_new_points = set(range(nm[0], nm[0] + nm[1])) - set_neighbors: set = set() - set_rev_neighbors: set = set() - - # Define neighbors and reverse neighbors of new data points - for i in set_new_points: - set_neighbors = set(set_neighbors) | set(neighborhoods[i]) - set_rev_neighbors = set(set_rev_neighbors) | set(rev_neighborhoods[i]) - - # Define points that need to update their local reachability distance because of new data points - set_upd_lrd = set_rev_neighbors - for j in set_rev_neighbors: - set_upd_lrd = set_upd_lrd | set(rev_neighborhoods[j]) - set_upd_lrd = set_upd_lrd | set_new_points - - # Define points that need to update their lof because of new data points - set_upd_lof = set_upd_lrd - for m in set_upd_lrd: - set_upd_lof = set_upd_lof | set(rev_neighborhoods[m]) - set_upd_lof = set_upd_lof - - return set_new_points, set_neighbors, set_rev_neighbors, set_upd_lrd, set_upd_lof - - @staticmethod - def calc_reach_dist_new_points( - set_index: set, - neighborhoods: dict, - rev_neighborhoods: dict, - reach_dist: dict, - dist_dict: dict, - k_dist: dict, - ): - """ - Calculate reachability distance from new points to neighbors and from neighbors to new points. - """ - for c in set_index: - for j in set(neighborhoods[c]): - reach_dist[c][j] = max(dist_dict[c][j], k_dist[j]) - for j in set(rev_neighborhoods[c]): - reach_dist[j][c] = max(dist_dict[j][c], k_dist[c]) - return reach_dist - - @staticmethod - def calc_reach_dist_other_points( - set_index: set, - rev_neighborhoods: dict, - reach_dist: dict, - dist_dict: dict, - k_dist: dict, - ): - """ - Calculate reachability distance from reverse neighbors of reverse neighbors ( RkNN(RkNN(NewPoints)) ) - to reverse neighbors ( RkNN(NewPoints) ). These values change due to the insertion of new points. - """ - for j in set_index: - for i in set(rev_neighborhoods[j]): - reach_dist[i][j] = max(dist_dict[i][j], k_dist[j]) - return reach_dist - - @staticmethod - def calc_local_reach_dist( - set_index: set, neighborhoods: dict, reach_dist: dict, local_reach_dist: dict - ): - """ - Calculate local reachability distance of affected points. - """ - for i in set_index: - local_reach_dist[i] = len(neighborhoods[i]) / sum( - [reach_dist[i][j] for j in neighborhoods[i]] - ) - return local_reach_dist - - @staticmethod - def calc_lof(set_index: set, neighborhoods: dict, local_reach: dict, lof: dict): - """ - Calculate local outlier factor (LOF) of affected points. - """ - for i in set_index: - lof[i] = sum([local_reach[j] for j in neighborhoods[i]]) / ( - len(neighborhoods[i]) * local_reach[i] - ) - return lof diff --git a/river/anomaly/test_ilof.py b/river/anomaly/test_ilof.py index b3855fba79..ac584173c1 100644 --- a/river/anomaly/test_ilof.py +++ b/river/anomaly/test_ilof.py @@ -32,7 +32,7 @@ def test_incremental_lof_scores(): df_train = pd.DataFrame({"observations": x_train_dict, "ground_truth": ground_truth}) x_pred = np.random.uniform(low=-5, high=5, size=(30, 2)) x_pred_dict = [{f"feature_{i + 1}": elem[i] for i in range(2)} for elem in x_pred] - incremental_lof = anomaly.LocalOutlierFactor(n_neighbors=20, verbose=False) + incremental_lof = anomaly.LocalOutlierFactor(n_neighbors=20) for x in df_train["observations"]: incremental_lof.learn_one(x) @@ -62,7 +62,7 @@ def test_batch_lof_scores(): batch_sizes = [20, 50, 100] for batch_size in batch_sizes: - ilof_river_batch = anomaly.LocalOutlierFactor(n_neighbors=20, verbose=False) + ilof_river_batch = anomaly.LocalOutlierFactor(n_neighbors=20) ilof_river_batch.learn_many(cc_df[0:batch_size]) ilof_scores_river_batch = np.array([v for v in ilof_river_batch.lof.values()]) diff --git a/river/test_estimators.py b/river/test_estimators.py index e5c3ded948..18aacf32f2 100644 --- a/river/test_estimators.py +++ b/river/test_estimators.py @@ -53,6 +53,7 @@ def iter_estimators_which_can_be_tested(): ignored = ( River2SKLBase, SKL2RiverBase, + anomaly.LocalOutlierFactor, # needs warm-start to work correctly compose.FuncTransformer, compose.Grouper, compose.Pipeline, From 4d3432f79694bde71d134a9e5a1f70d1846b21f8 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 17:58:58 +0700 Subject: [PATCH 101/347] Remove unnecessary comments. --- river/cluster/hcluster.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0589c914a7..7fc69e8399 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -10,9 +10,7 @@ # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: def __init__(self, key: int, data: np.ndarray = None): - # If it's a leaf : x data, else : None self.data = data - # Key of the node self.key = key # Children and parent self.left = None From 90a785803eb8c4600ab91f1e698ba5efb08269ba Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 18:08:39 +0700 Subject: [PATCH 102/347] Refactor description of the algorithm within Docstring. --- river/cluster/hcluster.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 7fc69e8399..cc987b2a06 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -26,19 +26,18 @@ def euclidean_distance(w_i, w_j): class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. - HierarchicalClustering is a stream hierarchical clustering algorithm. - The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. - It will (begining with the whole tree T) : + HierarchicalClustering is a stream hierarchical clustering algorithm. This algorithm [^1] inserts new nodes + near the nodes it is similar to without breaking clusters of very similar nodes. - * Compare the new node to the tree T : - * if T is just a leaf : merge - * else if the nodes of T are more similar between them than with the new node : merge - * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree - * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree + Beginning with the whole tree `T`, it will compare the new node to this respective tree: + * If `T` is just a leaf : merge + * else if the nodes of T are more similar between them than with the new node: merge + * else if the new node is more similar to the left subtree than to the right subtree: + redo from the first point with T = left subtree + * else (the new node is more similar to the right subtree than to the left subtree): + redo from the first point with T = right subtree - You can choose a window size to use only the recent points and not overload the tree. (default : 100) - - You can also choose a distance function to compare the nodes. (default : euclidean distance) + A certain window size can be chosen to use only the most recent points to make sure that the tree is not overloaded. Parameters ---------- @@ -56,7 +55,8 @@ class HierarchicalClustering(base.Clusterer): References ---------- - [^1]: Menon, Aditya & Rajagopalan, Anand & Sumengen, Baris & Citovsky, Gui & Cao, Qin & Kumar, Sanjiv. (2019). Online Hierarchical Clustering Approximations. + [^1]: Anand Rajagopalan, Aditya Krishna Menon, Qin Cao, Gui Citovsky, Baris Sumengen and Sanjiv Kumar (2019). Online + Hierarchical Clustering Approximations. arXiV:1909.09667. Available at: https://doi.org/10.48550/arXiv.1909.09667 Examples -------- From d00b1860fbb49ab5fb804ae5fc368209dbe06572 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 18:12:09 +0700 Subject: [PATCH 103/347] Refactor tests in Docstring. --- river/cluster/hcluster.py | 27 ++++++--------------------- 1 file changed, 6 insertions(+), 21 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index cc987b2a06..2ef8df8fb7 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -74,11 +74,7 @@ class HierarchicalClustering(base.Clusterer): ... HC = HC.learn_one(x) >>> HC.X - {'[1 1 1]': 1, - '[1 1 0]': 2, - '[20 20 20]': 4, - '[20. 20. 20.1]': 6, - '[0 1 1]': 8} + {'[1 1 1]': 1, '[1 1 0]': 2, '[20 20 20]': 4, '[20. 20. 20.1]': 6, '[0 1 1]': 8} >>> HC.n 9 @@ -93,24 +89,13 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 + Printed Hierarchical Clustering Tree. >>> HC.get_all_clusters() - [(1, [array([1, 1, 1])]), - (2, [array([1, 1, 0])]), - (4, [array([20, 20, 20])]), - (6, [array([20. , 20. , 20.1])]), - (8, [array([0, 1, 1])]), - (3, [1, 2]), - (5, [9, 7]), - (7, [4, 6]), - (9, [3, 8])] + [(1, [array([1, 1, 1])]), (2, [array([1, 1, 0])]), (4, [array([20, 20, 20])]), (6, [array([20. , 20. , 20.1])]), (8, [array([0, 1, 1])]), (3, [1, 2]), (5, [9, 7]), (7, [4, 6]), (9, [3, 8])] >>> HC.get_clusters_by_point() - {'[1 1 1]': [1, 3, 9, 5], - '[1 1 0]': [2, 3, 9, 5], - '[20 20 20]': [4, 7, 5], - '[20. 20. 20.1]': [6, 7, 5], - '[0 1 1]': [8, 9, 5]} + {'[1 1 1]': [1, 3, 9, 5], '[1 1 0]': [2, 3, 9, 5], '[20 20 20]': [4, 7, 5], '[20. 20. 20.1]': [6, 7, 5], '[0 1 1]': [8, 9, 5]} >>> HC.predict_one({0 : 20.1, 1 : 20, 2 : 20 }) ([10, 11, 5], 7) @@ -129,7 +114,7 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 - + Printed Hierarchical Clustering Tree. >>> HC = cluster.HierarchicalClustering(window_size=2) @@ -146,7 +131,7 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 - + Printed Hierarchical Clustering Tree. """ def __init__( From d99ffbbed52b5d2e618789eacfc2b318da0b11b3 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 18:13:42 +0700 Subject: [PATCH 104/347] Refactor merge_nodes function. --- river/cluster/hcluster.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 2ef8df8fb7..92f63ff425 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -184,29 +184,29 @@ def otd_clustering(self, tree, x): # Continue to search where to merge the new node in the left part of T self.otd_clustering(tree.left, x) - def merge_nodes(self, T, added_node): + def merge_nodes(self, tree, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node self.n += 1 new_node = BinaryTreeNode(self.n) # We add T and the added node as its children - new_node.left = T + new_node.left = tree new_node.right = added_node # The parent of the new node is the parent of T - new_node.parent = T.parent + new_node.parent = tree.parent # If T is not the root, we set the child of its parent as new node (instead of T) - if T.parent is not None: - if T.parent.left.key == T.key: - T.parent.left = new_node + if tree.parent is not None: + if tree.parent.left.key == tree.key: + tree.parent.left = new_node else: - T.parent.right = new_node + tree.parent.right = new_node # We add the new node as the parent of T and the added node - T.parent = new_node + tree.parent = new_node added_node.parent = new_node # We add the new node to the dict self.nodes[self.n] = new_node # If T was the root, the new node become the root - if self.root.key == T.key: + if self.root.key == tree.key: self.root = self.nodes[self.n] def learn_one(self, x): From 0cfe7c981d5a044f67e0ab821919594a1d29630f Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:46:31 +0700 Subject: [PATCH 105/347] Refactor comments in merge_nodes --- river/cluster/hcluster.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 92f63ff425..4054cf181a 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -144,7 +144,7 @@ def __init__( # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X: dict[np.ndarray, int] = {} + self.X: dict[str, int] = {} # Dict : key -> node self.nodes: dict[int, BinaryTreeNode] = {} # First node of the tree @@ -185,27 +185,27 @@ def otd_clustering(self, tree, x): self.otd_clustering(tree.left, x) def merge_nodes(self, tree, added_node): - # Merge a new node (added node) to the tree T - # We create the node that will be the parent of T and the added node + # Merge a new node (added node) to the tree + # We create the node that will be the parent of the tree and the added node self.n += 1 new_node = BinaryTreeNode(self.n) - # We add T and the added node as its children + # We add the tree and the added node as its children new_node.left = tree new_node.right = added_node - # The parent of the new node is the parent of T + # The parent of the new node is the parent of the tree new_node.parent = tree.parent - # If T is not the root, we set the child of its parent as new node (instead of T) + # If the tree is not the root, we set the child of its parent as new node (instead of T) if tree.parent is not None: if tree.parent.left.key == tree.key: tree.parent.left = new_node else: tree.parent.right = new_node - # We add the new node as the parent of T and the added node + # We add the new node as the parent of the tree and the added node tree.parent = new_node added_node.parent = new_node # We add the new node to the dict self.nodes[self.n] = new_node - # If T was the root, the new node become the root + # If the tree was the root, the new node become the root if self.root.key == tree.key: self.root = self.nodes[self.n] From d0f9f49d3f66cf63ef14cbbabd0511288af31042 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:47:10 +0700 Subject: [PATCH 106/347] Rename predict_otd. --- river/cluster/hcluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 4054cf181a..3a19972399 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -233,7 +233,7 @@ def learn_one(self, x): self.otd_clustering(self.root, x) return self - def predict_OTD(self, x, node, clusters): + def predict_otd(self, x, node, clusters): # get the list of predicted clusters for x if node is None: # If there is still no node in the tree @@ -255,10 +255,10 @@ def predict_OTD(self, x, node, clusters): node.left, BinaryTreeNode(self.n + 1, x) ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): # If the right part of the tree is closer to x than the left part, we continue in the right part - return self.predict_OTD(x, node.right, clusters) + return self.predict_otd(x, node.right, clusters) else: # If the left part of the tree is closer to x than the right part, we continue in the left part - return self.predict_OTD(x, node.left, clusters) + return self.predict_otd(x, node.left, clusters) def predict_one(self, x): """Predicts the clusters for a set of features `x`. From bef5f295ff03fa4a4a7a33d30d860876a397c2be Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:47:21 +0700 Subject: [PATCH 107/347] Simplify comments. --- river/cluster/hcluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 3a19972399..11f6e78794 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -213,7 +213,7 @@ def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree if len(self.X.keys()) >= self.window_size: - # If we have reached the maximum of data points, we delete the oldest one and add a node with the same key as the one we deleted + # Delete the oldest data point and add a node with the same key as the one deleted oldest_key = self.X[list(self.X.keys())[0]] oldest = self.nodes[oldest_key] if oldest.parent.left.key == oldest_key: @@ -239,13 +239,13 @@ def predict_otd(self, x, node, clusters): # If there is still no node in the tree return [1], -1 if node.data is not None: - # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) + # Add itself (n+1) and the key of the node that would merge x and node (n+2) clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( node, BinaryTreeNode(self.n + 1, x) ): - # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) + # Add itself (n+1) and the key of the node that would merge x and node (n+2) clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key else: From fd3f9b98b08b808cbddbefb9d1fe4e0783f06a5e Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:48:15 +0700 Subject: [PATCH 108/347] Modify __str__ printed output by adding "Printed Hierarchical Clustering Tree." at the end of the tree. --- river/cluster/hcluster.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 11f6e78794..0155b14189 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -358,8 +358,8 @@ def intra_subtree_similarity(self, tree): return r / nb def __str__(self): - self.printTree(self.root) - return "" + self.print_tree(self.root) + return "Printed Hierarchical Clustering Tree." def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python From 05f9cf4b6afc553af845f5bfb87fde14f2a48179 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:48:35 +0700 Subject: [PATCH 109/347] Rename predict_otd. --- river/cluster/hcluster.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0155b14189..1cb27da78c 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -274,8 +274,8 @@ def predict_one(self, x): """ x = utils.dict2numpy(x) - # We predict to which cluster x would be if we added it in the tree - r, merged = self.predict_OTD(x, self.root, []) + # We predict to which cluster x would be if we added in the tree + r, merged = self.predict_otd(x, self.root, []) r.reverse() return r, merged From d8da76b62761914137b8bb08d412fd5f515cb83f Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:49:25 +0700 Subject: [PATCH 110/347] Split comments and rename printTree to print_tree. --- river/cluster/hcluster.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 1cb27da78c..0ebac19e39 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -280,7 +280,8 @@ def predict_one(self, x): return r, merged def find_path(self, root, path, k): - # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + # find the path from root to k + # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if root is None: # No path @@ -300,7 +301,9 @@ def find_path(self, root, path, k): return False def lca(self, i, j): - # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + # Find the least common ancestor + # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + if self.root is None: return -1 @@ -361,12 +364,13 @@ def __str__(self): self.print_tree(self.root) return "Printed Hierarchical Clustering Tree." - def printTree(self, node, level=0): - # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python + def print_tree(self, node, level=0): + # Print node and its children + # Adapted from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: - self.printTree(node.right, level + 1) + self.print_tree(node.right, level + 1) print(" " * 4 * level + "-> " + str(node.key)) - self.printTree(node.left, level + 1) + self.print_tree(node.left, level + 1) def get_parents(self, node): # Get all the parents of the node (the clusters it belongs to) From 98eee8824b6b1bdee6f8c978dbd15837a39ec28a Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 00:05:35 +0700 Subject: [PATCH 111/347] Modify self.X to self.x_clusters. --- river/cluster/hcluster.py | 40 +++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0ebac19e39..ff522b72a3 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -50,7 +50,7 @@ class HierarchicalClustering(base.Clusterer): ---------- n number of nodes - X + x_clusters data points used by the algorithm with the key of the node representing them References @@ -73,7 +73,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - >>> HC.X + >>> HC.x_clusters {'[1 1 1]': 1, '[1 1 0]': 2, '[20 20 20]': 4, '[20. 20. 20.1]': 6, '[0 1 1]': 8} >>> HC.n @@ -121,7 +121,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - >>> HC.X + >>> HC.x_clusters {'[20. 20. 20.1]': 2, '[0 1 1]': 1} >>> HC.n @@ -144,7 +144,7 @@ def __init__( # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X: dict[str, int] = {} + self.x_clusters: dict[str, int] = {} # Dict : key -> node self.nodes: dict[int, BinaryTreeNode] = {} # First node of the tree @@ -161,23 +161,23 @@ def otd_clustering(self, tree, x): self.root = self.nodes[1] elif tree.data is not None: # If T is a leaf, we merge the two nodes together - self.merge_nodes(tree, self.nodes[self.X[x_string]]) + self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) elif tree.left is None: # If there is no node at the left of the intermediate node, we add it there - tree.left = self.nodes[self.X[x_string]] - self.nodes[self.X[x_string]].parent = tree + tree.left = self.nodes[self.x_clusters[x_string]] + self.nodes[self.x_clusters[x_string]].parent = tree elif tree.right is None: # If there is no node at the right of the intermediate node, we add it there - tree.right = self.nodes[self.X[x_string]] - self.nodes[self.X[x_string]].parent = tree + tree.right = self.nodes[self.x_clusters[x_string]] + self.nodes[self.x_clusters[x_string]].parent = tree elif self.intra_subtree_similarity(tree) < self.inter_subtree_similarity( - tree, self.nodes[self.X[x_string]] + tree, self.nodes[self.x_clusters[x_string]] ): # If the nodes in T are closer between them than with the new node, we merge T and the new node - self.merge_nodes(tree, self.nodes[self.X[x_string]]) + self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) elif self.inter_subtree_similarity( - tree.left, self.nodes[self.X[x_string]] - ) > self.inter_subtree_similarity(tree.right, self.nodes[self.X[x_string]]): + tree.left, self.nodes[self.x_clusters[x_string]] + ) > self.inter_subtree_similarity(tree.right, self.nodes[self.x_clusters[x_string]]): # Continue to search where to merge the new node in the right part of T self.otd_clustering(tree.right, x) else: @@ -212,22 +212,22 @@ def merge_nodes(self, tree, added_node): def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree - if len(self.X.keys()) >= self.window_size: + if len(self.x_clusters.keys()) >= self.window_size: # Delete the oldest data point and add a node with the same key as the one deleted - oldest_key = self.X[list(self.X.keys())[0]] + oldest_key = self.x_clusters[list(self.x_clusters.keys())[0]] oldest = self.nodes[oldest_key] if oldest.parent.left.key == oldest_key: oldest.parent.left = None else: oldest.parent.right = None del self.nodes[oldest_key] - del self.X[list(self.X.keys())[0]] - self.X[np.array2string(x)] = oldest_key + del self.x_clusters[list(self.x_clusters.keys())[0]] + self.x_clusters[np.array2string(x)] = oldest_key self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: # Else we just add a node self.n += 1 - self.X[np.array2string(x)] = self.n + self.x_clusters[np.array2string(x)] = self.n self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree self.otd_clustering(self.root, x) @@ -390,8 +390,8 @@ def get_clusters_by_point(self): """ # Get all the clusters each data point belong to clusters = {} - for x in self.X.keys(): - clusters[x] = self.get_parents(self.nodes[self.X[x]]) + for x in self.x_clusters.keys(): + clusters[x] = self.get_parents(self.nodes[self.x_clusters[x]]) return clusters def get_all_clusters(self): From d3d939832862f69bbeb633c3e619fba15abb7c08 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 00:06:25 +0700 Subject: [PATCH 112/347] Lexical changes. --- river/cluster/hcluster.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index ff522b72a3..053aa488cb 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -249,7 +249,7 @@ def predict_otd(self, x, node, clusters): clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key else: - # Else, x wouls be added in the tree, we add the key of node + # Else, x would be added in the tree, we add the key of node clusters.extend([node.key]) if self.inter_subtree_similarity( node.left, BinaryTreeNode(self.n + 1, x) @@ -381,7 +381,7 @@ def get_parents(self, node): return clusters def get_clusters_by_point(self): - """Returns the list of clusters (from the data point node to the root) for all of the data points. + """Returns the list of clusters (from the data point node to the root) for all data points. Returns ------- From f5cebe7c259999e6cedc4111c9ee38002d9c2ee7 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 00:06:50 +0700 Subject: [PATCH 113/347] Remove unnecessary comments. --- river/cluster/hcluster.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 053aa488cb..598a11bfdf 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -284,7 +284,6 @@ def find_path(self, root, path, k): # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if root is None: - # No path return False path.append(root) @@ -322,13 +321,12 @@ def lca(self, i, j): def leaves(self, v): # find all the leaves from node v + if v is None: - # No leaves return -1 if v.data is not None: - # If v is a leaf, returns itself return [v] - # Else, returns leaves from its children + leave_list = [] leave_list.extend(self.leaves(v.left)) leave_list.extend(self.leaves(v.right)) From 0539046b914ce27357fff2240c168d94187632e0 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 12 Sep 2023 10:11:14 +0200 Subject: [PATCH 114/347] Support pandas v2 (#1321) --- docs/releases/unreleased.md | 2 ++ river/compose/product.py | 39 ++++++++++++++++++++++------------ river/naive_bayes/bernoulli.py | 1 + 3 files changed, 29 insertions(+), 13 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 1b1690e26e..8a655bfa95 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,5 +1,7 @@ # Unreleased +River's mini-batch methods now support pandas v2. In particular, River conforms to pandas' new sparse API. + ## anomaly - Added `anomaly.LocalOutlierFactor`, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation. diff --git a/river/compose/product.py b/river/compose/product.py index 20291508d2..4ffa82a3be 100644 --- a/river/compose/product.py +++ b/river/compose/product.py @@ -88,27 +88,40 @@ def transform_one(self, x): def transform_many(self, X): outputs = [t.transform_many(X) for t in self.transformers.values()] - def get_fill_value(a): - if isinstance(a, pd.arrays.SparseArray): - return a.fill_value - return a.sparse.fill_value - def multiply(a, b): # Fast-track for sparse[uint8] * sparse[uint8] if a.dtype == pd.SparseDtype("uint8") and b.dtype == pd.SparseDtype("uint8"): return a & b - + # Fast-track for sparse[uint8] * numeric + if a.dtype == pd.SparseDtype("uint8"): + c = np.zeros_like(b) + true_mask = a.eq(1) + c[true_mask] = b[true_mask] + return pd.Series( + c, + index=b.index, + dtype=pd.SparseDtype(b.dtype, fill_value=0), + ) + # Fast-track for numeric * sparse[uint8] + if b.dtype == pd.SparseDtype("uint8"): + return multiply(b, a) # Fast-track for sparse * sparse - if pd.api.types.is_sparse(a) and pd.api.types.is_sparse(b): - return pd.arrays.SparseArray( - a * b, fill_value=get_fill_value(a) * get_fill_value(b) + if isinstance(a.dtype, pd.SparseDtype) and isinstance(b.dtype, pd.SparseDtype): + return pd.Series( + a * b, + index=a.index, + dtype=pd.SparseDtype( + b.dtype, fill_value=a.sparse.fill_value * b.sparse.fill_value + ), ) # Fast-track for sparse * numeric - if pd.api.types.is_sparse(a): - return pd.arrays.SparseArray(a * b, fill_value=get_fill_value(a)) + if isinstance(a.dtype, pd.SparseDtype): + return pd.Series( + a * b, dtype=pd.SparseDtype(fill_value=a.sparse.fill_value, dtype=b.dtype) + ) # Fast-track for numeric * sparse - if pd.api.types.is_sparse(b): - return pd.arrays.SparseArray(a * b, fill_value=get_fill_value(b)) + if isinstance(b.dtype, pd.SparseDtype): + return multiply(b, a) # Default return np.multiply(a, b) diff --git a/river/naive_bayes/bernoulli.py b/river/naive_bayes/bernoulli.py index d2cdc149ea..7ddba2acb0 100644 --- a/river/naive_bayes/bernoulli.py +++ b/river/naive_bayes/bernoulli.py @@ -288,4 +288,5 @@ def joint_log_likelihood_many(self, X: pd.DataFrame) -> pd.DataFrame: X @ (flp - neg_p).T + (np.log(self.p_class_many()) + neg_p.sum(axis=1).T).values, index=index, columns=self.class_counts.keys(), + dtype=float, ) From 9ef224ddf364b84614b6ffb10f17937f6e6f85c6 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 17:41:08 +0700 Subject: [PATCH 115/347] Refactor Docstring --- river/cluster/hcluster.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 598a11bfdf..322a7cccdc 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -68,18 +68,18 @@ class HierarchicalClustering(base.Clusterer): >>> X = [[1, 1, 1], [1, 1, 0], [20, 20, 20], [20, 20, 20.1], [0 , 1, 1]] - >>> HC = cluster.HierarchicalClustering() + >>> hierarchical_clustering = cluster.HierarchicalClustering() >>> for x, _ in stream.iter_array(X): - ... HC = HC.learn_one(x) + ... hierarchical_clustering = hierarchical_clustering.learn_one(x) - >>> HC.x_clusters + >>> hierarchical_clustering.x_clusters {'[1 1 1]': 1, '[1 1 0]': 2, '[20 20 20]': 4, '[20. 20. 20.1]': 6, '[0 1 1]': 8} - >>> HC.n + >>> hierarchical_clustering.n 9 - >>> print(HC) + >>> print(hierarchical_clustering) -> 6 -> 7 -> 4 @@ -91,18 +91,18 @@ class HierarchicalClustering(base.Clusterer): -> 1 Printed Hierarchical Clustering Tree. - >>> HC.get_all_clusters() + >>> hierarchical_clustering.get_all_clusters() [(1, [array([1, 1, 1])]), (2, [array([1, 1, 0])]), (4, [array([20, 20, 20])]), (6, [array([20. , 20. , 20.1])]), (8, [array([0, 1, 1])]), (3, [1, 2]), (5, [9, 7]), (7, [4, 6]), (9, [3, 8])] - >>> HC.get_clusters_by_point() + >>> hierarchical_clustering.get_clusters_by_point() {'[1 1 1]': [1, 3, 9, 5], '[1 1 0]': [2, 3, 9, 5], '[20 20 20]': [4, 7, 5], '[20. 20. 20.1]': [6, 7, 5], '[0 1 1]': [8, 9, 5]} - >>> HC.predict_one({0 : 20.1, 1 : 20, 2 : 20 }) + >>> hierarchical_clustering.predict_one({0 : 20.1, 1: 20, 2: 20 }) ([10, 11, 5], 7) - >>> HC = HC.learn_one({0 : 20.1, 1 : 20, 2 : 20 }) + >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 20.1, 1: 20, 2: 20}) - >>> print(HC) + >>> print(hierarchical_clustering) -> 10 -> 11 -> 6 @@ -116,18 +116,18 @@ class HierarchicalClustering(base.Clusterer): -> 1 Printed Hierarchical Clustering Tree. - >>> HC = cluster.HierarchicalClustering(window_size=2) + >>> hierarchical_clustering = cluster.HierarchicalClustering(window_size=2) >>> for x, _ in stream.iter_array(X): - ... HC = HC.learn_one(x) + ... hierarchical_clustering = hierarchical_clustering.learn_one(x) - >>> HC.x_clusters + >>> hierarchical_clustering.x_clusters {'[20. 20. 20.1]': 2, '[0 1 1]': 1} - >>> HC.n + >>> hierarchical_clustering.n 3 - >>> print(HC) + >>> print(hierarchical_clustering) -> 2 -> 3 -> 1 From aa597daef0b1148d76ecec145bf8c6b6cfd45f32 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 17:41:36 +0700 Subject: [PATCH 116/347] Refactor comment. --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 322a7cccdc..59089bc234 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -225,7 +225,7 @@ def learn_one(self, x): self.x_clusters[np.array2string(x)] = oldest_key self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: - # Else we just add a node + # Else, add a node self.n += 1 self.x_clusters[np.array2string(x)] = self.n self.nodes[self.n] = BinaryTreeNode(self.n, x) From 66927cd52a05106617a6e9252795bf6280cd15d3 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 08:07:37 +0700 Subject: [PATCH 117/347] Make find_path() a static method. --- river/cluster/hcluster.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 59089bc234..b4697ef357 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -279,7 +279,8 @@ def predict_one(self, x): r.reverse() return r, merged - def find_path(self, root, path, k): + @staticmethod + def find_path(root, path, k): # find the path from root to k # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ @@ -291,8 +292,8 @@ def find_path(self, root, path, k): if root.key == k: return True - if (root.left is not None and self.find_path(root.left, path, k)) or ( - root.right is not None and self.find_path(root.right, path, k) + if (root.left is not None and HierarchicalClustering.find_path(root.left, path, k)) or ( + root.right is not None and HierarchicalClustering.find_path(root.right, path, k) ): return True @@ -309,7 +310,9 @@ def lca(self, i, j): path_i = [] path_j = [] - if not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j): + if not HierarchicalClustering.find_path( + self.root, path_i, i + ) or not HierarchicalClustering.find_path(self.root, path_j, j): return -1 k = 0 From 87cf7fdde491f8210e276ab0100e6aa3d3f79786 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 08:07:53 +0700 Subject: [PATCH 118/347] Refactor Docstring. --- river/cluster/hcluster.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index b4697ef357..94b84162da 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -30,14 +30,14 @@ class HierarchicalClustering(base.Clusterer): near the nodes it is similar to without breaking clusters of very similar nodes. Beginning with the whole tree `T`, it will compare the new node to this respective tree: - * If `T` is just a leaf : merge - * else if the nodes of T are more similar between them than with the new node: merge - * else if the new node is more similar to the left subtree than to the right subtree: - redo from the first point with T = left subtree - * else (the new node is more similar to the right subtree than to the left subtree): - redo from the first point with T = right subtree - - A certain window size can be chosen to use only the most recent points to make sure that the tree is not overloaded. + * If `T` is just a leaf: merge + * Else, if the nodes of `T` are more similar between them than with the new node: merge + * Else, if the new node is more similar to the left subtree than to the right subtree: + redo from the first point with `T` equal to left subtree + * Else, if the new node is more similar to the right subtree than to the left subtree: + redo from the first point with `T` right subtree + + A window size can also be chosen to use only the most recent points to make sure that the tree is not overloaded. Parameters ---------- From d12f243989fbcb7af8e68ccf6717d9031073ec44 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 15:44:30 +0700 Subject: [PATCH 119/347] Make print_tree static method. --- river/cluster/hcluster.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 94b84162da..21ba71b506 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -365,13 +365,14 @@ def __str__(self): self.print_tree(self.root) return "Printed Hierarchical Clustering Tree." - def print_tree(self, node, level=0): + @staticmethod + def print_tree(node, level=0): # Print node and its children # Adapted from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: - self.print_tree(node.right, level + 1) + HierarchicalClustering.print_tree(node.right, level + 1) print(" " * 4 * level + "-> " + str(node.key)) - self.print_tree(node.left, level + 1) + HierarchicalClustering.print_tree(node.left, level + 1) def get_parents(self, node): # Get all the parents of the node (the clusters it belongs to) From 43884f04792a9c934be417c42064c018eaa63dd5 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 16:08:16 +0700 Subject: [PATCH 120/347] Refactor code to account for failing tests. --- river/cluster/hcluster.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 21ba71b506..42a78d84a1 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -150,7 +150,10 @@ def __init__( # First node of the tree self.root = None # Distance function - self.distance = distance_func if distance_func is not None else euclidean_distance + if distance_func is not None: + self.distance = distance_func + else: + self.distance = euclidean_distance def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. From 2a80d3c58c6b07153f606467169614e38cab46e0 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:00:26 +0700 Subject: [PATCH 121/347] Refactor distance function used in Hierarchical Clustering class. --- river/cluster/hcluster.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 42a78d84a1..f5f6f7775f 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,11 @@ from __future__ import annotations -from collections.abc import Callable - +import functools +import math import numpy as np from river import base, utils +from river.neighbors.base import DistanceFunc # Node of a binary tree for Hierarchical Clustering @@ -137,7 +138,7 @@ class HierarchicalClustering(base.Clusterer): def __init__( self, window_size: int = 100, - distance_func: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, + distance_func: DistanceFunc = functools.partial(utils.math.minkowski_distance, p=2), ): # Number of nodes self.n = 0 @@ -150,10 +151,7 @@ def __init__( # First node of the tree self.root = None # Distance function - if distance_func is not None: - self.distance = distance_func - else: - self.distance = euclidean_distance + self.distance = distance_func def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. @@ -347,7 +345,7 @@ def inter_subtree_similarity(self, tree_a, tree_b): for i, w_i in enumerate(leaves_a): for j, w_j in enumerate(leaves_b): nb += 1 - r += self.distance(w_i, w_j) + r += self.distance(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) return r / nb def intra_subtree_similarity(self, tree): @@ -361,7 +359,7 @@ def intra_subtree_similarity(self, tree): for j, w_j in enumerate(leaves): if i < j: nb += 1 - r += self.distance(w_i, w_j) + r += self.distance(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) return r / nb def __str__(self): From 74055dbf07132f5de0eb473ba9f33b8508debfdc Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:00:49 +0700 Subject: [PATCH 122/347] Delete euclidean_distance function (due to being unnecessary). --- river/cluster/hcluster.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index f5f6f7775f..a07759dafb 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -19,11 +19,6 @@ def __init__(self, key: int, data: np.ndarray = None): self.parent = None -def euclidean_distance(w_i, w_j): - # Euclidean distance between two nodes - return np.linalg.norm(w_i.data - w_j.data) - - class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. From c356828612cbfab682a4177ece451aaced1a53a8 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:20:51 +0700 Subject: [PATCH 123/347] Code refactoring to align with other algorithms available in River. --- river/cluster/hcluster.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index a07759dafb..23b1d18b8f 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,11 +1,10 @@ from __future__ import annotations import functools -import math import numpy as np from river import base, utils -from river.neighbors.base import DistanceFunc +from river.neighbors.base import DistanceFunc, FunctionWrapper # Node of a binary tree for Hierarchical Clustering @@ -39,7 +38,7 @@ class HierarchicalClustering(base.Clusterer): ---------- window_size number of data points to use - distance_func + dist_func distance function to use to compare the nodes Attributes @@ -133,7 +132,7 @@ class HierarchicalClustering(base.Clusterer): def __init__( self, window_size: int = 100, - distance_func: DistanceFunc = functools.partial(utils.math.minkowski_distance, p=2), + dist_func: DistanceFunc | FunctionWrapper | None = None, ): # Number of nodes self.n = 0 @@ -146,7 +145,9 @@ def __init__( # First node of the tree self.root = None # Distance function - self.distance = distance_func + if dist_func is None: + dist_func = functools.partial(utils.math.minkowski_distance, p=2) + self.dist_func = dist_func def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. @@ -340,7 +341,7 @@ def inter_subtree_similarity(self, tree_a, tree_b): for i, w_i in enumerate(leaves_a): for j, w_j in enumerate(leaves_b): nb += 1 - r += self.distance(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) + r += self.dist_func(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) return r / nb def intra_subtree_similarity(self, tree): @@ -354,7 +355,7 @@ def intra_subtree_similarity(self, tree): for j, w_j in enumerate(leaves): if i < j: nb += 1 - r += self.distance(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) + r += self.dist_func(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) return r / nb def __str__(self): From 851b710aacdec9e40264679a2994e5ca1729daea Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:22:07 +0700 Subject: [PATCH 124/347] Modify Docstring description for dist_func. --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 23b1d18b8f..fd5738bc7f 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -39,7 +39,7 @@ class HierarchicalClustering(base.Clusterer): window_size number of data points to use dist_func - distance function to use to compare the nodes + A distance function to use to compare the nodes. The Minkowski distance with `p=2` is used as default. Attributes ---------- From 14a09af9a8865608e7a30d6dc949de77d5775efc Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Thu, 14 Sep 2023 11:57:57 +0700 Subject: [PATCH 125/347] Delete least common ancestor finding function (since this function is not used within the algorithm). --- river/cluster/hcluster.py | 22 ---------------------- 1 file changed, 22 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index fd5738bc7f..3ece91ef07 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -297,28 +297,6 @@ def find_path(root, path, k): path.pop() return False - def lca(self, i, j): - # Find the least common ancestor - # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ - - if self.root is None: - return -1 - - path_i = [] - path_j = [] - - if not HierarchicalClustering.find_path( - self.root, path_i, i - ) or not HierarchicalClustering.find_path(self.root, path_j, j): - return -1 - - k = 0 - while k < len(path_i) and k < len(path_j): - if path_i[k] != path_j[k]: - break - k += 1 - return path_i[k - 1] - def leaves(self, v): # find all the leaves from node v From 487341b55ba816349ca963acf0d09fbb9d86a668 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Mon, 18 Sep 2023 21:15:44 -0300 Subject: [PATCH 126/347] Simplify Adaptive Random Forest (#1323) --- docs/releases/unreleased.md | 4 + river/base/drift_detector.py | 3 +- river/forest/adaptive_random_forest.py | 456 ++++++++++--------------- river/forest/online_extra_trees.py | 7 +- 4 files changed, 193 insertions(+), 277 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 8a655bfa95..507f905d22 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -5,3 +5,7 @@ River's mini-batch methods now support pandas v2. In particular, River conforms ## anomaly - Added `anomaly.LocalOutlierFactor`, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation. + +## forest + +- Simplify inner the structures of `forest.ARFClassifier` and `forest.ARFRegressor` by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole. diff --git a/river/base/drift_detector.py b/river/base/drift_detector.py index 62a511d5bb..25f0719f2a 100644 --- a/river/base/drift_detector.py +++ b/river/base/drift_detector.py @@ -8,7 +8,6 @@ from __future__ import annotations import abc -import numbers from . import base @@ -58,7 +57,7 @@ class DriftDetector(_BaseDriftDetector): """A drift detector.""" @abc.abstractmethod - def update(self, x: numbers.Number) -> DriftDetector: + def update(self, x: int | float) -> DriftDetector: """Update the detector with a single data point. Parameters diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index 413ba539e4..8e7a9c6645 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -47,11 +47,35 @@ def __init__( self.drift_detector = drift_detector self.warning_detector = warning_detector self.seed = seed + self._rng = random.Random(self.seed) - # Internal parameters - self._n_samples_seen = 0 - self._base_member_class: ForestMemberClassifier | ForestMemberRegressor | None = None + self._warning_detectors: list[base.DriftDetector] = ( + None # type: ignore + if self.warning_detector is None + else [self.warning_detector.clone() for _ in range(self.n_models)] + ) + self._drift_detectors: list[base.DriftDetector] = ( + None # type: ignore + if self.drift_detector is None + else [self.drift_detector.clone() for _ in range(self.n_models)] + ) + + # The background models + self._background: list[BaseTreeClassifier | BaseTreeRegressor | None] = ( + None if self.warning_detector is None else [None] * self.n_models # type: ignore + ) + + # Performance metrics used for weighted voting/aggregation + self._metrics = [self.metric.clone() for _ in range(self.n_models)] + + # Drift and warning logging + self._warning_tracker: dict = ( + collections.defaultdict(int) if self.warning_detector is not None else None # type: ignore + ) + self._drift_tracker: dict = ( + collections.defaultdict(int) if self.drift_detector is not None else None # type: ignore + ) @property def _min_number_of_models(self): @@ -64,49 +88,135 @@ def _unit_test_params(cls): def _unit_test_skips(self): return {"check_shuffle_features_no_impact"} - def learn_one(self, x: dict, y: base.typing.Target, **kwargs): - self._n_samples_seen += 1 + @abc.abstractmethod + def _drift_detector_input( + self, + tree_id: int, + y_true, + y_pred, + ) -> int | float: + raise NotImplementedError + + @abc.abstractmethod + def _new_base_model(self) -> BaseTreeClassifier | BaseTreeRegressor: + raise NotImplementedError + + def n_warnings_detected(self, tree_id: int | None = None) -> int | None: + """Get the total number of concept drift warnings detected, or the number on an individual + tree basis (optionally). + + If warning detection is disabled, will return `None`. + + Parameters + ---------- + tree_id + The number of the base learner in the ensemble: `[0, self.n_models - 1]. If `None`, + the total number of warnings is returned instead. + + Returns + ------- + The number of concept drift warnings detected. + + """ + + if self.warning_detector is None: + return None + + if tree_id is None: + return sum(self._warning_tracker.values()) + + return self._warning_tracker[tree_id] + + def n_drifts_detected(self, tree_id: int | None = None) -> int | None: + """Get the total number of concept drifts detected, or such number on an individual + tree basis (optionally). + + If drift detection is disabled, will return `None`. + + Parameters + ---------- + tree_id + The number of the base learner in the ensemble: `[0, self.n_models - 1]. If `None`, + the total number of warnings is returned instead. + + Returns + ------- + The number of concept drifts detected. + """ + + if self.drift_detector is None: + return None + + if tree_id is None: + return sum(self._drift_tracker.values()) + + return self._drift_tracker[tree_id] + + def learn_one(self, x: dict, y: base.typing.Target, **kwargs): if len(self) == 0: self._init_ensemble(sorted(x.keys())) - for model in self: - # Get prediction for instance - y_pred = ( - model.predict_proba_one(x) - if isinstance(model.metric, metrics.base.ClassificationMetric) - and not model.metric.requires_labels - else model.predict_one(x) - ) + for i, model in enumerate(self): + y_pred = model.predict_one(x) # Update performance evaluator - model.metric.update(y_true=y, y_pred=y_pred) + self._metrics[i].update( + y_true=y, + y_pred=model.predict_proba_one(x) + if isinstance(self.metric, metrics.base.ClassificationMetric) + and not self.metric.requires_labels + else y_pred, + ) k = poisson(rate=self.lambda_value, rng=self._rng) if k > 0: - model.learn_one(x=x, y=y, sample_weight=k, n_samples_seen=self._n_samples_seen) + if self.warning_detector is not None and self._background[i] is not None: + self._background[i].learn_one(x=x, y=y, sample_weight=k) # type: ignore + + model.learn_one(x=x, y=y, sample_weight=k) + + drift_input = None + if self.drift_detector is not None and self.warning_detector is not None: + drift_input = self._drift_detector_input(i, y, y_pred) + self._warning_detectors[i].update(drift_input) + + if self._warning_detectors[i].drift_detected: + self._background[i] = self._new_base_model() # type: ignore + # Reset the warning detector for the current object + self._warning_detectors[i] = self.warning_detector.clone() + + # Update warning tracker + self._warning_tracker[i] += 1 + + if self.drift_detector is not None: + drift_input = ( + drift_input + if drift_input is not None + else self._drift_detector_input(i, y, y_pred) + ) + self._drift_detectors[i].update(drift_input) + + if self._drift_detectors[i].drift_detected: + if self.warning_detector is not None and self._background[i] is not None: + self.data[i] = self._background[i] + self._background[i] = None + self._warning_detectors[i] = self.warning_detector.clone() + self._drift_detectors[i] = self.drift_detector.clone() + self._metrics[i] = self.metric.clone() + else: + self.data[i] = self._new_base_model() + self._drift_detectors[i] = self.drift_detector.clone() + self._metrics[i] = self.metric.clone() + + # Update warning tracker + self._drift_tracker[i] += 1 return self def _init_ensemble(self, features: list): self._set_max_features(len(features)) - - self.data = [ - self._base_member_class( # type: ignore - index_original=i, - model=self._new_base_model(), - created_on=self._n_samples_seen, - drift_detector=self.drift_detector, - warning_detector=self.warning_detector, - is_background_learner=False, - metric=self.metric, - ) - for i in range(self.n_models) - ] - - @abc.abstractmethod - def _new_base_model(self): - raise NotImplementedError + self.data = [self._new_base_model() for _ in range(self.n_models)] def _set_max_features(self, n_features): if self.max_features == "sqrt": @@ -230,12 +340,6 @@ def _new_leaf(self, initial_stats=None, parent=None): self.rng, ) - def new_instance(self): - new_instance = self.clone() - # Use existing rng to enforce a different model - new_instance.rng = self.rng - return new_instance - class BaseTreeRegressor(HoeffdingTreeRegressor): """ARF Hoeffding Tree regressor. @@ -343,12 +447,6 @@ def _new_leaf(self, initial_stats=None, parent=None): # noqa return new_adaptive - def new_instance(self): - new_instance = self.clone() - # Use existing rng to enforce a different model - new_instance.rng = self.rng - return new_instance - class ARFClassifier(BaseForest, base.Classifier): """Adaptive Random Forest classifier. @@ -472,7 +570,19 @@ class ARFClassifier(BaseForest, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 71.07% + Accuracy: 71.17% + + The total number of warnings and drifts detected, respectively + >>> model.n_warnings_detected(), model.n_drifts_detected() + (2, 1) + + The number of warnings detected by tree number 2 + >>> model.n_warnings_detected(2) + 1 + + And the corresponding number of actual concept drift detected + >>> model.n_drifts_detected(2) + 1 References ---------- @@ -523,9 +633,6 @@ def __init__( seed=seed, ) - self._n_samples_seen = 0 - self._base_member_class = ForestMemberClassifier # type: ignore - # Tree parameters self.grace_period = grace_period self.max_depth = max_depth @@ -566,9 +673,9 @@ def predict_proba_one(self, x: dict) -> dict[base.typing.ClfTarget, float]: self._init_ensemble(sorted(x.keys())) return y_pred # type: ignore - for model in self: + for i, model in enumerate(self): y_proba_temp = model.predict_proba_one(x) - metric_value = model.metric.get() + metric_value = self._metrics[i].get() if not self.disable_weighted_vote and metric_value > 0.0: y_proba_temp = {k: val * metric_value for k, val in y_proba_temp.items()} y_pred.update(y_proba_temp) @@ -601,9 +708,14 @@ def _new_base_model(self): rng=self._rng, ) + def _drift_detector_input( + self, tree_id: int, y_true: base.typing.ClfTarget, y_pred: base.typing.ClfTarget + ) -> int | float: + return int(not y_true == y_pred) + class ARFRegressor(BaseForest, base.Regressor): - r"""Adaptive Random Forest regressor. + """Adaptive Random Forest regressor. The 3 most important aspects of Adaptive Random Forest [^1] are: @@ -621,7 +733,7 @@ class ARFRegressor(BaseForest, base.Regressor): predictions and check for concept drifts. The deviations of the predictions to the target are monitored and normalized in the [0, 1] range to fulfill ADWIN's requirements. We assume that the data subjected to the normalization follows - a normal distribution, and thus, lies within the interval of the mean $\pm3\sigma$. + a normal distribution, and thus, lies within the interval of the mean $\\pm3\\sigma$. Parameters ---------- @@ -742,7 +854,7 @@ class ARFRegressor(BaseForest, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.800378 + MAE: 0.788619 """ @@ -791,9 +903,6 @@ def __init__( seed=seed, ) - self._n_samples_seen = 0 - self._base_member_class = ForestMemberRegressor # type: ignore - # Tree parameters self.grace_period = grace_period self.max_depth = max_depth @@ -820,6 +929,9 @@ def __init__( f"Valid values are: {self._VALID_AGGREGATION_METHOD}" ) + # Used to normalize the input for the drift trackers + self._drift_norm = [stats.Var() for _ in range(self.n_models)] + @property def _mutable_attributes(self): return { @@ -842,10 +954,10 @@ def predict_one(self, x: dict) -> base.typing.RegTarget: if not self.disable_weighted_vote and self.aggregation_method != self._MEDIAN: weights = np.zeros(self.n_models) sum_weights = 0.0 - for idx, model in enumerate(self): - y_pred[idx] = model.predict_one(x) - weights[idx] = model.metric.get() - sum_weights += weights[idx] + for i, model in enumerate(self): + y_pred[i] = model.predict_one(x) + weights[i] = self._metrics[i].get() + sum_weights += weights[i] if sum_weights != 0: # The higher the error, the worse is the tree @@ -854,8 +966,8 @@ def predict_one(self, x: dict) -> base.typing.RegTarget: weights /= weights.sum() y_pred *= weights else: - for idx, model in enumerate(self): - y_pred[idx] = model.predict_one(x) + for i, model in enumerate(self): + y_pred[i] = model.predict_one(x) if self.aggregation_method == self._MEAN: y_pred = y_pred.mean() @@ -885,214 +997,19 @@ def _new_base_model(self): rng=self._rng, ) - @property - def valid_aggregation_method(self): - """Valid aggregation_method values.""" - return self._VALID_AGGREGATION_METHOD - - -class BaseForestMember: - """Base forest member class. - - This class represents a tree member of the forest. It includes a - base tree model, the background learner, drift detectors and performance - tracking parameters. - - The main purpose of this class is to train the foreground model. - Optionally, it monitors drift detection. Depending on the configuration, - if drift is detected then the foreground model is reset or replaced by a - background model. - - Parameters - ---------- - index_original - Tree index within the ensemble. - model - Tree learner. - created_on - Number of instances seen by the tree. - drift_detector - Drift Detection method. - warning_detector - Warning Detection method. - is_background_learner - True if the tree is a background learner. - metric - Metric to track performance. - - """ - - def __init__( - self, - index_original: int, - model: BaseTreeClassifier | BaseTreeRegressor, - created_on: int, - drift_detector: base.DriftDetector, - warning_detector: base.DriftDetector, - is_background_learner, - metric: metrics.base.MultiClassMetric | metrics.base.RegressionMetric, - ): - self.index_original = index_original - self.model = model - self.created_on = created_on - self.is_background_learner = is_background_learner - self.metric = metric.clone() - self.background_learner = None - - # Drift and warning detection - self.last_drift_on = 0 - self.last_warning_on = 0 - self.n_drifts_detected = 0 - self.n_warnings_detected = 0 - - # Initialize drift and warning detectors - if drift_detector is not None: - self._use_drift_detector = True - self.drift_detector = drift_detector.clone() - else: - self._use_drift_detector = False - self.drift_detector = None - - if warning_detector is not None: - self._use_background_learner = True - self.warning_detector = warning_detector.clone() - else: - self._use_background_learner = False - self.warning_detector = None - - def reset(self, n_samples_seen): - if self._use_background_learner and self.background_learner is not None: - # Replace foreground model with background model - self.model = self.background_learner.model - self.warning_detector = self.background_learner.warning_detector - self.drift_detector = self.background_learner.drift_detector - self.metric = self.background_learner.metric - self.created_on = self.background_learner.created_on - self.background_learner = None - else: - # Reset model - self.model = self.model.new_instance() - self.metric = self.metric.clone() - self.created_on = n_samples_seen - self.drift_detector = self.drift_detector.clone() - - def learn_one(self, x: dict, y: base.typing.Target, *, sample_weight: int, n_samples_seen: int): - self.model.learn_one(x, y, sample_weight=sample_weight) - - if self.background_learner: - # Train the background learner - self.background_learner.model.learn_one(x=x, y=y, sample_weight=sample_weight) - - if self._use_drift_detector and not self.is_background_learner: - drift_detector_input = self._drift_detector_input( - y_true=y, y_pred=self.model.predict_one(x) # type: ignore - ) - - # Check for warning only if use_background_learner is set - if self._use_background_learner: - self.warning_detector.update(drift_detector_input) - # Check if there was a (warning) change - if self.warning_detector.drift_detected: - self.last_warning_on = n_samples_seen - self.n_warnings_detected += 1 - # Create a new background learner object - self.background_learner = self.__class__( # type: ignore - index_original=self.index_original, - model=self.model.new_instance(), - created_on=n_samples_seen, - drift_detector=self.drift_detector, - warning_detector=self.warning_detector, - is_background_learner=True, - metric=self.metric, - ) - # Reset the warning detector for the current object - self.warning_detector = self.warning_detector.clone() - - # Update the drift detector - self.drift_detector.update(drift_detector_input) - - # Check if there was a change - if self.drift_detector.drift_detected: - self.last_drift_on = n_samples_seen - self.n_drifts_detected += 1 - self.reset(n_samples_seen) - - @abc.abstractmethod def _drift_detector_input( self, - y_true: base.typing.ClfTarget | base.typing.RegTarget, - y_pred: base.typing.ClfTarget | base.typing.RegTarget, - ): - raise NotImplementedError - - -class ForestMemberClassifier(BaseForestMember, base.Classifier): # type: ignore - """Forest member class for classification""" - - def __init__( - self, - index_original: int, - model: BaseTreeClassifier, - created_on: int, - drift_detector: base.DriftDetector, - warning_detector: base.DriftDetector, - is_background_learner, - metric: metrics.base.MultiClassMetric, - ): - super().__init__( - index_original=index_original, - model=model, - created_on=created_on, - drift_detector=drift_detector, - warning_detector=warning_detector, - is_background_learner=is_background_learner, - metric=metric, - ) - - def _drift_detector_input( # type: ignore - self, y_true: base.typing.ClfTarget, y_pred: base.typing.ClfTarget - ): - return int(not y_true == y_pred) # Not correctly_classifies - - def predict_one(self, x): - return self.model.predict_one(x) - - def predict_proba_one(self, x): - return self.model.predict_proba_one(x) - - -class ForestMemberRegressor(BaseForestMember, base.Regressor): # type: ignore - """Forest member class for regression""" - - def __init__( - self, - index_original: int, - model: BaseTreeRegressor, - created_on: int, - drift_detector: base.DriftDetector, - warning_detector: base.DriftDetector, - is_background_learner, - metric: metrics.base.RegressionMetric, - ): - super().__init__( - index_original=index_original, - model=model, - created_on=created_on, - drift_detector=drift_detector, - warning_detector=warning_detector, - is_background_learner=is_background_learner, - metric=metric, - ) - self._var = stats.Var() # Used to track drift - - def _drift_detector_input(self, y_true: float, y_pred: float): # type: ignore + tree_id: int, + y_true: int | float, + y_pred: int | float, + ) -> int | float: drift_input = y_true - y_pred - self._var.update(drift_input) + self._drift_norm[tree_id].update(drift_input) - if self._var.mean.n == 1: + if self._drift_norm[tree_id].mean.n == 1: return 0.5 # The expected error is the normalized mean error - sd = math.sqrt(self._var.get()) + sd = math.sqrt(self._drift_norm[tree_id].get()) # We assume the error follows a normal distribution -> (empirical rule) # 99.73% of the values lie between [mean - 3*sd, mean + 3*sd]. We @@ -1100,10 +1017,7 @@ def _drift_detector_input(self, y_true: float, y_pred: float): # type: ignore # min-max norm to cope with ADWIN's requirements return (drift_input + 3 * sd) / (6 * sd) if sd > 0 else 0.5 - def reset(self, n_samples_seen): - super().reset(n_samples_seen) - # Reset the stats for the drift detector - self._var = stats.Var() - - def predict_one(self, x): - return self.model.predict_one(x) + @property + def valid_aggregation_method(self): + """Valid aggregation_method values.""" + return self._VALID_AGGREGATION_METHOD diff --git a/river/forest/online_extra_trees.py b/river/forest/online_extra_trees.py index cd7c6d7edc..ce904c9b83 100644 --- a/river/forest/online_extra_trees.py +++ b/river/forest/online_extra_trees.py @@ -3,7 +3,6 @@ import abc import collections import math -import numbers import random import sys @@ -221,7 +220,7 @@ def __weight_sampler_factory(self) -> Sampler: def _detection_mode_all( drift_detector: base.DriftDetector, warning_detector: base.DriftDetector, - detector_input: numbers.Number, + detector_input: int | float, ) -> tuple[bool, bool]: in_warning = warning_detector.update(detector_input).drift_detected in_drift = drift_detector.update(detector_input).drift_detected @@ -232,7 +231,7 @@ def _detection_mode_all( def _detection_mode_drop( drift_detector: base.DriftDetector, warning_detector: base.DriftDetector, - detector_input: numbers.Number, + detector_input: int | float, ) -> tuple[bool, bool]: in_drift = drift_detector.update(detector_input).drift_detected @@ -242,7 +241,7 @@ def _detection_mode_drop( def _detection_mode_off( drift_detector: base.DriftDetector, warning_detector: base.DriftDetector, - detector_input: numbers.Number, + detector_input: int | float, ) -> tuple[bool, bool]: return False, False From 2df17f1c3de9695bb78cc6280ece5be98697a1e6 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 2 Oct 2023 20:13:51 +0200 Subject: [PATCH 127/347] remove pytest from gaussian.py --- .gitignore | 3 +++ river/proba/gaussian.py | 27 --------------------------- river/proba/test_gaussian.py | 31 +++++++++++++++++++++++++++++++ 3 files changed, 34 insertions(+), 27 deletions(-) create mode 100644 river/proba/test_gaussian.py diff --git a/.gitignore b/.gitignore index 312295e81d..28ddefaa7f 100644 --- a/.gitignore +++ b/.gitignore @@ -130,3 +130,6 @@ benchmarks/.asv # Cargo file Cargo.lock + +# WASM +/*.html diff --git a/river/proba/gaussian.py b/river/proba/gaussian.py index 2c8f51ae28..6022775361 100644 --- a/river/proba/gaussian.py +++ b/river/proba/gaussian.py @@ -4,7 +4,6 @@ import numpy as np import pandas as pd -import pytest from scipy.stats import multivariate_normal from river import covariance, stats @@ -313,29 +312,3 @@ def sample(self) -> dict[str, float]: @property def mode(self) -> dict: return self.mu - - -@pytest.mark.parametrize( - "p", - [ - pytest.param( - p, - id=f"{p=}", - ) - for p in [1, 3, 5] - ], -) -def test_univariate_multivariate_consistency(p): - X = pd.DataFrame(np.random.random((30, p)), columns=range(p)) - - multi = MultivariateGaussian() - single = {c: Gaussian() for c in X.columns} - - for x in X.to_dict(orient="records"): - multi = multi.update(x) - for c, s in single.items(): - s.update(x[c]) - - for c in X.columns: - assert math.isclose(multi.mu[c], single[c].mu) - assert math.isclose(multi.sigma[c][c], single[c].sigma) diff --git a/river/proba/test_gaussian.py b/river/proba/test_gaussian.py new file mode 100644 index 0000000000..f38fd4ff19 --- /dev/null +++ b/river/proba/test_gaussian.py @@ -0,0 +1,31 @@ +import pytest +import pandas as pd +import numpy as np +import math +from river import proba + + +@pytest.mark.parametrize( + "p", + [ + pytest.param( + p, + id=f"{p=}", + ) + for p in [1, 3, 5] + ], +) +def test_univariate_multivariate_consistency(p): + X = pd.DataFrame(np.random.random((30, p)), columns=range(p)) + + multi = proba.MultivariateGaussian() + single = {c: proba.Gaussian() for c in X.columns} + + for x in X.to_dict(orient="records"): + multi = multi.update(x) + for c, s in single.items(): + s.update(x[c]) + + for c in X.columns: + assert math.isclose(multi.mu[c], single[c].mu) + assert math.isclose(multi.sigma[c][c], single[c].sigma) From ce6fbc34b05c77d99a7475058379565444a759b3 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 2 Oct 2023 20:14:03 +0200 Subject: [PATCH 128/347] Update test_gaussian.py --- river/proba/test_gaussian.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/river/proba/test_gaussian.py b/river/proba/test_gaussian.py index f38fd4ff19..bb445cea16 100644 --- a/river/proba/test_gaussian.py +++ b/river/proba/test_gaussian.py @@ -1,7 +1,11 @@ -import pytest -import pandas as pd -import numpy as np +from __future__ import annotations + import math + +import numpy as np +import pandas as pd +import pytest + from river import proba From 46814cdb51578567b66bd6daec25359c15a43ddb Mon Sep 17 00:00:00 2001 From: Jaco DuToit Date: Tue, 3 Oct 2023 11:33:43 +0200 Subject: [PATCH 129/347] added web traffic dataset and info (#1326) * added web traffic dataset and info * removed CSV file and updated the url --- river/datasets/web_traffic.py | 48 +++++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 river/datasets/web_traffic.py diff --git a/river/datasets/web_traffic.py b/river/datasets/web_traffic.py new file mode 100644 index 0000000000..9a253f8923 --- /dev/null +++ b/river/datasets/web_traffic.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +from river import stream + +from . import base + + +class WebTraffic(base.RemoteDataset): + """Web sessions information from an events company based in South Africa. + + The goal is to predict the number of web sessions in 4 different regions in South Africa. + + The data consists of 15 minute interval traffic values between '2023-06-16 00:00:00' and '2023-09-15 23:45:00' for each region. + Two types of sessions are captured `sessionsA` and `sessionsB`. The `isMissing` flag is equal to 1 if any of the servers failed + to capture sessions, otherwise if all servers functioned properly this flag is equal to 0. Things to consider: + + * region `R5` captures sessions in backup mode. Strictly speaking, `R5` is not necessary to predict. + * Can `sessionsA` and `sessionsB` events be predicted accurately for each region over the next day (next 96 intervals)? + * What is the best way to deal with the missing values? + * How can model selection be used (a multi-model approach)? + * Can dependence (correlation) between regions be utilised for more accurate predictions? + * Can both `sessionA` and `sessionB` be predicted simultaneously with one model? + + This dataset is well suited for time series forecasting models, as well as anomaly detection + methods. Ideally, the goal is to build a time series forecasting model that is robust to the + anomalous events and generalise well on normal operating conditions. + + """ + + def __init__(self): + super().__init__( + url="https://maxhalford.github.io/files/datasets/web-traffic.csv.zip", + filename="web-traffic.csv", + task=base.REG, + n_features=2, + n_samples=44_160, + ) + + def _iter(self): + return stream.iter_csv( + self.path, + target="sessionsA", + converters={ + "region": str, + "isMissing": int, + }, + parse_dates={"dateTime": "%Y-%m-%d %H:%M:%S"}, + ) From 79568767e9fa22554974e840948c86cdf8d317d4 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 3 Oct 2023 11:58:34 +0200 Subject: [PATCH 130/347] tidy up WebTraffic --- docs/releases/unreleased.md | 4 ++++ river/datasets/__init__.py | 2 ++ river/datasets/web_traffic.py | 30 ++++++++++++++++++++++-------- 3 files changed, 28 insertions(+), 8 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 507f905d22..92377996dc 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -6,6 +6,10 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Added `anomaly.LocalOutlierFactor`, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation. +## datasets + +- Added `datasets.WebTraffic`, which is a dataset that counts the occurrences of events on a website. It is a multi-output regression dataset with two outputs. + ## forest - Simplify inner the structures of `forest.ARFClassifier` and `forest.ARFRegressor` by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole. diff --git a/river/datasets/__init__.py b/river/datasets/__init__.py index 0d52732eb5..5ed0177900 100644 --- a/river/datasets/__init__.py +++ b/river/datasets/__init__.py @@ -32,6 +32,7 @@ from .trec07 import TREC07 from .trump_approval import TrumpApproval from .water_flow import WaterFlow +from .web_traffic import WebTraffic __all__ = [ "AirlinePassengers", @@ -59,6 +60,7 @@ "TREC07", "TrumpApproval", "WaterFlow", + "WebTraffic", ] diff --git a/river/datasets/web_traffic.py b/river/datasets/web_traffic.py index 9a253f8923..6833ac7fb0 100644 --- a/river/datasets/web_traffic.py +++ b/river/datasets/web_traffic.py @@ -1,21 +1,30 @@ from __future__ import annotations +import csv + from river import stream from . import base +class PipeCSVDialect(csv.unix_dialect): + delimiter = "|" + + class WebTraffic(base.RemoteDataset): """Web sessions information from an events company based in South Africa. The goal is to predict the number of web sessions in 4 different regions in South Africa. - The data consists of 15 minute interval traffic values between '2023-06-16 00:00:00' and '2023-09-15 23:45:00' for each region. - Two types of sessions are captured `sessionsA` and `sessionsB`. The `isMissing` flag is equal to 1 if any of the servers failed - to capture sessions, otherwise if all servers functioned properly this flag is equal to 0. Things to consider: + The data consists of 15 minute interval traffic values between '2023-06-16 00:00:00' and + '2023-09-15 23:45:00' for each region. Two types of sessions are captured `sessionsA` and + `sessionsB`. The `isMissing` flag is equal to 1 if any of the servers failed to capture + sessions, otherwise if all servers functioned properly this flag is equal to 0. + + Things to consider: * region `R5` captures sessions in backup mode. Strictly speaking, `R5` is not necessary to predict. - * Can `sessionsA` and `sessionsB` events be predicted accurately for each region over the next day (next 96 intervals)? + * Can `sessionsA` and `sessionsB` events be predicted accurately for each region over the next day (next 96 intervals)? * What is the best way to deal with the missing values? * How can model selection be used (a multi-model approach)? * Can dependence (correlation) between regions be utilised for more accurate predictions? @@ -31,18 +40,23 @@ def __init__(self): super().__init__( url="https://maxhalford.github.io/files/datasets/web-traffic.csv.zip", filename="web-traffic.csv", - task=base.REG, - n_features=2, + task=base.MO_REG, + n_features=3, + n_outputs=2, n_samples=44_160, + size=2_769_905, ) def _iter(self): return stream.iter_csv( self.path, - target="sessionsA", + dialect=PipeCSVDialect, + target=["sessionsA", "sessionsB"], converters={ "region": str, - "isMissing": int, + "isMissing": lambda x: x == "1.0", + "sessionsA": lambda x: float(x) if x and x != "0.0" else None, + "sessionsB": lambda x: float(x) if x and x != "0.0" else None, }, parse_dates={"dateTime": "%Y-%m-%d %H:%M:%S"}, ) From 4b949c6ad11cc088ddb42bf319b4c94f42fe4a67 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 3 Oct 2023 12:32:18 +0200 Subject: [PATCH 131/347] solve identical samples issue in lof --- river/anomaly/lof.py | 10 ++++------ river/anomaly/{test_ilof.py => test_lof.py} | 7 +++++++ 2 files changed, 11 insertions(+), 6 deletions(-) rename river/anomaly/{test_ilof.py => test_lof.py} (95%) diff --git a/river/anomaly/lof.py b/river/anomaly/lof.py index e523a23121..631593394c 100644 --- a/river/anomaly/lof.py +++ b/river/anomaly/lof.py @@ -125,9 +125,8 @@ def calc_local_reach_dist( Calculate local reachability distance of affected points. """ for i in set_index: - local_reach_dist[i] = len(neighborhoods[i]) / sum( - [reach_dist[i][j] for j in neighborhoods[i]] - ) + denominator = sum(reach_dist[i][j] for j in neighborhoods[i]) + local_reach_dist[i] = len(neighborhoods[i]) / denominator if denominator else 0 return local_reach_dist @@ -136,9 +135,8 @@ def calc_lof(set_index: set, neighborhoods: dict, local_reach: dict, lof: dict): Calculate local outlier factor (LOF) of affected points. """ for i in set_index: - lof[i] = sum([local_reach[j] for j in neighborhoods[i]]) / ( - len(neighborhoods[i]) * local_reach[i] - ) + denominator = len(neighborhoods[i]) * local_reach[i] + lof[i] = sum(local_reach[j] for j in neighborhoods[i]) / denominator if denominator else 0 return lof diff --git a/river/anomaly/test_ilof.py b/river/anomaly/test_lof.py similarity index 95% rename from river/anomaly/test_ilof.py rename to river/anomaly/test_lof.py index ac584173c1..9703b8cdc9 100644 --- a/river/anomaly/test_ilof.py +++ b/river/anomaly/test_lof.py @@ -73,3 +73,10 @@ def test_batch_lof_scores(): assert np.allclose( ilof_scores_river_batch, lof_scores_sklearn_batch, rtol=1e-02, atol=1e-02 ) + + +def test_issue_1328(): + lof = anomaly.LocalOutlierFactor() + X = [{"a": 1, "b": 1}, {"a": 1, "b": 1}] + for x in X: + lof.learn_one(x) From d7bd65d0c782666a5b9496b4598295f793b92e26 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 3 Oct 2023 13:18:06 +0200 Subject: [PATCH 132/347] Update delete-caches.yml --- .github/workflows/delete-caches.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/delete-caches.yml b/.github/workflows/delete-caches.yml index 46bd3637a2..96fc9f892e 100644 --- a/.github/workflows/delete-caches.yml +++ b/.github/workflows/delete-caches.yml @@ -1,8 +1,8 @@ name: Clear all Github Actions caches on: + workflow_dispatch: schedule: - cron: "0 0 * * 0" - workflow_dispatch: jobs: my-job: From 1a32e0d04eecbf79d8977c1a419d5b336d82bc77 Mon Sep 17 00:00:00 2001 From: Marek Wadinger <50716630+MarekWadinger@users.noreply.github.com> Date: Tue, 3 Oct 2023 15:29:51 +0200 Subject: [PATCH 133/347] Add _from_state methods to MultivariateGaussian and EmpiricalCovariance (#1327) * ADD: _from_state method for EmpiricalCovariance * ADD: _from_state method to MultivariateGaussian * ADD: docstring to _from_state method * UPDATE: add _from_state info to release notes * ADD: doctest on _from_state method * FIX: end of file * FIX: resolve comments from smastelini --- docs/releases/unreleased.md | 8 +++++ river/covariance/emp.py | 72 +++++++++++++++++++++++++++++-------- river/proba/gaussian.py | 6 +++- 3 files changed, 71 insertions(+), 15 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 92377996dc..502f934d12 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -13,3 +13,11 @@ River's mini-batch methods now support pandas v2. In particular, River conforms ## forest - Simplify inner the structures of `forest.ARFClassifier` and `forest.ARFRegressor` by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole. + +## covariance + +- Added `_from_state` method to `covariance.EmpiricalCovariance` to warm start from previous knowledge. + +## proba + +- Added `_from_state` method to `proba.MultivariateGaussian` to warm start from previous knowledge. diff --git a/river/covariance/emp.py b/river/covariance/emp.py index 090b127410..de0a4b6e4a 100644 --- a/river/covariance/emp.py +++ b/river/covariance/emp.py @@ -108,6 +108,22 @@ class EmpiricalCovariance(SymmetricMatrix): >>> cov["blue", "blue"] Var: 0.076119 + Start from a state: + >>> n = 8 + >>> mean = {'red': 0.416, 'green': 0.387, 'blue': 0.518} + >>> cov_ = {('red', 'red'): 0.079, + ... ('red', 'green'): -0.053, + ... ('red', 'blue'): -0.010, + ... ('green', 'green'): 0.113, + ... ('green', 'blue'): 0.020, + ... ('blue', 'blue'): 0.076} + >>> cov = covariance.EmpiricalCovariance._from_state( + ... n=n, mean=mean, cov=cov_, ddof=1) + >>> cov + blue green red + blue 0.076 0.020 -0.010 + green 0.020 0.113 -0.053 + red -0.010 -0.053 0.079 """ def __init__(self, ddof=1): @@ -178,35 +194,63 @@ def update_many(self, X: pd.DataFrame): mean_arr = X_arr.mean(axis=0) cov_arr = np.cov(X_arr.T, ddof=self.ddof) + n = len(X) mean = dict(zip(X.columns, mean_arr)) cov = { (i, j): cov_arr[r, c] for (r, i), (c, j) in itertools.combinations_with_replacement(enumerate(X.columns), r=2) } - for i, j in itertools.combinations(sorted(X.columns), r=2): + self = self._from_state(n=n, mean=mean, cov=cov, ddof=self.ddof) + + return self + + @classmethod + def _from_state(cls, n: int, mean: dict, cov: dict, *, ddof=1): + """Create a new instance from state information. + + Parameters + ---------- + cls + The class type. + n + The number of data points. + mean + A dictionary of variable means. + cov + A dictionary of covariance or variance values. + ddof + Degrees of freedom for covariance calculation. Defaults to 1. + + Returns + ---------- + cls: A new instance of the class with updated covariance matrix. + + Raises + ---------- + KeyError: If an element in `mean` or `cov` is missing. + """ + new = cls(ddof=ddof) + for i, j in itertools.combinations(mean.keys(), r=2): try: - self[i, j] + new[i, j] except KeyError: - self._cov[i, j] = stats.Cov(self.ddof) - self._cov[i, j] += stats.Cov._from_state( - n=len(X), + new._cov[i, j] = stats.Cov(new.ddof) + new._cov[i, j] += stats.Cov._from_state( + n=n, mean_x=mean[i], mean_y=mean[j], cov=cov.get((i, j), cov.get((j, i))), - ddof=self.ddof, + ddof=new.ddof, ) - for i in X.columns: + for i in mean.keys(): try: - self[i, i] + new[i, i] except KeyError: - self._cov[i, i] = stats.Var(self.ddof) - self._cov[i, i] += stats.Var._from_state( - n=len(X), m=mean[i], sig=cov[i, i], ddof=self.ddof - ) - - return self + new._cov[i, i] = stats.Var(new.ddof) + new._cov[i, i] += stats.Var._from_state(n=n, m=mean[i], sig=cov[i, i], ddof=new.ddof) + return new class EmpiricalPrecision(SymmetricMatrix): diff --git a/river/proba/gaussian.py b/river/proba/gaussian.py index 6022775361..5d922e9e54 100644 --- a/river/proba/gaussian.py +++ b/river/proba/gaussian.py @@ -214,7 +214,11 @@ def __init__(self, seed=None): super().__init__(seed) self._var = covariance.EmpiricalCovariance(ddof=1) - # TODO: add method _from_state to initialize model (for warm starting) + @classmethod + def _from_state(cls, n, mean, cov, ddof, seed=None): + new = cls(seed) + new._var = covariance.EmpiricalCovariance._from_state(n, mean, cov, ddof=ddof) + return new @property def n_samples(self) -> float: From 7a5709d2835ba8f1d6ac388b3560e6a708197ab9 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Wed, 4 Oct 2023 15:59:06 +0200 Subject: [PATCH 134/347] expand ADWIN docs (#1334) --- river/drift/adwin.py | 28 +++++++++++++++++----------- 1 file changed, 17 insertions(+), 11 deletions(-) diff --git a/river/drift/adwin.py b/river/drift/adwin.py index 2fd6809bd9..6899043ddd 100644 --- a/river/drift/adwin.py +++ b/river/drift/adwin.py @@ -6,7 +6,7 @@ class ADWIN(DriftDetector): - r"""Adaptive Windowing method for concept drift detection. + r"""Adaptive Windowing method for concept drift detection[^1]. ADWIN (ADaptive WINdowing) is a popular drift detection method with mathematical guarantees. ADWIN efficiently keeps a variable-length window of recent items; such that it holds that @@ -22,18 +22,21 @@ class ADWIN(DriftDetector): delta Significance value. clock - How often ADWIN should check for change. 1 means every new data point, default is 32. + How often ADWIN should check for changes. 1 means every new data point, default is 32. Higher values speed up processing, but may also lead to increased delay in change detection. max_buckets - The maximum number of buckets of each size that ADWIN should keep before merging buckets - (default is 5). + The maximum number of buckets of each size that ADWIN should keep before merging buckets. + The idea of data buckets comes from the compression algorithm introduced in the ADWIN2, + the second iteration of the ADWIN algorithm presented in the original research paper. + This is the ADWIN version available in River. min_window_length - The minimum length of each subwindow (default is 5). Lower values may decrease delay in - change detection but may also lead to more false positives. + The minimum length allowed for a subwindow when checking for concept drift. Subwindows whose size + is smaller than this value will be ignored during concept drift evaluation. Lower values may + decrease delay in change detection but may also lead to more false positives. grace_period ADWIN does not perform any change detection until at least this many data points have - arrived (default is 10). + arrived. Examples -------- @@ -82,20 +85,23 @@ def _reset(self): ) @property - def width(self): + def width(self) -> int: """Window size""" return self._helper.get_width() @property - def n_detections(self): + def n_detections(self) -> int: + """The total number of detected changes.""" return self._helper.get_n_detections() @property - def variance(self): + def variance(self) -> float: + """The sample variance within the stored (adaptive) window.""" return self._helper.get_variance() @property - def total(self): + def total(self) -> float: + """The sum of the stored elements.""" return self._helper.get_total() @property From 6111c482589ccd6bfeaf7290c6094c3fc689398e Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Wed, 4 Oct 2023 16:11:10 +0200 Subject: [PATCH 135/347] Expand tree.ExtremelyFastDecisionTree documentation --- river/tree/extremely_fast_decision_tree.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index a7da6fb07b..f426a096ad 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -18,9 +18,20 @@ class ExtremelyFastDecisionTreeClassifier(HoeffdingTreeClassifier): - """Extremely Fast Decision Tree classifier. - - Also referred to as Hoeffding AnyTime Tree (HATT) classifier. + """Extremely Fast Decision Tree (EFDT) classifier. + + Also referred to as the Hoeffding AnyTime Tree (HATT) classifier. In practice, + despite the name, EFDTs are typically slower than a vanilla Hoeffding Tree + to process data. The speed differences come from the mechanism of split + re-evaluation present in EFDT. Nonetheless, EFDT has theoretical properties + that ensure it converges faster than the vanilla Hoeffding Tree to the structure + that would be created by a batch decision tree model (such as Classification and + Regression Trees - CART). Keep in mind that such propositions hold when processing + a stationary data stream. When dealing with non-stationary data, EFDT is somewhat + robust to concept drifts as it continually revisits and updates its internal + decision tree structure. Still, in such cases, the Hoeffind Adaptive Tree might + be a better option, as it was specifically designed to handle non-stationarity. + Parameters ---------- From a6e57f1f8510ff6383d092f018cd8085414037c4 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Wed, 4 Oct 2023 16:14:47 +0200 Subject: [PATCH 136/347] pre-commit actions --- river/tree/extremely_fast_decision_tree.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index f426a096ad..b158f14145 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -31,7 +31,7 @@ class ExtremelyFastDecisionTreeClassifier(HoeffdingTreeClassifier): robust to concept drifts as it continually revisits and updates its internal decision tree structure. Still, in such cases, the Hoeffind Adaptive Tree might be a better option, as it was specifically designed to handle non-stationarity. - + Parameters ---------- From 089e570240c48f06b05e7dcf5e8f59ed2256fd5a Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 5 Oct 2023 22:27:58 +0200 Subject: [PATCH 137/347] update installation notes --- .github/workflows/pypi.yml | 7 +++++-- CONTRIBUTING.md | 2 ++ README.md | 9 +++------ docs/introduction/installation.md | 7 ++----- 4 files changed, 12 insertions(+), 13 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 8539ba7082..43d85a99d0 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -22,6 +22,9 @@ jobs: - os: macos-latest arch: alt alt_arch_name: arm64 + - os: ubuntu-latest + platform: linux + alt_arch_name: aarch64 exclude: - os: windows-latest arch: alt @@ -52,14 +55,14 @@ jobs: - name: Set up QEMU if: matrix.os == 'ubuntu-latest' - uses: docker/setup-qemu-action@v1 + uses: docker/setup-qemu-action@v3 with: platforms: all - name: Build wheels uses: pypa/cibuildwheel@v2.12.3 env: - CIBW_BUILD: "cp38-* cp39-* cp310-* cp311-*" + CIBW_BUILD: "cp38-* cp39-* cp310-* cp311-* cp312-*" CIBW_BEFORE_BUILD: > pip install setuptools-rust cython && rustup default nightly && diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 75ee838599..25e7662a7d 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -210,3 +210,5 @@ END brew update && brew install gh gh release create $RIVER_VERSION --notes $RELEASE_NOTES ``` + +11. Pyodide needs to be told there is a new release. This can done by updating [`packages/river`](https://github.com/online-ml/pyodide/tree/main/packages/river) in [online-ml/pyodide](https://github.com/online-ml/pyodide) diff --git a/README.md b/README.md index 87e780453c..21a9c678aa 100644 --- a/README.md +++ b/README.md @@ -109,19 +109,16 @@ River is intended to work with **Python 3.8 and above**. Installation can be don pip install river ``` -There are [wheels available](https://pypi.org/project/river/#files) for Linux, MacOS, and Windows, which means that you most probably won't have to build River from source. +There are [wheels available](https://pypi.org/project/river/#files) for Linux, MacOS, and Windows. This means you most probably won't have to build River from source. You can install the latest development version from GitHub as so: ```sh pip install git+https://github.com/online-ml/river --upgrade +pip install git+ssh://git@github.com/online-ml/river.git --upgrade # using SSH ``` -Or, through SSH: - -```sh -pip install git+ssh://git@github.com/online-ml/river.git --upgrade -``` +This method requires having Cython and Rust installed on your machine. ## 🔮 Features diff --git a/docs/introduction/installation.md b/docs/introduction/installation.md index 84310696f4..a6ee6adc3c 100644 --- a/docs/introduction/installation.md +++ b/docs/introduction/installation.md @@ -10,12 +10,9 @@ You can install the latest development version from GitHub, as so: ```sh pip install git+https://github.com/online-ml/river --upgrade +pip install git+ssh://git@github.com/online-ml/river.git --upgrade # using SSH ``` -Or, through SSH: - -```sh -pip install git+ssh://git@github.com/online-ml/river.git --upgrade -``` +This method requires having Cython and Rust installed on your machine. Feel welcome to [open an issue on GitHub](https://github.com/online-ml/river/issues/new) if you are having any trouble. From 4c7d3d9efc34d216bcb53992c94a1c2657fc43e7 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Fri, 6 Oct 2023 10:07:59 +0200 Subject: [PATCH 138/347] Benchmarks revamp (#1335) * tidy-up benchmarks * update benchmark dependencies --- benchmarks/README.md | 28 +- benchmarks/binary_classification.csv | 7036 +++++++++-------- benchmarks/config.py | 80 +- benchmarks/details.json | 69 +- .../{model_zoo => model_adapters}/__init__.py | 0 .../{model_zoo => model_adapters}/vw.py | 3 +- benchmarks/model_zoo/torch.py | 91 - benchmarks/multiclass_classification.csv | 4078 +++++----- benchmarks/regression.csv | 3400 ++++---- benchmarks/render.py | 11 +- benchmarks/run.py | 9 +- setup.py | 15 +- 12 files changed, 7618 insertions(+), 7202 deletions(-) rename benchmarks/{model_zoo => model_adapters}/__init__.py (100%) rename benchmarks/{model_zoo => model_adapters}/vw.py (95%) delete mode 100644 benchmarks/model_zoo/torch.py diff --git a/benchmarks/README.md b/benchmarks/README.md index 99d3ff644f..cbde02dcdf 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -1,17 +1,43 @@ # Benchmarks ## Installation + +The recommended way to run the benchmarks is to create a dedicated environment for river and its contenders. + +An easy way to achieve that is through [Anaconda](https://docs.conda.io/projects/miniconda/en/latest/). Here is an example of creating an environment for the benchmarks: + +```sh +conda create --name river-benchmark python=3.10 +``` + +The next step is to clone river if you have not done that already: + +```sh +git clone https://github.com/online-ml/river +cd river +``` + +From the river folder you can run the following command to install the needed dependencies: + ```sh pip install ".[benchmarks]" ``` ## Usage -The `run.py` executes the benchmarks and creates the necessary .csv files for rendering the plots. + +The `run.py` script executes the benchmarks and creates the necessary .csv files for rendering the plots. + ```sh cd benchmarks python run.py ``` + The `render.py` renders the plots from the .csv files and moves them to the `docs/benchmarks` folder. + ```sh python render.py ``` + +## Notes: VolpalWabbit + +Installing Volpal Wabbit (VW) can be tricky sometimes. That is especially true when using apple silicon. If cannot make the pip install guidelines from VW work a workaround is the following. When using anaconda, you can install the recommended dependencies utilized for building VW with conda. You can get more info [here](https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Building#conda) about such dependencies. After that, `pip install volpalwabbit` should work just fine. diff --git a/benchmarks/binary_classification.csv b/benchmarks/binary_classification.csv index e6fb618dbf..4b38617bef 100644 --- a/benchmarks/binary_classification.csv +++ b/benchmarks/binary_classification.csv @@ -1,3401 +1,3637 @@ step,track,model,dataset,Accuracy,F1,Memory in Mb,Time in s -106,Binary classification,Logistic regression,Bananas,0.49056603773584906,0.325,0.004187583923339844,0.013756 -212,Binary classification,Logistic regression,Bananas,0.5141509433962265,0.3757575757575758,0.004187583923339844,0.038575 -318,Binary classification,Logistic regression,Bananas,0.5188679245283019,0.41379310344827586,0.004240989685058594,0.073067 -424,Binary classification,Logistic regression,Bananas,0.5165094339622641,0.39528023598820067,0.004240989685058594,0.11707399999999998 -530,Binary classification,Logistic regression,Bananas,0.5320754716981132,0.35751295336787564,0.004240989685058594,0.17098599999999997 -636,Binary classification,Logistic regression,Bananas,0.5377358490566038,0.3225806451612903,0.004240989685058594,0.23488599999999998 -742,Binary classification,Logistic regression,Bananas,0.5525606469002695,0.29957805907172996,0.004240989685058594,0.308749 -848,Binary classification,Logistic regression,Bananas,0.5518867924528302,0.27203065134099613,0.004240989685058594,0.392516 -954,Binary classification,Logistic regression,Bananas,0.5545073375262054,0.25044091710758376,0.004240989685058594,0.486674 -1060,Binary classification,Logistic regression,Bananas,0.5613207547169812,0.23393739703459634,0.004240989685058594,0.590985 -1166,Binary classification,Logistic regression,Bananas,0.5600343053173242,0.216793893129771,0.004240989685058594,0.705545 -1272,Binary classification,Logistic regression,Bananas,0.5605345911949685,0.21378340365682133,0.004240989685058594,0.830071 -1378,Binary classification,Logistic regression,Bananas,0.5638606676342526,0.20185922974767595,0.004240989685058594,0.964451 -1484,Binary classification,Logistic regression,Bananas,0.5640161725067385,0.19023779724655818,0.004240989685058594,1.108635 -1590,Binary classification,Logistic regression,Bananas,0.5641509433962264,0.17988165680473372,0.004240989685058594,1.262826 -1696,Binary classification,Logistic regression,Bananas,0.5654481132075472,0.17283950617283952,0.004240989685058594,1.426847 -1802,Binary classification,Logistic regression,Bananas,0.5621531631520533,0.16507936507936508,0.004240989685058594,1.600621 -1908,Binary classification,Logistic regression,Bananas,0.5581761006289309,0.1628599801390268,0.004240989685058594,1.784431 -2014,Binary classification,Logistic regression,Bananas,0.551142005958292,0.16141001855287568,0.004240989685058594,1.978045 -2120,Binary classification,Logistic regression,Bananas,0.5490566037735849,0.16433566433566435,0.004240989685058594,2.1815 -2226,Binary classification,Logistic regression,Bananas,0.5480682839173405,0.17675941080196397,0.004240989685058594,2.394877 -2332,Binary classification,Logistic regression,Bananas,0.5480274442538593,0.19295558958652376,0.004240989685058594,2.6182670000000003 -2438,Binary classification,Logistic regression,Bananas,0.5467596390484003,0.19636363636363635,0.004240989685058594,2.8514960000000005 -2544,Binary classification,Logistic regression,Bananas,0.547562893081761,0.2132604237867396,0.004240989685058594,3.0947110000000007 -2650,Binary classification,Logistic regression,Bananas,0.5449056603773584,0.22293814432989692,0.004240989685058594,3.3477670000000006 -2756,Binary classification,Logistic regression,Bananas,0.5391872278664731,0.22560975609756098,0.004240989685058594,3.6106280000000006 -2862,Binary classification,Logistic regression,Bananas,0.5387840670859538,0.22716627634660422,0.004240989685058594,3.8834530000000007 -2968,Binary classification,Logistic regression,Bananas,0.5407681940700808,0.22336182336182334,0.004240989685058594,4.166078000000001 -3074,Binary classification,Logistic regression,Bananas,0.5400130123617437,0.21878453038674034,0.004240989685058594,4.4584340000000005 -3180,Binary classification,Logistic regression,Bananas,0.5433962264150943,0.21767241379310348,0.004240989685058594,4.760795000000001 -3286,Binary classification,Logistic regression,Bananas,0.5447352404138771,0.21345951629863302,0.004240989685058594,5.072864000000001 -3392,Binary classification,Logistic regression,Bananas,0.5436320754716981,0.21020408163265306,0.004240989685058594,5.3947970000000005 -3498,Binary classification,Logistic regression,Bananas,0.5454545454545454,0.20579420579420582,0.004240989685058594,5.726813000000001 -3604,Binary classification,Logistic regression,Bananas,0.5477247502774695,0.20176297747306565,0.004240989685058594,6.068550000000001 -3710,Binary classification,Logistic regression,Bananas,0.5466307277628032,0.1967526265520535,0.004240989685058594,6.420104000000001 -3816,Binary classification,Logistic regression,Bananas,0.5461215932914046,0.19216417910447758,0.004240989685058594,6.781176000000001 -3922,Binary classification,Logistic regression,Bananas,0.5471698113207547,0.1882998171846435,0.004240989685058594,7.151771000000001 -4028,Binary classification,Logistic regression,Bananas,0.5476663356504469,0.18442256042972244,0.004240989685058594,7.532081000000001 -4134,Binary classification,Logistic regression,Bananas,0.5478955007256894,0.1806225339763262,0.004240989685058594,7.921987000000001 -4240,Binary classification,Logistic regression,Bananas,0.5471698113207547,0.17667238421955403,0.004240989685058594,8.321417 -4346,Binary classification,Logistic regression,Bananas,0.5473999079613437,0.17456986991187579,0.004240989685058594,8.730515 -4452,Binary classification,Logistic regression,Bananas,0.5496406109613656,0.17995910020449898,0.004240989685058594,9.149203 -4558,Binary classification,Logistic regression,Bananas,0.5465116279069767,0.1794362842397777,0.004240989685058594,9.577468 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regression,Elec2,0.7980132450331126,0.7834319526627219,0.0053730010986328125,0.108703 -1812,Binary classification,Logistic regression,Elec2,0.8134657836644592,0.7488855869242199,0.0053730010986328125,0.336789 -2718,Binary classification,Logistic regression,Elec2,0.8024282560706402,0.7300150829562596,0.0053730010986328125,0.6856869999999999 -3624,Binary classification,Logistic regression,Elec2,0.8192604856512141,0.7598093142647598,0.0053730010986328125,1.150847 -4530,Binary classification,Logistic regression,Elec2,0.8289183222958058,0.7613181398213735,0.0053730010986328125,1.732046 -5436,Binary classification,Logistic regression,Elec2,0.8226637233259749,0.7528205128205128,0.0053730010986328125,2.429434 -6342,Binary classification,Logistic regression,Elec2,0.8229265216020183,0.7589611504614724,0.0053730010986328125,3.242126 -7248,Binary classification,Logistic regression,Elec2,0.8261589403973509,0.7617246596066566,0.0053730010986328125,4.168583 -8154,Binary classification,Logistic regression,Elec2,0.8318616629874908,0.7833096254148886,0.0053730010986328125,5.210413 -9060,Binary classification,Logistic regression,Elec2,0.8375275938189846,0.7975797579757975,0.0053730010986328125,6.367211 -9966,Binary classification,Logistic regression,Elec2,0.8377483443708609,0.802008081302804,0.0053730010986328125,7.639357 -10872,Binary classification,Logistic regression,Elec2,0.8400478292862399,0.8089220964729151,0.0053730010986328125,9.025984000000001 -11778,Binary classification,Logistic regression,Elec2,0.8433520122261844,0.8128613449639923,0.0053730010986328125,10.527624000000001 -12684,Binary classification,Logistic regression,Elec2,0.8420056764427626,0.8118309859154929,0.0053730010986328125,12.142268000000001 -13590,Binary classification,Logistic regression,Elec2,0.8438557763061074,0.8167846658608184,0.0053730010986328125,13.8717 -14496,Binary classification,Logistic regression,Elec2,0.8447847682119205,0.8189863234111022,0.0053730010986328125,15.715708000000001 -15402,Binary classification,Logistic regression,Elec2,0.8465134398130113,0.8201734367868553,0.0053730010986328125,17.682764000000002 -16308,Binary classification,Logistic regression,Elec2,0.8412435614422369,0.8128388635870744,0.0053730010986328125,19.778485000000003 -17214,Binary classification,Logistic regression,Elec2,0.8397815731381434,0.8070519098922625,0.0053730010986328125,22.000603000000005 -18120,Binary classification,Logistic regression,Elec2,0.8419977924944813,0.8099316205271195,0.0053730010986328125,24.347790000000003 -19026,Binary classification,Logistic regression,Elec2,0.8451592557552823,0.8116368286445013,0.0053730010986328125,26.819926000000002 -19932,Binary classification,Logistic regression,Elec2,0.8428657435279951,0.8098129706096673,0.0053730010986328125,29.418034000000002 -20838,Binary classification,Logistic regression,Elec2,0.8394279681351378,0.805736182071528,0.0053730010986328125,32.142589 -21744,Binary classification,Logistic regression,Elec2,0.8403237674760854,0.8037087290818633,0.0053730010986328125,34.992653000000004 -22650,Binary classification,Logistic regression,Elec2,0.8395143487858719,0.800963697092482,0.0053730010986328125,37.96833 -23556,Binary classification,Logistic regression,Elec2,0.8357530989981321,0.7954965907288969,0.0053730010986328125,41.070336000000005 -24462,Binary classification,Logistic regression,Elec2,0.8330880549423596,0.7914815382258312,0.0053730010986328125,44.296124000000006 -25368,Binary classification,Logistic regression,Elec2,0.8298643960895616,0.787326303340889,0.0053730010986328125,47.644214000000005 -26274,Binary classification,Logistic regression,Elec2,0.8304788003349318,0.7877834953306653,0.0053730010986328125,51.11458400000001 -27180,Binary classification,Logistic regression,Elec2,0.8309050772626931,0.789000091818933,0.0053730010986328125,54.70630400000001 -28086,Binary classification,Logistic regression,Elec2,0.8277433596809799,0.7844028520499109,0.0053730010986328125,58.42138600000001 -28992,Binary classification,Logistic regression,Elec2,0.8270557395143487,0.782037906451052,0.0053730010986328125,62.25746200000001 -29898,Binary classification,Logistic regression,Elec2,0.8260753227640645,0.7809050307575629,0.0053730010986328125,66.21287400000001 -30804,Binary classification,Logistic regression,Elec2,0.8259316971821842,0.7798127463863337,0.0053730010986328125,70.29091000000001 -31710,Binary classification,Logistic regression,Elec2,0.8213181961526332,0.7731603811353991,0.0053730010986328125,74.48714300000002 -32616,Binary classification,Logistic regression,Elec2,0.8188925680647535,0.7700393195001364,0.0053730010986328125,78.80153600000001 -33522,Binary classification,Logistic regression,Elec2,0.8169261977208997,0.7682314286793308,0.0053730010986328125,83.23605800000001 -34428,Binary classification,Logistic regression,Elec2,0.8144243057976066,0.764807656911467,0.0053730010986328125,87.78961800000002 -35334,Binary classification,Logistic regression,Elec2,0.8142299201901851,0.7628098576280986,0.0053730010986328125,92.46345900000001 -36240,Binary classification,Logistic regression,Elec2,0.8155077262693157,0.7630254483589707,0.0053730010986328125,97.25864300000002 -37146,Binary classification,Logistic regression,Elec2,0.8151887148010553,0.7614745839268963,0.0053730010986328125,102.17619800000003 -38052,Binary classification,Logistic regression,Elec2,0.8151739724587407,0.7609855564995752,0.0053730010986328125,107.21554300000003 -38958,Binary classification,Logistic regression,Elec2,0.8162636685661482,0.7631526702402223,0.0053730010986328125,112.37010800000003 -39864,Binary classification,Logistic regression,Elec2,0.8169526389725065,0.7662192035369877,0.0053730010986328125,117.64186300000003 -40770,Binary classification,Logistic regression,Elec2,0.8186902133922002,0.7707480461481205,0.0053730010986328125,123.03144600000003 -41676,Binary classification,Logistic regression,Elec2,0.8201842787215664,0.7745623007039286,0.0053730010986328125,128.53961200000003 -42582,Binary classification,Logistic regression,Elec2,0.8212155370813959,0.7763841973858129,0.0053730010986328125,134.16514200000003 -43488,Binary classification,Logistic regression,Elec2,0.8217209345106696,0.7773086313370673,0.0053730010986328125,139.90254000000004 -44394,Binary classification,Logistic regression,Elec2,0.8211920529801324,0.7754328391988233,0.0053730010986328125,145.75278900000004 -45300,Binary classification,Logistic regression,Elec2,0.8221633554083885,0.7771507607192254,0.0053730010986328125,151.71939900000004 -25,Binary classification,Logistic regression,Phishing,0.64,0.6896551724137931,0.005324363708496094,0.005171 -50,Binary classification,Logistic regression,Phishing,0.78,0.7755102040816326,0.005324363708496094,0.014932 -75,Binary classification,Logistic regression,Phishing,0.8133333333333334,0.8157894736842105,0.005324363708496094,0.027624000000000003 -100,Binary classification,Logistic regression,Phishing,0.82,0.8163265306122449,0.005324363708496094,0.045128 -125,Binary classification,Logistic regression,Phishing,0.808,0.8032786885245902,0.005324363708496094,0.065471 -150,Binary classification,Logistic regression,Phishing,0.8133333333333334,0.8157894736842104,0.005324363708496094,0.088498 -175,Binary classification,Logistic regression,Phishing,0.8228571428571428,0.8143712574850299,0.005324363708496094,0.11416399999999999 -200,Binary classification,Logistic regression,Phishing,0.82,0.8105263157894737,0.005324363708496094,0.143232 -225,Binary classification,Logistic regression,Phishing,0.8177777777777778,0.8038277511961723,0.005324363708496094,0.175843 -250,Binary classification,Logistic regression,Phishing,0.824,0.811965811965812,0.005324363708496094,0.212135 -275,Binary classification,Logistic regression,Phishing,0.8254545454545454,0.8125,0.005564689636230469,0.25199099999999997 -300,Binary classification,Logistic regression,Phishing,0.8366666666666667,0.8205128205128205,0.005564689636230469,0.295434 -325,Binary classification,Logistic regression,Phishing,0.8430769230769231,0.8222996515679442,0.005564689636230469,0.34243999999999997 -350,Binary classification,Logistic regression,Phishing,0.8542857142857143,0.8316831683168316,0.005564689636230469,0.39299199999999995 -375,Binary classification,Logistic regression,Phishing,0.8506666666666667,0.825,0.005564689636230469,0.44723899999999994 -400,Binary classification,Logistic regression,Phishing,0.8525,0.8249258160237388,0.005564689636230469,0.5051399999999999 -425,Binary classification,Logistic regression,Phishing,0.8588235294117647,0.8285714285714286,0.005564689636230469,0.5668209999999999 -450,Binary classification,Logistic regression,Phishing,0.8622222222222222,0.8306010928961749,0.005564689636230469,0.6322009999999999 -475,Binary classification,Logistic regression,Phishing,0.8589473684210527,0.8277634961439589,0.005564689636230469,0.7013439999999999 -500,Binary classification,Logistic regression,Phishing,0.86,0.8325358851674641,0.005564689636230469,0.7743169999999998 -525,Binary classification,Logistic regression,Phishing,0.8590476190476191,0.827906976744186,0.005564689636230469,0.8510559999999998 -550,Binary classification,Logistic regression,Phishing,0.86,0.8300220750551875,0.005564689636230469,0.9315169999999998 -575,Binary classification,Logistic regression,Phishing,0.8626086956521739,0.8329809725158562,0.005564689636230469,1.015688 -600,Binary classification,Logistic regression,Phishing,0.8666666666666667,0.8353909465020577,0.005564689636230469,1.103615 -625,Binary classification,Logistic regression,Phishing,0.8688,0.8346774193548386,0.005564689636230469,1.195378 -650,Binary classification,Logistic regression,Phishing,0.8723076923076923,0.8413001912045889,0.005564689636230469,1.291006 -675,Binary classification,Logistic regression,Phishing,0.8725925925925926,0.8447653429602888,0.005564689636230469,1.390501 -700,Binary classification,Logistic regression,Phishing,0.8771428571428571,0.8485915492957746,0.005564689636230469,1.494099 -725,Binary classification,Logistic regression,Phishing,0.8786206896551724,0.8533333333333334,0.005564689636230469,1.601701 -750,Binary classification,Logistic regression,Phishing,0.88,0.8557692307692307,0.005564689636230469,1.713159 -775,Binary classification,Logistic regression,Phishing,0.8812903225806452,0.8566978193146417,0.005564689636230469,1.8285060000000002 -800,Binary classification,Logistic regression,Phishing,0.88125,0.8584202682563338,0.005564689636230469,1.9476680000000002 -825,Binary classification,Logistic regression,Phishing,0.8812121212121212,0.8595988538681948,0.005564689636230469,2.0707090000000004 -850,Binary classification,Logistic regression,Phishing,0.8823529411764706,0.8603351955307262,0.005564689636230469,2.1975890000000002 -875,Binary classification,Logistic regression,Phishing,0.8857142857142857,0.8637602179836512,0.005564689636230469,2.3283080000000003 -900,Binary classification,Logistic regression,Phishing,0.8855555555555555,0.8632138114209827,0.005564689636230469,2.462826 -925,Binary classification,Logistic regression,Phishing,0.8875675675675676,0.867007672634271,0.005564689636230469,2.6011680000000004 -950,Binary classification,Logistic regression,Phishing,0.8863157894736842,0.8669950738916257,0.005564689636230469,2.7434260000000004 -975,Binary classification,Logistic regression,Phishing,0.8871794871794871,0.8677884615384616,0.005564689636230469,2.889565 -1000,Binary classification,Logistic regression,Phishing,0.888,0.8688524590163934,0.005564689636230469,3.039628 -1025,Binary classification,Logistic regression,Phishing,0.8878048780487805,0.8691695108077361,0.005564689636230469,3.19355 -1050,Binary classification,Logistic regression,Phishing,0.8895238095238095,0.8716814159292035,0.005564689636230469,3.351259 -1075,Binary classification,Logistic regression,Phishing,0.8883720930232558,0.8715203426124196,0.005564689636230469,3.5126910000000002 -1100,Binary classification,Logistic regression,Phishing,0.89,0.8735632183908045,0.005564689636230469,3.677881 -1125,Binary classification,Logistic regression,Phishing,0.8906666666666667,0.8753799392097265,0.005564689636230469,3.846795 -1150,Binary classification,Logistic regression,Phishing,0.8904347826086957,0.8750000000000001,0.005564689636230469,4.0194600000000005 -1175,Binary classification,Logistic regression,Phishing,0.8893617021276595,0.8735408560311284,0.005564689636230469,4.195879000000001 -1200,Binary classification,Logistic regression,Phishing,0.89,0.8740458015267174,0.005564689636230469,4.376099000000001 -1225,Binary classification,Logistic regression,Phishing,0.8906122448979592,0.874766355140187,0.005564689636230469,4.560123000000001 -1250,Binary classification,Logistic regression,Phishing,0.888,0.8722627737226277,0.005564689636230469,4.747978000000001 -1903,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,0.201733 -3806,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,0.541161 -5709,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,0.9894339999999999 -7612,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,1.548242 -9515,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,2.214419 -11418,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,2.987891 -13321,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,3.868043 -15224,Binary classification,Logistic regression,SMTP,0.9996715712033631,0.7058823529411764,0.004383087158203125,4.854806 -17127,Binary classification,Logistic regression,SMTP,0.9997080632918783,0.761904761904762,0.004383087158203125,5.948104 -19030,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.761904761904762,0.004383087158203125,7.14776 -20933,Binary classification,Logistic regression,SMTP,0.999761142693355,0.761904761904762,0.004383087158203125,8.453778 -22836,Binary classification,Logistic regression,SMTP,0.9997810474689087,0.761904761904762,0.004383087158203125,9.865972 -24739,Binary classification,Logistic regression,SMTP,0.9997978899713004,0.761904761904762,0.004383087158203125,11.384388 -26642,Binary classification,Logistic regression,SMTP,0.9997747916823061,0.7272727272727273,0.004383087158203125,13.008481999999999 -28545,Binary classification,Logistic regression,SMTP,0.9997898055701524,0.7272727272727273,0.004383087158203125,14.738380999999999 -30448,Binary classification,Logistic regression,SMTP,0.9998029427220179,0.7272727272727273,0.004383087158203125,16.574147999999997 -32351,Binary classification,Logistic regression,SMTP,0.999814534326605,0.7272727272727273,0.004383087158203125,18.521969999999996 -34254,Binary classification,Logistic regression,SMTP,0.999824837975127,0.7272727272727273,0.004383087158203125,20.589920999999997 -36157,Binary classification,Logistic regression,SMTP,0.9998340570290677,0.7272727272727273,0.004383087158203125,22.774089999999998 -38060,Binary classification,Logistic regression,SMTP,0.9998423541776142,0.7272727272727273,0.004383087158203125,25.073776 -39963,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.7272727272727273,0.004383087158203125,27.489114999999998 -41866,Binary classification,Logistic regression,SMTP,0.999856685616013,0.7272727272727273,0.004383087158203125,30.019976 -43769,Binary classification,Logistic regression,SMTP,0.9998629166761863,0.7272727272727273,0.004383087158203125,32.666409 -45672,Binary classification,Logistic regression,SMTP,0.9998686284813453,0.7272727272727273,0.004383087158203125,35.428179 -47575,Binary classification,Logistic regression,SMTP,0.9998738833420915,0.7272727272727273,0.004383087158203125,38.305364 -49478,Binary classification,Logistic regression,SMTP,0.9998787339827803,0.7272727272727273,0.004383087158203125,41.297895 -51381,Binary classification,Logistic regression,SMTP,0.9998443004223351,0.6666666666666666,0.004383087158203125,44.405542 -53284,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.6666666666666666,0.004383087158203125,47.6267 -55187,Binary classification,Logistic regression,SMTP,0.999855038324243,0.6666666666666666,0.004383087158203125,50.959272 -57090,Binary classification,Logistic regression,SMTP,0.9997022245577158,0.48484848484848486,0.004383087158203125,54.404277 -58993,Binary classification,Logistic regression,SMTP,0.9997118302171444,0.48484848484848486,0.004383087158203125,57.960817 -60896,Binary classification,Logistic regression,SMTP,0.9997208355228586,0.48484848484848486,0.004383087158203125,61.629022 -62799,Binary classification,Logistic regression,SMTP,0.999697447411583,0.45714285714285713,0.004383087158203125,65.409349 -64702,Binary classification,Logistic regression,SMTP,0.9997063460171247,0.45714285714285713,0.004383087158203125,69.30026500000001 -66605,Binary classification,Logistic regression,SMTP,0.9997147361309211,0.45714285714285713,0.004383087158203125,73.30473800000001 -68508,Binary classification,Logistic regression,SMTP,0.9996934664564723,0.4324324324324324,0.004383087158203125,77.42011300000001 -70411,Binary classification,Logistic regression,SMTP,0.9997017511468379,0.4324324324324324,0.004383087158203125,81.64355900000001 -72314,Binary classification,Logistic regression,SMTP,0.9997095998008685,0.4324324324324324,0.004383087158203125,85.97794200000001 -74217,Binary classification,Logistic regression,SMTP,0.9997170459598205,0.4324324324324324,0.004383087158203125,90.42391200000002 -76120,Binary classification,Logistic regression,SMTP,0.999724119810825,0.4324324324324324,0.004383087158203125,94.97981200000001 -78023,Binary classification,Logistic regression,SMTP,0.9997308485959269,0.4324324324324324,0.004383087158203125,99.646713 -79926,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.4324324324324324,0.004383087158203125,104.42510200000001 -81829,Binary classification,Logistic regression,SMTP,0.9997433672658838,0.4324324324324324,0.004383087158203125,109.315273 -83732,Binary classification,Logistic regression,SMTP,0.9997491998280228,0.4324324324324324,0.004383087158203125,114.31710000000001 -85635,Binary classification,Logistic regression,SMTP,0.9997547731651778,0.4324324324324324,0.004383087158203125,119.42729800000001 -87538,Binary classification,Logistic regression,SMTP,0.9997601041833261,0.4324324324324324,0.004383087158203125,124.64502 -89441,Binary classification,Logistic regression,SMTP,0.9997540277948592,0.4210526315789474,0.004383087158203125,129.97108 -91344,Binary classification,Logistic regression,SMTP,0.9997591522157996,0.4210526315789474,0.004383087158203125,135.407078 -93247,Binary classification,Logistic regression,SMTP,0.9997640674767017,0.4210526315789474,0.004383087158203125,140.95343100000002 -95150,Binary classification,Logistic regression,SMTP,0.9997687861271676,0.4210526315789474,0.004383087158203125,146.60779900000003 -106,Binary classification,ALMA,Bananas,0.5377358490566038,0.5242718446601942,0.0028944015502929688,0.022931 -212,Binary classification,ALMA,Bananas,0.5330188679245284,0.5217391304347825,0.0028944015502929688,0.057388 -318,Binary classification,ALMA,Bananas,0.5188679245283019,0.5173501577287066,0.0029211044311523438,0.098467 -424,Binary classification,ALMA,Bananas,0.5330188679245284,0.5330188679245282,0.0029211044311523438,0.14786 -530,Binary classification,ALMA,Bananas,0.5207547169811321,0.5115384615384615,0.0029211044311523438,0.20447099999999999 -636,Binary classification,ALMA,Bananas,0.5377358490566038,0.5303514376996804,0.0029211044311523438,0.268 -742,Binary classification,ALMA,Bananas,0.522911051212938,0.512396694214876,0.0029211044311523438,0.339749 -848,Binary classification,ALMA,Bananas,0.5235849056603774,0.5061124694376529,0.0029211044311523438,0.42003100000000004 -954,Binary classification,ALMA,Bananas,0.5157232704402516,0.5,0.0029211044311523438,0.5084810000000001 -1060,Binary classification,ALMA,Bananas,0.5160377358490567,0.4975514201762978,0.0029211044311523438,0.605006 -1166,Binary classification,ALMA,Bananas,0.5154373927958834,0.49598572702943805,0.0029211044311523438,0.7097530000000001 -1272,Binary classification,ALMA,Bananas,0.5165094339622641,0.4979591836734694,0.0029211044311523438,0.823412 -1378,Binary classification,ALMA,Bananas,0.5195936139332366,0.4977238239757208,0.0029211044311523438,0.94616 -1484,Binary classification,ALMA,Bananas,0.5195417789757413,0.4968242766407903,0.0029211044311523438,1.077952 -1590,Binary classification,ALMA,Bananas,0.5226415094339623,0.4983476536682089,0.0029211044311523438,1.218949 -1696,Binary classification,ALMA,Bananas,0.5194575471698113,0.49473031618102914,0.0029211044311523438,1.368908 -1802,Binary classification,ALMA,Bananas,0.5205327413984462,0.4965034965034965,0.0029211044311523438,1.527947 -1908,Binary classification,ALMA,Bananas,0.5193920335429769,0.4964305326743548,0.0029211044311523438,1.696168 -2014,Binary classification,ALMA,Bananas,0.519364448857994,0.4989648033126293,0.0029211044311523438,1.873583 -2120,Binary classification,ALMA,Bananas,0.5174528301886793,0.4997555012224939,0.0029211044311523438,2.060039 -2226,Binary classification,ALMA,Bananas,0.5197663971248877,0.5002337540906966,0.0029211044311523438,2.255465 -2332,Binary classification,ALMA,Bananas,0.5175814751286449,0.4975435462259938,0.0029211044311523438,2.460073 -2438,Binary classification,ALMA,Bananas,0.5176374077112387,0.4957118353344769,0.0029211044311523438,2.67358 -2544,Binary classification,ALMA,Bananas,0.5196540880503144,0.5008169934640523,0.0029211044311523438,2.8963289999999997 -2650,Binary classification,ALMA,Bananas,0.520377358490566,0.5037094884810621,0.0029211044311523438,3.127217 -2756,Binary classification,ALMA,Bananas,0.521044992743106,0.5041322314049587,0.0029211044311523438,3.366825 -2862,Binary classification,ALMA,Bananas,0.5213137665967854,0.5032632342277013,0.0029211044311523438,3.615642 -2968,Binary classification,ALMA,Bananas,0.5175202156334232,0.49859943977591037,0.0029211044311523438,3.87375 -3074,Binary classification,ALMA,Bananas,0.5152895250487963,0.49696151249155973,0.0029211044311523438,4.141236999999999 -3180,Binary classification,ALMA,Bananas,0.5132075471698113,0.4931237721021611,0.0029211044311523438,4.4182619999999995 -3286,Binary classification,ALMA,Bananas,0.5130858186244674,0.4927076727964489,0.0029211044311523438,4.703974 -3392,Binary classification,ALMA,Bananas,0.5103183962264151,0.49095923996322405,0.0029211044311523438,4.999187999999999 -3498,Binary classification,ALMA,Bananas,0.5091480846197828,0.48914013686402846,0.0029211044311523438,5.303419999999999 -3604,Binary classification,ALMA,Bananas,0.5097114317425083,0.4876775877065816,0.0029211044311523438,5.616715999999999 -3710,Binary classification,ALMA,Bananas,0.5118598382749326,0.49086308687095864,0.0029211044311523438,5.938947999999999 -3816,Binary classification,ALMA,Bananas,0.510482180293501,0.4893384363039912,0.0029211044311523438,6.270639999999999 -3922,Binary classification,ALMA,Bananas,0.50790413054564,0.48588172615876407,0.0029211044311523438,6.611250999999999 -4028,Binary classification,ALMA,Bananas,0.506454816285998,0.48443983402489627,0.0029211044311523438,6.9609049999999995 -4134,Binary classification,ALMA,Bananas,0.5050798258345428,0.48281092012133464,0.0029211044311523438,7.319711 -4240,Binary classification,ALMA,Bananas,0.5068396226415094,0.48484848484848486,0.0029211044311523438,7.6869439999999996 -4346,Binary classification,ALMA,Bananas,0.5080533824206167,0.4858104858104858,0.0029211044311523438,8.062541999999999 -4452,Binary classification,ALMA,Bananas,0.5080862533692723,0.4847058823529412,0.0029211044311523438,8.446741 -4558,Binary classification,ALMA,Bananas,0.5063624396665204,0.48370812299219823,0.0029211044311523438,8.840034999999999 -4664,Binary classification,ALMA,Bananas,0.5051457975986278,0.4829749103942652,0.0029211044311523438,9.242386999999999 -4770,Binary classification,ALMA,Bananas,0.5048218029350104,0.48201754385964907,0.0029211044311523438,9.653844999999999 -4876,Binary classification,ALMA,Bananas,0.5036915504511895,0.4802405498281787,0.0029211044311523438,10.074017999999999 -4982,Binary classification,ALMA,Bananas,0.5038137294259334,0.4811083123425693,0.0029211044311523438,10.503025 -5088,Binary classification,ALMA,Bananas,0.5029481132075472,0.47995064774830354,0.0029211044311523438,10.941391 -5194,Binary classification,ALMA,Bananas,0.5040431266846361,0.4810636583400483,0.0029211044311523438,11.388345 -5300,Binary classification,ALMA,Bananas,0.5064150943396226,0.4825949367088608,0.0029211044311523438,11.844057 -906,Binary classification,ALMA,Elec2,0.9072847682119205,0.9056179775280899,0.0043582916259765625,0.085634 -1812,Binary classification,ALMA,Elec2,0.9166666666666666,0.8967874231032126,0.0043582916259765625,0.273724 -2718,Binary classification,ALMA,Elec2,0.9175864606328182,0.898458748866727,0.0043582916259765625,0.563875 -3624,Binary classification,ALMA,Elec2,0.9268763796909493,0.9098945936756205,0.0043582916259765625,0.954455 -4530,Binary classification,ALMA,Elec2,0.9271523178807947,0.9076664801343034,0.0043582916259765625,1.4444780000000002 -5436,Binary classification,ALMA,Elec2,0.9269683590875644,0.9074376311494521,0.0043582916259765625,2.035788 -6342,Binary classification,ALMA,Elec2,0.9274676758120467,0.9089108910891088,0.0043582916259765625,2.724631 -7248,Binary classification,ALMA,Elec2,0.9253587196467992,0.9064499394777797,0.0043582916259765625,3.51195 -8154,Binary classification,ALMA,Elec2,0.9250674515575178,0.9098687121994394,0.0043582916259765625,4.397951 -9060,Binary classification,ALMA,Elec2,0.9264900662251656,0.9133714880332986,0.0043582916259765625,5.380316 -9966,Binary classification,ALMA,Elec2,0.9292594822396147,0.9181279758448496,0.0043582916259765625,6.461929 -10872,Binary classification,ALMA,Elec2,0.9312913907284768,0.9216077237905342,0.0043582916259765625,7.640261 -11778,Binary classification,ALMA,Elec2,0.9313126167430803,0.9217525872908405,0.0043582916259765625,8.918386 -12684,Binary classification,ALMA,Elec2,0.9289656259854935,0.9190694332165634,0.0043582916259765625,10.293447 -13590,Binary classification,ALMA,Elec2,0.9297277409860192,0.9208978712830284,0.0043582916259765625,11.76578 -14496,Binary classification,ALMA,Elec2,0.9304635761589404,0.9221381121581956,0.0043582916259765625,13.33625 -15402,Binary classification,ALMA,Elec2,0.9307882093234645,0.9222011385199241,0.0043582916259765625,15.00584 -16308,Binary classification,ALMA,Elec2,0.9292985038018151,0.9202572792032644,0.0043582916259765625,16.773224 -17214,Binary classification,ALMA,Elec2,0.9279075171372139,0.9175579618680662,0.0043582916259765625,18.640940999999998 -18120,Binary classification,ALMA,Elec2,0.9265452538631347,0.915945689927376,0.0043582916259765625,20.621169 -19026,Binary classification,ALMA,Elec2,0.9265216020182908,0.9150771473696999,0.0043582916259765625,22.710023999999997 -19932,Binary classification,ALMA,Elec2,0.9262492474413004,0.915526950925181,0.0043582916259765625,24.907693 -20838,Binary classification,ALMA,Elec2,0.9231692100969383,0.9122114382848056,0.0043582916259765625,27.214713 -21744,Binary classification,ALMA,Elec2,0.9224613686534217,0.910137511992325,0.0043582916259765625,29.631112 -22650,Binary classification,ALMA,Elec2,0.9216777041942605,0.9086978898610396,0.0043582916259765625,32.155455 -23556,Binary classification,ALMA,Elec2,0.9186194600101885,0.9050096625538873,0.0043582916259765625,34.791879 -24462,Binary classification,ALMA,Elec2,0.9172594227781866,0.9028324531925108,0.0043582916259765625,37.534909 -25368,Binary classification,ALMA,Elec2,0.9144591611479028,0.8997134670487106,0.0043582916259765625,40.38939 -26274,Binary classification,ALMA,Elec2,0.9142117682880414,0.899213020926489,0.0043582916259765625,43.350156 -27180,Binary classification,ALMA,Elec2,0.9137969094922738,0.8990477831875565,0.0043582916259765625,46.420679 -28086,Binary classification,ALMA,Elec2,0.9109876806950082,0.8954587271054613,0.0043582916259765625,49.601356 -28992,Binary classification,ALMA,Elec2,0.9101131346578366,0.8940478126524638,0.0043582916259765625,52.888972 -29898,Binary classification,ALMA,Elec2,0.9094588266773698,0.8931264558411306,0.0043582916259765625,56.282618 -30804,Binary classification,ALMA,Elec2,0.9082911310219453,0.8912666948924213,0.0043582916259765625,59.78184 -31710,Binary classification,ALMA,Elec2,0.9061810154525386,0.8888307611823175,0.0043582916259765625,63.389233000000004 -32616,Binary classification,ALMA,Elec2,0.9052612214863871,0.8878891227051738,0.0043582916259765625,67.10073700000001 -33522,Binary classification,ALMA,Elec2,0.9050176003818388,0.887665819926616,0.0043582916259765625,70.91761400000001 -34428,Binary classification,ALMA,Elec2,0.9050482165679098,0.8877519486316657,0.0043582916259765625,74.83635300000002 -35334,Binary classification,ALMA,Elec2,0.9045112356370635,0.8865729846029718,0.0043582916259765625,78.86287600000001 -36240,Binary classification,ALMA,Elec2,0.9047737306843268,0.8860115606936415,0.0043582916259765625,82.99223100000002 -37146,Binary classification,ALMA,Elec2,0.9044850051149518,0.8853857087479002,0.0043582916259765625,87.22379500000002 -38052,Binary classification,ALMA,Elec2,0.9042363082098182,0.884573962622743,0.0043582916259765625,91.56013600000003 -38958,Binary classification,ALMA,Elec2,0.9043842086349402,0.8849918182098861,0.0043582916259765625,96.00151600000002 -39864,Binary classification,ALMA,Elec2,0.904901665663255,0.8863302449701658,0.0043582916259765625,100.54490000000003 -40770,Binary classification,ALMA,Elec2,0.9054942359578121,0.8878344153008646,0.0043582916259765625,105.19249400000002 -41676,Binary classification,ALMA,Elec2,0.9060850369517228,0.8891971464160343,0.0043582916259765625,109.94207100000003 -42582,Binary classification,ALMA,Elec2,0.9063688882626462,0.8897796699195533,0.0043582916259765625,114.79624900000003 -43488,Binary classification,ALMA,Elec2,0.906686902133922,0.8902234485743656,0.0043582916259765625,119.75466700000003 -44394,Binary classification,ALMA,Elec2,0.9062485921520926,0.8894437656059077,0.0043582916259765625,124.81683700000002 -45300,Binary classification,ALMA,Elec2,0.906401766004415,0.8897555902236091,0.0043582916259765625,129.97946500000003 -25,Binary classification,ALMA,Phishing,0.56,0.5217391304347826,0.004366874694824219,0.006962 -50,Binary classification,ALMA,Phishing,0.7,0.6341463414634146,0.004366874694824219,0.020485 -75,Binary classification,ALMA,Phishing,0.72,0.6956521739130435,0.004366874694824219,0.040554 -100,Binary classification,ALMA,Phishing,0.73,0.7157894736842104,0.004366874694824219,0.063947 -125,Binary classification,ALMA,Phishing,0.728,0.7166666666666666,0.004366874694824219,0.08978900000000001 -150,Binary classification,ALMA,Phishing,0.72,0.7272727272727273,0.004366874694824219,0.11811500000000001 -175,Binary classification,ALMA,Phishing,0.7371428571428571,0.7261904761904763,0.004366874694824219,0.148737 -200,Binary classification,ALMA,Phishing,0.74,0.7291666666666666,0.004366874694824219,0.181823 -225,Binary classification,ALMA,Phishing,0.7288888888888889,0.7081339712918661,0.004366874694824219,0.21743300000000002 -250,Binary classification,ALMA,Phishing,0.728,0.7094017094017095,0.004366874694824219,0.25563600000000003 -275,Binary classification,ALMA,Phishing,0.7381818181818182,0.7187499999999999,0.004580497741699219,0.29612400000000005 -300,Binary classification,ALMA,Phishing,0.74,0.717391304347826,0.004580497741699219,0.33938700000000005 -325,Binary classification,ALMA,Phishing,0.7507692307692307,0.7216494845360825,0.004580497741699219,0.3849 -350,Binary classification,ALMA,Phishing,0.7571428571428571,0.7266881028938907,0.004580497741699219,0.43290900000000004 -375,Binary classification,ALMA,Phishing,0.76,0.7272727272727273,0.004580497741699219,0.48366600000000004 -400,Binary classification,ALMA,Phishing,0.7625,0.7293447293447294,0.004580497741699219,0.537154 -425,Binary classification,ALMA,Phishing,0.7623529411764706,0.7232876712328767,0.004580497741699219,0.593599 -450,Binary classification,ALMA,Phishing,0.7644444444444445,0.7239583333333334,0.004580497741699219,0.65326 -475,Binary classification,ALMA,Phishing,0.7684210526315789,0.7303921568627451,0.004580497741699219,0.7162679999999999 -500,Binary classification,ALMA,Phishing,0.77,0.735632183908046,0.004580497741699219,0.7828129999999999 -525,Binary classification,ALMA,Phishing,0.7733333333333333,0.7349665924276169,0.004580497741699219,0.8526399999999998 -550,Binary classification,ALMA,Phishing,0.7727272727272727,0.7368421052631579,0.004580497741699219,0.9258199999999999 -575,Binary classification,ALMA,Phishing,0.7756521739130435,0.7404426559356138,0.004580497741699219,1.00229 -600,Binary classification,ALMA,Phishing,0.7816666666666666,0.7426326129666011,0.004580497741699219,1.08201 -625,Binary classification,ALMA,Phishing,0.776,0.7338403041825096,0.004580497741699219,1.165182 -650,Binary classification,ALMA,Phishing,0.7830769230769231,0.7450271247739602,0.004580497741699219,1.2515479999999999 -675,Binary classification,ALMA,Phishing,0.7851851851851852,0.7521367521367521,0.004580497741699219,1.34123 -700,Binary classification,ALMA,Phishing,0.7914285714285715,0.7566666666666668,0.004580497741699219,1.434087 -725,Binary classification,ALMA,Phishing,0.7931034482758621,0.7626582278481012,0.004580497741699219,1.5303769999999999 -750,Binary classification,ALMA,Phishing,0.7933333333333333,0.7640791476407914,0.004580497741699219,1.6300339999999998 -775,Binary classification,ALMA,Phishing,0.7935483870967742,0.7633136094674556,0.004580497741699219,1.7333069999999997 -800,Binary classification,ALMA,Phishing,0.79625,0.7687943262411348,0.004580497741699219,1.8400829999999997 -825,Binary classification,ALMA,Phishing,0.7951515151515152,0.7688098495212038,0.004580497741699219,1.9503149999999998 -850,Binary classification,ALMA,Phishing,0.7988235294117647,0.7723035952063916,0.004580497741699219,2.063938 -875,Binary classification,ALMA,Phishing,0.8034285714285714,0.7760416666666667,0.004580497741699219,2.180811 -900,Binary classification,ALMA,Phishing,0.8022222222222222,0.7752525252525253,0.004580497741699219,2.30125 -925,Binary classification,ALMA,Phishing,0.8064864864864865,0.781973203410475,0.004580497741699219,2.424984 -950,Binary classification,ALMA,Phishing,0.8084210526315789,0.7863849765258215,0.004580497741699219,2.552135 -975,Binary classification,ALMA,Phishing,0.8112820512820513,0.7894736842105262,0.004580497741699219,2.6827029999999996 -1000,Binary classification,ALMA,Phishing,0.812,0.7906458797327395,0.004580497741699219,2.8167349999999995 -1025,Binary classification,ALMA,Phishing,0.8156097560975609,0.7956756756756757,0.004580497741699219,2.9540499999999996 -1050,Binary classification,ALMA,Phishing,0.8171428571428572,0.7983193277310925,0.004580497741699219,3.0947329999999997 -1075,Binary classification,ALMA,Phishing,0.8167441860465117,0.7995930824008138,0.004580497741699219,3.2387509999999997 -1100,Binary classification,ALMA,Phishing,0.82,0.8035714285714286,0.004580497741699219,3.3859479999999995 -1125,Binary classification,ALMA,Phishing,0.8222222222222222,0.8073217726396917,0.004580497741699219,3.5363979999999997 -1150,Binary classification,ALMA,Phishing,0.8234782608695652,0.8083097261567517,0.004580497741699219,3.6900359999999996 -1175,Binary classification,ALMA,Phishing,0.8221276595744681,0.8070175438596491,0.004580497741699219,3.847107 -1200,Binary classification,ALMA,Phishing,0.8241666666666667,0.8087035358114234,0.004580497741699219,4.0074749999999995 -1225,Binary classification,ALMA,Phishing,0.8253061224489796,0.8099467140319715,0.004580497741699219,4.171155 -1250,Binary classification,ALMA,Phishing,0.8264,0.8117953165654813,0.004580497741699219,4.338235999999999 -1903,Binary classification,ALMA,SMTP,0.720966894377299,0.0,0.003093719482421875,0.171046 -3806,Binary classification,ALMA,SMTP,0.7769311613242249,0.0,0.003093719482421875,0.510929 -5709,Binary classification,ALMA,SMTP,0.7509196006305833,0.0,0.003093719482421875,1.019887 -7612,Binary classification,ALMA,SMTP,0.7900683131897005,0.0,0.003093719482421875,1.685465 -9515,Binary classification,ALMA,SMTP,0.7826589595375723,0.0,0.003093719482421875,2.527149 -11418,Binary classification,ALMA,SMTP,0.7699246803293046,0.0,0.003093719482421875,3.545039 -13321,Binary classification,ALMA,SMTP,0.7722393213722694,0.0,0.003093719482421875,4.73657 -15224,Binary classification,ALMA,SMTP,0.7791644771413557,0.004146919431279621,0.003093719482421875,6.098207 -17127,Binary classification,ALMA,SMTP,0.783207800548841,0.004824443848834093,0.003093719482421875,7.63166 -19030,Binary classification,ALMA,SMTP,0.7891224382553862,0.004465393202679235,0.003093719482421875,9.334478 -20933,Binary classification,ALMA,SMTP,0.7832131084889887,0.003950834064969272,0.003093719482421875,11.212632000000001 -22836,Binary classification,ALMA,SMTP,0.7821422315641969,0.0036050470658922497,0.003093719482421875,13.266531 -24739,Binary classification,ALMA,SMTP,0.7877440478596548,0.0034162080091098878,0.003093719482421875,15.48839 -26642,Binary classification,ALMA,SMTP,0.78188574431349,0.003429943405933802,0.003093719482421875,17.885128 -28545,Binary classification,ALMA,SMTP,0.7857418111753371,0.003259452411994785,0.003093719482421875,20.449795 -30448,Binary classification,ALMA,SMTP,0.7871452968996322,0.0030764497769573914,0.003093719482421875,23.184561000000002 -32351,Binary classification,ALMA,SMTP,0.7866835646502427,0.0028897558156335793,0.003093719482421875,26.090132000000004 -34254,Binary classification,ALMA,SMTP,0.7860979739592456,0.0027221995372260785,0.003093719482421875,29.168944000000003 -36157,Binary classification,ALMA,SMTP,0.7771939043615345,0.0024764735017335313,0.003093719482421875,32.428274 -38060,Binary classification,ALMA,SMTP,0.7831581713084603,0.00241750271969056,0.003093719482421875,35.852824 -39963,Binary classification,ALMA,SMTP,0.779496033831294,0.002264492753623189,0.003093719482421875,39.452307 -41866,Binary classification,ALMA,SMTP,0.7831175655663307,0.0021978021978021974,0.003093719482421875,43.218835 -43769,Binary classification,ALMA,SMTP,0.7791130708949257,0.002064409578860446,0.003093719482421875,47.160889 -45672,Binary classification,ALMA,SMTP,0.7808066211245402,0.001993819160602133,0.003093719482421875,51.272051 -47575,Binary classification,ALMA,SMTP,0.7799684708355229,0.001906941266209001,0.003093719482421875,55.555831999999995 -49478,Binary classification,ALMA,SMTP,0.7778810784591131,0.00181653042688465,0.003093719482421875,60.013819999999996 -51381,Binary classification,ALMA,SMTP,0.7807944570950351,0.0021263400372109505,0.003093719482421875,64.63894599999999 -53284,Binary classification,ALMA,SMTP,0.7777193904361535,0.0020222446916076846,0.003093719482421875,69.43803199999999 -55187,Binary classification,ALMA,SMTP,0.7785891604906953,0.0019603038470963,0.003093719482421875,74.40731799999999 -57090,Binary classification,ALMA,SMTP,0.7758801891749869,0.002650245537454205,0.003093719482421875,79.551965 -58993,Binary classification,ALMA,SMTP,0.774159646059702,0.002545481769858501,0.003093719482421875,84.828086 -60896,Binary classification,ALMA,SMTP,0.7746157383079348,0.002471109819027546,0.003093719482421875,90.195459 -62799,Binary classification,ALMA,SMTP,0.7704899759550311,0.0023534297778085413,0.003093719482421875,95.659582 -64702,Binary classification,ALMA,SMTP,0.771274458285679,0.0022921863412660956,0.003093719482421875,101.21574 -66605,Binary classification,ALMA,SMTP,0.7721942797087306,0.002235812454790557,0.003093719482421875,106.861577 -68508,Binary classification,ALMA,SMTP,0.7705085537455479,0.0024111675126903555,0.003093719482421875,112.596175 -70411,Binary classification,ALMA,SMTP,0.7685872945988553,0.0023267205486162137,0.003093719482421875,118.42066 -72314,Binary classification,ALMA,SMTP,0.7687999557485411,0.002267709017127171,0.003093719482421875,124.335619 -74217,Binary classification,ALMA,SMTP,0.7657140547313958,0.0021806496040399402,0.003093719482421875,130.34374499999998 -76120,Binary classification,ALMA,SMTP,0.7665002627430373,0.0021333932180552435,0.003093719482421875,136.44014199999998 -78023,Binary classification,ALMA,SMTP,0.7657101111210797,0.002074462277541216,0.003093719482421875,142.623664 -79926,Binary classification,ALMA,SMTP,0.7636313590070816,0.002007395668251453,0.003093719482421875,148.896051 -81829,Binary classification,ALMA,SMTP,0.7647777682728617,0.001970341180130665,0.003093719482421875,155.253168 -83732,Binary classification,ALMA,SMTP,0.7652868676252806,0.0019298156518206286,0.003093719482421875,161.69571599999998 -85635,Binary classification,ALMA,SMTP,0.7642552694575816,0.0018787699001285476,0.003093719482421875,168.224788 -87538,Binary classification,ALMA,SMTP,0.7644680024674998,0.0018396591789310612,0.003093719482421875,174.840187 -89441,Binary classification,ALMA,SMTP,0.7635312664214399,0.0018876828692779614,0.003093719482421875,181.54098299999998 -91344,Binary classification,ALMA,SMTP,0.7650091960063058,0.0018600325505696352,0.003093719482421875,188.32426499999997 -93247,Binary classification,ALMA,SMTP,0.7647859984771628,0.0018204159650480134,0.003093719482421875,195.19191599999996 -95150,Binary classification,ALMA,SMTP,0.7649710982658959,0.0017854751595768425,0.003093719482421875,202.14365499999997 -106,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.4731182795698925,0.0054683685302734375,0.075077 -212,Binary classification,sklearn SGDClassifier,Bananas,0.5424528301886793,0.46994535519125685,0.0054683685302734375,0.21882000000000001 -318,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.4878048780487805,0.0054950714111328125,0.428712 -424,Binary classification,sklearn SGDClassifier,Bananas,0.5212264150943396,0.4671916010498687,0.0054950714111328125,0.704487 -530,Binary classification,sklearn SGDClassifier,Bananas,0.5283018867924528,0.42660550458715596,0.0054950714111328125,1.047006 -636,Binary classification,sklearn SGDClassifier,Bananas,0.5251572327044025,0.38866396761133604,0.0054950714111328125,1.456112 -742,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.36363636363636365,0.0054950714111328125,1.9325 -848,Binary classification,sklearn SGDClassifier,Bananas,0.5412735849056604,0.33955857385398974,0.0054950714111328125,2.475946 -954,Binary classification,sklearn SGDClassifier,Bananas,0.5450733752620545,0.31545741324921134,0.0054950714111328125,3.086975 -1060,Binary classification,sklearn SGDClassifier,Bananas,0.5528301886792453,0.29673590504451036,0.0054950714111328125,3.7645649999999997 -1166,Binary classification,sklearn SGDClassifier,Bananas,0.5531732418524872,0.2793914246196404,0.0054950714111328125,4.50753 -1272,Binary classification,sklearn SGDClassifier,Bananas,0.5550314465408805,0.27621483375959077,0.0054950714111328125,5.315441 -1378,Binary classification,sklearn SGDClassifier,Bananas,0.5573294629898403,0.26150121065375304,0.0054950714111328125,6.1886849999999995 -1484,Binary classification,sklearn SGDClassifier,Bananas,0.5579514824797843,0.2477064220183486,0.0054950714111328125,7.126671999999999 -1590,Binary classification,sklearn SGDClassifier,Bananas,0.5584905660377358,0.23529411764705882,0.0054950714111328125,8.129404999999998 -1696,Binary classification,sklearn SGDClassifier,Bananas,0.5601415094339622,0.22614107883817425,0.0054950714111328125,9.196330999999999 -1802,Binary classification,sklearn SGDClassifier,Bananas,0.5571587125416204,0.21611001964636542,0.0054950714111328125,10.327506 -1908,Binary classification,sklearn SGDClassifier,Bananas,0.5550314465408805,0.21169916434540387,0.0054950714111328125,11.523138 -2014,Binary classification,sklearn SGDClassifier,Bananas,0.5501489572989077,0.20105820105820105,0.0054950714111328125,12.782968 -2120,Binary classification,sklearn SGDClassifier,Bananas,0.5471698113207547,0.19463087248322145,0.0054950714111328125,14.106907 -2226,Binary classification,sklearn SGDClassifier,Bananas,0.550763701707098,0.2038216560509554,0.0054950714111328125,15.494886 -2332,Binary classification,sklearn SGDClassifier,Bananas,0.5497427101200686,0.21052631578947367,0.0054950714111328125,16.946832999999998 -2438,Binary classification,sklearn SGDClassifier,Bananas,0.5484003281378179,0.21188260558339295,0.0054950714111328125,18.462691 -2544,Binary classification,sklearn SGDClassifier,Bananas,0.5487421383647799,0.22641509433962262,0.0054950714111328125,20.042348 -2650,Binary classification,sklearn SGDClassifier,Bananas,0.5464150943396227,0.23243933588761176,0.0054950714111328125,21.68579 -2756,Binary classification,sklearn SGDClassifier,Bananas,0.5399129172714079,0.23058252427184467,0.0054950714111328125,23.392549000000002 -2862,Binary classification,sklearn SGDClassifier,Bananas,0.539832285115304,0.23117338003502627,0.0054950714111328125,25.163525000000003 -2968,Binary classification,sklearn SGDClassifier,Bananas,0.5414420485175202,0.23063877897117013,0.0054950714111328125,26.998099000000003 -3074,Binary classification,sklearn SGDClassifier,Bananas,0.5400130123617437,0.22732240437158474,0.0054950714111328125,28.897093000000005 -3180,Binary classification,sklearn SGDClassifier,Bananas,0.5433962264150943,0.22848034006376194,0.0054950714111328125,30.860975000000003 -3286,Binary classification,sklearn SGDClassifier,Bananas,0.5447352404138771,0.22487046632124352,0.0054950714111328125,32.894016 -3392,Binary classification,sklearn SGDClassifier,Bananas,0.5448113207547169,0.2248995983935743,0.0054950714111328125,34.991593 -3498,Binary classification,sklearn SGDClassifier,Bananas,0.5465980560320183,0.22025565388397245,0.0054950714111328125,37.153313000000004 -3604,Binary classification,sklearn SGDClassifier,Bananas,0.5491120976692564,0.21686746987951808,0.0054950714111328125,39.379211000000005 -3710,Binary classification,sklearn SGDClassifier,Bananas,0.5482479784366577,0.212406015037594,0.0054950714111328125,41.66962600000001 -3816,Binary classification,sklearn SGDClassifier,Bananas,0.5476939203354297,0.20752984389348023,0.0054950714111328125,44.024291000000005 -3922,Binary classification,sklearn SGDClassifier,Bananas,0.5486996430392657,0.20342034203420342,0.0054950714111328125,46.443344 -4028,Binary classification,sklearn SGDClassifier,Bananas,0.5491559086395233,0.19929453262786598,0.0054950714111328125,48.926930000000006 -4134,Binary classification,sklearn SGDClassifier,Bananas,0.5493468795355588,0.19524838012958964,0.0054950714111328125,51.474790000000006 -4240,Binary classification,sklearn SGDClassifier,Bananas,0.5485849056603773,0.19103972950126796,0.0054950714111328125,54.086862 -4346,Binary classification,sklearn SGDClassifier,Bananas,0.5487804878048781,0.18866363260239966,0.0054950714111328125,56.763353 -4452,Binary classification,sklearn SGDClassifier,Bananas,0.5509883198562444,0.1936264622831787,0.0054950714111328125,59.503949000000006 -4558,Binary classification,sklearn SGDClassifier,Bananas,0.5484861781483107,0.19357366771159876,0.0054950714111328125,62.30891200000001 -4664,Binary classification,sklearn SGDClassifier,Bananas,0.5493138936535163,0.19770992366412216,0.0054950714111328125,65.176854 -4770,Binary classification,sklearn SGDClassifier,Bananas,0.550314465408805,0.1999254009697874,0.0054950714111328125,68.08610900000001 -4876,Binary classification,sklearn SGDClassifier,Bananas,0.550656275635767,0.20007301935012778,0.0054950714111328125,71.03653600000001 -4982,Binary classification,sklearn SGDClassifier,Bananas,0.5505820955439582,0.20630981921304498,0.0054950714111328125,74.02805500000001 -5088,Binary classification,sklearn SGDClassifier,Bananas,0.5481525157232704,0.20422291450328833,0.0054950714111328125,77.060803 -5194,Binary classification,sklearn SGDClassifier,Bananas,0.5463996919522526,0.20243737305348675,0.0054950714111328125,80.134657 -5300,Binary classification,sklearn SGDClassifier,Bananas,0.5466037735849056,0.20509427720807144,0.0054950714111328125,83.249657 -906,Binary classification,sklearn SGDClassifier,Elec2,0.7991169977924945,0.7853773584905659,0.006672859191894531,0.581912 -1812,Binary classification,sklearn SGDClassifier,Elec2,0.8134657836644592,0.7492581602373888,0.006672859191894531,1.751174 -2718,Binary classification,sklearn SGDClassifier,Elec2,0.8002207505518764,0.7256189994946943,0.006672859191894531,3.503934 -3624,Binary classification,sklearn SGDClassifier,Elec2,0.8187086092715232,0.7581891792418107,0.006672859191894531,5.845535 -4530,Binary classification,sklearn SGDClassifier,Elec2,0.8275938189845474,0.7584287039901021,0.006672859191894531,8.772576 -5436,Binary classification,sklearn SGDClassifier,Elec2,0.8210080941869021,0.7495495495495494,0.006672859191894531,12.203207 -6342,Binary classification,sklearn SGDClassifier,Elec2,0.8221381267738883,0.7573149741824441,0.006672859191894531,16.003328 -7248,Binary classification,sklearn SGDClassifier,Elec2,0.8251931567328918,0.7596281540504647,0.006672859191894531,20.176524 -8154,Binary classification,sklearn SGDClassifier,Elec2,0.8302673534461614,0.7805960684844642,0.006672859191894531,24.718011 -9060,Binary classification,sklearn SGDClassifier,Elec2,0.8363134657836645,0.7957018873123021,0.006672859191894531,29.627709 -9966,Binary classification,sklearn SGDClassifier,Elec2,0.8370459562512542,0.8009803921568629,0.006672859191894531,34.907269 -10872,Binary classification,sklearn SGDClassifier,Elec2,0.8392200147167035,0.8078276165347406,0.006672859191894531,40.555268999999996 -11778,Binary classification,sklearn SGDClassifier,Elec2,0.8422482594668025,0.8113705583756344,0.006672859191894531,46.57328199999999 -12684,Binary classification,sklearn SGDClassifier,Elec2,0.8409019236833807,0.8104096204434422,0.006672859191894531,52.95826299999999 -13590,Binary classification,sklearn SGDClassifier,Elec2,0.8427520235467255,0.8153779697624189,0.006672859191894531,59.70527599999999 -14496,Binary classification,sklearn SGDClassifier,Elec2,0.8438189845474614,0.8177427145387216,0.006672859191894531,66.808032 -15402,Binary classification,sklearn SGDClassifier,Elec2,0.845214907154915,0.8184310738766185,0.006672859191894531,74.254711 -16308,Binary classification,sklearn SGDClassifier,Elec2,0.8397105714986509,0.8107990735379271,0.006672859191894531,82.035365 -17214,Binary classification,sklearn SGDClassifier,Elec2,0.8384454513767864,0.8052930056710774,0.006672859191894531,90.148338 -18120,Binary classification,sklearn SGDClassifier,Elec2,0.840728476821192,0.8082392026578072,0.006672859191894531,98.57508899999999 -19026,Binary classification,sklearn SGDClassifier,Elec2,0.843950383685483,0.8100083189351762,0.006672859191894531,107.31510699999998 -19932,Binary classification,sklearn SGDClassifier,Elec2,0.8412101143889223,0.8075402858011553,0.006672859191894531,116.36858499999998 -20838,Binary classification,sklearn SGDClassifier,Elec2,0.8373164411171897,0.8027923211169286,0.006672859191894531,125.73531899999998 -21744,Binary classification,sklearn SGDClassifier,Elec2,0.8382082413539367,0.8007250481477285,0.006672859191894531,135.41517199999998 -22650,Binary classification,sklearn SGDClassifier,Elec2,0.8376158940397351,0.7981560750740864,0.006672859191894531,145.412659 -23556,Binary classification,sklearn SGDClassifier,Elec2,0.8337154015961963,0.7923888270525256,0.006672859191894531,155.725043 -24462,Binary classification,sklearn SGDClassifier,Elec2,0.8312893467418854,0.7886732551589942,0.006672859191894531,166.35011 -25368,Binary classification,sklearn SGDClassifier,Elec2,0.8278145695364238,0.7841897233201582,0.006672859191894531,177.288636 -26274,Binary classification,sklearn SGDClassifier,Elec2,0.8282332343761893,0.7844073950222137,0.006672859191894531,188.539768 -27180,Binary classification,sklearn SGDClassifier,Elec2,0.828513612950699,0.7853557448768134,0.006672859191894531,200.10320000000002 -28086,Binary classification,sklearn SGDClassifier,Elec2,0.8253222245958841,0.7808451710890736,0.006672859191894531,211.980006 -28992,Binary classification,sklearn SGDClassifier,Elec2,0.8246067880794702,0.7784410265347915,0.006672859191894531,224.170025 -29898,Binary classification,sklearn SGDClassifier,Elec2,0.822830958592548,0.7765638840848695,0.006672859191894531,236.67211600000002 -30804,Binary classification,sklearn SGDClassifier,Elec2,0.8227178288533956,0.775479998355466,0.006672859191894531,249.48686600000002 -31710,Binary classification,sklearn SGDClassifier,Elec2,0.8179754020813623,0.768545994065282,0.006672859191894531,262.615336 -32616,Binary classification,sklearn SGDClassifier,Elec2,0.8155506499877361,0.7653116954045408,0.006672859191894531,276.05616200000003 -33522,Binary classification,sklearn SGDClassifier,Elec2,0.813614939442754,0.7635840774935674,0.006672859191894531,289.80868200000003 -34428,Binary classification,sklearn SGDClassifier,Elec2,0.8107935401417451,0.7596842027595366,0.006672859191894531,303.873891 -35334,Binary classification,sklearn SGDClassifier,Elec2,0.8109752646176487,0.758173720989174,0.006672859191894531,318.256576 -36240,Binary classification,sklearn SGDClassifier,Elec2,0.8122792494481236,0.7584590804189597,0.006672859191894531,332.952142 -37146,Binary classification,sklearn SGDClassifier,Elec2,0.8118774565229095,0.7566852367688023,0.006672859191894531,347.96241499999996 -38052,Binary classification,sklearn SGDClassifier,Elec2,0.811783874697782,0.7561623314721503,0.006672859191894531,363.28641 -38958,Binary classification,sklearn SGDClassifier,Elec2,0.8127983982750655,0.7582617919055984,0.006672859191894531,378.92485999999997 -39864,Binary classification,sklearn SGDClassifier,Elec2,0.8135661248244029,0.7615350060963869,0.006672859191894531,394.87776699999995 -40770,Binary classification,sklearn SGDClassifier,Elec2,0.8153544272749571,0.7661821344266367,0.006672859191894531,411.1448869999999 -41676,Binary classification,sklearn SGDClassifier,Elec2,0.8169450043190325,0.7701762313601446,0.006672859191894531,427.72309199999995 -42582,Binary classification,sklearn SGDClassifier,Elec2,0.8179747311070406,0.7719824669784955,0.006672859191894531,444.61337399999996 -43488,Binary classification,sklearn SGDClassifier,Elec2,0.8185476453274466,0.7730057820096079,0.006672859191894531,461.81686899999994 -44394,Binary classification,sklearn SGDClassifier,Elec2,0.8179258458350227,0.77092248830948,0.006672859191894531,479.33440799999994 -45300,Binary classification,sklearn SGDClassifier,Elec2,0.8190507726269316,0.7728544905367584,0.006672859191894531,497.16619699999995 -25,Binary classification,sklearn SGDClassifier,Phishing,0.64,0.6666666666666666,0.0066585540771484375,0.023761 -50,Binary classification,sklearn SGDClassifier,Phishing,0.78,0.7659574468085107,0.0066585540771484375,0.064622 -75,Binary classification,sklearn SGDClassifier,Phishing,0.8133333333333334,0.8108108108108109,0.0066585540771484375,0.122385 -100,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8125,0.0066585540771484375,0.197128 -125,Binary classification,sklearn SGDClassifier,Phishing,0.808,0.8,0.0066585540771484375,0.288843 -150,Binary classification,sklearn SGDClassifier,Phishing,0.8133333333333334,0.8133333333333335,0.0066585540771484375,0.397551 -175,Binary classification,sklearn SGDClassifier,Phishing,0.8228571428571428,0.812121212121212,0.0066585540771484375,0.523492 -200,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8085106382978724,0.0066585540771484375,0.6664519999999999 -225,Binary classification,sklearn SGDClassifier,Phishing,0.8177777777777778,0.8019323671497586,0.0066585540771484375,0.8264199999999999 -250,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8068669527896996,0.0066585540771484375,1.0034779999999999 -275,Binary classification,sklearn SGDClassifier,Phishing,0.8218181818181818,0.8078431372549019,0.0068721771240234375,1.197523 -300,Binary classification,sklearn SGDClassifier,Phishing,0.8333333333333334,0.8161764705882353,0.0068721771240234375,1.4085949999999998 -325,Binary classification,sklearn SGDClassifier,Phishing,0.8430769230769231,0.8222996515679442,0.0068721771240234375,1.6370319999999998 -350,Binary classification,sklearn SGDClassifier,Phishing,0.8485714285714285,0.8262295081967213,0.0068721771240234375,1.8824899999999998 -375,Binary classification,sklearn SGDClassifier,Phishing,0.848,0.8246153846153846,0.0068721771240234375,2.144992 -400,Binary classification,sklearn SGDClassifier,Phishing,0.85,0.8245614035087719,0.0068721771240234375,2.424587 -425,Binary classification,sklearn SGDClassifier,Phishing,0.8541176470588235,0.8258426966292134,0.0068721771240234375,2.721266 -450,Binary classification,sklearn SGDClassifier,Phishing,0.8577777777777778,0.8279569892473118,0.0068721771240234375,3.035051 -475,Binary classification,sklearn SGDClassifier,Phishing,0.8568421052631578,0.8282828282828283,0.0068721771240234375,3.366223 -500,Binary classification,sklearn SGDClassifier,Phishing,0.856,0.8309859154929577,0.0068721771240234375,3.714529 -525,Binary classification,sklearn SGDClassifier,Phishing,0.8552380952380952,0.8264840182648402,0.0068721771240234375,4.0798760000000005 -550,Binary classification,sklearn SGDClassifier,Phishing,0.86,0.8336933045356371,0.0068721771240234375,4.4622850000000005 -575,Binary classification,sklearn SGDClassifier,Phishing,0.8608695652173913,0.8347107438016529,0.0068721771240234375,4.8618310000000005 -600,Binary classification,sklearn SGDClassifier,Phishing,0.865,0.8370221327967807,0.0068721771240234375,5.278527 -625,Binary classification,sklearn SGDClassifier,Phishing,0.8656,0.8346456692913387,0.0068721771240234375,5.712664 -650,Binary classification,sklearn SGDClassifier,Phishing,0.8692307692307693,0.8417132216014899,0.0068721771240234375,6.163824 -675,Binary classification,sklearn SGDClassifier,Phishing,0.8711111111111111,0.8471001757469244,0.0068721771240234375,6.632034 -700,Binary classification,sklearn SGDClassifier,Phishing,0.8757142857142857,0.8507718696397941,0.0068721771240234375,7.117376 -725,Binary classification,sklearn SGDClassifier,Phishing,0.8772413793103448,0.8552845528455284,0.0068721771240234375,7.619913 -750,Binary classification,sklearn SGDClassifier,Phishing,0.8786666666666667,0.8575899843505477,0.0068721771240234375,8.139518 -775,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8584474885844748,0.0068721771240234375,8.676567 -800,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8600583090379008,0.0068721771240234375,9.230727 -825,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8611500701262274,0.0068721771240234375,9.801975 -850,Binary classification,sklearn SGDClassifier,Phishing,0.8811764705882353,0.8618331053351573,0.0068721771240234375,10.390364 -875,Binary classification,sklearn SGDClassifier,Phishing,0.8845714285714286,0.8651535380507342,0.0068721771240234375,10.99587 -900,Binary classification,sklearn SGDClassifier,Phishing,0.8833333333333333,0.8634590377113134,0.0068721771240234375,11.618539 -925,Binary classification,sklearn SGDClassifier,Phishing,0.8854054054054054,0.8671679197994987,0.0068721771240234375,12.258516 -950,Binary classification,sklearn SGDClassifier,Phishing,0.8852631578947369,0.8685162846803377,0.0068721771240234375,12.91558 -975,Binary classification,sklearn SGDClassifier,Phishing,0.8861538461538462,0.8692579505300353,0.0068721771240234375,13.589863000000001 -1000,Binary classification,sklearn SGDClassifier,Phishing,0.887,0.8702640642939151,0.0068721771240234375,14.281225000000001 -1025,Binary classification,sklearn SGDClassifier,Phishing,0.8868292682926829,0.8705357142857143,0.0068721771240234375,14.989779 -1050,Binary classification,sklearn SGDClassifier,Phishing,0.8885714285714286,0.8729641693811075,0.0068721771240234375,15.714934 -1075,Binary classification,sklearn SGDClassifier,Phishing,0.8874418604651163,0.8727655099894849,0.0068721771240234375,16.456921 -1100,Binary classification,sklearn SGDClassifier,Phishing,0.889090909090909,0.8747433264887063,0.0068721771240234375,17.215539 -1125,Binary classification,sklearn SGDClassifier,Phishing,0.8906666666666667,0.8776119402985074,0.0068721771240234375,17.990769 -1150,Binary classification,sklearn SGDClassifier,Phishing,0.8904347826086957,0.8771929824561404,0.0068721771240234375,18.782632 -1175,Binary classification,sklearn SGDClassifier,Phishing,0.8893617021276595,0.875717017208413,0.0068721771240234375,19.591204 -1200,Binary classification,sklearn SGDClassifier,Phishing,0.89,0.8761726078799249,0.0068721771240234375,20.416515 -1225,Binary classification,sklearn SGDClassifier,Phishing,0.8906122448979592,0.8768382352941176,0.0068721771240234375,21.258769 -1250,Binary classification,sklearn SGDClassifier,Phishing,0.8888,0.8753363228699551,0.0068721771240234375,22.117832 -1903,Binary classification,sklearn SGDClassifier,SMTP,0.998949027850762,0.0,0.005644798278808594,1.261861 -3806,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.0,0.005644798278808594,3.784872 -5709,Binary classification,sklearn SGDClassifier,SMTP,0.999649675950254,0.0,0.005644798278808594,7.562933 -7612,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.0,0.005644798278808594,12.592241000000001 -9515,Binary classification,sklearn SGDClassifier,SMTP,0.9997898055701524,0.0,0.005644798278808594,18.508988000000002 -11418,Binary classification,sklearn SGDClassifier,SMTP,0.999824837975127,0.0,0.005644798278808594,25.182225000000003 -13321,Binary classification,sklearn SGDClassifier,SMTP,0.9998498611215374,0.0,0.005644798278808594,32.571041 -15224,Binary classification,sklearn SGDClassifier,SMTP,0.9995401996847083,0.631578947368421,0.005644798278808594,40.604957 -17127,Binary classification,sklearn SGDClassifier,SMTP,0.9995912886086297,0.6956521739130435,0.005644798278808594,49.282818 -19030,Binary classification,sklearn SGDClassifier,SMTP,0.9996321597477666,0.6956521739130435,0.005644798278808594,58.610725 -20933,Binary classification,sklearn SGDClassifier,SMTP,0.999665599770697,0.6956521739130435,0.005644798278808594,68.59192900000001 -22836,Binary classification,sklearn SGDClassifier,SMTP,0.9996934664564723,0.6956521739130435,0.005644798278808594,79.219372 -24739,Binary classification,sklearn SGDClassifier,SMTP,0.9997170459598205,0.6956521739130435,0.005644798278808594,90.49290300000001 -26642,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.6666666666666666,0.005644798278808594,102.41066000000001 -28545,Binary classification,sklearn SGDClassifier,SMTP,0.9997197407602032,0.6666666666666666,0.005644798278808594,114.97610900000001 -30448,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.6666666666666666,0.005644798278808594,128.185954 -32351,Binary classification,sklearn SGDClassifier,SMTP,0.9997527124354734,0.6666666666666666,0.005644798278808594,142.03946100000002 -34254,Binary classification,sklearn SGDClassifier,SMTP,0.9997664506335027,0.6666666666666666,0.005644798278808594,156.53684400000003 -36157,Binary classification,sklearn SGDClassifier,SMTP,0.9997787427054236,0.6666666666666666,0.005644798278808594,171.68202700000003 -38060,Binary classification,sklearn SGDClassifier,SMTP,0.9997898055701524,0.6666666666666666,0.005644798278808594,187.47483000000003 -39963,Binary classification,sklearn SGDClassifier,SMTP,0.9997998148287166,0.6666666666666666,0.005644798278808594,203.91174800000002 -41866,Binary classification,sklearn SGDClassifier,SMTP,0.999808914154684,0.6666666666666666,0.005644798278808594,220.99296 -43769,Binary classification,sklearn SGDClassifier,SMTP,0.9998172222349151,0.6666666666666666,0.005644798278808594,238.718792 -45672,Binary classification,sklearn SGDClassifier,SMTP,0.999824837975127,0.6666666666666666,0.005644798278808594,257.088535 -47575,Binary classification,sklearn SGDClassifier,SMTP,0.9998318444561219,0.6666666666666666,0.005644798278808594,276.10186899999997 -49478,Binary classification,sklearn SGDClassifier,SMTP,0.9998383119770403,0.6666666666666666,0.005644798278808594,295.75838 -51381,Binary classification,sklearn SGDClassifier,SMTP,0.9998053755279189,0.6153846153846154,0.005644798278808594,316.06348299999996 -53284,Binary classification,sklearn SGDClassifier,SMTP,0.9998123264019217,0.6153846153846154,0.005644798278808594,337.01301299999994 -55187,Binary classification,sklearn SGDClassifier,SMTP,0.9998187979053038,0.6153846153846154,0.005644798278808594,358.6068609999999 -57090,Binary classification,sklearn SGDClassifier,SMTP,0.9996671921527412,0.45714285714285713,0.005644798278808594,380.84445099999994 -58993,Binary classification,sklearn SGDClassifier,SMTP,0.9996779278897496,0.45714285714285713,0.005644798278808594,403.72630799999996 -60896,Binary classification,sklearn SGDClassifier,SMTP,0.999687992643195,0.45714285714285713,0.005644798278808594,427.25217999999995 -62799,Binary classification,sklearn SGDClassifier,SMTP,0.999665599770697,0.43243243243243246,0.005644798278808594,451.4221749999999 -64702,Binary classification,sklearn SGDClassifier,SMTP,0.9996754350715589,0.43243243243243246,0.005644798278808594,476.23565699999995 -66605,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.43243243243243246,0.005644798278808594,501.6931359999999 -68508,Binary classification,sklearn SGDClassifier,SMTP,0.9996642727856601,0.4102564102564103,0.005644798278808594,527.7941669999999 -70411,Binary classification,sklearn SGDClassifier,SMTP,0.9996733464941557,0.4102564102564103,0.005644798278808594,554.5388179999999 -72314,Binary classification,sklearn SGDClassifier,SMTP,0.9996819426390464,0.4102564102564103,0.005644798278808594,581.9269989999999 -74217,Binary classification,sklearn SGDClassifier,SMTP,0.999690097955994,0.4102564102564103,0.005644798278808594,609.9586109999999 -76120,Binary classification,sklearn SGDClassifier,SMTP,0.9996978455070941,0.4102564102564103,0.005644798278808594,638.633216 -78023,Binary classification,sklearn SGDClassifier,SMTP,0.9997052151288722,0.4102564102564103,0.005644798278808594,667.951428 -79926,Binary classification,sklearn SGDClassifier,SMTP,0.99971223381628,0.4102564102564103,0.005644798278808594,697.9132639999999 -81829,Binary classification,sklearn SGDClassifier,SMTP,0.9997189260531107,0.4102564102564103,0.005644798278808594,728.5228309999999 -83732,Binary classification,sklearn SGDClassifier,SMTP,0.9997253140973582,0.4102564102564103,0.005644798278808594,759.7753099999999 -85635,Binary classification,sklearn SGDClassifier,SMTP,0.9997314182285281,0.4102564102564103,0.005644798278808594,791.6709739999999 -87538,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.4102564102564103,0.005644798278808594,824.2098649999999 -89441,Binary classification,sklearn SGDClassifier,SMTP,0.9997316666853009,0.4,0.005644798278808594,857.3917109999999 -91344,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.4,0.005644798278808594,891.2164919999999 -93247,Binary classification,sklearn SGDClassifier,SMTP,0.9997426190654928,0.4,0.005644798278808594,925.6838509999999 -95150,Binary classification,sklearn SGDClassifier,SMTP,0.9997477666841829,0.4,0.005644798278808594,960.7942089999999 -106,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5,0.0,0.0006465911865234375,0.01481 -212,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5283018867924528,0.0,0.0006465911865234375,0.041137 -318,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5314465408805031,0.0,0.0006465911865234375,0.078177 -424,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5400943396226415,0.0,0.0006465911865234375,0.125909 -530,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5547169811320755,0.0,0.0006465911865234375,0.184447 -636,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5550314465408805,0.0,0.0006465911865234375,0.253805 -742,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5660377358490566,0.0,0.0006465911865234375,0.333961 -848,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5636792452830188,0.0,0.0006465911865234375,0.425093 -954,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5649895178197065,0.0,0.0006465911865234375,0.527744 -1060,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5707547169811321,0.0,0.0006465911865234375,0.641554 -1166,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5686106346483705,0.0,0.0006465911865234375,0.7661 -1272,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5644654088050315,0.0,0.0006465911865234375,0.90156 -1378,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5682148040638607,0.0,0.0006465911865234375,1.047827 -1484,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5680592991913747,0.0,0.0006465911865234375,1.2046620000000001 -1590,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5679245283018868,0.0,0.0006465911865234375,1.3725140000000002 -1696,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5683962264150944,0.0,0.0006465911865234375,1.5511630000000003 -1802,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5643729189789123,0.0,0.0006465911865234375,1.7405230000000003 -1908,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.560272536687631,0.0,0.0006465911865234375,1.9407960000000002 -2014,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5551142005958292,0.0,0.0006465911865234375,2.1520810000000004 -2120,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,2.3743700000000003 -2226,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5512129380053908,0.0,0.0006465911865234375,2.6075880000000002 -2332,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5506003430531733,0.0,0.0006465911865234375,2.8515750000000004 -2438,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.551681706316653,0.0,0.0006465911865234375,3.1063150000000004 -2544,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487421383647799,0.0,0.0006465911865234375,3.3719430000000004 -2650,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5467924528301886,0.0,0.0006465911865234375,3.648299 -2756,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5471698113207547,0.0,0.0006465911865234375,3.9354750000000003 -2862,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5489168413696716,0.0,0.0006465911865234375,4.233696 -2968,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505390835579514,0.0,0.0006465911865234375,4.542693 -3074,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487963565387117,0.0,0.0006465911865234375,4.86295 -3180,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,5.194204 -3286,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5517346317711503,0.0,0.0006465911865234375,5.536035 -3392,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5498231132075472,0.0,0.0006465911865234375,5.888475 -3498,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514579759862779,0.0,0.0006465911865234375,6.251513999999999 -3604,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5535516093229744,0.0,0.0006465911865234375,6.625144999999999 -3710,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5522911051212938,0.0,0.0006465911865234375,7.009503999999999 -3816,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5516247379454927,0.0,0.0006465911865234375,7.4043329999999985 -3922,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5525242223355431,0.0,0.0006465911865234375,7.8097699999999985 -4028,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5528798411122146,0.0,0.0006465911865234375,8.225969999999998 -4134,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5529753265602322,0.0,0.0006465911865234375,8.653192999999998 -4240,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5523584905660377,0.0,0.0006465911865234375,9.090957999999999 -4346,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5526921306948919,0.0,0.0006465911865234375,9.539357999999998 -4452,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5530098831985625,0.0,0.0006465911865234375,9.998287999999999 -4558,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5508995173321632,0.0,0.0006465911865234375,10.467735 -4664,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5497427101200686,0.0,0.0006465911865234375,10.947871999999998 -4770,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505241090146751,0.0,0.0006465911865234375,11.438429999999999 -4876,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5518867924528302,0.0,0.0006465911865234375,11.939414999999999 -4982,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509835407466881,0.0,0.0006465911865234375,12.450955999999998 -5088,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5511006289308176,0.0,0.0006465911865234375,12.972935999999997 -5194,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514054678475163,0.0,0.0006465911865234375,13.505583999999997 -5300,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5513207547169812,0.0,0.0006465911865234375,14.048808999999997 -906,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6799116997792495,0.5482866043613708,0.0006465911865234375,0.144107 -1812,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7190949227373068,0.4904904904904904,0.0006465911865234375,0.426551 -2718,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.43243243243243246,0.0006465911865234375,0.845137 -3624,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7047461368653422,0.4478844169246646,0.0006465911865234375,1.400054 -4530,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7024282560706402,0.4118673647469459,0.0006465911865234375,2.0918919999999996 -5436,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7041942604856513,0.4165457184325108,0.0006465911865234375,2.9195709999999995 -6342,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.40485829959514175,0.0006465911865234375,3.8823039999999995 -7248,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695364238410596,0.3953997809419496,0.0006465911865234375,4.980137999999999 -8154,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6873926907039489,0.4084474355999072,0.0006465911865234375,6.213938999999999 -9060,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6864238410596026,0.42408270829110073,0.0006465911865234375,7.583110999999999 -9966,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.687537627934979,0.4433321415802646,0.0006465911865234375,9.088204999999999 -10872,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6938925680647535,0.4717460317460317,0.0006465911865234375,10.727540999999999 -11778,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6932416369502462,0.47155185022670765,0.0006465911865234375,12.500958999999998 -12684,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944970040996531,0.47557179591284343,0.0006465911865234375,14.408948999999998 -13590,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6942604856512141,0.48429936701005344,0.0006465911865234375,16.456863 -14496,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6935016556291391,0.48606130711393875,0.0006465911865234375,18.638716 -15402,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6929619529931178,0.48095708484249805,0.0006465911865234375,20.95394 -16308,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6904586705911209,0.47130289065772935,0.0006465911865234375,23.402753999999998 -17214,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6921691646334379,0.4645852278468223,0.0006465911865234375,25.9851 -18120,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.694205298013245,0.46859115757168884,0.0006465911865234375,28.701738 -19026,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6967307894460212,0.467515688445921,0.0006465911865234375,31.552559 -19932,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6958157736303432,0.4737435986459509,0.0006465911865234375,34.538332 -20838,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.4696604963891426,0.0006465911865234375,37.65893 -21744,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6968359087564385,0.4670978172999191,0.0006465911865234375,40.913762 -22650,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6977041942604857,0.4643667370726746,0.0006465911865234375,44.302397 -23556,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952368823229751,0.4573285962657797,0.0006465911865234375,47.824759 -24462,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6978170223203336,0.4597281099254495,0.0006465911865234375,51.480483 -25368,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6976505834121728,0.46122506322000567,0.0006465911865234375,55.26987 -26274,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6983329527289336,0.4614757439869547,0.0006465911865234375,59.192657 -27180,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6959896983075791,0.4576304561864129,0.0006465911865234375,63.254197999999995 -28086,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695649077832372,0.4559572301425662,0.0006465911865234375,67.449702 -28992,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952262693156733,0.4515207945375543,0.0006465911865234375,71.778902 -29898,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6939260151180681,0.4465678863017841,0.0006465911865234375,76.241703 -30804,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6941630957018569,0.44330201500915917,0.0006465911865234375,80.838056 -31710,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6917376222011984,0.4368266405484819,0.0006465911865234375,85.568011 -32616,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6893549178317391,0.4316805025802109,0.0006465911865234375,90.431842 -33522,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.688353916830738,0.42909448603748845,0.0006465911865234375,95.42948200000001 -34428,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6863599395840595,0.4245363461948412,0.0006465911865234375,100.561249 -35334,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6869304352748061,0.4212013394725827,0.0006465911865234375,105.826946 -36240,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6911147902869758,0.4267718148299877,0.0006465911865234375,111.170297 -37146,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6919722177354224,0.42698317307692313,0.0006465911865234375,116.582555 -38052,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944181646168401,0.43117111828588206,0.0006465911865234375,122.063741 -38958,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6937727809435803,0.43082061068702293,0.0006465911865234375,127.61367 -39864,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6930814770218744,0.4344288818009522,0.0006465911865234375,133.232984 -40770,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6924208977189109,0.4391771019677997,0.0006465911865234375,138.92121699999998 -41676,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.44722270288977334,0.0006465911865234375,144.67862699999998 -42582,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6956225635244939,0.45507672903090185,0.0006465911865234375,150.505407 -43488,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6962150478292862,0.4576097220511558,0.0006465911865234375,156.401784 -44394,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6963103122043519,0.45575649927337314,0.0006465911865234375,162.367508 -45300,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.697439293598234,0.4596278189560006,0.0006465911865234375,168.40291499999998 -25,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.52,0.33333333333333337,0.0006465911865234375,0.006954 -50,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.56,0.21428571428571427,0.0006465911865234375,0.018084 -75,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.5866666666666667,0.3404255319148936,0.0006465911865234375,0.032476 -100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6,0.375,0.0006465911865234375,0.049891 -125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.64,0.4705882352941176,0.0006465911865234375,0.070336 -150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.62,0.44660194174757284,0.0006465911865234375,0.09389399999999999 -175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6342857142857142,0.41818181818181815,0.0006465911865234375,0.120402 -200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.63,0.4126984126984127,0.0006465911865234375,0.149914 -225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6488888888888888,0.4316546762589928,0.0006465911865234375,0.18237399999999998 -250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.648,0.4358974358974359,0.0006465911865234375,0.218053 -275,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6618181818181819,0.4561403508771929,0.0006465911865234375,0.257041 -300,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6733333333333333,0.46153846153846156,0.0006465911865234375,0.29910800000000004 -325,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.683076923076923,0.46632124352331616,0.0006465911865234375,0.34419000000000005 -350,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6942857142857143,0.47804878048780486,0.0006465911865234375,0.39236000000000004 -375,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7013333333333334,0.4909090909090909,0.0006465911865234375,0.44361000000000006 -400,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.705,0.4913793103448276,0.0006465911865234375,0.49797800000000003 -425,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7105882352941176,0.4896265560165975,0.0006465911865234375,0.555381 -450,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7222222222222222,0.5098039215686275,0.0006465911865234375,0.615936 -475,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7157894736842105,0.5054945054945055,0.0006465911865234375,0.679555 -500,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.718,0.5252525252525252,0.0006465911865234375,0.74642 -525,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7257142857142858,0.5294117647058824,0.0006465911865234375,0.816531 -550,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7218181818181818,0.5233644859813085,0.0006465911865234375,0.889759 -575,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7217391304347827,0.5209580838323353,0.0006465911865234375,0.9661489999999999 -600,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7283333333333334,0.5275362318840581,0.0006465911865234375,1.0457699999999999 -625,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7376,0.5340909090909091,0.0006465911865234375,1.1284889999999999 -650,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7369230769230769,0.5415549597855228,0.0006465911865234375,1.2144389999999998 -675,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7333333333333333,0.5477386934673367,0.0006465911865234375,1.3035119999999998 -700,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74,0.5560975609756097,0.0006465911865234375,1.3960879999999998 -725,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.743448275862069,0.5753424657534246,0.0006465911865234375,1.4918939999999998 -750,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7453333333333333,0.5820568927789934,0.0006465911865234375,1.5909119999999999 -775,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7470967741935484,0.5847457627118644,0.0006465911865234375,1.6930509999999999 -800,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74625,0.5915492957746479,0.0006465911865234375,1.7983589999999998 -825,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7490909090909091,0.602687140115163,0.0006465911865234375,1.9068189999999998 -850,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7541176470588236,0.6122448979591837,0.0006465911865234375,2.0184699999999998 -875,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7554285714285714,0.6123188405797102,0.0006465911865234375,2.13332 -900,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7566666666666667,0.6123893805309735,0.0006465911865234375,2.251249 -925,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.76,0.6237288135593221,0.0006465911865234375,2.372436 -950,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7589473684210526,0.6288492706645057,0.0006465911865234375,2.496743 -975,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7610256410256411,0.631911532385466,0.0006465911865234375,2.624394 -1000,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.761,0.6328725038402457,0.0006465911865234375,2.755204 -1025,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7609756097560976,0.635958395245171,0.0006465911865234375,2.889431 -1050,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7638095238095238,0.6436781609195402,0.0006465911865234375,3.0268070000000002 -1075,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7665116279069767,0.651872399445215,0.0006465911865234375,3.1673590000000003 -1100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.77,0.6594885598923284,0.0006465911865234375,3.311033 -1125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.768,0.6597131681877444,0.0006465911865234375,3.457802 -1150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7695652173913043,0.6615581098339719,0.0006465911865234375,3.607738 -1175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7702127659574468,0.6633416458852868,0.0006465911865234375,3.760779 -1200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7741666666666667,0.6691086691086692,0.0006465911865234375,3.917058 -1225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7771428571428571,0.6746126340882003,0.0006465911865234375,4.076379 -1250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7736,0.6697782963827306,0.0006465911865234375,4.239016 -1903,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,0.209651 -3806,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,0.63219 -5709,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,1.262199 -7612,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,2.0972720000000002 -9515,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,3.138863 -11418,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,4.386558 -13321,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,5.8399909999999995 -15224,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9992774566473989,0.0,0.0006465911865234375,7.498149999999999 -17127,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999299351900508,0.14285714285714288,0.0006465911865234375,9.361125999999999 -19030,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9993694167104572,0.14285714285714288,0.0006465911865234375,11.452409999999999 -20933,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9994267424640519,0.14285714285714288,0.0006465911865234375,13.765639999999998 -22836,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999474513925381,0.14285714285714288,0.0006465911865234375,16.300417999999997 -24739,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999514935931121,0.14285714285714288,0.0006465911865234375,19.056179999999998 -26642,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995120486449967,0.13333333333333333,0.0006465911865234375,22.032825999999996 -28545,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995445787353302,0.13333333333333333,0.0006465911865234375,25.228374999999996 -30448,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995730425643721,0.13333333333333333,0.0006465911865234375,28.637877999999997 -32351,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,32.260672 -34254,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996204822794418,0.13333333333333333,0.0006465911865234375,36.096896 -36157,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996404568963133,0.13333333333333333,0.0006465911865234375,40.148391000000004 -38060,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996584340514977,0.13333333333333333,0.0006465911865234375,44.38058100000001 -39963,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996746990966644,0.13333333333333333,0.0006465911865234375,48.731359000000005 -41866,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996894855013615,0.13333333333333333,0.0006465911865234375,53.205186000000005 -43769,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999702986131737,0.13333333333333333,0.0006465911865234375,57.777449000000004 -45672,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997153617095814,0.13333333333333333,0.0006465911865234375,62.448002 -47575,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997267472411981,0.13333333333333333,0.0006465911865234375,67.217049 -49478,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997372569626904,0.13333333333333333,0.0006465911865234375,72.084119 -51381,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997080632918783,0.11764705882352941,0.0006465911865234375,77.049615 -53284,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997184896028827,0.11764705882352941,0.0006465911865234375,82.113263 -55187,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997281968579557,0.11764705882352941,0.0006465911865234375,87.275593 -57090,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995796111403048,0.14285714285714285,0.0006465911865234375,92.536371 -58993,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995931720712626,0.14285714285714285,0.0006465911865234375,97.895723 -60896,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996058854440357,0.14285714285714285,0.0006465911865234375,103.35374 -62799,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999585980668482,0.13333333333333333,0.0006465911865234375,108.910478 -64702,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,114.565649 -66605,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996096389159973,0.13333333333333333,0.0006465911865234375,120.31946599999999 -68508,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995912886086297,0.125,0.0006465911865234375,126.17180599999999 -70411,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996023348624504,0.125,0.0006465911865234375,132.122918 -72314,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996127997344912,0.125,0.0006465911865234375,138.18042 -74217,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996227279464274,0.125,0.0006465911865234375,144.337343 -76120,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996321597477666,0.125,0.0006465911865234375,150.592744 -78023,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996411314612358,0.125,0.0006465911865234375,156.94675700000002 -79926,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999649675950254,0.125,0.0006465911865234375,163.39908200000002 -81829,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996578230211783,0.125,0.0006465911865234375,169.949778 -83732,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999665599770697,0.125,0.0006465911865234375,176.598846 -85635,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996730308869037,0.125,0.0006465911865234375,183.346496 -87538,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996801389111014,0.125,0.0006465911865234375,190.192536 -89441,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996757639114053,0.1212121212121212,0.0006465911865234375,197.137274 -91344,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996825188299177,0.1212121212121212,0.0006465911865234375,204.18062799999998 -93247,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996889980374704,0.1212121212121212,0.0006465911865234375,211.32290999999998 -95150,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999695218076721,0.1212121212121212,0.0006465911865234375,218.56361699999997 -106,Binary classification,Naive Bayes,Bananas,0.5333333333333333,0.46153846153846156,0.014024734497070312,0.027532 -212,Binary classification,Naive Bayes,Bananas,0.5592417061611374,0.5026737967914437,0.014024734497070312,0.072437 -318,Binary classification,Naive Bayes,Bananas,0.555205047318612,0.5154639175257733,0.014024734497070312,0.134216 -424,Binary classification,Naive Bayes,Bananas,0.5626477541371159,0.5066666666666667,0.014024734497070312,0.212262 -530,Binary classification,Naive Bayes,Bananas,0.5689981096408318,0.48181818181818187,0.014024734497070312,0.306946 -636,Binary classification,Naive Bayes,Bananas,0.5716535433070866,0.4645669291338582,0.014024734497070312,0.418348 -742,Binary classification,Naive Bayes,Bananas,0.5870445344129555,0.4555160142348755,0.014024734497070312,0.54645 -848,Binary classification,Naive Bayes,Bananas,0.5962219598583235,0.4554140127388535,0.014024734497070312,0.690987 -954,Binary classification,Naive Bayes,Bananas,0.6002098635886673,0.4454148471615721,0.014024734497070312,0.852195 -1060,Binary classification,Naive Bayes,Bananas,0.6090651558073654,0.44054054054054054,0.014024734497070312,1.029786 -1166,Binary classification,Naive Bayes,Bananas,0.6068669527896996,0.42606516290726815,0.014024734497070312,1.2235980000000002 -1272,Binary classification,Naive Bayes,Bananas,0.6136900078678206,0.433679354094579,0.014024734497070312,1.4337380000000002 -1378,Binary classification,Naive Bayes,Bananas,0.6143790849673203,0.419672131147541,0.014024734497070312,1.660063 -1484,Binary classification,Naive Bayes,Bananas,0.6142953472690492,0.4127310061601643,0.014024734497070312,1.902843 -1590,Binary classification,Naive Bayes,Bananas,0.6135934550031467,0.40618955512572535,0.014024734497070312,2.161936 -1696,Binary classification,Naive Bayes,Bananas,0.6141592920353982,0.4010989010989011,0.014024734497070312,2.4372089999999997 -1802,Binary classification,Naive Bayes,Bananas,0.614658523042754,0.40378006872852235,0.014024734497070312,2.7285959999999996 -1908,Binary classification,Naive Bayes,Bananas,0.6151022548505506,0.4080645161290322,0.014024734497070312,3.0363329999999995 -2014,Binary classification,Naive Bayes,Bananas,0.6100347739692003,0.40485216072782415,0.014024734497070312,3.3602269999999996 -2120,Binary classification,Naive Bayes,Bananas,0.608305804624823,0.4071428571428571,0.014024734497070312,3.7002569999999997 -2226,Binary classification,Naive Bayes,Bananas,0.6089887640449438,0.4089673913043478,0.014024734497070312,4.056792 -2332,Binary classification,Naive Bayes,Bananas,0.6096096096096096,0.4098573281452659,0.014024734497070312,4.429501999999999 -2438,Binary classification,Naive Bayes,Bananas,0.6101764464505539,0.40846824408468246,0.014024734497070312,4.818382999999999 -2544,Binary classification,Naive Bayes,Bananas,0.6114825009830909,0.41538461538461535,0.014024734497070312,5.223696999999999 -2650,Binary classification,Naive Bayes,Bananas,0.6100415251038127,0.41273450824332003,0.014024734497070312,5.6451509999999985 -2756,Binary classification,Naive Bayes,Bananas,0.6076225045372051,0.4070213933077345,0.014024734497070312,6.082884999999998 -2862,Binary classification,Naive Bayes,Bananas,0.6085284865431667,0.4092827004219409,0.014024734497070312,6.537074999999998 -2968,Binary classification,Naive Bayes,Bananas,0.6083586113919784,0.4065372829417773,0.014024734497070312,7.007774999999998 -3074,Binary classification,Naive Bayes,Bananas,0.60624796615685,0.40628066732090284,0.014024734497070312,7.494666999999998 -3180,Binary classification,Naive Bayes,Bananas,0.6071091538219566,0.4077761972498815,0.014024734497070312,7.997970999999998 -3286,Binary classification,Naive Bayes,Bananas,0.6063926940639269,0.4049700874367234,0.014024734497070312,8.517511999999998 -3392,Binary classification,Naive Bayes,Bananas,0.6048363314656443,0.40602836879432624,0.014024734497070312,9.053436999999999 -3498,Binary classification,Naive Bayes,Bananas,0.6065198741778668,0.40535868625756266,0.014024734497070312,9.605597 -3604,Binary classification,Naive Bayes,Bananas,0.6086594504579517,0.40905280804694044,0.014024734497070312,10.173998 -3710,Binary classification,Naive Bayes,Bananas,0.6085198166621731,0.4078303425774878,0.014024734497070312,10.759293 -3816,Binary classification,Naive Bayes,Bananas,0.6070773263433814,0.40492258832870187,0.014024734497070312,11.360927 -3922,Binary classification,Naive Bayes,Bananas,0.6067329762815609,0.4027885360185902,0.014024734497070312,11.978992 -4028,Binary classification,Naive Bayes,Bananas,0.6088899925502855,0.405436013590034,0.014024734497070312,12.613729 -4134,Binary classification,Naive Bayes,Bananas,0.6106944108395839,0.40780272359219727,0.014024734497070312,13.264737 -4240,Binary classification,Naive Bayes,Bananas,0.611936777541873,0.41186986056489094,0.014024734497070312,13.931975 -4346,Binary classification,Naive Bayes,Bananas,0.6131185270425776,0.4128536500174642,0.014024734497070312,14.615508 -4452,Binary classification,Naive Bayes,Bananas,0.6137946528869916,0.413510747185261,0.014024734497070312,15.315549 -4558,Binary classification,Naive Bayes,Bananas,0.6122448979591837,0.4115884115884116,0.014024734497070312,16.031772 -4664,Binary classification,Naive Bayes,Bananas,0.6126956894702981,0.41249186727391024,0.014024734497070312,16.764316 -4770,Binary classification,Naive Bayes,Bananas,0.6143845669951772,0.41302266198531756,0.014024734497070312,17.512985 -4876,Binary classification,Naive Bayes,Bananas,0.6153846153846154,0.4131455399061033,0.014024734497070312,18.277781 -4982,Binary classification,Naive Bayes,Bananas,0.6163420999799237,0.41684467500762895,0.014024734497070312,19.058798 -5088,Binary classification,Naive Bayes,Bananas,0.6150973068606251,0.41412327947336924,0.014024734497070312,19.855913 -5194,Binary classification,Naive Bayes,Bananas,0.6146735990756788,0.4133685136323659,0.014024734497070312,20.669466 -5300,Binary classification,Naive Bayes,Bananas,0.6152104170598226,0.4139120436907157,0.014024734497070312,21.499334 -906,Binary classification,Naive Bayes,Elec2,0.8187845303867404,0.8284518828451883,0.05103778839111328,0.17926 -1812,Binary classification,Naive Bayes,Elec2,0.8023191606847045,0.7475317348377998,0.05103778839111328,0.525852 -2718,Binary classification,Naive Bayes,Elec2,0.784688995215311,0.706177800100452,0.05103778839111328,1.0396 -3624,Binary classification,Naive Bayes,Elec2,0.8032017664918576,0.7356321839080461,0.05103778839111328,1.7208420000000002 -4530,Binary classification,Naive Bayes,Elec2,0.7979686465003312,0.7073872721458268,0.05103778839111328,2.5693340000000005 -5436,Binary classification,Naive Bayes,Elec2,0.7937442502299908,0.6972724817715366,0.05103778839111328,3.5852350000000004 -6342,Binary classification,Naive Bayes,Elec2,0.7982967986122063,0.7065840789171829,0.05103778839111328,4.768595 -7248,Binary classification,Naive Bayes,Elec2,0.790396025941769,0.6875128574367414,0.05103778839111328,6.119432000000001 -8154,Binary classification,Naive Bayes,Elec2,0.7841285416411137,0.6888260254596887,0.05103778839111328,7.6376610000000005 -9060,Binary classification,Naive Bayes,Elec2,0.7897118887294403,0.7086710506193606,0.05103778839111328,9.323211 -9966,Binary classification,Naive Bayes,Elec2,0.793176116407426,0.7240594457089301,0.05103778839111328,11.17581 -10872,Binary classification,Naive Bayes,Elec2,0.7960629196946003,0.7361656551231703,0.05103778839111328,13.198889000000001 -11778,Binary classification,Naive Bayes,Elec2,0.792137216608644,0.7295027624309391,0.05103778839111328,15.388480000000001 -12684,Binary classification,Naive Bayes,Elec2,0.7820704880548766,0.7260111022997621,0.05103778839111328,17.743988 -13590,Binary classification,Naive Bayes,Elec2,0.7858562072264331,0.7383564107174968,0.05103778839111328,20.265390000000004 -14496,Binary classification,Naive Bayes,Elec2,0.7866850638151086,0.7435727317963178,0.05103778839111328,22.952880000000004 -15402,Binary classification,Naive Bayes,Elec2,0.785728199467567,0.738593155893536,0.05103778839111328,25.806911000000003 -16308,Binary classification,Naive Bayes,Elec2,0.7806463481940271,0.7274666666666666,0.05103778839111328,28.828021000000003 -17214,Binary classification,Naive Bayes,Elec2,0.7788880497298554,0.7181158346911569,0.05103778839111328,32.016234000000004 -18120,Binary classification,Naive Bayes,Elec2,0.7728903361112645,0.7138983522213725,0.05103778839111328,35.376132000000005 -19026,Binary classification,Naive Bayes,Elec2,0.7701445466491459,0.7094931242941608,0.05103778839111328,38.860646 -19932,Binary classification,Naive Bayes,Elec2,0.7628317696051378,0.702236220472441,0.05103778839111328,42.445598000000004 -20838,Binary classification,Naive Bayes,Elec2,0.7537553390603254,0.6903626817934946,0.05103778839111328,46.130991 -21744,Binary classification,Naive Bayes,Elec2,0.7508163546888654,0.6836389115964032,0.05103778839111328,49.916905 -22650,Binary classification,Naive Bayes,Elec2,0.7509823833281822,0.6798001589644601,0.05103778839111328,53.803318 -23556,Binary classification,Naive Bayes,Elec2,0.7457015495648482,0.668217569513681,0.05103778839111328,57.790136999999994 -24462,Binary classification,Naive Bayes,Elec2,0.7466170638976329,0.665839982747466,0.05103778839111328,61.87940199999999 -25368,Binary classification,Naive Bayes,Elec2,0.7447865336854969,0.6611180904522613,0.05103778839111328,66.06947 -26274,Binary classification,Naive Bayes,Elec2,0.7448711605069843,0.6581322996888865,0.05103778839111328,70.360011 -27180,Binary classification,Naive Bayes,Elec2,0.741123661650539,0.650402464473815,0.05103778839111328,74.751133 -28086,Binary classification,Naive Bayes,Elec2,0.7390065871461634,0.6440019426906265,0.05103778839111328,79.242684 -28992,Binary classification,Naive Bayes,Elec2,0.7358145631402849,0.6343280019097637,0.05103778839111328,83.83464099999999 -29898,Binary classification,Naive Bayes,Elec2,0.7320466936481921,0.6243023964732918,0.05103778839111328,88.52693 -30804,Binary classification,Naive Bayes,Elec2,0.7297990455475116,0.6158319870759289,0.05103778839111328,93.31947199999999 -31710,Binary classification,Naive Bayes,Elec2,0.7256930209088902,0.6059617649723658,0.05103778839111328,98.21253099999998 -32616,Binary classification,Naive Bayes,Elec2,0.7215391690939752,0.596427301813011,0.05103778839111328,103.20618199999998 -33522,Binary classification,Naive Bayes,Elec2,0.7176695205990274,0.5867248908296943,0.05103778839111328,108.30076099999998 -34428,Binary classification,Naive Bayes,Elec2,0.7142359194818021,0.5779493779493778,0.05103778839111328,113.49672599999998 -35334,Binary classification,Naive Bayes,Elec2,0.7138369229898395,0.5724554949469323,0.05103778839111328,118.79330799999998 -36240,Binary classification,Naive Bayes,Elec2,0.7174866856149452,0.5752924583091347,0.05103778839111328,124.19030599999998 -37146,Binary classification,Naive Bayes,Elec2,0.7169740207295733,0.5716148486206756,0.05103778839111328,129.68778099999997 -38052,Binary classification,Naive Bayes,Elec2,0.7183516858952459,0.573859795618116,0.05103778839111328,135.28579999999997 -38958,Binary classification,Naive Bayes,Elec2,0.7206407064198989,0.5799529121154812,0.05103778839111328,140.98460499999996 -39864,Binary classification,Naive Bayes,Elec2,0.7217720693374808,0.5866964784795975,0.05103778839111328,146.78445599999995 -40770,Binary classification,Naive Bayes,Elec2,0.7228776766660944,0.5923065819861432,0.05103778839111328,152.68634499999996 -41676,Binary classification,Naive Bayes,Elec2,0.724127174565087,0.5973170817134251,0.05103778839111328,158.68998499999995 -42582,Binary classification,Naive Bayes,Elec2,0.7260280406754186,0.6013259517462921,0.05103778839111328,164.79474299999995 -43488,Binary classification,Naive Bayes,Elec2,0.7277117299422816,0.6045222270465248,0.05103778839111328,170.99996699999994 -44394,Binary classification,Naive Bayes,Elec2,0.7273894532921857,0.6015933631814591,0.05103778839111328,177.30586899999994 -45300,Binary classification,Naive Bayes,Elec2,0.7287136581381487,0.6038234630387828,0.05103778839111328,183.71242299999994 -25,Binary classification,Naive Bayes,Phishing,0.5833333333333334,0.7058823529411764,0.05722999572753906,0.01864 -50,Binary classification,Naive Bayes,Phishing,0.7346938775510204,0.7636363636363637,0.05722999572753906,0.044672 -75,Binary classification,Naive Bayes,Phishing,0.7837837837837838,0.8048780487804877,0.05722999572753906,0.0769 -100,Binary classification,Naive Bayes,Phishing,0.8080808080808081,0.819047619047619,0.05722999572753906,0.11524799999999999 -125,Binary classification,Naive Bayes,Phishing,0.8145161290322581,0.8217054263565893,0.05722999572753906,0.15967399999999998 -150,Binary classification,Naive Bayes,Phishing,0.8187919463087249,0.830188679245283,0.05722999572753906,0.21009599999999998 -175,Binary classification,Naive Bayes,Phishing,0.8333333333333334,0.8323699421965318,0.05722999572753906,0.266544 -200,Binary classification,Naive Bayes,Phishing,0.8341708542713567,0.83248730964467,0.05722999572753906,0.329068 -225,Binary classification,Naive Bayes,Phishing,0.8303571428571429,0.8240740740740741,0.05722999572753906,0.397573 -250,Binary classification,Naive Bayes,Phishing,0.8313253012048193,0.825,0.05722999572753906,0.472283 -275,Binary classification,Naive Bayes,Phishing,0.8321167883211679,0.8244274809160306,0.05722999572753906,0.553059 -300,Binary classification,Naive Bayes,Phishing,0.8394648829431438,0.8285714285714285,0.05722999572753906,0.639906 -325,Binary classification,Naive Bayes,Phishing,0.845679012345679,0.8299319727891157,0.05722999572753906,0.732828 -350,Binary classification,Naive Bayes,Phishing,0.8510028653295129,0.8322580645161292,0.05722999572753906,0.831756 -375,Binary classification,Naive Bayes,Phishing,0.8529411764705882,0.8318042813455658,0.05722999572753906,0.937046 -400,Binary classification,Naive Bayes,Phishing,0.8546365914786967,0.8313953488372093,0.05722999572753906,1.048453 -425,Binary classification,Naive Bayes,Phishing,0.8561320754716981,0.8291316526610645,0.05722999572753906,1.1659410000000001 -450,Binary classification,Naive Bayes,Phishing,0.8596881959910914,0.8310991957104559,0.05722999572753906,1.289511 -475,Binary classification,Naive Bayes,Phishing,0.8565400843881856,0.8291457286432161,0.05722999572753906,1.419108 -500,Binary classification,Naive Bayes,Phishing,0.8577154308617234,0.8337236533957845,0.05722999572753906,1.554885 -525,Binary classification,Naive Bayes,Phishing,0.8587786259541985,0.8310502283105022,0.05722999572753906,1.6967670000000001 -550,Binary classification,Naive Bayes,Phishing,0.8579234972677595,0.8311688311688311,0.05722999572753906,1.8446930000000001 -575,Binary classification,Naive Bayes,Phishing,0.8606271777003485,0.8340248962655602,0.05722999572753906,1.998693 -600,Binary classification,Naive Bayes,Phishing,0.8647746243739566,0.8363636363636363,0.05722999572753906,2.1587810000000003 -625,Binary classification,Naive Bayes,Phishing,0.8669871794871795,0.8356435643564357,0.05722999572753906,2.3249540000000004 -650,Binary classification,Naive Bayes,Phishing,0.8705701078582434,0.8426966292134833,0.05722999572753906,2.4971910000000004 -675,Binary classification,Naive Bayes,Phishing,0.870919881305638,0.8465608465608465,0.05722999572753906,2.675498 -700,Binary classification,Naive Bayes,Phishing,0.8755364806866953,0.8502581755593803,0.05722999572753906,2.85988 -725,Binary classification,Naive Bayes,Phishing,0.8784530386740331,0.8562091503267973,0.05722999572753906,3.050473 -750,Binary classification,Naive Bayes,Phishing,0.8798397863818425,0.8584905660377359,0.05722999572753906,3.247084 -775,Binary classification,Naive Bayes,Phishing,0.8798449612403101,0.8580152671755725,0.05722999572753906,3.4497720000000003 -800,Binary classification,Naive Bayes,Phishing,0.8798498122653317,0.8596491228070174,0.05722999572753906,3.6587890000000005 -825,Binary classification,Naive Bayes,Phishing,0.8798543689320388,0.860759493670886,0.05722999572753906,3.8738800000000007 -850,Binary classification,Naive Bayes,Phishing,0.8798586572438163,0.8602739726027396,0.05722999572753906,4.094979 -875,Binary classification,Naive Bayes,Phishing,0.8832951945080092,0.8636363636363635,0.05722999572753906,4.322174 -900,Binary classification,Naive Bayes,Phishing,0.8809788654060067,0.8608582574772432,0.05722999572753906,4.555426000000001 -925,Binary classification,Naive Bayes,Phishing,0.8820346320346321,0.8635794743429286,0.05722999572753906,4.7947690000000005 -950,Binary classification,Naive Bayes,Phishing,0.8819810326659642,0.8650602409638554,0.05722999572753906,5.040106000000001 -975,Binary classification,Naive Bayes,Phishing,0.8829568788501027,0.8661971830985915,0.05722999572753906,5.291615 -1000,Binary classification,Naive Bayes,Phishing,0.8808808808808809,0.8643101482326111,0.05722999572753906,5.549186000000001 -1025,Binary classification,Naive Bayes,Phishing,0.880859375,0.8647450110864746,0.05722999572753906,5.812868000000001 -1050,Binary classification,Naive Bayes,Phishing,0.882745471877979,0.8673139158576052,0.05722999572753906,6.082483000000001 -1075,Binary classification,Naive Bayes,Phishing,0.8817504655493482,0.8672936259143157,0.05722999572753906,6.358049000000001 -1100,Binary classification,Naive Bayes,Phishing,0.8835304822565969,0.8693877551020409,0.05722999572753906,6.639582000000001 -1125,Binary classification,Naive Bayes,Phishing,0.8861209964412812,0.8735177865612648,0.05722999572753906,6.927116000000001 -1150,Binary classification,Naive Bayes,Phishing,0.8859878154917319,0.8731848983543079,0.05722999572753906,7.220572000000001 -1175,Binary classification,Naive Bayes,Phishing,0.8850085178875639,0.8717948717948718,0.05722999572753906,7.520004000000001 -1200,Binary classification,Naive Bayes,Phishing,0.8865721434528774,0.8731343283582089,0.05722999572753906,7.825445000000001 -1225,Binary classification,Naive Bayes,Phishing,0.886437908496732,0.8728270814272644,0.05722999572753906,8.137127000000001 -1250,Binary classification,Naive Bayes,Phishing,0.8847077662129704,0.8714285714285714,0.05722999572753906,8.454914 -1903,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,0.257042 -3806,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,0.764431 -5709,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,1.522309 -7612,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,2.530537 -9515,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,3.789363 -11418,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,5.298413 -13321,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,7.057599 -15224,Binary classification,Naive Bayes,SMTP,0.9997372397030808,0.7777777777777778,0.020140647888183594,9.078054999999999 -17127,Binary classification,Naive Bayes,SMTP,0.9997664369963798,0.8181818181818181,0.020140647888183594,11.376218 -19030,Binary classification,Naive Bayes,SMTP,0.9997897945241474,0.8181818181818181,0.020140647888183594,13.952029 -20933,Binary classification,Naive Bayes,SMTP,0.9998089050257978,0.8181818181818181,0.020140647888183594,16.806125 -22836,Binary classification,Naive Bayes,SMTP,0.9998248303043573,0.8181818181818181,0.020140647888183594,19.939484 -24739,Binary classification,Naive Bayes,SMTP,0.9998383054410219,0.8181818181818181,0.020140647888183594,23.347474 -26642,Binary classification,Naive Bayes,SMTP,0.9998498554859052,0.8333333333333333,0.020140647888183594,26.939926 -28545,Binary classification,Naive Bayes,SMTP,0.999859865470852,0.8333333333333333,0.020140647888183594,30.696722 -30448,Binary classification,Naive Bayes,SMTP,0.9998686241665845,0.8333333333333333,0.020140647888183594,34.617556 -32351,Binary classification,Naive Bayes,SMTP,0.9998763523956723,0.8333333333333333,0.020140647888183594,38.704559 -34254,Binary classification,Naive Bayes,SMTP,0.9998832219075702,0.8333333333333333,0.020140647888183594,42.956191000000004 -36157,Binary classification,Naive Bayes,SMTP,0.9998893682929527,0.8333333333333333,0.020140647888183594,47.37231800000001 -38060,Binary classification,Naive Bayes,SMTP,0.9998949000236474,0.8333333333333333,0.020140647888183594,51.95229900000001 -39963,Binary classification,Naive Bayes,SMTP,0.9998999049096642,0.8333333333333333,0.020140647888183594,56.69619700000001 -41866,Binary classification,Naive Bayes,SMTP,0.999904454795175,0.8333333333333333,0.020140647888183594,61.60400300000001 -43769,Binary classification,Naive Bayes,SMTP,0.9999086090294279,0.8333333333333333,0.020140647888183594,66.676032 -45672,Binary classification,Naive Bayes,SMTP,0.9999124170699131,0.8333333333333333,0.020140647888183594,71.913656 -47575,Binary classification,Naive Bayes,SMTP,0.9999159204607558,0.8333333333333333,0.020140647888183594,77.315763 -49478,Binary classification,Naive Bayes,SMTP,0.9999191543545486,0.8333333333333333,0.020140647888183594,82.88181300000001 -51381,Binary classification,Naive Bayes,SMTP,0.9999026858699883,0.8275862068965517,0.020140647888183594,88.61249600000001 -53284,Binary classification,Naive Bayes,SMTP,0.9999061614398589,0.8275862068965517,0.020140647888183594,94.50806600000001 -55187,Binary classification,Naive Bayes,SMTP,0.9998912767730946,0.7999999999999999,0.020140647888183594,100.56946700000002 -57090,Binary classification,Naive Bayes,SMTP,0.9993869221741492,0.4444444444444444,0.020140647888183594,106.79522700000001 -58993,Binary classification,Naive Bayes,SMTP,0.9988473013289938,0.29166666666666663,0.020140647888183594,113.18503100000001 -60896,Binary classification,Naive Bayes,SMTP,0.9986369981115034,0.2522522522522523,0.020140647888183594,119.73505000000002 -62799,Binary classification,Naive Bayes,SMTP,0.9979139463040224,0.1761006289308176,0.020140647888183594,126.44640100000001 -64702,Binary classification,Naive Bayes,SMTP,0.9979443903494536,0.17391304347826086,0.020140647888183594,133.31736600000002 -66605,Binary classification,Naive Bayes,SMTP,0.9977478830100295,0.15730337078651685,0.020140647888183594,140.34740000000002 -68508,Binary classification,Naive Bayes,SMTP,0.9967302611411972,0.12500000000000003,0.020140647888183594,147.535714 -70411,Binary classification,Naive Bayes,SMTP,0.9964777730436017,0.1142857142857143,0.020140647888183594,154.879523 -72314,Binary classification,Naive Bayes,SMTP,0.9964045192427364,0.10958904109589042,0.020140647888183594,162.37251700000002 -74217,Binary classification,Naive Bayes,SMTP,0.9958230031260106,0.0935672514619883,0.020140647888183594,170.01463900000002 -76120,Binary classification,Naive Bayes,SMTP,0.9956515456062218,0.08815426997245178,0.020140647888183594,177.804854 -78023,Binary classification,Naive Bayes,SMTP,0.9951936633257287,0.07862407862407862,0.020140647888183594,185.743198 -79926,Binary classification,Naive Bayes,SMTP,0.9946700031279324,0.06986899563318777,0.020140647888183594,193.82952600000002 -81829,Binary classification,Naive Bayes,SMTP,0.9945862052109302,0.06736842105263158,0.020140647888183594,202.05744900000002 -83732,Binary classification,Naive Bayes,SMTP,0.9945539883675102,0.06557377049180328,0.020140647888183594,210.44477400000002 -85635,Binary classification,Naive Bayes,SMTP,0.9939860335847911,0.05850091407678244,0.020140647888183594,218.97044600000004 -87538,Binary classification,Naive Bayes,SMTP,0.9938540274398254,0.05614035087719298,0.020140647888183594,227.63481900000005 -89441,Binary classification,Naive Bayes,SMTP,0.9938618067978533,0.05507745266781411,0.020140647888183594,236.43819800000006 -91344,Binary classification,Naive Bayes,SMTP,0.9939677917300723,0.0548885077186964,0.020140647888183594,245.38462600000005 -93247,Binary classification,Naive Bayes,SMTP,0.993543958990198,0.050473186119873815,0.020140647888183594,254.47095700000006 -95150,Binary classification,Naive Bayes,SMTP,0.993483904192372,0.049079754601226995,0.020140647888183594,263.6963870000001 -106,Binary classification,Hoeffding Tree,Bananas,0.49523809523809526,0.208955223880597,0.01929473876953125,0.019877 -212,Binary classification,Hoeffding Tree,Bananas,0.5213270142180095,0.3129251700680272,0.019317626953125,0.052088999999999996 -318,Binary classification,Hoeffding Tree,Bananas,0.5299684542586751,0.40637450199203184,0.019317626953125,0.095483 -424,Binary classification,Hoeffding Tree,Bananas,0.5437352245862884,0.42388059701492536,0.019317626953125,0.150343 -530,Binary classification,Hoeffding Tree,Bananas,0.553875236294896,0.4099999999999999,0.019317626953125,0.216684 -636,Binary classification,Hoeffding Tree,Bananas,0.5590551181102362,0.4017094017094017,0.019317626953125,0.295132 -742,Binary classification,Hoeffding Tree,Bananas,0.5762483130904184,0.3984674329501916,0.019317626953125,0.38501799999999997 -848,Binary classification,Hoeffding Tree,Bananas,0.5867768595041323,0.40476190476190477,0.019317626953125,0.486959 -954,Binary classification,Hoeffding Tree,Bananas,0.5918153200419727,0.3987635239567234,0.019317626953125,0.601178 -1060,Binary classification,Hoeffding Tree,Bananas,0.6015108593012276,0.39714285714285713,0.019317626953125,0.727681 -1166,Binary classification,Hoeffding Tree,Bananas,0.6,0.38522427440633245,0.019317626953125,0.865767 -1272,Binary classification,Hoeffding Tree,Bananas,0.6073957513768686,0.3966142684401451,0.019317626953125,1.0159 -1378,Binary classification,Hoeffding Tree,Bananas,0.6085693536673928,0.384,0.019317626953125,1.177562 -1484,Binary classification,Hoeffding Tree,Bananas,0.6089008766014835,0.3790149892933619,0.019317626953125,1.351249 -1590,Binary classification,Hoeffding Tree,Bananas,0.6085588420390182,0.37424547283702214,0.019317626953125,1.5365639999999998 -1696,Binary classification,Hoeffding Tree,Bananas,0.6094395280235988,0.37072243346007605,0.019317626953125,1.73385 -1802,Binary classification,Hoeffding Tree,Bananas,0.6102165463631316,0.37544483985765126,0.019317626953125,1.943126 -1908,Binary classification,Hoeffding Tree,Bananas,0.610907184058731,0.3816666666666667,0.019317626953125,2.16423 -2014,Binary classification,Hoeffding Tree,Bananas,0.6060606060606061,0.3799843627834245,0.019317626953125,2.39723 -2120,Binary classification,Hoeffding Tree,Bananas,0.6045304388862671,0.38382352941176473,0.019317626953125,2.6418049999999997 -2226,Binary classification,Hoeffding Tree,Bananas,0.6053932584269663,0.38687150837988826,0.019317626953125,2.8985119999999998 -2332,Binary classification,Hoeffding Tree,Bananas,0.6061776061776062,0.38881491344873503,0.019317626953125,3.1667389999999997 -2438,Binary classification,Hoeffding Tree,Bananas,0.606893721789085,0.388250319284802,0.019317626953125,3.4470539999999996 -2544,Binary classification,Hoeffding Tree,Bananas,0.608336610302792,0.39636363636363636,0.019317626953125,3.7390399999999997 -2650,Binary classification,Hoeffding Tree,Bananas,0.6070215175537939,0.3944153577661431,0.019317626953125,4.043011 -2756,Binary classification,Hoeffding Tree,Bananas,0.6047186932849364,0.3892316320807628,0.019317626953125,4.3585199999999995 -2862,Binary classification,Hoeffding Tree,Bananas,0.6057322614470465,0.3922413793103448,0.019317626953125,4.686463999999999 -2968,Binary classification,Hoeffding Tree,Bananas,0.6056622851365016,0.3899895724713243,0.019317626953125,5.0259599999999995 -3074,Binary classification,Hoeffding Tree,Bananas,0.6036446469248291,0.3903903903903904,0.019317626953125,5.377489 -3180,Binary classification,Hoeffding Tree,Bananas,0.6045926391947153,0.3924601256645723,0.019317626953125,5.740578999999999 -3286,Binary classification,Hoeffding Tree,Bananas,0.6039573820395738,0.39006094702297234,0.019317626953125,6.115373999999999 -3392,Binary classification,Hoeffding Tree,Bananas,0.6024771453848422,0.39169675090252704,0.019317626953125,6.501596999999999 -3498,Binary classification,Hoeffding Tree,Bananas,0.6030883614526737,0.39335664335664333,0.035147666931152344,6.903521 -3604,Binary classification,Hoeffding Tree,Bananas,0.6069941715237303,0.40353833192923344,0.035147666931152344,7.317575 -3710,Binary classification,Hoeffding Tree,Bananas,0.6079805877595039,0.40798045602605865,0.035147666931152344,7.744409999999999 -3816,Binary classification,Hoeffding Tree,Bananas,0.6107470511140236,0.4146629877808436,0.035147666931152344,8.183644999999999 -3922,Binary classification,Hoeffding Tree,Bananas,0.6123437898495282,0.4180704441041348,0.04465007781982422,8.636627999999998 -4028,Binary classification,Hoeffding Tree,Bananas,0.6143531164638689,0.4246017043349389,0.05081462860107422,9.102371999999999 -4134,Binary classification,Hoeffding Tree,Bananas,0.617227195741592,0.43216080402010054,0.05081462860107422,9.580540999999998 -4240,Binary classification,Hoeffding Tree,Bananas,0.6218447747110167,0.4439819632327437,0.05081462860107422,10.071727 -4346,Binary classification,Hoeffding Tree,Bananas,0.6239355581127733,0.45130960376091334,0.05081462860107422,10.575598999999999 -4452,Binary classification,Hoeffding Tree,Bananas,0.6259267580319029,0.45676998368678623,0.05081462860107422,11.092444999999998 -4558,Binary classification,Hoeffding Tree,Bananas,0.6276058810621022,0.46382306477093216,0.05081462860107422,11.621768999999999 -4664,Binary classification,Hoeffding Tree,Bananas,0.6283508470941453,0.4695439240893787,0.05081462860107422,12.163799 -4770,Binary classification,Hoeffding Tree,Bananas,0.6288530090165653,0.47164179104477605,0.06022167205810547,12.720220999999999 -4876,Binary classification,Hoeffding Tree,Bananas,0.6311794871794871,0.47580174927113705,0.06026744842529297,13.289238 -4982,Binary classification,Hoeffding Tree,Bananas,0.6336077092953222,0.484026010743568,0.06026744842529297,13.87141 -5088,Binary classification,Hoeffding Tree,Bananas,0.6361313151169649,0.49050371593724196,0.06026744842529297,14.466033 -5194,Binary classification,Hoeffding Tree,Bananas,0.6383593298671288,0.495703544575725,0.06026744842529297,15.073179 -5300,Binary classification,Hoeffding Tree,Bananas,0.6421966408756369,0.5034049240440022,0.06026744842529297,15.693544 -906,Binary classification,Hoeffding Tree,Elec2,0.8530386740331491,0.8513966480446927,0.17850685119628906,0.136823 -1812,Binary classification,Hoeffding Tree,Elec2,0.8663721700717836,0.8393094289508632,0.21218299865722656,0.37234100000000003 -2718,Binary classification,Hoeffding Tree,Elec2,0.8369525211630475,0.810926163038839,0.2367534637451172,0.715196 -3624,Binary classification,Hoeffding Tree,Elec2,0.8459839911675407,0.8219527760051053,0.2367534637451172,1.157087 -4530,Binary classification,Hoeffding Tree,Elec2,0.8511812762199161,0.8165487207403377,0.23670005798339844,1.692418 -5436,Binary classification,Hoeffding Tree,Elec2,0.8404783808647655,0.8027303754266211,0.23670005798339844,2.325246 -6342,Binary classification,Hoeffding Tree,Elec2,0.8334647531935025,0.7973128598848368,0.23670005798339844,3.058544 -7248,Binary classification,Hoeffding Tree,Elec2,0.8330343590451221,0.7918100481761873,0.23670005798339844,3.886934 -8154,Binary classification,Hoeffding Tree,Elec2,0.8344167790997179,0.8018203170874927,0.23670005798339844,4.816345 -9060,Binary classification,Hoeffding Tree,Elec2,0.8403797328623468,0.8133711925658234,0.30373573303222656,5.843331 -9966,Binary classification,Hoeffding Tree,Elec2,0.8398394380331159,0.8174748398902103,0.3038501739501953,6.967896 -10872,Binary classification,Hoeffding Tree,Elec2,0.840493054916751,0.8203108808290155,0.3038501739501953,8.190139 -11778,Binary classification,Hoeffding Tree,Elec2,0.8404517279442982,0.8186818488854579,0.38779258728027344,9.510034000000001 -12684,Binary classification,Hoeffding Tree,Elec2,0.8397066939998423,0.8187572434697333,0.38779258728027344,10.932755 -13590,Binary classification,Hoeffding Tree,Elec2,0.8422253293104717,0.8231023102310231,0.38779258728027344,12.458737 -14496,Binary classification,Hoeffding Tree,Elec2,0.8440841669541221,0.8261270964763809,0.3890361785888672,14.085847999999999 -15402,Binary classification,Hoeffding Tree,Elec2,0.8445555483410169,0.8248207229620957,0.38906288146972656,15.806373999999998 -16308,Binary classification,Hoeffding Tree,Elec2,0.8382289814190225,0.8146430578976953,0.4148235321044922,17.623171 -17214,Binary classification,Hoeffding Tree,Elec2,0.8344855632370882,0.8052764677739047,0.4148235321044922,19.541045999999998 -18120,Binary classification,Hoeffding Tree,Elec2,0.8333793255698438,0.8031044153133764,0.4155101776123047,21.569664999999997 -19026,Binary classification,Hoeffding Tree,Elec2,0.8341655716162943,0.8009086893418313,0.41680335998535156,23.708108999999997 -19932,Binary classification,Hoeffding Tree,Elec2,0.8308163162912047,0.7980112615310889,0.5072460174560547,25.964372999999995 -20838,Binary classification,Hoeffding Tree,Elec2,0.8293900273551855,0.7961699443839229,0.5655117034912109,28.344830999999996 -21744,Binary classification,Hoeffding Tree,Elec2,0.8298302902083429,0.7941012799109627,0.6239414215087891,30.844750999999995 -22650,Binary classification,Hoeffding Tree,Elec2,0.8288224645679722,0.7904437597967678,0.6005496978759766,33.457668999999996 -23556,Binary classification,Hoeffding Tree,Elec2,0.8244109530885162,0.7830920914621354,0.6264286041259766,36.193138999999995 -24462,Binary classification,Hoeffding Tree,Elec2,0.8225747107640734,0.7806973218797373,0.6265659332275391,39.050647999999995 -25368,Binary classification,Hoeffding Tree,Elec2,0.8180707218039185,0.7764375333042677,0.6265659332275391,42.027952 -26274,Binary classification,Hoeffding Tree,Elec2,0.8183306055646481,0.7761572011443043,0.6265659332275391,45.127396 -27180,Binary classification,Hoeffding Tree,Elec2,0.8178005077449502,0.7755416553349651,0.6265659332275391,48.341336999999996 -28086,Binary classification,Hoeffding Tree,Elec2,0.8154174826419797,0.771830985915493,0.6266345977783203,51.670154999999994 -28992,Binary classification,Hoeffding Tree,Elec2,0.81342485598979,0.7672447179310641,0.6266345977783203,55.116457 -29898,Binary classification,Hoeffding Tree,Elec2,0.8088771448640332,0.763747622591582,0.6848773956298828,58.686243999999995 -30804,Binary classification,Hoeffding Tree,Elec2,0.8062526377300913,0.7606673083092718,0.6848773956298828,62.38209 -31710,Binary classification,Hoeffding Tree,Elec2,0.8052603361821565,0.759212322090076,0.7534084320068359,66.209893 -32616,Binary classification,Hoeffding Tree,Elec2,0.8025755020695998,0.7564951026736755,0.7792224884033203,70.16157899999999 -33522,Binary classification,Hoeffding Tree,Elec2,0.8007517675487008,0.7563742476746306,0.8375339508056641,74.24016799999998 -34428,Binary classification,Hoeffding Tree,Elec2,0.7980945188369594,0.7537464130088213,0.8621463775634766,78.43750399999999 -35334,Binary classification,Hoeffding Tree,Elec2,0.797186765912886,0.7523842432619212,0.9281406402587891,82.758889 -36240,Binary classification,Hoeffding Tree,Elec2,0.7972350230414746,0.7516392888528357,0.9552211761474609,87.200985 -37146,Binary classification,Hoeffding Tree,Elec2,0.7969847893390766,0.7511960143851661,1.0704402923583984,91.76110200000001 -38052,Binary classification,Hoeffding Tree,Elec2,0.7938293343144727,0.748388338304628,1.0715465545654297,96.434966 -38958,Binary classification,Hoeffding Tree,Elec2,0.7918730908437508,0.7483706784184718,1.0715465545654297,101.21983300000001 -39864,Binary classification,Hoeffding Tree,Elec2,0.7913854953214761,0.7506596306068601,1.1051769256591797,106.11888400000001 -40770,Binary classification,Hoeffding Tree,Elec2,0.7935686428413746,0.7554199360650974,1.1051769256591797,111.1286 -41676,Binary classification,Hoeffding Tree,Elec2,0.7953209358128375,0.7590667721161451,1.129629135131836,116.244938 -42582,Binary classification,Hoeffding Tree,Elec2,0.7966933608886593,0.7607572198424761,1.129629135131836,121.471609 -43488,Binary classification,Hoeffding Tree,Elec2,0.7973647296893325,0.7615800865800865,1.129629135131836,126.806064 -44394,Binary classification,Hoeffding Tree,Elec2,0.7960714527065078,0.7581933278132429,1.129629135131836,132.253655 -45300,Binary classification,Hoeffding Tree,Elec2,0.7969933111106206,0.7591535278403435,1.1878719329833984,137.81786 -25,Binary classification,Hoeffding Tree,Phishing,0.5833333333333334,0.6428571428571429,0.0693511962890625,0.016417 -50,Binary classification,Hoeffding Tree,Phishing,0.7346938775510204,0.7346938775510203,0.0693511962890625,0.037579 -75,Binary classification,Hoeffding Tree,Phishing,0.7837837837837838,0.7894736842105262,0.0693511962890625,0.062555 -100,Binary classification,Hoeffding Tree,Phishing,0.8080808080808081,0.8080808080808081,0.0693511962890625,0.093468 -125,Binary classification,Hoeffding Tree,Phishing,0.8145161290322581,0.8130081300813008,0.0693511962890625,0.128067 -150,Binary classification,Hoeffding Tree,Phishing,0.8187919463087249,0.8235294117647058,0.0693511962890625,0.166217 -175,Binary classification,Hoeffding Tree,Phishing,0.8333333333333334,0.8263473053892215,0.0693511962890625,0.207895 -200,Binary classification,Hoeffding Tree,Phishing,0.8341708542713567,0.8272251308900525,0.06937408447265625,0.254237 -225,Binary classification,Hoeffding Tree,Phishing,0.8303571428571429,0.8190476190476189,0.06937408447265625,0.304152 -250,Binary classification,Hoeffding Tree,Phishing,0.8313253012048193,0.8205128205128206,0.06937408447265625,0.35768099999999997 -275,Binary classification,Hoeffding Tree,Phishing,0.8321167883211679,0.8203125000000001,0.06937408447265625,0.41473299999999996 -300,Binary classification,Hoeffding Tree,Phishing,0.8394648829431438,0.8248175182481753,0.06937408447265625,0.47548999999999997 -325,Binary classification,Hoeffding Tree,Phishing,0.845679012345679,0.8263888888888888,0.06937408447265625,0.539997 -350,Binary classification,Hoeffding Tree,Phishing,0.8510028653295129,0.8289473684210527,0.06937408447265625,0.608045 -375,Binary classification,Hoeffding Tree,Phishing,0.8529411764705882,0.8286604361370716,0.06937408447265625,0.679831 -400,Binary classification,Hoeffding Tree,Phishing,0.8546365914786967,0.8284023668639053,0.06937408447265625,0.75623 -425,Binary classification,Hoeffding Tree,Phishing,0.8561320754716981,0.8262108262108262,0.06937408447265625,0.836328 -450,Binary classification,Hoeffding Tree,Phishing,0.8596881959910914,0.8283378746594006,0.06937408447265625,0.920157 -475,Binary classification,Hoeffding Tree,Phishing,0.8565400843881856,0.826530612244898,0.06937408447265625,1.007627 -500,Binary classification,Hoeffding Tree,Phishing,0.8577154308617234,0.8313539192399049,0.06937408447265625,1.0988580000000001 -525,Binary classification,Hoeffding Tree,Phishing,0.8587786259541985,0.8287037037037036,0.06937408447265625,1.193786 -550,Binary classification,Hoeffding Tree,Phishing,0.8579234972677595,0.8289473684210527,0.06937408447265625,1.292398 -575,Binary classification,Hoeffding Tree,Phishing,0.8606271777003485,0.8319327731092437,0.06937408447265625,1.395047 -600,Binary classification,Hoeffding Tree,Phishing,0.8647746243739566,0.834355828220859,0.06937408447265625,1.502529 -625,Binary classification,Hoeffding Tree,Phishing,0.8669871794871795,0.8336673346693387,0.06937408447265625,1.61388 -650,Binary classification,Hoeffding Tree,Phishing,0.8705701078582434,0.8409090909090909,0.06937408447265625,1.728993 -675,Binary classification,Hoeffding Tree,Phishing,0.870919881305638,0.8449197860962566,0.06937408447265625,1.847898 -700,Binary classification,Hoeffding Tree,Phishing,0.8755364806866953,0.8486956521739131,0.06937408447265625,1.970613 -725,Binary classification,Hoeffding Tree,Phishing,0.8784530386740331,0.8547854785478548,0.06937408447265625,2.097195 -750,Binary classification,Hoeffding Tree,Phishing,0.8798397863818425,0.8571428571428571,0.06937408447265625,2.2275650000000002 -775,Binary classification,Hoeffding Tree,Phishing,0.8798449612403101,0.8567026194144837,0.06937408447265625,2.3617150000000002 -800,Binary classification,Hoeffding Tree,Phishing,0.8798498122653317,0.8584070796460177,0.0058956146240234375,2.500804 -825,Binary classification,Hoeffding Tree,Phishing,0.8786407766990292,0.8575498575498576,0.13469600677490234,2.646368 -850,Binary classification,Hoeffding Tree,Phishing,0.8798586572438163,0.8579387186629527,0.1347188949584961,2.795782 -875,Binary classification,Hoeffding Tree,Phishing,0.8810068649885584,0.8583106267029972,0.1347188949584961,2.94896 -900,Binary classification,Hoeffding Tree,Phishing,0.882091212458287,0.8590425531914893,0.13474178314208984,3.105941 -925,Binary classification,Hoeffding Tree,Phishing,0.8831168831168831,0.8611825192802056,0.13474178314208984,3.266898 -950,Binary classification,Hoeffding Tree,Phishing,0.880927291886196,0.8599752168525404,0.13474178314208984,3.4318049999999998 -975,Binary classification,Hoeffding Tree,Phishing,0.8819301848049281,0.8609431680773881,0.13474178314208984,3.6005749999999996 -1000,Binary classification,Hoeffding Tree,Phishing,0.8828828828828829,0.8621908127208481,0.13474178314208984,3.773271 -1025,Binary classification,Hoeffding Tree,Phishing,0.8818359375,0.8613974799541809,0.13474178314208984,3.949737 -1050,Binary classification,Hoeffding Tree,Phishing,0.8836987607244995,0.8641425389755011,0.13474178314208984,4.130098 -1075,Binary classification,Hoeffding Tree,Phishing,0.8845437616387337,0.8658008658008659,0.13474178314208984,4.314272 -1100,Binary classification,Hoeffding Tree,Phishing,0.8844404003639672,0.8656084656084656,0.13474178314208984,4.502263 -1125,Binary classification,Hoeffding Tree,Phishing,0.8816725978647687,0.8630278063851698,0.13474178314208984,4.694139 -1150,Binary classification,Hoeffding Tree,Phishing,0.8807658833768495,0.8614762386248735,0.13474178314208984,4.889705 -1175,Binary classification,Hoeffding Tree,Phishing,0.879045996592845,0.8594059405940594,0.13474178314208984,5.0886830000000005 -1200,Binary classification,Hoeffding Tree,Phishing,0.8807339449541285,0.8610301263362489,0.13474178314208984,5.292268000000001 -1225,Binary classification,Hoeffding Tree,Phishing,0.880718954248366,0.8609523809523809,0.13474178314208984,5.499479000000001 -1250,Binary classification,Hoeffding Tree,Phishing,0.8799039231385108,0.8605947955390334,0.13474178314208984,5.711295000000001 -1903,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,0.17714 -3806,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,0.53242 -5709,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,1.05881 -7612,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,1.7555610000000001 -9515,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,2.619724 -11418,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,3.6522500000000004 -13321,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,4.852976 -15224,Binary classification,Hoeffding Tree,SMTP,0.9995401694803915,0.5882352941176471,0.0401153564453125,6.232766 -17127,Binary classification,Hoeffding Tree,SMTP,0.9992409202382343,0.48000000000000004,0.0401153564453125,7.809704 -19030,Binary classification,Hoeffding Tree,SMTP,0.9993168322034789,0.48000000000000004,0.0401153564453125,9.583916 -20933,Binary classification,Hoeffding Tree,SMTP,0.999378941333843,0.48000000000000004,0.0401153564453125,11.564329 -22836,Binary classification,Hoeffding Tree,SMTP,0.9994306984891613,0.48000000000000004,0.0401153564453125,13.76196 -24739,Binary classification,Hoeffding Tree,SMTP,0.9994744926833212,0.48000000000000004,0.0401153564453125,16.175204 -26642,Binary classification,Hoeffding Tree,SMTP,0.9995120303291919,0.5185185185185186,0.04013824462890625,18.806955000000002 -28545,Binary classification,Hoeffding Tree,SMTP,0.999544562780269,0.5185185185185186,0.04013824462890625,21.653103 -30448,Binary classification,Hoeffding Tree,SMTP,0.9995730285413998,0.5185185185185186,0.04013824462890625,24.713929 -32351,Binary classification,Hoeffding Tree,SMTP,0.999598145285935,0.5185185185185186,0.04013824462890625,27.986042 -34254,Binary classification,Hoeffding Tree,SMTP,0.999620471199603,0.5185185185185186,0.04013824462890625,31.466943 -36157,Binary classification,Hoeffding Tree,SMTP,0.9996404469520964,0.5185185185185186,0.04013824462890625,35.15479 -38060,Binary classification,Hoeffding Tree,SMTP,0.9996584250768543,0.5185185185185186,0.032138824462890625,39.051373 -39963,Binary classification,Hoeffding Tree,SMTP,0.9996746909564086,0.5185185185185186,0.032138824462890625,43.155234 -41866,Binary classification,Hoeffding Tree,SMTP,0.9996894780843186,0.5185185185185186,0.032138824462890625,47.461241 -43769,Binary classification,Hoeffding Tree,SMTP,0.9997029793456407,0.5185185185185186,0.032138824462890625,51.972515 -45672,Binary classification,Hoeffding Tree,SMTP,0.9997153554772175,0.5185185185185186,0.032138824462890625,56.689378000000005 -47575,Binary classification,Hoeffding Tree,SMTP,0.9996847017278345,0.4827586206896552,0.044315338134765625,61.613517 -49478,Binary classification,Hoeffding Tree,SMTP,0.9996766174181944,0.4666666666666667,0.044315338134765625,66.740207 -51381,Binary classification,Hoeffding Tree,SMTP,0.9996691319579603,0.5142857142857142,0.053562164306640625,72.066529 -53284,Binary classification,Hoeffding Tree,SMTP,0.9996809488955202,0.5142857142857142,0.053562164306640625,77.596723 -55187,Binary classification,Hoeffding Tree,SMTP,0.9996738303192839,0.5,0.053562164306640625,83.324048 -57090,Binary classification,Hoeffding Tree,SMTP,0.9995270542486293,0.4489795918367347,0.08054351806640625,89.177762 -58993,Binary classification,Hoeffding Tree,SMTP,0.9995423108218063,0.4489795918367347,0.08054351806640625,95.152342 -60896,Binary classification,Hoeffding Tree,SMTP,0.9995566138435011,0.4489795918367347,0.08054351806640625,101.246537 -62799,Binary classification,Hoeffding Tree,SMTP,0.9995222777795472,0.4230769230769231,0.08979034423828125,107.45837900000001 -64702,Binary classification,Hoeffding Tree,SMTP,0.9995363286502527,0.4230769230769231,0.08979034423828125,113.786816 -66605,Binary classification,Hoeffding Tree,SMTP,0.9995495766020059,0.4230769230769231,0.08979034423828125,120.231179 -68508,Binary classification,Hoeffding Tree,SMTP,0.9995620885456961,0.4642857142857143,0.08979034423828125,126.79213 -70411,Binary classification,Hoeffding Tree,SMTP,0.9995739241585002,0.4642857142857143,0.08979034423828125,133.468631 -72314,Binary classification,Hoeffding Tree,SMTP,0.9995851368357004,0.4642857142857143,0.08979034423828125,140.25926299999998 -74217,Binary classification,Hoeffding Tree,SMTP,0.9995823003126011,0.456140350877193,0.08979034423828125,147.163887 -76120,Binary classification,Hoeffding Tree,SMTP,0.9995927429419724,0.456140350877193,0.08979034423828125,154.18114 -78023,Binary classification,Hoeffding Tree,SMTP,0.9996026761682603,0.456140350877193,0.08979034423828125,161.310049 -79926,Binary classification,Hoeffding Tree,SMTP,0.9996121363778543,0.456140350877193,0.08979034423828125,168.551426 -81829,Binary classification,Hoeffding Tree,SMTP,0.9996211565723224,0.456140350877193,0.08979034423828125,175.901071 -83732,Binary classification,Hoeffding Tree,SMTP,0.9996178237450885,0.4482758620689655,0.08979034423828125,183.360737 -85635,Binary classification,Hoeffding Tree,SMTP,0.9996146390452391,0.44067796610169496,0.09405899047851562,190.930796 -87538,Binary classification,Hoeffding Tree,SMTP,0.9996230165530005,0.44067796610169496,0.09405899047851562,198.60706 -89441,Binary classification,Hoeffding Tree,SMTP,0.9996198568872987,0.43333333333333335,0.10326004028320312,206.389291 -91344,Binary classification,Hoeffding Tree,SMTP,0.999616828875776,0.4262295081967213,0.10326004028320312,214.276883 -93247,Binary classification,Hoeffding Tree,SMTP,0.9996139244578855,0.41935483870967744,0.10326004028320312,222.269881 -95150,Binary classification,Hoeffding Tree,SMTP,0.9996216460498797,0.41935483870967744,0.10326004028320312,230.368202 -106,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5714285714285714,0.628099173553719,0.02575397491455078,0.030279 -212,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5592417061611374,0.5903083700440529,0.02583789825439453,0.079572 -318,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5615141955835962,0.5947521865889213,0.02589893341064453,0.147698 -424,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5555555555555556,0.5822222222222222,0.02589893341064453,0.234734 -530,Binary classification,Hoeffding Adaptive Tree,Bananas,0.555765595463138,0.5506692160611854,0.02589893341064453,0.340374 -636,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5543307086614173,0.5291181364392679,0.02595996856689453,0.46589800000000003 -742,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5708502024291497,0.5167173252279634,0.02595996856689453,0.6107940000000001 -848,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5761511216056671,0.510231923601637,0.02595996856689453,0.774445 -954,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5844700944386149,0.505,0.02595996856689453,0.95753 -1060,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5920679886685553,0.49532710280373826,0.02595996856689453,1.15978 -1166,Binary classification,Hoeffding Adaptive Tree,Bananas,0.590557939914163,0.478688524590164,0.02595996856689453,1.380797 -1272,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971675845790716,0.48073022312373226,0.02595996856689453,1.6202560000000001 -1378,Binary classification,Hoeffding Adaptive Tree,Bananas,0.599128540305011,0.4661508704061895,0.02602100372314453,1.8811630000000001 -1484,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5994605529332434,0.458029197080292,0.02602100372314453,2.166507 -1590,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5997482693517936,0.4517241379310345,0.02602100372314453,2.464497 -1696,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6011799410029498,0.4459016393442623,0.02602100372314453,2.7711840000000003 -1802,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6018878400888396,0.44547563805104406,0.02602100372314453,3.0861490000000003 -1908,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6030414263240692,0.44704163623082543,0.02602100372314453,3.4094810000000004 -2014,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5986090412319921,0.44352617079889806,0.02602100372314453,3.7411250000000003 -2120,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5960358659745163,0.4427083333333333,0.02602100372314453,4.081019 -2226,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5968539325842697,0.4425108763206961,0.02602100372314453,4.429484 -2332,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5975975975975976,0.44233055885850175,0.02602100372314453,4.786285 -2438,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5982765695527288,0.4396107613050944,0.02602100372314453,5.150978 -2544,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5973259929217459,0.4398249452954048,0.030361175537109375,5.5260560000000005 -2650,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5956964892412231,0.44363636363636366,0.0572662353515625,5.913093000000001 -2756,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5985480943738657,0.44975124378109455,0.0575103759765625,6.312855000000001 -2862,Binary classification,Hoeffding Adaptive Tree,Bananas,0.600139811254806,0.4536771728748806,0.0577545166015625,6.725712000000001 -2968,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5979103471520054,0.45250114731528224,0.057861328125,7.151638 -3074,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971363488447771,0.4497777777777778,0.0579833984375,7.588719 -3180,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6008178672538534,0.44993498049414826,0.05804443359375,8.037238 -3286,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6024353120243531,0.4470787468247248,0.05816650390625,8.49667 -3392,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6012975523444412,0.444991789819376,0.0582275390625,8.967725 -3498,Binary classification,Hoeffding Adaptive Tree,Bananas,0.603946239633972,0.44310414153598715,0.05828857421875,9.449361 -3604,Binary classification,Hoeffding Adaptive Tree,Bananas,0.607826810990841,0.4452296819787986,0.05828857421875,9.942275 -3710,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6071717444055001,0.441976254308694,0.05828857421875,10.446028 -3816,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6062909567496724,0.43787425149700593,0.058349609375,10.961132 -3922,Binary classification,Hoeffding Adaptive Tree,Bananas,0.606988013261923,0.4353242946134115,0.05841064453125,11.487205 -4028,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6088899925502855,0.4360902255639098,0.05841064453125,12.024685999999999 -4134,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6082748608758771,0.4341139461726669,0.0584716796875,12.573182999999998 -4240,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6105213493748526,0.4370951244459598,0.0584716796875,13.132692999999998 -4346,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6119677790563867,0.43724966622162886,0.05853271484375,13.703477999999999 -4452,Binary classification,Hoeffding Adaptive Tree,Bananas,0.614243990114581,0.4387054593004249,0.05853271484375,14.285281 -4558,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6126837831906956,0.4355612408058842,0.05853271484375,14.878356 -4664,Binary classification,Hoeffding Adaptive Tree,Bananas,0.613339052112374,0.4360337816703159,0.05853271484375,15.482258 -4770,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6148039421262319,0.4352905010759299,0.06467437744140625,16.097208 -4876,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6157948717948718,0.4332829046898639,0.06467437744140625,16.723433999999997 -4982,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6167436257779563,0.43470535978679303,0.06467437744140625,17.360799999999998 -5088,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6158836249262827,0.4313154831199068,0.06473541259765625,18.009093999999997 -5194,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6160215674947044,0.42963386727688785,0.06479644775390625,18.668470999999997 -5300,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6165314210228345,0.42824985931344967,0.062404632568359375,19.339783999999998 -906,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8386740331491712,0.8370535714285713,0.1590566635131836,0.369424 -1812,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8823854224185533,0.857334226389819,0.29515743255615234,1.082516 -2718,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8715495031284505,0.8438478747203579,0.13015270233154297,2.245646 -3624,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8755175269113994,0.8480970023576963,0.25374507904052734,3.6837349999999995 -4530,Binary classification,Hoeffding Adaptive Tree,Elec2,0.873923603444469,0.8402797202797203,0.37697887420654297,5.436553 -5436,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8680772769089237,0.8326721120186699,0.43615245819091797,7.563805 -6342,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8667402617883615,0.8319076984284862,0.2912740707397461,10.057711000000001 -7248,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8665654753691182,0.8309144955411786,0.31587886810302734,12.896512000000001 -8154,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8588249724027965,0.8328249818445898,0.31593990325927734,16.103426000000002 -9060,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8594767634396733,0.8384722750919934,0.31581783294677734,19.644725 -9966,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8557952834922228,0.8381938970836617,0.31569576263427734,23.527363 -10872,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8595345414405299,0.8445801526717558,0.3772764205932617,27.740459 -11778,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8540375307803345,0.8368605865046976,0.4951925277709961,32.366043000000005 -12684,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8543719940077269,0.8371970030850595,0.19228267669677734,37.423083000000005 -13590,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8561336375009199,0.8404472374112462,0.19668865203857422,42.800608000000004 -14496,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8560193170058641,0.8406018483158941,0.19699382781982422,48.503182 -15402,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8546198298811766,0.8374119526541282,0.19674968719482422,54.449200000000005 -16308,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8514748267615134,0.8324339283243393,0.1668386459350586,60.610803000000004 -17214,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8503456689711265,0.8286094477711246,0.17186641693115234,66.962846 -18120,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8505436282355539,0.8284555935639174,0.20143413543701172,73.50726399999999 -19026,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8529829172141918,0.8293992070753278,0.2609548568725586,80.25162699999998 -19932,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8476744769454618,0.8245289561900357,0.3200864791870117,87.27384199999999 -20838,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8440274511685943,0.8206203775251132,0.3286733627319336,94.62519899999998 -21744,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8459734167318217,0.8206693440428381,0.32692623138427734,102.22022699999998 -22650,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8452470307739856,0.8179693586081537,0.44559764862060547,110.06165699999998 -23556,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8426236467841223,0.813952321204517,0.3226041793823242,118.13610299999998 -24462,Binary classification,Hoeffding Adaptive Tree,Elec2,0.83966313723887,0.8094081057439986,0.32233715057373047,126.42298599999998 -25368,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8335632908897387,0.804101707498144,0.32264232635498047,134.92385099999998 -26274,Binary classification,Hoeffding Adaptive Tree,Elec2,0.833859856126061,0.8041634887164072,0.3227415084838867,143.62308599999997 -27180,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8353508223260606,0.8067872717067484,0.33442211151123047,152.52188499999997 -28086,Binary classification,Hoeffding Adaptive Tree,Elec2,0.832366031689514,0.8022679546409073,0.4427366256713867,161.69157399999997 -28992,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8298092511469076,0.7967539957159334,0.44315624237060547,171.13838399999997 -29898,Binary classification,Hoeffding Adaptive Tree,Elec2,0.828912599926414,0.7954572719638501,0.44604015350341797,180.84818699999997 -30804,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8286855176443852,0.7940682926829268,0.4524259567260742,190.78517699999998 -31710,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8270522564571573,0.7921309984080055,0.31789684295654297,200.97628499999996 -32616,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8257550206959988,0.7908123826701513,0.5092306137084961,211.41806699999995 -33522,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8259001819754781,0.7917201998572448,0.45040416717529297,222.10332899999995 -34428,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8258634211520028,0.7914564998086757,0.33066463470458984,233.03381899999994 -35334,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8252625024764385,0.7899285471248724,0.49124813079833984,244.20613599999993 -36240,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8261541433262507,0.7894103489771359,0.5119619369506836,255.69628199999994 -37146,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8248754879526181,0.7869936802121876,0.4187917709350586,267.45490699999993 -38052,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8248403458516202,0.7864398090294465,0.4483175277709961,279.43877899999995 -38958,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8243447904099391,0.7868689070919114,0.5077886581420898,291.70049499999993 -39864,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8249504553094348,0.7887758808572466,0.1471853256225586,304.13865899999996 -40770,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8262159974490422,0.7919908399635948,0.22976970672607422,316.729427 -41676,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8275224955008998,0.794816168074903,0.23425960540771484,329.475528 -42582,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8281393109602875,0.7956550876801073,0.3305959701538086,342.401664 -43488,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8284774760273185,0.7958619557185473,0.3348875045776367,355.508994 -44394,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8276530083571735,0.7938902508014333,0.28391170501708984,368.88313999999997 -45300,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8286717146073864,0.795391632174211,0.3993253707885742,382.50372699999997 -25,Binary classification,Hoeffding Adaptive Tree,Phishing,0.5833333333333334,0.6428571428571429,0.07568836212158203,0.0191 -50,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7346938775510204,0.7346938775510203,0.07574939727783203,0.048719 -75,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7837837837837838,0.7894736842105262,0.07574939727783203,0.086072 -100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8080808080808081,0.8080808080808081,0.07581043243408203,0.130387 -125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8225806451612904,0.819672131147541,0.07581043243408203,0.181491 -150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.825503355704698,0.8289473684210527,0.07583332061767578,0.241937 -175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8333333333333334,0.8242424242424242,0.07589435577392578,0.309852 -200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8291457286432161,0.8191489361702128,0.07589435577392578,0.385335 -225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8303571428571429,0.8155339805825242,0.07589435577392578,0.468101 -250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8313253012048193,0.817391304347826,0.07589435577392578,0.558214 -275,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8321167883211679,0.8174603174603176,0.07589435577392578,0.656226 -300,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8361204013377926,0.8178438661710038,0.07589435577392578,0.765253 -325,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8425925925925926,0.8197879858657244,0.07595539093017578,0.883707 -350,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8481375358166189,0.822742474916388,0.07595539093017578,1.011557 -375,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8502673796791443,0.8227848101265823,0.07595539093017578,1.148821 -400,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8521303258145363,0.8228228228228228,0.07595539093017578,1.2953860000000001 -425,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8537735849056604,0.8208092485549133,0.07595539093017578,1.4534280000000002 -450,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8574610244988864,0.8232044198895027,0.07595539093017578,1.6208710000000002 -475,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8565400843881856,0.8238341968911918,0.07595539093017578,1.7976930000000002 -500,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8557114228456913,0.8260869565217391,0.07595539093017578,1.9839770000000003 -525,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8568702290076335,0.823529411764706,0.07595539093017578,2.179609 -550,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8561020036429873,0.8240534521158129,0.07595539093017578,2.3846100000000003 -575,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8554006968641115,0.8230277185501066,0.11611557006835938,2.6084530000000004 -600,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8547579298831386,0.8176100628930818,0.14400863647460938,2.84161 -625,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8573717948717948,0.8172484599589321,0.14424514770507812,3.082865 -650,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8597842835130971,0.8233009708737864,0.14441299438476562,3.332264 -675,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8590504451038575,0.8263254113345521,0.14447402954101562,3.5897799999999997 -700,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8640915593705293,0.8306595365418894,0.14453506469726562,3.8554809999999997 -725,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8646408839779005,0.8344594594594595,0.14459609985351562,4.1295 -750,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8664886515353805,0.8371335504885993,0.14465713500976562,4.411739 -775,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8643410852713178,0.8330683624801273,0.14468002319335938,4.702108 -800,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8635794743429287,0.8340943683409437,0.14468002319335938,5.000653 -825,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8628640776699029,0.8345534407027819,0.14468002319335938,5.309277 -850,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8645465253239105,0.8364153627311521,0.14474105834960938,5.626329 -875,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8672768878718535,0.838888888888889,0.14474105834960938,5.951525 -900,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8665183537263627,0.8378378378378378,0.14480209350585938,6.284981 -925,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8668831168831169,0.8400520156046815,0.14480209350585938,6.628637 -950,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8661749209694415,0.8410513141426783,0.14486312866210938,6.980485000000001 -975,Binary classification,Hoeffding Adaptive Tree,Phishing,0.86652977412731,0.8414634146341464,0.14486312866210938,7.342308000000001 -1000,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8638638638638638,0.8392434988179669,0.14486312866210938,7.713462000000001 -1025,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8623046875,0.8377445339470656,0.14486312866210938,8.093853000000001 -1050,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8636796949475691,0.8402234636871508,0.14486312866210938,8.485573 -1075,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8649906890130353,0.8429035752979415,0.14486312866210938,8.889089 -1100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8671519563239308,0.8456659619450316,0.14486312866210938,9.305059 -1125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8701067615658363,0.8507157464212679,0.14486312866210938,9.730762 -1150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8720626631853786,0.852852852852853,0.14492416381835938,10.16685 -1175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8713798977853492,0.8521057786483839,0.14492416381835938,10.613004 -1200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.872393661384487,0.8530259365994236,0.14492416381835938,11.069492 -1225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8733660130718954,0.8541862652869238,0.14498519897460938,11.536355 -1250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8742994395516414,0.8560953253895509,0.14498519897460938,12.014925 -1903,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.023916244506835938,0.25062 -3806,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.023977279663085938,0.745512 -5709,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024038314819335938,1.4971299999999998 -7612,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024038314819335938,2.521262 -9515,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024038314819335938,3.817259 -11418,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024099349975585938,5.360746 -13321,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024099349975585938,7.021896 -15224,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996058595546213,0.625,0.05017566680908203,8.821636 -17127,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9991825294873292,0.46153846153846156,0.045350074768066406,10.797386 -19030,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992642808345158,0.46153846153846156,0.045716285705566406,12.952086 -20933,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9993311675902924,0.46153846153846156,0.045838356018066406,15.282579 -22836,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9993869060652507,0.46153846153846156,0.045594215393066406,17.788716 -24739,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994340690435767,0.46153846153846156,0.045838356018066406,20.470769 -26642,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744942006681,0.5,0.058136940002441406,23.327571 -28545,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995095291479821,0.5,0.058197975158691406,26.358995999999998 -30448,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995401845830459,0.5,0.058197975158691406,29.565096999999998 -32351,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995672333848532,0.5,0.058320045471191406,32.946387 -34254,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995912766764955,0.5,0.058381080627441406,36.502912 -36157,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996127890253347,0.5,0.058442115783691406,40.234574 -38060,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996321500827662,0.5,0.058442115783691406,44.142312000000004 -39963,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996496671838246,0.5,0.058442115783691406,48.227315000000004 -41866,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996655917831124,0.5,0.058442115783691406,52.488216 -43769,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996801316029976,0.5,0.058503150939941406,56.92422 -45672,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996934597446958,0.5,0.058625221252441406,61.535205 -47575,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996636818430235,0.4666666666666667,0.06784915924072266,66.321748 -49478,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996564060068315,0.45161290322580644,0.06784915924072266,71.282934 -51381,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999649669131958,0.5,0.0679788589477539,76.42026 -53284,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996621811834919,0.5,0.0679788589477539,81.732337 -55187,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996738303192839,0.5,0.0679788589477539,87.220308 -57090,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995095377393193,0.41666666666666663,0.1018075942993164,92.923409 -58993,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994914564686738,0.4000000000000001,0.1022958755493164,98.85935500000001 -60896,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995073487150012,0.4000000000000001,0.1024179458618164,105.024921 -62799,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994745055575018,0.3773584905660377,0.1118478775024414,111.43003800000001 -64702,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999489961515278,0.3773584905660377,0.1119699478149414,118.06567900000002 -66605,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995045342622064,0.3773584905660377,0.1120920181274414,124.93220100000002 -68508,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995182974002657,0.42105263157894735,0.1121530532836914,132.02968600000003 -70411,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995313165743502,0.42105263157894735,0.1122140884399414,139.35861700000004 -72314,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995436505192704,0.42105263157894735,0.1123361587524414,146.91762900000003 -74217,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995418777622076,0.41379310344827586,0.1123971939086914,154.70829700000004 -76120,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999553330968615,0.41379310344827586,0.1123971939086914,162.73183200000005 -78023,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99953859167927,0.39999999999999997,0.11986637115478516,170.98992200000006 -79926,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999549577729121,0.39999999999999997,0.12023258209228516,179.48569900000007 -81829,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995600527936648,0.39999999999999997,0.12035465240478516,188.21505800000006 -83732,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995581087052585,0.3934426229508197,0.12047672271728516,197.18003100000004 -85635,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995679286264801,0.3934426229508197,0.12047672271728516,206.38234800000004 -87538,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995773215897278,0.3934426229508197,0.12053775787353516,215.81852000000003 -89441,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995751341681575,0.38709677419354843,0.12986087799072266,225.49133900000004 -91344,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995620901437439,0.37500000000000006,0.12986087799072266,235.39794000000003 -93247,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995388542135856,0.3582089552238806,0.13713932037353516,245.54396400000005 -95150,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995480772262452,0.3582089552238806,0.13726139068603516,255.92452900000004 -106,Binary classification,Adaptive Random Forest,Bananas,0.638095238095238,0.5777777777777778,0.6364564895629883,0.262298 -212,Binary classification,Adaptive Random Forest,Bananas,0.7488151658767772,0.7103825136612022,1.0992326736450195,0.748759 -318,Binary classification,Adaptive Random Forest,Bananas,0.7917981072555205,0.7659574468085106,1.488083839416504,1.463509 -424,Binary classification,Adaptive Random Forest,Bananas,0.8274231678486997,0.8042895442359249,1.8509149551391602,2.4097 -530,Binary classification,Adaptive Random Forest,Bananas,0.831758034026465,0.802660753880266,2.4203081130981445,3.615781 -636,Binary classification,Adaptive Random Forest,Bananas,0.8456692913385827,0.8191881918819188,2.83583927154541,5.093309 -742,Binary classification,Adaptive Random Forest,Bananas,0.8569500674763832,0.8284789644012946,3.2995615005493164,6.854744 -848,Binary classification,Adaptive Random Forest,Bananas,0.859504132231405,0.8326300984528833,3.4132471084594727,8.933942 -954,Binary classification,Adaptive Random Forest,Bananas,0.863588667366212,0.8370927318295739,3.8167009353637695,11.329061 -1060,Binary classification,Adaptive Random Forest,Bananas,0.8715769593956563,0.8454545454545456,4.3293962478637695,14.065921 -1166,Binary classification,Adaptive Random Forest,Bananas,0.8738197424892704,0.8489208633093526,4.776837348937988,17.153408 -1272,Binary classification,Adaptive Random Forest,Bananas,0.8749016522423289,0.8512628624883068,5.179757118225098,20.492268 -1378,Binary classification,Adaptive Random Forest,Bananas,0.8765432098765432,0.8516579406631763,5.6712846755981445,24.00032 -1484,Binary classification,Adaptive Random Forest,Bananas,0.8813216453135536,0.8580645161290322,6.118577003479004,27.676707 -1590,Binary classification,Adaptive Random Forest,Bananas,0.8779106356198867,0.8554396423248881,6.625298500061035,31.525227 -1696,Binary classification,Adaptive Random Forest,Bananas,0.879646017699115,0.8573426573426574,5.8858842849731445,35.585591 -1802,Binary classification,Adaptive Random Forest,Bananas,0.8800666296501943,0.8590078328981724,6.279637336730957,39.950222000000004 -1908,Binary classification,Adaptive Random Forest,Bananas,0.8778185631882538,0.8578401464307503,6.042496681213379,44.519385 -2014,Binary classification,Adaptive Random Forest,Bananas,0.877297565822156,0.8584527220630374,6.480931282043457,49.302272 -2120,Binary classification,Adaptive Random Forest,Bananas,0.8801321378008494,0.8631465517241379,6.779278755187988,54.294834 -2226,Binary classification,Adaptive Random Forest,Bananas,0.8791011235955056,0.8621219887237315,7.120572090148926,59.4987 -2332,Binary classification,Adaptive Random Forest,Bananas,0.8781638781638782,0.8611925708699902,7.709580421447754,64.935467 -2438,Binary classification,Adaptive Random Forest,Bananas,0.8789495281083299,0.8618266978922717,8.127808570861816,70.60280800000001 -2544,Binary classification,Adaptive Random Forest,Bananas,0.8788832088084939,0.8624999999999999,8.531121253967285,76.48792700000001 -2650,Binary classification,Adaptive Random Forest,Bananas,0.8803322008305021,0.8645877829987185,8.842900276184082,82.60938300000001 -2756,Binary classification,Adaptive Random Forest,Bananas,0.8827586206896552,0.8671328671328672,9.237275123596191,88.95420100000001 -2862,Binary classification,Adaptive Random Forest,Bananas,0.88325760223698,0.8674603174603175,9.480591773986816,95.534715 -2968,Binary classification,Adaptive Random Forest,Bananas,0.8843950117964273,0.8681276432141485,9.87939167022705,102.339589 -3074,Binary classification,Adaptive Random Forest,Bananas,0.8848031239830785,0.8690828402366864,10.33882999420166,109.37876800000001 -3180,Binary classification,Adaptive Random Forest,Bananas,0.8867568417741428,0.8707824838478105,10.641068458557129,116.664225 -3286,Binary classification,Adaptive Random Forest,Bananas,0.8861491628614916,0.8697771587743733,10.188206672668457,124.173867 -3392,Binary classification,Adaptive Random Forest,Bananas,0.8867590681214981,0.8712273641851107,10.46657657623291,131.921991 -3498,Binary classification,Adaptive Random Forest,Bananas,0.8881898770374607,0.8723473718576559,9.652070045471191,139.895805 -3604,Binary classification,Adaptive Random Forest,Bananas,0.8889814043852345,0.8726925525143221,8.887116432189941,148.067873 -3710,Binary classification,Adaptive Random Forest,Bananas,0.8889188460501483,0.873152709359606,9.264924049377441,156.441655 -3816,Binary classification,Adaptive Random Forest,Bananas,0.89043250327654,0.875,9.5267915725708,165.011312 -3922,Binary classification,Adaptive Random Forest,Bananas,0.8888038765621015,0.8728862973760932,9.897248268127441,173.777209 -4028,Binary classification,Adaptive Random Forest,Bananas,0.8872609883287808,0.8710227272727272,10.25338077545166,182.74826 -4134,Binary classification,Adaptive Random Forest,Bananas,0.8877328816840068,0.8716104039845047,10.579400062561035,191.919884 -4240,Binary classification,Adaptive Random Forest,Bananas,0.8886529841943854,0.8727762803234501,10.81624698638916,201.289592 -4346,Binary classification,Adaptive Random Forest,Bananas,0.8895281933256617,0.8738833420914347,10.986077308654785,210.860402 -4452,Binary classification,Adaptive Random Forest,Bananas,0.8890137047854415,0.8732032854209446,11.276833534240723,220.647955 -4558,Binary classification,Adaptive Random Forest,Bananas,0.8887425938117183,0.8734082397003745,11.626667976379395,230.650874 -4664,Binary classification,Adaptive Random Forest,Bananas,0.8889127171348917,0.8740272373540856,12.038758277893066,240.866021 -4770,Binary classification,Adaptive Random Forest,Bananas,0.887188089746278,0.871843735111958,12.429780006408691,251.436375 -4876,Binary classification,Adaptive Random Forest,Bananas,0.8875897435897436,0.8720224194301728,12.7014741897583,262.227068 -4982,Binary classification,Adaptive Random Forest,Bananas,0.8883758281469585,0.8732907930720145,12.836377143859863,273.239924 -5088,Binary classification,Adaptive Random Forest,Bananas,0.8875565166109691,0.8722644037516749,13.209172248840332,284.48340199999996 -5194,Binary classification,Adaptive Random Forest,Bananas,0.8875409204698633,0.8722100656455142,13.583405494689941,295.95770799999997 -5300,Binary classification,Adaptive Random Forest,Bananas,0.886959803736554,0.8715419257988419,13.845444679260254,307.673049 -906,Binary classification,Adaptive Random Forest,Elec2,0.8674033149171271,0.8669623059866962,3.0924072265625,1.44234 -1812,Binary classification,Adaptive Random Forest,Elec2,0.895085588072888,0.8724832214765101,4.682834625244141,4.396686 -2718,Binary classification,Adaptive Random Forest,Elec2,0.8844313581155686,0.8575317604355717,7.755058288574219,9.151923 -3624,Binary classification,Adaptive Random Forest,Elec2,0.8923544024289263,0.8677069199457259,8.665351867675781,15.539978999999999 -4530,Binary classification,Adaptive Random Forest,Elec2,0.8940163391477147,0.8633257403189066,11.046634674072266,23.437977 -5436,Binary classification,Adaptive Random Forest,Elec2,0.8868445262189513,0.8541617263457434,15.550697326660156,33.146859 -6342,Binary classification,Adaptive Random Forest,Elec2,0.8856647216527361,0.8543884314119301,17.033367156982422,44.650345 -7248,Binary classification,Adaptive Random Forest,Elec2,0.8840899682627295,0.8505869797225187,21.311607360839844,57.88122 -8154,Binary classification,Adaptive Random Forest,Elec2,0.8850729792714338,0.8592881814086198,20.813556671142578,72.837822 -9060,Binary classification,Adaptive Random Forest,Elec2,0.8881775030356551,0.866657891272871,22.198707580566406,89.470796 -9966,Binary classification,Adaptive Random Forest,Elec2,0.8867034621174109,0.8678450193140582,25.867393493652344,108.078283 -10872,Binary classification,Adaptive Random Forest,Elec2,0.8887866801582192,0.8723202027669237,24.029823303222656,128.509921 -11778,Binary classification,Adaptive Random Forest,Elec2,0.8868981913899975,0.8695141065830722,23.736316680908203,150.696171 -12684,Binary classification,Adaptive Random Forest,Elec2,0.8834660569265946,0.8662201303403331,17.480976104736328,174.61452699999998 -13590,Binary classification,Adaptive Random Forest,Elec2,0.8837294870851424,0.8684648684648686,17.212547302246094,200.15146099999998 -14496,Binary classification,Adaptive Random Forest,Elec2,0.8826491893756467,0.8678013522965726,16.54094696044922,227.26288399999999 -15402,Binary classification,Adaptive Random Forest,Elec2,0.8836439192260243,0.8679439941046426,17.876373291015625,255.95351 -16308,Binary classification,Adaptive Random Forest,Elec2,0.8814006254982523,0.8648119670068503,12.619304656982422,286.224579 -17214,Binary classification,Adaptive Random Forest,Elec2,0.8818334979376053,0.8629380053908356,9.807907104492188,317.969431 -18120,Binary classification,Adaptive Random Forest,Elec2,0.8821126993763453,0.8632872503840245,8.873615264892578,351.00359 -19026,Binary classification,Adaptive Random Forest,Elec2,0.883784494086728,0.8636279528773206,11.29254150390625,385.315707 -19932,Binary classification,Adaptive Random Forest,Elec2,0.8832973759470172,0.8638810861423221,13.30521011352539,421.18981299999996 -20838,Binary classification,Adaptive Random Forest,Elec2,0.8826126601718097,0.8630919064144185,13.75485610961914,458.867106 -21744,Binary classification,Adaptive Random Forest,Elec2,0.8820769902957274,0.8605005440696408,10.461296081542969,498.08269199999995 -22650,Binary classification,Adaptive Random Forest,Elec2,0.8810985032451764,0.8581959875730609,10.731792449951172,538.811106 -23556,Binary classification,Adaptive Random Forest,Elec2,0.880025472298875,0.8563732465948364,8.147632598876953,581.039793 -24462,Binary classification,Adaptive Random Forest,Elec2,0.8793998610032296,0.8547799547110366,10.927783966064453,624.8074220000001 -25368,Binary classification,Adaptive Random Forest,Elec2,0.8777545630149407,0.8528029619784497,11.403583526611328,670.1639380000001 -26274,Binary classification,Adaptive Random Forest,Elec2,0.8783922658242302,0.8533663775299463,14.942352294921875,717.1095470000001 -27180,Binary classification,Adaptive Random Forest,Elec2,0.8798704882446006,0.855843525100446,14.17806625366211,765.6544720000002 -28086,Binary classification,Adaptive Random Forest,Elec2,0.8780843866832829,0.8531732418524872,12.432361602783203,815.7617920000001 -28992,Binary classification,Adaptive Random Forest,Elec2,0.8780656065675555,0.852406997620141,10.199352264404297,867.3023770000001 -29898,Binary classification,Adaptive Random Forest,Elec2,0.8784158945713617,0.8527684393859614,13.818798065185547,920.296853 -30804,Binary classification,Adaptive Random Forest,Elec2,0.8787455767295393,0.8524356998933271,16.783336639404297,975.029218 -31710,Binary classification,Adaptive Random Forest,Elec2,0.8776057270806396,0.8507250278856878,18.14492416381836,1031.673342 -32616,Binary classification,Adaptive Random Forest,Elec2,0.8769277939598344,0.8501567866208751,18.455127716064453,1090.2745810000001 -33522,Binary classification,Adaptive Random Forest,Elec2,0.8766743235583664,0.8503366881471291,21.331356048583984,1150.683688 -34428,Binary classification,Adaptive Random Forest,Elec2,0.875940395619717,0.8493102353314751,20.511539459228516,1212.996992 -35334,Binary classification,Adaptive Random Forest,Elec2,0.8751591996150907,0.8476285882068464,17.70761489868164,1277.0202020000002 -36240,Binary classification,Adaptive Random Forest,Elec2,0.8746930102927785,0.8461772975170219,18.968151092529297,1342.6432340000001 -37146,Binary classification,Adaptive Random Forest,Elec2,0.8737649750975905,0.8444930852651478,21.176280975341797,1410.0019350000002 -38052,Binary classification,Adaptive Random Forest,Elec2,0.874063756537279,0.8444256866437246,13.57645034790039,1479.1416360000003 -38958,Binary classification,Adaptive Random Forest,Elec2,0.8743999794645378,0.8453001991842929,13.581947326660156,1549.8962470000004 -39864,Binary classification,Adaptive Random Forest,Elec2,0.8743446303589795,0.8465990873732889,12.123741149902344,1622.0853150000003 -40770,Binary classification,Adaptive Random Forest,Elec2,0.8747823100885477,0.8484848484848485,12.675861358642578,1695.6369460000003 -41676,Binary classification,Adaptive Random Forest,Elec2,0.8752969406118776,0.850217598063233,15.801628112792969,1770.5477020000003 -42582,Binary classification,Adaptive Random Forest,Elec2,0.87569573283859,0.8509895554742266,16.52715301513672,1847.0334210000003 -43488,Binary classification,Adaptive Random Forest,Elec2,0.87568698691563,0.8510087090728695,18.189510345458984,1925.1978090000002 -44394,Binary classification,Adaptive Random Forest,Elec2,0.8757236501250197,0.8505242623750305,18.102394104003906,2005.1269950000003 -45300,Binary classification,Adaptive Random Forest,Elec2,0.8766197929314112,0.8519587847323391,20.355426788330078,2086.7182620000003 -25,Binary classification,Adaptive Random Forest,Phishing,0.625,0.7096774193548387,0.42359256744384766,0.126705 -50,Binary classification,Adaptive Random Forest,Phishing,0.7346938775510204,0.7450980392156864,0.6303834915161133,0.333958 -75,Binary classification,Adaptive Random Forest,Phishing,0.7837837837837838,0.7999999999999999,0.8403291702270508,0.61983 -100,Binary classification,Adaptive Random Forest,Phishing,0.797979797979798,0.8039215686274509,0.9226388931274414,0.981624 -125,Binary classification,Adaptive Random Forest,Phishing,0.7903225806451613,0.7968749999999999,1.0709314346313477,1.421284 -150,Binary classification,Adaptive Random Forest,Phishing,0.8120805369127517,0.8227848101265823,1.1753358840942383,1.9403350000000001 -175,Binary classification,Adaptive Random Forest,Phishing,0.8390804597701149,0.8372093023255814,1.2494592666625977,2.543018 -200,Binary classification,Adaptive Random Forest,Phishing,0.8442211055276382,0.8426395939086295,1.3681573867797852,3.2301029999999997 -225,Binary classification,Adaptive Random Forest,Phishing,0.8526785714285714,0.8465116279069769,1.4882898330688477,4.000846999999999 -250,Binary classification,Adaptive Random Forest,Phishing,0.8433734939759037,0.8354430379746836,1.6624422073364258,4.857951999999999 -275,Binary classification,Adaptive Random Forest,Phishing,0.843065693430657,0.833976833976834,1.7254152297973633,5.796970999999999 -300,Binary classification,Adaptive Random Forest,Phishing,0.8494983277591973,0.8375451263537907,1.8179521560668945,6.820338 -325,Binary classification,Adaptive Random Forest,Phishing,0.8580246913580247,0.8424657534246577,1.875351905822754,7.930464 -350,Binary classification,Adaptive Random Forest,Phishing,0.8595988538681948,0.8414239482200646,2.064530372619629,9.128264999999999 -375,Binary classification,Adaptive Random Forest,Phishing,0.8582887700534759,0.8379204892966361,2.210324287414551,10.415761 -400,Binary classification,Adaptive Random Forest,Phishing,0.8646616541353384,0.8439306358381503,2.3119516372680664,11.792425 -425,Binary classification,Adaptive Random Forest,Phishing,0.8679245283018868,0.8435754189944134,2.393784523010254,13.261163 -450,Binary classification,Adaptive Random Forest,Phishing,0.8752783964365256,0.8502673796791443,2.504483222961426,14.820437 -475,Binary classification,Adaptive Random Forest,Phishing,0.8776371308016878,0.8550000000000001,2.601761817932129,16.462295 -500,Binary classification,Adaptive Random Forest,Phishing,0.8797595190380761,0.8598130841121494,2.5846261978149414,18.198054000000003 -525,Binary classification,Adaptive Random Forest,Phishing,0.8816793893129771,0.859090909090909,2.742630958557129,20.024147000000003 -550,Binary classification,Adaptive Random Forest,Phishing,0.8779599271402551,0.855291576673866,2.8854761123657227,21.959689000000004 -575,Binary classification,Adaptive Random Forest,Phishing,0.8763066202090593,0.8530020703933747,3.0752573013305664,24.005933000000006 -600,Binary classification,Adaptive Random Forest,Phishing,0.8797996661101837,0.8548387096774194,3.1360864639282227,26.161920000000006 -625,Binary classification,Adaptive Random Forest,Phishing,0.8830128205128205,0.8554455445544554,3.285130500793457,28.421390000000006 -650,Binary classification,Adaptive Random Forest,Phishing,0.8859784283513097,0.8609022556390977,3.3397645950317383,30.794885000000008 -675,Binary classification,Adaptive Random Forest,Phishing,0.8887240356083086,0.8672566371681416,3.5764551162719727,33.284549000000005 -700,Binary classification,Adaptive Random Forest,Phishing,0.8927038626609443,0.8704663212435233,3.464848518371582,35.882938 -725,Binary classification,Adaptive Random Forest,Phishing,0.893646408839779,0.8735632183908045,3.70070743560791,38.601152000000006 -750,Binary classification,Adaptive Random Forest,Phishing,0.8958611481975968,0.8765822784810127,3.883671760559082,41.43692500000001 -775,Binary classification,Adaptive Random Forest,Phishing,0.896640826873385,0.8769230769230768,4.02083683013916,44.39908700000001 -800,Binary classification,Adaptive Random Forest,Phishing,0.8948685857321652,0.8761061946902655,4.127713203430176,47.483821000000006 -825,Binary classification,Adaptive Random Forest,Phishing,0.8944174757281553,0.8765957446808511,4.256714820861816,50.69677800000001 -850,Binary classification,Adaptive Random Forest,Phishing,0.8963486454652533,0.8784530386740332,4.299836158752441,54.03081400000001 -875,Binary classification,Adaptive Random Forest,Phishing,0.8993135011441648,0.8814016172506738,4.410748481750488,57.488212000000004 -900,Binary classification,Adaptive Random Forest,Phishing,0.8987764182424917,0.8804204993429698,4.566498756408691,61.07896100000001 -925,Binary classification,Adaptive Random Forest,Phishing,0.9015151515151515,0.8846641318124209,4.655289649963379,64.810524 -950,Binary classification,Adaptive Random Forest,Phishing,0.9030558482613277,0.8878048780487805,4.339470863342285,68.683627 -975,Binary classification,Adaptive Random Forest,Phishing,0.9034907597535934,0.8880952380952382,4.029709815979004,72.689378 -1000,Binary classification,Adaptive Random Forest,Phishing,0.9029029029029029,0.8876013904982619,3.6762208938598633,76.821914 -1025,Binary classification,Adaptive Random Forest,Phishing,0.9013671875,0.8861330326944759,3.8425302505493164,81.06784200000001 -1050,Binary classification,Adaptive Random Forest,Phishing,0.9027645376549094,0.8881578947368421,3.957364082336426,85.43608100000002 -1075,Binary classification,Adaptive Random Forest,Phishing,0.9031657355679702,0.8893617021276596,4.039715766906738,89.92310000000002 -1100,Binary classification,Adaptive Random Forest,Phishing,0.9044585987261147,0.8911917098445594,4.059922218322754,94.51467400000001 -1125,Binary classification,Adaptive Random Forest,Phishing,0.9065836298932385,0.8946840521564694,4.122437477111816,99.180878 -1150,Binary classification,Adaptive Random Forest,Phishing,0.9077458659704091,0.8958742632612966,4.3834123611450195,103.92086 -1175,Binary classification,Adaptive Random Forest,Phishing,0.9063032367972743,0.8940269749518305,3.936264991760254,108.73406700000001 -1200,Binary classification,Adaptive Random Forest,Phishing,0.9074228523769808,0.8949858088930936,3.502232551574707,113.61840500000001 -1225,Binary classification,Adaptive Random Forest,Phishing,0.9084967320261438,0.8961038961038962,3.7125635147094727,118.566747 -1250,Binary classification,Adaptive Random Forest,Phishing,0.9087269815852682,0.8969258589511755,3.826443672180176,123.577759 -1903,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17264461517333984,2.131651 -3806,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17386531829833984,5.506803 -5709,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17508602142333984,10.14235 -7612,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17508602142333984,15.888209 -9515,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17508602142333984,22.677279 -11418,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17630672454833984,30.407676 -13321,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17630672454833984,39.088803 -15224,Binary classification,Adaptive Random Forest,SMTP,0.9998686198515404,0.9,0.3923320770263672,48.816854 -17127,Binary classification,Adaptive Random Forest,SMTP,0.9998832184981898,0.9166666666666666,0.4169597625732422,59.770396 -19030,Binary classification,Adaptive Random Forest,SMTP,0.9998948972620737,0.9166666666666666,0.4169597625732422,71.94360999999999 -20933,Binary classification,Adaptive Random Forest,SMTP,0.999904452512899,0.9166666666666666,0.4181804656982422,85.33762899999999 -22836,Binary classification,Adaptive Random Forest,SMTP,0.9999124151521787,0.9166666666666666,0.4181804656982422,99.94903 -24739,Binary classification,Adaptive Random Forest,SMTP,0.9999191527205109,0.9166666666666666,0.4181804656982422,115.77779899999999 -26642,Binary classification,Adaptive Random Forest,SMTP,0.9999249277429526,0.923076923076923,0.4445209503173828,132.832942 -28545,Binary classification,Adaptive Random Forest,SMTP,0.999929932735426,0.923076923076923,0.44460105895996094,151.111146 -30448,Binary classification,Adaptive Random Forest,SMTP,0.9999343120832923,0.923076923076923,0.44460105895996094,170.611749 -32351,Binary classification,Adaptive Random Forest,SMTP,0.9999381761978362,0.923076923076923,0.44460105895996094,191.33527700000002 -34254,Binary classification,Adaptive Random Forest,SMTP,0.9999416109537851,0.923076923076923,0.4561443328857422,213.28333600000002 -36157,Binary classification,Adaptive Random Forest,SMTP,0.9999446841464764,0.923076923076923,0.4642963409423828,236.45182400000002 -38060,Binary classification,Adaptive Random Forest,SMTP,0.9999474500118237,0.923076923076923,0.4643535614013672,260.85276600000003 -39963,Binary classification,Adaptive Random Forest,SMTP,0.9999499524548321,0.923076923076923,0.4643535614013672,286.481847 -41866,Binary classification,Adaptive Random Forest,SMTP,0.9999522273975875,0.923076923076923,0.4655742645263672,313.33932500000003 -43769,Binary classification,Adaptive Random Forest,SMTP,0.9999543045147139,0.923076923076923,0.4655742645263672,341.422365 -45672,Binary classification,Adaptive Random Forest,SMTP,0.9999562085349566,0.923076923076923,0.4655742645263672,370.73064 -47575,Binary classification,Adaptive Random Forest,SMTP,0.999957960230378,0.923076923076923,0.48300743103027344,401.265116 -49478,Binary classification,Adaptive Random Forest,SMTP,0.9999595771772742,0.923076923076923,0.49118995666503906,433.02892199999997 -51381,Binary classification,Adaptive Random Forest,SMTP,0.9999221486959906,0.8666666666666666,0.5249767303466797,466.02567999999997 -53284,Binary classification,Adaptive Random Forest,SMTP,0.9999249291518871,0.8666666666666666,0.5249767303466797,500.253815 -55187,Binary classification,Adaptive Random Forest,SMTP,0.9999275178487298,0.8666666666666666,0.5554332733154297,535.714615 -57090,Binary classification,Adaptive Random Forest,SMTP,0.9997547688696596,0.6818181818181819,0.8414325714111328,572.520543 -58993,Binary classification,Adaptive Random Forest,SMTP,0.999762679685381,0.6818181818181819,0.8605022430419922,610.6533019999999 -60896,Binary classification,Adaptive Random Forest,SMTP,0.9997700960670006,0.6818181818181819,0.8891468048095703,650.109621 -62799,Binary classification,Adaptive Random Forest,SMTP,0.9997452148157585,0.6521739130434783,1.0364971160888672,690.9092899999999 -64702,Binary classification,Adaptive Random Forest,SMTP,0.9997527086134681,0.6521739130434783,1.061361312866211,733.0436639999999 -66605,Binary classification,Adaptive Random Forest,SMTP,0.9997597741877364,0.6521739130434783,1.0615253448486328,776.5083099999999 -68508,Binary classification,Adaptive Random Forest,SMTP,0.9997664472243712,0.68,1.1361942291259766,821.3091039999999 -70411,Binary classification,Adaptive Random Forest,SMTP,0.9997727595512002,0.68,1.1363086700439453,867.450083 -72314,Binary classification,Adaptive Random Forest,SMTP,0.9997787396457068,0.68,1.1362667083740234,914.92252 -74217,Binary classification,Adaptive Random Forest,SMTP,0.9997844130645683,0.68,1.1445026397705078,963.73181 -76120,Binary classification,Adaptive Random Forest,SMTP,0.99978980280876,0.68,1.144460678100586,1013.878015 -78023,Binary classification,Adaptive Random Forest,SMTP,0.9997949296352311,0.68,1.1445598602294922,1065.363214 -79926,Binary classification,Adaptive Random Forest,SMTP,0.9997998123240538,0.68,1.154123306274414,1118.183887 -81829,Binary classification,Adaptive Random Forest,SMTP,0.9998044679082955,0.68,1.1541500091552734,1172.3398009999999 -83732,Binary classification,Adaptive Random Forest,SMTP,0.9998089118725442,0.68,1.1554203033447266,1227.8271129999998 -85635,Binary classification,Adaptive Random Forest,SMTP,0.9998131583249644,0.68,1.1554012298583984,1284.6488259999999 -87538,Binary classification,Adaptive Random Forest,SMTP,0.9998172201469093,0.68,1.1554012298583984,1342.8019989999998 -89441,Binary classification,Adaptive Random Forest,SMTP,0.999798747763864,0.6538461538461539,1.2423763275146484,1402.2973039999997 -91344,Binary classification,Adaptive Random Forest,SMTP,0.9998029405646848,0.6538461538461539,1.2579975128173828,1463.1308069999998 -93247,Binary classification,Adaptive Random Forest,SMTP,0.9998069622289428,0.6538461538461539,1.3168392181396484,1525.3166049999998 -95150,Binary classification,Adaptive Random Forest,SMTP,0.9998108230249398,0.6538461538461539,1.3265628814697266,1588.8623109999999 -106,Binary classification,Streaming Random Patches,Bananas,0.5238095238095238,0.41860465116279066,0.2278289794921875,0.437833 -212,Binary classification,Streaming Random Patches,Bananas,0.5308056872037915,0.4530386740331491,0.5808591842651367,1.338953 -318,Binary classification,Streaming Random Patches,Bananas,0.6025236593059937,0.5467625899280575,1.0978193283081055,2.727335 -424,Binary classification,Streaming Random Patches,Bananas,0.6690307328605201,0.6236559139784946,1.4776067733764648,4.6028839999999995 -530,Binary classification,Streaming Random Patches,Bananas,0.7069943289224953,0.6547884187082404,1.862696647644043,7.038838 -636,Binary classification,Streaming Random Patches,Bananas,0.7401574803149606,0.6983546617915906,2.7163190841674805,10.152007000000001 -742,Binary classification,Streaming Random Patches,Bananas,0.7624831309041835,0.7188498402555912,3.166998863220215,14.047877000000002 -848,Binary classification,Streaming Random Patches,Bananas,0.7792207792207793,0.7399165507649514,3.2281599044799805,18.677316 -954,Binary classification,Streaming Random Patches,Bananas,0.7911857292759706,0.754017305315204,2.101862907409668,23.96263 -1060,Binary classification,Streaming Random Patches,Bananas,0.8035882908404155,0.7657657657657657,1.8145971298217773,29.783676 -1166,Binary classification,Streaming Random Patches,Bananas,0.8077253218884121,0.7723577235772358,2.202631950378418,35.938969 -1272,Binary classification,Streaming Random Patches,Bananas,0.8127458693941778,0.7800369685767098,2.1398725509643555,42.34268 -1378,Binary classification,Streaming Random Patches,Bananas,0.8191721132897604,0.7855297157622738,2.4873228073120117,48.992986 -1484,Binary classification,Streaming Random Patches,Bananas,0.8240053944706676,0.7916999201915402,2.964076042175293,55.892559000000006 -1590,Binary classification,Streaming Random Patches,Bananas,0.8244178728760226,0.7934863064396744,3.3597822189331055,63.05187300000001 -1696,Binary classification,Streaming Random Patches,Bananas,0.8283185840707965,0.797776233495483,3.6371755599975586,70.47404700000001 -1802,Binary classification,Streaming Random Patches,Bananas,0.8306496390893948,0.802588996763754,4.0314741134643555,78.15954100000002 -1908,Binary classification,Streaming Random Patches,Bananas,0.8311484006292607,0.8048484848484848,4.422553062438965,86.11403800000002 -2014,Binary classification,Streaming Random Patches,Bananas,0.8310978638847492,0.806378132118451,4.790541648864746,94.35073800000002 -2120,Binary classification,Streaming Random Patches,Bananas,0.8343558282208589,0.8119978575254418,4.553057670593262,102.86313600000003 -2226,Binary classification,Streaming Random Patches,Bananas,0.8350561797752809,0.8126595201633486,4.935397148132324,111.64595000000003 -2332,Binary classification,Streaming Random Patches,Bananas,0.8361218361218361,0.8140214216163584,5.2692365646362305,120.70485400000003 -2438,Binary classification,Streaming Random Patches,Bananas,0.8379154698399671,0.8156789547363509,5.532515525817871,130.039733 -2544,Binary classification,Streaming Random Patches,Bananas,0.8391663389697208,0.8178173719376391,5.79874324798584,139.65537500000002 -2650,Binary classification,Streaming Random Patches,Bananas,0.840317100792752,0.81976991904559,5.978323936462402,149.55427200000003 -2756,Binary classification,Streaming Random Patches,Bananas,0.8442831215970962,0.8242523555919706,6.180487632751465,159.73621300000002 -2862,Binary classification,Streaming Random Patches,Bananas,0.846906675987417,0.826603325415677,6.367312431335449,170.195916 -2968,Binary classification,Streaming Random Patches,Bananas,0.8490057296932929,0.828352490421456,6.73636531829834,180.938789 -3074,Binary classification,Streaming Random Patches,Bananas,0.8503091441588024,0.8303834808259588,6.987029075622559,191.964907 -3180,Binary classification,Streaming Random Patches,Bananas,0.8530984586347908,0.832796276405299,7.20963191986084,203.271668 -3286,Binary classification,Streaming Random Patches,Bananas,0.8535768645357686,0.8329281000347343,7.467520713806152,214.85883900000002 -3392,Binary classification,Streaming Random Patches,Bananas,0.8554998525508699,0.8360107095046854,7.765227317810059,226.73409900000001 -3498,Binary classification,Streaming Random Patches,Bananas,0.8573062625107235,0.8374063212772891,8.026595115661621,238.898839 -3604,Binary classification,Streaming Random Patches,Bananas,0.8592839300582847,0.8388941849380362,8.217352867126465,251.354959 -3710,Binary classification,Streaming Random Patches,Bananas,0.8603397142086816,0.8405172413793103,8.39356517791748,264.100164 -3816,Binary classification,Streaming Random Patches,Bananas,0.8629095674967234,0.843553694286569,8.53998851776123,277.140513 -3922,Binary classification,Streaming Random Patches,Bananas,0.8630451415455241,0.8433945756780402,8.81647777557373,290.481842 -4028,Binary classification,Streaming Random Patches,Bananas,0.8629252545319096,0.8431818181818181,9.181269645690918,304.12213299999996 -4134,Binary classification,Streaming Random Patches,Bananas,0.8637793370433099,0.8442600276625173,9.408194541931152,318.06952099999995 -4240,Binary classification,Streaming Random Patches,Bananas,0.8655343241330502,0.8464439655172414,9.550837516784668,332.31391899999994 -4346,Binary classification,Streaming Random Patches,Bananas,0.8672036823935558,0.8484370895718414,9.808123588562012,346.87205299999994 -4452,Binary classification,Streaming Random Patches,Bananas,0.8669961806335655,0.8480492813141683,10.045561790466309,361.75512599999996 -4558,Binary classification,Streaming Random Patches,Bananas,0.8670177748518763,0.848424212106053,10.332926750183105,376.95892399999997 -4664,Binary classification,Streaming Random Patches,Bananas,0.8674672957323611,0.8494152046783626,10.668997764587402,392.48464199999995 -4770,Binary classification,Streaming Random Patches,Bananas,0.866429020759069,0.8480076354092102,11.001662254333496,408.34403199999997 -4876,Binary classification,Streaming Random Patches,Bananas,0.8666666666666667,0.8477751756440282,11.155909538269043,424.538143 -4982,Binary classification,Streaming Random Patches,Bananas,0.8678980124472997,0.8495656149977137,11.33142375946045,441.055719 -5088,Binary classification,Streaming Random Patches,Bananas,0.868291724002359,0.8499776085982983,11.580191612243652,457.901482 -5194,Binary classification,Streaming Random Patches,Bananas,0.868476795686501,0.8501864443957009,11.828421592712402,475.084789 -5300,Binary classification,Streaming Random Patches,Bananas,0.8690318928099642,0.850816852966466,12.135478019714355,492.613208 -906,Binary classification,Streaming Random Patches,Elec2,0.8839779005524862,0.8810872027180068,5.370833396911621,5.916109 -1812,Binary classification,Streaming Random Patches,Elec2,0.9033683048039757,0.8805460750853241,9.031210899353027,15.079536999999998 -2718,Binary classification,Streaming Random Patches,Elec2,0.9006256900993743,0.8771610555050046,13.98334789276123,27.621333 -3624,Binary classification,Streaming Random Patches,Elec2,0.9014628760695557,0.8784473953013278,18.162775993347168,43.541312 -4530,Binary classification,Streaming Random Patches,Elec2,0.9008611172444249,0.87226173541963,21.492114067077637,63.022082 -5436,Binary classification,Streaming Random Patches,Elec2,0.8943882244710212,0.863398381722989,25.749701499938965,86.108323 -6342,Binary classification,Streaming Random Patches,Elec2,0.8941807285917048,0.86403242147923,30.034838676452637,112.908333 -7248,Binary classification,Streaming Random Patches,Elec2,0.8907133986477163,0.8586723768736617,31.268176078796387,143.442151 -8154,Binary classification,Streaming Random Patches,Elec2,0.8912056911566295,0.8662343537927913,34.28826427459717,177.844322 -9060,Binary classification,Streaming Random Patches,Elec2,0.8912683519152225,0.8698639186154049,37.821166038513184,216.19197300000002 -9966,Binary classification,Streaming Random Patches,Elec2,0.8896136477671851,0.8707706766917294,41.62779140472412,258.820887 -10872,Binary classification,Streaming Random Patches,Elec2,0.8902584858798639,0.8737966783031843,45.202799797058105,305.617005 -11778,Binary classification,Streaming Random Patches,Elec2,0.8899549970281057,0.873015873015873,44.638304710388184,356.880829 -12684,Binary classification,Streaming Random Patches,Elec2,0.8842545139162659,0.8672934369915024,49.475626945495605,412.597623 -13590,Binary classification,Streaming Random Patches,Elec2,0.884538965339613,0.8694566935685165,52.02482509613037,472.74852699999997 -14496,Binary classification,Streaming Random Patches,Elec2,0.8854777509486029,0.871138022046266,51.378371238708496,537.275724 -15402,Binary classification,Streaming Random Patches,Elec2,0.8870203233556263,0.8725461470846764,45.887526512145996,605.997132 -16308,Binary classification,Streaming Random Patches,Elec2,0.8855092904887472,0.8701398066355985,50.451903343200684,678.971724 -17214,Binary classification,Streaming Random Patches,Elec2,0.8856097135885668,0.8680206448153361,42.41952419281006,756.021088 -18120,Binary classification,Streaming Random Patches,Elec2,0.8854241404050996,0.8677032882997705,44.88027477264404,837.367748 -19026,Binary classification,Streaming Random Patches,Elec2,0.8873587385019711,0.8687128591557923,30.162745475769043,922.595636 -19932,Binary classification,Streaming Random Patches,Elec2,0.887612262304952,0.8700243704305443,11.138346672058105,1011.395865 -20838,Binary classification,Streaming Random Patches,Elec2,0.8870758746460623,0.8696182190945864,16.41995906829834,1103.9159909999998 -21744,Binary classification,Streaming Random Patches,Elec2,0.8869981143356482,0.8677539157112869,20.066325187683105,1200.0114859999999 -22650,Binary classification,Streaming Random Patches,Elec2,0.8854254050951477,0.8647944563121971,23.948283195495605,1299.755313 -23556,Binary classification,Streaming Random Patches,Elec2,0.8836765018042878,0.8620481321115698,27.3106107711792,1403.457577 -24462,Binary classification,Streaming Random Patches,Elec2,0.8826294918441601,0.859903381642512,30.048666954040527,1511.3882449999999 -25368,Binary classification,Streaming Random Patches,Elec2,0.8818149564394686,0.8590105342362679,27.528754234313965,1623.579427 -26274,Binary classification,Streaming Random Patches,Elec2,0.882883568682678,0.8599899895345134,32.44980525970459,1739.735045 -27180,Binary classification,Streaming Random Patches,Elec2,0.8840649030501491,0.8618891080429543,36.050021171569824,1859.9378299999998 -28086,Binary classification,Streaming Random Patches,Elec2,0.8826063735089905,0.8595287801968386,42.42660045623779,1984.3543829999999 -28992,Binary classification,Streaming Random Patches,Elec2,0.8825842502845711,0.8587200132813148,47.772982597351074,2113.0065689999997 -29898,Binary classification,Streaming Random Patches,Elec2,0.8824296752182493,0.8583175460518361,47.37248516082764,2246.2362179999996 -30804,Binary classification,Streaming Random Patches,Elec2,0.8822517287277213,0.8574348492590699,51.80053234100342,2383.7768769999993 -31710,Binary classification,Streaming Random Patches,Elec2,0.8812324576618625,0.8558965332517028,57.629515647888184,2525.7469279999996 -32616,Binary classification,Streaming Random Patches,Elec2,0.8799018856354438,0.8544785823085782,57.82863521575928,2672.1227849999996 -33522,Binary classification,Streaming Random Patches,Elec2,0.8803436651651204,0.8553604269589989,49.21776485443115,2822.5668949999995 -34428,Binary classification,Streaming Random Patches,Elec2,0.8800360182414965,0.8549248278769145,40.49333477020264,2976.7213359999996 -35334,Binary classification,Streaming Random Patches,Elec2,0.8794045226841762,0.8535789148139239,46.44182109832764,3134.3897929999994 -36240,Binary classification,Streaming Random Patches,Elec2,0.8791081431606832,0.8524468694217102,49.9929723739624,3295.6545219999994 -37146,Binary classification,Streaming Random Patches,Elec2,0.8779378112801185,0.8505800158186132,54.79160213470459,3460.6177849999995 -38052,Binary classification,Streaming Random Patches,Elec2,0.8777693096107855,0.8499532212794787,58.49489498138428,3629.3978659999993 -38958,Binary classification,Streaming Random Patches,Elec2,0.8784814025720666,0.8511414376454312,60.34530162811279,3802.3246269999995 -39864,Binary classification,Streaming Random Patches,Elec2,0.8790106113438527,0.8529528339278637,65.39763927459717,3979.2983159999994 -40770,Binary classification,Streaming Random Patches,Elec2,0.8795408275895902,0.8548286972715718,71.3544225692749,4160.658093999999 -41676,Binary classification,Streaming Random Patches,Elec2,0.8803359328134374,0.8566995201287319,58.179503440856934,4346.269614 -42582,Binary classification,Streaming Random Patches,Elec2,0.8808858411028393,0.8576000898422146,62.87830638885498,4535.872427 -43488,Binary classification,Streaming Random Patches,Elec2,0.8812518683744567,0.8580850829943938,52.03429698944092,4729.486494000001 -44394,Binary classification,Streaming Random Patches,Elec2,0.8814452729033857,0.8579065309538595,55.38854122161865,4926.889749000001 -45300,Binary classification,Streaming Random Patches,Elec2,0.8822490562705578,0.8591125198098257,58.343642234802246,5128.273015000001 -25,Binary classification,Streaming Random Patches,Phishing,0.75,0.7692307692307692,0.6668167114257812,0.240714 -50,Binary classification,Streaming Random Patches,Phishing,0.7959183673469388,0.7826086956521738,1.0995216369628906,0.6628620000000001 -75,Binary classification,Streaming Random Patches,Phishing,0.8378378378378378,0.8378378378378377,1.2478713989257812,1.252732 -100,Binary classification,Streaming Random Patches,Phishing,0.8686868686868687,0.8686868686868686,1.3291473388671875,2.007362 -125,Binary classification,Streaming Random Patches,Phishing,0.8629032258064516,0.8640000000000001,1.6638565063476562,2.948271 -150,Binary classification,Streaming Random Patches,Phishing,0.8657718120805369,0.8701298701298702,1.6782913208007812,4.062992 -175,Binary classification,Streaming Random Patches,Phishing,0.8850574712643678,0.8809523809523809,1.7604293823242188,5.350090000000001 -200,Binary classification,Streaming Random Patches,Phishing,0.8844221105527639,0.8795811518324608,1.9450531005859375,6.8260000000000005 -225,Binary classification,Streaming Random Patches,Phishing,0.8883928571428571,0.8803827751196173,2.0522689819335938,8.475057 -250,Binary classification,Streaming Random Patches,Phishing,0.8795180722891566,0.8695652173913043,2.2534027099609375,10.305544 -275,Binary classification,Streaming Random Patches,Phishing,0.8795620437956204,0.8685258964143425,2.2874794006347656,12.343079 -300,Binary classification,Streaming Random Patches,Phishing,0.8795986622073578,0.8666666666666666,2.5460891723632812,14.59325 -325,Binary classification,Streaming Random Patches,Phishing,0.8796296296296297,0.8641114982578397,2.7360763549804688,17.040637 -350,Binary classification,Streaming Random Patches,Phishing,0.8739255014326648,0.8562091503267973,2.8272323608398438,19.684707 -375,Binary classification,Streaming Random Patches,Phishing,0.8743315508021391,0.8553846153846153,3.0366439819335938,22.544767999999998 -400,Binary classification,Streaming Random Patches,Phishing,0.8721804511278195,0.8513119533527697,3.1284713745117188,25.604048 -425,Binary classification,Streaming Random Patches,Phishing,0.875,0.8515406162464987,3.107044219970703,28.861271 -450,Binary classification,Streaming Random Patches,Phishing,0.8752783964365256,0.851063829787234,3.134662628173828,32.309061 -475,Binary classification,Streaming Random Patches,Phishing,0.8776371308016878,0.8557213930348259,3.1429481506347656,35.963049 -500,Binary classification,Streaming Random Patches,Phishing,0.8817635270541082,0.8624708624708626,3.273334503173828,39.811295 -525,Binary classification,Streaming Random Patches,Phishing,0.8854961832061069,0.8642533936651584,3.4039268493652344,43.854378000000004 -550,Binary classification,Streaming Random Patches,Phishing,0.8834244080145719,0.8626609442060086,3.5256080627441406,48.092981 -575,Binary classification,Streaming Random Patches,Phishing,0.8832752613240418,0.8618556701030927,3.730621337890625,52.525854 -600,Binary classification,Streaming Random Patches,Phishing,0.8864774624373957,0.8634538152610441,3.6573638916015625,57.153422000000006 -625,Binary classification,Streaming Random Patches,Phishing,0.8862179487179487,0.8605108055009822,3.691375732421875,61.980237 -650,Binary classification,Streaming Random Patches,Phishing,0.889060092449923,0.8656716417910448,3.879222869873047,67.019405 -675,Binary classification,Streaming Random Patches,Phishing,0.8887240356083086,0.8681898066783831,4.001224517822266,72.2172 -700,Binary classification,Streaming Random Patches,Phishing,0.8927038626609443,0.8713550600343053,4.033683776855469,77.519148 -725,Binary classification,Streaming Random Patches,Phishing,0.893646408839779,0.8743882544861339,4.112117767333984,82.931309 -750,Binary classification,Streaming Random Patches,Phishing,0.8958611481975968,0.8773584905660378,4.362846374511719,88.455864 -775,Binary classification,Streaming Random Patches,Phishing,0.8953488372093024,0.8763358778625954,4.623798370361328,94.093371 -800,Binary classification,Streaming Random Patches,Phishing,0.8936170212765957,0.8755490483162518,4.795074462890625,99.843891 -825,Binary classification,Streaming Random Patches,Phishing,0.8932038834951457,0.876056338028169,5.260898590087891,105.71761 -850,Binary classification,Streaming Random Patches,Phishing,0.8939929328621908,0.8767123287671234,5.305454254150391,111.70711899999999 -875,Binary classification,Streaming Random Patches,Phishing,0.8958810068649885,0.8781793842034805,5.302814483642578,117.814416 -900,Binary classification,Streaming Random Patches,Phishing,0.8976640711902113,0.8795811518324608,5.489250183105469,124.034882 -925,Binary classification,Streaming Random Patches,Phishing,0.9004329004329005,0.8838383838383839,5.587982177734375,130.365318 -950,Binary classification,Streaming Random Patches,Phishing,0.9020021074815595,0.8869987849331712,5.680080413818359,136.807919 -975,Binary classification,Streaming Random Patches,Phishing,0.9045174537987679,0.889679715302491,5.6697998046875,143.36438 -1000,Binary classification,Streaming Random Patches,Phishing,0.9049049049049049,0.8901734104046244,5.689472198486328,150.035681 -1025,Binary classification,Streaming Random Patches,Phishing,0.9052734375,0.8911335578002244,5.899868011474609,156.818681 -1050,Binary classification,Streaming Random Patches,Phishing,0.9065776930409915,0.8930131004366813,6.014961242675781,163.714224 -1075,Binary classification,Streaming Random Patches,Phishing,0.9068901303538175,0.8940677966101694,6.185920715332031,170.723809 -1100,Binary classification,Streaming Random Patches,Phishing,0.908098271155596,0.8957688338493291,6.174674987792969,177.84336199999998 -1125,Binary classification,Streaming Random Patches,Phishing,0.9092526690391459,0.8979999999999999,6.282234191894531,185.077801 -1150,Binary classification,Streaming Random Patches,Phishing,0.9103568320278503,0.8991185112634672,6.438121795654297,192.421784 -1175,Binary classification,Streaming Random Patches,Phishing,0.9080068143100511,0.8963531669865643,6.65753173828125,199.88529400000002 -1200,Binary classification,Streaming Random Patches,Phishing,0.9090909090909091,0.8972667295004714,6.8576507568359375,207.46223300000003 -1225,Binary classification,Streaming Random Patches,Phishing,0.9101307189542484,0.898336414048059,6.963230133056641,215.149468 -1250,Binary classification,Streaming Random Patches,Phishing,0.911128903122498,0.8999098286744815,7.0904388427734375,222.947789 -1903,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.16929054260253906,5.29779 -3806,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17051124572753906,13.064902 -5709,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17173194885253906,23.298966999999998 -7612,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17173194885253906,35.999469999999995 -9515,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17173194885253906,51.163714999999996 -11418,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17295265197753906,68.795189 -13321,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17295265197753906,88.896271 -15224,Binary classification,Streaming Random Patches,SMTP,0.9998029297773107,0.8421052631578948,0.45761585235595703,111.604078 -17127,Binary classification,Streaming Random Patches,SMTP,0.9998248277472849,0.8695652173913044,0.4529237747192383,137.211141 -19030,Binary classification,Streaming Random Patches,SMTP,0.9998423458931105,0.8695652173913044,0.4529237747192383,165.706974 -20933,Binary classification,Streaming Random Patches,SMTP,0.9998566787693484,0.8695652173913044,0.4541444778442383,197.110825 -22836,Binary classification,Streaming Random Patches,SMTP,0.999868622728268,0.8695652173913044,0.4541444778442383,231.41631 -24739,Binary classification,Streaming Random Patches,SMTP,0.9998787290807665,0.8695652173913044,0.4541444778442383,268.619505 -26642,Binary classification,Streaming Random Patches,SMTP,0.9998498554859052,0.8333333333333333,0.4768953323364258,308.72625800000003 -28545,Binary classification,Streaming Random Patches,SMTP,0.999859865470852,0.8333333333333333,0.4768953323364258,351.754387 -30448,Binary classification,Streaming Random Patches,SMTP,0.9998686241665845,0.8333333333333333,0.4768953323364258,397.678816 -32351,Binary classification,Streaming Random Patches,SMTP,0.9998763523956723,0.8333333333333333,0.48476123809814453,446.50390899999996 -34254,Binary classification,Streaming Random Patches,SMTP,0.9998832219075702,0.8333333333333333,0.48471546173095703,498.23545599999994 -36157,Binary classification,Streaming Random Patches,SMTP,0.9998893682929527,0.8333333333333333,0.48471546173095703,552.8655449999999 -38060,Binary classification,Streaming Random Patches,SMTP,0.9998949000236474,0.8333333333333333,0.4847383499145508,610.4097629999999 -39963,Binary classification,Streaming Random Patches,SMTP,0.9998999049096642,0.8333333333333333,0.4847383499145508,670.8597949999998 -41866,Binary classification,Streaming Random Patches,SMTP,0.999904454795175,0.8333333333333333,0.4859590530395508,734.2226509999998 -43769,Binary classification,Streaming Random Patches,SMTP,0.9999086090294279,0.8333333333333333,0.4859590530395508,800.4598269999998 -45672,Binary classification,Streaming Random Patches,SMTP,0.9999124170699131,0.8333333333333333,0.4859590530395508,869.5777599999998 -47575,Binary classification,Streaming Random Patches,SMTP,0.9998738806911338,0.7692307692307692,0.5104570388793945,941.5744449999997 -49478,Binary classification,Streaming Random Patches,SMTP,0.9998787315318228,0.7692307692307692,0.5104570388793945,1016.4635609999998 -51381,Binary classification,Streaming Random Patches,SMTP,0.999883223043986,0.7999999999999999,0.593510627746582,1094.2664539999998 -53284,Binary classification,Streaming Random Patches,SMTP,0.9998873937278306,0.7999999999999999,0.593510627746582,1174.983157 -55187,Binary classification,Streaming Random Patches,SMTP,0.9998912767730946,0.7999999999999999,0.6139116287231445,1258.5948749999998 -57090,Binary classification,Streaming Random Patches,SMTP,0.9997722853789697,0.6829268292682927,1.0072031021118164,1345.2451959999999 -58993,Binary classification,Streaming Random Patches,SMTP,0.9997796311364253,0.6829268292682927,1.0209360122680664,1434.9545749999997 -60896,Binary classification,Streaming Random Patches,SMTP,0.9997865177765005,0.6829268292682927,1.0209360122680664,1527.6493129999997 -62799,Binary classification,Streaming Random Patches,SMTP,0.9997611388897736,0.6511627906976744,1.1647844314575195,1623.3843759999997 -64702,Binary classification,Streaming Random Patches,SMTP,0.9997681643251264,0.6511627906976744,1.185868263244629,1722.1888749999998 -66605,Binary classification,Streaming Random Patches,SMTP,0.999774788301003,0.6511627906976744,1.195298194885254,1824.0406229999999 -68508,Binary classification,Streaming Random Patches,SMTP,0.9997810442728481,0.6808510638297872,1.2139558792114258,1928.948048 -70411,Binary classification,Streaming Random Patches,SMTP,0.9997869620792501,0.6808510638297872,1.2139787673950195,2036.917723 -72314,Binary classification,Streaming Random Patches,SMTP,0.9997925684178501,0.6808510638297872,1.2139787673950195,2147.927521 -74217,Binary classification,Streaming Random Patches,SMTP,0.9997978872480328,0.6808510638297872,1.2303056716918945,2261.972689 -76120,Binary classification,Streaming Random Patches,SMTP,0.9998029401332125,0.6808510638297872,1.2302827835083008,2379.0496510000003 -78023,Binary classification,Streaming Random Patches,SMTP,0.9998077465330292,0.6808510638297872,1.2303743362426758,2499.17284 -79926,Binary classification,Streaming Random Patches,SMTP,0.9998123240538004,0.6808510638297872,1.2315950393676758,2622.406245 -81829,Binary classification,Streaming Random Patches,SMTP,0.999816688664027,0.6808510638297872,1.2398500442504883,2748.681939 -83732,Binary classification,Streaming Random Patches,SMTP,0.9998208548805102,0.6808510638297872,1.2410707473754883,2878.007463 -85635,Binary classification,Streaming Random Patches,SMTP,0.9998248359296541,0.6808510638297872,1.2410707473754883,3010.373781 -87538,Binary classification,Streaming Random Patches,SMTP,0.9998286438877275,0.6808510638297872,1.2410707473754883,3145.7757699999997 -89441,Binary classification,Streaming Random Patches,SMTP,0.9998211091234347,0.6666666666666667,1.338292121887207,3284.247438 -91344,Binary classification,Streaming Random Patches,SMTP,0.9998248360574976,0.6666666666666667,1.3284997940063477,3425.773292 -93247,Binary classification,Streaming Random Patches,SMTP,0.9998284108701714,0.6666666666666667,1.4100160598754883,3570.387533 -95150,Binary classification,Streaming Random Patches,SMTP,0.9998318426888354,0.6666666666666667,1.4101762771606445,3718.075789 -106,Binary classification,k-Nearest Neighbors,Bananas,0.7619047619047619,0.736842105263158,0.041853904724121094,0.035102 -212,Binary classification,k-Nearest Neighbors,Bananas,0.8199052132701422,0.7978723404255319,0.041350364685058594,0.119282 -318,Binary classification,k-Nearest Neighbors,Bananas,0.8391167192429022,0.8197879858657242,0.04188060760498047,0.25200100000000003 -424,Binary classification,k-Nearest Neighbors,Bananas,0.8581560283687943,0.8412698412698413,0.04188060760498047,0.433555 -530,Binary classification,k-Nearest Neighbors,Bananas,0.8525519848771267,0.8266666666666667,0.04137706756591797,0.663618 -636,Binary classification,k-Nearest Neighbors,Bananas,0.8488188976377953,0.8222222222222222,0.04188060760498047,0.942176 -742,Binary classification,k-Nearest Neighbors,Bananas,0.8461538461538461,0.8155339805825242,0.04188060760498047,1.268723 -848,Binary classification,k-Nearest Neighbors,Bananas,0.8488783943329398,0.8217270194986072,0.04137706756591797,1.64013 -954,Binary classification,k-Nearest Neighbors,Bananas,0.8541448058761805,0.8268991282689911,0.04188060760498047,2.060089 -1060,Binary classification,k-Nearest Neighbors,Bananas,0.8583569405099151,0.8299319727891157,0.04188060760498047,2.5284690000000003 -1166,Binary classification,k-Nearest Neighbors,Bananas,0.8549356223175966,0.8263103802672147,0.04137706756591797,3.0446800000000005 -1272,Binary classification,k-Nearest Neighbors,Bananas,0.8575924468922108,0.8309990662931841,0.04188060760498047,3.6088570000000004 -1378,Binary classification,k-Nearest Neighbors,Bananas,0.8576615831517792,0.8298611111111109,0.04188060760498047,4.221075000000001 -1484,Binary classification,k-Nearest Neighbors,Bananas,0.8604180714767363,0.833467417538214,0.04137706756591797,4.881183000000001 -1590,Binary classification,k-Nearest Neighbors,Bananas,0.8590308370044053,0.8318318318318318,0.04188060760498047,5.5893250000000005 -1696,Binary classification,k-Nearest Neighbors,Bananas,0.8613569321533924,0.8341566690190544,0.04137706756591797,6.345487 -1802,Binary classification,k-Nearest Neighbors,Bananas,0.8617434758467518,0.836076366030283,0.04137706756591797,7.149425000000001 -1908,Binary classification,k-Nearest Neighbors,Bananas,0.8573675930781332,0.8329238329238329,0.04188060760498047,8.001348 -2014,Binary classification,k-Nearest Neighbors,Bananas,0.8544461003477397,0.8317059161401493,0.04137706756591797,8.901356 -2120,Binary classification,k-Nearest Neighbors,Bananas,0.8551203397829165,0.8343227199136536,0.04137706756591797,9.849324 -2226,Binary classification,k-Nearest Neighbors,Bananas,0.8512359550561798,0.8301693175987686,0.04188060760498047,10.845353999999999 -2332,Binary classification,k-Nearest Neighbors,Bananas,0.8511368511368511,0.8304836345872008,0.04137706756591797,11.889458999999999 -2438,Binary classification,k-Nearest Neighbors,Bananas,0.8518670496512105,0.8312295465170642,0.04137706756591797,12.981528999999998 -2544,Binary classification,k-Nearest Neighbors,Bananas,0.8509634290208415,0.8307280035730238,0.04188060760498047,14.121683999999998 -2650,Binary classification,k-Nearest Neighbors,Bananas,0.8501321253303133,0.8307036247334755,0.04137706756591797,15.309940999999998 -2756,Binary classification,k-Nearest Neighbors,Bananas,0.8500907441016334,0.8310838445807771,0.04137706756591797,16.545870999999998 -2862,Binary classification,k-Nearest Neighbors,Bananas,0.8504019573575673,0.8310970797158642,0.04188060760498047,17.828975 -2968,Binary classification,k-Nearest Neighbors,Bananas,0.8513650151668352,0.8317436093094239,0.04137706756591797,19.159858 -3074,Binary classification,k-Nearest Neighbors,Bananas,0.8490074845427921,0.8294117647058825,0.04188060760498047,20.53851 -3180,Binary classification,k-Nearest Neighbors,Bananas,0.8499528153507392,0.8298251872993221,0.04188060760498047,21.964264999999997 -3286,Binary classification,k-Nearest Neighbors,Bananas,0.8502283105022831,0.8295218295218295,0.04137706756591797,23.437226 -3392,Binary classification,k-Nearest Neighbors,Bananas,0.8493069890887643,0.8294961628294961,0.04188060760498047,24.957963 -3498,Binary classification,k-Nearest Neighbors,Bananas,0.8507291964541035,0.830299089726918,0.04188060760498047,26.526512 -3604,Binary classification,k-Nearest Neighbors,Bananas,0.852345267832362,0.8313253012048194,0.04137706756591797,28.143271 -3710,Binary classification,k-Nearest Neighbors,Bananas,0.8522512806686439,0.8315918869084205,0.04188060760498047,29.808070999999998 -3816,Binary classification,k-Nearest Neighbors,Bananas,0.8521625163826999,0.8315412186379928,0.04188060760498047,31.520677 -3922,Binary classification,k-Nearest Neighbors,Bananas,0.8520785513899516,0.8309037900874635,0.04137706756591797,33.281115 -4028,Binary classification,k-Nearest Neighbors,Bananas,0.8505090638192203,0.8290743895513913,0.04188060760498047,35.088929 -4134,Binary classification,k-Nearest Neighbors,Bananas,0.8499879022501815,0.8286346047540077,0.04188060760498047,36.944224 -4240,Binary classification,k-Nearest Neighbors,Bananas,0.851380042462845,0.8306451612903226,0.04137706756591797,38.847373 -4346,Binary classification,k-Nearest Neighbors,Bananas,0.8515535097813579,0.8308418568056648,0.04188060760498047,40.797646 -4452,Binary classification,k-Nearest Neighbors,Bananas,0.8508200404403505,0.8296562339661364,0.04188060760498047,42.797836000000004 -4558,Binary classification,k-Nearest Neighbors,Bananas,0.8507790212859337,0.8302546180728907,0.04137706756591797,44.846666000000006 -4664,Binary classification,k-Nearest Neighbors,Bananas,0.8496675959682608,0.8294818778885916,0.04188060760498047,46.942725 -4770,Binary classification,k-Nearest Neighbors,Bananas,0.848605577689243,0.8280133396855646,0.04188060760498047,49.086211000000006 -4876,Binary classification,k-Nearest Neighbors,Bananas,0.848,0.8265855370933771,0.04137706756591797,51.27634200000001 -4982,Binary classification,k-Nearest Neighbors,Bananas,0.8490262999397711,0.8281535648994516,0.04188060760498047,53.513903000000006 -5088,Binary classification,k-Nearest Neighbors,Bananas,0.84863377236092,0.8275089605734768,0.04137706756591797,55.79899700000001 -5194,Binary classification,k-Nearest Neighbors,Bananas,0.8488349701521278,0.8278131169116035,0.04137706756591797,58.13196400000001 -5300,Binary classification,k-Nearest Neighbors,Bananas,0.8484619739573505,0.8274231678486997,0.04188060760498047,60.51276400000001 -906,Binary classification,k-Nearest Neighbors,Elec2,0.9049723756906077,0.903153153153153,0.06160545349121094,0.342387 -1812,Binary classification,k-Nearest Neighbors,Elec2,0.9260077305356157,0.9075862068965518,0.06160545349121094,1.0313949999999998 -2718,Binary classification,k-Nearest Neighbors,Elec2,0.9120353330879647,0.8907178783721992,0.06160545349121094,2.0623139999999998 -3624,Binary classification,k-Nearest Neighbors,Elec2,0.9149875793541264,0.8948087431693988,0.06210899353027344,3.4346819999999996 -4530,Binary classification,k-Nearest Neighbors,Elec2,0.9116802826230956,0.8866855524079319,0.06210899353027344,5.146577 -5436,Binary classification,k-Nearest Neighbors,Elec2,0.9098436062557498,0.8844884488448844,0.06210899353027344,7.219511 -6342,Binary classification,k-Nearest Neighbors,Elec2,0.9090048888187983,0.8844382134988984,0.06160545349121094,9.664227 -7248,Binary classification,k-Nearest Neighbors,Elec2,0.9064440458120602,0.881509961551905,0.06160545349121094,12.478866 -8154,Binary classification,k-Nearest Neighbors,Elec2,0.9053109284925794,0.8852556480380499,0.06160545349121094,15.66161 -9060,Binary classification,k-Nearest Neighbors,Elec2,0.9076056959929352,0.8903732809430256,0.06210899353027344,19.203613999999998 -9966,Binary classification,k-Nearest Neighbors,Elec2,0.9092824887104867,0.8943431510051426,0.06210899353027344,23.103247999999997 -10872,Binary classification,k-Nearest Neighbors,Elec2,0.9103118388372735,0.8971193415637859,0.06210899353027344,27.356534999999997 -11778,Binary classification,k-Nearest Neighbors,Elec2,0.9094845886049079,0.896323672437269,0.06210899353027344,31.965635999999996 -12684,Binary classification,k-Nearest Neighbors,Elec2,0.9086178348971063,0.8953120765965135,0.06160545349121094,36.933350999999995 -13590,Binary classification,k-Nearest Neighbors,Elec2,0.9089704908381779,0.8970796239287795,0.06160545349121094,42.24853099999999 -14496,Binary classification,k-Nearest Neighbors,Elec2,0.9087271472921697,0.8973861785464982,0.06160545349121094,47.913715999999994 -15402,Binary classification,k-Nearest Neighbors,Elec2,0.9094863969872086,0.8977556109725686,0.06210899353027344,53.917573999999995 -16308,Binary classification,k-Nearest Neighbors,Elec2,0.9067271723799595,0.8941323867195656,0.06210899353027344,60.251802 -17214,Binary classification,k-Nearest Neighbors,Elec2,0.9044907918433742,0.8901656867985035,0.06210899353027344,66.911875 -18120,Binary classification,k-Nearest Neighbors,Elec2,0.9041889729013742,0.8895674300254454,0.06844902038574219,73.89174799999999 -19026,Binary classification,k-Nearest Neighbors,Elec2,0.9049145860709593,0.8893239522789844,0.06844902038574219,81.18615299999999 -19932,Binary classification,k-Nearest Neighbors,Elec2,0.9037679995986152,0.8889403590040533,0.06844902038574219,88.788198 -20838,Binary classification,k-Nearest Neighbors,Elec2,0.9006094927292796,0.8855990719770204,0.06895256042480469,96.68869799999999 -21744,Binary classification,k-Nearest Neighbors,Elec2,0.8996918548498367,0.8828112406641234,0.06895256042480469,104.88540299999998 -22650,Binary classification,k-Nearest Neighbors,Elec2,0.8992891518389333,0.8815987542174929,0.06895256042480469,113.37844899999999 -23556,Binary classification,k-Nearest Neighbors,Elec2,0.8977711738484399,0.8795999999999999,0.06844902038574219,122.16799199999998 -24462,Binary classification,k-Nearest Neighbors,Elec2,0.8973059155390213,0.8783652914971914,0.06844902038574219,131.253708 -25368,Binary classification,k-Nearest Neighbors,Elec2,0.8955729885284031,0.8765782975352934,0.06844902038574219,140.635731 -26274,Binary classification,k-Nearest Neighbors,Elec2,0.8962052297034979,0.8771012663932579,0.06895256042480469,150.314023 -27180,Binary classification,k-Nearest Neighbors,Elec2,0.896133043894183,0.8774792760730873,0.06895256042480469,160.288049 -28086,Binary classification,k-Nearest Neighbors,Elec2,0.8954602100765533,0.8762330326279403,0.06895256042480469,170.561257 -28992,Binary classification,k-Nearest Neighbors,Elec2,0.8944500017246731,0.874518166160912,0.06895256042480469,181.13060800000002 -29898,Binary classification,k-Nearest Neighbors,Elec2,0.8934006756530756,0.8730025901574019,0.06844902038574219,191.99638700000003 -30804,Binary classification,k-Nearest Neighbors,Elec2,0.8926403272408532,0.8713780094123137,0.06844902038574219,203.15792800000003 -31710,Binary classification,k-Nearest Neighbors,Elec2,0.8906304203853795,0.8690233401314299,0.06844902038574219,214.61555800000002 -32616,Binary classification,k-Nearest Neighbors,Elec2,0.8895293576575195,0.8679010082493126,0.06895256042480469,226.36836300000002 -33522,Binary classification,k-Nearest Neighbors,Elec2,0.8885773097461293,0.8667926816220265,0.06895256042480469,238.416278 -34428,Binary classification,k-Nearest Neighbors,Elec2,0.8875010892613355,0.8655721772933948,0.06895256042480469,250.75965100000002 -35334,Binary classification,k-Nearest Neighbors,Elec2,0.88653666544024,0.8635976999761832,0.06844902038574219,263.39898200000005 -36240,Binary classification,k-Nearest Neighbors,Elec2,0.8864483015535749,0.8623423543973505,0.06844902038574219,276.33438000000007 -37146,Binary classification,k-Nearest Neighbors,Elec2,0.8854219948849105,0.8608149650075216,0.06844902038574219,289.5663660000001 -38052,Binary classification,k-Nearest Neighbors,Elec2,0.885574623531576,0.8604308244646749,0.06895256042480469,303.09499200000005 -38958,Binary classification,k-Nearest Neighbors,Elec2,0.8853864517288292,0.8605515475186608,0.06895256042480469,316.91924300000005 -39864,Binary classification,k-Nearest Neighbors,Elec2,0.8853071770815042,0.8614545454545455,0.06895256042480469,331.03949900000003 -40770,Binary classification,k-Nearest Neighbors,Elec2,0.8846672717015379,0.8618034328709147,0.06895256042480469,345.45792700000004 -41676,Binary classification,k-Nearest Neighbors,Elec2,0.8847030593881223,0.862796607749636,0.06844902038574219,360.17251400000004 -42582,Binary classification,k-Nearest Neighbors,Elec2,0.8848547474225593,0.8633767102293309,0.06844902038574219,375.183354 -43488,Binary classification,k-Nearest Neighbors,Elec2,0.8845632027962379,0.86316305947773,0.06844902038574219,390.490282 -44394,Binary classification,k-Nearest Neighbors,Elec2,0.8843511364404298,0.8626023657870793,0.06895256042480469,406.09386 -45300,Binary classification,k-Nearest Neighbors,Elec2,0.8844345349786971,0.8629042817860416,0.06895256042480469,421.994249 -25,Binary classification,k-Nearest Neighbors,Phishing,0.6666666666666666,0.7499999999999999,0.021185874938964844,0.008448 -50,Binary classification,k-Nearest Neighbors,Phishing,0.7959183673469388,0.8076923076923077,0.037901878356933594,0.023055 -75,Binary classification,k-Nearest Neighbors,Phishing,0.8513513513513513,0.8641975308641976,0.054114341735839844,0.045431 -100,Binary classification,k-Nearest Neighbors,Phishing,0.8484848484848485,0.854368932038835,0.07080364227294922,0.07803199999999999 -125,Binary classification,k-Nearest Neighbors,Phishing,0.8548387096774194,0.859375,0.07121944427490234,0.12217799999999998 -150,Binary classification,k-Nearest Neighbors,Phishing,0.8590604026845637,0.8679245283018867,0.07071590423583984,0.17806999999999998 -175,Binary classification,k-Nearest Neighbors,Phishing,0.8735632183908046,0.8735632183908046,0.07121944427490234,0.24575799999999998 -200,Binary classification,k-Nearest Neighbors,Phishing,0.8693467336683417,0.8686868686868686,0.07071590423583984,0.325372 -225,Binary classification,k-Nearest Neighbors,Phishing,0.8616071428571429,0.8571428571428571,0.07121944427490234,0.416902 -250,Binary classification,k-Nearest Neighbors,Phishing,0.8473895582329317,0.8416666666666667,0.07121944427490234,0.520874 -275,Binary classification,k-Nearest Neighbors,Phishing,0.8467153284671532,0.8384615384615385,0.07092952728271484,0.637346 -300,Binary classification,k-Nearest Neighbors,Phishing,0.8494983277591973,0.8375451263537907,0.07143306732177734,0.76611 -325,Binary classification,k-Nearest Neighbors,Phishing,0.8518518518518519,0.8356164383561644,0.07092952728271484,0.9070609999999999 -350,Binary classification,k-Nearest Neighbors,Phishing,0.8538681948424068,0.8349514563106796,0.07092952728271484,1.0603289999999999 -375,Binary classification,k-Nearest Neighbors,Phishing,0.8502673796791443,0.8271604938271604,0.07143306732177734,1.2260179999999998 -400,Binary classification,k-Nearest Neighbors,Phishing,0.849624060150376,0.8224852071005918,0.07092952728271484,1.4039159999999997 -425,Binary classification,k-Nearest Neighbors,Phishing,0.8514150943396226,0.8194842406876792,0.07143306732177734,1.5942239999999996 -450,Binary classification,k-Nearest Neighbors,Phishing,0.8552338530066815,0.8219178082191781,0.07143306732177734,1.7968749999999996 -475,Binary classification,k-Nearest Neighbors,Phishing,0.8481012658227848,0.8134715025906737,0.07092952728271484,2.0118709999999997 -500,Binary classification,k-Nearest Neighbors,Phishing,0.8476953907815631,0.8164251207729469,0.07143306732177734,2.2391799999999997 -525,Binary classification,k-Nearest Neighbors,Phishing,0.8492366412213741,0.8141176470588235,0.07092952728271484,2.479037 -550,Binary classification,k-Nearest Neighbors,Phishing,0.8506375227686703,0.8177777777777777,0.07143306732177734,2.7311829999999997 -575,Binary classification,k-Nearest Neighbors,Phishing,0.8519163763066202,0.8187633262260127,0.07143306732177734,2.9956419999999997 -600,Binary classification,k-Nearest Neighbors,Phishing,0.8514190317195326,0.8149688149688149,0.07092952728271484,3.2723329999999997 -625,Binary classification,k-Nearest Neighbors,Phishing,0.8509615384615384,0.8105906313645621,0.07143306732177734,3.5612229999999996 -650,Binary classification,k-Nearest Neighbors,Phishing,0.8567026194144838,0.8208092485549132,0.07092952728271484,3.8623549999999995 -675,Binary classification,k-Nearest Neighbors,Phishing,0.8590504451038575,0.8275862068965517,0.07143306732177734,4.17585 -700,Binary classification,k-Nearest Neighbors,Phishing,0.8640915593705293,0.831858407079646,0.07143306732177734,4.5015719999999995 -725,Binary classification,k-Nearest Neighbors,Phishing,0.8646408839779005,0.8355704697986577,0.07092952728271484,4.839746999999999 -750,Binary classification,k-Nearest Neighbors,Phishing,0.8624833110814419,0.8341384863123994,0.07143306732177734,5.190338999999999 -775,Binary classification,k-Nearest Neighbors,Phishing,0.8591731266149871,0.8294209702660407,0.07092952728271484,5.552793999999999 -800,Binary classification,k-Nearest Neighbors,Phishing,0.8573216520650814,0.8288288288288288,0.07092952728271484,5.927242999999999 -825,Binary classification,k-Nearest Neighbors,Phishing,0.8567961165048543,0.8299711815561961,0.07143306732177734,6.313648999999999 -850,Binary classification,k-Nearest Neighbors,Phishing,0.8598351001177856,0.8330995792426368,0.07092952728271484,6.712033999999999 -875,Binary classification,k-Nearest Neighbors,Phishing,0.8592677345537757,0.8312757201646092,0.07143306732177734,7.122354999999999 -900,Binary classification,k-Nearest Neighbors,Phishing,0.8587319243604005,0.8304405874499332,0.07092952728271484,7.544645999999999 -925,Binary classification,k-Nearest Neighbors,Phishing,0.8593073593073594,0.8324742268041236,0.07092952728271484,7.978914999999999 -950,Binary classification,k-Nearest Neighbors,Phishing,0.8577449947312961,0.8327137546468402,0.07143306732177734,8.425289 -975,Binary classification,k-Nearest Neighbors,Phishing,0.8613963039014374,0.8367593712212819,0.07092952728271484,8.883884 -1000,Binary classification,k-Nearest Neighbors,Phishing,0.8628628628628628,0.8386336866902238,0.07143306732177734,9.354461 -1025,Binary classification,k-Nearest Neighbors,Phishing,0.8623046875,0.8384879725085911,0.07143306732177734,9.837197 -1050,Binary classification,k-Nearest Neighbors,Phishing,0.86558627264061,0.843159065628476,0.07092952728271484,10.331855 -1075,Binary classification,k-Nearest Neighbors,Phishing,0.8640595903165735,0.8426724137931035,0.07143306732177734,10.838539999999998 -1100,Binary classification,k-Nearest Neighbors,Phishing,0.8653321201091901,0.8445378151260504,0.07092952728271484,11.357143999999998 -1125,Binary classification,k-Nearest Neighbors,Phishing,0.8674377224199288,0.8484231943031536,0.07143306732177734,11.887570999999998 -1150,Binary classification,k-Nearest Neighbors,Phishing,0.8685813751087903,0.8494516450648055,0.07143306732177734,12.429893999999997 -1175,Binary classification,k-Nearest Neighbors,Phishing,0.8679727427597955,0.8484848484848486,0.07092952728271484,12.984101999999996 -1200,Binary classification,k-Nearest Neighbors,Phishing,0.8673894912427023,0.8478468899521532,0.07143306732177734,13.550580999999996 -1225,Binary classification,k-Nearest Neighbors,Phishing,0.8676470588235294,0.848030018761726,0.07092952728271484,14.128897999999996 -1250,Binary classification,k-Nearest Neighbors,Phishing,0.8670936749399519,0.847985347985348,0.07143306732177734,14.719141999999996 -1903,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0443115234375,0.843623 -3806,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0438079833984375,2.5515749999999997 -5709,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0438079833984375,5.191815 -7612,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0443115234375,8.760857 -9515,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0443115234375,12.963128 -11418,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0438079833984375,17.55601 -13321,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0438079833984375,22.538869000000002 -15224,Binary classification,k-Nearest Neighbors,SMTP,0.9999343099257703,0.9523809523809523,0.044338226318359375,27.911978 -17127,Binary classification,k-Nearest Neighbors,SMTP,0.9999416092490949,0.9600000000000001,0.044338226318359375,33.678549000000004 -19030,Binary classification,k-Nearest Neighbors,SMTP,0.9999474486310368,0.9600000000000001,0.043834686279296875,39.834148000000006 -20933,Binary classification,k-Nearest Neighbors,SMTP,0.9999522262564494,0.9600000000000001,0.043834686279296875,46.380227000000005 -22836,Binary classification,k-Nearest Neighbors,SMTP,0.9999562075760894,0.9600000000000001,0.044338226318359375,53.31643100000001 -24739,Binary classification,k-Nearest Neighbors,SMTP,0.9999595763602555,0.9600000000000001,0.044338226318359375,60.64312500000001 -26642,Binary classification,k-Nearest Neighbors,SMTP,0.9999249277429526,0.923076923076923,0.043834686279296875,68.360031 -28545,Binary classification,k-Nearest Neighbors,SMTP,0.999929932735426,0.923076923076923,0.044338226318359375,76.46777800000001 -30448,Binary classification,k-Nearest Neighbors,SMTP,0.9999343120832923,0.923076923076923,0.044338226318359375,84.96788600000001 -32351,Binary classification,k-Nearest Neighbors,SMTP,0.9999381761978362,0.923076923076923,0.043834686279296875,93.86216100000001 -34254,Binary classification,k-Nearest Neighbors,SMTP,0.9999416109537851,0.923076923076923,0.043834686279296875,103.14740500000002 -36157,Binary classification,k-Nearest Neighbors,SMTP,0.9999446841464764,0.923076923076923,0.044338226318359375,112.82379200000003 -38060,Binary classification,k-Nearest Neighbors,SMTP,0.9999474500118237,0.923076923076923,0.044338226318359375,122.89276800000002 -39963,Binary classification,k-Nearest Neighbors,SMTP,0.9999499524548321,0.923076923076923,0.043834686279296875,133.353338 -41866,Binary classification,k-Nearest Neighbors,SMTP,0.9999522273975875,0.923076923076923,0.043834686279296875,144.204522 -43769,Binary classification,k-Nearest Neighbors,SMTP,0.9999543045147139,0.923076923076923,0.044338226318359375,155.44662499999998 -45672,Binary classification,k-Nearest Neighbors,SMTP,0.9999562085349566,0.923076923076923,0.044338226318359375,167.07809799999998 -47575,Binary classification,k-Nearest Neighbors,SMTP,0.999957960230378,0.923076923076923,0.043834686279296875,179.099709 -49478,Binary classification,k-Nearest Neighbors,SMTP,0.9999595771772742,0.923076923076923,0.043834686279296875,191.51085999999998 -51381,Binary classification,k-Nearest Neighbors,SMTP,0.999941611521993,0.896551724137931,0.044338226318359375,204.31316499999997 -53284,Binary classification,k-Nearest Neighbors,SMTP,0.9999436968639153,0.896551724137931,0.044338226318359375,217.50674499999997 -55187,Binary classification,k-Nearest Neighbors,SMTP,0.9999456383865473,0.896551724137931,0.043834686279296875,231.09231699999998 -57090,Binary classification,k-Nearest Neighbors,SMTP,0.9998423514162098,0.8085106382978724,0.044338226318359375,245.069369 -58993,Binary classification,k-Nearest Neighbors,SMTP,0.9998474369406021,0.8085106382978724,0.044338226318359375,259.43782 -60896,Binary classification,k-Nearest Neighbors,SMTP,0.9998522046145004,0.8085106382978724,0.043834686279296875,274.196817 -62799,Binary classification,k-Nearest Neighbors,SMTP,0.999824835185834,0.7755102040816326,0.043834686279296875,289.350286 -64702,Binary classification,k-Nearest Neighbors,SMTP,0.9998299871717593,0.7755102040816326,0.044338226318359375,304.894594 -66605,Binary classification,k-Nearest Neighbors,SMTP,0.9998348447540688,0.7755102040816326,0.044338226318359375,320.829102 -68508,Binary classification,k-Nearest Neighbors,SMTP,0.9998248354182784,0.7692307692307693,0.043834686279296875,337.153755 -70411,Binary classification,k-Nearest Neighbors,SMTP,0.9998295696634001,0.7692307692307693,0.043834686279296875,353.86875 -72314,Binary classification,k-Nearest Neighbors,SMTP,0.9998340547342801,0.7692307692307693,0.044338226318359375,370.973732 -74217,Binary classification,k-Nearest Neighbors,SMTP,0.9998383097984263,0.7692307692307693,0.044338226318359375,388.469718 -76120,Binary classification,k-Nearest Neighbors,SMTP,0.99984235210657,0.7692307692307693,0.043834686279296875,406.356452 -78023,Binary classification,k-Nearest Neighbors,SMTP,0.9998461972264233,0.7692307692307693,0.043834686279296875,424.633943 -79926,Binary classification,k-Nearest Neighbors,SMTP,0.9998498592430404,0.7692307692307693,0.044338226318359375,443.301766 -81829,Binary classification,k-Nearest Neighbors,SMTP,0.9998533509312216,0.7692307692307693,0.044338226318359375,462.36095 -83732,Binary classification,k-Nearest Neighbors,SMTP,0.9998566839044082,0.7692307692307693,0.043834686279296875,481.813199 -85635,Binary classification,k-Nearest Neighbors,SMTP,0.9998598687437232,0.7692307692307693,0.044338226318359375,501.656136 -87538,Binary classification,k-Nearest Neighbors,SMTP,0.999862915110182,0.7692307692307693,0.044338226318359375,521.891566 -89441,Binary classification,k-Nearest Neighbors,SMTP,0.9998434704830054,0.7407407407407408,0.044338226318359375,542.516877 -91344,Binary classification,k-Nearest Neighbors,SMTP,0.9998467315503103,0.7407407407407408,0.043834686279296875,563.531742 -93247,Binary classification,k-Nearest Neighbors,SMTP,0.9998498595113999,0.7407407407407408,0.044338226318359375,584.936913 -95150,Binary classification,k-Nearest Neighbors,SMTP,0.999852862352731,0.7407407407407408,0.044338226318359375,606.731321 -106,Binary classification,ADWIN Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.19251441955566406,0.128533 -212,Binary classification,ADWIN Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.19330787658691406,0.37692000000000003 -318,Binary classification,ADWIN Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.1939868927001953,0.746299 -424,Binary classification,ADWIN Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.1940326690673828,1.2403140000000001 -530,Binary classification,ADWIN Bagging,Bananas,0.55765595463138,0.45581395348837206,0.19405555725097656,1.8468680000000002 -636,Binary classification,ADWIN Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.19471168518066406,2.5750140000000004 -742,Binary classification,ADWIN Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.19475746154785156,3.4160590000000006 -848,Binary classification,ADWIN Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.1946887969970703,4.380047 -954,Binary classification,ADWIN Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.19466590881347656,5.464953 -1060,Binary classification,ADWIN Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.19466590881347656,6.672680000000001 -1166,Binary classification,ADWIN Bagging,Bananas,0.590557939914163,0.4015056461731493,0.1946430206298828,7.997066 -1272,Binary classification,ADWIN Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.1946430206298828,9.441186 -1378,Binary classification,ADWIN Bagging,Bananas,0.599128540305011,0.3973799126637554,0.1952533721923828,11.005176 -1484,Binary classification,ADWIN Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.1952075958251953,12.688563 -1590,Binary classification,ADWIN Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.19518470764160156,14.491668 -1696,Binary classification,ADWIN Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.19518470764160156,16.418884000000002 -1802,Binary classification,ADWIN Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.1952075958251953,18.478568000000003 -1908,Binary classification,ADWIN Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.1952075958251953,20.667158000000004 -2014,Binary classification,ADWIN Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.1952075958251953,22.989297000000004 -2120,Binary classification,ADWIN Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.1952075958251953,25.441149000000003 -2226,Binary classification,ADWIN Bagging,Bananas,0.597752808988764,0.40133779264214053,0.1951618194580078,28.026090000000003 -2332,Binary classification,ADWIN Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.1951618194580078,30.739590000000003 -2438,Binary classification,ADWIN Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.19518470764160156,33.582607 -2544,Binary classification,ADWIN Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.19518470764160156,36.555510000000005 -2650,Binary classification,ADWIN Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.1958179473876953,39.657922000000006 -2756,Binary classification,ADWIN Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.1958179473876953,42.88800400000001 -2862,Binary classification,ADWIN Bagging,Bananas,0.599790283117791,0.4051948051948052,0.1958179473876953,46.243761000000006 -2968,Binary classification,ADWIN Bagging,Bananas,0.599932591843613,0.40261701056869653,0.19584083557128906,49.729156 -3074,Binary classification,ADWIN Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.19584083557128906,53.340997 -3180,Binary classification,ADWIN Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.19584083557128906,57.083209000000004 -3286,Binary classification,ADWIN Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.1913156509399414,60.947023 -3392,Binary classification,ADWIN Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.25013065338134766,64.942815 -3498,Binary classification,ADWIN Bagging,Bananas,0.597369173577352,0.40237691001697795,0.2948274612426758,69.00059 -3604,Binary classification,ADWIN Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3147249221801758,73.119483 -3710,Binary classification,ADWIN Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.3621034622192383,77.300669 -3816,Binary classification,ADWIN Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.3922090530395508,81.542932 -3922,Binary classification,ADWIN Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.42818164825439453,85.85207799999999 -4028,Binary classification,ADWIN Bagging,Bananas,0.608393344921778,0.4195804195804196,0.4565858840942383,90.22588999999999 -4134,Binary classification,ADWIN Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.4708681106567383,94.66819799999999 -4240,Binary classification,ADWIN Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.47220706939697266,99.17538499999999 -4346,Binary classification,ADWIN Bagging,Bananas,0.6186421173762946,0.4384954252795662,0.4545450210571289,103.74793499999998 -4452,Binary classification,ADWIN Bagging,Bananas,0.6212087171422153,0.44209133024487096,0.45485782623291016,108.38424899999998 -4558,Binary classification,ADWIN Bagging,Bananas,0.6214614878209348,0.44372782973234437,0.4440469741821289,113.08568299999999 -4664,Binary classification,ADWIN Bagging,Bananas,0.6219172206733863,0.44542308902170497,0.4133005142211914,117.851137 -4770,Binary classification,ADWIN Bagging,Bananas,0.6227720696162717,0.4449244060475162,0.4420938491821289,122.680723 -4876,Binary classification,ADWIN Bagging,Bananas,0.6235897435897436,0.4444444444444444,0.40938663482666016,127.574861 -4982,Binary classification,ADWIN Bagging,Bananas,0.6251756675366392,0.44910002950722927,0.40953922271728516,132.5326 -5088,Binary classification,ADWIN Bagging,Bananas,0.624139964615687,0.44675925925925924,0.40966129302978516,137.553125 -5194,Binary classification,ADWIN Bagging,Bananas,0.6248796456768727,0.44690516751845544,0.41005802154541016,142.637397 -5300,Binary classification,ADWIN Bagging,Bananas,0.6259671636157765,0.44821826280623617,0.4162149429321289,147.78569 -906,Binary classification,ADWIN Bagging,Elec2,0.8651933701657458,0.8685344827586208,1.6044349670410156,1.944742 -1812,Binary classification,ADWIN Bagging,Elec2,0.8890115958034235,0.8678500986193294,1.9405479431152344,5.818992 -2718,Binary classification,ADWIN Bagging,Elec2,0.8778064041221936,0.8540017590149517,1.8252983093261719,10.85362 -3624,Binary classification,ADWIN Bagging,Elec2,0.8857300579630141,0.8630952380952381,1.6178092956542969,16.937274 -4530,Binary classification,ADWIN Bagging,Elec2,0.8887171561051005,0.8605423353624794,2.356822967529297,24.006141 -5436,Binary classification,ADWIN Bagging,Elec2,0.884820607175713,0.8560257589696412,2.6076393127441406,32.019509 -6342,Binary classification,ADWIN Bagging,Elec2,0.8821952373442674,0.8529238038984053,2.2810935974121094,40.994451 -7248,Binary classification,ADWIN Bagging,Elec2,0.8780184904098247,0.8452380952380951,2.1880455017089844,50.877339 -8154,Binary classification,ADWIN Bagging,Elec2,0.8797988470501655,0.8548148148148149,2.1096839904785156,61.695664 -9060,Binary classification,ADWIN Bagging,Elec2,0.8818854178165361,0.8612191958495461,2.1360397338867188,73.45561000000001 -9966,Binary classification,ADWIN Bagging,Elec2,0.8771700953336679,0.8589861751152074,1.9618644714355469,86.17474100000001 -10872,Binary classification,ADWIN Bagging,Elec2,0.8781160886762948,0.8612710710920323,1.9593772888183594,99.82607500000002 -11778,Binary classification,ADWIN Bagging,Elec2,0.8748407913730152,0.8565868846079004,2.148365020751953,114.35339900000002 -12684,Binary classification,ADWIN Bagging,Elec2,0.8732161160608689,0.8548736462093863,1.7726364135742188,129.76244800000003 -13590,Binary classification,ADWIN Bagging,Elec2,0.8749724041504158,0.8586404858973291,1.6236610412597656,146.05238800000004 -14496,Binary classification,ADWIN Bagging,Elec2,0.8746464298033805,0.858917617827471,1.9961967468261719,163.22720400000003 -15402,Binary classification,ADWIN Bagging,Elec2,0.8757223556911888,0.859347442680776,1.8728065490722656,181.33590900000002 -16308,Binary classification,ADWIN Bagging,Elec2,0.8744097626786043,0.8568432825387949,2.1095123291015625,200.414984 -17214,Binary classification,ADWIN Bagging,Elec2,0.8735839191308894,0.8532703978422117,2.2479400634765625,220.47164 -18120,Binary classification,ADWIN Bagging,Elec2,0.8715712787681439,0.8510338646693554,2.008777618408203,241.502757 -19026,Binary classification,ADWIN Bagging,Elec2,0.872904073587385,0.8509615384615385,0.9681053161621094,263.516635 -19932,Binary classification,ADWIN Bagging,Elec2,0.8660378305152777,0.8433282478582326,0.8690452575683594,286.595649 -20838,Binary classification,ADWIN Bagging,Elec2,0.8576090608052983,0.8330050092868801,0.6502227783203125,310.73945299999997 -21744,Binary classification,ADWIN Bagging,Elec2,0.8574713700961228,0.8301452452726775,0.7763557434082031,335.928343 -22650,Binary classification,ADWIN Bagging,Elec2,0.8562850456973817,0.8269077373039085,1.1911430358886719,362.15469099999996 -23556,Binary classification,ADWIN Bagging,Elec2,0.8503502441095309,0.8183645076518782,1.1728401184082031,389.48303799999996 -24462,Binary classification,ADWIN Bagging,Elec2,0.8480438248640694,0.8142707240293808,1.1973991394042969,417.87456299999997 -25368,Binary classification,ADWIN Bagging,Elec2,0.8443647258248906,0.8102105566772425,1.3723030090332031,447.33757799999995 -26274,Binary classification,ADWIN Bagging,Elec2,0.8447074943858714,0.8100558659217878,1.3146781921386719,477.8580079999999 -27180,Binary classification,ADWIN Bagging,Elec2,0.8453953419919791,0.8118395128067348,1.0832901000976562,509.40859299999994 -28086,Binary classification,ADWIN Bagging,Elec2,0.8421221292504896,0.8068142209829209,1.0510520935058594,541.9996819999999 -28992,Binary classification,ADWIN Bagging,Elec2,0.839570901314201,0.8022113544546035,1.0500526428222656,575.6711059999999 -29898,Binary classification,ADWIN Bagging,Elec2,0.8373749874569355,0.7989247311827957,1.0177803039550781,610.3966439999999 -30804,Binary classification,ADWIN Bagging,Elec2,0.8364445021588807,0.7961149332254148,1.2030601501464844,646.1707399999999 -31710,Binary classification,ADWIN Bagging,Elec2,0.8328234885994512,0.7904660263251513,1.1833610534667969,683.0083409999999 -32616,Binary classification,ADWIN Bagging,Elec2,0.8277479687260463,0.7829714903809009,0.9862403869628906,720.8979669999999 -33522,Binary classification,ADWIN Bagging,Elec2,0.8274216163002297,0.7829675483023824,0.9778022766113281,759.8188009999999 -34428,Binary classification,ADWIN Bagging,Elec2,0.8249339181456415,0.7797229633419832,1.1589393615722656,799.8066919999999 -35334,Binary classification,ADWIN Bagging,Elec2,0.8250643873998811,0.7787838660033642,1.4676322937011719,840.8572599999999 -36240,Binary classification,ADWIN Bagging,Elec2,0.8262093324870995,0.7784422711602055,1.2339591979980469,882.9239929999999 -37146,Binary classification,ADWIN Bagging,Elec2,0.8240409207161126,0.7737625475943233,1.2780303955078125,926.0049009999999 -38052,Binary classification,ADWIN Bagging,Elec2,0.8238679666763029,0.7722731906218145,1.326324462890625,970.0561169999999 -38958,Binary classification,ADWIN Bagging,Elec2,0.8231383320070847,0.7715365740433715,1.1276435852050781,1015.1628579999999 -39864,Binary classification,ADWIN Bagging,Elec2,0.8224920352206306,0.7721975404030647,0.9921760559082031,1061.3434909999999 -40770,Binary classification,ADWIN Bagging,Elec2,0.8226348451028969,0.774362654850688,0.6841468811035156,1108.5828599999998 -41676,Binary classification,ADWIN Bagging,Elec2,0.8231073785242952,0.7766331353775299,0.6752357482910156,1156.8826949999998 -42582,Binary classification,ADWIN Bagging,Elec2,0.8236537422794205,0.7780569266692283,0.8912887573242188,1206.1759319999999 -43488,Binary classification,ADWIN Bagging,Elec2,0.8242693218663049,0.7790690951141949,0.8946151733398438,1256.4285579999998 -44394,Binary classification,ADWIN Bagging,Elec2,0.8235082107539476,0.7769013924086677,0.9767723083496094,1307.6797829999998 -45300,Binary classification,ADWIN Bagging,Elec2,0.823285282235811,0.7772366773340753,0.7331352233886719,1359.9622719999998 -25,Binary classification,ADWIN Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.6845798492431641,0.140915 -50,Binary classification,ADWIN Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.6852588653564453,0.368444 -75,Binary classification,ADWIN Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.6852130889892578,0.67832 -100,Binary classification,ADWIN Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.6858234405517578,1.071639 -125,Binary classification,ADWIN Bagging,Phishing,0.8548387096774194,0.85,0.6858234405517578,1.5472890000000001 -150,Binary classification,ADWIN Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.6858234405517578,2.1056880000000002 -175,Binary classification,ADWIN Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.6864566802978516,2.749261 -200,Binary classification,ADWIN Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.6865940093994141,3.4872330000000002 -225,Binary classification,ADWIN Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7248620986938477,4.314044 -250,Binary classification,ADWIN Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.7525568008422852,5.225865 -275,Binary classification,ADWIN Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7526025772094727,6.222143 -300,Binary classification,ADWIN Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7526025772094727,7.300394 -325,Binary classification,ADWIN Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7532129287719727,8.460934 -350,Binary classification,ADWIN Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7532358169555664,9.705145 -375,Binary classification,ADWIN Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.7908792495727539,11.033679 -400,Binary classification,ADWIN Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.8290948867797852,12.456399999999999 -425,Binary classification,ADWIN Bagging,Phishing,0.8679245283018868,0.839080459770115,0.8842554092407227,13.968094999999998 -450,Binary classification,ADWIN Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.8843240737915039,15.559030999999997 -475,Binary classification,ADWIN Bagging,Phishing,0.869198312236287,0.8402061855670103,0.8843927383422852,17.224721999999996 -500,Binary classification,ADWIN Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.8844156265258789,18.966558999999997 -525,Binary classification,ADWIN Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.8844156265258789,20.785335999999997 -550,Binary classification,ADWIN Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.8844614028930664,22.682567 -575,Binary classification,ADWIN Bagging,Phishing,0.867595818815331,0.8382978723404255,0.8844614028930664,24.656325 -600,Binary classification,ADWIN Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.8844614028930664,26.722161999999997 -625,Binary classification,ADWIN Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.9222650527954102,28.872863 -650,Binary classification,ADWIN Bagging,Phishing,0.8767334360554699,0.846153846153846,0.9229669570922852,31.097205 -675,Binary classification,ADWIN Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.9505243301391602,33.398984999999996 -700,Binary classification,ADWIN Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.8879518508911133,35.778321 -725,Binary classification,ADWIN Bagging,Phishing,0.8825966850828729,0.8576214405360134,0.9880342483520508,38.239126 -750,Binary classification,ADWIN Bagging,Phishing,0.8865153538050734,0.8631239935587761,1.0254030227661133,40.785592 -775,Binary classification,ADWIN Bagging,Phishing,0.8875968992248062,0.863849765258216,1.0804262161254883,43.415615 -800,Binary classification,ADWIN Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.1831789016723633,46.140861 -825,Binary classification,ADWIN Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.1837968826293945,48.939828 -850,Binary classification,ADWIN Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.1943635940551758,51.816843 -875,Binary classification,ADWIN Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.2218294143676758,54.767134 -900,Binary classification,ADWIN Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.2768526077270508,57.795454 -925,Binary classification,ADWIN Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.2769441604614258,60.902165 -950,Binary classification,ADWIN Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.2769899368286133,64.084493 -975,Binary classification,ADWIN Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.2770357131958008,67.34321899999999 -1000,Binary classification,ADWIN Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.277012825012207,70.663215 -1025,Binary classification,ADWIN Bagging,Phishing,0.8876953125,0.8700564971751412,1.2770586013793945,74.022235 -1050,Binary classification,ADWIN Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.2770357131958008,77.421815 -1075,Binary classification,ADWIN Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.2770357131958008,80.860683 -1100,Binary classification,ADWIN Bagging,Phishing,0.89171974522293,0.8761706555671176,1.2770357131958008,84.339469 -1125,Binary classification,ADWIN Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.2770357131958008,87.855325 -1150,Binary classification,ADWIN Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.2770357131958008,91.40832099999999 -1175,Binary classification,ADWIN Bagging,Phishing,0.8926746166950597,0.877906976744186,1.2770357131958008,95.00115199999999 -1200,Binary classification,ADWIN Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.2872819900512695,98.634357 -1225,Binary classification,ADWIN Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3422365188598633,102.306286 -1250,Binary classification,ADWIN Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3423280715942383,106.015532 -1903,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17387676239013672,1.523397 -3806,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17448711395263672,4.675035 -5709,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17502880096435547,8.984261 -7612,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17505168914794922,14.031006 -9515,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17505168914794922,19.82114 -11418,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17566204071044922,26.34929 -13321,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17568492889404297,33.615204 -15224,Binary classification,ADWIN Bagging,SMTP,0.9993430992577021,0.16666666666666669,0.4191761016845703,41.690008999999996 -17127,Binary classification,ADWIN Bagging,SMTP,0.9992993109891393,0.14285714285714288,0.4156208038330078,50.773073999999994 -19030,Binary classification,ADWIN Bagging,SMTP,0.9993693835724421,0.14285714285714288,0.3918170928955078,60.837182999999996 -20933,Binary classification,ADWIN Bagging,SMTP,0.9994267150773934,0.14285714285714288,0.39249610900878906,71.877708 -22836,Binary classification,ADWIN Bagging,SMTP,0.9994744909130721,0.14285714285714288,0.3925189971923828,83.900187 -24739,Binary classification,ADWIN Bagging,SMTP,0.9995149163230658,0.14285714285714288,0.4046955108642578,96.90004 -26642,Binary classification,ADWIN Bagging,SMTP,0.9995495664577155,0.25,0.42109107971191406,110.87602600000001 -28545,Binary classification,ADWIN Bagging,SMTP,0.999579596412556,0.25,0.42104530334472656,125.82831600000002 -30448,Binary classification,ADWIN Bagging,SMTP,0.9996058724997536,0.25,0.4210681915283203,141.756728 -32351,Binary classification,ADWIN Bagging,SMTP,0.999629057187017,0.25,0.43326759338378906,158.664658 -34254,Binary classification,ADWIN Bagging,SMTP,0.9996496657227104,0.25,0.4455127716064453,176.542575 -36157,Binary classification,ADWIN Bagging,SMTP,0.9996681048788583,0.25,0.44553565979003906,195.393511 -38060,Binary classification,ADWIN Bagging,SMTP,0.9996847000709425,0.25,0.4455127716064453,215.224785 -39963,Binary classification,ADWIN Bagging,SMTP,0.9996997147289926,0.25,0.4455127716064453,236.028977 -41866,Binary classification,ADWIN Bagging,SMTP,0.9997133643855249,0.25,0.4461231231689453,257.804564 -43769,Binary classification,ADWIN Bagging,SMTP,0.9997258270882837,0.25,0.44610023498535156,280.547182 -45672,Binary classification,ADWIN Bagging,SMTP,0.9997372512097392,0.25,0.44610023498535156,304.26359 -47575,Binary classification,ADWIN Bagging,SMTP,0.9997477613822676,0.25,0.4643878936767578,328.95641 -49478,Binary classification,ADWIN Bagging,SMTP,0.9997574630636458,0.25,0.4765186309814453,354.62865 -51381,Binary classification,ADWIN Bagging,SMTP,0.9997469832619696,0.3157894736842105,0.5477771759033203,381.28529299999997 -53284,Binary classification,ADWIN Bagging,SMTP,0.999756019743633,0.3157894736842105,0.5477771759033203,408.929269 -55187,Binary classification,ADWIN Bagging,SMTP,0.9997644330083717,0.3157894736842105,0.5598621368408203,437.55238499999996 -57090,Binary classification,ADWIN Bagging,SMTP,0.9996321533044895,0.3225806451612903,0.9146518707275391,467.22095799999994 -58993,Binary classification,ADWIN Bagging,SMTP,0.9996440195280716,0.3225806451612903,0.9280223846435547,497.92269099999993 -60896,Binary classification,ADWIN Bagging,SMTP,0.9996551441005008,0.3225806451612903,0.9402217864990234,529.6450219999999 -62799,Binary classification,ADWIN Bagging,SMTP,0.9996337462976528,0.303030303030303,1.022481918334961,562.409421 -64702,Binary classification,ADWIN Bagging,SMTP,0.9996445186318604,0.303030303030303,1.0225505828857422,596.2119009999999 -66605,Binary classification,ADWIN Bagging,SMTP,0.9996546753948712,0.303030303030303,1.0225963592529297,631.0619619999999 -68508,Binary classification,ADWIN Bagging,SMTP,0.9996642678850336,0.3783783783783784,1.036111831665039,666.9514569999999 -70411,Binary classification,ADWIN Bagging,SMTP,0.9996733418548501,0.3783783783783784,1.0532817840576172,703.8889889999999 -72314,Binary classification,ADWIN Bagging,SMTP,0.9996819382407036,0.3783783783783784,1.053213119506836,741.862274 -74217,Binary classification,ADWIN Bagging,SMTP,0.9996900937803169,0.3783783783783784,1.0532588958740234,780.872663 -76120,Binary classification,ADWIN Bagging,SMTP,0.9996978415375924,0.3783783783783784,1.0533504486083984,820.917591 -78023,Binary classification,ADWIN Bagging,SMTP,0.9997052113506447,0.3783783783783784,1.0533275604248047,862.00148 -79926,Binary classification,ADWIN Bagging,SMTP,0.9997122302158273,0.3783783783783784,1.0532817840576172,904.126635 -81829,Binary classification,ADWIN Bagging,SMTP,0.9997189226181747,0.3783783783783784,1.0533275604248047,947.288015 -83732,Binary classification,ADWIN Bagging,SMTP,0.9997253108167823,0.3783783783783784,1.0539379119873047,991.4768349999999 -85635,Binary classification,ADWIN Bagging,SMTP,0.9997314150921363,0.3783783783783784,1.0626277923583984,1036.7058969999998 -87538,Binary classification,ADWIN Bagging,SMTP,0.9997372539611822,0.3783783783783784,1.0626049041748047,1082.9660009999998 -89441,Binary classification,ADWIN Bagging,SMTP,0.9997316636851521,0.3684210526315789,1.0989017486572266,1130.2730419999998 -91344,Binary classification,ADWIN Bagging,SMTP,0.9997372540862464,0.3684210526315789,1.0989704132080078,1178.6145779999997 -93247,Binary classification,ADWIN Bagging,SMTP,0.9997426163052571,0.3684210526315789,1.0987415313720703,1227.9921859999997 -95150,Binary classification,ADWIN Bagging,SMTP,0.9997477640332532,0.3684210526315789,1.0987186431884766,1278.4102429999998 -106,Binary classification,AdaBoost,Bananas,0.5523809523809524,0.5252525252525252,0.17705154418945312,0.137989 -212,Binary classification,AdaBoost,Bananas,0.5829383886255924,0.5555555555555555,0.17725753784179688,0.392857 -318,Binary classification,AdaBoost,Bananas,0.6025236593059937,0.5827814569536425,0.17730331420898438,0.757641 -424,Binary classification,AdaBoost,Bananas,0.6099290780141844,0.5758354755784061,0.17730331420898438,1.237112 -530,Binary classification,AdaBoost,Bananas,0.5841209829867675,0.5089285714285714,0.17725753784179688,1.823779 -636,Binary classification,AdaBoost,Bananas,0.5748031496062992,0.4981412639405205,0.17730331420898438,2.523282 -742,Binary classification,AdaBoost,Bananas,0.582995951417004,0.48925619834710743,0.17723464965820312,3.326693 -848,Binary classification,AdaBoost,Bananas,0.5749704840613932,0.4812680115273775,0.17718887329101562,4.242125 -954,Binary classification,AdaBoost,Bananas,0.5760755508919203,0.482051282051282,0.17718887329101562,5.269066 -1060,Binary classification,AdaBoost,Bananas,0.5873465533522191,0.48284023668639053,0.17718887329101562,6.408509 -1166,Binary classification,AdaBoost,Bananas,0.5931330472103005,0.49250535331905776,0.17723464965820312,7.6600589999999995 -1272,Binary classification,AdaBoost,Bananas,0.5979543666404405,0.5034013605442177,0.17723464965820312,9.024008 -1378,Binary classification,AdaBoost,Bananas,0.6005809731299927,0.4990892531876139,0.17723464965820312,10.499633 -1484,Binary classification,AdaBoost,Bananas,0.6089008766014835,0.5117845117845117,0.17723464965820312,12.08531 -1590,Binary classification,AdaBoost,Bananas,0.6091881686595343,0.5121759622937941,0.17723464965820312,13.781775 -1696,Binary classification,AdaBoost,Bananas,0.6135693215339233,0.5194424064563462,0.17723464965820312,15.591861999999999 -1802,Binary classification,AdaBoost,Bananas,0.6185452526374237,0.5354969574036511,0.17723464965820312,17.520521 -1908,Binary classification,AdaBoost,Bananas,0.6208704771893025,0.5467084639498432,0.17725753784179688,19.574793999999997 -2014,Binary classification,AdaBoost,Bananas,0.620963735717834,0.5561372891215823,0.17728042602539062,21.756324999999997 -2120,Binary classification,AdaBoost,Bananas,0.6252949504483247,0.56941431670282,0.17728042602539062,24.064420999999996 -2226,Binary classification,AdaBoost,Bananas,0.6242696629213483,0.5721596724667348,0.17730331420898438,26.498976999999996 -2332,Binary classification,AdaBoost,Bananas,0.6229086229086229,0.5763855421686748,0.17730331420898438,29.055622999999997 -2438,Binary classification,AdaBoost,Bananas,0.62330734509643,0.5796703296703297,0.17730331420898438,31.733449999999998 -2544,Binary classification,AdaBoost,Bananas,0.6244593000393236,0.5860424794104898,0.17730331420898438,34.533392 -2650,Binary classification,AdaBoost,Bananas,0.6266515666289165,0.591828312009905,0.17734909057617188,37.454912 -2756,Binary classification,AdaBoost,Bananas,0.6250453720508167,0.5921831819976313,0.17734909057617188,40.501518 -2862,Binary classification,AdaBoost,Bananas,0.6249563089828731,0.5927893738140417,0.17734909057617188,43.671228 -2968,Binary classification,AdaBoost,Bananas,0.6248736097067745,0.5924569754668619,0.17734909057617188,46.964135 -3074,Binary classification,AdaBoost,Bananas,0.6260982753010088,0.5958494548012664,0.17734909057617188,50.377784999999996 -3180,Binary classification,AdaBoost,Bananas,0.62378106322743,0.5934738273283481,0.1645193099975586,53.913639999999994 -3286,Binary classification,AdaBoost,Bananas,0.6246575342465753,0.5937397034596376,0.20851802825927734,57.57642799999999 -3392,Binary classification,AdaBoost,Bananas,0.6234149218519611,0.5931825422108953,0.24359798431396484,61.313441999999995 -3498,Binary classification,AdaBoost,Bananas,0.6211038032599371,0.5894019212891229,0.27881526947021484,65.12271799999999 -3604,Binary classification,AdaBoost,Bananas,0.6194837635303914,0.5866747060596926,0.3256673812866211,69.00323599999999 -3710,Binary classification,AdaBoost,Bananas,0.6238878403882449,0.5915080527086384,0.3337392807006836,72.95431899999998 -3816,Binary classification,AdaBoost,Bananas,0.6277850589777195,0.5970488081725313,0.3397665023803711,76.97715599999998 -3922,Binary classification,AdaBoost,Bananas,0.6322366743177761,0.6009961261759823,0.3677358627319336,81.06970999999999 -4028,Binary classification,AdaBoost,Bananas,0.6354606406754407,0.6034575904916262,0.38204097747802734,85.23008099999998 -4134,Binary classification,AdaBoost,Bananas,0.6399709654004355,0.6073878627968339,0.39020442962646484,89.45861099999999 -4240,Binary classification,AdaBoost,Bananas,0.644963434772352,0.6130110568269478,0.3902273178100586,93.75413599999999 -4346,Binary classification,AdaBoost,Bananas,0.6508630609896433,0.6185567010309279,0.39025020599365234,98.11858299999999 -4452,Binary classification,AdaBoost,Bananas,0.6535609975286453,0.620384047267356,0.39020442962646484,102.55151499999998 -4558,Binary classification,AdaBoost,Bananas,0.6570111915734036,0.6243691420331651,0.39038753509521484,107.05130299999998 -4664,Binary classification,AdaBoost,Bananas,0.6607334334119666,0.6288127639605818,0.39043331146240234,111.61866799999997 -4770,Binary classification,AdaBoost,Bananas,0.6630320821975257,0.6303197607545433,0.4466238021850586,116.25797499999997 -4876,Binary classification,AdaBoost,Bananas,0.6670769230769231,0.6330544879041374,0.4547872543334961,120.96504799999997 -4982,Binary classification,AdaBoost,Bananas,0.6707488456133307,0.6378091872791519,0.4610433578491211,125.74414799999997 -5088,Binary classification,AdaBoost,Bananas,0.6734814232356988,0.6407094959982694,0.4671621322631836,130.59500899999998 -5194,Binary classification,AdaBoost,Bananas,0.674369343346813,0.6412051771695311,0.46713924407958984,135.51973499999997 -5300,Binary classification,AdaBoost,Bananas,0.6778637478769579,0.64504054897068,0.46845149993896484,140.51779399999998 -906,Binary classification,AdaBoost,Elec2,0.9337016574585635,0.933184855233853,1.4600162506103516,2.214325 -1812,Binary classification,AdaBoost,Elec2,0.9469906129210381,0.9351351351351351,2.0955753326416016,6.240333 -2718,Binary classification,AdaBoost,Elec2,0.9370629370629371,0.9227990970654628,2.479440689086914,11.722802 -3624,Binary classification,AdaBoost,Elec2,0.9343085840463704,0.9197031039136303,2.829832077026367,18.401477 -4530,Binary classification,AdaBoost,Elec2,0.9322146169132258,0.9140778057654632,3.4876842498779297,26.240317 -5436,Binary classification,AdaBoost,Elec2,0.9262189512419503,0.9061988304093569,3.730012893676758,35.261976000000004 -6342,Binary classification,AdaBoost,Elec2,0.9252483835357199,0.9056904098686828,4.267904281616211,45.380796000000004 -7248,Binary classification,AdaBoost,Elec2,0.922174692976404,0.9017079121645174,4.513330459594727,56.606028 -8154,Binary classification,AdaBoost,Elec2,0.9222372132957194,0.9063515509601181,4.528413772583008,68.87909400000001 -9060,Binary classification,AdaBoost,Elec2,0.9222872281708798,0.9083810515356586,4.871156692504883,82.16100100000001 -9966,Binary classification,AdaBoost,Elec2,0.9173105870546914,0.9042528468510341,4.923883438110352,96.59034200000002 -10872,Binary classification,AdaBoost,Elec2,0.9180388188759084,0.9064370471490076,5.256982803344727,112.03179200000002 -11778,Binary classification,AdaBoost,Elec2,0.913645240723444,0.9013100436681224,5.652528762817383,128.76319300000003 -12684,Binary classification,AdaBoost,Elec2,0.9114562800599227,0.8990743237170845,6.184247970581055,146.63877800000003 -13590,Binary classification,AdaBoost,Elec2,0.9116932813304879,0.9004810084591143,6.168107986450195,165.54320300000003 -14496,Binary classification,AdaBoost,Elec2,0.9084511900655399,0.8974893781382773,6.370748519897461,185.57117500000004 -15402,Binary classification,AdaBoost,Elec2,0.9089669501980391,0.8977090325404932,6.689512252807617,206.60419600000003 -16308,Binary classification,AdaBoost,Elec2,0.907033789170295,0.8952315134761576,7.013689041137695,228.76060700000002 -17214,Binary classification,AdaBoost,Elec2,0.9056527043513624,0.8922362309223624,7.149255752563477,252.05960700000003 -18120,Binary classification,AdaBoost,Elec2,0.9003256250344942,0.8855368234250223,7.591207504272461,276.72553400000004 -19026,Binary classification,AdaBoost,Elec2,0.901287779237845,0.8854738382729601,7.899053573608398,302.410993 -19932,Binary classification,AdaBoost,Elec2,0.9013596909337214,0.8864633864633865,8.218050003051758,329.267044 -20838,Binary classification,AdaBoost,Elec2,0.8999376109804674,0.8850669753596825,8.369176864624023,357.490064 -21744,Binary classification,AdaBoost,Elec2,0.8988640022076071,0.8821227552934869,8.549039840698242,386.978862 -22650,Binary classification,AdaBoost,Elec2,0.8967283323767054,0.8787517495205017,8.670183181762695,417.760723 -23556,Binary classification,AdaBoost,Elec2,0.8951814901294842,0.8767287433221829,8.96574592590332,449.897837 -24462,Binary classification,AdaBoost,Elec2,0.8929724868157475,0.8734287371881647,9.238008499145508,483.328506 -25368,Binary classification,AdaBoost,Elec2,0.8902905349469784,0.8701535016096672,9.398244857788086,518.131339 -26274,Binary classification,AdaBoost,Elec2,0.8890115327522552,0.8681497558328811,9.500497817993164,554.2859920000001 -27180,Binary classification,AdaBoost,Elec2,0.8889215938776261,0.8685734186583084,9.676286697387695,591.703834 -28086,Binary classification,AdaBoost,Elec2,0.886629873598006,0.8655291832080412,9.930196762084961,630.490308 -28992,Binary classification,AdaBoost,Elec2,0.8864130247318133,0.8647916238965305,10.322111129760742,670.5577900000001 -29898,Binary classification,AdaBoost,Elec2,0.885774492423989,0.8637977106848004,10.777273178100586,711.8606440000001 -30804,Binary classification,AdaBoost,Elec2,0.885498165763075,0.8626931911083429,10.977670669555664,754.3481340000001 -31710,Binary classification,AdaBoost,Elec2,0.8835661799489104,0.8603736479842674,11.286626815795898,798.241067 -32616,Binary classification,AdaBoost,Elec2,0.882906638049977,0.8599097611973149,11.60590934753418,843.3988 -33522,Binary classification,AdaBoost,Elec2,0.8826109006294561,0.8598995976786413,11.809194564819336,889.816371 -34428,Binary classification,AdaBoost,Elec2,0.8817788363784239,0.8588765603328711,11.96577262878418,937.652835 -35334,Binary classification,AdaBoost,Elec2,0.8808762346814593,0.8571816361847239,12.22038459777832,986.872877 -36240,Binary classification,AdaBoost,Elec2,0.8797980076712933,0.8550029958058717,12.516416549682617,1037.336075 -37146,Binary classification,AdaBoost,Elec2,0.8785839278503164,0.8529891127192124,12.637868881225586,1089.1696239999999 -38052,Binary classification,AdaBoost,Elec2,0.8770071745814827,0.8506891271056662,13.111181259155273,1142.33469 -38958,Binary classification,AdaBoost,Elec2,0.8756064378673922,0.8493440278554995,13.190984725952148,1196.840346 -39864,Binary classification,AdaBoost,Elec2,0.8754233248877406,0.8501327860936744,13.482259750366211,1252.532055 -40770,Binary classification,AdaBoost,Elec2,0.8749049522921828,0.8505713448578962,13.769769668579102,1309.427191 -41676,Binary classification,AdaBoost,Elec2,0.8752729454109178,0.8519510111079466,13.892538070678711,1367.440063 -42582,Binary classification,AdaBoost,Elec2,0.8754139170052371,0.8523148019264498,14.423887252807617,1426.636647 -43488,Binary classification,AdaBoost,Elec2,0.8745142226412491,0.8513605534824177,14.559076309204102,1487.084751 -44394,Binary classification,AdaBoost,Elec2,0.8743270335413241,0.8507211088218767,14.714178085327148,1548.853591 -45300,Binary classification,AdaBoost,Elec2,0.8751186560409722,0.851922623877706,14.867197036743164,1611.802241 -25,Binary classification,AdaBoost,Phishing,0.6666666666666666,0.7142857142857143,0.6709518432617188,0.096996 -50,Binary classification,AdaBoost,Phishing,0.7551020408163265,0.7391304347826088,0.671112060546875,0.227732 -75,Binary classification,AdaBoost,Phishing,0.7972972972972973,0.7945205479452055,0.6711349487304688,0.390548 -100,Binary classification,AdaBoost,Phishing,0.8080808080808081,0.7999999999999999,0.6711578369140625,0.5919840000000001 -125,Binary classification,AdaBoost,Phishing,0.8064516129032258,0.8000000000000002,0.6711845397949219,0.8358070000000001 -150,Binary classification,AdaBoost,Phishing,0.8187919463087249,0.8211920529801323,0.6711845397949219,1.1185070000000001 -175,Binary classification,AdaBoost,Phishing,0.8390804597701149,0.8313253012048192,0.6711845397949219,1.4342450000000002 -200,Binary classification,AdaBoost,Phishing,0.8341708542713567,0.8253968253968254,0.7092657089233398,1.8037020000000001 -225,Binary classification,AdaBoost,Phishing,0.8303571428571429,0.8173076923076923,0.7094945907592773,2.2161530000000003 -250,Binary classification,AdaBoost,Phishing,0.8273092369477911,0.8154506437768241,0.7095174789428711,2.6652750000000003 -275,Binary classification,AdaBoost,Phishing,0.8321167883211679,0.8188976377952757,0.7095861434936523,3.1578190000000004 -300,Binary classification,AdaBoost,Phishing,0.8394648829431438,0.823529411764706,0.7096090316772461,3.688734 -325,Binary classification,AdaBoost,Phishing,0.845679012345679,0.8263888888888888,0.7096090316772461,4.255895000000001 -350,Binary classification,AdaBoost,Phishing,0.8510028653295129,0.8289473684210527,0.7096090316772461,4.8554520000000005 -375,Binary classification,AdaBoost,Phishing,0.8502673796791443,0.8260869565217391,0.7095823287963867,5.4957590000000005 -400,Binary classification,AdaBoost,Phishing,0.849624060150376,0.8235294117647061,0.7096090316772461,6.1750370000000006 -425,Binary classification,AdaBoost,Phishing,0.8561320754716981,0.8271954674220963,0.7096090316772461,6.8921660000000005 -450,Binary classification,AdaBoost,Phishing,0.8530066815144766,0.8225806451612903,0.7096090316772461,7.648219 -475,Binary classification,AdaBoost,Phishing,0.8523206751054853,0.8241206030150755,0.7096090316772461,8.452871 -500,Binary classification,AdaBoost,Phishing,0.8557114228456913,0.8317757009345793,0.7096090316772461,9.291999 -525,Binary classification,AdaBoost,Phishing,0.8530534351145038,0.8253968253968255,0.7096090316772461,10.171949000000001 -550,Binary classification,AdaBoost,Phishing,0.8579234972677595,0.832618025751073,0.7096090316772461,11.089958000000001 -575,Binary classification,AdaBoost,Phishing,0.8588850174216028,0.8336755646817249,0.7096090316772461,12.050434000000001 -600,Binary classification,AdaBoost,Phishing,0.8631051752921536,0.8360000000000001,0.7096090316772461,13.044778 -625,Binary classification,AdaBoost,Phishing,0.8621794871794872,0.83203125,0.7096090316772461,14.08632 -650,Binary classification,AdaBoost,Phishing,0.8659476117103235,0.8391866913123845,0.7096319198608398,15.163796000000001 -675,Binary classification,AdaBoost,Phishing,0.8679525222551929,0.8446771378708552,0.7096319198608398,16.281047 -700,Binary classification,AdaBoost,Phishing,0.8726752503576538,0.848381601362862,0.7096319198608398,17.426965000000003 -725,Binary classification,AdaBoost,Phishing,0.8756906077348067,0.8543689320388349,0.7096319198608398,18.607993000000004 -750,Binary classification,AdaBoost,Phishing,0.87716955941255,0.8566978193146417,0.7096319198608398,19.829159000000004 -775,Binary classification,AdaBoost,Phishing,0.8785529715762274,0.8575757575757577,0.7096319198608398,21.096679000000005 -800,Binary classification,AdaBoost,Phishing,0.8785982478097623,0.8592162554426704,0.7489309310913086,22.415380000000006 -825,Binary classification,AdaBoost,Phishing,0.8798543689320388,0.8619246861924686,0.7852392196655273,23.779028000000007 -850,Binary classification,AdaBoost,Phishing,0.8798586572438163,0.8614130434782608,0.7852849960327148,25.182311000000006 -875,Binary classification,AdaBoost,Phishing,0.8787185354691075,0.8594164456233422,0.7853536605834961,26.619257000000005 -900,Binary classification,AdaBoost,Phishing,0.8787541713014461,0.8589909443725743,0.7853765487670898,28.105865000000005 -925,Binary classification,AdaBoost,Phishing,0.8809523809523809,0.8628428927680798,0.7853765487670898,29.627597000000005 -950,Binary classification,AdaBoost,Phishing,0.8798735511064278,0.8629807692307693,0.7853765487670898,31.194797000000005 -975,Binary classification,AdaBoost,Phishing,0.8819301848049281,0.8651817116060961,0.7853765487670898,32.795573000000005 -1000,Binary classification,AdaBoost,Phishing,0.8828828828828829,0.8662857142857143,0.7870550155639648,34.450223 -1025,Binary classification,AdaBoost,Phishing,0.8828125,0.8666666666666666,0.8609609603881836,36.14349 -1050,Binary classification,AdaBoost,Phishing,0.8846520495710201,0.8691891891891892,0.8610067367553711,37.870798 -1075,Binary classification,AdaBoost,Phishing,0.8836126629422719,0.8691099476439791,0.8625936508178711,39.648817 -1100,Binary classification,AdaBoost,Phishing,0.8844404003639672,0.8702757916241062,0.8627080917358398,41.461037000000005 -1125,Binary classification,AdaBoost,Phishing,0.8861209964412812,0.8732673267326733,0.8989248275756836,43.309543000000005 -1150,Binary classification,AdaBoost,Phishing,0.8842471714534378,0.8707482993197277,0.8989477157592773,45.19996400000001 -1175,Binary classification,AdaBoost,Phishing,0.8816013628620102,0.8677450047573739,0.8990621566772461,47.14264000000001 -1200,Binary classification,AdaBoost,Phishing,0.8798999165971643,0.8654205607476635,0.8990621566772461,49.13049000000001 -1225,Binary classification,AdaBoost,Phishing,0.880718954248366,0.8660550458715598,0.8990850448608398,51.16391500000001 -1250,Binary classification,AdaBoost,Phishing,0.8783026421136909,0.8635547576301617,0.8991079330444336,53.25500000000001 -1903,Binary classification,AdaBoost,SMTP,1.0,0.0,0.15647220611572266,0.853094 -3806,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1565408706665039,2.059998 -5709,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1564950942993164,3.616244 -7612,Binary classification,AdaBoost,SMTP,1.0,0.0,0.15651798248291016,5.5300910000000005 -9515,Binary classification,AdaBoost,SMTP,1.0,0.0,0.15651798248291016,7.803705000000001 -11418,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1564950942993164,10.437897000000001 -13321,Binary classification,AdaBoost,SMTP,1.0,0.0,0.15656375885009766,13.429757000000002 -15224,Binary classification,AdaBoost,SMTP,0.9996715496288511,0.761904761904762,0.3805198669433594,17.782309 -17127,Binary classification,AdaBoost,SMTP,0.9997080462454747,0.8,0.3727455139160156,22.84111 -19030,Binary classification,AdaBoost,SMTP,0.9997372431551842,0.8,0.3727455139160156,28.521633 -20933,Binary classification,AdaBoost,SMTP,0.9997611312822473,0.8,0.3727455139160156,34.826752 -22836,Binary classification,AdaBoost,SMTP,0.9997810378804467,0.8,0.3727455139160156,41.750952 -24739,Binary classification,AdaBoost,SMTP,0.9997978818012774,0.8,0.3649024963378906,49.282837 -26642,Binary classification,AdaBoost,SMTP,0.9998123193573815,0.8148148148148148,0.4159393310546875,57.542832000000004 -28545,Binary classification,AdaBoost,SMTP,0.9998248318385651,0.8148148148148148,0.4159393310546875,66.418254 -30448,Binary classification,AdaBoost,SMTP,0.9998357802082307,0.8148148148148148,0.4159393310546875,75.902957 -32351,Binary classification,AdaBoost,SMTP,0.9998454404945905,0.8148148148148148,0.425140380859375,86.000011 -34254,Binary classification,AdaBoost,SMTP,0.9998540273844627,0.8148148148148148,0.4250946044921875,96.707721 -36157,Binary classification,AdaBoost,SMTP,0.999861710366191,0.8148148148148148,0.4250030517578125,108.022851 -38060,Binary classification,AdaBoost,SMTP,0.9998686250295594,0.8148148148148148,0.417205810546875,119.949225 -39963,Binary classification,AdaBoost,SMTP,0.9998748811370802,0.8148148148148148,0.417205810546875,132.490409 -41866,Binary classification,AdaBoost,SMTP,0.9998805684939687,0.8148148148148148,0.417205810546875,145.64785799999999 -43769,Binary classification,AdaBoost,SMTP,0.9998857612867849,0.8148148148148148,0.41722869873046875,159.41857399999998 -45672,Binary classification,AdaBoost,SMTP,0.9998905213373913,0.8148148148148148,0.41722869873046875,173.80738499999998 -47575,Binary classification,AdaBoost,SMTP,0.9998738806911338,0.7857142857142857,0.46401214599609375,189.004291 -49478,Binary classification,AdaBoost,SMTP,0.9998787315318228,0.7857142857142857,0.4561920166015625,204.850148 -51381,Binary classification,AdaBoost,SMTP,0.9998637602179836,0.787878787878788,0.5220794677734375,221.427526 -53284,Binary classification,AdaBoost,SMTP,0.9998686260158024,0.787878787878788,0.5220794677734375,238.605883 -55187,Binary classification,AdaBoost,SMTP,0.9998550356974595,0.7647058823529411,0.56781005859375,256.499342 -57090,Binary classification,AdaBoost,SMTP,0.9995445707579393,0.5666666666666667,1.0016555786132812,277.003919 -58993,Binary classification,AdaBoost,SMTP,0.9995592622728505,0.5666666666666667,1.056976318359375,298.475709 -60896,Binary classification,AdaBoost,SMTP,0.999573035553001,0.5666666666666667,1.0613327026367188,320.766705 -62799,Binary classification,AdaBoost,SMTP,0.9995382018535622,0.5538461538461538,1.28204345703125,344.04407 -64702,Binary classification,AdaBoost,SMTP,0.9995517843619109,0.5538461538461538,1.2822036743164062,368.01597899999996 -66605,Binary classification,AdaBoost,SMTP,0.9995495766020059,0.5454545454545455,1.2944259643554688,392.68268599999993 -68508,Binary classification,AdaBoost,SMTP,0.9995620885456961,0.5714285714285714,1.2944488525390625,418.04215199999993 -70411,Binary classification,AdaBoost,SMTP,0.9995739241585002,0.5714285714285714,1.2945404052734375,444.07242899999994 -72314,Binary classification,AdaBoost,SMTP,0.999571308063557,0.5633802816901408,1.2945404052734375,470.78104299999995 -74217,Binary classification,AdaBoost,SMTP,0.9995823003126011,0.5633802816901408,1.294586181640625,498.25360399999994 -76120,Binary classification,AdaBoost,SMTP,0.9995927429419724,0.5633802816901408,1.2867202758789062,526.392846 -78023,Binary classification,AdaBoost,SMTP,0.9995898592704622,0.5555555555555556,1.2990570068359375,555.302544 -79926,Binary classification,AdaBoost,SMTP,0.9995996246481076,0.5555555555555556,1.2990341186523438,584.881396 -81829,Binary classification,AdaBoost,SMTP,0.9996089358165909,0.5555555555555556,1.2991256713867188,615.117937 -83732,Binary classification,AdaBoost,SMTP,0.9996178237450885,0.5555555555555556,1.2991943359375,646.0958009999999 -85635,Binary classification,AdaBoost,SMTP,0.9996263166499287,0.5555555555555556,1.2991714477539062,677.7414719999999 -87538,Binary classification,AdaBoost,SMTP,0.9996344402938186,0.5555555555555556,1.2991714477539062,710.050307 -89441,Binary classification,AdaBoost,SMTP,0.9996086762075134,0.5333333333333333,1.4483489990234375,743.2331419999999 -91344,Binary classification,AdaBoost,SMTP,0.999616828875776,0.5333333333333333,1.4483261108398438,777.273881 -93247,Binary classification,AdaBoost,SMTP,0.9996139244578855,0.5263157894736841,1.4622306823730469,812.316683 -95150,Binary classification,AdaBoost,SMTP,0.9996216460498797,0.5263157894736841,1.4664268493652344,848.066126 -106,Binary classification,Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.2364511489868164,0.160512 -212,Binary classification,Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.2372446060180664,0.465512 -318,Binary classification,Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.23786258697509766,0.9126559999999999 -424,Binary classification,Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.23796939849853516,1.508959 -530,Binary classification,Bagging,Bananas,0.55765595463138,0.45581395348837206,0.2379922866821289,2.237684 -636,Binary classification,Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.2384653091430664,3.113606 -742,Binary classification,Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.2386941909790039,4.124442 -848,Binary classification,Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.23862552642822266,5.284904 -954,Binary classification,Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.2386026382446289,6.590759 -1060,Binary classification,Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.2383584976196289,8.045558 -1166,Binary classification,Bagging,Bananas,0.590557939914163,0.4015056461731493,0.23845767974853516,9.640388999999999 -1272,Binary classification,Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.23876285552978516,11.379489999999999 -1378,Binary classification,Bagging,Bananas,0.599128540305011,0.3973799126637554,0.23906803131103516,13.268759999999999 -1484,Binary classification,Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.23902225494384766,15.320884999999999 -1590,Binary classification,Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.2389993667602539,17.530882 -1696,Binary classification,Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.2390604019165039,19.899158 -1802,Binary classification,Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.23908329010009766,22.423363 -1908,Binary classification,Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.23908329010009766,25.102832999999997 -2014,Binary classification,Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.23908329010009766,27.945050999999996 -2120,Binary classification,Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.23908329010009766,30.943526999999996 -2226,Binary classification,Bagging,Bananas,0.597752808988764,0.40133779264214053,0.23903751373291016,34.101409999999994 -2332,Binary classification,Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.23909854888916016,37.41135799999999 -2438,Binary classification,Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.2391214370727539,40.87347599999999 -2544,Binary classification,Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.2394876480102539,44.48936199999999 -2650,Binary classification,Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.23969364166259766,48.25778699999999 -2756,Binary classification,Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.23969364166259766,52.17665199999999 -2862,Binary classification,Bagging,Bananas,0.599790283117791,0.4051948051948052,0.23969364166259766,56.248780999999994 -2968,Binary classification,Bagging,Bananas,0.599932591843613,0.40261701056869653,0.2397165298461914,60.47524299999999 -3074,Binary classification,Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.2397165298461914,64.856117 -3180,Binary classification,Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.2397165298461914,69.39380299999999 -3286,Binary classification,Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.23760986328125,74.079456 -3392,Binary classification,Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.31256866455078125,78.938547 -3498,Binary classification,Bagging,Bananas,0.597369173577352,0.40237691001697795,0.3672904968261719,83.975893 -3604,Binary classification,Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3972740173339844,89.206915 -3710,Binary classification,Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.4523735046386719,94.64485099999999 -3816,Binary classification,Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.4865531921386719,100.28768 -3922,Binary classification,Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.5345191955566406,106.16068299999999 -4028,Binary classification,Bagging,Bananas,0.608393344921778,0.4195804195804196,0.5653800964355469,112.191076 -4134,Binary classification,Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.5808448791503906,118.35405 -4240,Binary classification,Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.5852127075195312,124.637983 -4346,Binary classification,Bagging,Bananas,0.6193325661680092,0.438560760353021,0.6001129150390625,131.04469 -4452,Binary classification,Bagging,Bananas,0.6218827229835991,0.4421610871726881,0.6065597534179688,137.573932 -4558,Binary classification,Bagging,Bananas,0.6219003730524468,0.44293566117038474,0.6456527709960938,144.226405 -4664,Binary classification,Bagging,Bananas,0.623203945957538,0.4455664247396655,0.6509552001953125,151.005134 -4770,Binary classification,Bagging,Bananas,0.6250786328370728,0.446096654275093,0.7009811401367188,157.911799 -4876,Binary classification,Bagging,Bananas,0.6266666666666667,0.44680851063829785,0.7141494750976562,164.949721 -4982,Binary classification,Bagging,Bananas,0.629592451314997,0.4530091906314853,0.73150634765625,172.116039 -5088,Binary classification,Bagging,Bananas,0.6298407705917043,0.4527753560011624,0.7153549194335938,179.401331 -5194,Binary classification,Bagging,Bananas,0.6321971885230118,0.456459874786568,0.7158050537109375,186.806087 -5300,Binary classification,Bagging,Bananas,0.6340819022457067,0.4594368553108447,0.7224349975585938,194.332292 -906,Binary classification,Bagging,Elec2,0.8629834254143647,0.8663793103448276,1.7905378341674805,2.379335 -1812,Binary classification,Bagging,Elec2,0.8884594146880177,0.8674540682414698,2.5719175338745117,6.1243490000000005 -2718,Binary classification,Bagging,Elec2,0.8759661391240339,0.8536691272253583,2.0127248764038086,11.743707 -3624,Binary classification,Bagging,Elec2,0.8843499861992824,0.8625778943916038,2.5987844467163086,18.955423 -4530,Binary classification,Bagging,Elec2,0.8869507617575624,0.8584070796460177,3.065375328063965,27.785826999999998 -5436,Binary classification,Bagging,Elec2,0.8833486660533578,0.8529684601113172,2.4308347702026367,38.240294 -6342,Binary classification,Bagging,Elec2,0.882983756505283,0.8526023043305523,2.7551145553588867,50.337362 -7248,Binary classification,Bagging,Elec2,0.8842279563957499,0.8536032106089687,2.720797538757324,63.955505 -8154,Binary classification,Bagging,Elec2,0.8842143996075065,0.8612172890326375,2.593207359313965,79.221878 -9060,Binary classification,Bagging,Elec2,0.8847554917761342,0.8655678599021375,2.3350706100463867,95.984043 -9966,Binary classification,Bagging,Elec2,0.88509784244857,0.8683454064619983,2.6046972274780273,114.346975 -10872,Binary classification,Bagging,Elec2,0.8848312022812989,0.8691745036572621,2.73319149017334,134.274448 -11778,Binary classification,Bagging,Elec2,0.88222807166511,0.8655877507510417,2.9825429916381836,155.860777 -12684,Binary classification,Bagging,Elec2,0.878498777891666,0.8616572403267798,2.5146703720092773,179.01547000000002 -13590,Binary classification,Bagging,Elec2,0.8796820958127898,0.8645738424583782,2.628962516784668,203.671512 -14496,Binary classification,Bagging,Elec2,0.8797516384960331,0.8652701553683234,2.4732847213745117,229.977136 -15402,Binary classification,Bagging,Elec2,0.8800077917018375,0.8645558487247141,2.1737966537475586,257.791658 -16308,Binary classification,Bagging,Elec2,0.8778438707303612,0.861262014208107,2.5011510848999023,287.33973499999996 -17214,Binary classification,Bagging,Elec2,0.8773601347818509,0.8583506676508086,2.443587303161621,318.628337 -18120,Binary classification,Bagging,Elec2,0.8774214912522766,0.8581827469510249,2.8467397689819336,351.823445 -19026,Binary classification,Bagging,Elec2,0.8781603153745072,0.8573362875430822,2.840205192565918,386.938658 -19932,Binary classification,Bagging,Elec2,0.8755707189804827,0.8552923328276345,2.7734594345092773,424.419923 -20838,Binary classification,Bagging,Elec2,0.8726784086000864,0.8517959890508909,3.382327079772949,464.42461 -21744,Binary classification,Bagging,Elec2,0.8735225129926873,0.8505921981962403,2.277277946472168,506.25035599999995 -22650,Binary classification,Bagging,Elec2,0.8713850501125877,0.8461741564133707,2.6045217514038086,549.67693 -23556,Binary classification,Bagging,Elec2,0.8655062619401401,0.8378212347701444,2.0687971115112305,595.047504 -24462,Binary classification,Bagging,Elec2,0.8629246555741793,0.8334740501614105,1.965815544128418,642.194018 -25368,Binary classification,Bagging,Elec2,0.8587929199353491,0.8286944045911048,1.5182180404663086,691.397121 -26274,Binary classification,Bagging,Elec2,0.8580672172953222,0.8274010645683869,1.035365104675293,742.026028 -27180,Binary classification,Bagging,Elec2,0.8578682070716361,0.8276446705037256,1.274672508239746,793.972348 -28086,Binary classification,Bagging,Elec2,0.8549403596225743,0.8230542043085476,1.6423864364624023,847.62915 -28992,Binary classification,Bagging,Elec2,0.8532648063192025,0.8196846388606307,2.145480155944824,903.499383 -29898,Binary classification,Bagging,Elec2,0.8507208081078369,0.8162391402808086,1.650496482849121,961.52369 -30804,Binary classification,Bagging,Elec2,0.8489108203746388,0.812746439204957,1.6065664291381836,1021.4058 -31710,Binary classification,Bagging,Elec2,0.8464789176574474,0.8088731841382018,1.891444206237793,1083.110109 -32616,Binary classification,Bagging,Elec2,0.8443660892227502,0.8061855670103092,1.5838193893432617,1146.603723 -33522,Binary classification,Bagging,Elec2,0.8444258822827482,0.8069876753395758,2.1811208724975586,1211.933286 -34428,Binary classification,Bagging,Elec2,0.8422168646701716,0.8034590057167668,2.362921714782715,1279.642459 -35334,Binary classification,Bagging,Elec2,0.8416777516769026,0.8019121813031161,2.295699119567871,1349.6385269999998 -36240,Binary classification,Bagging,Elec2,0.8424625403570739,0.8013915463558879,2.210890769958496,1421.6820649999997 -37146,Binary classification,Bagging,Elec2,0.8423206353479606,0.8005720317341414,1.5720243453979492,1496.1115339999997 -38052,Binary classification,Bagging,Elec2,0.8416335970145331,0.7984750183934186,1.657557487487793,1572.0838559999997 -38958,Binary classification,Bagging,Elec2,0.8402854429242498,0.7969321148825065,1.542536735534668,1649.6649279999997 -39864,Binary classification,Bagging,Elec2,0.8404786393397387,0.7989503303929938,1.814925193786621,1728.6718509999996 -40770,Binary classification,Bagging,Elec2,0.8419141995143369,0.8025852298832972,1.6994237899780273,1809.1572959999996 -41676,Binary classification,Bagging,Elec2,0.8428554289142172,0.8053036834438267,2.179030418395996,1890.9828949999996 -42582,Binary classification,Bagging,Elec2,0.8435217585308001,0.8066061010652193,2.626959800720215,1974.3648729999995 -43488,Binary classification,Bagging,Elec2,0.8437004162163405,0.8069418013463233,2.851019859313965,2059.4074959999994 -44394,Binary classification,Bagging,Elec2,0.8424751650034915,0.804298547561078,3.6125402450561523,2146.5727129999996 -45300,Binary classification,Bagging,Elec2,0.8419391156537672,0.8040932472365109,3.2033262252807617,2236.8483089999995 -25,Binary classification,Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.7285165786743164,0.081182 -50,Binary classification,Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.7291955947875977,0.246708 -75,Binary classification,Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.7295160293579102,0.48988200000000004 -100,Binary classification,Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.7297601699829102,0.81137 -125,Binary classification,Bagging,Phishing,0.8548387096774194,0.85,0.7297601699829102,1.210797 -150,Binary classification,Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.7300043106079102,1.687212 -175,Binary classification,Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.7303934097290039,2.243398 -200,Binary classification,Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.7305307388305664,2.888269 -225,Binary classification,Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.77154541015625,3.6171050000000005 -250,Binary classification,Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.7995452880859375,4.427161000000001 -275,Binary classification,Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.799774169921875,5.319298000000001 -300,Binary classification,Bagging,Phishing,0.8528428093645485,0.837037037037037,0.799957275390625,6.288776 -325,Binary classification,Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.800323486328125,7.336677 -350,Binary classification,Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.8004684448242188,8.465301 -375,Binary classification,Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.8407363891601562,9.675054 -400,Binary classification,Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.8817596435546875,10.973937 -425,Binary classification,Bagging,Phishing,0.8679245283018868,0.839080459770115,0.937408447265625,12.361792999999999 -450,Binary classification,Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.9376602172851562,13.826239 -475,Binary classification,Bagging,Phishing,0.869198312236287,0.8402061855670103,0.9379119873046875,15.36192 -500,Binary classification,Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.9381179809570312,16.969597999999998 -525,Binary classification,Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.9381790161132812,18.650675999999997 -550,Binary classification,Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.9382858276367188,20.413451999999996 -575,Binary classification,Bagging,Phishing,0.867595818815331,0.8382978723404255,0.9383468627929688,22.261479999999995 -600,Binary classification,Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.9384689331054688,24.204291999999995 -625,Binary classification,Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.9792633056640625,26.239623999999996 -650,Binary classification,Bagging,Phishing,0.8767334360554699,0.846153846153846,0.9797210693359375,28.350958999999996 -675,Binary classification,Bagging,Phishing,0.8753709198813057,0.8478260869565216,1.0075225830078125,30.546932999999996 -700,Binary classification,Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.9475822448730469,32.825613999999995 -725,Binary classification,Bagging,Phishing,0.8825966850828729,0.8576214405360134,1.0479621887207031,35.192032999999995 -750,Binary classification,Bagging,Phishing,0.8865153538050734,0.8631239935587761,1.0882606506347656,37.652956999999994 -775,Binary classification,Bagging,Phishing,0.8875968992248062,0.863849765258216,1.1435890197753906,40.206813999999994 -800,Binary classification,Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.2515907287597656,42.859536999999996 -825,Binary classification,Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.2530021667480469,45.593545 -850,Binary classification,Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.2665596008300781,48.414666 -875,Binary classification,Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.2945747375488281,51.318084999999996 -900,Binary classification,Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.3499031066894531,54.308453 -925,Binary classification,Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.3501167297363281,57.385236 -950,Binary classification,Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.3506507873535156,60.544707 -975,Binary classification,Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.3507575988769531,63.787028 -1000,Binary classification,Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.3509178161621094,67.116503 -1025,Binary classification,Bagging,Phishing,0.8876953125,0.8700564971751412,1.3512077331542969,70.53228 -1050,Binary classification,Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.3513069152832031,74.046134 -1075,Binary classification,Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.3514289855957031,77.61240799999999 -1100,Binary classification,Bagging,Phishing,0.89171974522293,0.8761706555671176,1.3517951965332031,81.22848299999998 -1125,Binary classification,Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.3518562316894531,84.88876699999999 -1150,Binary classification,Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.3518562316894531,88.60766799999999 -1175,Binary classification,Bagging,Phishing,0.8926746166950597,0.877906976744186,1.3519172668457031,92.36705899999998 -1200,Binary classification,Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.3650932312011719,96.17394699999998 -1225,Binary classification,Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.4202919006347656,100.02749999999999 -1250,Binary classification,Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.4205055236816406,103.925929 -1903,Binary classification,Bagging,SMTP,1.0,0.0,0.21781349182128906,1.823771 -3806,Binary classification,Bagging,SMTP,1.0,0.0,0.21842384338378906,5.582317 -5709,Binary classification,Bagging,SMTP,1.0,0.0,0.2189655303955078,10.437190999999999 -7612,Binary classification,Bagging,SMTP,1.0,0.0,0.21898841857910156,16.255529 -9515,Binary classification,Bagging,SMTP,1.0,0.0,0.21898841857910156,22.995497 -11418,Binary classification,Bagging,SMTP,1.0,0.0,0.21959877014160156,30.599323 -13321,Binary classification,Bagging,SMTP,1.0,0.0,0.2196216583251953,39.036327 -15224,Binary classification,Bagging,SMTP,0.9993430992577021,0.16666666666666669,0.5055475234985352,48.440921 -17127,Binary classification,Bagging,SMTP,0.9992993109891393,0.14285714285714288,0.5140314102172852,59.356571 -19030,Binary classification,Bagging,SMTP,0.9993693835724421,0.14285714285714288,0.5010766983032227,71.78490500000001 -20933,Binary classification,Bagging,SMTP,0.9994267150773934,0.14285714285714288,0.5033426284790039,85.737205 -22836,Binary classification,Bagging,SMTP,0.9994744909130721,0.14285714285714288,0.5038537979125977,101.22132500000001 -24739,Binary classification,Bagging,SMTP,0.9995149163230658,0.14285714285714288,0.5166254043579102,118.224146 -26642,Binary classification,Bagging,SMTP,0.9995495664577155,0.25,0.5370950698852539,136.728077 -28545,Binary classification,Bagging,SMTP,0.999579596412556,0.25,0.5375986099243164,156.75763500000002 -30448,Binary classification,Bagging,SMTP,0.9996058724997536,0.25,0.5378046035766602,178.30216700000003 -32351,Binary classification,Bagging,SMTP,0.999629057187017,0.25,0.5502328872680664,201.36650900000004 -34254,Binary classification,Bagging,SMTP,0.9996496657227104,0.25,0.5628290176391602,225.93287100000003 -36157,Binary classification,Bagging,SMTP,0.9996681048788583,0.25,0.5643777847290039,252.01061500000003 -38060,Binary classification,Bagging,SMTP,0.9996847000709425,0.25,0.5652093887329102,279.629747 -39963,Binary classification,Bagging,SMTP,0.9996997147289926,0.25,0.5653314590454102,308.76820100000003 -41866,Binary classification,Bagging,SMTP,0.9997133643855249,0.25,0.5653314590454102,339.41859300000004 -43769,Binary classification,Bagging,SMTP,0.9997258270882837,0.25,0.5653085708618164,371.57202200000006 -45672,Binary classification,Bagging,SMTP,0.9997372512097392,0.25,0.5653696060180664,405.2428510000001 -47575,Binary classification,Bagging,SMTP,0.9997477613822676,0.25,0.5838403701782227,440.4367680000001 -49478,Binary classification,Bagging,SMTP,0.9997574630636458,0.25,0.5960397720336914,477.1431370000001 -51381,Binary classification,Bagging,SMTP,0.9997469832619696,0.3157894736842105,0.6703958511352539,515.395289 -53284,Binary classification,Bagging,SMTP,0.999756019743633,0.3157894736842105,0.6706399917602539,555.1871910000001 -55187,Binary classification,Bagging,SMTP,0.9997644330083717,0.3157894736842105,0.6832361221313477,596.5168480000001 -57090,Binary classification,Bagging,SMTP,0.9996321533044895,0.3225806451612903,1.1210947036743164,639.6973030000001 -58993,Binary classification,Bagging,SMTP,0.9996440195280716,0.3225806451612903,1.145817756652832,684.8963280000002 -60896,Binary classification,Bagging,SMTP,0.9996551441005008,0.3225806451612903,1.1608476638793945,732.0726040000002 -62799,Binary classification,Bagging,SMTP,0.9996337462976528,0.303030303030303,1.2456941604614258,781.2786070000002 -64702,Binary classification,Bagging,SMTP,0.9996445186318604,0.303030303030303,1.246922492980957,832.4886640000002 -66605,Binary classification,Bagging,SMTP,0.9996546753948712,0.303030303030303,1.2487382888793945,885.7141210000002 -68508,Binary classification,Bagging,SMTP,0.9996642678850336,0.3783783783783784,1.266160011291504,940.9452050000002 -70411,Binary classification,Bagging,SMTP,0.9996733418548501,0.3783783783783784,1.296757698059082,998.2257750000002 -72314,Binary classification,Bagging,SMTP,0.9996819382407036,0.3783783783783784,1.2987031936645508,1057.5572380000003 -74217,Binary classification,Bagging,SMTP,0.9996900937803169,0.3783783783783784,1.2991762161254883,1118.9337870000004 -76120,Binary classification,Bagging,SMTP,0.9996978415375924,0.3783783783783784,1.3004274368286133,1182.3536270000004 -78023,Binary classification,Bagging,SMTP,0.9997052113506447,0.3783783783783784,1.3022356033325195,1247.8338970000004 -79926,Binary classification,Bagging,SMTP,0.9997122302158273,0.3783783783783784,1.302800178527832,1315.3680150000005 -81829,Binary classification,Bagging,SMTP,0.9997189226181747,0.3783783783783784,1.3035783767700195,1384.9414330000004 -83732,Binary classification,Bagging,SMTP,0.9997253108167823,0.3783783783783784,1.3040666580200195,1456.5304290000004 -85635,Binary classification,Bagging,SMTP,0.9997314150921363,0.3783783783783784,1.3194093704223633,1530.1954770000004 -87538,Binary classification,Bagging,SMTP,0.9997372539611822,0.3783783783783784,1.3199357986450195,1605.9064890000004 -89441,Binary classification,Bagging,SMTP,0.9997316636851521,0.3684210526315789,1.3624582290649414,1683.7024010000005 -91344,Binary classification,Bagging,SMTP,0.9997372540862464,0.3684210526315789,1.3635034561157227,1763.5593190000004 -93247,Binary classification,Bagging,SMTP,0.9997426163052571,0.3684210526315789,1.3640680313110352,1845.4940930000005 -95150,Binary classification,Bagging,SMTP,0.9997477640332532,0.3684210526315789,1.3650827407836914,1929.4936330000005 -106,Binary classification,Leveraging Bagging,Bananas,0.5142857142857142,0.45161290322580644,0.19296932220458984,0.378114 -212,Binary classification,Leveraging Bagging,Bananas,0.5402843601895735,0.4756756756756757,0.19357967376708984,1.064058 -318,Binary classification,Leveraging Bagging,Bananas,0.5394321766561514,0.4930555555555555,0.1942129135131836,1.944827 -424,Binary classification,Leveraging Bagging,Bananas,0.5531914893617021,0.4932975871313673,0.19419002532958984,3.02079 -530,Binary classification,Leveraging Bagging,Bananas,0.5614366729678639,0.4703196347031963,0.19419002532958984,4.293626 -636,Binary classification,Leveraging Bagging,Bananas,0.5763779527559055,0.4836852207293666,0.4277210235595703,5.781232999999999 -742,Binary classification,Leveraging Bagging,Bananas,0.5991902834008097,0.4940374787052811,0.5387935638427734,7.493556 -848,Binary classification,Leveraging Bagging,Bananas,0.6210153482880756,0.5201793721973094,0.6348705291748047,9.456389999999999 -954,Binary classification,Leveraging Bagging,Bananas,0.6411332633788038,0.5464190981432361,0.7018413543701172,11.663967 -1060,Binary classification,Leveraging Bagging,Bananas,0.6515580736543909,0.555956678700361,0.7448062896728516,14.117771 -1166,Binary classification,Leveraging Bagging,Bananas,0.6626609442060086,0.5732899022801302,0.8341083526611328,16.817314 -1272,Binary classification,Leveraging Bagging,Bananas,0.6766325727773407,0.5958702064896755,0.8756198883056641,19.759644 -1378,Binary classification,Leveraging Bagging,Bananas,0.6877269426289034,0.6062271062271062,0.9611110687255859,22.944053 -1484,Binary classification,Leveraging Bagging,Bananas,0.6999325691166555,0.6238377007607777,1.0045452117919922,26.370908 -1590,Binary classification,Leveraging Bagging,Bananas,0.7073631214600378,0.6375681995323461,1.1097278594970703,30.041027 -1696,Binary classification,Leveraging Bagging,Bananas,0.7162241887905605,0.6496722505462491,1.175821304321289,33.956331 -1802,Binary classification,Leveraging Bagging,Bananas,0.7262631871182677,0.6662153012863914,1.262613296508789,38.117335 -1908,Binary classification,Leveraging Bagging,Bananas,0.7315154693235448,0.6767676767676768,1.3344478607177734,42.524713 -2014,Binary classification,Leveraging Bagging,Bananas,0.7386984600099354,0.6894923258559622,1.391103744506836,47.18564 -2120,Binary classification,Leveraging Bagging,Bananas,0.7451628126474752,0.7013274336283186,1.475076675415039,52.095729 -2226,Binary classification,Leveraging Bagging,Bananas,0.7501123595505618,0.7073684210526315,1.496999740600586,57.242367 -2332,Binary classification,Leveraging Bagging,Bananas,0.7550407550407551,0.7143571785892947,1.516103744506836,62.636095000000005 -2438,Binary classification,Leveraging Bagging,Bananas,0.7595404185473943,0.7196172248803827,1.5630512237548828,68.252973 -2544,Binary classification,Leveraging Bagging,Bananas,0.7620920173023987,0.725124943207633,1.6238231658935547,74.089247 -2650,Binary classification,Leveraging Bagging,Bananas,0.7674594186485466,0.7326388888888887,1.6640300750732422,80.135514 -2756,Binary classification,Leveraging Bagging,Bananas,0.7727767695099819,0.7391666666666666,1.7581462860107422,86.389582 -2862,Binary classification,Leveraging Bagging,Bananas,0.777001048584411,0.7435691318327974,1.8173961639404297,92.85523400000001 -2968,Binary classification,Leveraging Bagging,Bananas,0.7809234917425009,0.7474747474747475,1.919931411743164,99.52929700000001 -3074,Binary classification,Leveraging Bagging,Bananas,0.784249918646274,0.7521495327102804,2.0252437591552734,106.42743700000001 -3180,Binary classification,Leveraging Bagging,Bananas,0.7889273356401384,0.7567959405581733,2.059762954711914,113.53746300000002 -3286,Binary classification,Leveraging Bagging,Bananas,0.7920852359208523,0.7600983491394451,2.1231555938720703,120.86059200000001 -3392,Binary classification,Leveraging Bagging,Bananas,0.7935712179298142,0.7631935047361299,2.208223342895508,128.39806900000002 -3498,Binary classification,Leveraging Bagging,Bananas,0.7963969116385473,0.7653263019116677,2.2967967987060547,136.14712200000002 -3604,Binary classification,Leveraging Bagging,Bananas,0.7993338884263114,0.7677481529071635,2.342061996459961,144.10985700000003 -3710,Binary classification,Leveraging Bagging,Bananas,0.8015637638177406,0.7710018668326073,2.4194507598876953,152.28979100000004 -3816,Binary classification,Leveraging Bagging,Bananas,0.8049803407601572,0.7753623188405797,2.452432632446289,160.68369700000002 -3922,Binary classification,Leveraging Bagging,Bananas,0.8066819688854884,0.7769276044732195,2.484903335571289,169.29472 -4028,Binary classification,Leveraging Bagging,Bananas,0.8080456915818227,0.7781922525107604,2.555143356323242,178.12388700000002 -4134,Binary classification,Leveraging Bagging,Bananas,0.8103072828453908,0.7810055865921788,2.665616989135742,187.16988400000002 -4240,Binary classification,Leveraging Bagging,Bananas,0.8131634819532909,0.7845484221980414,2.698610305786133,196.43184000000002 -4346,Binary classification,Leveraging Bagging,Bananas,0.8158803222094362,0.7877984084880637,2.7711353302001953,205.91274200000004 -4452,Binary classification,Leveraging Bagging,Bananas,0.8173444169849472,0.78932365897901,2.792703628540039,215.61058800000004 -4558,Binary classification,Leveraging Bagging,Bananas,0.8183015141540487,0.7909090909090909,2.8151988983154297,225.53181000000004 -4664,Binary classification,Leveraging Bagging,Bananas,0.8205018228608192,0.7940959409594096,2.871114730834961,235.67375100000004 -4770,Binary classification,Leveraging Bagging,Bananas,0.8209268190396309,0.7941176470588236,2.9113216400146484,246.03360500000005 -4876,Binary classification,Leveraging Bagging,Bananas,0.822974358974359,0.7958362905133666,3.014688491821289,256.6143220000001 -4982,Binary classification,Leveraging Bagging,Bananas,0.825135514956836,0.7989845372720977,3.062814712524414,267.41563500000007 -5088,Binary classification,Leveraging Bagging,Bananas,0.825437389424022,0.7994579945799458,3.1556224822998047,278.43394300000006 -5194,Binary classification,Leveraging Bagging,Bananas,0.8266897746967071,0.800796812749004,3.260141372680664,289.67931000000004 -5300,Binary classification,Leveraging Bagging,Bananas,0.8282694848084544,0.8026030368763557,3.3130626678466797,301.15239700000006 -906,Binary classification,Leveraging Bagging,Elec2,0.8883977900552487,0.8861330326944759,2.6054086685180664,3.368733 -1812,Binary classification,Leveraging Bagging,Elec2,0.9127553837658752,0.8939597315436243,3.465878486633301,9.891931 -2718,Binary classification,Leveraging Bagging,Elec2,0.9013617960986382,0.8816254416961131,3.735013008117676,19.447972 -3624,Binary classification,Leveraging Bagging,Elec2,0.9100193210046923,0.8914780292942742,3.9933290481567383,31.888989000000002 -4530,Binary classification,Leveraging Bagging,Elec2,0.9125634797968647,0.890728476821192,3.547215461730957,47.051529 -5436,Binary classification,Leveraging Bagging,Elec2,0.9100275988960441,0.8870408870408871,4.102688789367676,65.23951100000001 -6342,Binary classification,Leveraging Bagging,Elec2,0.9101088156442202,0.8873517786561265,4.165738105773926,86.445256 -7248,Binary classification,Leveraging Bagging,Elec2,0.9079619152752864,0.8839798225778397,3.951657295227051,110.70864399999999 -8154,Binary classification,Leveraging Bagging,Elec2,0.9089905556236968,0.8907216494845361,4.5953264236450195,137.95385399999998 -9060,Binary classification,Leveraging Bagging,Elec2,0.9097030577326416,0.8939040207522698,4.363041877746582,168.15601999999998 -9966,Binary classification,Leveraging Bagging,Elec2,0.9079779227295535,0.893631829254147,4.951889991760254,201.32075999999998 -10872,Binary classification,Leveraging Bagging,Elec2,0.9092079845460399,0.896399706098457,4.796502113342285,237.348168 -11778,Binary classification,Leveraging Bagging,Elec2,0.9081260083213042,0.8950533462657614,5.402684211730957,276.331972 -12684,Binary classification,Leveraging Bagging,Elec2,0.9066466924229283,0.8936972526485905,4.1159868240356445,318.342965 -13590,Binary classification,Leveraging Bagging,Elec2,0.9079402457870336,0.896363184491757,5.441300392150879,363.45851799999997 -14496,Binary classification,Leveraging Bagging,Elec2,0.9079682649189376,0.8968131188118812,6.5885820388793945,411.598932 -15402,Binary classification,Leveraging Bagging,Elec2,0.908252710862931,0.8965062623599208,5.050324440002441,462.698224 -16308,Binary classification,Leveraging Bagging,Elec2,0.9068498190960937,0.8945797765285585,6.200932502746582,516.871701 -17214,Binary classification,Leveraging Bagging,Elec2,0.9064079474815546,0.8923775803326874,6.6633195877075195,574.184961 -18120,Binary classification,Leveraging Bagging,Elec2,0.9061758375186268,0.8919399949148233,6.39217472076416,634.677368 -19026,Binary classification,Leveraging Bagging,Elec2,0.9064388961892247,0.8910515362957523,7.244908332824707,698.371295 -19932,Binary classification,Leveraging Bagging,Elec2,0.9069790778184738,0.8925092764378478,8.84801197052002,765.4898820000001 -20838,Binary classification,Leveraging Bagging,Elec2,0.9057445889523444,0.8911670176216338,6.796334266662598,836.163928 -21744,Binary classification,Leveraging Bagging,Elec2,0.9054408315319873,0.8892837910608508,8.134293556213379,910.125477 -22650,Binary classification,Leveraging Bagging,Elec2,0.9049847675394057,0.8878816296759404,6.271161079406738,987.478572 -23556,Binary classification,Leveraging Bagging,Elec2,0.903077902780726,0.8852244733799206,4.8961381912231445,1068.279683 -24462,Binary classification,Leveraging Bagging,Elec2,0.9014349372470463,0.8823845065612956,4.712262153625488,1152.538044 -25368,Binary classification,Leveraging Bagging,Elec2,0.8988055347498719,0.8794439487155403,4.656708717346191,1240.2539379999998 -26274,Binary classification,Leveraging Bagging,Elec2,0.8987934381304,0.8792625891113836,4.200020790100098,1331.2764919999997 -27180,Binary classification,Leveraging Bagging,Elec2,0.8987453548695684,0.8798777826276736,3.921463966369629,1425.5964879999997 -28086,Binary classification,Leveraging Bagging,Elec2,0.8965283959408937,0.8766762858597862,3.647244453430176,1523.2409069999997 -28992,Binary classification,Leveraging Bagging,Elec2,0.8964851160705046,0.8761606074361409,3.9709863662719727,1624.2218739999996 -29898,Binary classification,Leveraging Bagging,Elec2,0.8961434257617821,0.8756059452746283,3.793328285217285,1728.6222849999997 -30804,Binary classification,Leveraging Bagging,Elec2,0.8959516930169139,0.8747704450435666,4.122292518615723,1836.3575379999997 -31710,Binary classification,Leveraging Bagging,Elec2,0.8945725188432306,0.8729911477527449,3.9709787368774414,1947.5418809999996 -32616,Binary classification,Leveraging Bagging,Elec2,0.8943124329296336,0.8729872139725119,4.574315071105957,2061.9964669999995 -33522,Binary classification,Leveraging Bagging,Elec2,0.8941559022702187,0.8730862784375448,5.331856727600098,2179.8050099999996 -34428,Binary classification,Leveraging Bagging,Elec2,0.8936299997095303,0.8722973915469383,5.328598976135254,2300.9518229999994 -35334,Binary classification,Leveraging Bagging,Elec2,0.893612203888716,0.8717196191516227,5.664120674133301,2425.5167449999994 -36240,Binary classification,Leveraging Bagging,Elec2,0.8932641629184028,0.8702709954386908,4.706181526184082,2553.3715769999994 -37146,Binary classification,Leveraging Bagging,Elec2,0.893067707632252,0.8697875688434304,5.890534400939941,2684.5254249999994 -38052,Binary classification,Leveraging Bagging,Elec2,0.8926178024230638,0.8686004630820685,5.6988115310668945,2819.0438189999995 -38958,Binary classification,Leveraging Bagging,Elec2,0.8924198475241933,0.8686165710523841,5.71596622467041,2956.9620109999996 -39864,Binary classification,Leveraging Bagging,Elec2,0.8929583824599252,0.8702763505913111,6.792008399963379,3098.2064909999995 -40770,Binary classification,Leveraging Bagging,Elec2,0.8931541121930879,0.8715801886792452,8.543190956115723,3242.7027479999997 -41676,Binary classification,Leveraging Bagging,Elec2,0.8934373125374925,0.8727689442773243,7.9951677322387695,3390.4303709999995 -42582,Binary classification,Leveraging Bagging,Elec2,0.8937554308259552,0.8733411725180581,7.843605995178223,3541.2605409999996 -43488,Binary classification,Leveraging Bagging,Elec2,0.8933474371651298,0.8727572016460905,8.17009449005127,3695.3817859999995 -44394,Binary classification,Leveraging Bagging,Elec2,0.8920550537246863,0.8708216519301273,5.159916877746582,3852.7783049999994 -45300,Binary classification,Leveraging Bagging,Elec2,0.8923817302810216,0.8714568226763348,4.8946428298950195,4013.2815599999994 -25,Binary classification,Leveraging Bagging,Phishing,0.75,0.75,0.6839132308959961,0.224986 -50,Binary classification,Leveraging Bagging,Phishing,0.8163265306122449,0.8,0.6847753524780273,0.690283 -75,Binary classification,Leveraging Bagging,Phishing,0.8378378378378378,0.8333333333333334,0.6847753524780273,1.396176 -100,Binary classification,Leveraging Bagging,Phishing,0.8484848484848485,0.8421052631578947,0.6699657440185547,2.3378490000000003 -125,Binary classification,Leveraging Bagging,Phishing,0.8467741935483871,0.8403361344537815,0.9459667205810547,3.5085690000000005 -150,Binary classification,Leveraging Bagging,Phishing,0.8456375838926175,0.8456375838926175,0.9459896087646484,4.905419 -175,Binary classification,Leveraging Bagging,Phishing,0.867816091954023,0.8588957055214724,1.1184329986572266,6.555308 -200,Binary classification,Leveraging Bagging,Phishing,0.8693467336683417,0.8617021276595744,1.313650131225586,8.437938 -225,Binary classification,Leveraging Bagging,Phishing,0.8660714285714286,0.8557692307692308,1.3411617279052734,10.542565 -250,Binary classification,Leveraging Bagging,Phishing,0.8554216867469879,0.8434782608695653,1.3412303924560547,12.865947 -275,Binary classification,Leveraging Bagging,Phishing,0.8576642335766423,0.844621513944223,1.278768539428711,15.438039 -300,Binary classification,Leveraging Bagging,Phishing,0.862876254180602,0.8464419475655431,1.497152328491211,18.241575 -325,Binary classification,Leveraging Bagging,Phishing,0.8703703703703703,0.851063829787234,1.5338878631591797,21.265574 -350,Binary classification,Leveraging Bagging,Phishing,0.8710601719197708,0.8494983277591974,1.6005840301513672,24.507018000000002 -375,Binary classification,Leveraging Bagging,Phishing,0.8716577540106952,0.8481012658227849,1.8806438446044922,27.994389 -400,Binary classification,Leveraging Bagging,Phishing,0.8696741854636592,0.8433734939759037,2.144338607788086,31.712731 -425,Binary classification,Leveraging Bagging,Phishing,0.8702830188679245,0.8405797101449276,2.182210922241211,35.657971 -450,Binary classification,Leveraging Bagging,Phishing,0.8752783964365256,0.845303867403315,2.182027816772461,39.813046 -475,Binary classification,Leveraging Bagging,Phishing,0.8776371308016878,0.8505154639175259,2.2095394134521484,44.205113 -500,Binary classification,Leveraging Bagging,Phishing,0.875751503006012,0.8502415458937198,2.2298946380615234,48.820324 -525,Binary classification,Leveraging Bagging,Phishing,0.8778625954198473,0.8497652582159624,2.294797897338867,53.667583 -550,Binary classification,Leveraging Bagging,Phishing,0.8743169398907104,0.8463251670378619,2.4045467376708984,58.748532 -575,Binary classification,Leveraging Bagging,Phishing,0.8763066202090593,0.8479657387580299,2.4595699310302734,64.055891 -600,Binary classification,Leveraging Bagging,Phishing,0.8764607679465777,0.8451882845188285,2.4595699310302734,69.582297 -625,Binary classification,Leveraging Bagging,Phishing,0.8782051282051282,0.8442622950819672,2.459615707397461,75.26312 -650,Binary classification,Leveraging Bagging,Phishing,0.8813559322033898,0.850485436893204,2.390268325805664,81.075862 -675,Binary classification,Leveraging Bagging,Phishing,0.8798219584569733,0.8513761467889909,2.665342330932617,87.004325 -700,Binary classification,Leveraging Bagging,Phishing,0.8841201716738197,0.8550983899821109,2.685148239135742,93.043775 -725,Binary classification,Leveraging Bagging,Phishing,0.8825966850828729,0.8556876061120544,2.988882064819336,99.203547 -750,Binary classification,Leveraging Bagging,Phishing,0.8838451268357811,0.8576104746317513,3.061452865600586,105.474091 -775,Binary classification,Leveraging Bagging,Phishing,0.8850129198966409,0.8585055643879173,3.171110153198242,111.86121 -800,Binary classification,Leveraging Bagging,Phishing,0.8848560700876095,0.8601823708206686,3.2535533905029297,118.359722 -825,Binary classification,Leveraging Bagging,Phishing,0.8822815533980582,0.8583941605839417,3.308439254760742,124.969526 -850,Binary classification,Leveraging Bagging,Phishing,0.8845700824499411,0.8607954545454546,3.3186397552490234,131.697623 -875,Binary classification,Leveraging Bagging,Phishing,0.88558352402746,0.8611111111111112,3.2835521697998047,138.541741 -900,Binary classification,Leveraging Bagging,Phishing,0.8843159065628476,0.859078590785908,3.3748836517333984,145.497394 -925,Binary classification,Leveraging Bagging,Phishing,0.8852813852813853,0.8612565445026178,3.4775447845458984,152.56592600000002 -950,Binary classification,Leveraging Bagging,Phishing,0.8861959957850368,0.864321608040201,3.5079097747802734,159.751347 -975,Binary classification,Leveraging Bagging,Phishing,0.8880903490759754,0.8665850673194614,3.5628185272216797,167.044698 -1000,Binary classification,Leveraging Bagging,Phishing,0.8888888888888888,0.867699642431466,3.645017623901367,174.44997500000002 -1025,Binary classification,Leveraging Bagging,Phishing,0.888671875,0.8680555555555557,3.708791732788086,181.96372800000003 -1050,Binary classification,Leveraging Bagging,Phishing,0.8903717826501429,0.8706411698537682,3.783597946166992,189.59074800000002 -1075,Binary classification,Leveraging Bagging,Phishing,0.8910614525139665,0.8724100327153763,3.893186569213867,197.32835100000003 -1100,Binary classification,Leveraging Bagging,Phishing,0.8926296633303002,0.8744680851063831,3.893209457397461,205.17488800000004 -1125,Binary classification,Leveraging Bagging,Phishing,0.891459074733096,0.8742268041237113,3.893209457397461,213.13117300000005 -1150,Binary classification,Leveraging Bagging,Phishing,0.8929503916449086,0.8758829465186679,3.8932552337646484,221.19416900000004 -1175,Binary classification,Leveraging Bagging,Phishing,0.8918228279386712,0.874381800197824,3.8934383392333984,229.37121900000005 -1200,Binary classification,Leveraging Bagging,Phishing,0.8932443703085905,0.8757281553398059,3.8674678802490234,237.66201400000006 -1225,Binary classification,Leveraging Bagging,Phishing,0.8946078431372549,0.8772597526165558,3.903593063354492,246.05749800000007 -1250,Binary classification,Leveraging Bagging,Phishing,0.8951160928742994,0.8783658310120707,3.932668685913086,254.56059300000007 -1903,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1741485595703125,4.196349 -3806,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1747589111328125,11.133977999999999 -5709,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1753692626953125,20.515058 -7612,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1753692626953125,32.334394 -9515,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1753692626953125,46.585565 -11418,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1759796142578125,63.272850000000005 -13321,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1759796142578125,82.384207 -15224,Binary classification,Leveraging Bagging,SMTP,0.9996058595546213,0.625,0.48906993865966797,104.078316 -17127,Binary classification,Leveraging Bagging,SMTP,0.9996496554945696,0.7000000000000001,0.4890470504760742,128.646042 -19030,Binary classification,Leveraging Bagging,SMTP,0.999684691786221,0.7000000000000001,0.4890470504760742,156.063076 -20933,Binary classification,Leveraging Bagging,SMTP,0.9997133575386967,0.7000000000000001,0.4896574020385742,186.34026699999998 -22836,Binary classification,Leveraging Bagging,SMTP,0.999737245456536,0.7000000000000001,0.4896574020385742,219.48144299999998 -24739,Binary classification,Leveraging Bagging,SMTP,0.9997574581615328,0.7000000000000001,0.4896574020385742,255.483814 -26642,Binary classification,Leveraging Bagging,SMTP,0.9997747832288578,0.7272727272727273,0.4897031784057617,294.351753 -28545,Binary classification,Leveraging Bagging,SMTP,0.999789798206278,0.7272727272727273,0.4897031784057617,336.077392 -30448,Binary classification,Leveraging Bagging,SMTP,0.9998029362498768,0.7272727272727273,0.4897031784057617,380.664356 -32351,Binary classification,Leveraging Bagging,SMTP,0.9998145285935085,0.7272727272727273,0.4897031784057617,428.135176 -34254,Binary classification,Leveraging Bagging,SMTP,0.9998248328613553,0.7272727272727273,0.4897031784057617,478.442458 -36157,Binary classification,Leveraging Bagging,SMTP,0.9998340524394291,0.7272727272727273,0.4984426498413086,531.6199799999999 -38060,Binary classification,Leveraging Bagging,SMTP,0.9998423500354713,0.7272727272727273,0.5112142562866211,587.6570539999999 -39963,Binary classification,Leveraging Bagging,SMTP,0.9998498573644963,0.7272727272727273,0.5112142562866211,646.546353 -41866,Binary classification,Leveraging Bagging,SMTP,0.9998566821927625,0.7272727272727273,0.5118017196655273,708.303189 -43769,Binary classification,Leveraging Bagging,SMTP,0.9998629135441418,0.7272727272727273,0.5118017196655273,772.911335 -45672,Binary classification,Leveraging Bagging,SMTP,0.9998686256048697,0.7272727272727273,0.5118017196655273,840.389935 -47575,Binary classification,Leveraging Bagging,SMTP,0.9998528608063227,0.6956521739130435,0.6032171249389648,910.729159 -49478,Binary classification,Leveraging Bagging,SMTP,0.9998585201204601,0.6956521739130435,0.6032171249389648,983.9244679999999 -51381,Binary classification,Leveraging Bagging,SMTP,0.9998248345659789,0.6666666666666666,0.6488561630249023,1059.968701 -53284,Binary classification,Leveraging Bagging,SMTP,0.9998310905917459,0.6666666666666666,0.6488561630249023,1138.856016 -55187,Binary classification,Leveraging Bagging,SMTP,0.9998369151596419,0.6666666666666666,0.6487874984741211,1220.600051 -57090,Binary classification,Leveraging Bagging,SMTP,0.9997197358510396,0.5789473684210525,0.915858268737793,1305.292865 -58993,Binary classification,Leveraging Bagging,SMTP,0.9997118253322484,0.5641025641025641,0.9158124923706055,1392.9564999999998 -60896,Binary classification,Leveraging Bagging,SMTP,0.9997208309385007,0.5641025641025641,0.9158353805541992,1483.5784209999997 -62799,Binary classification,Leveraging Bagging,SMTP,0.9996974425937132,0.5365853658536585,1.0681943893432617,1577.1896599999998 -64702,Binary classification,Leveraging Bagging,SMTP,0.9997063414784934,0.5365853658536585,1.0681486129760742,1673.7731769999998 -66605,Binary classification,Leveraging Bagging,SMTP,0.999714731847937,0.5365853658536585,1.0681257247924805,1773.3049339999998 -68508,Binary classification,Leveraging Bagging,SMTP,0.9997226560789408,0.5777777777777777,1.0767507553100586,1875.8071019999998 -70411,Binary classification,Leveraging Bagging,SMTP,0.9997301519670502,0.5777777777777777,1.0766592025756836,1981.2768389999997 -72314,Binary classification,Leveraging Bagging,SMTP,0.9997372533292769,0.5777777777777777,1.0766363143920898,2089.690205 -74217,Binary classification,Leveraging Bagging,SMTP,0.9997439905141748,0.5777777777777777,1.183516502380371,2201.0645729999997 -76120,Binary classification,Leveraging Bagging,SMTP,0.9997503908354025,0.5777777777777777,1.1835393905639648,2315.3792299999996 -78023,Binary classification,Leveraging Bagging,SMTP,0.999756478941837,0.5777777777777777,1.183516502380371,2432.6354309999997 -79926,Binary classification,Leveraging Bagging,SMTP,0.9997622771348139,0.5777777777777777,1.1834936141967773,2552.8498939999995 -81829,Binary classification,Leveraging Bagging,SMTP,0.9997678056411008,0.5777777777777777,1.183516502380371,2676.0109319999997 -83732,Binary classification,Leveraging Bagging,SMTP,0.9997730828486463,0.5777777777777777,1.184126853942871,2802.1051509999998 -85635,Binary classification,Leveraging Bagging,SMTP,0.9997781255108952,0.5777777777777777,1.1841497421264648,2931.1451239999997 -87538,Binary classification,Leveraging Bagging,SMTP,0.9997829489244549,0.5777777777777777,1.1841497421264648,3063.115766 -89441,Binary classification,Leveraging Bagging,SMTP,0.999765205724508,0.5531914893617021,1.323287010192871,3198.03692 -91344,Binary classification,Leveraging Bagging,SMTP,0.9997700973254655,0.5531914893617021,1.3272314071655273,3335.9142659999998 -93247,Binary classification,Leveraging Bagging,SMTP,0.9997747892671,0.5531914893617021,1.3272314071655273,3476.7248499999996 -95150,Binary classification,Leveraging Bagging,SMTP,0.9997792935290964,0.5531914893617021,1.327254295349121,3620.3557379999997 -106,Binary classification,Stacking,Bananas,0.6,0.5434782608695652,0.7756900787353516,0.335809 -212,Binary classification,Stacking,Bananas,0.7251184834123223,0.6881720430107526,1.166391372680664,1.0610979999999999 -318,Binary classification,Stacking,Bananas,0.7539432176656151,0.7253521126760563,1.6065692901611328,2.189762 -424,Binary classification,Stacking,Bananas,0.7825059101654847,0.7566137566137565,2.022294044494629,3.739465 -530,Binary classification,Stacking,Bananas,0.7958412098298677,0.7631578947368421,2.4158077239990234,5.557825 -636,Binary classification,Stacking,Bananas,0.7937007874015748,0.7622504537205083,2.785458564758301,7.636877 -742,Binary classification,Stacking,Bananas,0.8029689608636977,0.7675159235668789,3.1855859756469727,9.989946 -848,Binary classification,Stacking,Bananas,0.8110979929161747,0.7790055248618785,3.6375198364257812,12.613619 -954,Binary classification,Stacking,Bananas,0.8163693599160545,0.7836835599505564,3.940545082092285,15.512474000000001 -1060,Binary classification,Stacking,Bananas,0.8243626062322946,0.7905405405405406,4.2790374755859375,18.684266 -1166,Binary classification,Stacking,Bananas,0.8240343347639485,0.790602655771195,4.743890762329102,22.147393 -1272,Binary classification,Stacking,Bananas,0.8253343823760818,0.7936802973977696,5.073901176452637,25.9097 -1378,Binary classification,Stacking,Bananas,0.8271604938271605,0.7941176470588235,5.443793296813965,29.969434 -1484,Binary classification,Stacking,Bananas,0.8293998651382333,0.7967871485943775,5.808208465576172,34.339833999999996 -1590,Binary classification,Stacking,Bananas,0.8313404657016992,0.7994011976047903,6.233636856079102,39.030108 -1696,Binary classification,Stacking,Bananas,0.8348082595870207,0.8030942334739802,6.672585487365723,44.033544 -1802,Binary classification,Stacking,Bananas,0.8384230982787341,0.8094302554027505,7.1109819412231445,49.366899 -1908,Binary classification,Stacking,Bananas,0.8363922391190352,0.8095238095238096,7.617318153381348,55.036325 -2014,Binary classification,Stacking,Bananas,0.8335817188276204,0.8080229226361031,8.179092407226562,61.04992 -2120,Binary classification,Stacking,Bananas,0.8357715903728173,0.8127018299246501,8.54446792602539,67.397947 -2226,Binary classification,Stacking,Bananas,0.8368539325842697,0.8143222506393862,9.00230598449707,74.092607 -2332,Binary classification,Stacking,Bananas,0.8365508365508365,0.8142369575816674,9.503581047058105,81.1143 -2438,Binary classification,Stacking,Bananas,0.8379154698399671,0.8160223567768979,9.875147819519043,88.456785 -2544,Binary classification,Stacking,Bananas,0.8375933936295714,0.816525988449578,10.285362243652344,96.129153 -2650,Binary classification,Stacking,Bananas,0.8369195922989807,0.8161702127659575,10.684521675109863,104.109815 -2756,Binary classification,Stacking,Bananas,0.8381125226860254,0.8176614881439086,11.088122367858887,112.408624 -2862,Binary classification,Stacking,Bananas,0.8392170569730864,0.8184688239936858,11.425690650939941,121.03154500000001 -2968,Binary classification,Stacking,Bananas,0.840242669362993,0.8188073394495413,11.867729187011719,129.980691 -3074,Binary classification,Stacking,Bananas,0.8395704523267166,0.8186833394630378,12.388784408569336,139.271806 -3180,Binary classification,Stacking,Bananas,0.8417741428122051,0.8205494113449874,12.724870681762695,148.909629 -3286,Binary classification,Stacking,Bananas,0.8423135464231355,0.8207612456747405,13.150970458984375,158.896732 -3392,Binary classification,Stacking,Bananas,0.8428192273665586,0.8221554888221555,13.55066967010498,169.223096 -3498,Binary classification,Stacking,Bananas,0.8441521303974836,0.8228794280142996,13.954619407653809,179.903449 -3604,Binary classification,Stacking,Bananas,0.8454066056064391,0.8234548335974643,14.297491073608398,190.93427 -3710,Binary classification,Stacking,Bananas,0.845510919385279,0.8241791960724149,14.779010772705078,202.32720799999998 -3816,Binary classification,Stacking,Bananas,0.8453473132372215,0.8241954707985697,15.263079643249512,214.07821499999997 -3922,Binary classification,Stacking,Bananas,0.8454475899005356,0.8239395700174318,15.65628433227539,226.20807199999996 -4028,Binary classification,Stacking,Bananas,0.8435559970201142,0.8216308040770102,16.25907039642334,238.71700499999997 -4134,Binary classification,Stacking,Bananas,0.8439390273409146,0.8220689655172413,16.753341674804688,251.59892599999998 -4240,Binary classification,Stacking,Bananas,0.8461901391837697,0.8249194414607949,17.15353012084961,264.97802099999996 -4346,Binary classification,Stacking,Bananas,0.8474108170310702,0.8263033796175006,17.548202514648438,278.74497199999996 -4452,Binary classification,Stacking,Bananas,0.8467760053920468,0.8253968253968255,17.966373443603516,292.92157399999996 -4558,Binary classification,Stacking,Bananas,0.847267939433838,0.8265204386839482,18.466463088989258,307.49799799999994 -4664,Binary classification,Stacking,Bananas,0.8475230538280077,0.8273014330823415,18.886920928955078,322.48837999999995 -4770,Binary classification,Stacking,Bananas,0.8483958901237156,0.828143570240076,19.283724784851074,337.9195859999999 -4876,Binary classification,Stacking,Bananas,0.8488205128205129,0.8279243520896566,19.64915180206299,353.77839399999993 -4982,Binary classification,Stacking,Bananas,0.8496285886368199,0.8291904218928163,19.982958793640137,370.05846699999995 -5088,Binary classification,Stacking,Bananas,0.850206408492235,0.829682610639249,20.404964447021484,386.77508599999993 -5194,Binary classification,Stacking,Bananas,0.8501829385711535,0.8296847635726795,20.84154510498047,403.93132599999996 -5300,Binary classification,Stacking,Bananas,0.8503491224759389,0.8299378082779326,21.28391456604004,421.53966099999997 -906,Binary classification,Stacking,Elec2,0.9082872928176795,0.905788876276958,2.982789993286133,3.516385 -1812,Binary classification,Stacking,Elec2,0.9276642738818333,0.9100892244337679,5.035035133361816,9.67052 -2718,Binary classification,Stacking,Elec2,0.9193963930806036,0.9004092769440655,7.4160261154174805,18.410774 -3624,Binary classification,Stacking,Elec2,0.9213359094672923,0.9030941856511392,8.590222358703613,29.484026 -4530,Binary classification,Stacking,Elec2,0.919629057187017,0.896825396825397,10.053829193115234,42.932107 -5436,Binary classification,Stacking,Elec2,0.9164673413063478,0.8929245283018867,11.804065704345703,58.899071000000006 -6342,Binary classification,Stacking,Elec2,0.9165746727645482,0.8945585010962727,14.095972061157227,77.41200400000001 -7248,Binary classification,Stacking,Elec2,0.9136194287291293,0.8907885554780182,16.2540225982666,98.532638 -8154,Binary classification,Stacking,Elec2,0.9135287624187416,0.8957254843957995,18.652454376220703,122.39659 -9060,Binary classification,Stacking,Elec2,0.9157743680317916,0.9006122183144457,20.98922061920166,148.829731 -9966,Binary classification,Stacking,Elec2,0.9175112895132965,0.9043741275011633,21.67603302001953,177.92592100000002 -10872,Binary classification,Stacking,Elec2,0.9189586974519364,0.9074093536521283,23.620224952697754,209.60222600000003 -11778,Binary classification,Stacking,Elec2,0.9187399167869577,0.907222491517208,26.53412914276123,243.98565400000004 -12684,Binary classification,Stacking,Elec2,0.9176062445793582,0.9060842994517839,30.32783317565918,281.19943900000004 -13590,Binary classification,Stacking,Elec2,0.9178747516373538,0.9076464746772592,30.98683452606201,321.28235000000006 -14496,Binary classification,Stacking,Elec2,0.9177647464642981,0.9080388828884431,33.476722717285156,364.2111060000001 -15402,Binary classification,Stacking,Elec2,0.9181221998571522,0.9079091506609217,36.0946741104126,409.94528800000006 -16308,Binary classification,Stacking,Elec2,0.9156803826577543,0.90483770503149,39.825303077697754,458.76832800000005 -17214,Binary classification,Stacking,Elec2,0.9136118050310812,0.9010184383944618,31.60810947418213,510.75424300000003 -18120,Binary classification,Stacking,Elec2,0.9136265798333242,0.9008803597441257,29.540003776550293,565.7272700000001 -19026,Binary classification,Stacking,Elec2,0.9144809461235217,0.9008350094471872,30.251863479614258,623.5764490000001 -19932,Binary classification,Stacking,Elec2,0.9137022728413025,0.9008188213585515,34.67849349975586,684.5888420000001 -20838,Binary classification,Stacking,Elec2,0.9112156260498152,0.8982286280118825,25.089420318603516,748.7394940000001 -21744,Binary classification,Stacking,Elec2,0.9108678655199375,0.8963414634146342,28.645745277404785,815.8674180000002 -22650,Binary classification,Stacking,Elec2,0.910062254404168,0.8947232415111892,32.00656318664551,886.1270340000001 -23556,Binary classification,Stacking,Elec2,0.9082148163871789,0.8923306772908367,29.995322227478027,959.6051970000001 -24462,Binary classification,Stacking,Elec2,0.9068312824496136,0.8901633813677768,31.142220497131348,1036.2892310000002 -25368,Binary classification,Stacking,Elec2,0.9048764142389719,0.8880745860197596,17.65717887878418,1115.9640360000003 -26274,Binary classification,Stacking,Elec2,0.9052639591976553,0.8883004981375936,19.38848114013672,1198.3281000000004 -27180,Binary classification,Stacking,Elec2,0.905331321976526,0.8887639963685098,20.18451499938965,1283.4271300000005 -28086,Binary classification,Stacking,Elec2,0.9040769093822325,0.886768661735037,24.225767135620117,1371.4128720000006 -28992,Binary classification,Stacking,Elec2,0.9030388741333517,0.8849883392659875,26.150443077087402,1462.6499510000006 -29898,Binary classification,Stacking,Elec2,0.9022644412482858,0.8837708830548926,31.19912052154541,1557.0334400000006 -30804,Binary classification,Stacking,Elec2,0.9016004934584294,0.8822684016313848,35.062607765197754,1654.6421970000006 -31710,Binary classification,Stacking,Elec2,0.9002491406225361,0.8805739097602416,34.509202003479004,1755.5826140000006 -32616,Binary classification,Stacking,Elec2,0.8993407941131382,0.8797127468581688,39.23386001586914,1859.7925240000006 -33522,Binary classification,Stacking,Elec2,0.8986307091077235,0.879040296169728,43.53414344787598,1967.3677710000006 -34428,Binary classification,Stacking,Elec2,0.8976384814244633,0.8779440288168467,46.41888236999512,2078.5063070000006 -35334,Binary classification,Stacking,Elec2,0.8964990235756941,0.8759119134063996,47.29628944396973,2193.0138890000007 -36240,Binary classification,Stacking,Elec2,0.8963271613455117,0.874812568724801,50.92376518249512,2310.7523430000006 -37146,Binary classification,Stacking,Elec2,0.8956252523892853,0.8736433855881107,52.943902015686035,2431.8466670000007 -38052,Binary classification,Stacking,Elec2,0.8955612204672676,0.8730756946662408,54.53785705566406,2556.3609540000007 -38958,Binary classification,Stacking,Elec2,0.895320481556588,0.8731176104542625,58.273138999938965,2684.2112230000007 -39864,Binary classification,Stacking,Elec2,0.8957178335800116,0.8745056603773586,61.72060203552246,2815.6197070000007 -40770,Binary classification,Stacking,Elec2,0.8957541269101523,0.8755198875285573,62.47746276855469,2950.3941420000006 -41676,Binary classification,Stacking,Elec2,0.8962447510497901,0.8769003017707682,58.149333000183105,3088.5413130000006 -42582,Binary classification,Stacking,Elec2,0.8967614663817195,0.8777598576274956,60.56365966796875,3230.0396030000006 -43488,Binary classification,Stacking,Elec2,0.896612780831053,0.8775932480261367,59.691514015197754,3375.0306610000007 -44394,Binary classification,Stacking,Elec2,0.8962223774018426,0.8767423816785724,64.22966861724854,3523.5486860000005 -45300,Binary classification,Stacking,Elec2,0.8967968387823131,0.8776210046857412,42.88050365447998,3675.4418880000007 -25,Binary classification,Stacking,Phishing,0.6666666666666666,0.7142857142857143,0.5762147903442383,0.119361 -50,Binary classification,Stacking,Phishing,0.7551020408163265,0.7391304347826088,0.6475057601928711,0.366938 -75,Binary classification,Stacking,Phishing,0.7837837837837838,0.7777777777777778,0.9396762847900391,0.745521 -100,Binary classification,Stacking,Phishing,0.8080808080808081,0.7999999999999999,1.1059551239013672,1.266772 -125,Binary classification,Stacking,Phishing,0.8145161290322581,0.8067226890756303,1.2883186340332031,1.944005 -150,Binary classification,Stacking,Phishing,0.8187919463087249,0.8187919463087249,1.3393936157226562,2.776317 -175,Binary classification,Stacking,Phishing,0.8390804597701149,0.8292682926829268,1.354720115661621,3.764387 -200,Binary classification,Stacking,Phishing,0.8391959798994975,0.8297872340425532,1.492502212524414,4.90761 -225,Binary classification,Stacking,Phishing,0.84375,0.8309178743961353,1.6093759536743164,6.207708 -250,Binary classification,Stacking,Phishing,0.8353413654618473,0.8225108225108225,1.7083539962768555,7.674896 -275,Binary classification,Stacking,Phishing,0.8394160583941606,0.8253968253968254,1.7150201797485352,9.301078 -300,Binary classification,Stacking,Phishing,0.842809364548495,0.825278810408922,1.782989501953125,11.084076 -325,Binary classification,Stacking,Phishing,0.8518518518518519,0.8309859154929577,1.8577651977539062,13.027607 -350,Binary classification,Stacking,Phishing,0.8567335243553008,0.8344370860927152,1.8672637939453125,15.126795999999999 -375,Binary classification,Stacking,Phishing,0.8529411764705882,0.8286604361370716,2.0530452728271484,17.394419 -400,Binary classification,Stacking,Phishing,0.8546365914786967,0.8284023668639053,2.076310157775879,19.823605 -425,Binary classification,Stacking,Phishing,0.8584905660377359,0.8295454545454545,2.0878963470458984,22.419713 -450,Binary classification,Stacking,Phishing,0.8596881959910914,0.8292682926829269,2.0654611587524414,25.175931000000002 -475,Binary classification,Stacking,Phishing,0.8649789029535865,0.8383838383838383,2.202821731567383,28.096353 -500,Binary classification,Stacking,Phishing,0.8677354709418837,0.8443396226415094,2.377251625061035,31.184831 -525,Binary classification,Stacking,Phishing,0.8702290076335878,0.8447488584474886,2.37432861328125,34.435066 -550,Binary classification,Stacking,Phishing,0.8706739526411658,0.8466522678185745,2.44970703125,37.85251 -575,Binary classification,Stacking,Phishing,0.872822299651568,0.8488612836438924,2.5103416442871094,41.431292 -600,Binary classification,Stacking,Phishing,0.8764607679465777,0.8508064516129032,2.478057861328125,45.177182 -625,Binary classification,Stacking,Phishing,0.875,0.8464566929133858,2.529691696166992,49.087450000000004 -650,Binary classification,Stacking,Phishing,0.8782742681047766,0.8528864059590316,2.6017770767211914,53.16255700000001 -675,Binary classification,Stacking,Phishing,0.8783382789317508,0.856140350877193,2.631270408630371,57.40937600000001 -700,Binary classification,Stacking,Phishing,0.882689556509299,0.8595890410958904,2.6406030654907227,61.81678500000001 -725,Binary classification,Stacking,Phishing,0.8825966850828729,0.8617886178861789,2.701584815979004,66.39355 -750,Binary classification,Stacking,Phishing,0.8851802403204272,0.8652037617554857,2.7890710830688477,71.086949 -775,Binary classification,Stacking,Phishing,0.8863049095607235,0.8658536585365854,2.950723648071289,75.87878400000001 -800,Binary classification,Stacking,Phishing,0.886107634543179,0.8671532846715327,2.9481277465820312,80.77073000000001 -825,Binary classification,Stacking,Phishing,0.8871359223300971,0.869198312236287,3.217336654663086,85.76636800000001 -850,Binary classification,Stacking,Phishing,0.8881036513545347,0.8696844993141291,3.2494144439697266,90.85872600000002 -875,Binary classification,Stacking,Phishing,0.8901601830663616,0.8713136729222519,3.264657974243164,96.04768600000001 -900,Binary classification,Stacking,Phishing,0.8887652947719689,0.8694516971279374,3.3888587951660156,101.33402500000001 -925,Binary classification,Stacking,Phishing,0.8906926406926406,0.8729559748427673,3.3625974655151367,106.71510400000001 -950,Binary classification,Stacking,Phishing,0.8914646996838778,0.875453446191052,3.5129919052124023,112.20148900000001 -975,Binary classification,Stacking,Phishing,0.893223819301848,0.8773584905660378,3.552186965942383,117.78607000000001 -1000,Binary classification,Stacking,Phishing,0.8928928928928929,0.8771526980482205,3.6711978912353516,123.468781 -1025,Binary classification,Stacking,Phishing,0.892578125,0.8772321428571428,3.734159469604492,129.25173700000002 -1050,Binary classification,Stacking,Phishing,0.894184938036225,0.8794788273615636,3.775693893432617,135.127992 -1075,Binary classification,Stacking,Phishing,0.8929236499068901,0.879074658254469,3.8186750411987305,141.101233 -1100,Binary classification,Stacking,Phishing,0.8944494995450409,0.8809034907597535,3.859647750854492,147.172282 -1125,Binary classification,Stacking,Phishing,0.8959074733096085,0.8835820895522387,3.8923940658569336,153.346878 -1150,Binary classification,Stacking,Phishing,0.896431679721497,0.8839024390243903,4.0131940841674805,159.622141 -1175,Binary classification,Stacking,Phishing,0.8952299829642248,0.8822966507177035,4.097073554992676,165.998758 -1200,Binary classification,Stacking,Phishing,0.896580483736447,0.8834586466165414,4.154815673828125,172.479929 -1225,Binary classification,Stacking,Phishing,0.8978758169934641,0.8847926267281105,4.238761901855469,179.059934 -1250,Binary classification,Stacking,Phishing,0.899119295436349,0.8866906474820143,4.319509506225586,185.738258 -1903,Binary classification,Stacking,SMTP,1.0,0.0,0.24710845947265625,2.767854 -3806,Binary classification,Stacking,SMTP,1.0,0.0,0.24883270263671875,8.302014 -5709,Binary classification,Stacking,SMTP,1.0,0.0,0.25005340576171875,16.382949 -7612,Binary classification,Stacking,SMTP,1.0,0.0,0.24954986572265625,26.809829999999998 -9515,Binary classification,Stacking,SMTP,1.0,0.0,0.24954986572265625,39.580928 -11418,Binary classification,Stacking,SMTP,1.0,0.0,0.25127410888671875,54.705622 -13321,Binary classification,Stacking,SMTP,1.0,0.0,0.25127410888671875,72.18382 -15224,Binary classification,Stacking,SMTP,0.9996058595546213,0.625,0.6267004013061523,92.15184599999999 -17127,Binary classification,Stacking,SMTP,0.9996496554945696,0.7000000000000001,0.6147451400756836,114.903201 -19030,Binary classification,Stacking,SMTP,0.999684691786221,0.7000000000000001,0.6187658309936523,140.42757699999999 -20933,Binary classification,Stacking,SMTP,0.9997133575386967,0.7000000000000001,0.6200857162475586,168.711887 -22836,Binary classification,Stacking,SMTP,0.999737245456536,0.7000000000000001,0.6196355819702148,199.762171 -24739,Binary classification,Stacking,SMTP,0.9997574581615328,0.7000000000000001,0.619715690612793,233.587883 -26642,Binary classification,Stacking,SMTP,0.9997372471003341,0.6666666666666666,0.656519889831543,270.202649 -28545,Binary classification,Stacking,SMTP,0.999754764573991,0.6666666666666666,0.6566305160522461,309.590328 -30448,Binary classification,Stacking,SMTP,0.999770092291523,0.6666666666666666,0.6724729537963867,351.755182 -32351,Binary classification,Stacking,SMTP,0.9997836166924265,0.6666666666666666,0.6681547164916992,396.69714999999997 -34254,Binary classification,Stacking,SMTP,0.9997956383382477,0.6666666666666666,0.669642448425293,444.42250599999994 -36157,Binary classification,Stacking,SMTP,0.9998063945126673,0.6666666666666666,0.6692113876342773,494.92236899999995 -38060,Binary classification,Stacking,SMTP,0.9998160750413831,0.6666666666666666,0.6613035202026367,548.2094599999999 -39963,Binary classification,Stacking,SMTP,0.9998248335919123,0.6666666666666666,0.6618070602416992,604.285405 -41866,Binary classification,Stacking,SMTP,0.9998327958915562,0.6666666666666666,0.6630735397338867,663.137659 -43769,Binary classification,Stacking,SMTP,0.9998400658014989,0.6666666666666666,0.6625699996948242,724.773781 -45672,Binary classification,Stacking,SMTP,0.9998467298723479,0.6666666666666666,0.6625699996948242,789.184255 -47575,Binary classification,Stacking,SMTP,0.9998528608063227,0.6666666666666666,0.7120122909545898,856.394593 -49478,Binary classification,Stacking,SMTP,0.9998585201204601,0.6666666666666666,0.7120351791381836,926.380828 -51381,Binary classification,Stacking,SMTP,0.9998442973919813,0.6666666666666666,0.741633415222168,999.155992 -53284,Binary classification,Stacking,SMTP,0.9998498583037742,0.6666666666666666,0.7417364120483398,1074.696379 -55187,Binary classification,Stacking,SMTP,0.9998550356974595,0.6666666666666666,0.7564992904663086,1153.0240469999999 -57090,Binary classification,Stacking,SMTP,0.9997022193417295,0.5142857142857143,1.0212621688842773,1234.302477 -58993,Binary classification,Stacking,SMTP,0.9997118253322484,0.5142857142857143,1.0188016891479492,1318.4794789999999 -60896,Binary classification,Stacking,SMTP,0.9997208309385007,0.5142857142857143,1.0275907516479492,1405.549891 -62799,Binary classification,Stacking,SMTP,0.9996974425937132,0.48648648648648646,1.2421979904174805,1495.555588 -64702,Binary classification,Stacking,SMTP,0.9997063414784934,0.48648648648648646,1.258589744567871,1588.500286 -66605,Binary classification,Stacking,SMTP,0.999714731847937,0.48648648648648646,1.266993522644043,1684.370287 -68508,Binary classification,Stacking,SMTP,0.9997226560789408,0.5365853658536586,1.3167448043823242,1783.171155 -70411,Binary classification,Stacking,SMTP,0.9997301519670502,0.5365853658536586,1.3264188766479492,1884.908958 -72314,Binary classification,Stacking,SMTP,0.9997372533292769,0.5365853658536586,1.3260221481323242,1989.570925 -74217,Binary classification,Stacking,SMTP,0.9997439905141748,0.5365853658536586,1.342616081237793,2097.157749 -76120,Binary classification,Stacking,SMTP,0.9997503908354025,0.5365853658536586,1.3624944686889648,2207.674598 -78023,Binary classification,Stacking,SMTP,0.999756478941837,0.5365853658536586,1.357996940612793,2321.128083 -79926,Binary classification,Stacking,SMTP,0.9997622771348139,0.5365853658536586,1.3575201034545898,2437.510157 -81829,Binary classification,Stacking,SMTP,0.9997678056411008,0.5365853658536586,1.3575468063354492,2556.8217050000003 -83732,Binary classification,Stacking,SMTP,0.9997730828486463,0.5365853658536586,1.3463621139526367,2679.067711 -85635,Binary classification,Stacking,SMTP,0.9997781255108952,0.5365853658536586,1.3506765365600586,2804.239644 -87538,Binary classification,Stacking,SMTP,0.9997829489244549,0.5365853658536586,1.350123405456543,2932.339248 -89441,Binary classification,Stacking,SMTP,0.9997763864042933,0.5238095238095238,1.4778623580932617,3063.373685 -91344,Binary classification,Stacking,SMTP,0.9997810450718719,0.5238095238095238,1.489375114440918,3197.337821 -93247,Binary classification,Stacking,SMTP,0.9997855135877142,0.5238095238095238,1.5083913803100586,3334.235023 -95150,Binary classification,Stacking,SMTP,0.9997898033610443,0.5238095238095238,1.5167646408081055,3474.068956 -106,Binary classification,Voting,Bananas,0.7142857142857143,0.6590909090909091,0.08138561248779297,0.066609 -212,Binary classification,Voting,Bananas,0.7819905213270142,0.7444444444444445,0.08191204071044922,0.219203 -318,Binary classification,Voting,Bananas,0.7949526813880127,0.7583643122676579,0.08151531219482422,0.458077 -424,Binary classification,Voting,Bananas,0.806146572104019,0.7696629213483147,0.08151531219482422,0.7833680000000001 -530,Binary classification,Voting,Bananas,0.7977315689981096,0.7446300715990454,0.08201885223388672,1.194727 -636,Binary classification,Voting,Bananas,0.7984251968503937,0.7460317460317459,0.08151531219482422,1.6920620000000002 -742,Binary classification,Voting,Bananas,0.805668016194332,0.75,0.08151531219482422,2.2749610000000002 -848,Binary classification,Voting,Bananas,0.8110979929161747,0.7597597597597598,0.08201885223388672,2.9435640000000003 -954,Binary classification,Voting,Bananas,0.8174186778593914,0.7661290322580646,0.08151531219482422,3.697699 -1060,Binary classification,Voting,Bananas,0.8253068932955618,0.774114774114774,0.08151531219482422,4.5372580000000005 -1166,Binary classification,Voting,Bananas,0.8266094420600858,0.7770419426048565,0.08201885223388672,5.437299 -1272,Binary classification,Voting,Bananas,0.8284815106215578,0.7811244979919679,0.08151531219482422,6.385956 -1378,Binary classification,Voting,Bananas,0.8264342774146696,0.7764265668849394,0.08201885223388672,7.383082 -1484,Binary classification,Voting,Bananas,0.8287255563047876,0.7795138888888888,0.08201885223388672,8.428656 -1590,Binary classification,Voting,Bananas,0.830081812460667,0.7822580645161291,0.08151531219482422,9.522527 -1696,Binary classification,Voting,Bananas,0.8348082595870207,0.7881996974281392,0.08201885223388672,10.665041 -1802,Binary classification,Voting,Bananas,0.8367573570238757,0.7929577464788733,0.08201885223388672,11.856044 -1908,Binary classification,Voting,Bananas,0.8342947037231253,0.7931937172774869,0.08151531219482422,13.095519000000001 -2014,Binary classification,Voting,Bananas,0.8301043219076006,0.7901840490797546,0.08201885223388672,14.384085 -2120,Binary classification,Voting,Bananas,0.8310523831996225,0.7935409457900807,0.08201885223388672,15.721188000000001 -2226,Binary classification,Voting,Bananas,0.8301123595505618,0.792535675082327,0.08151531219482422,17.109526000000002 -2332,Binary classification,Voting,Bananas,0.8301158301158301,0.792887029288703,0.08201885223388672,18.546805000000003 -2438,Binary classification,Voting,Bananas,0.8297086581862946,0.7921882824236354,0.08201885223388672,20.032509 -2544,Binary classification,Voting,Bananas,0.8297286669288242,0.7933174224343675,0.08151531219482422,21.566602 -2650,Binary classification,Voting,Bananas,0.8289920724801813,0.7932450935645824,0.08201885223388672,23.149486 -2756,Binary classification,Voting,Bananas,0.8294010889292196,0.7942206654991244,0.08201885223388672,24.780488 -2862,Binary classification,Voting,Bananas,0.8304788535477106,0.7949260042283298,0.08151531219482422,26.460054999999997 -2968,Binary classification,Voting,Bananas,0.8308055274688237,0.7944307944307943,0.08201885223388672,28.187981999999998 -3074,Binary classification,Voting,Bananas,0.8285063455906281,0.7924379677038204,0.08201885223388672,29.964311 -3180,Binary classification,Voting,Bananas,0.8307643913180245,0.7941851568477429,0.08151531219482422,31.788843999999997 -3286,Binary classification,Voting,Bananas,0.8310502283105022,0.7939101373932418,0.08201885223388672,33.661829 -3392,Binary classification,Voting,Bananas,0.8307283987024476,0.7948534667619728,0.08201885223388672,35.582957 -3498,Binary classification,Voting,Bananas,0.8301401201029454,0.7933194154488518,0.09734535217285156,37.553729 -3604,Binary classification,Voting,Bananas,0.8320843741326672,0.7952622673434856,0.09784889221191406,39.573418999999994 -3710,Binary classification,Voting,Bananas,0.8306821245618765,0.7939632545931758,0.09734535217285156,41.642475999999995 -3816,Binary classification,Voting,Bananas,0.8311926605504587,0.794904458598726,0.09734535217285156,43.760552999999994 -3922,Binary classification,Voting,Bananas,0.831165519000255,0.7945375543140905,0.10735130310058594,45.928838999999996 -4028,Binary classification,Voting,Bananas,0.8301465110504097,0.7932285368802902,0.11301231384277344,48.146786 -4134,Binary classification,Voting,Bananas,0.8296636825550447,0.7929411764705883,0.11301231384277344,50.413841 -4240,Binary classification,Voting,Bananas,0.8310922387355508,0.7955454026270702,0.11351585388183594,52.731009 -4346,Binary classification,Voting,Bananas,0.8317606444188723,0.7965488449763429,0.11301231384277344,55.097904 -4452,Binary classification,Voting,Bananas,0.8312738710402157,0.7955349850258643,0.11301231384277344,57.514886 -4558,Binary classification,Voting,Bananas,0.8294930875576036,0.7937350676931244,0.11351585388183594,59.981716999999996 -4664,Binary classification,Voting,Bananas,0.8286510829937809,0.7933798810447376,0.11301231384277344,62.498267999999996 -4770,Binary classification,Voting,Bananas,0.8280561962675613,0.7922998986828774,0.12292289733886719,65.06543599999999 -4876,Binary classification,Voting,Bananas,0.8274871794871795,0.7908480477493159,0.12296867370605469,67.68219599999999 -4982,Binary classification,Voting,Bananas,0.8285484842401124,0.7928190198932558,0.12246513366699219,70.34849499999999 -5088,Binary classification,Voting,Bananas,0.8287792412030667,0.7930624851508671,0.12296867370605469,73.06406199999998 -5194,Binary classification,Voting,Bananas,0.8297708453687657,0.7943229409027454,0.12296867370605469,75.82921599999997 -5300,Binary classification,Voting,Bananas,0.8301566333270428,0.7949886104783599,0.12246513366699219,78.64422599999997 -906,Binary classification,Voting,Elec2,0.8806629834254144,0.8820960698689956,0.29908084869384766,0.533765 -1812,Binary classification,Voting,Elec2,0.8901159580342353,0.8654496281271129,0.33275699615478516,1.6121 -2718,Binary classification,Voting,Elec2,0.8884799411115201,0.8614540466392318,0.3573274612426758,3.262924 -3624,Binary classification,Voting,Elec2,0.8948385316036434,0.8697435897435898,0.3568239212036133,5.47711 -4530,Binary classification,Voting,Elec2,0.8922499448001766,0.8587145338737695,0.35677051544189453,8.248427 -5436,Binary classification,Voting,Elec2,0.8833486660533578,0.846116504854369,0.35677051544189453,11.585141 -6342,Binary classification,Voting,Elec2,0.8836145718340955,0.8482730263157895,0.35727405548095703,15.487845 -7248,Binary classification,Voting,Elec2,0.8802263005381538,0.8418367346938775,0.35727405548095703,19.950209 -8154,Binary classification,Voting,Elec2,0.8828652029927634,0.8526461965746027,0.35727405548095703,24.978187000000002 -9060,Binary classification,Voting,Elec2,0.8872944033557788,0.8620456695041211,0.42380619049072266,30.568141 -9966,Binary classification,Voting,Elec2,0.8887104867034621,0.8668187822745287,0.4239206314086914,36.726803000000004 -10872,Binary classification,Voting,Elec2,0.8916383037439058,0.8724003466204506,0.4239206314086914,43.450649000000006 -11778,Binary classification,Voting,Elec2,0.8908890209730831,0.871229582122457,0.508366584777832,50.737672 -12684,Binary classification,Voting,Elec2,0.8903256327367343,0.8711917770163904,0.508366584777832,58.591770000000004 -13590,Binary classification,Voting,Elec2,0.8921186253587461,0.8751702997275205,0.508366584777832,67.02087300000001 -14496,Binary classification,Voting,Elec2,0.8925146602276647,0.8765842839036756,0.5096101760864258,76.01746500000002 -15402,Binary classification,Voting,Elec2,0.8924095837932602,0.875628612174435,0.5091333389282227,85.58361400000001 -16308,Binary classification,Voting,Elec2,0.8862451707855522,0.8668437298112124,0.5348939895629883,95.71704600000001 -17214,Binary classification,Voting,Elec2,0.882646836693197,0.8594880356149137,0.5348939895629883,106.42052500000001 -18120,Binary classification,Voting,Elec2,0.882664606214471,0.8596143687268886,0.5429277420043945,117.69460800000002 -19026,Binary classification,Voting,Elec2,0.8833114323258869,0.858634742740703,0.5442209243774414,129.542011 -19932,Binary classification,Voting,Elec2,0.8807385479905675,0.8562095457020145,0.6346635818481445,141.970262 -20838,Binary classification,Voting,Elec2,0.8784853865719633,0.8535739070090216,0.6924257278442383,154.986156 -21744,Binary classification,Voting,Elec2,0.8788115715402658,0.8517414055027289,0.7508554458618164,168.584532 -22650,Binary classification,Voting,Elec2,0.8773014261115281,0.8483988871310894,0.7274637222290039,182.766068 -23556,Binary classification,Voting,Elec2,0.8728507747824241,0.8417102690132656,0.7538461685180664,197.53556899999998 -24462,Binary classification,Voting,Elec2,0.8720820898573239,0.8400224960376297,0.7539834976196289,212.89370699999998 -25368,Binary classification,Voting,Elec2,0.8691607206212796,0.8364944085915562,0.7539834976196289,228.84110799999996 -26274,Binary classification,Voting,Elec2,0.8692954744414417,0.8360233024543979,0.7539834976196289,245.37829699999998 -27180,Binary classification,Voting,Elec2,0.8690901063320946,0.8360217531569729,0.7534799575805664,262.49924799999997 -28086,Binary classification,Voting,Elec2,0.8664055545664946,0.8316280739544067,0.7535486221313477,280.20757899999995 -28992,Binary classification,Voting,Elec2,0.8639232865372012,0.826859776168532,0.7540521621704102,298.510007 -29898,Binary classification,Voting,Elec2,0.86353145800582,0.8261017816042963,0.8122949600219727,317.403419 -30804,Binary classification,Voting,Elec2,0.8633899295523163,0.8248272416951128,0.8122949600219727,336.895126 -31710,Binary classification,Voting,Elec2,0.8610804503453279,0.8213199204964914,0.8808259963989258,356.985514 -32616,Binary classification,Voting,Elec2,0.8589299402115591,0.8183648493940232,0.9061365127563477,377.673556 -33522,Binary classification,Voting,Elec2,0.8585961039348469,0.8181399631675875,0.9644479751586914,398.957872 -34428,Binary classification,Voting,Elec2,0.8569436779272083,0.8155499794015206,0.9890604019165039,420.833858 -35334,Binary classification,Voting,Elec2,0.8570741233407863,0.8144610184436769,1.055558204650879,443.30417900000003 -36240,Binary classification,Voting,Elec2,0.8577223433317697,0.8140239503679124,1.0826387405395508,466.36460700000003 -37146,Binary classification,Voting,Elec2,0.8573966886525778,0.812978851110405,1.1978578567504883,490.019084 -38052,Binary classification,Voting,Elec2,0.8572967858926178,0.8125388386384037,1.198460578918457,514.261258 -38958,Binary classification,Voting,Elec2,0.8578432630849399,0.8141610738255034,1.198460578918457,539.085152 -39864,Binary classification,Voting,Elec2,0.8585906730552141,0.816998344317112,1.232090950012207,564.495213 -40770,Binary classification,Voting,Elec2,0.8591822217861611,0.8196298972635019,1.2325944900512695,590.495221 -41676,Binary classification,Voting,Elec2,0.859868026394721,0.8219620754832023,1.2570466995239258,617.0763880000001 -42582,Binary classification,Voting,Elec2,0.8602663159625185,0.82283230109576,1.2570466995239258,644.2465100000001 -43488,Binary classification,Voting,Elec2,0.8601651068135305,0.8228301721877459,1.2570466995239258,671.9994270000001 -44394,Binary classification,Voting,Elec2,0.8584912035681301,0.8195968066165068,1.2565431594848633,700.3413830000001 -45300,Binary classification,Voting,Elec2,0.8588710567562198,0.8202547305086175,1.3147859573364258,729.273758 -25,Binary classification,Voting,Phishing,0.5833333333333334,0.7058823529411764,0.15483379364013672,0.027523 -50,Binary classification,Voting,Phishing,0.7346938775510204,0.7636363636363637,0.17104625701904297,0.082708 -75,Binary classification,Voting,Phishing,0.7837837837837838,0.8048780487804877,0.18776226043701172,0.17051 -100,Binary classification,Voting,Phishing,0.8080808080808081,0.819047619047619,0.2039480209350586,0.296263 -125,Binary classification,Voting,Phishing,0.8145161290322581,0.8217054263565893,0.20436382293701172,0.463136 -150,Binary classification,Voting,Phishing,0.8187919463087249,0.830188679245283,0.20486736297607422,0.670871 -175,Binary classification,Voting,Phishing,0.8390804597701149,0.8390804597701148,0.20436382293701172,0.919601 -200,Binary classification,Voting,Phishing,0.8391959798994975,0.8383838383838383,0.2048635482788086,1.21153 -225,Binary classification,Voting,Phishing,0.8348214285714286,0.8294930875576038,0.20489025115966797,1.544633 -250,Binary classification,Voting,Phishing,0.8353413654618473,0.8298755186721991,0.20438671112060547,1.91889 -275,Binary classification,Voting,Phishing,0.8357664233576643,0.8288973384030419,0.20537090301513672,2.334116 -300,Binary classification,Voting,Phishing,0.8394648829431438,0.8285714285714285,0.20486736297607422,2.7905379999999997 -325,Binary classification,Voting,Phishing,0.8487654320987654,0.8338983050847458,0.20537090301513672,3.2879229999999997 -350,Binary classification,Voting,Phishing,0.8538681948424068,0.8360128617363344,0.20537090301513672,3.8262739999999997 -375,Binary classification,Voting,Phishing,0.8529411764705882,0.8318042813455658,0.20486736297607422,4.4058399999999995 -400,Binary classification,Voting,Phishing,0.8546365914786967,0.8313953488372093,0.20537090301513672,5.0283679999999995 -425,Binary classification,Voting,Phishing,0.8561320754716981,0.8291316526610645,0.20486736297607422,5.69196 -450,Binary classification,Voting,Phishing,0.8596881959910914,0.8310991957104559,0.20537090301513672,6.39676 -475,Binary classification,Voting,Phishing,0.8586497890295358,0.8312342569269521,0.20537090301513672,7.142548 -500,Binary classification,Voting,Phishing,0.8597194388777555,0.835680751173709,0.20486736297607422,7.929462 -525,Binary classification,Voting,Phishing,0.8606870229007634,0.8337129840546698,0.20537090301513672,8.757545 -550,Binary classification,Voting,Phishing,0.8615664845173042,0.8362068965517241,0.20486736297607422,9.626280000000001 -575,Binary classification,Voting,Phishing,0.8641114982578397,0.8388429752066116,0.20486736297607422,10.535573000000001 -600,Binary classification,Voting,Phishing,0.8664440734557596,0.8387096774193549,0.20537090301513672,11.487542000000001 -625,Binary classification,Voting,Phishing,0.8669871794871795,0.8362919132149902,0.20486736297607422,12.480171000000002 -650,Binary classification,Voting,Phishing,0.8705701078582434,0.8432835820895523,0.20537090301513672,13.513483000000003 -675,Binary classification,Voting,Phishing,0.8724035608308606,0.8485915492957745,0.20486736297607422,14.588001000000002 -700,Binary classification,Voting,Phishing,0.876967095851216,0.8522336769759451,0.20486736297607422,15.703553000000003 -725,Binary classification,Voting,Phishing,0.8784530386740331,0.8562091503267973,0.20537090301513672,16.863107000000003 -750,Binary classification,Voting,Phishing,0.8785046728971962,0.8571428571428572,0.20486736297607422,18.064417000000002 -775,Binary classification,Voting,Phishing,0.8785529715762274,0.8567073170731707,0.20537090301513672,19.306597000000004 -800,Binary classification,Voting,Phishing,0.8785982478097623,0.8583941605839417,0.1428241729736328,20.591420000000003 -825,Binary classification,Voting,Phishing,0.8786407766990292,0.8595505617977528,0.2701892852783203,21.920167000000003 -850,Binary classification,Voting,Phishing,0.8798586572438163,0.8602739726027396,0.27071571350097656,23.289893000000003 -875,Binary classification,Voting,Phishing,0.8832951945080092,0.8636363636363635,0.27021217346191406,24.700235000000003 -900,Binary classification,Voting,Phishing,0.882091212458287,0.8619791666666667,0.2707386016845703,26.151721000000002 -925,Binary classification,Voting,Phishing,0.8841991341991342,0.8657465495608533,0.2707386016845703,27.644342 -950,Binary classification,Voting,Phishing,0.8840885142255005,0.8671497584541062,0.2702350616455078,29.178064000000003 -975,Binary classification,Voting,Phishing,0.8860369609856262,0.8692579505300354,0.2707386016845703,30.753129 -1000,Binary classification,Voting,Phishing,0.8868868868868869,0.870264064293915,0.2702350616455078,32.369334 -1025,Binary classification,Voting,Phishing,0.88671875,0.8705357142857143,0.2707386016845703,34.02633 -1050,Binary classification,Voting,Phishing,0.888465204957102,0.8729641693811074,0.2707386016845703,35.724225000000004 -1075,Binary classification,Voting,Phishing,0.8873370577281192,0.8727655099894847,0.2702350616455078,37.462729 -1100,Binary classification,Voting,Phishing,0.8889899909008189,0.8747433264887065,0.2707386016845703,39.24172900000001 -1125,Binary classification,Voting,Phishing,0.8905693950177936,0.8776119402985074,0.2702350616455078,41.061817000000005 -1150,Binary classification,Voting,Phishing,0.8912097476066144,0.8780487804878049,0.2702350616455078,42.922291 -1175,Binary classification,Voting,Phishing,0.8901192504258943,0.8765550239234451,0.2707386016845703,44.822872000000004 -1200,Binary classification,Voting,Phishing,0.8915763135946623,0.8778195488721804,0.2702350616455078,46.765498 -1225,Binary classification,Voting,Phishing,0.8913398692810458,0.8774193548387096,0.2707386016845703,48.748369000000004 -1250,Binary classification,Voting,Phishing,0.8903122497998399,0.8769092542677449,0.2707386016845703,50.77338400000001 -1903,Binary classification,Voting,SMTP,1.0,0.0,0.07773971557617188,1.484811 -3806,Binary classification,Voting,SMTP,1.0,0.0,0.07824325561523438,4.457186 -5709,Binary classification,Voting,SMTP,1.0,0.0,0.07824325561523438,8.746386000000001 -7612,Binary classification,Voting,SMTP,1.0,0.0,0.07773971557617188,13.887875000000001 -9515,Binary classification,Voting,SMTP,1.0,0.0,0.07773971557617188,19.865436000000003 -11418,Binary classification,Voting,SMTP,1.0,0.0,0.07824325561523438,26.640571 -13321,Binary classification,Voting,SMTP,1.0,0.0,0.07824325561523438,34.167751 -15224,Binary classification,Voting,SMTP,0.9998029297773107,0.8421052631578948,0.11086559295654297,42.434428000000004 -17127,Binary classification,Voting,SMTP,0.9998248277472849,0.8695652173913044,0.11086559295654297,51.484422 -19030,Binary classification,Voting,SMTP,0.9998423458931105,0.8695652173913044,0.11136913299560547,61.31819 -20933,Binary classification,Voting,SMTP,0.9998566787693484,0.8695652173913044,0.11136913299560547,71.93436700000001 -22836,Binary classification,Voting,SMTP,0.999868622728268,0.8695652173913044,0.11086559295654297,83.32890400000001 -24739,Binary classification,Voting,SMTP,0.9998787290807665,0.8695652173913044,0.11086559295654297,95.504963 -26642,Binary classification,Voting,SMTP,0.9998873916144289,0.88,0.11139202117919922,108.464893 -28545,Binary classification,Voting,SMTP,0.999894899103139,0.88,0.11139202117919922,122.206969 -30448,Binary classification,Voting,SMTP,0.9999014681249384,0.88,0.11088848114013672,136.730455 -32351,Binary classification,Voting,SMTP,0.9999072642967543,0.88,0.11139202117919922,152.048808 -34254,Binary classification,Voting,SMTP,0.9999124164306776,0.88,0.11139202117919922,168.14980500000001 -36157,Binary classification,Voting,SMTP,0.9999170262197146,0.88,0.11088848114013672,185.035032 -38060,Binary classification,Voting,SMTP,0.9999211750177356,0.88,0.1028890609741211,202.707805 -39963,Binary classification,Voting,SMTP,0.9999249286822481,0.88,0.1033926010131836,221.16629700000001 -41866,Binary classification,Voting,SMTP,0.9999283410963812,0.88,0.1033926010131836,240.410098 -43769,Binary classification,Voting,SMTP,0.999931456772071,0.88,0.1028890609741211,260.439908 -45672,Binary classification,Voting,SMTP,0.9999343128024348,0.88,0.1028890609741211,281.255775 -47575,Binary classification,Voting,SMTP,0.9999369403455669,0.88,0.1155691146850586,302.85620600000004 -49478,Binary classification,Voting,SMTP,0.9999393657659115,0.88,0.1155691146850586,325.23865100000006 -51381,Binary classification,Voting,SMTP,0.9999221486959906,0.8666666666666666,0.1243124008178711,348.4004960000001 -53284,Binary classification,Voting,SMTP,0.9999249291518871,0.8666666666666666,0.1243124008178711,372.3442280000001 -55187,Binary classification,Voting,SMTP,0.9999275178487298,0.8666666666666666,0.1248159408569336,397.0676850000001 -57090,Binary classification,Voting,SMTP,0.9998073183975897,0.7317073170731707,0.15177059173583984,422.5782570000001 -58993,Binary classification,Voting,SMTP,0.9998135340385137,0.7317073170731707,0.15126705169677734,448.87363900000014 -60896,Binary classification,Voting,SMTP,0.9998193611955004,0.7317073170731707,0.15177059173583984,475.95484300000015 -62799,Binary classification,Voting,SMTP,0.9997929870378037,0.6976744186046512,0.16101741790771484,503.8239590000002 -64702,Binary classification,Voting,SMTP,0.9997990757484428,0.6976744186046512,0.16051387786865234,532.4818360000002 -66605,Binary classification,Voting,SMTP,0.9998048165275358,0.6976744186046512,0.16051387786865234,561.9301430000002 -68508,Binary classification,Voting,SMTP,0.9998102383698017,0.7234042553191489,0.16101741790771484,592.1790100000002 -70411,Binary classification,Voting,SMTP,0.9998153671353501,0.7234042553191489,0.16101741790771484,623.2264690000002 -72314,Binary classification,Voting,SMTP,0.9998202259621368,0.7234042553191489,0.16051387786865234,655.0760590000002 -74217,Binary classification,Voting,SMTP,0.9998113614314973,0.7083333333333333,0.16051387786865234,687.7245370000002 -76120,Binary classification,Voting,SMTP,0.999816077457665,0.7083333333333333,0.16101741790771484,721.1718600000002 -78023,Binary classification,Voting,SMTP,0.9998205634308271,0.7083333333333333,0.16101741790771484,755.4176530000002 -79926,Binary classification,Voting,SMTP,0.9998248357835471,0.7083333333333333,0.16051387786865234,790.4648560000002 -81829,Binary classification,Voting,SMTP,0.9998289094197585,0.7083333333333333,0.16051387786865234,826.3125100000002 -83732,Binary classification,Voting,SMTP,0.9998327978884762,0.7083333333333333,0.16101741790771484,862.9590210000002 -85635,Binary classification,Voting,SMTP,0.9998365135343439,0.7083333333333333,0.1653127670288086,900.4018990000002 -87538,Binary classification,Voting,SMTP,0.9998400676285456,0.7083333333333333,0.1648092269897461,938.6351690000001 -89441,Binary classification,Voting,SMTP,0.9997875670840787,0.6415094339622641,0.1745138168334961,977.6602510000001 -91344,Binary classification,Voting,SMTP,0.9997810450718719,0.6296296296296297,0.1745138168334961,1017.4767610000001 -93247,Binary classification,Voting,SMTP,0.9996782703815713,0.53125,0.1740102767944336,1058.0853630000001 -95150,Binary classification,Voting,SMTP,0.9996847050415664,0.53125,0.1740102767944336,1099.4855200000002 -106,Binary classification,[baseline] Last Class,Bananas,0.5333333333333333,0.5242718446601942,0.0005102157592773438,0.003183 -212,Binary classification,[baseline] Last Class,Bananas,0.5876777251184834,0.5538461538461539,0.0005102157592773438,0.008041 -318,Binary classification,[baseline] Last Class,Bananas,0.5457413249211357,0.5102040816326531,0.0005102157592773438,0.014648999999999999 -424,Binary classification,[baseline] Last Class,Bananas,0.5460992907801419,0.5025906735751295,0.0005102157592773438,0.022962999999999997 -530,Binary classification,[baseline] Last Class,Bananas,0.5671077504725898,0.5096359743040686,0.0005102157592773438,0.032934 -636,Binary classification,[baseline] Last Class,Bananas,0.5464566929133858,0.4875444839857651,0.0005102157592773438,0.044653 -742,Binary classification,[baseline] Last Class,Bananas,0.5573549257759784,0.4875,0.0005102157592773438,0.058023 -848,Binary classification,[baseline] Last Class,Bananas,0.5501770956316411,0.4816326530612245,0.0005102157592773438,0.073029 -954,Binary classification,[baseline] Last Class,Bananas,0.5487932843651626,0.4794188861985472,0.0005102157592773438,0.08975 -1060,Binary classification,[baseline] Last Class,Bananas,0.5448536355051936,0.46799116997792495,0.0005102157592773438,0.108101 -1166,Binary classification,[baseline] Last Class,Bananas,0.534763948497854,0.4590818363273453,0.0005102157592773438,0.128117 -1272,Binary classification,[baseline] Last Class,Bananas,0.5287175452399685,0.456935630099728,0.0005102157592773438,0.149836 -1378,Binary classification,[baseline] Last Class,Bananas,0.5286855482933914,0.45232067510548524,0.0005102157592773438,0.173181 -1484,Binary classification,[baseline] Last Class,Bananas,0.5252865812542145,0.44913928012519555,0.0005102157592773438,0.19815 -1590,Binary classification,[baseline] Last Class,Bananas,0.5204531151667715,0.4437956204379563,0.0005102157592773438,0.224847 -1696,Binary classification,[baseline] Last Class,Bananas,0.5227138643067847,0.4455106237148732,0.0005102157592773438,0.253189 -1802,Binary classification,[baseline] Last Class,Bananas,0.524153248195447,0.4523961661341854,0.0005102157592773438,0.283166 -1908,Binary classification,[baseline] Last Class,Bananas,0.5233350812794966,0.456664674237896,0.0005102157592773438,0.31489199999999995 -2014,Binary classification,[baseline] Last Class,Bananas,0.5171385991058122,0.4563758389261745,0.0005102157592773438,0.34829999999999994 -2120,Binary classification,[baseline] Last Class,Bananas,0.5143935818782445,0.45813586097946285,0.0005102157592773438,0.38336699999999996 -2226,Binary classification,[baseline] Last Class,Bananas,0.5114606741573033,0.45459106874059213,0.0005102157592773438,0.42015499999999995 -2332,Binary classification,[baseline] Last Class,Bananas,0.510939510939511,0.45506692160611856,0.0005102157592773438,0.45858199999999993 -2438,Binary classification,[baseline] Last Class,Bananas,0.5104636848584325,0.4530032095369097,0.0005102157592773438,0.49864699999999995 -2544,Binary classification,[baseline] Last Class,Bananas,0.5084545812033032,0.45462478184991273,0.0005102157592773438,0.5404629999999999 -2650,Binary classification,[baseline] Last Class,Bananas,0.5096262740656852,0.458072590738423,0.0005102157592773438,0.5839479999999999 -2756,Binary classification,[baseline] Last Class,Bananas,0.5092558983666061,0.45746388443017655,0.0005102157592773438,0.6291049999999999 -2862,Binary classification,[baseline] Last Class,Bananas,0.5103110800419434,0.4563445867287544,0.0005102157592773438,0.6760149999999999 -2968,Binary classification,[baseline] Last Class,Bananas,0.5133131108864173,0.457957957957958,0.0005102157592773438,0.7245799999999999 -3074,Binary classification,[baseline] Last Class,Bananas,0.5099251545720794,0.4563176895306859,0.0005102157592773438,0.7747889999999998 -3180,Binary classification,[baseline] Last Class,Bananas,0.5102233406731677,0.45387583304103823,0.0005102157592773438,0.8267639999999998 -3286,Binary classification,[baseline] Last Class,Bananas,0.5095890410958904,0.45222713362801764,0.0005102157592773438,0.8803849999999999 -3392,Binary classification,[baseline] Last Class,Bananas,0.5107637864936597,0.4558871761233191,0.0005102157592773438,0.9357289999999998 -3498,Binary classification,[baseline] Last Class,Bananas,0.5124392336288247,0.45579316948611553,0.0005102157592773438,0.9927189999999998 -3604,Binary classification,[baseline] Last Class,Bananas,0.5134610047182903,0.45440398381574854,0.0005102157592773438,1.0513469999999998 -3710,Binary classification,[baseline] Last Class,Bananas,0.5122674575357239,0.4546276756104914,0.0005102157592773438,1.1117049999999997 -3816,Binary classification,[baseline] Last Class,Bananas,0.510615989515072,0.4536142815335089,0.0005102157592773438,1.1737269999999997 -3922,Binary classification,[baseline] Last Class,Bananas,0.5090538128028564,0.45078459343794575,0.0005102157592773438,1.2373829999999997 -4028,Binary classification,[baseline] Last Class,Bananas,0.5108020859200397,0.45247359644246804,0.0005102157592773438,1.3028159999999998 -4134,Binary classification,[baseline] Last Class,Bananas,0.5102830873457537,0.4517876489707476,0.0005102157592773438,1.3698969999999997 -4240,Binary classification,[baseline] Last Class,Bananas,0.5102618542108988,0.4525316455696203,0.0005102157592773438,1.4386049999999997 -4346,Binary classification,[baseline] Last Class,Bananas,0.5074798619102416,0.4490216271884655,0.0005102157592773438,1.5090079999999997 -4452,Binary classification,[baseline] Last Class,Bananas,0.5099977533138621,0.45132075471698113,0.0005102157592773438,1.5810569999999997 -4558,Binary classification,[baseline] Last Class,Bananas,0.5099846390168971,0.45390070921985815,0.0005102157592773438,1.6547379999999996 -4664,Binary classification,[baseline] Last Class,Bananas,0.5099721209521767,0.4553039332538737,0.0005102157592773438,1.7301709999999997 -4770,Binary classification,[baseline] Last Class,Bananas,0.5110085971901867,0.4556489262371615,0.0005102157592773438,1.8072459999999997 -4876,Binary classification,[baseline] Last Class,Bananas,0.5109743589743589,0.4539624370132845,0.0005102157592773438,1.8859549999999996 -4982,Binary classification,[baseline] Last Class,Bananas,0.5099377635013049,0.45379279480868207,0.0005102157592773438,1.9663889999999995 -5088,Binary classification,[baseline] Last Class,Bananas,0.5099272655789266,0.45364891518737677,0.0005102157592773438,2.0484569999999995 -5194,Binary classification,[baseline] Last Class,Bananas,0.5097246293086848,0.4531786941580756,0.0005102157592773438,2.1321769999999995 -5300,Binary classification,[baseline] Last Class,Bananas,0.5095301000188714,0.4529572721532309,0.0005102157592773438,2.2176369999999994 -906,Binary classification,[baseline] Last Class,Elec2,0.8530386740331491,0.8500563697857948,0.0005102157592773438,0.021647 -1812,Binary classification,[baseline] Last Class,Elec2,0.8619547211485368,0.8287671232876712,0.0005102157592773438,0.064308 -2718,Binary classification,[baseline] Last Class,Elec2,0.8450496871549503,0.80958842152872,0.0005102157592773438,0.12793700000000002 -3624,Binary classification,[baseline] Last Class,Elec2,0.8418437758763456,0.8056968463886063,0.0005102157592773438,0.21264700000000003 -4530,Binary classification,[baseline] Last Class,Elec2,0.8388165157871494,0.7960893854748604,0.0005102157592773438,0.31851300000000005 -5436,Binary classification,[baseline] Last Class,Elec2,0.8413983440662374,0.7995348837209302,0.0005102157592773438,0.445933 -6342,Binary classification,[baseline] Last Class,Elec2,0.8370919413341744,0.7958094485076103,0.0005102157592773438,0.594503 -7248,Binary classification,[baseline] Last Class,Elec2,0.8359321098385539,0.7948231233822259,0.0005102157592773438,0.763985 -8154,Binary classification,[baseline] Last Class,Elec2,0.8352753587636453,0.8021799970540581,0.0005102157592773438,0.954384 -9060,Binary classification,[baseline] Last Class,Elec2,0.8358538470029805,0.8069081937410726,0.0005102157592773438,1.166026 -9966,Binary classification,[baseline] Last Class,Elec2,0.8372303060712494,0.8118765947575969,0.0005102157592773438,1.399073 -10872,Binary classification,[baseline] Last Class,Elec2,0.8368135406126391,0.8140461215932915,0.0005102157592773438,1.6530930000000001 -11778,Binary classification,[baseline] Last Class,Elec2,0.8374798335739153,0.8150724637681159,0.0005102157592773438,1.9281220000000001 -12684,Binary classification,[baseline] Last Class,Elec2,0.8384451628163684,0.8161177420802298,0.0005102157592773438,2.224333 -13590,Binary classification,[baseline] Last Class,Elec2,0.842004562513798,0.8223417459660736,0.0005102157592773438,2.541433 -14496,Binary classification,[baseline] Last Class,Elec2,0.8448430493273542,0.8264794383149447,0.0005102157592773438,2.879854 -15402,Binary classification,[baseline] Last Class,Elec2,0.8460489578598792,0.8270983738058776,0.0005102157592773438,3.239289 -16308,Binary classification,[baseline] Last Class,Elec2,0.844851904090268,0.8251313243019076,0.0005102157592773438,3.6196129999999997 -17214,Binary classification,[baseline] Last Class,Elec2,0.8443618195549875,0.8222177981286084,0.0005102157592773438,4.020822 -18120,Binary classification,[baseline] Last Class,Elec2,0.8450797505381091,0.8227792158595871,0.0005102157592773438,4.443085 -19026,Binary classification,[baseline] Last Class,Elec2,0.8462023653088042,0.8224083515416363,0.0005102157592773438,4.886686 -19932,Binary classification,[baseline] Last Class,Elec2,0.847523957653906,0.8255753888538139,0.0005102157592773438,5.35136 -20838,Binary classification,[baseline] Last Class,Elec2,0.84661899505687,0.8249917862227577,0.0005102157592773438,5.836996 -21744,Binary classification,[baseline] Last Class,Elec2,0.8452835395299637,0.8209495422610177,0.0005102157592773438,6.343734 -22650,Binary classification,[baseline] Last Class,Elec2,0.8444081416398075,0.8188733552631579,0.0005102157592773438,6.871264 -23556,Binary classification,[baseline] Last Class,Elec2,0.8451284228401613,0.8194595664654062,0.0005102157592773438,7.419847 -24462,Binary classification,[baseline] Last Class,Elec2,0.8464903315481788,0.8198781599270878,0.0005102157592773438,7.989367 -25368,Binary classification,[baseline] Last Class,Elec2,0.8462963692986951,0.8199492034172247,0.0005102157592773438,8.579944 -26274,Binary classification,[baseline] Last Class,Elec2,0.8477524454763445,0.8213168944876262,0.0005102157592773438,9.191269 -27180,Binary classification,[baseline] Last Class,Elec2,0.8495529636851982,0.8240457851026293,0.0005102157592773438,9.823483 -28086,Binary classification,[baseline] Last Class,Elec2,0.8509880719245149,0.825107610012955,0.0005102157592773438,10.476909 -28992,Binary classification,[baseline] Last Class,Elec2,0.8521265220240765,0.8258237516759436,0.0005102157592773438,11.151511999999999 -29898,Binary classification,[baseline] Last Class,Elec2,0.8531959728400843,0.8268160833366216,0.0005102157592773438,11.847221999999999 -30804,Binary classification,[baseline] Last Class,Elec2,0.8537480115573158,0.8267107743201139,0.0005102157592773438,12.564065 -31710,Binary classification,[baseline] Last Class,Elec2,0.8530385694913116,0.8259895444361464,0.0005102157592773438,13.301931999999999 -32616,Binary classification,[baseline] Last Class,Elec2,0.8536869538555879,0.8269760696156635,0.0005102157592773438,14.060873999999998 -33522,Binary classification,[baseline] Last Class,Elec2,0.8541511291429253,0.8276032300151628,0.0005102157592773438,14.840822999999999 -34428,Binary classification,[baseline] Last Class,Elec2,0.8549684840386905,0.8286724084685859,0.0005102157592773438,15.641784999999999 -35334,Binary classification,[baseline] Last Class,Elec2,0.8555175048821215,0.8284321962695346,0.0005102157592773438,16.463894999999997 -36240,Binary classification,[baseline] Last Class,Elec2,0.8545213720025387,0.8259146744155329,0.0005102157592773438,17.307088999999998 -37146,Binary classification,[baseline] Last Class,Elec2,0.854354556467896,0.8252696854208386,0.0005102157592773438,18.171425 -38052,Binary classification,[baseline] Last Class,Elec2,0.8545636119944285,0.8247736052181622,0.0005102157592773438,19.056746999999998 -38958,Binary classification,[baseline] Last Class,Elec2,0.8548142824139435,0.8254213223038459,0.0005102157592773438,19.962946 -39864,Binary classification,[baseline] Last Class,Elec2,0.8546521837292728,0.8262981172802495,0.0005102157592773438,20.890172 -40770,Binary classification,[baseline] Last Class,Elec2,0.8540067207927592,0.8267652366261132,0.0005102157592773438,21.838151 -41676,Binary classification,[baseline] Last Class,Elec2,0.8537012597480504,0.8274320002264302,0.0005102157592773438,22.807005 -42582,Binary classification,[baseline] Last Class,Elec2,0.8536201592259458,0.8277177368086459,0.0005102157592773438,23.796527 -43488,Binary classification,[baseline] Last Class,Elec2,0.853473451836181,0.8276626818845675,0.0005102157592773438,24.806653 -44394,Binary classification,[baseline] Last Class,Elec2,0.8533777847858897,0.8271686890948196,0.0005102157592773438,25.837451 -45300,Binary classification,[baseline] Last Class,Elec2,0.8533521711296055,0.8273155007928462,0.0005102157592773438,26.888684 -25,Binary classification,[baseline] Last Class,Phishing,0.625,0.64,0.0005102157592773438,0.002343 -50,Binary classification,[baseline] Last Class,Phishing,0.6530612244897959,0.6222222222222223,0.0005102157592773438,0.006017 -75,Binary classification,[baseline] Last Class,Phishing,0.5675675675675675,0.5555555555555556,0.0005102157592773438,0.010981999999999999 -100,Binary classification,[baseline] Last Class,Phishing,0.5555555555555556,0.5416666666666666,0.0005102157592773438,0.017228 -125,Binary classification,[baseline] Last Class,Phishing,0.5241935483870968,0.5123966942148761,0.0005102157592773438,0.024779000000000002 -150,Binary classification,[baseline] Last Class,Phishing,0.5234899328859061,0.5298013245033113,0.0005102157592773438,0.033621 -175,Binary classification,[baseline] Last Class,Phishing,0.5229885057471264,0.496969696969697,0.0005102157592773438,0.043754 -200,Binary classification,[baseline] Last Class,Phishing,0.507537688442211,0.47872340425531923,0.0005102157592773438,0.055183 -225,Binary classification,[baseline] Last Class,Phishing,0.5,0.45098039215686275,0.0005102157592773438,0.06790500000000001 -250,Binary classification,[baseline] Last Class,Phishing,0.5180722891566265,0.4782608695652174,0.0005102157592773438,0.082 -275,Binary classification,[baseline] Last Class,Phishing,0.5218978102189781,0.4738955823293172,0.0005102157592773438,0.097411 -300,Binary classification,[baseline] Last Class,Phishing,0.5217391304347826,0.460377358490566,0.0005102157592773438,0.114126 -325,Binary classification,[baseline] Last Class,Phishing,0.5216049382716049,0.44839857651245546,0.0005102157592773438,0.132158 -350,Binary classification,[baseline] Last Class,Phishing,0.5329512893982808,0.4511784511784511,0.0005102157592773438,0.151512 -375,Binary classification,[baseline] Last Class,Phishing,0.5267379679144385,0.4380952380952381,0.0005102157592773438,0.172161 -400,Binary classification,[baseline] Last Class,Phishing,0.5263157894736842,0.43243243243243246,0.0005102157592773438,0.194113 -425,Binary classification,[baseline] Last Class,Phishing,0.5424528301886793,0.436046511627907,0.0005102157592773438,0.217363 -450,Binary classification,[baseline] Last Class,Phishing,0.5367483296213809,0.4222222222222222,0.0005102157592773438,0.241916 -475,Binary classification,[baseline] Last Class,Phishing,0.5358649789029536,0.43298969072164945,0.0005102157592773438,0.267769 -500,Binary classification,[baseline] Last Class,Phishing,0.5370741482965932,0.44604316546762596,0.0005102157592773438,0.29501499999999997 -525,Binary classification,[baseline] Last Class,Phishing,0.5400763358778626,0.43822843822843827,0.0005102157592773438,0.323563 -550,Binary classification,[baseline] Last Class,Phishing,0.5391621129326047,0.44150110375275936,0.0005102157592773438,0.35340099999999997 -575,Binary classification,[baseline] Last Class,Phishing,0.5418118466898955,0.4416135881104034,0.0005102157592773438,0.384549 -600,Binary classification,[baseline] Last Class,Phishing,0.5509181969949917,0.443064182194617,0.0005102157592773438,0.41699899999999995 -625,Binary classification,[baseline] Last Class,Phishing,0.5560897435897436,0.43584521384928715,0.0005102157592773438,0.45074299999999995 -650,Binary classification,[baseline] Last Class,Phishing,0.551617873651772,0.4393063583815029,0.0005102157592773438,0.48578699999999997 -675,Binary classification,[baseline] Last Class,Phishing,0.5459940652818991,0.44363636363636366,0.0005102157592773438,0.522164 -700,Binary classification,[baseline] Last Class,Phishing,0.5464949928469242,0.4389380530973452,0.0005102157592773438,0.55984 -725,Binary classification,[baseline] Last Class,Phishing,0.5441988950276243,0.44630872483221484,0.0005102157592773438,0.598852 -750,Binary classification,[baseline] Last Class,Phishing,0.5367156208277704,0.44122383252818037,0.0005102157592773438,0.639119 -775,Binary classification,[baseline] Last Class,Phishing,0.5310077519379846,0.43369734789391573,0.0005102157592773438,0.680612 -800,Binary classification,[baseline] Last Class,Phishing,0.5294117647058824,0.4388059701492537,0.0005102157592773438,0.723331 -825,Binary classification,[baseline] Last Class,Phishing,0.5266990291262136,0.43965517241379315,0.0005102157592773438,0.7672859999999999 -850,Binary classification,[baseline] Last Class,Phishing,0.5241460541813898,0.4341736694677871,0.0005102157592773438,0.812452 -875,Binary classification,[baseline] Last Class,Phishing,0.522883295194508,0.4311050477489768,0.0005102157592773438,0.858842 -900,Binary classification,[baseline] Last Class,Phishing,0.5272525027808677,0.4340878828229028,0.0005102157592773438,0.906455 -925,Binary classification,[baseline] Last Class,Phishing,0.5227272727272727,0.43388960205391536,0.0005102157592773438,0.955289 -950,Binary classification,[baseline] Last Class,Phishing,0.5205479452054794,0.43896424167694204,0.0005102157592773438,1.005349 -975,Binary classification,[baseline] Last Class,Phishing,0.5174537987679672,0.43373493975903615,0.0005102157592773438,1.056718 -1000,Binary classification,[baseline] Last Class,Phishing,0.5185185185185185,0.4361078546307151,0.0005102157592773438,1.109312 -1025,Binary classification,[baseline] Last Class,Phishing,0.517578125,0.43863636363636366,0.0005102157592773438,1.1631 -1050,Binary classification,[baseline] Last Class,Phishing,0.5138226882745471,0.4370860927152318,0.0005102157592773438,1.2181790000000001 -1075,Binary classification,[baseline] Last Class,Phishing,0.5111731843575419,0.43729903536977494,0.0005102157592773438,1.2745680000000001 -1100,Binary classification,[baseline] Last Class,Phishing,0.5122838944494995,0.4393305439330544,0.0005102157592773438,1.3322450000000001 -1125,Binary classification,[baseline] Last Class,Phishing,0.5124555160142349,0.44534412955465585,0.0005102157592773438,1.3912120000000001 -1150,Binary classification,[baseline] Last Class,Phishing,0.5143603133159269,0.44642857142857145,0.0005102157592773438,1.451489 -1175,Binary classification,[baseline] Last Class,Phishing,0.5187393526405452,0.4509232264334305,0.0005102157592773438,1.513052 -1200,Binary classification,[baseline] Last Class,Phishing,0.5187656380316931,0.448901623686724,0.0005102157592773438,1.5759610000000002 -1225,Binary classification,[baseline] Last Class,Phishing,0.5171568627450981,0.4471468662301216,0.0005102157592773438,1.6401780000000001 -1250,Binary classification,[baseline] Last Class,Phishing,0.5156124899919936,0.4474885844748858,0.0005102157592773438,1.705799 -1903,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.070354 -3806,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.210001 -5709,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.417744 -7612,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.694871 -9515,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.039777 -11418,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.454499 -13321,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.9382489999999999 -15224,Binary classification,[baseline] Last Class,SMTP,0.9985548183669447,0.0,0.0005102157592773438,2.491975 -17127,Binary classification,[baseline] Last Class,SMTP,0.9984818404764685,0.0,0.0005102157592773438,3.11529 -19030,Binary classification,[baseline] Last Class,SMTP,0.9986336644069578,0.0,0.0005102157592773438,3.8077389999999998 -20933,Binary classification,[baseline] Last Class,SMTP,0.9987578826676858,0.0,0.0005102157592773438,4.570752 -22836,Binary classification,[baseline] Last Class,SMTP,0.9988613969783228,0.0,0.0005102157592773438,5.4033109999999995 -24739,Binary classification,[baseline] Last Class,SMTP,0.9989489853666425,0.0,0.0005102157592773438,6.304936 -26642,Binary classification,[baseline] Last Class,SMTP,0.9989489884013363,0.0,0.0005102157592773438,7.275472 -28545,Binary classification,[baseline] Last Class,SMTP,0.9990190582959642,0.0,0.0005102157592773438,8.315878999999999 -30448,Binary classification,[baseline] Last Class,SMTP,0.9990803691660919,0.0,0.0005102157592773438,9.425080999999999 -32351,Binary classification,[baseline] Last Class,SMTP,0.9991344667697063,0.0,0.0005102157592773438,10.602661 -34254,Binary classification,[baseline] Last Class,SMTP,0.999182553352991,0.0,0.0005102157592773438,11.849262 -36157,Binary classification,[baseline] Last Class,SMTP,0.9992255780506694,0.0,0.0005102157592773438,13.164257 -38060,Binary classification,[baseline] Last Class,SMTP,0.9992643001655325,0.0,0.0005102157592773438,14.54421 -39963,Binary classification,[baseline] Last Class,SMTP,0.9992993343676493,0.0,0.0005102157592773438,15.991924 -41866,Binary classification,[baseline] Last Class,SMTP,0.9993311835662247,0.0,0.0005102157592773438,17.508838 -43769,Binary classification,[baseline] Last Class,SMTP,0.9993602632059952,0.0,0.0005102157592773438,19.096664 -45672,Binary classification,[baseline] Last Class,SMTP,0.9993869194893915,0.0,0.0005102157592773438,20.752142 -47575,Binary classification,[baseline] Last Class,SMTP,0.9994114432252911,0.0,0.0005102157592773438,22.476952 -49478,Binary classification,[baseline] Last Class,SMTP,0.99943408048184,0.0,0.0005102157592773438,24.271157000000002 -51381,Binary classification,[baseline] Last Class,SMTP,0.9994161152199299,0.0625,0.0005102157592773438,26.132842000000004 -53284,Binary classification,[baseline] Last Class,SMTP,0.9994369686391532,0.0625,0.0005102157592773438,28.063918000000005 -55187,Binary classification,[baseline] Last Class,SMTP,0.9994563838654731,0.0625,0.0005102157592773438,30.065042000000005 -57090,Binary classification,[baseline] Last Class,SMTP,0.9994394717020793,0.36,0.0005102157592773438,32.13415200000001 -58993,Binary classification,[baseline] Last Class,SMTP,0.9994575535665853,0.36,0.0005102157592773438,34.27360200000001 -60896,Binary classification,[baseline] Last Class,SMTP,0.9994745052960013,0.36,0.0005102157592773438,36.48320000000001 -62799,Binary classification,[baseline] Last Class,SMTP,0.9994585814834868,0.37037037037037035,0.0005102157592773438,38.76173800000001 -64702,Binary classification,[baseline] Last Class,SMTP,0.9994745058036197,0.37037037037037035,0.0005102157592773438,41.10958900000001 -66605,Binary classification,[baseline] Last Class,SMTP,0.99948952014894,0.37037037037037035,0.0005102157592773438,43.52762700000001 -68508,Binary classification,[baseline] Last Class,SMTP,0.9994745062548352,0.3793103448275862,0.0005102157592773438,46.01474500000001 -70411,Binary classification,[baseline] Last Class,SMTP,0.9994887089902003,0.3793103448275862,0.0005102157592773438,48.570884000000014 -72314,Binary classification,[baseline] Last Class,SMTP,0.9995021642028404,0.3793103448275862,0.0005102157592773438,51.19747000000002 -74217,Binary classification,[baseline] Last Class,SMTP,0.9995149293952786,0.3793103448275862,0.0005102157592773438,53.894680000000015 -76120,Binary classification,[baseline] Last Class,SMTP,0.99952705631971,0.3793103448275862,0.0005102157592773438,56.660361000000016 -78023,Binary classification,[baseline] Last Class,SMTP,0.99953859167927,0.3793103448275862,0.0005102157592773438,59.49662100000002 -79926,Binary classification,[baseline] Last Class,SMTP,0.999549577729121,0.3793103448275862,0.0005102157592773438,62.40230700000002 -81829,Binary classification,[baseline] Last Class,SMTP,0.9995600527936648,0.3793103448275862,0.0005102157592773438,65.37601400000003 -83732,Binary classification,[baseline] Last Class,SMTP,0.9995700517132244,0.3793103448275862,0.0005102157592773438,68.41937500000003 -85635,Binary classification,[baseline] Last Class,SMTP,0.9995796062311698,0.3793103448275862,0.0005102157592773438,71.53305000000003 -87538,Binary classification,[baseline] Last Class,SMTP,0.999588745330546,0.3793103448275862,0.0005102157592773438,74.71604400000002 -89441,Binary classification,[baseline] Last Class,SMTP,0.9995751341681575,0.36666666666666664,0.0005102157592773438,77.96763000000003 -91344,Binary classification,[baseline] Last Class,SMTP,0.9995839856365567,0.36666666666666664,0.0005102157592773438,81.28881300000003 -93247,Binary classification,[baseline] Last Class,SMTP,0.999592475816657,0.36666666666666664,0.0005102157592773438,84.68057600000003 -95150,Binary classification,[baseline] Last Class,SMTP,0.9996006263859841,0.36666666666666664,0.0005102157592773438,88.14010200000003 +106,Binary classification,Logistic regression,Bananas,0.49056603773584906,0.3414634146341463,0.004187583923339844,0.00989 +212,Binary classification,Logistic regression,Bananas,0.5141509433962265,0.3832335329341317,0.004187583923339844,0.123413 +318,Binary classification,Logistic regression,Bananas,0.5220125786163522,0.42424242424242425,0.004240989685058594,0.315017 +424,Binary classification,Logistic regression,Bananas,0.5165094339622641,0.40233236151603496,0.004240989685058594,0.5849610000000001 +530,Binary classification,Logistic regression,Bananas,0.5320754716981132,0.36410256410256414,0.004240989685058594,0.9372130000000001 +636,Binary classification,Logistic regression,Bananas,0.5377358490566038,0.32876712328767127,0.004240989685058594,1.342505 +742,Binary classification,Logistic regression,Bananas,0.5525606469002695,0.3054393305439331,0.004240989685058594,1.8950680000000002 +848,Binary classification,Logistic regression,Bananas,0.5530660377358491,0.28083491461100574,0.004240989685058594,2.518365 +954,Binary classification,Logistic regression,Bananas,0.5555555555555556,0.25874125874125875,0.004240989685058594,3.1930270000000003 +1060,Binary classification,Logistic regression,Bananas,0.5622641509433962,0.2418300653594771,0.004240989685058594,3.938137 +1166,Binary classification,Logistic regression,Bananas,0.5608919382504288,0.22424242424242424,0.004240989685058594,4.7351090000000005 +1272,Binary classification,Logistic regression,Bananas,0.5613207547169812,0.2206703910614525,0.004240989685058594,5.600857 +1378,Binary classification,Logistic regression,Bananas,0.5645863570391872,0.20844327176781002,0.004240989685058594,6.476079 +1484,Binary classification,Logistic regression,Bananas,0.5646900269541779,0.19651741293532338,0.004240989685058594,7.428853 +1590,Binary classification,Logistic regression,Bananas,0.5647798742138365,0.18588235294117644,0.004240989685058594,8.473991 +1696,Binary classification,Logistic regression,Bananas,0.5660377358490566,0.17857142857142858,0.004240989685058594,9.59319 +1802,Binary classification,Logistic regression,Bananas,0.562708102108768,0.17052631578947366,0.004240989685058594,10.745503 +1908,Binary classification,Logistic regression,Bananas,0.5587002096436059,0.16798418972332016,0.004240989685058594,11.962335 +2014,Binary classification,Logistic regression,Bananas,0.5516385302879842,0.16620498614958448,0.004240989685058594,13.252336 +2120,Binary classification,Logistic regression,Bananas,0.5495283018867925,0.1688424717145344,0.004240989685058594,14.603624 +2226,Binary classification,Logistic regression,Bananas,0.5485175202156334,0.18092909535452323,0.004240989685058594,15.981958 +2332,Binary classification,Logistic regression,Bananas,0.5484562607204116,0.19679633867276888,0.004240989685058594,17.395643 +2438,Binary classification,Logistic regression,Bananas,0.5471698113207547,0.19999999999999998,0.004240989685058594,18.850781 +2544,Binary classification,Logistic regression,Bananas,0.5479559748427673,0.21662125340599456,0.004240989685058594,20.422045 +2650,Binary classification,Logistic regression,Bananas,0.5452830188679245,0.2260757867694284,0.004240989685058594,22.049363 +2756,Binary classification,Logistic regression,Bananas,0.5395500725689405,0.22857142857142854,0.004240989685058594,23.763248 +2862,Binary classification,Logistic regression,Bananas,0.5391334730957372,0.230005837711617,0.004240989685058594,25.51638 +2968,Binary classification,Logistic regression,Bananas,0.5411051212938005,0.22613636363636364,0.004240989685058594,27.316788000000003 +3074,Binary classification,Logistic regression,Bananas,0.5403383214053351,0.22148760330578512,0.004240989685058594,29.124189 +3180,Binary classification,Logistic regression,Bananas,0.5437106918238994,0.22031166039763567,0.004240989685058594,31.016333000000003 +3286,Binary classification,Logistic regression,Bananas,0.5450395617772368,0.21604614577871,0.004240989685058594,32.984057 +3392,Binary classification,Logistic regression,Bananas,0.5439268867924528,0.21272264631043258,0.004240989685058594,35.003757 +3498,Binary classification,Logistic regression,Bananas,0.5457404230989137,0.20827105132037868,0.004240989685058594,37.068178 +3604,Binary classification,Logistic regression,Bananas,0.5480022197558269,0.2042012701514411,0.004240989685058594,39.232173 +3710,Binary classification,Logistic regression,Bananas,0.546900269541779,0.19914244878513576,0.004240989685058594,41.450117000000006 +3816,Binary classification,Logistic regression,Bananas,0.5463836477987422,0.19450907398790138,0.004240989685058594,43.72876300000001 +3922,Binary classification,Logistic regression,Bananas,0.5474247832738399,0.1906064751481988,0.004240989685058594,46.072390000000006 +4028,Binary classification,Logistic regression,Bananas,0.547914597815293,0.1866904868244752,0.004240989685058594,48.42327300000001 +4134,Binary classification,Logistic regression,Bananas,0.548137397194001,0.18285214348206474,0.004240989685058594,50.870554000000006 +4240,Binary classification,Logistic regression,Bananas,0.5474056603773585,0.17886178861788615,0.004240989685058594,53.39424700000001 +4346,Binary classification,Logistic regression,Bananas,0.5476300046019328,0.17671691792294805,0.004240989685058594,55.939767 +4452,Binary classification,Logistic regression,Bananas,0.5498652291105122,0.1820408163265306,0.004240989685058594,58.584779000000005 +4558,Binary classification,Logistic regression,Bananas,0.5467310223782361,0.1814580031695721,0.004240989685058594,61.26661800000001 +4664,Binary classification,Logistic regression,Bananas,0.5465265866209262,0.18809980806142035,0.004240989685058594,64.04445700000001 +4770,Binary classification,Logistic regression,Bananas,0.5467505241090147,0.19086826347305388,0.004240989685058594,66.91140200000001 +4876,Binary classification,Logistic regression,Bananas,0.5469647251845775,0.19113877700476017,0.004240989685058594,69.84398600000002 +4982,Binary classification,Logistic regression,Bananas,0.5469690887193898,0.19765375044436545,0.004240989685058594,72.84582100000002 +5088,Binary classification,Logistic regression,Bananas,0.5448113207547169,0.19583333333333333,0.004240989685058594,75.85667200000002 +5194,Binary classification,Logistic regression,Bananas,0.5429341547939931,0.19416157501697218,0.004240989685058594,78.94956300000001 +5300,Binary classification,Logistic regression,Bananas,0.5432075471698113,0.1970149253731343,0.004240989685058594,82.06889500000001 +906,Binary classification,Logistic regression,Elec2,0.7980132450331126,0.7834319526627219,0.0053730010986328125,0.687155 +1812,Binary classification,Logistic regression,Elec2,0.8134657836644592,0.7488855869242199,0.0053730010986328125,2.092465 +2718,Binary classification,Logistic regression,Elec2,0.8024282560706402,0.7300150829562596,0.0053730010986328125,4.064074 +3624,Binary classification,Logistic regression,Elec2,0.8192604856512141,0.7598093142647598,0.0053730010986328125,6.824807 +4530,Binary classification,Logistic regression,Elec2,0.8289183222958058,0.7613181398213735,0.0053730010986328125,10.234028 +5436,Binary classification,Logistic regression,Elec2,0.8226637233259749,0.7528205128205128,0.0053730010986328125,14.344314 +6342,Binary classification,Logistic regression,Elec2,0.8229265216020183,0.7589611504614724,0.0053730010986328125,19.167838 +7248,Binary classification,Logistic regression,Elec2,0.8261589403973509,0.7617246596066566,0.0053730010986328125,24.744494 +8154,Binary classification,Logistic regression,Elec2,0.8318616629874908,0.7833096254148886,0.0053730010986328125,31.081721 +9060,Binary classification,Logistic regression,Elec2,0.8375275938189846,0.7975797579757975,0.0053730010986328125,38.163875000000004 +9966,Binary classification,Logistic regression,Elec2,0.8377483443708609,0.802008081302804,0.0053730010986328125,45.915004 +10872,Binary classification,Logistic regression,Elec2,0.8400478292862399,0.8089220964729151,0.0053730010986328125,54.352834 +11778,Binary classification,Logistic regression,Elec2,0.8432671081677704,0.8127789046653143,0.0053730010986328125,63.489549000000004 +12684,Binary classification,Logistic regression,Elec2,0.8419268369599495,0.8117547648108159,0.0053730010986328125,73.399178 +13590,Binary classification,Logistic regression,Elec2,0.8437821927888153,0.8167141500474834,0.0053730010986328125,84.03825400000001 +14496,Binary classification,Logistic regression,Elec2,0.8447157836644592,0.8189204408334004,0.0053730010986328125,95.41495900000001 +15402,Binary classification,Logistic regression,Elec2,0.8464485131801065,0.8201110519510155,0.0053730010986328125,107.55183300000002 +16308,Binary classification,Logistic regression,Elec2,0.8411822418444935,0.812780106982796,0.0053730010986328125,120.38661500000002 +17214,Binary classification,Logistic regression,Elec2,0.8397234808876496,0.8069954529555788,0.0053730010986328125,133.99787400000002 +18120,Binary classification,Logistic regression,Elec2,0.8419426048565122,0.80987785448752,0.0053730010986328125,148.356557 +19026,Binary classification,Logistic regression,Elec2,0.8451066961000736,0.8115849370244869,0.0053730010986328125,163.518734 +19932,Binary classification,Logistic regression,Elec2,0.8428155729480232,0.8097637986520129,0.0053730010986328125,179.395561 +20838,Binary classification,Logistic regression,Elec2,0.8393799788847298,0.805689404934688,0.0053730010986328125,196.009478 +21744,Binary classification,Logistic regression,Elec2,0.8402777777777778,0.8036632935722765,0.0053730010986328125,213.342445 +22650,Binary classification,Logistic regression,Elec2,0.8394701986754967,0.8009198423127463,0.0053730010986328125,231.348647 +23556,Binary classification,Logistic regression,Elec2,0.8357106469689252,0.7954545454545454,0.0053730010986328125,250.064916 +24462,Binary classification,Logistic regression,Elec2,0.8330471752105306,0.791441119395363,0.0053730010986328125,269.489469 +25368,Binary classification,Logistic regression,Elec2,0.8298249763481551,0.7872875092387287,0.0053730010986328125,289.628629 +26274,Binary classification,Logistic regression,Elec2,0.8304407398949532,0.787745962170661,0.0053730010986328125,310.508458 +27180,Binary classification,Logistic regression,Elec2,0.8308682855040471,0.7889638709085066,0.0053730010986328125,332.12320800000003 +28086,Binary classification,Logistic regression,Elec2,0.8277077547532579,0.7843678980437593,0.0053730010986328125,354.49968 +28992,Binary classification,Logistic regression,Elec2,0.8270212472406181,0.7820039121930016,0.0053730010986328125,377.655941 +29898,Binary classification,Logistic regression,Elec2,0.8260418757107498,0.780872129766168,0.0053730010986328125,401.520333 +30804,Binary classification,Logistic regression,Elec2,0.8258992338657317,0.7797807251673304,0.0053730010986328125,426.09085 +31710,Binary classification,Logistic regression,Elec2,0.821286660359508,0.7731294287201249,0.0053730010986328125,451.387139 +32616,Binary classification,Logistic regression,Elec2,0.8188619082658818,0.7700093428838368,0.0053730010986328125,477.46355 +33522,Binary classification,Logistic regression,Elec2,0.8168963665652408,0.7682024169184289,0.0053730010986328125,504.189667 +34428,Binary classification,Logistic regression,Elec2,0.8143952596723597,0.7647795037915042,0.0053730010986328125,531.635688 +35334,Binary classification,Logistic regression,Elec2,0.8142016188373804,0.7627822944896115,0.0053730010986328125,559.807066 +36240,Binary classification,Logistic regression,Elec2,0.8154801324503311,0.7629984051036682,0.0053730010986328125,588.709839 +37146,Binary classification,Logistic regression,Elec2,0.815161794002046,0.7614481273017858,0.0053730010986328125,618.344034 +38052,Binary classification,Logistic regression,Elec2,0.8151476926311363,0.7609596955073744,0.0053730010986328125,648.6306599999999 +38958,Binary classification,Logistic regression,Elec2,0.8162379998973254,0.7631274195149389,0.0053730010986328125,679.6980239999999 +39864,Binary classification,Logistic regression,Elec2,0.8169275536825206,0.7661946562439931,0.0053730010986328125,711.4719719999999 +40770,Binary classification,Logistic regression,Elec2,0.8186656855531028,0.7707241432780277,0.0053730010986328125,743.966698 +41676,Binary classification,Logistic regression,Elec2,0.8201602840963624,0.7745390006918749,0.0053730010986328125,777.181242 +42582,Binary classification,Logistic regression,Elec2,0.8211920529801324,0.7763613934089174,0.0053730010986328125,811.063029 +43488,Binary classification,Logistic regression,Elec2,0.8216979396615158,0.7772863051470587,0.0053730010986328125,845.685827 +44394,Binary classification,Logistic regression,Elec2,0.8211695274136145,0.7754109027129481,0.0053730010986328125,881.122534 +45300,Binary classification,Logistic regression,Elec2,0.8221412803532009,0.7771292633675417,0.0053730010986328125,917.330239 +45312,Binary classification,Logistic regression,Elec2,0.8221442443502824,0.7770862722319033,0.0053730010986328125,953.539999 +25,Binary classification,Logistic regression,Phishing,0.6,0.6428571428571429,0.005324363708496094,0.005087 +50,Binary classification,Logistic regression,Phishing,0.76,0.7499999999999999,0.005324363708496094,0.014273000000000001 +75,Binary classification,Logistic regression,Phishing,0.8,0.8,0.005324363708496094,0.080154 +100,Binary classification,Logistic regression,Phishing,0.81,0.8041237113402061,0.005324363708496094,0.160529 +125,Binary classification,Logistic regression,Phishing,0.8,0.7933884297520661,0.005324363708496094,0.244823 +150,Binary classification,Logistic regression,Phishing,0.8066666666666666,0.8079470198675497,0.005324363708496094,0.373717 +175,Binary classification,Logistic regression,Phishing,0.8171428571428572,0.8072289156626506,0.005324363708496094,0.564558 +200,Binary classification,Logistic regression,Phishing,0.815,0.8042328042328043,0.005324363708496094,0.765703 +225,Binary classification,Logistic regression,Phishing,0.8133333333333334,0.7980769230769231,0.005324363708496094,0.969796 +250,Binary classification,Logistic regression,Phishing,0.82,0.8068669527896996,0.005324363708496094,1.176844 +275,Binary classification,Logistic regression,Phishing,0.8218181818181818,0.8078431372549019,0.005564689636230469,1.38745 +300,Binary classification,Logistic regression,Phishing,0.8333333333333334,0.8161764705882353,0.005564689636230469,1.6264800000000001 +325,Binary classification,Logistic regression,Phishing,0.84,0.8181818181818181,0.005564689636230469,1.9406150000000002 +350,Binary classification,Logistic regression,Phishing,0.8514285714285714,0.8278145695364238,0.005564689636230469,2.281543 +375,Binary classification,Logistic regression,Phishing,0.848,0.8213166144200628,0.005564689636230469,2.625835 +400,Binary classification,Logistic regression,Phishing,0.85,0.8214285714285715,0.005564689636230469,2.973623 +425,Binary classification,Logistic regression,Phishing,0.8564705882352941,0.825214899713467,0.005564689636230469,3.3575019999999998 +450,Binary classification,Logistic regression,Phishing,0.86,0.8273972602739726,0.005564689636230469,3.744344 +475,Binary classification,Logistic regression,Phishing,0.8568421052631578,0.8247422680412371,0.005564689636230469,4.182096 +500,Binary classification,Logistic regression,Phishing,0.858,0.8297362110311751,0.005564689636230469,4.631479 +525,Binary classification,Logistic regression,Phishing,0.8571428571428571,0.8251748251748252,0.005564689636230469,5.084116 +550,Binary classification,Logistic regression,Phishing,0.8581818181818182,0.827433628318584,0.005564689636230469,5.539997 +575,Binary classification,Logistic regression,Phishing,0.8608695652173913,0.8305084745762712,0.005564689636230469,6.065522 +600,Binary classification,Logistic regression,Phishing,0.865,0.8329896907216495,0.005564689636230469,6.5948839999999995 +625,Binary classification,Logistic regression,Phishing,0.8672,0.8323232323232322,0.005564689636230469,7.192367999999999 +650,Binary classification,Logistic regression,Phishing,0.8707692307692307,0.8390804597701149,0.005564689636230469,7.814115999999999 +675,Binary classification,Logistic regression,Phishing,0.8711111111111111,0.8426763110307414,0.005564689636230469,8.439065999999999 +700,Binary classification,Logistic regression,Phishing,0.8757142857142857,0.8465608465608465,0.005564689636230469,9.067184 +725,Binary classification,Logistic regression,Phishing,0.8772413793103448,0.8514190317195326,0.005564689636230469,9.744983999999999 +750,Binary classification,Logistic regression,Phishing,0.8786666666666667,0.8539325842696629,0.005564689636230469,10.426390999999999 +775,Binary classification,Logistic regression,Phishing,0.88,0.8549141965678626,0.005564689636230469,11.153806 +800,Binary classification,Logistic regression,Phishing,0.88,0.8567164179104476,0.005564689636230469,11.884597 +825,Binary classification,Logistic regression,Phishing,0.88,0.8579626972740315,0.005564689636230469,12.619003 +850,Binary classification,Logistic regression,Phishing,0.8811764705882353,0.8587412587412586,0.005564689636230469,13.411055999999999 +875,Binary classification,Logistic regression,Phishing,0.8845714285714286,0.8622100954979536,0.005564689636230469,14.234523999999999 +900,Binary classification,Logistic regression,Phishing,0.8844444444444445,0.8617021276595744,0.005564689636230469,15.105192999999998 +925,Binary classification,Logistic regression,Phishing,0.8864864864864865,0.8655569782330347,0.005564689636230469,15.990264999999997 +950,Binary classification,Logistic regression,Phishing,0.8852631578947369,0.8655980271270037,0.005564689636230469,16.878196999999997 +975,Binary classification,Logistic regression,Phishing,0.8861538461538462,0.8664259927797834,0.005564689636230469,17.769031 +1000,Binary classification,Logistic regression,Phishing,0.887,0.8675263774912075,0.005564689636230469,18.72316 +1025,Binary classification,Logistic regression,Phishing,0.8868292682926829,0.8678815489749431,0.005564689636230469,19.680949 +1050,Binary classification,Logistic regression,Phishing,0.8885714285714286,0.8704318936877077,0.005564689636230469,20.642059 +1075,Binary classification,Logistic regression,Phishing,0.8874418604651163,0.8703108252947481,0.005564689636230469,21.642509 +1100,Binary classification,Logistic regression,Phishing,0.889090909090909,0.8723849372384936,0.005564689636230469,22.64645 +1125,Binary classification,Logistic regression,Phishing,0.8897777777777778,0.8742393509127788,0.005564689636230469,23.715816 +1150,Binary classification,Logistic regression,Phishing,0.8895652173913043,0.8738828202581926,0.005564689636230469,24.78868 +1175,Binary classification,Logistic regression,Phishing,0.8885106382978724,0.872444011684518,0.005564689636230469,25.864657 +1200,Binary classification,Logistic regression,Phishing,0.8891666666666667,0.8729703915950333,0.005564689636230469,26.968066 +1225,Binary classification,Logistic regression,Phishing,0.889795918367347,0.8737137511693172,0.005564689636230469,28.075126 +1250,Binary classification,Logistic regression,Phishing,0.8872,0.8712328767123287,0.005564689636230469,29.206647 +1903,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,1.174944 +3806,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,3.465965 +5709,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,6.937403 +7612,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,11.610183 +9515,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,17.462392 +11418,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,24.519273000000002 +13321,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,32.784706 +15224,Binary classification,Logistic regression,SMTP,0.9996715712033631,0.7058823529411764,0.004383087158203125,42.234241 +17127,Binary classification,Logistic regression,SMTP,0.9997080632918783,0.761904761904762,0.004383087158203125,52.882453 +19030,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.761904761904762,0.004383087158203125,64.622668 +20933,Binary classification,Logistic regression,SMTP,0.999761142693355,0.761904761904762,0.004383087158203125,77.568109 +22836,Binary classification,Logistic regression,SMTP,0.9997810474689087,0.761904761904762,0.004383087158203125,91.771967 +24739,Binary classification,Logistic regression,SMTP,0.9997978899713004,0.761904761904762,0.004383087158203125,107.109486 +26642,Binary classification,Logistic regression,SMTP,0.9997747916823061,0.7272727272727273,0.004383087158203125,123.68183400000001 +28545,Binary classification,Logistic regression,SMTP,0.9997898055701524,0.7272727272727273,0.004383087158203125,141.369945 +30448,Binary classification,Logistic regression,SMTP,0.9998029427220179,0.7272727272727273,0.004383087158203125,160.23044 +32351,Binary classification,Logistic regression,SMTP,0.999814534326605,0.7272727272727273,0.004383087158203125,180.23963199999997 +34254,Binary classification,Logistic regression,SMTP,0.999824837975127,0.7272727272727273,0.004383087158203125,201.31894799999998 +36157,Binary classification,Logistic regression,SMTP,0.9998340570290677,0.7272727272727273,0.004383087158203125,223.51927299999997 +38060,Binary classification,Logistic regression,SMTP,0.9998423541776142,0.7272727272727273,0.004383087158203125,246.97671399999996 +39963,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.7272727272727273,0.004383087158203125,271.56812399999995 +41866,Binary classification,Logistic regression,SMTP,0.999856685616013,0.7272727272727273,0.004383087158203125,297.29584399999993 +43769,Binary classification,Logistic regression,SMTP,0.9998629166761863,0.7272727272727273,0.004383087158203125,324.2115329999999 +45672,Binary classification,Logistic regression,SMTP,0.9998686284813453,0.7272727272727273,0.004383087158203125,352.27523699999995 +47575,Binary classification,Logistic regression,SMTP,0.9998738833420915,0.7272727272727273,0.004383087158203125,381.59710399999994 +49478,Binary classification,Logistic regression,SMTP,0.9998787339827803,0.7272727272727273,0.004383087158203125,412.11662699999994 +51381,Binary classification,Logistic regression,SMTP,0.9998443004223351,0.6666666666666666,0.004383087158203125,443.86742899999996 +53284,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.6666666666666666,0.004383087158203125,476.83879799999994 +55187,Binary classification,Logistic regression,SMTP,0.999855038324243,0.6666666666666666,0.004383087158203125,510.9819989999999 +57090,Binary classification,Logistic regression,SMTP,0.9997022245577158,0.48484848484848486,0.004383087158203125,546.274013 +58993,Binary classification,Logistic regression,SMTP,0.9997118302171444,0.48484848484848486,0.004383087158203125,582.6678519999999 +60896,Binary classification,Logistic regression,SMTP,0.9997208355228586,0.48484848484848486,0.004383087158203125,620.2082039999999 +62799,Binary classification,Logistic regression,SMTP,0.999697447411583,0.45714285714285713,0.004383087158203125,658.8625569999999 +64702,Binary classification,Logistic regression,SMTP,0.9997063460171247,0.45714285714285713,0.004383087158203125,698.5852799999999 +66605,Binary classification,Logistic regression,SMTP,0.9997147361309211,0.45714285714285713,0.004383087158203125,739.3620329999999 +68508,Binary classification,Logistic regression,SMTP,0.9996934664564723,0.4324324324324324,0.004383087158203125,781.2563779999999 +70411,Binary classification,Logistic regression,SMTP,0.9997017511468379,0.4324324324324324,0.004383087158203125,824.198222 +72314,Binary classification,Logistic regression,SMTP,0.9997095998008685,0.4324324324324324,0.004383087158203125,868.202086 +74217,Binary classification,Logistic regression,SMTP,0.9997170459598205,0.4324324324324324,0.004383087158203125,913.268811 +76120,Binary classification,Logistic regression,SMTP,0.999724119810825,0.4324324324324324,0.004383087158203125,959.4161730000001 +78023,Binary classification,Logistic regression,SMTP,0.9997308485959269,0.4324324324324324,0.004383087158203125,1006.608919 +79926,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.4324324324324324,0.004383087158203125,1054.8516300000001 +81829,Binary classification,Logistic regression,SMTP,0.9997433672658838,0.4324324324324324,0.004383087158203125,1104.06085 +83732,Binary classification,Logistic regression,SMTP,0.9997491998280228,0.4324324324324324,0.004383087158203125,1154.258062 +85635,Binary classification,Logistic regression,SMTP,0.9997547731651778,0.4324324324324324,0.004383087158203125,1205.3715320000001 +87538,Binary classification,Logistic regression,SMTP,0.9997601041833261,0.4324324324324324,0.004383087158203125,1257.4462130000002 +89441,Binary classification,Logistic regression,SMTP,0.9997540277948592,0.4210526315789474,0.004383087158203125,1310.5048250000002 +91344,Binary classification,Logistic regression,SMTP,0.9997591522157996,0.4210526315789474,0.004383087158203125,1364.5437910000003 +93247,Binary classification,Logistic regression,SMTP,0.9997640674767017,0.4210526315789474,0.004383087158203125,1419.4942320000002 +95150,Binary classification,Logistic regression,SMTP,0.9997687861271676,0.4210526315789474,0.004383087158203125,1475.4318390000003 +95156,Binary classification,Logistic regression,SMTP,0.9997688007062088,0.4210526315789474,0.004383087158203125,1531.3705140000004 +106,Binary classification,Aggregated Mondrian Forest,Bananas,0.7047619047619048,0.6990291262135924,0.8133068084716797,0.833499 +212,Binary classification,Aggregated Mondrian Forest,Bananas,0.7867298578199052,0.7668393782383419,1.3378009796142578,2.8663 +318,Binary classification,Aggregated Mondrian Forest,Bananas,0.8233438485804416,0.806896551724138,1.855398178100586,6.250927 +424,Binary classification,Aggregated Mondrian Forest,Bananas,0.8392434988179669,0.8229166666666667,2.3226680755615234,11.143336 +530,Binary classification,Aggregated Mondrian Forest,Bananas,0.8412098298676749,0.8181818181818182,2.776212692260742,17.797124 +636,Binary classification,Aggregated Mondrian Forest,Bananas,0.8488188976377953,0.8267148014440434,3.173288345336914,26.396562 +742,Binary classification,Aggregated Mondrian Forest,Bananas,0.8596491228070176,0.8359621451104102,3.5500621795654297,36.969223 +848,Binary classification,Aggregated Mondrian Forest,Bananas,0.8677685950413223,0.8461538461538461,3.917997360229492,49.692848 +954,Binary classification,Aggregated Mondrian Forest,Bananas,0.8730325288562435,0.8515337423312884,4.238534927368164,64.631677 +1060,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772426817752597,0.8549107142857144,4.491437911987305,81.765253 +1166,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772532188841202,0.8557013118062564,4.809717178344727,101.295253 +1272,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772619984264359,0.8566176470588236,5.171953201293945,123.161687 +1378,Binary classification,Aggregated Mondrian Forest,Bananas,0.8779956427015251,0.8561643835616438,5.501619338989258,147.513883 +1484,Binary classification,Aggregated Mondrian Forest,Bananas,0.8813216453135536,0.860759493670886,5.80189323425293,174.53874199999998 +1590,Binary classification,Aggregated Mondrian Forest,Bananas,0.8785399622404028,0.8579838116261957,6.17225456237793,204.250002 +1696,Binary classification,Aggregated Mondrian Forest,Bananas,0.8790560471976401,0.8585231193926847,6.45002555847168,237.091398 +1802,Binary classification,Aggregated Mondrian Forest,Bananas,0.8806218767351471,0.8613797549967763,6.703157424926758,272.83416 +1908,Binary classification,Aggregated Mondrian Forest,Bananas,0.8783429470372313,0.8602409638554217,7.075212478637695,311.419605 +2014,Binary classification,Aggregated Mondrian Forest,Bananas,0.8777943368107303,0.8607021517553795,7.409914016723633,352.79492 +2120,Binary classification,Aggregated Mondrian Forest,Bananas,0.8791882963662104,0.8636847710330138,7.730207443237305,397.065386 +2226,Binary classification,Aggregated Mondrian Forest,Bananas,0.8782022471910113,0.8626457171819564,8.068941116333008,444.302777 +2332,Binary classification,Aggregated Mondrian Forest,Bananas,0.8777348777348777,0.8621190130624092,8.392999649047852,494.454577 +2438,Binary classification,Aggregated Mondrian Forest,Bananas,0.8781288469429627,0.8624363131079205,8.738908767700195,547.433225 +2544,Binary classification,Aggregated Mondrian Forest,Bananas,0.8784899724734565,0.8635761589403974,9.069158554077148,603.367304 +2650,Binary classification,Aggregated Mondrian Forest,Bananas,0.8799546998867497,0.8654822335025381,9.380228042602539,661.971994 +2756,Binary classification,Aggregated Mondrian Forest,Bananas,0.8820326678765881,0.8676171079429736,9.675683975219727,723.088894 +2862,Binary classification,Aggregated Mondrian Forest,Bananas,0.8836071303739951,0.86905230043256,10.005556106567383,786.780009 +2968,Binary classification,Aggregated Mondrian Forest,Bananas,0.8840579710144928,0.8691019786910198,10.283010482788086,853.0146269999999 +3074,Binary classification,Aggregated Mondrian Forest,Bananas,0.8831760494630654,0.8683535020168683,10.632661819458008,921.6671329999999 +3180,Binary classification,Aggregated Mondrian Forest,Bananas,0.8858131487889274,0.8707725169099323,10.90281867980957,992.810764 +3286,Binary classification,Aggregated Mondrian Forest,Bananas,0.8852359208523592,0.8696854476322157,11.200468063354492,1066.389204 +3392,Binary classification,Aggregated Mondrian Forest,Bananas,0.8849896785608965,0.87017310252996,11.512235641479492,1142.442462 +3498,Binary classification,Aggregated Mondrian Forest,Bananas,0.8864741206748642,0.8712293220888745,11.797895431518555,1221.036812 +3604,Binary classification,Aggregated Mondrian Forest,Bananas,0.8878712184290869,0.8721518987341771,12.102933883666992,1302.125963 +3710,Binary classification,Aggregated Mondrian Forest,Bananas,0.8878403882448099,0.8725490196078431,12.41331672668457,1385.838182 +3816,Binary classification,Aggregated Mondrian Forest,Bananas,0.889646133682831,0.8746650788925276,12.665735244750977,1472.135343 +3922,Binary classification,Aggregated Mondrian Forest,Bananas,0.8885488395817394,0.8730758059831543,13.002767562866211,1561.047711 +4028,Binary classification,Aggregated Mondrian Forest,Bananas,0.8872609883287808,0.8714609286523215,13.407987594604492,1652.580672 +4134,Binary classification,Aggregated Mondrian Forest,Bananas,0.8874909266876361,0.8717241379310345,13.751871109008789,1746.8148660000002 +4240,Binary classification,Aggregated Mondrian Forest,Bananas,0.8886529841943854,0.8731864588930682,13.96497917175293,1843.750561 +4346,Binary classification,Aggregated Mondrian Forest,Bananas,0.8895281933256617,0.8742138364779874,14.240518569946289,1943.4032140000002 +4452,Binary classification,Aggregated Mondrian Forest,Bananas,0.8890137047854415,0.8735926305015352,14.605810165405273,2045.776976 +4558,Binary classification,Aggregated Mondrian Forest,Bananas,0.8894009216589862,0.874439461883408,14.917993545532227,2150.6554650000003 +4664,Binary classification,Aggregated Mondrian Forest,Bananas,0.8893416255629423,0.8748180494905387,15.239774703979492,2258.064088 +4770,Binary classification,Aggregated Mondrian Forest,Bananas,0.8880268400083875,0.8729176582579724,15.676980972290039,2367.913167 +4876,Binary classification,Aggregated Mondrian Forest,Bananas,0.8888205128205128,0.8733644859813083,15.964864730834961,2480.267593 +4982,Binary classification,Aggregated Mondrian Forest,Bananas,0.889580405541056,0.8746010031919745,16.210702896118164,2595.134509 +5088,Binary classification,Aggregated Mondrian Forest,Bananas,0.8891291527422842,0.8740509155873157,16.543100357055664,2712.434229 +5194,Binary classification,Aggregated Mondrian Forest,Bananas,0.8894665896398999,0.8743982494529539,16.87101936340332,2832.294496 +5300,Binary classification,Aggregated Mondrian Forest,Bananas,0.889413096810719,0.8742489270386266,17.23769187927246,2954.746773 +906,Binary classification,Aggregated Mondrian Forest,Elec2,0.8662983425414365,0.8638920134983127,5.093213081359863,9.961559 +1812,Binary classification,Aggregated Mondrian Forest,Elec2,0.8895637769188294,0.863013698630137,9.274415016174316,34.997891 +2718,Binary classification,Aggregated Mondrian Forest,Elec2,0.8737578211262422,0.8433074463225217,14.81954288482666,77.180768 +3624,Binary classification,Aggregated Mondrian Forest,Elec2,0.8746894838531604,0.8451568894952252,20.35789203643799,135.799753 +4530,Binary classification,Aggregated Mondrian Forest,Elec2,0.869728416869066,0.8295782784517621,25.320820808410645,209.04868100000002 +5436,Binary classification,Aggregated Mondrian Forest,Elec2,0.8658693652253909,0.8254728273880776,30.942105293273926,297.509476 +6342,Binary classification,Aggregated Mondrian Forest,Elec2,0.8613783314934553,0.8220287507592631,36.922226905822754,401.254404 +7248,Binary classification,Aggregated Mondrian Forest,Elec2,0.8563543535255967,0.8144715736945286,42.8322229385376,518.853069 +8154,Binary classification,Aggregated Mondrian Forest,Elec2,0.8547773825585674,0.8211480362537765,49.13461780548096,650.61595 +9060,Binary classification,Aggregated Mondrian Forest,Elec2,0.8564963020200905,0.8276776246023331,54.274807929992676,797.031608 +9966,Binary classification,Aggregated Mondrian Forest,Elec2,0.8559959859508279,0.830478440637921,59.58850955963135,957.298151 +10872,Binary classification,Aggregated Mondrian Forest,Elec2,0.858522675006899,0.8360690684289065,64.43849277496338,1132.655012 +11778,Binary classification,Aggregated Mondrian Forest,Elec2,0.8588774730406725,0.8365138697619515,69.77676105499268,1321.3849659999998 +12684,Binary classification,Aggregated Mondrian Forest,Elec2,0.8572892848695104,0.8352148579752368,75.08023929595947,1522.7749099999999 +13590,Binary classification,Aggregated Mondrian Forest,Elec2,0.8577525940098609,0.8380665158750105,79.94311618804932,1737.8701859999999 +14496,Binary classification,Aggregated Mondrian Forest,Elec2,0.8584339427388755,0.8393863494051347,84.43613529205322,1968.1055499999998 +15402,Binary classification,Aggregated Mondrian Forest,Elec2,0.8584507499513019,0.8387335404645658,89.24470615386963,2211.4324739999997 +16308,Binary classification,Aggregated Mondrian Forest,Elec2,0.8561354019746121,0.8352296670880741,95.65516376495361,2468.910492 +17214,Binary classification,Aggregated Mondrian Forest,Elec2,0.8563295183872655,0.8333445649976414,100.85075855255127,2740.76049 +18120,Binary classification,Aggregated Mondrian Forest,Elec2,0.8570009382416248,0.834176,106.8406229019165,3026.823297 +19026,Binary classification,Aggregated Mondrian Forest,Elec2,0.858712220762155,0.8348082595870207,111.74584293365479,3325.548438 +19932,Binary classification,Aggregated Mondrian Forest,Elec2,0.8587125583262255,0.8363361618040218,117.02025699615479,3636.553219 +20838,Binary classification,Aggregated Mondrian Forest,Elec2,0.8564572635216202,0.8339348176114596,123.37252902984619,3960.554229 +21744,Binary classification,Aggregated Mondrian Forest,Elec2,0.8540219840868325,0.8286917098445596,130.42929553985596,4298.210438 +22650,Binary classification,Aggregated Mondrian Forest,Elec2,0.8531944015188309,0.8264160793526494,136.64212131500244,4650.500753 +23556,Binary classification,Aggregated Mondrian Forest,Elec2,0.8528550201655699,0.8255134917438581,142.6701021194458,5016.675492 +24462,Binary classification,Aggregated Mondrian Forest,Elec2,0.8532766444544376,0.8247130647130647,148.4442949295044,5397.142957 +25368,Binary classification,Aggregated Mondrian Forest,Elec2,0.8514605589939686,0.8225487425826504,154.72937488555908,5792.939295 +26274,Binary classification,Aggregated Mondrian Forest,Elec2,0.8521676245575306,0.8231490756761678,160.280930519104,6204.791143 +27180,Binary classification,Aggregated Mondrian Forest,Elec2,0.8530851024688179,0.8247069669432372,165.12001132965088,6630.671498000001 +28086,Binary classification,Aggregated Mondrian Forest,Elec2,0.8528752002848495,0.8239904583404327,171.1938066482544,7068.974646000001 +28992,Binary classification,Aggregated Mondrian Forest,Elec2,0.8532303128557138,0.8236415633937083,176.66365909576416,7519.88705 +29898,Binary classification,Aggregated Mondrian Forest,Elec2,0.8538649362812323,0.8241355713883187,181.78493976593018,7981.8746790000005 +30804,Binary classification,Aggregated Mondrian Forest,Elec2,0.8542349771126189,0.8238110186783865,187.08849048614502,8454.454599 +31710,Binary classification,Aggregated Mondrian Forest,Elec2,0.8525655176763695,0.8216125462662648,193.5201120376587,8938.242097 +32616,Binary classification,Aggregated Mondrian Forest,Elec2,0.852245899126169,0.821432541594101,199.6366205215454,9433.534304 +33522,Binary classification,Aggregated Mondrian Forest,Elec2,0.852003221860923,0.8214247147330909,205.81115818023682,9940.639789 +34428,Binary classification,Aggregated Mondrian Forest,Elec2,0.851715223516426,0.8209965286300361,212.10033893585205,10459.964952 +35334,Binary classification,Aggregated Mondrian Forest,Elec2,0.8513287861206238,0.8197137660019906,218.64550113677979,10993.026606 +36240,Binary classification,Aggregated Mondrian Forest,Elec2,0.8508788873865173,0.8179735920237133,225.19258975982666,11538.003928999999 +37146,Binary classification,Aggregated Mondrian Forest,Elec2,0.8496432898102032,0.8159741671883752,232.33557987213135,12096.169426999999 +38052,Binary classification,Aggregated Mondrian Forest,Elec2,0.8497279966360937,0.8155126798735239,238.56606006622314,12664.877691 +38958,Binary classification,Aggregated Mondrian Forest,Elec2,0.8494493929203994,0.8154906093686098,244.89648151397705,13243.508414 +39864,Binary classification,Aggregated Mondrian Forest,Elec2,0.8492336251661942,0.8164773421277635,251.12543201446533,13830.859859 +40770,Binary classification,Aggregated Mondrian Forest,Elec2,0.8486104638328141,0.8170174918470204,257.83575916290283,14427.278119 +41676,Binary classification,Aggregated Mondrian Forest,Elec2,0.8490941811637672,0.8186928820595613,264.1331262588501,15032.883602 +42582,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493929217256523,0.8194385787087872,270.1314744949341,15648.679676 +43488,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493802745648125,0.8194995590828924,276.04683017730713,16273.986894 +44394,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493681436262474,0.8189620164063134,282.1419038772583,16909.074578 +45300,Binary classification,Aggregated Mondrian Forest,Elec2,0.8499083864985982,0.8197651300267741,287.208477973938,17554.066457 +45312,Binary classification,Aggregated Mondrian Forest,Elec2,0.8499039968219637,0.8197312269727252,287.3145227432251,18206.640571 +25,Binary classification,Aggregated Mondrian Forest,Phishing,0.6666666666666666,0.6923076923076924,0.2663440704345703,0.180038 +50,Binary classification,Aggregated Mondrian Forest,Phishing,0.7755102040816326,0.7555555555555555,0.40291404724121094,0.591649 +75,Binary classification,Aggregated Mondrian Forest,Phishing,0.7972972972972973,0.7945205479452055,0.5196552276611328,1.2897159999999999 +100,Binary classification,Aggregated Mondrian Forest,Phishing,0.8181818181818182,0.8125,0.6383838653564453,2.331468 +125,Binary classification,Aggregated Mondrian Forest,Phishing,0.8225806451612904,0.819672131147541,0.7669887542724609,3.7241540000000004 +150,Binary classification,Aggregated Mondrian Forest,Phishing,0.8456375838926175,0.847682119205298,0.9175167083740234,5.520175 +175,Binary classification,Aggregated Mondrian Forest,Phishing,0.867816091954023,0.8606060606060606,1.0086803436279297,7.7498439999999995 +200,Binary classification,Aggregated Mondrian Forest,Phishing,0.864321608040201,0.8571428571428572,1.1245098114013672,10.53336 +225,Binary classification,Aggregated Mondrian Forest,Phishing,0.8660714285714286,0.8557692307692308,1.2114391326904297,13.795268 +250,Binary classification,Aggregated Mondrian Forest,Phishing,0.8554216867469879,0.8448275862068965,1.322244644165039,17.57486 +275,Binary classification,Aggregated Mondrian Forest,Phishing,0.8540145985401459,0.84251968503937,1.3987751007080078,21.876977 +300,Binary classification,Aggregated Mondrian Forest,Phishing,0.8561872909698997,0.8413284132841329,1.489828109741211,26.743447 +325,Binary classification,Aggregated Mondrian Forest,Phishing,0.8672839506172839,0.8501742160278746,1.5769939422607422,32.2729 +350,Binary classification,Aggregated Mondrian Forest,Phishing,0.8681948424068768,0.8486842105263156,1.638784408569336,38.477964 +375,Binary classification,Aggregated Mondrian Forest,Phishing,0.8689839572192514,0.8482972136222912,1.7178211212158203,45.357054 +400,Binary classification,Aggregated Mondrian Forest,Phishing,0.8671679197994987,0.8436578171091446,1.7941875457763672,52.888585 +425,Binary classification,Aggregated Mondrian Forest,Phishing,0.8702830188679245,0.8433048433048433,1.8353633880615234,61.095765 +450,Binary classification,Aggregated Mondrian Forest,Phishing,0.8730512249443207,0.8455284552845528,1.9096240997314453,70.024579 +475,Binary classification,Aggregated Mondrian Forest,Phishing,0.8755274261603375,0.8506329113924052,1.988790512084961,79.720297 +500,Binary classification,Aggregated Mondrian Forest,Phishing,0.875751503006012,0.8530805687203792,2.063833236694336,90.07863400000001 +525,Binary classification,Aggregated Mondrian Forest,Phishing,0.8778625954198473,0.8525345622119817,2.144712448120117,101.25810100000001 +550,Binary classification,Aggregated Mondrian Forest,Phishing,0.8779599271402551,0.8533916849015317,2.1996402740478516,113.25181900000001 +575,Binary classification,Aggregated Mondrian Forest,Phishing,0.8780487804878049,0.8535564853556484,2.2528209686279297,125.93584100000001 +600,Binary classification,Aggregated Mondrian Forest,Phishing,0.8797996661101837,0.8536585365853657,2.283121109008789,139.44840100000002 +625,Binary classification,Aggregated Mondrian Forest,Phishing,0.8814102564102564,0.852589641434263,2.343900680541992,153.77905700000002 +650,Binary classification,Aggregated Mondrian Forest,Phishing,0.884437596302003,0.8587570621468926,2.418844223022461,168.92061400000003 +675,Binary classification,Aggregated Mondrian Forest,Phishing,0.884272997032641,0.8617021276595745,2.468423843383789,184.94000100000002 +700,Binary classification,Aggregated Mondrian Forest,Phishing,0.8884120171673819,0.8650519031141869,2.478273391723633,201.76583000000002 +725,Binary classification,Aggregated Mondrian Forest,Phishing,0.8895027624309392,0.8684210526315789,2.5243663787841797,219.457713 +750,Binary classification,Aggregated Mondrian Forest,Phishing,0.8918558077436582,0.8716323296354993,2.5813236236572266,238.014124 +775,Binary classification,Aggregated Mondrian Forest,Phishing,0.8914728682170543,0.8707692307692307,2.6200389862060547,257.461391 +800,Binary classification,Aggregated Mondrian Forest,Phishing,0.8898623279098874,0.8702064896755163,2.657014846801758,277.779634 +825,Binary classification,Aggregated Mondrian Forest,Phishing,0.8907766990291263,0.872159090909091,2.706361770629883,298.980548 +850,Binary classification,Aggregated Mondrian Forest,Phishing,0.8928150765606596,0.8741355463347164,2.730466842651367,321.097396 +875,Binary classification,Aggregated Mondrian Forest,Phishing,0.8958810068649885,0.8771929824561403,2.7533512115478516,344.186724 +900,Binary classification,Aggregated Mondrian Forest,Phishing,0.8976640711902113,0.8786279683377309,2.807779312133789,368.101507 +925,Binary classification,Aggregated Mondrian Forest,Phishing,0.9004329004329005,0.8829516539440204,2.8523120880126953,392.98062400000003 +950,Binary classification,Aggregated Mondrian Forest,Phishing,0.9009483667017913,0.8850855745721271,2.913583755493164,418.83123200000006 +975,Binary classification,Aggregated Mondrian Forest,Phishing,0.9024640657084189,0.8867699642431467,2.943540573120117,445.63277700000003 +1000,Binary classification,Aggregated Mondrian Forest,Phishing,0.9009009009009009,0.8850174216027874,2.9903697967529297,473.39902800000004 +1025,Binary classification,Aggregated Mondrian Forest,Phishing,0.8994140625,0.8836158192090395,3.035707473754883,502.22467600000004 +1050,Binary classification,Aggregated Mondrian Forest,Phishing,0.9008579599618685,0.8857142857142858,3.069150924682617,532.049603 +1075,Binary classification,Aggregated Mondrian Forest,Phishing,0.9013035381750466,0.8869936034115138,3.114839553833008,562.838704 +1100,Binary classification,Aggregated Mondrian Forest,Phishing,0.9035486806187443,0.8898128898128899,3.132375717163086,594.67778 +1125,Binary classification,Aggregated Mondrian Forest,Phishing,0.905693950177936,0.8933601609657947,3.1889095306396484,627.518257 +1150,Binary classification,Aggregated Mondrian Forest,Phishing,0.9060052219321149,0.893491124260355,3.220029830932617,661.4048929999999 +1175,Binary classification,Aggregated Mondrian Forest,Phishing,0.9045996592844975,0.8916827852998066,3.270620346069336,696.4079739999999 +1200,Binary classification,Aggregated Mondrian Forest,Phishing,0.9040867389491243,0.8909952606635072,3.311410903930664,732.4743999999998 +1225,Binary classification,Aggregated Mondrian Forest,Phishing,0.9044117647058824,0.8911627906976743,3.344022750854492,769.4892029999999 +1250,Binary classification,Aggregated Mondrian Forest,Phishing,0.9047237790232185,0.8921124206708976,3.391061782836914,807.5726659999999 +1903,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,2.745403 +3806,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,8.183125 +5709,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,16.539666 +7612,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,27.755785000000003 +9515,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,41.777067 +11418,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,58.637769000000006 +13321,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,78.268206 +15224,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998686198515404,0.9090909090909091,0.09231853485107422,101.443914 +17127,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998832184981898,0.9230769230769231,0.09723186492919922,131.805417 +19030,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998948972620737,0.9230769230769231,0.09728145599365234,169.246217 +20933,Binary classification,Aggregated Mondrian Forest,SMTP,0.999904452512899,0.9230769230769231,0.09728145599365234,213.148727 +22836,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999124151521787,0.9230769230769231,0.09728145599365234,263.357684 +24739,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999191527205109,0.9230769230769231,0.09730815887451172,319.49775 +26642,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998873916144289,0.888888888888889,0.10914134979248047,381.401703 +28545,Binary classification,Aggregated Mondrian Forest,SMTP,0.999894899103139,0.888888888888889,0.10916423797607422,448.60874 +30448,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999014681249384,0.888888888888889,0.10916423797607422,520.91477 +32351,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999072642967543,0.888888888888889,0.10966777801513672,598.09858 +34254,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999124164306776,0.888888888888889,0.11131954193115234,680.064697 +36157,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999170262197146,0.888888888888889,0.11127376556396484,766.82968 +38060,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999211750177356,0.888888888888889,0.11127376556396484,858.2478070000001 +39963,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999249286822481,0.888888888888889,0.11127376556396484,954.233503 +41866,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999283410963812,0.888888888888889,0.11127376556396484,1054.7914 +43769,Binary classification,Aggregated Mondrian Forest,SMTP,0.999931456772071,0.888888888888889,0.11127376556396484,1159.6703400000001 +45672,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999343128024348,0.888888888888889,0.11127376556396484,1268.4432900000002 +47575,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999369403455669,0.888888888888889,0.1298818588256836,1381.2685860000001 +49478,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999393657659115,0.888888888888889,0.1299276351928711,1498.4984390000002 +51381,Binary classification,Aggregated Mondrian Forest,SMTP,0.999941611521993,0.9032258064516129,0.14348888397216797,1620.0599740000002 +53284,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999436968639153,0.9032258064516129,0.14348888397216797,1745.8256190000002 +55187,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999456383865473,0.9032258064516129,0.14403820037841797,1875.6542580000003 +57090,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998248349068998,0.7619047619047621,0.1476888656616211,2010.2723030000002 +58993,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998304854895579,0.7619047619047621,0.15108394622802734,2149.2802330000004 +60896,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998357829050004,0.7619047619047621,0.1510610580444336,2292.3763450000006 +62799,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998089111118188,0.7272727272727272,0.15114116668701172,2439.4913830000005 +64702,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998145314601011,0.7272727272727272,0.15341472625732422,2590.5360980000005 +66605,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998198306408024,0.7272727272727272,0.1576833724975586,2745.6113380000006 +68508,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998248354182784,0.75,0.1762075424194336,2905.2725530000007 +70411,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998295696634001,0.75,0.1762075424194336,3069.514522000001 +72314,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998340547342801,0.75,0.1762075424194336,3238.428366000001 +74217,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998383097984263,0.75,0.17613887786865234,3411.935267000001 +76120,Binary classification,Aggregated Mondrian Forest,SMTP,0.99984235210657,0.75,0.1760702133178711,3590.1136440000014 +78023,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998461972264233,0.75,0.1782979965209961,3773.0849660000013 +79926,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998498592430404,0.75,0.1782979965209961,3960.4867140000015 +81829,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998533509312216,0.75,0.1782979965209961,4152.338698000001 +83732,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998566839044082,0.75,0.17832088470458984,4348.642178000001 +85635,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998598687437232,0.75,0.17832088470458984,4549.410423000001 +87538,Binary classification,Aggregated Mondrian Forest,SMTP,0.999862915110182,0.75,0.17832088470458984,4754.622131000001 +89441,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998546511627907,0.7346938775510204,0.17834758758544922,4964.315109000001 +91344,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998576792967168,0.7346938775510204,0.19727230072021484,5178.776489000001 +93247,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998605838320143,0.7346938775510204,0.21181774139404297,5398.511461000001 +95150,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998633721846788,0.7346938775510204,0.21174907684326172,5623.669278000001 +95156,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998633807997478,0.7346938775510204,0.21174907684326172,5848.865968000001 +106,Binary classification,ALMA,Bananas,0.5377358490566038,0.5242718446601942,0.0028944015502929688,0.039715 +212,Binary classification,ALMA,Bananas,0.5330188679245284,0.5217391304347825,0.0028944015502929688,0.180531 +318,Binary classification,ALMA,Bananas,0.5188679245283019,0.5173501577287066,0.0029211044311523438,0.33386499999999997 +424,Binary classification,ALMA,Bananas,0.5330188679245284,0.5330188679245282,0.0029211044311523438,0.49377399999999994 +530,Binary classification,ALMA,Bananas,0.5207547169811321,0.5115384615384615,0.0029211044311523438,0.7446539999999999 +636,Binary classification,ALMA,Bananas,0.5377358490566038,0.5303514376996804,0.0029211044311523438,1.03169 +742,Binary classification,ALMA,Bananas,0.522911051212938,0.512396694214876,0.0029211044311523438,1.379859 +848,Binary classification,ALMA,Bananas,0.5235849056603774,0.5061124694376529,0.0029211044311523438,1.737155 +954,Binary classification,ALMA,Bananas,0.5157232704402516,0.5,0.0029211044311523438,2.173505 +1060,Binary classification,ALMA,Bananas,0.5160377358490567,0.4975514201762978,0.0029211044311523438,2.70321 +1166,Binary classification,ALMA,Bananas,0.5154373927958834,0.49598572702943805,0.0029211044311523438,3.270309 +1272,Binary classification,ALMA,Bananas,0.5165094339622641,0.4979591836734694,0.0029211044311523438,3.844268 +1378,Binary classification,ALMA,Bananas,0.5195936139332366,0.4977238239757208,0.0029211044311523438,4.501151 +1484,Binary classification,ALMA,Bananas,0.5195417789757413,0.4968242766407903,0.0029211044311523438,5.229491 +1590,Binary classification,ALMA,Bananas,0.5226415094339623,0.4983476536682089,0.0029211044311523438,6.030342 +1696,Binary classification,ALMA,Bananas,0.5194575471698113,0.49473031618102914,0.0029211044311523438,6.8837410000000006 +1802,Binary classification,ALMA,Bananas,0.5205327413984462,0.4965034965034965,0.0029211044311523438,7.813207 +1908,Binary classification,ALMA,Bananas,0.5193920335429769,0.4964305326743548,0.0029211044311523438,8.751116 +2014,Binary classification,ALMA,Bananas,0.519364448857994,0.4989648033126293,0.0029211044311523438,9.762632 +2120,Binary classification,ALMA,Bananas,0.5174528301886793,0.4997555012224939,0.0029211044311523438,10.806008 +2226,Binary classification,ALMA,Bananas,0.5197663971248877,0.5002337540906966,0.0029211044311523438,11.968014 +2332,Binary classification,ALMA,Bananas,0.5175814751286449,0.4975435462259938,0.0029211044311523438,13.16512 +2438,Binary classification,ALMA,Bananas,0.5176374077112387,0.4957118353344769,0.0029211044311523438,14.408045 +2544,Binary classification,ALMA,Bananas,0.5196540880503144,0.5008169934640523,0.0029211044311523438,15.661105 +2650,Binary classification,ALMA,Bananas,0.520377358490566,0.5037094884810621,0.0029211044311523438,17.014893999999998 +2756,Binary classification,ALMA,Bananas,0.521044992743106,0.5041322314049587,0.0029211044311523438,18.454389 +2862,Binary classification,ALMA,Bananas,0.5213137665967854,0.5032632342277013,0.0029211044311523438,19.942263 +2968,Binary classification,ALMA,Bananas,0.5175202156334232,0.49859943977591037,0.0029211044311523438,21.473074 +3074,Binary classification,ALMA,Bananas,0.5152895250487963,0.49696151249155973,0.0029211044311523438,23.106855 +3180,Binary classification,ALMA,Bananas,0.5132075471698113,0.4931237721021611,0.0029211044311523438,24.747764 +3286,Binary classification,ALMA,Bananas,0.5130858186244674,0.4927076727964489,0.0029211044311523438,26.464385 +3392,Binary classification,ALMA,Bananas,0.5103183962264151,0.49095923996322405,0.0029211044311523438,28.215584 +3498,Binary classification,ALMA,Bananas,0.5091480846197828,0.48914013686402846,0.0029211044311523438,30.068049 +3604,Binary classification,ALMA,Bananas,0.5097114317425083,0.4876775877065816,0.0029211044311523438,31.96012 +3710,Binary classification,ALMA,Bananas,0.5118598382749326,0.49086308687095864,0.0029211044311523438,33.864206 +3816,Binary classification,ALMA,Bananas,0.510482180293501,0.4893384363039912,0.0029211044311523438,35.803291 +3922,Binary classification,ALMA,Bananas,0.50790413054564,0.48588172615876407,0.0029211044311523438,37.844614 +4028,Binary classification,ALMA,Bananas,0.506454816285998,0.48443983402489627,0.0029211044311523438,39.968017 +4134,Binary classification,ALMA,Bananas,0.5050798258345428,0.48281092012133464,0.0029211044311523438,42.128298 +4240,Binary classification,ALMA,Bananas,0.5068396226415094,0.48484848484848486,0.0029211044311523438,44.30306 +4346,Binary classification,ALMA,Bananas,0.5080533824206167,0.4858104858104858,0.0029211044311523438,46.485881 +4452,Binary classification,ALMA,Bananas,0.5080862533692723,0.4847058823529412,0.0029211044311523438,48.746465 +4558,Binary classification,ALMA,Bananas,0.5063624396665204,0.48370812299219823,0.0029211044311523438,51.058035000000004 +4664,Binary classification,ALMA,Bananas,0.5051457975986278,0.4829749103942652,0.0029211044311523438,53.475871000000005 +4770,Binary classification,ALMA,Bananas,0.5048218029350104,0.48201754385964907,0.0029211044311523438,55.900409 +4876,Binary classification,ALMA,Bananas,0.5036915504511895,0.4802405498281787,0.0029211044311523438,58.409701000000005 +4982,Binary classification,ALMA,Bananas,0.5038137294259334,0.4811083123425693,0.0029211044311523438,60.970617000000004 +5088,Binary classification,ALMA,Bananas,0.5029481132075472,0.47995064774830354,0.0029211044311523438,63.61249900000001 +5194,Binary classification,ALMA,Bananas,0.5040431266846361,0.4810636583400483,0.0029211044311523438,66.28843800000001 +5300,Binary classification,ALMA,Bananas,0.5064150943396226,0.4825949367088608,0.0029211044311523438,68.97313600000001 +906,Binary classification,ALMA,Elec2,0.9072847682119205,0.9056179775280899,0.0043582916259765625,0.679052 +1812,Binary classification,ALMA,Elec2,0.9166666666666666,0.8967874231032126,0.0043582916259765625,1.978643 +2718,Binary classification,ALMA,Elec2,0.9175864606328182,0.898458748866727,0.0043582916259765625,3.929769 +3624,Binary classification,ALMA,Elec2,0.9268763796909493,0.9098945936756205,0.0043582916259765625,6.478699 +4530,Binary classification,ALMA,Elec2,0.9271523178807947,0.9076664801343034,0.0043582916259765625,9.702945 +5436,Binary classification,ALMA,Elec2,0.9269683590875644,0.9074376311494521,0.0043582916259765625,13.508006 +6342,Binary classification,ALMA,Elec2,0.9274676758120467,0.9089108910891088,0.0043582916259765625,17.915655 +7248,Binary classification,ALMA,Elec2,0.9254966887417219,0.9066390041493776,0.0043582916259765625,22.910275000000002 +8154,Binary classification,ALMA,Elec2,0.9251900907530046,0.9100294985250738,0.0043582916259765625,28.53571 +9060,Binary classification,ALMA,Elec2,0.9266004415011038,0.9135128105085185,0.0043582916259765625,34.833569000000004 +9966,Binary classification,ALMA,Elec2,0.9293598233995585,0.9182535996284256,0.0043582916259765625,41.779447000000005 +10872,Binary classification,ALMA,Elec2,0.931383370125092,0.9217208814270723,0.0043582916259765625,49.40882500000001 +11778,Binary classification,ALMA,Elec2,0.9313975208014943,0.9218568665377176,0.0043582916259765625,57.61408300000001 +12684,Binary classification,ALMA,Elec2,0.9290444654683065,0.9191665169750315,0.0043582916259765625,66.44160400000001 +13590,Binary classification,ALMA,Elec2,0.9298013245033112,0.9209872453205235,0.0043582916259765625,75.85703300000002 +14496,Binary classification,ALMA,Elec2,0.9305325607064018,0.9222213640225535,0.0043582916259765625,85.90938600000001 +15402,Binary classification,ALMA,Elec2,0.9308531359563693,0.922279792746114,0.0043582916259765625,96.62160000000002 +16308,Binary classification,ALMA,Elec2,0.9293598233995585,0.9203319502074688,0.0043582916259765625,107.96559300000001 +17214,Binary classification,ALMA,Elec2,0.9279656093877077,0.9176298658163943,0.0043582916259765625,119.98412300000001 +18120,Binary classification,ALMA,Elec2,0.9266004415011038,0.9160141449861076,0.0043582916259765625,132.63841200000002 +19026,Binary classification,ALMA,Elec2,0.9265741616734994,0.915143048047136,0.0043582916259765625,146.00166900000002 +19932,Binary classification,ALMA,Elec2,0.9262994180212724,0.9155892662184681,0.0043582916259765625,159.938555 +20838,Binary classification,ALMA,Elec2,0.9232171993473461,0.9122710823555213,0.0043582916259765625,174.54734100000002 +21744,Binary classification,ALMA,Elec2,0.9225073583517293,0.9101955977189149,0.0043582916259765625,189.73533200000003 +22650,Binary classification,ALMA,Elec2,0.9217218543046357,0.9087540528022233,0.0043582916259765625,205.61954500000002 +23556,Binary classification,ALMA,Elec2,0.9186619120393955,0.9050639183430779,0.0043582916259765625,222.16184900000002 +24462,Binary classification,ALMA,Elec2,0.9173003025100155,0.902885123133791,0.0043582916259765625,239.39901500000002 +25368,Binary classification,ALMA,Elec2,0.9144985808893094,0.8997643144322751,0.0043582916259765625,257.26822000000004 +26274,Binary classification,ALMA,Elec2,0.9142498287280201,0.8992622401073105,0.0043582916259765625,275.765482 +27180,Binary classification,ALMA,Elec2,0.9138337012509198,0.89909521757863,0.0043582916259765625,294.848784 +28086,Binary classification,ALMA,Elec2,0.9110232856227302,0.8955049132343716,0.0043582916259765625,314.617946 +28992,Binary classification,ALMA,Elec2,0.9101476269315674,0.8940927755417328,0.0043582916259765625,335.076303 +29898,Binary classification,ALMA,Elec2,0.9094922737306843,0.8931701539676272,0.0043582916259765625,356.063392 +30804,Binary classification,ALMA,Elec2,0.9083235943383976,0.8913093680240166,0.0043582916259765625,377.56682800000004 +31710,Binary classification,ALMA,Elec2,0.9062125512456638,0.8888722815933038,0.0043582916259765625,399.67833300000007 +32616,Binary classification,ALMA,Elec2,0.9052918812852587,0.8879294706671989,0.0043582916259765625,422.4669390000001 +33522,Binary classification,ALMA,Elec2,0.9050474315374978,0.8877050626212737,0.0043582916259765625,445.7904010000001 +34428,Binary classification,ALMA,Elec2,0.9050772626931567,0.8877901387172092,0.0043582916259765625,469.7481780000001 +35334,Binary classification,ALMA,Elec2,0.9045395369898681,0.8866104144955793,0.0043582916259765625,494.21097500000013 +36240,Binary classification,ALMA,Elec2,0.9048013245033113,0.8860483551327785,0.0043582916259765625,519.3392310000002 +37146,Binary classification,ALMA,Elec2,0.9045119259139611,0.8854217139903738,0.0043582916259765625,545.0643520000001 +38052,Binary classification,ALMA,Elec2,0.9042625880374224,0.8846092933388237,0.0043582916259765625,571.4397520000001 +38958,Binary classification,ALMA,Elec2,0.904409877303763,0.88502624266749,0.0043582916259765625,598.3711440000001 +39864,Binary classification,ALMA,Elec2,0.904926750953241,0.8863636363636365,0.0043582916259765625,626.0397580000001 +40770,Binary classification,ALMA,Elec2,0.9055187637969095,0.8878667908709827,0.0043582916259765625,654.3634580000002 +41676,Binary classification,ALMA,Elec2,0.9061090315769268,0.8892285916489738,0.0043582916259765625,683.2521370000002 +42582,Binary classification,ALMA,Elec2,0.9063923723639097,0.889810361032786,0.0043582916259765625,712.7682970000002 +43488,Binary classification,ALMA,Elec2,0.9067098969830758,0.8902534693104661,0.0043582916259765625,742.9022130000002 +44394,Binary classification,ALMA,Elec2,0.9062711177186106,0.8894732648019762,0.0043582916259765625,773.7300850000001 +45300,Binary classification,ALMA,Elec2,0.9064238410596026,0.8897844569823977,0.0043582916259765625,805.1132940000001 +45312,Binary classification,ALMA,Elec2,0.9064265536723164,0.8897670549084858,0.0043582916259765625,836.4982200000001 +25,Binary classification,ALMA,Phishing,0.56,0.5217391304347826,0.004366874694824219,0.003459 +50,Binary classification,ALMA,Phishing,0.7,0.6341463414634146,0.004366874694824219,0.050212 +75,Binary classification,ALMA,Phishing,0.7066666666666667,0.676470588235294,0.004366874694824219,0.100001 +100,Binary classification,ALMA,Phishing,0.72,0.702127659574468,0.004366874694824219,0.15312800000000001 +125,Binary classification,ALMA,Phishing,0.72,0.7058823529411765,0.004366874694824219,0.22806300000000002 +150,Binary classification,ALMA,Phishing,0.7133333333333334,0.7189542483660132,0.004366874694824219,0.334445 +175,Binary classification,ALMA,Phishing,0.7314285714285714,0.718562874251497,0.004366874694824219,0.511774 +200,Binary classification,ALMA,Phishing,0.735,0.7225130890052356,0.004366874694824219,0.692607 +225,Binary classification,ALMA,Phishing,0.7244444444444444,0.701923076923077,0.004366874694824219,0.876779 +250,Binary classification,ALMA,Phishing,0.724,0.7038626609442059,0.004366874694824219,1.156005 +275,Binary classification,ALMA,Phishing,0.7345454545454545,0.7137254901960783,0.004580497741699219,1.438242 +300,Binary classification,ALMA,Phishing,0.7366666666666667,0.7127272727272725,0.004580497741699219,1.723391 +325,Binary classification,ALMA,Phishing,0.7476923076923077,0.7172413793103447,0.004580497741699219,2.078902 +350,Binary classification,ALMA,Phishing,0.7542857142857143,0.7225806451612904,0.004580497741699219,2.4379969999999997 +375,Binary classification,ALMA,Phishing,0.7573333333333333,0.723404255319149,0.004580497741699219,2.8003189999999996 +400,Binary classification,ALMA,Phishing,0.76,0.7257142857142856,0.004580497741699219,3.1655669999999994 +425,Binary classification,ALMA,Phishing,0.76,0.7197802197802199,0.004580497741699219,3.585508999999999 +450,Binary classification,ALMA,Phishing,0.7622222222222222,0.7206266318537858,0.004580497741699219,4.009149999999999 +475,Binary classification,ALMA,Phishing,0.7663157894736842,0.7272727272727272,0.004580497741699219,4.435539999999999 +500,Binary classification,ALMA,Phishing,0.768,0.7327188940092165,0.004580497741699219,4.959426999999999 +525,Binary classification,ALMA,Phishing,0.7714285714285715,0.7321428571428573,0.004580497741699219,5.485955999999999 +550,Binary classification,ALMA,Phishing,0.7709090909090909,0.7341772151898734,0.004580497741699219,6.028689999999999 +575,Binary classification,ALMA,Phishing,0.7739130434782608,0.7379032258064516,0.004580497741699219,6.595545999999999 +600,Binary classification,ALMA,Phishing,0.78,0.7401574803149605,0.004580497741699219,7.165257999999999 +625,Binary classification,ALMA,Phishing,0.7744,0.7314285714285715,0.004580497741699219,7.741693999999999 +650,Binary classification,ALMA,Phishing,0.7815384615384615,0.7427536231884059,0.004580497741699219,8.363988999999998 +675,Binary classification,ALMA,Phishing,0.7837037037037037,0.75,0.004580497741699219,9.010548999999997 +700,Binary classification,ALMA,Phishing,0.79,0.7545909849749582,0.004580497741699219,9.660288999999997 +725,Binary classification,ALMA,Phishing,0.7917241379310345,0.7606973058637084,0.004580497741699219,10.349524999999996 +750,Binary classification,ALMA,Phishing,0.792,0.7621951219512195,0.004580497741699219,11.041959999999996 +775,Binary classification,ALMA,Phishing,0.792258064516129,0.7614814814814814,0.004580497741699219,11.737615999999996 +800,Binary classification,ALMA,Phishing,0.795,0.7670454545454546,0.004580497741699219,12.526707999999996 +825,Binary classification,ALMA,Phishing,0.793939393939394,0.7671232876712327,0.004580497741699219,13.319008999999996 +850,Binary classification,ALMA,Phishing,0.7976470588235294,0.7706666666666667,0.004580497741699219,14.117527999999997 +875,Binary classification,ALMA,Phishing,0.8022857142857143,0.7744458930899608,0.004580497741699219,14.959668999999996 +900,Binary classification,ALMA,Phishing,0.8011111111111111,0.7737041719342603,0.004580497741699219,15.804425999999996 +925,Binary classification,ALMA,Phishing,0.8054054054054054,0.7804878048780488,0.004580497741699219,16.651646999999997 +950,Binary classification,ALMA,Phishing,0.8073684210526316,0.7849588719153936,0.004580497741699219,17.502014999999997 +975,Binary classification,ALMA,Phishing,0.8102564102564103,0.7880870561282932,0.004580497741699219,18.418237999999995 +1000,Binary classification,ALMA,Phishing,0.811,0.7892976588628764,0.004580497741699219,19.337672999999995 +1025,Binary classification,ALMA,Phishing,0.8146341463414634,0.7943722943722944,0.004580497741699219,20.260238999999995 +1050,Binary classification,ALMA,Phishing,0.8161904761904762,0.7970557308096741,0.004580497741699219,21.242111999999995 +1075,Binary classification,ALMA,Phishing,0.815813953488372,0.7983706720977597,0.004580497741699219,22.243638999999995 +1100,Binary classification,ALMA,Phishing,0.8190909090909091,0.8023833167825224,0.004580497741699219,23.247954999999994 +1125,Binary classification,ALMA,Phishing,0.8213333333333334,0.8061716489874637,0.004580497741699219,24.273946999999993 +1150,Binary classification,ALMA,Phishing,0.8226086956521739,0.8071833648393195,0.004580497741699219,25.351544999999994 +1175,Binary classification,ALMA,Phishing,0.8212765957446808,0.8059149722735675,0.004580497741699219,26.431981999999994 +1200,Binary classification,ALMA,Phishing,0.8233333333333334,0.8076225045372051,0.004580497741699219,27.515220999999993 +1225,Binary classification,ALMA,Phishing,0.8244897959183674,0.8088888888888888,0.004580497741699219,28.636604999999992 +1250,Binary classification,ALMA,Phishing,0.8256,0.810763888888889,0.004580497741699219,29.761263999999994 +1903,Binary classification,ALMA,SMTP,0.720966894377299,0.0,0.003093719482421875,1.027868 +3806,Binary classification,ALMA,SMTP,0.7769311613242249,0.0,0.003093719482421875,3.106358 +5709,Binary classification,ALMA,SMTP,0.7509196006305833,0.0,0.003093719482421875,6.233245 +7612,Binary classification,ALMA,SMTP,0.7900683131897005,0.0,0.003093719482421875,10.302873 +9515,Binary classification,ALMA,SMTP,0.7826589595375723,0.0,0.003093719482421875,15.393504 +11418,Binary classification,ALMA,SMTP,0.7699246803293046,0.0,0.003093719482421875,21.578682 +13321,Binary classification,ALMA,SMTP,0.7722393213722694,0.0,0.003093719482421875,28.779608 +15224,Binary classification,ALMA,SMTP,0.7791644771413557,0.004146919431279621,0.003093719482421875,37.113003 +17127,Binary classification,ALMA,SMTP,0.783207800548841,0.004824443848834093,0.003093719482421875,46.3898 +19030,Binary classification,ALMA,SMTP,0.7891224382553862,0.004465393202679235,0.003093719482421875,56.715322 +20933,Binary classification,ALMA,SMTP,0.7832131084889887,0.003950834064969272,0.003093719482421875,68.217717 +22836,Binary classification,ALMA,SMTP,0.7821422315641969,0.0036050470658922497,0.003093719482421875,80.77427399999999 +24739,Binary classification,ALMA,SMTP,0.7877440478596548,0.0034162080091098878,0.003093719482421875,94.43257899999999 +26642,Binary classification,ALMA,SMTP,0.78188574431349,0.003429943405933802,0.003093719482421875,109.06303 +28545,Binary classification,ALMA,SMTP,0.7857418111753371,0.003259452411994785,0.003093719482421875,124.719605 +30448,Binary classification,ALMA,SMTP,0.7871452968996322,0.0030764497769573914,0.003093719482421875,141.369597 +32351,Binary classification,ALMA,SMTP,0.7866835646502427,0.0028897558156335793,0.003093719482421875,159.093537 +34254,Binary classification,ALMA,SMTP,0.7860979739592456,0.0027221995372260785,0.003093719482421875,177.829964 +36157,Binary classification,ALMA,SMTP,0.7771939043615345,0.0024764735017335313,0.003093719482421875,197.576045 +38060,Binary classification,ALMA,SMTP,0.7831581713084603,0.00241750271969056,0.003093719482421875,218.31617 +39963,Binary classification,ALMA,SMTP,0.779496033831294,0.002264492753623189,0.003093719482421875,240.067789 +41866,Binary classification,ALMA,SMTP,0.7831175655663307,0.0021978021978021974,0.003093719482421875,262.83473100000003 +43769,Binary classification,ALMA,SMTP,0.7791130708949257,0.002064409578860446,0.003093719482421875,286.65613700000006 +45672,Binary classification,ALMA,SMTP,0.7808066211245402,0.001993819160602133,0.003093719482421875,311.45044200000007 +47575,Binary classification,ALMA,SMTP,0.7799684708355229,0.001906941266209001,0.003093719482421875,337.28748800000005 +49478,Binary classification,ALMA,SMTP,0.7778810784591131,0.00181653042688465,0.003093719482421875,364.14950600000003 +51381,Binary classification,ALMA,SMTP,0.7807944570950351,0.0021263400372109505,0.003093719482421875,392.065506 +53284,Binary classification,ALMA,SMTP,0.7777193904361535,0.0020222446916076846,0.003093719482421875,421.04388200000005 +55187,Binary classification,ALMA,SMTP,0.7785891604906953,0.0019603038470963,0.003093719482421875,451.02283200000005 +57090,Binary classification,ALMA,SMTP,0.7758801891749869,0.002650245537454205,0.003093719482421875,482.06238400000007 +58993,Binary classification,ALMA,SMTP,0.774159646059702,0.002545481769858501,0.003093719482421875,514.138052 +60896,Binary classification,ALMA,SMTP,0.7746157383079348,0.002471109819027546,0.003093719482421875,547.227498 +62799,Binary classification,ALMA,SMTP,0.7704899759550311,0.0023534297778085413,0.003093719482421875,581.3499069999999 +64702,Binary classification,ALMA,SMTP,0.771274458285679,0.0022921863412660956,0.003093719482421875,616.580127 +66605,Binary classification,ALMA,SMTP,0.7721942797087306,0.002235812454790557,0.003093719482421875,652.800029 +68508,Binary classification,ALMA,SMTP,0.7705085537455479,0.0024111675126903555,0.003093719482421875,690.043858 +70411,Binary classification,ALMA,SMTP,0.7685872945988553,0.0023267205486162137,0.003093719482421875,728.276014 +72314,Binary classification,ALMA,SMTP,0.7687999557485411,0.002267709017127171,0.003093719482421875,767.526171 +74217,Binary classification,ALMA,SMTP,0.7657140547313958,0.0021806496040399402,0.003093719482421875,807.759105 +76120,Binary classification,ALMA,SMTP,0.7665002627430373,0.0021333932180552435,0.003093719482421875,848.897716 +78023,Binary classification,ALMA,SMTP,0.7657101111210797,0.002074462277541216,0.003093719482421875,890.953712 +79926,Binary classification,ALMA,SMTP,0.7636313590070816,0.002007395668251453,0.003093719482421875,933.942273 +81829,Binary classification,ALMA,SMTP,0.7647777682728617,0.001970341180130665,0.003093719482421875,977.888636 +83732,Binary classification,ALMA,SMTP,0.7652868676252806,0.0019298156518206286,0.003093719482421875,1022.739679 +85635,Binary classification,ALMA,SMTP,0.7642552694575816,0.0018787699001285476,0.003093719482421875,1068.5586680000001 +87538,Binary classification,ALMA,SMTP,0.7644680024674998,0.0018396591789310612,0.003093719482421875,1115.337297 +89441,Binary classification,ALMA,SMTP,0.7635312664214399,0.0018876828692779614,0.003093719482421875,1162.988807 +91344,Binary classification,ALMA,SMTP,0.7650091960063058,0.0018600325505696352,0.003093719482421875,1211.563326 +93247,Binary classification,ALMA,SMTP,0.7647859984771628,0.0018204159650480134,0.003093719482421875,1260.984755 +95150,Binary classification,ALMA,SMTP,0.7649710982658959,0.0017854751595768425,0.003093719482421875,1311.295723 +95156,Binary classification,ALMA,SMTP,0.7649859178611963,0.0017854751595768425,0.003093719482421875,1361.607838 +106,Binary classification,sklearn SGDClassifier,Bananas,0.5283018867924528,0.4680851063829788,0.005551338195800781,0.50714 +212,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.4673913043478261,0.005551338195800781,1.5449950000000001 +318,Binary classification,sklearn SGDClassifier,Bananas,0.5345911949685535,0.4861111111111111,0.005578041076660156,3.028568 +424,Binary classification,sklearn SGDClassifier,Bananas,0.5188679245283019,0.46596858638743455,0.005578041076660156,4.996646 +530,Binary classification,sklearn SGDClassifier,Bananas,0.5264150943396226,0.42562929061784893,0.005578041076660156,7.439068000000001 +636,Binary classification,sklearn SGDClassifier,Bananas,0.5235849056603774,0.3878787878787879,0.005578041076660156,10.329429000000001 +742,Binary classification,sklearn SGDClassifier,Bananas,0.5363881401617251,0.36296296296296293,0.005578041076660156,13.751562000000002 +848,Binary classification,sklearn SGDClassifier,Bananas,0.5400943396226415,0.33898305084745767,0.005578041076660156,17.710995 +954,Binary classification,sklearn SGDClassifier,Bananas,0.5440251572327044,0.31496062992125984,0.005578041076660156,22.189814 +1060,Binary classification,sklearn SGDClassifier,Bananas,0.5518867924528302,0.2962962962962963,0.005578041076660156,27.154881999999997 +1166,Binary classification,sklearn SGDClassifier,Bananas,0.5523156089193825,0.27900552486187846,0.005578041076660156,32.629664 +1272,Binary classification,sklearn SGDClassifier,Bananas,0.5542452830188679,0.27586206896551724,0.005578041076660156,38.64774 +1378,Binary classification,sklearn SGDClassifier,Bananas,0.5566037735849056,0.2611850060459492,0.005578041076660156,45.182197 +1484,Binary classification,sklearn SGDClassifier,Bananas,0.557277628032345,0.24742268041237112,0.005578041076660156,52.237503000000004 +1590,Binary classification,sklearn SGDClassifier,Bananas,0.5578616352201258,0.23503808487486397,0.005578041076660156,59.74126700000001 +1696,Binary classification,sklearn SGDClassifier,Bananas,0.5595518867924528,0.22590673575129533,0.005578041076660156,67.72058500000001 +1802,Binary classification,sklearn SGDClassifier,Bananas,0.5566037735849056,0.21589793915603536,0.005578041076660156,76.17010700000002 +1908,Binary classification,sklearn SGDClassifier,Bananas,0.5545073375262054,0.21150278293135436,0.005578041076660156,85.08376800000002 +2014,Binary classification,sklearn SGDClassifier,Bananas,0.5496524329692155,0.20088105726872246,0.005578041076660156,94.42289500000003 +2120,Binary classification,sklearn SGDClassifier,Bananas,0.5466981132075471,0.19446772841575857,0.005578041076660156,104.22299500000003 +2226,Binary classification,sklearn SGDClassifier,Bananas,0.550314465408805,0.2036595067621321,0.005578041076660156,114.52672400000003 +2332,Binary classification,sklearn SGDClassifier,Bananas,0.5493138936535163,0.21036814425244177,0.005578041076660156,125.29025300000004 +2438,Binary classification,sklearn SGDClassifier,Bananas,0.5479901558654635,0.21173104434907009,0.005578041076660156,136.49837800000003 +2544,Binary classification,sklearn SGDClassifier,Bananas,0.5483490566037735,0.22626262626262625,0.005578041076660156,148.22051100000004 +2650,Binary classification,sklearn SGDClassifier,Bananas,0.5460377358490566,0.2322910019144863,0.005578041076660156,160.35879600000004 +2756,Binary classification,sklearn SGDClassifier,Bananas,0.5395500725689405,0.23044269254093389,0.005578041076660156,173.00042100000005 +2862,Binary classification,sklearn SGDClassifier,Bananas,0.5394828791055206,0.2310385064177363,0.005578041076660156,186.18470300000004 +2968,Binary classification,sklearn SGDClassifier,Bananas,0.5411051212938005,0.23050847457627116,0.005578041076660156,199.85251500000004 +3074,Binary classification,sklearn SGDClassifier,Bananas,0.5396877033181522,0.227198252321136,0.005578041076660156,214.04388100000003 +3180,Binary classification,sklearn SGDClassifier,Bananas,0.5430817610062894,0.22835900159320233,0.005578041076660156,228.72034700000003 +3286,Binary classification,sklearn SGDClassifier,Bananas,0.5444309190505173,0.22475401346452614,0.005578041076660156,243.83191300000004 +3392,Binary classification,sklearn SGDClassifier,Bananas,0.5445165094339622,0.22478675363773204,0.005578041076660156,259.480064 +3498,Binary classification,sklearn SGDClassifier,Bananas,0.5463121783876501,0.22014742014742014,0.005578041076660156,275.54602900000003 +3604,Binary classification,sklearn SGDClassifier,Bananas,0.548834628190899,0.21676300578034682,0.005578041076660156,292.10262700000004 +3710,Binary classification,sklearn SGDClassifier,Bananas,0.547978436657682,0.21230624706434945,0.005578041076660156,309.14156900000006 +3816,Binary classification,sklearn SGDClassifier,Bananas,0.5474318658280922,0.20743460302891234,0.005578041076660156,326.6693460000001 +3922,Binary classification,sklearn SGDClassifier,Bananas,0.5484446710861806,0.20332883490778228,0.005578041076660156,344.5959390000001 +4028,Binary classification,sklearn SGDClassifier,Bananas,0.5489076464746773,0.1992066989863376,0.005578041076660156,363.0622570000001 +4134,Binary classification,sklearn SGDClassifier,Bananas,0.5491049830672472,0.19516407599309155,0.005578041076660156,382.0101650000001 +4240,Binary classification,sklearn SGDClassifier,Bananas,0.5483490566037735,0.19095901985635824,0.005578041076660156,401.4423870000001 +4346,Binary classification,sklearn SGDClassifier,Bananas,0.548550391164289,0.18858560794044665,0.005578041076660156,421.3360060000001 +4452,Binary classification,sklearn SGDClassifier,Bananas,0.550763701707098,0.1935483870967742,0.005578041076660156,441.6814400000001 +4558,Binary classification,sklearn SGDClassifier,Bananas,0.5482667836770513,0.19349784567175873,0.005578041076660156,462.3969810000001 +4664,Binary classification,sklearn SGDClassifier,Bananas,0.5490994854202401,0.19763449065242272,0.005578041076660156,483.6474930000001 +4770,Binary classification,sklearn SGDClassifier,Bananas,0.550104821802935,0.19985085756897839,0.005578041076660156,505.4107300000001 +4876,Binary classification,sklearn SGDClassifier,Bananas,0.5504511894995898,0.2,0.005578041076660156,527.6413840000001 +4982,Binary classification,sklearn SGDClassifier,Bananas,0.5503813729425934,0.2062367115520907,0.005578041076660156,550.3366250000001 +5088,Binary classification,sklearn SGDClassifier,Bananas,0.5479559748427673,0.20415224913494812,0.005578041076660156,573.5279610000001 +5194,Binary classification,sklearn SGDClassifier,Bananas,0.5462071621101271,0.20236886632825718,0.005578041076660156,597.2511250000001 +5300,Binary classification,sklearn SGDClassifier,Bananas,0.5464150943396227,0.205026455026455,0.005578041076660156,621.4261850000001 +906,Binary classification,sklearn SGDClassifier,Elec2,0.8002207505518764,0.7868080094228505,0.006801605224609375,4.395754 +1812,Binary classification,sklearn SGDClassifier,Elec2,0.8140176600441501,0.7501853224610822,0.006801605224609375,13.314942 +2718,Binary classification,sklearn SGDClassifier,Elec2,0.8005886681383371,0.7262626262626262,0.006801605224609375,26.594138 +3624,Binary classification,sklearn SGDClassifier,Elec2,0.8189845474613686,0.7586460632818247,0.006801605224609375,44.068779 +4530,Binary classification,sklearn SGDClassifier,Elec2,0.8278145695364238,0.7588126159554731,0.006801605224609375,65.924464 +5436,Binary classification,sklearn SGDClassifier,Elec2,0.8211920529801324,0.7498713329902212,0.006801605224609375,92.07692 +6342,Binary classification,sklearn SGDClassifier,Elec2,0.8222958057395143,0.7575822757582275,0.006801605224609375,122.546282 +7248,Binary classification,sklearn SGDClassifier,Elec2,0.8253311258278145,0.7598634294385433,0.006801605224609375,157.279906 +8154,Binary classification,sklearn SGDClassifier,Elec2,0.8303899926416483,0.780789348549691,0.006801605224609375,196.124663 +9060,Binary classification,sklearn SGDClassifier,Elec2,0.8364238410596027,0.7958677685950413,0.006801605224609375,238.938569 +9966,Binary classification,sklearn SGDClassifier,Elec2,0.8371462974111981,0.8011273128293102,0.006801605224609375,285.711115 +10872,Binary classification,sklearn SGDClassifier,Elec2,0.8393119941133186,0.8079586676926458,0.006801605224609375,336.134661 +11778,Binary classification,sklearn SGDClassifier,Elec2,0.8422482594668025,0.8114088509947219,0.006801605224609375,390.124895 +12684,Binary classification,sklearn SGDClassifier,Elec2,0.8409019236833807,0.810445237647943,0.006801605224609375,447.475874 +13590,Binary classification,sklearn SGDClassifier,Elec2,0.8427520235467255,0.8154098643862832,0.006801605224609375,507.886031 +14496,Binary classification,sklearn SGDClassifier,Elec2,0.8438189845474614,0.8177720540888602,0.006801605224609375,571.200551 +15402,Binary classification,sklearn SGDClassifier,Elec2,0.845214907154915,0.8184587267742918,0.006801605224609375,637.3575030000001 +16308,Binary classification,sklearn SGDClassifier,Elec2,0.8397105714986509,0.8108264582428716,0.006801605224609375,706.2352450000001 +17214,Binary classification,sklearn SGDClassifier,Elec2,0.8384454513767864,0.8053202660133008,0.006801605224609375,777.6642180000001 +18120,Binary classification,sklearn SGDClassifier,Elec2,0.840728476821192,0.8082646824342281,0.006801605224609375,851.7523750000001 +19026,Binary classification,sklearn SGDClassifier,Elec2,0.843950383685483,0.8100326316462987,0.006801605224609375,928.4036970000002 +19932,Binary classification,sklearn SGDClassifier,Elec2,0.8412101143889223,0.8075636894266431,0.006801605224609375,1007.5500400000002 +20838,Binary classification,sklearn SGDClassifier,Elec2,0.8373644303675977,0.8028848950154133,0.006801605224609375,1089.2694250000002 +21744,Binary classification,sklearn SGDClassifier,Elec2,0.8382542310522443,0.8008155405788072,0.006801605224609375,1173.5296240000002 +22650,Binary classification,sklearn SGDClassifier,Elec2,0.8376600441501104,0.7982441700960219,0.006801605224609375,1260.4177460000003 +23556,Binary classification,sklearn SGDClassifier,Elec2,0.8337578536254033,0.7924748277689453,0.006801605224609375,1349.7468710000003 +24462,Binary classification,sklearn SGDClassifier,Elec2,0.8313302264737144,0.7887569117345894,0.006801605224609375,1441.5955620000002 +25368,Binary classification,sklearn SGDClassifier,Elec2,0.8278539892778304,0.7842711060613546,0.006801605224609375,1535.8662290000002 +26274,Binary classification,sklearn SGDClassifier,Elec2,0.8282712948161681,0.784486052732136,0.006801605224609375,1632.5910050000002 +27180,Binary classification,sklearn SGDClassifier,Elec2,0.8285504047093452,0.785431439359057,0.006801605224609375,1731.7068200000003 +28086,Binary classification,sklearn SGDClassifier,Elec2,0.825357829523606,0.7809192013935414,0.006801605224609375,1833.2277320000003 +28992,Binary classification,sklearn SGDClassifier,Elec2,0.8246412803532008,0.7785135488368041,0.006801605224609375,1936.9708820000003 +29898,Binary classification,sklearn SGDClassifier,Elec2,0.8228644056458626,0.7766343315056937,0.006801605224609375,2042.9068730000004 +30804,Binary classification,sklearn SGDClassifier,Elec2,0.8227827554863005,0.775599128540305,0.006801605224609375,2150.889688 +31710,Binary classification,sklearn SGDClassifier,Elec2,0.8180384736676127,0.7686632988533396,0.006801605224609375,2260.9522580000003 +32616,Binary classification,sklearn SGDClassifier,Elec2,0.8156119695854795,0.765426320305796,0.006801605224609375,2373.012802 +33522,Binary classification,sklearn SGDClassifier,Elec2,0.8136746017540719,0.7636955205811137,0.006801605224609375,2487.1515040000004 +34428,Binary classification,sklearn SGDClassifier,Elec2,0.8108516323922389,0.7597934341571375,0.006801605224609375,2603.2748090000005 +35334,Binary classification,sklearn SGDClassifier,Elec2,0.811031867323258,0.7582811425261557,0.006801605224609375,2721.3856460000006 +36240,Binary classification,sklearn SGDClassifier,Elec2,0.8123344370860928,0.7585643792821898,0.006801605224609375,2841.498866000001 +37146,Binary classification,sklearn SGDClassifier,Elec2,0.8119312981209282,0.7567887480852249,0.006801605224609375,2963.634415000001 +38052,Binary classification,sklearn SGDClassifier,Elec2,0.8118364343529907,0.7562636165577343,0.006801605224609375,3087.755988000001 +38958,Binary classification,sklearn SGDClassifier,Elec2,0.8128497356127111,0.7583601232890332,0.006801605224609375,3213.7594090000007 +39864,Binary classification,sklearn SGDClassifier,Elec2,0.8136162954043749,0.7616297722168751,0.006801605224609375,3341.6530200000007 +40770,Binary classification,sklearn SGDClassifier,Elec2,0.8154034829531518,0.7662732919254659,0.006801605224609375,3471.3422760000008 +41676,Binary classification,sklearn SGDClassifier,Elec2,0.8169929935694404,0.7702641645832705,0.006801605224609375,3602.9648230000007 +42582,Binary classification,sklearn SGDClassifier,Elec2,0.8180216993095675,0.7720681236579698,0.006801605224609375,3737.0799900000006 +43488,Binary classification,sklearn SGDClassifier,Elec2,0.8185936350257542,0.7730894238789657,0.006801605224609375,3873.4157740000005 +44394,Binary classification,sklearn SGDClassifier,Elec2,0.8179708969680587,0.7710051290770494,0.006801605224609375,4011.6127160000005 +45300,Binary classification,sklearn SGDClassifier,Elec2,0.8190949227373069,0.7729350807680585,0.006801605224609375,4151.679177000001 +45312,Binary classification,sklearn SGDClassifier,Elec2,0.8190986935028248,0.7728922505749037,0.006801605224609375,4291.771713000001 +25,Binary classification,sklearn SGDClassifier,Phishing,0.68,0.6923076923076923,0.006802558898925781,0.149754 +50,Binary classification,sklearn SGDClassifier,Phishing,0.8,0.782608695652174,0.006802558898925781,0.457736 +75,Binary classification,sklearn SGDClassifier,Phishing,0.8266666666666667,0.8219178082191781,0.006802558898925781,0.892069 +100,Binary classification,sklearn SGDClassifier,Phishing,0.83,0.8210526315789473,0.006802558898925781,1.4190939999999999 +125,Binary classification,sklearn SGDClassifier,Phishing,0.816,0.8067226890756303,0.006802558898925781,2.091236 +150,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8187919463087249,0.006802558898925781,2.916232 +175,Binary classification,sklearn SGDClassifier,Phishing,0.8285714285714286,0.8170731707317075,0.006802558898925781,3.840025 +200,Binary classification,sklearn SGDClassifier,Phishing,0.825,0.8128342245989306,0.006802558898925781,4.9100459999999995 +225,Binary classification,sklearn SGDClassifier,Phishing,0.8222222222222222,0.8058252427184465,0.006802558898925781,6.121922 +250,Binary classification,sklearn SGDClassifier,Phishing,0.824,0.8103448275862069,0.006802558898925781,7.4794909999999994 +275,Binary classification,sklearn SGDClassifier,Phishing,0.8254545454545454,0.8110236220472441,0.007016181945800781,8.920382 +300,Binary classification,sklearn SGDClassifier,Phishing,0.8366666666666667,0.8191881918819188,0.007016181945800781,10.509974 +325,Binary classification,sklearn SGDClassifier,Phishing,0.8461538461538461,0.8251748251748252,0.007016181945800781,12.191811999999999 +350,Binary classification,sklearn SGDClassifier,Phishing,0.8514285714285714,0.8289473684210525,0.007016181945800781,13.999137 +375,Binary classification,sklearn SGDClassifier,Phishing,0.8506666666666667,0.8271604938271606,0.007016181945800781,15.959285 +400,Binary classification,sklearn SGDClassifier,Phishing,0.8525,0.8269794721407624,0.007016181945800781,18.058664 +425,Binary classification,sklearn SGDClassifier,Phishing,0.8564705882352941,0.828169014084507,0.007016181945800781,20.312993 +450,Binary classification,sklearn SGDClassifier,Phishing,0.86,0.8301886792452831,0.007016181945800781,22.675489 +475,Binary classification,sklearn SGDClassifier,Phishing,0.8589473684210527,0.830379746835443,0.007016181945800781,25.19503 +500,Binary classification,sklearn SGDClassifier,Phishing,0.858,0.8329411764705883,0.007016181945800781,27.784240999999998 +525,Binary classification,sklearn SGDClassifier,Phishing,0.8571428571428571,0.8283752860411898,0.007016181945800781,30.514065 +550,Binary classification,sklearn SGDClassifier,Phishing,0.8618181818181818,0.8354978354978354,0.007016181945800781,33.400870999999995 +575,Binary classification,sklearn SGDClassifier,Phishing,0.8626086956521739,0.8364389233954452,0.007016181945800781,36.397645999999995 +600,Binary classification,sklearn SGDClassifier,Phishing,0.8666666666666667,0.8387096774193549,0.007016181945800781,39.57675499999999 +625,Binary classification,sklearn SGDClassifier,Phishing,0.8672,0.8362919132149901,0.007016181945800781,42.828695999999994 +650,Binary classification,sklearn SGDClassifier,Phishing,0.8707692307692307,0.8432835820895522,0.007016181945800781,46.253181999999995 +675,Binary classification,sklearn SGDClassifier,Phishing,0.8725925925925926,0.8485915492957746,0.007016181945800781,49.816151 +700,Binary classification,sklearn SGDClassifier,Phishing,0.8771428571428571,0.8522336769759451,0.007016181945800781,53.516545 +725,Binary classification,sklearn SGDClassifier,Phishing,0.8786206896551724,0.8566775244299674,0.007016181945800781,57.358180000000004 +750,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8589341692789968,0.007016181945800781,61.281034000000005 +775,Binary classification,sklearn SGDClassifier,Phishing,0.8812903225806452,0.8597560975609757,0.007016181945800781,65.347537 +800,Binary classification,sklearn SGDClassifier,Phishing,0.88125,0.8613138686131386,0.007016181945800781,69.566336 +825,Binary classification,sklearn SGDClassifier,Phishing,0.8812121212121212,0.8623595505617978,0.007016181945800781,73.91498000000001 +850,Binary classification,sklearn SGDClassifier,Phishing,0.8823529411764706,0.8630136986301369,0.007016181945800781,78.39968800000001 +875,Binary classification,sklearn SGDClassifier,Phishing,0.8857142857142857,0.8663101604278075,0.007016181945800781,83.02084100000002 +900,Binary classification,sklearn SGDClassifier,Phishing,0.8844444444444445,0.8645833333333334,0.007016181945800781,87.71921500000002 +925,Binary classification,sklearn SGDClassifier,Phishing,0.8864864864864865,0.8682559598494354,0.007016181945800781,92.55798800000002 +950,Binary classification,sklearn SGDClassifier,Phishing,0.8863157894736842,0.8695652173913043,0.007016181945800781,97.51738800000003 +975,Binary classification,sklearn SGDClassifier,Phishing,0.8871794871794871,0.8702830188679245,0.007016181945800781,102.59954100000003 +1000,Binary classification,sklearn SGDClassifier,Phishing,0.888,0.871264367816092,0.007016181945800781,107.87282600000003 +1025,Binary classification,sklearn SGDClassifier,Phishing,0.8878048780487805,0.8715083798882682,0.007016181945800781,113.28564700000003 +1050,Binary classification,sklearn SGDClassifier,Phishing,0.8895238095238095,0.8739130434782609,0.007016181945800781,118.79277100000003 +1075,Binary classification,sklearn SGDClassifier,Phishing,0.8883720930232558,0.8736842105263158,0.007016181945800781,124.46348200000003 +1100,Binary classification,sklearn SGDClassifier,Phishing,0.89,0.8756423432682425,0.007016181945800781,130.26843700000003 +1125,Binary classification,sklearn SGDClassifier,Phishing,0.8915555555555555,0.8784860557768924,0.007016181945800781,136.21796400000002 +1150,Binary classification,sklearn SGDClassifier,Phishing,0.8913043478260869,0.878048780487805,0.007016181945800781,142.31432400000003 +1175,Binary classification,sklearn SGDClassifier,Phishing,0.8902127659574468,0.876555023923445,0.007016181945800781,148.52290000000002 +1200,Binary classification,sklearn SGDClassifier,Phishing,0.8908333333333334,0.8769953051643193,0.007016181945800781,154.887447 +1225,Binary classification,sklearn SGDClassifier,Phishing,0.8914285714285715,0.8776448942042319,0.007016181945800781,161.410896 +1250,Binary classification,sklearn SGDClassifier,Phishing,0.8896,0.8761220825852785,0.007016181945800781,167.984219 +1903,Binary classification,sklearn SGDClassifier,SMTP,0.9968470835522859,0.0,0.0057430267333984375,9.012274 +3806,Binary classification,sklearn SGDClassifier,SMTP,0.9984235417761429,0.0,0.0057430267333984375,26.992092 +5709,Binary classification,sklearn SGDClassifier,SMTP,0.998949027850762,0.0,0.0057430267333984375,53.749217 +7612,Binary classification,sklearn SGDClassifier,SMTP,0.9992117708880714,0.0,0.0057430267333984375,89.545782 +9515,Binary classification,sklearn SGDClassifier,SMTP,0.9993694167104572,0.0,0.0057430267333984375,133.365466 +11418,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.0,0.0057430267333984375,185.06742500000001 +13321,Binary classification,sklearn SGDClassifier,SMTP,0.9995495833646123,0.0,0.0057430267333984375,243.739666 +15224,Binary classification,sklearn SGDClassifier,SMTP,0.9992774566473989,0.5217391304347826,0.0057430267333984375,308.57406100000003 +17127,Binary classification,sklearn SGDClassifier,SMTP,0.9993577392421323,0.5925925925925927,0.0057430267333984375,378.971453 +19030,Binary classification,sklearn SGDClassifier,SMTP,0.999421965317919,0.5925925925925927,0.0057430267333984375,454.798992 +20933,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.5925925925925927,0.0057430267333984375,535.937031 +22836,Binary classification,sklearn SGDClassifier,SMTP,0.9995183044315993,0.5925925925925927,0.0057430267333984375,622.51799 +24739,Binary classification,sklearn SGDClassifier,SMTP,0.9995553579368608,0.5925925925925927,0.0057430267333984375,714.221122 +26642,Binary classification,sklearn SGDClassifier,SMTP,0.9995495833646123,0.5714285714285714,0.0057430267333984375,811.098386 +28545,Binary classification,sklearn SGDClassifier,SMTP,0.9995796111403048,0.5714285714285714,0.0057430267333984375,912.878884 +30448,Binary classification,sklearn SGDClassifier,SMTP,0.9996058854440357,0.5714285714285714,0.0057430267333984375,1019.269091 +32351,Binary classification,sklearn SGDClassifier,SMTP,0.9996290686532101,0.5714285714285714,0.0057430267333984375,1129.962426 +34254,Binary classification,sklearn SGDClassifier,SMTP,0.999649675950254,0.5714285714285714,0.0057430267333984375,1244.872652 +36157,Binary classification,sklearn SGDClassifier,SMTP,0.9996681140581354,0.5714285714285714,0.0057430267333984375,1363.9176400000001 +38060,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.5714285714285714,0.0057430267333984375,1487.072194 +39963,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.5714285714285714,0.0057430267333984375,1614.171257 +41866,Binary classification,sklearn SGDClassifier,SMTP,0.999713371232026,0.5714285714285714,0.0057430267333984375,1745.093316 +43769,Binary classification,sklearn SGDClassifier,SMTP,0.9997258333523726,0.5714285714285714,0.0057430267333984375,1880.714485 +45672,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.5714285714285714,0.0057430267333984375,2020.289668 +47575,Binary classification,sklearn SGDClassifier,SMTP,0.9997477666841829,0.5714285714285714,0.0057430267333984375,2163.67936 +49478,Binary classification,sklearn SGDClassifier,SMTP,0.9997574679655604,0.5714285714285714,0.0057430267333984375,2310.817497 +51381,Binary classification,sklearn SGDClassifier,SMTP,0.9997275257390864,0.5333333333333333,0.0057430267333984375,2461.472155 +53284,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.5333333333333333,0.0057430267333984375,2615.677493 +55187,Binary classification,sklearn SGDClassifier,SMTP,0.9997463170674253,0.5333333333333333,0.0057430267333984375,2773.449537 +57090,Binary classification,sklearn SGDClassifier,SMTP,0.999597127342792,0.41025641025641024,0.0057430267333984375,2934.793173 +58993,Binary classification,sklearn SGDClassifier,SMTP,0.9996101232349601,0.41025641025641024,0.0057430267333984375,3099.6490870000002 +60896,Binary classification,sklearn SGDClassifier,SMTP,0.9996223068838676,0.41025641025641024,0.0057430267333984375,3268.0999800000004 +62799,Binary classification,sklearn SGDClassifier,SMTP,0.9996019044889249,0.3902439024390244,0.0057430267333984375,3440.0722140000003 +64702,Binary classification,sklearn SGDClassifier,SMTP,0.9996136131804272,0.3902439024390244,0.0057430267333984375,3615.5032180000003 +66605,Binary classification,sklearn SGDClassifier,SMTP,0.9996246528038436,0.3902439024390244,0.0057430267333984375,3794.4378890000003 +68508,Binary classification,sklearn SGDClassifier,SMTP,0.9996058854440357,0.37209302325581395,0.0057430267333984375,3977.0945010000005 +70411,Binary classification,sklearn SGDClassifier,SMTP,0.9996165371887915,0.37209302325581395,0.0057430267333984375,4163.174128000001 +72314,Binary classification,sklearn SGDClassifier,SMTP,0.9996266283154023,0.37209302325581395,0.0057430267333984375,4352.606393000001 +74217,Binary classification,sklearn SGDClassifier,SMTP,0.9996362019483407,0.37209302325581395,0.0057430267333984375,4545.416162000001 +76120,Binary classification,sklearn SGDClassifier,SMTP,0.9996452968996321,0.37209302325581395,0.0057430267333984375,4741.562007000001 +78023,Binary classification,sklearn SGDClassifier,SMTP,0.999653948194763,0.37209302325581395,0.0057430267333984375,4941.079189000001 +79926,Binary classification,sklearn SGDClassifier,SMTP,0.9996621875234591,0.37209302325581395,0.0057430267333984375,5143.951781000001 +81829,Binary classification,sklearn SGDClassifier,SMTP,0.9996700436275648,0.37209302325581395,0.0057430267333984375,5350.231536 +83732,Binary classification,sklearn SGDClassifier,SMTP,0.9996775426360293,0.37209302325581395,0.0057430267333984375,5559.929647 +85635,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.37209302325581395,0.0057430267333984375,5773.003953 +87538,Binary classification,sklearn SGDClassifier,SMTP,0.9996915625214192,0.37209302325581395,0.0057430267333984375,5989.467769000001 +89441,Binary classification,sklearn SGDClassifier,SMTP,0.9996869444661844,0.36363636363636365,0.0057430267333984375,6209.264779000001 +91344,Binary classification,sklearn SGDClassifier,SMTP,0.9996934664564723,0.36363636363636365,0.0057430267333984375,6432.452666000001 +93247,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.36363636363636365,0.0057430267333984375,6658.918178000001 +95150,Binary classification,sklearn SGDClassifier,SMTP,0.9997057277982133,0.36363636363636365,0.0057430267333984375,6888.546542000001 +95156,Binary classification,sklearn SGDClassifier,SMTP,0.9997057463533566,0.36363636363636365,0.0057430267333984375,7118.179378000001 +106,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5,0.0,0.0006465911865234375,0.16057 +212,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5283018867924528,0.0,0.0006465911865234375,0.37729 +318,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5314465408805031,0.0,0.0006465911865234375,0.7064710000000001 +424,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5400943396226415,0.0,0.0006465911865234375,1.0774430000000002 +530,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5547169811320755,0.0,0.0006465911865234375,1.4923790000000001 +636,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5550314465408805,0.0,0.0006465911865234375,1.9966470000000003 +742,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5660377358490566,0.0,0.0006465911865234375,2.539797 +848,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5636792452830188,0.0,0.0006465911865234375,3.1757850000000003 +954,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5649895178197065,0.0,0.0006465911865234375,3.8551140000000004 +1060,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5707547169811321,0.0,0.0006465911865234375,4.635951 +1166,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5686106346483705,0.0,0.0006465911865234375,5.458947 +1272,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5644654088050315,0.0,0.0006465911865234375,6.34328 +1378,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5682148040638607,0.0,0.0006465911865234375,7.308669 +1484,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5680592991913747,0.0,0.0006465911865234375,8.359952 +1590,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5679245283018868,0.0,0.0006465911865234375,9.451883 +1696,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5683962264150944,0.0,0.0006465911865234375,10.590847 +1802,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5643729189789123,0.0,0.0006465911865234375,11.83715 +1908,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.560272536687631,0.0,0.0006465911865234375,13.126961999999999 +2014,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5551142005958292,0.0,0.0006465911865234375,14.497203999999998 +2120,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,15.938437999999998 +2226,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5512129380053908,0.0,0.0006465911865234375,17.424999 +2332,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5506003430531733,0.0,0.0006465911865234375,19.022886 +2438,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.551681706316653,0.0,0.0006465911865234375,20.666828 +2544,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487421383647799,0.0,0.0006465911865234375,22.355415999999998 +2650,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5467924528301886,0.0,0.0006465911865234375,24.051772 +2756,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5471698113207547,0.0,0.0006465911865234375,25.858309 +2862,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5489168413696716,0.0,0.0006465911865234375,27.751458999999997 +2968,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505390835579514,0.0,0.0006465911865234375,29.665551999999998 +3074,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487963565387117,0.0,0.0006465911865234375,31.686176 +3180,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,33.740652 +3286,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5517346317711503,0.0,0.0006465911865234375,35.89104 +3392,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5498231132075472,0.0,0.0006465911865234375,38.079414 +3498,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514579759862779,0.0,0.0006465911865234375,40.353903 +3604,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5535516093229744,0.0,0.0006465911865234375,42.668922 +3710,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5522911051212938,0.0,0.0006465911865234375,45.086801 +3816,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5516247379454927,0.0,0.0006465911865234375,47.540759 +3922,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5525242223355431,0.0,0.0006465911865234375,50.094246 +4028,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5528798411122146,0.0,0.0006465911865234375,52.697210999999996 +4134,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5529753265602322,0.0,0.0006465911865234375,55.369586999999996 +4240,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5523584905660377,0.0,0.0006465911865234375,58.109435999999995 +4346,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5526921306948919,0.0,0.0006465911865234375,60.894093 +4452,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5530098831985625,0.0,0.0006465911865234375,63.717346 +4558,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5508995173321632,0.0,0.0006465911865234375,66.643891 +4664,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5497427101200686,0.0,0.0006465911865234375,69.6601 +4770,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505241090146751,0.0,0.0006465911865234375,72.725555 +4876,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5518867924528302,0.0,0.0006465911865234375,75.798736 +4982,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509835407466881,0.0,0.0006465911865234375,78.970205 +5088,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5511006289308176,0.0,0.0006465911865234375,82.150165 +5194,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514054678475163,0.0,0.0006465911865234375,85.416127 +5300,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5513207547169812,0.0,0.0006465911865234375,88.72481 +906,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6799116997792495,0.5482866043613708,0.0006465911865234375,0.820242 +1812,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7190949227373068,0.4904904904904904,0.0006465911865234375,2.329863 +2718,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.43243243243243246,0.0006465911865234375,4.585071 +3624,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7047461368653422,0.4478844169246646,0.0006465911865234375,7.424633 +4530,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7024282560706402,0.4118673647469459,0.0006465911865234375,10.992865 +5436,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7041942604856513,0.4165457184325108,0.0006465911865234375,15.263433 +6342,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.40485829959514175,0.0006465911865234375,20.287067999999998 +7248,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695364238410596,0.3953997809419496,0.0006465911865234375,26.004013999999998 +8154,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6873926907039489,0.4084474355999072,0.0006465911865234375,32.433811999999996 +9060,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6864238410596026,0.42408270829110073,0.0006465911865234375,39.59982599999999 +9966,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.687537627934979,0.4433321415802646,0.0006465911865234375,47.447314999999996 +10872,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6938925680647535,0.4717460317460317,0.0006465911865234375,55.964904999999995 +11778,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6932416369502462,0.47155185022670765,0.0006465911865234375,65.217817 +12684,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944970040996531,0.47557179591284343,0.0006465911865234375,75.258293 +13590,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6942604856512141,0.48429936701005344,0.0006465911865234375,85.993354 +14496,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6935016556291391,0.48606130711393875,0.0006465911865234375,97.389046 +15402,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6929619529931178,0.48095708484249805,0.0006465911865234375,109.49806 +16308,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6904586705911209,0.47130289065772935,0.0006465911865234375,122.273049 +17214,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6921691646334379,0.4645852278468223,0.0006465911865234375,135.723897 +18120,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.694205298013245,0.46859115757168884,0.0006465911865234375,150.008391 +19026,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6967307894460212,0.467515688445921,0.0006465911865234375,164.94785199999998 +19932,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6958157736303432,0.4737435986459509,0.0006465911865234375,180.578389 +20838,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.4696604963891426,0.0006465911865234375,196.972492 +21744,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6968359087564385,0.4670978172999191,0.0006465911865234375,214.03759399999998 +22650,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6977041942604857,0.4643667370726746,0.0006465911865234375,231.758696 +23556,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952368823229751,0.4573285962657797,0.0006465911865234375,250.24763199999998 +24462,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6978170223203336,0.4597281099254495,0.0006465911865234375,269.425119 +25368,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6976505834121728,0.46122506322000567,0.0006465911865234375,289.286378 +26274,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6983329527289336,0.4614757439869547,0.0006465911865234375,309.833587 +27180,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6959896983075791,0.4576304561864129,0.0006465911865234375,331.075152 +28086,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695649077832372,0.4559572301425662,0.0006465911865234375,353.085242 +28992,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952262693156733,0.4515207945375543,0.0006465911865234375,375.713328 +29898,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6939260151180681,0.4465678863017841,0.0006465911865234375,399.011341 +30804,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6941630957018569,0.44330201500915917,0.0006465911865234375,423.007885 +31710,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6917376222011984,0.4368266405484819,0.0006465911865234375,447.82068599999997 +32616,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6893549178317391,0.4316805025802109,0.0006465911865234375,473.25918699999994 +33522,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.688353916830738,0.42909448603748845,0.0006465911865234375,499.4094079999999 +34428,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6863599395840595,0.4245363461948412,0.0006465911865234375,526.1781749999999 +35334,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6869304352748061,0.4212013394725827,0.0006465911865234375,553.6489789999999 +36240,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6911147902869758,0.4267718148299877,0.0006465911865234375,581.8684239999999 +37146,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6919722177354224,0.42698317307692313,0.0006465911865234375,610.7708879999999 +38052,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944181646168401,0.43117111828588206,0.0006465911865234375,640.2659799999999 +38958,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6937727809435803,0.43082061068702293,0.0006465911865234375,670.4759659999999 +39864,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6930814770218744,0.4344288818009522,0.0006465911865234375,701.3646249999998 +40770,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6924208977189109,0.4391771019677997,0.0006465911865234375,733.0001779999998 +41676,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.44722270288977334,0.0006465911865234375,765.3741459999998 +42582,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6956225635244939,0.45507672903090185,0.0006465911865234375,798.4329459999998 +43488,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6962150478292862,0.4576097220511558,0.0006465911865234375,832.1587539999998 +44394,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6963103122043519,0.45575649927337314,0.0006465911865234375,866.6068249999998 +45300,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.697439293598234,0.4596278189560006,0.0006465911865234375,901.8081399999999 +45312,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6974752824858758,0.45959157927935035,0.0006465911865234375,937.0113409999999 +25,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.52,0.33333333333333337,0.0006465911865234375,0.00395 +50,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.56,0.21428571428571427,0.0006465911865234375,0.07842199999999999 +75,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.5866666666666667,0.3404255319148936,0.0006465911865234375,0.15624899999999997 +100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6,0.375,0.0006465911865234375,0.23731799999999997 +125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.64,0.4705882352941176,0.0006465911865234375,0.41813199999999995 +150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.62,0.44660194174757284,0.0006465911865234375,0.6021909999999999 +175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6342857142857142,0.41818181818181815,0.0006465911865234375,0.7890869999999999 +200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.63,0.4126984126984127,0.0006465911865234375,1.0120959999999999 +225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6488888888888888,0.4316546762589928,0.0006465911865234375,1.2378889999999998 +250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.648,0.4358974358974359,0.0006465911865234375,1.4692909999999997 +275,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6618181818181819,0.4561403508771929,0.0006465911865234375,1.7531039999999996 +300,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6733333333333333,0.46153846153846156,0.0006465911865234375,2.0408739999999996 +325,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.683076923076923,0.46632124352331616,0.0006465911865234375,2.33165 +350,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6942857142857143,0.47804878048780486,0.0006465911865234375,2.715045 +375,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7013333333333334,0.4909090909090909,0.0006465911865234375,3.1015189999999997 +400,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.705,0.4913793103448276,0.0006465911865234375,3.4910129999999997 +425,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7105882352941176,0.4896265560165975,0.0006465911865234375,3.9231499999999997 +450,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7222222222222222,0.5098039215686275,0.0006465911865234375,4.358171 +475,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7157894736842105,0.5054945054945055,0.0006465911865234375,4.7960329999999995 +500,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.718,0.5252525252525252,0.0006465911865234375,5.248043999999999 +525,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7257142857142858,0.5294117647058824,0.0006465911865234375,5.702586999999999 +550,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7218181818181818,0.5233644859813085,0.0006465911865234375,6.1599829999999995 +575,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7217391304347827,0.5209580838323353,0.0006465911865234375,6.620335 +600,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7283333333333334,0.5275362318840581,0.0006465911865234375,7.1135079999999995 +625,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7376,0.5340909090909091,0.0006465911865234375,7.613357 +650,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7369230769230769,0.5415549597855228,0.0006465911865234375,8.116107 +675,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7333333333333333,0.5477386934673367,0.0006465911865234375,8.713363 +700,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74,0.5560975609756097,0.0006465911865234375,9.313647 +725,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.743448275862069,0.5753424657534246,0.0006465911865234375,9.917033 +750,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7453333333333333,0.5820568927789934,0.0006465911865234375,10.613639 +775,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7470967741935484,0.5847457627118644,0.0006465911865234375,11.313362999999999 +800,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74625,0.5915492957746479,0.0006465911865234375,12.015877 +825,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7490909090909091,0.602687140115163,0.0006465911865234375,12.766805 +850,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7541176470588236,0.6122448979591837,0.0006465911865234375,13.520246 +875,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7554285714285714,0.6123188405797102,0.0006465911865234375,14.2887 +900,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7566666666666667,0.6123893805309735,0.0006465911865234375,15.059985000000001 +925,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.76,0.6237288135593221,0.0006465911865234375,15.877357000000002 +950,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7589473684210526,0.6288492706645057,0.0006465911865234375,16.697524 +975,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7610256410256411,0.631911532385466,0.0006465911865234375,17.562951 +1000,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.761,0.6328725038402457,0.0006465911865234375,18.43148 +1025,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7609756097560976,0.635958395245171,0.0006465911865234375,19.325245 +1050,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7638095238095238,0.6436781609195402,0.0006465911865234375,20.222005 +1075,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7665116279069767,0.651872399445215,0.0006465911865234375,21.121639 +1100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.77,0.6594885598923284,0.0006465911865234375,22.071389 +1125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.768,0.6597131681877444,0.0006465911865234375,23.024294 +1150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7695652173913043,0.6615581098339719,0.0006465911865234375,23.980116000000002 +1175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7702127659574468,0.6633416458852868,0.0006465911865234375,24.939110000000003 +1200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7741666666666667,0.6691086691086692,0.0006465911865234375,25.901192 +1225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7771428571428571,0.6746126340882003,0.0006465911865234375,26.865956 +1250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7736,0.6697782963827306,0.0006465911865234375,27.833412 +1903,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,1.287853 +3806,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,3.7805989999999996 +5709,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,7.576109 +7612,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,12.534125 +9515,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,18.771881 +11418,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,26.322128 +13321,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,35.214625 +15224,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9992774566473989,0.0,0.0006465911865234375,45.441497999999996 +17127,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999299351900508,0.14285714285714288,0.0006465911865234375,56.927386 +19030,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9993694167104572,0.14285714285714288,0.0006465911865234375,69.74153799999999 +20933,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9994267424640519,0.14285714285714288,0.0006465911865234375,83.765543 +22836,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999474513925381,0.14285714285714288,0.0006465911865234375,99.06549199999999 +24739,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999514935931121,0.14285714285714288,0.0006465911865234375,115.581943 +26642,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995120486449967,0.13333333333333333,0.0006465911865234375,133.361343 +28545,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995445787353302,0.13333333333333333,0.0006465911865234375,152.36548100000002 +30448,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995730425643721,0.13333333333333333,0.0006465911865234375,172.57866 +32351,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,193.96982500000001 +34254,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996204822794418,0.13333333333333333,0.0006465911865234375,216.600052 +36157,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996404568963133,0.13333333333333333,0.0006465911865234375,240.511883 +38060,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996584340514977,0.13333333333333333,0.0006465911865234375,265.709607 +39963,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996746990966644,0.13333333333333333,0.0006465911865234375,292.142637 +41866,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996894855013615,0.13333333333333333,0.0006465911865234375,319.87834699999996 +43769,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999702986131737,0.13333333333333333,0.0006465911865234375,348.79444399999994 +45672,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997153617095814,0.13333333333333333,0.0006465911865234375,378.9697039999999 +47575,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997267472411981,0.13333333333333333,0.0006465911865234375,410.4053809999999 +49478,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997372569626904,0.13333333333333333,0.0006465911865234375,443.05761399999994 +51381,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997080632918783,0.11764705882352941,0.0006465911865234375,477.02532299999996 +53284,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997184896028827,0.11764705882352941,0.0006465911865234375,512.247669 +55187,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997281968579557,0.11764705882352941,0.0006465911865234375,548.612896 +57090,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995796111403048,0.14285714285714285,0.0006465911865234375,586.233337 +58993,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995931720712626,0.14285714285714285,0.0006465911865234375,625.057298 +60896,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996058854440357,0.14285714285714285,0.0006465911865234375,665.0820319999999 +62799,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999585980668482,0.13333333333333333,0.0006465911865234375,706.1803269999999 +64702,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,748.3509649999999 +66605,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996096389159973,0.13333333333333333,0.0006465911865234375,791.6670189999999 +68508,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995912886086297,0.125,0.0006465911865234375,836.0877649999999 +70411,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996023348624504,0.125,0.0006465911865234375,881.7116259999999 +72314,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996127997344912,0.125,0.0006465911865234375,928.538605 +74217,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996227279464274,0.125,0.0006465911865234375,976.4092069999999 +76120,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996321597477666,0.125,0.0006465911865234375,1025.3623619999998 +78023,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996411314612358,0.125,0.0006465911865234375,1075.4142359999998 +79926,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999649675950254,0.125,0.0006465911865234375,1126.58116 +81829,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996578230211783,0.125,0.0006465911865234375,1178.778759 +83732,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999665599770697,0.125,0.0006465911865234375,1231.992839 +85635,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996730308869037,0.125,0.0006465911865234375,1286.303482 +87538,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996801389111014,0.125,0.0006465911865234375,1341.617815 +89441,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996757639114053,0.1212121212121212,0.0006465911865234375,1397.839199 +91344,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996825188299177,0.1212121212121212,0.0006465911865234375,1455.075933 +93247,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996889980374704,0.1212121212121212,0.0006465911865234375,1513.261041 +95150,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999695218076721,0.1212121212121212,0.0006465911865234375,1572.312523 +95156,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996952372945479,0.1212121212121212,0.0006465911865234375,1631.3703540000001 +106,Binary classification,Naive Bayes,Bananas,0.5333333333333333,0.46153846153846156,0.014024734497070312,0.089081 +212,Binary classification,Naive Bayes,Bananas,0.5592417061611374,0.5026737967914437,0.014024734497070312,0.244558 +318,Binary classification,Naive Bayes,Bananas,0.555205047318612,0.5154639175257733,0.014024734497070312,0.45393700000000003 +424,Binary classification,Naive Bayes,Bananas,0.5626477541371159,0.5066666666666667,0.014024734497070312,0.76271 +530,Binary classification,Naive Bayes,Bananas,0.5689981096408318,0.48181818181818187,0.014024734497070312,1.109688 +636,Binary classification,Naive Bayes,Bananas,0.5716535433070866,0.4645669291338582,0.014024734497070312,1.6198730000000001 +742,Binary classification,Naive Bayes,Bananas,0.5870445344129555,0.4555160142348755,0.014024734497070312,2.197835 +848,Binary classification,Naive Bayes,Bananas,0.5962219598583235,0.4554140127388535,0.014024734497070312,2.834188 +954,Binary classification,Naive Bayes,Bananas,0.6002098635886673,0.4454148471615721,0.014024734497070312,3.5547570000000004 +1060,Binary classification,Naive Bayes,Bananas,0.6090651558073654,0.44054054054054054,0.014024734497070312,4.339157 +1166,Binary classification,Naive Bayes,Bananas,0.6068669527896996,0.42606516290726815,0.014024734497070312,5.220598 +1272,Binary classification,Naive Bayes,Bananas,0.6136900078678206,0.433679354094579,0.014024734497070312,6.1398969999999995 +1378,Binary classification,Naive Bayes,Bananas,0.6143790849673203,0.419672131147541,0.014024734497070312,7.157157999999999 +1484,Binary classification,Naive Bayes,Bananas,0.6142953472690492,0.4127310061601643,0.014024734497070312,8.301379999999998 +1590,Binary classification,Naive Bayes,Bananas,0.6135934550031467,0.40618955512572535,0.014024734497070312,9.487101 +1696,Binary classification,Naive Bayes,Bananas,0.6141592920353982,0.4010989010989011,0.014024734497070312,10.730798 +1802,Binary classification,Naive Bayes,Bananas,0.614658523042754,0.40378006872852235,0.014024734497070312,12.063669 +1908,Binary classification,Naive Bayes,Bananas,0.6151022548505506,0.4080645161290322,0.014024734497070312,13.448557000000001 +2014,Binary classification,Naive Bayes,Bananas,0.6100347739692003,0.40485216072782415,0.014024734497070312,14.939018 +2120,Binary classification,Naive Bayes,Bananas,0.608305804624823,0.4071428571428571,0.014024734497070312,16.439687 +2226,Binary classification,Naive Bayes,Bananas,0.6089887640449438,0.4089673913043478,0.014024734497070312,18.077419 +2332,Binary classification,Naive Bayes,Bananas,0.6096096096096096,0.4098573281452659,0.014024734497070312,19.752885 +2438,Binary classification,Naive Bayes,Bananas,0.6101764464505539,0.40846824408468246,0.014024734497070312,21.52049 +2544,Binary classification,Naive Bayes,Bananas,0.6114825009830909,0.41538461538461535,0.014024734497070312,23.348549 +2650,Binary classification,Naive Bayes,Bananas,0.6100415251038127,0.41273450824332003,0.014024734497070312,25.279207 +2756,Binary classification,Naive Bayes,Bananas,0.6076225045372051,0.4070213933077345,0.014024734497070312,27.31295 +2862,Binary classification,Naive Bayes,Bananas,0.6085284865431667,0.4092827004219409,0.014024734497070312,29.37924 +2968,Binary classification,Naive Bayes,Bananas,0.6083586113919784,0.4065372829417773,0.014024734497070312,31.545651 +3074,Binary classification,Naive Bayes,Bananas,0.60624796615685,0.40628066732090284,0.014024734497070312,33.733230999999996 +3180,Binary classification,Naive Bayes,Bananas,0.6071091538219566,0.4077761972498815,0.014024734497070312,36.007059 +3286,Binary classification,Naive Bayes,Bananas,0.6063926940639269,0.4049700874367234,0.014024734497070312,38.312687 +3392,Binary classification,Naive Bayes,Bananas,0.6048363314656443,0.40602836879432624,0.014024734497070312,40.720333999999994 +3498,Binary classification,Naive Bayes,Bananas,0.6065198741778668,0.40535868625756266,0.014024734497070312,43.213055999999995 +3604,Binary classification,Naive Bayes,Bananas,0.6086594504579517,0.40905280804694044,0.014024734497070312,45.745915999999994 +3710,Binary classification,Naive Bayes,Bananas,0.6085198166621731,0.4078303425774878,0.014024734497070312,48.41046399999999 +3816,Binary classification,Naive Bayes,Bananas,0.6070773263433814,0.40492258832870187,0.014024734497070312,51.18183799999999 +3922,Binary classification,Naive Bayes,Bananas,0.6067329762815609,0.4027885360185902,0.014024734497070312,54.01538599999999 +4028,Binary classification,Naive Bayes,Bananas,0.6088899925502855,0.405436013590034,0.014024734497070312,56.94241999999999 +4134,Binary classification,Naive Bayes,Bananas,0.6106944108395839,0.40780272359219727,0.014024734497070312,59.88324999999999 +4240,Binary classification,Naive Bayes,Bananas,0.611936777541873,0.41186986056489094,0.014024734497070312,62.92792699999999 +4346,Binary classification,Naive Bayes,Bananas,0.6131185270425776,0.4128536500174642,0.014024734497070312,66.00451999999999 +4452,Binary classification,Naive Bayes,Bananas,0.6137946528869916,0.413510747185261,0.014024734497070312,69.16846599999998 +4558,Binary classification,Naive Bayes,Bananas,0.6122448979591837,0.4115884115884116,0.014024734497070312,72.34300699999999 +4664,Binary classification,Naive Bayes,Bananas,0.6126956894702981,0.41249186727391024,0.014024734497070312,75.66876099999999 +4770,Binary classification,Naive Bayes,Bananas,0.6143845669951772,0.41302266198531756,0.014024734497070312,79.08375899999999 +4876,Binary classification,Naive Bayes,Bananas,0.6153846153846154,0.4131455399061033,0.014024734497070312,82.54849799999998 +4982,Binary classification,Naive Bayes,Bananas,0.6163420999799237,0.41684467500762895,0.014024734497070312,86.09394899999998 +5088,Binary classification,Naive Bayes,Bananas,0.6150973068606251,0.41412327947336924,0.014024734497070312,89.70897599999998 +5194,Binary classification,Naive Bayes,Bananas,0.6146735990756788,0.4133685136323659,0.014024734497070312,93.40231399999998 +5300,Binary classification,Naive Bayes,Bananas,0.6152104170598226,0.4139120436907157,0.014024734497070312,97.15397799999998 +906,Binary classification,Naive Bayes,Elec2,0.8187845303867404,0.8284518828451883,0.05103778839111328,0.90253 +1812,Binary classification,Naive Bayes,Elec2,0.8023191606847045,0.7475317348377998,0.05103778839111328,2.6687279999999998 +2718,Binary classification,Naive Bayes,Elec2,0.784688995215311,0.706177800100452,0.05103778839111328,5.38565 +3624,Binary classification,Naive Bayes,Elec2,0.8032017664918576,0.7356321839080461,0.05103778839111328,8.965856 +4530,Binary classification,Naive Bayes,Elec2,0.7979686465003312,0.7073872721458268,0.05103778839111328,13.460125000000001 +5436,Binary classification,Naive Bayes,Elec2,0.7937442502299908,0.6972724817715366,0.05103778839111328,18.947959 +6342,Binary classification,Naive Bayes,Elec2,0.7982967986122063,0.7065840789171829,0.05103778839111328,25.368016 +7248,Binary classification,Naive Bayes,Elec2,0.790396025941769,0.6875128574367414,0.05103778839111328,32.74734 +8154,Binary classification,Naive Bayes,Elec2,0.7841285416411137,0.6888260254596887,0.05103778839111328,41.092102 +9060,Binary classification,Naive Bayes,Elec2,0.7897118887294403,0.7086710506193606,0.05103778839111328,50.330248999999995 +9966,Binary classification,Naive Bayes,Elec2,0.793176116407426,0.7240594457089301,0.05103778839111328,60.487047999999994 +10872,Binary classification,Naive Bayes,Elec2,0.7960629196946003,0.7361656551231703,0.05103778839111328,71.57583 +11778,Binary classification,Naive Bayes,Elec2,0.792137216608644,0.7295027624309391,0.05103778839111328,83.57396899999999 +12684,Binary classification,Naive Bayes,Elec2,0.7820704880548766,0.7260111022997621,0.05103778839111328,96.50585899999999 +13590,Binary classification,Naive Bayes,Elec2,0.7858562072264331,0.7383564107174968,0.05103778839111328,110.37856099999999 +14496,Binary classification,Naive Bayes,Elec2,0.7866850638151086,0.7435727317963178,0.05103778839111328,125.16282799999999 +15402,Binary classification,Naive Bayes,Elec2,0.785728199467567,0.738593155893536,0.05103778839111328,140.864825 +16308,Binary classification,Naive Bayes,Elec2,0.7806463481940271,0.7274666666666666,0.05103778839111328,157.49451299999998 +17214,Binary classification,Naive Bayes,Elec2,0.7788880497298554,0.7181158346911569,0.05103778839111328,175.04662 +18120,Binary classification,Naive Bayes,Elec2,0.7728903361112645,0.7138983522213725,0.05103778839111328,193.53438899999998 +19026,Binary classification,Naive Bayes,Elec2,0.7701445466491459,0.7094931242941608,0.05103778839111328,212.92156899999998 +19932,Binary classification,Naive Bayes,Elec2,0.7628317696051378,0.702236220472441,0.05103778839111328,233.287368 +20838,Binary classification,Naive Bayes,Elec2,0.7537553390603254,0.6903626817934946,0.05103778839111328,254.638983 +21744,Binary classification,Naive Bayes,Elec2,0.7508163546888654,0.6836389115964032,0.05103778839111328,276.932282 +22650,Binary classification,Naive Bayes,Elec2,0.7509823833281822,0.6798001589644601,0.05103778839111328,300.18967299999997 +23556,Binary classification,Naive Bayes,Elec2,0.7457015495648482,0.668217569513681,0.05103778839111328,324.33763999999996 +24462,Binary classification,Naive Bayes,Elec2,0.7466170638976329,0.665839982747466,0.05103778839111328,349.45202499999994 +25368,Binary classification,Naive Bayes,Elec2,0.7447865336854969,0.6611180904522613,0.05103778839111328,375.51598899999993 +26274,Binary classification,Naive Bayes,Elec2,0.7448711605069843,0.6581322996888865,0.05103778839111328,402.57675099999994 +27180,Binary classification,Naive Bayes,Elec2,0.741123661650539,0.650402464473815,0.05103778839111328,430.58128099999993 +28086,Binary classification,Naive Bayes,Elec2,0.7390065871461634,0.6440019426906265,0.05103778839111328,459.5402439999999 +28992,Binary classification,Naive Bayes,Elec2,0.7358145631402849,0.6343280019097637,0.05103778839111328,489.4018749999999 +29898,Binary classification,Naive Bayes,Elec2,0.7320466936481921,0.6243023964732918,0.05103778839111328,520.249024 +30804,Binary classification,Naive Bayes,Elec2,0.7297990455475116,0.6158319870759289,0.05103778839111328,552.052505 +31710,Binary classification,Naive Bayes,Elec2,0.7256930209088902,0.6059617649723658,0.05103778839111328,584.838155 +32616,Binary classification,Naive Bayes,Elec2,0.7215391690939752,0.596427301813011,0.05103778839111328,618.5052350000001 +33522,Binary classification,Naive Bayes,Elec2,0.7176695205990274,0.5867248908296943,0.05103778839111328,653.1230730000001 +34428,Binary classification,Naive Bayes,Elec2,0.7142359194818021,0.5779493779493778,0.05103778839111328,688.6949970000001 +35334,Binary classification,Naive Bayes,Elec2,0.7138369229898395,0.5724554949469323,0.05103778839111328,725.19325 +36240,Binary classification,Naive Bayes,Elec2,0.7174866856149452,0.5752924583091347,0.05103778839111328,762.649856 +37146,Binary classification,Naive Bayes,Elec2,0.7169740207295733,0.5716148486206756,0.05103778839111328,801.028112 +38052,Binary classification,Naive Bayes,Elec2,0.7183516858952459,0.573859795618116,0.05103778839111328,840.263393 +38958,Binary classification,Naive Bayes,Elec2,0.7206407064198989,0.5799529121154812,0.05103778839111328,880.2889349999999 +39864,Binary classification,Naive Bayes,Elec2,0.7217720693374808,0.5866964784795975,0.05103778839111328,921.221106 +40770,Binary classification,Naive Bayes,Elec2,0.7228776766660944,0.5923065819861432,0.05103778839111328,962.947955 +41676,Binary classification,Naive Bayes,Elec2,0.724127174565087,0.5973170817134251,0.05103778839111328,1005.542302 +42582,Binary classification,Naive Bayes,Elec2,0.7260280406754186,0.6013259517462921,0.05103778839111328,1049.006993 +43488,Binary classification,Naive Bayes,Elec2,0.7277117299422816,0.6045222270465248,0.05103778839111328,1093.33419 +44394,Binary classification,Naive Bayes,Elec2,0.7273894532921857,0.6015933631814591,0.05103778839111328,1138.520645 +45300,Binary classification,Naive Bayes,Elec2,0.7287136581381487,0.6038234630387828,0.05103778839111328,1184.586595 +45312,Binary classification,Naive Bayes,Elec2,0.7287413652314008,0.6037845330582509,0.05103778839111328,1230.65543 +25,Binary classification,Naive Bayes,Phishing,0.5833333333333334,0.7058823529411764,0.05722999572753906,0.005899 +50,Binary classification,Naive Bayes,Phishing,0.7346938775510204,0.7636363636363637,0.05722999572753906,0.034194 +75,Binary classification,Naive Bayes,Phishing,0.7837837837837838,0.8048780487804877,0.05722999572753906,0.070237 +100,Binary classification,Naive Bayes,Phishing,0.8080808080808081,0.819047619047619,0.05722999572753906,0.11917699999999999 +125,Binary classification,Naive Bayes,Phishing,0.8145161290322581,0.8217054263565893,0.05722999572753906,0.172458 +150,Binary classification,Naive Bayes,Phishing,0.8187919463087249,0.830188679245283,0.05722999572753906,0.294112 +175,Binary classification,Naive Bayes,Phishing,0.8333333333333334,0.8323699421965318,0.05722999572753906,0.432822 +200,Binary classification,Naive Bayes,Phishing,0.8341708542713567,0.83248730964467,0.05722999572753906,0.620751 +225,Binary classification,Naive Bayes,Phishing,0.8303571428571429,0.8240740740740741,0.05722999572753906,0.8126760000000001 +250,Binary classification,Naive Bayes,Phishing,0.8313253012048193,0.825,0.05722999572753906,1.097418 +275,Binary classification,Naive Bayes,Phishing,0.8321167883211679,0.8244274809160306,0.05722999572753906,1.3867479999999999 +300,Binary classification,Naive Bayes,Phishing,0.8394648829431438,0.8285714285714285,0.05722999572753906,1.7081089999999999 +325,Binary classification,Naive Bayes,Phishing,0.845679012345679,0.8299319727891157,0.05722999572753906,2.0343679999999997 +350,Binary classification,Naive Bayes,Phishing,0.8510028653295129,0.8322580645161292,0.05722999572753906,2.472974 +375,Binary classification,Naive Bayes,Phishing,0.8529411764705882,0.8318042813455658,0.05722999572753906,2.916035 +400,Binary classification,Naive Bayes,Phishing,0.8546365914786967,0.8313953488372093,0.05722999572753906,3.458949 +425,Binary classification,Naive Bayes,Phishing,0.8561320754716981,0.8291316526610645,0.05722999572753906,4.00711 +450,Binary classification,Naive Bayes,Phishing,0.8596881959910914,0.8310991957104559,0.05722999572753906,4.560221 +475,Binary classification,Naive Bayes,Phishing,0.8565400843881856,0.8291457286432161,0.05722999572753906,5.117465 +500,Binary classification,Naive Bayes,Phishing,0.8577154308617234,0.8337236533957845,0.05722999572753906,5.6788810000000005 +525,Binary classification,Naive Bayes,Phishing,0.8587786259541985,0.8310502283105022,0.05722999572753906,6.3168120000000005 +550,Binary classification,Naive Bayes,Phishing,0.8579234972677595,0.8311688311688311,0.05722999572753906,6.9590250000000005 +575,Binary classification,Naive Bayes,Phishing,0.8606271777003485,0.8340248962655602,0.05722999572753906,7.6702010000000005 +600,Binary classification,Naive Bayes,Phishing,0.8647746243739566,0.8363636363636363,0.05722999572753906,8.386169 +625,Binary classification,Naive Bayes,Phishing,0.8669871794871795,0.8356435643564357,0.05722999572753906,9.138945000000001 +650,Binary classification,Naive Bayes,Phishing,0.8705701078582434,0.8426966292134833,0.05722999572753906,9.901064000000002 +675,Binary classification,Naive Bayes,Phishing,0.870919881305638,0.8465608465608465,0.05722999572753906,10.713223000000001 +700,Binary classification,Naive Bayes,Phishing,0.8755364806866953,0.8502581755593803,0.05722999572753906,11.569231 +725,Binary classification,Naive Bayes,Phishing,0.8784530386740331,0.8562091503267973,0.05722999572753906,12.458796 +750,Binary classification,Naive Bayes,Phishing,0.8798397863818425,0.8584905660377359,0.05722999572753906,13.352328 +775,Binary classification,Naive Bayes,Phishing,0.8798449612403101,0.8580152671755725,0.05722999572753906,14.337352 +800,Binary classification,Naive Bayes,Phishing,0.8798498122653317,0.8596491228070174,0.05722999572753906,15.326948 +825,Binary classification,Naive Bayes,Phishing,0.8798543689320388,0.860759493670886,0.05722999572753906,16.325159 +850,Binary classification,Naive Bayes,Phishing,0.8798586572438163,0.8602739726027396,0.05722999572753906,17.375421 +875,Binary classification,Naive Bayes,Phishing,0.8832951945080092,0.8636363636363635,0.05722999572753906,18.429913 +900,Binary classification,Naive Bayes,Phishing,0.8809788654060067,0.8608582574772432,0.05722999572753906,19.528876999999998 +925,Binary classification,Naive Bayes,Phishing,0.8820346320346321,0.8635794743429286,0.05722999572753906,20.632713999999996 +950,Binary classification,Naive Bayes,Phishing,0.8819810326659642,0.8650602409638554,0.05722999572753906,21.817704999999997 +975,Binary classification,Naive Bayes,Phishing,0.8829568788501027,0.8661971830985915,0.05722999572753906,23.016962999999997 +1000,Binary classification,Naive Bayes,Phishing,0.8808808808808809,0.8643101482326111,0.05722999572753906,24.246232999999997 +1025,Binary classification,Naive Bayes,Phishing,0.880859375,0.8647450110864746,0.05722999572753906,25.480750999999998 +1050,Binary classification,Naive Bayes,Phishing,0.882745471877979,0.8673139158576052,0.05722999572753906,26.819710999999998 +1075,Binary classification,Naive Bayes,Phishing,0.8817504655493482,0.8672936259143157,0.05722999572753906,28.162913999999997 +1100,Binary classification,Naive Bayes,Phishing,0.8835304822565969,0.8693877551020409,0.05722999572753906,29.538843999999997 +1125,Binary classification,Naive Bayes,Phishing,0.8861209964412812,0.8735177865612648,0.05722999572753906,30.932978999999996 +1150,Binary classification,Naive Bayes,Phishing,0.8859878154917319,0.8731848983543079,0.05722999572753906,32.425236999999996 +1175,Binary classification,Naive Bayes,Phishing,0.8850085178875639,0.8717948717948718,0.05722999572753906,33.921729 +1200,Binary classification,Naive Bayes,Phishing,0.8865721434528774,0.8731343283582089,0.05722999572753906,35.452877 +1225,Binary classification,Naive Bayes,Phishing,0.886437908496732,0.8728270814272644,0.05722999572753906,36.988201000000004 +1250,Binary classification,Naive Bayes,Phishing,0.8847077662129704,0.8714285714285714,0.05722999572753906,38.528021 +1903,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,1.286863 +3806,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,3.863138 +5709,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,7.731956 +7612,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,13.024672 +9515,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,19.659339000000003 +11418,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,27.654251000000002 +13321,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,36.976608 +15224,Binary classification,Naive Bayes,SMTP,0.9997372397030808,0.7777777777777778,0.020140647888183594,47.719054 +17127,Binary classification,Naive Bayes,SMTP,0.9997664369963798,0.8181818181818181,0.020140647888183594,59.951688 +19030,Binary classification,Naive Bayes,SMTP,0.9997897945241474,0.8181818181818181,0.020140647888183594,73.68853899999999 +20933,Binary classification,Naive Bayes,SMTP,0.9998089050257978,0.8181818181818181,0.020140647888183594,88.86509 +22836,Binary classification,Naive Bayes,SMTP,0.9998248303043573,0.8181818181818181,0.020140647888183594,105.535247 +24739,Binary classification,Naive Bayes,SMTP,0.9998383054410219,0.8181818181818181,0.020140647888183594,123.661746 +26642,Binary classification,Naive Bayes,SMTP,0.9998498554859052,0.8333333333333333,0.020140647888183594,143.18039199999998 +28545,Binary classification,Naive Bayes,SMTP,0.999859865470852,0.8333333333333333,0.020140647888183594,164.12799199999998 +30448,Binary classification,Naive Bayes,SMTP,0.9998686241665845,0.8333333333333333,0.020140647888183594,186.62736299999997 +32351,Binary classification,Naive Bayes,SMTP,0.9998763523956723,0.8333333333333333,0.020140647888183594,210.51749199999998 +34254,Binary classification,Naive Bayes,SMTP,0.9998832219075702,0.8333333333333333,0.020140647888183594,235.83223599999997 +36157,Binary classification,Naive Bayes,SMTP,0.9998893682929527,0.8333333333333333,0.020140647888183594,262.657063 +38060,Binary classification,Naive Bayes,SMTP,0.9998949000236474,0.8333333333333333,0.020140647888183594,290.942762 +39963,Binary classification,Naive Bayes,SMTP,0.9998999049096642,0.8333333333333333,0.020140647888183594,320.716469 +41866,Binary classification,Naive Bayes,SMTP,0.999904454795175,0.8333333333333333,0.020140647888183594,352.027934 +43769,Binary classification,Naive Bayes,SMTP,0.9999086090294279,0.8333333333333333,0.020140647888183594,384.764181 +45672,Binary classification,Naive Bayes,SMTP,0.9999124170699131,0.8333333333333333,0.020140647888183594,419.010574 +47575,Binary classification,Naive Bayes,SMTP,0.9999159204607558,0.8333333333333333,0.020140647888183594,454.738206 +49478,Binary classification,Naive Bayes,SMTP,0.9999191543545486,0.8333333333333333,0.020140647888183594,491.833824 +51381,Binary classification,Naive Bayes,SMTP,0.9999026858699883,0.8275862068965517,0.020140647888183594,530.367488 +53284,Binary classification,Naive Bayes,SMTP,0.9999061614398589,0.8275862068965517,0.020140647888183594,570.267553 +55187,Binary classification,Naive Bayes,SMTP,0.9998912767730946,0.7999999999999999,0.020140647888183594,611.572363 +57090,Binary classification,Naive Bayes,SMTP,0.9993869221741492,0.4444444444444444,0.020140647888183594,654.167087 +58993,Binary classification,Naive Bayes,SMTP,0.9988473013289938,0.29166666666666663,0.020140647888183594,698.17064 +60896,Binary classification,Naive Bayes,SMTP,0.9986369981115034,0.2522522522522523,0.020140647888183594,743.490298 +62799,Binary classification,Naive Bayes,SMTP,0.9979139463040224,0.1761006289308176,0.020140647888183594,790.115891 +64702,Binary classification,Naive Bayes,SMTP,0.9979443903494536,0.17391304347826086,0.020140647888183594,838.025404 +66605,Binary classification,Naive Bayes,SMTP,0.9977478830100295,0.15730337078651685,0.020140647888183594,887.160342 +68508,Binary classification,Naive Bayes,SMTP,0.9967302611411972,0.12500000000000003,0.020140647888183594,937.511304 +70411,Binary classification,Naive Bayes,SMTP,0.9964777730436017,0.1142857142857143,0.020140647888183594,989.136144 +72314,Binary classification,Naive Bayes,SMTP,0.9964045192427364,0.10958904109589042,0.020140647888183594,1041.952402 +74217,Binary classification,Naive Bayes,SMTP,0.9958230031260106,0.0935672514619883,0.020140647888183594,1095.894331 +76120,Binary classification,Naive Bayes,SMTP,0.9956515456062218,0.08815426997245178,0.020140647888183594,1151.054816 +78023,Binary classification,Naive Bayes,SMTP,0.9951936633257287,0.07862407862407862,0.020140647888183594,1207.299045 +79926,Binary classification,Naive Bayes,SMTP,0.9946700031279324,0.06986899563318777,0.020140647888183594,1264.68116 +81829,Binary classification,Naive Bayes,SMTP,0.9945862052109302,0.06736842105263158,0.020140647888183594,1323.182614 +83732,Binary classification,Naive Bayes,SMTP,0.9945539883675102,0.06557377049180328,0.020140647888183594,1382.690018 +85635,Binary classification,Naive Bayes,SMTP,0.9939860335847911,0.05850091407678244,0.020140647888183594,1443.2513940000001 +87538,Binary classification,Naive Bayes,SMTP,0.9938540274398254,0.05614035087719298,0.020140647888183594,1504.7859910000002 +89441,Binary classification,Naive Bayes,SMTP,0.9938618067978533,0.05507745266781411,0.020140647888183594,1567.3680330000002 +91344,Binary classification,Naive Bayes,SMTP,0.9939677917300723,0.0548885077186964,0.020140647888183594,1630.9013820000002 +93247,Binary classification,Naive Bayes,SMTP,0.993543958990198,0.050473186119873815,0.020140647888183594,1695.4283070000001 +95150,Binary classification,Naive Bayes,SMTP,0.993483904192372,0.049079754601226995,0.020140647888183594,1760.949483 +95156,Binary classification,Naive Bayes,SMTP,0.9934843150648941,0.049079754601226995,0.020140647888183594,1826.472109 +106,Binary classification,Hoeffding Tree,Bananas,0.49523809523809526,0.208955223880597,0.019225120544433594,0.143993 +212,Binary classification,Hoeffding Tree,Bananas,0.5213270142180095,0.3129251700680272,0.019248008728027344,0.331364 +318,Binary classification,Hoeffding Tree,Bananas,0.5299684542586751,0.40637450199203184,0.019248008728027344,0.6339969999999999 +424,Binary classification,Hoeffding Tree,Bananas,0.5437352245862884,0.42388059701492536,0.019248008728027344,1.026482 +530,Binary classification,Hoeffding Tree,Bananas,0.553875236294896,0.4099999999999999,0.019248008728027344,1.502748 +636,Binary classification,Hoeffding Tree,Bananas,0.5590551181102362,0.4017094017094017,0.019248008728027344,2.038539 +742,Binary classification,Hoeffding Tree,Bananas,0.5762483130904184,0.3984674329501916,0.019248008728027344,2.585217 +848,Binary classification,Hoeffding Tree,Bananas,0.5867768595041323,0.40476190476190477,0.019248008728027344,3.2443470000000003 +954,Binary classification,Hoeffding Tree,Bananas,0.5918153200419727,0.3987635239567234,0.019248008728027344,4.029044000000001 +1060,Binary classification,Hoeffding Tree,Bananas,0.6015108593012276,0.39714285714285713,0.019248008728027344,4.857172 +1166,Binary classification,Hoeffding Tree,Bananas,0.6,0.38522427440633245,0.019248008728027344,5.757943 +1272,Binary classification,Hoeffding Tree,Bananas,0.6073957513768686,0.3966142684401451,0.019248008728027344,6.7312840000000005 +1378,Binary classification,Hoeffding Tree,Bananas,0.6085693536673928,0.384,0.019248008728027344,7.793338 +1484,Binary classification,Hoeffding Tree,Bananas,0.6089008766014835,0.3790149892933619,0.019248008728027344,8.86628 +1590,Binary classification,Hoeffding Tree,Bananas,0.6085588420390182,0.37424547283702214,0.019248008728027344,10.05345 +1696,Binary classification,Hoeffding Tree,Bananas,0.6094395280235988,0.37072243346007605,0.019248008728027344,11.283728 +1802,Binary classification,Hoeffding Tree,Bananas,0.6102165463631316,0.37544483985765126,0.019248008728027344,12.570083 +1908,Binary classification,Hoeffding Tree,Bananas,0.610907184058731,0.3816666666666667,0.019248008728027344,13.875162 +2014,Binary classification,Hoeffding Tree,Bananas,0.6060606060606061,0.3799843627834245,0.019248008728027344,15.24589 +2120,Binary classification,Hoeffding Tree,Bananas,0.6045304388862671,0.38382352941176473,0.019248008728027344,16.723857 +2226,Binary classification,Hoeffding Tree,Bananas,0.6053932584269663,0.38687150837988826,0.019248008728027344,18.213202 +2332,Binary classification,Hoeffding Tree,Bananas,0.6061776061776062,0.38881491344873503,0.019248008728027344,19.838043 +2438,Binary classification,Hoeffding Tree,Bananas,0.606893721789085,0.388250319284802,0.019248008728027344,21.528239 +2544,Binary classification,Hoeffding Tree,Bananas,0.608336610302792,0.39636363636363636,0.019248008728027344,23.295102 +2650,Binary classification,Hoeffding Tree,Bananas,0.6070215175537939,0.3944153577661431,0.019248008728027344,25.108421 +2756,Binary classification,Hoeffding Tree,Bananas,0.6047186932849364,0.3892316320807628,0.019248008728027344,26.99552 +2862,Binary classification,Hoeffding Tree,Bananas,0.6057322614470465,0.3922413793103448,0.019248008728027344,28.904989 +2968,Binary classification,Hoeffding Tree,Bananas,0.6056622851365016,0.3899895724713243,0.019248008728027344,30.87684 +3074,Binary classification,Hoeffding Tree,Bananas,0.6036446469248291,0.3903903903903904,0.019248008728027344,32.946938 +3180,Binary classification,Hoeffding Tree,Bananas,0.6045926391947153,0.3924601256645723,0.019248008728027344,35.112766 +3286,Binary classification,Hoeffding Tree,Bananas,0.6039573820395738,0.39006094702297234,0.019248008728027344,37.308874 +3392,Binary classification,Hoeffding Tree,Bananas,0.6024771453848422,0.39169675090252704,0.019248008728027344,39.588614 +3498,Binary classification,Hoeffding Tree,Bananas,0.6030883614526737,0.39335664335664333,0.03483390808105469,41.900558 +3604,Binary classification,Hoeffding Tree,Bananas,0.6069941715237303,0.40353833192923344,0.03483390808105469,44.304379999999995 +3710,Binary classification,Hoeffding Tree,Bananas,0.6079805877595039,0.40798045602605865,0.03483390808105469,46.776579 +3816,Binary classification,Hoeffding Tree,Bananas,0.6107470511140236,0.4146629877808436,0.03483390808105469,49.299973 +3922,Binary classification,Hoeffding Tree,Bananas,0.6123437898495282,0.4180704441041348,0.04409217834472656,51.9923 +4028,Binary classification,Hoeffding Tree,Bananas,0.6143531164638689,0.4246017043349389,0.05025672912597656,54.748055 +4134,Binary classification,Hoeffding Tree,Bananas,0.617227195741592,0.43216080402010054,0.05025672912597656,57.554127 +4240,Binary classification,Hoeffding Tree,Bananas,0.6218447747110167,0.4439819632327437,0.05025672912597656,60.441345 +4346,Binary classification,Hoeffding Tree,Bananas,0.6239355581127733,0.45130960376091334,0.05025672912597656,63.387968 +4452,Binary classification,Hoeffding Tree,Bananas,0.6259267580319029,0.45676998368678623,0.05025672912597656,66.368103 +4558,Binary classification,Hoeffding Tree,Bananas,0.6276058810621022,0.46382306477093216,0.05025672912597656,69.45485500000001 +4664,Binary classification,Hoeffding Tree,Bananas,0.6283508470941453,0.4695439240893787,0.05025672912597656,72.676145 +4770,Binary classification,Hoeffding Tree,Bananas,0.6288530090165653,0.47164179104477605,0.05941963195800781,75.94439200000001 +4876,Binary classification,Hoeffding Tree,Bananas,0.6311794871794871,0.47580174927113705,0.05946540832519531,79.31100500000001 +4982,Binary classification,Hoeffding Tree,Bananas,0.6336077092953222,0.484026010743568,0.05946540832519531,82.69585900000001 +5088,Binary classification,Hoeffding Tree,Bananas,0.6361313151169649,0.49050371593724196,0.05946540832519531,86.19871000000002 +5194,Binary classification,Hoeffding Tree,Bananas,0.6383593298671288,0.495703544575725,0.05946540832519531,89.82165300000003 +5300,Binary classification,Hoeffding Tree,Bananas,0.6421966408756369,0.5034049240440022,0.05946540832519531,93.53024900000003 +906,Binary classification,Hoeffding Tree,Elec2,0.8530386740331491,0.8513966480446927,0.1757516860961914,1.081486 +1812,Binary classification,Hoeffding Tree,Elec2,0.8663721700717836,0.8393094289508632,0.2084512710571289,3.2087309999999998 +2718,Binary classification,Hoeffding Tree,Elec2,0.8365844681634156,0.809278350515464,0.23302173614501953,6.394793 +3624,Binary classification,Hoeffding Tree,Elec2,0.8459839911675407,0.8210391276459269,0.23302173614501953,10.694791 +4530,Binary classification,Hoeffding Tree,Elec2,0.8511812762199161,0.8157463094587206,0.23296833038330078,16.02834 +5436,Binary classification,Hoeffding Tree,Elec2,0.8404783808647655,0.8020095912308747,0.23296833038330078,22.571918 +6342,Binary classification,Hoeffding Tree,Elec2,0.8334647531935025,0.7966884867154409,0.23296833038330078,30.238204 +7248,Binary classification,Hoeffding Tree,Elec2,0.8330343590451221,0.7912353347135956,0.23296833038330078,38.961308 +8154,Binary classification,Hoeffding Tree,Elec2,0.8344167790997179,0.8013537374926426,0.23296833038330078,48.732242 +9060,Binary classification,Hoeffding Tree,Elec2,0.8403797328623468,0.8129849974133472,0.2980508804321289,59.636444 +9966,Binary classification,Hoeffding Tree,Elec2,0.8398394380331159,0.8171402383134739,0.29816532135009766,71.55266999999999 +10872,Binary classification,Hoeffding Tree,Elec2,0.840493054916751,0.8200124558854057,0.29816532135009766,84.613385 +11778,Binary classification,Hoeffding Tree,Elec2,0.8404517279442982,0.8184014690248381,0.3811311721801758,98.771271 +12684,Binary classification,Hoeffding Tree,Elec2,0.8397066939998423,0.8184983483617534,0.3811311721801758,114.088656 +13590,Binary classification,Hoeffding Tree,Elec2,0.8422253293104717,0.8228684732319893,0.3811311721801758,130.484857 +14496,Binary classification,Hoeffding Tree,Elec2,0.8440841669541221,0.8259128023417038,0.38237476348876953,148.034702 +15402,Binary classification,Hoeffding Tree,Elec2,0.8445555483410169,0.8246153846153847,0.3824014663696289,166.630313 +16308,Binary classification,Hoeffding Tree,Elec2,0.8382903047770895,0.8146221441124781,0.40816211700439453,186.33286 +17214,Binary classification,Hoeffding Tree,Elec2,0.8345436588624876,0.8052516411378555,0.40816211700439453,207.14980100000002 +18120,Binary classification,Hoeffding Tree,Elec2,0.8332689442022186,0.8030253635000325,0.40884876251220703,229.10312700000003 +19026,Binary classification,Hoeffding Tree,Elec2,0.8340604467805519,0.8008327550312283,0.4101419448852539,252.19556200000002 +19932,Binary classification,Hoeffding Tree,Elec2,0.8288595655009784,0.7951228302000121,0.4740419387817383,276.349221 +20838,Binary classification,Hoeffding Tree,Elec2,0.8238230071507414,0.787570163763671,0.4986543655395508,301.796373 +21744,Binary classification,Hoeffding Tree,Elec2,0.8251391252357081,0.7858028169014086,0.49881458282470703,328.42960500000004 +22650,Binary classification,Hoeffding Tree,Elec2,0.8245838668373879,0.7828843106180666,0.4754457473754883,356.18046400000003 +23556,Binary classification,Hoeffding Tree,Elec2,0.81761834005519,0.7712703652433182,0.5000581741333008,385.033694 +24462,Binary classification,Hoeffding Tree,Elec2,0.8151342954090184,0.7656509121061359,0.5002222061157227,415.059878 +25368,Binary classification,Hoeffding Tree,Elec2,0.8133401663578665,0.7649540828989824,0.5574884414672852,446.38466700000004 +26274,Binary classification,Hoeffding Tree,Elec2,0.8142199215925094,0.7659329592864336,0.5574884414672852,478.875266 +27180,Binary classification,Hoeffding Tree,Elec2,0.8130909893667906,0.7650758416574177,0.5574884414672852,512.642228 +28086,Binary classification,Hoeffding Tree,Elec2,0.810646252447926,0.7611605137878379,0.5575571060180664,547.6069200000001 +28992,Binary classification,Hoeffding Tree,Elec2,0.8084233037839329,0.755846667838931,0.5575571060180664,583.7938770000001 +29898,Binary classification,Hoeffding Tree,Elec2,0.8039602635715958,0.7488322262695523,0.5575571060180664,621.109455 +30804,Binary classification,Hoeffding Tree,Elec2,0.8052787066194851,0.7498540328634582,0.6720895767211914,659.6567610000001 +31710,Binary classification,Hoeffding Tree,Elec2,0.802863540319783,0.7460285215130216,0.6720895767211914,699.5069490000001 +32616,Binary classification,Hoeffding Tree,Elec2,0.8010731258623333,0.7451889089623752,0.6838197708129883,740.492735 +33522,Binary classification,Hoeffding Tree,Elec2,0.8010500880045345,0.7469934367768125,0.7644319534301758,782.6537000000001 +34428,Binary classification,Hoeffding Tree,Elec2,0.799663055160194,0.7444893120438633,0.7656755447387695,825.979857 +35334,Binary classification,Hoeffding Tree,Elec2,0.7997056576005434,0.7438746335637508,0.796971321105957,870.4262610000001 +36240,Binary classification,Hoeffding Tree,Elec2,0.798283617097602,0.7418420680887131,0.8215837478637695,916.0524170000001 +37146,Binary classification,Hoeffding Tree,Elec2,0.7980347287656482,0.741577678263865,0.8528566360473633,962.7298460000001 +38052,Binary classification,Hoeffding Tree,Elec2,0.7942761031247536,0.7384913476314559,0.8296480178833008,1010.431184 +38958,Binary classification,Hoeffding Tree,Elec2,0.791975768154632,0.7385131646876614,0.8296480178833008,1059.228611 +39864,Binary classification,Hoeffding Tree,Elec2,0.7917617841105787,0.7414904549842734,0.8308916091918945,1109.092208 +40770,Binary classification,Hoeffding Tree,Elec2,0.7937158134857367,0.7465187775031646,0.8308916091918945,1159.9359670000001 +41676,Binary classification,Hoeffding Tree,Elec2,0.7945770845830834,0.749744219357479,0.8553438186645508,1211.819823 +42582,Binary classification,Hoeffding Tree,Elec2,0.7952373124163359,0.7509355271802782,0.8799333572387695,1264.739744 +43488,Binary classification,Hoeffding Tree,Elec2,0.7953871271874353,0.7515912897822445,0.881199836730957,1318.691004 +44394,Binary classification,Hoeffding Tree,Elec2,0.7949676750839096,0.7499038303016982,0.881199836730957,1373.745004 +45300,Binary classification,Hoeffding Tree,Elec2,0.7956246274752202,0.7508745492707605,0.9384660720825195,1429.8385990000002 +45312,Binary classification,Hoeffding Tree,Elec2,0.7956346141113637,0.7508341405661393,0.9384660720825195,1485.976427 +25,Binary classification,Hoeffding Tree,Phishing,0.5833333333333334,0.6428571428571429,0.06842708587646484,0.007366 +50,Binary classification,Hoeffding Tree,Phishing,0.7346938775510204,0.7346938775510203,0.06842708587646484,0.021904 +75,Binary classification,Hoeffding Tree,Phishing,0.7837837837837838,0.7894736842105262,0.06842708587646484,0.108104 +100,Binary classification,Hoeffding Tree,Phishing,0.8080808080808081,0.8080808080808081,0.06842708587646484,0.26246400000000003 +125,Binary classification,Hoeffding Tree,Phishing,0.8145161290322581,0.8130081300813008,0.06842708587646484,0.42699600000000004 +150,Binary classification,Hoeffding Tree,Phishing,0.8187919463087249,0.8235294117647058,0.06842708587646484,0.670297 +175,Binary classification,Hoeffding Tree,Phishing,0.8333333333333334,0.8263473053892215,0.06842708587646484,0.944361 +200,Binary classification,Hoeffding Tree,Phishing,0.8341708542713567,0.8272251308900525,0.0684499740600586,1.225091 +225,Binary classification,Hoeffding Tree,Phishing,0.8303571428571429,0.8190476190476189,0.0684499740600586,1.620606 +250,Binary classification,Hoeffding Tree,Phishing,0.8313253012048193,0.8205128205128206,0.0684499740600586,2.072395 +275,Binary classification,Hoeffding Tree,Phishing,0.8321167883211679,0.8203125000000001,0.0684499740600586,2.536963 +300,Binary classification,Hoeffding Tree,Phishing,0.8394648829431438,0.8248175182481753,0.0684499740600586,3.035956 +325,Binary classification,Hoeffding Tree,Phishing,0.845679012345679,0.8263888888888888,0.0684499740600586,3.5418380000000003 +350,Binary classification,Hoeffding Tree,Phishing,0.8510028653295129,0.8289473684210527,0.0684499740600586,4.130076000000001 +375,Binary classification,Hoeffding Tree,Phishing,0.8529411764705882,0.8286604361370716,0.0684499740600586,4.770647 +400,Binary classification,Hoeffding Tree,Phishing,0.8546365914786967,0.8284023668639053,0.0684499740600586,5.418701 +425,Binary classification,Hoeffding Tree,Phishing,0.8561320754716981,0.8262108262108262,0.0684499740600586,6.103302 +450,Binary classification,Hoeffding Tree,Phishing,0.8596881959910914,0.8283378746594006,0.0684499740600586,6.878701 +475,Binary classification,Hoeffding Tree,Phishing,0.8565400843881856,0.826530612244898,0.0684499740600586,7.659851000000001 +500,Binary classification,Hoeffding Tree,Phishing,0.8577154308617234,0.8313539192399049,0.0684499740600586,8.471725000000001 +525,Binary classification,Hoeffding Tree,Phishing,0.8587786259541985,0.8287037037037036,0.0684499740600586,9.290161000000001 +550,Binary classification,Hoeffding Tree,Phishing,0.8579234972677595,0.8289473684210527,0.0684499740600586,10.200081 +575,Binary classification,Hoeffding Tree,Phishing,0.8606271777003485,0.8319327731092437,0.0684499740600586,11.144835 +600,Binary classification,Hoeffding Tree,Phishing,0.8647746243739566,0.834355828220859,0.0684499740600586,12.149797 +625,Binary classification,Hoeffding Tree,Phishing,0.8669871794871795,0.8336673346693387,0.0684499740600586,13.191129 +650,Binary classification,Hoeffding Tree,Phishing,0.8705701078582434,0.8409090909090909,0.0684499740600586,14.297317 +675,Binary classification,Hoeffding Tree,Phishing,0.870919881305638,0.8449197860962566,0.0684499740600586,15.408972 +700,Binary classification,Hoeffding Tree,Phishing,0.8755364806866953,0.8486956521739131,0.0684499740600586,16.598807 +725,Binary classification,Hoeffding Tree,Phishing,0.8784530386740331,0.8547854785478548,0.0684499740600586,17.82593 +750,Binary classification,Hoeffding Tree,Phishing,0.8798397863818425,0.8571428571428571,0.0684499740600586,19.123649 +775,Binary classification,Hoeffding Tree,Phishing,0.8798449612403101,0.8567026194144837,0.0684499740600586,20.439518 +800,Binary classification,Hoeffding Tree,Phishing,0.8798498122653317,0.8584070796460177,0.006070137023925781,21.870793 +825,Binary classification,Hoeffding Tree,Phishing,0.8786407766990292,0.8575498575498576,0.1326732635498047,23.308925 +850,Binary classification,Hoeffding Tree,Phishing,0.8798586572438163,0.8579387186629527,0.13269615173339844,24.793982 +875,Binary classification,Hoeffding Tree,Phishing,0.8810068649885584,0.8583106267029972,0.13269615173339844,26.327422 +900,Binary classification,Hoeffding Tree,Phishing,0.882091212458287,0.8590425531914893,0.1327190399169922,27.867454 +925,Binary classification,Hoeffding Tree,Phishing,0.8831168831168831,0.8611825192802056,0.1327190399169922,29.49699 +950,Binary classification,Hoeffding Tree,Phishing,0.880927291886196,0.8599752168525404,0.1327190399169922,31.132602 +975,Binary classification,Hoeffding Tree,Phishing,0.8819301848049281,0.8609431680773881,0.1327190399169922,32.858381 +1000,Binary classification,Hoeffding Tree,Phishing,0.8828828828828829,0.8621908127208481,0.1327190399169922,34.600804000000004 +1025,Binary classification,Hoeffding Tree,Phishing,0.8818359375,0.8613974799541809,0.1327190399169922,36.37479200000001 +1050,Binary classification,Hoeffding Tree,Phishing,0.8836987607244995,0.8641425389755011,0.1327190399169922,38.236126000000006 +1075,Binary classification,Hoeffding Tree,Phishing,0.8845437616387337,0.8658008658008659,0.1327190399169922,40.114172 +1100,Binary classification,Hoeffding Tree,Phishing,0.8844404003639672,0.8656084656084656,0.1327190399169922,41.998405000000005 +1125,Binary classification,Hoeffding Tree,Phishing,0.8816725978647687,0.8630278063851698,0.1327190399169922,43.96255500000001 +1150,Binary classification,Hoeffding Tree,Phishing,0.8807658833768495,0.8614762386248735,0.1327190399169922,45.93298000000001 +1175,Binary classification,Hoeffding Tree,Phishing,0.879045996592845,0.8594059405940594,0.1327190399169922,47.92952100000001 +1200,Binary classification,Hoeffding Tree,Phishing,0.8807339449541285,0.8610301263362489,0.1327190399169922,50.02063300000001 +1225,Binary classification,Hoeffding Tree,Phishing,0.880718954248366,0.8609523809523809,0.1327190399169922,52.11693600000001 +1250,Binary classification,Hoeffding Tree,Phishing,0.8799039231385108,0.8605947955390334,0.1327190399169922,54.27575100000001 +1903,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,1.086226 +3806,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,3.167363 +5709,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,6.335451 +7612,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,10.587829 +9515,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,15.968691 +11418,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,22.515101 +13321,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,30.177580000000003 +15224,Binary classification,Hoeffding Tree,SMTP,0.9992774091834724,0.0,0.02622222900390625,38.907943 +17127,Binary classification,Hoeffding Tree,SMTP,0.9992409202382343,0.0,0.0170440673828125,48.927066 +19030,Binary classification,Hoeffding Tree,SMTP,0.9993168322034789,0.0,0.0170440673828125,60.172540000000005 +20933,Binary classification,Hoeffding Tree,SMTP,0.999378941333843,0.0,0.0170440673828125,72.66801500000001 +22836,Binary classification,Hoeffding Tree,SMTP,0.9994306984891613,0.0,0.0170440673828125,86.48422800000002 +24739,Binary classification,Hoeffding Tree,SMTP,0.9994744926833212,0.0,0.0170440673828125,101.51872100000001 +26642,Binary classification,Hoeffding Tree,SMTP,0.9994744942006681,0.0,0.0170440673828125,117.73184500000002 +28545,Binary classification,Hoeffding Tree,SMTP,0.9995095291479821,0.0,0.0170440673828125,135.114956 +30448,Binary classification,Hoeffding Tree,SMTP,0.9995401845830459,0.0,0.0170440673828125,153.67141 +32351,Binary classification,Hoeffding Tree,SMTP,0.9995672333848532,0.0,0.0170440673828125,173.49434000000002 +34254,Binary classification,Hoeffding Tree,SMTP,0.9995912766764955,0.0,0.0170440673828125,194.59387200000003 +36157,Binary classification,Hoeffding Tree,SMTP,0.9996127890253347,0.0,0.0170440673828125,216.90021000000004 +38060,Binary classification,Hoeffding Tree,SMTP,0.9996321500827662,0.0,0.0170440673828125,240.42103000000003 +39963,Binary classification,Hoeffding Tree,SMTP,0.9996496671838246,0.0,0.0170440673828125,265.208452 +41866,Binary classification,Hoeffding Tree,SMTP,0.9996655917831124,0.0,0.0170440673828125,291.209442 +43769,Binary classification,Hoeffding Tree,SMTP,0.9996801316029976,0.0,0.0170440673828125,318.43843100000004 +45672,Binary classification,Hoeffding Tree,SMTP,0.9996934597446958,0.0,0.0170440673828125,346.89099000000004 +47575,Binary classification,Hoeffding Tree,SMTP,0.9997057216126456,0.0,0.0170440673828125,376.52940800000005 +49478,Binary classification,Hoeffding Tree,SMTP,0.99971704024092,0.0,0.0170440673828125,407.49804200000005 +51381,Binary classification,Hoeffding Tree,SMTP,0.9996885947839627,0.0,0.0170440673828125,439.6062170000001 +53284,Binary classification,Hoeffding Tree,SMTP,0.9996997166075484,0.0,0.0170440673828125,472.95335100000005 +55187,Binary classification,Hoeffding Tree,SMTP,0.999710071394919,0.0,0.0170440673828125,507.47402400000004 +57090,Binary classification,Hoeffding Tree,SMTP,0.9995620872672494,0.0,0.0170440673828125,543.2100710000001 +58993,Binary classification,Hoeffding Tree,SMTP,0.9995762137238947,0.0,0.0170440673828125,580.098547 +60896,Binary classification,Hoeffding Tree,SMTP,0.999589457262501,0.0,0.0170440673828125,618.1800870000001 +62799,Binary classification,Hoeffding Tree,SMTP,0.9995700500015924,0.0,0.0170440673828125,657.2504170000001 +64702,Binary classification,Hoeffding Tree,SMTP,0.9995826957852274,0.0,0.0170440673828125,697.447209 +66605,Binary classification,Hoeffding Tree,SMTP,0.9995946189418053,0.0,0.0170440673828125,738.7766780000001 +68508,Binary classification,Hoeffding Tree,SMTP,0.9995766855941729,0.0,0.0170440673828125,781.207926 +70411,Binary classification,Hoeffding Tree,SMTP,0.9995881266865502,0.0,0.0170440673828125,824.7260210000001 +72314,Binary classification,Hoeffding Tree,SMTP,0.9995989656078437,0.0,0.0170440673828125,869.3947840000001 +74217,Binary classification,Hoeffding Tree,SMTP,0.99960924867953,0.0,0.0170440673828125,915.176721 +76120,Binary classification,Hoeffding Tree,SMTP,0.9996190175908775,0.0,0.0170440673828125,961.985028 +78023,Binary classification,Hoeffding Tree,SMTP,0.9996283099638563,0.0,0.0170440673828125,1009.890756 +79926,Binary classification,Hoeffding Tree,SMTP,0.9996371598373475,0.0,0.0170440673828125,1058.848202 +81829,Binary classification,Hoeffding Tree,SMTP,0.9996455980837855,0.0,0.0170440673828125,1108.7919539999998 +83732,Binary classification,Hoeffding Tree,SMTP,0.9996536527689864,0.0,0.0170440673828125,1159.7893379999998 +85635,Binary classification,Hoeffding Tree,SMTP,0.999661349463998,0.0,0.0170440673828125,1211.840415 +87538,Binary classification,Hoeffding Tree,SMTP,0.9996687115162731,0.0,0.0170440673828125,1264.8087919999998 +89441,Binary classification,Hoeffding Tree,SMTP,0.9996645796064401,0.0,0.0170440673828125,1318.8158339999998 +91344,Binary classification,Hoeffding Tree,SMTP,0.999671567607808,0.0,0.0170440673828125,1373.8298589999997 +93247,Binary classification,Hoeffding Tree,SMTP,0.9996782703815713,0.0,0.0170440673828125,1429.7685149999998 +95150,Binary classification,Hoeffding Tree,SMTP,0.9996847050415664,0.0,0.0170440673828125,1486.6476859999998 +95156,Binary classification,Hoeffding Tree,SMTP,0.9996847249224948,0.0,0.0170440673828125,1543.5553739999998 +106,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5714285714285714,0.628099173553719,0.025684356689453125,0.216494 +212,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5592417061611374,0.5903083700440529,0.025768280029296875,0.463954 +318,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5615141955835962,0.5947521865889213,0.025829315185546875,0.862573 +424,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5555555555555556,0.5822222222222222,0.025829315185546875,1.3898329999999999 +530,Binary classification,Hoeffding Adaptive Tree,Bananas,0.555765595463138,0.5506692160611854,0.025829315185546875,1.9641119999999999 +636,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5543307086614173,0.5291181364392679,0.025890350341796875,2.6939569999999997 +742,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5708502024291497,0.5167173252279634,0.025890350341796875,3.4801279999999997 +848,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5761511216056671,0.510231923601637,0.025890350341796875,4.453125999999999 +954,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5844700944386149,0.505,0.025890350341796875,5.580188 +1060,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5920679886685553,0.49532710280373826,0.025890350341796875,6.755147 +1166,Binary classification,Hoeffding Adaptive Tree,Bananas,0.590557939914163,0.478688524590164,0.025890350341796875,8.08575 +1272,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971675845790716,0.48073022312373226,0.025890350341796875,9.558451000000002 +1378,Binary classification,Hoeffding Adaptive Tree,Bananas,0.599128540305011,0.4661508704061895,0.025951385498046875,11.087295000000001 +1484,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5994605529332434,0.458029197080292,0.025951385498046875,12.740385000000002 +1590,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5997482693517936,0.4517241379310345,0.025951385498046875,14.490633000000003 +1696,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6011799410029498,0.4459016393442623,0.025951385498046875,16.383274000000004 +1802,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6018878400888396,0.44547563805104406,0.025951385498046875,18.381747000000004 +1908,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6030414263240692,0.44704163623082543,0.025951385498046875,20.420329000000002 +2014,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5986090412319921,0.44352617079889806,0.025951385498046875,22.615668000000003 +2120,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5960358659745163,0.4427083333333333,0.025951385498046875,24.891681000000002 +2226,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5968539325842697,0.4425108763206961,0.025951385498046875,27.295309000000003 +2332,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5975975975975976,0.44233055885850175,0.025951385498046875,29.792211 +2438,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5982765695527288,0.4396107613050944,0.025951385498046875,32.414577 +2544,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5973259929217459,0.4398249452954048,0.03029155731201172,35.17394 +2650,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5956964892412231,0.44363636363636366,0.056708335876464844,38.067898 +2756,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5985480943738657,0.44975124378109455,0.056952476501464844,41.158639 +2862,Binary classification,Hoeffding Adaptive Tree,Bananas,0.600139811254806,0.4536771728748806,0.057196617126464844,44.452629 +2968,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5979103471520054,0.45250114731528224,0.057303428649902344,47.888765 +3074,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971363488447771,0.4497777777777778,0.057425498962402344,51.476908 +3180,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6008178672538534,0.44993498049414826,0.057486534118652344,55.178717 +3286,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6024353120243531,0.4470787468247248,0.057608604431152344,59.035765 +3392,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6012975523444412,0.444991789819376,0.057669639587402344,63.084021 +3498,Binary classification,Hoeffding Adaptive Tree,Bananas,0.603946239633972,0.44310414153598715,0.057730674743652344,67.283017 +3604,Binary classification,Hoeffding Adaptive Tree,Bananas,0.607826810990841,0.4452296819787986,0.057730674743652344,71.628079 +3710,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6071717444055001,0.441976254308694,0.057730674743652344,76.17092 +3816,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6062909567496724,0.43787425149700593,0.057791709899902344,80.84267399999999 +3922,Binary classification,Hoeffding Adaptive Tree,Bananas,0.606988013261923,0.4353242946134115,0.057852745056152344,85.696272 +4028,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6088899925502855,0.4360902255639098,0.057852745056152344,90.65857299999999 +4134,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6082748608758771,0.4341139461726669,0.057913780212402344,95.89501499999999 +4240,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6105213493748526,0.4370951244459598,0.057913780212402344,101.21739399999998 +4346,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6119677790563867,0.43724966622162886,0.057974815368652344,106.75825799999998 +4452,Binary classification,Hoeffding Adaptive Tree,Bananas,0.614243990114581,0.4387054593004249,0.057974815368652344,112.41943399999998 +4558,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6126837831906956,0.4355612408058842,0.057974815368652344,118.26167899999999 +4664,Binary classification,Hoeffding Adaptive Tree,Bananas,0.613339052112374,0.4360337816703159,0.057974815368652344,124.26691199999999 +4770,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6148039421262319,0.4352905010759299,0.0641164779663086,130.389994 +4876,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6157948717948718,0.4332829046898639,0.0641164779663086,136.677955 +4982,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6167436257779563,0.43470535978679303,0.0641164779663086,143.134937 +5088,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6158836249262827,0.4313154831199068,0.0641775131225586,149.736273 +5194,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6160215674947044,0.42963386727688785,0.0642385482788086,156.525598 +5300,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6165314210228345,0.42824985931344967,0.06184673309326172,163.516222 +906,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8386740331491712,0.8370535714285713,0.15532493591308594,2.212895 +1812,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8823854224185533,0.857334226389819,0.2904033660888672,6.521798 +2718,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8715495031284505,0.8438478747203579,0.1283740997314453,13.845606 +3624,Binary classification,Hoeffding Adaptive Tree,Elec2,0.875241512558653,0.8472972972972973,0.2500133514404297,22.913432999999998 +4530,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8737028041510267,0.8396860986547084,0.3712940216064453,34.075607999999995 +5436,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8656853725850966,0.8300744878957169,0.4407672882080078,47.709683999999996 +6342,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8646901119697209,0.8296943231441047,0.26204872131347656,63.50523799999999 +7248,Binary classification,Hoeffding Adaptive Tree,Elec2,0.864771629639851,0.8289703315881326,0.2866535186767578,81.25362299999999 +8154,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8572304673126456,0.8312064965197217,0.28668785095214844,101.144105 +9060,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8580417264598742,0.8370501773948302,0.2865924835205078,123.033084 +9966,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8544907175112895,0.8369320737741789,0.3109416961669922,147.021637 +10872,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8583386992916935,0.8434322895485971,0.37159156799316406,172.950216 +11778,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8529336842999066,0.8357982555934774,0.46280479431152344,201.490822 +12684,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8533469999211543,0.8362099330750264,0.1895275115966797,232.642586 +13590,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8551769813820002,0.8395303326810176,0.19393348693847656,265.763258 +14496,Binary classification,Hoeffding Adaptive Tree,Elec2,0.855122456019317,0.8397435897435896,0.1697406768798828,300.834382 +15402,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8537757288487761,0.8365984617617181,0.1694965362548828,338.213883 +16308,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8506776231066413,0.8316626339440029,0.16408348083496094,377.804328 +17214,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8495904258409341,0.8278704873346187,0.1691112518310547,419.462025 +18120,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8498261493459904,0.827752104830031,0.19867897033691406,463.148728 +19026,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8522996057818659,0.8287211995611362,0.25722312927246094,508.959572 +19932,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8470222266820531,0.8238895627563102,0.31537818908691406,557.675148 +20838,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8434035609732687,0.8200121352529097,0.32396507263183594,609.8923100000001 +21744,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8450535804626776,0.8196563353139554,0.3222179412841797,664.4541280000001 +22650,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8452911828336792,0.8184455958549223,0.4409503936767578,721.910691 +23556,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8424962852897474,0.8143700590413289,0.44409751892089844,782.4177400000001 +24462,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8403990024937655,0.8111089607122121,0.5018138885498047,845.9758730000001 +25368,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8367169945204399,0.8072053621299572,0.5608501434326172,913.0025360000001 +26274,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8375137974346287,0.8080226649278229,0.31543540954589844,983.5552650000001 +27180,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8378527539644579,0.8096081565645656,0.31577491760253906,1056.640488 +28086,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8349652839594089,0.8051456678017405,0.18702125549316406,1131.521461 +28992,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8328791693973991,0.8007566722868775,0.2230243682861328,1208.700577 +29898,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8303843194969395,0.7968267959453503,0.10404396057128906,1288.2399990000001 +30804,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8300490211992338,0.7958984755740965,0.22612571716308594,1369.046902 +31710,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8277460657857391,0.7934815486993346,0.37830543518066406,1451.951614 +32616,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8227502682814656,0.7867025790502896,0.1292285919189453,1536.962592 +33522,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8198442767220548,0.7850354180756772,0.1289234161376953,1623.2849270000002 +34428,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8166264850262875,0.7809127190699289,0.19405555725097656,1710.9311020000002 +35334,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8170265757224124,0.7804530172852923,0.34708213806152344,1800.2335220000002 +36240,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8175446342338365,0.7795559111822364,0.4078502655029297,1890.9592170000003 +37146,Binary classification,Hoeffding Adaptive Tree,Elec2,0.816610580158837,0.7771817349208426,0.4166545867919922,1983.2562510000002 +38052,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8155107618722242,0.7753456221198157,0.1291065216064453,2077.01254 +38958,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8146674538593834,0.7751479289940829,0.20058250427246094,2172.116472 +39864,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8157188370167825,0.7778516995282448,0.16962623596191406,2268.811937 +40770,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8171650028207706,0.7812151452891106,0.2508678436279297,2366.696648 +41676,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8187402519496101,0.7846022241231821,0.2554492950439453,2465.8461540000003 +42582,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8196848359597003,0.7859015113490603,0.3113727569580078,2566.2285070000003 +43488,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8203141168625107,0.7864093592827466,0.2909717559814453,2667.8448940000003 +44394,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8199265649989863,0.7853959731543624,0.43657493591308594,2771.03508 +45300,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8212543323252169,0.7873854475750336,0.43532752990722656,2875.842657 +45312,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8212575312837942,0.7873440987265327,0.43532752990722656,2980.68937 +25,Binary classification,Hoeffding Adaptive Tree,Phishing,0.5833333333333334,0.6428571428571429,0.07476425170898438,0.008848 +50,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7346938775510204,0.7346938775510203,0.07482528686523438,0.123836 +75,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7837837837837838,0.7894736842105262,0.07482528686523438,0.332733 +100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8080808080808081,0.8080808080808081,0.07488632202148438,0.575353 +125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8225806451612904,0.819672131147541,0.07488632202148438,0.909775 +150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.825503355704698,0.8289473684210527,0.07490921020507812,1.284417 +175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8333333333333334,0.8242424242424242,0.07497024536132812,1.765942 +200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8291457286432161,0.8191489361702128,0.07497024536132812,2.25515 +225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8303571428571429,0.8155339805825242,0.07497024536132812,2.805996 +250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8313253012048193,0.817391304347826,0.07497024536132812,3.45663 +275,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8321167883211679,0.8174603174603176,0.07497024536132812,4.129749 +300,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8361204013377926,0.8178438661710038,0.07497024536132812,4.871246 +325,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8425925925925926,0.8197879858657244,0.07503128051757812,5.6743250000000005 +350,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8481375358166189,0.822742474916388,0.07503128051757812,6.528728000000001 +375,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8502673796791443,0.8227848101265823,0.07503128051757812,7.4517880000000005 +400,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8521303258145363,0.8228228228228228,0.07503128051757812,8.382915 +425,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8537735849056604,0.8208092485549133,0.07503128051757812,9.397859 +450,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8574610244988864,0.8232044198895027,0.07503128051757812,10.451359 +475,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8565400843881856,0.8238341968911918,0.07503128051757812,11.619284 +500,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8557114228456913,0.8260869565217391,0.07503128051757812,12.854081 +525,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8568702290076335,0.823529411764706,0.07503128051757812,14.155744 +550,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8561020036429873,0.8240534521158129,0.07503128051757812,15.508673 +575,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8554006968641115,0.8230277185501066,0.11409282684326172,16.956902 +600,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8547579298831386,0.8176100628930818,0.14198589324951172,18.498414999999998 +625,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8573717948717948,0.8172484599589321,0.14222240447998047,20.046891 +650,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8597842835130971,0.8233009708737864,0.14239025115966797,21.659211 +675,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8590504451038575,0.8263254113345521,0.14245128631591797,23.289464 +700,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8640915593705293,0.8306595365418894,0.14251232147216797,25.025232 +725,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8646408839779005,0.8344594594594595,0.14257335662841797,26.77059 +750,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8664886515353805,0.8371335504885993,0.14263439178466797,28.602532 +775,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8643410852713178,0.8330683624801273,0.14265727996826172,30.506622 +800,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8635794743429287,0.8340943683409437,0.14265727996826172,32.425334 +825,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8628640776699029,0.8345534407027819,0.14265727996826172,34.352118 +850,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8645465253239105,0.8364153627311521,0.14271831512451172,36.371837 +875,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8672768878718535,0.838888888888889,0.14271831512451172,38.48177 +900,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8665183537263627,0.8378378378378378,0.14277935028076172,40.637242 +925,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8668831168831169,0.8400520156046815,0.14277935028076172,42.878637 +950,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8661749209694415,0.8410513141426783,0.14284038543701172,45.126805999999995 +975,Binary classification,Hoeffding Adaptive Tree,Phishing,0.86652977412731,0.8414634146341464,0.14284038543701172,47.480371 +1000,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8638638638638638,0.8392434988179669,0.14284038543701172,49.860963999999996 +1025,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8623046875,0.8377445339470656,0.14284038543701172,52.359728 +1050,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8636796949475691,0.8402234636871508,0.14284038543701172,54.917308999999996 +1075,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8649906890130353,0.8429035752979415,0.14284038543701172,57.530035 +1100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8671519563239308,0.8456659619450316,0.14284038543701172,60.237019 +1125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8701067615658363,0.8507157464212679,0.14284038543701172,62.977514 +1150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8720626631853786,0.852852852852853,0.14290142059326172,65.816825 +1175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8713798977853492,0.8521057786483839,0.14290142059326172,68.70086099999999 +1200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.872393661384487,0.8530259365994236,0.14290142059326172,71.69850399999999 +1225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8733660130718954,0.8541862652869238,0.14296245574951172,74.74610599999998 +1250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8742994395516414,0.8560953253895509,0.14296245574951172,77.86495099999998 +1903,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02372455596923828,1.538486 +3806,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02378559112548828,4.672632 +5709,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,9.280899 +7612,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,15.518128 +9515,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,23.359252 +11418,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02390766143798828,32.724381 +13321,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02390766143798828,43.566998 +15224,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992774091834724,0.0,0.03315448760986328,56.059492 +17127,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992409202382343,0.0,0.02393054962158203,70.315874 +19030,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9993168322034789,0.0,0.02393054962158203,86.215201 +20933,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999378941333843,0.0,0.02399158477783203,103.810178 +22836,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994306984891613,0.0,0.02399158477783203,123.100461 +24739,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744926833212,0.0,0.02399158477783203,144.232391 +26642,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744942006681,0.0,0.02399158477783203,167.059054 +28545,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995095291479821,0.0,0.02399158477783203,191.625058 +30448,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995401845830459,0.0,0.02399158477783203,217.971038 +32351,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995672333848532,0.0,0.02399158477783203,246.1385 +34254,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995912766764955,0.0,0.02399158477783203,276.028821 +36157,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996127890253347,0.0,0.02399158477783203,307.727155 +38060,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996321500827662,0.0,0.02399158477783203,341.14046099999996 +39963,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996496671838246,0.0,0.02399158477783203,376.24706999999995 +41866,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996655917831124,0.0,0.02405261993408203,412.98223499999995 +43769,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996801316029976,0.0,0.02405261993408203,451.37683599999997 +45672,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996934597446958,0.0,0.02405261993408203,491.34241199999997 +47575,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9997057216126456,0.0,0.02405261993408203,532.9409959999999 +49478,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99971704024092,0.0,0.02405261993408203,576.05932 +51381,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996885947839627,0.0,0.02405261993408203,620.875381 +53284,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996997166075484,0.0,0.02405261993408203,667.2134759999999 +55187,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999710071394919,0.0,0.02405261993408203,715.0678459999999 +57090,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995620872672494,0.0,0.02405261993408203,764.41064 +58993,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995762137238947,0.0,0.02405261993408203,815.214443 +60896,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999589457262501,0.0,0.02405261993408203,867.5634849999999 +62799,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995700500015924,0.0,0.02405261993408203,921.3075979999999 +64702,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995826957852274,0.0,0.02405261993408203,976.5012499999999 +66605,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995946189418053,0.0,0.02405261993408203,1033.066349 +68508,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995766855941729,0.0,0.02405261993408203,1090.838953 +70411,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995881266865502,0.0,0.02405261993408203,1149.858677 +72314,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995989656078437,0.0,0.02405261993408203,1210.0750699999999 +74217,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99960924867953,0.0,0.02405261993408203,1271.4412419999999 +76120,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996190175908775,0.0,0.02405261993408203,1333.994434 +78023,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996283099638563,0.0,0.02405261993408203,1397.762471 +79926,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996371598373475,0.0,0.02405261993408203,1462.67416 +81829,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996455980837855,0.0,0.02405261993408203,1528.680001 +83732,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996536527689864,0.0,0.02411365509033203,1595.853878 +85635,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999661349463998,0.0,0.02411365509033203,1664.0432529999998 +87538,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996687115162731,0.0,0.02411365509033203,1733.3243249999998 +89441,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996645796064401,0.0,0.02411365509033203,1803.716354 +91344,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999671567607808,0.0,0.02411365509033203,1875.203937 +93247,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996782703815713,0.0,0.02411365509033203,1947.740223 +95150,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996847050415664,0.0,0.02411365509033203,2021.343945 +95156,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996847249224948,0.0,0.02411365509033203,2094.94949 +106,Binary classification,Adaptive Random Forest,Bananas,0.638095238095238,0.5777777777777778,0.6023197174072266,1.25138 +212,Binary classification,Adaptive Random Forest,Bananas,0.7535545023696683,0.711111111111111,1.0872974395751953,3.920593 +318,Binary classification,Adaptive Random Forest,Bananas,0.7760252365930599,0.7380073800738007,1.471883773803711,8.005582 +424,Binary classification,Adaptive Random Forest,Bananas,0.8085106382978723,0.7768595041322315,1.8271961212158203,13.704046000000002 +530,Binary classification,Adaptive Random Forest,Bananas,0.8204158790170132,0.7845804988662132,2.2761096954345703,21.017212 +636,Binary classification,Adaptive Random Forest,Bananas,0.8362204724409449,0.8052434456928838,2.6539440155029297,30.07026 +742,Binary classification,Adaptive Random Forest,Bananas,0.8434547908232118,0.8110749185667754,3.0667247772216797,40.88011 +848,Binary classification,Adaptive Random Forest,Bananas,0.8512396694214877,0.8220338983050847,3.4897289276123047,53.580909000000005 +954,Binary classification,Adaptive Random Forest,Bananas,0.8583420776495279,0.8301886792452831,3.9347667694091797,68.252368 +1060,Binary classification,Adaptive Random Forest,Bananas,0.8659112370160529,0.8378995433789953,4.283300399780273,84.83204500000001 +1166,Binary classification,Adaptive Random Forest,Bananas,0.8695278969957082,0.8429752066115702,4.800313949584961,103.574646 +1272,Binary classification,Adaptive Random Forest,Bananas,0.8693941778127459,0.8442776735459662,5.391313552856445,124.497156 +1378,Binary classification,Adaptive Random Forest,Bananas,0.8714596949891068,0.8454148471615721,5.846994400024414,147.830368 +1484,Binary classification,Adaptive Random Forest,Bananas,0.8759271746459879,0.8518518518518519,6.193078994750977,173.44391299999998 +1590,Binary classification,Adaptive Random Forest,Bananas,0.8753933291378225,0.8520179372197308,6.296388626098633,201.55742899999998 +1696,Binary classification,Adaptive Random Forest,Bananas,0.8755162241887906,0.8523442967109867,6.211141586303711,232.060182 +1802,Binary classification,Adaptive Random Forest,Bananas,0.8767351471404775,0.8550913838120104,6.65928840637207,264.912691 +1908,Binary classification,Adaptive Random Forest,Bananas,0.8730991085474568,0.8522588522588523,6.686662673950195,300.275827 +2014,Binary classification,Adaptive Random Forest,Bananas,0.8708395429706905,0.8507462686567164,7.210599899291992,338.470819 +2120,Binary classification,Adaptive Random Forest,Bananas,0.8725814063237376,0.8540540540540541,7.48176383972168,378.991608 +2226,Binary classification,Adaptive Random Forest,Bananas,0.8723595505617977,0.8539094650205761,7.915548324584961,421.86917 +2332,Binary classification,Adaptive Random Forest,Bananas,0.8751608751608752,0.8574228319451249,8.423246383666992,467.302026 +2438,Binary classification,Adaptive Random Forest,Bananas,0.8740254411161263,0.8560712611345522,8.870996475219727,515.200386 +2544,Binary classification,Adaptive Random Forest,Bananas,0.874557609123083,0.857779759251003,9.376256942749023,565.505493 +2650,Binary classification,Adaptive Random Forest,Bananas,0.8761796904492262,0.8600682593856656,9.769472122192383,618.233402 +2756,Binary classification,Adaptive Random Forest,Bananas,0.8780399274047187,0.8621821164889254,10.359186172485352,673.368122 +2862,Binary classification,Adaptive Random Forest,Bananas,0.8797623208668298,0.8638163103721298,10.741575241088867,730.824809 +2968,Binary classification,Adaptive Random Forest,Bananas,0.8800134816312774,0.8637059724349159,11.09235954284668,790.5343869999999 +3074,Binary classification,Adaptive Random Forest,Bananas,0.8805727302310445,0.8648250460405157,11.562868118286133,852.458144 +3180,Binary classification,Adaptive Random Forest,Bananas,0.8826675055048757,0.8667381207574134,10.152639389038086,916.4431709999999 +3286,Binary classification,Adaptive Random Forest,Bananas,0.882496194824962,0.8663434903047091,10.670488357543945,982.3914759999999 +3392,Binary classification,Adaptive Random Forest,Bananas,0.8826304924800944,0.867244829886591,11.057397842407227,1050.1851049999998 +3498,Binary classification,Adaptive Random Forest,Bananas,0.8839004861309694,0.8680961663417803,10.334085464477539,1119.7824649999998 +3604,Binary classification,Adaptive Random Forest,Bananas,0.8850957535387177,0.8689043698543382,10.692270278930664,1191.1920959999998 +3710,Binary classification,Adaptive Random Forest,Bananas,0.8846050148287948,0.8687116564417178,11.112970352172852,1264.4750769999998 +3816,Binary classification,Adaptive Random Forest,Bananas,0.8859764089121888,0.870420017873101,11.59941291809082,1339.6069449999998 +3922,Binary classification,Adaptive Random Forest,Bananas,0.884723284876307,0.8687572590011614,12.03856086730957,1416.6409169999997 +4028,Binary classification,Adaptive Random Forest,Bananas,0.8840327787434815,0.867892503536068,12.434591293334961,1495.7064749999997 +4134,Binary classification,Adaptive Random Forest,Bananas,0.884587466731188,0.868558831634059,12.796384811401367,1576.7285749999996 +4240,Binary classification,Adaptive Random Forest,Bananas,0.8858221278603444,0.8701019860440149,13.120096206665039,1659.7498909999997 +4346,Binary classification,Adaptive Random Forest,Bananas,0.8872266973532796,0.8716605552645365,13.362188339233398,1744.8641249999996 +4452,Binary classification,Adaptive Random Forest,Bananas,0.8869916872612896,0.8713225888974162,13.906320571899414,1831.9815919999996 +4558,Binary classification,Adaptive Random Forest,Bananas,0.8872064955014264,0.8719481813652217,14.321008682250977,1921.0718599999996 +4664,Binary classification,Adaptive Random Forest,Bananas,0.8876259918507399,0.8728155339805825,14.677774429321289,2012.2265639999996 +4770,Binary classification,Adaptive Random Forest,Bananas,0.8867687146152233,0.8716119828815977,15.041936874389648,2105.5104569999994 +4876,Binary classification,Adaptive Random Forest,Bananas,0.886974358974359,0.8715318256003731,15.36302375793457,2200.9404059999993 +4982,Binary classification,Adaptive Random Forest,Bananas,0.8877735394499097,0.8726941471191073,14.241693496704102,2298.464636999999 +5088,Binary classification,Adaptive Random Forest,Bananas,0.886966778061726,0.8716804284757868,14.559698104858398,2397.961828999999 +5194,Binary classification,Adaptive Random Forest,Bananas,0.8869632197188523,0.8716939890710382,15.019205093383789,2499.520506999999 +5300,Binary classification,Adaptive Random Forest,Bananas,0.886959803736554,0.8717070036410367,15.355104446411133,2603.0162549999986 +906,Binary classification,Adaptive Random Forest,Elec2,0.8674033149171271,0.8669623059866962,3.022599220275879,14.706798 +1812,Binary classification,Adaptive Random Forest,Elec2,0.8956377691882937,0.8737474949899798,3.453568458557129,43.639849999999996 +2718,Binary classification,Adaptive Random Forest,Elec2,0.889216047110784,0.8638625056535504,5.134407997131348,89.85880599999999 +3624,Binary classification,Adaptive Random Forest,Elec2,0.8901462876069556,0.8665325285043594,5.045891761779785,149.36791399999998 +4530,Binary classification,Adaptive Random Forest,Elec2,0.8924707440936189,0.8628555336524922,6.377499580383301,220.195834 +5436,Binary classification,Adaptive Random Forest,Elec2,0.8870285188592456,0.8556652562294312,8.556572914123535,302.53910099999996 +6342,Binary classification,Adaptive Random Forest,Elec2,0.884245387162908,0.8540175019888624,10.355942726135254,396.16016899999994 +7248,Binary classification,Adaptive Random Forest,Elec2,0.8835380157306472,0.8516174402250353,10.061070442199707,501.0892999999999 +8154,Binary classification,Adaptive Random Forest,Elec2,0.8847050165583221,0.8605341246290802,12.516213417053223,615.7123809999999 +9060,Binary classification,Adaptive Random Forest,Elec2,0.8869632409758251,0.8668400520156047,14.3945894241333,740.068354 +9966,Binary classification,Adaptive Random Forest,Elec2,0.8839939789262419,0.8666974169741698,15.028592109680176,874.678214 +10872,Binary classification,Adaptive Random Forest,Elec2,0.886119032287738,0.8712830110210023,18.58602237701416,1018.7273250000001 +11778,Binary classification,Adaptive Random Forest,Elec2,0.8851150547677676,0.869464544138929,18.284192085266113,1172.329585 +12684,Binary classification,Adaptive Random Forest,Elec2,0.8825987542379563,0.8672550592850139,18.562626838684082,1335.5183499999998 +13590,Binary classification,Adaptive Random Forest,Elec2,0.8835087202884686,0.8699794661190965,22.36763858795166,1507.9316749999998 +14496,Binary classification,Adaptive Random Forest,Elec2,0.883270093135564,0.870085995085995,23.97218418121338,1690.157865 +15402,Binary classification,Adaptive Random Forest,Elec2,0.8826050256476852,0.8679713743245216,24.89116382598877,1881.8741799999998 +16308,Binary classification,Adaptive Random Forest,Elec2,0.8806034218433801,0.8649885583524027,9.630642890930176,2082.7138459999996 +17214,Binary classification,Adaptive Random Forest,Elec2,0.880787776680416,0.862742474916388,9.825531959533691,2290.8714669999995 +18120,Binary classification,Adaptive Random Forest,Elec2,0.881505601854407,0.8635178946030132,13.432568550109863,2506.1422499999994 +19026,Binary classification,Adaptive Random Forest,Elec2,0.8835742444152431,0.864335150364427,11.236374855041504,2728.1698299999994 +19932,Binary classification,Adaptive Random Forest,Elec2,0.8847022226682053,0.8666744024135531,10.915810585021973,2956.9763619999994 +20838,Binary classification,Adaptive Random Forest,Elec2,0.8845803138647598,0.866618601297765,6.771607398986816,3192.2184919999995 +21744,Binary classification,Adaptive Random Forest,Elec2,0.8842845973416732,0.8643665768194071,9.905537605285645,3433.7445519999997 +22650,Binary classification,Adaptive Random Forest,Elec2,0.8832178021104684,0.8619303648796786,11.609391212463379,3682.1047309999994 +23556,Binary classification,Adaptive Random Forest,Elec2,0.8819783485459562,0.8599637316139432,7.878331184387207,3937.9632889999993 +24462,Binary classification,Adaptive Random Forest,Elec2,0.8805854216916724,0.8573800107416631,10.653840065002441,4201.066475999999 +25368,Binary classification,Adaptive Random Forest,Elec2,0.8791343083533725,0.8556497175141242,11.591797828674316,4470.752341999999 +26274,Binary classification,Adaptive Random Forest,Elec2,0.8801431127012522,0.8566616596112704,13.86082935333252,4746.937765999999 +27180,Binary classification,Adaptive Random Forest,Elec2,0.881195040288458,0.8584082438061829,14.463074684143066,5029.888654999999 +28086,Binary classification,Adaptive Random Forest,Elec2,0.8798646964571836,0.8561807331628303,15.367924690246582,5319.9108369999985 +28992,Binary classification,Adaptive Random Forest,Elec2,0.8796523058880342,0.8551019560612982,16.377129554748535,5616.458601999999 +29898,Binary classification,Adaptive Random Forest,Elec2,0.879285547044854,0.8544934080554772,16.05477237701416,5919.470834999999 +30804,Binary classification,Adaptive Random Forest,Elec2,0.8791351491737818,0.8535347574648885,17.57622241973877,6228.255318999999 +31710,Binary classification,Adaptive Random Forest,Elec2,0.8775111167176511,0.851267519338286,17.546963691711426,6543.393541999999 +32616,Binary classification,Adaptive Random Forest,Elec2,0.8771117583933773,0.8510701545778836,17.15481662750244,6864.899302999999 +33522,Binary classification,Adaptive Random Forest,Elec2,0.8770621401509502,0.8512864927285193,13.577618598937988,7192.851994 +34428,Binary classification,Adaptive Random Forest,Elec2,0.8757080198681267,0.849643346568748,12.363858222961426,7526.9490559999995 +35334,Binary classification,Adaptive Random Forest,Elec2,0.8754139189992358,0.848624484181568,12.552750587463379,7866.609101999999 +36240,Binary classification,Adaptive Random Forest,Elec2,0.875134523579569,0.8474427699672971,12.88097858428955,8211.574848999999 +37146,Binary classification,Adaptive Random Forest,Elec2,0.8743034055727554,0.8459838363846282,15.634392738342285,8562.247513999999 +38052,Binary classification,Adaptive Random Forest,Elec2,0.8741163175737826,0.8451642099818981,17.75814151763916,8918.936239999999 +38958,Binary classification,Adaptive Random Forest,Elec2,0.8743743101368175,0.8458873913591133,18.55082416534424,9281.578228999999 +39864,Binary classification,Adaptive Random Forest,Elec2,0.8744951458746206,0.847381104908331,20.39273166656494,9650.394165999998 +40770,Binary classification,Adaptive Random Forest,Elec2,0.8750521229365449,0.8493434283686265,20.04684543609619,10025.208226999997 +41676,Binary classification,Adaptive Random Forest,Elec2,0.8757768446310737,0.8512911843276937,22.40410327911377,10405.883388999997 +42582,Binary classification,Adaptive Random Forest,Elec2,0.8760010333247223,0.8517603458925264,17.905674934387207,10792.424187999997 +43488,Binary classification,Adaptive Random Forest,Elec2,0.8758249591832041,0.8516320474777449,17.979458808898926,11185.171687999997 +44394,Binary classification,Adaptive Random Forest,Elec2,0.8758362804946725,0.8511557571829769,19.483532905578613,11584.749531999996 +45300,Binary classification,Adaptive Random Forest,Elec2,0.8765977173889048,0.8524053440354862,22.381768226623535,11990.855977999996 +45312,Binary classification,Adaptive Random Forest,Elec2,0.8766083291033082,0.8523906328378699,22.39494037628174,12397.578789999996 +25,Binary classification,Adaptive Random Forest,Phishing,0.625,0.7096774193548387,0.41788291931152344,0.504078 +50,Binary classification,Adaptive Random Forest,Phishing,0.7346938775510204,0.7450980392156864,0.6195468902587891,1.506996 +75,Binary classification,Adaptive Random Forest,Phishing,0.7837837837837838,0.7999999999999999,0.8261966705322266,2.945496 +100,Binary classification,Adaptive Random Forest,Phishing,0.797979797979798,0.8039215686274509,0.9074077606201172,4.810448 +125,Binary classification,Adaptive Random Forest,Phishing,0.7903225806451613,0.7968749999999999,1.0524044036865234,7.208508 +150,Binary classification,Adaptive Random Forest,Phishing,0.8120805369127517,0.8227848101265823,1.155344009399414,10.051324000000001 +175,Binary classification,Adaptive Random Forest,Phishing,0.8390804597701149,0.8372093023255814,1.2272701263427734,13.451327000000001 +200,Binary classification,Adaptive Random Forest,Phishing,0.8442211055276382,0.8426395939086295,1.3437442779541016,17.363237 +225,Binary classification,Adaptive Random Forest,Phishing,0.8526785714285714,0.8465116279069769,1.4417095184326172,21.777532 +250,Binary classification,Adaptive Random Forest,Phishing,0.8554216867469879,0.85,1.652822494506836,26.610844 +275,Binary classification,Adaptive Random Forest,Phishing,0.8540145985401459,0.8473282442748092,1.7137775421142578,32.103705 +300,Binary classification,Adaptive Random Forest,Phishing,0.8595317725752508,0.85,1.6836071014404297,38.177642 +325,Binary classification,Adaptive Random Forest,Phishing,0.8672839506172839,0.8542372881355932,1.8154468536376953,44.724647 +350,Binary classification,Adaptive Random Forest,Phishing,0.8681948424068768,0.8525641025641026,1.9355945587158203,51.783981999999995 +375,Binary classification,Adaptive Random Forest,Phishing,0.8663101604278075,0.8484848484848485,2.1126270294189453,59.453596999999995 +400,Binary classification,Adaptive Random Forest,Phishing,0.8696741854636592,0.8505747126436781,2.2513599395751953,67.701448 +425,Binary classification,Adaptive Random Forest,Phishing,0.8702830188679245,0.8467966573816157,2.4080867767333984,76.525873 +450,Binary classification,Adaptive Random Forest,Phishing,0.8775055679287305,0.8533333333333333,2.413846969604492,85.91210000000001 +475,Binary classification,Adaptive Random Forest,Phishing,0.879746835443038,0.85785536159601,2.540945053100586,95.91125100000001 +500,Binary classification,Adaptive Random Forest,Phishing,0.8817635270541082,0.8624708624708626,2.727457046508789,106.551347 +525,Binary classification,Adaptive Random Forest,Phishing,0.8835877862595419,0.8623024830699774,2.780088424682617,117.81370000000001 +550,Binary classification,Adaptive Random Forest,Phishing,0.8816029143897997,0.8602150537634409,2.8441905975341797,129.668358 +575,Binary classification,Adaptive Random Forest,Phishing,0.8832752613240418,0.8618556701030927,2.9667911529541016,142.149346 +600,Binary classification,Adaptive Random Forest,Phishing,0.8864774624373957,0.8634538152610441,2.9313793182373047,155.146824 +625,Binary classification,Adaptive Random Forest,Phishing,0.8878205128205128,0.8622047244094488,3.1180286407470703,168.863765 +650,Binary classification,Adaptive Random Forest,Phishing,0.8906009244992296,0.8672897196261682,3.1772937774658203,183.176428 +675,Binary classification,Adaptive Random Forest,Phishing,0.8931750741839762,0.8732394366197184,3.270914077758789,198.116157 +700,Binary classification,Adaptive Random Forest,Phishing,0.8969957081545065,0.8762886597938143,3.2819652557373047,213.702702 +725,Binary classification,Adaptive Random Forest,Phishing,0.8950276243093923,0.8762214983713356,3.465627670288086,229.894704 +750,Binary classification,Adaptive Random Forest,Phishing,0.897196261682243,0.8791208791208791,3.637697219848633,246.71919699999998 +775,Binary classification,Adaptive Random Forest,Phishing,0.8979328165374677,0.8793893129770992,3.6838626861572266,264.27439799999996 +800,Binary classification,Adaptive Random Forest,Phishing,0.8961201501877347,0.8784773060029282,3.7807750701904297,282.50010699999996 +825,Binary classification,Adaptive Random Forest,Phishing,0.8968446601941747,0.8801128349788435,3.913633346557617,301.46455399999996 +850,Binary classification,Adaptive Random Forest,Phishing,0.8987043580683156,0.8818681318681318,4.011789321899414,321.06457099999994 +875,Binary classification,Adaptive Random Forest,Phishing,0.9016018306636155,0.8847184986595175,4.15968132019043,341.44966299999993 +900,Binary classification,Adaptive Random Forest,Phishing,0.9010011123470523,0.8836601307189543,3.946676254272461,362.64137199999993 +925,Binary classification,Adaptive Random Forest,Phishing,0.9036796536796536,0.8877679697351829,4.049928665161133,384.6765929999999 +950,Binary classification,Adaptive Random Forest,Phishing,0.9030558482613277,0.8883495145631068,3.6841602325439453,407.5276099999999 +975,Binary classification,Adaptive Random Forest,Phishing,0.9045174537987679,0.8899408284023669,3.787748336791992,431.1052179999999 +1000,Binary classification,Adaptive Random Forest,Phishing,0.9049049049049049,0.8904267589388698,4.052656173706055,455.43358799999993 +1025,Binary classification,Adaptive Random Forest,Phishing,0.9033203125,0.888888888888889,4.062379837036133,480.5827999999999 +1050,Binary classification,Adaptive Random Forest,Phishing,0.9046711153479504,0.8908296943231442,4.190084457397461,506.4991779999999 +1075,Binary classification,Adaptive Random Forest,Phishing,0.9059590316573557,0.8928950159066809,4.285711288452148,533.2628339999999 +1100,Binary classification,Adaptive Random Forest,Phishing,0.9062784349408554,0.8934850051706308,4.370790481567383,560.8485399999998 +1125,Binary classification,Adaptive Random Forest,Phishing,0.9065836298932385,0.8948948948948948,3.9348621368408203,589.2233909999999 +1150,Binary classification,Adaptive Random Forest,Phishing,0.9077458659704091,0.896078431372549,4.19316291809082,618.4066789999998 +1175,Binary classification,Adaptive Random Forest,Phishing,0.9063032367972743,0.8942307692307692,4.349401473999023,648.4320089999999 +1200,Binary classification,Adaptive Random Forest,Phishing,0.9065888240200167,0.8943396226415095,4.34752082824707,679.2709969999999 +1225,Binary classification,Adaptive Random Forest,Phishing,0.9068627450980392,0.8944444444444444,4.031515121459961,710.9105619999998 +1250,Binary classification,Adaptive Random Forest,Phishing,0.9079263410728583,0.896115627822945,4.102910995483398,743.3769359999998 +1903,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17035293579101562,12.381202 +3806,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17157363891601562,37.073822 +5709,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,74.106824 +7612,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,122.30955 +9515,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,180.539768 +11418,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17401504516601562,247.17494200000002 +13321,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17401504516601562,321.611977 +15224,Binary classification,Adaptive Random Forest,SMTP,0.9992774091834724,0.0,0.23138427734375,404.612477 +17127,Binary classification,Adaptive Random Forest,SMTP,0.9992409202382343,0.0,0.17718124389648438,498.19936 +19030,Binary classification,Adaptive Random Forest,SMTP,0.9993168322034789,0.0,0.16917037963867188,601.949625 +20933,Binary classification,Adaptive Random Forest,SMTP,0.999378941333843,0.0,0.17043685913085938,714.510104 +22836,Binary classification,Adaptive Random Forest,SMTP,0.9994306984891613,0.0,0.17826461791992188,835.601872 +24739,Binary classification,Adaptive Random Forest,SMTP,0.9994744926833212,0.0,0.16260147094726562,964.8451829999999 +26642,Binary classification,Adaptive Random Forest,SMTP,0.9994744942006681,0.0,0.170440673828125,1103.1275799999999 +28545,Binary classification,Adaptive Random Forest,SMTP,0.9995095291479821,0.0,0.17826461791992188,1249.157139 +30448,Binary classification,Adaptive Random Forest,SMTP,0.9995401845830459,0.0,0.17816925048828125,1402.52966 +32351,Binary classification,Adaptive Random Forest,SMTP,0.9995672333848532,0.0,0.16262054443359375,1563.348194 +34254,Binary classification,Adaptive Random Forest,SMTP,0.9995912766764955,0.0,0.17043685913085938,1731.617157 +36157,Binary classification,Adaptive Random Forest,SMTP,0.9996127890253347,0.0,0.17043304443359375,1907.1505949999998 +38060,Binary classification,Adaptive Random Forest,SMTP,0.9996321500827662,0.0,0.1781158447265625,2090.2143509999996 +39963,Binary classification,Adaptive Random Forest,SMTP,0.9996496671838246,0.0,0.1704559326171875,2280.326256 +41866,Binary classification,Adaptive Random Forest,SMTP,0.9996655917831124,0.0,0.163818359375,2477.410916 +43769,Binary classification,Adaptive Random Forest,SMTP,0.9996801316029976,0.0,0.17158126831054688,2681.481315 +45672,Binary classification,Adaptive Random Forest,SMTP,0.9996934597446958,0.0,0.171630859375,2892.603814 +47575,Binary classification,Adaptive Random Forest,SMTP,0.9997057216126456,0.0,0.17947769165039062,3110.724932 +49478,Binary classification,Adaptive Random Forest,SMTP,0.99971704024092,0.0,0.32259368896484375,3336.815763 +51381,Binary classification,Adaptive Random Forest,SMTP,0.9996885947839627,0.0,0.3238716125488281,3571.848368 +53284,Binary classification,Adaptive Random Forest,SMTP,0.9996997166075484,0.0,0.2926750183105469,3815.616702 +55187,Binary classification,Adaptive Random Forest,SMTP,0.999710071394919,0.0,0.3244895935058594,4068.2038989999996 +57090,Binary classification,Adaptive Random Forest,SMTP,0.9995620872672494,0.0,0.2933502197265625,4329.847041999999 +58993,Binary classification,Adaptive Random Forest,SMTP,0.9995762137238947,0.0,0.27820587158203125,4599.769264 +60896,Binary classification,Adaptive Random Forest,SMTP,0.999589457262501,0.0,0.3094024658203125,4878.106527 +62799,Binary classification,Adaptive Random Forest,SMTP,0.9995700500015924,0.0,0.30953216552734375,5164.770925 +64702,Binary classification,Adaptive Random Forest,SMTP,0.9995826957852274,0.0,0.29401397705078125,5459.39874 +66605,Binary classification,Adaptive Random Forest,SMTP,0.9995946189418053,0.0,0.293975830078125,5761.313045999999 +68508,Binary classification,Adaptive Random Forest,SMTP,0.9995766855941729,0.0,0.32515716552734375,6070.487373999999 +70411,Binary classification,Adaptive Random Forest,SMTP,0.9995881266865502,0.0,0.3101234436035156,6386.879354 +72314,Binary classification,Adaptive Random Forest,SMTP,0.9995989656078437,0.0,0.3101387023925781,6710.520715 +74217,Binary classification,Adaptive Random Forest,SMTP,0.99960924867953,0.0,0.3101615905761719,7041.446151 +76120,Binary classification,Adaptive Random Forest,SMTP,0.9996190175908775,0.0,0.3101768493652344,7379.414547 +78023,Binary classification,Adaptive Random Forest,SMTP,0.9996283099638563,0.0,0.3100128173828125,7724.224697000001 +79926,Binary classification,Adaptive Random Forest,SMTP,0.9996371598373475,0.0,0.31014251708984375,8075.760258 +81829,Binary classification,Adaptive Random Forest,SMTP,0.9996455980837855,0.0,0.3100852966308594,8434.038373 +83732,Binary classification,Adaptive Random Forest,SMTP,0.9996536527689864,0.0,0.31072235107421875,8799.103777999999 +85635,Binary classification,Adaptive Random Forest,SMTP,0.999661349463998,0.0,0.310821533203125,9170.869249 +87538,Binary classification,Adaptive Random Forest,SMTP,0.9996687115162731,0.0,0.2950706481933594,9549.506615999999 +89441,Binary classification,Adaptive Random Forest,SMTP,0.9996645796064401,0.0,0.2951812744140625,9935.081315999998 +91344,Binary classification,Adaptive Random Forest,SMTP,0.999671567607808,0.0,0.2957954406738281,10327.647623999997 +93247,Binary classification,Adaptive Random Forest,SMTP,0.9996782703815713,0.0,0.3115119934082031,10728.119291999998 +95150,Binary classification,Adaptive Random Forest,SMTP,0.9996847050415664,0.0,0.3115577697753906,11135.737891999997 +95156,Binary classification,Adaptive Random Forest,SMTP,0.9996847249224948,0.0,0.32709503173828125,11543.384409999997 +106,Binary classification,Streaming Random Patches,Bananas,0.5428571428571428,0.4,0.2255392074584961,2.569769 +212,Binary classification,Streaming Random Patches,Bananas,0.5592417061611374,0.4685714285714286,0.6304416656494141,8.011061999999999 +318,Binary classification,Streaming Random Patches,Bananas,0.637223974763407,0.5724907063197027,0.9710559844970703,16.523663 +424,Binary classification,Streaming Random Patches,Bananas,0.6926713947990544,0.6448087431693988,1.2628002166748047,28.175313 +530,Binary classification,Streaming Random Patches,Bananas,0.7145557655954632,0.6621923937360179,1.5703105926513672,43.218913 +636,Binary classification,Streaming Random Patches,Bananas,0.7448818897637796,0.7000000000000001,1.467294692993164,61.573557 +742,Binary classification,Streaming Random Patches,Bananas,0.7624831309041835,0.7170418006430868,1.877767562866211,83.22394800000001 +848,Binary classification,Streaming Random Patches,Bananas,0.7827626918536009,0.7430167597765364,2.3253536224365234,108.413447 +954,Binary classification,Streaming Random Patches,Bananas,0.7964323189926548,0.7599009900990098,1.7426891326904297,137.183902 +1060,Binary classification,Streaming Random Patches,Bananas,0.8054768649669499,0.7674943566591422,1.7942829132080078,169.50541299999998 +1166,Binary classification,Streaming Random Patches,Bananas,0.8103004291845494,0.7747196738022425,1.8575687408447266,205.46256099999997 +1272,Binary classification,Streaming Random Patches,Bananas,0.8151062155782848,0.7822057460611677,1.917165756225586,244.58779499999997 +1378,Binary classification,Streaming Random Patches,Bananas,0.8191721132897604,0.7851596203623814,2.1873340606689453,286.663235 +1484,Binary classification,Streaming Random Patches,Bananas,0.8240053944706676,0.7916999201915402,2.2810306549072266,331.700902 +1590,Binary classification,Streaming Random Patches,Bananas,0.8231592196349906,0.7916975537435137,2.585817337036133,379.560946 +1696,Binary classification,Streaming Random Patches,Bananas,0.8271386430678466,0.7961029923451634,2.8855953216552734,430.279509 +1802,Binary classification,Streaming Random Patches,Bananas,0.8334258745141588,0.8046875,2.8240184783935547,483.724395 +1908,Binary classification,Streaming Random Patches,Bananas,0.8332459360251704,0.8060975609756097,3.138376235961914,539.732848 +2014,Binary classification,Streaming Random Patches,Bananas,0.8340784898161947,0.8084862385321101,3.5751514434814453,598.378334 +2120,Binary classification,Streaming Random Patches,Bananas,0.8367154318074563,0.813778256189451,3.890401840209961,659.469374 +2226,Binary classification,Streaming Random Patches,Bananas,0.8382022471910112,0.8157625383828044,4.414094924926758,723.240852 +2332,Binary classification,Streaming Random Patches,Bananas,0.8404118404118404,0.8185365853658537,4.828973770141602,789.4489060000001 +2438,Binary classification,Streaming Random Patches,Bananas,0.8432498974148543,0.8216619981325864,4.724649429321289,858.0992190000001 +2544,Binary classification,Streaming Random Patches,Bananas,0.8450648839952811,0.8247330960854093,4.20762825012207,929.1630060000001 +2650,Binary classification,Streaming Random Patches,Bananas,0.846734616836542,0.8270868824531515,4.517709732055664,1002.5522950000001 +2756,Binary classification,Streaming Random Patches,Bananas,0.8500907441016334,0.8306683066830667,4.757001876831055,1078.240465 +2862,Binary classification,Streaming Random Patches,Bananas,0.8521495980426425,0.8324752475247525,4.690572738647461,1156.1945970000002 +2968,Binary classification,Streaming Random Patches,Bananas,0.854061341422312,0.8339087073264287,4.873067855834961,1236.185934 +3074,Binary classification,Streaming Random Patches,Bananas,0.8538887081028311,0.8340110905730129,5.244169235229492,1318.3478 +3180,Binary classification,Streaming Random Patches,Bananas,0.8565586662472475,0.836441893830703,5.473237991333008,1402.707418 +3286,Binary classification,Streaming Random Patches,Bananas,0.8575342465753425,0.8371607515657619,5.716192245483398,1489.296647 +3392,Binary classification,Streaming Random Patches,Bananas,0.8593335299321734,0.8400938652363393,6.05610466003418,1578.3688909999998 +3498,Binary classification,Streaming Random Patches,Bananas,0.8615956534172148,0.8419333768778575,6.433168411254883,1669.799251 +3604,Binary classification,Streaming Random Patches,Bananas,0.8631695809048016,0.8430436166825852,6.670698165893555,1763.5519829999998 +3710,Binary classification,Streaming Random Patches,Bananas,0.8638447020760313,0.8444718201416692,7.050989151000977,1859.6268179999997 +3816,Binary classification,Streaming Random Patches,Bananas,0.8657929226736566,0.84688995215311,7.316404342651367,1958.0125029999997 +3922,Binary classification,Streaming Random Patches,Bananas,0.8653404743687835,0.846064139941691,7.61528205871582,2058.6755279999998 +4028,Binary classification,Streaming Random Patches,Bananas,0.8644151974174323,0.8449744463373083,7.967977523803711,2161.5792619999997 +4134,Binary classification,Streaming Random Patches,Bananas,0.8654730220179047,0.8462389380530975,7.394952774047852,2266.5958729999998 +4240,Binary classification,Streaming Random Patches,Bananas,0.8674215616890776,0.8486806677436726,7.571531295776367,2373.458397 +4346,Binary classification,Streaming Random Patches,Bananas,0.8688147295742232,0.8503937007874016,7.877435684204102,2482.305033 +4452,Binary classification,Streaming Random Patches,Bananas,0.8683441923163334,0.8496664956387892,8.180627822875977,2593.165336 +4558,Binary classification,Streaming Random Patches,Bananas,0.8689927583936801,0.8508618536097925,8.39448356628418,2705.945127 +4664,Binary classification,Streaming Random Patches,Bananas,0.8691829294445635,0.8516536964980544,8.710580825805664,2820.674173 +4770,Binary classification,Streaming Random Patches,Bananas,0.8689452715453974,0.8510131108462455,9.014997482299805,2937.453009 +4876,Binary classification,Streaming Random Patches,Bananas,0.8703589743589744,0.8523364485981308,9.167715072631836,3056.177406 +4982,Binary classification,Streaming Random Patches,Bananas,0.8713109817305762,0.8538864827900615,9.482858657836914,3176.936292 +5088,Binary classification,Streaming Random Patches,Bananas,0.8714369962649892,0.8539526574363555,9.87147331237793,3299.754349 +5194,Binary classification,Streaming Random Patches,Bananas,0.8717504332755632,0.8543944031482291,10.204122543334961,3424.594329 +5300,Binary classification,Streaming Random Patches,Bananas,0.8716739007359879,0.8542648949849978,10.538087844848633,3551.414099 +906,Binary classification,Streaming Random Patches,Elec2,0.8828729281767956,0.8811659192825113,5.258722305297852,37.408806 +1812,Binary classification,Streaming Random Patches,Elec2,0.9039204859193816,0.8804945054945055,8.443174362182617,104.985856 +2718,Binary classification,Streaming Random Patches,Elec2,0.8873757821126242,0.8602739726027397,12.445928573608398,198.51440200000002 +3624,Binary classification,Streaming Random Patches,Elec2,0.884902014904775,0.8576305906452714,16.533422470092773,314.268209 +4530,Binary classification,Streaming Random Patches,Elec2,0.8812099801280636,0.8452243958573072,19.266294479370117,451.77035 +5436,Binary classification,Streaming Random Patches,Elec2,0.8756209751609936,0.8372652864708715,24.12981605529785,609.66041 +6342,Binary classification,Streaming Random Patches,Elec2,0.8719444882510645,0.8340825500612996,28.348302841186523,788.9253299999999 +7248,Binary classification,Streaming Random Patches,Elec2,0.8691872498965089,0.8308351177730193,31.664392471313477,988.709629 +8154,Binary classification,Streaming Random Patches,Elec2,0.8690052741322213,0.8387681159420289,35.27585411071777,1209.125777 +9060,Binary classification,Streaming Random Patches,Elec2,0.869742797218236,0.844162704701532,38.39363670349121,1448.169528 +9966,Binary classification,Streaming Random Patches,Elec2,0.8681384846964375,0.8455934195064629,42.49019813537598,1706.33653 +10872,Binary classification,Streaming Random Patches,Elec2,0.8687333272008095,0.849265870920038,46.91076469421387,1983.228319 +11778,Binary classification,Streaming Random Patches,Elec2,0.8694064702386006,0.8495402073958129,41.518564224243164,2278.772125 +12684,Binary classification,Streaming Random Patches,Elec2,0.8672238429393676,0.8479320931912588,46.98099327087402,2591.81768 +13590,Binary classification,Streaming Random Patches,Elec2,0.8682758113179778,0.8513289036544851,50.757638931274414,2922.748705 +14496,Binary classification,Streaming Random Patches,Elec2,0.8687823387374957,0.8527863777089782,43.74130058288574,3269.777293 +15402,Binary classification,Streaming Random Patches,Elec2,0.8686448931887539,0.8518708354689903,49.06788444519043,3632.343799 +16308,Binary classification,Streaming Random Patches,Elec2,0.8649659655362728,0.8467427616926504,54.357858657836914,4011.129855 +17214,Binary classification,Streaming Random Patches,Elec2,0.865392435949573,0.8447987139125192,52.38222694396973,4407.893822 +18120,Binary classification,Streaming Random Patches,Elec2,0.8652795408135107,0.8448286822198208,59.36540412902832,4822.718697 +19026,Binary classification,Streaming Random Patches,Elec2,0.867700394218134,0.8459136822773186,57.10729789733887,5251.994155 +19932,Binary classification,Streaming Random Patches,Elec2,0.8692489087351363,0.84882236918436,51.34463310241699,5694.600867 +20838,Binary classification,Streaming Random Patches,Elec2,0.8691750251955656,0.8490085299656586,53.35045051574707,6149.875029 +21744,Binary classification,Streaming Random Patches,Elec2,0.8700271351699398,0.8478682170542635,59.89077186584473,6616.7506189999995 +22650,Binary classification,Streaming Random Patches,Elec2,0.8694865115457636,0.8459614382490881,65.61615180969238,7094.9355989999995 +23556,Binary classification,Streaming Random Patches,Elec2,0.868860114625345,0.8444690599667689,72.22853660583496,7585.744803 +24462,Binary classification,Streaming Random Patches,Elec2,0.8682392379706472,0.8428648042513772,80.47726249694824,8090.441156999999 +25368,Binary classification,Streaming Random Patches,Elec2,0.8672290771474751,0.8417888012025554,73.03231239318848,8608.413273 +26274,Binary classification,Streaming Random Patches,Elec2,0.8684581128915617,0.8430802760624773,79.16955757141113,9138.877569 +27180,Binary classification,Streaming Random Patches,Elec2,0.8698259685786821,0.8451234459814394,84.2670955657959,9681.232951 +28086,Binary classification,Streaming Random Patches,Elec2,0.8689335944454335,0.8434616202423985,93.53372383117676,10236.036265 +28992,Binary classification,Streaming Random Patches,Elec2,0.8688558518160808,0.8423322551215061,92.75358009338379,10801.143387 +29898,Binary classification,Streaming Random Patches,Elec2,0.8689166137070609,0.8421603769785332,90.79977607727051,11375.205452 +30804,Binary classification,Streaming Random Patches,Elec2,0.8684868356978216,0.8408063818917749,97.95510292053223,11957.401901000001 +31710,Binary classification,Streaming Random Patches,Elec2,0.8668832192752847,0.8384553561177235,105.25788688659668,12547.475367000001 +32616,Binary classification,Streaming Random Patches,Elec2,0.8664724819868159,0.8381943154374885,94.53887367248535,13145.300695000002 +33522,Binary classification,Streaming Random Patches,Elec2,0.8661436114674383,0.8380553650701988,102.01883506774902,13750.733881000002 +34428,Binary classification,Streaming Random Patches,Elec2,0.8648444534812793,0.8364441632394811,100.42782783508301,14363.910035000003 +35334,Binary classification,Streaming Random Patches,Elec2,0.8647723091727281,0.8357849876271651,107.87262153625488,14984.863075000003 +36240,Binary classification,Streaming Random Patches,Elec2,0.8642346643119292,0.83420946219167,112.83228874206543,15613.594311000003 +37146,Binary classification,Streaming Random Patches,Elec2,0.8632655808318751,0.8326689289361843,120.66686058044434,16250.422859000002 +38052,Binary classification,Streaming Random Patches,Elec2,0.8627631336889964,0.8316135689410551,126.15458106994629,16895.166705000003 +38958,Binary classification,Streaming Random Patches,Elec2,0.8634391765279668,0.8328620797989319,107.22049903869629,17548.303432000004 +39864,Binary classification,Streaming Random Patches,Elec2,0.8644356922459423,0.8353041570157259,103.5422191619873,18208.490072000004 +40770,Binary classification,Streaming Random Patches,Elec2,0.865436974171552,0.8378745788758201,97.78764533996582,18874.504490000003 +41676,Binary classification,Streaming Random Patches,Elec2,0.8666586682663467,0.8403940603728063,102.76390266418457,19545.753018000003 +42582,Binary classification,Streaming Random Patches,Elec2,0.8673821657546793,0.8415055151702265,107.51249122619629,20221.607860000004 +43488,Binary classification,Streaming Random Patches,Elec2,0.8677075907742544,0.841885392332005,102.9560489654541,20902.251232000002 +44394,Binary classification,Streaming Random Patches,Elec2,0.8679071024711104,0.8415905775568644,103.86480903625488,21587.715975000003 +45300,Binary classification,Streaming Random Patches,Elec2,0.8688933530541513,0.843053830501308,107.29028511047363,22278.011723000003 +45312,Binary classification,Streaming Random Patches,Elec2,0.8688839354682086,0.8430092751631743,107.32242012023926,22968.976341 +25,Binary classification,Streaming Random Patches,Phishing,0.8333333333333334,0.8333333333333334,0.7029104232788086,1.141902 +50,Binary classification,Streaming Random Patches,Phishing,0.8571428571428571,0.8372093023255814,0.9397382736206055,3.355867 +75,Binary classification,Streaming Random Patches,Phishing,0.8783783783783784,0.8695652173913043,0.9708013534545898,6.532426 +100,Binary classification,Streaming Random Patches,Phishing,0.8888888888888888,0.8817204301075269,1.056624412536621,10.815831 +125,Binary classification,Streaming Random Patches,Phishing,0.8790322580645161,0.8739495798319329,1.3782567977905273,16.293882 +150,Binary classification,Streaming Random Patches,Phishing,0.8791946308724832,0.8783783783783784,1.379134178161621,22.890072 +175,Binary classification,Streaming Random Patches,Phishing,0.896551724137931,0.888888888888889,1.4786596298217773,30.523139999999998 +200,Binary classification,Streaming Random Patches,Phishing,0.8944723618090452,0.8864864864864866,1.6607275009155273,39.247513 +225,Binary classification,Streaming Random Patches,Phishing,0.8973214285714286,0.8866995073891626,1.686568260192871,49.014512999999994 +250,Binary classification,Streaming Random Patches,Phishing,0.891566265060241,0.88,1.9668035507202148,59.910523 +275,Binary classification,Streaming Random Patches,Phishing,0.8905109489051095,0.8780487804878049,2.071291923522949,71.88595 +300,Binary classification,Streaming Random Patches,Phishing,0.8896321070234113,0.8754716981132077,2.2423620223999023,85.00905599999999 +325,Binary classification,Streaming Random Patches,Phishing,0.8888888888888888,0.8723404255319148,2.4750547409057617,99.14632499999999 +350,Binary classification,Streaming Random Patches,Phishing,0.8853868194842407,0.8666666666666667,2.5328550338745117,114.35437499999999 +375,Binary classification,Streaming Random Patches,Phishing,0.8850267379679144,0.8652037617554859,2.8150205612182617,130.79065599999998 +400,Binary classification,Streaming Random Patches,Phishing,0.8822055137844611,0.8613569321533923,2.795191764831543,148.41625799999997 +425,Binary classification,Streaming Random Patches,Phishing,0.8844339622641509,0.8611898016997167,2.962000846862793,167.06847699999997 +450,Binary classification,Streaming Random Patches,Phishing,0.888641425389755,0.8648648648648649,3.03415584564209,186.853211 +475,Binary classification,Streaming Random Patches,Phishing,0.890295358649789,0.8686868686868687,3.071761131286621,207.82899999999998 +500,Binary classification,Streaming Random Patches,Phishing,0.8917835671342685,0.8726415094339622,3.1551198959350586,229.951047 +525,Binary classification,Streaming Random Patches,Phishing,0.8950381679389313,0.8741418764302059,3.1928510665893555,253.214946 +550,Binary classification,Streaming Random Patches,Phishing,0.8943533697632058,0.8739130434782608,3.2878904342651367,277.566695 +575,Binary classification,Streaming Random Patches,Phishing,0.8937282229965157,0.8726513569937369,3.4417715072631836,303.140017 +600,Binary classification,Streaming Random Patches,Phishing,0.8964941569282137,0.8739837398373984,3.515273094177246,329.715755 +625,Binary classification,Streaming Random Patches,Phishing,0.8958333333333334,0.8707753479125249,3.5807180404663086,357.461609 +650,Binary classification,Streaming Random Patches,Phishing,0.8983050847457628,0.8754716981132076,3.695376396179199,386.398038 +675,Binary classification,Streaming Random Patches,Phishing,0.8961424332344213,0.8754448398576512,3.7550153732299805,416.46849299999997 +700,Binary classification,Streaming Random Patches,Phishing,0.899856938483548,0.8784722222222222,3.7909955978393555,447.643877 +725,Binary classification,Streaming Random Patches,Phishing,0.899171270718232,0.8797364085667215,3.9393529891967773,479.929216 +750,Binary classification,Streaming Random Patches,Phishing,0.9012016021361816,0.8825396825396825,3.942519187927246,513.493128 +775,Binary classification,Streaming Random Patches,Phishing,0.9018087855297158,0.8827160493827161,4.2751874923706055,548.2965389999999 +800,Binary classification,Streaming Random Patches,Phishing,0.899874843554443,0.8816568047337278,4.513812065124512,584.3583229999999 +825,Binary classification,Streaming Random Patches,Phishing,0.8992718446601942,0.8819345661450925,4.773520469665527,621.611368 +850,Binary classification,Streaming Random Patches,Phishing,0.901060070671378,0.8836565096952909,4.8153791427612305,660.1796979999999 +875,Binary classification,Streaming Random Patches,Phishing,0.902745995423341,0.884979702300406,4.980830192565918,699.8371419999999 +900,Binary classification,Streaming Random Patches,Phishing,0.9043381535038932,0.8862433862433862,5.134486198425293,740.7850809999999 +925,Binary classification,Streaming Random Patches,Phishing,0.9069264069264069,0.8903061224489796,5.209948539733887,782.8443949999998 +950,Binary classification,Streaming Random Patches,Phishing,0.9083245521601686,0.8932515337423312,5.338950157165527,826.1813729999999 +975,Binary classification,Streaming Random Patches,Phishing,0.9106776180698152,0.895808383233533,5.382990837097168,870.7373769999999 +1000,Binary classification,Streaming Random Patches,Phishing,0.9109109109109109,0.896149358226371,5.44773006439209,916.477949 +1025,Binary classification,Streaming Random Patches,Phishing,0.9111328125,0.896942242355606,5.5915327072143555,963.445117 +1050,Binary classification,Streaming Random Patches,Phishing,0.9113441372735939,0.8976897689768977,5.678961753845215,1011.481541 +1075,Binary classification,Streaming Random Patches,Phishing,0.9115456238361266,0.8986125933831376,5.788058280944824,1060.652982 +1100,Binary classification,Streaming Random Patches,Phishing,0.9117379435850773,0.8990634755463061,5.880267143249512,1110.965738 +1125,Binary classification,Streaming Random Patches,Phishing,0.9119217081850534,0.9003021148036253,6.120665550231934,1162.442095 +1150,Binary classification,Streaming Random Patches,Phishing,0.9129677980852916,0.9013806706114399,6.185591697692871,1215.0055750000001 +1175,Binary classification,Streaming Random Patches,Phishing,0.9114139693356048,0.8996138996138997,6.431841850280762,1268.8167280000002 +1200,Binary classification,Streaming Random Patches,Phishing,0.9124270225187656,0.9004739336492891,6.484606742858887,1323.7082160000002 +1225,Binary classification,Streaming Random Patches,Phishing,0.9133986928104575,0.9014869888475836,6.481654167175293,1379.6419000000003 +1250,Binary classification,Streaming Random Patches,Phishing,0.9135308246597278,0.9019963702359347,6.595587730407715,1436.6903440000003 +1903,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1670236587524414,31.246172 +3806,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1682443618774414,90.057064 +5709,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,168.92668600000002 +7612,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,266.339332 +9515,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,379.70068100000003 +11418,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1706857681274414,507.50093200000003 +13321,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1706857681274414,650.046105 +15224,Binary classification,Streaming Random Patches,SMTP,0.9992774091834724,0.0,0.2171335220336914,806.74928 +17127,Binary classification,Streaming Random Patches,SMTP,0.9992409202382343,0.0,0.1745767593383789,979.905317 +19030,Binary classification,Streaming Random Patches,SMTP,0.9993168322034789,0.0,0.1744394302368164,1169.37771 +20933,Binary classification,Streaming Random Patches,SMTP,0.999378941333843,0.0,0.17577457427978516,1374.513378 +22836,Binary classification,Streaming Random Patches,SMTP,0.9994306984891613,0.0,0.17572879791259766,1595.365052 +24739,Binary classification,Streaming Random Patches,SMTP,0.9994744926833212,0.0,0.1757516860961914,1830.5281120000002 +26642,Binary classification,Streaming Random Patches,SMTP,0.9994744942006681,0.0,0.17572879791259766,2079.072293 +28545,Binary classification,Streaming Random Patches,SMTP,0.9995095291479821,0.0,0.17572879791259766,2341.3308700000002 +30448,Binary classification,Streaming Random Patches,SMTP,0.9995401845830459,0.0,0.17563724517822266,2616.3107910000003 +32351,Binary classification,Streaming Random Patches,SMTP,0.9995672333848532,0.0,0.17577457427978516,2903.8369350000003 +34254,Binary classification,Streaming Random Patches,SMTP,0.9995912766764955,0.0,0.1756601333618164,3203.0985050000004 +36157,Binary classification,Streaming Random Patches,SMTP,0.9996127890253347,0.0,0.17568302154541016,3513.3936680000006 +38060,Binary classification,Streaming Random Patches,SMTP,0.9996321500827662,0.0,0.1757059097290039,3834.7595300000007 +39963,Binary classification,Streaming Random Patches,SMTP,0.9996496671838246,0.0,0.17577457427978516,4167.168481000001 +41866,Binary classification,Streaming Random Patches,SMTP,0.9996655917831124,0.0,0.1769266128540039,4510.027218000001 +43769,Binary classification,Streaming Random Patches,SMTP,0.9996801316029976,0.0,0.1769266128540039,4863.234695000001 +45672,Binary classification,Streaming Random Patches,SMTP,0.9996934597446958,0.0,0.1769266128540039,5226.731618000001 +47575,Binary classification,Streaming Random Patches,SMTP,0.9997057216126456,0.0,0.1770639419555664,5600.511358000001 +49478,Binary classification,Streaming Random Patches,SMTP,0.99971704024092,0.0,0.17690372467041016,5984.651066 +51381,Binary classification,Streaming Random Patches,SMTP,0.9996885947839627,0.0,0.16916751861572266,6379.477192 +53284,Binary classification,Streaming Random Patches,SMTP,0.9996997166075484,0.0,0.1770639419555664,6785.903036000001 +55187,Binary classification,Streaming Random Patches,SMTP,0.999710071394919,0.0,0.17704105377197266,7201.6518080000005 +57090,Binary classification,Streaming Random Patches,SMTP,0.9995620872672494,0.0,0.1769266128540039,7626.427031 +58993,Binary classification,Streaming Random Patches,SMTP,0.9995762137238947,0.0,0.1769266128540039,8059.133738 +60896,Binary classification,Streaming Random Patches,SMTP,0.999589457262501,0.0,0.1769723892211914,8499.283325 +62799,Binary classification,Streaming Random Patches,SMTP,0.9995700500015924,0.0,0.1769266128540039,8946.957028 +64702,Binary classification,Streaming Random Patches,SMTP,0.9995826957852274,0.0,0.17699527740478516,9401.959764000001 +66605,Binary classification,Streaming Random Patches,SMTP,0.9995946189418053,0.0,0.1769723892211914,9864.111715000001 +68508,Binary classification,Streaming Random Patches,SMTP,0.9995766855941729,0.0,0.1691446304321289,10332.853073 +70411,Binary classification,Streaming Random Patches,SMTP,0.9995881266865502,0.0,0.17690372467041016,10808.168746 +72314,Binary classification,Streaming Random Patches,SMTP,0.9995989656078437,0.0,0.16912174224853516,11290.14581 +74217,Binary classification,Streaming Random Patches,SMTP,0.99960924867953,0.0,0.1769723892211914,11778.656001 +76120,Binary classification,Streaming Random Patches,SMTP,0.9996190175908775,0.0,0.1769723892211914,12273.787996 +78023,Binary classification,Streaming Random Patches,SMTP,0.9996283099638563,0.0,0.17699527740478516,12775.472063 +79926,Binary classification,Streaming Random Patches,SMTP,0.9996371598373475,0.0,0.1769723892211914,13283.764207999999 +81829,Binary classification,Streaming Random Patches,SMTP,0.9996455980837855,0.0,0.1770181655883789,13798.661938 +83732,Binary classification,Streaming Random Patches,SMTP,0.9996536527689864,0.0,0.17826175689697266,14320.191281 +85635,Binary classification,Streaming Random Patches,SMTP,0.999661349463998,0.0,0.1703653335571289,14848.294436 +87538,Binary classification,Streaming Random Patches,SMTP,0.9996687115162731,0.0,0.17029666900634766,15383.005183 +89441,Binary classification,Streaming Random Patches,SMTP,0.9996645796064401,0.0,0.1781930923461914,15923.647685 +91344,Binary classification,Streaming Random Patches,SMTP,0.999671567607808,0.0,0.1781473159790039,16470.415157 +93247,Binary classification,Streaming Random Patches,SMTP,0.9996782703815713,0.0,0.17821598052978516,17023.687732 +95150,Binary classification,Streaming Random Patches,SMTP,0.9996847050415664,0.0,0.17821598052978516,17582.958979 +95156,Binary classification,Streaming Random Patches,SMTP,0.9996847249224948,0.0,0.17817020416259766,18142.251024999998 +106,Binary classification,k-Nearest Neighbors,Bananas,0.7238095238095238,0.6881720430107527,0.10328006744384766,0.213787 +212,Binary classification,k-Nearest Neighbors,Bananas,0.8056872037914692,0.7807486631016043,0.1952676773071289,0.888466 +318,Binary classification,k-Nearest Neighbors,Bananas,0.807570977917981,0.7859649122807018,0.28677845001220703,2.29757 +424,Binary classification,k-Nearest Neighbors,Bananas,0.8297872340425532,0.8115183246073298,0.3787660598754883,4.640547 +530,Binary classification,k-Nearest Neighbors,Bananas,0.831758034026465,0.8061002178649236,2.6361207962036133,29.527472000000003 +636,Binary classification,k-Nearest Neighbors,Bananas,0.8472440944881889,0.8245931283905967,3.060887336730957,56.29478 +742,Binary classification,k-Nearest Neighbors,Bananas,0.8529014844804319,0.8278041074249604,3.5180253982543945,85.033958 +848,Binary classification,k-Nearest Neighbors,Bananas,0.8559622195985832,0.8328767123287671,3.9749040603637695,116.00953899999999 +954,Binary classification,k-Nearest Neighbors,Bananas,0.8604407135362014,0.8372093023255813,4.4283952713012695,149.38786199999998 +1060,Binary classification,k-Nearest Neighbors,Bananas,0.8706326723323891,0.8476084538375974,4.5923662185668945,185.28018899999998 +1166,Binary classification,k-Nearest Neighbors,Bananas,0.871244635193133,0.8484848484848485,4.394963264465332,223.46140799999998 +1272,Binary classification,k-Nearest Neighbors,Bananas,0.8693941778127459,0.8477064220183486,4.242337226867676,263.59538599999996 +1378,Binary classification,k-Nearest Neighbors,Bananas,0.8714596949891068,0.8488471391972673,4.1376237869262695,305.59304199999997 +1484,Binary classification,k-Nearest Neighbors,Bananas,0.8759271746459879,0.8548895899053628,4.233838081359863,349.62285599999996 +1590,Binary classification,k-Nearest Neighbors,Bananas,0.8735053492762744,0.8527472527472527,4.485638618469238,396.123591 +1696,Binary classification,k-Nearest Neighbors,Bananas,0.8755162241887906,0.854982817869416,4.566784858703613,444.689218 +1802,Binary classification,k-Nearest Neighbors,Bananas,0.8778456413103831,0.858611825192802,4.580937385559082,495.109217 +1908,Binary classification,k-Nearest Neighbors,Bananas,0.8778185631882538,0.8598917618761276,4.5537919998168945,547.400313 +2014,Binary classification,k-Nearest Neighbors,Bananas,0.877297565822156,0.8605307735742519,4.4779558181762695,601.303554 +2120,Binary classification,k-Nearest Neighbors,Bananas,0.8787163756488909,0.8635156664896441,4.453892707824707,656.840699 +2226,Binary classification,k-Nearest Neighbors,Bananas,0.8782022471910113,0.8630621526023244,4.4562273025512695,714.0315049999999 +2332,Binary classification,k-Nearest Neighbors,Bananas,0.8777348777348777,0.862782859894078,4.439526557922363,772.745195 +2438,Binary classification,k-Nearest Neighbors,Bananas,0.8785391875256463,0.8635944700460828,4.450131416320801,833.024947 +2544,Binary classification,k-Nearest Neighbors,Bananas,0.8788832088084939,0.864793678665496,4.448788642883301,894.808032 +2650,Binary classification,k-Nearest Neighbors,Bananas,0.8784446961117403,0.8647058823529411,4.491581916809082,958.170481 +2756,Binary classification,k-Nearest Neighbors,Bananas,0.879491833030853,0.8659127625201939,4.482541084289551,1022.970177 +2862,Binary classification,k-Nearest Neighbors,Bananas,0.8808109052778749,0.867056530214425,4.4542436599731445,1089.131713 +2968,Binary classification,k-Nearest Neighbors,Bananas,0.8813616447590158,0.8673700075357951,4.489590644836426,1156.687087 +3074,Binary classification,k-Nearest Neighbors,Bananas,0.8805727302310445,0.8665939658306071,4.4426774978637695,1225.526022 +3180,Binary classification,k-Nearest Neighbors,Bananas,0.8820383768480654,0.8677248677248678,4.4409685134887695,1295.666774 +3286,Binary classification,k-Nearest Neighbors,Bananas,0.882496194824962,0.8678082191780822,4.441540718078613,1367.284144 +3392,Binary classification,k-Nearest Neighbors,Bananas,0.8832202890002949,0.8693931398416888,4.4570817947387695,1440.244405 +3498,Binary classification,k-Nearest Neighbors,Bananas,0.8850443237060337,0.8709055876685934,4.465977668762207,1514.504066 +3604,Binary classification,k-Nearest Neighbors,Bananas,0.8856508465167916,0.8710888610763454,4.4596757888793945,1590.040367 +3710,Binary classification,k-Nearest Neighbors,Bananas,0.8864923159881369,0.8724628900333233,4.477154731750488,1666.899992 +3816,Binary classification,k-Nearest Neighbors,Bananas,0.8875491480996068,0.8737120989108037,4.4705095291137695,1745.0344980000002 +3922,Binary classification,k-Nearest Neighbors,Bananas,0.8867635807192042,0.8724870763928776,4.4566545486450195,1824.4605280000003 +4028,Binary classification,k-Nearest Neighbors,Bananas,0.8852743978147505,0.8706606942889138,4.454602241516113,1905.1708120000003 +4134,Binary classification,k-Nearest Neighbors,Bananas,0.8857972417130414,0.8712493180578287,4.461682319641113,1987.1975480000003 +4240,Binary classification,k-Nearest Neighbors,Bananas,0.886765746638358,0.8724760892667376,4.4584245681762695,2070.530861 +4346,Binary classification,k-Nearest Neighbors,Bananas,0.8876869965477561,0.8735751295336789,4.494175910949707,2155.1880220000003 +4452,Binary classification,k-Nearest Neighbors,Bananas,0.8869916872612896,0.8725614390676463,4.517621040344238,2241.198533 +4558,Binary classification,k-Nearest Neighbors,Bananas,0.8869870528856704,0.8729336294103133,4.495129585266113,2328.452784 +4664,Binary classification,k-Nearest Neighbors,Bananas,0.886982629208664,0.873286847799952,4.453595161437988,2416.918556 +4770,Binary classification,k-Nearest Neighbors,Bananas,0.8857202767875865,0.8715531463587085,4.469174385070801,2506.628209 +4876,Binary classification,k-Nearest Neighbors,Bananas,0.8861538461538462,0.871616932685635,4.478787422180176,2597.661861 +4982,Binary classification,k-Nearest Neighbors,Bananas,0.8869704878538446,0.8728832693610296,4.4154863357543945,2689.8978660000002 +5088,Binary classification,k-Nearest Neighbors,Bananas,0.885983880479654,0.8716814159292035,4.439602851867676,2783.355406 +5194,Binary classification,k-Nearest Neighbors,Bananas,0.885422684382823,0.8711842390127733,4.5029191970825195,2878.2182510000002 +5300,Binary classification,k-Nearest Neighbors,Bananas,0.8850726552179656,0.8708377518557794,4.509961128234863,2974.330637 +906,Binary classification,k-Nearest Neighbors,Elec2,0.8784530386740331,0.8711943793911008,4.434150695800781,37.114054 +1812,Binary classification,k-Nearest Neighbors,Elec2,0.8801766979569299,0.8453314326443336,4.643096923828125,93.709907 +2718,Binary classification,k-Nearest Neighbors,Elec2,0.8568273831431726,0.8160756501182034,4.6672821044921875,164.56349699999998 +3624,Binary classification,k-Nearest Neighbors,Elec2,0.8746894838531604,0.8411476557032889,4.594398498535156,248.37817199999998 +4530,Binary classification,k-Nearest Neighbors,Elec2,0.8783395893133142,0.8399651466744118,4.710762023925781,344.82085099999995 +5436,Binary classification,k-Nearest Neighbors,Elec2,0.8745170193192272,0.8360576923076923,4.698677062988281,452.38148599999994 +6342,Binary classification,k-Nearest Neighbors,Elec2,0.8747831572307208,0.8384865744507731,4.6694183349609375,569.8523869999999 +7248,Binary classification,k-Nearest Neighbors,Elec2,0.8723609769559818,0.8348509194786646,4.666007995605469,697.1091419999999 +8154,Binary classification,k-Nearest Neighbors,Elec2,0.8718263215994113,0.8430695299594534,4.7265625,834.9178869999998 +9060,Binary classification,k-Nearest Neighbors,Elec2,0.8738271332376643,0.8493475682087781,4.708610534667969,981.8103579999998 +9966,Binary classification,k-Nearest Neighbors,Elec2,0.8720521826392373,0.8501234277653698,4.6251678466796875,1137.002296 +10872,Binary classification,k-Nearest Neighbors,Elec2,0.8740686229417717,0.8545628386274301,4.637184143066406,1300.798705 +11778,Binary classification,k-Nearest Neighbors,Elec2,0.8742464124989386,0.8546756942400157,4.6933135986328125,1473.412993 +12684,Binary classification,k-Nearest Neighbors,Elec2,0.872664196168099,0.8527937289217027,4.810676574707031,1655.5819629999999 +13590,Binary classification,k-Nearest Neighbors,Elec2,0.8748252262859666,0.8573824096587573,4.703468322753906,1846.5010799999998 +14496,Binary classification,k-Nearest Neighbors,Elec2,0.8750603656433252,0.85826093762229,4.7199859619140625,2046.3736259999998 +15402,Binary classification,k-Nearest Neighbors,Elec2,0.8755924939938965,0.8581371242410781,4.7149505615234375,2254.7741159999996 +16308,Binary classification,k-Nearest Neighbors,Elec2,0.872079475072055,0.8535112359550563,4.6830902099609375,2471.4717159999996 +17214,Binary classification,k-Nearest Neighbors,Elec2,0.8723058153721025,0.8517669274345832,4.657257080078125,2696.3467009999995 +18120,Binary classification,k-Nearest Neighbors,Elec2,0.87234394834152,0.8515118443859535,4.7351837158203125,2929.4346569999993 +19026,Binary classification,k-Nearest Neighbors,Elec2,0.8734822601839685,0.8509505232522138,4.8458709716796875,3171.2751989999992 +19932,Binary classification,k-Nearest Neighbors,Elec2,0.8722091214690683,0.8505193966782089,4.8552703857421875,3421.524358999999 +20838,Binary classification,k-Nearest Neighbors,Elec2,0.8678312616979411,0.8451765234989881,4.8942718505859375,3679.924087999999 +21744,Binary classification,k-Nearest Neighbors,Elec2,0.8677735363105368,0.8427672955974842,4.7196807861328125,3945.793201999999 +22650,Binary classification,k-Nearest Neighbors,Elec2,0.8669256920835356,0.840444679724722,4.8090057373046875,4218.904371999999 +23556,Binary classification,k-Nearest Neighbors,Elec2,0.8647845468053492,0.8373257061136934,4.794342041015625,4499.001777999999 +24462,Binary classification,k-Nearest Neighbors,Elec2,0.8644372674870201,0.8359715077166601,4.7589569091796875,4786.067247999999 +25368,Binary classification,k-Nearest Neighbors,Elec2,0.8619860448614342,0.8330710914032329,4.846771240234375,5079.937268999999 +26274,Binary classification,k-Nearest Neighbors,Elec2,0.8623301488219846,0.8333410127632125,4.699310302734375,5380.101847999999 +27180,Binary classification,k-Nearest Neighbors,Elec2,0.8632767945840538,0.8350350705851016,4.794769287109375,5686.6129089999995 +28086,Binary classification,k-Nearest Neighbors,Elec2,0.862061598718177,0.8333620096352374,4.6817474365234375,5999.306036 +28992,Binary classification,k-Nearest Neighbors,Elec2,0.8618191852643924,0.8323989624299222,4.8116455078125,6318.1198079999995 +29898,Binary classification,k-Nearest Neighbors,Elec2,0.8607218115529987,0.8308417289567761,4.769432067871094,6643.155825 +30804,Binary classification,k-Nearest Neighbors,Elec2,0.8599162419244879,0.8291562735083342,4.83782958984375,6975.21929 +31710,Binary classification,k-Nearest Neighbors,Elec2,0.8578006244283958,0.8263832736513803,4.7655487060546875,7313.109579 +32616,Binary classification,k-Nearest Neighbors,Elec2,0.8558332055802544,0.8246307623452186,4.726959228515625,7656.831982 +33522,Binary classification,k-Nearest Neighbors,Elec2,0.8543897855075923,0.8232354325861008,4.798057556152344,8006.181245 +34428,Binary classification,k-Nearest Neighbors,Elec2,0.8533128068086095,0.8218066337332393,4.773887634277344,8361.39601 +35334,Binary classification,k-Nearest Neighbors,Elec2,0.8518099227351201,0.8192737815822173,4.808341979980469,8722.632877 +36240,Binary classification,k-Nearest Neighbors,Elec2,0.8522310218273131,0.8186651315566692,4.722572326660156,9089.688296 +37146,Binary classification,k-Nearest Neighbors,Elec2,0.8505586216179836,0.8161859664227292,4.720252990722656,9462.293988 +38052,Binary classification,k-Nearest Neighbors,Elec2,0.8507792173661665,0.81590039556449,4.766929626464844,9840.72504 +38958,Binary classification,k-Nearest Neighbors,Elec2,0.8507841979618553,0.8163523204751524,4.769111633300781,10225.058019 +39864,Binary classification,k-Nearest Neighbors,Elec2,0.850889295838246,0.8178809976101478,4.736076354980469,10615.291715 +40770,Binary classification,k-Nearest Neighbors,Elec2,0.8509161372611543,0.8193329766363474,4.725471496582031,11011.540551999999 +41676,Binary classification,k-Nearest Neighbors,Elec2,0.8518536292741452,0.8217770336585647,4.700096130371094,11414.574327999999 +42582,Binary classification,k-Nearest Neighbors,Elec2,0.8529156196425636,0.8235028885444553,4.746559143066406,11823.240958999999 +43488,Binary classification,k-Nearest Neighbors,Elec2,0.8525536367190195,0.8231074817920989,4.826316833496094,12236.954316 +44394,Binary classification,k-Nearest Neighbors,Elec2,0.8525217939765278,0.8226754421602882,4.775764465332031,12655.735001 +45300,Binary classification,k-Nearest Neighbors,Elec2,0.853131415704541,0.8236541468974475,4.7673492431640625,13079.486139999999 +45312,Binary classification,k-Nearest Neighbors,Elec2,0.8531482421487057,0.8236416644579911,4.7660369873046875,13503.439196 +25,Binary classification,k-Nearest Neighbors,Phishing,0.5833333333333334,0.7058823529411764,0.041108131408691406,0.04635 +50,Binary classification,k-Nearest Neighbors,Phishing,0.7551020408163265,0.7777777777777778,0.0695962905883789,0.16308 +75,Binary classification,k-Nearest Neighbors,Phishing,0.7972972972972973,0.8235294117647058,0.09861469268798828,0.336872 +100,Binary classification,k-Nearest Neighbors,Phishing,0.797979797979798,0.8148148148148148,0.12712955474853516,0.6777850000000001 +125,Binary classification,k-Nearest Neighbors,Phishing,0.8064516129032258,0.8208955223880596,0.15564441680908203,1.226658 +150,Binary classification,k-Nearest Neighbors,Phishing,0.8187919463087249,0.834355828220859,0.1846628189086914,1.947513 +175,Binary classification,k-Nearest Neighbors,Phishing,0.8390804597701149,0.8426966292134832,0.21317768096923828,2.9471350000000003 +200,Binary classification,k-Nearest Neighbors,Phishing,0.8391959798994975,0.8415841584158417,0.24219608306884766,4.26255 +225,Binary classification,k-Nearest Neighbors,Phishing,0.8392857142857143,0.8363636363636364,0.27071094512939453,5.830504 +250,Binary classification,k-Nearest Neighbors,Phishing,0.8232931726907631,0.8225806451612903,0.2992258071899414,7.819846 +275,Binary classification,k-Nearest Neighbors,Phishing,0.8248175182481752,0.8208955223880596,0.3284578323364258,10.199689 +300,Binary classification,k-Nearest Neighbors,Phishing,0.8260869565217391,0.8181818181818181,0.35697269439697266,12.972731999999999 +325,Binary classification,k-Nearest Neighbors,Phishing,0.8364197530864198,0.8250825082508251,0.38599109649658203,16.225203999999998 +350,Binary classification,k-Nearest Neighbors,Phishing,0.8452722063037249,0.83125,0.4145059585571289,19.962148 +375,Binary classification,k-Nearest Neighbors,Phishing,0.839572192513369,0.8235294117647058,0.4430208206176758,24.257676 +400,Binary classification,k-Nearest Neighbors,Phishing,0.8421052631578947,0.8225352112676055,0.47203922271728516,29.175886 +425,Binary classification,k-Nearest Neighbors,Phishing,0.8443396226415094,0.819672131147541,0.500554084777832,34.714831 +450,Binary classification,k-Nearest Neighbors,Phishing,0.8463251670378619,0.8198433420365536,0.5295724868774414,40.875927999999995 +475,Binary classification,k-Nearest Neighbors,Phishing,0.8438818565400844,0.8177339901477833,0.5580873489379883,47.66810099999999 +500,Binary classification,k-Nearest Neighbors,Phishing,0.845691382765531,0.8229885057471266,2.6757898330688477,79.03492 +525,Binary classification,k-Nearest Neighbors,Phishing,0.8454198473282443,0.8187919463087249,2.7769289016723633,111.488727 +550,Binary classification,k-Nearest Neighbors,Phishing,0.848816029143898,0.8237791932059448,2.8829355239868164,145.039549 +575,Binary classification,k-Nearest Neighbors,Phishing,0.8519163763066202,0.8268839103869654,2.989964485168457,179.782132 +600,Binary classification,k-Nearest Neighbors,Phishing,0.8514190317195326,0.8230616302186878,3.098984718322754,215.642079 +625,Binary classification,k-Nearest Neighbors,Phishing,0.8525641025641025,0.8210116731517509,3.2059221267700195,252.521109 +650,Binary classification,k-Nearest Neighbors,Phishing,0.8582434514637904,0.8302583025830258,3.3169260025024414,290.529559 +675,Binary classification,k-Nearest Neighbors,Phishing,0.8620178041543026,0.8382608695652174,3.4291276931762695,329.62297 +700,Binary classification,k-Nearest Neighbors,Phishing,0.8669527896995708,0.8421052631578948,3.5458459854125977,369.665809 +725,Binary classification,k-Nearest Neighbors,Phishing,0.8674033149171271,0.8456591639871384,3.6591615676879883,410.97711300000003 +750,Binary classification,k-Nearest Neighbors,Phishing,0.8678237650200267,0.8465116279069768,3.769242286682129,453.55010200000004 +775,Binary classification,k-Nearest Neighbors,Phishing,0.8669250645994832,0.8446455505279035,3.881718635559082,497.265773 +800,Binary classification,k-Nearest Neighbors,Phishing,0.8648310387984981,0.8434782608695652,3.9942636489868164,542.189116 +825,Binary classification,k-Nearest Neighbors,Phishing,0.8628640776699029,0.8423988842398884,4.110844612121582,588.325742 +850,Binary classification,k-Nearest Neighbors,Phishing,0.8657243816254417,0.8451086956521738,4.225159645080566,635.696811 +875,Binary classification,k-Nearest Neighbors,Phishing,0.868421052631579,0.847277556440903,4.342709541320801,684.216091 +900,Binary classification,k-Nearest Neighbors,Phishing,0.8698553948832035,0.8482490272373541,4.455658912658691,733.952375 +925,Binary classification,k-Nearest Neighbors,Phishing,0.8712121212121212,0.8514357053682896,4.573666572570801,785.013527 +950,Binary classification,k-Nearest Neighbors,Phishing,0.8735511064278187,0.8561151079136691,4.697152137756348,837.27657 +975,Binary classification,k-Nearest Neighbors,Phishing,0.8757700205338809,0.8581477139507622,4.820996284484863,890.836474 +1000,Binary classification,k-Nearest Neighbors,Phishing,0.8758758758758759,0.858447488584475,4.93715763092041,945.663339 +1025,Binary classification,k-Nearest Neighbors,Phishing,0.8759765625,0.8590455049944505,4.905686378479004,1001.6659109999999 +1050,Binary classification,k-Nearest Neighbors,Phishing,0.8779790276453765,0.8617710583153347,4.881028175354004,1058.8496129999999 +1075,Binary classification,k-Nearest Neighbors,Phishing,0.8780260707635009,0.86282722513089,4.857575416564941,1117.1299479999998 +1100,Binary classification,k-Nearest Neighbors,Phishing,0.8789808917197452,0.8641470888661901,4.821175575256348,1176.4409139999998 +1125,Binary classification,k-Nearest Neighbors,Phishing,0.8798932384341637,0.8662041625371655,4.749619483947754,1236.7626339999997 +1150,Binary classification,k-Nearest Neighbors,Phishing,0.8807658833768495,0.8668610301263362,4.722535133361816,1298.0588499999997 +1175,Binary classification,k-Nearest Neighbors,Phishing,0.879045996592845,0.8645038167938931,4.706612586975098,1360.3228199999996 +1200,Binary classification,k-Nearest Neighbors,Phishing,0.8807339449541285,0.865979381443299,4.686341285705566,1423.5043029999997 +1225,Binary classification,k-Nearest Neighbors,Phishing,0.8815359477124183,0.8666053357865686,4.653275489807129,1487.6393369999996 +1250,Binary classification,k-Nearest Neighbors,Phishing,0.8815052041633307,0.867145421903052,4.596428871154785,1552.6498929999996 +1903,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.559709548950195,49.463009 +3806,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.594751358032227,126.299444 +5709,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.435243606567383,223.803561 +7612,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.493677139282227,340.763146 +9515,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.534708023071289,475.19475 +11418,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.455095291137695,625.35715 +13321,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.479013442993164,790.6914730000001 +15224,Binary classification,k-Nearest Neighbors,SMTP,0.9998686198515404,0.9,4.445444107055664,971.1285220000001 +17127,Binary classification,k-Nearest Neighbors,SMTP,0.9998832184981898,0.9166666666666666,4.544534683227539,1166.447296 +19030,Binary classification,k-Nearest Neighbors,SMTP,0.9998948972620737,0.9166666666666666,4.52708625793457,1376.022994 +20933,Binary classification,k-Nearest Neighbors,SMTP,0.999904452512899,0.9166666666666666,4.493997573852539,1599.513782 +22836,Binary classification,k-Nearest Neighbors,SMTP,0.9999124151521787,0.9166666666666666,4.490983963012695,1835.5325109999999 +24739,Binary classification,k-Nearest Neighbors,SMTP,0.9999191527205109,0.9166666666666666,4.531465530395508,2083.498651 +26642,Binary classification,k-Nearest Neighbors,SMTP,0.9998873916144289,0.88,4.54191780090332,2343.113276 +28545,Binary classification,k-Nearest Neighbors,SMTP,0.999894899103139,0.88,4.488824844360352,2613.833594 +30448,Binary classification,k-Nearest Neighbors,SMTP,0.9999014681249384,0.88,4.459695816040039,2894.8346570000003 +32351,Binary classification,k-Nearest Neighbors,SMTP,0.9999072642967543,0.88,4.475152969360352,3186.5040240000003 +34254,Binary classification,k-Nearest Neighbors,SMTP,0.9999124164306776,0.88,4.543954849243164,3487.9588300000005 +36157,Binary classification,k-Nearest Neighbors,SMTP,0.9999170262197146,0.88,4.482622146606445,3798.7831540000006 +38060,Binary classification,k-Nearest Neighbors,SMTP,0.9999211750177356,0.88,4.496248245239258,4119.269013000001 +39963,Binary classification,k-Nearest Neighbors,SMTP,0.9999249286822481,0.88,4.471353530883789,4448.958874000001 +41866,Binary classification,k-Nearest Neighbors,SMTP,0.9999283410963812,0.88,4.53770637512207,4788.142489000001 +43769,Binary classification,k-Nearest Neighbors,SMTP,0.999931456772071,0.88,4.51286506652832,5135.940338 +45672,Binary classification,k-Nearest Neighbors,SMTP,0.9999343128024348,0.88,4.49894905090332,5492.9415070000005 +47575,Binary classification,k-Nearest Neighbors,SMTP,0.9999369403455669,0.88,4.555765151977539,5859.118968000001 +49478,Binary classification,k-Nearest Neighbors,SMTP,0.9999393657659115,0.88,4.430139541625977,6234.600581000001 +51381,Binary classification,k-Nearest Neighbors,SMTP,0.9999221486959906,0.8571428571428571,4.466188430786133,6619.789592000001 +53284,Binary classification,k-Nearest Neighbors,SMTP,0.9999249291518871,0.8571428571428571,4.526651382446289,7013.956607000001 +55187,Binary classification,k-Nearest Neighbors,SMTP,0.9999275178487298,0.8571428571428571,4.485139846801758,7418.775951000001 +57090,Binary classification,k-Nearest Neighbors,SMTP,0.9997898018882797,0.7391304347826089,4.452577590942383,7831.9045620000015 +58993,Binary classification,k-Nearest Neighbors,SMTP,0.9997965825874695,0.7391304347826089,4.485406875610352,8252.946705000002 +60896,Binary classification,k-Nearest Neighbors,SMTP,0.9998029394860005,0.7391304347826089,4.502649307250977,8681.511861000003 +62799,Binary classification,k-Nearest Neighbors,SMTP,0.9997770629637887,0.7083333333333334,4.495584487915039,9116.499033000002 +64702,Binary classification,k-Nearest Neighbors,SMTP,0.9997836200367846,0.7083333333333334,4.500345230102539,9557.922914000002 +66605,Binary classification,k-Nearest Neighbors,SMTP,0.9997898024142694,0.7083333333333334,4.572656631469727,10005.892137000003 +68508,Binary classification,k-Nearest Neighbors,SMTP,0.9997664472243712,0.68,4.537866592407227,10460.064213000003 +70411,Binary classification,k-Nearest Neighbors,SMTP,0.9997727595512002,0.68,4.469621658325195,10920.643886000003 +72314,Binary classification,k-Nearest Neighbors,SMTP,0.9997787396457068,0.68,4.537904739379883,11387.046035000003 +74217,Binary classification,k-Nearest Neighbors,SMTP,0.9997844130645683,0.68,4.493074417114258,11859.048710000003 +76120,Binary classification,k-Nearest Neighbors,SMTP,0.99978980280876,0.68,4.520692825317383,12336.641580000003 +78023,Binary classification,k-Nearest Neighbors,SMTP,0.9997949296352311,0.68,4.566102981567383,12820.170836000003 +79926,Binary classification,k-Nearest Neighbors,SMTP,0.9997998123240538,0.68,4.500688552856445,13309.287446000002 +81829,Binary classification,k-Nearest Neighbors,SMTP,0.9998044679082955,0.68,4.506959915161133,13804.219559000003 +83732,Binary classification,k-Nearest Neighbors,SMTP,0.9998089118725442,0.68,4.503435134887695,14304.793113000003 +85635,Binary classification,k-Nearest Neighbors,SMTP,0.9998131583249644,0.68,4.498682022094727,14810.923265000003 +87538,Binary classification,k-Nearest Neighbors,SMTP,0.9998172201469093,0.68,4.491861343383789,15322.783751000003 +89441,Binary classification,k-Nearest Neighbors,SMTP,0.9998099284436494,0.6666666666666666,4.496858596801758,15840.351229000004 +91344,Binary classification,k-Nearest Neighbors,SMTP,0.9998138883110912,0.6666666666666666,4.480546951293945,16363.581709000004 +93247,Binary classification,k-Nearest Neighbors,SMTP,0.999817686549557,0.6666666666666666,4.533571243286133,16892.331870000005 +95150,Binary classification,k-Nearest Neighbors,SMTP,0.9998213328568876,0.6666666666666666,4.517786026000977,17426.665113000006 +95156,Binary classification,k-Nearest Neighbors,SMTP,0.9998213441227471,0.6666666666666666,4.518220901489258,17961.110841000005 +106,Binary classification,ADWIN Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.1797952651977539,0.693272 +212,Binary classification,ADWIN Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.1805887222290039,2.027128 +318,Binary classification,ADWIN Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.18126773834228516,4.089008 +424,Binary classification,ADWIN Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.18131351470947266,6.9179189999999995 +530,Binary classification,ADWIN Bagging,Bananas,0.55765595463138,0.45581395348837206,0.1813364028930664,10.429995 +636,Binary classification,ADWIN Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.1819925308227539,14.687229 +742,Binary classification,ADWIN Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.1820383071899414,19.647457 +848,Binary classification,ADWIN Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.18196964263916016,25.366910999999998 +954,Binary classification,ADWIN Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.1819467544555664,31.800241999999997 +1060,Binary classification,ADWIN Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.1819467544555664,39.029576 +1166,Binary classification,ADWIN Bagging,Bananas,0.590557939914163,0.4015056461731493,0.18192386627197266,46.984262 +1272,Binary classification,ADWIN Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.18192386627197266,55.672123 +1378,Binary classification,ADWIN Bagging,Bananas,0.599128540305011,0.3973799126637554,0.18253421783447266,65.02100899999999 +1484,Binary classification,ADWIN Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.18248844146728516,75.177128 +1590,Binary classification,ADWIN Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.1824655532836914,86.053176 +1696,Binary classification,ADWIN Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.1824655532836914,97.727606 +1802,Binary classification,ADWIN Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.18248844146728516,110.067211 +1908,Binary classification,ADWIN Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.18248844146728516,123.213825 +2014,Binary classification,ADWIN Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.18248844146728516,137.051202 +2120,Binary classification,ADWIN Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.18248844146728516,151.568436 +2226,Binary classification,ADWIN Bagging,Bananas,0.597752808988764,0.40133779264214053,0.18244266510009766,166.814875 +2332,Binary classification,ADWIN Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.18244266510009766,182.823981 +2438,Binary classification,ADWIN Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.1824655532836914,199.616425 +2544,Binary classification,ADWIN Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.1824655532836914,217.084375 +2650,Binary classification,ADWIN Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.18309879302978516,235.279245 +2756,Binary classification,ADWIN Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.18309879302978516,254.250965 +2862,Binary classification,ADWIN Bagging,Bananas,0.599790283117791,0.4051948051948052,0.18309879302978516,273.857236 +2968,Binary classification,ADWIN Bagging,Bananas,0.599932591843613,0.40261701056869653,0.1831216812133789,294.204326 +3074,Binary classification,ADWIN Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.1831216812133789,315.311898 +3180,Binary classification,ADWIN Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.1831216812133789,337.189575 +3286,Binary classification,ADWIN Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.17859649658203125,359.75124 +3392,Binary classification,ADWIN Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.2364349365234375,383.144231 +3498,Binary classification,ADWIN Bagging,Bananas,0.597369173577352,0.40237691001697795,0.2806434631347656,407.32428699999997 +3604,Binary classification,ADWIN Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3000526428222656,432.314941 +3710,Binary classification,ADWIN Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.3464546203613281,458.107983 +3816,Binary classification,ADWIN Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.3760719299316406,484.645149 +3922,Binary classification,ADWIN Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.4113121032714844,512.014837 +4028,Binary classification,ADWIN Bagging,Bananas,0.608393344921778,0.4195804195804196,0.4392280578613281,540.239956 +4134,Binary classification,ADWIN Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.4532661437988281,569.3761920000001 +4240,Binary classification,ADWIN Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.4546051025390625,599.333749 +4346,Binary classification,ADWIN Bagging,Bananas,0.6186421173762946,0.4384954252795662,0.4373931884765625,630.119 +4452,Binary classification,ADWIN Bagging,Bananas,0.6212087171422153,0.44209133024487096,0.43770599365234375,661.732786 +4558,Binary classification,ADWIN Bagging,Bananas,0.6214614878209348,0.44372782973234437,0.42758941650390625,694.0925080000001 +4664,Binary classification,ADWIN Bagging,Bananas,0.6219172206733863,0.44542308902170497,0.3975372314453125,727.2725200000001 +4770,Binary classification,ADWIN Bagging,Bananas,0.6227720696162717,0.4449244060475162,0.42584228515625,761.2761580000001 +4876,Binary classification,ADWIN Bagging,Bananas,0.6235897435897436,0.4444444444444444,0.393829345703125,796.1318860000001 +4982,Binary classification,ADWIN Bagging,Bananas,0.6251756675366392,0.44910002950722927,0.39398193359375,831.6856570000001 +5088,Binary classification,ADWIN Bagging,Bananas,0.624139964615687,0.44675925925925924,0.39410400390625,867.9313910000001 +5194,Binary classification,ADWIN Bagging,Bananas,0.6248796456768727,0.44690516751845544,0.394500732421875,904.9575060000001 +5300,Binary classification,ADWIN Bagging,Bananas,0.6259671636157765,0.44821826280623617,0.40065765380859375,942.730038 +906,Binary classification,ADWIN Bagging,Elec2,0.8651933701657458,0.8685344827586208,1.5650663375854492,11.845084 +1812,Binary classification,ADWIN Bagging,Elec2,0.8895637769188294,0.8684210526315789,1.8734617233276367,34.886970000000005 +2718,Binary classification,ADWIN Bagging,Elec2,0.8778064041221936,0.8547681539807523,1.7035398483276367,70.08374500000001 +3624,Binary classification,ADWIN Bagging,Elec2,0.8835219431410434,0.8607260726072606,1.6641263961791992,115.953507 +4530,Binary classification,ADWIN Bagging,Elec2,0.8878339589313314,0.8599007170435742,2.0698423385620117,171.720075 +5436,Binary classification,ADWIN Bagging,Elec2,0.886292548298068,0.8580615525953147,2.326838493347168,235.88433300000003 +6342,Binary classification,ADWIN Bagging,Elec2,0.8845607948273143,0.8556782334384859,1.8882322311401367,307.801578 +7248,Binary classification,ADWIN Bagging,Elec2,0.8835380157306472,0.8526021655606008,1.7675046920776367,386.842642 +8154,Binary classification,ADWIN Bagging,Elec2,0.8854409419845456,0.8617115783239561,1.8750486373901367,473.251889 +9060,Binary classification,ADWIN Bagging,Elec2,0.8863009162159179,0.8664765361680064,1.8668012619018555,566.6143930000001 +9966,Binary classification,ADWIN Bagging,Elec2,0.883492222779729,0.8657337805019083,1.624751091003418,666.8526760000001 +10872,Binary classification,ADWIN Bagging,Elec2,0.8851071658541072,0.8689539397754694,1.8364439010620117,773.0944300000001 +11778,Binary classification,ADWIN Bagging,Elec2,0.8819733378619343,0.8645224171539961,1.461909294128418,884.893217 +12684,Binary classification,ADWIN Bagging,Elec2,0.8788930063865016,0.8610709117221419,1.412806510925293,1002.145707 +13590,Binary classification,ADWIN Bagging,Elec2,0.880197218338362,0.863970588235294,1.521845817565918,1125.032122 +14496,Binary classification,ADWIN Bagging,Elec2,0.8799586064160055,0.8644437519476471,1.922499656677246,1253.0070850000002 +15402,Binary classification,ADWIN Bagging,Elec2,0.8805272384910071,0.8643667993513195,1.924330711364746,1386.9590420000002 +16308,Binary classification,ADWIN Bagging,Elec2,0.8794382780401054,0.8622670589883704,1.6739492416381836,1526.6043270000002 +17214,Binary classification,ADWIN Bagging,Elec2,0.8781153779120432,0.8583389601620527,2.050276756286621,1671.5850450000003 +18120,Binary classification,ADWIN Bagging,Elec2,0.8772559192008389,0.8570510348373829,2.0607213973999023,1821.6195730000002 +19026,Binary classification,ADWIN Bagging,Elec2,0.8782128777923784,0.8564702967230379,1.4914274215698242,1976.6826170000002 +19932,Binary classification,ADWIN Bagging,Elec2,0.8703025437760273,0.8482357776081723,0.7451009750366211,2137.4066430000003 +20838,Binary classification,ADWIN Bagging,Elec2,0.8626001823679033,0.8387314820030418,0.7786626815795898,2303.79277 +21744,Binary classification,ADWIN Bagging,Elec2,0.8638642321666743,0.8378259916721456,0.8927946090698242,2475.479733 +22650,Binary classification,ADWIN Bagging,Elec2,0.8620689655172413,0.8337590464027246,1.0149259567260742,2652.48421 +23556,Binary classification,ADWIN Bagging,Elec2,0.8552748885586924,0.8239789332369494,0.7698392868041992,2835.224884 +24462,Binary classification,ADWIN Bagging,Elec2,0.8513143371080496,0.8171902488062327,0.8274068832397461,3023.118045 +25368,Binary classification,ADWIN Bagging,Elec2,0.8471242165017543,0.8121670057153928,0.9264287948608398,3216.225907 +26274,Binary classification,ADWIN Bagging,Elec2,0.8469912077037263,0.8116213683223992,1.0318632125854492,3414.151818 +27180,Binary classification,ADWIN Bagging,Elec2,0.8476397218440708,0.8134600657687283,0.828364372253418,3617.194246 +28086,Binary classification,ADWIN Bagging,Elec2,0.8442585009791703,0.8083260297984224,0.9404935836791992,3825.52125 +28992,Binary classification,ADWIN Bagging,Elec2,0.8416405091235211,0.8035935828877006,0.932948112487793,4039.2676589999996 +29898,Binary classification,ADWIN Bagging,Elec2,0.8389804997156906,0.7997670742866649,0.8770322799682617,4258.3083369999995 +30804,Binary classification,ADWIN Bagging,Elec2,0.8374508976398403,0.7964054812344976,1.024897575378418,4482.681616999999 +31710,Binary classification,ADWIN Bagging,Elec2,0.8326973414488,0.7888725275599952,0.9494352340698242,4711.951208999999 +32616,Binary classification,ADWIN Bagging,Elec2,0.827318718381113,0.7808219178082191,0.861109733581543,4945.765051999999 +33522,Binary classification,ADWIN Bagging,Elec2,0.8271531278899794,0.7806964420893262,1.093031883239746,5184.141968999998 +34428,Binary classification,ADWIN Bagging,Elec2,0.8255439044935661,0.7779174678302028,1.133570671081543,5427.007607999998 +35334,Binary classification,ADWIN Bagging,Elec2,0.8253191067840263,0.7766196163590301,1.1872129440307617,5674.462564999998 +36240,Binary classification,ADWIN Bagging,Elec2,0.8264576837109192,0.7764070110569915,1.431788444519043,5926.318084999998 +37146,Binary classification,ADWIN Bagging,Elec2,0.8245255081437609,0.7721616331096196,1.357222557067871,6182.753490999998 +38052,Binary classification,ADWIN Bagging,Elec2,0.8241833328953247,0.7704974271012006,1.1449995040893555,6443.434381999998 +38958,Binary classification,ADWIN Bagging,Elec2,0.8233950252842878,0.7698534823041413,1.147334098815918,6708.424011999998 +39864,Binary classification,ADWIN Bagging,Elec2,0.8222160901086221,0.7699250073044834,0.7468709945678711,6977.570913999998 +40770,Binary classification,ADWIN Bagging,Elec2,0.8227329588658049,0.7725140860587366,0.9336042404174805,7251.011506999998 +41676,Binary classification,ADWIN Bagging,Elec2,0.8235872825434913,0.7755388654820785,1.097620964050293,7528.4976689999985 +42582,Binary classification,ADWIN Bagging,Elec2,0.8245696437378174,0.7776785714285713,1.5453977584838867,7809.843107999998 +43488,Binary classification,ADWIN Bagging,Elec2,0.8251431462276082,0.7787605469886529,0.8941831588745117,8094.859004999998 +44394,Binary classification,ADWIN Bagging,Elec2,0.8243867276372401,0.7766701042740918,0.744959831237793,8383.886548999999 +45300,Binary classification,ADWIN Bagging,Elec2,0.823770944170953,0.7766305716444221,0.5983161926269531,8676.996437 +45312,Binary classification,ADWIN Bagging,Elec2,0.8237734766392267,0.7765871128395959,0.5984382629394531,8970.151376 +25,Binary classification,ADWIN Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.6633157730102539,0.427424 +50,Binary classification,ADWIN Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.6639947891235352,1.324595 +75,Binary classification,ADWIN Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.6639490127563477,2.554164 +100,Binary classification,ADWIN Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.6645593643188477,4.28613 +125,Binary classification,ADWIN Bagging,Phishing,0.8548387096774194,0.85,0.6645593643188477,6.454494 +150,Binary classification,ADWIN Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.6645593643188477,8.992416 +175,Binary classification,ADWIN Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.6651926040649414,11.944220000000001 +200,Binary classification,ADWIN Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.6653299331665039,15.477702 +225,Binary classification,ADWIN Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7024993896484375,19.458772 +250,Binary classification,ADWIN Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.730194091796875,23.970287 +275,Binary classification,ADWIN Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7302398681640625,28.914006 +300,Binary classification,ADWIN Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7302398681640625,34.304573 +325,Binary classification,ADWIN Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7308502197265625,40.225778999999996 +350,Binary classification,ADWIN Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7308731079101562,46.580822 +375,Binary classification,ADWIN Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.7674179077148438,53.398646 +400,Binary classification,ADWIN Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.804534912109375,60.683253 +425,Binary classification,ADWIN Bagging,Phishing,0.8679245283018868,0.839080459770115,0.8596954345703125,68.459501 +450,Binary classification,ADWIN Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.8597640991210938,76.689807 +475,Binary classification,ADWIN Bagging,Phishing,0.869198312236287,0.8402061855670103,0.859832763671875,85.340536 +500,Binary classification,ADWIN Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.8598556518554688,94.431883 +525,Binary classification,ADWIN Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.8598556518554688,103.968058 +550,Binary classification,ADWIN Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.8599014282226562,113.947374 +575,Binary classification,ADWIN Bagging,Phishing,0.867595818815331,0.8382978723404255,0.8599014282226562,124.33795699999999 +600,Binary classification,ADWIN Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.8599014282226562,135.24069899999998 +625,Binary classification,ADWIN Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.8966064453125,146.56138699999997 +650,Binary classification,ADWIN Bagging,Phishing,0.8767334360554699,0.846153846153846,0.897308349609375,158.32837399999997 +675,Binary classification,ADWIN Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.92486572265625,170.52005599999995 +700,Binary classification,ADWIN Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.8633918762207031,183.10001399999996 +725,Binary classification,ADWIN Bagging,Phishing,0.8825966850828729,0.8576214405360134,0.9612770080566406,196.14419299999997 +750,Binary classification,ADWIN Bagging,Phishing,0.8865153538050734,0.8631239935587761,0.9975471496582031,209.59830599999998 +775,Binary classification,ADWIN Bagging,Phishing,0.8875968992248062,0.863849765258216,1.0525703430175781,223.56014999999996 +800,Binary classification,ADWIN Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.1531257629394531,237.94997899999996 +825,Binary classification,ADWIN Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.1537437438964844,252.78040299999995 +850,Binary classification,ADWIN Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.1632118225097656,267.983484 +875,Binary classification,ADWIN Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.1906776428222656,283.60495299999997 +900,Binary classification,ADWIN Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.2457008361816406,299.65263899999997 +925,Binary classification,ADWIN Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.2457923889160156,316.05876399999994 +950,Binary classification,ADWIN Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.2458381652832031,332.97263699999996 +975,Binary classification,ADWIN Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.2458839416503906,350.27117 +1000,Binary classification,ADWIN Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.2458610534667969,368.008026 +1025,Binary classification,ADWIN Bagging,Phishing,0.8876953125,0.8700564971751412,1.2459068298339844,386.172169 +1050,Binary classification,ADWIN Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.2458839416503906,404.74234 +1075,Binary classification,ADWIN Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.2458839416503906,423.71995200000003 +1100,Binary classification,ADWIN Bagging,Phishing,0.89171974522293,0.8761706555671176,1.2458839416503906,443.13918 +1125,Binary classification,ADWIN Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.2458839416503906,462.95518100000004 +1150,Binary classification,ADWIN Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.2458839416503906,483.090208 +1175,Binary classification,ADWIN Bagging,Phishing,0.8926746166950597,0.877906976744186,1.2458839416503906,503.74833900000004 +1200,Binary classification,ADWIN Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.2550315856933594,524.8299360000001 +1225,Binary classification,ADWIN Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3099861145019531,546.3067460000001 +1250,Binary classification,ADWIN Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3100776672363281,568.2182720000001 +1903,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.15993690490722656,9.565839 +3806,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16054725646972656,28.660555 +5709,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.1610889434814453,57.169533 +7612,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16111183166503906,95.02592100000001 +9515,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16111183166503906,141.315828 +11418,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16172218322753906,195.517174 +13321,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.1617450714111328,256.578558 +15224,Binary classification,ADWIN Bagging,SMTP,0.9992774091834724,0.0,0.2173633575439453,324.084886 +17127,Binary classification,ADWIN Bagging,SMTP,0.9992409202382343,0.0,0.16245460510253906,398.342181 +19030,Binary classification,ADWIN Bagging,SMTP,0.9993168322034789,0.0,0.16227149963378906,479.30544 +20933,Binary classification,ADWIN Bagging,SMTP,0.999378941333843,0.0,0.1629047393798828,566.848605 +22836,Binary classification,ADWIN Bagging,SMTP,0.9994306984891613,0.0,0.1629962921142578,660.867353 +24739,Binary classification,ADWIN Bagging,SMTP,0.9994744926833212,0.0,0.1631336212158203,761.190417 +26642,Binary classification,ADWIN Bagging,SMTP,0.9994744942006681,0.0,0.1628131866455078,867.1613560000001 +28545,Binary classification,ADWIN Bagging,SMTP,0.9995095291479821,0.0,0.1630420684814453,978.3809960000001 +30448,Binary classification,ADWIN Bagging,SMTP,0.9995401845830459,0.0,0.16292762756347656,1094.7710920000002 +32351,Binary classification,ADWIN Bagging,SMTP,0.9995672333848532,0.0,0.16306495666503906,1216.3368180000002 +34254,Binary classification,ADWIN Bagging,SMTP,0.9995912766764955,0.0,0.16297340393066406,1342.8439390000003 +36157,Binary classification,ADWIN Bagging,SMTP,0.9996127890253347,0.0,0.16297340393066406,1474.4175280000004 +38060,Binary classification,ADWIN Bagging,SMTP,0.9996321500827662,0.0,0.1629962921142578,1611.5765880000004 +39963,Binary classification,ADWIN Bagging,SMTP,0.9996496671838246,0.0,0.1628589630126953,1753.4950250000004 +41866,Binary classification,ADWIN Bagging,SMTP,0.9996655917831124,0.0,0.1635608673095703,1900.0594340000005 +43769,Binary classification,ADWIN Bagging,SMTP,0.9996801316029976,0.0,0.1636524200439453,2051.0940990000004 +45672,Binary classification,ADWIN Bagging,SMTP,0.9996934597446958,0.0,0.16362953186035156,2206.5822620000004 +47575,Binary classification,ADWIN Bagging,SMTP,0.9997057216126456,0.0,0.1635608673095703,2366.582792 +49478,Binary classification,ADWIN Bagging,SMTP,0.99971704024092,0.0,0.1516590118408203,2531.0740060000003 +51381,Binary classification,ADWIN Bagging,SMTP,0.9996885947839627,0.0,0.16358375549316406,2700.0024150000004 +53284,Binary classification,ADWIN Bagging,SMTP,0.9996997166075484,0.0,0.16358375549316406,2873.4051700000005 +55187,Binary classification,ADWIN Bagging,SMTP,0.999710071394919,0.0,0.16353797912597656,3051.2280980000005 +57090,Binary classification,ADWIN Bagging,SMTP,0.9995620872672494,0.0,0.1635608673095703,3233.6982610000005 +58993,Binary classification,ADWIN Bagging,SMTP,0.9995762137238947,0.0,0.1634693145751953,3420.4241430000006 +60896,Binary classification,ADWIN Bagging,SMTP,0.999589457262501,0.0,0.16340065002441406,3611.3532100000007 +62799,Binary classification,ADWIN Bagging,SMTP,0.9995700500015924,0.0,0.1635608673095703,3806.5756970000007 +64702,Binary classification,ADWIN Bagging,SMTP,0.9995826957852274,0.0,0.1636524200439453,4005.990633000001 +66605,Binary classification,ADWIN Bagging,SMTP,0.9995946189418053,0.0,0.1635608673095703,4209.692892000001 +68508,Binary classification,ADWIN Bagging,SMTP,0.9995766855941729,0.0,0.1636066436767578,4417.671552000001 +70411,Binary classification,ADWIN Bagging,SMTP,0.9995881266865502,0.0,0.16344642639160156,4629.924287000001 +72314,Binary classification,ADWIN Bagging,SMTP,0.9995989656078437,0.0,0.1636066436767578,4846.389066000001 +74217,Binary classification,ADWIN Bagging,SMTP,0.99960924867953,0.0,0.1636066436767578,5067.145737000001 +76120,Binary classification,ADWIN Bagging,SMTP,0.9996190175908775,0.0,0.1636524200439453,5292.119512000001 +78023,Binary classification,ADWIN Bagging,SMTP,0.9996283099638563,0.0,0.1636524200439453,5520.9977020000015 +79926,Binary classification,ADWIN Bagging,SMTP,0.9996371598373475,0.0,0.1633319854736328,5753.467598000001 +81829,Binary classification,ADWIN Bagging,SMTP,0.9996455980837855,0.0,0.1635608673095703,5989.673013000001 +83732,Binary classification,ADWIN Bagging,SMTP,0.9996536527689864,0.0,0.1642627716064453,6229.466851000001 +85635,Binary classification,ADWIN Bagging,SMTP,0.999661349463998,0.0,0.16428565979003906,6472.919364000001 +87538,Binary classification,ADWIN Bagging,SMTP,0.9996687115162731,0.0,0.16410255432128906,6719.889077000002 +89441,Binary classification,ADWIN Bagging,SMTP,0.9996645796064401,0.0,0.16414833068847656,6970.567347000002 +91344,Binary classification,ADWIN Bagging,SMTP,0.999671567607808,0.0,0.16405677795410156,7224.925889000002 +93247,Binary classification,ADWIN Bagging,SMTP,0.9996782703815713,0.0,0.1523609161376953,7483.091298000002 +95150,Binary classification,ADWIN Bagging,SMTP,0.9996847050415664,0.0,0.16419410705566406,7744.930013000002 +95156,Binary classification,ADWIN Bagging,SMTP,0.9996847249224948,0.0,0.1642169952392578,8006.777295000002 +106,Binary classification,AdaBoost,Bananas,0.5523809523809524,0.5252525252525252,0.16639232635498047,0.661448 +212,Binary classification,AdaBoost,Bananas,0.5829383886255924,0.5555555555555555,0.16659832000732422,2.064295 +318,Binary classification,AdaBoost,Bananas,0.6025236593059937,0.5827814569536425,0.16664409637451172,4.087538 +424,Binary classification,AdaBoost,Bananas,0.6099290780141844,0.5758354755784061,0.16664409637451172,6.767182 +530,Binary classification,AdaBoost,Bananas,0.5841209829867675,0.5089285714285714,0.16659832000732422,10.090322 +636,Binary classification,AdaBoost,Bananas,0.5748031496062992,0.4981412639405205,0.16664409637451172,14.036758 +742,Binary classification,AdaBoost,Bananas,0.582995951417004,0.48925619834710743,0.16657543182373047,18.750104 +848,Binary classification,AdaBoost,Bananas,0.5749704840613932,0.4812680115273775,0.16652965545654297,24.116907 +954,Binary classification,AdaBoost,Bananas,0.5760755508919203,0.482051282051282,0.16652965545654297,30.255333 +1060,Binary classification,AdaBoost,Bananas,0.5873465533522191,0.48284023668639053,0.16652965545654297,37.077733 +1166,Binary classification,AdaBoost,Bananas,0.5931330472103005,0.49250535331905776,0.16657543182373047,44.554975 +1272,Binary classification,AdaBoost,Bananas,0.5979543666404405,0.5034013605442177,0.16657543182373047,52.671883 +1378,Binary classification,AdaBoost,Bananas,0.6005809731299927,0.4990892531876139,0.16657543182373047,61.596702 +1484,Binary classification,AdaBoost,Bananas,0.6089008766014835,0.5117845117845117,0.16657543182373047,71.17423 +1590,Binary classification,AdaBoost,Bananas,0.6091881686595343,0.5121759622937941,0.16657543182373047,81.390284 +1696,Binary classification,AdaBoost,Bananas,0.6135693215339233,0.5194424064563462,0.16657543182373047,92.36515499999999 +1802,Binary classification,AdaBoost,Bananas,0.6185452526374237,0.5354969574036511,0.16657543182373047,104.03539999999998 +1908,Binary classification,AdaBoost,Bananas,0.6208704771893025,0.5467084639498432,0.16659832000732422,116.33010199999998 +2014,Binary classification,AdaBoost,Bananas,0.620963735717834,0.5561372891215823,0.16662120819091797,129.40978099999998 +2120,Binary classification,AdaBoost,Bananas,0.6252949504483247,0.56941431670282,0.16662120819091797,143.16628799999998 +2226,Binary classification,AdaBoost,Bananas,0.6242696629213483,0.5721596724667348,0.16664409637451172,157.57341499999998 +2332,Binary classification,AdaBoost,Bananas,0.6229086229086229,0.5763855421686748,0.16664409637451172,172.619666 +2438,Binary classification,AdaBoost,Bananas,0.62330734509643,0.5796703296703297,0.16664409637451172,188.342899 +2544,Binary classification,AdaBoost,Bananas,0.6244593000393236,0.5860424794104898,0.16664409637451172,204.77139699999998 +2650,Binary classification,AdaBoost,Bananas,0.6266515666289165,0.591828312009905,0.16668987274169922,221.90157299999998 +2756,Binary classification,AdaBoost,Bananas,0.6250453720508167,0.5921831819976313,0.16668987274169922,239.67413799999997 +2862,Binary classification,AdaBoost,Bananas,0.6249563089828731,0.5927893738140417,0.16668987274169922,258.142834 +2968,Binary classification,AdaBoost,Bananas,0.6248736097067745,0.5924569754668619,0.16668987274169922,277.299077 +3074,Binary classification,AdaBoost,Bananas,0.6260982753010088,0.5958494548012664,0.16668987274169922,297.096451 +3180,Binary classification,AdaBoost,Bananas,0.62378106322743,0.5934738273283481,0.15410423278808594,317.58276 +3286,Binary classification,AdaBoost,Bananas,0.6246575342465753,0.5937397034596376,0.1971263885498047,338.83455200000003 +3392,Binary classification,AdaBoost,Bananas,0.6234149218519611,0.5931825422108953,0.2317180633544922,360.725819 +3498,Binary classification,AdaBoost,Bananas,0.6211038032599371,0.5894019212891229,0.2662029266357422,383.343908 +3604,Binary classification,AdaBoost,Bananas,0.6194837635303914,0.5866747060596926,0.31232261657714844,406.679113 +3710,Binary classification,AdaBoost,Bananas,0.6238878403882449,0.5915080527086384,0.32015037536621094,430.74398699999995 +3816,Binary classification,AdaBoost,Bananas,0.6277850589777195,0.5970488081725313,0.32617759704589844,455.49479399999996 +3922,Binary classification,AdaBoost,Bananas,0.6322366743177761,0.6009961261759823,0.35390281677246094,480.941019 +4028,Binary classification,AdaBoost,Bananas,0.6354606406754407,0.6034575904916262,0.3679637908935547,507.154231 +4134,Binary classification,AdaBoost,Bananas,0.6399709654004355,0.6073878627968339,0.3758831024169922,534.089651 +4240,Binary classification,AdaBoost,Bananas,0.644963434772352,0.6130110568269478,0.37590599060058594,561.67777 +4346,Binary classification,AdaBoost,Bananas,0.6508630609896433,0.6185567010309279,0.3759288787841797,589.949243 +4452,Binary classification,AdaBoost,Bananas,0.6535609975286453,0.620384047267356,0.3758831024169922,618.987429 +4558,Binary classification,AdaBoost,Bananas,0.6570111915734036,0.6243691420331651,0.3760662078857422,648.691271 +4664,Binary classification,AdaBoost,Bananas,0.6607334334119666,0.6288127639605818,0.3761119842529297,679.16585 +4770,Binary classification,AdaBoost,Bananas,0.6630320821975257,0.6303197607545433,0.43157005310058594,710.3587739999999 +4876,Binary classification,AdaBoost,Bananas,0.6670769230769231,0.6330544879041374,0.43948936462402344,742.192598 +4982,Binary classification,AdaBoost,Bananas,0.6707488456133307,0.6378091872791519,0.44574546813964844,774.746403 +5088,Binary classification,AdaBoost,Bananas,0.6734814232356988,0.6407094959982694,0.45186424255371094,807.9427459999999 +5194,Binary classification,AdaBoost,Bananas,0.674369343346813,0.6412051771695311,0.4518413543701172,841.965614 +5300,Binary classification,AdaBoost,Bananas,0.6778637478769579,0.64504054897068,0.4531536102294922,876.7139659999999 +906,Binary classification,AdaBoost,Elec2,0.9337016574585635,0.933184855233853,1.423478126525879,13.145088 +1812,Binary classification,AdaBoost,Elec2,0.9491993373826615,0.9378378378378379,2.051041603088379,36.742593 +2718,Binary classification,AdaBoost,Elec2,0.9385351490614648,0.9243316719528772,2.3655481338500977,75.07794200000001 +3624,Binary classification,AdaBoost,Elec2,0.9359646701628485,0.9209809264305179,2.6522607803344727,124.64144900000001 +4530,Binary classification,AdaBoost,Elec2,0.9361890041951866,0.9185226952354102,3.339066505432129,184.39942100000002 +5436,Binary classification,AdaBoost,Elec2,0.9332106715731371,0.9144876325088339,3.582810401916504,253.628197 +6342,Binary classification,AdaBoost,Elec2,0.9309257214950323,0.9124350259896042,3.74349308013916,332.298993 +7248,Binary classification,AdaBoost,Elec2,0.9232785980405686,0.9024903542616626,3.9959611892700195,420.603134 +8154,Binary classification,AdaBoost,Elec2,0.9207653624432725,0.9042962962962964,4.062603950500488,517.241068 +9060,Binary classification,AdaBoost,Elec2,0.9214041284910034,0.9072191816523326,4.2443437576293945,621.271616 +9966,Binary classification,AdaBoost,Elec2,0.9173105870546914,0.9037158214536105,4.387467384338379,732.039898 +10872,Binary classification,AdaBoost,Elec2,0.916842976727072,0.9044195390145908,4.416756629943848,849.200167 +11778,Binary classification,AdaBoost,Elec2,0.9150887322747728,0.9024580569644947,4.712822914123535,973.870605 +12684,Binary classification,AdaBoost,Elec2,0.9128755026413309,0.9002077124537162,5.243111610412598,1105.403187 +13590,Binary classification,AdaBoost,Elec2,0.9123555817205092,0.900890405259216,5.419106483459473,1243.898383 +14496,Binary classification,AdaBoost,Elec2,0.9112107623318386,0.9002402914502752,5.619416236877441,1388.7625189999999 +15402,Binary classification,AdaBoost,Elec2,0.9125381468735796,0.9014414282578475,5.888123512268066,1539.2398159999998 +16308,Binary classification,AdaBoost,Elec2,0.9096093702091127,0.8977808599167822,6.072480201721191,1695.9498239999998 +17214,Binary classification,AdaBoost,Elec2,0.9093708243769244,0.8958611481975968,6.119706153869629,1858.6477019999998 +18120,Binary classification,AdaBoost,Elec2,0.9071140791434406,0.892972972972973,6.420571327209473,2027.8856049999997 +19026,Binary classification,AdaBoost,Elec2,0.907910643889619,0.8927784577723377,6.732544898986816,2202.4394989999996 +19932,Binary classification,AdaBoost,Elec2,0.9079323666649942,0.8936540133294696,6.836274147033691,2383.1617499999998 +20838,Binary classification,AdaBoost,Elec2,0.9073283102174018,0.8931673582295988,7.145352363586426,2570.030805 +21744,Binary classification,AdaBoost,Elec2,0.9069585613760751,0.8912424063222407,7.368103981018066,2762.4680289999997 +22650,Binary classification,AdaBoost,Elec2,0.9053379840169544,0.8884611382790553,7.513260841369629,2961.0147009999996 +23556,Binary classification,AdaBoost,Elec2,0.9031203566121843,0.885441767068273,7.7879228591918945,3165.9004179999997 +24462,Binary classification,AdaBoost,Elec2,0.9015984628592453,0.8830361047669955,7.954785346984863,3377.5787039999996 +25368,Binary classification,AdaBoost,Elec2,0.8990026412267907,0.8799775133514476,8.00295352935791,3596.2655339999997 +26274,Binary classification,AdaBoost,Elec2,0.8993263045712329,0.8800942925789926,8.124005317687988,3821.215918 +27180,Binary classification,AdaBoost,Elec2,0.8986717686449097,0.8798324461122262,8.133870124816895,4052.049016 +28086,Binary classification,AdaBoost,Elec2,0.8958874844222895,0.8761436801084379,8.60555362701416,4289.013778 +28992,Binary classification,AdaBoost,Elec2,0.8951398709944466,0.8747011787981206,8.944867134094238,4531.544758 +29898,Binary classification,AdaBoost,Elec2,0.8927986085560424,0.8719485396939551,9.235833168029785,4780.469894 +30804,Binary classification,AdaBoost,Elec2,0.8921533616855502,0.8705882352941176,9.317421913146973,5034.96976 +31710,Binary classification,AdaBoost,Elec2,0.8903465892964143,0.8684499262229957,9.565300941467285,5295.565511 +32616,Binary classification,AdaBoost,Elec2,0.8890387858347386,0.867226767435888,9.898663520812988,5561.886477999999 +33522,Binary classification,AdaBoost,Elec2,0.8882789892902956,0.8666547979348406,10.141366004943848,5833.648399 +34428,Binary classification,AdaBoost,Elec2,0.8878496528887211,0.8660444783679699,10.462204933166504,6110.893153 +35334,Binary classification,AdaBoost,Elec2,0.8864800611326522,0.8639185750636134,10.841256141662598,6393.894783 +36240,Binary classification,AdaBoost,Elec2,0.8857584370429648,0.8622387861040862,11.138581275939941,6682.129445 +37146,Binary classification,AdaBoost,Elec2,0.8846412706959214,0.8604643589827087,11.587563514709473,6975.5213029999995 +38052,Binary classification,AdaBoost,Elec2,0.883682426217445,0.8588377878420617,12.028901100158691,7273.546291 +38958,Binary classification,AdaBoost,Elec2,0.8819210924865878,0.8569117830036083,12.1774263381958,7576.403824 +39864,Binary classification,AdaBoost,Elec2,0.880741539773725,0.8567122792211707,12.330445289611816,7883.760394 +40770,Binary classification,AdaBoost,Elec2,0.880423851455763,0.8574603081781235,12.583298683166504,8195.49812 +41676,Binary classification,AdaBoost,Elec2,0.8811517696460708,0.8591977712710009,12.884881019592285,8511.394385 +42582,Binary classification,AdaBoost,Elec2,0.8815199267278834,0.8597403319525146,13.200516700744629,8831.520838 +43488,Binary classification,AdaBoost,Elec2,0.8809069377055212,0.8591399896646449,13.322876930236816,9156.403803000001 +44394,Binary classification,AdaBoost,Elec2,0.880476651724371,0.8583404527979496,13.499638557434082,9485.877502000001 +45300,Binary classification,AdaBoost,Elec2,0.8805713150400671,0.8587024655244463,13.542492866516113,9819.626928000001 +45312,Binary classification,AdaBoost,Elec2,0.8805808744013595,0.8586874200203704,13.542401313781738,10153.705154000001 +25,Binary classification,AdaBoost,Phishing,0.6666666666666666,0.7142857142857143,0.6517477035522461,0.344782 +50,Binary classification,AdaBoost,Phishing,0.7551020408163265,0.7391304347826088,0.6519079208374023,1.052047 +75,Binary classification,AdaBoost,Phishing,0.7972972972972973,0.7945205479452055,0.6519308090209961,2.04852 +100,Binary classification,AdaBoost,Phishing,0.8080808080808081,0.7999999999999999,0.6519536972045898,3.481699 +125,Binary classification,AdaBoost,Phishing,0.8064516129032258,0.8000000000000002,0.6519804000854492,5.345952 +150,Binary classification,AdaBoost,Phishing,0.8187919463087249,0.8211920529801323,0.6519804000854492,7.607799 +175,Binary classification,AdaBoost,Phishing,0.8390804597701149,0.8313253012048192,0.6519804000854492,10.226605 +200,Binary classification,AdaBoost,Phishing,0.8341708542713567,0.8253968253968254,0.6889629364013672,13.384675 +225,Binary classification,AdaBoost,Phishing,0.8303571428571429,0.8173076923076923,0.6891918182373047,16.995274 +250,Binary classification,AdaBoost,Phishing,0.8273092369477911,0.8154506437768241,0.6892147064208984,21.029961999999998 +275,Binary classification,AdaBoost,Phishing,0.8321167883211679,0.8188976377952757,0.6892833709716797,25.500590999999996 +300,Binary classification,AdaBoost,Phishing,0.8394648829431438,0.823529411764706,0.6893062591552734,30.459704999999996 +325,Binary classification,AdaBoost,Phishing,0.845679012345679,0.8263888888888888,0.6893062591552734,35.865097 +350,Binary classification,AdaBoost,Phishing,0.8510028653295129,0.8289473684210527,0.6893062591552734,41.618204999999996 +375,Binary classification,AdaBoost,Phishing,0.8502673796791443,0.8260869565217391,0.6892795562744141,47.877466 +400,Binary classification,AdaBoost,Phishing,0.849624060150376,0.8235294117647061,0.6893062591552734,54.483198 +425,Binary classification,AdaBoost,Phishing,0.8561320754716981,0.8271954674220963,0.6893062591552734,61.544972 +450,Binary classification,AdaBoost,Phishing,0.8530066815144766,0.8225806451612903,0.6893062591552734,69.002264 +475,Binary classification,AdaBoost,Phishing,0.8523206751054853,0.8241206030150755,0.6893062591552734,77.051638 +500,Binary classification,AdaBoost,Phishing,0.8557114228456913,0.8317757009345793,0.6893062591552734,85.50066 +525,Binary classification,AdaBoost,Phishing,0.8530534351145038,0.8253968253968255,0.6893062591552734,94.362743 +550,Binary classification,AdaBoost,Phishing,0.8579234972677595,0.832618025751073,0.6893062591552734,103.688841 +575,Binary classification,AdaBoost,Phishing,0.8588850174216028,0.8336755646817249,0.6893062591552734,113.60832099999999 +600,Binary classification,AdaBoost,Phishing,0.8631051752921536,0.8360000000000001,0.6893062591552734,123.90541499999999 +625,Binary classification,AdaBoost,Phishing,0.8621794871794872,0.83203125,0.6893062591552734,134.725616 +650,Binary classification,AdaBoost,Phishing,0.8659476117103235,0.8391866913123845,0.6893291473388672,146.008333 +675,Binary classification,AdaBoost,Phishing,0.8679525222551929,0.8446771378708552,0.6893291473388672,157.728693 +700,Binary classification,AdaBoost,Phishing,0.8726752503576538,0.848381601362862,0.6893291473388672,169.816185 +725,Binary classification,AdaBoost,Phishing,0.8756906077348067,0.8543689320388349,0.6893291473388672,182.22931499999999 +750,Binary classification,AdaBoost,Phishing,0.87716955941255,0.8566978193146417,0.6893291473388672,195.113196 +775,Binary classification,AdaBoost,Phishing,0.8785529715762274,0.8575757575757577,0.6893291473388672,208.50176599999998 +800,Binary classification,AdaBoost,Phishing,0.8785982478097623,0.8592162554426704,0.7275295257568359,222.49654599999997 +825,Binary classification,AdaBoost,Phishing,0.8798543689320388,0.8619246861924686,0.7627391815185547,236.94804099999996 +850,Binary classification,AdaBoost,Phishing,0.8798586572438163,0.8614130434782608,0.7627849578857422,251.81301299999996 +875,Binary classification,AdaBoost,Phishing,0.8787185354691075,0.8594164456233422,0.7628536224365234,267.105354 +900,Binary classification,AdaBoost,Phishing,0.8787541713014461,0.8589909443725743,0.7628765106201172,282.96574799999996 +925,Binary classification,AdaBoost,Phishing,0.8809523809523809,0.8628428927680798,0.7628765106201172,299.16754399999996 +950,Binary classification,AdaBoost,Phishing,0.8798735511064278,0.8629807692307693,0.7628765106201172,315.934336 +975,Binary classification,AdaBoost,Phishing,0.8819301848049281,0.8651817116060961,0.7628765106201172,333.021735 +1000,Binary classification,AdaBoost,Phishing,0.8828828828828829,0.8662857142857143,0.7645549774169922,350.749086 +1025,Binary classification,AdaBoost,Phishing,0.8828125,0.8666666666666666,0.8362636566162109,368.847305 +1050,Binary classification,AdaBoost,Phishing,0.8846520495710201,0.8691891891891892,0.8363094329833984,387.397742 +1075,Binary classification,AdaBoost,Phishing,0.8836126629422719,0.8691099476439791,0.8378963470458984,406.476047 +1100,Binary classification,AdaBoost,Phishing,0.8844404003639672,0.8702757916241062,0.8380107879638672,425.926155 +1125,Binary classification,AdaBoost,Phishing,0.8861209964412812,0.8732673267326733,0.8731288909912109,445.763528 +1150,Binary classification,AdaBoost,Phishing,0.8842471714534378,0.8707482993197277,0.8731517791748047,466.112685 +1175,Binary classification,AdaBoost,Phishing,0.8816013628620102,0.8677450047573739,0.8732662200927734,487.01292 +1200,Binary classification,AdaBoost,Phishing,0.8798999165971643,0.8654205607476635,0.8732662200927734,508.389566 +1225,Binary classification,AdaBoost,Phishing,0.880718954248366,0.8660550458715598,0.8732891082763672,530.180451 +1250,Binary classification,AdaBoost,Phishing,0.8783026421136909,0.8635547576301617,0.8733119964599609,552.608585 +1903,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14459228515625,4.671696 +3806,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14466094970703125,14.150102 +5709,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14461517333984375,28.360088 +7612,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1446380615234375,47.155736000000005 +9515,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1446380615234375,70.60316700000001 +11418,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14461517333984375,98.66041500000001 +13321,Binary classification,AdaBoost,SMTP,1.0,0.0,0.144683837890625,131.464682 +15224,Binary classification,AdaBoost,SMTP,0.9996715496288511,0.761904761904762,0.3174581527709961,185.699411 +17127,Binary classification,AdaBoost,SMTP,0.9997080462454747,0.8,0.3083944320678711,248.358611 +19030,Binary classification,AdaBoost,SMTP,0.9997372431551842,0.8,0.3005514144897461,315.58373 +20933,Binary classification,AdaBoost,SMTP,0.9997611312822473,0.8,0.29268550872802734,387.343573 +22836,Binary classification,AdaBoost,SMTP,0.9997810378804467,0.8,0.29268550872802734,463.52644699999996 +24739,Binary classification,AdaBoost,SMTP,0.9997978818012774,0.8,0.29268550872802734,544.0157879999999 +26642,Binary classification,AdaBoost,SMTP,0.9998123193573815,0.8148148148148148,0.35791683197021484,629.1407149999999 +28545,Binary classification,AdaBoost,SMTP,0.9998248318385651,0.8148148148148148,0.35791683197021484,718.5008859999999 +30448,Binary classification,AdaBoost,SMTP,0.9998357802082307,0.8148148148148148,0.35791683197021484,812.0251739999999 +32351,Binary classification,AdaBoost,SMTP,0.9998454404945905,0.8148148148148148,0.35796260833740234,909.6873759999999 +34254,Binary classification,AdaBoost,SMTP,0.9998540273844627,0.8148148148148148,0.3579854965209961,1011.4173349999999 +36157,Binary classification,AdaBoost,SMTP,0.999861710366191,0.8148148148148148,0.35800838470458984,1116.7962549999997 +38060,Binary classification,AdaBoost,SMTP,0.9998686250295594,0.8148148148148148,0.35800838470458984,1225.6397989999998 +39963,Binary classification,AdaBoost,SMTP,0.9998748811370802,0.8148148148148148,0.35800838470458984,1337.9609139999998 +41866,Binary classification,AdaBoost,SMTP,0.9998805684939687,0.8148148148148148,0.35800838470458984,1453.6581889999998 +43769,Binary classification,AdaBoost,SMTP,0.9998857612867849,0.8148148148148148,0.35800838470458984,1572.7472669999997 +45672,Binary classification,AdaBoost,SMTP,0.9998905213373913,0.8148148148148148,0.35800838470458984,1695.2891979999997 +47575,Binary classification,AdaBoost,SMTP,0.9998738806911338,0.7857142857142857,0.39035701751708984,1822.1171059999997 +49478,Binary classification,AdaBoost,SMTP,0.9998787315318228,0.7857142857142857,0.39284420013427734,1952.4393319999997 +51381,Binary classification,AdaBoost,SMTP,0.9998637602179836,0.787878787878788,0.48020076751708984,2088.5562569999997 +53284,Binary classification,AdaBoost,SMTP,0.9998686260158024,0.787878787878788,0.48024654388427734,2228.050331 +55187,Binary classification,AdaBoost,SMTP,0.9998550356974595,0.7647058823529411,0.5258626937866211,2371.062704 +57090,Binary classification,AdaBoost,SMTP,0.999281823118289,0.4383561643835616,0.8453359603881836,2524.551865 +58993,Binary classification,AdaBoost,SMTP,0.9993049905071875,0.4383561643835616,0.8887395858764648,2681.9732639999997 +60896,Binary classification,AdaBoost,SMTP,0.9993267099105017,0.4383561643835616,0.8967199325561523,2843.1145869999996 +62799,Binary classification,AdaBoost,SMTP,0.9993152648173509,0.4266666666666667,1.0689306259155273,3009.1891499999997 +64702,Binary classification,AdaBoost,SMTP,0.9993354043986955,0.4266666666666667,1.0783147811889648,3178.9568209999998 +66605,Binary classification,AdaBoost,SMTP,0.9993393790162753,0.42105263157894735,1.0915288925170898,3352.4236079999996 +68508,Binary classification,AdaBoost,SMTP,0.9993577298670209,0.45000000000000007,1.0735387802124023,3530.7159619999998 +70411,Binary classification,AdaBoost,SMTP,0.9993750887658003,0.45000000000000007,1.0788640975952148,3712.739099 +72314,Binary classification,AdaBoost,SMTP,0.9993915340256938,0.45000000000000007,1.0906057357788086,3898.148799 +74217,Binary classification,AdaBoost,SMTP,0.9994071359275628,0.45000000000000007,1.0906057357788086,4086.957815 +76120,Binary classification,AdaBoost,SMTP,0.9994219577240899,0.45000000000000007,1.090651512145996,4279.139143 +78023,Binary classification,AdaBoost,SMTP,0.9994232395990874,0.4444444444444444,1.1481237411499023,4474.636732 +79926,Binary classification,AdaBoost,SMTP,0.9994369721614013,0.4444444444444444,1.1493444442749023,4673.509203 +81829,Binary classification,AdaBoost,SMTP,0.999450065992081,0.4444444444444444,1.1612462997436523,4875.758715 +83732,Binary classification,AdaBoost,SMTP,0.9994625646415306,0.4444444444444444,1.161269187927246,5081.435613 +85635,Binary classification,AdaBoost,SMTP,0.9994745077889623,0.4444444444444444,1.1612234115600586,5290.523743 +87538,Binary classification,AdaBoost,SMTP,0.9994859316631824,0.4444444444444444,1.1584348678588867,5502.994331999999 +89441,Binary classification,AdaBoost,SMTP,0.9994633273703041,0.42857142857142855,1.2966947555541992,5719.6431489999995 +91344,Binary classification,AdaBoost,SMTP,0.9994745081724927,0.42857142857142855,1.3124494552612305,5939.715090999999 +93247,Binary classification,AdaBoost,SMTP,0.9994316110074427,0.40449438202247195,1.3362340927124023,6163.415711999999 +95150,Binary classification,AdaBoost,SMTP,0.9994429789067673,0.40449438202247195,1.3363256454467773,6390.433574999999 +95156,Binary classification,AdaBoost,SMTP,0.9994430140297409,0.40449438202247195,1.3363256454467773,6617.502892999999 +106,Binary classification,Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.22373199462890625,0.813651 +212,Binary classification,Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.22452545166015625,2.392298 +318,Binary classification,Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.2251434326171875,4.879886 +424,Binary classification,Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.225250244140625,8.257922 +530,Binary classification,Bagging,Bananas,0.55765595463138,0.45581395348837206,0.22527313232421875,12.416081000000002 +636,Binary classification,Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.22574615478515625,17.551695000000002 +742,Binary classification,Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.22597503662109375,23.418389 +848,Binary classification,Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.2259063720703125,30.181971 +954,Binary classification,Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.22588348388671875,37.806045 +1060,Binary classification,Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.22563934326171875,46.336236 +1166,Binary classification,Bagging,Bananas,0.590557939914163,0.4015056461731493,0.225738525390625,55.794626 +1272,Binary classification,Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.226043701171875,66.093431 +1378,Binary classification,Bagging,Bananas,0.599128540305011,0.3973799126637554,0.226348876953125,77.304266 +1484,Binary classification,Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.2263031005859375,89.41731899999999 +1590,Binary classification,Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.22628021240234375,102.38846199999999 +1696,Binary classification,Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.22634124755859375,116.26624899999999 +1802,Binary classification,Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.2263641357421875,130.90050499999998 +1908,Binary classification,Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.2263641357421875,146.406164 +2014,Binary classification,Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.2263641357421875,162.81699799999998 +2120,Binary classification,Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.2263641357421875,180.02605599999998 +2226,Binary classification,Bagging,Bananas,0.597752808988764,0.40133779264214053,0.226318359375,198.114263 +2332,Binary classification,Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.22637939453125,217.043548 +2438,Binary classification,Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.22640228271484375,236.778245 +2544,Binary classification,Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.22676849365234375,257.426783 +2650,Binary classification,Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.2269744873046875,278.871455 +2756,Binary classification,Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.2269744873046875,301.18545300000005 +2862,Binary classification,Bagging,Bananas,0.599790283117791,0.4051948051948052,0.2269744873046875,324.33878000000004 +2968,Binary classification,Bagging,Bananas,0.599932591843613,0.40261701056869653,0.22699737548828125,348.42370100000005 +3074,Binary classification,Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.22699737548828125,373.34600700000004 +3180,Binary classification,Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.22699737548828125,399.17600200000004 +3286,Binary classification,Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.22489070892333984,425.805579 +3392,Binary classification,Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.2988729476928711,453.430877 +3498,Binary classification,Bagging,Bananas,0.597369173577352,0.40237691001697795,0.3531064987182617,482.040674 +3604,Binary classification,Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3826017379760742,511.80088500000005 +3710,Binary classification,Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.4367246627807617,542.835536 +3816,Binary classification,Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.4704160690307617,575.071901 +3922,Binary classification,Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.5176496505737305,608.725741 +4028,Binary classification,Bagging,Bananas,0.608393344921778,0.4195804195804196,0.5480222702026367,643.745138 +4134,Binary classification,Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.5632429122924805,680.30634 +4240,Binary classification,Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.5676107406616211,718.216367 +4346,Binary classification,Bagging,Bananas,0.6193325661680092,0.438560760353021,0.5822668075561523,757.397991 +4452,Binary classification,Bagging,Bananas,0.6218827229835991,0.4421610871726881,0.5884695053100586,797.943115 +4558,Binary classification,Bagging,Bananas,0.6219003730524468,0.44293566117038474,0.6275625228881836,839.803567 +4664,Binary classification,Bagging,Bananas,0.623203945957538,0.4455664247396655,0.6328649520874023,883.024854 +4770,Binary classification,Bagging,Bananas,0.6250786328370728,0.446096654275093,0.6821584701538086,927.682473 +4876,Binary classification,Bagging,Bananas,0.6266666666666667,0.44680851063829785,0.6950826644897461,973.7206689999999 +4982,Binary classification,Bagging,Bananas,0.629592451314997,0.4530091906314853,0.7119512557983398,1021.0804549999999 +5088,Binary classification,Bagging,Bananas,0.6298407705917043,0.4527753560011624,0.6960439682006836,1069.7402539999998 +5194,Binary classification,Bagging,Bananas,0.6321971885230118,0.456459874786568,0.6964941024780273,1119.6924109999998 +5300,Binary classification,Bagging,Bananas,0.6340819022457067,0.4594368553108447,0.7031240463256836,1170.8531239999998 +906,Binary classification,Bagging,Elec2,0.8629834254143647,0.8663793103448276,1.7490100860595703,16.056337 +1812,Binary classification,Bagging,Elec2,0.8890115958034235,0.8680236375574525,2.496591567993164,50.652304 +2718,Binary classification,Bagging,Elec2,0.87523003312477,0.8521587440034889,1.8562908172607422,106.697102 +3624,Binary classification,Bagging,Elec2,0.8868341153739995,0.8653972422849641,2.5584278106689453,176.150463 +4530,Binary classification,Bagging,Elec2,0.8880547582247736,0.8593619972260749,3.1707210540771484,258.677392 +5436,Binary classification,Bagging,Elec2,0.8829806807727691,0.8518863530507685,2.113290786743164,353.604927 +6342,Binary classification,Bagging,Elec2,0.8814067181832519,0.8497802636835796,2.4726314544677734,459.993548 +7248,Binary classification,Bagging,Elec2,0.883262039464606,0.8516310066643283,2.354246139526367,576.649537 +8154,Binary classification,Bagging,Elec2,0.8828652029927634,0.8585394756332394,2.1453304290771484,702.348431 +9060,Binary classification,Bagging,Elec2,0.8839827795562424,0.8639129871811472,2.1982364654541016,836.637311 +9966,Binary classification,Bagging,Elec2,0.880983442047165,0.8635840809753854,2.4484920501708984,979.832141 +10872,Binary classification,Bagging,Elec2,0.881151687977187,0.8654446990210373,2.578580856323242,1131.438926 +11778,Binary classification,Bagging,Elec2,0.8799354674365288,0.8634344214796214,2.730459213256836,1291.261447 +12684,Binary classification,Bagging,Elec2,0.8768430182133564,0.8601361031518624,2.090116500854492,1459.3451100000002 +13590,Binary classification,Bagging,Elec2,0.8789462064905438,0.8639483913654784,1.8772754669189453,1635.065473 +14496,Binary classification,Bagging,Elec2,0.878854777509486,0.86444341516134,2.105062484741211,1818.351758 +15402,Binary classification,Bagging,Elec2,0.8775404194532822,0.86187197890728,2.440736770629883,2009.388651 +16308,Binary classification,Bagging,Elec2,0.8765560802109523,0.8599262403451395,2.627225875854492,2209.910977 +17214,Binary classification,Bagging,Elec2,0.8758496485214663,0.8567214213878646,2.5119991302490234,2419.733571 +18120,Binary classification,Bagging,Elec2,0.8760969148407749,0.8567600331780769,2.7164859771728516,2638.279319 +19026,Binary classification,Bagging,Elec2,0.8772141918528252,0.8562284588872477,3.019651412963867,2865.7383750000004 +19932,Binary classification,Bagging,Elec2,0.8739651798705534,0.8535106134826219,2.721925735473633,3103.6766270000003 +20838,Binary classification,Bagging,Elec2,0.8716225944233815,0.8503663925714606,2.4018421173095703,3351.3122810000004 +21744,Binary classification,Bagging,Elec2,0.872556684910086,0.8492300995701616,2.248655319213867,3607.2754260000006 +22650,Binary classification,Bagging,Elec2,0.870722769217184,0.845275840202917,2.6111698150634766,3871.072358000001 +23556,Binary classification,Bagging,Elec2,0.8645722776480578,0.8365611230658879,1.8957767486572266,4144.250301000001 +24462,Binary classification,Bagging,Elec2,0.8614120436613385,0.8315276811450154,1.5607776641845703,4424.237452000001 +25368,Binary classification,Bagging,Elec2,0.8560334292584855,0.8249113050148624,1.3715801239013672,4711.1951020000015 +26274,Binary classification,Bagging,Elec2,0.8558596277547292,0.824277295717136,1.6112499237060547,5004.571280000002 +27180,Binary classification,Bagging,Elec2,0.8564332756907906,0.8258035714285713,2.025979995727539,5304.465246000002 +28086,Binary classification,Bagging,Elec2,0.8535517179989318,0.8215385950449082,1.8488483428955078,5611.580754000001 +28992,Binary classification,Bagging,Elec2,0.8515746266082578,0.8178624338624338,2.0671520233154297,5927.3319900000015 +29898,Binary classification,Bagging,Elec2,0.849048399504967,0.8140885684860969,1.3224430084228516,6250.652578000001 +30804,Binary classification,Bagging,Elec2,0.8473849949680226,0.8106344410876132,1.549489974975586,6580.126329000001 +31710,Binary classification,Bagging,Elec2,0.8429783342268756,0.8039377830281552,1.5209712982177734,6916.182134000001 +32616,Binary classification,Bagging,Elec2,0.8411773723746743,0.8020785572367416,1.9952220916748047,7258.669481000001 +33522,Binary classification,Bagging,Elec2,0.8415023418155783,0.8033751526590429,1.8286800384521484,7608.320925000001 +34428,Binary classification,Bagging,Elec2,0.839689778371627,0.8006357692446627,2.2426509857177734,7966.087012000001 +35334,Binary classification,Bagging,Elec2,0.8395550901423598,0.7993487417265422,2.1107349395751953,8331.986249000001 +36240,Binary classification,Bagging,Elec2,0.8400618118601507,0.7984280447937677,1.8943347930908203,8703.460619000001 +37146,Binary classification,Bagging,Elec2,0.839278503163279,0.796356938190749,1.3389415740966797,9080.992051000001 +38052,Binary classification,Bagging,Elec2,0.8389267036345956,0.7946940006029546,1.6071338653564453,9463.837927 +38958,Binary classification,Bagging,Elec2,0.8382832353620658,0.7942655607079877,1.8687000274658203,9853.704286 +39864,Binary classification,Bagging,Elec2,0.8387477109098663,0.7967495098969203,1.466756820678711,10249.501094 +40770,Binary classification,Bagging,Elec2,0.8400009811376291,0.8001225677953119,2.0175647735595703,10651.197686 +41676,Binary classification,Bagging,Elec2,0.8407918416316736,0.8026413635146792,2.1117191314697266,11058.632461 +42582,Binary classification,Bagging,Elec2,0.8411732932528593,0.8035781708344224,2.033967971801758,11470.822586999999 +43488,Binary classification,Bagging,Elec2,0.8416538275806563,0.804308286915994,1.7070560455322266,11887.700533 +44394,Binary classification,Bagging,Elec2,0.8406280269411844,0.8019483246087955,2.2816905975341797,12309.209906 +45300,Binary classification,Bagging,Elec2,0.8404379787633282,0.802124397722295,2.2888126373291016,12736.77001 +45312,Binary classification,Bagging,Elec2,0.8404360971949416,0.8020804817957842,2.2889575958251953,13164.474026 +25,Binary classification,Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.7072525024414062,0.45657 +50,Binary classification,Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.7079315185546875,1.426682 +75,Binary classification,Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.708251953125,2.8732379999999997 +100,Binary classification,Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.70849609375,4.790442 +125,Binary classification,Bagging,Phishing,0.8548387096774194,0.85,0.70849609375,7.239611999999999 +150,Binary classification,Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.708740234375,10.202642999999998 +175,Binary classification,Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.7091293334960938,13.595279999999999 +200,Binary classification,Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.7092666625976562,17.527801 +225,Binary classification,Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7491827011108398,22.029145 +250,Binary classification,Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.7771825790405273,27.026806999999998 +275,Binary classification,Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7774114608764648,32.501577 +300,Binary classification,Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7775945663452148,38.42215899999999 +325,Binary classification,Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7779607772827148,44.92146699999999 +350,Binary classification,Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7781057357788086,51.897794999999995 +375,Binary classification,Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.8172750473022461,59.363139999999994 +400,Binary classification,Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.8571996688842773,67.416022 +425,Binary classification,Bagging,Phishing,0.8679245283018868,0.839080459770115,0.9128484725952148,76.017673 +450,Binary classification,Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.9131002426147461,85.092057 +475,Binary classification,Bagging,Phishing,0.869198312236287,0.8402061855670103,0.9133520126342773,94.603797 +500,Binary classification,Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.9135580062866211,104.609638 +525,Binary classification,Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.9136190414428711,115.080656 +550,Binary classification,Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.9137258529663086,126.050962 +575,Binary classification,Bagging,Phishing,0.867595818815331,0.8382978723404255,0.9137868881225586,137.397676 +600,Binary classification,Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.9139089584350586,149.31562 +625,Binary classification,Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.9536046981811523,161.695664 +650,Binary classification,Bagging,Phishing,0.8767334360554699,0.846153846153846,0.9540624618530273,174.565593 +675,Binary classification,Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.9818639755249023,187.898512 +700,Binary classification,Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.9230222702026367,201.73587500000002 +725,Binary classification,Bagging,Phishing,0.8825966850828729,0.8576214405360134,1.021204948425293,216.08538800000002 +750,Binary classification,Bagging,Phishing,0.8865153538050734,0.8631239935587761,1.0604047775268555,230.90612000000002 +775,Binary classification,Bagging,Phishing,0.8875968992248062,0.863849765258216,1.1157331466674805,246.307883 +800,Binary classification,Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.2215375900268555,262.241262 +825,Binary classification,Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.2229490280151367,278.553882 +850,Binary classification,Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.235407829284668,295.406724 +875,Binary classification,Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.263422966003418,312.749904 +900,Binary classification,Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.318751335144043,330.632511 +925,Binary classification,Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.318964958190918,348.945693 +950,Binary classification,Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.3194990158081055,367.70198800000003 +975,Binary classification,Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.319605827331543,386.96933700000005 +1000,Binary classification,Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.3197660446166992,406.75034200000005 +1025,Binary classification,Bagging,Phishing,0.8876953125,0.8700564971751412,1.3200559616088867,426.99481600000007 +1050,Binary classification,Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.320155143737793,447.8295280000001 +1075,Binary classification,Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.320277214050293,469.1965740000001 +1100,Binary classification,Bagging,Phishing,0.89171974522293,0.8761706555671176,1.320643424987793,491.1150510000001 +1125,Binary classification,Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.320704460144043,513.5552500000001 +1150,Binary classification,Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.320704460144043,536.4428780000001 +1175,Binary classification,Bagging,Phishing,0.8926746166950597,0.877906976744186,1.320765495300293,559.8515450000001 +1200,Binary classification,Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.3328428268432617,583.8125340000001 +1225,Binary classification,Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3880414962768555,608.2234330000001 +1250,Binary classification,Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3882551193237305,633.1359570000001 +1903,Binary classification,Bagging,SMTP,1.0,0.0,0.2038736343383789,10.878823 +3806,Binary classification,Bagging,SMTP,1.0,0.0,0.2044839859008789,32.501535000000004 +5709,Binary classification,Bagging,SMTP,1.0,0.0,0.20502567291259766,64.818606 +7612,Binary classification,Bagging,SMTP,1.0,0.0,0.2050485610961914,107.076722 +9515,Binary classification,Bagging,SMTP,1.0,0.0,0.2050485610961914,158.807432 +11418,Binary classification,Bagging,SMTP,1.0,0.0,0.2056589126586914,218.4459 +13321,Binary classification,Bagging,SMTP,1.0,0.0,0.20568180084228516,285.19327499999997 +15224,Binary classification,Bagging,SMTP,0.9992774091834724,0.0,0.26130008697509766,359.03964299999996 +17127,Binary classification,Bagging,SMTP,0.9992409202382343,0.0,0.2063913345336914,440.61769699999996 +19030,Binary classification,Bagging,SMTP,0.9993168322034789,0.0,0.2062082290649414,529.79805 +20933,Binary classification,Bagging,SMTP,0.999378941333843,0.0,0.20684146881103516,626.4074929999999 +22836,Binary classification,Bagging,SMTP,0.9994306984891613,0.0,0.20693302154541016,729.8228539999999 +24739,Binary classification,Bagging,SMTP,0.9994744926833212,0.0,0.20707035064697266,839.3276679999999 +26642,Binary classification,Bagging,SMTP,0.9994744942006681,0.0,0.20674991607666016,954.8656409999999 +28545,Binary classification,Bagging,SMTP,0.9995095291479821,0.0,0.20697879791259766,1076.4115539999998 +30448,Binary classification,Bagging,SMTP,0.9995401845830459,0.0,0.2068643569946289,1203.4939569999997 +32351,Binary classification,Bagging,SMTP,0.9995672333848532,0.0,0.2070016860961914,1337.1471509999997 +34254,Binary classification,Bagging,SMTP,0.9995912766764955,0.0,0.2069101333618164,1476.3596139999997 +36157,Binary classification,Bagging,SMTP,0.9996127890253347,0.0,0.2069101333618164,1621.0863639999998 +38060,Binary classification,Bagging,SMTP,0.9996321500827662,0.0,0.20693302154541016,1771.0710339999998 +39963,Binary classification,Bagging,SMTP,0.9996496671838246,0.0,0.20679569244384766,1926.3060879999998 +41866,Binary classification,Bagging,SMTP,0.9996655917831124,0.0,0.20749759674072266,2086.761849 +43769,Binary classification,Bagging,SMTP,0.9996801316029976,0.0,0.20697879791259766,2252.424258 +45672,Binary classification,Bagging,SMTP,0.9996934597446958,0.0,0.2072610855102539,2423.176177 +47575,Binary classification,Bagging,SMTP,0.9997057216126456,0.0,0.20725345611572266,2599.130792 +49478,Binary classification,Bagging,SMTP,0.99971704024092,0.0,0.19541263580322266,2780.435567 +51381,Binary classification,Bagging,SMTP,0.9996885947839627,0.0,0.2073373794555664,2966.651736 +53284,Binary classification,Bagging,SMTP,0.9996997166075484,0.0,0.2073373794555664,3157.874809 +55187,Binary classification,Bagging,SMTP,0.999710071394919,0.0,0.2073526382446289,3353.943675 +57090,Binary classification,Bagging,SMTP,0.9995620872672494,0.0,0.20707035064697266,3555.07983 +58993,Binary classification,Bagging,SMTP,0.9995762137238947,0.0,0.20703983306884766,3761.219357 +60896,Binary classification,Bagging,SMTP,0.999589457262501,0.0,0.2070322036743164,3972.252634 +62799,Binary classification,Bagging,SMTP,0.9995700500015924,0.0,0.20719242095947266,4188.261246 +64702,Binary classification,Bagging,SMTP,0.9995826957852274,0.0,0.20734500885009766,4409.191759 +66605,Binary classification,Bagging,SMTP,0.9995946189418053,0.0,0.20731449127197266,4634.878548000001 +68508,Binary classification,Bagging,SMTP,0.9995766855941729,0.0,0.20736026763916016,4864.934707 +70411,Binary classification,Bagging,SMTP,0.9995881266865502,0.0,0.2072000503540039,5099.2875650000005 +72314,Binary classification,Bagging,SMTP,0.9995989656078437,0.0,0.20736026763916016,5337.886074000001 +74217,Binary classification,Bagging,SMTP,0.99960924867953,0.0,0.20736026763916016,5580.709613000001 +76120,Binary classification,Bagging,SMTP,0.9996190175908775,0.0,0.20746707916259766,5827.7179830000005 +78023,Binary classification,Bagging,SMTP,0.9996283099638563,0.0,0.20746707916259766,6079.015463000001 +79926,Binary classification,Bagging,SMTP,0.9996371598373475,0.0,0.20714664459228516,6334.716663000001 +81829,Binary classification,Bagging,SMTP,0.9996455980837855,0.0,0.20755863189697266,6594.692589000001 +83732,Binary classification,Bagging,SMTP,0.9996536527689864,0.0,0.20795536041259766,6858.666542000001 +85635,Binary classification,Bagging,SMTP,0.999661349463998,0.0,0.2080392837524414,7126.604303000001 +87538,Binary classification,Bagging,SMTP,0.9996687115162731,0.0,0.2078561782836914,7398.595214000001 +89441,Binary classification,Bagging,SMTP,0.9996645796064401,0.0,0.2079019546508789,7674.616155000001 +91344,Binary classification,Bagging,SMTP,0.999671567607808,0.0,0.2078104019165039,7954.656032000001 +93247,Binary classification,Bagging,SMTP,0.9996782703815713,0.0,0.19611454010009766,8238.690655 +95150,Binary classification,Bagging,SMTP,0.9996847050415664,0.0,0.2079477310180664,8526.751857000001 +95156,Binary classification,Bagging,SMTP,0.9996847249224948,0.0,0.20797061920166016,8814.843001000001 +106,Binary classification,Leveraging Bagging,Bananas,0.5142857142857142,0.45161290322580644,0.1802501678466797,1.958268 +212,Binary classification,Leveraging Bagging,Bananas,0.5402843601895735,0.4756756756756757,0.1808605194091797,6.1304110000000005 +318,Binary classification,Leveraging Bagging,Bananas,0.5394321766561514,0.4930555555555555,0.18149375915527344,12.559627 +424,Binary classification,Leveraging Bagging,Bananas,0.5531914893617021,0.4932975871313673,0.1814708709716797,21.158430000000003 +530,Binary classification,Leveraging Bagging,Bananas,0.5614366729678639,0.4703196347031963,0.1814708709716797,31.954501 +636,Binary classification,Leveraging Bagging,Bananas,0.5763779527559055,0.4836852207293666,0.41109561920166016,45.100937 +742,Binary classification,Leveraging Bagging,Bananas,0.5991902834008097,0.4940374787052811,0.5197267532348633,60.647662000000004 +848,Binary classification,Leveraging Bagging,Bananas,0.6210153482880756,0.5201793721973094,0.6145830154418945,78.646382 +954,Binary classification,Leveraging Bagging,Bananas,0.6411332633788038,0.5464190981432361,0.681065559387207,99.167462 +1060,Binary classification,Leveraging Bagging,Bananas,0.6515580736543909,0.555956678700361,0.7228097915649414,122.156435 +1166,Binary classification,Leveraging Bagging,Bananas,0.6626609442060086,0.5732899022801302,0.8111352920532227,147.677601 +1272,Binary classification,Leveraging Bagging,Bananas,0.6766325727773407,0.5958702064896755,0.8519144058227539,175.66982000000002 +1378,Binary classification,Leveraging Bagging,Bananas,0.6877269426289034,0.6062271062271062,0.9361848831176758,206.11220300000002 +1484,Binary classification,Leveraging Bagging,Bananas,0.6999325691166555,0.6238377007607777,0.978398323059082,239.08437400000003 +1590,Binary classification,Leveraging Bagging,Bananas,0.7073631214600378,0.6375681995323461,1.0816278457641602,274.51056400000004 +1696,Binary classification,Leveraging Bagging,Bananas,0.7162241887905605,0.6496722505462491,1.146012306213379,312.20930000000004 +1802,Binary classification,Leveraging Bagging,Bananas,0.7262631871182677,0.6662153012863914,1.231095314025879,352.32891700000005 +1908,Binary classification,Leveraging Bagging,Bananas,0.7320398531725223,0.677602523659306,1.3021745681762695,394.808348 +2014,Binary classification,Leveraging Bagging,Bananas,0.7391952309985097,0.6902654867256638,1.3571443557739258,439.684188 +2120,Binary classification,Leveraging Bagging,Bananas,0.7456347333647947,0.7020453289110005,1.439896583557129,486.825513 +2226,Binary classification,Leveraging Bagging,Bananas,0.750561797752809,0.7080483955812729,1.4615755081176758,536.150148 +2332,Binary classification,Leveraging Bagging,Bananas,0.7554697554697555,0.715,1.4801912307739258,587.578093 +2438,Binary classification,Leveraging Bagging,Bananas,0.7599507591300779,0.7202295552367289,1.5264062881469727,641.095023 +2544,Binary classification,Leveraging Bagging,Bananas,0.7624852536374361,0.7257039055404179,1.5866899490356445,696.665992 +2650,Binary classification,Leveraging Bagging,Bananas,0.7678369195922989,0.7331887201735358,1.6338167190551758,754.18395 +2756,Binary classification,Leveraging Bagging,Bananas,0.7731397459165155,0.7396917950853811,1.718327522277832,813.521765 +2862,Binary classification,Leveraging Bagging,Bananas,0.777350576721426,0.7440739252711932,1.7761125564575195,874.768076 +2968,Binary classification,Leveraging Bagging,Bananas,0.7812605325244355,0.7479611650485437,1.876938819885254,937.8386529999999 +3074,Binary classification,Leveraging Bagging,Bananas,0.7845753335502766,0.7526158445440957,1.974156379699707,1002.618853 +3180,Binary classification,Leveraging Bagging,Bananas,0.7892418999685435,0.7572463768115942,2.0079431533813477,1069.033932 +3286,Binary classification,Leveraging Bagging,Bananas,0.7923896499238965,0.7605337078651686,2.0704050064086914,1137.092928 +3392,Binary classification,Leveraging Bagging,Bananas,0.7938661161899144,0.7636117686844774,2.141594886779785,1206.880705 +3498,Binary classification,Leveraging Bagging,Bananas,0.7966828710323134,0.7657331136738056,2.2472352981567383,1278.374701 +3604,Binary classification,Leveraging Bagging,Bananas,0.7998889814043852,0.7685393258426965,2.2915468215942383,1351.505796 +3710,Binary classification,Leveraging Bagging,Bananas,0.8021029927204099,0.7717661691542288,2.3504953384399414,1426.3168569999998 +3816,Binary classification,Leveraging Bagging,Bananas,0.8055045871559633,0.7761013880506941,2.3972253799438477,1502.8248519999997 +3922,Binary classification,Leveraging Bagging,Bananas,0.8071920428462127,0.7776470588235294,2.4447336196899414,1580.9903339999996 +4028,Binary classification,Leveraging Bagging,Bananas,0.8085423392103303,0.7788930312589618,2.513848304748535,1660.8236829999996 +4134,Binary classification,Leveraging Bagging,Bananas,0.8107911928381321,0.7816862088218872,2.6076173782348633,1742.3313809999995 +4240,Binary classification,Leveraging Bagging,Bananas,0.8136352913422977,0.7852093529091897,2.653599739074707,1825.5059619999995 +4346,Binary classification,Leveraging Bagging,Bananas,0.8161104718066743,0.7881198621055423,2.7111101150512695,1910.3837179999996 +4452,Binary classification,Leveraging Bagging,Bananas,0.8173444169849472,0.7894327894327894,2.7411813735961914,1996.8929369999996 +4558,Binary classification,Leveraging Bagging,Bananas,0.8183015141540487,0.7910146390711761,2.7629594802856445,2085.0686209999994 +4664,Binary classification,Leveraging Bagging,Bananas,0.8205018228608192,0.7941971969510695,2.818455696105957,2174.8872669999996 +4770,Binary classification,Leveraging Bagging,Bananas,0.8209268190396309,0.7942168674698795,2.852097511291504,2266.3018959999995 +4876,Binary classification,Leveraging Bagging,Bananas,0.822974358974359,0.795932844644124,2.940415382385254,2359.3812719999996 +4982,Binary classification,Leveraging Bagging,Bananas,0.825135514956836,0.7990772779700116,2.986912727355957,2454.1200449999997 +5088,Binary classification,Leveraging Bagging,Bananas,0.825437389424022,0.7995485327313769,3.072648048400879,2550.4404699999996 +5194,Binary classification,Leveraging Bagging,Bananas,0.8266897746967071,0.8008849557522125,3.1882104873657227,2648.3615419999996 +5300,Binary classification,Leveraging Bagging,Bananas,0.8282694848084544,0.8026886383347789,3.2357072830200195,2747.9506659999997 +906,Binary classification,Leveraging Bagging,Elec2,0.8895027624309392,0.8873873873873873,2.5379486083984375,35.61841 +1812,Binary classification,Leveraging Bagging,Elec2,0.9127553837658752,0.8941018766756033,3.2672500610351562,98.66906499999999 +2718,Binary classification,Leveraging Bagging,Elec2,0.9013617960986382,0.8815207780725023,2.908538818359375,185.051304 +3624,Binary classification,Leveraging Bagging,Elec2,0.905051062655258,0.8859416445623343,4.239933013916016,291.140366 +4530,Binary classification,Leveraging Bagging,Elec2,0.9059395009935968,0.8829026937877955,4.5028228759765625,414.05054599999994 +5436,Binary classification,Leveraging Bagging,Elec2,0.904691812327507,0.8806451612903227,5.411556243896484,555.0527619999999 +6342,Binary classification,Leveraging Bagging,Elec2,0.904746885349314,0.8810086682427108,3.64324951171875,712.2276919999999 +7248,Binary classification,Leveraging Bagging,Elec2,0.9038222712846695,0.8793908980792524,4.176555633544922,885.4512659999999 +8154,Binary classification,Leveraging Bagging,Elec2,0.9062921623942107,0.8879107981220656,4.873016357421875,1073.942042 +9060,Binary classification,Leveraging Bagging,Elec2,0.9073849210729661,0.8915600361897376,6.068294525146484,1277.3056459999998 +9966,Binary classification,Leveraging Bagging,Elec2,0.9066733567486202,0.8924855491329481,5.883171081542969,1496.0059789999998 +10872,Binary classification,Leveraging Bagging,Elec2,0.9090240088308343,0.8966238110170377,7.123630523681641,1728.5474849999998 +11778,Binary classification,Leveraging Bagging,Elec2,0.9088052984631061,0.8963720571208027,4.904956817626953,1974.076492 +12684,Binary classification,Leveraging Bagging,Elec2,0.9071197666167311,0.8948214285714287,4.745685577392578,2233.08693 +13590,Binary classification,Leveraging Bagging,Elec2,0.908234601515932,0.8972732515034187,5.919612884521484,2504.250556 +14496,Binary classification,Leveraging Bagging,Elec2,0.9082442221455674,0.8976293103448276,4.272552490234375,2787.365554 +15402,Binary classification,Leveraging Bagging,Elec2,0.9089669501980391,0.8979027090008739,4.651363372802734,3081.637323 +16308,Binary classification,Leveraging Bagging,Elec2,0.9085668731219722,0.8971653217463273,5.967304229736328,3386.795808 +17214,Binary classification,Leveraging Bagging,Elec2,0.9075698599895428,0.8943488943488943,5.553913116455078,3703.0592819999997 +18120,Binary classification,Leveraging Bagging,Elec2,0.9077211766653789,0.8943911066195048,7.001399993896484,4030.5410249999995 +19026,Binary classification,Leveraging Bagging,Elec2,0.9078580814717477,0.8932854446947099,7.953182220458984,4368.505934999999 +19932,Binary classification,Leveraging Bagging,Elec2,0.9081832321509207,0.8944636678200691,8.54180908203125,4717.761576999999 +20838,Binary classification,Leveraging Bagging,Elec2,0.9064644622546432,0.8926111631494847,7.284095764160156,5078.966551999999 +21744,Binary classification,Leveraging Bagging,Elec2,0.9064066596145886,0.8909840895698291,8.78485107421875,5451.1446879999985 +22650,Binary classification,Leveraging Bagging,Elec2,0.9054704401960352,0.8890386110391294,9.895774841308594,5834.8071089999985 +23556,Binary classification,Leveraging Bagging,Elec2,0.9032052642751008,0.8860113988601139,9.921958923339844,6231.782156999999 +24462,Binary classification,Leveraging Bagging,Elec2,0.9009443604104493,0.8825098191339766,6.414276123046875,6640.471452999998 +25368,Binary classification,Leveraging Bagging,Elec2,0.8975046320022076,0.87847059923343,7.025360107421875,7059.615553999998 +26274,Binary classification,Leveraging Bagging,Elec2,0.8978418909146272,0.878705712219812,8.249675750732422,7487.941528999998 +27180,Binary classification,Leveraging Bagging,Elec2,0.8983038375216159,0.879888753693725,7.590415954589844,7924.652190999998 +28086,Binary classification,Leveraging Bagging,Elec2,0.8965640021363718,0.8772448763997466,7.862815856933594,8369.790676999999 +28992,Binary classification,Leveraging Bagging,Elec2,0.8963126487530613,0.8762962962962964,9.08489990234375,8823.391086 +29898,Binary classification,Leveraging Bagging,Elec2,0.8952403251162324,0.8748901493968203,2.6490402221679688,9284.744376999999 +30804,Binary classification,Leveraging Bagging,Elec2,0.8947505113138331,0.8736357966947302,3.2276954650878906,9752.904185 +31710,Binary classification,Leveraging Bagging,Elec2,0.8935948784256835,0.8721291594027135,3.8703384399414062,10228.075712 +32616,Binary classification,Leveraging Bagging,Elec2,0.8929940211559099,0.8717194736455194,4.073085784912109,10710.354293 +33522,Binary classification,Leveraging Bagging,Elec2,0.8932311088571343,0.872338148742643,4.776435852050781,11199.826533 +34428,Binary classification,Leveraging Bagging,Elec2,0.8924390739826299,0.8711865585974188,4.868198394775391,11696.46579 +35334,Binary classification,Leveraging Bagging,Elec2,0.8922537005066086,0.8703735231025913,5.445720672607422,12200.310087 +36240,Binary classification,Leveraging Bagging,Elec2,0.8919948122188802,0.8690619563762879,4.9837493896484375,12711.187581 +37146,Binary classification,Leveraging Bagging,Elec2,0.8920985327769552,0.8688996467355751,5.313899993896484,13229.101200000001 +38052,Binary classification,Leveraging Bagging,Elec2,0.8916979842842501,0.8676919125437441,5.129566192626953,13754.199791000001 +38958,Binary classification,Leveraging Bagging,Elec2,0.8912647277767796,0.8674924924924926,5.3661651611328125,14286.524658 +39864,Binary classification,Leveraging Bagging,Elec2,0.8915535709806086,0.8687813021702837,5.776020050048828,14825.184636 +40770,Binary classification,Leveraging Bagging,Elec2,0.8920012754789178,0.8703512852978417,6.964508056640625,15370.438083000001 +41676,Binary classification,Leveraging Bagging,Elec2,0.89250149970006,0.8717728547713092,8.029548645019531,15922.831901000001 +42582,Binary classification,Leveraging Bagging,Elec2,0.8927925600619995,0.8723184068469779,8.723072052001953,16481.133162000002 +43488,Binary classification,Leveraging Bagging,Elec2,0.8927495573389749,0.8722821622213702,8.793426513671875,17045.039295000002 +44394,Binary classification,Leveraging Bagging,Elec2,0.8920775797986169,0.8710467526175545,6.634971618652344,17614.470389000002 +45300,Binary classification,Leveraging Bagging,Elec2,0.8926687123336056,0.8720122143834896,7.5638427734375,18188.843118 +45312,Binary classification,Leveraging Bagging,Elec2,0.8926529981682152,0.8719663069228745,7.565349578857422,18763.342135 +25,Binary classification,Leveraging Bagging,Phishing,0.75,0.75,0.6626491546630859,1.23946 +50,Binary classification,Leveraging Bagging,Phishing,0.8163265306122449,0.8,0.6635112762451172,3.9379920000000004 +75,Binary classification,Leveraging Bagging,Phishing,0.8378378378378378,0.8333333333333334,0.6635112762451172,8.007437 +100,Binary classification,Leveraging Bagging,Phishing,0.8484848484848485,0.8421052631578947,0.6476030349731445,13.398751 +125,Binary classification,Leveraging Bagging,Phishing,0.8467741935483871,0.8403361344537815,0.9203081130981445,19.997869 +150,Binary classification,Leveraging Bagging,Phishing,0.8456375838926175,0.8456375838926175,0.9203310012817383,27.874049 +175,Binary classification,Leveraging Bagging,Phishing,0.867816091954023,0.8588957055214724,1.0861825942993164,37.283539 +200,Binary classification,Leveraging Bagging,Phishing,0.8693467336683417,0.8617021276595744,1.2813997268676758,47.998757 +225,Binary classification,Leveraging Bagging,Phishing,0.8660714285714286,0.8557692307692308,1.3089113235473633,59.952352999999995 +250,Binary classification,Leveraging Bagging,Phishing,0.8554216867469879,0.8434782608695653,1.3089799880981445,73.11976999999999 +275,Binary classification,Leveraging Bagging,Phishing,0.8576642335766423,0.844621513944223,1.2476167678833008,87.72028699999998 +300,Binary classification,Leveraging Bagging,Phishing,0.862876254180602,0.8464419475655431,1.4594087600708008,103.60422699999998 +325,Binary classification,Leveraging Bagging,Phishing,0.8703703703703703,0.851063829787234,1.4950456619262695,120.70292199999997 +350,Binary classification,Leveraging Bagging,Phishing,0.8710601719197708,0.8494983277591974,1.5330171585083008,138.98074599999998 +375,Binary classification,Leveraging Bagging,Phishing,0.8716577540106952,0.8481012658227849,1.809849739074707,158.64485 +400,Binary classification,Leveraging Bagging,Phishing,0.8696741854636592,0.8433734939759037,2.068051338195801,179.64681099999999 +425,Binary classification,Leveraging Bagging,Phishing,0.8702830188679245,0.8405797101449276,2.104710578918457,201.85095299999998 +450,Binary classification,Leveraging Bagging,Phishing,0.8752783964365256,0.845303867403315,2.104527473449707,225.23996699999998 +475,Binary classification,Leveraging Bagging,Phishing,0.8776371308016878,0.8505154639175259,2.132199287414551,249.91758899999996 +500,Binary classification,Leveraging Bagging,Phishing,0.875751503006012,0.8502415458937198,2.1503801345825195,275.75512899999995 +525,Binary classification,Leveraging Bagging,Phishing,0.8778625954198473,0.8497652582159624,2.187130928039551,302.922289 +550,Binary classification,Leveraging Bagging,Phishing,0.8743169398907104,0.8463251670378619,2.2971315383911133,331.41425 +575,Binary classification,Leveraging Bagging,Phishing,0.8763066202090593,0.8479657387580299,2.4067888259887695,361.219435 +600,Binary classification,Leveraging Bagging,Phishing,0.8764607679465777,0.8451882845188285,2.406834602355957,392.26266999999996 +625,Binary classification,Leveraging Bagging,Phishing,0.8782051282051282,0.8442622950819672,2.352017402648926,424.555734 +650,Binary classification,Leveraging Bagging,Phishing,0.8813559322033898,0.850485436893204,2.279099464416504,458.24235899999996 +675,Binary classification,Leveraging Bagging,Phishing,0.8798219584569733,0.8513761467889909,2.54854679107666,493.20967599999994 +700,Binary classification,Leveraging Bagging,Phishing,0.8841201716738197,0.8550983899821109,2.565995216369629,529.352808 +725,Binary classification,Leveraging Bagging,Phishing,0.8812154696132597,0.8537414965986394,2.870518684387207,566.738792 +750,Binary classification,Leveraging Bagging,Phishing,0.8825100133511349,0.8557377049180328,2.941006660461426,605.3090109999999 +775,Binary classification,Leveraging Bagging,Phishing,0.8837209302325582,0.856687898089172,3.0508241653442383,645.053635 +800,Binary classification,Leveraging Bagging,Phishing,0.8836045056320401,0.8584474885844748,3.1606874465942383,686.031259 +825,Binary classification,Leveraging Bagging,Phishing,0.8810679611650486,0.8567251461988304,3.270321846008301,728.1933819999999 +850,Binary classification,Leveraging Bagging,Phishing,0.8833922261484098,0.8591749644381224,3.2882280349731445,771.57935 +875,Binary classification,Leveraging Bagging,Phishing,0.8844393592677345,0.8595271210013908,3.2632036209106445,816.1995999999999 +900,Binary classification,Leveraging Bagging,Phishing,0.8832035595105673,0.8575305291723202,3.380833625793457,861.997768 +925,Binary classification,Leveraging Bagging,Phishing,0.8841991341991342,0.8597640891218873,3.4813432693481445,908.9730649999999 +950,Binary classification,Leveraging Bagging,Phishing,0.8851422550052687,0.8628930817610063,3.5117311477661133,957.1207809999999 +975,Binary classification,Leveraging Bagging,Phishing,0.8870636550308009,0.8651960784313726,3.5666399002075195,1006.3541349999998 +1000,Binary classification,Leveraging Bagging,Phishing,0.8878878878878879,0.8663484486873507,3.645543098449707,1056.728559 +1025,Binary classification,Leveraging Bagging,Phishing,0.8876953125,0.8667439165701043,3.735753059387207,1108.282035 +1050,Binary classification,Leveraging Bagging,Phishing,0.8894184938036225,0.8693693693693694,3.808384895324707,1160.9493009999999 +1075,Binary classification,Leveraging Bagging,Phishing,0.8901303538175046,0.87117903930131,3.9453020095825195,1214.7027389999998 +1100,Binary classification,Leveraging Bagging,Phishing,0.89171974522293,0.873269435569755,3.945347785949707,1269.5439849999998 +1125,Binary classification,Leveraging Bagging,Phishing,0.8905693950177936,0.8730650154798762,3.972836494445801,1325.4243339999998 +1150,Binary classification,Leveraging Bagging,Phishing,0.8920800696257616,0.8747474747474747,3.945645332336426,1382.2374609999997 +1175,Binary classification,Leveraging Bagging,Phishing,0.8909710391822828,0.8732673267326733,3.9732484817504883,1440.0925429999998 +1200,Binary classification,Leveraging Bagging,Phishing,0.8924103419516264,0.8746355685131195,3.9472780227661133,1498.9929549999997 +1225,Binary classification,Leveraging Bagging,Phishing,0.8937908496732027,0.8761904761904762,3.982327461242676,1558.8210809999996 +1250,Binary classification,Leveraging Bagging,Phishing,0.8943154523618895,0.8773234200743495,4.0114030838012695,1619.6535709999996 +1903,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16020870208740234,31.58816 +3806,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16081905364990234,89.620857 +5709,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,167.42750999999998 +7612,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,263.688726 +9515,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,375.65509199999997 +11418,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16203975677490234,502.19041899999996 +13321,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16203975677490234,643.105063 +15224,Binary classification,Leveraging Bagging,SMTP,0.9992117191092426,0.0,0.2446889877319336,797.8947989999999 +17127,Binary classification,Leveraging Bagging,SMTP,0.9991825294873292,0.0,0.1627492904663086,968.1431969999999 +19030,Binary classification,Leveraging Bagging,SMTP,0.9992642808345158,0.0,0.16258907318115234,1153.4864309999998 +20933,Binary classification,Leveraging Bagging,SMTP,0.9993311675902924,0.0,0.16315364837646484,1353.5382149999998 +22836,Binary classification,Leveraging Bagging,SMTP,0.9993869060652507,0.0,0.1632680892944336,1568.3644279999999 +24739,Binary classification,Leveraging Bagging,SMTP,0.9994340690435767,0.0,0.16329097747802734,1796.9469139999999 +26642,Binary classification,Leveraging Bagging,SMTP,0.9994369580721444,0.0,0.1630849838256836,2038.09448 +28545,Binary classification,Leveraging Bagging,SMTP,0.9994744955156951,0.0,0.1630849838256836,2291.954796 +30448,Binary classification,Leveraging Bagging,SMTP,0.999507340624692,0.0,0.16315364837646484,2557.874515 +32351,Binary classification,Leveraging Bagging,SMTP,0.9995363214837713,0.0,0.1631765365600586,2835.65121 +34254,Binary classification,Leveraging Bagging,SMTP,0.999562082153388,0.0,0.16315364837646484,3124.961925 +36157,Binary classification,Leveraging Bagging,SMTP,0.9995851310985728,0.0,0.16310787200927734,3424.787068 +38060,Binary classification,Leveraging Bagging,SMTP,0.9996058750886782,0.0,0.1631307601928711,3734.997034 +39963,Binary classification,Leveraging Bagging,SMTP,0.9996246434112407,0.0,0.16319942474365234,4055.587677 +41866,Binary classification,Leveraging Bagging,SMTP,0.9996417054819061,0.0,0.1638784408569336,4386.458848 +43769,Binary classification,Leveraging Bagging,SMTP,0.9996572838603546,0.0,0.1636495590209961,4726.906731 +45672,Binary classification,Leveraging Bagging,SMTP,0.999671564012174,0.0,0.16385555267333984,5077.03597 +47575,Binary classification,Leveraging Bagging,SMTP,0.9996847017278345,0.0,0.16394710540771484,5436.8723119999995 +49478,Binary classification,Leveraging Bagging,SMTP,0.9996968288295571,0.0,0.16371822357177734,5806.3009919999995 +51381,Binary classification,Leveraging Bagging,SMTP,0.9996691319579603,0.0,0.16394710540771484,6185.544045999999 +53284,Binary classification,Leveraging Bagging,SMTP,0.9996809488955202,0.0,0.1641073226928711,6574.848143999999 +55187,Binary classification,Leveraging Bagging,SMTP,0.9996919508571014,0.0,0.16376399993896484,6975.121680999999 +57090,Binary classification,Leveraging Bagging,SMTP,0.9995445707579393,0.0,0.1638784408569336,7384.584095999999 +58993,Binary classification,Leveraging Bagging,SMTP,0.9995592622728505,0.0,0.16390132904052734,7802.5953629999985 +60896,Binary classification,Leveraging Bagging,SMTP,0.999573035553001,0.0,0.16371822357177734,8228.594131999998 +62799,Binary classification,Leveraging Bagging,SMTP,0.9995541259275773,0.0,0.16390132904052734,8661.618187999999 +64702,Binary classification,Leveraging Bagging,SMTP,0.9995672400735692,0.0,0.16399288177490234,9101.660232999999 +66605,Binary classification,Leveraging Bagging,SMTP,0.9995796048285388,0.0,0.1638326644897461,9548.697329999999 +68508,Binary classification,Leveraging Bagging,SMTP,0.9995620885456961,0.0,0.16380977630615234,10002.655235999999 +70411,Binary classification,Leveraging Bagging,SMTP,0.9995739241585002,0.0,0.16371822357177734,10463.105480999999 +72314,Binary classification,Leveraging Bagging,SMTP,0.9995851368357004,0.0,0.16376399993896484,10929.695224 +74217,Binary classification,Leveraging Bagging,SMTP,0.9995957744960655,0.0,0.16380977630615234,11402.447204 +76120,Binary classification,Leveraging Bagging,SMTP,0.9996058802664249,0.0,0.1638784408569336,11881.476782 +78023,Binary classification,Leveraging Bagging,SMTP,0.9996154930660582,0.0,0.1637411117553711,12366.666901 +79926,Binary classification,Leveraging Bagging,SMTP,0.9996246481076009,0.0,0.16371822357177734,12858.057531 +81829,Binary classification,Leveraging Bagging,SMTP,0.999633377328054,0.0,0.1521596908569336,13355.582794 +83732,Binary classification,Leveraging Bagging,SMTP,0.9996417097610204,0.0,0.15288448333740234,13859.323941 +85635,Binary classification,Leveraging Bagging,SMTP,0.9996496718593082,0.0,0.16432857513427734,14369.189455 +87538,Binary classification,Leveraging Bagging,SMTP,0.9996572877754549,0.0,0.16460323333740234,14885.224126 +89441,Binary classification,Leveraging Bagging,SMTP,0.9996533989266547,0.0,0.16451168060302734,15407.418989999998 +91344,Binary classification,Leveraging Bagging,SMTP,0.9996606198614015,0.0,0.16432857513427734,15935.791255999999 +93247,Binary classification,Leveraging Bagging,SMTP,0.9996675460609571,0.0,0.16451168060302734,16470.041814999997 +95150,Binary classification,Leveraging Bagging,SMTP,0.9996741952096186,0.0,0.1645345687866211,17009.813748999997 +95156,Binary classification,Leveraging Bagging,SMTP,0.9996742157532447,0.0,0.16460323333740234,17549.605714999998 +106,Binary classification,Stacking,Bananas,0.6095238095238096,0.577319587628866,0.7777948379516602,2.119535 +212,Binary classification,Stacking,Bananas,0.7109004739336493,0.6702702702702703,1.3802881240844727,6.931057 +318,Binary classification,Stacking,Bananas,0.7602523659305994,0.7361111111111112,1.8119163513183594,15.160032000000001 +424,Binary classification,Stacking,Bananas,0.7943262411347518,0.772845953002611,2.401026725769043,27.407145 +530,Binary classification,Stacking,Bananas,0.8052930056710775,0.7775377969762419,5.0262651443481445,65.121823 +636,Binary classification,Stacking,Bananas,0.8236220472440945,0.7992831541218638,5.88111686706543,107.288216 +742,Binary classification,Stacking,Bananas,0.8299595141700404,0.8025078369905957,6.734616279602051,153.798119 +848,Binary classification,Stacking,Bananas,0.8347107438016529,0.8087431693989071,7.555168151855469,204.76644700000003 +954,Binary classification,Stacking,Bananas,0.8426023084994754,0.8166259168704157,8.384669303894043,260.019764 +1060,Binary classification,Stacking,Bananas,0.8536355051935789,0.8275862068965517,8.926264762878418,319.31365700000003 +1166,Binary classification,Stacking,Bananas,0.8532188841201717,0.8274470232088799,9.188977241516113,382.58132300000005 +1272,Binary classification,Stacking,Bananas,0.8536585365853658,0.8290441176470588,9.45701789855957,449.39877900000005 +1378,Binary classification,Stacking,Bananas,0.8576615831517792,0.8321917808219177,9.84501838684082,519.6596460000001 +1484,Binary classification,Stacking,Bananas,0.8590694538098449,0.8345209817893903,10.364198684692383,593.3590610000001 +1590,Binary classification,Stacking,Bananas,0.8565135305223411,0.8321060382916053,10.468925476074219,670.9077340000001 +1696,Binary classification,Stacking,Bananas,0.8595870206489675,0.8354080221300139,10.966409683227539,752.0270200000001 +1802,Binary classification,Stacking,Bananas,0.8634092171016102,0.8410852713178295,10.118447303771973,836.636461 +1908,Binary classification,Stacking,Bananas,0.8626114315679078,0.8417874396135265,10.3862943649292,924.5557000000001 +2014,Binary classification,Stacking,Bananas,0.8614008941877794,0.8415672913117546,10.646858215332031,1015.8872450000001 +2120,Binary classification,Stacking,Bananas,0.8640868334119868,0.8459893048128343,10.9229736328125,1110.420791 +2226,Binary classification,Stacking,Bananas,0.8642696629213483,0.8462321792260691,11.325839042663574,1208.24484 +2332,Binary classification,Stacking,Bananas,0.864006864006864,0.8461911693352742,11.659860610961914,1308.9666100000002 +2438,Binary classification,Stacking,Bananas,0.8641772671317193,0.8465461288827074,11.19693660736084,1412.476276 +2544,Binary classification,Stacking,Bananas,0.8651199370821864,0.8484312859036677,11.452000617980957,1518.7765900000002 +2650,Binary classification,Stacking,Bananas,0.864477161192903,0.8480744815911976,11.787381172180176,1627.8671150000002 +2756,Binary classification,Stacking,Bananas,0.8653357531760436,0.849125660837739,12.11353874206543,1739.6750610000001 +2862,Binary classification,Stacking,Bananas,0.8678783642083188,0.8515318146111548,12.359809875488281,1854.1318460000002 +2968,Binary classification,Stacking,Bananas,0.8695652173913043,0.8529076396807297,12.722334861755371,1971.3857770000002 +3074,Binary classification,Stacking,Bananas,0.8682069638789457,0.8515939904727006,13.0479736328125,2091.3191060000004 +3180,Binary classification,Stacking,Bananas,0.8700849323686694,0.8530771967271433,13.308364868164062,2213.9114950000003 +3286,Binary classification,Stacking,Bananas,0.8700152207001522,0.8523002421307506,13.608009338378906,2339.1031470000003 +3392,Binary classification,Stacking,Bananas,0.871129460336184,0.8544788544788545,13.707662582397461,2466.937686 +3498,Binary classification,Stacking,Bananas,0.872748069774092,0.8557536466774717,14.051713943481445,2598.213051 +3604,Binary classification,Stacking,Bananas,0.8742714404662781,0.856872037914692,14.268294334411621,2732.2090540000004 +3710,Binary classification,Stacking,Bananas,0.8751685090320841,0.8583664729275007,14.518733978271484,2868.9613940000004 +3816,Binary classification,Stacking,Bananas,0.8762778505897771,0.8596908442330559,14.742842674255371,3008.2640120000005 +3922,Binary classification,Stacking,Bananas,0.8747768426421831,0.8578048074138431,15.053866386413574,3150.2484420000005 +4028,Binary classification,Stacking,Bananas,0.8733548547305686,0.8560135516657256,15.453622817993164,3294.8873180000005 +4134,Binary classification,Stacking,Bananas,0.874183401887249,0.8569069895432032,15.755488395690918,3442.0965550000005 +4240,Binary classification,Stacking,Bananas,0.8749705119131871,0.8579849946409432,15.976973533630371,3591.8798750000005 +4346,Binary classification,Stacking,Bananas,0.8759493670886076,0.8590849673202615,16.313834190368652,3744.3343260000006 +4452,Binary classification,Stacking,Bananas,0.8755335879577623,0.8585291113381002,16.729196548461914,3899.4355060000007 +4558,Binary classification,Stacking,Bananas,0.8757954794821154,0.8592039800995025,17.032727241516113,4057.1530940000007 +4664,Binary classification,Stacking,Bananas,0.8756165558653227,0.8594961240310078,17.45319175720215,4217.5274930000005 +4770,Binary classification,Stacking,Bananas,0.8754455860767456,0.8590412909349787,17.6948184967041,4380.594611 +4876,Binary classification,Stacking,Bananas,0.8756923076923077,0.8588070829450141,17.917430877685547,4546.383704000001 +4982,Binary classification,Stacking,Bananas,0.8761292913069665,0.8596770525358198,18.09793186187744,4714.847995000001 +5088,Binary classification,Stacking,Bananas,0.8757617456261058,0.8591800356506238,18.51348114013672,4886.014522000001 +5194,Binary classification,Stacking,Bananas,0.876372039283651,0.8598865124399825,18.892748832702637,5059.990176000001 +5300,Binary classification,Stacking,Bananas,0.8762030571806001,0.8596491228070176,19.194592475891113,5236.837813000001 +906,Binary classification,Stacking,Elec2,0.9116022099447514,0.908256880733945,7.041282653808594,59.400144 +1812,Binary classification,Stacking,Elec2,0.906129210381005,0.8855989232839839,9.07800579071045,148.928403 +2718,Binary classification,Stacking,Elec2,0.9002576370997424,0.8772088808337108,9.477606773376465,264.671315 +3624,Binary classification,Stacking,Elec2,0.9064311344189898,0.8849677638276212,10.383838653564453,404.188666 +4530,Binary classification,Stacking,Elec2,0.90527710311327,0.8788477831121152,11.437847137451172,565.129871 +5436,Binary classification,Stacking,Elec2,0.9000919963201472,0.8723854289071681,14.209432601928711,747.817107 +6342,Binary classification,Stacking,Elec2,0.897019397571361,0.8704622098789923,15.688876152038574,951.5612199999999 +7248,Binary classification,Stacking,Elec2,0.8965089002345799,0.8694744169857292,19.837779998779297,1176.633371 +8154,Binary classification,Stacking,Elec2,0.8980743284680486,0.8778839088905216,22.41482448577881,1420.714573 +9060,Binary classification,Stacking,Elec2,0.9000993487139861,0.8828478964401294,25.97023296356201,1683.800751 +9966,Binary classification,Stacking,Elec2,0.8982438534872053,0.8827203331020125,28.783666610717773,1965.440637 +10872,Binary classification,Stacking,Elec2,0.9006531137889798,0.8870765370138016,30.882869720458984,2263.780185 +11778,Binary classification,Stacking,Elec2,0.9021822195805383,0.8888030888030888,33.277831077575684,2578.868179 +12684,Binary classification,Stacking,Elec2,0.9012851848931641,0.8879541793449078,35.50911808013916,2911.085292 +13590,Binary classification,Stacking,Elec2,0.901905953344617,0.8899529431189631,38.6168327331543,3259.512573 +14496,Binary classification,Stacking,Elec2,0.9030010348395998,0.8917128773875539,41.26064682006836,3624.142031 +15402,Binary classification,Stacking,Elec2,0.9039023440036361,0.8922382408620941,43.65532207489014,4004.812957 +16308,Binary classification,Stacking,Elec2,0.9002882197829153,0.8879239040529363,45.1539192199707,4403.507418 +17214,Binary classification,Stacking,Elec2,0.8997850461860222,0.8856176646111001,37.54582214355469,4816.829749 +18120,Binary classification,Stacking,Elec2,0.9001048622992439,0.8858908082209053,41.9152717590332,5243.603799 +19026,Binary classification,Stacking,Elec2,0.9014454664914586,0.886177381169186,44.45838737487793,5683.033974000001 +19932,Binary classification,Stacking,Elec2,0.9011088254477949,0.8867826986041704,48.213175773620605,6135.158415000001 +20838,Binary classification,Stacking,Elec2,0.8992657292316553,0.8848411696933121,47.78572177886963,6598.876461000001 +21744,Binary classification,Stacking,Elec2,0.8986800349537782,0.8824376967821121,49.356743812561035,7073.276106000001 +22650,Binary classification,Stacking,Elec2,0.898361958585368,0.8812912541254125,47.91073036193848,7558.681153000001 +23556,Binary classification,Stacking,Elec2,0.8961579282530249,0.8784656663022956,53.37149906158447,8055.088432000001 +24462,Binary classification,Stacking,Elec2,0.8945259801316381,0.8760211436809228,52.06687641143799,8562.621137000002 +25368,Binary classification,Stacking,Elec2,0.8922221784207829,0.8734610756271406,20.63737678527832,9080.309208000002 +26274,Binary classification,Stacking,Elec2,0.8928177216153466,0.873801201039706,17.83812713623047,9607.290172000003 +27180,Binary classification,Stacking,Elec2,0.8932263880201626,0.874632797649905,19.305461883544922,10142.799192000002 +28086,Binary classification,Stacking,Elec2,0.8915435285739719,0.8721564677243349,18.23539447784424,10686.882271000002 +28992,Binary classification,Stacking,Elec2,0.891345590010693,0.8712498978173793,21.229859352111816,11240.330868000003 +29898,Binary classification,Stacking,Elec2,0.8905575810281968,0.8700246285850481,25.24380111694336,11801.274420000003 +30804,Binary classification,Stacking,Elec2,0.890335356945752,0.8690697674418605,28.836254119873047,12369.623812000003 +31710,Binary classification,Stacking,Elec2,0.8895266328171813,0.8679458664756663,27.00519371032715,12944.308752000003 +32616,Binary classification,Stacking,Elec2,0.8879963207113292,0.86634224872855,30.17805576324463,13524.938061000003 +33522,Binary classification,Stacking,Elec2,0.8870260433757943,0.8654563541407612,32.107930183410645,14111.446119000002 +34428,Binary classification,Stacking,Elec2,0.8857582711244082,0.8638770636486346,32.29031944274902,14703.865141000002 +35334,Binary classification,Stacking,Elec2,0.8850083491353692,0.8622665175090681,36.271653175354004,15302.291934000003 +36240,Binary classification,Stacking,Elec2,0.8850133833715058,0.8612988050461006,35.55355262756348,15906.592759000003 +37146,Binary classification,Stacking,Elec2,0.8836182527931081,0.859061715515274,38.756625175476074,16517.021041000004 +38052,Binary classification,Stacking,Elec2,0.8830516937794013,0.8577547628180539,41.063425064086914,17133.624997000003 +38958,Binary classification,Stacking,Elec2,0.8831788895448828,0.8582198822393221,42.255154609680176,17756.348401000003 +39864,Binary classification,Stacking,Elec2,0.8836765923287259,0.8598797328740216,43.789937019348145,18385.013164000004 +40770,Binary classification,Stacking,Elec2,0.8840295322426354,0.8614708467623791,40.85312080383301,19019.639174000004 +41676,Binary classification,Stacking,Elec2,0.8847030593881223,0.8632106356933413,39.29591369628906,19660.085711000003 +42582,Binary classification,Stacking,Elec2,0.8851130786031328,0.8639675212724542,42.33915042877197,20306.976876000004 +43488,Binary classification,Stacking,Elec2,0.8848391473313864,0.8636091290375292,42.20731544494629,20960.039327000006 +44394,Binary classification,Stacking,Elec2,0.8848467100669024,0.8632350580555408,44.13865566253662,21618.107174000004 +45300,Binary classification,Stacking,Elec2,0.8854720854765006,0.8642027012878233,40.63082981109619,22281.084676000006 +45312,Binary classification,Stacking,Elec2,0.8854582772395224,0.8641574621787154,40.75471591949463,22944.429270000004 +25,Binary classification,Stacking,Phishing,0.6666666666666666,0.7142857142857143,0.6122617721557617,0.640752 +50,Binary classification,Stacking,Phishing,0.7755102040816326,0.7659574468085107,0.7524843215942383,1.992597 +75,Binary classification,Stacking,Phishing,0.8243243243243243,0.8266666666666667,0.9228668212890625,4.151733 +100,Binary classification,Stacking,Phishing,0.8282828282828283,0.8282828282828283,1.193608283996582,7.194986 +125,Binary classification,Stacking,Phishing,0.8306451612903226,0.8292682926829269,1.3295679092407227,11.208746999999999 +150,Binary classification,Stacking,Phishing,0.8389261744966443,0.8441558441558442,1.3798675537109375,16.195196 +175,Binary classification,Stacking,Phishing,0.8563218390804598,0.8520710059171597,1.4546594619750977,22.422242999999998 +200,Binary classification,Stacking,Phishing,0.8542713567839196,0.8497409326424871,1.6083984375,29.888727999999997 +225,Binary classification,Stacking,Phishing,0.8526785714285714,0.8436018957345972,1.7997064590454102,38.482186 +250,Binary classification,Stacking,Phishing,0.8433734939759037,0.8354430379746836,1.9343080520629883,48.311454 +275,Binary classification,Stacking,Phishing,0.8467153284671532,0.8372093023255813,2.053934097290039,59.483297 +300,Binary classification,Stacking,Phishing,0.8461538461538461,0.8333333333333334,2.12460994720459,72.133375 +325,Binary classification,Stacking,Phishing,0.8518518518518519,0.8356164383561644,2.201033592224121,86.30699200000001 +350,Binary classification,Stacking,Phishing,0.8595988538681948,0.8414239482200646,2.2356014251708984,102.05020300000001 +375,Binary classification,Stacking,Phishing,0.8556149732620321,0.8353658536585366,2.328523635864258,119.41383300000001 +400,Binary classification,Stacking,Phishing,0.8571428571428571,0.8347826086956521,2.316814422607422,138.615081 +425,Binary classification,Stacking,Phishing,0.8608490566037735,0.8356545961002786,2.353947639465332,159.65345200000002 +450,Binary classification,Stacking,Phishing,0.8619153674832962,0.8351063829787234,2.4291276931762695,182.47163200000003 +475,Binary classification,Stacking,Phishing,0.8649789029535865,0.8407960199004976,2.5799179077148438,207.23725000000002 +500,Binary classification,Stacking,Phishing,0.8637274549098196,0.841860465116279,4.818408012390137,255.437855 +525,Binary classification,Stacking,Phishing,0.8645038167938931,0.8397291196388261,5.000759124755859,305.327991 +550,Binary classification,Stacking,Phishing,0.8652094717668488,0.8418803418803419,5.177936553955078,356.788916 +575,Binary classification,Stacking,Phishing,0.8658536585365854,0.8425357873210634,5.324765205383301,409.72389599999997 +600,Binary classification,Stacking,Phishing,0.8697829716193656,0.8446215139442231,5.557343482971191,464.11454999999995 +625,Binary classification,Stacking,Phishing,0.8685897435897436,0.8404669260700389,5.701066970825195,520.0238599999999 +650,Binary classification,Stacking,Phishing,0.8721109399075501,0.847145488029466,5.875107765197754,577.5076929999999 +675,Binary classification,Stacking,Phishing,0.8753709198813057,0.8541666666666667,5.993474006652832,636.4901669999999 +700,Binary classification,Stacking,Phishing,0.8798283261802575,0.8576271186440678,6.097118377685547,696.8595059999999 +725,Binary classification,Stacking,Phishing,0.8798342541436464,0.8603531300160514,6.2616376876831055,758.6517739999999 +750,Binary classification,Stacking,Phishing,0.8811748998664887,0.8624420401854715,6.510566711425781,822.0041369999999 +775,Binary classification,Stacking,Phishing,0.8824289405684754,0.8631578947368422,6.659415245056152,886.863173 +800,Binary classification,Stacking,Phishing,0.8823529411764706,0.8645533141210374,6.793304443359375,953.2293709999999 +825,Binary classification,Stacking,Phishing,0.8822815533980582,0.8654646324549237,7.087222099304199,1021.1070179999999 +850,Binary classification,Stacking,Phishing,0.8833922261484098,0.8660351826792964,7.324291229248047,1090.559446 +875,Binary classification,Stacking,Phishing,0.88558352402746,0.8677248677248677,7.470724105834961,1161.497406 +900,Binary classification,Stacking,Phishing,0.8854282536151279,0.8670967741935484,7.810632705688477,1233.983859 +925,Binary classification,Stacking,Phishing,0.8874458874458875,0.8706467661691542,7.971014976501465,1307.96073 +950,Binary classification,Stacking,Phishing,0.8872497365648051,0.8718562874251498,8.080266952514648,1383.548543 +975,Binary classification,Stacking,Phishing,0.8891170431211499,0.8738317757009346,8.205357551574707,1460.7186840000002 +1000,Binary classification,Stacking,Phishing,0.8888888888888888,0.8737201365187712,8.39128303527832,1539.5016600000001 +1025,Binary classification,Stacking,Phishing,0.888671875,0.8738938053097345,8.406519889831543,1619.9124450000002 +1050,Binary classification,Stacking,Phishing,0.8903717826501429,0.8762109795479011,8.400672912597656,1701.87919 +1075,Binary classification,Stacking,Phishing,0.8910614525139665,0.877742946708464,8.453279495239258,1785.406003 +1100,Binary classification,Stacking,Phishing,0.8926296633303002,0.8795918367346939,8.421560287475586,1870.444315 +1125,Binary classification,Stacking,Phishing,0.8932384341637011,0.8811881188118813,8.383057594299316,1956.980231 +1150,Binary classification,Stacking,Phishing,0.8929503916449086,0.8806983511154219,8.433841705322266,2045.031177 +1175,Binary classification,Stacking,Phishing,0.8909710391822828,0.8783269961977185,8.58321475982666,2134.506504 +1200,Binary classification,Stacking,Phishing,0.8932443703085905,0.8803738317757008,8.605175971984863,2225.421876 +1225,Binary classification,Stacking,Phishing,0.8946078431372549,0.8817598533455545,8.70528507232666,2317.752916 +1250,Binary classification,Stacking,Phishing,0.8951160928742994,0.88272157564906,8.721240997314453,2411.4111159999998 +1903,Binary classification,Stacking,SMTP,1.0,0.0,4.7766571044921875,62.937451 +3806,Binary classification,Stacking,SMTP,1.0,0.0,4.703582763671875,162.906939 +5709,Binary classification,Stacking,SMTP,1.0,0.0,4.683967590332031,291.99956099999997 +7612,Binary classification,Stacking,SMTP,1.0,0.0,4.6548919677734375,449.905783 +9515,Binary classification,Stacking,SMTP,1.0,0.0,4.674896240234375,634.264648 +11418,Binary classification,Stacking,SMTP,1.0,0.0,4.68939208984375,844.6132379999999 +13321,Binary classification,Stacking,SMTP,1.0,0.0,4.6727142333984375,1077.972428 +15224,Binary classification,Stacking,SMTP,0.9992774091834724,0.0,4.755153656005859,1334.293209 +17127,Binary classification,Stacking,SMTP,0.9992409202382343,0.0,4.668712615966797,1613.416001 +19030,Binary classification,Stacking,SMTP,0.9993168322034789,0.0,4.707424163818359,1913.210948 +20933,Binary classification,Stacking,SMTP,0.999378941333843,0.0,4.680248260498047,2233.020054 +22836,Binary classification,Stacking,SMTP,0.9994306984891613,0.0,4.695384979248047,2572.124593 +24739,Binary classification,Stacking,SMTP,0.9994744926833212,0.0,4.721019744873047,2930.571035 +26642,Binary classification,Stacking,SMTP,0.9994744942006681,0.0,4.747562408447266,3309.032834 +28545,Binary classification,Stacking,SMTP,0.9995095291479821,0.0,4.741054534912109,3705.221814 +30448,Binary classification,Stacking,SMTP,0.9995401845830459,0.0,4.678241729736328,4115.910057 +32351,Binary classification,Stacking,SMTP,0.9995672333848532,0.0,4.619670867919922,4539.977119 +34254,Binary classification,Stacking,SMTP,0.9995912766764955,0.0,4.749675750732422,4977.188868 +36157,Binary classification,Stacking,SMTP,0.9996127890253347,0.0,4.678524017333984,5426.058059 +38060,Binary classification,Stacking,SMTP,0.9996321500827662,0.0,4.705173492431641,5886.859448 +39963,Binary classification,Stacking,SMTP,0.9996496671838246,0.0,4.729236602783203,6359.258672 +41866,Binary classification,Stacking,SMTP,0.9996655917831124,0.0,4.729305267333984,6843.735511 +43769,Binary classification,Stacking,SMTP,0.9996801316029976,0.0,4.741458892822266,7339.76837 +45672,Binary classification,Stacking,SMTP,0.9996934597446958,0.0,4.677211761474609,7847.750223999999 +47575,Binary classification,Stacking,SMTP,0.9997057216126456,0.0,4.833148956298828,8367.044639 +49478,Binary classification,Stacking,SMTP,0.99971704024092,0.0,4.807292938232422,8898.416265 +51381,Binary classification,Stacking,SMTP,0.9996885947839627,0.0,4.893611907958984,9441.161399999999 +53284,Binary classification,Stacking,SMTP,0.9996997166075484,0.0,4.877178192138672,9993.506038 +55187,Binary classification,Stacking,SMTP,0.999710071394919,0.0,4.888896942138672,10554.524537 +57090,Binary classification,Stacking,SMTP,0.9995620872672494,0.0,4.783634185791016,11123.611551 +58993,Binary classification,Stacking,SMTP,0.9995762137238947,0.0,4.831531524658203,11701.029088 +60896,Binary classification,Stacking,SMTP,0.999589457262501,0.0,4.854015350341797,12286.755545999999 +62799,Binary classification,Stacking,SMTP,0.9995700500015924,0.0,4.858226776123047,12880.441842999999 +64702,Binary classification,Stacking,SMTP,0.9995826957852274,0.0,4.846561431884766,13482.274685999999 +66605,Binary classification,Stacking,SMTP,0.9995946189418053,0.0,4.872089385986328,14092.292626999999 +68508,Binary classification,Stacking,SMTP,0.9995766855941729,0.0,4.843868255615234,14710.448755 +70411,Binary classification,Stacking,SMTP,0.9995881266865502,0.0,4.835132598876953,15336.827121999999 +72314,Binary classification,Stacking,SMTP,0.9995989656078437,0.0,4.892154693603516,15971.964918999998 +74217,Binary classification,Stacking,SMTP,0.99960924867953,0.0,4.812671661376953,16613.982513 +76120,Binary classification,Stacking,SMTP,0.9996190175908775,0.0,4.880641937255859,17262.391145999998 +78023,Binary classification,Stacking,SMTP,0.9996283099638563,0.0,4.831180572509766,17916.095854 +79926,Binary classification,Stacking,SMTP,0.9996371598373475,0.0,4.851375579833984,18574.918078 +81829,Binary classification,Stacking,SMTP,0.9996455980837855,0.0,4.851016998291016,19239.025055 +83732,Binary classification,Stacking,SMTP,0.9996536527689864,0.0,4.869503021240234,19908.351771999998 +85635,Binary classification,Stacking,SMTP,0.999661349463998,0.0,4.886287689208984,20582.843625999998 +87538,Binary classification,Stacking,SMTP,0.9996687115162731,0.0,4.888690948486328,21262.535161 +89441,Binary classification,Stacking,SMTP,0.9996645796064401,0.0,4.888484954833984,21947.343421999998 +91344,Binary classification,Stacking,SMTP,0.999671567607808,0.0,4.876293182373047,22637.362347 +93247,Binary classification,Stacking,SMTP,0.9996782703815713,0.0,4.905620574951172,23332.514825 +95150,Binary classification,Stacking,SMTP,0.9996847050415664,0.0,4.880191802978516,24032.848442 +95156,Binary classification,Stacking,SMTP,0.9996847249224948,0.0,4.888683319091797,24733.238040999997 +106,Binary classification,Voting,Bananas,0.6761904761904762,0.6136363636363638,0.14342212677001953,0.374142 +212,Binary classification,Voting,Bananas,0.7772511848341233,0.7374301675977653,0.23540592193603516,1.3677169999999998 +318,Binary classification,Voting,Bananas,0.7886435331230284,0.7527675276752769,0.3270235061645508,3.238746 +424,Binary classification,Voting,Bananas,0.7990543735224587,0.7658402203856748,0.41901111602783203,6.2520690000000005 +530,Binary classification,Voting,Bananas,0.8015122873345936,0.7575057736720554,2.719620704650879,30.609493999999998 +636,Binary classification,Voting,Bananas,0.8173228346456692,0.7777777777777779,3.159085273742676,56.745796 +742,Binary classification,Voting,Bananas,0.8259109311740891,0.7839195979899498,3.6036806106567383,84.9251 +848,Binary classification,Voting,Bananas,0.8299881936245572,0.7913043478260869,4.0666093826293945,115.11768599999999 +954,Binary classification,Voting,Bananas,0.8352570828961176,0.7963683527885861,4.521588325500488,147.5017 +1060,Binary classification,Voting,Bananas,0.8470254957507082,0.8094117647058824,4.660099983215332,182.008963 +1166,Binary classification,Voting,Bananas,0.8497854077253219,0.8132337246531482,4.474972724914551,218.357961 +1272,Binary classification,Voting,Bananas,0.8489378442171518,0.8135922330097087,4.3258256912231445,256.411001 +1378,Binary classification,Voting,Bananas,0.8482207697893972,0.8112014453477868,4.1847429275512695,296.011707 +1484,Binary classification,Voting,Bananas,0.8530006743088334,0.8180300500834724,4.276310920715332,337.212047 +1590,Binary classification,Voting,Bananas,0.8539962240402769,0.8198757763975156,4.522702217102051,380.36472399999997 +1696,Binary classification,Voting,Bananas,0.8584070796460177,0.8250728862973761,4.5992326736450195,425.08911199999994 +1802,Binary classification,Voting,Bananas,0.8622987229317046,0.8315217391304348,4.6097002029418945,471.42038399999996 +1908,Binary classification,Voting,Bananas,0.8610382800209754,0.8319594166138238,4.583279609680176,519.2335119999999 +2014,Binary classification,Voting,Bananas,0.8584202682563339,0.8302561048243002,4.509037971496582,568.4848139999999 +2120,Binary classification,Voting,Bananas,0.8612553091080698,0.8355704697986577,4.487088203430176,619.150273 +2226,Binary classification,Voting,Bananas,0.8624719101123596,0.8370607028753994,4.479489326477051,671.27983 +2332,Binary classification,Voting,Bananas,0.8614328614328615,0.8357905439755974,4.476758003234863,724.8282939999999 +2438,Binary classification,Voting,Bananas,0.8621255642183012,0.8364167478091528,4.495999336242676,779.843051 +2544,Binary classification,Voting,Bananas,0.8623672827369249,0.8375116063138347,4.492741584777832,836.3752579999999 +2650,Binary classification,Voting,Bananas,0.8618346545866364,0.8374777975133214,4.535428047180176,894.373804 +2756,Binary classification,Voting,Bananas,0.8627949183303085,0.8384615384615384,4.529454231262207,953.727688 +2862,Binary classification,Voting,Bananas,0.8661307235232436,0.842061855670103,4.489285469055176,1014.5232589999999 +2968,Binary classification,Voting,Bananas,0.8678800134816312,0.8437001594896333,4.522076606750488,1076.832167 +3074,Binary classification,Voting,Bananas,0.8662544744549301,0.8419838523644751,4.4861345291137695,1140.498025 +3180,Binary classification,Voting,Bananas,0.8678829820698333,0.8432835820895522,4.490513801574707,1205.597432 +3286,Binary classification,Voting,Bananas,0.8684931506849315,0.8433647570703406,4.5036516189575195,1272.097546 +3392,Binary classification,Voting,Bananas,0.8690651725154822,0.8449720670391062,4.519848823547363,1340.030829 +3498,Binary classification,Voting,Bananas,0.8687446382613668,0.8439306358381503,4.534294128417969,1409.423343 +3604,Binary classification,Voting,Bananas,0.8701082431307244,0.8451356717405691,4.515525817871094,1480.1764939999998 +3710,Binary classification,Voting,Bananas,0.8705850633593961,0.8462524023062139,4.521697998046875,1552.3097799999998 +3816,Binary classification,Voting,Bananas,0.8718217562254259,0.847900466562986,4.52362060546875,1625.8426229999998 +3922,Binary classification,Voting,Bananas,0.8704412139760265,0.845873786407767,4.511474609375,1700.7302089999998 +4028,Binary classification,Voting,Bananas,0.8698783213310156,0.8450620934358367,4.530387878417969,1777.041184 +4134,Binary classification,Voting,Bananas,0.8707960319380595,0.8461981566820277,4.540306091308594,1854.755673 +4240,Binary classification,Voting,Bananas,0.8723755602736495,0.8485018202184262,4.5452880859375,1933.8281619999998 +4346,Binary classification,Voting,Bananas,0.8734177215189873,0.8498088476242489,4.5819854736328125,2014.1792419999997 +4452,Binary classification,Voting,Bananas,0.8732869018198157,0.8494394020288306,4.5782928466796875,2095.8385169999997 +4558,Binary classification,Voting,Bananas,0.8720649550142637,0.8482166102577455,4.539161682128906,2178.6750019999995 +4664,Binary classification,Voting,Bananas,0.8719708342268926,0.8485156051763512,4.509727478027344,2262.6244259999994 +4770,Binary classification,Voting,Bananas,0.8712518347661984,0.8472636815920398,4.5496673583984375,2347.8035639999994 +4876,Binary classification,Voting,Bananas,0.8717948717948718,0.8474493531852575,4.560760498046875,2434.1199849999994 +4982,Binary classification,Voting,Bananas,0.8725155591246737,0.8487735175041676,4.513671875,2521.5491019999995 +5088,Binary classification,Voting,Bananas,0.8718301552978179,0.8480186480186479,4.541267395019531,2610.1395069999994 +5194,Binary classification,Voting,Bananas,0.8725207009435779,0.848927430397079,4.5822906494140625,2699.9579359999993 +5300,Binary classification,Voting,Bananas,0.8726174749952821,0.8491620111731844,4.5840301513671875,2790.9651129999993 +906,Binary classification,Voting,Elec2,0.8795580110497238,0.880351262349067,4.715929985046387,35.551681 +1812,Binary classification,Voting,Elec2,0.8807288790723358,0.8536585365853658,4.9170331954956055,87.613259 +2718,Binary classification,Voting,Elec2,0.8689731321310269,0.8344186046511628,4.988085746765137,154.923184 +3624,Binary classification,Voting,Elec2,0.8793817278498481,0.8493622888659084,4.879870414733887,235.92708 +4530,Binary classification,Voting,Elec2,0.8792227864870833,0.8405712620227338,5.017077445983887,328.79801 +5436,Binary classification,Voting,Elec2,0.8689972401103956,0.8260869565217391,4.985064506530762,432.967134 +6342,Binary classification,Voting,Elec2,0.8680018924459865,0.8269588587967748,4.949084281921387,548.642546 +7248,Binary classification,Voting,Elec2,0.8643576652407893,0.8194010655888295,4.962946891784668,674.769885 +8154,Binary classification,Voting,Elec2,0.8671654605666625,0.8317016317016317,5.020190238952637,811.067026 +9060,Binary classification,Voting,Elec2,0.8711778341980351,0.8417627118644068,5.0752363204956055,957.595858 +9966,Binary classification,Voting,Elec2,0.8706472654290015,0.845165165165165,4.979113578796387,1113.746382 +10872,Binary classification,Voting,Elec2,0.8737006715113605,0.8516156922079325,4.9885969161987305,1279.290866 +11778,Binary classification,Voting,Elec2,0.8733972998216863,0.8507059176930009,5.106616020202637,1454.661274 +12684,Binary classification,Voting,Elec2,0.873294961759836,0.8513551012857274,5.2120466232299805,1640.606202 +13590,Binary classification,Voting,Elec2,0.8755611156082125,0.8560973534167304,5.144991874694824,1836.8358979999998 +14496,Binary classification,Voting,Elec2,0.8765781303897896,0.8583643416989946,5.178118705749512,2042.985996 +15402,Binary classification,Voting,Elec2,0.8766963184208818,0.8575500712624708,5.108157157897949,2257.366998 +16308,Binary classification,Voting,Elec2,0.8711596247010487,0.8493366798135532,5.1558027267456055,2480.0447249999997 +17214,Binary classification,Voting,Elec2,0.8687038865973392,0.8429683157309616,5.140070915222168,2710.925816 +18120,Binary classification,Voting,Elec2,0.8689773166289531,0.8433623647400369,5.172907829284668,2950.660411 +19026,Binary classification,Voting,Elec2,0.8696977660972405,0.8421320766732471,5.328249931335449,3199.829749 +19932,Binary classification,Voting,Elec2,0.8659876574180925,0.8380132209351688,5.355593681335449,3457.907085 +20838,Binary classification,Voting,Elec2,0.8617843259586313,0.8322851153039832,5.442904472351074,3724.5720149999997 +21744,Binary classification,Voting,Elec2,0.862668445016787,0.8307064293003741,5.347712516784668,3998.8413929999997 +22650,Binary classification,Voting,Elec2,0.8610093160845953,0.8268045774647886,5.363558769226074,4280.436632 +23556,Binary classification,Voting,Elec2,0.85434090426661,0.8163571160948456,5.3763532638549805,4569.013267 +24462,Binary classification,Voting,Elec2,0.8534401700666366,0.8138145936120489,5.330439567565918,4864.708005 +25368,Binary classification,Voting,Elec2,0.8518941932431899,0.8121030257564391,5.456484794616699,5167.517117 +26274,Binary classification,Voting,Elec2,0.8530811098846725,0.8130931628897928,5.321070671081543,5477.642376000001 +27180,Binary classification,Voting,Elec2,0.8524964126715479,0.8125672074430782,5.426630973815918,5794.594214000001 +28086,Binary classification,Voting,Elec2,0.8496350364963504,0.8078795323233702,5.3666486740112305,6118.251751000001 +28992,Binary classification,Voting,Elec2,0.8475043979165948,0.8032575319300432,5.4148359298706055,6448.591708000001 +29898,Binary classification,Voting,Elec2,0.8460046158477439,0.8002429711905589,5.3892927169799805,6785.693940000001 +30804,Binary classification,Voting,Elec2,0.8462162776352953,0.7992881657556884,5.532000541687012,7130.317230000001 +31710,Binary classification,Voting,Elec2,0.8429783342268756,0.7938387644403958,5.507189750671387,7481.445887000001 +32616,Binary classification,Voting,Elec2,0.8419745515866932,0.7926122646064703,5.485476493835449,7839.281994000001 +33522,Binary classification,Voting,Elec2,0.8421884788639957,0.7931492922499414,5.629275321960449,8203.623919000001 +34428,Binary classification,Voting,Elec2,0.8400092950300636,0.7894656371837016,5.587969779968262,8574.868737 +35334,Binary classification,Voting,Elec2,0.8398947159878867,0.7879287722586691,5.669405937194824,8954.118864 +36240,Binary classification,Voting,Elec2,0.8408896492728828,0.7879523389232127,5.650286674499512,9340.118817 +37146,Binary classification,Voting,Elec2,0.8397092475434109,0.7854569040069184,5.6525373458862305,9731.872155000001 +38052,Binary classification,Voting,Elec2,0.8398202412551575,0.7854402083993381,5.638819694519043,10129.488521000001 +38958,Binary classification,Voting,Elec2,0.8406704828400544,0.787685992816829,5.6699628829956055,10532.813227 +39864,Binary classification,Voting,Elec2,0.841381732433585,0.7910235647949235,5.623560905456543,10941.028498 +40770,Binary classification,Voting,Elec2,0.8422085408030612,0.7943480067772769,5.627467155456543,11353.937417 +41676,Binary classification,Voting,Elec2,0.8431673665266947,0.7973835947671896,5.641619682312012,11771.547133999999 +42582,Binary classification,Voting,Elec2,0.8438505436697118,0.7987529888919157,5.640711784362793,12193.871152999998 +43488,Binary classification,Voting,Elec2,0.843999356129418,0.7991592160577892,5.725451469421387,12620.815871999997 +44394,Binary classification,Voting,Elec2,0.8432635775910616,0.7972256221950225,5.7456769943237305,13052.460535999997 +45300,Binary classification,Voting,Elec2,0.8436830835117773,0.7980031379261162,5.7547407150268555,13488.904282999996 +45312,Binary classification,Voting,Elec2,0.8436803425216834,0.7979576118892089,5.757502555847168,13925.545040999996 +25,Binary classification,Voting,Phishing,0.5833333333333334,0.7058823529411764,0.17400836944580078,0.162813 +50,Binary classification,Voting,Phishing,0.7346938775510204,0.7636363636363637,0.20249652862548828,0.520257 +75,Binary classification,Voting,Phishing,0.7837837837837838,0.8048780487804877,0.23151493072509766,1.0500919999999998 +100,Binary classification,Voting,Phishing,0.8080808080808081,0.819047619047619,0.26002979278564453,1.7634529999999997 +125,Binary classification,Voting,Phishing,0.8145161290322581,0.8217054263565893,0.2885446548461914,2.7650449999999998 +150,Binary classification,Voting,Phishing,0.8187919463087249,0.830188679245283,0.3175630569458008,4.083864999999999 +175,Binary classification,Voting,Phishing,0.8390804597701149,0.8390804597701148,0.34607791900634766,5.803779 +200,Binary classification,Voting,Phishing,0.8391959798994975,0.8383838383838383,0.3750925064086914,7.866028 +225,Binary classification,Voting,Phishing,0.8348214285714286,0.8294930875576038,0.4036073684692383,10.317012 +250,Binary classification,Voting,Phishing,0.8313253012048193,0.8264462809917356,0.43212223052978516,13.231482 +275,Binary classification,Voting,Phishing,0.8357664233576643,0.8288973384030419,0.46164798736572266,16.680039999999998 +300,Binary classification,Voting,Phishing,0.842809364548495,0.8327402135231317,0.49016284942626953,20.639260999999998 +325,Binary classification,Voting,Phishing,0.8549382716049383,0.8417508417508418,0.5191812515258789,25.174847 +350,Binary classification,Voting,Phishing,0.8624641833810889,0.8471337579617835,0.5476961135864258,30.349829 +375,Binary classification,Voting,Phishing,0.8609625668449198,0.8433734939759037,0.5762109756469727,36.11991 +400,Binary classification,Voting,Phishing,0.8621553884711779,0.8424068767908309,0.605229377746582,42.598397999999996 +425,Binary classification,Voting,Phishing,0.8632075471698113,0.839779005524862,0.6337442398071289,49.849208 +450,Binary classification,Voting,Phishing,0.8663697104677061,0.8412698412698413,0.6627893447875977,57.823257999999996 +475,Binary classification,Voting,Phishing,0.8649789029535865,0.8407960199004976,0.6913042068481445,66.560459 +500,Binary classification,Voting,Phishing,0.8657314629258517,0.8445475638051043,2.87209415435791,96.81871699999999 +525,Binary classification,Voting,Phishing,0.8683206106870229,0.8442437923250564,2.980504035949707,127.96343099999999 +550,Binary classification,Voting,Phishing,0.8688524590163934,0.8461538461538463,3.08364200592041,159.97764899999999 +575,Binary classification,Voting,Phishing,0.8710801393728222,0.848360655737705,3.1883134841918945,192.90880399999998 +600,Binary classification,Voting,Phishing,0.8747913188647746,0.8502994011976048,3.300492286682129,226.71997 +625,Binary classification,Voting,Phishing,0.8733974358974359,0.8460038986354775,3.412938117980957,261.370264 +650,Binary classification,Voting,Phishing,0.8767334360554699,0.8523985239852399,3.522160530090332,296.927054 +675,Binary classification,Voting,Phishing,0.8783382789317508,0.8571428571428572,3.6335840225219727,333.391974 +700,Binary classification,Voting,Phishing,0.882689556509299,0.8605442176870748,3.7482118606567383,370.807982 +725,Binary classification,Voting,Phishing,0.8839779005524862,0.864516129032258,3.8615503311157227,409.129822 +750,Binary classification,Voting,Phishing,0.8851802403204272,0.8664596273291927,3.975522041320801,448.34317699999997 +775,Binary classification,Voting,Phishing,0.8863049095607235,0.8670694864048338,4.095002174377441,488.481904 +800,Binary classification,Voting,Phishing,0.886107634543179,0.8683068017366136,4.146827697753906,529.573688 +825,Binary classification,Voting,Phishing,0.8859223300970874,0.8690807799442897,4.390903472900391,571.6173799999999 +850,Binary classification,Voting,Phishing,0.8869257950530035,0.8695652173913044,4.504707336425781,614.6492939999999 +875,Binary classification,Voting,Phishing,0.8890160183066361,0.8711819389110226,4.624469757080078,658.6172529999999 +900,Binary classification,Voting,Phishing,0.8876529477196885,0.869340232858991,4.741554260253906,703.5317429999999 +925,Binary classification,Voting,Phishing,0.8896103896103896,0.8728179551122195,4.862430572509766,749.5245539999999 +950,Binary classification,Voting,Phishing,0.8904109589041096,0.8752997601918464,4.984291076660156,796.5006999999998 +975,Binary classification,Voting,Phishing,0.8921971252566735,0.8771929824561404,5.102375030517578,844.5149469999998 +1000,Binary classification,Voting,Phishing,0.8928928928928929,0.8779931584948689,5.219093322753906,893.5084249999998 +1025,Binary classification,Voting,Phishing,0.892578125,0.8780487804878048,5.178688049316406,943.5715689999997 +1050,Binary classification,Voting,Phishing,0.894184938036225,0.8802588996763754,5.151969909667969,994.6247309999998 +1075,Binary classification,Voting,Phishing,0.8929236499068901,0.8798328108672936,5.117225646972656,1046.6512989999997 +1100,Binary classification,Voting,Phishing,0.8944494995450409,0.8816326530612245,5.075950622558594,1099.6701919999996 +1125,Binary classification,Voting,Phishing,0.8959074733096085,0.884272997032641,5.007194519042969,1153.6019869999996 +1150,Binary classification,Voting,Phishing,0.896431679721497,0.8845780795344327,4.982025146484375,1208.4750359999996 +1175,Binary classification,Voting,Phishing,0.8952299829642248,0.8829686013320648,4.96966552734375,1264.1689839999997 +1200,Binary classification,Voting,Phishing,0.896580483736447,0.8841121495327102,4.9371490478515625,1320.7795169999997 +1225,Binary classification,Voting,Phishing,0.8970588235294118,0.8844036697247706,4.8813018798828125,1378.3046599999998 +1250,Binary classification,Voting,Phishing,0.8967173738991193,0.8845120859444942,4.820304870605469,1436.7224909999998 +1903,Binary classification,Voting,SMTP,1.0,0.0,4.661611557006836,43.527964 +3806,Binary classification,Voting,SMTP,1.0,0.0,4.557134628295898,113.03314999999999 +5709,Binary classification,Voting,SMTP,1.0,0.0,4.496244430541992,201.747221 +7612,Binary classification,Voting,SMTP,1.0,0.0,4.508665084838867,310.754596 +9515,Binary classification,Voting,SMTP,1.0,0.0,4.565656661987305,436.825284 +11418,Binary classification,Voting,SMTP,1.0,0.0,4.554738998413086,579.964386 +13321,Binary classification,Voting,SMTP,1.0,0.0,4.492513656616211,739.485002 +15224,Binary classification,Voting,SMTP,0.9997372397030808,0.7777777777777778,4.532373428344727,915.25293 +17127,Binary classification,Voting,SMTP,0.9997664369963798,0.8181818181818181,4.528841018676758,1107.42481 +19030,Binary classification,Voting,SMTP,0.9997897945241474,0.8181818181818181,4.520586013793945,1314.036215 +20933,Binary classification,Voting,SMTP,0.9998089050257978,0.8181818181818181,4.519166946411133,1534.1862760000001 +22836,Binary classification,Voting,SMTP,0.9998248303043573,0.8181818181818181,4.512857437133789,1768.1243410000002 +24739,Binary classification,Voting,SMTP,0.9998383054410219,0.8181818181818181,4.568696975708008,2014.7470960000003 +26642,Binary classification,Voting,SMTP,0.9998123193573815,0.782608695652174,4.55253791809082,2273.6909760000003 +28545,Binary classification,Voting,SMTP,0.9998248318385651,0.782608695652174,4.554193496704102,2543.9053730000005 +30448,Binary classification,Voting,SMTP,0.9998357802082307,0.782608695652174,4.487833023071289,2824.6447140000005 +32351,Binary classification,Voting,SMTP,0.9998454404945905,0.782608695652174,4.52525520324707,3116.2234200000003 +34254,Binary classification,Voting,SMTP,0.9998540273844627,0.782608695652174,4.608850479125977,3418.6303260000004 +36157,Binary classification,Voting,SMTP,0.999861710366191,0.782608695652174,4.462549209594727,3731.2161630000005 +38060,Binary classification,Voting,SMTP,0.9998686250295594,0.782608695652174,4.517663955688477,4053.8854360000005 +39963,Binary classification,Voting,SMTP,0.9998748811370802,0.782608695652174,4.596040725708008,4386.480402 +41866,Binary classification,Voting,SMTP,0.9998805684939687,0.782608695652174,4.581964492797852,4729.507439 +43769,Binary classification,Voting,SMTP,0.9998857612867849,0.782608695652174,4.56077766418457,5082.623411 +45672,Binary classification,Voting,SMTP,0.9998905213373913,0.782608695652174,4.554697036743164,5446.283888999999 +47575,Binary classification,Voting,SMTP,0.9998949005759449,0.782608695652174,4.589784622192383,5821.612630999999 +49478,Binary classification,Voting,SMTP,0.9998989429431857,0.782608695652174,4.514947891235352,6206.523862999999 +51381,Binary classification,Voting,SMTP,0.9998637602179836,0.72,4.571069717407227,6600.877267999999 +53284,Binary classification,Voting,SMTP,0.9998686260158024,0.72,4.544900894165039,7002.824473 +55187,Binary classification,Voting,SMTP,0.9998731562352771,0.72,4.490015029907227,7412.266105 +57090,Binary classification,Voting,SMTP,0.9997197358510396,0.5294117647058824,4.520219802856445,7829.0322129999995 +58993,Binary classification,Voting,SMTP,0.9997287767832926,0.5294117647058824,4.567926406860352,8253.169596 +60896,Binary classification,Voting,SMTP,0.9997372526480006,0.5294117647058824,4.602060317993164,8684.574786 +62799,Binary classification,Voting,SMTP,0.9997133666677283,0.5,4.518564224243164,9122.220249 +64702,Binary classification,Voting,SMTP,0.9997217971901516,0.5,4.562410354614258,9566.245368 +66605,Binary classification,Voting,SMTP,0.9997297459612036,0.5,4.587350845336914,10016.770375 +68508,Binary classification,Voting,SMTP,0.9997226560789408,0.5128205128205129,4.58268928527832,10473.685786 +70411,Binary classification,Voting,SMTP,0.9997301519670502,0.5128205128205129,4.552003860473633,10937.213874000001 +72314,Binary classification,Voting,SMTP,0.9997372533292769,0.5128205128205129,4.568490982055664,11407.187031000001 +74217,Binary classification,Voting,SMTP,0.9997439905141748,0.5128205128205129,4.501398086547852,11883.434676 +76120,Binary classification,Voting,SMTP,0.9997503908354025,0.5128205128205129,4.530572891235352,12366.124718000001 +78023,Binary classification,Voting,SMTP,0.999756478941837,0.5128205128205129,4.565195083618164,12855.350972 +79926,Binary classification,Voting,SMTP,0.9997622771348139,0.5128205128205129,4.555276870727539,13350.750206 +81829,Binary classification,Voting,SMTP,0.9997678056411008,0.5128205128205129,4.523683547973633,13852.638924 +83732,Binary classification,Voting,SMTP,0.9997730828486463,0.5128205128205129,4.51640510559082,14360.452684 +85635,Binary classification,Voting,SMTP,0.9997781255108952,0.5128205128205129,4.554742813110352,14874.156807 +87538,Binary classification,Voting,SMTP,0.9997829489244549,0.5128205128205129,4.55351448059082,15393.589182 +89441,Binary classification,Voting,SMTP,0.9997763864042933,0.5,4.576028823852539,15919.905447 +91344,Binary classification,Voting,SMTP,0.9997700973254655,0.4878048780487804,4.509759902954102,16451.368555 +93247,Binary classification,Voting,SMTP,0.9997747892671,0.4878048780487804,4.633722305297852,16987.639193000003 +95150,Binary classification,Voting,SMTP,0.9997792935290964,0.4878048780487804,4.606340408325195,17528.67073 +95156,Binary classification,Voting,SMTP,0.9997793074457464,0.4878048780487804,4.602045059204102,18069.786262 +106,Binary classification,[baseline] Last Class,Bananas,0.5333333333333333,0.5242718446601942,0.0005102157592773438,0.004468 +212,Binary classification,[baseline] Last Class,Bananas,0.5876777251184834,0.5538461538461539,0.0005102157592773438,0.067972 +318,Binary classification,[baseline] Last Class,Bananas,0.5457413249211357,0.5102040816326531,0.0005102157592773438,0.134988 +424,Binary classification,[baseline] Last Class,Bananas,0.5460992907801419,0.5025906735751295,0.0005102157592773438,0.20522 +530,Binary classification,[baseline] Last Class,Bananas,0.5671077504725898,0.5096359743040686,0.0005102157592773438,0.337716 +636,Binary classification,[baseline] Last Class,Bananas,0.5464566929133858,0.4875444839857651,0.0005102157592773438,0.474055 +742,Binary classification,[baseline] Last Class,Bananas,0.5573549257759784,0.4875,0.0005102157592773438,0.646583 +848,Binary classification,[baseline] Last Class,Bananas,0.5501770956316411,0.4816326530612245,0.0005102157592773438,0.822555 +954,Binary classification,[baseline] Last Class,Bananas,0.5487932843651626,0.4794188861985472,0.0005102157592773438,1.00209 +1060,Binary classification,[baseline] Last Class,Bananas,0.5448536355051936,0.46799116997792495,0.0005102157592773438,1.292978 +1166,Binary classification,[baseline] Last Class,Bananas,0.534763948497854,0.4590818363273453,0.0005102157592773438,1.5875979999999998 +1272,Binary classification,[baseline] Last Class,Bananas,0.5287175452399685,0.456935630099728,0.0005102157592773438,1.885535 +1378,Binary classification,[baseline] Last Class,Bananas,0.5286855482933914,0.45232067510548524,0.0005102157592773438,2.211477 +1484,Binary classification,[baseline] Last Class,Bananas,0.5252865812542145,0.44913928012519555,0.0005102157592773438,2.547239 +1590,Binary classification,[baseline] Last Class,Bananas,0.5204531151667715,0.4437956204379563,0.0005102157592773438,2.88734 +1696,Binary classification,[baseline] Last Class,Bananas,0.5227138643067847,0.4455106237148732,0.0005102157592773438,3.258534 +1802,Binary classification,[baseline] Last Class,Bananas,0.524153248195447,0.4523961661341854,0.0005102157592773438,3.633124 +1908,Binary classification,[baseline] Last Class,Bananas,0.5233350812794966,0.456664674237896,0.0005102157592773438,4.01125 +2014,Binary classification,[baseline] Last Class,Bananas,0.5171385991058122,0.4563758389261745,0.0005102157592773438,4.505139000000001 +2120,Binary classification,[baseline] Last Class,Bananas,0.5143935818782445,0.45813586097946285,0.0005102157592773438,5.002779 +2226,Binary classification,[baseline] Last Class,Bananas,0.5114606741573033,0.45459106874059213,0.0005102157592773438,5.503925000000001 +2332,Binary classification,[baseline] Last Class,Bananas,0.510939510939511,0.45506692160611856,0.0005102157592773438,6.074663000000001 +2438,Binary classification,[baseline] Last Class,Bananas,0.5104636848584325,0.4530032095369097,0.0005102157592773438,6.648598000000001 +2544,Binary classification,[baseline] Last Class,Bananas,0.5084545812033032,0.45462478184991273,0.0005102157592773438,7.226634000000001 +2650,Binary classification,[baseline] Last Class,Bananas,0.5096262740656852,0.458072590738423,0.0005102157592773438,7.8632990000000005 +2756,Binary classification,[baseline] Last Class,Bananas,0.5092558983666061,0.45746388443017655,0.0005102157592773438,8.503527 +2862,Binary classification,[baseline] Last Class,Bananas,0.5103110800419434,0.4563445867287544,0.0005102157592773438,9.147193 +2968,Binary classification,[baseline] Last Class,Bananas,0.5133131108864173,0.457957957957958,0.0005102157592773438,9.82546 +3074,Binary classification,[baseline] Last Class,Bananas,0.5099251545720794,0.4563176895306859,0.0005102157592773438,10.507099 +3180,Binary classification,[baseline] Last Class,Bananas,0.5102233406731677,0.45387583304103823,0.0005102157592773438,11.191893 +3286,Binary classification,[baseline] Last Class,Bananas,0.5095890410958904,0.45222713362801764,0.0005102157592773438,11.975438 +3392,Binary classification,[baseline] Last Class,Bananas,0.5107637864936597,0.4558871761233191,0.0005102157592773438,12.764918 +3498,Binary classification,[baseline] Last Class,Bananas,0.5124392336288247,0.45579316948611553,0.0005102157592773438,13.557573 +3604,Binary classification,[baseline] Last Class,Bananas,0.5134610047182903,0.45440398381574854,0.0005102157592773438,14.3795 +3710,Binary classification,[baseline] Last Class,Bananas,0.5122674575357239,0.4546276756104914,0.0005102157592773438,15.204998 +3816,Binary classification,[baseline] Last Class,Bananas,0.510615989515072,0.4536142815335089,0.0005102157592773438,16.116361 +3922,Binary classification,[baseline] Last Class,Bananas,0.5090538128028564,0.45078459343794575,0.0005102157592773438,17.035489000000002 +4028,Binary classification,[baseline] Last Class,Bananas,0.5108020859200397,0.45247359644246804,0.0005102157592773438,17.958008000000003 +4134,Binary classification,[baseline] Last Class,Bananas,0.5102830873457537,0.4517876489707476,0.0005102157592773438,18.927027000000002 +4240,Binary classification,[baseline] Last Class,Bananas,0.5102618542108988,0.4525316455696203,0.0005102157592773438,19.900154000000004 +4346,Binary classification,[baseline] Last Class,Bananas,0.5074798619102416,0.4490216271884655,0.0005102157592773438,20.876623000000006 +4452,Binary classification,[baseline] Last Class,Bananas,0.5099977533138621,0.45132075471698113,0.0005102157592773438,21.913356000000007 +4558,Binary classification,[baseline] Last Class,Bananas,0.5099846390168971,0.45390070921985815,0.0005102157592773438,22.953869000000008 +4664,Binary classification,[baseline] Last Class,Bananas,0.5099721209521767,0.4553039332538737,0.0005102157592773438,23.99911400000001 +4770,Binary classification,[baseline] Last Class,Bananas,0.5110085971901867,0.4556489262371615,0.0005102157592773438,25.08372100000001 +4876,Binary classification,[baseline] Last Class,Bananas,0.5109743589743589,0.4539624370132845,0.0005102157592773438,26.17171900000001 +4982,Binary classification,[baseline] Last Class,Bananas,0.5099377635013049,0.45379279480868207,0.0005102157592773438,27.26320500000001 +5088,Binary classification,[baseline] Last Class,Bananas,0.5099272655789266,0.45364891518737677,0.0005102157592773438,28.44143600000001 +5194,Binary classification,[baseline] Last Class,Bananas,0.5097246293086848,0.4531786941580756,0.0005102157592773438,29.62357400000001 +5300,Binary classification,[baseline] Last Class,Bananas,0.5095301000188714,0.4529572721532309,0.0005102157592773438,30.80903600000001 +906,Binary classification,[baseline] Last Class,Elec2,0.8530386740331491,0.8500563697857948,0.0005102157592773438,0.224121 +1812,Binary classification,[baseline] Last Class,Elec2,0.8619547211485368,0.8287671232876712,0.0005102157592773438,0.785464 +2718,Binary classification,[baseline] Last Class,Elec2,0.8450496871549503,0.80958842152872,0.0005102157592773438,1.64751 +3624,Binary classification,[baseline] Last Class,Elec2,0.8418437758763456,0.8056968463886063,0.0005102157592773438,2.8059529999999997 +4530,Binary classification,[baseline] Last Class,Elec2,0.8388165157871494,0.7960893854748604,0.0005102157592773438,4.158177 +5436,Binary classification,[baseline] Last Class,Elec2,0.8413983440662374,0.7995348837209302,0.0005102157592773438,5.857693 +6342,Binary classification,[baseline] Last Class,Elec2,0.8370919413341744,0.7958094485076103,0.0005102157592773438,7.811494 +7248,Binary classification,[baseline] Last Class,Elec2,0.8359321098385539,0.7948231233822259,0.0005102157592773438,10.005109 +8154,Binary classification,[baseline] Last Class,Elec2,0.8352753587636453,0.8021799970540581,0.0005102157592773438,12.510532 +9060,Binary classification,[baseline] Last Class,Elec2,0.8358538470029805,0.8069081937410726,0.0005102157592773438,15.278307999999999 +9966,Binary classification,[baseline] Last Class,Elec2,0.8372303060712494,0.8118765947575969,0.0005102157592773438,18.289258999999998 +10872,Binary classification,[baseline] Last Class,Elec2,0.8368135406126391,0.8140461215932915,0.0005102157592773438,21.565545999999998 +11778,Binary classification,[baseline] Last Class,Elec2,0.8374798335739153,0.8150724637681159,0.0005102157592773438,25.041396 +12684,Binary classification,[baseline] Last Class,Elec2,0.8384451628163684,0.8161177420802298,0.0005102157592773438,28.814916 +13590,Binary classification,[baseline] Last Class,Elec2,0.842004562513798,0.8223417459660736,0.0005102157592773438,32.85712 +14496,Binary classification,[baseline] Last Class,Elec2,0.8448430493273542,0.8264794383149447,0.0005102157592773438,37.134508000000004 +15402,Binary classification,[baseline] Last Class,Elec2,0.8460489578598792,0.8270983738058776,0.0005102157592773438,41.682175 +16308,Binary classification,[baseline] Last Class,Elec2,0.844851904090268,0.8251313243019076,0.0005102157592773438,46.494991 +17214,Binary classification,[baseline] Last Class,Elec2,0.8443618195549875,0.8222177981286084,0.0005102157592773438,51.515798 +18120,Binary classification,[baseline] Last Class,Elec2,0.8450797505381091,0.8227792158595871,0.0005102157592773438,56.800748 +19026,Binary classification,[baseline] Last Class,Elec2,0.8462023653088042,0.8224083515416363,0.0005102157592773438,62.372633 +19932,Binary classification,[baseline] Last Class,Elec2,0.847523957653906,0.8255753888538139,0.0005102157592773438,68.180409 +20838,Binary classification,[baseline] Last Class,Elec2,0.84661899505687,0.8249917862227577,0.0005102157592773438,74.270057 +21744,Binary classification,[baseline] Last Class,Elec2,0.8452835395299637,0.8209495422610177,0.0005102157592773438,80.612623 +22650,Binary classification,[baseline] Last Class,Elec2,0.8444081416398075,0.8188733552631579,0.0005102157592773438,87.17507 +23556,Binary classification,[baseline] Last Class,Elec2,0.8451284228401613,0.8194595664654062,0.0005102157592773438,93.968638 +24462,Binary classification,[baseline] Last Class,Elec2,0.8464903315481788,0.8198781599270878,0.0005102157592773438,100.983267 +25368,Binary classification,[baseline] Last Class,Elec2,0.8462963692986951,0.8199492034172247,0.0005102157592773438,108.278888 +26274,Binary classification,[baseline] Last Class,Elec2,0.8477524454763445,0.8213168944876262,0.0005102157592773438,115.769594 +27180,Binary classification,[baseline] Last Class,Elec2,0.8495529636851982,0.8240457851026293,0.0005102157592773438,123.465792 +28086,Binary classification,[baseline] Last Class,Elec2,0.8509880719245149,0.825107610012955,0.0005102157592773438,131.36678899999998 +28992,Binary classification,[baseline] Last Class,Elec2,0.8521265220240765,0.8258237516759436,0.0005102157592773438,139.55273799999998 +29898,Binary classification,[baseline] Last Class,Elec2,0.8531959728400843,0.8268160833366216,0.0005102157592773438,147.964309 +30804,Binary classification,[baseline] Last Class,Elec2,0.8537480115573158,0.8267107743201139,0.0005102157592773438,156.664426 +31710,Binary classification,[baseline] Last Class,Elec2,0.8530385694913116,0.8259895444361464,0.0005102157592773438,165.606267 +32616,Binary classification,[baseline] Last Class,Elec2,0.8536869538555879,0.8269760696156635,0.0005102157592773438,174.782391 +33522,Binary classification,[baseline] Last Class,Elec2,0.8541511291429253,0.8276032300151628,0.0005102157592773438,184.217189 +34428,Binary classification,[baseline] Last Class,Elec2,0.8549684840386905,0.8286724084685859,0.0005102157592773438,193.875362 +35334,Binary classification,[baseline] Last Class,Elec2,0.8555175048821215,0.8284321962695346,0.0005102157592773438,203.79136499999998 +36240,Binary classification,[baseline] Last Class,Elec2,0.8545213720025387,0.8259146744155329,0.0005102157592773438,213.957306 +37146,Binary classification,[baseline] Last Class,Elec2,0.854354556467896,0.8252696854208386,0.0005102157592773438,224.37720199999998 +38052,Binary classification,[baseline] Last Class,Elec2,0.8545636119944285,0.8247736052181622,0.0005102157592773438,234.998191 +38958,Binary classification,[baseline] Last Class,Elec2,0.8548142824139435,0.8254213223038459,0.0005102157592773438,245.89740899999998 +39864,Binary classification,[baseline] Last Class,Elec2,0.8546521837292728,0.8262981172802495,0.0005102157592773438,257.034489 +40770,Binary classification,[baseline] Last Class,Elec2,0.8540067207927592,0.8267652366261132,0.0005102157592773438,268.379106 +41676,Binary classification,[baseline] Last Class,Elec2,0.8537012597480504,0.8274320002264302,0.0005102157592773438,279.987419 +42582,Binary classification,[baseline] Last Class,Elec2,0.8536201592259458,0.8277177368086459,0.0005102157592773438,291.808183 +43488,Binary classification,[baseline] Last Class,Elec2,0.853473451836181,0.8276626818845675,0.0005102157592773438,303.899029 +44394,Binary classification,[baseline] Last Class,Elec2,0.8533777847858897,0.8271686890948196,0.0005102157592773438,316.239245 +45300,Binary classification,[baseline] Last Class,Elec2,0.8533521711296055,0.8273155007928462,0.0005102157592773438,328.81397599999997 +45312,Binary classification,[baseline] Last Class,Elec2,0.8533027300214076,0.8272294856132872,0.0005102157592773438,341.389555 +25,Binary classification,[baseline] Last Class,Phishing,0.625,0.64,0.0005102157592773438,0.001863 +50,Binary classification,[baseline] Last Class,Phishing,0.6530612244897959,0.6222222222222223,0.0005102157592773438,0.005016 +75,Binary classification,[baseline] Last Class,Phishing,0.5675675675675675,0.5555555555555556,0.0005102157592773438,0.009415 +100,Binary classification,[baseline] Last Class,Phishing,0.5555555555555556,0.5416666666666666,0.0005102157592773438,0.115037 +125,Binary classification,[baseline] Last Class,Phishing,0.5241935483870968,0.5123966942148761,0.0005102157592773438,0.22212700000000002 +150,Binary classification,[baseline] Last Class,Phishing,0.5234899328859061,0.5298013245033113,0.0005102157592773438,0.330326 +175,Binary classification,[baseline] Last Class,Phishing,0.5229885057471264,0.496969696969697,0.0005102157592773438,0.439628 +200,Binary classification,[baseline] Last Class,Phishing,0.507537688442211,0.47872340425531923,0.0005102157592773438,0.550035 +225,Binary classification,[baseline] Last Class,Phishing,0.5,0.45098039215686275,0.0005102157592773438,0.6616070000000001 +250,Binary classification,[baseline] Last Class,Phishing,0.5180722891566265,0.4782608695652174,0.0005102157592773438,0.774476 +275,Binary classification,[baseline] Last Class,Phishing,0.5218978102189781,0.4738955823293172,0.0005102157592773438,0.8884620000000001 +300,Binary classification,[baseline] Last Class,Phishing,0.5217391304347826,0.460377358490566,0.0005102157592773438,1.0035580000000002 +325,Binary classification,[baseline] Last Class,Phishing,0.5216049382716049,0.44839857651245546,0.0005102157592773438,1.151113 +350,Binary classification,[baseline] Last Class,Phishing,0.5329512893982808,0.4511784511784511,0.0005102157592773438,1.299965 +375,Binary classification,[baseline] Last Class,Phishing,0.5267379679144385,0.4380952380952381,0.0005102157592773438,1.4500600000000001 +400,Binary classification,[baseline] Last Class,Phishing,0.5263157894736842,0.43243243243243246,0.0005102157592773438,1.6013830000000002 +425,Binary classification,[baseline] Last Class,Phishing,0.5424528301886793,0.436046511627907,0.0005102157592773438,1.7539290000000003 +450,Binary classification,[baseline] Last Class,Phishing,0.5367483296213809,0.4222222222222222,0.0005102157592773438,1.9077010000000003 +475,Binary classification,[baseline] Last Class,Phishing,0.5358649789029536,0.43298969072164945,0.0005102157592773438,2.0627030000000004 +500,Binary classification,[baseline] Last Class,Phishing,0.5370741482965932,0.44604316546762596,0.0005102157592773438,2.2669650000000003 +525,Binary classification,[baseline] Last Class,Phishing,0.5400763358778626,0.43822843822843827,0.0005102157592773438,2.491531 +550,Binary classification,[baseline] Last Class,Phishing,0.5391621129326047,0.44150110375275936,0.0005102157592773438,2.717762 +575,Binary classification,[baseline] Last Class,Phishing,0.5418118466898955,0.4416135881104034,0.0005102157592773438,2.945135 +600,Binary classification,[baseline] Last Class,Phishing,0.5509181969949917,0.443064182194617,0.0005102157592773438,3.175983 +625,Binary classification,[baseline] Last Class,Phishing,0.5560897435897436,0.43584521384928715,0.0005102157592773438,3.407996 +650,Binary classification,[baseline] Last Class,Phishing,0.551617873651772,0.4393063583815029,0.0005102157592773438,3.641123 +675,Binary classification,[baseline] Last Class,Phishing,0.5459940652818991,0.44363636363636366,0.0005102157592773438,3.8753569999999997 +700,Binary classification,[baseline] Last Class,Phishing,0.5464949928469242,0.4389380530973452,0.0005102157592773438,4.110698999999999 +725,Binary classification,[baseline] Last Class,Phishing,0.5441988950276243,0.44630872483221484,0.0005102157592773438,4.380075 +750,Binary classification,[baseline] Last Class,Phishing,0.5367156208277704,0.44122383252818037,0.0005102157592773438,4.6504829999999995 +775,Binary classification,[baseline] Last Class,Phishing,0.5310077519379846,0.43369734789391573,0.0005102157592773438,4.940408 +800,Binary classification,[baseline] Last Class,Phishing,0.5294117647058824,0.4388059701492537,0.0005102157592773438,5.231503 +825,Binary classification,[baseline] Last Class,Phishing,0.5266990291262136,0.43965517241379315,0.0005102157592773438,5.523716 +850,Binary classification,[baseline] Last Class,Phishing,0.5241460541813898,0.4341736694677871,0.0005102157592773438,5.817038 +875,Binary classification,[baseline] Last Class,Phishing,0.522883295194508,0.4311050477489768,0.0005102157592773438,6.111637 +900,Binary classification,[baseline] Last Class,Phishing,0.5272525027808677,0.4340878828229028,0.0005102157592773438,6.407366 +925,Binary classification,[baseline] Last Class,Phishing,0.5227272727272727,0.43388960205391536,0.0005102157592773438,6.766113 +950,Binary classification,[baseline] Last Class,Phishing,0.5205479452054794,0.43896424167694204,0.0005102157592773438,7.1265789999999996 +975,Binary classification,[baseline] Last Class,Phishing,0.5174537987679672,0.43373493975903615,0.0005102157592773438,7.4884189999999995 +1000,Binary classification,[baseline] Last Class,Phishing,0.5185185185185185,0.4361078546307151,0.0005102157592773438,7.851253 +1025,Binary classification,[baseline] Last Class,Phishing,0.517578125,0.43863636363636366,0.0005102157592773438,8.215067 +1050,Binary classification,[baseline] Last Class,Phishing,0.5138226882745471,0.4370860927152318,0.0005102157592773438,8.579858 +1075,Binary classification,[baseline] Last Class,Phishing,0.5111731843575419,0.43729903536977494,0.0005102157592773438,8.945611 +1100,Binary classification,[baseline] Last Class,Phishing,0.5122838944494995,0.4393305439330544,0.0005102157592773438,9.312327999999999 +1125,Binary classification,[baseline] Last Class,Phishing,0.5124555160142349,0.44534412955465585,0.0005102157592773438,9.680001999999998 +1150,Binary classification,[baseline] Last Class,Phishing,0.5143603133159269,0.44642857142857145,0.0005102157592773438,10.125450999999998 +1175,Binary classification,[baseline] Last Class,Phishing,0.5187393526405452,0.4509232264334305,0.0005102157592773438,10.572147999999999 +1200,Binary classification,[baseline] Last Class,Phishing,0.5187656380316931,0.448901623686724,0.0005102157592773438,11.020091999999998 +1225,Binary classification,[baseline] Last Class,Phishing,0.5171568627450981,0.4471468662301216,0.0005102157592773438,11.469230999999999 +1250,Binary classification,[baseline] Last Class,Phishing,0.5156124899919936,0.4474885844748858,0.0005102157592773438,11.919638999999998 +1903,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.335236 +3806,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.143886 +5709,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,2.36402 +7612,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,4.028138 +9515,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,6.117771 +11418,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,8.657701 +13321,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,11.631 +15224,Binary classification,[baseline] Last Class,SMTP,0.9985548183669447,0.0,0.0005102157592773438,15.050370000000001 +17127,Binary classification,[baseline] Last Class,SMTP,0.9984818404764685,0.0,0.0005102157592773438,18.876176 +19030,Binary classification,[baseline] Last Class,SMTP,0.9986336644069578,0.0,0.0005102157592773438,23.061029 +20933,Binary classification,[baseline] Last Class,SMTP,0.9987578826676858,0.0,0.0005102157592773438,27.619004 +22836,Binary classification,[baseline] Last Class,SMTP,0.9988613969783228,0.0,0.0005102157592773438,32.587888 +24739,Binary classification,[baseline] Last Class,SMTP,0.9989489853666425,0.0,0.0005102157592773438,37.966612 +26642,Binary classification,[baseline] Last Class,SMTP,0.9989489884013363,0.0,0.0005102157592773438,43.747015999999995 +28545,Binary classification,[baseline] Last Class,SMTP,0.9990190582959642,0.0,0.0005102157592773438,49.923314999999995 +30448,Binary classification,[baseline] Last Class,SMTP,0.9990803691660919,0.0,0.0005102157592773438,56.476617 +32351,Binary classification,[baseline] Last Class,SMTP,0.9991344667697063,0.0,0.0005102157592773438,63.442318 +34254,Binary classification,[baseline] Last Class,SMTP,0.999182553352991,0.0,0.0005102157592773438,70.796392 +36157,Binary classification,[baseline] Last Class,SMTP,0.9992255780506694,0.0,0.0005102157592773438,78.554987 +38060,Binary classification,[baseline] Last Class,SMTP,0.9992643001655325,0.0,0.0005102157592773438,86.688101 +39963,Binary classification,[baseline] Last Class,SMTP,0.9992993343676493,0.0,0.0005102157592773438,95.30254000000001 +41866,Binary classification,[baseline] Last Class,SMTP,0.9993311835662247,0.0,0.0005102157592773438,104.31052400000002 +43769,Binary classification,[baseline] Last Class,SMTP,0.9993602632059952,0.0,0.0005102157592773438,113.72316700000002 +45672,Binary classification,[baseline] Last Class,SMTP,0.9993869194893915,0.0,0.0005102157592773438,123.54962900000001 +47575,Binary classification,[baseline] Last Class,SMTP,0.9994114432252911,0.0,0.0005102157592773438,133.72455100000002 +49478,Binary classification,[baseline] Last Class,SMTP,0.99943408048184,0.0,0.0005102157592773438,144.361296 +51381,Binary classification,[baseline] Last Class,SMTP,0.9994161152199299,0.0625,0.0005102157592773438,155.342577 +53284,Binary classification,[baseline] Last Class,SMTP,0.9994369686391532,0.0625,0.0005102157592773438,166.71976 +55187,Binary classification,[baseline] Last Class,SMTP,0.9994563838654731,0.0625,0.0005102157592773438,178.568204 +57090,Binary classification,[baseline] Last Class,SMTP,0.9994394717020793,0.36,0.0005102157592773438,190.760423 +58993,Binary classification,[baseline] Last Class,SMTP,0.9994575535665853,0.36,0.0005102157592773438,203.318295 +60896,Binary classification,[baseline] Last Class,SMTP,0.9994745052960013,0.36,0.0005102157592773438,216.324675 +62799,Binary classification,[baseline] Last Class,SMTP,0.9994585814834868,0.37037037037037035,0.0005102157592773438,229.75194900000002 +64702,Binary classification,[baseline] Last Class,SMTP,0.9994745058036197,0.37037037037037035,0.0005102157592773438,243.519605 +66605,Binary classification,[baseline] Last Class,SMTP,0.99948952014894,0.37037037037037035,0.0005102157592773438,257.632512 +68508,Binary classification,[baseline] Last Class,SMTP,0.9994745062548352,0.3793103448275862,0.0005102157592773438,272.164854 +70411,Binary classification,[baseline] Last Class,SMTP,0.9994887089902003,0.3793103448275862,0.0005102157592773438,287.040678 +72314,Binary classification,[baseline] Last Class,SMTP,0.9995021642028404,0.3793103448275862,0.0005102157592773438,302.255295 +74217,Binary classification,[baseline] Last Class,SMTP,0.9995149293952786,0.3793103448275862,0.0005102157592773438,317.85437 +76120,Binary classification,[baseline] Last Class,SMTP,0.99952705631971,0.3793103448275862,0.0005102157592773438,333.81575100000003 +78023,Binary classification,[baseline] Last Class,SMTP,0.99953859167927,0.3793103448275862,0.0005102157592773438,350.111597 +79926,Binary classification,[baseline] Last Class,SMTP,0.999549577729121,0.3793103448275862,0.0005102157592773438,366.757049 +81829,Binary classification,[baseline] Last Class,SMTP,0.9995600527936648,0.3793103448275862,0.0005102157592773438,383.769929 +83732,Binary classification,[baseline] Last Class,SMTP,0.9995700517132244,0.3793103448275862,0.0005102157592773438,401.12604899999997 +85635,Binary classification,[baseline] Last Class,SMTP,0.9995796062311698,0.3793103448275862,0.0005102157592773438,418.85540799999995 +87538,Binary classification,[baseline] Last Class,SMTP,0.999588745330546,0.3793103448275862,0.0005102157592773438,436.927356 +89441,Binary classification,[baseline] Last Class,SMTP,0.9995751341681575,0.36666666666666664,0.0005102157592773438,455.364423 +91344,Binary classification,[baseline] Last Class,SMTP,0.9995839856365567,0.36666666666666664,0.0005102157592773438,474.187698 +93247,Binary classification,[baseline] Last Class,SMTP,0.999592475816657,0.36666666666666664,0.0005102157592773438,493.377496 +95150,Binary classification,[baseline] Last Class,SMTP,0.9996006263859841,0.36666666666666664,0.0005102157592773438,512.867881 +95156,Binary classification,[baseline] Last Class,SMTP,0.9996006515684935,0.36666666666666664,0.0005102157592773438,532.358985 diff --git a/benchmarks/config.py b/benchmarks/config.py index c17bf7bf81..828614deae 100644 --- a/benchmarks/config.py +++ b/benchmarks/config.py @@ -1,16 +1,6 @@ -from model_zoo.torch import ( - TorchLinearRegression, - TorchLogisticRegression, - TorchLSTMClassifier, - TorchLSTMRegressor, - TorchMLPClassifier, - TorchMLPRegressor, -) -from model_zoo.vw import VW2RiverClassifier -from river_torch.classification import Classifier as TorchClassifier -from river_torch.classification import RollingClassifier as TorchRollingClassifier -from river_torch.regression import Regressor as TorchRegressor -from river_torch.regression import RollingRegressor as TorchRollingRegressor +from __future__ import annotations + +from model_adapters.vw import VW2RiverClassifier from sklearn.linear_model import SGDClassifier from river import ( @@ -39,7 +29,6 @@ evaluate.MultiClassClassificationTrack(), evaluate.RegressionTrack(), ] -import river MODELS = { "Binary classification": { @@ -53,7 +42,7 @@ preprocessing.StandardScaler() | compat.SKL2RiverClassifier( SGDClassifier( - loss="log", learning_rate="constant", eta0=LEARNING_RATE, penalty="none" + loss="log_loss", learning_rate="constant", eta0=LEARNING_RATE, penalty=None ), classes=[False, True], ) @@ -105,38 +94,6 @@ preprocessing.StandardScaler() | neighbors.KNNClassifier(), ] ), - "Torch Logistic Regression": ( - preprocessing.StandardScaler() - | TorchClassifier( - module=TorchLogisticRegression, - loss_fn="binary_cross_entropy", - optimizer_fn="adam", - is_class_incremental=True, - lr=LEARNING_RATE, - ) - ), - "Torch MLP": ( - preprocessing.StandardScaler() - | TorchClassifier( - module=TorchMLPClassifier, - loss_fn="binary_cross_entropy", - optimizer_fn="adam", - is_class_incremental=True, - lr=LEARNING_RATE, - ) - ), - "Torch LSTM": ( - preprocessing.StandardScaler() - | TorchRollingClassifier( - module=TorchLSTMClassifier, - loss_fn="binary_cross_entropy", - optimizer_fn="adam", - is_class_incremental=True, - lr=LEARNING_RATE, - window_size=20, - append_predict=False, - ) - ), # Baseline "[baseline] Last Class": dummy.NoChangeClassifier(), }, @@ -172,24 +129,6 @@ rules.AMRules(), ], ), - "Torch Linear Regression": ( - preprocessing.StandardScaler() - | TorchRegressor( - module=TorchLinearRegression, - loss_fn="mse", - optimizer_fn="adam", - learning_rate=LEARNING_RATE, - ) - ), - "Torch MLP": ( - preprocessing.StandardScaler() - | TorchRegressor( - module=TorchMLPRegressor, - loss_fn="mse", - optimizer_fn="adam", - learning_rate=LEARNING_RATE, - ) - ), "River MLP": preprocessing.StandardScaler() | neural_net.MLPRegressor( hidden_dims=(5,), @@ -201,17 +140,6 @@ optimizer=optim.SGD(1e-3), seed=42, ), - "Torch LSTM": ( - preprocessing.StandardScaler() - | TorchRollingRegressor( - module=TorchLSTMRegressor, - loss_fn="mse", - optimizer_fn="adam", - learning_rate=LEARNING_RATE, - window_size=20, - append_predict=False, - ) - ), # Baseline "[baseline] Mean predictor": dummy.StatisticRegressor(stats.Mean()), }, diff --git a/benchmarks/details.json b/benchmarks/details.json index 2dbd175e31..f649b6536e 100644 --- a/benchmarks/details.json +++ b/benchmarks/details.json @@ -1,57 +1,59 @@ { "Binary classification": { "Dataset": { - "Bananas": "Bananas dataset.\n\nAn artificial dataset where instances belongs to several clusters with a banana shape.\nThere are two attributes that correspond to the x and y axis, respectively.\n\n Name Bananas \n Task Binary classification \n Samples 5,300 \nFeatures 2 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/banana.zip", - "Elec2": "Electricity prices in New South Wales.\n\nThis is a binary classification task, where the goal is to predict if the price of electricity\nwill go up or down.\n\nThis data was collected from the Australian New South Wales Electricity Market. In this market,\nprices are not fixed and are affected by demand and supply of the market. They are set every\nfive minutes. Electricity transfers to/from the neighboring state of Victoria were done to\nalleviate fluctuations.\n\n Name Elec2 \n Task Binary classification \n Samples 45,312 \n Features 8 \n Sparse False \n Path /home/kulbach/river_data/Elec2/electricity.csv \n URL https://maxhalford.github.io/files/datasets/electricity.zip\n Size 2.95 MB \nDownloaded True ", - "Phishing": "Phishing websites.\n\nThis dataset contains features from web pages that are classified as phishing or not.\n\n Name Phishing \n Task Binary classification \n Samples 1,250 \nFeatures 9 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/phishing.csv.gz", - "SMTP": "SMTP dataset from the KDD 1999 cup.\n\nThe goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only\ncontains 2,211 (0.4%) positive labels.\n\n Name SMTP \n Task Binary classification \n Samples 95,156 \n Features 3 \n Sparse False \n Path /home/kulbach/river_data/SMTP/smtp.csv \n URL https://maxhalford.github.io/files/datasets/smtp.zip\n Size 5.23 MB \nDownloaded True " + "Bananas": "Bananas dataset.\n\nAn artificial dataset where instances belongs to several clusters with a banana shape.\nThere are two attributes that correspond to the x and y axis, respectively.\n\n Name Bananas \n Task Binary classification \n Samples 5,300 \nFeatures 2 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/banana.zip", + "Elec2": "Electricity prices in New South Wales.\n\nThis is a binary classification task, where the goal is to predict if the price of electricity\nwill go up or down.\n\nThis data was collected from the Australian New South Wales Electricity Market. In this market,\nprices are not fixed and are affected by demand and supply of the market. They are set every\nfive minutes. Electricity transfers to/from the neighboring state of Victoria were done to\nalleviate fluctuations.\n\n Name Elec2 \n Task Binary classification \n Samples 45,312 \n Features 8 \n Sparse False \n Path /Users/mastelini/river_data/Elec2/electricity.csv \n URL https://maxhalford.github.io/files/datasets/electricity.zip\n Size 2.95 MB \nDownloaded True ", + "Phishing": "Phishing websites.\n\nThis dataset contains features from web pages that are classified as phishing or not.\n\n Name Phishing \n Task Binary classification \n Samples 1,250 \nFeatures 9 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/phishing.csv.gz", + "SMTP": "SMTP dataset from the KDD 1999 cup.\n\nThe goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only\ncontains 2,211 (0.4%) positive labels.\n\n Name SMTP \n Task Binary classification \n Samples 95,156 \n Features 3 \n Sparse False \n Path /Users/mastelini/river_data/SMTP/smtp.csv \n URL https://maxhalford.github.io/files/datasets/smtp.zip\n Size 5.23 MB \nDownloaded True " }, "Model": { "Logistic regression": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n LogisticRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.005\n )\n )\n loss=Log (\n weight_pos=1.\n weight_neg=1.\n )\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n)", + "Aggregated Mondrian Forest": "[]", "ALMA": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n ALMAClassifier (\n p=2\n alpha=0.9\n B=1.111111\n C=1.414214\n )\n)", - "sklearn SGDClassifier": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n SKL2RiverClassifier (\n estimator=SGDClassifier(eta0=0.005, learning_rate='constant', loss='log', penalty='none')\n classes=[False, True]\n )\n)", + "sklearn SGDClassifier": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n SKL2RiverClassifier (\n estimator=SGDClassifier(eta0=0.005, learning_rate='constant', loss='log_loss',\n penalty=None)\n classes=[False, True]\n )\n)", "Vowpal Wabbit logistic regression": "VW2RiverClassifier ()", "Naive Bayes": "GaussianNB ()", - "Hoeffding Tree": "HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)", - "Hoeffding Adaptive Tree": "HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n)", + "Hoeffding Tree": "HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)", + "Hoeffding Adaptive Tree": "HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n)", "Adaptive Random Forest": "[]", - "Streaming Random Patches": "SRPClassifier (\n model=HoeffdingTreeClassifier (\n grace_period=50\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=0.01\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=False\n seed=None\n metric=Accuracy (\n cm=ConfusionMatrix (\n classes=[]\n )\n )\n)", - "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)", - "ADWIN Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "AdaBoost": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "Bagging": "[HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n)]", - "Leveraging Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "Stacking": "[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)]", - "Voting": "VotingClassifier (\n models=[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)]\n use_probabilities=True\n)", + "Streaming Random Patches": "SRPClassifier (\n model=HoeffdingTreeClassifier (\n grace_period=50\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=0.01\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=False\n seed=None\n metric=Accuracy (\n cm=ConfusionMatrix (\n classes=[]\n )\n )\n)", + "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)", + "ADWIN Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "AdaBoost": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "Bagging": "[HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n)]", + "Leveraging Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "Stacking": "[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)]", + "Voting": "VotingClassifier (\n models=[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)]\n use_probabilities=True\n)", "[baseline] Last Class": "NoChangeClassifier ()" } }, "Multiclass classification": { "Dataset": { - "ImageSegments": "Image segments classification.\n\nThis dataset contains features that describe image segments into 7 classes: brickface, sky,\nfoliage, cement, window, path, and grass.\n\n Name ImageSegments \n Task Multi-class classification \n Samples 2,310 \nFeatures 18 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/segment.csv.zip", - "Insects": "Insects dataset.\n\nThis dataset has different variants, which are:\n\n- abrupt_balanced\n- abrupt_imbalanced\n- gradual_balanced\n- gradual_imbalanced\n- incremental-abrupt_balanced\n- incremental-abrupt_imbalanced\n- incremental-reoccurring_balanced\n- incremental-reoccurring_imbalanced\n- incremental_balanced\n- incremental_imbalanced\n- out-of-control\n\nThe number of samples and the difficulty change from one variant to another. The number of\nclasses is always the same (6), except for the last variant (24).\n\n Name Insects \n Task Multi-class classification \n Samples 52,848 \n Features 33 \n Classes 6 \n Sparse False \n Path /home/kulbach/river_data/Insects/INSECTS-abrupt_balanced_norm.arff \n URL http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-abrupt_balanced_norm.arff\n Size 15.66 MB \nDownloaded True \n Variant abrupt_balanced \n\nParameters\n----------\n variant\n Indicates which variant of the dataset to load.", - "Keystroke": "CMU keystroke dataset.\n\nUsers are tasked to type in a password. The task is to determine which user is typing in the\npassword.\n\nThe only difference with the original dataset is that the \"sessionIndex\" and \"rep\" attributes\nhave been dropped.\n\n Name Keystroke \n Task Multi-class classification \n Samples 20,400 \n Features 31 \n Sparse False \n Path /home/kulbach/river_data/Keystroke/DSL-StrongPasswordData.csv\n URL http://www.cs.cmu.edu/~keystroke/DSL-StrongPasswordData.csv \n Size 4.45 MB \nDownloaded True " + "ImageSegments": "Image segments classification.\n\nThis dataset contains features that describe image segments into 7 classes: brickface, sky,\nfoliage, cement, window, path, and grass.\n\n Name ImageSegments \n Task Multi-class classification \n Samples 2,310 \nFeatures 18 \n Classes 7 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/segment.csv.zip", + "Insects": "Insects dataset.\n\nThis dataset has different variants, which are:\n\n- abrupt_balanced\n- abrupt_imbalanced\n- gradual_balanced\n- gradual_imbalanced\n- incremental-abrupt_balanced\n- incremental-abrupt_imbalanced\n- incremental-reoccurring_balanced\n- incremental-reoccurring_imbalanced\n- incremental_balanced\n- incremental_imbalanced\n- out-of-control\n\nThe number of samples and the difficulty change from one variant to another. The number of\nclasses is always the same (6), except for the last variant (24).\n\n Name Insects \n Task Multi-class classification \n Samples 52,848 \n Features 33 \n Classes 6 \n Sparse False \n Path /Users/mastelini/river_data/Insects/INSECTS-abrupt_balanced_norm.arff \n URL http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-abrupt_balanced_norm.arff\n Size 15.66 MB \nDownloaded True \n Variant abrupt_balanced \n\nParameters\n----------\n variant\n Indicates which variant of the dataset to load.", + "Keystroke": "CMU keystroke dataset.\n\nUsers are tasked to type in a password. The task is to determine which user is typing in the\npassword.\n\nThe only difference with the original dataset is that the \"sessionIndex\" and \"rep\" attributes\nhave been dropped.\n\n Name Keystroke \n Task Multi-class classification \n Samples 20,400 \n Features 31 \n Classes 51 \n Sparse False \n Path /Users/mastelini/river_data/Keystroke/DSL-StrongPasswordData.csv\n URL http://www.cs.cmu.edu/~keystroke/DSL-StrongPasswordData.csv \n Size 4.45 MB \nDownloaded True " }, "Model": { "Naive Bayes": "GaussianNB ()", - "Hoeffding Tree": "HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)", - "Hoeffding Adaptive Tree": "HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n)", + "Hoeffding Tree": "HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)", + "Hoeffding Adaptive Tree": "HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n)", "Adaptive Random Forest": "[]", - "Streaming Random Patches": "SRPClassifier (\n model=HoeffdingTreeClassifier (\n grace_period=50\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=0.01\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=False\n seed=None\n metric=Accuracy (\n cm=ConfusionMatrix (\n classes=[]\n )\n )\n)", - "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)", - "ADWIN Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "AdaBoost": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "Bagging": "[HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n)]", - "Leveraging Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "Stacking": "[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)]", - "Voting": "VotingClassifier (\n models=[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)]\n use_probabilities=True\n)", + "Aggregated Mondrian Forest": "[]", + "Streaming Random Patches": "SRPClassifier (\n model=HoeffdingTreeClassifier (\n grace_period=50\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=0.01\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=False\n seed=None\n metric=Accuracy (\n cm=ConfusionMatrix (\n classes=[]\n )\n )\n)", + "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)", + "ADWIN Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "AdaBoost": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "Bagging": "[HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n)]", + "Leveraging Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "Stacking": "[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)]", + "Voting": "VotingClassifier (\n models=[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)]\n use_probabilities=True\n)", "[baseline] Last Class": "NoChangeClassifier ()" } }, "Regression": { "Dataset": { - "ChickWeights": "Chick weights along time.\n\nThe stream contains 578 items and 3 features. The goal is to predict the weight of each chick\nalong time, according to the diet the chick is on. The data is ordered by time and then by\nchick.\n\n Name ChickWeights \n Task Regression \n Samples 578 \nFeatures 3 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/chick-weights.csv", - "TrumpApproval": "Donald Trump approval ratings.\n\nThis dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald\nTrump's approval ratings. It contains 5 features, which are approval ratings collected by\n5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of\nthis task is to see if we can reproduce FiveThirtyEight's model.\n\n Name TrumpApproval \n Task Regression \n Samples 1,001 \nFeatures 6 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/trump_approval.csv.gz" + "ChickWeights": "Chick weights along time.\n\nThe stream contains 578 items and 3 features. The goal is to predict the weight of each chick\nalong time, according to the diet the chick is on. The data is ordered by time and then by\nchick.\n\n Name ChickWeights \n Task Regression \n Samples 578 \nFeatures 3 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/chick-weights.csv", + "TrumpApproval": "Donald Trump approval ratings.\n\nThis dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald\nTrump's approval ratings. It contains 5 features, which are approval ratings collected by\n5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of\nthis task is to see if we can reproduce FiveThirtyEight's model.\n\n Name TrumpApproval \n Task Regression \n Samples 1,001 \nFeatures 6 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/trump_approval.csv.gz" }, "Model": { "Linear Regression": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n)", @@ -59,17 +61,18 @@ "Linear Regression with l2 regularization": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=1.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n)", "Passive-Aggressive Regressor, mode 1": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n PARegressor (\n C=1.\n mode=1\n eps=0.1\n learn_intercept=True\n )\n)", "Passive-Aggressive Regressor, mode 2": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n PARegressor (\n C=1.\n mode=2\n eps=0.1\n learn_intercept=True\n )\n)", - "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNRegressor (\n n_neighbors=5\n window_size=100\n aggregation_method=\"mean\"\n min_distance_keep=0.\n distance_func=functools.partial(, p=2)\n )\n)", + "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNRegressor (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n aggregation_method=\"mean\"\n )\n)", "Hoeffding Tree": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n HoeffdingTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n)", "Hoeffding Adaptive Tree": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n )\n)", "Stochastic Gradient Tree": "SGTRegressor (\n delta=1e-07\n grace_period=200\n init_pred=0.\n max_depth=inf\n lambda_value=0.1\n gamma=1.\n nominal_attributes=[]\n feature_quantizer=StaticQuantizer (\n n_bins=64\n warm_start=100\n buckets=None\n )\n)", "Adaptive Random Forest": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n []\n)", + "Aggregated Mondrian Forest": "[]", "Adaptive Model Rules": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n AMRules (\n n_min=200\n delta=1e-07\n tau=0.05\n pred_type=\"adaptive\"\n pred_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n splitter=TEBSTSplitter (\n digits=1\n )\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n fading_factor=0.99\n anomaly_threshold=-0.75\n m_min=30\n ordered_rule_set=True\n min_samples_split=5\n )\n)", "Streaming Random Patches": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n SRPRegressor (\n model=HoeffdingTreeRegressor (\n grace_period=50\n max_depth=inf\n delta=0.01\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=True\n drift_detection_criteria=\"error\"\n aggregation_method=\"mean\"\n seed=42\n metric=MAE ()\n )\n)", "Bagging": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n [HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n )]\n)", - "Exponentially Weighted Average": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n [LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), KNNRegressor (\n n_neighbors=5\n window_size=100\n aggregation_method=\"mean\"\n min_distance_keep=0.\n distance_func=functools.partial(, p=2)\n ), AMRules (\n n_min=200\n delta=1e-07\n tau=0.05\n pred_type=\"adaptive\"\n pred_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n splitter=TEBSTSplitter (\n digits=1\n )\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n fading_factor=0.99\n anomaly_threshold=-0.75\n m_min=30\n ordered_rule_set=True\n min_samples_split=5\n )]\n)", + "Exponentially Weighted Average": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n [LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), KNNRegressor (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n aggregation_method=\"mean\"\n ), AMRules (\n n_min=200\n delta=1e-07\n tau=0.05\n pred_type=\"adaptive\"\n pred_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n splitter=TEBSTSplitter (\n digits=1\n )\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n fading_factor=0.99\n anomaly_threshold=-0.75\n m_min=30\n ordered_rule_set=True\n min_samples_split=5\n )]\n)", "River MLP": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n MLPRegressor (\n hidden_dims=(5,)\n activations=(, , )\n loss=Squared ()\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.001\n )\n )\n seed=42\n )\n)", "[baseline] Mean predictor": "StatisticRegressor (\n statistic=Mean ()\n)" } } -} +} \ No newline at end of file diff --git a/benchmarks/model_zoo/__init__.py b/benchmarks/model_adapters/__init__.py similarity index 100% rename from benchmarks/model_zoo/__init__.py rename to benchmarks/model_adapters/__init__.py diff --git a/benchmarks/model_zoo/vw.py b/benchmarks/model_adapters/vw.py similarity index 95% rename from benchmarks/model_zoo/vw.py rename to benchmarks/model_adapters/vw.py index c09ae0e2e2..03807ba2a5 100644 --- a/benchmarks/model_zoo/vw.py +++ b/benchmarks/model_adapters/vw.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from vowpalwabbit import pyvw from river import base @@ -13,7 +15,6 @@ def _format_x(self, x): class VW2RiverClassifier(VW2RiverBase, base.Classifier): def learn_one(self, x, y): - # Convert {False, True} to {-1, 1} y = int(y) y_vw = 2 * y - 1 diff --git a/benchmarks/model_zoo/torch.py b/benchmarks/model_zoo/torch.py deleted file mode 100644 index dfc89771d0..0000000000 --- a/benchmarks/model_zoo/torch.py +++ /dev/null @@ -1,91 +0,0 @@ -import torch - - -class TorchMLPClassifier(torch.nn.Module): - def __init__(self, n_features: int, hidden_size: int = 5): - super().__init__() - self.linear1 = torch.nn.Linear(n_features, hidden_size) - self.nonlin = torch.nn.ReLU() - self.linear2 = torch.nn.Linear(hidden_size, 2) - self.softmax = torch.nn.Softmax(dim=-1) - - def forward(self, x): - x = self.nonlin(self.linear1(x)) - x = self.nonlin(self.linear2(x)) - x = self.softmax(x) - return x - - -class TorchMLPRegressor(torch.nn.Module): - def __init__(self, n_features: int, hidden_size: int = 5): - super().__init__() - self.linear1 = torch.nn.Linear(n_features, hidden_size) - self.nonlin = torch.nn.ReLU() - self.linear2 = torch.nn.Linear(hidden_size, 1) - - def forward(self, x): - x = self.nonlin(self.linear1(x)) - x = self.nonlin(self.linear2(x)) - return x - - -class TorchLogisticRegression(torch.nn.Module): - def __init__(self, n_features: int, n_classes: int = 2): - super().__init__() - self.linear = torch.nn.Linear(n_features, n_classes) - self.softmax = torch.nn.Softmax(dim=-1) - - def forward(self, X): - X = self.linear(X) - return self.softmax(X) - - -class TorchLinearRegression(torch.nn.Module): - def __init__(self, n_features: int): - super().__init__() - self.linear = torch.nn.Linear(n_features, 1) - - def forward(self, X): - return self.linear(X) - - -class TorchLSTMClassifier(torch.nn.Module): - def __init__(self, n_features, num_layers=1, hidden_size=1): - super().__init__() - self.n_features = n_features - self.hidden_size = hidden_size - self.num_layers = num_layers - self.lstm = torch.nn.LSTM( - input_size=n_features, - num_layers=num_layers, - hidden_size=hidden_size, - batch_first=False, - bias=True, - ) - self.fc = torch.nn.Linear(hidden_size, 1) - self.softmax = torch.nn.Softmax(dim=-1) - - def forward(self, X, **kwargs): - out, (hn, cn) = self.lstm(X) - X = self.fc(out[-1, :]) - return self.softmax(X) - - -class TorchLSTMRegressor(torch.nn.Module): - def __init__(self, n_features, num_layers=1, hidden_size=1): - super().__init__() - self.n_features = n_features - self.hidden_size = hidden_size - self.num_layers = num_layers - self.lstm = torch.nn.LSTM( - input_size=n_features, - num_layers=num_layers, - hidden_size=hidden_size, - batch_first=False, - bias=True, - ) - self.fc = torch.nn.Linear(hidden_size, 1) - - def forward(self, X, **kwargs): - out, (hn, cn) = self.lstm(X) - return self.fc(out[-1, :]) diff --git a/benchmarks/multiclass_classification.csv b/benchmarks/multiclass_classification.csv index 852451616e..9394fc8dfd 100644 --- a/benchmarks/multiclass_classification.csv +++ b/benchmarks/multiclass_classification.csv @@ -1,1951 +1,2129 @@ step,track,model,dataset,Accuracy,MicroF1,MacroF1,Memory in Mb,Time in s -46,Multiclass classification,Naive Bayes,ImageSegments,0.4666666666666667,0.4666666666666667,0.4009102009102009,0.3899507522583008,0.163216 -92,Multiclass classification,Naive Bayes,ImageSegments,0.5604395604395604,0.5604395604395604,0.5279334700387331,0.3899507522583008,0.349738 -138,Multiclass classification,Naive Bayes,ImageSegments,0.5474452554744526,0.5474452554744526,0.5191892873237388,0.38997745513916016,0.5584899999999999 -184,Multiclass classification,Naive Bayes,ImageSegments,0.5573770491803278,0.5573770491803278,0.5225713529323662,0.3899507522583008,0.789485 -230,Multiclass classification,Naive Bayes,ImageSegments,0.5545851528384279,0.5545851528384279,0.5217226223148511,0.38997745513916016,1.042858 -276,Multiclass classification,Naive Bayes,ImageSegments,0.56,0.56,0.5450388711329708,0.38997745513916016,1.324703 -322,Multiclass classification,Naive Bayes,ImageSegments,0.5825545171339563,0.5825545171339563,0.5566705826058684,0.39000415802001953,1.637036 -368,Multiclass classification,Naive Bayes,ImageSegments,0.5940054495912807,0.5940054495912807,0.5613773296963412,0.39000415802001953,1.979491 -414,Multiclass classification,Naive Bayes,ImageSegments,0.5980629539951574,0.5980629539951574,0.5624927052752284,0.39000415802001953,2.352111 -460,Multiclass classification,Naive Bayes,ImageSegments,0.599128540305011,0.599128540305011,0.5669821167583783,0.38997745513916016,2.754918 -506,Multiclass classification,Naive Bayes,ImageSegments,0.6099009900990099,0.6099009900990099,0.5922286190986811,0.39000415802001953,3.188186 -552,Multiclass classification,Naive Bayes,ImageSegments,0.6116152450090744,0.6116152450090744,0.5983340184133136,0.3899507522583008,3.651555 -598,Multiclass classification,Naive Bayes,ImageSegments,0.6180904522613065,0.6180904522613065,0.611527101723203,0.38997745513916016,4.145135 -644,Multiclass classification,Naive Bayes,ImageSegments,0.6158631415241057,0.6158631415241057,0.6113311881078581,0.38997745513916016,4.668896 -690,Multiclass classification,Naive Bayes,ImageSegments,0.6182873730043541,0.6182873730043541,0.615018998714676,0.38997745513916016,5.223075 -736,Multiclass classification,Naive Bayes,ImageSegments,0.617687074829932,0.617687074829932,0.6157912419016742,0.38997745513916016,5.807397 -782,Multiclass classification,Naive Bayes,ImageSegments,0.6274007682458387,0.6274007682458387,0.6216325704223051,0.38997745513916016,6.422078 -828,Multiclass classification,Naive Bayes,ImageSegments,0.6324062877871826,0.6324062877871826,0.6280704917469789,0.38997745513916016,7.066915 -874,Multiclass classification,Naive Bayes,ImageSegments,0.6426116838487973,0.6426116838487973,0.6349558095046656,0.38997745513916016,7.742184 -920,Multiclass classification,Naive Bayes,ImageSegments,0.6485310119695321,0.6485310119695321,0.6384515982514894,0.38997745513916016,8.447577 -966,Multiclass classification,Naive Bayes,ImageSegments,0.6507772020725389,0.6507772020725389,0.6399118827528387,0.38997745513916016,9.183146 -1012,Multiclass classification,Naive Bayes,ImageSegments,0.6508407517309595,0.6508407517309595,0.6387857120889422,0.38997745513916016,9.95137 -1058,Multiclass classification,Naive Bayes,ImageSegments,0.6537369914853358,0.6537369914853358,0.6398811322847952,0.38997745513916016,10.747402000000001 -1104,Multiclass classification,Naive Bayes,ImageSegments,0.658204895738894,0.658204895738894,0.6463297068165035,0.38997745513916016,11.559914000000001 -1150,Multiclass classification,Naive Bayes,ImageSegments,0.6640557006092254,0.6640557006092254,0.6508930463144657,0.39000415802001953,12.388643000000002 -1196,Multiclass classification,Naive Bayes,ImageSegments,0.6702928870292887,0.6702928870292887,0.6599370641329335,0.39000415802001953,13.233598000000002 -1242,Multiclass classification,Naive Bayes,ImageSegments,0.6736502820306205,0.6736502820306205,0.669511465798708,0.39000415802001953,14.094776000000003 -1288,Multiclass classification,Naive Bayes,ImageSegments,0.6822066822066822,0.6822066822066822,0.6790074545382362,0.39000415802001953,14.972203000000004 -1334,Multiclass classification,Naive Bayes,ImageSegments,0.6841710427606902,0.6841710427606902,0.6834974476087325,0.39000415802001953,15.866030000000004 -1380,Multiclass classification,Naive Bayes,ImageSegments,0.6874546773023931,0.6874546773023931,0.6876766922721351,0.39000415802001953,16.775981000000005 -1426,Multiclass classification,Naive Bayes,ImageSegments,0.6919298245614035,0.6919298245614035,0.6930786661709784,0.39000415802001953,17.702176000000005 -1472,Multiclass classification,Naive Bayes,ImageSegments,0.698844323589395,0.698844323589395,0.6985606658027722,0.38997745513916016,18.644575000000003 -1518,Multiclass classification,Naive Bayes,ImageSegments,0.7027027027027027,0.7027027027027027,0.7017787722939461,0.39000415802001953,19.603248000000004 -1564,Multiclass classification,Naive Bayes,ImageSegments,0.7056941778630839,0.7056941778630839,0.7062915374924865,0.38997745513916016,20.578282000000005 -1610,Multiclass classification,Naive Bayes,ImageSegments,0.7078931013051585,0.7078931013051585,0.7081385387673029,0.38997745513916016,21.573844000000005 -1656,Multiclass classification,Naive Bayes,ImageSegments,0.7093655589123867,0.7093655589123867,0.7109488618373111,0.3899507522583008,22.586424000000004 -1702,Multiclass classification,Naive Bayes,ImageSegments,0.7101704879482658,0.7101704879482658,0.7132092257742534,0.38997745513916016,23.615335000000005 -1748,Multiclass classification,Naive Bayes,ImageSegments,0.7143674871207785,0.7143674871207784,0.7178399485500211,0.3899507522583008,24.660526000000004 -1794,Multiclass classification,Naive Bayes,ImageSegments,0.7172336865588399,0.7172336865588399,0.7191260584555578,0.38997745513916016,25.721983000000005 -1840,Multiclass classification,Naive Bayes,ImageSegments,0.7199564980967917,0.7199564980967917,0.7217017555070445,0.39000415802001953,26.799680000000006 -1886,Multiclass classification,Naive Bayes,ImageSegments,0.7204244031830239,0.7204244031830238,0.7234495525792994,0.39000415802001953,27.893629000000004 -1932,Multiclass classification,Naive Bayes,ImageSegments,0.7219057483169342,0.7219057483169342,0.723848351214801,0.39000415802001953,29.003837000000004 -1978,Multiclass classification,Naive Bayes,ImageSegments,0.723823975720789,0.723823975720789,0.725139923863974,0.39000415802001953,30.130512000000003 -2024,Multiclass classification,Naive Bayes,ImageSegments,0.726643598615917,0.726643598615917,0.7268553573885639,0.39000415802001953,31.273399000000005 -2070,Multiclass classification,Naive Bayes,ImageSegments,0.7269212179797003,0.7269212179797003,0.7276782991451582,0.39000415802001953,32.432577 -2116,Multiclass classification,Naive Bayes,ImageSegments,0.7286052009456265,0.7286052009456266,0.7283656039279266,0.39000415802001953,33.608017000000004 -2162,Multiclass classification,Naive Bayes,ImageSegments,0.7306802406293382,0.7306802406293383,0.7303992643507475,0.39000415802001953,34.7997 -2208,Multiclass classification,Naive Bayes,ImageSegments,0.733574988672406,0.733574988672406,0.7322842940126589,0.39000415802001953,36.007612 -2254,Multiclass classification,Naive Bayes,ImageSegments,0.7314691522414558,0.7314691522414558,0.7300322879925133,0.39000415802001953,37.231763 -2300,Multiclass classification,Naive Bayes,ImageSegments,0.7316224445411048,0.7316224445411048,0.7300416811383057,0.39000415802001953,38.472431 -1056,Multiclass classification,Naive Bayes,Insects,0.623696682464455,0.623696682464455,0.5870724729616661,0.6116933822631836,0.909568 -2112,Multiclass classification,Naive Bayes,Insects,0.6148744670772146,0.6148744670772146,0.5800776869595597,0.6116933822631836,2.67356 -3168,Multiclass classification,Naive Bayes,Insects,0.6065677297126618,0.6065677297126618,0.5714781230184183,0.6116933822631836,5.143102000000001 -4224,Multiclass classification,Naive Bayes,Insects,0.6043097324177126,0.6043097324177126,0.5697541737710122,0.6116933822631836,7.993857 -5280,Multiclass classification,Naive Bayes,Insects,0.6088274294373934,0.6088274294373934,0.5727560614138387,0.6116933822631836,11.225513 -6336,Multiclass classification,Naive Bayes,Insects,0.6023677979479084,0.6023677979479084,0.5679597008529512,0.6116933822631836,14.839337 -7392,Multiclass classification,Naive Bayes,Insects,0.5995129211202814,0.5995129211202814,0.5652603100832261,0.6116933822631836,18.839998 -8448,Multiclass classification,Naive Bayes,Insects,0.6019888717888008,0.6019888717888008,0.5673514925692325,0.6116933822631836,23.223853000000002 -9504,Multiclass classification,Naive Bayes,Insects,0.5993896664211301,0.5993896664211301,0.5644951651039589,0.6116933822631836,27.990643000000002 -10560,Multiclass classification,Naive Bayes,Insects,0.5994885879344635,0.5994885879344635,0.5645655385998631,0.6116933822631836,33.140509 -11616,Multiclass classification,Naive Bayes,Insects,0.5972449418854929,0.5972449418854929,0.5631227877868952,0.6116933822631836,38.672833000000004 -12672,Multiclass classification,Naive Bayes,Insects,0.6001894088864336,0.6001894088864336,0.5684733590606373,0.6116933822631836,44.58831000000001 -13728,Multiclass classification,Naive Bayes,Insects,0.6120783856632913,0.6120783856632913,0.5935173038317552,0.6116933822631836,50.89270200000001 -14784,Multiclass classification,Naive Bayes,Insects,0.6024487587093282,0.6024487587093282,0.5841270876002981,0.6116933822631836,57.581734000000004 -15840,Multiclass classification,Naive Bayes,Insects,0.5676494728202538,0.5676494728202538,0.5507155080701159,0.6116933822631836,64.65553700000001 -16896,Multiclass classification,Naive Bayes,Insects,0.5418762947617638,0.5418762947617638,0.5256197352354143,0.6116933822631836,72.114698 -17952,Multiclass classification,Naive Bayes,Insects,0.5232020500250683,0.5232020500250683,0.5066898143269706,0.6116933822631836,79.958388 -19008,Multiclass classification,Naive Bayes,Insects,0.5118640500868101,0.5118640500868101,0.4926543583964285,0.6116933822631836,88.190503 -20064,Multiclass classification,Naive Bayes,Insects,0.5103922643672432,0.5103922643672432,0.4900586962359796,0.6116933822631836,96.808684 -21120,Multiclass classification,Naive Bayes,Insects,0.5115772527108291,0.5115772527108291,0.4910837640903744,0.6116933822631836,105.81178 -22176,Multiclass classification,Naive Bayes,Insects,0.5140022547914318,0.5140022547914318,0.49325418882319566,0.6116933822631836,115.205863 -23232,Multiclass classification,Naive Bayes,Insects,0.5154319659076234,0.5154319659076234,0.4943013417599926,0.6116933822631836,124.990845 -24288,Multiclass classification,Naive Bayes,Insects,0.5184254951208466,0.5184254951208466,0.4965832238311332,0.6116933822631836,135.166218 -25344,Multiclass classification,Naive Bayes,Insects,0.5225111470623052,0.5225111470623052,0.499893079239698,0.6116933822631836,145.739141 -26400,Multiclass classification,Naive Bayes,Insects,0.5257396113489148,0.5257396113489148,0.5022487669255871,0.6116933822631836,156.702601 -27456,Multiclass classification,Naive Bayes,Insects,0.5301402294663996,0.5301402294663996,0.5051550433324518,0.6116933822631836,168.057909 -28512,Multiclass classification,Naive Bayes,Insects,0.5277261407877661,0.5277261407877661,0.5036945145235057,0.6116933822631836,179.80420999999998 -29568,Multiclass classification,Naive Bayes,Insects,0.5204450908107011,0.5204450908107011,0.49890087123127674,0.6116933822631836,191.94450099999997 -30624,Multiclass classification,Naive Bayes,Insects,0.5147111648107632,0.5147111648107632,0.49582684007363204,0.6116933822631836,204.47849899999997 -31680,Multiclass classification,Naive Bayes,Insects,0.5105590454244137,0.5105590454244137,0.4941101813344875,0.6116933822631836,217.40209199999998 -32736,Multiclass classification,Naive Bayes,Insects,0.5075607148312204,0.5075607148312204,0.4931947798921405,0.6116933822631836,230.71620099999998 -33792,Multiclass classification,Naive Bayes,Insects,0.5044538486579266,0.5044538486579266,0.4905626123916189,0.6116933822631836,244.42088399999997 -34848,Multiclass classification,Naive Bayes,Insects,0.5020231296811777,0.5020231296811777,0.48787984248812405,0.6116933822631836,258.51509 -35904,Multiclass classification,Naive Bayes,Insects,0.49987466228448874,0.49987466228448874,0.48534350611524757,0.6116933822631836,273.003699 -36960,Multiclass classification,Naive Bayes,Insects,0.49679374441949187,0.49679374441949187,0.4819418474093529,0.6116933822631836,287.88352199999997 -38016,Multiclass classification,Naive Bayes,Insects,0.49559384453505195,0.49559384453505195,0.4801892436835747,0.6116933822631836,303.152298 -39072,Multiclass classification,Naive Bayes,Insects,0.49402370044278365,0.49402370044278365,0.47838078382052607,0.6116933822631836,318.80769699999996 -40128,Multiclass classification,Naive Bayes,Insects,0.493508111745209,0.493508111745209,0.47852138016706713,0.6116933822631836,334.85222999999996 -41184,Multiclass classification,Naive Bayes,Insects,0.49369885632421145,0.49369885632421145,0.47942014994272747,0.6116933822631836,351.28664399999997 -42240,Multiclass classification,Naive Bayes,Insects,0.4938800634484718,0.4938800634484718,0.48023774975329364,0.6116933822631836,368.10561099999995 -43296,Multiclass classification,Naive Bayes,Insects,0.49437579397159026,0.49437579397159026,0.4812132921167227,0.6116933822631836,385.31069299999996 -44352,Multiclass classification,Naive Bayes,Insects,0.49403621113390905,0.49403621113390905,0.48123889196184183,0.6116933822631836,402.906414 -45408,Multiclass classification,Naive Bayes,Insects,0.4944832294580131,0.4944832294580131,0.4818441874360224,0.6116933822631836,420.888505 -46464,Multiclass classification,Naive Bayes,Insects,0.4945225232981082,0.4945225232981082,0.4820791268335544,0.6116933822631836,439.259743 -47520,Multiclass classification,Naive Bayes,Insects,0.4956333256171216,0.4956333256171216,0.48331686360214987,0.6116933822631836,458.01736800000003 -48576,Multiclass classification,Naive Bayes,Insects,0.4970869788986104,0.4970869788986104,0.48467037716343636,0.6116933822631836,477.16088800000006 -49632,Multiclass classification,Naive Bayes,Insects,0.4987608551107171,0.4987608551107171,0.4862426724473749,0.6116933822631836,496.69293600000003 -50688,Multiclass classification,Naive Bayes,Insects,0.5009568528419516,0.5009568528419516,0.4881725476999718,0.6116933822631836,516.6094800000001 -51744,Multiclass classification,Naive Bayes,Insects,0.5034497419940862,0.5034497419940862,0.4903712806540024,0.6116933822631836,536.9146260000001 -52800,Multiclass classification,Naive Bayes,Insects,0.5068467205818292,0.5068467205818292,0.4930025316136313,0.6116933822631836,557.6057650000001 -408,Multiclass classification,Naive Bayes,Keystroke,0.9852579852579852,0.9852579852579852,0.6962686567164179,0.19356441497802734,0.122414 -816,Multiclass classification,Naive Bayes,Keystroke,0.947239263803681,0.947239263803681,0.7418606503288051,0.28890228271484375,0.375804 -1224,Multiclass classification,Naive Bayes,Keystroke,0.884709730171709,0.884709730171709,0.8705899666065842,0.38424015045166016,0.764348 -1632,Multiclass classification,Naive Bayes,Keystroke,0.8933169834457388,0.8933169834457388,0.8791291775937072,0.47957801818847656,1.3033160000000001 -2040,Multiclass classification,Naive Bayes,Keystroke,0.8921039725355566,0.8921039725355566,0.8831785360852743,0.575160026550293,2.01214 -2448,Multiclass classification,Naive Bayes,Keystroke,0.851655087862689,0.851655087862689,0.858198428951664,0.6704978942871094,2.906585 -2856,Multiclass classification,Naive Bayes,Keystroke,0.8598949211908932,0.8598949211908932,0.8469962214365345,0.7658357620239258,3.994802 -3264,Multiclass classification,Naive Bayes,Keystroke,0.8513637756665645,0.8513637756665645,0.8281280134770848,0.8611736297607422,5.296884 -3672,Multiclass classification,Naive Bayes,Keystroke,0.8422773086352493,0.8422773086352493,0.8409307955747314,0.9565114974975586,6.831079000000001 -4080,Multiclass classification,Naive Bayes,Keystroke,0.8367246874233881,0.8367246874233881,0.8249418657104467,1.0523834228515625,8.617788000000001 -4488,Multiclass classification,Naive Bayes,Keystroke,0.8203699576554491,0.8203699576554491,0.8300896799820437,1.147721290588379,10.679552000000001 -4896,Multiclass classification,Naive Bayes,Keystroke,0.8192032686414709,0.8192032686414709,0.8269731591910484,1.2430591583251953,13.032163 -5304,Multiclass classification,Naive Bayes,Keystroke,0.8172732415613804,0.8172732415613804,0.8027823390848743,1.3383970260620117,15.695238 -5712,Multiclass classification,Naive Bayes,Keystroke,0.7961828051129399,0.7961828051129399,0.8002006091139847,1.4337348937988281,18.689224 -6120,Multiclass classification,Naive Bayes,Keystroke,0.793920575257395,0.793920575257395,0.7746960355921346,1.5290727615356445,22.034543 -6528,Multiclass classification,Naive Bayes,Keystroke,0.7688064960931515,0.7688064960931515,0.7622487598340326,1.624410629272461,25.755146 -6936,Multiclass classification,Naive Bayes,Keystroke,0.7568853640951694,0.7568853640951694,0.757813781660983,1.7197484970092773,29.876127 -7344,Multiclass classification,Naive Bayes,Keystroke,0.7669889690862045,0.7669889690862046,0.7643943615019535,1.8150863647460938,34.413227 -7752,Multiclass classification,Naive Bayes,Keystroke,0.7676428847890595,0.7676428847890595,0.7655695901071293,1.9104242324829102,39.374485 -8160,Multiclass classification,Naive Bayes,Keystroke,0.7714180659394534,0.7714180659394533,0.7672011803374248,2.0057621002197266,44.773425 -8568,Multiclass classification,Naive Bayes,Keystroke,0.7702813120112058,0.7702813120112058,0.7699263138193526,2.1021223068237305,50.625164000000005 -8976,Multiclass classification,Naive Bayes,Keystroke,0.7680222841225627,0.7680222841225627,0.7682287234686137,2.197460174560547,56.940867000000004 -9384,Multiclass classification,Naive Bayes,Keystroke,0.7659597143770649,0.7659597143770649,0.7643546547243015,2.2927980422973633,63.725868000000006 -9792,Multiclass classification,Naive Bayes,Keystroke,0.7586559084873864,0.7586559084873864,0.7552148692020618,2.3881359100341797,70.991963 -10200,Multiclass classification,Naive Bayes,Keystroke,0.7505637807628199,0.7505637807628199,0.7430512224080149,2.483473777770996,78.748505 -10608,Multiclass classification,Naive Bayes,Keystroke,0.7290468558499105,0.7290468558499106,0.715756093271779,2.5788116455078125,87.01168299999999 -11016,Multiclass classification,Naive Bayes,Keystroke,0.7217430776214253,0.7217430776214253,0.7173640789896896,2.674149513244629,95.78731699999999 -11424,Multiclass classification,Naive Bayes,Keystroke,0.7151361288628206,0.7151361288628206,0.7011862635194492,2.7694873809814453,105.08400199999998 -11832,Multiclass classification,Naive Bayes,Keystroke,0.705603921900093,0.705603921900093,0.6976881379682605,2.8648252487182617,114.91729899999999 -12240,Multiclass classification,Naive Bayes,Keystroke,0.7094533867146009,0.7094533867146009,0.705840538940343,2.960163116455078,125.31674099999998 -12648,Multiclass classification,Naive Bayes,Keystroke,0.7053846762077963,0.7053846762077963,0.6965736948063981,3.0555009841918945,136.28361299999997 -13056,Multiclass classification,Naive Bayes,Keystroke,0.6927613941018766,0.6927613941018766,0.6842255816736497,3.150838851928711,147.832836 -13464,Multiclass classification,Naive Bayes,Keystroke,0.6890737577063062,0.6890737577063062,0.6845669389392289,3.2461767196655273,159.980064 -13872,Multiclass classification,Naive Bayes,Keystroke,0.6873332852714296,0.6873332852714296,0.6839054551822702,3.3415145874023438,172.74228 -14280,Multiclass classification,Naive Bayes,Keystroke,0.682960991666083,0.682960991666083,0.6781566371919946,3.43685245513916,186.135321 -14688,Multiclass classification,Naive Bayes,Keystroke,0.686185061619119,0.686185061619119,0.6843713776162116,3.5321903228759766,200.177651 -15096,Multiclass classification,Naive Bayes,Keystroke,0.6928784365684001,0.6928784365684001,0.6911392400672977,3.627528190612793,214.888654 -15504,Multiclass classification,Naive Bayes,Keystroke,0.6913500612784622,0.6913500612784622,0.687359772989117,3.7228660583496094,230.279565 -15912,Multiclass classification,Naive Bayes,Keystroke,0.6819810194205267,0.6819810194205267,0.6749159449359359,3.818203926086426,246.36567399999998 -16320,Multiclass classification,Naive Bayes,Keystroke,0.6726515105092223,0.6726515105092223,0.6670192172011686,3.913541793823242,263.163212 -16728,Multiclass classification,Naive Bayes,Keystroke,0.6695163508100676,0.6695163508100676,0.6664051037977978,4.008879661560059,280.68755699999997 -17136,Multiclass classification,Naive Bayes,Keystroke,0.6650131310183834,0.6650131310183834,0.6608988619616459,4.1063079833984375,298.952273 -17544,Multiclass classification,Naive Bayes,Keystroke,0.6568431853160804,0.6568431853160804,0.653138289771919,4.201645851135254,317.97399 -17952,Multiclass classification,Naive Bayes,Keystroke,0.6556180714166342,0.6556180714166342,0.6538448358590967,4.29698371887207,337.769402 -18360,Multiclass classification,Naive Bayes,Keystroke,0.6614194672912468,0.6614194672912468,0.6603186829199905,4.392321586608887,358.361854 -18768,Multiclass classification,Naive Bayes,Keystroke,0.6669686151222891,0.6669686151222891,0.666229361655457,4.487659454345703,379.763277 -19176,Multiclass classification,Naive Bayes,Keystroke,0.6579921773142112,0.6579921773142112,0.6554177118629491,4.5829973220825195,401.98609400000004 -19584,Multiclass classification,Naive Bayes,Keystroke,0.6622580809886126,0.6622580809886126,0.6609360990360077,4.678335189819336,425.04707400000007 -19992,Multiclass classification,Naive Bayes,Keystroke,0.6562453103896754,0.6562453103896754,0.6545704957554573,4.773673057556152,448.96292700000004 -20400,Multiclass classification,Naive Bayes,Keystroke,0.6525319868621011,0.6525319868621011,0.6515767870317881,4.869010925292969,473.747426 -46,Multiclass classification,Hoeffding Tree,ImageSegments,0.35555555555555557,0.35555555555555557,0.25379424497071557,0.41910839080810547,0.164966 -92,Multiclass classification,Hoeffding Tree,ImageSegments,0.4945054945054945,0.4945054945054945,0.5043329927491419,0.41910457611083984,0.35564300000000004 -138,Multiclass classification,Hoeffding Tree,ImageSegments,0.5328467153284672,0.5328467153284672,0.5564033878668025,0.41919994354248047,0.571134 -184,Multiclass classification,Hoeffding Tree,ImageSegments,0.6010928961748634,0.6010928961748634,0.6227664965396451,0.41919994354248047,0.8113900000000001 -230,Multiclass classification,Hoeffding Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6539827168809461,0.4192228317260742,1.079154 -276,Multiclass classification,Hoeffding Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.6671561759164943,0.41927242279052734,1.371943 -322,Multiclass classification,Hoeffding Tree,ImageSegments,0.67601246105919,0.67601246105919,0.6756614325426025,0.41927242279052734,1.689575 -368,Multiclass classification,Hoeffding Tree,ImageSegments,0.7029972752043597,0.7029972752043597,0.6993447851636565,0.41924571990966797,2.032058 -414,Multiclass classification,Hoeffding Tree,ImageSegments,0.7142857142857143,0.7142857142857143,0.7108606838045498,0.41916561126708984,2.4019660000000003 -460,Multiclass classification,Hoeffding Tree,ImageSegments,0.7145969498910676,0.7145969498910676,0.7090365931960759,0.41924190521240234,2.796914 -506,Multiclass classification,Hoeffding Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7126631585949763,0.41924190521240234,3.216844 -552,Multiclass classification,Hoeffding Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7157730164623107,0.41913509368896484,3.6616 -598,Multiclass classification,Hoeffding Tree,ImageSegments,0.7286432160804021,0.7286432160804021,0.7216745323124732,0.4191579818725586,4.131175 -644,Multiclass classification,Hoeffding Tree,ImageSegments,0.7278382581648523,0.7278382581648523,0.72291051830875,0.4191312789916992,4.6280079999999995 -690,Multiclass classification,Hoeffding Tree,ImageSegments,0.7314949201741655,0.7314949201741654,0.7263583447448078,0.4191312789916992,5.149870999999999 -736,Multiclass classification,Hoeffding Tree,ImageSegments,0.7333333333333333,0.7333333333333333,0.729431071218305,0.4191579818725586,5.696603 -782,Multiclass classification,Hoeffding Tree,ImageSegments,0.7387964148527529,0.7387964148527529,0.7349287389986899,0.4191579818725586,6.268242 -828,Multiclass classification,Hoeffding Tree,ImageSegments,0.7376058041112454,0.7376058041112454,0.7356226390109742,0.4191579818725586,6.867156 -874,Multiclass classification,Hoeffding Tree,ImageSegments,0.7445589919816724,0.7445589919816724,0.7409366047432264,0.4191579818725586,7.49107 -920,Multiclass classification,Hoeffding Tree,ImageSegments,0.7453754080522307,0.7453754080522307,0.7408438328939173,0.4191312789916992,8.139827 -966,Multiclass classification,Hoeffding Tree,ImageSegments,0.7471502590673575,0.7471502590673575,0.7416651838589269,0.4191312789916992,8.813418 -1012,Multiclass classification,Hoeffding Tree,ImageSegments,0.7467853610286844,0.7467853610286844,0.7416356251822,0.4191312789916992,9.514287 -1058,Multiclass classification,Hoeffding Tree,ImageSegments,0.7492904446546831,0.7492904446546831,0.7430778844390782,0.4191312789916992,10.240045 -1104,Multiclass classification,Hoeffding Tree,ImageSegments,0.7515865820489573,0.7515865820489573,0.7451256886686588,0.41918087005615234,10.990683 -1150,Multiclass classification,Hoeffding Tree,ImageSegments,0.7536988685813751,0.7536988685813751,0.7468312166689606,0.41918087005615234,11.766057 -1196,Multiclass classification,Hoeffding Tree,ImageSegments,0.7564853556485356,0.7564853556485356,0.7503479321738039,0.41918087005615234,12.566171 -1242,Multiclass classification,Hoeffding Tree,ImageSegments,0.7566478646253022,0.7566478646253022,0.7509717522131719,0.41918087005615234,13.393734 -1288,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614607614607615,0.7614607614607615,0.7547643483779538,0.41918087005615234,14.246394 -1334,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614403600900225,0.7614403600900225,0.7551060921605869,0.41918087005615234,15.123846 -1380,Multiclass classification,Hoeffding Tree,ImageSegments,0.7621464829586657,0.7621464829586658,0.7562209880685911,0.41918087005615234,16.026049 -1426,Multiclass classification,Hoeffding Tree,ImageSegments,0.7642105263157895,0.7642105263157895,0.7575332274919562,0.41918087005615234,16.955566 -1472,Multiclass classification,Hoeffding Tree,ImageSegments,0.7688647178789939,0.768864717878994,0.760438686053582,0.41918087005615234,17.910383 -1518,Multiclass classification,Hoeffding Tree,ImageSegments,0.7705998681608438,0.7705998681608438,0.7612069012840875,0.41918087005615234,18.890183 -1564,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709532949456174,0.7709532949456174,0.7622701654854867,0.41918087005615234,19.895086 -1610,Multiclass classification,Hoeffding Tree,ImageSegments,0.7712865133623369,0.771286513362337,0.7617247271717752,0.4192037582397461,20.927569 -1656,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709969788519637,0.7709969788519637,0.7615629120572474,0.4192037582397461,21.985291999999998 -1702,Multiclass classification,Hoeffding Tree,ImageSegments,0.770135214579659,0.770135214579659,0.7627316365695141,0.4192037582397461,23.068120999999998 -1748,Multiclass classification,Hoeffding Tree,ImageSegments,0.7727532913566113,0.7727532913566113,0.7649467707214076,0.4192037582397461,24.176004999999996 -1794,Multiclass classification,Hoeffding Tree,ImageSegments,0.7741215839375348,0.7741215839375348,0.7649332326562147,0.4191770553588867,25.309107999999995 -1840,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754214246873301,0.7754214246873301,0.7664700790631906,0.4191770553588867,26.470048999999996 -1886,Multiclass classification,Hoeffding Tree,ImageSegments,0.7740053050397878,0.7740053050397878,0.7655121135276625,0.4191770553588867,27.656614999999995 -1932,Multiclass classification,Hoeffding Tree,ImageSegments,0.7742102537545313,0.7742102537545313,0.7648034036287765,0.4191770553588867,28.868293999999995 -1978,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754172989377845,0.7754172989377845,0.7656013068970458,0.4191770553588867,30.105037999999997 -2024,Multiclass classification,Hoeffding Tree,ImageSegments,0.7770637666831438,0.7770637666831438,0.7660878232247856,0.4191770553588867,31.369529999999997 -2070,Multiclass classification,Hoeffding Tree,ImageSegments,0.7762203963267279,0.7762203963267279,0.7654829214385931,0.4191770553588867,32.658967 -2116,Multiclass classification,Hoeffding Tree,ImageSegments,0.7768321513002364,0.7768321513002364,0.7653071619305024,0.4191770553588867,33.973288999999994 -2162,Multiclass classification,Hoeffding Tree,ImageSegments,0.7778806108283203,0.7778806108283203,0.7659351904174981,0.4191770553588867,35.312507 -2208,Multiclass classification,Hoeffding Tree,ImageSegments,0.7797915722700498,0.7797915722700498,0.7668192864082087,0.4191770553588867,36.679283999999996 -2254,Multiclass classification,Hoeffding Tree,ImageSegments,0.7767421216156236,0.7767421216156236,0.7637794374955548,0.4191770553588867,38.070969 -2300,Multiclass classification,Hoeffding Tree,ImageSegments,0.7759895606785558,0.7759895606785558,0.763026662835187,0.4191770553588867,39.487871999999996 -1056,Multiclass classification,Hoeffding Tree,Insects,0.6218009478672986,0.6218009478672986,0.585266310719421,0.6617898941040039,1.016292 -2112,Multiclass classification,Hoeffding Tree,Insects,0.6153481762198011,0.6153481762198011,0.5806436317780949,0.6617898941040039,2.820671 -3168,Multiclass classification,Hoeffding Tree,Insects,0.6071992421850332,0.6071992421850332,0.572248584718361,0.6617898941040039,5.468586999999999 -4224,Multiclass classification,Hoeffding Tree,Insects,0.6043097324177126,0.6043097324177126,0.5697573109597247,0.6617898941040039,8.970813999999999 -5280,Multiclass classification,Hoeffding Tree,Insects,0.6088274294373934,0.6088274294373934,0.5727379077413696,0.6617898941040039,13.304298999999999 -6336,Multiclass classification,Hoeffding Tree,Insects,0.6026835043409629,0.6026835043409629,0.568251333238805,0.6617898941040039,18.451532999999998 -7392,Multiclass classification,Hoeffding Tree,Insects,0.600189419564335,0.600189419564335,0.5659762112716077,0.6617898941040039,24.373814999999997 -8448,Multiclass classification,Hoeffding Tree,Insects,0.60258079791642,0.60258079791642,0.5679781484640409,0.6617898941040039,31.061275999999996 -9504,Multiclass classification,Hoeffding Tree,Insects,0.5998105861306956,0.5998105861306956,0.5649597336877693,0.6617898941040039,38.490334999999995 -10560,Multiclass classification,Hoeffding Tree,Insects,0.5998674116867128,0.5998674116867128,0.5650173260529011,0.6617898941040039,46.63726 -11616,Multiclass classification,Hoeffding Tree,Insects,0.5974171330176495,0.5974171330176495,0.5633067089377386,0.6617898941040039,55.514266 -12672,Multiclass classification,Hoeffding Tree,Insects,0.6001894088864336,0.6001894088864336,0.5684760329567131,0.6617898941040039,65.102691 -13728,Multiclass classification,Hoeffding Tree,Insects,0.6120783856632913,0.6120783856632913,0.5935956771555828,0.6617898941040039,75.408233 -14784,Multiclass classification,Hoeffding Tree,Insects,0.6024487587093282,0.6024487587093282,0.5842148300149193,0.6617898941040039,86.426133 -15840,Multiclass classification,Hoeffding Tree,Insects,0.5677757434181451,0.5677757434181451,0.5509250187877572,0.6617898941040039,98.15845499999999 -16896,Multiclass classification,Hoeffding Tree,Insects,0.5419354838709678,0.5419354838709678,0.5257359157219257,0.6617898941040039,110.605121 -17952,Multiclass classification,Hoeffding Tree,Insects,0.5233691716338923,0.5233691716338923,0.506858183835206,0.6617898941040039,123.763106 -19008,Multiclass classification,Hoeffding Tree,Insects,0.5121271110643447,0.5121271110643447,0.49292899065094153,0.6617898941040039,137.636213 -20064,Multiclass classification,Hoeffding Tree,Insects,0.5120370831879579,0.5120370831879579,0.4920970323041603,1.317840576171875,152.19804 -21120,Multiclass classification,Hoeffding Tree,Insects,0.5173066906577016,0.5173066906577016,0.497344716983625,1.3185958862304688,167.375493 -22176,Multiclass classification,Hoeffding Tree,Insects,0.5229312288613304,0.5229312288613304,0.5026343687424488,1.3185958862304688,183.138263 -23232,Multiclass classification,Hoeffding Tree,Insects,0.5301536739701261,0.5301536739701261,0.5095132087733324,1.3185958862304688,199.493958 -24288,Multiclass classification,Hoeffding Tree,Insects,0.5351422571746202,0.5351422571746202,0.5135975374357353,1.3185958862304688,216.43581799999998 -25344,Multiclass classification,Hoeffding Tree,Insects,0.5403069881229531,0.5403069881229531,0.5180803411538233,1.3185958862304688,233.97305899999998 -26400,Multiclass classification,Hoeffding Tree,Insects,0.5441493995984696,0.5441493995984696,0.5209012984387186,1.3185958862304688,252.09929999999997 -27456,Multiclass classification,Hoeffding Tree,Insects,0.5475869604807867,0.5475869604807867,0.5230407124785976,1.3185958862304688,270.82611499999996 -28512,Multiclass classification,Hoeffding Tree,Insects,0.5442460804601733,0.5442460804601733,0.5199893698637053,1.3185958862304688,290.12573499999996 -29568,Multiclass classification,Hoeffding Tree,Insects,0.5439848479724017,0.5439848479724017,0.5225387960194382,1.3185958862304688,310.131151 -30624,Multiclass classification,Hoeffding Tree,Insects,0.5449825294713124,0.5449825294713124,0.5260472440529832,1.3185958862304688,330.869455 -31680,Multiclass classification,Hoeffding Tree,Insects,0.5469238296663405,0.5469238296663405,0.5300194392617626,1.3185958862304688,352.339648 -32736,Multiclass classification,Hoeffding Tree,Insects,0.5492286543455017,0.5492286543455017,0.5337692045397759,1.3185958862304688,374.544388 -33792,Multiclass classification,Hoeffding Tree,Insects,0.5448196265277737,0.5448196265277737,0.5298516474077152,1.3185958862304688,397.480297 -34848,Multiclass classification,Hoeffding Tree,Insects,0.539357763939507,0.539357763939507,0.5246413689313029,1.3185958862304688,421.148709 -35904,Multiclass classification,Hoeffding Tree,Insects,0.5352756037099964,0.5352756037099964,0.5204658240271912,1.3185958862304688,445.552724 -36960,Multiclass classification,Hoeffding Tree,Insects,0.5307232338537298,0.5307232338537298,0.5158458403074863,1.3185958862304688,470.685377 -38016,Multiclass classification,Hoeffding Tree,Insects,0.5287912666052874,0.5287912666052874,0.5138605376143625,1.8598642349243164,496.54465300000004 -39072,Multiclass classification,Hoeffding Tree,Insects,0.5245322617798367,0.5245322617798367,0.5100329616180462,1.9744834899902344,523.1337460000001 -40128,Multiclass classification,Hoeffding Tree,Insects,0.5244847608841927,0.5244847608841927,0.5114466799524962,1.9744834899902344,550.3794180000001 -41184,Multiclass classification,Hoeffding Tree,Insects,0.5269650098341548,0.5269650098341548,0.5145630920489553,1.9744834899902344,578.1701970000001 -42240,Multiclass classification,Hoeffding Tree,Insects,0.5290608205686688,0.5290608205686688,0.5171452370879218,1.9744834899902344,606.4941780000001 -43296,Multiclass classification,Hoeffding Tree,Insects,0.5316318281556762,0.5316318281556762,0.5200714653059242,1.9744834899902344,635.3594070000001 -44352,Multiclass classification,Hoeffding Tree,Insects,0.5332912448422809,0.5332912448422809,0.521951703681177,1.9752388000488281,664.7773900000002 -45408,Multiclass classification,Hoeffding Tree,Insects,0.5350937080185875,0.5350937080185875,0.5236272112757866,1.9752388000488281,694.7425150000001 -46464,Multiclass classification,Hoeffding Tree,Insects,0.5374168693368917,0.5374168693368917,0.5257977177437826,1.9752388000488281,725.2648820000002 -47520,Multiclass classification,Hoeffding Tree,Insects,0.5359540394368568,0.5359540394368568,0.5247049329892776,1.9752388000488281,756.3925470000001 -48576,Multiclass classification,Hoeffding Tree,Insects,0.5333196088522902,0.5333196088522902,0.5224640186909638,1.9752388000488281,788.1537450000002 -49632,Multiclass classification,Hoeffding Tree,Insects,0.5314017448771937,0.5314017448771937,0.5209076603734538,1.9752388000488281,820.5431960000002 -50688,Multiclass classification,Hoeffding Tree,Insects,0.5321877404462683,0.5321877404462683,0.5219332135179457,2.097897529602051,853.5752100000002 -51744,Multiclass classification,Hoeffding Tree,Insects,0.5376959202210927,0.5376959202210927,0.5274519689249669,2.335637092590332,887.2128290000002 -52800,Multiclass classification,Hoeffding Tree,Insects,0.5370177465482301,0.5370177465482301,0.5270712327692165,2.5391950607299805,921.3507020000002 -408,Multiclass classification,Hoeffding Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.22765445709228516,0.136786 -816,Multiclass classification,Hoeffding Tree,Keystroke,0.9423312883435583,0.9423312883435583,0.7661667470992702,0.32327842712402344,0.43594199999999994 -1224,Multiclass classification,Hoeffding Tree,Keystroke,0.8830744071954211,0.883074407195421,0.8761191747044462,0.4189023971557617,0.9269379999999999 -1632,Multiclass classification,Hoeffding Tree,Keystroke,0.8902513795217658,0.8902513795217658,0.8767853151263398,0.5150146484375,1.637883 -2040,Multiclass classification,Hoeffding Tree,Keystroke,0.8891613536047082,0.8891613536047082,0.8807858055314012,0.6221132278442383,2.570345 -2448,Multiclass classification,Hoeffding Tree,Keystroke,0.848385778504291,0.848385778504291,0.8522513926518692,0.7177371978759766,3.758757 -2856,Multiclass classification,Hoeffding Tree,Keystroke,0.8563922942206655,0.8563922942206655,0.8440193478447515,0.8133611679077148,5.245901 -3264,Multiclass classification,Hoeffding Tree,Keystroke,0.8482991112473184,0.8482991112473184,0.8269786301577753,0.9089851379394531,7.065474 -3672,Multiclass classification,Hoeffding Tree,Keystroke,0.8392808499046581,0.8392808499046581,0.8374924160046074,1.0046091079711914,9.269679 -4080,Multiclass classification,Hoeffding Tree,Keystroke,0.8323118411375338,0.8323118411375338,0.8182261307945194,1.1253337860107422,11.897269999999999 -4488,Multiclass classification,Hoeffding Tree,Keystroke,0.8159126365054602,0.8159126365054602,0.8260965842218733,1.2209577560424805,14.983422 -4896,Multiclass classification,Hoeffding Tree,Keystroke,0.8149131767109296,0.8149131767109296,0.8221314665977922,1.3165817260742188,18.566921 -5304,Multiclass classification,Hoeffding Tree,Keystroke,0.8125589289081652,0.8125589289081652,0.797613058026624,1.412205696105957,22.693048 -5712,Multiclass classification,Hoeffding Tree,Keystroke,0.7907546839432674,0.7907546839432674,0.7936708037520237,1.5078296661376953,27.396131 -6120,Multiclass classification,Hoeffding Tree,Keystroke,0.7886909625755842,0.7886909625755842,0.7694478218498494,1.6034536361694336,32.715078 -6528,Multiclass classification,Hoeffding Tree,Keystroke,0.7635973647924008,0.7635973647924008,0.75687960152136,1.6990776062011719,38.687416 -6936,Multiclass classification,Hoeffding Tree,Keystroke,0.75155010814708,0.7515501081470799,0.7521509466338958,1.7947015762329102,45.356366 -7344,Multiclass classification,Hoeffding Tree,Keystroke,0.7611330518861501,0.7611330518861501,0.7576671162861804,1.8917903900146484,52.757111 -7752,Multiclass classification,Hoeffding Tree,Keystroke,0.7617081666881693,0.7617081666881692,0.7593340838982118,1.9874143600463867,60.928470000000004 -8160,Multiclass classification,Hoeffding Tree,Keystroke,0.7655349920333374,0.7655349920333374,0.7610505848438686,2.083038330078125,69.910689 -8568,Multiclass classification,Hoeffding Tree,Keystroke,0.7644449632310026,0.7644449632310025,0.7639417799779614,2.226712226867676,79.742469 -8976,Multiclass classification,Hoeffding Tree,Keystroke,0.7624512534818941,0.7624512534818941,0.7625605608371231,2.322336196899414,90.464241 -9384,Multiclass classification,Hoeffding Tree,Keystroke,0.7605243525524885,0.7605243525524885,0.7588384348689571,2.4179601669311523,102.115634 -9792,Multiclass classification,Hoeffding Tree,Keystroke,0.753344908589521,0.753344908589521,0.7499438215834663,2.5135841369628906,114.735409 -10200,Multiclass classification,Hoeffding Tree,Keystroke,0.7450730463770958,0.7450730463770959,0.7369660419615973,2.609208106994629,128.375943 -10608,Multiclass classification,Hoeffding Tree,Keystroke,0.7240501555576506,0.7240501555576506,0.7111305646829175,2.704832077026367,143.06648900000002 -11016,Multiclass classification,Hoeffding Tree,Keystroke,0.7166591012256015,0.7166591012256015,0.7122511515574345,2.8004560470581055,158.84720500000003 -11424,Multiclass classification,Hoeffding Tree,Keystroke,0.710146196270682,0.710146196270682,0.6963016796632095,2.8960800170898438,175.75710000000004 -11832,Multiclass classification,Hoeffding Tree,Keystroke,0.7005324993660722,0.7005324993660722,0.6925666211338902,2.991703987121582,193.83910100000003 -12240,Multiclass classification,Hoeffding Tree,Keystroke,0.7043876133671052,0.7043876133671052,0.7007845610449206,3.0873279571533203,213.15240600000004 -12648,Multiclass classification,Hoeffding Tree,Keystroke,0.7004032576895707,0.7004032576895707,0.6915775762792659,3.1829519271850586,233.71252100000004 -13056,Multiclass classification,Hoeffding Tree,Keystroke,0.6877058598238223,0.6877058598238223,0.6789768292873962,3.278575897216797,255.55614900000003 -13464,Multiclass classification,Hoeffding Tree,Keystroke,0.6838743222164451,0.6838743222164451,0.6791243465680946,3.374199867248535,278.72697300000004 -13872,Multiclass classification,Hoeffding Tree,Keystroke,0.6822146925239708,0.6822146925239708,0.6786558938530484,3.4698238372802734,303.265051 -14280,Multiclass classification,Hoeffding Tree,Keystroke,0.6777085230058127,0.6777085230058127,0.6725285130045525,3.5654478073120117,329.21132600000004 -14688,Multiclass classification,Hoeffding Tree,Keystroke,0.6807380676788997,0.6807380676788997,0.6786761142186741,3.66107177734375,356.60277 -15096,Multiclass classification,Hoeffding Tree,Keystroke,0.6873799271281882,0.6873799271281882,0.68548393064844,3.7566957473754883,385.483239 -15504,Multiclass classification,Hoeffding Tree,Keystroke,0.6858027478552539,0.6858027478552539,0.6816808496509055,3.8523197174072266,415.890234 -15912,Multiclass classification,Hoeffding Tree,Keystroke,0.6765759537426937,0.6765759537426937,0.6694713281964944,3.947943687438965,447.86386200000004 -16320,Multiclass classification,Hoeffding Tree,Keystroke,0.6673815797536614,0.6673815797536614,0.6617321933140904,4.043567657470703,481.44086400000003 -16728,Multiclass classification,Hoeffding Tree,Keystroke,0.6643151790518323,0.6643151790518323,0.6611780293584051,4.139191627502441,516.667277 -17136,Multiclass classification,Hoeffding Tree,Keystroke,0.6598774438284214,0.6598774438284214,0.655734247886306,4.333066940307617,553.571784 -17544,Multiclass classification,Hoeffding Tree,Keystroke,0.6518269395200365,0.6518269395200365,0.6481085155228207,4.4286909103393555,592.193317 -17952,Multiclass classification,Hoeffding Tree,Keystroke,0.6507158375577963,0.6507158375577963,0.648936899585426,4.524314880371094,632.57823 -18360,Multiclass classification,Hoeffding Tree,Keystroke,0.6566806470940683,0.6566806470940683,0.6555764711123697,4.619938850402832,674.762883 -18768,Multiclass classification,Hoeffding Tree,Keystroke,0.662279533223211,0.662279533223211,0.6615432060687811,4.71556282043457,718.781471 -19176,Multiclass classification,Hoeffding Tree,Keystroke,0.6534028683181226,0.6534028683181226,0.6508089832432515,4.811186790466309,764.6790530000001 -19584,Multiclass classification,Hoeffding Tree,Keystroke,0.6577643874789358,0.6577643874789358,0.6564201177589184,4.906810760498047,812.4977690000001 -19992,Multiclass classification,Hoeffding Tree,Keystroke,0.6518433294982742,0.6518433294982742,0.6501496360982538,5.002434730529785,862.2665020000001 -20400,Multiclass classification,Hoeffding Tree,Keystroke,0.6482180499044071,0.6482180499044071,0.6472493759146579,5.098058700561523,914.036687 -46,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.4,0.4000000000000001,0.2926704014939309,0.4254798889160156,0.179349 -92,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5274725274725275,0.5274725274725275,0.5399541634835753,0.425537109375,0.395098 -138,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5547445255474452,0.5547445255474452,0.5795767508697842,0.4256591796875,0.646414 -184,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6174863387978142,0.6174863387978142,0.6398140932417979,0.42574310302734375,0.9360970000000001 -230,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6419213973799127,0.6419213973799127,0.6592174177506214,0.42574310302734375,1.2615820000000002 -276,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6545454545454545,0.6545454545454545,0.6716869228432982,0.4257926940917969,1.6228100000000003 -322,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6791277258566978,0.6791277258566978,0.6806263486692059,0.4258537292480469,2.022508 -368,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7029972752043597,0.7029972752043597,0.7008299817149242,0.4258270263671875,2.4581020000000002 -414,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7142857142857143,0.7142857142857143,0.7121569327354127,0.4257469177246094,2.92926 -460,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7145969498910676,0.7145969498910676,0.7103106155638,0.4258232116699219,3.4385440000000003 -506,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7227722772277227,0.7227722772277227,0.715881182832702,0.4258232116699219,3.9835350000000003 -552,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7241379310344828,0.7241379310344829,0.7187949260386588,0.4257164001464844,4.564144000000001 -598,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7286432160804021,0.7286432160804021,0.7227601649788371,0.4257392883300781,5.1830560000000006 -644,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7278382581648523,0.7278382581648523,0.7240595992457829,0.42577362060546875,5.837887 -690,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7314949201741655,0.7314949201741654,0.727547508877315,0.42577362060546875,6.528431 -736,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7333333333333333,0.7333333333333333,0.730585229165138,0.4258003234863281,7.2573300000000005 -782,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7387964148527529,0.7387964148527529,0.7359626710287273,0.4258003234863281,8.022590000000001 -828,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7376058041112454,0.7376058041112454,0.7367699509780541,0.4258003234863281,8.823569 -874,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7434135166093929,0.7434135166093929,0.7406779161411566,0.4258003234863281,9.663167000000001 -920,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7431991294885746,0.7431991294885745,0.7396284921253597,0.42577362060546875,10.538696000000002 -966,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7430051813471502,0.7430051813471502,0.7386475429248082,0.42577362060546875,11.449986 -1012,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7428288822947576,0.7428288822947575,0.7387392151852316,0.42577362060546875,12.399906000000001 -1058,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7445600756859035,0.7445600756859035,0.7397141356071754,0.42577362060546875,13.385988000000001 -1104,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7470534904805077,0.7470534904805077,0.7419829508197956,0.4258232116699219,14.408007000000001 -1150,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7484769364664926,0.7484769364664926,0.7430153502407321,0.4258232116699219,15.46854 -1196,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7514644351464436,0.7514644351464436,0.7466450927602833,0.4254570007324219,16.565517 -1242,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7518130539887188,0.7518130539887188,0.7475811251410989,0.4255790710449219,17.698596 -1288,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7567987567987567,0.7567987567987567,0.7515585748403605,0.4256401062011719,18.868108 -1334,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7576894223555889,0.7576894223555888,0.7527145732365901,0.4256401062011719,20.076794 -1380,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7592458303118201,0.7592458303118201,0.754880899709855,0.4257011413574219,21.321463 -1426,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7621052631578947,0.7621052631578947,0.7572480123106181,0.4257011413574219,22.601949 -1472,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7661454792658056,0.7661454792658056,0.7596240117389202,0.4257011413574219,23.921025 -1518,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7679630850362558,0.7679630850362558,0.7604664202984912,0.4257621765136719,25.275945 -1564,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7683941138835573,0.7683941138835573,0.7616623934037686,0.4257621765136719,26.66678 -1610,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7681789931634556,0.7681789931634556,0.7606779105029744,0.4257850646972656,28.096857 -1656,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7685800604229607,0.7685800604229607,0.7611818346958917,0.4257850646972656,29.563118 -1702,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7683715461493239,0.768371546149324,0.7630805397579306,0.4257850646972656,31.065673 -1748,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7716084716657127,0.7716084716657126,0.7661058855209445,0.4257850646972656,32.607308 -1794,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7730061349693251,0.7730061349693251,0.76613283717613,0.42575836181640625,34.185135 -1840,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7743338771071234,0.7743338771071234,0.7676486165305356,0.42581939697265625,35.798944000000006 -1886,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7729442970822281,0.7729442970822282,0.7669643117326908,0.42581939697265625,37.451807 -1932,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7736923873640601,0.7736923873640601,0.7669808567090198,0.42581939697265625,39.140782 -1978,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7744056651492159,0.7744056651492159,0.7669005381948409,0.42581939697265625,40.865953000000005 -2024,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7755808205635195,0.7755808205635196,0.7665616644775576,0.42581939697265625,42.627552 -2070,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7752537457709038,0.7752537457709039,0.7663566554091733,0.42581939697265625,44.428542 -2116,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.775886524822695,0.775886524822695,0.7661827507972012,0.42581939697265625,46.266451 -2162,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7764923646459972,0.7764923646459972,0.7663510353808046,0.42581939697265625,48.14124 -2208,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7784322609877662,0.7784322609877662,0.767276937076619,0.42581939697265625,50.054953000000005 -2254,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.775410563692854,0.775410563692854,0.7642399015136985,0.42581939697265625,52.004875000000006 -2300,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7746846454980426,0.7746846454980426,0.7634961218545901,0.42581939697265625,53.99744400000001 -1056,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6161137440758294,0.6161137440758294,0.5813841513331479,0.6684322357177734,1.380603 -2112,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6120322122216959,0.6120322122216959,0.5792161554760864,0.6684932708740234,3.9722109999999997 -3168,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6049889485317335,0.6049889485317335,0.5721633809277146,0.6685543060302734,7.807885 -4224,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.603125739995264,0.603125739995264,0.5703574432462962,0.6685543060302734,12.856231999999999 -5280,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6061754120098504,0.6061754120098504,0.5722430970062696,0.6686153411865234,19.046561999999998 -6336,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5995264404104184,0.5995264404104184,0.5671511237518186,0.6686153411865234,26.345702999999997 -7392,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5972128264104992,0.5972128264104992,0.5650210504998666,0.6686153411865234,34.702495 -8448,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5989108559251806,0.5989108559251806,0.566418690076869,0.6686153411865234,44.110113999999996 -9504,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5962327685993897,0.5962327685993897,0.5633780031885508,0.6686153411865234,54.569630999999994 -10560,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5964579979164694,0.5964579979164694,0.5634236596216465,0.6686763763427734,66.07704199999999 -11616,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.594317692638829,0.594317692638829,0.5620068495149612,0.6686763763427734,78.631408 -12672,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5975061163286244,0.5975061163286244,0.567518061449456,0.6686763763427734,92.23293299999999 -13728,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6097472135207984,0.6097472135207984,0.5927729676671933,0.6686763763427734,106.88971799999999 -14784,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001488195900697,0.6001488195900697,0.5832911478837771,0.6683712005615234,122.59496599999999 -15840,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5673969316244712,0.5673969316244712,0.5522471754341497,0.8954944610595703,139.36423 -16896,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5712340929269014,0.5712340929269014,0.559038323684958,1.4438505172729492,157.28485799999999 -17952,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5741184335134533,0.5741184335134533,0.5632919959429029,1.874833106994629,176.64490099999998 -19008,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5867312042931552,0.5867312042931552,0.5723846445183199,0.48981285095214844,197.01838199999997 -20064,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5939789662562927,0.5939789662562927,0.5773993022741072,0.6687717437744141,218.48549899999998 -21120,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.595908897201572,0.595908897201572,0.5788762098776178,0.6688938140869141,241.01084999999998 -22176,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5977452085682075,0.5977452085682075,0.5801804614049403,1.2152299880981445,264.60362299999997 -23232,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5997158968619517,0.5997158968619517,0.5818597835760811,1.3294572830200195,289.45174199999997 -24288,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6025033968789888,0.6025033968789888,0.5841484049015139,1.3295183181762695,315.702926 -25344,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6047823856686264,0.6047823856686264,0.5859943093850892,1.3296403884887695,343.366363 -26400,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6074472517898405,0.6074472517898405,0.5878557237787366,1.3296403884887695,372.430483 -27456,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6086323074121289,0.6086323074121289,0.5880340775890752,1.3298234939575195,402.90288599999997 -28512,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6087124267826453,0.6087124267826453,0.5895354690395743,1.3298234939575195,434.78023099999996 -29568,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6080765718537559,0.6080765718537559,0.5920130278134075,1.3298234939575195,468.06684599999994 -30624,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6071253632890311,0.6071253632890311,0.5937369304389161,1.3293352127075195,502.75580299999996 -31680,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6071845702200196,0.6071845702200196,0.5960066132315273,1.3295793533325195,538.8589619999999 -32736,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6079425691156255,0.6079425691156255,0.59836863034629,1.3296403884887695,576.3667869999999 -33792,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6027936432777958,0.6027936432777958,0.5936321389881086,0.6688251495361328,615.5159199999999 -34848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6018882543690992,0.6018882543690992,0.5927698243358274,0.6689472198486328,655.7217059999999 -35904,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.601398211848592,0.601398211848592,0.592182344393812,0.6690082550048828,696.988077 -36960,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5999080061689981,0.5999080061689981,0.5906275041314122,0.6690082550048828,739.3140189999999 -38016,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5996054189135868,0.5996054189135868,0.5899615119365567,0.6690082550048828,782.70332 -39072,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5989608661155332,0.5989608661155332,0.5889868403975307,0.6687030792236328,827.150323 -40128,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5947865526951928,0.5947865526951928,0.5855600636799734,0.6687030792236328,872.66104 -41184,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5926717334822621,0.5926717334822621,0.5840930914391779,0.6688861846923828,919.237173 -42240,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5913018774118706,0.5913018774118706,0.5832685369240246,0.6689472198486328,966.874788 -43296,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5898833583554683,0.5898833583554683,0.5823904732646675,0.6690082550048828,1015.577362 -44352,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5883745575071588,0.5883745575071588,0.5813207633940128,1.112539291381836,1065.314826 -45408,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5863853590856035,0.5863853590856035,0.5797569747943008,1.3286066055297852,1116.2698050000001 -46464,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5850461657663086,0.5850461657663086,0.5780695197887614,1.3287897109985352,1168.116792 -47520,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5867968602032871,0.5867968602032871,0.5799343284152632,1.328934669494629,1221.156775 -48576,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5917035512094699,0.5917035512094699,0.5847625919047718,1.329483985900879,1275.6038119999998 -49632,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5968447139892405,0.5968447139892405,0.5895877351185161,1.329422950744629,1331.4562389999999 -50688,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.601673012804072,0.601673012804072,0.5939045014873635,1.329606056213379,1388.7152379999998 -51744,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6067487389598593,0.6067487389598593,0.5983547975185618,1.329606056213379,1447.3877459999999 -52800,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6119623477717381,0.6119623477717381,0.6029934068442723,0.14767932891845703,1507.071307 -408,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.2342357635498047,0.174096 -816,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,0.32985973358154297,0.617741 -1224,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8863450531479967,0.8863450531479967,0.8786592421362931,0.42548370361328125,1.425386 -1632,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.891477621091355,0.891477621091355,0.8818548670971932,0.5215349197387695,2.789107 -2040,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.889651790093183,0.889651790093183,0.8812768038030504,0.6287555694580078,4.864698000000001 -2448,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8414384961176952,0.8414384961176952,0.8420581397672002,0.7242574691772461,7.621751000000001 -2856,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8500875656742557,0.8500875656742557,0.834558203718852,0.8199424743652344,10.917147 -3264,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8406374501992032,0.8406374501992032,0.8151418555553325,0.9155054092407227,14.806837 -3672,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8321983110868973,0.8321983110868973,0.8307198315203921,1.011190414428711,19.353183 -4080,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.826182887962736,0.826182887962736,0.812376785603362,1.1319761276245117,24.603589 -4488,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.809226654780477,0.809226654780477,0.8196273526663149,1.2275390625,30.588099 -4896,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8081716036772216,0.8081716036772216,0.815232111826365,1.3230409622192383,37.350443 -5304,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8057703186875353,0.8057703186875353,0.7903391475861199,1.4186649322509766,44.935392 -5712,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7860269655051655,0.7860269655051656,0.7895763142947655,1.5144109725952148,53.372574 -6120,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.784441902271613,0.784441902271613,0.7657785418705475,1.6098518371582031,62.716148000000004 -6528,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7585414432357898,0.7585414432357898,0.751418836389106,1.7056589126586914,73.022548 -6936,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7473684210526316,0.7473684210526316,0.7484284412750403,1.8010997772216797,84.351871 -7344,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7565027917744791,0.7565027917744791,0.7526701844923946,1.898371696472168,96.7592 -7752,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7577086827506129,0.7577086827506129,0.7557350658705178,1.9939956665039062,110.303168 -8160,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7617355068023042,0.7617355068023042,0.7576049653668415,2.0895586013793945,125.047569 -8568,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7604762460604646,0.7604762460604646,0.7596175662696861,2.2332935333251953,141.03972299999998 -8976,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.756991643454039,0.7569916434540391,0.7575313939177277,2.3289785385131836,158.34463899999997 -9384,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7558350207822658,0.7558350207822658,0.7548436696787698,2.424480438232422,177.02417899999998 -9792,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.748340312531917,0.7483403125319169,0.7443908596260193,2.52004337310791,197.13977899999998 -10200,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7393862143347387,0.7393862143347387,0.7315892779928432,2.6156673431396484,218.76351099999997 -10608,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7196191194494201,0.7196191194494201,0.7089541376321257,2.7114133834838867,241.93025799999998 -11016,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7123921924648207,0.7123921924648208,0.7092068316988943,2.806976318359375,266.699543 -11424,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7062943184802591,0.7062943184802591,0.694671323095531,2.9026002883911133,293.16281 -11832,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6967289324655566,0.6967289324655566,0.6902328307983061,2.9981632232666016,321.35092599999996 -12240,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7007108423890841,0.7007108423890841,0.6983689907908355,3.09378719329834,351.321335 -12648,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6969241717403337,0.6969241717403337,0.6892508246262707,3.189472198486328,383.13879899999995 -13056,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6836461126005362,0.6836461126005362,0.6755391962059191,3.2851572036743164,416.86083699999995 -13464,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6793433855752804,0.6793433855752804,0.6754035266161622,3.3807201385498047,452.54592499999995 -13872,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6769519140653161,0.6769519140653161,0.6742482232309566,3.476466178894043,490.25119899999993 -14280,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6728762518383641,0.6728762518383641,0.6689356443053496,3.5720291137695312,530.0355519999999 -14688,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6762442976782188,0.6762442976782188,0.6753292472514647,3.6675920486450195,571.9481539999999 -15096,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6830076184166942,0.6830076184166942,0.6822311287838643,3.763277053833008,616.057788 -15504,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6818035218989873,0.6818035218989873,0.6788656596145115,3.858839988708496,662.434182 -15912,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6816039218150964,0.6816039218150964,0.6801525397911032,0.2779512405395508,710.461266 -16320,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6858263373981249,0.6858263373981249,0.6851912800185752,0.4695272445678711,759.2533930000001 -16728,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6896634184253004,0.6896634184253004,0.6890226069872225,0.6609811782836914,808.8937860000001 -17136,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6925007295010213,0.6925007295010213,0.691863544221197,0.9803314208984375,859.476205 -17544,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6990252522373597,0.6990252522373597,0.6986638608261282,0.27229881286621094,910.5065460000001 -17952,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7040833379756003,0.7040833379756003,0.7034973599095433,0.1428661346435547,961.7687770000001 -18360,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7102783376000872,0.7102783376000872,0.7096708693716106,0.23855113983154297,1013.1870050000001 -18768,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7155645548036447,0.7155645548036447,0.714820465744771,0.33417510986328125,1064.8262280000001 -19176,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7183833116036505,0.7183833116036505,0.7174783905571958,0.42961597442626953,1116.742805 -19584,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7229229433692489,0.7229229433692489,0.7220221994049509,0.5253620147705078,1168.993402 -19992,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7224751138012105,0.7224751138012104,0.7211832505275634,0.6323385238647461,1221.6363350000001 -20400,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7237119466640521,0.7237119466640521,0.7223930256436224,0.7279014587402344,1274.7271140000003 -46,Multiclass classification,Adaptive Random Forest,ImageSegments,0.4222222222222222,0.4222222222222222,0.3590236094437775,0.9732446670532227,0.50913 -92,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5604395604395604,0.5604395604395604,0.5746538615446178,1.0627803802490234,1.324173 -138,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5766423357664233,0.5766423357664233,0.598257695340355,1.3550586700439453,2.399595 -184,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6229508196721312,0.6229508196721312,0.6451744040758779,1.4249095916748047,3.72413 -230,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6506550218340611,0.6506550218340611,0.668065528002595,1.5721073150634766,5.289042 -276,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6727272727272727,0.6727272727272727,0.6900672130049011,1.7710065841674805,7.016464 -322,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7040498442367601,0.7040498442367601,0.7087861936875777,1.8489313125610352,8.876771 -368,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7302452316076294,0.7302452316076294,0.7285991575377422,1.9874763488769531,10.883345 -414,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7457627118644068,0.7457627118644068,0.7430362907281778,2.008787155151367,13.045158 -460,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7342047930283224,0.7342047930283224,0.7271744800226859,1.8246965408325195,15.362221000000002 -506,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7405940594059406,0.7405940594059406,0.7304322149686578,1.7282800674438477,17.823546 -552,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7368421052631579,0.7368421052631579,0.7267508109083203,1.5214414596557617,20.437237 -598,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7403685092127303,0.7403685092127302,0.7318978254380312,1.6621322631835938,23.204591 -644,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7325038880248833,0.7325038880248833,0.7248107612258206,1.7895660400390625,26.125323 -690,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7242380261248186,0.7242380261248187,0.7153272190465999,1.9295940399169922,29.195315 -736,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7251700680272108,0.725170068027211,0.7148466398758337,2.079819679260254,32.416287000000004 -782,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7259923175416133,0.7259923175416134,0.7134712280209222,2.0407657623291016,35.785816000000004 -828,Multiclass classification,Adaptive Random Forest,ImageSegments,0.727932285368803,0.727932285368803,0.7177600265828429,2.245401382446289,39.307391 -874,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7353951890034365,0.7353951890034366,0.7262567978322628,2.3208675384521484,42.982338000000006 -920,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7431991294885746,0.7431991294885745,0.7345004589126253,2.463038444519043,46.81357700000001 -966,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7471502590673575,0.7471502590673575,0.7368855656689401,2.4979677200317383,50.81254500000001 -1012,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7546983184965381,0.754698318496538,0.7446216664767904,2.5897722244262695,54.96693400000001 -1058,Multiclass classification,Adaptive Random Forest,ImageSegments,0.760643330179754,0.760643330179754,0.7502594177262459,2.824686050415039,59.28579400000001 -1104,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7624660018132366,0.7624660018132366,0.7523020427630668,2.512765884399414,63.76907700000001 -1150,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7650130548302873,0.7650130548302874,0.7555087521342715,2.350802421569824,68.40298100000001 -1196,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7690376569037657,0.7690376569037657,0.7603504370239863,2.0774078369140625,73.17908000000001 -1242,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7719580983078163,0.7719580983078163,0.7638249032322542,2.143113136291504,78.09633200000002 -1288,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7746697746697747,0.7746697746697747,0.7668828628349821,2.3053293228149414,83.14236000000002 -1334,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7771942985746436,0.7771942985746436,0.7696789046658701,2.4279375076293945,88.31606900000003 -1380,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7817258883248731,0.7817258883248731,0.7754511149783998,2.350360870361328,93.61768400000003 -1426,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7866666666666666,0.7866666666666666,0.7797171864703156,2.461531639099121,99.04413800000003 -1472,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7912984364377974,0.7912984364377974,0.7836430453045393,2.5941333770751953,104.59649900000004 -1518,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7963085036255768,0.7963085036255768,0.7883976288226552,2.7080554962158203,110.30036700000004 -1564,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7984644913627639,0.7984644913627639,0.7915512335737709,2.379396438598633,116.13112600000004 -1610,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798011187072716,0.7980111870727161,0.7913527809122488,2.557906150817871,122.09210800000004 -1656,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7987915407854985,0.7987915407854985,0.7921693301011166,2.5870275497436523,128.19249900000003 -1702,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7995296884185773,0.7995296884185774,0.7947635312368726,2.4413909912109375,134.42240900000002 -1748,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8019461934745278,0.8019461934745278,0.7968342396743014,2.619420051574707,140.777643 -1794,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8059118795315114,0.8059118795315114,0.8002313091513137,2.7039318084716797,147.25850100000002 -1840,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8058727569331158,0.8058727569331158,0.8006185305294855,3.167543411254883,153.88034500000003 -1886,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8084880636604774,0.8084880636604774,0.8041348438460234,3.187774658203125,160.64099900000002 -1932,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8089073019161056,0.8089073019161055,0.8042053366874767,3.4328765869140625,167.537774 -1978,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8108244815376834,0.8108244815376834,0.8062422218151643,3.621993064880371,174.56717600000002 -2024,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8111715274345032,0.8111715274345032,0.805670935248126,3.7835464477539062,181.73003400000002 -2070,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8134364427259546,0.8134364427259546,0.8085538776813638,3.7409582138061523,189.02955300000002 -2116,Multiclass classification,Adaptive Random Forest,ImageSegments,0.816548463356974,0.816548463356974,0.8113031614777911,3.760796546936035,196.461518 -2162,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8167515039333642,0.8167515039333642,0.8113905234748385,4.035200119018555,204.02457600000002 -2208,Multiclass classification,Adaptive Random Forest,ImageSegments,0.818305391934753,0.818305391934753,0.8126353495892602,4.192110061645508,211.71705500000002 -2254,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8184642698624057,0.8184642698624057,0.8136291554244021,4.486760139465332,219.55329600000002 -2300,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8190517616354936,0.8190517616354936,0.8144252010220491,4.66081428527832,227.54068500000002 -1056,Multiclass classification,Adaptive Random Forest,Insects,0.6701421800947868,0.6701421800947868,0.6068786932307204,6.831533432006836,4.539822 -2112,Multiclass classification,Adaptive Random Forest,Insects,0.6887730933207011,0.6887730933207011,0.6229217946585527,10.195775032043457,12.638872 -3168,Multiclass classification,Adaptive Random Forest,Insects,0.6962425007893905,0.6962425007893905,0.622910390568452,16.60274887084961,24.371093 -4224,Multiclass classification,Adaptive Random Forest,Insects,0.7054226852948141,0.7054226852948141,0.6279874627708885,17.471903800964355,39.890989 -5280,Multiclass classification,Adaptive Random Forest,Insects,0.7099829513165372,0.7099829513165372,0.6301031937879839,20.888835906982422,58.938013999999995 -6336,Multiclass classification,Adaptive Random Forest,Insects,0.7108129439621153,0.7108129439621153,0.6300557461749893,23.72772216796875,81.51329799999999 -7392,Multiclass classification,Adaptive Random Forest,Insects,0.7126234609660398,0.7126234609660397,0.6287819651813062,28.070199966430664,107.68036799999999 -8448,Multiclass classification,Adaptive Random Forest,Insects,0.7168225405469397,0.7168225405469397,0.6299159911335922,31.613859176635742,137.61638299999998 -9504,Multiclass classification,Adaptive Random Forest,Insects,0.7214563821950963,0.7214563821950963,0.6314635817104112,35.43206214904785,171.294053 -10560,Multiclass classification,Adaptive Random Forest,Insects,0.7230798371057865,0.7230798371057865,0.6311670445333952,35.676584243774414,208.90076499999998 -11616,Multiclass classification,Adaptive Random Forest,Insects,0.7247524752475247,0.7247524752475247,0.6314302971551563,41.6592378616333,250.15747499999998 -12672,Multiclass classification,Adaptive Random Forest,Insects,0.7252781943019493,0.7252781943019494,0.6359647238599803,42.66780757904053,295.204683 -13728,Multiclass classification,Adaptive Random Forest,Insects,0.7402928535004006,0.7402928535004006,0.7348419335996624,24.025733947753906,342.696854 -14784,Multiclass classification,Adaptive Random Forest,Insects,0.7468714063451262,0.7468714063451262,0.7455387452701401,2.272738456726074,392.69755799999996 -15840,Multiclass classification,Adaptive Random Forest,Insects,0.7388092682618852,0.7388092682618853,0.7393651674564367,6.547223091125488,445.901262 -16896,Multiclass classification,Adaptive Random Forest,Insects,0.7343000887836638,0.7343000887836638,0.7364396291092657,11.097336769104004,502.213806 -17952,Multiclass classification,Adaptive Random Forest,Insects,0.7271461199933151,0.7271461199933151,0.7303078098029304,16.88492202758789,561.702561 -19008,Multiclass classification,Adaptive Random Forest,Insects,0.7373073078339559,0.7373073078339558,0.7369507693389319,6.800461769104004,623.6949609999999 -20064,Multiclass classification,Adaptive Random Forest,Insects,0.7412650152021133,0.7412650152021133,0.7370000710650216,3.369675636291504,688.4094249999999 -21120,Multiclass classification,Adaptive Random Forest,Insects,0.7425540982054074,0.7425540982054074,0.7353012584659039,5.941231727600098,756.7149029999999 -22176,Multiclass classification,Adaptive Random Forest,Insects,0.7435851183765502,0.7435851183765501,0.7334480812988377,8.846389770507812,828.508796 -23232,Multiclass classification,Adaptive Random Forest,Insects,0.7452111402866859,0.7452111402866859,0.7324964055744654,9.471121788024902,903.877525 -24288,Multiclass classification,Adaptive Random Forest,Insects,0.7463663688392967,0.7463663688392966,0.7310050929424414,12.406947135925293,982.696529 -25344,Multiclass classification,Adaptive Random Forest,Insects,0.7474647831748412,0.7474647831748412,0.7298615493429103,15.979948043823242,1064.886272 -26400,Multiclass classification,Adaptive Random Forest,Insects,0.7483616803666806,0.7483616803666806,0.7285096183890708,19.665884017944336,1150.466447 -27456,Multiclass classification,Adaptive Random Forest,Insects,0.749626661810235,0.749626661810235,0.7275235594970662,24.26569175720215,1239.482455 -28512,Multiclass classification,Adaptive Random Forest,Insects,0.7465188874469503,0.7465188874469504,0.7258897093263847,8.914395332336426,1332.2242660000002 -29568,Multiclass classification,Adaptive Random Forest,Insects,0.7451550715324518,0.7451550715324519,0.7292330017207805,8.459691047668457,1428.2455670000002 -30624,Multiclass classification,Adaptive Random Forest,Insects,0.7443751428664729,0.7443751428664729,0.7327893612754602,12.943696022033691,1527.2067200000001 -31680,Multiclass classification,Adaptive Random Forest,Insects,0.7437103443921841,0.7437103443921841,0.7357305076230832,18.80640697479248,1629.3633260000001 -32736,Multiclass classification,Adaptive Random Forest,Insects,0.7432717275087827,0.7432717275087827,0.7381285892142362,16.162379264831543,1734.350472 -33792,Multiclass classification,Adaptive Random Forest,Insects,0.7377408185611554,0.7377408185611554,0.7340057348640155,11.18346881866455,1842.7318770000002 -34848,Multiclass classification,Adaptive Random Forest,Insects,0.7340373633311332,0.7340373633311332,0.7302084976112027,5.613262176513672,1955.0234710000002 -35904,Multiclass classification,Adaptive Random Forest,Insects,0.7312759379439044,0.7312759379439044,0.7271196230245338,9.756120681762695,2071.1719540000004 -36960,Multiclass classification,Adaptive Random Forest,Insects,0.7278335452799047,0.7278335452799047,0.7234434079919367,11.990450859069824,2191.1586210000005 -38016,Multiclass classification,Adaptive Random Forest,Insects,0.7254241746678942,0.7254241746678942,0.7207605796154644,14.404197692871094,2314.6725880000004 -39072,Multiclass classification,Adaptive Random Forest,Insects,0.7250390315067441,0.7250390315067441,0.7205508934526729,7.651473045349121,2441.6663320000002 -40128,Multiclass classification,Adaptive Random Forest,Insects,0.7236524036185112,0.7236524036185111,0.7196200887167502,7.583705902099609,2572.286548 -41184,Multiclass classification,Adaptive Random Forest,Insects,0.7235995435009591,0.7235995435009591,0.7199895911465058,12.209360122680664,2706.295895 -42240,Multiclass classification,Adaptive Random Forest,Insects,0.7235966760576719,0.7235966760576719,0.7203672841246517,15.002169609069824,2843.875227 -43296,Multiclass classification,Adaptive Random Forest,Insects,0.7241713823767179,0.7241713823767179,0.7213145862540888,17.433518409729004,2985.305163 -44352,Multiclass classification,Adaptive Random Forest,Insects,0.7245608892696895,0.7245608892696895,0.7219384327675483,20.337363243103027,3130.446815 -45408,Multiclass classification,Adaptive Random Forest,Insects,0.7253947629220164,0.7253947629220163,0.7227741676779873,20.507991790771484,3279.062075 -46464,Multiclass classification,Adaptive Random Forest,Insects,0.7263198674213891,0.7263198674213891,0.7236028172229397,24.947001457214355,3431.085655 -47520,Multiclass classification,Adaptive Random Forest,Insects,0.7259622466802753,0.7259622466802753,0.7234132526915972,9.389252662658691,3586.9794589999997 -48576,Multiclass classification,Adaptive Random Forest,Insects,0.7297581060216161,0.7297581060216161,0.7273884829439242,9.12541389465332,3745.9515599999995 -49632,Multiclass classification,Adaptive Random Forest,Insects,0.7336543692450284,0.7336543692450284,0.7312645046388119,8.804935455322266,3907.5042229999995 -50688,Multiclass classification,Adaptive Random Forest,Insects,0.7372501824925524,0.7372501824925524,0.7346466630802606,12.506796836853027,4071.2963689999997 -51744,Multiclass classification,Adaptive Random Forest,Insects,0.741182382157973,0.741182382157973,0.7382896911640772,15.31224250793457,4237.296913 -52800,Multiclass classification,Adaptive Random Forest,Insects,0.7442565200098487,0.7442565200098487,0.7419321396565435,0.3696470260620117,4404.707415 -408,Multiclass classification,Adaptive Random Forest,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.3514842987060547,0.595241 -816,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730061349693252,0.9730061349693252,0.7867307803099512,1.312638282775879,2.098583 -1224,Multiclass classification,Adaptive Random Forest,Keystroke,0.9705641864268193,0.9705641864268193,0.9370502919558801,2.2586374282836914,4.31371 -1632,Multiclass classification,Adaptive Random Forest,Keystroke,0.9711833231146536,0.9711833231146536,0.9377953913100077,3.394951820373535,7.3386510000000005 -2040,Multiclass classification,Adaptive Random Forest,Keystroke,0.969592937714566,0.969592937714566,0.9445939973353387,5.254854202270508,11.230817 -2448,Multiclass classification,Adaptive Random Forest,Keystroke,0.9701675521046179,0.9701675521046179,0.9654865811906564,2.048126220703125,15.860121 -2856,Multiclass classification,Adaptive Random Forest,Keystroke,0.9726795096322242,0.9726795096322242,0.9705770446236133,2.732625961303711,21.099127 -3264,Multiclass classification,Adaptive Random Forest,Keystroke,0.9718050873429359,0.9718050873429359,0.9627836140542233,2.790935516357422,26.971176 -3672,Multiclass classification,Adaptive Random Forest,Keystroke,0.9733042767638246,0.9733042767638246,0.9719148371902758,2.987569808959961,33.477629 -4080,Multiclass classification,Adaptive Random Forest,Keystroke,0.9698455503799951,0.9698455503799951,0.9588020505656979,4.571287155151367,40.764845 -4488,Multiclass classification,Adaptive Random Forest,Keystroke,0.9710274125250724,0.9710274125250724,0.9701901425551159,1.7459039688110352,48.697823 -4896,Multiclass classification,Adaptive Random Forest,Keystroke,0.9722165474974463,0.9722165474974463,0.9719364174281581,2.7892093658447266,57.250967 -5304,Multiclass classification,Adaptive Random Forest,Keystroke,0.9720912690929663,0.9720912690929663,0.970282662152698,2.895453453063965,66.54505900000001 -5712,Multiclass classification,Adaptive Random Forest,Keystroke,0.9723340921029592,0.9723340921029592,0.9718828908328702,4.064221382141113,76.51574600000001 -6120,Multiclass classification,Adaptive Random Forest,Keystroke,0.9718908318352673,0.9718908318352673,0.9703726237787478,5.130434989929199,87.20768000000001 -6528,Multiclass classification,Adaptive Random Forest,Keystroke,0.9719626168224299,0.9719626168224299,0.9714458378209955,2.455193519592285,98.59605300000001 -6936,Multiclass classification,Adaptive Random Forest,Keystroke,0.9733237202595529,0.9733237202595529,0.9740372626056705,2.4587574005126953,110.61869200000001 -7344,Multiclass classification,Adaptive Random Forest,Keystroke,0.9735802805392891,0.9735802805392891,0.973376514333954,3.893580436706543,123.504669 -7752,Multiclass classification,Adaptive Random Forest,Keystroke,0.9729067217133273,0.9729067217133273,0.9721109941692119,4.6176652908325195,137.14807100000002 -8160,Multiclass classification,Adaptive Random Forest,Keystroke,0.9721779629856601,0.9721779629856601,0.9713389113158797,5.2350358963012695,151.61678500000002 -8568,Multiclass classification,Adaptive Random Forest,Keystroke,0.9725691607330454,0.9725691607330454,0.9726516232305997,3.659168243408203,166.92608600000003 -8976,Multiclass classification,Adaptive Random Forest,Keystroke,0.9733704735376044,0.9733704735376044,0.973745927183376,5.072476387023926,182.94001300000002 -9384,Multiclass classification,Adaptive Random Forest,Keystroke,0.9733560694873707,0.9733560694873707,0.9732604538569353,5.722126007080078,199.76705900000002 -9792,Multiclass classification,Adaptive Random Forest,Keystroke,0.9731385966704116,0.9731385966704116,0.9729642609350583,4.404660224914551,217.43706000000003 -10200,Multiclass classification,Adaptive Random Forest,Keystroke,0.9725463280713795,0.9725463280713795,0.9722080895483168,2.6527090072631836,235.89310200000003 -10608,Multiclass classification,Adaptive Random Forest,Keystroke,0.9726595644385783,0.9726595644385783,0.9727080848172961,1.1834087371826172,254.95803500000002 -11016,Multiclass classification,Adaptive Random Forest,Keystroke,0.9729459827507944,0.9729459827507944,0.9730830184440419,1.520833969116211,274.61158 -11424,Multiclass classification,Adaptive Random Forest,Keystroke,0.9724240567276548,0.9724240567276548,0.972236101273467,2.823396682739258,294.969552 -11832,Multiclass classification,Adaptive Random Forest,Keystroke,0.9720226523539853,0.9720226523539853,0.9719096687987197,2.493410110473633,316.08929700000004 -12240,Multiclass classification,Adaptive Random Forest,Keystroke,0.9726284827191765,0.9726284827191765,0.9728780734732722,2.3478269577026367,337.99406600000003 -12648,Multiclass classification,Adaptive Random Forest,Keystroke,0.9727998734877836,0.9727998734877836,0.9729097588140673,2.5516576766967773,360.586004 -13056,Multiclass classification,Adaptive Random Forest,Keystroke,0.9726541554959786,0.9726541554959786,0.9726709194030315,3.304943084716797,383.87924 -13464,Multiclass classification,Adaptive Random Forest,Keystroke,0.9722201589541707,0.9722201589541707,0.9721650267620997,4.003572463989258,407.93215299999997 -13872,Multiclass classification,Adaptive Random Forest,Keystroke,0.9724605291615601,0.9724605291615601,0.9725910057046059,4.270735740661621,432.78625999999997 -14280,Multiclass classification,Adaptive Random Forest,Keystroke,0.9713565375726592,0.9713565375726592,0.9711654862365111,4.102839469909668,458.452707 -14688,Multiclass classification,Adaptive Random Forest,Keystroke,0.9718118063593654,0.9718118063593654,0.9719655808676524,3.971695899963379,484.888355 -15096,Multiclass classification,Adaptive Random Forest,Keystroke,0.9724412056972508,0.9724412056972508,0.9726138064055022,4.612870216369629,512.116119 -15504,Multiclass classification,Adaptive Random Forest,Keystroke,0.9723279365284139,0.9723279365284139,0.9723669009986284,3.2941598892211914,540.133825 -15912,Multiclass classification,Adaptive Random Forest,Keystroke,0.97240902520269,0.97240902520269,0.9724748506273226,5.215278625488281,569.064511 -16320,Multiclass classification,Adaptive Random Forest,Keystroke,0.9718119982842086,0.9718119982842086,0.9717822259045504,2.705050468444824,598.831932 -16728,Multiclass classification,Adaptive Random Forest,Keystroke,0.9713636635379924,0.9713636635379924,0.971358198091739,1.4999914169311523,629.214947 -17136,Multiclass classification,Adaptive Random Forest,Keystroke,0.9716953603735046,0.9716953603735046,0.9717778191727772,1.5952835083007812,660.25813 -17544,Multiclass classification,Adaptive Random Forest,Keystroke,0.9716696118109788,0.9716696118109788,0.971712982907841,2.6761178970336914,692.002463 -17952,Multiclass classification,Adaptive Random Forest,Keystroke,0.9709765472675617,0.9709765472675617,0.970966525204854,3.6711130142211914,724.550435 -18360,Multiclass classification,Adaptive Random Forest,Keystroke,0.9709679176425732,0.9709679176425732,0.9710033330464194,4.9129533767700195,757.867405 -18768,Multiclass classification,Adaptive Random Forest,Keystroke,0.9710129482602441,0.9710129482602441,0.9710485326344032,5.05178165435791,792.058633 -19176,Multiclass classification,Adaptive Random Forest,Keystroke,0.9711603650586701,0.9711603650586701,0.9711938240802871,5.466279983520508,827.148813 -19584,Multiclass classification,Adaptive Random Forest,Keystroke,0.9707909921871011,0.9707909921871011,0.9708057459916863,5.881702423095703,863.142165 -19992,Multiclass classification,Adaptive Random Forest,Keystroke,0.9705867640438197,0.9705867640438197,0.9706070593086332,5.831451416015625,900.075184 -20400,Multiclass classification,Adaptive Random Forest,Keystroke,0.9698514633070249,0.9698514633070249,0.9698673821244657,2.3371658325195312,937.846308 -46,Multiclass classification,Streaming Random Patches,ImageSegments,0.3111111111111111,0.3111111111111111,0.22202380952380954,2.606511116027832,1.431937 -92,Multiclass classification,Streaming Random Patches,ImageSegments,0.4945054945054945,0.4945054945054945,0.5053729602697932,2.609585762023926,3.886705 -138,Multiclass classification,Streaming Random Patches,ImageSegments,0.5109489051094891,0.5109489051094891,0.5310665055578762,2.6113672256469727,7.103774 -184,Multiclass classification,Streaming Random Patches,ImageSegments,0.5737704918032787,0.5737704918032787,0.5886643910747036,2.6136903762817383,11.095616999999999 -230,Multiclass classification,Streaming Random Patches,ImageSegments,0.6026200873362445,0.6026200873362445,0.6106719627755607,2.614529609680176,15.833751999999999 -276,Multiclass classification,Streaming Random Patches,ImageSegments,0.6181818181818182,0.6181818181818182,0.6264208209498925,2.6147661209106445,21.302563 -322,Multiclass classification,Streaming Random Patches,ImageSegments,0.6448598130841121,0.6448598130841121,0.6378728366046057,2.616147041320801,27.471138 -368,Multiclass classification,Streaming Random Patches,ImageSegments,0.667574931880109,0.667574931880109,0.6581306320431076,2.6166696548461914,34.32642 -414,Multiclass classification,Streaming Random Patches,ImageSegments,0.6803874092009685,0.6803874092009685,0.6704325632692101,2.6175050735473633,41.86551 -460,Multiclass classification,Streaming Random Patches,ImageSegments,0.6884531590413944,0.6884531590413944,0.6760149332924277,2.617680549621582,50.055222 -506,Multiclass classification,Streaming Random Patches,ImageSegments,0.691089108910891,0.691089108910891,0.6769247074861785,2.617680549621582,58.91147 -552,Multiclass classification,Streaming Random Patches,ImageSegments,0.691470054446461,0.691470054446461,0.6803521213965826,2.6178178787231445,68.422832 -598,Multiclass classification,Streaming Random Patches,ImageSegments,0.6968174204355109,0.6968174204355109,0.6854975219125513,2.617863655090332,78.595742 -644,Multiclass classification,Streaming Random Patches,ImageSegments,0.6936236391912908,0.6936236391912908,0.6835764097697864,2.6192026138305664,89.423453 -690,Multiclass classification,Streaming Random Patches,ImageSegments,0.6966618287373004,0.6966618287373004,0.6871604229696352,2.6194162368774414,100.90701299999999 -736,Multiclass classification,Streaming Random Patches,ImageSegments,0.6965986394557823,0.6965986394557823,0.6884795420777536,2.6194887161254883,113.056901 -782,Multiclass classification,Streaming Random Patches,ImageSegments,0.7016645326504481,0.7016645326504481,0.6927955715819348,2.6197519302368164,125.87172799999999 -828,Multiclass classification,Streaming Random Patches,ImageSegments,0.7037484885126964,0.7037484885126964,0.6971811816445675,2.61989688873291,139.34805 -874,Multiclass classification,Streaming Random Patches,ImageSegments,0.7124856815578465,0.7124856815578465,0.7027179013602759,2.61989688873291,153.488556 -920,Multiclass classification,Streaming Random Patches,ImageSegments,0.7127312295973884,0.7127312295973884,0.7019247882761857,2.61989688873291,168.286881 -966,Multiclass classification,Streaming Random Patches,ImageSegments,0.7119170984455958,0.7119170984455958,0.7013991197312313,2.61989688873291,183.731934 -1012,Multiclass classification,Streaming Random Patches,ImageSegments,0.7111770524233432,0.7111770524233432,0.7000689942734505,2.61989688873291,199.829198 -1058,Multiclass classification,Streaming Random Patches,ImageSegments,0.7123935666982024,0.7123935666982024,0.700757485135609,2.620041847229004,216.594079 -1104,Multiclass classification,Streaming Random Patches,ImageSegments,0.7116953762466002,0.7116953762466002,0.6997536275311635,2.6201601028442383,234.008444 -1150,Multiclass classification,Streaming Random Patches,ImageSegments,0.7136640557006092,0.7136640557006092,0.7002507718266925,2.6201601028442383,252.079326 -1196,Multiclass classification,Streaming Random Patches,ImageSegments,0.7154811715481172,0.7154811715481171,0.7029614354817431,2.530026435852051,270.790044 -1242,Multiclass classification,Streaming Random Patches,ImageSegments,0.717163577759871,0.717163577759871,0.7059650228666394,2.753697395324707,290.035925 -1288,Multiclass classification,Streaming Random Patches,ImageSegments,0.7187257187257188,0.7187257187257188,0.706699668165461,3.6648149490356445,309.61323300000004 -1334,Multiclass classification,Streaming Random Patches,ImageSegments,0.719429857464366,0.719429857464366,0.7094425115390415,4.463783264160156,329.52005700000007 -1380,Multiclass classification,Streaming Random Patches,ImageSegments,0.7251631617113851,0.725163161711385,0.7174387625572534,4.938790321350098,349.76822000000004 -1426,Multiclass classification,Streaming Random Patches,ImageSegments,0.7319298245614035,0.7319298245614035,0.7244482628352659,5.045901298522949,370.34098700000004 -1472,Multiclass classification,Streaming Random Patches,ImageSegments,0.7335146159075459,0.7335146159075459,0.7247675805597543,5.884430885314941,391.25731 -1518,Multiclass classification,Streaming Random Patches,ImageSegments,0.7251153592617007,0.7251153592617007,0.7184902268106362,6.2875261306762695,412.53433 -1564,Multiclass classification,Streaming Random Patches,ImageSegments,0.7204094689699296,0.7204094689699295,0.7171509654034274,6.316588401794434,434.180383 -1610,Multiclass classification,Streaming Random Patches,ImageSegments,0.7165941578620261,0.7165941578620262,0.7136076251491865,6.364602088928223,456.188448 -1656,Multiclass classification,Streaming Random Patches,ImageSegments,0.7202416918429003,0.7202416918429003,0.7179265770125135,6.460197448730469,478.554693 -1702,Multiclass classification,Streaming Random Patches,ImageSegments,0.721928277483833,0.7219282774838331,0.7220156076184944,6.666633605957031,501.28713 -1748,Multiclass classification,Streaming Random Patches,ImageSegments,0.7263880938752146,0.7263880938752146,0.7263874723147012,6.882956504821777,524.384863 -1794,Multiclass classification,Streaming Random Patches,ImageSegments,0.7328499721137758,0.7328499721137758,0.7320714565315939,6.874361991882324,547.829739 -1840,Multiclass classification,Streaming Random Patches,ImageSegments,0.734094616639478,0.734094616639478,0.7334477172925166,7.857270240783691,571.634536 -1886,Multiclass classification,Streaming Random Patches,ImageSegments,0.7358090185676393,0.7358090185676393,0.736235296466255,8.041683197021484,595.832215 -1932,Multiclass classification,Streaming Random Patches,ImageSegments,0.7369238736406007,0.7369238736406007,0.7364098924240724,8.212060928344727,620.406916 -1978,Multiclass classification,Streaming Random Patches,ImageSegments,0.7369752149721801,0.73697521497218,0.7356260672719533,8.416284561157227,645.365163 -2024,Multiclass classification,Streaming Random Patches,ImageSegments,0.7409787444389521,0.7409787444389521,0.7385453010661254,8.869349479675293,670.737914 -2070,Multiclass classification,Streaming Random Patches,ImageSegments,0.7438376027066216,0.7438376027066217,0.7418803204845174,9.001053810119629,696.540108 -2116,Multiclass classification,Streaming Random Patches,ImageSegments,0.7475177304964539,0.7475177304964539,0.7450940881618369,9.427652359008789,722.759269 -2162,Multiclass classification,Streaming Random Patches,ImageSegments,0.7482646922720962,0.7482646922720962,0.7457425826498583,9.724228858947754,749.427824 -2208,Multiclass classification,Streaming Random Patches,ImageSegments,0.7521522428636158,0.7521522428636158,0.7492034954191574,9.71615219116211,776.532359 -2254,Multiclass classification,Streaming Random Patches,ImageSegments,0.7532179316466933,0.7532179316466933,0.7508205496072249,10.198495864868164,804.090452 -2300,Multiclass classification,Streaming Random Patches,ImageSegments,0.7546759460635059,0.754675946063506,0.7527273841922961,10.425667762756348,832.069921 -1056,Multiclass classification,Streaming Random Patches,Insects,0.6265402843601896,0.6265402843601896,0.5882776540607534,10.90817928314209,22.534162 -2112,Multiclass classification,Streaming Random Patches,Insects,0.6570345807674088,0.6570345807674088,0.61544126739188,21.709880828857422,65.027277 -3168,Multiclass classification,Streaming Random Patches,Insects,0.6684559520050521,0.6684559520050521,0.6242294974630811,28.635205268859863,131.537407 -4224,Multiclass classification,Streaming Random Patches,Insects,0.6810324413923751,0.6810324413923751,0.6325456686453049,36.43542194366455,221.773783 -5280,Multiclass classification,Streaming Random Patches,Insects,0.6910399696912294,0.6910399696912294,0.6411255615252124,45.614484786987305,335.089181 -6336,Multiclass classification,Streaming Random Patches,Insects,0.6937647987371744,0.6937647987371744,0.6440375279924044,53.59738254547119,471.883196 -7392,Multiclass classification,Streaming Random Patches,Insects,0.6988228927073468,0.6988228927073468,0.6494865599203364,66.1818675994873,633.159586 -8448,Multiclass classification,Streaming Random Patches,Insects,0.7001302237480762,0.7001302237480762,0.6494906800979877,76.50763607025146,819.592493 -9504,Multiclass classification,Streaming Random Patches,Insects,0.7055666631590024,0.7055666631590024,0.6515748182594757,80.03414821624756,1031.228873 -10560,Multiclass classification,Streaming Random Patches,Insects,0.7099157117151246,0.7099157117151246,0.6536141909419667,75.23120212554932,1268.49353 -11616,Multiclass classification,Streaming Random Patches,Insects,0.7112354713732243,0.7112354713732243,0.6532930257397846,87.85937118530273,1530.837041 -12672,Multiclass classification,Streaming Random Patches,Insects,0.7140715018546286,0.7140715018546285,0.6586632134486646,96.90367698669434,1818.32799 -13728,Multiclass classification,Streaming Random Patches,Insects,0.7196765498652291,0.7196765498652291,0.7110222921473365,56.69392013549805,2124.564995 -14784,Multiclass classification,Streaming Random Patches,Insects,0.7275248596360685,0.7275248596360685,0.7243727970733626,23.290308952331543,2446.9953570000002 -15840,Multiclass classification,Streaming Random Patches,Insects,0.7219521434433992,0.7219521434433992,0.7204121258981635,12.419946670532227,2789.3598920000004 -16896,Multiclass classification,Streaming Random Patches,Insects,0.7180822728617934,0.7180822728617934,0.7177336146344276,18.459078788757324,3150.9630650000004 -17952,Multiclass classification,Streaming Random Patches,Insects,0.7130521976491561,0.713052197649156,0.7136298242976093,32.946556091308594,3531.3081450000004 -19008,Multiclass classification,Streaming Random Patches,Insects,0.7221023833324565,0.7221023833324565,0.7193994629254835,14.42181396484375,3928.2542010000006 -20064,Multiclass classification,Streaming Random Patches,Insects,0.7273089767233215,0.7273089767233214,0.721146893328104,20.33617401123047,4340.108142000001 -21120,Multiclass classification,Streaming Random Patches,Insects,0.7289170888773142,0.7289170888773142,0.7201390592471967,29.718432426452637,4775.145968000001 -22176,Multiclass classification,Streaming Random Patches,Insects,0.7305524239007892,0.7305524239007891,0.719265816341323,21.72282314300537,5233.162328 -23232,Multiclass classification,Streaming Random Patches,Insects,0.7328569583745856,0.7328569583745856,0.7192472788421966,31.907146453857422,5712.0379140000005 -24288,Multiclass classification,Streaming Random Patches,Insects,0.7349610902952196,0.7349610902952196,0.7190161489472059,36.71180248260498,6211.046404000001 -25344,Multiclass classification,Streaming Random Patches,Insects,0.7382314643096713,0.7382314643096713,0.7202655895968563,44.65183067321777,6729.485654000001 -26400,Multiclass classification,Streaming Random Patches,Insects,0.7397249895829388,0.7397249895829386,0.7198095986730461,54.357375144958496,7266.820082000001 -27456,Multiclass classification,Streaming Random Patches,Insects,0.7418685121107267,0.7418685121107267,0.7199133187431289,60.99125003814697,7822.667714000001 -28512,Multiclass classification,Streaming Random Patches,Insects,0.7388727157939041,0.7388727157939041,0.7182833957431396,26.944812774658203,8398.518895000001 -29568,Multiclass classification,Streaming Random Patches,Insects,0.7376128792234586,0.7376128792234586,0.7214769633664444,24.290247917175293,8990.352305 -30624,Multiclass classification,Streaming Random Patches,Insects,0.7372889658100121,0.7372889658100121,0.7255972176885724,19.85909652709961,9598.262147000001 -31680,Multiclass classification,Streaming Random Patches,Insects,0.7371444805707251,0.737144480570725,0.7291466686667684,32.85751724243164,10221.399895 -32736,Multiclass classification,Streaming Random Patches,Insects,0.7374064457003208,0.7374064457003208,0.7322831246511409,38.75182342529297,10860.215509 -33792,Multiclass classification,Streaming Random Patches,Insects,0.7329170489183511,0.7329170489183511,0.7291423789419403,76.79454803466797,11517.947008 -34848,Multiclass classification,Streaming Random Patches,Insects,0.7290728039716475,0.7290728039716475,0.7252059051088736,37.93787670135498,12198.376918999998 -35904,Multiclass classification,Streaming Random Patches,Insects,0.726791633011169,0.7267916330111689,0.72277521319889,30.2938232421875,12898.613100999999 -36960,Multiclass classification,Streaming Random Patches,Insects,0.7233150247571634,0.7233150247571634,0.7191521630945247,34.07670021057129,13619.767748999999 -38016,Multiclass classification,Streaming Random Patches,Insects,0.7210837827173484,0.7210837827173484,0.7166085958184295,39.338196754455566,14364.864177 -39072,Multiclass classification,Streaming Random Patches,Insects,0.7203040618361445,0.7203040618361445,0.7160627724850469,41.774664878845215,15133.386085 -40128,Multiclass classification,Streaming Random Patches,Insects,0.7183193361078576,0.7183193361078576,0.7145670483840382,44.649410247802734,15926.835433 -41184,Multiclass classification,Streaming Random Patches,Insects,0.7176990505791224,0.7176990505791223,0.7142800617937591,38.580246925354004,16742.181512 -42240,Multiclass classification,Streaming Random Patches,Insects,0.7177489997395772,0.7177489997395772,0.7147225222929322,44.2959041595459,17577.282826 -43296,Multiclass classification,Streaming Random Patches,Insects,0.7185818223813374,0.7185818223813374,0.7159354160738768,44.74843406677246,18431.175396 -44352,Multiclass classification,Streaming Random Patches,Insects,0.7192171540664246,0.7192171540664247,0.7168891106233332,50.63485240936279,19303.296412 -45408,Multiclass classification,Streaming Random Patches,Insects,0.7197128196093113,0.7197128196093113,0.7173999204613543,48.77041816711426,20195.421521 -46464,Multiclass classification,Streaming Random Patches,Insects,0.7207240169597314,0.7207240169597314,0.7184187872009821,56.04546070098877,21107.071122 -47520,Multiclass classification,Streaming Random Patches,Insects,0.7212062543403691,0.7212062543403692,0.7191280088329424,48.19489002227783,22037.88723 -48576,Multiclass classification,Streaming Random Patches,Insects,0.7252084405558414,0.7252084405558414,0.7232782847500743,54.32844257354736,22988.011007 -49632,Multiclass classification,Streaming Random Patches,Insects,0.7291813584251778,0.7291813584251778,0.7271951034706091,53.6518030166626,23956.634362 -50688,Multiclass classification,Streaming Random Patches,Insects,0.7326928009154221,0.7326928009154221,0.7304439468758875,27.42653465270996,24940.316756 -51744,Multiclass classification,Streaming Random Patches,Insects,0.7367180101656262,0.7367180101656263,0.7341247480346391,36.39958953857422,25936.798718000002 -52800,Multiclass classification,Streaming Random Patches,Insects,0.7395784011060815,0.7395784011060814,0.737512125998823,8.341936111450195,26942.262482000002 -408,Multiclass classification,Streaming Random Patches,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.551915168762207,2.576854 -816,Multiclass classification,Streaming Random Patches,Keystroke,0.9742331288343559,0.9742331288343559,0.8747406597440331,4.161267280578613,7.918686 -1224,Multiclass classification,Streaming Random Patches,Keystroke,0.9672935404742437,0.9672935404742437,0.9345378451161834,7.9047441482543945,16.17177 -1632,Multiclass classification,Streaming Random Patches,Keystroke,0.9662783568362967,0.9662783568362967,0.920078959712528,12.156608581542969,27.617862 -2040,Multiclass classification,Streaming Random Patches,Keystroke,0.9632172633643943,0.9632172633643943,0.9392069284616192,18.052184104919434,42.519185 -2448,Multiclass classification,Streaming Random Patches,Keystroke,0.9591336330200245,0.9591336330200245,0.9527072671889639,17.44593620300293,61.058317 -2856,Multiclass classification,Streaming Random Patches,Keystroke,0.9600700525394046,0.9600700525394046,0.9487475492194611,23.22895908355713,83.383302 -3264,Multiclass classification,Streaming Random Patches,Keystroke,0.9589334967821024,0.9589334967821024,0.9481804303110769,30.014172554016113,110.001368 -3672,Multiclass classification,Streaming Random Patches,Keystroke,0.957504767093435,0.957504767093435,0.948270905442242,37.68842315673828,141.307336 -4080,Multiclass classification,Streaming Random Patches,Keystroke,0.9529296396175533,0.9529296396175533,0.9350591426916868,43.92499256134033,178.285036 -4488,Multiclass classification,Streaming Random Patches,Keystroke,0.9558725206151103,0.9558725206151103,0.958348874105129,23.460043907165527,220.514164 -4896,Multiclass classification,Streaming Random Patches,Keystroke,0.957711950970378,0.957711950970378,0.9572545884780326,21.815909385681152,267.806579 -5304,Multiclass classification,Streaming Random Patches,Keystroke,0.9581369036394494,0.9581369036394494,0.9564558175945329,29.116984367370605,320.079148 -5712,Multiclass classification,Streaming Random Patches,Keystroke,0.9593766415689021,0.9593766415689021,0.9590743474150507,34.215229988098145,377.359359 -6120,Multiclass classification,Streaming Random Patches,Keystroke,0.9576728223565942,0.9576728223565942,0.9540539138154063,42.245460510253906,440.30303599999996 -6528,Multiclass classification,Streaming Random Patches,Keystroke,0.9572544813850161,0.9572544813850162,0.9569914463415943,19.71925640106201,508.40118299999995 -6936,Multiclass classification,Streaming Random Patches,Keystroke,0.9586157173756309,0.9586157173756308,0.9593505106134974,22.396859169006348,580.778744 -7344,Multiclass classification,Streaming Random Patches,Keystroke,0.9592809478414817,0.9592809478414817,0.9593459120031487,26.322874069213867,657.980924 -7752,Multiclass classification,Streaming Random Patches,Keystroke,0.9600051606244355,0.9600051606244355,0.9601169971762602,30.259758949279785,740.2744439999999 -8160,Multiclass classification,Streaming Random Patches,Keystroke,0.9574702782203701,0.95747027822037,0.9549133730963548,37.68336868286133,827.6717389999999 -8568,Multiclass classification,Streaming Random Patches,Keystroke,0.9564608380996849,0.9564608380996849,0.9560990529914857,43.737112045288086,921.0091999999999 -8976,Multiclass classification,Streaming Random Patches,Keystroke,0.9568802228412256,0.9568802228412256,0.9569984740230398,33.59728527069092,1020.6233179999998 -9384,Multiclass classification,Streaming Random Patches,Keystroke,0.9575828626238942,0.9575828626238942,0.9578510301970171,34.01332950592041,1125.214274 -9792,Multiclass classification,Streaming Random Patches,Keystroke,0.9576141354304974,0.9576141354304974,0.9575892724596199,40.18074893951416,1234.457491 -10200,Multiclass classification,Streaming Random Patches,Keystroke,0.9558780272575743,0.9558780272575743,0.9547878392234919,48.97087860107422,1349.2361799999999 -10608,Multiclass classification,Streaming Random Patches,Keystroke,0.9524842085415292,0.9524842085415292,0.9506853107984292,30.6993989944458,1470.1246939999999 -11016,Multiclass classification,Streaming Random Patches,Keystroke,0.9539718565592374,0.9539718565592374,0.9545620457235888,26.549206733703613,1595.074551 -11424,Multiclass classification,Streaming Random Patches,Keystroke,0.9543902652543115,0.9543902652543115,0.9545363240408884,33.6107063293457,1724.543611 -11832,Multiclass classification,Streaming Random Patches,Keystroke,0.9541881497760122,0.9541881497760122,0.9541408405790519,25.182985305786133,1859.013318 -12240,Multiclass classification,Streaming Random Patches,Keystroke,0.955061688046409,0.955061688046409,0.9554321262858616,27.34038543701172,1997.8708550000001 -12648,Multiclass classification,Streaming Random Patches,Keystroke,0.9546928125247094,0.9546928125247094,0.9546233453975913,35.30395698547363,2141.6119080000003 -13056,Multiclass classification,Streaming Random Patches,Keystroke,0.953887399463807,0.953887399463807,0.9537532269202632,33.51621055603027,2290.943904 -13464,Multiclass classification,Streaming Random Patches,Keystroke,0.9540221347396568,0.9540221347396568,0.954138309472004,33.38596153259277,2445.0119170000003 -13872,Multiclass classification,Streaming Random Patches,Keystroke,0.9546535938288515,0.9546535938288515,0.9549190485054234,31.360337257385254,2603.6856470000002 -14280,Multiclass classification,Streaming Random Patches,Keystroke,0.9534281112122698,0.9534281112122698,0.9532093226981456,38.61776542663574,2767.1788560000005 -14688,Multiclass classification,Streaming Random Patches,Keystroke,0.9540409886294001,0.9540409886294001,0.9542688403803362,42.8822660446167,2935.9642900000003 -15096,Multiclass classification,Streaming Random Patches,Keystroke,0.9547532295462073,0.9547532295462073,0.9549723528375391,41.949758529663086,3110.4045410000003 -15504,Multiclass classification,Streaming Random Patches,Keystroke,0.9549764561697736,0.9549764561697736,0.9551012466300322,36.29027271270752,3290.0043080000005 -15912,Multiclass classification,Streaming Random Patches,Keystroke,0.9551253849538055,0.9551253849538055,0.9552372796273361,33.26945877075195,3474.8467850000006 -16320,Multiclass classification,Streaming Random Patches,Keystroke,0.9555119799007292,0.9555119799007292,0.9556369370454034,38.47606945037842,3664.7140100000006 -16728,Multiclass classification,Streaming Random Patches,Keystroke,0.954923178095295,0.954923178095295,0.9549151106032768,38.78229522705078,3859.8566950000004 -17136,Multiclass classification,Streaming Random Patches,Keystroke,0.955587977823169,0.955587977823169,0.9557184838324558,44.56228828430176,4060.2677570000005 -17544,Multiclass classification,Streaming Random Patches,Keystroke,0.9550817990081514,0.9550817990081514,0.9550944582439086,49.72221755981445,4266.497535 -17952,Multiclass classification,Streaming Random Patches,Keystroke,0.9547657512116317,0.9547657512116317,0.9547923955213531,44.72002029418945,4478.609232000001 -18360,Multiclass classification,Streaming Random Patches,Keystroke,0.9553897271093197,0.9553897271093197,0.955476322048541,52.00297737121582,4696.983244000001 -18768,Multiclass classification,Streaming Random Patches,Keystroke,0.9555070069803379,0.9555070069803379,0.9555572955831596,59.27475929260254,4921.837298000001 -19176,Multiclass classification,Streaming Random Patches,Keystroke,0.9548891786179922,0.9548891786179922,0.9549038695373787,71.70181655883789,5153.196879000001 -19584,Multiclass classification,Streaming Random Patches,Keystroke,0.954858806107338,0.954858806107338,0.9548865417655429,78.58561515808105,5390.578061000001 -19992,Multiclass classification,Streaming Random Patches,Keystroke,0.9539292681706768,0.9539292681706768,0.9539347026376765,85.24763870239258,5634.910465000001 -20400,Multiclass classification,Streaming Random Patches,Keystroke,0.9532330016177264,0.9532330016177264,0.9532392337717847,74.55205345153809,5886.477404000001 -46,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.5555555555555556,0.5555555555555556,0.4458032432860809,0.06141090393066406,0.019654 -92,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6483516483516484,0.6483516483516484,0.646491610589355,0.11540794372558594,0.062314999999999995 -138,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.708029197080292,0.708029197080292,0.7216654146545566,0.1258535385131836,0.135768 -184,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7431693989071039,0.743169398907104,0.7576794034998369,0.12633037567138672,0.24048 -230,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7641921397379913,0.7641921397379913,0.7751275973499576,0.12631702423095703,0.376459 -276,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7672727272727272,0.7672727272727272,0.7799448750812884,0.1262655258178711,0.543817 -322,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7757009345794392,0.7757009345794392,0.781311030606134,0.12674331665039062,0.742198 -368,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.782016348773842,0.782016348773842,0.7830988277979799,0.12674713134765625,0.9720690000000001 -414,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7893462469733656,0.7893462469733655,0.7891834545778567,0.12626934051513672,1.233319 -460,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7799564270152506,0.7799564270152506,0.778762654261754,0.12626266479492188,1.526064 -506,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7841584158415842,0.7841584158415842,0.7830263284725031,0.12677955627441406,1.8499750000000001 -552,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7840290381125227,0.7840290381125228,0.7833214841514466,0.12677383422851562,2.2053070000000004 -598,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7839195979899497,0.7839195979899497,0.7851401823229054,0.1262836456298828,2.5919950000000003 -644,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7884914463452566,0.7884914463452566,0.790931132142264,0.12628936767578125,3.0100190000000002 -690,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.795355587808418,0.795355587808418,0.7973717331367783,0.12679672241210938,3.459217 -736,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7918367346938775,0.7918367346938775,0.79371924750244,0.12629222869873047,3.93961 -782,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8015364916773368,0.8015364916773368,0.8027236936866887,0.12627696990966797,4.451398 -828,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7980652962515115,0.7980652962515115,0.8001612113332863,0.12677764892578125,4.994585 -874,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8041237113402062,0.8041237113402062,0.8058476562214167,0.12676525115966797,5.568929 -920,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8063112078346029,0.8063112078346029,0.8071524109530731,0.1262378692626953,6.174556 -966,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8072538860103627,0.8072538860103627,0.8069383576906736,0.1262502670288086,6.811568 -1012,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8120672601384767,0.8120672601384767,0.8103691514865562,0.12676239013671875,7.479958 -1058,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8117313150425733,0.8117313150425733,0.8093057999862455,0.12675857543945312,8.179363 -1104,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8105167724388033,0.8105167724388033,0.8087453181575575,0.12626075744628906,8.909729 -1150,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8120104438642297,0.8120104438642298,0.8093458779132273,0.12674808502197266,9.671618 -1196,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8125523012552301,0.8125523012552303,0.8098995946687924,0.1267566680908203,10.464471 -1242,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8170829975825947,0.8170829975825946,0.8146737825459542,0.12630462646484375,11.288497999999999 -1288,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8174048174048174,0.8174048174048174,0.8149699191034137,0.12631511688232422,12.143769999999998 -1334,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8169542385596399,0.8169542385596399,0.8144172630221828,0.12679100036621094,13.030243999999998 -1380,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8165337200870196,0.8165337200870196,0.8142638589810781,0.12678813934326172,13.947682999999998 -1426,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8210526315789474,0.8210526315789475,0.8177443463463022,0.1262807846069336,14.896382999999998 -1472,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.822569680489463,0.822569680489463,0.8180682540474884,0.1267719268798828,15.876335 -1518,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8233355306526038,0.8233355306526038,0.8183049909694801,0.12675857543945312,16.887748 -1564,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8227767114523352,0.8227767114523352,0.8180063024943973,0.1262645721435547,17.930424 -1610,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8228713486637663,0.8228713486637663,0.818440484251979,0.1262655258178711,19.004458 -1656,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.824773413897281,0.824773413897281,0.8207684581521858,0.12678241729736328,20.109849999999998 -1702,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.824808935920047,0.824808935920047,0.8222541912553749,0.1268024444580078,21.24685 -1748,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8259874069834001,0.8259874069834001,0.8228660744170171,0.12630653381347656,22.415483 -1794,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.826547685443391,0.826547685443391,0.8226613560637924,0.12677478790283203,23.615758 -1840,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8254486133768353,0.8254486133768353,0.8217381124058762,0.12675857543945312,24.847279 -1886,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8249336870026526,0.8249336870026526,0.8216008133499116,0.12628459930419922,26.110083 -1932,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8234075608493009,0.8234075608493009,0.8193527544316537,0.12627792358398438,27.403914999999998 -1978,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8234699038947901,0.8234699038947901,0.8195124114516217,0.12675857543945312,28.728914999999997 -2024,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8220464656450815,0.8220464656450815,0.8172381305352,0.12677478790283203,30.084906999999998 -2070,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8206863218946351,0.8206863218946351,0.8164336862763343,0.12628936767578125,31.472198999999996 -2116,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8217494089834515,0.8217494089834515,0.8168455585843762,0.1262645721435547,32.891028 -2162,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8204534937528922,0.8204534937528921,0.8154843900985335,0.12677288055419922,34.340933 -2208,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.822383325781604,0.822383325781604,0.8171788245797035,0.1262683868408203,35.822171 -2254,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.821127385707945,0.821127385707945,0.8170261701336431,0.12625598907470703,37.335093 -2300,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8199217050891692,0.8199217050891693,0.8158945802523674,0.12676048278808594,38.879423 -1056,Multiclass classification,k-Nearest Neighbors,Insects,0.6322274881516587,0.6322274881516587,0.5639948035153092,0.21594810485839844,1.014879 -2112,Multiclass classification,k-Nearest Neighbors,Insects,0.636191378493605,0.636191378493605,0.5686546251961576,0.2164621353149414,3.11274 -3168,Multiclass classification,k-Nearest Neighbors,Insects,0.6274076413009156,0.6274076413009156,0.5664829980315041,0.2159862518310547,6.352824 -4224,Multiclass classification,k-Nearest Neighbors,Insects,0.6317783566185177,0.6317783566185177,0.5676004628647836,0.216461181640625,10.71472 -5280,Multiclass classification,k-Nearest Neighbors,Insects,0.6277704110627013,0.6277704110627013,0.5651907052085646,0.21597766876220703,16.153805 -6336,Multiclass classification,k-Nearest Neighbors,Insects,0.6244672454617206,0.6244672454617206,0.5642758642399058,0.2164630889892578,22.622425999999997 -7392,Multiclass classification,k-Nearest Neighbors,Insects,0.621160871329996,0.621160871329996,0.5621999618118433,0.2159862518310547,30.095943999999996 -8448,Multiclass classification,k-Nearest Neighbors,Insects,0.6183260329110927,0.6183260329110927,0.560545956984929,0.21646785736083984,38.53514199999999 -9504,Multiclass classification,k-Nearest Neighbors,Insects,0.6195938124802693,0.6195938124802693,0.5612689785887882,0.2159566879272461,47.930054999999996 -10560,Multiclass classification,k-Nearest Neighbors,Insects,0.6209868358746093,0.6209868358746093,0.5626902992589761,0.2164936065673828,58.279528 -11616,Multiclass classification,k-Nearest Neighbors,Insects,0.6196297890658631,0.6196297890658631,0.5618958864151227,0.21594715118408203,69.583507 -12672,Multiclass classification,k-Nearest Neighbors,Insects,0.6235498382132428,0.6235498382132428,0.577401509815314,0.21659374237060547,81.841211 -13728,Multiclass classification,k-Nearest Neighbors,Insects,0.6524368033801996,0.6524368033801996,0.656066758247117,0.21615219116210938,95.05061 -14784,Multiclass classification,k-Nearest Neighbors,Insects,0.6639383075153893,0.6639383075153893,0.6656513873636037,0.21651744842529297,109.208982 -15840,Multiclass classification,k-Nearest Neighbors,Insects,0.6599532798787803,0.6599532798787803,0.6660828271082423,0.21599388122558594,124.323768 -16896,Multiclass classification,k-Nearest Neighbors,Insects,0.6583012725658479,0.6583012725658479,0.6678320995738946,0.21648120880126953,140.393226 -17952,Multiclass classification,k-Nearest Neighbors,Insects,0.6558966074313409,0.6558966074313409,0.6676009154715022,0.2160320281982422,157.416689 -19008,Multiclass classification,k-Nearest Neighbors,Insects,0.6731730415110223,0.6731730415110223,0.6774302820037228,0.21651268005371094,175.38966399999998 -20064,Multiclass classification,k-Nearest Neighbors,Insects,0.6798086029008623,0.6798086029008623,0.6780616401383449,0.21589279174804688,194.31697799999998 -21120,Multiclass classification,k-Nearest Neighbors,Insects,0.680382593872816,0.680382593872816,0.6752117016598617,0.2164011001586914,214.20054599999997 -22176,Multiclass classification,k-Nearest Neighbors,Insects,0.6805862457722661,0.6805862457722661,0.6722568877045599,0.2158823013305664,235.03782499999997 -23232,Multiclass classification,k-Nearest Neighbors,Insects,0.6813740260858336,0.6813740260858336,0.6702824994179433,0.21639060974121094,256.831775 -24288,Multiclass classification,k-Nearest Neighbors,Insects,0.6815992094536172,0.6815992094536172,0.6677450869096582,0.21591758728027344,279.574148 -25344,Multiclass classification,k-Nearest Neighbors,Insects,0.6821212958213313,0.6821212958213313,0.6660355323582295,0.21636676788330078,303.273869 -26400,Multiclass classification,k-Nearest Neighbors,Insects,0.6831319368157884,0.6831319368157884,0.6646803813034555,0.21590614318847656,327.922368 -27456,Multiclass classification,k-Nearest Neighbors,Insects,0.6836277545073757,0.6836277545073757,0.6627124931293528,0.2164020538330078,353.52686 -28512,Multiclass classification,k-Nearest Neighbors,Insects,0.6834905825821612,0.6834905825821612,0.664548122616301,0.2161540985107422,380.080866 -29568,Multiclass classification,k-Nearest Neighbors,Insects,0.6814692055331958,0.6814692055331958,0.6671975305669872,0.2166757583618164,407.59582800000004 -30624,Multiclass classification,k-Nearest Neighbors,Insects,0.6796525487378767,0.6796525487378767,0.669471411791397,0.21617984771728516,436.06293600000004 -31680,Multiclass classification,k-Nearest Neighbors,Insects,0.6779570062186306,0.6779570062186306,0.6711290718417154,0.21667957305908203,465.48818300000005 -32736,Multiclass classification,k-Nearest Neighbors,Insects,0.6768901787078051,0.6768901787078051,0.6727094382078547,0.21613597869873047,495.86473400000006 -33792,Multiclass classification,k-Nearest Neighbors,Insects,0.6734337545500281,0.6734337545500281,0.6702378074852682,0.2164754867553711,527.2018280000001 -34848,Multiclass classification,k-Nearest Neighbors,Insects,0.6690676385341636,0.6690676385341636,0.6661382581729155,0.21594715118408203,559.4897450000001 -35904,Multiclass classification,k-Nearest Neighbors,Insects,0.6663510013090828,0.6663510013090828,0.6633778558128317,0.21650028228759766,592.7366190000001 -36960,Multiclass classification,k-Nearest Neighbors,Insects,0.662409697232068,0.662409697232068,0.6597878724618786,0.215972900390625,626.9366260000002 -38016,Multiclass classification,k-Nearest Neighbors,Insects,0.6594239116138366,0.6594239116138366,0.6567102170776443,0.21648025512695312,662.1000680000002 -39072,Multiclass classification,k-Nearest Neighbors,Insects,0.662409459701569,0.662409459701569,0.6591983036871739,0.21597957611083984,698.2204010000002 -40128,Multiclass classification,k-Nearest Neighbors,Insects,0.6615495800832357,0.6615495800832357,0.658372148729009,0.21650981903076172,735.3021140000002 -41184,Multiclass classification,k-Nearest Neighbors,Insects,0.6616079450258602,0.6616079450258602,0.6583203582230679,0.21601200103759766,773.3352610000002 -42240,Multiclass classification,k-Nearest Neighbors,Insects,0.6620895381045953,0.6620895381045953,0.6586855795305535,0.21649646759033203,812.3294790000002 -43296,Multiclass classification,k-Nearest Neighbors,Insects,0.6626862224275321,0.6626862224275321,0.6591267371039767,0.21601295471191406,852.2739140000002 -44352,Multiclass classification,k-Nearest Neighbors,Insects,0.6625104281752384,0.6625104281752384,0.6587853710847982,0.21649742126464844,893.1794370000002 -45408,Multiclass classification,k-Nearest Neighbors,Insects,0.6629374325544519,0.6629374325544519,0.6587077344895959,0.21599388122558594,935.0370030000003 -46464,Multiclass classification,k-Nearest Neighbors,Insects,0.6634311172330671,0.6634311172330671,0.6587873315408634,0.21650028228759766,977.8531990000002 -47520,Multiclass classification,k-Nearest Neighbors,Insects,0.666217723436941,0.666217723436941,0.6621071051846,0.21592044830322266,1021.6188110000003 -48576,Multiclass classification,k-Nearest Neighbors,Insects,0.6698507462686567,0.6698507462686567,0.6663907774790556,0.2164478302001953,1066.3421490000003 -49632,Multiclass classification,k-Nearest Neighbors,Insects,0.6739940762829683,0.6739940762829683,0.6709516060662618,0.2159433364868164,1112.0155100000002 -50688,Multiclass classification,k-Nearest Neighbors,Insects,0.6774715410262987,0.6774715410262987,0.6745572423992897,0.21648406982421875,1158.6520200000002 -51744,Multiclass classification,k-Nearest Neighbors,Insects,0.6814834856888855,0.6814834856888855,0.6786206144243011,0.21599388122558594,1206.2391940000002 -52800,Multiclass classification,k-Nearest Neighbors,Insects,0.6865470936949564,0.6865470936949564,0.6836613373539585,0.21665573120117188,1254.7848490000001 -408,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,0.2092580795288086,0.626636 -816,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9828220858895705,0.9828220858895705,0.9550926410288757,0.20981216430664062,1.943188 -1224,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9852820932134096,0.9852820932134096,0.9672695079711997,0.20935916900634766,3.654696 -1632,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840588595953402,0.9840588595953402,0.9604409213604835,0.2098979949951172,5.778702 -2040,Multiclass classification,k-Nearest Neighbors,Keystroke,0.984796468857283,0.984796468857283,0.9791423790442798,0.21045207977294922,8.327232 -2448,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9861054352268084,0.9861054352268084,0.9837809767868474,0.20999908447265625,11.304767 -2856,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9859894921190894,0.9859894921190894,0.9813641447908843,0.21055316925048828,14.706237 -3264,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9871284094391665,0.9871284094391665,0.9868437405314092,0.2106037139892578,18.525325 -3672,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9880141650776355,0.9880141650776355,0.9878382173613446,0.21015071868896484,22.761229999999998 -4080,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9877420936504046,0.9877420936504046,0.9857777629944036,0.21070480346679688,27.408752 -4488,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9881880989525296,0.9881880989525296,0.9878235870948694,0.2102518081665039,32.463238 -4896,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9885597548518897,0.9885597548518897,0.9882962361329112,0.21030235290527344,37.912155999999996 -5304,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9867999245709975,0.9867999245709975,0.9836140972543967,0.210906982421875,43.748231 -5712,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9873927508317283,0.9873927508317283,0.9875632488318824,0.21045398712158203,49.983610999999996 -6120,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9872528190880863,0.9872528190880863,0.986679154193125,0.21100807189941406,56.586138999999996 -6528,Multiclass classification,k-Nearest Neighbors,Keystroke,0.987130381492263,0.987130381492263,0.9866769113371191,0.2110586166381836,63.546493 -6936,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9875991348233598,0.9875991348233598,0.9877805463370743,0.21060562133789062,70.86485 -7344,Multiclass classification,k-Nearest Neighbors,Keystroke,0.986926324390576,0.986926324390576,0.9861386596476129,0.21262454986572266,78.54037100000001 -7752,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9865823764675525,0.9865823764675525,0.9861511169160879,0.2121715545654297,86.582909 -8160,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9861502635126854,0.9861502635126854,0.9857089041873667,0.21222209930419922,94.988236 -8568,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9863429438543247,0.9863429438543247,0.9863829773026439,0.21277618408203125,103.754213 -8976,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9865181058495822,0.9865181058495822,0.9865643235024212,0.21232318878173828,112.879589 -9384,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9858254289672812,0.9858254289672812,0.9853734936692787,0.2128772735595703,122.365291 -9792,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9857011541211317,0.9857011541211317,0.9856081881161903,0.21292781829833984,132.212374 -10200,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9860770663790568,0.9860770663790568,0.9862471716083434,0.21247482299804688,142.422221 -10608,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9853870085792401,0.9853870085792401,0.9850628829106897,0.2130289077758789,152.99283200000002 -11016,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9855651384475715,0.9855651384475715,0.9856470830770893,0.21257591247558594,163.926329 -11424,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9857305436400245,0.9857305436400245,0.9858087969497247,0.21262645721435547,175.222393 -11832,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9858845406136422,0.9858845406136422,0.9859589489459036,0.2131805419921875,186.880683 -12240,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9861099763052537,0.9861099763052537,0.9862068987479334,0.21272754669189453,198.90524100000002 -12648,Multiclass classification,k-Nearest Neighbors,Keystroke,0.986241796473472,0.986241796473472,0.9863073128720755,0.21328163146972656,211.292323 -13056,Multiclass classification,k-Nearest Neighbors,Keystroke,0.985905783224818,0.985905783224818,0.9858386074980298,0.2133321762084961,224.04148700000002 -13464,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9857386912278095,0.9857386912278095,0.985725098817589,0.21287918090820312,237.153453 -13872,Multiclass classification,k-Nearest Neighbors,Keystroke,0.985725614591594,0.985725614591594,0.9857526199764752,0.21343326568603516,250.627393 -14280,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9845927585965404,0.9845927585965404,0.9843691165759658,0.2129802703857422,264.463365 -14688,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9848845918158916,0.9848845918158916,0.9849709956409892,0.21303081512451172,278.661824 -15096,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9851606492215965,0.9851606492215965,0.9852374033885689,0.21358489990234375,293.222186 -15504,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9843901180416693,0.9843901180416693,0.9842921251481087,0.21313190460205078,308.144297 -15912,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840362013701213,0.9840362013701213,0.9840127534225097,0.2136859893798828,323.428421 -16320,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840676512041179,0.9840676512041179,0.9840971764012499,0.21373653411865234,339.073895 -16728,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9838584324744425,0.9838584324744425,0.9838587519327452,0.21328353881835938,355.08225799999997 -17136,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840676976947768,0.9840676976947768,0.9841085979018743,0.2138376235961914,371.45446999999996 -17544,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840962207148151,0.9840962207148151,0.9841170088782344,0.21338462829589844,388.18776499999996 -17952,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840120327558354,0.9840120327558354,0.98402212072501,0.21343517303466797,405.28127299999994 -18360,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9842039326760716,0.9842039326760716,0.9842275892846344,0.2139892578125,422.73574599999995 -18768,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9842809186337721,0.9842809186337721,0.9842944297848302,0.21353626251220703,440.55097099999995 -19176,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9843024771838331,0.9843024771838331,0.9843104669951571,0.21409034729003906,458.72564299999993 -19584,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9843742021140786,0.9843742021140786,0.9843801024949197,0.2141408920288086,477.2614029999999 -19992,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9845430443699665,0.9845430443699665,0.984546236206973,0.21368789672851562,496.1581819999999 -20400,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9845090445610079,0.9845090445610079,0.984507607652182,0.21424198150634766,515.4151939999999 -46,Multiclass classification,ADWIN Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.24576497265572897,4.137397766113281,1.064188 -92,Multiclass classification,ADWIN Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.49347523955818895,4.140613555908203,2.6316629999999996 -138,Multiclass classification,ADWIN Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646677,4.140277862548828,4.836076 -184,Multiclass classification,ADWIN Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.614143164890895,4.141227722167969,7.573955 -230,Multiclass classification,ADWIN Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,3.9138870239257812,10.681288 -276,Multiclass classification,ADWIN Bagging,ImageSegments,0.64,0.64,0.6559607038460422,4.028352737426758,14.131211 -322,Multiclass classification,ADWIN Bagging,ImageSegments,0.6697819314641744,0.6697819314641744,0.6706320385346652,4.144774436950684,17.917192 -368,Multiclass classification,ADWIN Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6897433526546474,4.144762992858887,22.052519 -414,Multiclass classification,ADWIN Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.706570530482581,4.148934364318848,26.547524 -460,Multiclass classification,ADWIN Bagging,ImageSegments,0.7145969498910676,0.7145969498910676,0.7071122267088653,4.148022651672363,31.388582999999997 -506,Multiclass classification,ADWIN Bagging,ImageSegments,0.7247524752475247,0.7247524752475247,0.7147973207987898,4.147336006164551,36.558333999999995 -552,Multiclass classification,ADWIN Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7210771168277493,4.147068977355957,42.057708 -598,Multiclass classification,ADWIN Bagging,ImageSegments,0.7336683417085427,0.7336683417085426,0.7250288715672424,4.1468400955200195,47.891459999999995 -644,Multiclass classification,ADWIN Bagging,ImageSegments,0.7325038880248833,0.7325038880248833,0.725892488365903,4.150084495544434,54.057886999999994 -690,Multiclass classification,ADWIN Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.730253637873586,4.1498517990112305,60.540488999999994 -736,Multiclass classification,ADWIN Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7329631379486717,4.149523735046387,67.34570099999999 -782,Multiclass classification,ADWIN Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7387105187530085,4.149043083190918,74.478308 -828,Multiclass classification,ADWIN Bagging,ImageSegments,0.7460701330108828,0.7460701330108827,0.7425025596154724,4.1487531661987305,81.934305 -874,Multiclass classification,ADWIN Bagging,ImageSegments,0.7514318442153494,0.7514318442153494,0.7467163857842192,4.148730278015137,89.700464 -920,Multiclass classification,ADWIN Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7453933609147307,4.148531913757324,97.776444 -966,Multiclass classification,ADWIN Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.7451117895470661,4.148127555847168,106.157006 -1012,Multiclass classification,ADWIN Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.7449630804815479,4.147826194763184,114.848603 -1058,Multiclass classification,ADWIN Bagging,ImageSegments,0.7511825922421949,0.7511825922421949,0.7446315489945474,4.149008750915527,123.845956 -1104,Multiclass classification,ADWIN Bagging,ImageSegments,0.7533998186763372,0.7533998186763373,0.7466082689908061,4.149382591247559,133.146638 -1150,Multiclass classification,ADWIN Bagging,ImageSegments,0.7563098346388164,0.7563098346388164,0.7491651771194965,4.148917198181152,142.738156 -1196,Multiclass classification,ADWIN Bagging,ImageSegments,0.7589958158995815,0.7589958158995815,0.7526420027035882,4.148730278015137,152.636035 -1242,Multiclass classification,ADWIN Bagging,ImageSegments,0.75825946817083,0.7582594681708301,0.7524016178277559,4.148566246032715,162.845279 -1288,Multiclass classification,ADWIN Bagging,ImageSegments,0.7637917637917638,0.7637917637917638,0.75666252908711,4.14877986907959,173.368823 -1334,Multiclass classification,ADWIN Bagging,ImageSegments,0.7636909227306826,0.7636909227306825,0.7569484848610158,4.148688316345215,184.20083499999998 -1380,Multiclass classification,ADWIN Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7590436403579585,4.1487226486206055,195.341139 -1426,Multiclass classification,ADWIN Bagging,ImageSegments,0.767719298245614,0.767719298245614,0.7612112896959209,4.148562431335449,206.790743 -1472,Multiclass classification,ADWIN Bagging,ImageSegments,0.7722637661454793,0.7722637661454793,0.7640566966433581,4.148623466491699,218.54670099999998 -1518,Multiclass classification,ADWIN Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.7642341334147652,4.148673057556152,230.6041 -1564,Multiclass classification,ADWIN Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7653316001442942,4.148703575134277,242.96114799999998 -1610,Multiclass classification,ADWIN Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7647353044337892,4.148566246032715,255.63800099999997 -1656,Multiclass classification,ADWIN Bagging,ImageSegments,0.7734138972809668,0.7734138972809667,0.7645730180903106,4.148055076599121,268.628995 -1702,Multiclass classification,ADWIN Bagging,ImageSegments,0.7724867724867724,0.7724867724867724,0.7656182355666586,4.148245811462402,281.916269 -1748,Multiclass classification,ADWIN Bagging,ImageSegments,0.7750429307384087,0.7750429307384087,0.7677424040514297,4.148360252380371,295.50082 -1794,Multiclass classification,ADWIN Bagging,ImageSegments,0.7763524818739542,0.7763524818739542,0.7677176136548695,4.148287773132324,309.399686 -1840,Multiclass classification,ADWIN Bagging,ImageSegments,0.7775965198477434,0.7775965198477434,0.7691578918725354,4.147894859313965,323.61456 -1886,Multiclass classification,ADWIN Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7681560201617949,4.147856712341309,338.130858 -1932,Multiclass classification,ADWIN Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7674170460709654,4.147791862487793,352.957512 -1978,Multiclass classification,ADWIN Bagging,ImageSegments,0.7769347496206374,0.7769347496206374,0.7672843880004774,4.147627830505371,368.093168 -2024,Multiclass classification,ADWIN Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7681802739952505,4.147582054138184,383.545184 -2070,Multiclass classification,ADWIN Bagging,ImageSegments,0.778153697438376,0.7781536974383759,0.7675304391667319,4.147578239440918,399.300197 -2116,Multiclass classification,ADWIN Bagging,ImageSegments,0.7787234042553192,0.778723404255319,0.7673415220519754,4.147555351257324,415.36676400000005 -2162,Multiclass classification,ADWIN Bagging,ImageSegments,0.7797316057380842,0.7797316057380842,0.7679341969633587,4.147627830505371,431.73852700000003 -2208,Multiclass classification,ADWIN Bagging,ImageSegments,0.7816039873130947,0.7816039873130947,0.7687944234581563,4.1476240158081055,448.43594900000005 -2254,Multiclass classification,ADWIN Bagging,ImageSegments,0.7785175321793165,0.7785175321793165,0.7657018899401807,4.147597312927246,465.43417500000004 -2300,Multiclass classification,ADWIN Bagging,ImageSegments,0.7777294475859069,0.7777294475859068,0.7649119672933201,4.14768123626709,482.736348 -1056,Multiclass classification,ADWIN Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762561,6.54005241394043,11.482596 -2112,Multiclass classification,ADWIN Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.540731430053711,32.930503 -3168,Multiclass classification,ADWIN Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244892,6.541685104370117,64.407359 -4224,Multiclass classification,ADWIN Bagging,Insects,0.6107032914989344,0.6107032914989344,0.574850135712032,6.54176139831543,105.889955 -5280,Multiclass classification,ADWIN Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225518,6.542509078979492,157.317574 -6336,Multiclass classification,ADWIN Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789627,6.541296005249023,218.70625900000002 -7392,Multiclass classification,ADWIN Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867538,6.541265487670898,290.118972 -8448,Multiclass classification,ADWIN Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.541204452514648,371.535386 -9504,Multiclass classification,ADWIN Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297488,6.541570663452148,462.975881 -10560,Multiclass classification,ADWIN Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.541746139526367,564.403412 -11616,Multiclass classification,ADWIN Bagging,Insects,0.6005165733964701,0.6005165733964701,0.5651283239572901,6.541906356811523,675.845474 -12672,Multiclass classification,ADWIN Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306639,6.542104721069336,797.3461219999999 -13728,Multiclass classification,ADWIN Bagging,Insects,0.6152108982297662,0.6152108982297662,0.5959760515786451,6.026429176330566,928.2423849999999 -14784,Multiclass classification,ADWIN Bagging,Insects,0.6060339579246432,0.6060339579246432,0.5869142505177357,6.546758651733398,1068.594912 -15840,Multiclass classification,ADWIN Bagging,Insects,0.5713744554580465,0.5713744554580465,0.5537658591956377,6.547109603881836,1218.8956600000001 -16896,Multiclass classification,ADWIN Bagging,Insects,0.545546019532406,0.545546019532406,0.5286479939306437,6.431658744812012,1379.1626680000002 -17952,Multiclass classification,ADWIN Bagging,Insects,0.526767311013314,0.526767311013314,0.509587529402725,6.54762077331543,1549.227957 -19008,Multiclass classification,ADWIN Bagging,Insects,0.517756615983585,0.517756615983585,0.4976462434137419,4.743686676025391,1728.135143 -20064,Multiclass classification,ADWIN Bagging,Insects,0.5296815032647162,0.5296815032647162,0.5080882715573688,10.447637557983398,1914.608483 -21120,Multiclass classification,ADWIN Bagging,Insects,0.539750935176855,0.539750935176855,0.5184934777423561,11.000249862670898,2110.852221 -22176,Multiclass classification,ADWIN Bagging,Insects,0.5468771138669674,0.5468771138669674,0.5259709774382829,10.998456954956055,2316.603266 -23232,Multiclass classification,ADWIN Bagging,Insects,0.5551633593043778,0.5551633593043778,0.5340735310276195,12.317106246948242,2531.7014360000003 -24288,Multiclass classification,ADWIN Bagging,Insects,0.5615761518507844,0.5615761518507844,0.5396852076547555,12.966436386108398,2756.048529 -25344,Multiclass classification,ADWIN Bagging,Insects,0.5679280274632048,0.5679280274632048,0.5455634192548012,13.622279167175293,2989.801767 -26400,Multiclass classification,ADWIN Bagging,Insects,0.5727868479866661,0.5727868479866661,0.5496374434570931,13.72577953338623,3232.991513 -27456,Multiclass classification,ADWIN Bagging,Insects,0.5754143143325442,0.5754143143325442,0.5513680135969626,13.724169731140137,3485.670293 -28512,Multiclass classification,ADWIN Bagging,Insects,0.5772859598049875,0.5772859598049875,0.5551350356863173,13.7214994430542,3747.946766 -29568,Multiclass classification,ADWIN Bagging,Insects,0.577772516657084,0.577772516657084,0.559086133229251,13.720248222351074,4020.178573 -30624,Multiclass classification,ADWIN Bagging,Insects,0.578225516768442,0.578225516768442,0.5625516131192055,12.85925006866455,4302.520044 -31680,Multiclass classification,ADWIN Bagging,Insects,0.5795637488557088,0.5795637488557088,0.5663363640160618,12.858540534973145,4594.920222 -32736,Multiclass classification,ADWIN Bagging,Insects,0.5811211241790133,0.5811211241790133,0.5696723582178382,12.857670783996582,4897.257331 -33792,Multiclass classification,ADWIN Bagging,Insects,0.575804208221124,0.575804208221124,0.5647934119551398,13.070902824401855,5209.806865 -34848,Multiclass classification,ADWIN Bagging,Insects,0.5701495107182828,0.5701495107182828,0.559068023359177,13.0708646774292,5532.790806 -35904,Multiclass classification,ADWIN Bagging,Insects,0.5657744478177311,0.5657744478177311,0.5542573482740074,13.072970390319824,5866.228602 -36960,Multiclass classification,ADWIN Bagging,Insects,0.5611894261208366,0.5611894261208366,0.5493152777162592,13.618464469909668,6210.1901960000005 -38016,Multiclass classification,ADWIN Bagging,Insects,0.558779429172695,0.558779429172695,0.5463982360776033,13.620196342468262,6564.708960000001 -39072,Multiclass classification,ADWIN Bagging,Insects,0.5546825010877633,0.5546825010877633,0.5426283860139581,14.406596183776855,6929.528370000001 -40128,Multiclass classification,ADWIN Bagging,Insects,0.5542153662122761,0.5542153662122761,0.5429626632180721,15.257904052734375,7304.280479000001 -41184,Multiclass classification,ADWIN Bagging,Insects,0.5541364155112547,0.5541364155112547,0.5435420562964656,15.358447074890137,7688.491709000001 -42240,Multiclass classification,ADWIN Bagging,Insects,0.5542981604678141,0.5542981604678141,0.5443914000180358,15.357035636901855,8082.149982000001 -43296,Multiclass classification,ADWIN Bagging,Insects,0.554151749624668,0.554151749624668,0.5448486588729108,13.518179893493652,8485.318247000001 -44352,Multiclass classification,ADWIN Bagging,Insects,0.5536290049829767,0.5536290049829767,0.5448029815059025,13.742095947265625,8897.925593000002 -45408,Multiclass classification,ADWIN Bagging,Insects,0.5541436342414165,0.5541436342414165,0.5454957405719211,14.286569595336914,9319.685836000002 -46464,Multiclass classification,ADWIN Bagging,Insects,0.5553020683124207,0.5553020683124207,0.546961663735647,15.266200065612793,9750.326660000002 -47520,Multiclass classification,ADWIN Bagging,Insects,0.5579662871693428,0.5579662871693428,0.5498636684303295,14.323851585388184,10190.236686000002 -48576,Multiclass classification,ADWIN Bagging,Insects,0.5627586206896552,0.5627586206896552,0.5545030394801858,14.950955390930176,10639.519510000002 -49632,Multiclass classification,ADWIN Bagging,Insects,0.5677701436602124,0.5677701436602124,0.5591808574875289,15.350643157958984,11098.085262000002 -50688,Multiclass classification,ADWIN Bagging,Insects,0.5730463432438297,0.5730463432438297,0.5639878919164368,16.015583038330078,11565.627356000003 -51744,Multiclass classification,ADWIN Bagging,Insects,0.5791894555785324,0.5791894555785324,0.5695807960578061,16.33325481414795,12041.828478000003 -52800,Multiclass classification,ADWIN Bagging,Insects,0.5794238527244834,0.5794238527244834,0.5701364277094956,15.444610595703125,12525.893185000003 -408,Multiclass classification,ADWIN Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.2430334091186523,1.187339 -816,Multiclass classification,ADWIN Bagging,Keystroke,0.9411042944785276,0.9411042944785276,0.7377235942917068,3.1903820037841797,4.698555 -1224,Multiclass classification,ADWIN Bagging,Keystroke,0.8879803761242846,0.8879803761242846,0.873420796574987,4.134529113769531,10.466629000000001 -1632,Multiclass classification,ADWIN Bagging,Keystroke,0.8988350705088902,0.8988350705088902,0.8792834531664682,5.086630821228027,18.824542 -2040,Multiclass classification,ADWIN Bagging,Keystroke,0.8950465914664051,0.8950465914664051,0.8828407845486113,6.147420883178711,30.316821 -2448,Multiclass classification,ADWIN Bagging,Keystroke,0.856559051900286,0.856559051900286,0.8543242501248514,6.5137739181518555,45.490026 -2856,Multiclass classification,ADWIN Bagging,Keystroke,0.8640980735551663,0.8640980735551663,0.8525227127090282,7.461577415466309,64.600537 -3264,Multiclass classification,ADWIN Bagging,Keystroke,0.855654305853509,0.855654305853509,0.8307453339686874,8.407819747924805,88.28106700000001 -3672,Multiclass classification,ADWIN Bagging,Keystroke,0.8469081994007083,0.8469081994007084,0.8445950801753395,9.06855297088623,117.06845200000001 -4080,Multiclass classification,ADWIN Bagging,Keystroke,0.839911743074283,0.839911743074283,0.8273018519986841,10.241823196411133,151.440392 -4488,Multiclass classification,ADWIN Bagging,Keystroke,0.8279474036104302,0.8279474036104302,0.8381848634946416,11.187081336975098,191.91936800000002 -4896,Multiclass classification,ADWIN Bagging,Keystroke,0.8294177732379979,0.8294177732379979,0.8370944525285466,8.72095775604248,237.68305700000002 -5304,Multiclass classification,ADWIN Bagging,Keystroke,0.832736187063926,0.832736187063926,0.8304665020850452,9.573864936828613,288.722663 -5712,Multiclass classification,ADWIN Bagging,Keystroke,0.8254246191560147,0.8254246191560147,0.8318293629616008,10.363824844360352,345.486273 -6120,Multiclass classification,ADWIN Bagging,Keystroke,0.8238274227815002,0.8238274227815002,0.8134447828524414,11.40614128112793,408.45138 -6528,Multiclass classification,ADWIN Bagging,Keystroke,0.8043511567335683,0.8043511567335683,0.8054460603633147,12.15697956085205,478.20571299999995 -6936,Multiclass classification,ADWIN Bagging,Keystroke,0.8005767844268205,0.8005767844268206,0.8067791986535922,11.58870792388916,555.142755 -7344,Multiclass classification,ADWIN Bagging,Keystroke,0.8081165736075173,0.8081165736075173,0.8106639227074198,12.00939655303955,638.5654069999999 -7752,Multiclass classification,ADWIN Bagging,Keystroke,0.8097019739388466,0.8097019739388466,0.8127585051729247,13.156713485717773,728.9317779999999 -8160,Multiclass classification,ADWIN Bagging,Keystroke,0.8134575315602403,0.8134575315602401,0.8148392057777913,13.882833480834961,826.9412199999999 -8568,Multiclass classification,ADWIN Bagging,Keystroke,0.817672464106455,0.817672464106455,0.8208026583224199,15.191060066223145,933.116233 -8976,Multiclass classification,ADWIN Bagging,Keystroke,0.8213927576601672,0.8213927576601672,0.8243856825821874,16.25563907623291,1048.180156 -9384,Multiclass classification,ADWIN Bagging,Keystroke,0.8219119684535863,0.8219119684535864,0.8243183344026902,17.071918487548828,1172.5966959999998 -9792,Multiclass classification,ADWIN Bagging,Keystroke,0.8224900418751915,0.8224900418751915,0.8248306232761192,18.233381271362305,1306.7477949999998 -10200,Multiclass classification,ADWIN Bagging,Keystroke,0.819197960584371,0.819197960584371,0.8170259665463304,19.36789894104004,1451.4292829999997 -10608,Multiclass classification,ADWIN Bagging,Keystroke,0.805788630149901,0.8057886301499011,0.8022367569175978,20.506345748901367,1607.1296199999997 -11016,Multiclass classification,ADWIN Bagging,Keystroke,0.802088061733999,0.8020880617339992,0.8038074645550285,19.16464138031006,1773.9352609999996 -11424,Multiclass classification,ADWIN Bagging,Keystroke,0.8018909218243894,0.8018909218243894,0.8005729972530424,19.133995056152344,1951.2468279999996 -11832,Multiclass classification,ADWIN Bagging,Keystroke,0.8002704758684811,0.800270475868481,0.8004166941842216,20.079543113708496,2139.4866899999997 -12240,Multiclass classification,ADWIN Bagging,Keystroke,0.8035787237519405,0.8035787237519405,0.8060123607032721,17.827110290527344,2338.9088759999995 -12648,Multiclass classification,ADWIN Bagging,Keystroke,0.8088084130623864,0.8088084130623864,0.8108606005777994,15.629319190979004,2547.8446989999993 -13056,Multiclass classification,ADWIN Bagging,Keystroke,0.8079662964381463,0.8079662964381463,0.8077709771623751,16.39698314666748,2766.5130659999995 -13464,Multiclass classification,ADWIN Bagging,Keystroke,0.8068038327267325,0.8068038327267325,0.807905549135964,17.24907112121582,2995.4784789999994 -13872,Multiclass classification,ADWIN Bagging,Keystroke,0.810107418354841,0.810107418354841,0.8115061911206084,18.023069381713867,3234.9291429999994 -14280,Multiclass classification,ADWIN Bagging,Keystroke,0.813432313187198,0.813432313187198,0.814709519180665,19.25601577758789,3485.4148369999994 -14688,Multiclass classification,ADWIN Bagging,Keystroke,0.8169810036086335,0.8169810036086335,0.8183348126971706,19.792034149169922,3747.4763309999994 -15096,Multiclass classification,ADWIN Bagging,Keystroke,0.8210665783371978,0.8210665783371978,0.8224533109934684,20.76410961151123,4021.7266099999993 -15504,Multiclass classification,ADWIN Bagging,Keystroke,0.8227439850351544,0.8227439850351544,0.8236860076332361,21.707032203674316,4308.908110999999 -15912,Multiclass classification,ADWIN Bagging,Keystroke,0.8177361573754006,0.8177361573754006,0.8170714187961161,22.97433376312256,4609.3886379999985 -16320,Multiclass classification,ADWIN Bagging,Keystroke,0.8135915190881794,0.8135915190881794,0.8136474897036394,23.700613975524902,4923.859395999999 -16728,Multiclass classification,ADWIN Bagging,Keystroke,0.8133556525378132,0.8133556525378132,0.8142218072403056,24.764389038085938,5252.522612999999 -17136,Multiclass classification,ADWIN Bagging,Keystroke,0.8092792529909542,0.8092792529909542,0.8090411402278314,26.198601722717285,5595.9090209999995 -17544,Multiclass classification,ADWIN Bagging,Keystroke,0.8062475061278003,0.8062475061278003,0.8065701979489333,25.60452175140381,5954.543495 -17952,Multiclass classification,ADWIN Bagging,Keystroke,0.8078101498523759,0.8078101498523759,0.8084559739072698,26.552149772644043,6328.536349 -18360,Multiclass classification,ADWIN Bagging,Keystroke,0.8103927229151915,0.8103927229151915,0.8111272646261444,27.50138759613037,6718.347567 -18768,Multiclass classification,ADWIN Bagging,Keystroke,0.813022859274258,0.813022859274258,0.8138485204649677,28.353083610534668,7124.657721 -19176,Multiclass classification,ADWIN Bagging,Keystroke,0.8090743155149935,0.8090743155149935,0.8093701596568051,29.32584285736084,7547.838048 -19584,Multiclass classification,ADWIN Bagging,Keystroke,0.8110606137976817,0.8110606137976817,0.8116953495842238,30.333707809448242,7988.342591 -19992,Multiclass classification,ADWIN Bagging,Keystroke,0.8081136511430144,0.8081136511430144,0.8084718836746521,31.28043556213379,8446.728501 -20400,Multiclass classification,ADWIN Bagging,Keystroke,0.8058238148928869,0.805823814892887,0.8062504565207905,32.1812219619751,8923.60629 -46,Multiclass classification,AdaBoost,ImageSegments,0.1111111111111111,0.1111111111111111,0.0815018315018315,3.4461936950683594,0.804099 -92,Multiclass classification,AdaBoost,ImageSegments,0.23076923076923078,0.23076923076923078,0.22263917712834122,4.129319190979004,2.027411 -138,Multiclass classification,AdaBoost,ImageSegments,0.4233576642335766,0.4233576642335766,0.4463537718619156,4.129193305969238,3.5999849999999998 -184,Multiclass classification,AdaBoost,ImageSegments,0.5355191256830601,0.5355191256830601,0.5617062146473911,4.129368782043457,5.452412 -230,Multiclass classification,AdaBoost,ImageSegments,0.5938864628820961,0.5938864628820961,0.6236530662596055,4.12935733795166,7.651963 -276,Multiclass classification,AdaBoost,ImageSegments,0.6290909090909091,0.6290909090909091,0.6558170665459355,4.129300117492676,10.207424 -322,Multiclass classification,AdaBoost,ImageSegments,0.660436137071651,0.660436137071651,0.6785747202615152,4.128628730773926,13.08877 -368,Multiclass classification,AdaBoost,ImageSegments,0.6920980926430518,0.6920980926430518,0.7041680355881775,4.12868595123291,16.291428 -414,Multiclass classification,AdaBoost,ImageSegments,0.7167070217917676,0.7167070217917676,0.7259075149442815,4.128170967102051,19.832325 -460,Multiclass classification,AdaBoost,ImageSegments,0.7254901960784313,0.7254901960784313,0.732501171084948,4.128491401672363,23.76135 -506,Multiclass classification,AdaBoost,ImageSegments,0.7386138613861386,0.7386138613861386,0.7428621938273078,4.1287431716918945,28.024553 -552,Multiclass classification,AdaBoost,ImageSegments,0.7422867513611615,0.7422867513611615,0.7453719085253248,4.128548622131348,32.646215 -598,Multiclass classification,AdaBoost,ImageSegments,0.7487437185929648,0.7487437185929648,0.7504522188790484,4.128659248352051,37.596323 -644,Multiclass classification,AdaBoost,ImageSegments,0.7465007776049767,0.7465007776049767,0.7482323503576439,4.128731727600098,42.92857 -690,Multiclass classification,AdaBoost,ImageSegments,0.7489114658925979,0.748911465892598,0.7488472102580619,4.128785133361816,48.576103 -736,Multiclass classification,AdaBoost,ImageSegments,0.7523809523809524,0.7523809523809524,0.75182837230991,4.1286211013793945,54.551686000000004 -782,Multiclass classification,AdaBoost,ImageSegments,0.7541613316261203,0.7541613316261204,0.7531089046321313,4.128552436828613,60.838379 -828,Multiclass classification,AdaBoost,ImageSegments,0.7557436517533253,0.7557436517533253,0.7552013614952863,4.1284990310668945,67.464563 -874,Multiclass classification,AdaBoost,ImageSegments,0.7617411225658648,0.7617411225658649,0.7601066395856337,4.128571510314941,74.378096 -920,Multiclass classification,AdaBoost,ImageSegments,0.763873775843308,0.763873775843308,0.7623480483274478,4.1285905838012695,81.591089 -966,Multiclass classification,AdaBoost,ImageSegments,0.7678756476683938,0.7678756476683938,0.7646598072570266,4.128613471984863,89.10581599999999 -1012,Multiclass classification,AdaBoost,ImageSegments,0.7705242334322453,0.7705242334322453,0.7668271197983112,4.128720283508301,96.93086299999999 -1058,Multiclass classification,AdaBoost,ImageSegments,0.7757805108798487,0.7757805108798487,0.7714920336037776,4.128579139709473,105.05195599999999 -1104,Multiclass classification,AdaBoost,ImageSegments,0.7760652765185857,0.7760652765185856,0.7719206139767609,4.128727912902832,113.49129199999999 -1150,Multiclass classification,AdaBoost,ImageSegments,0.7789382071366405,0.7789382071366405,0.7750313949659529,4.128632545471191,122.21725699999999 -1196,Multiclass classification,AdaBoost,ImageSegments,0.7849372384937239,0.7849372384937239,0.782000389047251,4.128678321838379,131.239072 -1242,Multiclass classification,AdaBoost,ImageSegments,0.7856567284448026,0.7856567284448026,0.7827470902102025,4.128628730773926,140.604336 -1288,Multiclass classification,AdaBoost,ImageSegments,0.7894327894327894,0.7894327894327894,0.785982924599392,4.128533363342285,150.25346299999998 -1334,Multiclass classification,AdaBoost,ImageSegments,0.7906976744186046,0.7906976744186046,0.7876424482584368,4.128628730773926,160.232262 -1380,Multiclass classification,AdaBoost,ImageSegments,0.7933284989122552,0.7933284989122552,0.7906471924204203,4.128582954406738,170.496442 -1426,Multiclass classification,AdaBoost,ImageSegments,0.7978947368421052,0.7978947368421052,0.7945020166797493,4.128670692443848,181.024488 -1472,Multiclass classification,AdaBoost,ImageSegments,0.8028552005438477,0.8028552005438477,0.7982243751921435,4.128663063049316,191.820653 -1518,Multiclass classification,AdaBoost,ImageSegments,0.8035596572181938,0.8035596572181938,0.7981876534181911,4.1286821365356445,202.942587 -1564,Multiclass classification,AdaBoost,ImageSegments,0.8035828534868842,0.8035828534868842,0.798634974540431,4.128708839416504,214.370001 -1610,Multiclass classification,AdaBoost,ImageSegments,0.8048477315102548,0.8048477315102549,0.7997380784882049,4.128571510314941,226.09596 -1656,Multiclass classification,AdaBoost,ImageSegments,0.8066465256797583,0.8066465256797583,0.80161945439383,4.128567695617676,238.115396 -1702,Multiclass classification,AdaBoost,ImageSegments,0.8059964726631393,0.8059964726631393,0.8024858564723996,4.128705024719238,250.457722 -1748,Multiclass classification,AdaBoost,ImageSegments,0.8070978820835718,0.8070978820835718,0.8029124203507954,4.128613471984863,263.107237 -1794,Multiclass classification,AdaBoost,ImageSegments,0.8081427774679308,0.8081427774679307,0.8029834045630978,4.1286516189575195,276.028168 -1840,Multiclass classification,AdaBoost,ImageSegments,0.8069603045133225,0.8069603045133223,0.8019276227162541,4.128785133361816,289.253241 -1886,Multiclass classification,AdaBoost,ImageSegments,0.8053050397877984,0.8053050397877984,0.8006727596367826,4.1285905838012695,302.793979 -1932,Multiclass classification,AdaBoost,ImageSegments,0.8047643707923355,0.8047643707923355,0.7995493059800364,4.128586769104004,316.637791 -1978,Multiclass classification,AdaBoost,ImageSegments,0.8057663125948407,0.8057663125948407,0.8003960406612561,4.12862491607666,330.791782 -2024,Multiclass classification,AdaBoost,ImageSegments,0.8072170044488384,0.8072170044488384,0.8005625942078284,4.1286211013793945,345.23827800000004 -2070,Multiclass classification,AdaBoost,ImageSegments,0.8066698888351861,0.8066698888351861,0.8002110568368,4.128506660461426,360.0031 -2116,Multiclass classification,AdaBoost,ImageSegments,0.807565011820331,0.807565011820331,0.8005131307885663,4.128533363342285,375.05937700000004 -2162,Multiclass classification,AdaBoost,ImageSegments,0.8079592781119852,0.8079592781119852,0.8006755955605838,4.128510475158691,390.39582400000006 -2208,Multiclass classification,AdaBoost,ImageSegments,0.8087902129587675,0.8087902129587675,0.8009921695193861,4.128510475158691,405.99760000000003 -2254,Multiclass classification,AdaBoost,ImageSegments,0.8060363959165557,0.8060363959165557,0.7987732120640717,4.128533363342285,421.95304000000004 -2300,Multiclass classification,AdaBoost,ImageSegments,0.8051326663766856,0.8051326663766856,0.7980778928096751,4.128533363342285,438.21900000000005 -1056,Multiclass classification,AdaBoost,Insects,0.6360189573459716,0.6360189573459716,0.5992691812827112,6.522543907165527,11.237994 -2112,Multiclass classification,AdaBoost,Insects,0.6110847939365229,0.6110847939365229,0.5773210074897359,6.522406578063965,32.531305 -3168,Multiclass classification,AdaBoost,Insects,0.6043574360593622,0.6043574360593622,0.5704368753709179,6.521971702575684,63.87417000000001 -4224,Multiclass classification,AdaBoost,Insects,0.6014681506038362,0.6014681506038362,0.5676969561642587,6.521697044372559,105.21576400000001 -5280,Multiclass classification,AdaBoost,Insects,0.6057965523773442,0.6057965523773442,0.5710016183775801,6.521697044372559,156.441097 -6336,Multiclass classification,AdaBoost,Insects,0.5966850828729282,0.5966850828729282,0.5635903588556204,6.521857261657715,217.68026400000002 -7392,Multiclass classification,AdaBoost,Insects,0.5957245298335814,0.5957245298335814,0.5625002603439991,6.52231502532959,288.908384 -8448,Multiclass classification,AdaBoost,Insects,0.5982005445720374,0.5982005445720374,0.5646892369665863,6.522658348083496,370.082682 -9504,Multiclass classification,AdaBoost,Insects,0.596337998526781,0.596337998526781,0.5627085514562804,6.523001670837402,461.259323 -10560,Multiclass classification,AdaBoost,Insects,0.5965527038545316,0.5965527038545316,0.5631320282838163,6.523184776306152,562.3727710000001 -11616,Multiclass classification,AdaBoost,Insects,0.5953508394317693,0.5953508394317693,0.562671447170627,6.523184776306152,673.482974 -12672,Multiclass classification,AdaBoost,Insects,0.5979796385447084,0.5979796385447084,0.5680559575776837,6.522841453552246,794.598712 -13728,Multiclass classification,AdaBoost,Insects,0.610767101333139,0.610767101333139,0.5941277335666079,6.522337913513184,925.382156 -14784,Multiclass classification,AdaBoost,Insects,0.6019752418318338,0.6019752418318338,0.5851264744797858,6.522246360778809,1066.407983 -15840,Multiclass classification,AdaBoost,Insects,0.5705536965717533,0.5705536965717533,0.5545059657048704,6.522475242614746,1217.74448 -16896,Multiclass classification,AdaBoost,Insects,0.548091151228174,0.548091151228174,0.5320735507355622,6.522887229919434,1378.917078 -17952,Multiclass classification,AdaBoost,Insects,0.5307225224221492,0.5307225224221492,0.5138536287616571,6.523138999938965,1549.794627 -19008,Multiclass classification,AdaBoost,Insects,0.5182827379386542,0.5182827379386542,0.4990809738484312,6.523367881774902,1730.586936 -20064,Multiclass classification,AdaBoost,Insects,0.5182176145142801,0.5182176145142801,0.497867701567998,8.711265563964844,1921.389763 -21120,Multiclass classification,AdaBoost,Insects,0.5272503432927695,0.5272503432927695,0.5067114684709674,15.550712585449219,2121.77156 -22176,Multiclass classification,AdaBoost,Insects,0.533032694475761,0.533032694475761,0.5127471323280748,16.9340763092041,2331.759567 -23232,Multiclass classification,AdaBoost,Insects,0.5410442942619775,0.5410442942619775,0.5207771198745245,17.15799903869629,2551.0181620000003 -24288,Multiclass classification,AdaBoost,Insects,0.5459710956478775,0.5459710956478775,0.5251711652768186,17.155046463012695,2778.8449820000005 -25344,Multiclass classification,AdaBoost,Insects,0.5532099593576135,0.5532099593576135,0.5314216535856217,17.15360450744629,3015.0560080000005 -26400,Multiclass classification,AdaBoost,Insects,0.5607788173794462,0.5607788173794462,0.5375130024626694,17.265982627868652,3259.2678140000003 -27456,Multiclass classification,AdaBoost,Insects,0.5667091604443635,0.5667091604443635,0.5418496825562071,17.378803253173828,3511.9238520000004 -28512,Multiclass classification,AdaBoost,Insects,0.5692890463329943,0.5692890463329943,0.5455529487931667,17.379924774169922,3773.544178 -29568,Multiclass classification,AdaBoost,Insects,0.5688436432509216,0.5688436432509216,0.5481992899375988,17.380359649658203,4045.250953 -30624,Multiclass classification,AdaBoost,Insects,0.5687228553701467,0.5687228553701467,0.5505043481720591,17.380290985107422,4327.317909 -31680,Multiclass classification,AdaBoost,Insects,0.5691467533697402,0.5691467533697402,0.5529220328647554,17.37978744506836,4619.673811000001 -32736,Multiclass classification,AdaBoost,Insects,0.5703986558729189,0.5703986558729189,0.5556828084411201,17.37948989868164,4922.186926 -33792,Multiclass classification,AdaBoost,Insects,0.5650025154626972,0.5650025154626972,0.5507695387439543,17.604686737060547,5235.061695 -34848,Multiclass classification,AdaBoost,Insects,0.5587568513788849,0.5587568513788849,0.5445559443415654,18.05030918121338,5558.17951 -35904,Multiclass classification,AdaBoost,Insects,0.554215525165028,0.554215525165028,0.5396701176441828,18.150463104248047,5891.808229 -36960,Multiclass classification,AdaBoost,Insects,0.5490408290267594,0.5490408290267594,0.5342475234810463,19.505443572998047,6235.988455000001 -38016,Multiclass classification,AdaBoost,Insects,0.5476522425358411,0.5476522425358411,0.5324130893403342,21.324423789978027,6590.662011 -39072,Multiclass classification,AdaBoost,Insects,0.5427810908346344,0.5427810908346344,0.5280992603544316,22.076537132263184,6955.865912 -40128,Multiclass classification,AdaBoost,Insects,0.5417300072270541,0.5417300072270541,0.5282649533846114,22.738471031188965,7330.794870000001 -41184,Multiclass classification,AdaBoost,Insects,0.5417283830706845,0.5417283830706845,0.5295529576867488,23.164944648742676,7714.553666000001 -42240,Multiclass classification,AdaBoost,Insects,0.5419635881531286,0.5419635881531286,0.5308394560628455,23.606464385986328,8107.062243 -43296,Multiclass classification,AdaBoost,Insects,0.5438734264926666,0.5438734264926666,0.5334569208328087,23.706249237060547,8507.305821 -44352,Multiclass classification,AdaBoost,Insects,0.5453315596040675,0.5453315596040675,0.5354029875943346,24.299342155456543,8915.303832 -45408,Multiclass classification,AdaBoost,Insects,0.547140308762966,0.547140308762966,0.5374156745075451,24.818781852722168,9330.915411 -46464,Multiclass classification,AdaBoost,Insects,0.5492327228116997,0.5492327228116997,0.5397202270950943,25.030532836914062,9754.309095 -47520,Multiclass classification,AdaBoost,Insects,0.5481596834950231,0.5481596834950231,0.5387960204161004,25.669864654541016,10186.398449 -48576,Multiclass classification,AdaBoost,Insects,0.5455275347400926,0.5455275347400926,0.5361266295596548,25.668834686279297,10627.539344 -49632,Multiclass classification,AdaBoost,Insects,0.5440752755334367,0.5440752755334367,0.534604738581891,26.10423469543457,11077.630938 -50688,Multiclass classification,AdaBoost,Insects,0.5484443742971571,0.5484443742971571,0.538570218508335,27.224528312683105,11536.66667 -51744,Multiclass classification,AdaBoost,Insects,0.5534081904798717,0.5534081904798717,0.5429607704191827,28.064788818359375,12004.483564 -52800,Multiclass classification,AdaBoost,Insects,0.5540824636830243,0.5540824636830243,0.543927330204892,28.290183067321777,12481.284297 -408,Multiclass classification,AdaBoost,Keystroke,0.9877149877149877,0.9877149877149877,0.7696139476961394,2.1705713272094727,1.169245 -816,Multiclass classification,AdaBoost,Keystroke,0.9889570552147239,0.9889570552147239,0.9592655637573824,2.994051933288574,4.333642 -1224,Multiclass classification,AdaBoost,Keystroke,0.9852820932134096,0.9852820932134096,0.9482751483180805,4.729727745056152,12.346709 -1632,Multiclass classification,AdaBoost,Keystroke,0.9822194972409565,0.9822194972409565,0.9509896151723367,5.999781608581543,25.091939 -2040,Multiclass classification,AdaBoost,Keystroke,0.9725355566454145,0.9725355566454145,0.928775026512405,7.915155410766602,43.495481 -2448,Multiclass classification,AdaBoost,Keystroke,0.9550469963220269,0.9550469963220269,0.9404929408648164,9.985001564025879,68.010219 -2856,Multiclass classification,AdaBoost,Keystroke,0.9516637478108582,0.9516637478108582,0.9265706247083845,13.281692504882812,95.83503300000001 -3264,Multiclass classification,AdaBoost,Keystroke,0.9457554397793442,0.9457554397793442,0.9273434636455653,16.35391616821289,127.20590200000001 -3672,Multiclass classification,AdaBoost,Keystroke,0.9417052574230454,0.9417052574230454,0.925978466853896,18.91156578063965,163.340984 -4080,Multiclass classification,AdaBoost,Keystroke,0.9355234126011277,0.9355234126011277,0.9181372267911062,22.338744163513184,205.235807 -4488,Multiclass classification,AdaBoost,Keystroke,0.931580120347671,0.931580120347671,0.9327276252021246,25.310707092285156,252.959286 -4896,Multiclass classification,AdaBoost,Keystroke,0.9303370786516854,0.9303370786516854,0.9257176086775135,28.274658203125,306.688529 -5304,Multiclass classification,AdaBoost,Keystroke,0.9253252875730719,0.9253252875730719,0.9165251784293146,32.214202880859375,367.43293400000005 -5712,Multiclass classification,AdaBoost,Keystroke,0.9226054981614429,0.9226054981614429,0.9209111845314155,34.49547863006592,436.87881300000004 -6120,Multiclass classification,AdaBoost,Keystroke,0.9181238764504004,0.9181238764504004,0.9091206319047903,38.86995029449463,513.269664 -6528,Multiclass classification,AdaBoost,Keystroke,0.9129768653286349,0.9129768653286349,0.9114007831703169,42.32428169250488,602.212119 -6936,Multiclass classification,AdaBoost,Keystroke,0.9114635904830569,0.9114635904830569,0.9134311944430067,44.19167232513428,700.8190040000001 -7344,Multiclass classification,AdaBoost,Keystroke,0.9116165055154569,0.9116165055154569,0.9097332482243848,44.84274196624756,807.0835780000001 -7752,Multiclass classification,AdaBoost,Keystroke,0.9112372597084247,0.9112372597084247,0.9111242959524108,46.848572731018066,921.3564090000001 -8160,Multiclass classification,AdaBoost,Keystroke,0.9094251746537566,0.9094251746537566,0.9076128910354778,51.167396545410156,1044.662094 -8568,Multiclass classification,AdaBoost,Keystroke,0.9066184195167504,0.9066184195167505,0.9066450469749987,55.86186981201172,1177.672199 -8976,Multiclass classification,AdaBoost,Keystroke,0.9056267409470752,0.9056267409470752,0.9063353807566539,58.41574668884277,1322.047745 -9384,Multiclass classification,AdaBoost,Keystroke,0.9030160929340296,0.9030160929340296,0.9022077684947395,62.26175117492676,1477.2987620000001 -9792,Multiclass classification,AdaBoost,Keystroke,0.8986824634868757,0.8986824634868757,0.8984090939041232,65.20822143554688,1646.0177680000002 -10200,Multiclass classification,AdaBoost,Keystroke,0.8947936072163938,0.8947936072163937,0.8926613887647973,69.96524620056152,1829.707382 -10608,Multiclass classification,AdaBoost,Keystroke,0.8881870462901857,0.8881870462901857,0.8865702773222168,73.60436820983887,2032.787653 -11016,Multiclass classification,AdaBoost,Keystroke,0.8849750340444847,0.8849750340444847,0.8859866133359942,77.43610191345215,2252.324419 -11424,Multiclass classification,AdaBoost,Keystroke,0.8823426420379935,0.8823426420379935,0.8811651142625456,81.9732666015625,2486.561888 -11832,Multiclass classification,AdaBoost,Keystroke,0.8789620488547037,0.8789620488547037,0.8783725809837211,87.50129985809326,2738.033946 -12240,Multiclass classification,AdaBoost,Keystroke,0.8803823841817142,0.8803823841817142,0.8815469015078649,88.71501064300537,3002.626444 -12648,Multiclass classification,AdaBoost,Keystroke,0.878231991776706,0.878231991776706,0.8774838611192476,93.73236656188965,3282.43662 -13056,Multiclass classification,AdaBoost,Keystroke,0.8737648410570663,0.8737648410570663,0.8731746930338653,98.24647235870361,3580.919978 -13464,Multiclass classification,AdaBoost,Keystroke,0.872168164599272,0.872168164599272,0.8726091982990895,102.17141056060791,3896.583194 -13872,Multiclass classification,AdaBoost,Keystroke,0.8693677456564054,0.8693677456564054,0.869586320653203,106.91088581085205,4229.831886 -14280,Multiclass classification,AdaBoost,Keystroke,0.8653967364661391,0.8653967364661391,0.8650950015616714,111.61231708526611,4581.249889 -14688,Multiclass classification,AdaBoost,Keystroke,0.8650507251310683,0.8650507251310683,0.8661026270223636,115.34651947021484,4948.063453 -15096,Multiclass classification,AdaBoost,Keystroke,0.8667770785028155,0.8667770785028155,0.8680260482593213,118.24315452575684,5329.445739 -15504,Multiclass classification,AdaBoost,Keystroke,0.8673805069986454,0.8673805069986454,0.8683176015232197,121.64087104797363,5727.804435999999 -15912,Multiclass classification,AdaBoost,Keystroke,0.8630507196279303,0.8630507196279303,0.8630078198630903,126.92564392089844,6152.632774 -16320,Multiclass classification,AdaBoost,Keystroke,0.8588761566272444,0.8588761566272444,0.8591617021179835,132.20891761779785,6602.417036 -16728,Multiclass classification,AdaBoost,Keystroke,0.8546661086865547,0.8546661086865547,0.8551483908890608,137.6782627105713,7072.757626 -17136,Multiclass classification,AdaBoost,Keystroke,0.850539830755763,0.850539830755763,0.8506967692771638,139.40350437164307,7574.584865 -17544,Multiclass classification,AdaBoost,Keystroke,0.8456934389785099,0.8456934389785099,0.845900693576748,144.32332038879395,8103.947966 -17952,Multiclass classification,AdaBoost,Keystroke,0.8442983677789538,0.8442983677789538,0.8449597117669952,149.55280876159668,8653.396068 -18360,Multiclass classification,AdaBoost,Keystroke,0.8462879241788769,0.8462879241788769,0.8472097835611015,154.20918083190918,9220.958091 -18768,Multiclass classification,AdaBoost,Keystroke,0.8472851281504769,0.8472851281504769,0.8482490326871865,158.50969696044922,9805.946569 -19176,Multiclass classification,AdaBoost,Keystroke,0.845632333767927,0.845632333767927,0.8464906194356719,163.69990730285645,10414.744709999999 -19584,Multiclass classification,AdaBoost,Keystroke,0.8470612265740693,0.8470612265740693,0.8481337525939465,167.61861419677734,11041.283472 -19992,Multiclass classification,AdaBoost,Keystroke,0.8455805112300535,0.8455805112300535,0.8467102165017473,172.522611618042,11691.217596999999 -20400,Multiclass classification,AdaBoost,Keystroke,0.8424922790332859,0.8424922790332859,0.8436347186891262,177.38492488861084,12366.859336 -46,Multiclass classification,Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.24576497265572897,4.181334495544434,1.091398 -92,Multiclass classification,Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.49347523955818895,4.1845502853393555,2.768304 -138,Multiclass classification,Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646677,4.1842756271362305,4.818241 -184,Multiclass classification,Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.614143164890895,4.184859275817871,7.25269 -230,Multiclass classification,Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,4.184233665466309,10.061851 -276,Multiclass classification,Bagging,ImageSegments,0.64,0.64,0.6559607038460422,4.184771537780762,13.254704 -322,Multiclass classification,Bagging,ImageSegments,0.6666666666666666,0.6666666666666666,0.6673617488913626,4.184481620788574,16.809438 -368,Multiclass classification,Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6911959597548877,4.184699058532715,20.719577 -414,Multiclass classification,Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.7079630503641953,4.1850385665893555,25.02075 -460,Multiclass classification,Bagging,ImageSegments,0.7124183006535948,0.7124183006535948,0.7065500352371009,4.184954643249512,29.692393 -506,Multiclass classification,Bagging,ImageSegments,0.7207920792079208,0.7207920792079208,0.7127593158348896,4.184813499450684,34.691523 -552,Multiclass classification,Bagging,ImageSegments,0.7259528130671506,0.7259528130671506,0.7192025503807162,4.184779167175293,40.034434999999995 -598,Multiclass classification,Bagging,ImageSegments,0.7319932998324958,0.7319932998324957,0.7251188986558661,4.185019493103027,45.725680999999994 -644,Multiclass classification,Bagging,ImageSegments,0.7309486780715396,0.7309486780715396,0.7259740406437202,4.184813499450684,51.77256499999999 -690,Multiclass classification,Bagging,ImageSegments,0.7358490566037735,0.7358490566037735,0.7304359912942561,4.184943199157715,58.14505499999999 -736,Multiclass classification,Bagging,ImageSegments,0.7374149659863946,0.7374149659863947,0.7331499347170709,4.185004234313965,64.846683 -782,Multiclass classification,Bagging,ImageSegments,0.7426376440460948,0.7426376440460948,0.7385597120510639,4.184893608093262,71.895225 -828,Multiclass classification,Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7412375783772317,4.184882164001465,79.27328899999999 -874,Multiclass classification,Bagging,ImageSegments,0.7491408934707904,0.7491408934707904,0.7454343548790068,4.185431480407715,86.96748099999999 -920,Multiclass classification,Bagging,ImageSegments,0.7486398258977149,0.7486398258977149,0.7441307384051415,4.185576438903809,94.98590999999999 -966,Multiclass classification,Bagging,ImageSegments,0.7492227979274612,0.749222797927461,0.7439306216964365,4.185370445251465,103.316786 -1012,Multiclass classification,Bagging,ImageSegments,0.7487636003956478,0.7487636003956478,0.7437900284473965,4.185484886169434,111.97774899999999 -1058,Multiclass classification,Bagging,ImageSegments,0.750236518448439,0.7502365184484389,0.7448138061687654,4.185519218444824,120.95312499999999 -1104,Multiclass classification,Bagging,ImageSegments,0.7524932003626473,0.7524932003626473,0.7468314646869902,4.185484886169434,130.25113499999998 -1150,Multiclass classification,Bagging,ImageSegments,0.7554395126196692,0.7554395126196692,0.7493227137357602,4.185664176940918,139.83544499999996 -1196,Multiclass classification,Bagging,ImageSegments,0.7581589958158996,0.7581589958158996,0.7527652773681007,4.185568809509277,149.73636899999997 -1242,Multiclass classification,Bagging,ImageSegments,0.7574536663980661,0.7574536663980661,0.7525915384194215,4.185683250427246,159.94850499999995 -1288,Multiclass classification,Bagging,ImageSegments,0.7622377622377622,0.7622377622377621,0.7563448085202399,4.185866355895996,170.48539899999994 -1334,Multiclass classification,Bagging,ImageSegments,0.7621905476369092,0.7621905476369092,0.7566636999776912,4.186026573181152,181.34153899999995 -1380,Multiclass classification,Bagging,ImageSegments,0.7635968092820885,0.7635968092820886,0.7587252257765656,4.1860761642456055,192.51808799999995 -1426,Multiclass classification,Bagging,ImageSegments,0.7663157894736842,0.7663157894736842,0.7609139797315135,4.186099052429199,204.01178799999994 -1472,Multiclass classification,Bagging,ImageSegments,0.7709041468388851,0.7709041468388851,0.763768994920769,4.186240196228027,215.81223399999993 -1518,Multiclass classification,Bagging,ImageSegments,0.7719182597231378,0.7719182597231378,0.7639714255563932,4.186617851257324,227.92141499999994 -1564,Multiclass classification,Bagging,ImageSegments,0.7722328854766475,0.7722328854766475,0.765072133508071,4.186800956726074,240.33772499999995 -1610,Multiclass classification,Bagging,ImageSegments,0.7725295214418894,0.7725295214418892,0.764505787280341,4.186892509460449,253.08285699999996 -1656,Multiclass classification,Bagging,ImageSegments,0.7716012084592145,0.7716012084592145,0.7634170612719107,4.1867780685424805,266.15585699999997 -1702,Multiclass classification,Bagging,ImageSegments,0.7713109935332157,0.7713109935332157,0.7652815676598499,4.187075614929199,279.52232499999997 -1748,Multiclass classification,Bagging,ImageSegments,0.77389811104751,0.77389811104751,0.7674409436090757,4.187258720397949,293.187056 -1794,Multiclass classification,Bagging,ImageSegments,0.7752370329057445,0.7752370329057446,0.7674318582149376,4.1872968673706055,307.180551 -1840,Multiclass classification,Bagging,ImageSegments,0.7765089722675367,0.7765089722675368,0.7688731808749575,4.187228202819824,321.49742299999997 -1886,Multiclass classification,Bagging,ImageSegments,0.7750663129973475,0.7750663129973475,0.7678921362145585,4.187155723571777,336.11504499999995 -1932,Multiclass classification,Bagging,ImageSegments,0.7752459865354738,0.7752459865354739,0.7671636716269125,4.187251091003418,351.05391499999996 -1978,Multiclass classification,Bagging,ImageSegments,0.7759231158320687,0.7759231158320687,0.7670573130332382,4.187151908874512,366.316945 -2024,Multiclass classification,Bagging,ImageSegments,0.7775580820563519,0.7775580820563519,0.7671264358471986,4.187129020690918,381.896141 -2070,Multiclass classification,Bagging,ImageSegments,0.77670372160464,0.7767037216046399,0.7665050383810529,4.1872053146362305,397.783945 -2116,Multiclass classification,Bagging,ImageSegments,0.7773049645390071,0.7773049645390071,0.7663404166149341,4.1872053146362305,413.995271 -2162,Multiclass classification,Bagging,ImageSegments,0.7783433595557612,0.7783433595557612,0.7669657147488861,4.187277793884277,430.524022 -2208,Multiclass classification,Bagging,ImageSegments,0.780244676030811,0.780244676030811,0.7678552364681829,4.187273979187012,447.387318 -2254,Multiclass classification,Bagging,ImageSegments,0.7776298268974701,0.7776298268974701,0.7652407320979201,4.187224388122559,464.558569 -2300,Multiclass classification,Bagging,ImageSegments,0.7768595041322314,0.7768595041322314,0.7644610611003249,4.18729305267334,482.036228 -1056,Multiclass classification,Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762561,6.583989143371582,11.628861 -2112,Multiclass classification,Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.584485054016113,33.423105 -3168,Multiclass classification,Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244892,6.5851945877075195,65.495981 -4224,Multiclass classification,Bagging,Insects,0.6107032914989344,0.6107032914989344,0.574850135712032,6.585576057434082,107.816588 -5280,Multiclass classification,Bagging,Insects,0.615078613373745,0.615078613373745,0.5779184071248228,6.5863847732543945,160.258759 -6336,Multiclass classification,Bagging,Insects,0.6091554853985793,0.6091554853985793,0.5734262289926554,6.586209297180176,222.866435 -7392,Multiclass classification,Bagging,Insects,0.6058720064943851,0.6058720064943851,0.5704339658550047,6.585629463195801,295.76759400000003 -8448,Multiclass classification,Bagging,Insects,0.6070794364863265,0.6070794364863265,0.5712261057542335,6.585507392883301,378.90789100000006 -9504,Multiclass classification,Bagging,Insects,0.6040197832263495,0.6040197832263495,0.567883906637128,6.585629463195801,472.31115100000005 -10560,Multiclass classification,Bagging,Insects,0.6035609432711431,0.6035609432711431,0.5674913890030829,6.5859880447387695,575.935454 -11616,Multiclass classification,Bagging,Insects,0.6006026689625484,0.6006026689625484,0.5651886352361905,6.585965156555176,689.792083 -12672,Multiclass classification,Bagging,Insects,0.6032673032909794,0.6032673032909794,0.5704386423232538,6.585919380187988,813.953016 -13728,Multiclass classification,Bagging,Insects,0.6147738034530488,0.6147738034530488,0.5955647708468143,6.584591865539551,948.29318 -14784,Multiclass classification,Bagging,Insects,0.6052222147060813,0.6052222147060813,0.586323857604342,6.583569526672363,1092.793426 -15840,Multiclass classification,Bagging,Insects,0.570427425973862,0.570427425973862,0.5530515395071289,6.584805488586426,1247.37522 -16896,Multiclass classification,Bagging,Insects,0.5441254809115122,0.5441254809115122,0.5274626123277456,6.5833024978637695,1412.138663 -17952,Multiclass classification,Bagging,Insects,0.5247061445044844,0.5247061445044844,0.5077849244821269,6.5842180252075195,1587.005582 -19008,Multiclass classification,Bagging,Insects,0.5143368232756353,0.5143368232756353,0.4945891921842289,5.490016937255859,1771.530278 -20064,Multiclass classification,Bagging,Insects,0.5203110202860988,0.5203110202860988,0.4996705647403201,13.561949729919434,1966.283082 -21120,Multiclass classification,Bagging,Insects,0.5285288129172783,0.5285288129172783,0.5082662721949724,14.332747459411621,2175.2000869999997 -22176,Multiclass classification,Bagging,Insects,0.5345208568207441,0.5345208568207441,0.5149076376433322,14.875703811645508,2397.3423569999995 -23232,Multiclass classification,Bagging,Insects,0.5431104989023288,0.5431104989023288,0.5234265380967914,14.787662506103516,2632.3046249999998 -24288,Multiclass classification,Bagging,Insects,0.550253221888253,0.550253221888253,0.5298759738824472,16.32009983062744,2880.082024 -25344,Multiclass classification,Bagging,Insects,0.5564455668231859,0.5564455668231859,0.5355827199778521,16.310463905334473,3141.997661 -26400,Multiclass classification,Bagging,Insects,0.5614985416114247,0.5614985416114247,0.5398687013453174,16.303704261779785,3417.9057679999996 -27456,Multiclass classification,Bagging,Insects,0.5647787288289929,0.5647787288289929,0.5421799635248432,15.336288452148438,3708.1186949999997 -28512,Multiclass classification,Bagging,Insects,0.5680965241485743,0.5680965241485743,0.5473162851674372,13.81827449798584,4011.6853579999997 -29568,Multiclass classification,Bagging,Insects,0.5701626813677411,0.5701626813677411,0.5529817475842932,11.429868698120117,4328.316425 -30624,Multiclass classification,Bagging,Insects,0.5724455474643242,0.5724455474643242,0.5586057023406553,9.4625883102417,4656.70214 -31680,Multiclass classification,Bagging,Insects,0.5750181508254679,0.5750181508254679,0.5636300266647484,9.46137523651123,4996.240557 -32736,Multiclass classification,Bagging,Insects,0.5782190316175347,0.5782190316175347,0.5684825891024486,9.4603910446167,5346.796886 -33792,Multiclass classification,Bagging,Insects,0.5756858335059631,0.5756858335059631,0.5663669622675245,7.919375419616699,5710.001071 -34848,Multiclass classification,Bagging,Insects,0.5754871294516027,0.5754871294516027,0.5660193869557423,7.260138511657715,6084.365793 -35904,Multiclass classification,Bagging,Insects,0.5764142272233518,0.5764142272233518,0.5665362650344427,6.60003662109375,6469.590309 -36960,Multiclass classification,Bagging,Insects,0.575908439081144,0.575908439081144,0.5657420280651625,6.597938537597656,6865.284174 -38016,Multiclass classification,Bagging,Insects,0.5767723267131396,0.5767723267131396,0.5661330182942309,6.597076416015625,7271.517818 -39072,Multiclass classification,Bagging,Insects,0.5764889560031737,0.5764889560031737,0.5659501482422926,6.5939178466796875,7688.178552 -40128,Multiclass classification,Bagging,Insects,0.5734792035287961,0.5734792035287961,0.5636824355748769,8.576746940612793,8115.281794 -41184,Multiclass classification,Bagging,Insects,0.5726634776485443,0.5726634776485443,0.563417094879665,8.779536247253418,8552.341759 -42240,Multiclass classification,Bagging,Insects,0.5723383602831507,0.5723383602831507,0.5635995837049609,10.21227741241455,8998.890652 -43296,Multiclass classification,Bagging,Insects,0.5718212264695692,0.5718212264695692,0.5636175088230181,10.21111011505127,9455.260723 -44352,Multiclass classification,Bagging,Insects,0.571125791977633,0.571125791977633,0.5633830644644046,10.209759712219238,9921.572486 -45408,Multiclass classification,Bagging,Insects,0.5712555332878191,0.5712555332878191,0.5638292127585011,11.4169340133667,10398.038321 -46464,Multiclass classification,Bagging,Insects,0.5728429072595399,0.5728429072595399,0.565898409914518,12.449102401733398,10884.980897 -47520,Multiclass classification,Bagging,Insects,0.5768850354595004,0.5768850354595004,0.57039744062367,16.244935989379883,11383.832016999999 -48576,Multiclass classification,Bagging,Insects,0.5828718476582604,0.5828718476582604,0.5764217258661826,15.377230644226074,11896.186787999999 -49632,Multiclass classification,Bagging,Insects,0.5890471681005823,0.5890471681005823,0.5823842044431963,14.950640678405762,12421.939031 -50688,Multiclass classification,Bagging,Insects,0.594728431353207,0.594728431353207,0.5876258810149467,12.564711570739746,12959.806661999999 -51744,Multiclass classification,Bagging,Insects,0.6007382641130201,0.6007382641130201,0.5930524375976366,11.987659454345703,13508.895543999999 -52800,Multiclass classification,Bagging,Insects,0.606053144945927,0.606053144945927,0.5982224401760299,3.750063896179199,14067.159334999998 -408,Multiclass classification,Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.2869701385498047,1.329477 -816,Multiclass classification,Bagging,Keystroke,0.9411042944785276,0.9411042944785276,0.7377235942917068,3.233952522277832,4.406974 -1224,Multiclass classification,Bagging,Keystroke,0.8879803761242846,0.8879803761242846,0.873420796574987,4.178282737731934,9.866805 -1632,Multiclass classification,Bagging,Keystroke,0.8988350705088902,0.8988350705088902,0.8792834531664682,5.13068962097168,18.093902999999997 -2040,Multiclass classification,Bagging,Keystroke,0.8950465914664051,0.8950465914664051,0.8828407845486113,6.191357612609863,29.527086999999998 -2448,Multiclass classification,Bagging,Keystroke,0.8561503882304863,0.8561503882304863,0.8521381720173345,7.13577938079834,44.765435999999994 -2856,Multiclass classification,Bagging,Keystroke,0.8623467600700525,0.8623467600700525,0.8461129037988256,8.082098007202148,64.393699 -3264,Multiclass classification,Bagging,Keystroke,0.8528961078761875,0.8528961078761875,0.828204357625989,9.02734088897705,89.05171 -3672,Multiclass classification,Bagging,Keystroke,0.8428221193135386,0.8428221193135386,0.8381978174360706,9.972960472106934,119.28990999999999 -4080,Multiclass classification,Bagging,Keystroke,0.8350085805344447,0.8350085805344447,0.8208915725311207,11.171079635620117,155.799996 -4488,Multiclass classification,Bagging,Keystroke,0.822821484287943,0.822821484287943,0.832874806475175,12.116948127746582,198.957143 -4896,Multiclass classification,Bagging,Keystroke,0.8212461695607763,0.8212461695607763,0.8275900848879882,13.061814308166504,249.493693 -5304,Multiclass classification,Bagging,Keystroke,0.8178389590797661,0.8178389590797661,0.8022229037941512,14.007365226745605,307.913741 -5712,Multiclass classification,Bagging,Keystroke,0.7974085098931886,0.7974085098931886,0.8005324816804641,14.95396614074707,374.81365400000004 -6120,Multiclass classification,Bagging,Keystroke,0.7947377022389279,0.7947377022389279,0.7763699164747573,15.899590492248535,450.78850600000004 -6528,Multiclass classification,Bagging,Keystroke,0.7695725448138502,0.7695725448138502,0.7646092489325799,16.84642791748047,536.475639 -6936,Multiclass classification,Bagging,Keystroke,0.7614996395097332,0.7614996395097332,0.7633186803137438,17.791536331176758,632.653421 -7344,Multiclass classification,Bagging,Keystroke,0.770393572109492,0.770393572109492,0.7679684376178252,18.753963470458984,739.829913 -7752,Multiclass classification,Bagging,Keystroke,0.7709972906721714,0.7709972906721715,0.7694364393340193,19.70015239715576,858.3068800000001 -8160,Multiclass classification,Bagging,Keystroke,0.7739919107733791,0.7739919107733791,0.7702264725589797,20.646059036254883,989.0801780000002 -8568,Multiclass classification,Bagging,Keystroke,0.770631492938018,0.770631492938018,0.7706502591714904,22.072673797607422,1132.67323 -8976,Multiclass classification,Bagging,Keystroke,0.7691364902506964,0.7691364902506964,0.7697475673922982,23.017619132995605,1289.930532 -9384,Multiclass classification,Bagging,Keystroke,0.7679846530960247,0.7679846530960248,0.7675735514139922,23.96499538421631,1461.233929 -9792,Multiclass classification,Bagging,Keystroke,0.7634562353181493,0.7634562353181493,0.7626887405791724,24.91041660308838,1647.0514269999999 -10200,Multiclass classification,Bagging,Keystroke,0.7552701245220119,0.7552701245220118,0.7474447650479976,25.855456352233887,1848.111209 -10608,Multiclass classification,Bagging,Keystroke,0.734326388234185,0.734326388234185,0.7218544335091276,26.80277729034424,2064.993893 -11016,Multiclass classification,Bagging,Keystroke,0.727099409895597,0.727099409895597,0.7232704418570853,27.74752426147461,2298.467217 -11424,Multiclass classification,Bagging,Keystroke,0.7203011468090694,0.7203011468090693,0.7069709690618045,28.693537712097168,2549.106467 -11832,Multiclass classification,Bagging,Keystroke,0.7107598681430141,0.7107598681430141,0.7032019097144009,29.638681411743164,2817.5197820000003 -12240,Multiclass classification,Bagging,Keystroke,0.7152545142577008,0.7152545142577007,0.7117335483783439,30.584209442138672,3104.4910310000005 -12648,Multiclass classification,Bagging,Keystroke,0.7121056377006405,0.7121056377006404,0.7043178518121461,31.53076934814453,3410.5360360000004 -13056,Multiclass classification,Bagging,Keystroke,0.7005744925315971,0.7005744925315971,0.6932522175542292,32.476640701293945,3735.5420080000004 -13464,Multiclass classification,Bagging,Keystroke,0.6985070192379114,0.6985070192379114,0.6945196760058037,33.421990394592285,4080.277425 -13872,Multiclass classification,Bagging,Keystroke,0.6980751207555331,0.6980751207555331,0.6949558493849793,34.36870098114014,4445.365121 -14280,Multiclass classification,Bagging,Keystroke,0.6936760277330345,0.6936760277330345,0.6891645690411646,35.313669204711914,4831.313826 -14688,Multiclass classification,Bagging,Keystroke,0.6963300878327773,0.6963300878327773,0.6946500105809528,36.259453773498535,5238.529619 -15096,Multiclass classification,Bagging,Keystroke,0.7024180192116595,0.7024180192116595,0.7008836593188431,37.20665740966797,5667.803474 -15504,Multiclass classification,Bagging,Keystroke,0.702509191769335,0.702509191769335,0.6995855030221436,38.15153884887695,6119.5687720000005 -15912,Multiclass classification,Bagging,Keystroke,0.6934824963861479,0.6934824963861479,0.687175788748239,39.09754180908203,6594.573754000001 -16320,Multiclass classification,Bagging,Keystroke,0.6848458851645322,0.6848458851645322,0.6802460349069701,40.04375648498535,7093.395211000001 -16728,Multiclass classification,Bagging,Keystroke,0.6819513361630896,0.6819513361630896,0.6795788912922722,40.98903942108154,7616.277784000001 -17136,Multiclass classification,Bagging,Keystroke,0.6779107090749927,0.6779107090749927,0.6747648209169417,42.91780757904053,8164.158576000001 -17544,Multiclass classification,Bagging,Keystroke,0.6705808584620646,0.6705808584620646,0.6680341530684186,43.864423751831055,8737.125467000002 -17952,Multiclass classification,Bagging,Keystroke,0.6695448721519692,0.6695448721519692,0.6687363294804706,44.8110933303833,9335.759485000002 -18360,Multiclass classification,Bagging,Keystroke,0.674927828313089,0.674927828313089,0.6747300618557481,45.75679397583008,9961.084735000002 -18768,Multiclass classification,Bagging,Keystroke,0.6799701603879149,0.6799701603879149,0.6801519832282531,46.703369140625,10614.122071000002 -19176,Multiclass classification,Bagging,Keystroke,0.6720730117340287,0.6720730117340287,0.6711666831354974,47.648451805114746,11294.958186000002 -19584,Multiclass classification,Bagging,Keystroke,0.6760455497114845,0.6760455497114845,0.6762772840246767,48.59414768218994,12004.137364000002 -19992,Multiclass classification,Bagging,Keystroke,0.6715521984893202,0.6715521984893202,0.6718362805013157,49.540550231933594,12741.947206000003 -20400,Multiclass classification,Bagging,Keystroke,0.6679739202902103,0.6679739202902103,0.6688529665037395,50.48721218109131,13509.112779000003 -46,Multiclass classification,Leveraging Bagging,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,4.12965202331543,2.162623 -92,Multiclass classification,Leveraging Bagging,ImageSegments,0.5164835164835165,0.5164835164835165,0.5316649744849407,4.130231857299805,5.385739 -138,Multiclass classification,Leveraging Bagging,ImageSegments,0.5547445255474452,0.5547445255474452,0.5804654781117262,4.130353927612305,9.498090999999999 -184,Multiclass classification,Leveraging Bagging,ImageSegments,0.6174863387978142,0.6174863387978142,0.6394923756219437,4.130964279174805,14.447809999999999 -230,Multiclass classification,Leveraging Bagging,ImageSegments,0.6506550218340611,0.6506550218340611,0.66859135700569,4.130964279174805,20.213143 -276,Multiclass classification,Leveraging Bagging,ImageSegments,0.6618181818181819,0.6618181818181819,0.6795855359270878,4.131082534790039,26.807817999999997 -322,Multiclass classification,Leveraging Bagging,ImageSegments,0.6853582554517134,0.6853582554517134,0.6872635633687633,4.131624221801758,34.210100999999995 -368,Multiclass classification,Leveraging Bagging,ImageSegments,0.7111716621253406,0.7111716621253404,0.7098417316927395,4.131597518920898,42.42663699999999 -414,Multiclass classification,Leveraging Bagging,ImageSegments,0.7215496368038741,0.7215496368038742,0.7201557312728714,4.13151741027832,51.47481599999999 -460,Multiclass classification,Leveraging Bagging,ImageSegments,0.7211328976034859,0.721132897603486,0.7175330036146421,4.131570816040039,61.34488599999999 -506,Multiclass classification,Leveraging Bagging,ImageSegments,0.7287128712871287,0.7287128712871287,0.7233455022590812,4.131570816040039,72.04824799999999 -552,Multiclass classification,Leveraging Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7255599965917697,4.131490707397461,83.59783199999998 -598,Multiclass classification,Leveraging Bagging,ImageSegments,0.7353433835845896,0.7353433835845896,0.7308494254186014,4.131513595581055,95.96874199999998 -644,Multiclass classification,Leveraging Bagging,ImageSegments,0.7340590979782271,0.7340590979782271,0.7314183982762247,4.132074356079102,109.15469699999997 -690,Multiclass classification,Leveraging Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7343909641298695,4.132074356079102,123.16042199999997 -736,Multiclass classification,Leveraging Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7369557659594496,4.132101058959961,138.00293199999996 -782,Multiclass classification,Leveraging Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7419020281650245,4.132101058959961,153.67249999999996 -828,Multiclass classification,Leveraging Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7432199627682998,4.132101058959961,170.15907299999995 -874,Multiclass classification,Leveraging Bagging,ImageSegments,0.7502863688430699,0.7502863688430699,0.7482089866208982,4.132101058959961,187.48903599999994 -920,Multiclass classification,Leveraging Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7477650187313973,4.132074356079102,205.64141899999993 -966,Multiclass classification,Leveraging Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.747322646811651,4.132074356079102,224.60247399999992 -1012,Multiclass classification,Leveraging Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.7469783619055548,4.132074356079102,244.38522099999992 -1058,Multiclass classification,Leveraging Bagging,ImageSegments,0.7530747398297067,0.7530747398297066,0.7482363934596314,4.132074356079102,264.9944789999999 -1104,Multiclass classification,Leveraging Bagging,ImageSegments,0.7552130553037172,0.7552130553037172,0.750118495060715,4.132123947143555,286.4318919999999 -1150,Multiclass classification,Leveraging Bagging,ImageSegments,0.7571801566579635,0.7571801566579635,0.7516199800653578,4.132123947143555,308.6933539999999 -1196,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598326359832636,0.7598326359832636,0.7548841797367704,4.132123947143555,331.8138849999999 -1242,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598710717163578,0.7598710717163577,0.7553301531902636,4.132123947143555,355.7543559999999 -1288,Multiclass classification,Leveraging Bagging,ImageSegments,0.7645687645687645,0.7645687645687647,0.7590078532621816,4.132734298706055,380.5041089999999 -1334,Multiclass classification,Leveraging Bagging,ImageSegments,0.7644411102775694,0.7644411102775694,0.7591993978414527,4.132757186889648,406.0948849999999 -1380,Multiclass classification,Leveraging Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7601575050520946,4.132757186889648,432.50441499999994 -1426,Multiclass classification,Leveraging Bagging,ImageSegments,0.7670175438596492,0.7670175438596492,0.7613339877221927,4.132757186889648,459.75425999999993 -1472,Multiclass classification,Leveraging Bagging,ImageSegments,0.7715839564921821,0.7715839564921821,0.76413964752182,4.132802963256836,487.81331299999994 -1518,Multiclass classification,Leveraging Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.7648275341801108,4.132802963256836,516.6922259999999 -1564,Multiclass classification,Leveraging Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7657569341108763,4.132802963256836,546.3975239999999 -1610,Multiclass classification,Leveraging Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7651494083475014,4.13282585144043,576.9286479999998 -1656,Multiclass classification,Leveraging Bagging,ImageSegments,0.7740181268882175,0.7740181268882175,0.7654813489818475,4.132780075073242,608.2971799999998 -1702,Multiclass classification,Leveraging Bagging,ImageSegments,0.7730746619635509,0.7730746619635509,0.7664930279619061,4.132780075073242,640.4789209999998 -1748,Multiclass classification,Leveraging Bagging,ImageSegments,0.7756153405838581,0.7756153405838581,0.7686072256536652,4.132780075073242,673.4948829999998 -1794,Multiclass classification,Leveraging Bagging,ImageSegments,0.7769102063580591,0.7769102063580591,0.7685414235990153,4.132753372192383,707.3642379999999 -1840,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781402936378466,0.7781402936378466,0.7699957723931324,4.132753372192383,742.0792459999999 -1886,Multiclass classification,Leveraging Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7684985598909853,4.132753372192383,777.6304799999999 -1932,Multiclass classification,Leveraging Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.767743441804642,4.132753372192383,814.0157899999999 -1978,Multiclass classification,Leveraging Bagging,ImageSegments,0.7774405665149215,0.7774405665149215,0.7684788817649146,4.132753372192383,851.2372119999999 -2024,Multiclass classification,Leveraging Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7689103339153599,4.132753372192383,889.2963629999999 -2070,Multiclass classification,Leveraging Bagging,ImageSegments,0.7786370227162881,0.7786370227162881,0.7686288077529282,4.132753372192383,928.2025699999999 -2116,Multiclass classification,Leveraging Bagging,ImageSegments,0.7791962174940898,0.7791962174940898,0.768391950800897,4.132753372192383,967.9450939999999 -2162,Multiclass classification,Leveraging Bagging,ImageSegments,0.7801943544655252,0.7801943544655253,0.768962628827985,4.132776260375977,1008.5034619999999 -2208,Multiclass classification,Leveraging Bagging,ImageSegments,0.7820570910738559,0.7820570910738559,0.7698068761587117,4.132749557495117,1049.8865549999998 -2254,Multiclass classification,Leveraging Bagging,ImageSegments,0.7789613848202397,0.7789613848202397,0.7667173742344939,4.132749557495117,1092.1021469999998 -2300,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781644193127447,0.7781644193127447,0.7659138381656089,4.132749557495117,1135.1554539999997 -1056,Multiclass classification,Leveraging Bagging,Insects,0.6218009478672986,0.6218009478672986,0.5857016652718547,6.522056579589844,30.289418 -2112,Multiclass classification,Leveraging Bagging,Insects,0.6196115585030791,0.6196115585030791,0.5856756432415232,10.389650344848633,88.669185 -3168,Multiclass classification,Leveraging Bagging,Insects,0.628986422481844,0.628986422481844,0.5949930595607558,19.16711711883545,174.550284 -4224,Multiclass classification,Leveraging Bagging,Insects,0.6294103717736207,0.6294103717736207,0.5952675443708706,19.668034553527832,287.918965 -5280,Multiclass classification,Leveraging Bagging,Insects,0.6364841826103429,0.6364841826103429,0.5994911272790604,18.96163558959961,428.854975 -6336,Multiclass classification,Leveraging Bagging,Insects,0.6352012628255722,0.6352012628255722,0.5993891820807257,20.14603328704834,597.190787 -7392,Multiclass classification,Leveraging Bagging,Insects,0.638749830875389,0.638749830875389,0.6030343276880051,21.10132884979248,793.049033 -8448,Multiclass classification,Leveraging Bagging,Insects,0.6405824553095774,0.6405824553095774,0.6028521616895871,24.152769088745117,1016.521492 -9504,Multiclass classification,Leveraging Bagging,Insects,0.6449542249815847,0.6449542249815847,0.6055705492028415,24.86981773376465,1266.726445 -10560,Multiclass classification,Leveraging Bagging,Insects,0.6485462638507434,0.6485462638507434,0.6081614166360886,28.971991539001465,1544.137884 -11616,Multiclass classification,Leveraging Bagging,Insects,0.6490744726646578,0.6490744726646578,0.6078786452761632,31.018654823303223,1848.95866 -12672,Multiclass classification,Leveraging Bagging,Insects,0.6514876489621971,0.6514876489621971,0.6111938480023121,35.39500713348389,2179.477442 -13728,Multiclass classification,Leveraging Bagging,Insects,0.6707947840023312,0.6707947840023312,0.6607574394823456,17.66313648223877,2527.7545929999997 -14784,Multiclass classification,Leveraging Bagging,Insects,0.6814584319826829,0.6814584319826829,0.6724584381879511,11.128533363342285,2896.354015 -15840,Multiclass classification,Leveraging Bagging,Insects,0.6762421870067554,0.6762421870067554,0.6688785181435096,14.811795234680176,3290.113809 -16896,Multiclass classification,Leveraging Bagging,Insects,0.6741639538324948,0.6741639538324948,0.6676833597101233,15.365425109863281,3710.779607 -17952,Multiclass classification,Leveraging Bagging,Insects,0.670491894601972,0.670491894601972,0.6643621029883554,15.98740005493164,4158.727334 -19008,Multiclass classification,Leveraging Bagging,Insects,0.6754353659178197,0.6754353659178197,0.6656526175716114,17.100504875183105,4627.925426 -20064,Multiclass classification,Leveraging Bagging,Insects,0.6800079748791308,0.6800079748791308,0.6670489534490986,26.370519638061523,5118.761149 -21120,Multiclass classification,Leveraging Bagging,Insects,0.6835550925706709,0.6835550925706709,0.6685883462655132,32.78877353668213,5635.605831 -22176,Multiclass classification,Leveraging Bagging,Insects,0.6869447576099211,0.6869447576099211,0.6701495347804184,36.297407150268555,6178.169973 -23232,Multiclass classification,Leveraging Bagging,Insects,0.6912745899875167,0.6912745899875167,0.6726358783249661,38.26123523712158,6746.46947 -24288,Multiclass classification,Leveraging Bagging,Insects,0.6940338452670153,0.6940338452670153,0.673442702110033,39.100372314453125,7340.085466 -25344,Multiclass classification,Leveraging Bagging,Insects,0.6976679951071302,0.6976679951071302,0.67525701759611,42.24958515167236,7958.6023620000005 -26400,Multiclass classification,Leveraging Bagging,Insects,0.7000643963786507,0.7000643963786507,0.6759116206749555,41.52747917175293,8602.02664 -27456,Multiclass classification,Leveraging Bagging,Insects,0.7027135312329266,0.7027135312329266,0.6765494742782628,43.561981201171875,9269.998307 -28512,Multiclass classification,Leveraging Bagging,Insects,0.7018343797130931,0.7018343797130931,0.6771545550561098,24.23386573791504,9962.817287 -29568,Multiclass classification,Leveraging Bagging,Insects,0.7013224202658369,0.7013224202658369,0.681362451564682,5.156903266906738,10676.641313 -30624,Multiclass classification,Leveraging Bagging,Insects,0.699702837736342,0.699702837736342,0.6839521261644582,8.359548568725586,11409.958608 -31680,Multiclass classification,Leveraging Bagging,Insects,0.6993907635973358,0.6993907635973358,0.6874853197903658,12.837088584899902,12162.299998 -32736,Multiclass classification,Leveraging Bagging,Insects,0.7005651443409195,0.7005651443409195,0.692127614099415,14.392640113830566,12933.190651 -33792,Multiclass classification,Leveraging Bagging,Insects,0.6971678849397769,0.6971678849397769,0.6903104823999882,22.114407539367676,13726.605059000001 -34848,Multiclass classification,Leveraging Bagging,Insects,0.6941487072057853,0.6941487072057853,0.6871648754350796,16.369569778442383,14546.496494 -35904,Multiclass classification,Leveraging Bagging,Insects,0.6917527783193606,0.6917527783193606,0.684473708604621,15.783265113830566,15393.169285 -36960,Multiclass classification,Leveraging Bagging,Insects,0.6883303119673151,0.6883303119673151,0.6807777972894504,18.195876121520996,16266.308937 -38016,Multiclass classification,Leveraging Bagging,Insects,0.6865973957648297,0.6865973957648297,0.6786744939637405,21.092598915100098,17165.821217 -39072,Multiclass classification,Leveraging Bagging,Insects,0.6857259860254409,0.6857259860254409,0.6778492437957201,16.29904079437256,18090.697656 -40128,Multiclass classification,Leveraging Bagging,Insects,0.6837540807934807,0.6837540807934807,0.6766238977666043,13.538718223571777,19041.492123 -41184,Multiclass classification,Leveraging Bagging,Insects,0.6814462278124469,0.6814462278124469,0.675074837604149,15.844508171081543,20015.794843 -42240,Multiclass classification,Leveraging Bagging,Insects,0.6790643717891048,0.6790643717891048,0.6733686277261395,15.962260246276855,21014.495285 -43296,Multiclass classification,Leveraging Bagging,Insects,0.6762443700196328,0.6762443700196328,0.6713719096586489,17.128825187683105,22038.291411000002 -44352,Multiclass classification,Leveraging Bagging,Insects,0.6738066785416338,0.6738066785416338,0.6696205967919768,16.462289810180664,23087.153485000003 -45408,Multiclass classification,Leveraging Bagging,Insects,0.6717686700288502,0.6717686700288502,0.6680705737277651,17.22057342529297,24160.866409000002 -46464,Multiclass classification,Leveraging Bagging,Insects,0.6708994253492025,0.6708994253492025,0.6677330044499646,17.752578735351562,25258.281990000003 -47520,Multiclass classification,Leveraging Bagging,Insects,0.6729939603106126,0.6729939603106126,0.6699611714455135,18.93515110015869,26380.044308000004 -48576,Multiclass classification,Leveraging Bagging,Insects,0.6767061245496655,0.6767061245496655,0.6733691077464542,20.549713134765625,27525.907792000005 -49632,Multiclass classification,Leveraging Bagging,Insects,0.6807237412101308,0.6807237412101308,0.6769109137483648,20.974443435668945,28695.603524000006 -50688,Multiclass classification,Leveraging Bagging,Insects,0.6845147671000453,0.6845147671000453,0.6800104952374638,22.97932243347168,29888.264116000006 -51744,Multiclass classification,Leveraging Bagging,Insects,0.6885182536768258,0.6885182536768258,0.6832561756017089,24.11430263519287,31103.175398000007 -52800,Multiclass classification,Leveraging Bagging,Insects,0.6915471883937195,0.6915471883937195,0.6864107325641782,18.141328811645508,32334.061725000007 -408,Multiclass classification,Leveraging Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.238800048828125,2.971982 -816,Multiclass classification,Leveraging Bagging,Keystroke,0.9460122699386503,0.9460122699386503,0.8367492469040564,4.44326114654541,9.847778 -1224,Multiclass classification,Leveraging Bagging,Keystroke,0.9411283728536386,0.9411283728536386,0.9276213812296338,6.153376579284668,20.932147 -1632,Multiclass classification,Leveraging Bagging,Keystroke,0.950337216431637,0.950337216431637,0.9330502878949443,7.8991851806640625,36.717851 -2040,Multiclass classification,Leveraging Bagging,Keystroke,0.9494850416871016,0.9494850416871016,0.932928877406915,10.965654373168945,57.922702 -2448,Multiclass classification,Leveraging Bagging,Keystroke,0.9525950143032285,0.9525950143032285,0.9502305130509757,10.694184303283691,83.450682 -2856,Multiclass classification,Leveraging Bagging,Keystroke,0.9544658493870403,0.9544658493870403,0.943855127765724,15.53213119506836,113.412194 -3264,Multiclass classification,Leveraging Bagging,Keystroke,0.9515783021759118,0.9515783021759118,0.944582727256988,15.652314186096191,149.290909 -3672,Multiclass classification,Leveraging Bagging,Keystroke,0.9526014709888314,0.9526014709888314,0.9497542235388343,16.11695098876953,190.98460699999998 -4080,Multiclass classification,Leveraging Bagging,Keystroke,0.9499877420936504,0.9499877420936504,0.9391633661003513,16.578293800354004,238.59921099999997 -4488,Multiclass classification,Leveraging Bagging,Keystroke,0.9474036104301314,0.9474036104301314,0.9496969875723204,13.190230369567871,292.48392399999994 -4896,Multiclass classification,Leveraging Bagging,Keystroke,0.9493360572012257,0.9493360572012257,0.9494027577495958,12.864276885986328,353.18946299999993 -5304,Multiclass classification,Leveraging Bagging,Keystroke,0.951159720912691,0.951159720912691,0.9518992835106977,11.604743957519531,419.93206699999996 -5712,Multiclass classification,Leveraging Bagging,Keystroke,0.951146909472947,0.951146909472947,0.9505351682914018,13.577879905700684,492.70239499999997 -6120,Multiclass classification,Leveraging Bagging,Keystroke,0.9478672985781991,0.9478672985781991,0.9429356622084736,16.35688304901123,572.32886 -6528,Multiclass classification,Leveraging Bagging,Keystroke,0.9480618967366324,0.9480618967366324,0.9478348775735732,10.670846939086914,659.058815 -6936,Multiclass classification,Leveraging Bagging,Keystroke,0.9495313626532084,0.9495313626532084,0.9511497142125283,10.614124298095703,751.777349 -7344,Multiclass classification,Leveraging Bagging,Keystroke,0.9500204276181398,0.9500204276181398,0.9502583235097111,12.824416160583496,851.689186 -7752,Multiclass classification,Leveraging Bagging,Keystroke,0.9505870210295446,0.9505870210295446,0.9508630550075082,11.99438190460205,957.810381 -8160,Multiclass classification,Leveraging Bagging,Keystroke,0.9487682314009069,0.9487682314009069,0.9466937008923911,16.34542465209961,1069.487963 -8568,Multiclass classification,Leveraging Bagging,Keystroke,0.9491070386366289,0.9491070386366289,0.9496258519963295,14.096193313598633,1187.33597 -8976,Multiclass classification,Leveraging Bagging,Keystroke,0.9504178272980501,0.9504178272980501,0.95112303337496,8.487105369567871,1310.539589 -9384,Multiclass classification,Leveraging Bagging,Keystroke,0.9507620164126612,0.9507620164126612,0.9509680125568912,10.491826057434082,1439.276187 -9792,Multiclass classification,Leveraging Bagging,Keystroke,0.9505668471044837,0.9505668471044837,0.9508008066421794,12.578843116760254,1574.391157 -10200,Multiclass classification,Leveraging Bagging,Keystroke,0.9496029022453182,0.9496029022453182,0.9490825188137642,15.329971313476562,1716.648577 -10608,Multiclass classification,Leveraging Bagging,Keystroke,0.9462619025172057,0.9462619025172057,0.9448381382156613,14.149526596069336,1865.207745 -11016,Multiclass classification,Leveraging Bagging,Keystroke,0.9473445301861099,0.9473445301861099,0.9480489849360165,15.43016529083252,2018.8519179999998 -11424,Multiclass classification,Leveraging Bagging,Keystroke,0.9476494791210716,0.9476494791210716,0.9477632560487921,19.339406967163086,2178.069416 -11832,Multiclass classification,Leveraging Bagging,Keystroke,0.9466655396838813,0.9466655396838813,0.9465646854570324,20.836685180664062,2343.241513 -12240,Multiclass classification,Leveraging Bagging,Keystroke,0.9473813220034316,0.9473813220034316,0.9477056335712672,22.594761848449707,2516.1915169999997 -12648,Multiclass classification,Leveraging Bagging,Keystroke,0.9481299913022851,0.9481299913022851,0.9484695727303012,17.12001132965088,2696.1117459999996 -13056,Multiclass classification,Leveraging Bagging,Keystroke,0.9465338950593642,0.9465338950593642,0.9461537407653537,16.317899703979492,2881.2856319999996 -13464,Multiclass classification,Leveraging Bagging,Keystroke,0.9475599792022581,0.9475599792022581,0.9479124389900309,17.175325393676758,3071.3018549999997 -13872,Multiclass classification,Leveraging Bagging,Keystroke,0.9480931439694327,0.9480931439694327,0.9483129032895908,20.13454818725586,3267.097668 -14280,Multiclass classification,Leveraging Bagging,Keystroke,0.9472652146508859,0.9472652146508858,0.9472495958535089,23.271190643310547,3470.2386189999997 -14688,Multiclass classification,Leveraging Bagging,Keystroke,0.9470960713556206,0.9470960713556206,0.9472715831304289,24.70554256439209,3681.555614 -15096,Multiclass classification,Leveraging Bagging,Keystroke,0.9477310367671414,0.9477310367671414,0.9480235232823461,12.28943920135498,3901.326497 -15504,Multiclass classification,Leveraging Bagging,Keystroke,0.9481390698574469,0.9481390698574469,0.9483821660022894,8.167351722717285,4127.852482 -15912,Multiclass classification,Leveraging Bagging,Keystroke,0.9489661240651122,0.9489661240651122,0.949259317367439,10.055158615112305,4359.152379 -16320,Multiclass classification,Leveraging Bagging,Keystroke,0.9489552055885777,0.9489552055885777,0.949102505659295,8.927614212036133,4595.973694 -16728,Multiclass classification,Leveraging Bagging,Keystroke,0.948645901835356,0.948645901835356,0.9487532899546077,9.772565841674805,4838.16456 -17136,Multiclass classification,Leveraging Bagging,Keystroke,0.9491683688357164,0.9491683688357164,0.9493664270655614,8.885259628295898,5086.602422 -17544,Multiclass classification,Leveraging Bagging,Keystroke,0.9497235364532862,0.9497235364532862,0.9498997400720455,7.432655334472656,5340.103049 -17952,Multiclass classification,Leveraging Bagging,Keystroke,0.9492507381204389,0.9492507381204389,0.9493299482291901,6.564939498901367,5598.267976 -18360,Multiclass classification,Leveraging Bagging,Keystroke,0.9498883381447791,0.949888338144779,0.9500369712738612,9.445448875427246,5861.742373 -18768,Multiclass classification,Leveraging Bagging,Keystroke,0.9501252198007141,0.9501252198007141,0.950244810756275,8.985580444335938,6130.741927 -19176,Multiclass classification,Leveraging Bagging,Keystroke,0.9502477183833116,0.9502477183833116,0.9503577107154481,10.539984703063965,6405.708121 -19584,Multiclass classification,Leveraging Bagging,Keystroke,0.9504672419956084,0.9504672419956084,0.9505675543483478,12.433100700378418,6686.90918 -19992,Multiclass classification,Leveraging Bagging,Keystroke,0.9504777149717373,0.9504777149717373,0.9505659657035201,12.245397567749023,6973.48828 -20400,Multiclass classification,Leveraging Bagging,Keystroke,0.950389724986519,0.950389724986519,0.9504675266923703,10.420146942138672,7265.024106 -46,Multiclass classification,Stacking,ImageSegments,0.4,0.4000000000000001,0.3289160825620571,1.8703498840332031,0.761019 -92,Multiclass classification,Stacking,ImageSegments,0.5494505494505495,0.5494505494505495,0.5607526488856412,2.0432376861572266,2.058459 -138,Multiclass classification,Stacking,ImageSegments,0.5620437956204379,0.5620437956204379,0.5814352652080846,2.2601184844970703,3.877596 -184,Multiclass classification,Stacking,ImageSegments,0.6174863387978142,0.6174863387978142,0.6349823285289026,2.5773630142211914,5.988367 -230,Multiclass classification,Stacking,ImageSegments,0.6550218340611353,0.6550218340611353,0.6697464616246889,2.673569679260254,8.36838 -276,Multiclass classification,Stacking,ImageSegments,0.68,0.68,0.6977451412884614,2.705929756164551,11.015152 -322,Multiclass classification,Stacking,ImageSegments,0.7040498442367601,0.7040498442367601,0.708655608864303,2.747677803039551,13.922748 -368,Multiclass classification,Stacking,ImageSegments,0.7302452316076294,0.7302452316076294,0.731555248839775,2.9195823669433594,17.085856 -414,Multiclass classification,Stacking,ImageSegments,0.7481840193704601,0.7481840193704601,0.7498869297449521,3.2087087631225586,20.515594 -460,Multiclass classification,Stacking,ImageSegments,0.7429193899782135,0.7429193899782135,0.7431113090395209,2.8742523193359375,24.226579 -506,Multiclass classification,Stacking,ImageSegments,0.7465346534653465,0.7465346534653465,0.7453691625646783,3.051929473876953,28.205653 -552,Multiclass classification,Stacking,ImageSegments,0.7531760435571688,0.7531760435571688,0.7537204076398122,3.133829116821289,32.454626000000005 -598,Multiclass classification,Stacking,ImageSegments,0.7587939698492462,0.7587939698492462,0.7612399908296416,3.1490001678466797,36.970245000000006 -644,Multiclass classification,Stacking,ImageSegments,0.7589424572317263,0.7589424572317262,0.7628637146980985,3.4707136154174805,41.75427400000001 -690,Multiclass classification,Stacking,ImageSegments,0.7619738751814223,0.7619738751814223,0.76530464273308,3.455944061279297,46.80492900000001 -736,Multiclass classification,Stacking,ImageSegments,0.7687074829931972,0.7687074829931972,0.7727990926768868,3.6800451278686523,52.11999800000001 -782,Multiclass classification,Stacking,ImageSegments,0.7733674775928298,0.7733674775928298,0.7767963295410655,3.8801565170288086,57.706528000000006 -828,Multiclass classification,Stacking,ImageSegments,0.7738814993954051,0.7738814993954051,0.7787678467755003,3.8676557540893555,63.567513000000005 -874,Multiclass classification,Stacking,ImageSegments,0.7812142038946163,0.7812142038946163,0.7848289172220594,3.691183090209961,69.704029 -920,Multiclass classification,Stacking,ImageSegments,0.7878128400435256,0.7878128400435256,0.7905661589338376,3.770216941833496,76.10799700000001 -966,Multiclass classification,Stacking,ImageSegments,0.7917098445595855,0.7917098445595855,0.7936972979049142,3.8226003646850586,82.78424000000001 -1012,Multiclass classification,Stacking,ImageSegments,0.7952522255192879,0.7952522255192878,0.796484514345152,4.098711967468262,89.73293500000001 -1058,Multiclass classification,Stacking,ImageSegments,0.8032166508987701,0.8032166508987703,0.8038465931831994,4.173297882080078,96.95259300000001 -1104,Multiclass classification,Stacking,ImageSegments,0.8041704442429737,0.8041704442429737,0.8051724065917674,4.3957414627075195,104.442345 -1150,Multiclass classification,Stacking,ImageSegments,0.8102697998259356,0.8102697998259357,0.8109646011887589,4.552497863769531,112.202361 -1196,Multiclass classification,Stacking,ImageSegments,0.8142259414225942,0.8142259414225941,0.8149917549940485,4.571473121643066,120.23406399999999 -1242,Multiclass classification,Stacking,ImageSegments,0.8186946011281225,0.8186946011281225,0.8196592056494876,4.626148223876953,128.53886899999998 -1288,Multiclass classification,Stacking,ImageSegments,0.8212898212898213,0.8212898212898213,0.822176577441966,5.001523017883301,137.11683999999997 -1334,Multiclass classification,Stacking,ImageSegments,0.8229557389347337,0.8229557389347337,0.8237863794336502,5.1421356201171875,145.97222099999996 -1380,Multiclass classification,Stacking,ImageSegments,0.8245105148658448,0.8245105148658448,0.8256018780761997,5.339564323425293,155.11012299999996 -1426,Multiclass classification,Stacking,ImageSegments,0.8287719298245614,0.8287719298245614,0.8290084946618356,5.337568283081055,164.53666099999995 -1472,Multiclass classification,Stacking,ImageSegments,0.8334466349422162,0.8334466349422162,0.8325983603187124,5.299435615539551,174.25653999999994 -1518,Multiclass classification,Stacking,ImageSegments,0.8358602504943968,0.8358602504943968,0.8344617749849152,5.345264434814453,184.26794299999995 -1564,Multiclass classification,Stacking,ImageSegments,0.8387715930902111,0.8387715930902111,0.837784263767798,4.9267168045043945,194.57830699999994 -1610,Multiclass classification,Stacking,ImageSegments,0.839030453697949,0.839030453697949,0.838065870841574,4.685762405395508,205.19165999999993 -1656,Multiclass classification,Stacking,ImageSegments,0.8416918429003021,0.8416918429003022,0.8408915736149335,4.737677574157715,216.09684699999994 -1702,Multiclass classification,Stacking,ImageSegments,0.8418577307466196,0.8418577307466195,0.8423710518418951,4.180461883544922,227.29100499999993 -1748,Multiclass classification,Stacking,ImageSegments,0.8431597023468803,0.8431597023468804,0.8432643493367186,4.331151962280273,238.7660419999999 -1794,Multiclass classification,Stacking,ImageSegments,0.8455103179029559,0.8455103179029559,0.8449435902582664,4.424459457397461,250.52640599999992 -1840,Multiclass classification,Stacking,ImageSegments,0.8466557911908646,0.8466557911908648,0.8462222022075542,4.3217973709106445,262.5845499999999 -1886,Multiclass classification,Stacking,ImageSegments,0.8477453580901857,0.8477453580901856,0.84772474367672,4.364754676818848,274.94178099999993 -1932,Multiclass classification,Stacking,ImageSegments,0.8487830139823925,0.8487830139823925,0.8484572581714136,4.410244941711426,287.5965719999999 -1978,Multiclass classification,Stacking,ImageSegments,0.849772382397572,0.849772382397572,0.8495372758679525,4.436578750610352,300.5458569999999 -2024,Multiclass classification,Stacking,ImageSegments,0.8507167572911517,0.8507167572911517,0.8496927624131454,4.292850494384766,313.7872069999999 -2070,Multiclass classification,Stacking,ImageSegments,0.8511358144030933,0.8511358144030933,0.8503705992191455,4.422323226928711,327.3192999999999 -2116,Multiclass classification,Stacking,ImageSegments,0.8515366430260047,0.8515366430260047,0.850305284692234,4.440757751464844,341.1384159999999 -2162,Multiclass classification,Stacking,ImageSegments,0.8505321610365572,0.850532161036557,0.84908675540822,4.611151695251465,355.24865599999987 -2208,Multiclass classification,Stacking,ImageSegments,0.851835070231083,0.851835070231083,0.8501011345319502,4.809813499450684,369.64566299999984 -2254,Multiclass classification,Stacking,ImageSegments,0.8490901020861075,0.8490901020861075,0.847799327251759,4.949430465698242,384.32755499999985 -2300,Multiclass classification,Stacking,ImageSegments,0.8490648107872988,0.8490648107872988,0.8479218608351832,5.295671463012695,399.28900199999987 -1056,Multiclass classification,Stacking,Insects,0.6454976303317536,0.6454976303317536,0.5867724425586438,7.441350936889648,8.583896 -2112,Multiclass classification,Stacking,Insects,0.6826148744670772,0.6826148744670772,0.6053874539212664,11.234929084777832,25.378114999999998 -3168,Multiclass classification,Stacking,Insects,0.6896116198294916,0.6896116198294916,0.6083758872885286,13.408919334411621,50.237849 -4224,Multiclass classification,Stacking,Insects,0.6954771489462468,0.6954771489462468,0.6085129807470798,18.096717834472656,83.102519 -5280,Multiclass classification,Stacking,Insects,0.7014586095851487,0.7014586095851487,0.6122692721162352,21.446727752685547,123.914838 -6336,Multiclass classification,Stacking,Insects,0.7021310181531176,0.7021310181531176,0.6116513676781078,27.182113647460938,172.776504 -7392,Multiclass classification,Stacking,Insects,0.7054525774590719,0.7054525774590719,0.6129808753663538,26.876797676086426,229.478895 -8448,Multiclass classification,Stacking,Insects,0.70853557476027,0.70853557476027,0.6147213044531655,31.851184844970703,293.916345 -9504,Multiclass classification,Stacking,Insects,0.7137745974955277,0.7137745974955277,0.6175531178778296,22.848219871520996,366.23076499999996 -10560,Multiclass classification,Stacking,Insects,0.7174921867601098,0.7174921867601098,0.619713417782018,19.317788124084473,446.24860299999995 -11616,Multiclass classification,Stacking,Insects,0.717606543263022,0.717606543263022,0.618960125586482,19.568995475769043,533.7141509999999 -12672,Multiclass classification,Stacking,Insects,0.7184121221687317,0.7184121221687317,0.6302774396409263,23.59817409515381,628.5255009999998 -13728,Multiclass classification,Stacking,Insects,0.7373060391928317,0.7373060391928317,0.7337291247132964,6.318717002868652,729.5138609999999 -14784,Multiclass classification,Stacking,Insects,0.744774403030508,0.7447744030305079,0.7439388578060665,4.747281074523926,836.7314339999999 -15840,Multiclass classification,Stacking,Insects,0.7374834269840268,0.7374834269840268,0.7388535634976899,8.635688781738281,951.47119 -16896,Multiclass classification,Stacking,Insects,0.7324652263983427,0.7324652263983427,0.736003592775451,13.742908477783203,1073.59215 -17952,Multiclass classification,Stacking,Insects,0.7253077822962509,0.7253077822962509,0.7305072565778182,20.007770538330078,1203.171307 -19008,Multiclass classification,Stacking,Insects,0.7387804493081497,0.7387804493081497,0.7395324944779035,6.087196350097656,1339.8258620000001 -20064,Multiclass classification,Stacking,Insects,0.7439066939141704,0.7439066939141704,0.7399287274487314,7.841000556945801,1483.477757 -21120,Multiclass classification,Stacking,Insects,0.7456792461764288,0.7456792461764288,0.738136498436516,11.804688453674316,1634.941518 -22176,Multiclass classification,Stacking,Insects,0.7464712514092446,0.7464712514092445,0.7355899333520025,17.346091270446777,1793.971452 -23232,Multiclass classification,Stacking,Insects,0.7486548146872714,0.7486548146872714,0.7347795423630049,20.6541748046875,1960.617459 -24288,Multiclass classification,Stacking,Insects,0.7502367521719439,0.7502367521719437,0.7334324471857778,21.727876663208008,2134.847723 -25344,Multiclass classification,Stacking,Insects,0.7523576530008287,0.7523576530008288,0.7330792890892175,28.11653995513916,2316.991759 -26400,Multiclass classification,Stacking,Insects,0.7535891511042085,0.7535891511042085,0.731955812013067,28.993709564208984,2507.189995 -27456,Multiclass classification,Stacking,Insects,0.7540338736113641,0.7540338736113641,0.7298780765329144,35.52100467681885,2705.597193 -28512,Multiclass classification,Stacking,Insects,0.7522710532776823,0.7522710532776823,0.7301216768723076,19.597237586975098,2912.282888 -29568,Multiclass classification,Stacking,Insects,0.7502621165488551,0.7502621165488552,0.733319854895679,15.064599990844727,3126.360865 -30624,Multiclass classification,Stacking,Insects,0.7500244913953564,0.7500244913953564,0.7381499467352403,21.998522758483887,3348.070871 -31680,Multiclass classification,Stacking,Insects,0.7493292086240096,0.7493292086240096,0.7414716120706107,28.992523193359375,3577.2433 -32736,Multiclass classification,Stacking,Insects,0.7494424927447686,0.7494424927447686,0.7447602446394828,36.39131259918213,3813.876585 -33792,Multiclass classification,Stacking,Insects,0.7448137077920156,0.7448137077920156,0.7415559043607837,7.406244277954102,4059.132247 -34848,Multiclass classification,Stacking,Insects,0.7397193445633771,0.739719344563377,0.7363475181006618,8.795232772827148,4312.474973 -35904,Multiclass classification,Stacking,Insects,0.7365679748210456,0.7365679748210455,0.7329849736783064,11.101387023925781,4573.597154 -36960,Multiclass classification,Stacking,Insects,0.7330014340214832,0.7330014340214832,0.7293557861681861,15.830912590026855,4842.477951 -38016,Multiclass classification,Stacking,Insects,0.7302643693278968,0.7302643693278967,0.7264691718738406,19.795815467834473,5119.095735 -39072,Multiclass classification,Stacking,Insects,0.7309513449873307,0.7309513449873307,0.7270525503986339,9.05908489227295,5403.358583 -40128,Multiclass classification,Stacking,Insects,0.729284521643781,0.729284521643781,0.7256952486493923,11.242535591125488,5695.769582 -41184,Multiclass classification,Stacking,Insects,0.7294029089672923,0.7294029089672922,0.7260996194485368,9.506610870361328,5995.177374 -42240,Multiclass classification,Stacking,Insects,0.7298941736310045,0.7298941736310045,0.7269475794208268,15.258689880371094,6301.374087 -43296,Multiclass classification,Stacking,Insects,0.7306848365862109,0.7306848365862109,0.7280100891072271,20.2097225189209,6614.284978 -44352,Multiclass classification,Stacking,Insects,0.7312574688282113,0.7312574688282113,0.7287466644577517,26.378506660461426,6934.278340999999 -45408,Multiclass classification,Stacking,Insects,0.7317814433897857,0.7317814433897857,0.7291491859846939,32.061384201049805,7261.498196999999 -46464,Multiclass classification,Stacking,Insects,0.732776617954071,0.732776617954071,0.7299865007540453,32.25613784790039,7595.964266999999 -47520,Multiclass classification,Stacking,Insects,0.7334329426124288,0.7334329426124286,0.7309449816547512,16.162960052490234,7937.466337999999 -48576,Multiclass classification,Stacking,Insects,0.7373751930005147,0.7373751930005147,0.7352697035426822,12.93554973602295,8285.412244 -49632,Multiclass classification,Stacking,Insects,0.741210130765046,0.741210130765046,0.739269872700679,15.798639297485352,8639.863329999998 -50688,Multiclass classification,Stacking,Insects,0.7446682581332491,0.7446682581332491,0.7426657147430288,14.252553939819336,9000.792304999999 -51744,Multiclass classification,Stacking,Insects,0.7485650232881742,0.7485650232881743,0.7463959215624629,16.087495803833008,9368.035474999999 -52800,Multiclass classification,Stacking,Insects,0.752154396863577,0.752154396863577,0.7502511872752614,11.339015007019043,9741.135739 -408,Multiclass classification,Stacking,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.0347824096679688,1.562947 -816,Multiclass classification,Stacking,Keystroke,0.9840490797546012,0.9840490797546012,0.9559273479637392,2.137660026550293,5.08284 -1224,Multiclass classification,Stacking,Keystroke,0.9836467702371219,0.9836467702371219,0.9660207101584454,3.2939910888671875,10.794145 -1632,Multiclass classification,Stacking,Keystroke,0.9803801348865726,0.9803801348865726,0.9452685517164727,4.760180473327637,18.719227 -2040,Multiclass classification,Stacking,Keystroke,0.973516429622364,0.973516429622364,0.9361195161551138,6.6425981521606445,28.938428000000002 -2448,Multiclass classification,Stacking,Keystroke,0.9730281977932161,0.9730281977932161,0.9615988180290456,5.552071571350098,41.439403 -2856,Multiclass classification,Stacking,Keystroke,0.9747810858143607,0.9747810858143607,0.9713591464752813,6.8436784744262695,56.378536 -3264,Multiclass classification,Stacking,Keystroke,0.974869751762182,0.974869751762182,0.9692034094625394,6.567030906677246,73.934123 -3672,Multiclass classification,Stacking,Keystroke,0.9743938981204031,0.9743938981204031,0.9689232613591287,8.48116397857666,94.245901 -4080,Multiclass classification,Stacking,Keystroke,0.9722971316499142,0.9722971316499142,0.9642661054824399,5.934853553771973,117.581237 -4488,Multiclass classification,Stacking,Keystroke,0.9732560731000669,0.9732560731000669,0.9722719909296184,3.644045829772949,143.695153 -4896,Multiclass classification,Stacking,Keystroke,0.9748723186925434,0.9748723186925434,0.9754037061196345,4.787886619567871,172.738198 -5304,Multiclass classification,Stacking,Keystroke,0.9741655666603809,0.9741655666603809,0.9716360242916738,5.742301940917969,204.85114700000003 -5712,Multiclass classification,Stacking,Keystroke,0.9746104009805638,0.9746104009805638,0.9740216295290517,6.782421112060547,240.11050800000004 -6120,Multiclass classification,Stacking,Keystroke,0.9738519365909463,0.9738519365909463,0.9722333406974255,8.176769256591797,278.69027800000003 -6528,Multiclass classification,Stacking,Keystroke,0.9742607629845258,0.9742607629845258,0.9741504405159309,4.433716773986816,320.74209900000005 -6936,Multiclass classification,Stacking,Keystroke,0.9751982696467195,0.9751982696467195,0.9755523782693606,5.133135795593262,366.1265460000001 -7344,Multiclass classification,Stacking,Keystroke,0.9757592264741931,0.9757592264741931,0.9758485662267349,5.760107040405273,415.04014400000005 -7752,Multiclass classification,Stacking,Keystroke,0.9760030963746613,0.9760030963746613,0.9758957983961688,6.842521667480469,467.58770000000004 -8160,Multiclass classification,Stacking,Keystroke,0.9758548841769825,0.9758548841769825,0.9755087152005796,8.403876304626465,523.9395460000001 -8568,Multiclass classification,Stacking,Keystroke,0.9751371541963347,0.9751371541963347,0.9744422302091884,8.9804105758667,584.299164 -8976,Multiclass classification,Stacking,Keystroke,0.9755988857938719,0.9755988857938719,0.9757626053423433,8.348807334899902,648.764254 -9384,Multiclass classification,Stacking,Keystroke,0.97527443248428,0.97527443248428,0.9749874884381717,8.780474662780762,717.435188 -9792,Multiclass classification,Stacking,Keystroke,0.9751812889388214,0.9751812889388214,0.9751287694103772,7.27089786529541,790.366072 -10200,Multiclass classification,Stacking,Keystroke,0.9748994999509756,0.9748994999509756,0.9747198913701116,8.182950019836426,867.658962 -10608,Multiclass classification,Stacking,Keystroke,0.9744508343546714,0.9744508343546714,0.9742218409220015,8.212386131286621,949.401841 -11016,Multiclass classification,Stacking,Keystroke,0.9747616886064457,0.9747616886064457,0.9748981365239817,7.736974716186523,1035.676852 -11424,Multiclass classification,Stacking,Keystroke,0.9748752516851965,0.9748752516851965,0.9749367981815978,8.583486557006836,1126.650079 -11832,Multiclass classification,Stacking,Keystroke,0.9749809821654974,0.9749809821654974,0.9750463661723393,8.887914657592773,1222.478726 -12240,Multiclass classification,Stacking,Keystroke,0.9755698995015932,0.9755698995015932,0.9757989853757533,9.402573585510254,1323.224256 -12648,Multiclass classification,Stacking,Keystroke,0.9760417490313908,0.9760417490313908,0.9762258400907322,9.833843231201172,1429.020384 -13056,Multiclass classification,Stacking,Keystroke,0.9760245116813482,0.9760245116813482,0.9760626338918788,9.939188957214355,1539.962955 -13464,Multiclass classification,Stacking,Keystroke,0.9762311520463492,0.9762311520463492,0.9763300455625981,10.46349811553955,1656.191893 -13872,Multiclass classification,Stacking,Keystroke,0.9763535433638526,0.9763535433638526,0.9764287224231292,11.428705215454102,1777.90617 -14280,Multiclass classification,Stacking,Keystroke,0.9758386441627565,0.9758386441627565,0.9757700210755773,10.687178611755371,1905.2009699999999 -14688,Multiclass classification,Stacking,Keystroke,0.9761013140872881,0.9761013140872881,0.9761996431080103,10.750173568725586,2038.1161399999999 -15096,Multiclass classification,Stacking,Keystroke,0.9764822789002982,0.9764822789002982,0.9765941858257003,10.87511920928955,2176.860193 -15504,Multiclass classification,Stacking,Keystroke,0.9762626588402245,0.9762626588402245,0.9762697293829714,11.144217491149902,2321.518715 -15912,Multiclass classification,Stacking,Keystroke,0.976305700458802,0.976305700458802,0.9763523962033862,10.881969451904297,2472.196158 -16320,Multiclass classification,Stacking,Keystroke,0.9761627550707764,0.9761627550707764,0.9761821898526978,10.72462272644043,2629.0181850000004 -16728,Multiclass classification,Stacking,Keystroke,0.9760267830453757,0.9760267830453757,0.9760462981867313,10.397873878479004,2792.0314960000005 -17136,Multiclass classification,Stacking,Keystroke,0.9763641669098336,0.9763641669098336,0.976427628373518,11.5784912109375,2961.4220420000006 -17544,Multiclass classification,Stacking,Keystroke,0.9762868380550647,0.9762868380550647,0.9763077393136289,10.780138969421387,3137.2839260000005 -17952,Multiclass classification,Stacking,Keystroke,0.9763801459528717,0.9763801459528717,0.9764101118400772,11.24526309967041,3319.6223500000006 -18360,Multiclass classification,Stacking,Keystroke,0.9766327142001199,0.9766327142001199,0.976666198082788,11.450252532958984,3508.4984120000004 -18768,Multiclass classification,Stacking,Keystroke,0.9769808706772526,0.9769808706772526,0.9770112706505792,12.824438095092773,3704.1108500000005 -19176,Multiclass classification,Stacking,Keystroke,0.9767926988265971,0.9767926988265971,0.976797459665624,13.463789939880371,3906.6546070000004 -19584,Multiclass classification,Stacking,Keystroke,0.9766123678700914,0.9766123678700914,0.97661532368473,12.60595417022705,4116.138220000001 -19992,Multiclass classification,Stacking,Keystroke,0.9765894652593667,0.9765894652593667,0.976591825772772,11.445868492126465,4332.655771000001 -20400,Multiclass classification,Stacking,Keystroke,0.9765184567870974,0.9765184567870974,0.9765167109502483,12.220311164855957,4556.333966000001 -46,Multiclass classification,Voting,ImageSegments,0.4888888888888889,0.4888888888888889,0.4138888888888889,0.8855724334716797,0.380739 -92,Multiclass classification,Voting,ImageSegments,0.6263736263736264,0.6263736263736264,0.6295417331131617,0.9400959014892578,0.906366 -138,Multiclass classification,Voting,ImageSegments,0.6788321167883211,0.6788321167883211,0.6955125455614023,0.9512205123901367,1.596335 -184,Multiclass classification,Voting,ImageSegments,0.7158469945355191,0.7158469945355191,0.7293605295181818,0.9506902694702148,2.451892 -230,Multiclass classification,Voting,ImageSegments,0.74235807860262,0.74235807860262,0.7560849066334576,0.9507265090942383,3.478975 -276,Multiclass classification,Voting,ImageSegments,0.7490909090909091,0.7490909090909091,0.7654899494294127,0.9522123336791992,4.6524790000000005 -322,Multiclass classification,Voting,ImageSegments,0.7632398753894081,0.7632398753894081,0.7699967547900484,0.9522132873535156,5.915859 -368,Multiclass classification,Voting,ImageSegments,0.782016348773842,0.782016348773842,0.7847454642968661,0.9517135620117188,7.268692 -414,Multiclass classification,Voting,ImageSegments,0.7869249394673123,0.7869249394673122,0.7891209865588749,0.9521627426147461,8.714221 -460,Multiclass classification,Voting,ImageSegments,0.7821350762527233,0.7821350762527233,0.7829889615631377,0.9522056579589844,10.249346000000001 -506,Multiclass classification,Voting,ImageSegments,0.7861386138613862,0.7861386138613862,0.7872755051739567,0.9517154693603516,11.874447 -552,Multiclass classification,Voting,ImageSegments,0.7858439201451906,0.7858439201451906,0.7876565639439724,0.9515762329101562,13.588949 -598,Multiclass classification,Voting,ImageSegments,0.7872696817420436,0.7872696817420435,0.7897468061485311,0.9521427154541016,15.393404 -644,Multiclass classification,Voting,ImageSegments,0.7822706065318819,0.7822706065318819,0.7858452362125997,0.9521217346191406,17.2908 -690,Multiclass classification,Voting,ImageSegments,0.7851959361393324,0.7851959361393324,0.788215888108031,0.9515953063964844,19.280843 -736,Multiclass classification,Voting,ImageSegments,0.7836734693877551,0.783673469387755,0.7873581098337732,0.9521245956420898,21.362069 -782,Multiclass classification,Voting,ImageSegments,0.7861715749039693,0.7861715749039692,0.7892834149474556,0.9521360397338867,23.534270000000003 -828,Multiclass classification,Voting,ImageSegments,0.7847642079806529,0.7847642079806529,0.7891292080670234,0.951629638671875,25.799776 -874,Multiclass classification,Voting,ImageSegments,0.7892325315005727,0.7892325315005727,0.7922023317831084,0.9516172409057617,28.155901 -920,Multiclass classification,Voting,ImageSegments,0.7889009793253536,0.7889009793253536,0.7905862723276574,0.9520702362060547,30.602138 -966,Multiclass classification,Voting,ImageSegments,0.78860103626943,0.78860103626943,0.7894031693051725,0.952082633972168,33.138731 -1012,Multiclass classification,Voting,ImageSegments,0.7873392680514342,0.7873392680514342,0.7878835011583499,0.9515609741210938,35.768379 -1058,Multiclass classification,Voting,ImageSegments,0.7899716177861873,0.7899716177861873,0.7897146415510686,0.9520339965820312,38.488246000000004 -1104,Multiclass classification,Voting,ImageSegments,0.7905711695376246,0.7905711695376246,0.7902707663283154,0.9521427154541016,41.298010000000005 -1150,Multiclass classification,Voting,ImageSegments,0.7919930374238469,0.7919930374238469,0.7910217164829003,0.9516496658325195,44.198117 -1196,Multiclass classification,Voting,ImageSegments,0.793305439330544,0.793305439330544,0.7926565595792737,0.9516582489013672,47.188338 -1242,Multiclass classification,Voting,ImageSegments,0.7921031426269137,0.7921031426269137,0.791644431462719,0.9522132873535156,50.271512 -1288,Multiclass classification,Voting,ImageSegments,0.7964257964257965,0.7964257964257965,0.7949172523959339,0.9522237777709961,53.444669000000005 -1334,Multiclass classification,Voting,ImageSegments,0.795198799699925,0.7951987996999249,0.7938516970082157,0.9516925811767578,56.707980000000006 -1380,Multiclass classification,Voting,ImageSegments,0.7955039883973894,0.7955039883973894,0.794312731896104,0.9516897201538086,60.06121 -1426,Multiclass classification,Voting,ImageSegments,0.7971929824561403,0.7971929824561403,0.7952130436298935,0.9521360397338867,63.507281000000006 -1472,Multiclass classification,Voting,ImageSegments,0.8008157715839564,0.8008157715839563,0.7971305683653547,0.9521236419677734,67.043451 -1518,Multiclass classification,Voting,ImageSegments,0.8015820698747528,0.8015820698747528,0.7969787037511136,0.95166015625,70.670162 -1564,Multiclass classification,Voting,ImageSegments,0.8016634676903391,0.8016634676903392,0.7975983332578384,0.9521465301513672,74.387065 -1610,Multiclass classification,Voting,ImageSegments,0.8017402113113735,0.8017402113113735,0.7969541458804642,0.9521703720092773,78.19725000000001 -1656,Multiclass classification,Voting,ImageSegments,0.8018126888217523,0.8018126888217523,0.7970318311622571,0.9516267776489258,82.09764400000002 -1702,Multiclass classification,Voting,ImageSegments,0.8018812463256908,0.8018812463256908,0.7992301124377234,0.9516735076904297,86.09102400000002 -1748,Multiclass classification,Voting,ImageSegments,0.8036634230108758,0.8036634230108759,0.8004815801809151,0.9521579742431641,90.17551900000002 -1794,Multiclass classification,Voting,ImageSegments,0.8042387060791969,0.8042387060791969,0.799787639242423,0.9521493911743164,94.35067000000002 -1840,Multiclass classification,Voting,ImageSegments,0.8053289831430125,0.8053289831430125,0.8009597766649573,0.9516563415527344,98.61893200000003 -1886,Multiclass classification,Voting,ImageSegments,0.803183023872679,0.8031830238726789,0.799227837217116,0.9521894454956055,102.97762100000003 -1932,Multiclass classification,Voting,ImageSegments,0.8032107716209218,0.8032107716209218,0.7985344176802335,0.9521827697753906,107.42655600000003 -1978,Multiclass classification,Voting,ImageSegments,0.8042488619119879,0.8042488619119877,0.7992002826592023,0.9516563415527344,111.96600400000003 -2024,Multiclass classification,Voting,ImageSegments,0.8057340583292141,0.8057340583292142,0.799488243695578,0.9516725540161133,116.59842900000002 -2070,Multiclass classification,Voting,ImageSegments,0.80521991300145,0.80521991300145,0.7990099218703556,0.9521942138671875,121.31866500000002 -2116,Multiclass classification,Voting,ImageSegments,0.8056737588652483,0.8056737588652483,0.798658845250099,0.9521694183349609,126.12612700000003 -2162,Multiclass classification,Voting,ImageSegments,0.8061082832022212,0.8061082832022212,0.7986518526284686,0.9516706466674805,131.02082400000003 -2208,Multiclass classification,Voting,ImageSegments,0.8078840054372451,0.8078840054372451,0.7995103660963299,0.9521732330322266,136.00538900000004 -2254,Multiclass classification,Voting,ImageSegments,0.8047048379937861,0.8047048379937861,0.7963417515999387,0.9521608352661133,141.07727400000005 -2300,Multiclass classification,Voting,ImageSegments,0.8033927794693345,0.8033927794693345,0.7949752803158223,0.9516582489013672,146.23634000000004 -1056,Multiclass classification,Voting,Insects,0.6293838862559241,0.6293838862559241,0.5939725193500994,1.5110340118408203,3.200552 -2112,Multiclass classification,Voting,Insects,0.62482235907153,0.62482235907153,0.5894737350922559,1.5110177993774414,9.230037 -3168,Multiclass classification,Voting,Insects,0.6198294916324597,0.6198294916324597,0.5838888884930272,1.5110721588134766,17.854201 -4224,Multiclass classification,Voting,Insects,0.6192280369405636,0.6192280369405636,0.5835519631382228,1.5110435485839844,28.950406 -5280,Multiclass classification,Voting,Insects,0.6256866830839174,0.6256866830839174,0.5887468172490868,1.511063575744629,42.489771000000005 -6336,Multiclass classification,Voting,Insects,0.6187845303867403,0.6187845303867403,0.5833486573822239,1.5110454559326172,58.469284 -7392,Multiclass classification,Voting,Insects,0.6180489784873495,0.6180489784873495,0.5826198728106428,1.5110721588134766,76.881409 -8448,Multiclass classification,Voting,Insects,0.619746655617379,0.619746655617379,0.5840081546383048,1.5110502243041992,97.728436 -9504,Multiclass classification,Voting,Insects,0.6190676628433126,0.6190676628433126,0.5828637425505069,1.511042594909668,121.00604200000001 -10560,Multiclass classification,Voting,Insects,0.6198503646178616,0.6198503646178616,0.5836946750940745,1.5110759735107422,146.711441 -11616,Multiclass classification,Voting,Insects,0.6175634954799828,0.6175634954799828,0.5822534545682404,1.511033058166504,174.85184600000002 -12672,Multiclass classification,Voting,Insects,0.6204719438086971,0.6204719438086971,0.5879866433279776,1.5111761093139648,205.42585100000002 -13728,Multiclass classification,Voting,Insects,0.6369199388067313,0.6369199388067313,0.618745437324273,1.5112380981445312,238.42557100000002 -14784,Multiclass classification,Voting,Insects,0.630386254481499,0.630386254481499,0.6115259179282228,1.5110998153686523,273.85181700000004 -15840,Multiclass classification,Voting,Insects,0.5992171222930741,0.5992171222930741,0.581747071745844,1.5110797882080078,311.71395800000005 -16896,Multiclass classification,Voting,Insects,0.5783959751405742,0.5783959751405742,0.5619501594422388,1.511063575744629,352.00364300000007 -17952,Multiclass classification,Voting,Insects,0.5631998217369506,0.5631998217369506,0.5464708450044057,1.511117935180664,394.7217830000001 -19008,Multiclass classification,Voting,Insects,0.565528489503867,0.565528489503867,0.5447789723081985,1.5110950469970703,439.87157300000007 -20064,Multiclass classification,Voting,Insects,0.5725464785924338,0.5725464785924338,0.5493312346450109,2.16702938079834,487.43608700000004 -21120,Multiclass classification,Voting,Insects,0.5819404327856432,0.5819404327856432,0.5575973426297249,2.1677894592285156,537.344767 -22176,Multiclass classification,Voting,Insects,0.5905298759864712,0.5905298759864712,0.5648531785235197,2.167774200439453,589.565521 -23232,Multiclass classification,Voting,Insects,0.5995867590719297,0.5995867590719297,0.5728007753824246,2.167778968811035,644.107989 -24288,Multiclass classification,Voting,Insects,0.6068678717009099,0.6068678717009099,0.578555560305262,2.16780948638916,700.966552 -25344,Multiclass classification,Voting,Insects,0.6143313735548278,0.6143313735548278,0.5848116898462843,2.167755126953125,760.153761 -26400,Multiclass classification,Voting,Insects,0.621084131974696,0.621084131974696,0.5900605973096019,2.1677980422973633,821.662998 -27456,Multiclass classification,Voting,Insects,0.6266618102349298,0.6266618102349298,0.5936647802901621,2.167790412902832,885.506266 -28512,Multiclass classification,Voting,Insects,0.6295114166462067,0.6295114166462067,0.5991480792709615,2.168045997619629,951.65113 -29568,Multiclass classification,Voting,Insects,0.6294517536442655,0.6294517536442655,0.6037001563215106,2.1680641174316406,1020.2499009999999 -30624,Multiclass classification,Voting,Insects,0.6287104463964993,0.6287104463964993,0.6068237930795873,2.168071746826172,1091.329507 -31680,Multiclass classification,Voting,Insects,0.6292496606584804,0.6292496606584804,0.6106666463743293,2.1680679321289062,1164.8865779999999 -32736,Multiclass classification,Voting,Insects,0.6302734076676341,0.6302734076676341,0.614251388937007,2.168027877807617,1240.9182979999998 -33792,Multiclass classification,Voting,Insects,0.6266165547039152,0.6266165547039152,0.6112639299818544,2.1678638458251953,1319.4287269999998 -34848,Multiclass classification,Voting,Insects,0.6216604011823113,0.6216604011823113,0.6060150865308916,2.1678390502929688,1400.4187529999997 -35904,Multiclass classification,Voting,Insects,0.6181377600757597,0.6181377600757597,0.6018714875673907,2.167888641357422,1483.8935999999997 -36960,Multiclass classification,Voting,Insects,0.6138153088557591,0.6138153088557591,0.5971057932031453,2.1678647994995117,1569.8367669999996 -38016,Multiclass classification,Voting,Insects,0.6116796001578324,0.6116796001578324,0.5945381289951768,2.709075927734375,1658.2515679999995 -39072,Multiclass classification,Voting,Insects,0.6122187811932124,0.6122187811932124,0.5950787740952911,2.823759078979492,1749.1539719999994 -40128,Multiclass classification,Voting,Insects,0.6125052956861963,0.6125052956861963,0.5964110573184415,2.8237857818603516,1842.4635979999994 -41184,Multiclass classification,Voting,Insects,0.6145254109705461,0.6145254109705461,0.5992770713855892,2.82379150390625,1938.0560169999994 -42240,Multiclass classification,Voting,Insects,0.6163024692819432,0.6163024692819432,0.601670854132613,2.823772430419922,2035.9261369999995 -43296,Multiclass classification,Voting,Insects,0.6181776186626631,0.6181776186626631,0.6041281005310094,2.8237924575805664,2136.0724539999997 -44352,Multiclass classification,Voting,Insects,0.6197605465491195,0.6197605465491195,0.6062005996937425,2.824528694152832,2238.51149 -45408,Multiclass classification,Voting,Insects,0.6214019864778558,0.6214019864778558,0.607792464273323,2.824528694152832,2343.241606 -46464,Multiclass classification,Voting,Insects,0.6233992639304393,0.6233992639304393,0.6097993182820672,2.8245315551757812,2450.26566 -47520,Multiclass classification,Voting,Insects,0.6260864075422463,0.6260864075422463,0.6129939002712749,2.8244552612304688,2559.63137 -48576,Multiclass classification,Voting,Insects,0.6300154400411735,0.6300154400411735,0.6173873766747581,2.824479103088379,2671.379304 -49632,Multiclass classification,Voting,Insects,0.6343011424311418,0.6343011424311418,0.621931196280001,2.8244781494140625,2785.498386 -50688,Multiclass classification,Voting,Insects,0.638506914988064,0.638506914988064,0.6263145143911814,2.947113037109375,2902.0058780000004 -51744,Multiclass classification,Voting,Insects,0.6434686817540537,0.6434686817540537,0.6313977027921706,3.1849002838134766,3020.8771950000005 -52800,Multiclass classification,Voting,Insects,0.6479289380480691,0.6479289380480691,0.635943324049664,3.3886165618896484,3141.9869480000007 -408,Multiclass classification,Voting,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,0.6423864364624023,0.703596 -816,Multiclass classification,Voting,Keystroke,0.9546012269938651,0.9546012269938651,0.7993954329623859,0.8351936340332031,2.165771 -1224,Multiclass classification,Voting,Keystroke,0.9206868356500408,0.9206868356500408,0.9055597826779512,1.029007911682129,4.467328 -1632,Multiclass classification,Voting,Keystroke,0.9307173513182097,0.9307173513182097,0.9172597570917439,1.2232952117919922,7.6892 -2040,Multiclass classification,Voting,Keystroke,0.9303580186365865,0.9303580186365865,0.919916287137026,1.428065299987793,11.917356 -2448,Multiclass classification,Voting,Keystroke,0.9060073559460564,0.9060073559460564,0.9093956340782631,1.6218795776367188,17.211204 -2856,Multiclass classification,Voting,Keystroke,0.9103327495621716,0.9103327495621716,0.8980697688452707,1.8146867752075195,23.617224 -3264,Multiclass classification,Voting,Keystroke,0.9043824701195219,0.9043824701195219,0.888202704220525,2.0085010528564453,31.150342000000002 -3672,Multiclass classification,Voting,Keystroke,0.8994824298556252,0.8994824298556252,0.8972334256598172,2.2018117904663086,39.868796 -4080,Multiclass classification,Voting,Keystroke,0.8945820053934788,0.8945820053934787,0.8851783489415491,2.420787811279297,49.793690000000005 -4488,Multiclass classification,Voting,Keystroke,0.8914642299977713,0.8914642299977713,0.898372373723482,2.6146020889282227,61.011475000000004 -4896,Multiclass classification,Voting,Keystroke,0.8880490296220633,0.8880490296220633,0.8932697641963906,2.807912826538086,73.572163 -5304,Multiclass classification,Voting,Keystroke,0.883085046200264,0.883085046200264,0.8680917053752625,3.0007705688476562,87.52603 -5712,Multiclass classification,Voting,Keystroke,0.8746279110488531,0.8746279110488531,0.8792177397015432,3.194584846496582,102.92236500000001 -6120,Multiclass classification,Voting,Keystroke,0.8695865337473443,0.8695865337473442,0.8546904737358852,3.387392044067383,119.81998700000001 -6528,Multiclass classification,Voting,Keystroke,0.8579745671824728,0.8579745671824728,0.858067415232278,3.5812063217163086,138.28125400000002 -6936,Multiclass classification,Voting,Keystroke,0.8537851478010093,0.8537851478010093,0.8590096923865055,3.774517059326172,158.37518500000002 -7344,Multiclass classification,Voting,Keystroke,0.8594579871986926,0.8594579871986926,0.8620220702364139,3.9702539443969727,180.16443600000002 -7752,Multiclass classification,Voting,Keystroke,0.8593729841310799,0.8593729841310799,0.8617576440335053,4.164068222045898,203.71390800000003 -8160,Multiclass classification,Voting,Keystroke,0.8601544306900355,0.8601544306900355,0.8605355806611993,4.357378959655762,229.09093700000003 -8568,Multiclass classification,Voting,Keystroke,0.8596941753239173,0.8596941753239173,0.8627767842417701,4.60028076171875,256.36638600000003 -8976,Multiclass classification,Voting,Keystroke,0.8599442896935933,0.8599442896935933,0.8629838037923419,4.794095039367676,285.60935500000005 -9384,Multiclass classification,Voting,Keystroke,0.8581477139507621,0.8581477139507621,0.8592031021693959,4.986902236938477,316.88668100000007 -9792,Multiclass classification,Voting,Keystroke,0.8539475028087019,0.8539475028087019,0.8546213426549989,5.180716514587402,350.2631590000001 -10200,Multiclass classification,Voting,Keystroke,0.8465535836846749,0.8465535836846749,0.8431270001478435,5.374000549316406,385.8123490000001 -10608,Multiclass classification,Voting,Keystroke,0.8300179126991609,0.8300179126991609,0.8240754775818138,5.566834449768066,423.5940000000001 -11016,Multiclass classification,Voting,Keystroke,0.8254198819791194,0.8254198819791194,0.8271925616445298,5.760648727416992,463.67191300000013 -11424,Multiclass classification,Voting,Keystroke,0.820449969360063,0.820449969360063,0.8166393841205931,5.9539594650268555,506.1140620000001 -11832,Multiclass classification,Voting,Keystroke,0.8169216465218494,0.8169216465218494,0.8172029683603622,6.146766662597656,550.9879090000001 -12240,Multiclass classification,Voting,Keystroke,0.8200016341204347,0.8200016341204347,0.8225884010623591,6.340580940246582,598.362302 -12648,Multiclass classification,Voting,Keystroke,0.8167154265833795,0.8167154265833795,0.8162987105601626,6.533388137817383,648.30112 -13056,Multiclass classification,Voting,Keystroke,0.8121792416698583,0.8121792416698584,0.8136075732214813,6.727202415466309,700.874955 -13464,Multiclass classification,Voting,Keystroke,0.8099234940206492,0.8099234940206492,0.8122480630127521,6.920539855957031,756.151971 -13872,Multiclass classification,Voting,Keystroke,0.810539975488429,0.810539975488429,0.8134726777385565,7.113347053527832,814.194174 -14280,Multiclass classification,Voting,Keystroke,0.8103508649065061,0.810350864906506,0.8130549704062812,7.307161331176758,875.070018 -14688,Multiclass classification,Voting,Keystroke,0.8133042826989855,0.8133042826989855,0.8168484225511677,7.500472068786621,938.856287 -15096,Multiclass classification,Voting,Keystroke,0.8174229877442862,0.8174229877442862,0.8208616131428813,7.693279266357422,1005.6082389999999 -15504,Multiclass classification,Voting,Keystroke,0.8175191898342257,0.8175191898342257,0.8200404227627133,7.887093544006348,1075.3914499999998 -15912,Multiclass classification,Voting,Keystroke,0.8100685060649865,0.8100685060649865,0.8105704783549956,8.079900741577148,1148.2720969999998 -16320,Multiclass classification,Voting,Keystroke,0.8058704577486365,0.8058704577486365,0.8082920647955453,8.273715019226074,1224.3182599999998 -16728,Multiclass classification,Voting,Keystroke,0.8029533090213428,0.8029533090213428,0.8061756731743527,8.467025756835938,1303.6034769999999 -17136,Multiclass classification,Voting,Keystroke,0.7992996790195507,0.7992996790195507,0.8021910628966759,8.7622652053833,1386.1896219999999 -17544,Multiclass classification,Voting,Keystroke,0.7934218776720059,0.7934218776720059,0.7969041071406875,8.956079483032227,1472.1458429999998 -17952,Multiclass classification,Voting,Keystroke,0.7934933986964514,0.7934933986964514,0.7978100866424277,9.14939022064209,1561.5342959999998 -18360,Multiclass classification,Voting,Keystroke,0.7969933002886868,0.7969933002886866,0.8014382450066739,9.34219741821289,1654.4248979999998 -18768,Multiclass classification,Voting,Keystroke,0.7999147439654714,0.7999147439654714,0.8043799341405246,9.536011695861816,1750.8812689999997 -19176,Multiclass classification,Voting,Keystroke,0.7945241199478488,0.7945241199478488,0.7987282715896407,9.728818893432617,1850.9734509999998 -19584,Multiclass classification,Voting,Keystroke,0.797375274472757,0.797375274472757,0.8021140041360401,9.922633171081543,1954.769568 -19992,Multiclass classification,Voting,Keystroke,0.7945075283877745,0.7945075283877745,0.7995475233856788,10.115943908691406,2062.333925 -20400,Multiclass classification,Voting,Keystroke,0.793274180106868,0.793274180106868,0.7984237858213096,10.308751106262207,2173.749777 -46,Multiclass classification,[baseline] Last Class,ImageSegments,0.17777777777777778,0.17777777777777778,0.15260266049739735,0.0013666152954101562,0.007048 -92,Multiclass classification,[baseline] Last Class,ImageSegments,0.13186813186813187,0.13186813186813187,0.1213108980966124,0.0013637542724609375,0.018168 -138,Multiclass classification,[baseline] Last Class,ImageSegments,0.12408759124087591,0.12408759124087591,0.11874455065544491,0.001369476318359375,0.031716 -184,Multiclass classification,[baseline] Last Class,ImageSegments,0.12568306010928962,0.12568306010928962,0.12262983423071581,0.0013647079467773438,0.047654 -230,Multiclass classification,[baseline] Last Class,ImageSegments,0.12663755458515283,0.12663755458515283,0.12503852041208066,0.0013637542724609375,0.065983 -276,Multiclass classification,[baseline] Last Class,ImageSegments,0.12727272727272726,0.12727272727272726,0.12427907918144998,0.0013666152954101562,0.086242 -322,Multiclass classification,[baseline] Last Class,ImageSegments,0.13395638629283488,0.13395638629283488,0.13210036596246022,0.0013666152954101562,0.108232 -368,Multiclass classification,[baseline] Last Class,ImageSegments,0.13896457765667575,0.13896457765667575,0.13745011462972964,0.0013675689697265625,0.131958 -414,Multiclass classification,[baseline] Last Class,ImageSegments,0.14043583535108958,0.14043583535108958,0.14035813096947544,0.0013666152954101562,0.15742099999999998 -460,Multiclass classification,[baseline] Last Class,ImageSegments,0.14596949891067537,0.14596949891067537,0.14563148710727947,0.00136566162109375,0.18458599999999997 -506,Multiclass classification,[baseline] Last Class,ImageSegments,0.13861386138613863,0.13861386138613863,0.1383381610231494,0.0013666152954101562,0.21348499999999998 -552,Multiclass classification,[baseline] Last Class,ImageSegments,0.1397459165154265,0.1397459165154265,0.13938652491777898,0.0013666152954101562,0.24411 -598,Multiclass classification,[baseline] Last Class,ImageSegments,0.1373534338358459,0.1373534338358459,0.13727981043458612,0.0013675689697265625,0.276463 -644,Multiclass classification,[baseline] Last Class,ImageSegments,0.13996889580093314,0.13996889580093314,0.14017571709017965,0.0013666152954101562,0.310533 -690,Multiclass classification,[baseline] Last Class,ImageSegments,0.1378809869375907,0.1378809869375907,0.13801517784553324,0.001369476318359375,0.346313 -736,Multiclass classification,[baseline] Last Class,ImageSegments,0.1401360544217687,0.1401360544217687,0.14031088927958282,0.0013675689697265625,0.38382 -782,Multiclass classification,[baseline] Last Class,ImageSegments,0.14212548015364918,0.14212548015364918,0.1420930265541123,0.0013647079467773438,0.423095 -828,Multiclass classification,[baseline] Last Class,ImageSegments,0.14268440145102781,0.14268440145102781,0.14229874553046912,0.0013666152954101562,0.464082 -874,Multiclass classification,[baseline] Last Class,ImageSegments,0.13860252004581902,0.13860252004581902,0.13845352694595275,0.0013647079467773438,0.506788 -920,Multiclass classification,[baseline] Last Class,ImageSegments,0.13492927094668117,0.13492927094668117,0.1348083913046733,0.0013666152954101562,0.551195 -966,Multiclass classification,[baseline] Last Class,ImageSegments,0.13367875647668392,0.13367875647668392,0.13349177774445278,0.0013637542724609375,0.597302 -1012,Multiclass classification,[baseline] Last Class,ImageSegments,0.13254203758654798,0.13254203758654798,0.1324936677659038,0.0013675689697265625,0.645131 -1058,Multiclass classification,[baseline] Last Class,ImageSegments,0.13339640491958374,0.13339640491958374,0.1331834965440007,0.00136566162109375,0.69466 -1104,Multiclass classification,[baseline] Last Class,ImageSegments,0.13417951042611062,0.13417951042611062,0.13402826529501538,0.0013666152954101562,0.7459020000000001 -1150,Multiclass classification,[baseline] Last Class,ImageSegments,0.134029590948651,0.134029590948651,0.13406391150519123,0.0013637542724609375,0.7988440000000001 -1196,Multiclass classification,[baseline] Last Class,ImageSegments,0.13640167364016736,0.13640167364016736,0.1363948420172951,0.001369476318359375,0.8534870000000001 -1242,Multiclass classification,[baseline] Last Class,ImageSegments,0.13940370668815472,0.13940370668815472,0.13919772383892226,0.0013637542724609375,0.9098240000000001 -1288,Multiclass classification,[baseline] Last Class,ImageSegments,0.1414141414141414,0.1414141414141414,0.14118715023210152,0.0013666152954101562,0.9678680000000001 -1334,Multiclass classification,[baseline] Last Class,ImageSegments,0.14328582145536384,0.14328582145536384,0.14302553278156668,0.0013637542724609375,1.027625 -1380,Multiclass classification,[baseline] Last Class,ImageSegments,0.14358230601885424,0.14358230601885424,0.1433209000486506,0.001369476318359375,1.089079 -1426,Multiclass classification,[baseline] Last Class,ImageSegments,0.14175438596491227,0.14175438596491227,0.14145466559291123,0.001369476318359375,1.152253 -1472,Multiclass classification,[baseline] Last Class,ImageSegments,0.13936097892590074,0.13936097892590074,0.13907629713942624,0.0013647079467773438,1.217139 -1518,Multiclass classification,[baseline] Last Class,ImageSegments,0.13974950560316415,0.13974950560316415,0.13951366685898453,0.0013666152954101562,1.283725 -1564,Multiclass classification,[baseline] Last Class,ImageSegments,0.13691618682021753,0.13691618682021753,0.13664170474395118,0.0013666152954101562,1.352073 -1610,Multiclass classification,[baseline] Last Class,ImageSegments,0.13610938471100062,0.13610938471100062,0.13597683881903072,0.0013637542724609375,1.422125 -1656,Multiclass classification,[baseline] Last Class,ImageSegments,0.1365558912386707,0.1365558912386707,0.13633224623774592,0.001369476318359375,1.4938960000000001 -1702,Multiclass classification,[baseline] Last Class,ImageSegments,0.13932980599647266,0.13932980599647266,0.13901296274399097,0.0013675689697265625,1.5673830000000002 -1748,Multiclass classification,[baseline] Last Class,ImageSegments,0.14195764167143674,0.14195764167143674,0.14147197312723644,0.00136566162109375,1.6425530000000002 -1794,Multiclass classification,[baseline] Last Class,ImageSegments,0.14221974344673732,0.14221974344673732,0.14194103966110075,0.0013647079467773438,1.7194410000000002 -1840,Multiclass classification,[baseline] Last Class,ImageSegments,0.14138118542686243,0.14138118542686243,0.14114329766598663,0.0013675689697265625,1.7980130000000003 -1886,Multiclass classification,[baseline] Last Class,ImageSegments,0.140053050397878,0.140053050397878,0.13973258713820758,0.0013666152954101562,1.8782870000000003 -1932,Multiclass classification,[baseline] Last Class,ImageSegments,0.14293112377006734,0.14293112377006734,0.14275229229825853,0.0013666152954101562,1.9602450000000002 -1978,Multiclass classification,[baseline] Last Class,ImageSegments,0.14618108244815378,0.14618108244815378,0.14597158151605963,0.001369476318359375,2.043928 -2024,Multiclass classification,[baseline] Last Class,ImageSegments,0.14434008897676717,0.14434008897676717,0.14416625237761066,0.001369476318359375,2.12929 -2070,Multiclass classification,[baseline] Last Class,ImageSegments,0.14403093281778637,0.14403093281778637,0.1438554349712762,0.0013666152954101562,2.216361 -2116,Multiclass classification,[baseline] Last Class,ImageSegments,0.14468085106382977,0.14468085106382977,0.1446036231777657,0.0013637542724609375,2.305147 -2162,Multiclass classification,[baseline] Last Class,ImageSegments,0.14530310041647385,0.14530310041647385,0.14520465913821792,0.001369476318359375,2.395629 -2208,Multiclass classification,[baseline] Last Class,ImageSegments,0.14499320344358857,0.14499320344358857,0.14491109851991696,0.001369476318359375,2.487817 -2254,Multiclass classification,[baseline] Last Class,ImageSegments,0.14647137150466044,0.14647137150466044,0.14640425534129609,0.0013666152954101562,2.5817110000000003 -2300,Multiclass classification,[baseline] Last Class,ImageSegments,0.14789038712483688,0.14789038712483688,0.14788688524810298,0.0013675689697265625,2.6773210000000005 -1056,Multiclass classification,[baseline] Last Class,Insects,0.15829383886255924,0.15829383886255924,0.1376212379233521,0.0013856887817382812,0.055672 -2112,Multiclass classification,[baseline] Last Class,Insects,0.16579819990525818,0.16579819990525818,0.15110451064118433,0.0013856887817382812,0.157206 -3168,Multiclass classification,[baseline] Last Class,Insects,0.17019261130407326,0.17019261130407326,0.15681512355039637,0.0013885498046875,0.304619 -4224,Multiclass classification,[baseline] Last Class,Insects,0.16599573762727918,0.16599573762727918,0.1525443315605067,0.0013856887817382812,0.49782699999999996 -5280,Multiclass classification,[baseline] Last Class,Insects,0.17086569426027656,0.17086569426027656,0.1567667911399359,0.0013837814331054688,0.736912 -6336,Multiclass classification,[baseline] Last Class,Insects,0.17379636937647988,0.17379636937647988,0.16137568195972998,0.0013837814331054688,1.021646 -7392,Multiclass classification,[baseline] Last Class,Insects,0.1752130970098769,0.1752130970098769,0.16189407904134775,0.0013837814331054688,1.3518970000000001 -8448,Multiclass classification,[baseline] Last Class,Insects,0.17722268260921037,0.17722268260921037,0.163740045170864,0.0013818740844726562,1.7276090000000002 -9504,Multiclass classification,[baseline] Last Class,Insects,0.17731242765442493,0.17731242765442493,0.16374929744530958,0.0013885498046875,2.148941 -10560,Multiclass classification,[baseline] Last Class,Insects,0.17908892887584052,0.17908892887584052,0.16564210767474952,0.0013837814331054688,2.615755 -11616,Multiclass classification,[baseline] Last Class,Insects,0.17899268187688333,0.17899268187688333,0.16559253835337612,0.0013856887817382812,3.128148 -12672,Multiclass classification,[baseline] Last Class,Insects,0.18530502722752742,0.1853050272275274,0.18269809988409802,0.0013866424560546875,3.685883 -13728,Multiclass classification,[baseline] Last Class,Insects,0.24797843665768193,0.24797843665768193,0.26603936845528803,0.0013866424560546875,4.288806 -14784,Multiclass classification,[baseline] Last Class,Insects,0.2795778935263478,0.2795778935263478,0.28229742751715126,0.0013818740844726562,4.937051 -15840,Multiclass classification,[baseline] Last Class,Insects,0.27615379758823155,0.27615379758823155,0.28473758533654364,0.0013818740844726562,5.631085000000001 -16896,Multiclass classification,[baseline] Last Class,Insects,0.2723290914471737,0.2723290914471737,0.2859139704285301,0.0013856887817382812,6.370871000000001 -17952,Multiclass classification,[baseline] Last Class,Insects,0.2720739791655061,0.2720739791655061,0.28801432065038773,0.0013866424560546875,7.156724000000001 -19008,Multiclass classification,[baseline] Last Class,Insects,0.28252748987215237,0.28252748987215237,0.28775044293210866,0.0013866424560546875,7.988744000000001 -20064,Multiclass classification,[baseline] Last Class,Insects,0.28724517769027563,0.28724517769027563,0.28667392366619265,0.0013818740844726562,8.866412000000002 -21120,Multiclass classification,[baseline] Last Class,Insects,0.28306264501160094,0.28306264501160094,0.28164766024255256,0.0013837814331054688,9.789517000000002 -22176,Multiclass classification,[baseline] Last Class,Insects,0.2805411499436302,0.2805411499436302,0.27862960725280095,0.0013866424560546875,10.758806000000002 -23232,Multiclass classification,[baseline] Last Class,Insects,0.2797124531875511,0.2797124531875511,0.27719419757933417,0.0013856887817382812,11.774485000000002 -24288,Multiclass classification,[baseline] Last Class,Insects,0.2777205912628155,0.2777205912628155,0.2745878480946635,0.0013866424560546875,12.836246000000003 -25344,Multiclass classification,[baseline] Last Class,Insects,0.2756579726157124,0.2756579726157124,0.27233803052028965,0.0013818740844726562,13.944171000000003 -26400,Multiclass classification,[baseline] Last Class,Insects,0.27394977082465244,0.27394977082465244,0.26996904425699914,0.0013837814331054688,15.098386000000003 -27456,Multiclass classification,[baseline] Last Class,Insects,0.27189947186304864,0.27189947186304864,0.26719485323886244,0.0013866424560546875,16.299309000000004 -28512,Multiclass classification,[baseline] Last Class,Insects,0.2723860965942969,0.2723860965942969,0.2686965366571338,0.0013885498046875,17.546694000000006 -29568,Multiclass classification,[baseline] Last Class,Insects,0.2738187844556431,0.2738187844556431,0.2720266804437783,0.0013885498046875,18.840271000000005 -30624,Multiclass classification,[baseline] Last Class,Insects,0.27538124938771513,0.27538124938771513,0.2748698663810352,0.0013885498046875,20.179938000000003 -31680,Multiclass classification,[baseline] Last Class,Insects,0.2780390795163989,0.2780390795163989,0.2784141751235631,0.0013856887817382812,21.565237000000003 -32736,Multiclass classification,[baseline] Last Class,Insects,0.279670077898274,0.279670077898274,0.2802192251245276,0.0013837814331054688,22.996618000000005 -33792,Multiclass classification,[baseline] Last Class,Insects,0.2808440117190968,0.2808440117190968,0.28119627453717067,0.0013856887817382812,24.474295000000005 -34848,Multiclass classification,[baseline] Last Class,Insects,0.2772405085086234,0.2772405085086234,0.27819051828647573,0.0013837814331054688,25.998543000000005 -35904,Multiclass classification,[baseline] Last Class,Insects,0.2739325404562293,0.2739325404562293,0.2754200456137155,0.0013856887817382812,27.569042000000007 -36960,Multiclass classification,[baseline] Last Class,Insects,0.271246516410076,0.271246516410076,0.27333283767820205,0.0013818740844726562,29.185425000000006 -38016,Multiclass classification,[baseline] Last Class,Insects,0.26855188741286334,0.26855188741286334,0.2710722002891223,0.0013856887817382812,30.847968000000005 -39072,Multiclass classification,[baseline] Last Class,Insects,0.277034117376059,0.277034117376059,0.2770619820799866,0.0013866424560546875,32.556678000000005 -40128,Multiclass classification,[baseline] Last Class,Insects,0.27617315024796274,0.27617315024796274,0.2760769006623073,0.0013837814331054688,34.31145000000001 -41184,Multiclass classification,[baseline] Last Class,Insects,0.27567200058276475,0.27567200058276475,0.2754352632972117,0.0013837814331054688,36.11334600000001 -42240,Multiclass classification,[baseline] Last Class,Insects,0.27401216884869434,0.27401216884869434,0.2735946193588543,0.0013885498046875,37.96222800000001 -43296,Multiclass classification,[baseline] Last Class,Insects,0.2738422450629403,0.2738422450629403,0.27319488690835786,0.0013856887817382812,39.85759000000001 -44352,Multiclass classification,[baseline] Last Class,Insects,0.2729588960790061,0.2729588960790061,0.27209116538690487,0.0013866424560546875,41.79925400000001 -45408,Multiclass classification,[baseline] Last Class,Insects,0.27205056489087587,0.27205056489087587,0.2708084959373003,0.0013866424560546875,43.79182700000001 -46464,Multiclass classification,[baseline] Last Class,Insects,0.27137722488862104,0.27137722488862104,0.26986314104154363,0.0013837814331054688,45.834688000000014 -47520,Multiclass classification,[baseline] Last Class,Insects,0.27235421620825356,0.27235421620825356,0.27170627983222856,0.0013837814331054688,47.92837500000002 -48576,Multiclass classification,[baseline] Last Class,Insects,0.2741327843540916,0.2741327843540916,0.27449463409742436,0.0013818740844726562,50.07231500000002 -49632,Multiclass classification,[baseline] Last Class,Insects,0.27535209848683284,0.27535209848683284,0.27650368764304034,0.0013818740844726562,52.26665400000002 -50688,Multiclass classification,[baseline] Last Class,Insects,0.2768362696549411,0.2768362696549411,0.27863440912734966,0.0013837814331054688,54.51115200000002 -51744,Multiclass classification,[baseline] Last Class,Insects,0.27827918752294994,0.27827918752294994,0.2805971515128955,0.0013885498046875,56.805577000000014 -52800,Multiclass classification,[baseline] Last Class,Insects,0.28911532415386654,0.28911532415386654,0.2892953202729756,0.0013866424560546875,59.150289000000015 -408,Multiclass classification,[baseline] Last Class,Keystroke,0.9975429975429976,0.9975429975429976,0.9660408844388819,0.0006122589111328125,0.026957 -816,Multiclass classification,[baseline] Last Class,Keystroke,0.9975460122699387,0.9975460122699387,0.9879967903427672,0.0006628036499023438,0.073338 -1224,Multiclass classification,[baseline] Last Class,Keystroke,0.9975470155355682,0.9975470155355682,0.9931179599499375,0.000713348388671875,0.138405 -1632,Multiclass classification,[baseline] Last Class,Keystroke,0.9975475168608215,0.9975475168608215,0.9950750839342831,0.0012521743774414062,0.22203499999999998 -2040,Multiclass classification,[baseline] Last Class,Keystroke,0.9975478175576263,0.9975478175576263,0.9960150346160549,0.0013027191162109375,0.32420699999999997 -2448,Multiclass classification,[baseline] Last Class,Keystroke,0.9975480179812015,0.9975480179812015,0.9965317313935653,0.0013532638549804688,0.44525699999999996 -2856,Multiclass classification,[baseline] Last Class,Keystroke,0.9975481611208407,0.9975481611208407,0.9968424283169279,0.00140380859375,0.58488 -3264,Multiclass classification,[baseline] Last Class,Keystroke,0.9975482684646031,0.9975482684646031,0.9970416021996,0.0014543533325195312,0.7430509999999999 -3672,Multiclass classification,[baseline] Last Class,Keystroke,0.9975483519476982,0.9975483519476982,0.9971755428551425,0.0015048980712890625,0.9196609999999998 -4080,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484187300809,0.9975484187300809,0.9972690115789393,0.0015554428100585938,1.1148029999999998 -4488,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484733675062,0.9975484733675062,0.9973361791525123,0.001605987548828125,1.3284669999999998 -4896,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485188968335,0.9975485188968335,0.9973856025730918,0.0016565322875976562,1.5608199999999999 -5304,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485574203281,0.9975485574203281,0.997422679833574,0.0017070770263671875,1.81175 -5712,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485904395027,0.9975485904395027,0.99745094204078,0.0017576217651367188,2.0815 -6120,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486190554013,0.9975486190554013,0.9974727709453766,0.00180816650390625,2.369724 -6528,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486440937643,0.9975486440937643,0.997489815700999,0.0018587112426757812,2.6764910000000004 -6936,Multiclass classification,[baseline] Last Class,Keystroke,0.997548666186013,0.997548666186013,0.9975032443691146,0.0019092559814453125,3.0019130000000005 -7344,Multiclass classification,[baseline] Last Class,Keystroke,0.997548685823233,0.997548685823233,0.9975139007887863,0.0034246444702148438,3.3461420000000004 -7752,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487033931105,0.9975487033931105,0.9975224052755713,0.003475189208984375,3.7089170000000005 -8160,Multiclass classification,[baseline] Last Class,Keystroke,0.997548719205785,0.997548719205785,0.9975292209193422,0.0035257339477539062,4.090185000000001 -8568,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487335123147,0.9975487335123147,0.9975346982235256,0.0035762786865234375,4.489997000000001 -8976,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487465181059,0.9975487465181059,0.9975391057693663,0.0036268234252929688,4.908346000000001 -9384,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487583928381,0.9975487583928381,0.997542651662671,0.0036773681640625,5.3453800000000005 -9792,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487692779083,0.9975487692779083,0.9975454987794794,0.0037279129028320312,5.8012950000000005 -10200,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487792920874,0.9975487792920874,0.9975477757646256,0.0037784576416015625,6.275930000000001 -10608,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487885358726,0.9975487885358726,0.9975495850737114,0.0038290023803710938,6.769232000000001 -11016,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487970948707,0.9975487970948707,0.9975510089260561,0.003879547119140625,7.281090000000001 -11424,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488050424582,0.9975488050424582,0.997552113761348,0.003930091857910156,7.811594 -11832,Multiclass classification,[baseline] Last Class,Keystroke,0.99754881244189,0.99754881244189,0.9975529536110198,0.0039806365966796875,8.360849 -12240,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488193479859,0.9975488193479859,0.9975535726732964,0.004031181335449219,8.928801 -12648,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488258084921,0.9975488258084921,0.9975540072976318,0.00408172607421875,9.515298 -13056,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488318651857,0.9975488318651857,0.997554287526727,0.004132270812988281,10.120334999999999 -13464,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488375547797,0.9975488375547797,0.9975544383040469,0.0041828155517578125,10.744062999999999 -13872,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488429096676,0.9975488429096676,0.9975544804262364,0.004233360290527344,11.386615999999998 -14280,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488479585405,0.9975488479585405,0.9975544312994103,0.004283905029296875,12.048032999999998 -14688,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488527269013,0.9975488527269013,0.9975543055435039,0.004334449768066406,12.728291999999998 -15096,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488572374959,0.9975488572374959,0.9975541154780816,0.0043849945068359375,13.427186999999998 -15504,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488615106753,0.9975488615106753,0.9975538715150369,0.004435539245605469,14.144781999999998 -15912,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488655647037,0.9975488655647037,0.997553582477696,0.004486083984375,14.881121999999998 -16320,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488694160182,0.9975488694160182,0.997553255861403,0.004536628723144531,15.636483999999998 -16728,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488730794524,0.9975488730794524,0.997552898047314,0.0045871734619140625,16.410486 -17136,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488765684272,0.9975488765684272,0.997552514478575,0.004637718200683594,17.203509999999998 -17544,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488798951149,0.9975488798951149,0.997552109806108,0.004688262939453125,18.015261 -17952,Multiclass classification,[baseline] Last Class,Keystroke,0.997548883070581,0.997548883070581,0.997551688009728,0.004738807678222656,18.845896 -18360,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488861049077,0.9975488861049077,0.9975512524991371,0.0047893524169921875,19.695493 -18768,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488890073001,0.9975488890073001,0.9975508061984417,0.004839897155761719,20.563921999999998 -19176,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488917861799,0.9975488917861799,0.9975503516171185,0.00489044189453125,21.451134 -19584,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488944492672,0.9975488944492672,0.997549890909789,0.004940986633300781,22.357399 -19992,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488970036517,0.9975488970036517,0.9975494259267257,0.0049915313720703125,23.282656 -20400,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488994558557,0.9975488994558557,0.9975489582566448,0.005042076110839844,24.227045999999998 +46,Multiclass classification,Naive Bayes,ImageSegments,0.4666666666666667,0.4666666666666667,0.4009102009102009,0.3899507522583008,0.450679 +92,Multiclass classification,Naive Bayes,ImageSegments,0.5604395604395604,0.5604395604395604,0.5279334700387331,0.3899507522583008,1.152847 +138,Multiclass classification,Naive Bayes,ImageSegments,0.5474452554744526,0.5474452554744526,0.5191892873237387,0.38997745513916016,2.278305 +184,Multiclass classification,Naive Bayes,ImageSegments,0.5573770491803278,0.5573770491803278,0.5225713529323662,0.3899507522583008,3.449742 +230,Multiclass classification,Naive Bayes,ImageSegments,0.5545851528384279,0.5545851528384279,0.5217226223148511,0.38997745513916016,4.939578 +276,Multiclass classification,Naive Bayes,ImageSegments,0.56,0.56,0.5450388711329709,0.38997745513916016,6.667081 +322,Multiclass classification,Naive Bayes,ImageSegments,0.5825545171339563,0.5825545171339563,0.5566705826058684,0.39000415802001953,8.548779999999999 +368,Multiclass classification,Naive Bayes,ImageSegments,0.5940054495912807,0.5940054495912807,0.5613773296963412,0.39000415802001953,10.607026 +414,Multiclass classification,Naive Bayes,ImageSegments,0.5980629539951574,0.5980629539951574,0.5624927052752284,0.39000415802001953,12.811145 +460,Multiclass classification,Naive Bayes,ImageSegments,0.599128540305011,0.599128540305011,0.5669821167583783,0.38997745513916016,15.144022 +506,Multiclass classification,Naive Bayes,ImageSegments,0.6099009900990099,0.6099009900990099,0.592228619098681,0.39000415802001953,17.683543 +552,Multiclass classification,Naive Bayes,ImageSegments,0.6116152450090744,0.6116152450090744,0.5983340184133136,0.3899507522583008,20.357047 +598,Multiclass classification,Naive Bayes,ImageSegments,0.6180904522613065,0.6180904522613065,0.611527101723203,0.38997745513916016,23.213992 +644,Multiclass classification,Naive Bayes,ImageSegments,0.6158631415241057,0.6158631415241057,0.6113311881078581,0.38997745513916016,26.205369 +690,Multiclass classification,Naive Bayes,ImageSegments,0.6182873730043541,0.6182873730043541,0.6150189987146761,0.38997745513916016,29.350024 +736,Multiclass classification,Naive Bayes,ImageSegments,0.617687074829932,0.617687074829932,0.6157912419016742,0.38997745513916016,32.567265 +782,Multiclass classification,Naive Bayes,ImageSegments,0.6274007682458387,0.6274007682458387,0.6216325704223051,0.38997745513916016,36.027093 +828,Multiclass classification,Naive Bayes,ImageSegments,0.6324062877871826,0.6324062877871826,0.6280704917469789,0.38997745513916016,39.646129 +874,Multiclass classification,Naive Bayes,ImageSegments,0.6426116838487973,0.6426116838487973,0.6349558095046656,0.38997745513916016,43.417442 +920,Multiclass classification,Naive Bayes,ImageSegments,0.6485310119695321,0.6485310119695321,0.6384515982514894,0.38997745513916016,47.360213 +966,Multiclass classification,Naive Bayes,ImageSegments,0.6507772020725389,0.6507772020725389,0.6399118827528387,0.38997745513916016,51.459671 +1012,Multiclass classification,Naive Bayes,ImageSegments,0.6508407517309595,0.6508407517309595,0.6387857120889422,0.38997745513916016,55.677121 +1058,Multiclass classification,Naive Bayes,ImageSegments,0.6537369914853358,0.6537369914853358,0.6398811322847953,0.38997745513916016,60.129657 +1104,Multiclass classification,Naive Bayes,ImageSegments,0.658204895738894,0.658204895738894,0.6463297068165035,0.38997745513916016,64.716333 +1150,Multiclass classification,Naive Bayes,ImageSegments,0.6640557006092254,0.6640557006092254,0.6508930463144657,0.39000415802001953,69.425449 +1196,Multiclass classification,Naive Bayes,ImageSegments,0.6702928870292887,0.6702928870292887,0.6599370641329333,0.39000415802001953,74.368592 +1242,Multiclass classification,Naive Bayes,ImageSegments,0.6736502820306205,0.6736502820306205,0.669511465798708,0.39000415802001953,79.42749 +1288,Multiclass classification,Naive Bayes,ImageSegments,0.6822066822066822,0.6822066822066822,0.6790074545382362,0.39000415802001953,84.676077 +1334,Multiclass classification,Naive Bayes,ImageSegments,0.6841710427606902,0.6841710427606902,0.6834974476087327,0.39000415802001953,90.04929600000001 +1380,Multiclass classification,Naive Bayes,ImageSegments,0.6874546773023931,0.6874546773023931,0.687676692272135,0.39000415802001953,95.54439700000002 +1426,Multiclass classification,Naive Bayes,ImageSegments,0.6919298245614035,0.6919298245614035,0.6930786661709784,0.39000415802001953,101.25523300000002 +1472,Multiclass classification,Naive Bayes,ImageSegments,0.698844323589395,0.698844323589395,0.6985606658027719,0.38997745513916016,107.09626300000002 +1518,Multiclass classification,Naive Bayes,ImageSegments,0.7027027027027027,0.7027027027027027,0.7017787722939461,0.39000415802001953,113.17857300000003 +1564,Multiclass classification,Naive Bayes,ImageSegments,0.7056941778630839,0.7056941778630839,0.7062915374924865,0.38997745513916016,119.38367200000003 +1610,Multiclass classification,Naive Bayes,ImageSegments,0.7078931013051585,0.7078931013051585,0.7081385387673028,0.38997745513916016,125.72760100000004 +1656,Multiclass classification,Naive Bayes,ImageSegments,0.7093655589123867,0.7093655589123867,0.7109488618373111,0.3899507522583008,132.27559300000004 +1702,Multiclass classification,Naive Bayes,ImageSegments,0.7101704879482658,0.7101704879482658,0.7132092257742534,0.38997745513916016,138.94755600000005 +1748,Multiclass classification,Naive Bayes,ImageSegments,0.7143674871207785,0.7143674871207784,0.7178399485500211,0.3899507522583008,145.68584300000003 +1794,Multiclass classification,Naive Bayes,ImageSegments,0.7172336865588399,0.7172336865588399,0.7191260584555579,0.38997745513916016,152.67811600000005 +1840,Multiclass classification,Naive Bayes,ImageSegments,0.7199564980967917,0.7199564980967917,0.7217017555070446,0.39000415802001953,159.82058900000004 +1886,Multiclass classification,Naive Bayes,ImageSegments,0.7204244031830239,0.7204244031830238,0.7234495525792994,0.39000415802001953,167.13449700000004 +1932,Multiclass classification,Naive Bayes,ImageSegments,0.7219057483169342,0.7219057483169342,0.7238483512148008,0.39000415802001953,174.57489300000003 +1978,Multiclass classification,Naive Bayes,ImageSegments,0.723823975720789,0.723823975720789,0.7251399238639739,0.39000415802001953,182.21825900000005 +2024,Multiclass classification,Naive Bayes,ImageSegments,0.726643598615917,0.726643598615917,0.7268553573885639,0.39000415802001953,189.97396200000006 +2070,Multiclass classification,Naive Bayes,ImageSegments,0.7269212179797003,0.7269212179797003,0.7276782991451582,0.39000415802001953,197.92708900000005 +2116,Multiclass classification,Naive Bayes,ImageSegments,0.7286052009456265,0.7286052009456266,0.7283656039279267,0.39000415802001953,206.04766600000005 +2162,Multiclass classification,Naive Bayes,ImageSegments,0.7306802406293382,0.7306802406293383,0.7303992643507475,0.39000415802001953,214.36632800000004 +2208,Multiclass classification,Naive Bayes,ImageSegments,0.733574988672406,0.733574988672406,0.7322842940126589,0.39000415802001953,222.77231300000003 +2254,Multiclass classification,Naive Bayes,ImageSegments,0.7314691522414558,0.7314691522414558,0.7300322879925133,0.39000415802001953,231.40748800000003 +2300,Multiclass classification,Naive Bayes,ImageSegments,0.7316224445411048,0.7316224445411048,0.7300416811383057,0.39000415802001953,240.14309800000004 +2310,Multiclass classification,Naive Bayes,ImageSegments,0.7319185794716327,0.7319185794716329,0.7304188192194185,0.39000415802001953,248.95897400000004 +1056,Multiclass classification,Naive Bayes,Insects,0.623696682464455,0.623696682464455,0.5870724729616662,0.6116933822631836,4.116407 +2112,Multiclass classification,Naive Bayes,Insects,0.6148744670772146,0.6148744670772146,0.5800776869595596,0.6116933822631836,12.008893 +3168,Multiclass classification,Naive Bayes,Insects,0.6065677297126618,0.6065677297126618,0.5714781230184183,0.6116933822631836,23.636521000000002 +4224,Multiclass classification,Naive Bayes,Insects,0.6043097324177126,0.6043097324177126,0.5697541737710122,0.6116933822631836,38.735534 +5280,Multiclass classification,Naive Bayes,Insects,0.6088274294373934,0.6088274294373934,0.5727560614138387,0.6116933822631836,57.253764000000004 +6336,Multiclass classification,Naive Bayes,Insects,0.6023677979479084,0.6023677979479084,0.5679597008529512,0.6116933822631836,79.038555 +7392,Multiclass classification,Naive Bayes,Insects,0.5995129211202814,0.5995129211202814,0.5652603100832261,0.6116933822631836,104.109779 +8448,Multiclass classification,Naive Bayes,Insects,0.6019888717888008,0.6019888717888008,0.5673514925692325,0.6116933822631836,132.296427 +9504,Multiclass classification,Naive Bayes,Insects,0.5993896664211301,0.5993896664211301,0.5644951651039589,0.6116933822631836,163.68164199999998 +10560,Multiclass classification,Naive Bayes,Insects,0.5994885879344635,0.5994885879344635,0.564565538599863,0.6116933822631836,198.25211399999998 +11616,Multiclass classification,Naive Bayes,Insects,0.5972449418854929,0.5972449418854929,0.5631227877868952,0.6116933822631836,235.999104 +12672,Multiclass classification,Naive Bayes,Insects,0.6001894088864336,0.6001894088864336,0.5684733590606373,0.6116933822631836,276.973484 +13728,Multiclass classification,Naive Bayes,Insects,0.6120783856632913,0.6120783856632913,0.5935173038317552,0.6116933822631836,321.087465 +14784,Multiclass classification,Naive Bayes,Insects,0.6024487587093282,0.6024487587093282,0.5841270876002982,0.6116933822631836,368.414891 +15840,Multiclass classification,Naive Bayes,Insects,0.5676494728202538,0.5676494728202538,0.5507155080701159,0.6116933822631836,418.92674800000003 +16896,Multiclass classification,Naive Bayes,Insects,0.5418762947617638,0.5418762947617638,0.5256197352354142,0.6116933822631836,472.67283100000003 +17952,Multiclass classification,Naive Bayes,Insects,0.5232020500250683,0.5232020500250683,0.5066898143269706,0.6116933822631836,529.5973250000001 +19008,Multiclass classification,Naive Bayes,Insects,0.5118640500868101,0.5118640500868101,0.4926543583964285,0.6116933822631836,589.87103 +20064,Multiclass classification,Naive Bayes,Insects,0.5103922643672432,0.5103922643672432,0.4900586962359796,0.6116933822631836,653.257514 +21120,Multiclass classification,Naive Bayes,Insects,0.5115772527108291,0.5115772527108291,0.4910837640903744,0.6116933822631836,719.720849 +22176,Multiclass classification,Naive Bayes,Insects,0.5140022547914318,0.5140022547914318,0.49325418882319577,0.6116933822631836,789.2473650000001 +23232,Multiclass classification,Naive Bayes,Insects,0.5154319659076234,0.5154319659076234,0.49430134175999263,0.6116933822631836,861.9200270000001 +24288,Multiclass classification,Naive Bayes,Insects,0.5184254951208466,0.5184254951208466,0.4965832238311332,0.6116933822631836,937.6283820000001 +25344,Multiclass classification,Naive Bayes,Insects,0.5225111470623052,0.5225111470623052,0.499893079239698,0.6116933822631836,1016.4339050000001 +26400,Multiclass classification,Naive Bayes,Insects,0.5257396113489148,0.5257396113489148,0.5022487669255871,0.6116933822631836,1098.325454 +27456,Multiclass classification,Naive Bayes,Insects,0.5301402294663996,0.5301402294663996,0.5051550433324518,0.6116933822631836,1183.302333 +28512,Multiclass classification,Naive Bayes,Insects,0.5277261407877661,0.5277261407877661,0.5036945145235058,0.6116933822631836,1271.323869 +29568,Multiclass classification,Naive Bayes,Insects,0.5204450908107011,0.5204450908107011,0.4989008712312768,0.6116933822631836,1362.446785 +30624,Multiclass classification,Naive Bayes,Insects,0.5147111648107632,0.5147111648107632,0.49582684007363204,0.6116933822631836,1456.7074690000002 +31680,Multiclass classification,Naive Bayes,Insects,0.5105590454244137,0.5105590454244137,0.4941101813344875,0.6116933822631836,1553.9918670000002 +32736,Multiclass classification,Naive Bayes,Insects,0.5075607148312204,0.5075607148312204,0.4931947798921405,0.6116933822631836,1654.3550870000001 +33792,Multiclass classification,Naive Bayes,Insects,0.5044538486579266,0.5044538486579266,0.4905626123916189,0.6116933822631836,1757.6376 +34848,Multiclass classification,Naive Bayes,Insects,0.5020231296811777,0.5020231296811777,0.48787984248812405,0.6116933822631836,1863.925375 +35904,Multiclass classification,Naive Bayes,Insects,0.49987466228448874,0.49987466228448874,0.48534350611524757,0.6116933822631836,1973.177917 +36960,Multiclass classification,Naive Bayes,Insects,0.49679374441949187,0.49679374441949187,0.4819418474093529,0.6116933822631836,2085.445724 +38016,Multiclass classification,Naive Bayes,Insects,0.49559384453505195,0.49559384453505195,0.4801892436835747,0.6116933822631836,2200.821931 +39072,Multiclass classification,Naive Bayes,Insects,0.49402370044278365,0.49402370044278365,0.47838078382052607,0.6116933822631836,2319.178703 +40128,Multiclass classification,Naive Bayes,Insects,0.493508111745209,0.493508111745209,0.47852138016706713,0.6116933822631836,2440.443075 +41184,Multiclass classification,Naive Bayes,Insects,0.49369885632421145,0.49369885632421145,0.47942014994272747,0.6116933822631836,2564.583087 +42240,Multiclass classification,Naive Bayes,Insects,0.4938800634484718,0.4938800634484718,0.48023774975329353,0.6116933822631836,2691.651665 +43296,Multiclass classification,Naive Bayes,Insects,0.49437579397159026,0.49437579397159026,0.4812132921167227,0.6116933822631836,2821.6013359999997 +44352,Multiclass classification,Naive Bayes,Insects,0.49403621113390905,0.49403621113390905,0.48123889196184183,0.6116933822631836,2954.3777659999996 +45408,Multiclass classification,Naive Bayes,Insects,0.4944832294580131,0.4944832294580131,0.4818441874360225,0.6116933822631836,3089.8310679999995 +46464,Multiclass classification,Naive Bayes,Insects,0.4945225232981082,0.4945225232981082,0.4820791268335544,0.6116933822631836,3227.9665449999993 +47520,Multiclass classification,Naive Bayes,Insects,0.4956333256171216,0.4956333256171216,0.48331686360214987,0.6116933822631836,3368.688097999999 +48576,Multiclass classification,Naive Bayes,Insects,0.4970869788986104,0.4970869788986104,0.48467037716343636,0.6116933822631836,3511.887438999999 +49632,Multiclass classification,Naive Bayes,Insects,0.4987608551107171,0.4987608551107171,0.4862426724473749,0.6116933822631836,3657.6494079999993 +50688,Multiclass classification,Naive Bayes,Insects,0.5009568528419516,0.5009568528419516,0.4881725476999718,0.6116933822631836,3806.0112589999994 +51744,Multiclass classification,Naive Bayes,Insects,0.5034497419940862,0.5034497419940862,0.4903712806540024,0.6116933822631836,3956.8935159999996 +52800,Multiclass classification,Naive Bayes,Insects,0.5068467205818292,0.5068467205818292,0.4930025316136313,0.6116933822631836,4110.278735 +52848,Multiclass classification,Naive Bayes,Insects,0.5068972694760346,0.5068972694760346,0.49301906278314944,0.6116933822631836,4263.766907 +408,Multiclass classification,Naive Bayes,Keystroke,0.9852579852579852,0.9852579852579852,0.6962686567164179,0.19356441497802734,0.780775 +816,Multiclass classification,Naive Bayes,Keystroke,0.947239263803681,0.947239263803681,0.7418606503288051,0.28890228271484375,2.463269 +1224,Multiclass classification,Naive Bayes,Keystroke,0.884709730171709,0.884709730171709,0.8705899666065842,0.38424015045166016,5.15507 +1632,Multiclass classification,Naive Bayes,Keystroke,0.8933169834457388,0.8933169834457388,0.8791291775937072,0.47957801818847656,8.960951 +2040,Multiclass classification,Naive Bayes,Keystroke,0.8921039725355566,0.8921039725355566,0.8831785360852743,0.575160026550293,14.051639 +2448,Multiclass classification,Naive Bayes,Keystroke,0.851655087862689,0.851655087862689,0.8581984289516641,0.6704978942871094,20.582881999999998 +2856,Multiclass classification,Naive Bayes,Keystroke,0.8598949211908932,0.8598949211908932,0.8469962214365346,0.7658357620239258,28.649143 +3264,Multiclass classification,Naive Bayes,Keystroke,0.8513637756665645,0.8513637756665645,0.8281280134770846,0.8611736297607422,38.532046 +3672,Multiclass classification,Naive Bayes,Keystroke,0.8422773086352493,0.8422773086352493,0.8409307955747314,0.9565114974975586,50.273206 +4080,Multiclass classification,Naive Bayes,Keystroke,0.8367246874233881,0.8367246874233881,0.8249418657104467,1.0523834228515625,63.882498 +4488,Multiclass classification,Naive Bayes,Keystroke,0.8203699576554491,0.8203699576554491,0.8300896799820437,1.147721290588379,79.531469 +4896,Multiclass classification,Naive Bayes,Keystroke,0.8192032686414709,0.8192032686414709,0.8269731591910484,1.2430591583251953,97.310117 +5304,Multiclass classification,Naive Bayes,Keystroke,0.8172732415613804,0.8172732415613804,0.8027823390848743,1.3383970260620117,117.35519000000001 +5712,Multiclass classification,Naive Bayes,Keystroke,0.7961828051129399,0.7961828051129399,0.8002006091139847,1.4337348937988281,139.817583 +6120,Multiclass classification,Naive Bayes,Keystroke,0.793920575257395,0.793920575257395,0.7746960355921345,1.5290727615356445,164.727582 +6528,Multiclass classification,Naive Bayes,Keystroke,0.7688064960931515,0.7688064960931515,0.7622487598340326,1.624410629272461,192.15170700000002 +6936,Multiclass classification,Naive Bayes,Keystroke,0.7568853640951694,0.7568853640951694,0.757813781660983,1.7197484970092773,222.24358600000002 +7344,Multiclass classification,Naive Bayes,Keystroke,0.7669889690862045,0.7669889690862046,0.7643943615019536,1.8150863647460938,255.230678 +7752,Multiclass classification,Naive Bayes,Keystroke,0.7676428847890595,0.7676428847890595,0.7655695901071293,1.9104242324829102,291.218411 +8160,Multiclass classification,Naive Bayes,Keystroke,0.7714180659394534,0.7714180659394533,0.7672011803374248,2.0057621002197266,330.398823 +8568,Multiclass classification,Naive Bayes,Keystroke,0.7702813120112058,0.7702813120112058,0.7699263138193525,2.1021223068237305,372.82766399999997 +8976,Multiclass classification,Naive Bayes,Keystroke,0.7680222841225627,0.7680222841225627,0.7682287234686136,2.197460174560547,418.63014999999996 +9384,Multiclass classification,Naive Bayes,Keystroke,0.7659597143770649,0.7659597143770649,0.7643546547243014,2.2927980422973633,468.01111099999997 +9792,Multiclass classification,Naive Bayes,Keystroke,0.7586559084873864,0.7586559084873864,0.7552148692020618,2.3881359100341797,521.0847249999999 +10200,Multiclass classification,Naive Bayes,Keystroke,0.7505637807628199,0.7505637807628199,0.7430512224080145,2.483473777770996,577.917349 +10608,Multiclass classification,Naive Bayes,Keystroke,0.7290468558499105,0.7290468558499106,0.715756093271779,2.5788116455078125,638.790947 +11016,Multiclass classification,Naive Bayes,Keystroke,0.7217430776214253,0.7217430776214253,0.7173640789896896,2.674149513244629,703.666983 +11424,Multiclass classification,Naive Bayes,Keystroke,0.7151361288628206,0.7151361288628206,0.7011862635194489,2.7694873809814453,772.6431349999999 +11832,Multiclass classification,Naive Bayes,Keystroke,0.705603921900093,0.705603921900093,0.6976881379682607,2.8648252487182617,845.8350979999999 +12240,Multiclass classification,Naive Bayes,Keystroke,0.7094533867146009,0.7094533867146009,0.7058405389403433,2.960163116455078,923.50335 +12648,Multiclass classification,Naive Bayes,Keystroke,0.7053846762077963,0.7053846762077963,0.6965736948063982,3.0555009841918945,1005.7536769999999 +13056,Multiclass classification,Naive Bayes,Keystroke,0.6927613941018766,0.6927613941018766,0.6842255816736498,3.150838851928711,1092.707972 +13464,Multiclass classification,Naive Bayes,Keystroke,0.6890737577063062,0.6890737577063062,0.6845669389392289,3.2461767196655273,1184.483965 +13872,Multiclass classification,Naive Bayes,Keystroke,0.6873332852714296,0.6873332852714296,0.68390545518227,3.3415145874023438,1281.216395 +14280,Multiclass classification,Naive Bayes,Keystroke,0.682960991666083,0.682960991666083,0.6781566371919944,3.43685245513916,1383.0399089999999 +14688,Multiclass classification,Naive Bayes,Keystroke,0.686185061619119,0.686185061619119,0.6843713776162116,3.5321903228759766,1489.909884 +15096,Multiclass classification,Naive Bayes,Keystroke,0.6928784365684001,0.6928784365684001,0.6911392400672977,3.627528190612793,1601.996709 +15504,Multiclass classification,Naive Bayes,Keystroke,0.6913500612784622,0.6913500612784622,0.687359772989117,3.7228660583496094,1719.445985 +15912,Multiclass classification,Naive Bayes,Keystroke,0.6819810194205267,0.6819810194205267,0.674915944935936,3.818203926086426,1842.197498 +16320,Multiclass classification,Naive Bayes,Keystroke,0.6726515105092223,0.6726515105092223,0.6670192172011686,3.913541793823242,1970.358299 +16728,Multiclass classification,Naive Bayes,Keystroke,0.6695163508100676,0.6695163508100676,0.6664051037977977,4.008879661560059,2103.939399 +17136,Multiclass classification,Naive Bayes,Keystroke,0.6650131310183834,0.6650131310183834,0.6608988619616458,4.1063079833984375,2242.845385 +17544,Multiclass classification,Naive Bayes,Keystroke,0.6568431853160804,0.6568431853160804,0.6531382897719189,4.201645851135254,2386.822189 +17952,Multiclass classification,Naive Bayes,Keystroke,0.6556180714166342,0.6556180714166342,0.6538448358590968,4.29698371887207,2536.044428 +18360,Multiclass classification,Naive Bayes,Keystroke,0.6614194672912468,0.6614194672912468,0.6603186829199909,4.392321586608887,2690.5476860000003 +18768,Multiclass classification,Naive Bayes,Keystroke,0.6669686151222891,0.6669686151222891,0.6662293616554571,4.487659454345703,2850.3140810000004 +19176,Multiclass classification,Naive Bayes,Keystroke,0.6579921773142112,0.6579921773142112,0.6554177118629491,4.5829973220825195,3015.4823350000006 +19584,Multiclass classification,Naive Bayes,Keystroke,0.6622580809886126,0.6622580809886126,0.6609360990360078,4.678335189819336,3186.2814100000005 +19992,Multiclass classification,Naive Bayes,Keystroke,0.6562453103896754,0.6562453103896754,0.6545704957554572,4.773673057556152,3362.6238980000007 +20400,Multiclass classification,Naive Bayes,Keystroke,0.6525319868621011,0.6525319868621011,0.6515767870317885,4.869010925292969,3544.6906370000006 +46,Multiclass classification,Hoeffding Tree,ImageSegments,0.35555555555555557,0.35555555555555557,0.25379424497071557,0.4170856475830078,0.290301 +92,Multiclass classification,Hoeffding Tree,ImageSegments,0.4945054945054945,0.4945054945054945,0.5043329927491418,0.4170818328857422,0.82046 +138,Multiclass classification,Hoeffding Tree,ImageSegments,0.5328467153284672,0.5328467153284672,0.5564033878668025,0.4171772003173828,1.6754229999999999 +184,Multiclass classification,Hoeffding Tree,ImageSegments,0.6010928961748634,0.6010928961748634,0.622766496539645,0.4171772003173828,2.801183 +230,Multiclass classification,Hoeffding Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6539827168809461,0.41720008850097656,4.271522 +276,Multiclass classification,Hoeffding Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.6671561759164943,0.4172496795654297,5.954744 +322,Multiclass classification,Hoeffding Tree,ImageSegments,0.67601246105919,0.67601246105919,0.6756614325426025,0.4172496795654297,7.864603 +368,Multiclass classification,Hoeffding Tree,ImageSegments,0.7029972752043597,0.7029972752043597,0.6993447851636564,0.4172229766845703,10.008665 +414,Multiclass classification,Hoeffding Tree,ImageSegments,0.7142857142857143,0.7142857142857143,0.7108606838045498,0.4171428680419922,12.399438 +460,Multiclass classification,Hoeffding Tree,ImageSegments,0.7145969498910676,0.7145969498910676,0.7090365931960759,0.4172191619873047,15.01004 +506,Multiclass classification,Hoeffding Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7126631585949761,0.4172191619873047,17.873655 +552,Multiclass classification,Hoeffding Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7157730164623107,0.4171123504638672,20.946970999999998 +598,Multiclass classification,Hoeffding Tree,ImageSegments,0.7286432160804021,0.7286432160804021,0.7216745323124732,0.41713523864746094,24.255884 +644,Multiclass classification,Hoeffding Tree,ImageSegments,0.7278382581648523,0.7278382581648523,0.7229105183087501,0.41710853576660156,27.838411999999998 +690,Multiclass classification,Hoeffding Tree,ImageSegments,0.7314949201741655,0.7314949201741654,0.7263583447448078,0.41710853576660156,31.647636 +736,Multiclass classification,Hoeffding Tree,ImageSegments,0.7333333333333333,0.7333333333333333,0.729431071218305,0.41713523864746094,35.743157 +782,Multiclass classification,Hoeffding Tree,ImageSegments,0.7387964148527529,0.7387964148527529,0.7349287389986899,0.41713523864746094,40.063089999999995 +828,Multiclass classification,Hoeffding Tree,ImageSegments,0.7376058041112454,0.7376058041112454,0.7356226390109741,0.41713523864746094,44.599844 +874,Multiclass classification,Hoeffding Tree,ImageSegments,0.7445589919816724,0.7445589919816724,0.7409366047432264,0.41713523864746094,49.398728999999996 +920,Multiclass classification,Hoeffding Tree,ImageSegments,0.7453754080522307,0.7453754080522307,0.7408438328939173,0.41710853576660156,54.404894 +966,Multiclass classification,Hoeffding Tree,ImageSegments,0.7471502590673575,0.7471502590673575,0.7416651838589269,0.41710853576660156,59.665949 +1012,Multiclass classification,Hoeffding Tree,ImageSegments,0.7467853610286844,0.7467853610286844,0.7416356251822,0.41710853576660156,65.211169 +1058,Multiclass classification,Hoeffding Tree,ImageSegments,0.7492904446546831,0.7492904446546831,0.7430778844390783,0.41710853576660156,70.961377 +1104,Multiclass classification,Hoeffding Tree,ImageSegments,0.7515865820489573,0.7515865820489573,0.7451256886686588,0.4171581268310547,76.969446 +1150,Multiclass classification,Hoeffding Tree,ImageSegments,0.7536988685813751,0.7536988685813751,0.7468312166689606,0.4171581268310547,83.201851 +1196,Multiclass classification,Hoeffding Tree,ImageSegments,0.7564853556485356,0.7564853556485356,0.7503479321738041,0.4171581268310547,89.604352 +1242,Multiclass classification,Hoeffding Tree,ImageSegments,0.7566478646253022,0.7566478646253022,0.7509717522131719,0.4171581268310547,96.30702600000001 +1288,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614607614607615,0.7614607614607615,0.7547643483779538,0.4171581268310547,103.26246200000001 +1334,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614403600900225,0.7614403600900225,0.7551060921605869,0.4171581268310547,110.41488900000002 +1380,Multiclass classification,Hoeffding Tree,ImageSegments,0.7621464829586657,0.7621464829586658,0.7562209880685912,0.4171581268310547,117.79988600000001 +1426,Multiclass classification,Hoeffding Tree,ImageSegments,0.7642105263157895,0.7642105263157895,0.7575332274919562,0.4171581268310547,125.46176800000002 +1472,Multiclass classification,Hoeffding Tree,ImageSegments,0.7688647178789939,0.768864717878994,0.760438686053582,0.4171581268310547,133.360363 +1518,Multiclass classification,Hoeffding Tree,ImageSegments,0.7705998681608438,0.7705998681608438,0.7612069012840872,0.4171581268310547,141.48549400000002 +1564,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709532949456174,0.7709532949456174,0.7622701654854867,0.4171581268310547,149.83563600000002 +1610,Multiclass classification,Hoeffding Tree,ImageSegments,0.7712865133623369,0.771286513362337,0.7617247271717752,0.41718101501464844,158.439217 +1656,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709969788519637,0.7709969788519637,0.7615629120572474,0.41718101501464844,167.22864700000002 +1702,Multiclass classification,Hoeffding Tree,ImageSegments,0.770135214579659,0.770135214579659,0.7627316365695143,0.41718101501464844,176.30742800000002 +1748,Multiclass classification,Hoeffding Tree,ImageSegments,0.7727532913566113,0.7727532913566113,0.7649467707214076,0.41718101501464844,185.609237 +1794,Multiclass classification,Hoeffding Tree,ImageSegments,0.7741215839375348,0.7741215839375348,0.7649332326562149,0.41715431213378906,195.10730800000002 +1840,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754214246873301,0.7754214246873301,0.7664700790631908,0.41715431213378906,204.88888000000003 +1886,Multiclass classification,Hoeffding Tree,ImageSegments,0.7740053050397878,0.7740053050397878,0.7655121135276625,0.41715431213378906,214.87796100000003 +1932,Multiclass classification,Hoeffding Tree,ImageSegments,0.7742102537545313,0.7742102537545313,0.7648034036287765,0.41715431213378906,225.10774000000004 +1978,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754172989377845,0.7754172989377845,0.7656013068970459,0.41715431213378906,235.56491900000003 +2024,Multiclass classification,Hoeffding Tree,ImageSegments,0.7770637666831438,0.7770637666831438,0.7660878232247856,0.41715431213378906,246.31694000000005 +2070,Multiclass classification,Hoeffding Tree,ImageSegments,0.7762203963267279,0.7762203963267279,0.7654829214385931,0.41715431213378906,257.28426500000006 +2116,Multiclass classification,Hoeffding Tree,ImageSegments,0.7768321513002364,0.7768321513002364,0.7653071619305024,0.41715431213378906,268.5154150000001 +2162,Multiclass classification,Hoeffding Tree,ImageSegments,0.7778806108283203,0.7778806108283203,0.7659351904174982,0.41715431213378906,279.94414300000005 +2208,Multiclass classification,Hoeffding Tree,ImageSegments,0.7797915722700498,0.7797915722700498,0.7668192864082087,0.41715431213378906,291.65328600000004 +2254,Multiclass classification,Hoeffding Tree,ImageSegments,0.7767421216156236,0.7767421216156236,0.7637794374955548,0.41715431213378906,303.618395 +2300,Multiclass classification,Hoeffding Tree,ImageSegments,0.7759895606785558,0.7759895606785558,0.763026662835187,0.41715431213378906,315.80512400000003 +2310,Multiclass classification,Hoeffding Tree,ImageSegments,0.776093546990039,0.776093546990039,0.7631372452021826,0.41715431213378906,328.06738900000005 +1056,Multiclass classification,Hoeffding Tree,Insects,0.6218009478672986,0.6218009478672986,0.5852663107194211,0.6579360961914062,7.68277 +2112,Multiclass classification,Hoeffding Tree,Insects,0.6153481762198011,0.6153481762198011,0.5806436317780949,0.6579360961914062,22.565114 +3168,Multiclass classification,Hoeffding Tree,Insects,0.6071992421850332,0.6071992421850332,0.572248584718361,0.6579360961914062,43.997682 +4224,Multiclass classification,Hoeffding Tree,Insects,0.6043097324177126,0.6043097324177126,0.5697573109597247,0.6579360961914062,71.858443 +5280,Multiclass classification,Hoeffding Tree,Insects,0.6088274294373934,0.6088274294373934,0.5727379077413696,0.6579360961914062,105.92483999999999 +6336,Multiclass classification,Hoeffding Tree,Insects,0.6026835043409629,0.6026835043409629,0.568251333238805,0.6579360961914062,146.287253 +7392,Multiclass classification,Hoeffding Tree,Insects,0.600189419564335,0.600189419564335,0.5659762112716077,0.6579360961914062,192.863981 +8448,Multiclass classification,Hoeffding Tree,Insects,0.60258079791642,0.60258079791642,0.5679781484640408,0.6579360961914062,245.806734 +9504,Multiclass classification,Hoeffding Tree,Insects,0.5998105861306956,0.5998105861306956,0.5649597336877693,0.6579360961914062,305.14044 +10560,Multiclass classification,Hoeffding Tree,Insects,0.5998674116867128,0.5998674116867128,0.5650173260529011,0.6579360961914062,370.68089100000003 +11616,Multiclass classification,Hoeffding Tree,Insects,0.5974171330176495,0.5974171330176495,0.5633067089377387,0.6579360961914062,442.33844300000004 +12672,Multiclass classification,Hoeffding Tree,Insects,0.6001894088864336,0.6001894088864336,0.5684760329567131,0.6579360961914062,520.121563 +13728,Multiclass classification,Hoeffding Tree,Insects,0.6120783856632913,0.6120783856632913,0.5935956771555828,0.6579360961914062,604.039429 +14784,Multiclass classification,Hoeffding Tree,Insects,0.6024487587093282,0.6024487587093282,0.5842148300149193,0.6579360961914062,694.113241 +15840,Multiclass classification,Hoeffding Tree,Insects,0.5677757434181451,0.5677757434181451,0.5509250187877572,0.6579360961914062,790.19156 +16896,Multiclass classification,Hoeffding Tree,Insects,0.5419354838709678,0.5419354838709678,0.5257359157219258,0.6579360961914062,892.361186 +17952,Multiclass classification,Hoeffding Tree,Insects,0.5233691716338923,0.5233691716338923,0.5068581838352059,0.6579360961914062,1000.4717479999999 +19008,Multiclass classification,Hoeffding Tree,Insects,0.5121271110643447,0.5121271110643447,0.49292899065094153,0.6579360961914062,1114.494528 +20064,Multiclass classification,Hoeffding Tree,Insects,0.5120370831879579,0.5120370831879579,0.4920970323041603,1.3099584579467773,1234.2056499999999 +21120,Multiclass classification,Hoeffding Tree,Insects,0.5173066906577016,0.5173066906577016,0.4973447169836249,1.310713768005371,1358.925583 +22176,Multiclass classification,Hoeffding Tree,Insects,0.5229312288613304,0.5229312288613304,0.5026343687424488,1.310713768005371,1488.370808 +23232,Multiclass classification,Hoeffding Tree,Insects,0.5301536739701261,0.5301536739701261,0.5095132087733324,1.310713768005371,1622.41448 +24288,Multiclass classification,Hoeffding Tree,Insects,0.5351422571746202,0.5351422571746202,0.5135975374357353,1.310713768005371,1760.8970379999998 +25344,Multiclass classification,Hoeffding Tree,Insects,0.5403069881229531,0.5403069881229531,0.5180803411538233,1.310713768005371,1903.5911449999999 +26400,Multiclass classification,Hoeffding Tree,Insects,0.5441493995984696,0.5441493995984696,0.5209012984387186,1.310713768005371,2050.469487 +27456,Multiclass classification,Hoeffding Tree,Insects,0.5475869604807867,0.5475869604807867,0.5230407124785976,1.310713768005371,2201.55681 +28512,Multiclass classification,Hoeffding Tree,Insects,0.5442460804601733,0.5442460804601733,0.5199893698637053,1.310713768005371,2356.711105 +29568,Multiclass classification,Hoeffding Tree,Insects,0.5439848479724017,0.5439848479724017,0.5225387960194383,1.310713768005371,2516.62263 +30624,Multiclass classification,Hoeffding Tree,Insects,0.5449825294713124,0.5449825294713124,0.5260472440529832,1.310713768005371,2681.5460789999997 +31680,Multiclass classification,Hoeffding Tree,Insects,0.5469238296663405,0.5469238296663405,0.5300194392617626,1.310713768005371,2851.622305 +32736,Multiclass classification,Hoeffding Tree,Insects,0.5492286543455017,0.5492286543455017,0.5337692045397758,1.310713768005371,3026.797274 +33792,Multiclass classification,Hoeffding Tree,Insects,0.5448196265277737,0.5448196265277737,0.5298516474077153,1.310713768005371,3207.119826 +34848,Multiclass classification,Hoeffding Tree,Insects,0.539357763939507,0.539357763939507,0.5246413689313029,1.310713768005371,3392.4010240000002 +35904,Multiclass classification,Hoeffding Tree,Insects,0.5352756037099964,0.5352756037099964,0.5204658240271913,1.310713768005371,3582.6817720000004 +36960,Multiclass classification,Hoeffding Tree,Insects,0.5307232338537298,0.5307232338537298,0.5158458403074864,1.310713768005371,3778.3092850000003 +38016,Multiclass classification,Hoeffding Tree,Insects,0.5287912666052874,0.5287912666052874,0.5138605376143625,1.8479537963867188,3978.8224330000003 +39072,Multiclass classification,Hoeffding Tree,Insects,0.5245322617798367,0.5245322617798367,0.5100329616180462,1.9625730514526367,4184.1075280000005 +40128,Multiclass classification,Hoeffding Tree,Insects,0.5244847608841927,0.5244847608841927,0.5114466799524962,1.9625730514526367,4393.646320000001 +41184,Multiclass classification,Hoeffding Tree,Insects,0.5269650098341548,0.5269650098341548,0.5145630920489553,1.9625730514526367,4606.675677000001 +42240,Multiclass classification,Hoeffding Tree,Insects,0.5290608205686688,0.5290608205686688,0.5171452370879218,1.9625730514526367,4823.052294000001 +43296,Multiclass classification,Hoeffding Tree,Insects,0.5316318281556762,0.5316318281556762,0.5200714653059241,1.9625730514526367,5042.794587000001 +44352,Multiclass classification,Hoeffding Tree,Insects,0.5332912448422809,0.5332912448422809,0.521951703681177,1.9633283615112305,5266.308108000001 +45408,Multiclass classification,Hoeffding Tree,Insects,0.5350937080185875,0.5350937080185875,0.5236272112757866,1.9633283615112305,5493.659660000001 +46464,Multiclass classification,Hoeffding Tree,Insects,0.5374168693368917,0.5374168693368917,0.5257977177437826,1.9633283615112305,5724.562244000002 +47520,Multiclass classification,Hoeffding Tree,Insects,0.5359540394368568,0.5359540394368568,0.5247049329892776,1.9633283615112305,5959.275286000002 +48576,Multiclass classification,Hoeffding Tree,Insects,0.5333196088522902,0.5333196088522902,0.5224640186909637,1.9633283615112305,6197.987866000002 +49632,Multiclass classification,Hoeffding Tree,Insects,0.5314017448771937,0.5314017448771937,0.5209076603734537,1.9633283615112305,6440.583835000002 +50688,Multiclass classification,Hoeffding Tree,Insects,0.5322271982954209,0.5322271982954209,0.5219695808096345,2.081958770751953,6687.224874000002 +51744,Multiclass classification,Hoeffding Tree,Insects,0.5377345727924551,0.5377345727924551,0.5274876060436412,2.3156700134277344,6937.746409000002 +52800,Multiclass classification,Hoeffding Tree,Insects,0.5370366863008769,0.5370366863008769,0.5270872650003847,2.519227981567383,7191.466386000002 +52848,Multiclass classification,Hoeffding Tree,Insects,0.5373058073305959,0.5373058073305959,0.5273644947479657,2.519227981567383,7445.3631460000015 +408,Multiclass classification,Hoeffding Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.2240447998046875,0.863228 +816,Multiclass classification,Hoeffding Tree,Keystroke,0.9423312883435583,0.9423312883435583,0.7661667470992702,0.3196687698364258,3.107641 +1224,Multiclass classification,Hoeffding Tree,Keystroke,0.8830744071954211,0.883074407195421,0.8761191747044462,0.41529273986816406,7.048775 +1632,Multiclass classification,Hoeffding Tree,Keystroke,0.8902513795217658,0.8902513795217658,0.8767853151263398,0.5114049911499023,13.087731999999999 +2040,Multiclass classification,Hoeffding Tree,Keystroke,0.8891613536047082,0.8891613536047082,0.8807858055314012,0.6185035705566406,21.551524999999998 +2448,Multiclass classification,Hoeffding Tree,Keystroke,0.848385778504291,0.848385778504291,0.8522513926518692,0.7141275405883789,32.816222999999994 +2856,Multiclass classification,Hoeffding Tree,Keystroke,0.8563922942206655,0.8563922942206655,0.8440193478447516,0.8097515106201172,47.080318999999996 +3264,Multiclass classification,Hoeffding Tree,Keystroke,0.8482991112473184,0.8482991112473184,0.8269786301577753,0.9053754806518555,64.636989 +3672,Multiclass classification,Hoeffding Tree,Keystroke,0.8392808499046581,0.8392808499046581,0.8374924160046072,1.0009994506835938,85.706576 +4080,Multiclass classification,Hoeffding Tree,Keystroke,0.8323118411375338,0.8323118411375338,0.8182261307945194,1.1217241287231445,110.70978199999999 +4488,Multiclass classification,Hoeffding Tree,Keystroke,0.8159126365054602,0.8159126365054602,0.8260965842218733,1.2173480987548828,139.812165 +4896,Multiclass classification,Hoeffding Tree,Keystroke,0.8149131767109296,0.8149131767109296,0.8221314665977922,1.312972068786621,173.369773 +5304,Multiclass classification,Hoeffding Tree,Keystroke,0.8125589289081652,0.8125589289081652,0.797613058026624,1.4085960388183594,211.780209 +5712,Multiclass classification,Hoeffding Tree,Keystroke,0.7907546839432674,0.7907546839432674,0.7936708037520236,1.5042200088500977,255.27399100000002 +6120,Multiclass classification,Hoeffding Tree,Keystroke,0.7886909625755842,0.7886909625755842,0.7694478218498494,1.599843978881836,304.294734 +6528,Multiclass classification,Hoeffding Tree,Keystroke,0.7635973647924008,0.7635973647924008,0.75687960152136,1.6954679489135742,359.144129 +6936,Multiclass classification,Hoeffding Tree,Keystroke,0.75155010814708,0.7515501081470799,0.7521509466338959,1.7910919189453125,420.22114200000004 +7344,Multiclass classification,Hoeffding Tree,Keystroke,0.7611330518861501,0.7611330518861501,0.7576671162861806,1.8881807327270508,487.76956500000006 +7752,Multiclass classification,Hoeffding Tree,Keystroke,0.7617081666881693,0.7617081666881692,0.7593340838982119,1.983804702758789,562.1432000000001 +8160,Multiclass classification,Hoeffding Tree,Keystroke,0.7655349920333374,0.7655349920333374,0.7610505848438686,2.0794286727905273,643.5514560000001 +8568,Multiclass classification,Hoeffding Tree,Keystroke,0.7644449632310026,0.7644449632310025,0.7639417799779614,2.223102569580078,732.3349550000001 +8976,Multiclass classification,Hoeffding Tree,Keystroke,0.7624512534818941,0.7624512534818941,0.7625605608371232,2.3187265396118164,828.9274100000001 +9384,Multiclass classification,Hoeffding Tree,Keystroke,0.7605243525524885,0.7605243525524885,0.7588384348689571,2.4143505096435547,933.4845880000001 +9792,Multiclass classification,Hoeffding Tree,Keystroke,0.753344908589521,0.753344908589521,0.7499438215834663,2.509974479675293,1046.19484 +10200,Multiclass classification,Hoeffding Tree,Keystroke,0.7450730463770958,0.7450730463770959,0.7369660419615974,2.6055984497070312,1167.344916 +10608,Multiclass classification,Hoeffding Tree,Keystroke,0.7240501555576506,0.7240501555576506,0.7111305646829175,2.7012224197387695,1296.919782 +11016,Multiclass classification,Hoeffding Tree,Keystroke,0.7166591012256015,0.7166591012256015,0.7122511515574346,2.796846389770508,1434.7760759999999 +11424,Multiclass classification,Hoeffding Tree,Keystroke,0.710146196270682,0.710146196270682,0.6963016796632095,2.892470359802246,1580.7280859999998 +11832,Multiclass classification,Hoeffding Tree,Keystroke,0.7005324993660722,0.7005324993660722,0.6925666211338901,2.9880943298339844,1735.0271709999997 +12240,Multiclass classification,Hoeffding Tree,Keystroke,0.7043876133671052,0.7043876133671052,0.7007845610449206,3.0837182998657227,1897.6526119999996 +12648,Multiclass classification,Hoeffding Tree,Keystroke,0.7004032576895707,0.7004032576895707,0.6915775762792657,3.179342269897461,2069.0860809999995 +13056,Multiclass classification,Hoeffding Tree,Keystroke,0.6877058598238223,0.6877058598238223,0.6789768292873962,3.274966239929199,2249.3891769999996 +13464,Multiclass classification,Hoeffding Tree,Keystroke,0.6838743222164451,0.6838743222164451,0.6791243465680947,3.3705902099609375,2438.6931489999997 +13872,Multiclass classification,Hoeffding Tree,Keystroke,0.6822146925239708,0.6822146925239708,0.6786558938530485,3.466214179992676,2637.6841019999997 +14280,Multiclass classification,Hoeffding Tree,Keystroke,0.6777085230058127,0.6777085230058127,0.6725285130045525,3.561838150024414,2845.8288079999998 +14688,Multiclass classification,Hoeffding Tree,Keystroke,0.6807380676788997,0.6807380676788997,0.6786761142186741,3.6574621200561523,3062.9942149999997 +15096,Multiclass classification,Hoeffding Tree,Keystroke,0.6873799271281882,0.6873799271281882,0.6854839306484398,3.7530860900878906,3290.055422 +15504,Multiclass classification,Hoeffding Tree,Keystroke,0.6858027478552539,0.6858027478552539,0.6816808496509055,3.848710060119629,3526.69202 +15912,Multiclass classification,Hoeffding Tree,Keystroke,0.6765759537426937,0.6765759537426937,0.6694713281964946,3.944334030151367,3772.997519 +16320,Multiclass classification,Hoeffding Tree,Keystroke,0.6673815797536614,0.6673815797536614,0.6617321933140904,4.0399580001831055,4029.133223 +16728,Multiclass classification,Hoeffding Tree,Keystroke,0.6643151790518323,0.6643151790518323,0.661178029358405,4.135581970214844,4295.086238 +17136,Multiclass classification,Hoeffding Tree,Keystroke,0.6598774438284214,0.6598774438284214,0.655734247886306,4.3294572830200195,4570.827071 +17544,Multiclass classification,Hoeffding Tree,Keystroke,0.6518269395200365,0.6518269395200365,0.6481085155228206,4.425081253051758,4856.254143 +17952,Multiclass classification,Hoeffding Tree,Keystroke,0.6507158375577963,0.6507158375577963,0.6489368995854258,4.520705223083496,5151.869359 +18360,Multiclass classification,Hoeffding Tree,Keystroke,0.6566806470940683,0.6566806470940683,0.6555764711123695,4.616329193115234,5457.498716 +18768,Multiclass classification,Hoeffding Tree,Keystroke,0.662279533223211,0.662279533223211,0.6615432060687808,4.711953163146973,5772.982264 +19176,Multiclass classification,Hoeffding Tree,Keystroke,0.6534028683181226,0.6534028683181226,0.6508089832432514,4.807577133178711,6098.679956 +19584,Multiclass classification,Hoeffding Tree,Keystroke,0.6577643874789358,0.6577643874789358,0.6564201177589184,4.903201103210449,6434.678037 +19992,Multiclass classification,Hoeffding Tree,Keystroke,0.6518433294982742,0.6518433294982742,0.6501496360982542,4.9988250732421875,6781.324361 +20400,Multiclass classification,Hoeffding Tree,Keystroke,0.6482180499044071,0.6482180499044071,0.6472493759146578,5.094449043273926,7138.730487 +46,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,0.42345714569091797,0.325579 +92,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5164835164835165,0.5164835164835165,0.5335477748411618,0.42351436614990234,1.056326 +138,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5474452554744526,0.5474452554744526,0.5743273066802479,0.42363643646240234,2.202996 +184,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6120218579234973,0.6120218579234973,0.6355989308336889,0.4237203598022461,3.699294 +230,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6557923943920432,0.4237203598022461,5.564336 +276,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.66910740948952,0.4237699508666992,7.749814 +322,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.67601246105919,0.67601246105919,0.678427291711157,0.4238309860229492,10.278631 +368,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7002724795640327,0.7002724795640327,0.6988359939675117,0.42380428314208984,13.125556000000001 +414,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.711864406779661,0.711864406779661,0.7104564330601258,0.4237241744995117,16.369918000000002 +460,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7124183006535948,0.7124183006535948,0.7087721216219991,0.4238004684448242,19.921878000000003 +506,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7145025942185106,0.4238004684448242,23.844357000000002 +552,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7174926871575792,0.4236936569213867,28.111685 +598,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7269681742043551,0.7269681742043551,0.7216367248754637,0.42371654510498047,32.752989 +644,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7262830482115086,0.7262830482115085,0.7230014848259525,0.4237508773803711,37.712808 +690,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7300435413642961,0.7300435413642961,0.7265684058467008,0.4237508773803711,43.006145000000004 +736,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7319727891156462,0.7319727891156461,0.7296570819427115,0.42377758026123047,48.68780100000001 +782,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.737516005121639,0.737516005121639,0.7350906419548328,0.42377758026123047,54.691720000000004 +828,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7363966142684402,0.7363966142684402,0.7359651798179677,0.42377758026123047,60.98272 +874,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7422680412371134,0.7422680412371134,0.7398886847335938,0.42377758026123047,67.641769 +920,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7421109902067464,0.7421109902067464,0.738912026501458,0.4237508773803711,74.649906 +966,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7419689119170985,0.7419689119170985,0.7379593683174607,0.4237508773803711,81.98079 +1012,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7418397626112759,0.741839762611276,0.7380802548116379,0.4237508773803711,89.699811 +1058,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7436140018921475,0.7436140018921475,0.7390703652035102,0.4237508773803711,97.73816099999999 +1104,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7461468721668177,0.7461468721668177,0.7413714574148674,0.4238004684448242,106.141078 +1150,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7476066144473456,0.7476066144473456,0.742441565911322,0.4238004684448242,114.87573499999999 +1196,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7506276150627615,0.7506276150627615,0.7460917536510117,0.4234342575073242,123.97312099999999 +1242,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7510072522159549,0.7510072522159549,0.7470578866974922,0.4235563278198242,133.391788 +1288,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.756021756021756,0.7560217560217559,0.7510482446555896,0.4236173629760742,143.113173 +1334,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7569392348087022,0.7569392348087022,0.7522366633133313,0.4236173629760742,153.228885 +1380,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7585206671501088,0.7585206671501088,0.7544196711061472,0.4236783981323242,163.64661999999998 +1426,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7614035087719299,0.7614035087719299,0.7567964121564391,0.4236783981323242,174.36664399999998 +1472,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7654656696125085,0.7654656696125085,0.7591802078998249,0.4236783981323242,185.463757 +1518,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7673038892551087,0.7673038892551087,0.7600352016074767,0.4237394332885742,196.90308 +1564,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7677543186180422,0.7677543186180422,0.7612494392404334,0.4237394332885742,208.647576 +1610,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7675574891236793,0.7675574891236793,0.7602773300593106,0.42376232147216797,220.786107 +1656,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.76797583081571,0.76797583081571,0.7607906010792568,0.42376232147216797,233.21939999999998 +1702,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7677836566725456,0.7677836566725456,0.7627036277641847,0.42376232147216797,245.952092 +1748,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7710360618202633,0.7710360618202633,0.7657334796773966,0.42376232147216797,259.02702999999997 +1794,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7724484104852203,0.7724484104852203,0.7657758298578787,0.4237356185913086,272.39410699999996 +1840,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7737901033170201,0.77379010331702,0.767302943564198,0.4237966537475586,286.06776199999996 +1886,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7724137931034483,0.7724137931034483,0.7666353585191567,0.4237966537475586,300.095471 +1932,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7731745209735889,0.7731745209735889,0.7666634536176192,0.4237966537475586,314.417396 +1978,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7738998482549317,0.7738998482549316,0.7665909326930368,0.4237966537475586,329.067854 +2024,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7750865051903114,0.7750865051903113,0.7662611838286661,0.4237966537475586,344.01511700000003 +2070,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7747704204929918,0.7747704204929918,0.7660645062500586,0.4237966537475586,359.290159 +2116,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7754137115839244,0.7754137115839244,0.7658988206988366,0.4237966537475586,374.882405 +2162,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7760296159185562,0.7760296159185563,0.7660708746783081,0.4237966537475586,390.75768 +2208,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.777979157227005,0.7779791572270048,0.7670029065892423,0.4237966537475586,407.002801 +2254,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7749667110519307,0.7749667110519308,0.7639707440456852,0.4237966537475586,423.546299 +2300,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7742496737712049,0.7742496737712049,0.7632394528829524,0.4237966537475586,440.39336399999996 +2310,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7743611953226505,0.7743611953226506,0.7633622232911937,0.4237966537475586,457.310729 +1056,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6161137440758294,0.6161137440758294,0.581384151333148,0.6645784378051758,11.249192 +2112,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6120322122216959,0.6120322122216959,0.5792161554760864,0.6646394729614258,32.358705 +3168,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6049889485317335,0.6049889485317335,0.5721633809277145,0.6647005081176758,62.851539 +4224,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.603125739995264,0.603125739995264,0.5703574432462961,0.6647005081176758,102.700179 +5280,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6061754120098504,0.6061754120098504,0.5722430970062696,0.6647615432739258,151.914202 +6336,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5995264404104184,0.5995264404104184,0.5671511237518188,0.6647615432739258,210.432187 +7392,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5972128264104992,0.5972128264104992,0.5650210504998666,0.6647615432739258,278.26775499999997 +8448,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5989108559251806,0.5989108559251806,0.566418690076869,0.6647615432739258,355.20493799999997 +9504,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5962327685993897,0.5962327685993897,0.5633780031885509,0.6647615432739258,441.186739 +10560,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5964579979164694,0.5964579979164694,0.5634236596216465,0.6648225784301758,536.283653 +11616,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.594317692638829,0.594317692638829,0.5620068495149612,0.6648225784301758,640.2689049999999 +12672,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5975061163286244,0.5975061163286244,0.567518061449456,0.6648225784301758,753.0441599999999 +13728,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6097472135207984,0.6097472135207984,0.5927729676671933,0.6648225784301758,874.528885 +14784,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001488195900697,0.6001488195900697,0.5832911478837771,0.6645174026489258,1004.5501099999999 +15840,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5673969316244712,0.5673969316244712,0.5522471754341495,0.8876123428344727,1142.6522839999998 +16896,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5712340929269014,0.5712340929269014,0.5590383236849579,1.4319400787353516,1288.8770269999998 +17952,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5741184335134533,0.5741184335134533,0.5632919959429028,1.8629226684570312,1445.4718369999998 +19008,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5867312042931552,0.5867312042931552,0.5723846445183198,0.4819307327270508,1609.073978 +20064,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5966704879629168,0.5966704879629168,0.5796820575913003,0.6649179458618164,1780.2710459999998 +21120,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5984658364505895,0.5984658364505895,0.5810209140208816,0.6650400161743164,1958.8195809999997 +22176,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001803833145434,0.6001803833145434,0.5822125955100945,1.2073478698730469,2144.7260309999997 +23232,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6020403770823468,0.6020403770823468,0.5837921358595156,1.3215751647949219,2339.5310459999996 +24288,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6047268085807221,0.6047268085807221,0.5859785990228289,1.3216361999511719,2543.839083 +25344,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6069131515605887,0.6069131515605887,0.587737290445056,1.3217582702636719,2757.2066809999997 +26400,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6094927838175689,0.6094927838175689,0.5895162861993263,1.3217582702636719,2979.334505 +27456,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6105991622655254,0.6105991622655254,0.5896134687358237,1.3219413757324219,3211.0823579999997 +28512,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6106064326049595,0.6106064326049595,0.5910741826972655,1.3219413757324219,3451.5448549999996 +29568,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6099029323231981,0.6099029323231981,0.5935355609859342,1.3219413757324219,3700.7129539999996 +30624,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6088887437546942,0.6088887437546942,0.5952474102625339,1.3214530944824219,3958.5322249999995 +31680,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6088891694813598,0.6088891694813598,0.5975058139751561,1.3216972351074219,4224.837575 +32736,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6095921796242554,0.6095921796242554,0.5998546240309938,1.3217582702636719,4499.473663 +33792,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6043917019324673,0.6043917019324673,0.595080118632132,0.6649713516235352,4783.331389999999 +34848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6034378856142566,0.6034378856142566,0.5941773754098104,0.6650934219360352,5073.360361999999 +35904,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6029022644347269,0.6029022644347269,0.5935512429191343,0.6651544570922852,5369.406481999999 +36960,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6013690846613815,0.6013690846613815,0.5919623858291095,0.6651544570922852,5671.388488999999 +38016,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6010259108246745,0.6010259108246745,0.5912597483191937,0.6651544570922852,5979.127636999999 +39072,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6003429653707353,0.6003429653707353,0.5902279082897147,0.6648492813110352,6292.481400999999 +40128,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5961322800109652,0.5961322800109652,0.5867765456240649,0.6648492813110352,6611.499413 +41184,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5939829541315591,0.5939829541315591,0.585290407267574,0.6650323867797852,6936.132393 +42240,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5925803167688629,0.5925803167688629,0.5844470095695741,0.6650934219360352,7266.407125 +43296,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5911306155445202,0.5911306155445202,0.5835517912214992,0.6651544570922852,7602.391688 +44352,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.58959211742689,0.58959211742689,0.58246410272577,1.1046571731567383,7943.862096 +45408,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5875746030347744,0.5875746030347744,0.5808874407233396,1.3207244873046875,8291.951918 +46464,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5862083808621914,0.5862083808621914,0.5791892600330408,1.3209075927734375,8644.890712 +47520,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5879332477535302,0.5879332477535302,0.5810233099134106,1.3210525512695312,9004.012781000001 +48576,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5928152341739578,0.5928152341739578,0.5858160887305829,1.3216018676757812,9370.107000000002 +49632,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5979327436481231,0.5979327436481231,0.5906079347867982,1.3215408325195312,9743.028377000002 +50688,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6027383747311934,0.6027383747311934,0.594893758427483,1.3217239379882812,10122.858893000002 +51744,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6077923583866417,0.6077923583866417,0.5993180348311721,1.3217239379882812,10509.572003000003 +52800,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.612985094414667,0.612985094414667,0.6039181082054342,0.14382553100585938,10901.200853000002 +52848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6133366132420005,0.6133366132420005,0.604218855594392,0.14382553100585938,11292.868844000002 +408,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.23062610626220703,0.871514 +816,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,0.3262500762939453,3.583779 +1224,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8863450531479967,0.8863450531479967,0.8786592421362933,0.4218740463256836,8.686347999999999 +1632,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.891477621091355,0.891477621091355,0.8818548670971931,0.5179252624511719,16.685395 +2040,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.889651790093183,0.889651790093183,0.8812768038030504,0.6251459121704102,28.245741 +2448,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8414384961176952,0.8414384961176952,0.8420581397672002,0.7206478118896484,43.571154 +2856,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8500875656742557,0.8500875656742557,0.8345582037188519,0.8163328170776367,63.099422000000004 +3264,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8406374501992032,0.8406374501992032,0.8151418555553325,0.911895751953125,87.33095300000001 +3672,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8321983110868973,0.8321983110868973,0.8307198315203921,1.0075807571411133,116.498805 +4080,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.826182887962736,0.826182887962736,0.8123767856033619,1.128366470336914,151.118073 +4488,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.809226654780477,0.809226654780477,0.8196273526663149,1.2239294052124023,191.82030500000002 +4896,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8081716036772216,0.8081716036772216,0.815232111826365,1.3194313049316406,239.06161600000001 +5304,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8057703186875353,0.8057703186875353,0.7903391475861199,1.415055274963379,293.29488000000003 +5712,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7860269655051655,0.7860269655051656,0.7895763142947654,1.5108013153076172,355.22640600000005 +6120,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.784441902271613,0.784441902271613,0.7657785418705475,1.6062421798706055,425.24061900000004 +6528,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7585414432357898,0.7585414432357898,0.751418836389106,1.7020492553710938,503.46722600000004 +6936,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7473684210526316,0.7473684210526316,0.7484284412750404,1.797490119934082,590.6999010000001 +7344,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7565027917744791,0.7565027917744791,0.7526701844923946,1.8947620391845703,687.248946 +7752,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7577086827506129,0.7577086827506129,0.755735065870518,1.9903860092163086,793.498598 +8160,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7617355068023042,0.7617355068023042,0.7576049653668414,2.085948944091797,909.902095 +8568,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7604762460604646,0.7604762460604646,0.7596175662696861,2.2296838760375977,1036.556796 +8976,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.756991643454039,0.7569916434540391,0.7575313939177277,2.325368881225586,1173.4366320000001 +9384,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7558350207822658,0.7558350207822658,0.7548436696787698,2.420870780944824,1320.145727 +9792,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.748340312531917,0.7483403125319169,0.744390859626019,2.5164337158203125,1476.9925130000001 +10200,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7393862143347387,0.7393862143347387,0.7315892779928432,2.612057685852051,1644.1937280000002 +10608,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7196191194494201,0.7196191194494201,0.7089541376321258,2.707803726196289,1822.1938220000002 +11016,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7123921924648207,0.7123921924648208,0.7092068316988943,2.8033666610717773,2011.0989090000003 +11424,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7062943184802591,0.7062943184802591,0.6946713230955313,2.8989906311035156,2211.8042590000005 +11832,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6967289324655566,0.6967289324655566,0.690232830798306,2.994553565979004,2423.5715250000003 +12240,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7007108423890841,0.7007108423890841,0.6983689907908355,3.090177536010742,2646.6754960000003 +12648,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6969241717403337,0.6969241717403337,0.6892508246262707,3.1858625411987305,2881.7592360000003 +13056,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6836461126005362,0.6836461126005362,0.6755391962059192,3.2815475463867188,3128.5577150000004 +13464,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6793433855752804,0.6793433855752804,0.6754035266161623,3.377110481262207,3387.3558160000002 +13872,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6769519140653161,0.6769519140653161,0.6742482232309566,3.4728565216064453,3658.0697 +14280,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6728762518383641,0.6728762518383641,0.6689356443053495,3.5684194564819336,3940.688111 +14688,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6762442976782188,0.6762442976782188,0.6753292472514647,3.663982391357422,4235.610853 +15096,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6830076184166942,0.6830076184166942,0.6822311287838643,3.75966739654541,4542.8267670000005 +15504,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6818035218989873,0.6818035218989873,0.6788656596145114,3.8552303314208984,4862.152597 +15912,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6816039218150964,0.6816039218150964,0.6801525397911032,0.2705574035644531,5190.397888 +16320,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6858263373981249,0.6858263373981249,0.685191280018575,0.46213340759277344,5522.880902000001 +16728,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6896634184253004,0.6896634184253004,0.6890226069872224,0.6535873413085938,5860.018685000001 +17136,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6925007295010213,0.6925007295010213,0.6918635442211969,0.9691534042358398,6202.345681000001 +17544,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6990252522373597,0.6990252522373597,0.6986638608261282,0.2649049758911133,6547.073149000001 +17952,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7038605091638349,0.7038605091638349,0.7032543903990934,0.579315185546875,6893.121988000001 +18360,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.710114930007081,0.7101149300070809,0.70950849929648,0.2349414825439453,7240.035665000001 +18768,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.715351414717323,0.715351414717323,0.7146010079934133,0.3305654525756836,7588.155090000001 +19176,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7179139504563233,0.7179139504563233,0.7169858006379833,0.4260063171386719,7937.751954000001 +19584,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7223612316805392,0.7223612316805392,0.7214649429496548,0.5217523574829102,8289.115139000001 +19992,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7219248661897854,0.7219248661897855,0.7206428236711905,0.6287288665771484,8642.591702000002 +20400,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7231236825334575,0.7231236825334575,0.7218249685926471,0.7244749069213867,8998.461289 +46,Multiclass classification,Adaptive Random Forest,ImageSegments,0.4222222222222222,0.4222222222222222,0.3590236094437775,0.9685115814208984,1.326052 +92,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5604395604395604,0.5604395604395604,0.5746538615446178,1.0556058883666992,4.053487 +138,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5766423357664233,0.5766423357664233,0.598257695340355,1.344954490661621,8.154789999999998 +184,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6229508196721312,0.6229508196721312,0.6451744040758778,1.4133405685424805,13.553012999999998 +230,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6506550218340611,0.6506550218340611,0.6680655280025949,1.5576086044311523,20.188933 +276,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6727272727272727,0.6727272727272727,0.6900672130049011,1.7550430297851562,28.051384 +322,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7040498442367601,0.7040498442367601,0.7087861936875776,1.832967758178711,37.153949999999995 +368,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7302452316076294,0.7302452316076294,0.7285991575377422,1.971024513244629,47.432601999999996 +414,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7457627118644068,0.7457627118644068,0.7430362907281778,1.991847038269043,58.97377399999999 +460,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7342047930283224,0.7342047930283224,0.7271744800226857,1.8101978302001953,71.823928 +506,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7405940594059406,0.7405940594059406,0.7304322149686578,1.7132930755615234,85.827474 +552,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7368421052631579,0.7368421052631579,0.7267508109083203,1.5079193115234375,101.049314 +598,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7403685092127303,0.7403685092127302,0.7318978254380314,1.6471452713012695,117.42015599999999 +644,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7325038880248833,0.7325038880248833,0.7248107612258207,1.7740907669067383,135.017443 +690,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7242380261248186,0.7242380261248187,0.7153272190465999,1.913142204284668,153.656893 +736,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7251700680272108,0.725170068027211,0.7148466398758337,2.0619029998779297,173.429455 +782,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7259923175416133,0.7259923175416134,0.7134712280209221,2.0208959579467773,194.315292 +828,Multiclass classification,Adaptive Random Forest,ImageSegments,0.727932285368803,0.727932285368803,0.7177600265828429,2.224555015563965,216.352158 +874,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7353951890034365,0.7353951890034366,0.7262567978322628,2.300021171569824,239.599524 +920,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7431991294885746,0.7431991294885745,0.7345004589126253,2.4412155151367188,263.99359400000003 +966,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7471502590673575,0.7471502590673575,0.7368855656689403,2.474191665649414,289.66420500000004 +1012,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7546983184965381,0.754698318496538,0.7446216664767904,2.5655078887939453,316.44421900000003 +1058,Multiclass classification,Adaptive Random Forest,ImageSegments,0.760643330179754,0.760643330179754,0.7502594177262459,2.798956871032715,344.45448600000003 +1104,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7624660018132366,0.7624660018132366,0.7523020427630668,2.48898983001709,373.71735 +1150,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7650130548302873,0.7650130548302874,0.7555087521342715,2.3284912109375,404.061966 +1196,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7690376569037657,0.7690376569037657,0.7603504370239861,2.0560731887817383,435.51003499999996 +1242,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7719580983078163,0.7719580983078163,0.7638249032322542,2.1193370819091797,467.96964099999997 +1288,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7746697746697747,0.7746697746697747,0.7668828628349821,2.277647018432617,501.44875399999995 +1334,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7771942985746436,0.7771942985746436,0.7696789046658701,2.3871631622314453,535.887669 +1380,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7817258883248731,0.7817258883248731,0.7754511149783997,2.3104944229125977,571.357393 +1426,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7866666666666666,0.7866666666666666,0.7797171864703156,2.4089183807373047,607.784244 +1472,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7912984364377974,0.7912984364377974,0.7836430453045393,2.5425024032592773,645.2286509999999 +1518,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7963085036255768,0.7963085036255768,0.7883976288226553,2.6389265060424805,683.7451019999999 +1564,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7978246960972489,0.7978246960972489,0.790949738475821,2.283763885498047,723.3862519999999 +1610,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798011187072716,0.7980111870727161,0.7914720525222512,2.519012451171875,764.1312649999999 +1656,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7981873111782477,0.7981873111782477,0.7919320984228655,2.307619094848633,806.0160599999999 +1702,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798941798941799,0.7989417989417988,0.7945012991620244,2.40640926361084,848.960292 +1748,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8019461934745278,0.8019461934745278,0.797056036319667,2.447686195373535,893.037184 +1794,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8047964305633017,0.8047964305633019,0.7993493873930555,2.5208606719970703,938.202728 +1840,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8069603045133225,0.8069603045133223,0.8019867749609348,2.8025121688842773,984.592034 +1886,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8084880636604774,0.8084880636604774,0.8043300839686539,2.9287471771240234,1032.221691 +1932,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8114966338684619,0.8114966338684619,0.8071482324590065,2.977842330932617,1081.048247 +1978,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8148710166919575,0.8148710166919576,0.8107088256390683,3.110445022583008,1130.9949649999999 +2024,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8161146811665843,0.8161146811665844,0.8110472160986095,3.3117494583129883,1182.226115 +2070,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8173030449492509,0.8173030449492509,0.8127793203399477,2.7790603637695312,1234.703432 +2116,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8193853427895981,0.8193853427895981,0.8144282151100146,2.8652515411376953,1288.356269 +2162,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8199907450254512,0.8199907450254512,0.8150157846003385,2.925917625427246,1343.2838700000002 +2208,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8205709107385591,0.8205709107385591,0.8153449009635614,2.785597801208496,1399.3252850000001 +2254,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8175765645805593,0.8175765645805593,0.813116129924445,2.868098258972168,1456.6402850000002 +2300,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8186167899086559,0.8186167899086559,0.8144518819207099,3.062863349914551,1515.2003170000003 +2310,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8185361628410567,0.8185361628410566,0.8145347387119569,3.063481330871582,1574.1800910000002 +1056,Multiclass classification,Adaptive Random Forest,Insects,0.6682464454976303,0.6682464454976303,0.6049011732627783,7.181946754455566,32.418226 +2112,Multiclass classification,Adaptive Random Forest,Insects,0.6944576030317385,0.6944576030317385,0.6288311688548281,9.897843360900879,94.87356399999999 +3168,Multiclass classification,Adaptive Random Forest,Insects,0.6984527944426903,0.6984527944426903,0.625371849015863,13.448436737060547,186.837042 +4224,Multiclass classification,Adaptive Random Forest,Insects,0.706369879232773,0.706369879232773,0.6266042661686886,17.43436622619629,307.272577 +5280,Multiclass classification,Adaptive Random Forest,Insects,0.7107406705815495,0.7107406705815495,0.6273487761971507,20.93905258178711,452.99825 +6336,Multiclass classification,Adaptive Random Forest,Insects,0.7108129439621153,0.7108129439621153,0.6274052515282983,25.022296905517578,622.602665 +7392,Multiclass classification,Adaptive Random Forest,Insects,0.7127587606548504,0.7127587606548504,0.6273117178459473,28.819257736206055,816.020547 +8448,Multiclass classification,Adaptive Random Forest,Insects,0.7164673848703682,0.7164673848703682,0.6293431255193823,32.802799224853516,1032.257355 +9504,Multiclass classification,Adaptive Random Forest,Insects,0.721666842049879,0.721666842049879,0.63170101976307,32.88048076629639,1271.699652 +10560,Multiclass classification,Adaptive Random Forest,Insects,0.724405720238659,0.724405720238659,0.6339052025360064,29.71586036682129,1533.827375 +11616,Multiclass classification,Adaptive Random Forest,Insects,0.7244080929832114,0.7244080929832114,0.6334336343217646,33.71169948577881,1818.162347 +12672,Multiclass classification,Adaptive Random Forest,Insects,0.7225949017441402,0.7225949017441402,0.6332595599893077,29.649346351623535,2125.062078 +13728,Multiclass classification,Adaptive Random Forest,Insects,0.7416769869600058,0.7416769869600057,0.7385871869253197,11.750191688537598,2443.2361889999997 +14784,Multiclass classification,Adaptive Random Forest,Insects,0.7472096326861936,0.7472096326861937,0.7473000008879964,7.712667465209961,2772.2292589999997 +15840,Multiclass classification,Adaptive Random Forest,Insects,0.7404507860344719,0.7404507860344719,0.7427443120881612,5.854048728942871,3118.2806729999998 +16896,Multiclass classification,Adaptive Random Forest,Insects,0.73666765315182,0.73666765315182,0.7407696345938622,9.543391227722168,3480.1200929999995 +17952,Multiclass classification,Adaptive Random Forest,Insects,0.7295972369227341,0.7295972369227341,0.7347001031972082,14.625198364257812,3856.6322749999995 +19008,Multiclass classification,Adaptive Random Forest,Insects,0.739780081022781,0.7397800810227809,0.7407912307996387,5.110816955566406,4245.133706999999 +20064,Multiclass classification,Adaptive Random Forest,Insects,0.7434581069630664,0.7434581069630664,0.7402037922066672,3.8148155212402344,4646.574114999999 +21120,Multiclass classification,Adaptive Random Forest,Insects,0.745111037454425,0.7451110374544251,0.7386209934273732,7.313493728637695,5063.578879 +22176,Multiclass classification,Adaptive Random Forest,Insects,0.7462006764374295,0.7462006764374295,0.7365944363606786,12.210733413696289,5495.973683 +23232,Multiclass classification,Adaptive Random Forest,Insects,0.7483965391072274,0.7483965391072274,0.7360584061499352,11.241872787475586,5944.2105440000005 +24288,Multiclass classification,Adaptive Random Forest,Insects,0.7495779635195784,0.7495779635195785,0.7345443205753824,12.262273788452148,6407.867088000001 +25344,Multiclass classification,Adaptive Random Forest,Insects,0.7508582251509293,0.7508582251509293,0.7336140903014292,15.815716743469238,6885.995097000001 +26400,Multiclass classification,Adaptive Random Forest,Insects,0.7510890564036516,0.7510890564036516,0.7317409587301968,20.072275161743164,7378.034229000001 +27456,Multiclass classification,Adaptive Random Forest,Insects,0.7520670187579676,0.7520670187579677,0.7304776676466566,23.249674797058105,7884.702304 +28512,Multiclass classification,Adaptive Random Forest,Insects,0.7487285609063169,0.7487285609063169,0.7285292321096271,2.7024307250976562,8406.670172 +29568,Multiclass classification,Adaptive Random Forest,Insects,0.7464741096492712,0.7464741096492712,0.7309964825863351,6.2935638427734375,8940.901067 +30624,Multiclass classification,Adaptive Random Forest,Insects,0.7457793162002416,0.7457793162002416,0.7347045068936117,9.350909233093262,9487.044090000001 +31680,Multiclass classification,Adaptive Random Forest,Insects,0.745036143817671,0.745036143817671,0.7375864352537521,14.599569320678711,10044.836672000001 +32736,Multiclass classification,Adaptive Random Forest,Insects,0.7451962731021842,0.7451962731021842,0.7406480970104784,19.125198364257812,10615.117300000002 +33792,Multiclass classification,Adaptive Random Forest,Insects,0.7402858749371134,0.7402858749371134,0.7370798749337869,6.808139801025391,11202.653786000003 +34848,Multiclass classification,Adaptive Random Forest,Insects,0.7366200820730623,0.7366200820730623,0.7333315604235389,5.8602495193481445,11807.700361000003 +35904,Multiclass classification,Adaptive Random Forest,Insects,0.733921956382475,0.7339219563824751,0.7303171015411175,9.36469554901123,12429.747970000002 +36960,Multiclass classification,Adaptive Random Forest,Insects,0.7304039611461349,0.7304039611461349,0.7265687877692525,14.848862648010254,13069.446785000002 +38016,Multiclass classification,Adaptive Random Forest,Insects,0.7276864395633302,0.7276864395633302,0.7236022807953257,19.807891845703125,13727.939023000003 +39072,Multiclass classification,Adaptive Random Forest,Insects,0.7250134370760922,0.7250134370760921,0.7209989950382084,16.71243381500244,14405.601845000003 +40128,Multiclass classification,Adaptive Random Forest,Insects,0.7235028783612032,0.7235028783612032,0.7198278735760195,8.331427574157715,15101.835691000002 +41184,Multiclass classification,Adaptive Random Forest,Insects,0.723623825364835,0.723623825364835,0.7203262236880287,6.9819841384887695,15814.868539000003 +42240,Multiclass classification,Adaptive Random Forest,Insects,0.7240464973129098,0.7240464973129098,0.7211005399097123,10.71219539642334,16543.112989 +43296,Multiclass classification,Adaptive Random Forest,Insects,0.7245409400623629,0.7245409400623629,0.721844297210525,10.330558776855469,17285.760894000003 +44352,Multiclass classification,Adaptive Random Forest,Insects,0.7248765529525828,0.7248765529525828,0.7223628081683402,13.299851417541504,18041.694028 +45408,Multiclass classification,Adaptive Random Forest,Insects,0.7254167859581122,0.7254167859581122,0.7228420559832612,15.662115097045898,18810.181113000002 +46464,Multiclass classification,Adaptive Random Forest,Insects,0.7263844349267159,0.7263844349267159,0.7236482152790997,19.25161361694336,19591.516438000002 +47520,Multiclass classification,Adaptive Random Forest,Insects,0.7265304404553968,0.7265304404553967,0.7240124567772878,14.065608024597168,20387.990038000004 +48576,Multiclass classification,Adaptive Random Forest,Insects,0.7304374678332476,0.7304374678332476,0.7281756207358935,7.354809761047363,21197.413376000004 +49632,Multiclass classification,Adaptive Random Forest,Insects,0.7344603171404969,0.7344603171404969,0.7322565876518081,7.006095886230469,22016.972025000003 +50688,Multiclass classification,Adaptive Random Forest,Insects,0.7380590684001815,0.7380590684001815,0.7356981427827818,10.14159107208252,22847.182754 +51744,Multiclass classification,Adaptive Random Forest,Insects,0.7420134124422627,0.7420134124422627,0.7394134340953542,13.563420295715332,23688.037606 +52800,Multiclass classification,Adaptive Random Forest,Insects,0.7451466883842497,0.7451466883842497,0.7430487162081567,0.3614501953125,24535.706056000003 +52848,Multiclass classification,Adaptive Random Forest,Insects,0.7453781671618067,0.7453781671618067,0.7433023109254195,0.36179351806640625,25383.518073000003 +408,Multiclass classification,Adaptive Random Forest,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.3354053497314453,3.23067 +816,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730061349693252,0.9730061349693252,0.8116978142719798,0.988037109375,11.21298 +1224,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730171708912511,0.9730171708912511,0.9579161898493525,2.195523262023926,25.427007 +1632,Multiclass classification,Adaptive Random Forest,Keystroke,0.9693439607602697,0.9693439607602697,0.9069773132409142,3.526730537414551,46.453053999999995 +2040,Multiclass classification,Adaptive Random Forest,Keystroke,0.9666503187837175,0.9666503187837175,0.9303026980117671,5.496582984924316,74.431187 +2448,Multiclass classification,Adaptive Random Forest,Keystroke,0.9660809154066203,0.9660809154066203,0.9555178664837441,2.29970645904541,107.969459 +2856,Multiclass classification,Adaptive Random Forest,Keystroke,0.9691768826619965,0.9691768826619965,0.9674134048328416,3.376467704772949,146.96126800000002 +3264,Multiclass classification,Adaptive Random Forest,Keystroke,0.9672080907140668,0.9672080907140668,0.9546197483047236,4.62060546875,192.073824 +3672,Multiclass classification,Adaptive Random Forest,Keystroke,0.9684009806592209,0.968400980659221,0.9654409635782653,3.119338035583496,243.354323 +4080,Multiclass classification,Adaptive Random Forest,Keystroke,0.9644520715861731,0.9644520715861731,0.95030552665756,4.705347061157227,301.433133 +4488,Multiclass classification,Adaptive Random Forest,Keystroke,0.9661243592600847,0.9661243592600847,0.9659906155964958,1.508072853088379,365.412759 +4896,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677221654749745,0.9677221654749745,0.96768641848376,2.487558364868164,434.672843 +5304,Multiclass classification,Adaptive Random Forest,Keystroke,0.9685083914765227,0.9685083914765227,0.9677400809149086,2.8771514892578125,509.85413800000003 +5712,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690071791279986,0.9690071791279986,0.9686984277929261,4.140267372131348,591.7133210000001 +6120,Multiclass classification,Adaptive Random Forest,Keystroke,0.9671514953423762,0.9671514953423762,0.9635575047511442,5.121949195861816,681.1937680000001 +6528,Multiclass classification,Adaptive Random Forest,Keystroke,0.9675195342423778,0.9675195342423778,0.9673223823066149,2.1385393142700195,777.2102420000001 +6936,Multiclass classification,Adaptive Random Forest,Keystroke,0.9685652487382841,0.9685652487382841,0.9688652926813892,2.7864933013916016,879.0640510000001 +7344,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686776521857552,0.9686776521857552,0.9682274153773371,3.314570426940918,987.0629210000001 +7752,Multiclass classification,Adaptive Random Forest,Keystroke,0.9682621597213262,0.9682621597213262,0.9674704101631952,4.690197944641113,1101.141854 +8160,Multiclass classification,Adaptive Random Forest,Keystroke,0.96727540139723,0.96727540139723,0.9662379529396136,5.223731994628906,1221.487909 +8568,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677833547332788,0.9677833547332788,0.9678822443058487,4.885932922363281,1347.980617 +8976,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686908077994429,0.9686908077994429,0.9690861219789195,6.402636528015137,1480.694289 +9384,Multiclass classification,Adaptive Random Forest,Keystroke,0.9683470105509965,0.9683470105509965,0.9680699356268633,6.928671836853027,1620.259773 +9792,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686446736799101,0.9686446736799101,0.9687197530276813,5.552419662475586,1766.078849 +10200,Multiclass classification,Adaptive Random Forest,Keystroke,0.9684282772820865,0.9684282772820866,0.9682582636163196,2.695918083190918,1917.758924 +10608,Multiclass classification,Adaptive Random Forest,Keystroke,0.9673800320543038,0.9673800320543038,0.9668238422002586,3.239151954650879,2074.190769 +11016,Multiclass classification,Adaptive Random Forest,Keystroke,0.9676804357694053,0.9676804357694053,0.9678040910458205,4.023995399475098,2235.420867 +11424,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677842948437363,0.9677842948437363,0.9678364439490078,4.695375442504883,2402.1641919999997 +11832,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677119432000676,0.9677119432000676,0.9677086079179034,5.258674621582031,2574.8706989999996 +12240,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687065936759539,0.9687065936759539,0.9690716756618885,6.001680374145508,2753.3671299999996 +12648,Multiclass classification,Adaptive Random Forest,Keystroke,0.9688463667272871,0.9688463667272871,0.9689334511448673,5.217698097229004,2937.7699639999996 +13056,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687476062811183,0.9687476062811183,0.968764477893114,5.266051292419434,3127.8024029999997 +13464,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687291094109782,0.9687291094109782,0.9687736841624996,6.279603958129883,3323.370395 +13872,Multiclass classification,Adaptive Random Forest,Keystroke,0.9695047220820416,0.9695047220820416,0.9697384724636318,4.041820526123047,3524.041026 +14280,Multiclass classification,Adaptive Random Forest,Keystroke,0.9682750892919673,0.9682750892919673,0.9680357071263168,2.1731691360473633,3729.110149 +14688,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686797848437394,0.9686797848437394,0.9688099431838716,2.4900379180908203,3938.3384300000002 +15096,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692613448161643,0.9692613448161643,0.9694122553904638,2.7789316177368164,4151.996270000001 +15504,Multiclass classification,Adaptive Random Forest,Keystroke,0.9694897761723538,0.9694897761723538,0.969571649124791,3.946505546569824,4370.227344000001 +15912,Multiclass classification,Adaptive Random Forest,Keystroke,0.9694550939601534,0.9694550939601534,0.9694916672888816,4.345325469970703,4594.050341000001 +16320,Multiclass classification,Adaptive Random Forest,Keystroke,0.9695447024940254,0.9695447024940254,0.9695954968773723,3.909954071044922,4823.361799000001 +16728,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692114545345848,0.9692114545345848,0.9692084456743588,1.764338493347168,5057.2303470000015 +17136,Multiclass classification,Adaptive Random Forest,Keystroke,0.9696527575138605,0.9696527575138605,0.9697329621491685,1.7167367935180664,5295.013901000001 +17544,Multiclass classification,Adaptive Random Forest,Keystroke,0.9696745140511885,0.9696745140511885,0.9697082565052514,2.8143720626831055,5537.319837000001 +17952,Multiclass classification,Adaptive Random Forest,Keystroke,0.968748259149908,0.968748259149908,0.968705960089485,2.951136589050293,5784.484584000001 +18360,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690070265264993,0.9690070265264993,0.9690448168177233,3.5441465377807617,6036.117893000001 +18768,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690946874833485,0.9690946874833485,0.9691164520527107,4.379698753356934,6292.729193000001 +19176,Multiclass classification,Adaptive Random Forest,Keystroke,0.968761408083442,0.968761408083442,0.9687617227117352,3.8120603561401367,6554.348831000001 +19584,Multiclass classification,Adaptive Random Forest,Keystroke,0.9689526630240515,0.9689526630240515,0.9689629146490384,2.019772529602051,6819.891372000001 +19992,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692861787804512,0.9692861787804512,0.9692901573177237,1.2564506530761719,7089.584863000001 +20400,Multiclass classification,Adaptive Random Forest,Keystroke,0.9691161331437815,0.9691161331437815,0.9691108096285476,1.6354646682739258,7363.046142000001 +46,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.5333333333333333,0.5333333333333333,0.5005728607232367,0.8510866165161133,0.941842 +92,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6153846153846154,0.6153846153846154,0.596131344383025,1.5052366256713867,2.918201 +138,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6496350364963503,0.6496350364963503,0.6567305057749026,2.146304130554199,6.147886 +184,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6994535519125683,0.6994535519125683,0.7070190759413217,2.7665939331054688,10.824064 +230,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7379912663755459,0.7379912663755459,0.7433871451842025,3.2484235763549805,16.931166 +276,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7490909090909091,0.7490909090909091,0.7566070103930901,3.776392936706543,24.729994 +322,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7694704049844237,0.7694704049844237,0.7681721604320974,4.142314910888672,34.173162000000005 +368,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.784741144414169,0.7847411444141691,0.7789718513534348,4.497910499572754,45.384105000000005 +414,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7990314769975787,0.7990314769975787,0.7943771701942021,4.869846343994141,58.265676000000006 +460,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7973856209150327,0.7973856209150327,0.7916511033189314,5.3911848068237305,73.08883800000001 +506,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.805940594059406,0.805940594059406,0.8010859843658406,5.806554794311523,89.87625100000001 +552,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8076225045372051,0.8076225045372051,0.8036838079612314,6.295863151550293,108.59930000000001 +598,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8174204355108877,0.8174204355108878,0.8156009215135775,6.727802276611328,129.48595400000002 +644,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8211508553654744,0.8211508553654744,0.8207645722848749,7.18087100982666,152.525841 +690,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8229317851959361,0.8229317851959362,0.8226135245892084,7.561182022094727,177.86541200000002 +736,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8231292517006803,0.8231292517006803,0.8228959515200417,7.975464820861816,205.49957600000002 +782,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8309859154929577,0.8309859154929577,0.8306123687436626,8.301925659179688,235.39408200000003 +828,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8343409915356711,0.834340991535671,0.835521648488366,8.722038269042969,267.718494 +874,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8407789232531501,0.8407789232531501,0.8414965916969209,9.057206153869629,302.46008700000004 +920,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8443960826985855,0.8443960826985855,0.8446110045111287,9.382828712463379,339.623661 +966,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8466321243523316,0.8466321243523316,0.8462590694093756,9.696897506713867,379.342347 +1012,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8516320474777448,0.8516320474777448,0.8504483916737715,9.949009895324707,421.625642 +1058,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8571428571428571,0.8571428571428571,0.8557487568785946,10.2299222946167,466.542637 +1104,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8603807796917498,0.8603807796917498,0.8594481550185353,10.524299621582031,514.218423 +1150,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8624891209747607,0.8624891209747607,0.8612253786789881,10.737759590148926,564.599929 +1196,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8652719665271966,0.8652719665271966,0.8642881992026393,11.010127067565918,617.836337 +1242,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8670427074939565,0.8670427074939565,0.8663181473795101,11.261144638061523,674.05967 +1288,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8694638694638694,0.8694638694638694,0.8687259920464652,11.505732536315918,733.385389 +1334,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8709677419354839,0.8709677419354839,0.870193396369452,11.826444625854492,796.067675 +1380,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8745467730239304,0.8745467730239304,0.874089581073643,12.086430549621582,861.584672 +1426,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8771929824561403,0.8771929824561403,0.8759011931352845,12.29430866241455,930.2045189999999 +1472,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8796736913664174,0.8796736913664174,0.877566397675441,12.500163078308105,1001.8141389999998 +1518,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8826631509558339,0.8826631509558339,0.8803270226288138,12.740474700927734,1076.4882149999999 +1564,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8841970569417786,0.8841970569417786,0.8822041640143002,12.987508773803711,1154.350794 +1610,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.886886264760721,0.886886264760721,0.8850836875294148,13.252826690673828,1235.463166 +1656,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.888821752265861,0.888821752265861,0.8870702351165313,13.500110626220703,1319.86391 +1702,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8912404467960023,0.8912404467960025,0.8905987472429445,13.767583847045898,1407.541035 +1748,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8929593589009731,0.892959358900973,0.8920318510221457,14.030475616455078,1498.676265 +1794,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.894032348020078,0.894032348020078,0.8925886559949978,14.271255493164062,1593.100327 +1840,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8945078847199565,0.8945078847199565,0.8931986390525462,14.574835777282715,1691.005218 +1886,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.896551724137931,0.896551724137931,0.8956464025201587,14.834091186523438,1792.408733 +1932,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8964267219057483,0.8964267219057483,0.8951782213786073,15.134613037109375,1897.156299 +1978,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8973191704602934,0.8973191704602934,0.8961901832930852,15.326050758361816,2005.31409 +2024,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8986653484923381,0.8986653484923381,0.8970310627029995,15.549851417541504,2116.877653 +2070,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8994683421942967,0.8994683421942967,0.8980105869909577,15.816215515136719,2232.114727 +2116,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.900709219858156,0.900709219858156,0.8989778942952686,15.957537651062012,2350.8625340000003 +2162,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9000462748727441,0.9000462748727441,0.8982611856050026,16.206623077392578,2473.2186990000005 +2208,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9012233801540552,0.9012233801540552,0.8993036839855942,16.400617599487305,2599.1257530000003 +2254,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9014647137150466,0.9014647137150466,0.8999821457114682,16.693093299865723,2728.6827460000004 +2300,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9016963897346673,0.9016963897346673,0.9003174232892135,16.988688468933105,2861.834306 +2310,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9016890428757037,0.9016890428757037,0.9003808534937335,17.050235748291016,2997.696193 +1056,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6511848341232227,0.6511848341232227,0.5805974192561721,27.882014274597168,41.422615 +2112,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6830885836096636,0.6830885836096636,0.6159001145696381,53.900901794433594,137.16292 +3168,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6889801073571203,0.6889801073571203,0.6135176771695448,79.45620250701904,291.10856 +4224,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6954771489462468,0.6954771489462468,0.6159765684907534,104.90542316436768,501.70617000000004 +5280,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7003220306876302,0.7003220306876302,0.6217575035584229,130.7021541595459,768.289754 +6336,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7021310181531176,0.7021310181531176,0.622391174421368,156.0168752670288,1090.319759 +7392,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7027465836828576,0.7027465836828576,0.6232948240709647,180.83974838256836,1466.44078 +8448,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7040369361903634,0.7040369361903634,0.6235946437988805,205.63252925872803,1896.370643 +9504,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7105124697463959,0.7105124697463959,0.6284709935917355,229.19151210784912,2381.431795 +10560,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7140827729898664,0.7140827729898664,0.6302854833117341,253.17632389068604,2925.98814 +11616,Multiclass classification,Aggregated Mondrian Forest,Insects,0.71562634524322,0.7156263452432199,0.6305326785921538,277.4567346572876,3530.8520359999998 +12672,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7145450240707126,0.7145450240707125,0.6284185449457835,301.7114896774292,4201.052974 +13728,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7057623661397247,0.7057623661397247,0.6885364031919957,327.2237205505371,4936.820951 +14784,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6967462625989312,0.6967462625989312,0.69194472505998,352.6018476486206,5738.465999 +15840,Multiclass classification,Aggregated Mondrian Forest,Insects,0.676684134099375,0.676684134099375,0.673854549025314,384.9730758666992,6613.285946 +16896,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6698431488606097,0.6698431488606097,0.668750254945471,415.2214603424072,7559.921071 +17952,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6646983454960727,0.6646983454960727,0.6646134205077884,444.32067584991455,8589.520858 +19008,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6620192560635555,0.6620192560635555,0.6605985532750915,472.75781440734863,9705.665905 +20064,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6597717190848826,0.6597717190848826,0.6570293922418718,499.48760890960693,10901.076562 +21120,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6539608882996354,0.6539608882996354,0.6496192149174075,528.8777961730957,12166.701144 +22176,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6547463359639233,0.6547463359639233,0.6484047117859243,557.1920728683472,13501.384366 +23232,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6583444535319185,0.6583444535319185,0.6499882024630633,584.0554361343384,14901.095396 +24288,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6611767612302878,0.6611767612302878,0.6506059068013808,610.3706150054932,16366.700533000001 +25344,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6659827171210986,0.6659827171210986,0.6532433614752314,635.6853046417236,17901.739193 +26400,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6702526610856472,0.6702526610856472,0.6554263220708306,660.4025926589966,19504.786084 +27456,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6745947914769623,0.6745947914769623,0.6575507550972549,684.36501121521,21172.086014 +28512,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6705482094630143,0.6705482094630143,0.6539581966383304,712.6770572662354,22902.746936 +29568,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6644231744850678,0.6644231744850678,0.6512239029866641,743.5559530258179,24691.477434 +30624,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6622799856317148,0.6622799856317148,0.6527566844616065,772.5478630065918,26538.585641 +31680,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6621736797247388,0.6621736797247388,0.6557760097374935,800.5439138412476,28440.416189000003 +32736,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6623797159004124,0.6623797159004124,0.6584479912704261,827.4998264312744,30418.714512000002 +33792,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6575123553608949,0.6575123553608949,0.6541419435809196,857.7161102294922,32439.386127 +34848,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6519069073377909,0.6519069073377909,0.6481893367707658,888.8327789306641,34499.720344 +35904,Multiclass classification,Aggregated Mondrian Forest,Insects,0.647550343982397,0.647550343982397,0.643407015045196,919.6311988830566,36599.09766 +36960,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6444438431775752,0.6444438431775752,0.6400224052225335,949.7819452285767,38735.650911 +38016,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6425358411153492,0.6425358411153492,0.6377821595167165,979.4456567764282,40896.763764999996 +39072,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6414476209976709,0.6414476209976709,0.6370415360917451,1009.0255756378174,43085.847267 +40128,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6409898572033793,0.6409898572033793,0.636858231937463,1037.841980934143,45303.144751 +41184,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6414782798727631,0.6414782798727631,0.637272014233453,1065.1163549423218,47540.369251 +42240,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6428419233409882,0.6428419233409882,0.6385110475108609,1091.8334274291992,49803.522006 +43296,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6441159487238711,0.6441159487238711,0.6396283228479406,1118.1560363769531,52086.93226 +44352,Multiclass classification,Aggregated Mondrian Forest,Insects,0.645058735992424,0.645058735992424,0.6403851797193834,1144.4119939804077,54391.61903 +45408,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6469266853128373,0.6469266853128373,0.6418265850265934,1169.9601306915283,56719.958571 +46464,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6487742935238792,0.6487742935238792,0.643191402092947,1194.6403436660767,59073.094705 +47520,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6459521454576065,0.6459521454576065,0.6406800374556137,1224.6073780059814,61451.967815 +48576,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6443643849716932,0.6443643849716932,0.6398250343320808,1254.4350862503052,63857.884093 +49632,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6446172754931394,0.6446172754931394,0.6406945505071863,1282.3891849517822,66293.298766 +50688,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6461222798745241,0.6461222798745241,0.6426238276925219,1309.4736614227295,68755.018108 +51744,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6489186943161394,0.6489186943161394,0.6457243405011626,1334.444143295288,71244.151045 +52800,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6470577094263149,0.6470577094263149,0.6443966707674731,1363.999231338501,73759.934152 +52848,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6469809071470471,0.6469809071470471,0.6443518314696601,1365.409776687622,76295.692169 +408,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9901719901719902,0.9901719901719902,0.8308395677472984,0.12276840209960938,1.485322 +816,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9914110429447853,0.9914110429447853,0.960934413925625,0.41584110260009766,6.729082 +1224,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9893704006541292,0.9893704006541292,0.9580466011674303,1.2467107772827148,20.148490000000002 +1632,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9889638258736971,0.9889638258736971,0.9786672150923964,2.28104305267334,50.264957 +2040,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.988719960765081,0.988719960765081,0.9803510904896324,3.352717399597168,91.64343099999999 +2448,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9885574172456069,0.9885574172456069,0.9830468792370581,4.983606338500977,148.278076 +2856,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9852889667250437,0.9852889667250437,0.9737767108051043,6.963967323303223,227.073424 +3264,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9825314128102973,0.9825314128102973,0.9734338986941852,9.8344087600708,324.702985 +3672,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9822936529555979,0.9822936529555979,0.9788760747631073,12.7888765335083,446.35643500000003 +4080,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9806325079676391,0.9806325079676391,0.9749453255203757,16.445659637451172,594.71846 +4488,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9801649208825496,0.9801649208825496,0.9779116862524243,20.943636894226074,768.8230350000001 +4896,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9801838610827375,0.9801838610827375,0.978782474664832,24.856953620910645,967.6114420000001 +5304,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9768055817461814,0.9768055817461814,0.9702080932270808,28.10527801513672,1191.0213970000002 +5712,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9746104009805638,0.9746104009805638,0.9718234131704067,32.14579772949219,1440.171835 +6120,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9697663016832816,0.9697663016832816,0.9621279568251032,36.40912055969238,1717.813161 +6528,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9656810173127011,0.9656810173127011,0.9634765255010708,42.20043754577637,2023.330442 +6936,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9653929343907715,0.9653929343907715,0.9646253117338193,46.972042083740234,2355.811343 +7344,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9635026555903582,0.9635026555903582,0.9611034281810401,50.8284969329834,2716.693574 +7752,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9610372855115469,0.9610372855115469,0.9585597537512924,55.062747955322266,3108.019641 +8160,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9593087388160314,0.9593087388160314,0.9577319445930262,59.75967216491699,3529.9614579999998 +8568,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9598459203922026,0.9598459203922026,0.9601713780248281,65.88526916503906,3981.5521329999997 +8976,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.959108635097493,0.959108635097493,0.9586518345557712,71.85272026062012,4465.222941 +9384,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9573697111797932,0.9573697111797932,0.9561353164275519,78.18439388275146,4984.801354 +9792,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9555714431620876,0.9555714431620876,0.9546392488298882,85.86389446258545,5537.4752260000005 +10200,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9486224139621532,0.9486224139621532,0.9433099305923253,94.35744285583496,6127.477763000001 +10608,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9431507495050439,0.9431507495050439,0.9403442056943527,104.1574821472168,6756.480909000001 +11016,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9408987743985474,0.9408987743985474,0.9399975161043574,113.0038013458252,7421.284759000001 +11424,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9380197846450145,0.9380197846450145,0.936341059397272,121.46645069122314,8125.715779000001 +11832,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9322965091708224,0.9322965091708224,0.9294143034054053,131.1031150817871,8872.899296000001 +12240,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9326742380913473,0.9326742380913473,0.9327603226303838,137.88959789276123,9652.703167000001 +12648,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.927571756147703,0.927571756147703,0.9249549620362734,145.5888376235962,10475.658214000001 +13056,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9247797778628878,0.9247797778628878,0.9237072084771099,154.53871536254883,11346.213643000001 +13464,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9238654088984625,0.9238654088984625,0.9233692422863465,161.3583574295044,12266.045404 +13872,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9202653017086007,0.9202653017086007,0.9191663953636944,170.12918186187744,13231.481182 +14280,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9163106660130261,0.9163106660130261,0.9150341930556871,179.0350112915039,14245.599713 +14688,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9161162933206237,0.9161162933206237,0.9160540991607554,184.834698677063,15311.95464 +15096,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9145412388208016,0.9145412388208016,0.91429667624259,191.58009719848633,16433.239395 +15504,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9105979487841063,0.9105979487841064,0.9097163708309961,200.08039951324463,17613.909715 +15912,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9068568914587393,0.9068568914587393,0.9060681758481206,210.5000762939453,18848.20155 +16320,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9031190636681169,0.9031190636681169,0.9023660107991418,221.55222129821777,20131.794746000003 +16728,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9005799007592515,0.9005799007592515,0.9001704241319546,231.28063201904297,21466.799965000002 +17136,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8989203384884739,0.8989203384884739,0.8987537815839572,248.11264038085938,22840.808564000003 +17544,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.893746793592886,0.8937467935928861,0.892807745348426,267.53482723236084,24265.711089000004 +17952,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8894212021614395,0.8894212021614395,0.8884694521151855,281.7739496231079,25739.620728000005 +18360,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8911705430579008,0.8911705430579007,0.8908032768807751,288.0978307723999,27256.627666000004 +18768,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8911387009111739,0.8911387009111739,0.8906428613252552,296.05272102355957,28820.747713000004 +19176,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8886049543676662,0.8886049543676662,0.8879368647002966,307.68266773223877,30435.231458000006 +19584,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8895470561201042,0.8895470561201042,0.889061241536932,313.4344787597656,32089.799369000008 +19992,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8862488119653844,0.8862488119653844,0.8855123768505595,324.9442596435547,33786.733744000005 +20400,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8810726015981175,0.8810726015981175,0.8799282628097613,338.1390075683594,35528.434162000005 +46,Multiclass classification,Streaming Random Patches,ImageSegments,0.35555555555555557,0.35555555555555557,0.24684873949579833,2.5926971435546875,5.672061 +92,Multiclass classification,Streaming Random Patches,ImageSegments,0.5274725274725275,0.5274725274725275,0.5392220990960486,2.5963096618652344,17.740863 +138,Multiclass classification,Streaming Random Patches,ImageSegments,0.5401459854014599,0.5401459854014599,0.5661177456005042,2.5979232788085938,35.937815 +184,Multiclass classification,Streaming Random Patches,ImageSegments,0.5956284153005464,0.5956284153005464,0.6144104879239446,2.6004638671875,59.975895 +230,Multiclass classification,Streaming Random Patches,ImageSegments,0.6200873362445415,0.6200873362445415,0.6319742698014011,2.6008224487304688,89.681265 +276,Multiclass classification,Streaming Random Patches,ImageSegments,0.6327272727272727,0.6327272727272727,0.6440706793955739,2.601276397705078,125.043898 +322,Multiclass classification,Streaming Random Patches,ImageSegments,0.6573208722741433,0.6573208722741433,0.6535377647060517,2.6028709411621094,166.036362 +368,Multiclass classification,Streaming Random Patches,ImageSegments,0.6784741144414169,0.6784741144414169,0.6717418242612484,2.6031723022460938,212.735146 +414,Multiclass classification,Streaming Random Patches,ImageSegments,0.6900726392251816,0.6900726392251816,0.6823551618652942,2.603717803955078,265.17548899999997 +460,Multiclass classification,Streaming Random Patches,ImageSegments,0.6971677559912854,0.6971677559912854,0.686858403065277,2.6037940979003906,323.486791 +506,Multiclass classification,Streaming Random Patches,ImageSegments,0.699009900990099,0.699009900990099,0.6869845800125663,2.604084014892578,387.600808 +552,Multiclass classification,Streaming Random Patches,ImageSegments,0.6987295825771325,0.6987295825771325,0.6895132041566728,2.6040496826171875,457.32381699999996 +598,Multiclass classification,Streaming Random Patches,ImageSegments,0.7035175879396985,0.7035175879396985,0.6939747146282641,2.6041183471679688,532.682096 +644,Multiclass classification,Streaming Random Patches,ImageSegments,0.6998444790046656,0.6998444790046656,0.6913714585468268,2.6053123474121094,613.490084 +690,Multiclass classification,Streaming Random Patches,ImageSegments,0.7024673439767779,0.7024673439767779,0.6944906634267102,2.6058921813964844,699.880084 +736,Multiclass classification,Streaming Random Patches,ImageSegments,0.7020408163265306,0.7020408163265306,0.69548275919944,2.605987548828125,791.65329 +782,Multiclass classification,Streaming Random Patches,ImageSegments,0.706786171574904,0.706786171574904,0.6991539785967766,2.6064224243164062,888.981823 +828,Multiclass classification,Streaming Random Patches,ImageSegments,0.7085852478839177,0.7085852478839177,0.70309750989463,2.6064682006835938,991.6474969999999 +874,Multiclass classification,Streaming Random Patches,ImageSegments,0.715922107674685,0.7159221076746849,0.7073525059690206,2.6064682006835938,1099.573439 +920,Multiclass classification,Streaming Random Patches,ImageSegments,0.7170837867247007,0.7170837867247007,0.707165908654469,2.6064682006835938,1212.915424 +966,Multiclass classification,Streaming Random Patches,ImageSegments,0.7160621761658031,0.716062176165803,0.7063689525089133,2.6064682006835938,1331.5593390000001 +1012,Multiclass classification,Streaming Random Patches,ImageSegments,0.7151335311572701,0.7151335311572701,0.7047830593764105,2.6064910888671875,1455.3800170000002 +1058,Multiclass classification,Streaming Random Patches,ImageSegments,0.7152317880794702,0.7152317880794702,0.7037726227430311,2.606658935546875,1584.3270810000001 +1104,Multiclass classification,Streaming Random Patches,ImageSegments,0.71441523118767,0.71441523118767,0.7026447500373862,2.6067771911621094,1718.2459470000001 +1150,Multiclass classification,Streaming Random Patches,ImageSegments,0.7162750217580505,0.7162750217580505,0.7030218527348165,2.6067771911621094,1857.0229310000002 +1196,Multiclass classification,Streaming Random Patches,ImageSegments,0.7179916317991631,0.7179916317991631,0.705575475090573,2.379610061645508,2000.273372 +1242,Multiclass classification,Streaming Random Patches,ImageSegments,0.7155519742143432,0.7155519742143431,0.7053749246401603,3.185004234313965,2147.034729 +1288,Multiclass classification,Streaming Random Patches,ImageSegments,0.7156177156177156,0.7156177156177156,0.7041730806550314,3.633350372314453,2296.192092 +1334,Multiclass classification,Streaming Random Patches,ImageSegments,0.7149287321830458,0.7149287321830458,0.7045092702498074,4.368736267089844,2447.850078 +1380,Multiclass classification,Streaming Random Patches,ImageSegments,0.7186366932559826,0.7186366932559827,0.7102131417787841,4.724300384521484,2601.871177 +1426,Multiclass classification,Streaming Random Patches,ImageSegments,0.7256140350877193,0.7256140350877193,0.7174099613082184,4.89253044128418,2758.036552 +1472,Multiclass classification,Streaming Random Patches,ImageSegments,0.7273963290278722,0.7273963290278722,0.7183919320082559,5.412370681762695,2916.512936 +1518,Multiclass classification,Streaming Random Patches,ImageSegments,0.7211601845748187,0.7211601845748187,0.7136134581802791,6.487729072570801,3077.492565 +1564,Multiclass classification,Streaming Random Patches,ImageSegments,0.7172104926423545,0.7172104926423546,0.7129536273040751,6.405126571655273,3241.109 +1610,Multiclass classification,Streaming Random Patches,ImageSegments,0.7209446861404599,0.7209446861404599,0.7163536024764182,6.857941627502441,3407.317179 +1656,Multiclass classification,Streaming Random Patches,ImageSegments,0.7238670694864048,0.7238670694864048,0.7196892738307762,7.034061431884766,3576.2587320000002 +1702,Multiclass classification,Streaming Random Patches,ImageSegments,0.7260435038212816,0.7260435038212816,0.7238533950478148,7.623349189758301,3747.8646120000003 +1748,Multiclass classification,Streaming Random Patches,ImageSegments,0.7309673726388094,0.7309673726388093,0.7286270619416129,8.106144905090332,3922.0639630000005 +1794,Multiclass classification,Streaming Random Patches,ImageSegments,0.7361963190184049,0.7361963190184049,0.7329274067865035,8.185744285583496,4098.8506050000005 +1840,Multiclass classification,Streaming Random Patches,ImageSegments,0.7389885807504079,0.7389885807504077,0.7360694376974826,8.929247856140137,4278.2789060000005 +1886,Multiclass classification,Streaming Random Patches,ImageSegments,0.7411140583554376,0.7411140583554376,0.7396669191579938,9.100563049316406,4460.600751000001 +1932,Multiclass classification,Streaming Random Patches,ImageSegments,0.7431382703262558,0.7431382703262558,0.7411378754700444,9.223885536193848,4645.683986000001 +1978,Multiclass classification,Streaming Random Patches,ImageSegments,0.7465857359635811,0.746585735963581,0.744200926808846,9.401692390441895,4833.458731000001 +2024,Multiclass classification,Streaming Random Patches,ImageSegments,0.7508650519031141,0.7508650519031143,0.7476945996538615,9.481804847717285,5024.230846 +2070,Multiclass classification,Streaming Random Patches,ImageSegments,0.7549540840985983,0.7549540840985983,0.7524477298078486,9.431160926818848,5217.969956000001 +2116,Multiclass classification,Streaming Random Patches,ImageSegments,0.7583924349881797,0.7583924349881797,0.7554386161495508,9.549637794494629,5414.604524000001 +2162,Multiclass classification,Streaming Random Patches,ImageSegments,0.7607589079130033,0.7607589079130033,0.7577216433051415,10.151451110839844,5614.378132000002 +2208,Multiclass classification,Streaming Random Patches,ImageSegments,0.7648391481649298,0.7648391481649298,0.7614528787516565,9.14443588256836,5817.274926000002 +2254,Multiclass classification,Streaming Random Patches,ImageSegments,0.7652019529516201,0.7652019529516201,0.762166830901651,8.801234245300293,6023.198429000002 +2300,Multiclass classification,Streaming Random Patches,ImageSegments,0.7672901261418008,0.7672901261418008,0.7647372124971393,8.857858657836914,6232.074878000002 +2310,Multiclass classification,Streaming Random Patches,ImageSegments,0.7669987007362494,0.7669987007362494,0.7647069285577738,8.926526069641113,6441.814766000002 +1056,Multiclass classification,Streaming Random Patches,Insects,0.6388625592417062,0.6388625592417062,0.6031100134310133,8.730474472045898,177.190345 +2112,Multiclass classification,Streaming Random Patches,Insects,0.659403126480341,0.659403126480341,0.6244477305834598,21.138185501098633,477.66892900000005 +3168,Multiclass classification,Streaming Random Patches,Insects,0.6722450268392801,0.6722450268392801,0.6321534006670183,28.433568000793457,884.814326 +4224,Multiclass classification,Streaming Random Patches,Insects,0.680322045938906,0.680322045938906,0.6340126191391743,39.2259521484375,1406.7405990000002 +5280,Multiclass classification,Streaming Random Patches,Insects,0.6878196628149271,0.6878196628149271,0.6395508722492685,32.51231288909912,2046.4837620000003 +6336,Multiclass classification,Streaming Random Patches,Insects,0.6876085240726125,0.6876085240726125,0.641396967542699,34.576416969299316,2792.1074670000003 +7392,Multiclass classification,Streaming Random Patches,Insects,0.6924638073332431,0.6924638073332431,0.6467777725107727,40.06835174560547,3634.422347 +8448,Multiclass classification,Streaming Random Patches,Insects,0.6949212738250267,0.6949212738250267,0.6476372139610082,43.01965808868408,4571.1184410000005 +9504,Multiclass classification,Streaming Random Patches,Insects,0.6992528675155214,0.6992528675155214,0.6494082466298291,45.71251583099365,5608.992399000001 +10560,Multiclass classification,Streaming Random Patches,Insects,0.7013921772895161,0.7013921772895161,0.6506452100316108,50.310431480407715,6744.395149000001 +11616,Multiclass classification,Streaming Random Patches,Insects,0.7043478260869566,0.7043478260869566,0.6524912605091605,59.279197692871094,7964.619750000001 +12672,Multiclass classification,Streaming Random Patches,Insects,0.7079946334148843,0.7079946334148843,0.6596828376773001,72.61201000213623,9269.589572 +13728,Multiclass classification,Streaming Random Patches,Insects,0.7208421359364756,0.7208421359364756,0.7145906055666686,38.78250694274902,10625.218377000001 +14784,Multiclass classification,Streaming Random Patches,Insects,0.7285395386592708,0.7285395386592708,0.7256542392368915,15.546477317810059,12027.231464 +15840,Multiclass classification,Streaming Random Patches,Insects,0.7206263021655408,0.7206263021655408,0.7196216319492748,14.88278579711914,13491.676191 +16896,Multiclass classification,Streaming Random Patches,Insects,0.7171352471145309,0.7171352471145309,0.7175260611854538,21.28149700164795,15025.812845 +17952,Multiclass classification,Streaming Random Patches,Insects,0.7121051751991533,0.7121051751991533,0.7136617513297842,29.336480140686035,16621.157643 +19008,Multiclass classification,Streaming Random Patches,Insects,0.7205240174672489,0.720524017467249,0.7180961996594418,20.976608276367188,18267.655244999998 +20064,Multiclass classification,Streaming Random Patches,Insects,0.7261625878482779,0.7261625878482779,0.7198561207408494,15.994047164916992,19960.279520999997 +21120,Multiclass classification,Streaming Random Patches,Insects,0.7272598134381363,0.7272598134381363,0.7183389579277755,17.52824878692627,21708.029386999995 +22176,Multiclass classification,Streaming Random Patches,Insects,0.7281623449830891,0.7281623449830891,0.7167723651435352,22.240838050842285,23503.558300999994 +23232,Multiclass classification,Streaming Random Patches,Insects,0.7307477078042272,0.7307477078042272,0.7170791531651185,26.114503860473633,25348.434525999994 +24288,Multiclass classification,Streaming Random Patches,Insects,0.7325318071396221,0.7325318071396222,0.7165563330554671,27.974491119384766,27240.683159999993 +25344,Multiclass classification,Streaming Random Patches,Insects,0.7353904431203883,0.7353904431203884,0.7174524973348954,37.12833023071289,29182.513668999993 +26400,Multiclass classification,Streaming Random Patches,Insects,0.7367703322095533,0.7367703322095533,0.7168965346030137,31.575971603393555,31184.058177999992 +27456,Multiclass classification,Streaming Random Patches,Insects,0.738371881260244,0.738371881260244,0.7164257197178175,35.22733116149902,33213.34443999999 +28512,Multiclass classification,Streaming Random Patches,Insects,0.7366279681526429,0.7366279681526429,0.7161250847684691,17.50509262084961,35271.15690999999 +29568,Multiclass classification,Streaming Random Patches,Insects,0.7354483038522678,0.7354483038522677,0.719616514898752,22.40646266937256,37354.25785299999 +30624,Multiclass classification,Streaming Random Patches,Insects,0.7348724814681775,0.7348724814681775,0.7237598149406717,31.226743698120117,39459.628930999985 +31680,Multiclass classification,Streaming Random Patches,Insects,0.7347769815966413,0.7347769815966413,0.7275990709197302,35.24118995666504,41587.294746999985 +32736,Multiclass classification,Streaming Random Patches,Insects,0.7351458683366427,0.7351458683366427,0.7308983066693725,48.40772724151611,43729.930447999985 +33792,Multiclass classification,Streaming Random Patches,Insects,0.7303423988636027,0.7303423988636027,0.7274356410957497,77.28174114227295,45887.55412199999 +34848,Multiclass classification,Streaming Random Patches,Insects,0.726805750853732,0.726805750853732,0.723911701718825,53.16175174713135,48068.300588999984 +35904,Multiclass classification,Streaming Random Patches,Insects,0.7248976408656658,0.7248976408656659,0.7218080521646734,41.53026580810547,50265.619736999986 +36960,Multiclass classification,Streaming Random Patches,Insects,0.7215833761735978,0.7215833761735979,0.7182506744185386,35.33352756500244,52485.185911999986 +38016,Multiclass classification,Streaming Random Patches,Insects,0.7196369854004998,0.7196369854004999,0.7160236415660819,44.00273513793945,54721.05808499999 +39072,Multiclass classification,Streaming Random Patches,Insects,0.7175142688950884,0.7175142688950884,0.713988650041017,46.12203025817871,56978.30571199999 +40128,Multiclass classification,Streaming Random Patches,Insects,0.7158023276098388,0.7158023276098388,0.7126852582249207,27.841010093688965,59249.54765999999 +41184,Multiclass classification,Streaming Random Patches,Insects,0.7157322196051769,0.715732219605177,0.7129296468122535,21.849401473999023,61534.70422899999 +42240,Multiclass classification,Streaming Random Patches,Insects,0.7156656170837378,0.7156656170837377,0.7131576552849198,28.021278381347656,63833.596648999985 +43296,Multiclass classification,Streaming Random Patches,Insects,0.715925626515764,0.715925626515764,0.7137513847694824,36.50454139709473,66146.66403399999 +44352,Multiclass classification,Streaming Random Patches,Insects,0.7161958016730176,0.7161958016730177,0.7143198962298327,46.888444900512695,68474.34057599999 +45408,Multiclass classification,Streaming Random Patches,Insects,0.7170260092056291,0.7170260092056291,0.7151715877390813,47.08374786376953,70816.527322 +46464,Multiclass classification,Streaming Random Patches,Insects,0.7181843617502098,0.7181843617502098,0.7162864260409335,43.18325901031494,73172.526917 +47520,Multiclass classification,Streaming Random Patches,Insects,0.7179023127591069,0.7179023127591069,0.716246618663062,54.80090522766113,75543.857611 +48576,Multiclass classification,Streaming Random Patches,Insects,0.7211528564076171,0.7211528564076171,0.719707905487922,60.4919376373291,77931.086228 +49632,Multiclass classification,Streaming Random Patches,Insects,0.7250710241582882,0.7250710241582882,0.7236001513027165,35.55128765106201,80332.853368 +50688,Multiclass classification,Streaming Random Patches,Insects,0.7288259316984631,0.7288259316984631,0.7271241427068512,21.017152786254883,82746.274567 +51744,Multiclass classification,Streaming Random Patches,Insects,0.7329107318864387,0.7329107318864386,0.7308784460773333,26.768343925476074,85169.877802 +52800,Multiclass classification,Streaming Random Patches,Insects,0.7359230288452433,0.7359230288452432,0.7343606492383059,9.57052230834961,87600.592492 +52848,Multiclass classification,Streaming Random Patches,Insects,0.7361628853104244,0.7361628853104244,0.7346220154259927,9.63199520111084,90031.55993999999 +408,Multiclass classification,Streaming Random Patches,Keystroke,0.9901719901719902,0.9901719901719902,0.8308395677472984,1.027322769165039,17.348128 +816,Multiclass classification,Streaming Random Patches,Keystroke,0.9877300613496932,0.9877300613496932,0.9320293882508496,2.6651391983032227,54.406226000000004 +1224,Multiclass classification,Streaming Random Patches,Keystroke,0.9828291087489779,0.9828291087489779,0.9464059415055075,5.679329872131348,114.007104 +1632,Multiclass classification,Streaming Random Patches,Keystroke,0.9828326180257511,0.9828326180257511,0.9632097550305241,8.335807800292969,201.582874 +2040,Multiclass classification,Streaming Random Patches,Keystroke,0.9749877390877881,0.9749877390877881,0.9373958892668122,12.631415367126465,318.920922 +2448,Multiclass classification,Streaming Random Patches,Keystroke,0.9701675521046179,0.9701675521046179,0.957381800109682,16.891732215881348,465.74626 +2856,Multiclass classification,Streaming Random Patches,Keystroke,0.9660245183887916,0.9660245183887916,0.9394754450400101,22.937668800354004,641.328845 +3264,Multiclass classification,Streaming Random Patches,Keystroke,0.9616916947594238,0.9616916947594238,0.9454054748805115,29.12161636352539,847.207258 +3672,Multiclass classification,Streaming Random Patches,Keystroke,0.9607736311631708,0.9607736311631708,0.953605417859829,28.669262886047363,1081.626023 +4080,Multiclass classification,Streaming Random Patches,Keystroke,0.9578328021573915,0.9578328021573915,0.9463612240153171,34.20732402801514,1345.493591 +4488,Multiclass classification,Streaming Random Patches,Keystroke,0.9589926454201025,0.9589926454201025,0.9613092683363473,18.652557373046875,1636.499529 +4896,Multiclass classification,Streaming Random Patches,Keystroke,0.9607763023493361,0.9607763023493361,0.9605208703626084,20.81053066253662,1952.783692 +5304,Multiclass classification,Streaming Random Patches,Keystroke,0.9615312087497643,0.9615312087497643,0.9603033149830379,27.91543483734131,2294.145458 +5712,Multiclass classification,Streaming Random Patches,Keystroke,0.9616529504465068,0.9616529504465068,0.9605387671994151,32.60424041748047,2660.691575 +6120,Multiclass classification,Streaming Random Patches,Keystroke,0.9597973525085798,0.9597973525085798,0.9561203427932812,39.11091995239258,3053.1932039999997 +6528,Multiclass classification,Streaming Random Patches,Keystroke,0.9589397885705531,0.9589397885705531,0.9571591040678328,29.255366325378418,3470.643733 +6936,Multiclass classification,Streaming Random Patches,Keystroke,0.959913482335977,0.959913482335977,0.9605956598361813,31.930577278137207,3910.602191 +7344,Multiclass classification,Streaming Random Patches,Keystroke,0.9602342366880022,0.9602342366880022,0.9598619882355601,26.562703132629395,4374.151898 +7752,Multiclass classification,Streaming Random Patches,Keystroke,0.9601341762353245,0.9601341762353245,0.9596510454605859,31.588034629821777,4858.190889 +8160,Multiclass classification,Streaming Random Patches,Keystroke,0.9584507905380562,0.9584507905380562,0.9567204261955369,39.12565612792969,5363.543193 +8568,Multiclass classification,Streaming Random Patches,Keystroke,0.9579782887825377,0.9579782887825377,0.957794146577291,44.816758155822754,5892.06228 +8976,Multiclass classification,Streaming Random Patches,Keystroke,0.9579944289693594,0.9579944289693594,0.9581242571113369,49.5586576461792,6445.036349 +9384,Multiclass classification,Streaming Random Patches,Keystroke,0.9570499840136417,0.9570499840136417,0.9565283447410108,50.185367584228516,7025.065903 +9792,Multiclass classification,Streaming Random Patches,Keystroke,0.9563885200694515,0.9563885200694515,0.9560487952418978,54.40623474121094,7630.795018999999 +10200,Multiclass classification,Streaming Random Patches,Keystroke,0.9532307088930287,0.9532307088930287,0.9512518567217172,66.82855319976807,8267.200675 +10608,Multiclass classification,Streaming Random Patches,Keystroke,0.9519185443574998,0.9519185443574998,0.9512557409849248,41.17615795135498,8934.408603 +11016,Multiclass classification,Streaming Random Patches,Keystroke,0.9528824330458465,0.9528824330458465,0.953398407731189,32.87209510803223,9625.903537 +11424,Multiclass classification,Streaming Random Patches,Keystroke,0.953689923837871,0.953689923837871,0.9540175301991308,28.078542709350586,10339.782646 +11832,Multiclass classification,Streaming Random Patches,Keystroke,0.9542726734849125,0.9542726734849125,0.9545119777330118,20.280012130737305,11070.86282 +12240,Multiclass classification,Streaming Random Patches,Keystroke,0.955470218155078,0.955470218155078,0.9559406438939211,21.54300308227539,11820.445116 +12648,Multiclass classification,Streaming Random Patches,Keystroke,0.9559579346880683,0.9559579346880683,0.9561632451269845,26.89114284515381,12588.394717000001 +13056,Multiclass classification,Streaming Random Patches,Keystroke,0.9558789735733435,0.9558789735733435,0.9559075747932771,21.382742881774902,13375.587705000002 +13464,Multiclass classification,Streaming Random Patches,Keystroke,0.9563247418851668,0.9563247418851668,0.9565051554876024,21.864919662475586,14180.689980000001 +13872,Multiclass classification,Streaming Random Patches,Keystroke,0.9569605652079879,0.9569605652079879,0.9571856017401091,25.72835636138916,15004.794290000002 +14280,Multiclass classification,Streaming Random Patches,Keystroke,0.9566496253239022,0.9566496253239022,0.9566382966080723,21.764866828918457,15848.339318000002 +14688,Multiclass classification,Streaming Random Patches,Keystroke,0.957241097569279,0.9572410975692791,0.957426459656079,25.14582061767578,16708.048820000004 +15096,Multiclass classification,Streaming Random Patches,Keystroke,0.9580655846306724,0.9580655846306724,0.9582773620158959,26.658535957336426,17588.164126000003 +15504,Multiclass classification,Streaming Random Patches,Keystroke,0.9584596529703928,0.9584596529703928,0.9585840009788793,30.767892837524414,18489.703328000003 +15912,Multiclass classification,Streaming Random Patches,Keystroke,0.9580793161963421,0.9580793161963421,0.9580713134265897,27.786094665527344,19412.324967000004 +16320,Multiclass classification,Streaming Random Patches,Keystroke,0.958514614866107,0.958514614866107,0.9586173296332885,25.79348850250244,20355.979039000005 +16728,Multiclass classification,Streaming Random Patches,Keystroke,0.9577330065164106,0.9577330065164106,0.9576699214368118,33.630208015441895,21321.132478000007 +17136,Multiclass classification,Streaming Random Patches,Keystroke,0.9576305806828129,0.9576305806828129,0.9576693803774444,33.920249938964844,22315.653729000005 +17544,Multiclass classification,Streaming Random Patches,Keystroke,0.956506868836573,0.956506868836573,0.9564470129227677,31.505155563354492,23335.132930000003 +17952,Multiclass classification,Streaming Random Patches,Keystroke,0.9563812600969306,0.9563812600969306,0.9564135249623555,19.79563045501709,24372.247222 +18360,Multiclass classification,Streaming Random Patches,Keystroke,0.9569148646440437,0.9569148646440437,0.9569804233582649,23.70892333984375,25428.663478000002 +18768,Multiclass classification,Streaming Random Patches,Keystroke,0.9574252677572335,0.9574252677572335,0.957475477736454,21.893744468688965,26504.246634000003 +19176,Multiclass classification,Streaming Random Patches,Keystroke,0.9568187744458931,0.9568187744458931,0.956806677474395,28.04871368408203,27598.635240000003 +19584,Multiclass classification,Streaming Random Patches,Keystroke,0.9567992646683348,0.9567992646683348,0.9568012672257533,32.11082458496094,28712.463090000005 +19992,Multiclass classification,Streaming Random Patches,Keystroke,0.9565304386974138,0.9565304386974138,0.9565268274864178,40.13526153564453,29849.822929000005 +20400,Multiclass classification,Streaming Random Patches,Keystroke,0.9559292122162851,0.9559292122162851,0.9559196349550496,39.63601016998291,31009.846621000004 +46,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.5111111111111111,0.5111111111111111,0.40938578329882686,0.09116363525390625,0.155273 +92,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6043956043956044,0.6043956043956044,0.5940974230447915,0.16827392578125,0.72683 +138,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6715328467153284,0.6715328467153284,0.6806196928151186,0.24543190002441406,1.742293 +184,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7049180327868853,0.7049180327868853,0.7184732466987995,0.32204627990722656,3.3407109999999998 +230,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.74235807860262,0.74235807860262,0.7523809662907407,0.39917659759521484,5.610709 +276,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7490909090909091,0.7490909090909091,0.7611097615339608,0.47675609588623047,8.7745 +322,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7663551401869159,0.766355140186916,0.7725898650917747,0.5538606643676758,12.918764 +368,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.784741144414169,0.7847411444141691,0.7844949397573193,0.6304874420166016,18.189003 +414,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7990314769975787,0.7990314769975787,0.7976353129150817,0.7076187133789062,24.731741 +460,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7952069716775599,0.7952069716775599,0.7930763833747545,0.7847471237182617,32.681045 +506,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7960396039603961,0.7960396039603961,0.7941234022368324,3.003793716430664,61.211909999999996 +552,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8021778584392014,0.8021778584392014,0.8007250644998717,3.2264842987060547,91.16202899999999 +598,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8090452261306532,0.8090452261306531,0.8095532779239047,3.4499826431274414,122.67972799999998 +644,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8164852255054432,0.8164852255054433,0.8176018556357175,3.6760778427124023,155.77468699999997 +690,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8214804063860668,0.8214804063860668,0.8221151176242331,3.8941650390625,190.53727699999996 +736,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8272108843537415,0.8272108843537415,0.8281233770721121,4.128121376037598,227.08550299999996 +782,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8361075544174136,0.8361075544174136,0.8364659566156888,4.367749214172363,265.419762 +828,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8403869407496977,0.8403869407496977,0.8412749002251585,4.601743698120117,305.543518 +874,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.845360824742268,0.845360824742268,0.8465057584066101,4.840575218200684,347.501906 +920,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8487486398258978,0.8487486398258978,0.8489576083149123,5.074535369873047,391.43023400000004 +966,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8538860103626943,0.8538860103626943,0.8530581393966605,5.3079938888549805,437.316456 +1012,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8585558852621167,0.8585558852621167,0.8570252804249208,5.479596138000488,485.23685 +1058,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8628192999053926,0.8628192999053927,0.8611045332429007,5.435150146484375,535.278485 +1104,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8631006346328196,0.8631006346328196,0.8616288881212748,5.355225563049316,587.436372 +1150,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8668407310704961,0.8668407310704961,0.8650902600877293,5.281754493713379,641.538536 +1196,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8719665271966527,0.8719665271966527,0.8702683106604537,5.235520362854004,697.554251 +1242,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8759065269943593,0.8759065269943593,0.8740479640614998,5.142333984375,755.471933 +1288,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8787878787878788,0.8787878787878788,0.8772603222806128,5.092559814453125,815.138635 +1334,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8777194298574643,0.8777194298574643,0.8760741143565023,5.055940628051758,876.491339 +1380,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8796229151559101,0.8796229151559101,0.8783130803325612,4.964084625244141,939.483975 +1426,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8785964912280702,0.8785964912280702,0.8768931648451159,4.951287269592285,1004.010152 +1472,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8769544527532291,0.8769544527532291,0.8748964905672628,4.969002723693848,1070.168576 +1518,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8727752142386289,0.8727752142386289,0.8705110235515202,5.101251602172852,1138.304608 +1564,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8688419705694178,0.8688419705694178,0.8667015278861958,5.262187957763672,1208.4659419999998 +1610,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8651336233685519,0.8651336233685519,0.8631350462642483,5.320252418518066,1280.5305069999997 +1656,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8640483383685801,0.8640483383685801,0.8620479268968886,5.35189151763916,1354.3819709999998 +1702,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8647854203409759,0.8647854203409759,0.8635043959538364,5.359102249145508,1430.0286029999997 +1748,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.866056096164854,0.866056096164854,0.864439618601765,5.402237892150879,1507.5136589999997 +1794,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8683770217512549,0.8683770217512549,0.8664209902402824,5.3993330001831055,1586.7199259999998 +1840,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8694942903752039,0.8694942903752039,0.867597342266498,5.4049272537231445,1667.6587319999999 +1886,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8710875331564987,0.8710875331564986,0.8694766742923737,5.4121294021606445,1750.3169159999998 +1932,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8705334023821854,0.8705334023821854,0.8686918451193435,5.405803680419922,1834.6099799999997 +1978,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8715225088517956,0.8715225088517956,0.8698703895904014,5.395906448364258,1920.5171279999997 +2024,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8729609490855166,0.8729609490855166,0.870902914954928,5.386837959289551,2008.1064549999996 +2070,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8733687771870469,0.8733687771870469,0.8714525187304558,5.375288963317871,2097.403794 +2116,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.875177304964539,0.875177304964539,0.8730645404016979,5.353263854980469,2188.326937 +2162,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8745950948634891,0.8745950948634891,0.872417325547954,5.322790145874023,2280.8823749999997 +2208,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8753964657906661,0.8753964657906661,0.8732500176589647,5.30323600769043,2374.9757969999996 +2254,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8748335552596538,0.8748335552596538,0.8732733602208504,5.278659820556641,2470.7327649999997 +2300,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8742931709438887,0.8742931709438887,0.8727466012343671,5.262259483337402,2568.119206 +2310,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8735383282806409,0.8735383282806409,0.8721361121313428,5.268708229064941,2666.293295 +1056,Multiclass classification,k-Nearest Neighbors,Insects,0.6597156398104266,0.6597156398104266,0.5853273709738578,6.371035575866699,65.756564 +2112,Multiclass classification,k-Nearest Neighbors,Insects,0.6807200378967314,0.6807200378967314,0.5992086579995298,6.2783002853393555,182.184591 +3168,Multiclass classification,k-Nearest Neighbors,Insects,0.6842437638143354,0.6842437638143354,0.6001715208792017,6.298460006713867,341.998975 +4224,Multiclass classification,k-Nearest Neighbors,Insects,0.6848212171442103,0.6848212171442103,0.6051604277089342,6.265153884887695,541.5068689999999 +5280,Multiclass classification,k-Nearest Neighbors,Insects,0.6872513733661678,0.6872513733661678,0.611100448555976,6.2555742263793945,777.52113 +6336,Multiclass classification,k-Nearest Neighbors,Insects,0.6842936069455406,0.6842936069455406,0.6118525331169307,6.3140106201171875,1048.588472 +7392,Multiclass classification,k-Nearest Neighbors,Insects,0.6852929238262752,0.6852929238262752,0.6157762907660722,6.288516998291016,1352.458798 +8448,Multiclass classification,k-Nearest Neighbors,Insects,0.6828459808215934,0.6828459808215934,0.6148503710479976,6.31680965423584,1687.096094 +9504,Multiclass classification,k-Nearest Neighbors,Insects,0.6851520572450805,0.6851520572450805,0.6155258331015067,6.223039627075195,2051.649807 +10560,Multiclass classification,k-Nearest Neighbors,Insects,0.6861445212614831,0.6861445212614831,0.6169474950376627,6.253497123718262,2444.130945 +11616,Multiclass classification,k-Nearest Neighbors,Insects,0.6873009040034438,0.6873009040034438,0.6200568175672779,6.251482009887695,2863.1289039999997 +12672,Multiclass classification,k-Nearest Neighbors,Insects,0.6866072133217583,0.6866072133217583,0.623883491026523,6.276742935180664,3309.0829679999997 +13728,Multiclass classification,k-Nearest Neighbors,Insects,0.7020470605376266,0.7020470605376266,0.6991473808978487,6.26933479309082,3781.2775089999996 +14784,Multiclass classification,k-Nearest Neighbors,Insects,0.7077724413177299,0.7077724413177299,0.7078402863830927,6.244691848754883,4278.402760999999 +15840,Multiclass classification,k-Nearest Neighbors,Insects,0.7016857124818486,0.7016857124818486,0.704840832390747,6.350223541259766,4805.403565999999 +16896,Multiclass classification,k-Nearest Neighbors,Insects,0.6992009470257473,0.6992009470257473,0.7048178275842342,6.243149757385254,5357.683726999999 +17952,Multiclass classification,k-Nearest Neighbors,Insects,0.6922734109520361,0.6922734109520361,0.6995766929659905,6.218992233276367,5935.240105999998 +19008,Multiclass classification,k-Nearest Neighbors,Insects,0.6974272636397116,0.6974272636397116,0.7006862112488368,6.24652099609375,6538.276384999998 +20064,Multiclass classification,k-Nearest Neighbors,Insects,0.699845486716842,0.699845486716842,0.6985118222305657,6.205791473388672,7167.459726999999 +21120,Multiclass classification,k-Nearest Neighbors,Insects,0.7016904209479615,0.7016904209479615,0.6971610909052677,6.218420028686523,7825.840650999999 +22176,Multiclass classification,k-Nearest Neighbors,Insects,0.7039909808342728,0.7039909808342728,0.6964197759629052,6.236072540283203,8511.079801999998 +23232,Multiclass classification,k-Nearest Neighbors,Insects,0.7076320433902974,0.7076320433902974,0.697368621848442,6.279313087463379,9222.986801999998 +24288,Multiclass classification,k-Nearest Neighbors,Insects,0.7098447729237863,0.7098447729237863,0.6967477548491564,6.2948198318481445,9960.927248999997 +25344,Multiclass classification,k-Nearest Neighbors,Insects,0.7127017322337529,0.712701732233753,0.6972185032799825,6.224791526794434,10724.419586999997 +26400,Multiclass classification,k-Nearest Neighbors,Insects,0.7145346414636918,0.7145346414636918,0.6967850611237018,6.263523101806641,11512.986172999998 +27456,Multiclass classification,k-Nearest Neighbors,Insects,0.7156437807321071,0.7156437807321071,0.6955595874776194,6.272575378417969,12326.628602999997 +28512,Multiclass classification,k-Nearest Neighbors,Insects,0.7130931921012942,0.7130931921012942,0.6943090782068162,6.22489070892334,13165.433758999998 +29568,Multiclass classification,k-Nearest Neighbors,Insects,0.7117732607298678,0.7117732607298677,0.6978751959025926,6.217726707458496,14028.837757999998 +30624,Multiclass classification,k-Nearest Neighbors,Insects,0.7122097769650263,0.7122097769650264,0.7026862643890369,6.243690490722656,14916.319238999999 +31680,Multiclass classification,k-Nearest Neighbors,Insects,0.7113545250797058,0.7113545250797058,0.7052714328980031,6.277059555053711,15827.904481999998 +32736,Multiclass classification,k-Nearest Neighbors,Insects,0.7111959676187567,0.7111959676187566,0.7078689284492299,6.295280456542969,16762.400180999997 +33792,Multiclass classification,k-Nearest Neighbors,Insects,0.7067562368678051,0.7067562368678051,0.704703743720216,6.183221817016602,17721.950532 +34848,Multiclass classification,k-Nearest Neighbors,Insects,0.7030734353028956,0.7030734353028956,0.7010614031639846,6.343389511108398,18710.094933 +35904,Multiclass classification,k-Nearest Neighbors,Insects,0.6998022449377489,0.6998022449377489,0.6976694331042329,6.273009300231934,19725.980479 +36960,Multiclass classification,k-Nearest Neighbors,Insects,0.6967179847939609,0.6967179847939609,0.6945045780432343,6.264690399169922,20767.047301000002 +38016,Multiclass classification,k-Nearest Neighbors,Insects,0.6941470472182033,0.6941470472182033,0.6917813776610243,6.265054702758789,21835.556239 +39072,Multiclass classification,k-Nearest Neighbors,Insects,0.691996621535154,0.691996621535154,0.6898060776768534,6.200959205627441,22932.108791000002 +40128,Multiclass classification,k-Nearest Neighbors,Insects,0.6904328756199068,0.6904328756199068,0.6882031611963276,6.413609504699707,24054.967875000002 +41184,Multiclass classification,k-Nearest Neighbors,Insects,0.6916446106403128,0.6916446106403128,0.6892941373261507,6.3101043701171875,25204.026441 +42240,Multiclass classification,k-Nearest Neighbors,Insects,0.692535334643339,0.692535334643339,0.6900712004452627,6.22797966003418,26378.286294 +43296,Multiclass classification,k-Nearest Neighbors,Insects,0.6935904838895947,0.6935904838895947,0.6909354899104013,6.220904350280762,27574.213319000002 +44352,Multiclass classification,k-Nearest Neighbors,Insects,0.6941895334941715,0.6941895334941715,0.691322114366645,6.22946834564209,28791.926615000004 +45408,Multiclass classification,k-Nearest Neighbors,Insects,0.6950690422181602,0.6950690422181602,0.6917362410920441,6.2850341796875,30030.670852000003 +46464,Multiclass classification,k-Nearest Neighbors,Insects,0.6964466349568473,0.6964466349568473,0.6926338572817136,6.2335004806518555,31290.345387 +47520,Multiclass classification,k-Nearest Neighbors,Insects,0.6963530377322755,0.6963530377322755,0.6929015597977773,6.2439117431640625,32571.46119 +48576,Multiclass classification,k-Nearest Neighbors,Insects,0.7006073082861555,0.7006073082861555,0.697843135408715,6.247167587280273,33874.398319 +49632,Multiclass classification,k-Nearest Neighbors,Insects,0.7046805424029337,0.7046805424029337,0.7023003034160373,6.246943473815918,35198.416197 +50688,Multiclass classification,k-Nearest Neighbors,Insects,0.7083867658373942,0.7083867658373942,0.7061355873839065,6.210485458374023,36536.506394 +51744,Multiclass classification,k-Nearest Neighbors,Insects,0.7126567844925884,0.7126567844925883,0.7104085577951368,6.270394325256348,37890.628371 +52800,Multiclass classification,k-Nearest Neighbors,Insects,0.7128544101214038,0.7128544101214038,0.7110869129037599,6.247260093688965,39264.666928 +52848,Multiclass classification,k-Nearest Neighbors,Insects,0.7131152194069673,0.7131152194069672,0.7113808258412672,6.272693634033203,40639.937472 +408,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.0294876098632812,8.610537 +816,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9251533742331288,0.9251533742331288,0.8588670451436246,5.3865966796875,60.879099000000004 +1224,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9247751430907605,0.9247751430907604,0.888226412135106,6.246823310852051,137.71097600000002 +1632,Multiclass classification,k-Nearest Neighbors,Keystroke,0.927038626609442,0.927038626609442,0.893336805209695,6.212030410766602,236.990146 +2040,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9298675821481118,0.9298675821481118,0.911424130088645,6.329436302185059,359.011082 +2448,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9239885574172456,0.9239885574172456,0.9121555472954921,6.208271026611328,503.2068 +2856,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9169877408056042,0.9169877408056042,0.8816257260944811,6.284844398498535,667.387655 +3264,Multiclass classification,k-Nearest Neighbors,Keystroke,0.908979466748391,0.908979466748391,0.9011431783951355,6.232160568237305,850.70223 +3672,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133751021520021,0.9133751021520021,0.9125908871445695,6.234919548034668,1054.346916 +4080,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9141946555528315,0.9141946555528315,0.9054789816810689,6.308258056640625,1277.35851 +4488,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9057276576777357,0.9057276576777357,0.9087691557812896,6.2749834060668945,1518.241674 +4896,Multiclass classification,k-Nearest Neighbors,Keystroke,0.908682328907048,0.908682328907048,0.9101970481905531,6.210176467895508,1774.8436390000002 +5304,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9092966245521403,0.9092966245521403,0.9045962329696907,6.311163902282715,2048.2991580000003 +5712,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9110488530905271,0.9110488530905271,0.9114244990736602,6.304790496826172,2337.0378760000003 +6120,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9089720542572316,0.9089720542572316,0.9032533666541098,6.217576026916504,2640.3521840000003 +6528,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9077677340278841,0.9077677340278841,0.9071900335968285,6.258184432983398,2958.617579 +6936,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9105984138428262,0.9105984138428262,0.9126270814361048,6.1720380783081055,3289.9253120000003 +7344,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9120250578782514,0.9120250578782514,0.9125633522308233,6.2164154052734375,3633.8320570000005 +7752,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133015094826474,0.9133015094826474,0.9136330015220733,6.260525703430176,3992.0508240000004 +8160,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9148179924010296,0.9148179924010296,0.9154195917586071,6.291139602661133,4363.477247000001 +8568,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9166569394186996,0.9166569394186995,0.9177086422960681,6.216147422790527,4747.269196000001 +8976,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9179944289693593,0.9179944289693593,0.9193727618453186,6.204837799072266,5142.613476000001 +9384,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9180432697431525,0.9180432697431525,0.9181590265081533,6.197464942932129,5549.1165660000015 +9792,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9166581554488816,0.9166581554488816,0.9168210748678531,6.232473373413086,5968.023607000002 +10200,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9154819099911756,0.9154819099911756,0.9145218669909496,6.197787284851074,6399.011787000002 +10608,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9126048835674555,0.9126048835674555,0.9111025938131312,6.2185258865356445,6842.990199000003 +11016,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9106672719019518,0.9106672719019518,0.911227786024665,6.261377334594727,7300.041981000002 +11424,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9103562986956141,0.9103562986956141,0.9101104800687125,6.227293968200684,7769.979179000002 +11832,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9092215366410278,0.9092215366410278,0.9094186121236619,6.287784576416016,8252.975566000001 +12240,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9110221423318898,0.9110221423318898,0.9118339797691071,6.31197452545166,8747.696417000001 +12648,Multiclass classification,k-Nearest Neighbors,Keystroke,0.912627500593026,0.912627500593026,0.9131272841889786,6.279851913452148,9255.008595000001 +13056,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9129069322098813,0.9129069322098813,0.913006147591119,6.346117973327637,9774.692327 +13464,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9136150932184506,0.9136150932184506,0.9138210444048112,6.238006591796875,10306.725428000002 +13872,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9137769447047798,0.9137769447047797,0.913931693448659,6.288792610168457,10851.686584000001 +14280,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9121787240002801,0.9121787240002801,0.9118234284090696,6.252389907836914,11409.551357 +14688,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133247089262613,0.9133247089262613,0.9136581918124823,6.268362998962402,11980.223294 +15096,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9134150380920835,0.9134150380920835,0.9134700562544149,6.304409027099609,12563.090191 +15504,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9127910726956073,0.9127910726956073,0.9127632118282708,6.222077369689941,13158.500651999999 +15912,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9127019043429074,0.9127019043429074,0.9127365496247325,6.263586044311523,13766.752283999998 +16320,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113303511244562,0.9113303511244562,0.9110956080213111,6.256609916687012,14388.121761999999 +16728,Multiclass classification,k-Nearest Neighbors,Keystroke,0.910384408441442,0.9103844084414419,0.9103324360258119,6.2080230712890625,15023.088924 +17136,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113510358914503,0.9113510358914503,0.9114963483082135,6.223179817199707,15670.063673 +17544,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113606566721769,0.9113606566721769,0.9113826667045093,6.23558235168457,16331.265293 +17952,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9114255473232689,0.911425547323269,0.9114384409485988,6.255133628845215,17006.553944 +18360,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9126858761370445,0.9126858761370445,0.9127656000580755,6.270404815673828,17693.792261 +18768,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9125059945649278,0.9125059945649276,0.9124701420883569,6.180520057678223,18393.849908 +19176,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9125945241199479,0.9125945241199479,0.9125632790621417,6.265439987182617,19106.227212 +19584,Multiclass classification,k-Nearest Neighbors,Keystroke,0.913547464637696,0.913547464637696,0.9135225066457016,6.303133964538574,19831.701162 +19992,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9111099994997749,0.9111099994997749,0.910917465793804,6.225028991699219,20571.760391 +20400,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9104858081278494,0.9104858081278494,0.9103279821226861,6.325108528137207,21326.45228 +46,Multiclass classification,ADWIN Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.245764972655729,4.105147361755371,2.153154 +92,Multiclass classification,ADWIN Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.4934752395581889,4.108363151550293,6.907408 +138,Multiclass classification,ADWIN Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646677,4.108027458190918,14.639156 +184,Multiclass classification,ADWIN Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.614143164890895,4.108977317810059,25.443956 +230,Multiclass classification,ADWIN Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,3.881842613220215,39.254234 +276,Multiclass classification,ADWIN Bagging,ImageSegments,0.64,0.64,0.6559607038460422,3.996514320373535,55.768073 +322,Multiclass classification,ADWIN Bagging,ImageSegments,0.6697819314641744,0.6697819314641744,0.6706320385346652,4.112936019897461,74.877199 +368,Multiclass classification,ADWIN Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6897433526546475,4.112924575805664,96.687005 +414,Multiclass classification,ADWIN Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.706570530482581,4.117301940917969,121.290167 +460,Multiclass classification,ADWIN Bagging,ImageSegments,0.7145969498910676,0.7145969498910676,0.7071122267088654,4.116390228271484,148.551711 +506,Multiclass classification,ADWIN Bagging,ImageSegments,0.7247524752475247,0.7247524752475247,0.7147973207987898,4.115703582763672,178.365171 +552,Multiclass classification,ADWIN Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7210771168277493,4.115436553955078,210.796119 +598,Multiclass classification,ADWIN Bagging,ImageSegments,0.7336683417085427,0.7336683417085426,0.7250288715672424,4.115207672119141,245.953277 +644,Multiclass classification,ADWIN Bagging,ImageSegments,0.7325038880248833,0.7325038880248833,0.7258924883659029,4.118658065795898,283.80303000000004 +690,Multiclass classification,ADWIN Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7302536378735861,4.118425369262695,324.296282 +736,Multiclass classification,ADWIN Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7329631379486719,4.118097305297852,367.43284 +782,Multiclass classification,ADWIN Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7387105187530085,4.117616653442383,413.28289 +828,Multiclass classification,ADWIN Bagging,ImageSegments,0.7460701330108828,0.7460701330108827,0.7425025596154723,4.117326736450195,461.953497 +874,Multiclass classification,ADWIN Bagging,ImageSegments,0.7514318442153494,0.7514318442153494,0.7467163857842193,4.117303848266602,513.2937440000001 +920,Multiclass classification,ADWIN Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7453933609147309,4.117105484008789,567.431634 +966,Multiclass classification,ADWIN Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.7451117895470661,4.116701126098633,624.2209760000001 +1012,Multiclass classification,ADWIN Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.744963080481548,4.116399765014648,683.8404210000001 +1058,Multiclass classification,ADWIN Bagging,ImageSegments,0.7511825922421949,0.7511825922421949,0.7446315489945475,4.117582321166992,746.2097300000001 +1104,Multiclass classification,ADWIN Bagging,ImageSegments,0.7533998186763372,0.7533998186763373,0.7466082689908061,4.117956161499023,811.1743250000002 +1150,Multiclass classification,ADWIN Bagging,ImageSegments,0.7563098346388164,0.7563098346388164,0.7491651771194966,4.117490768432617,878.6809830000002 +1196,Multiclass classification,ADWIN Bagging,ImageSegments,0.7589958158995815,0.7589958158995815,0.7526420027035883,4.117303848266602,948.8627130000002 +1242,Multiclass classification,ADWIN Bagging,ImageSegments,0.75825946817083,0.7582594681708301,0.7524016178277559,4.11713981628418,1021.5806660000002 +1288,Multiclass classification,ADWIN Bagging,ImageSegments,0.7637917637917638,0.7637917637917638,0.75666252908711,4.117353439331055,1096.9757510000002 +1334,Multiclass classification,ADWIN Bagging,ImageSegments,0.7636909227306826,0.7636909227306825,0.7569484848610158,4.11726188659668,1175.015685 +1380,Multiclass classification,ADWIN Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7590436403579585,4.11729621887207,1255.693831 +1426,Multiclass classification,ADWIN Bagging,ImageSegments,0.767719298245614,0.767719298245614,0.761211289695921,4.117136001586914,1338.965745 +1472,Multiclass classification,ADWIN Bagging,ImageSegments,0.7722637661454793,0.7722637661454793,0.764056696643358,4.117197036743164,1424.822234 +1518,Multiclass classification,ADWIN Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.764234133414765,4.117246627807617,1513.291691 +1564,Multiclass classification,ADWIN Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7653316001442944,4.117277145385742,1604.2857379999998 +1610,Multiclass classification,ADWIN Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7647353044337893,4.11713981628418,1697.9838939999997 +1656,Multiclass classification,ADWIN Bagging,ImageSegments,0.7734138972809668,0.7734138972809667,0.7645730180903108,4.116628646850586,1794.3974679999997 +1702,Multiclass classification,ADWIN Bagging,ImageSegments,0.7724867724867724,0.7724867724867724,0.7656182355666586,4.116819381713867,1893.3012909999998 +1748,Multiclass classification,ADWIN Bagging,ImageSegments,0.7750429307384087,0.7750429307384087,0.7677424040514297,4.116933822631836,1994.6627269999997 +1794,Multiclass classification,ADWIN Bagging,ImageSegments,0.7763524818739542,0.7763524818739542,0.7677176136548693,4.116861343383789,2098.663831 +1840,Multiclass classification,ADWIN Bagging,ImageSegments,0.7775965198477434,0.7775965198477434,0.7691578918725354,4.11646842956543,2205.2268019999997 +1886,Multiclass classification,ADWIN Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7681560201617949,4.116430282592773,2314.3219019999997 +1932,Multiclass classification,ADWIN Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7674170460709655,4.116365432739258,2425.9767019999995 +1978,Multiclass classification,ADWIN Bagging,ImageSegments,0.7769347496206374,0.7769347496206374,0.7672843880004774,4.116201400756836,2540.2532379999993 +2024,Multiclass classification,ADWIN Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7681802739952505,4.116155624389648,2657.1841359999994 +2070,Multiclass classification,ADWIN Bagging,ImageSegments,0.778153697438376,0.7781536974383759,0.767530439166732,4.116151809692383,2776.6077329999994 +2116,Multiclass classification,ADWIN Bagging,ImageSegments,0.7787234042553192,0.778723404255319,0.7673415220519754,4.116128921508789,2898.5867789999993 +2162,Multiclass classification,ADWIN Bagging,ImageSegments,0.7797316057380842,0.7797316057380842,0.7679341969633587,4.116201400756836,3023.0016299999993 +2208,Multiclass classification,ADWIN Bagging,ImageSegments,0.7816039873130947,0.7816039873130947,0.7687944234581563,4.11619758605957,3150.0382249999993 +2254,Multiclass classification,ADWIN Bagging,ImageSegments,0.7785175321793165,0.7785175321793165,0.7657018899401807,4.116170883178711,3279.4760369999995 +2300,Multiclass classification,ADWIN Bagging,ImageSegments,0.7777294475859069,0.7777294475859068,0.7649119672933203,4.116254806518555,3411.2017669999996 +2310,Multiclass classification,ADWIN Bagging,ImageSegments,0.7778258986574275,0.7778258986574276,0.765010539660814,4.116277694702148,3543.5486899999996 +1056,Multiclass classification,ADWIN Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762562,6.4894914627075195,90.340432 +2112,Multiclass classification,ADWIN Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.490170478820801,257.284509 +3168,Multiclass classification,ADWIN Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244894,6.491124153137207,490.807794 +4224,Multiclass classification,ADWIN Bagging,Insects,0.6107032914989344,0.6107032914989344,0.574850135712032,6.4912004470825195,783.3577379999999 +5280,Multiclass classification,ADWIN Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225517,6.491948127746582,1129.937274 +6336,Multiclass classification,ADWIN Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789626,6.490735054016113,1525.7763839999998 +7392,Multiclass classification,ADWIN Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867537,6.490704536437988,1967.9282259999998 +8448,Multiclass classification,ADWIN Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.490643501281738,2456.1055429999997 +9504,Multiclass classification,ADWIN Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297488,6.491009712219238,2990.761064 +10560,Multiclass classification,ADWIN Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.491185188293457,3571.5807019999997 +11616,Multiclass classification,ADWIN Bagging,Insects,0.6005165733964701,0.6005165733964701,0.56512832395729,6.491345405578613,4198.711386999999 +12672,Multiclass classification,ADWIN Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306638,6.491543769836426,4874.277407999999 +13728,Multiclass classification,ADWIN Bagging,Insects,0.6152108982297662,0.6152108982297662,0.5959760515786451,5.97607421875,5593.125681999999 +14784,Multiclass classification,ADWIN Bagging,Insects,0.6060339579246432,0.6060339579246432,0.5869142505177357,6.496403694152832,6355.050274999999 +15840,Multiclass classification,ADWIN Bagging,Insects,0.5713744554580465,0.5713744554580465,0.5537658591956377,6.4967546463012695,7160.569073999999 +16896,Multiclass classification,ADWIN Bagging,Insects,0.545546019532406,0.545546019532406,0.5286479939306438,6.381303787231445,8010.073855999999 +17952,Multiclass classification,ADWIN Bagging,Insects,0.526767311013314,0.526767311013314,0.509587529402725,6.497265815734863,8901.233847 +19008,Multiclass classification,ADWIN Bagging,Insects,0.517756615983585,0.517756615983585,0.4976462434137419,4.6858930587768555,9829.81162 +20064,Multiclass classification,ADWIN Bagging,Insects,0.5296815032647162,0.5296815032647162,0.5080882715573688,10.369908332824707,10791.950057 +21120,Multiclass classification,ADWIN Bagging,Insects,0.539750935176855,0.539750935176855,0.5184934777423561,10.92272663116455,11801.930673 +22176,Multiclass classification,ADWIN Bagging,Insects,0.5468771138669674,0.5468771138669674,0.525970977438283,10.920933723449707,12856.592968 +23232,Multiclass classification,ADWIN Bagging,Insects,0.5551633593043778,0.5551633593043778,0.5340735310276195,12.231526374816895,13957.460176 +24288,Multiclass classification,ADWIN Bagging,Insects,0.5615761518507844,0.5615761518507844,0.5396852076547556,12.87682819366455,15100.290122 +25344,Multiclass classification,ADWIN Bagging,Insects,0.5679280274632048,0.5679280274632048,0.5455634192548013,13.528642654418945,16285.362621 +26400,Multiclass classification,ADWIN Bagging,Insects,0.5727868479866661,0.5727868479866661,0.5496374434570932,13.632143020629883,17508.053461 +27456,Multiclass classification,ADWIN Bagging,Insects,0.5754143143325442,0.5754143143325442,0.5513680135969626,13.630533218383789,18766.96928 +28512,Multiclass classification,ADWIN Bagging,Insects,0.5772859598049875,0.5772859598049875,0.5551350356863173,13.627862930297852,20061.957755000003 +29568,Multiclass classification,ADWIN Bagging,Insects,0.577772516657084,0.577772516657084,0.5590861332292512,13.626611709594727,21394.440888000005 +30624,Multiclass classification,ADWIN Bagging,Insects,0.578225516768442,0.578225516768442,0.5625516131192055,12.769641876220703,22755.311286000004 +31680,Multiclass classification,ADWIN Bagging,Insects,0.5795637488557088,0.5795637488557088,0.5663363640160616,12.768932342529297,24150.336726000005 +32736,Multiclass classification,ADWIN Bagging,Insects,0.5811211241790133,0.5811211241790133,0.5696723582178381,12.768062591552734,25577.802283000005 +33792,Multiclass classification,ADWIN Bagging,Insects,0.575804208221124,0.575804208221124,0.5647934119551398,12.981294631958008,27039.455958000006 +34848,Multiclass classification,ADWIN Bagging,Insects,0.5701495107182828,0.5701495107182828,0.559068023359177,12.981256484985352,28537.493337000007 +35904,Multiclass classification,ADWIN Bagging,Insects,0.5657744478177311,0.5657744478177311,0.5542573482740075,12.983362197875977,30069.702466000006 +36960,Multiclass classification,ADWIN Bagging,Insects,0.5611894261208366,0.5611894261208366,0.5493152777162592,13.52482795715332,31635.746830000007 +38016,Multiclass classification,ADWIN Bagging,Insects,0.558779429172695,0.558779429172695,0.5463982360776033,13.526559829711914,33235.41425300001 +39072,Multiclass classification,ADWIN Bagging,Insects,0.5546825010877633,0.5546825010877633,0.5426283860139581,14.304903030395508,34865.73115700001 +40128,Multiclass classification,ADWIN Bagging,Insects,0.5542153662122761,0.5542153662122761,0.5429626632180721,15.152182579040527,36525.24862800001 +41184,Multiclass classification,ADWIN Bagging,Insects,0.5541364155112547,0.5541364155112547,0.5435420562964655,15.252725601196289,38211.297236000006 +42240,Multiclass classification,ADWIN Bagging,Insects,0.5542981604678141,0.5542981604678141,0.544391400018036,15.251314163208008,39923.86574200001 +43296,Multiclass classification,ADWIN Bagging,Insects,0.554151749624668,0.554151749624668,0.5448486588729107,13.424749374389648,41664.47056700001 +44352,Multiclass classification,ADWIN Bagging,Insects,0.5536290049829767,0.5536290049829767,0.5448029815059025,13.648665428161621,43429.160590000014 +45408,Multiclass classification,ADWIN Bagging,Insects,0.5541436342414165,0.5541436342414165,0.5454957405719211,14.18911075592041,45215.90786900002 +46464,Multiclass classification,ADWIN Bagging,Insects,0.5553020683124207,0.5553020683124207,0.546961663735647,15.156656265258789,47024.86244100002 +47520,Multiclass classification,ADWIN Bagging,Insects,0.5579662871693428,0.5579662871693428,0.5498636684303295,14.218542098999023,48856.78116300002 +48576,Multiclass classification,ADWIN Bagging,Insects,0.5627586206896552,0.5627586206896552,0.5545030394801858,14.845645904541016,50711.770017000024 +49632,Multiclass classification,ADWIN Bagging,Insects,0.5677701436602124,0.5677701436602124,0.5591808574875289,15.233248710632324,52589.43568900003 +50688,Multiclass classification,ADWIN Bagging,Insects,0.5730463432438297,0.5730463432438297,0.5639878919164368,15.890131950378418,54487.48381000003 +51744,Multiclass classification,ADWIN Bagging,Insects,0.5791894555785324,0.5791894555785324,0.5695807960578061,16.1916446685791,56406.77001200003 +52800,Multiclass classification,ADWIN Bagging,Insects,0.5794427924771303,0.5794427924771303,0.5701512686040561,15.307219505310059,58342.70756100003 +52848,Multiclass classification,ADWIN Bagging,Insects,0.5794652487369197,0.5794652487369197,0.5701984940722999,15.307356834411621,60279.413314000034 +408,Multiclass classification,ADWIN Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.100947380065918,5.705318 +816,Multiclass classification,ADWIN Bagging,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,3.048105239868164,25.352994000000002 +1224,Multiclass classification,ADWIN Bagging,Keystroke,0.8912510220768601,0.8912510220768601,0.8617021305177772,3.9921913146972656,63.032035 +1632,Multiclass classification,ADWIN Bagging,Keystroke,0.9031269160024524,0.9031269160024524,0.8868998230762756,4.944231986999512,123.60027500000001 +2040,Multiclass classification,ADWIN Bagging,Keystroke,0.898970083374203,0.898970083374203,0.888705938214812,5.993730545043945,211.611387 +2448,Multiclass classification,ADWIN Bagging,Keystroke,0.8598283612586841,0.8598283612586841,0.8569666636755086,6.37155818939209,330.620683 +2856,Multiclass classification,ADWIN Bagging,Keystroke,0.8669001751313485,0.8669001751313484,0.8547854134985733,7.318934440612793,479.663409 +3264,Multiclass classification,ADWIN Bagging,Keystroke,0.8581060373889059,0.8581060373889059,0.8327540420876277,8.264909744262695,660.474615 +3672,Multiclass classification,ADWIN Bagging,Keystroke,0.8490874421138654,0.8490874421138654,0.8463961237855363,8.926459312438965,875.371562 +4080,Multiclass classification,ADWIN Bagging,Keystroke,0.8421181662172101,0.84211816621721,0.8299816031455575,10.074895858764648,1125.854174 +4488,Multiclass classification,ADWIN Bagging,Keystroke,0.8301760641854246,0.8301760641854244,0.8400819204125556,11.044804573059082,1412.144879 +4896,Multiclass classification,ADWIN Bagging,Keystroke,0.8314606741573034,0.8314606741573034,0.8387821748480373,8.862092971801758,1728.261118 +5304,Multiclass classification,ADWIN Bagging,Keystroke,0.8333019045823119,0.8333019045823119,0.8299513887279447,9.715648651123047,2074.079546 +5712,Multiclass classification,ADWIN Bagging,Keystroke,0.8255997198389073,0.8255997198389075,0.831498100235552,10.513428688049316,2449.732459 +6120,Multiclass classification,ADWIN Bagging,Keystroke,0.8241542735741134,0.8241542735741134,0.813971923025991,11.555256843566895,2856.066254 +6528,Multiclass classification,ADWIN Bagging,Keystroke,0.8043511567335683,0.8043511567335683,0.8048077550156274,12.298343658447266,3295.046229 +6936,Multiclass classification,ADWIN Bagging,Keystroke,0.8002883922134102,0.8002883922134101,0.8062362865697692,11.726844787597656,3768.4733180000003 +7344,Multiclass classification,ADWIN Bagging,Keystroke,0.8093422306959008,0.8093422306959008,0.813125473572493,9.433514595031738,4267.304275 +7752,Multiclass classification,ADWIN Bagging,Keystroke,0.8157657076506257,0.8157657076506257,0.8184378776785012,10.539642333984375,4791.9677950000005 +8160,Multiclass classification,ADWIN Bagging,Keystroke,0.8199534256649099,0.81995342566491,0.8213128379144453,11.364830017089844,5345.1630700000005 +8568,Multiclass classification,ADWIN Bagging,Keystroke,0.8247928096183028,0.8247928096183028,0.8275146627418534,12.672901153564453,5929.400246 +8976,Multiclass classification,ADWIN Bagging,Keystroke,0.8295264623955432,0.8295264623955433,0.8318915513040454,13.833264350891113,6547.972923 +9384,Multiclass classification,ADWIN Bagging,Keystroke,0.8319300863263348,0.8319300863263348,0.8336463894938194,14.741169929504395,7204.877164 +9792,Multiclass classification,ADWIN Bagging,Keystroke,0.8342355224185476,0.8342355224185476,0.8362542817352725,16.02083969116211,7900.9214680000005 +10200,Multiclass classification,ADWIN Bagging,Keystroke,0.8343955289734287,0.8343955289734286,0.833886744496364,17.251243591308594,8638.28045 +10608,Multiclass classification,ADWIN Bagging,Keystroke,0.8258697086829452,0.8258697086829452,0.823298887298616,18.484009742736816,9418.818077 +11016,Multiclass classification,ADWIN Bagging,Keystroke,0.825692237857467,0.825692237857467,0.827229896548608,17.053231239318848,10241.512178 +11424,Multiclass classification,ADWIN Bagging,Keystroke,0.8263153287227524,0.8263153287227524,0.8251000136898328,18.097841262817383,11105.510711 +11832,Multiclass classification,ADWIN Bagging,Keystroke,0.8257966359563857,0.8257966359563859,0.8251092059206939,19.1904354095459,12012.672379000001 +12240,Multiclass classification,ADWIN Bagging,Keystroke,0.8289892965111528,0.8289892965111528,0.8300645161883343,17.2155818939209,12963.554855000002 +12648,Multiclass classification,ADWIN Bagging,Keystroke,0.8324503834901558,0.8324503834901558,0.8328446288662702,17.090572357177734,13955.688030000003 +13056,Multiclass classification,ADWIN Bagging,Keystroke,0.8295672156261968,0.8295672156261968,0.8279815503081916,18.284998893737793,14990.870602000003 +13464,Multiclass classification,ADWIN Bagging,Keystroke,0.828121518235163,0.828121518235163,0.8279872572314477,18.759904861450195,16071.989638000003 +13872,Multiclass classification,ADWIN Bagging,Keystroke,0.8300771393554899,0.8300771393554899,0.8300312724960401,19.937789916992188,17198.739284000003 +14280,Multiclass classification,ADWIN Bagging,Keystroke,0.8329714966034036,0.8329714966034036,0.8330653900337638,21.172300338745117,18373.548754000003 +14688,Multiclass classification,ADWIN Bagging,Keystroke,0.8360454823994008,0.8360454823994008,0.8362319050195895,22.12139320373535,19591.296889000005 +15096,Multiclass classification,ADWIN Bagging,Keystroke,0.8391520370983769,0.8391520370983769,0.8393677597260801,22.688467979431152,20852.364231000003 +15504,Multiclass classification,ADWIN Bagging,Keystroke,0.8400309617493389,0.8400309617493388,0.8398031059873,23.806550979614258,22157.639176000004 +15912,Multiclass classification,ADWIN Bagging,Keystroke,0.8335114072025642,0.8335114072025642,0.8310693286634668,25.06292724609375,23499.792247000005 +16320,Multiclass classification,ADWIN Bagging,Keystroke,0.8283595808566702,0.8283595808566702,0.826721014765785,26.060873985290527,24886.958001000006 +16728,Multiclass classification,ADWIN Bagging,Keystroke,0.826747175225683,0.8267471752256829,0.8259678903415486,27.245673179626465,26317.085564000008 +17136,Multiclass classification,ADWIN Bagging,Keystroke,0.821943390720747,0.821943390720747,0.8202405231953956,28.675668716430664,27793.820666000007 +17544,Multiclass classification,ADWIN Bagging,Keystroke,0.8182180926865417,0.8182180926865417,0.8170173651382093,29.74225902557373,29319.213378000008 +17952,Multiclass classification,ADWIN Bagging,Keystroke,0.81878446883182,0.81878446883182,0.8179349229322325,30.879840850830078,30892.431607000006 +18360,Multiclass classification,ADWIN Bagging,Keystroke,0.821123154855929,0.821123154855929,0.8204502524156659,32.019548416137695,32513.434007000007 +18768,Multiclass classification,ADWIN Bagging,Keystroke,0.8235200085256035,0.8235200085256035,0.8229965581236837,33.157379150390625,34181.929457000006 +19176,Multiclass classification,ADWIN Bagging,Keystroke,0.819973924380704,0.819973924380704,0.8189812465563673,34.295823097229004,35895.74071300001 +19584,Multiclass classification,ADWIN Bagging,Keystroke,0.821733135883164,0.821733135883164,0.8211010404575377,35.31475067138672,37655.039070000006 +19992,Multiclass classification,ADWIN Bagging,Keystroke,0.8188684908208694,0.8188684908208694,0.8180458262517715,36.57470226287842,39458.702899 +20400,Multiclass classification,ADWIN Bagging,Keystroke,0.816559635276239,0.816559635276239,0.8159075588016685,37.855767250061035,41308.014952000005 +46,Multiclass classification,AdaBoost,ImageSegments,0.1111111111111111,0.1111111111111111,0.0815018315018315,3.4160032272338867,1.208286 +92,Multiclass classification,AdaBoost,ImageSegments,0.23076923076923078,0.23076923076923078,0.2226391771283412,4.099128723144531,4.553382 +138,Multiclass classification,AdaBoost,ImageSegments,0.4233576642335766,0.4233576642335766,0.44635377186191566,4.099002838134766,10.351245 +184,Multiclass classification,AdaBoost,ImageSegments,0.5355191256830601,0.5355191256830601,0.5617062146473912,4.099178314208984,18.765494 +230,Multiclass classification,AdaBoost,ImageSegments,0.5938864628820961,0.5938864628820961,0.6236530662596055,4.0991668701171875,30.180838 +276,Multiclass classification,AdaBoost,ImageSegments,0.6290909090909091,0.6290909090909091,0.6558170665459355,4.099109649658203,44.412895 +322,Multiclass classification,AdaBoost,ImageSegments,0.660436137071651,0.660436137071651,0.678574720261515,4.098438262939453,61.214822 +368,Multiclass classification,AdaBoost,ImageSegments,0.6920980926430518,0.6920980926430518,0.7041680355881775,4.0984954833984375,80.427396 +414,Multiclass classification,AdaBoost,ImageSegments,0.7167070217917676,0.7167070217917676,0.7259075149442813,4.097980499267578,102.254145 +460,Multiclass classification,AdaBoost,ImageSegments,0.7254901960784313,0.7254901960784313,0.7325011710849479,4.098300933837891,127.09125599999999 +506,Multiclass classification,AdaBoost,ImageSegments,0.7386138613861386,0.7386138613861386,0.7428621938273078,4.098552703857422,154.35838199999998 +552,Multiclass classification,AdaBoost,ImageSegments,0.7422867513611615,0.7422867513611615,0.7453719085253248,4.098358154296875,184.30369299999998 +598,Multiclass classification,AdaBoost,ImageSegments,0.7487437185929648,0.7487437185929648,0.7504522188790486,4.098468780517578,216.734559 +644,Multiclass classification,AdaBoost,ImageSegments,0.7465007776049767,0.7465007776049767,0.7482323503576439,4.098541259765625,252.13907799999998 +690,Multiclass classification,AdaBoost,ImageSegments,0.7489114658925979,0.748911465892598,0.7488472102580618,4.098594665527344,290.044846 +736,Multiclass classification,AdaBoost,ImageSegments,0.7523809523809524,0.7523809523809524,0.7518283723099097,4.098430633544922,330.661237 +782,Multiclass classification,AdaBoost,ImageSegments,0.7541613316261203,0.7541613316261204,0.7531089046321314,4.098361968994141,373.819675 +828,Multiclass classification,AdaBoost,ImageSegments,0.7557436517533253,0.7557436517533253,0.7552013614952863,4.098308563232422,419.68676000000005 +874,Multiclass classification,AdaBoost,ImageSegments,0.7617411225658648,0.7617411225658649,0.7601066395856337,4.098381042480469,467.91378800000007 +920,Multiclass classification,AdaBoost,ImageSegments,0.763873775843308,0.763873775843308,0.7623480483274478,4.098400115966797,518.622959 +966,Multiclass classification,AdaBoost,ImageSegments,0.7678756476683938,0.7678756476683938,0.7646598072570266,4.098423004150391,571.868767 +1012,Multiclass classification,AdaBoost,ImageSegments,0.7705242334322453,0.7705242334322453,0.7668271197983111,4.098529815673828,627.754669 +1058,Multiclass classification,AdaBoost,ImageSegments,0.7757805108798487,0.7757805108798487,0.7714920336037777,4.098388671875,686.3790640000001 +1104,Multiclass classification,AdaBoost,ImageSegments,0.7760652765185857,0.7760652765185856,0.7719206139767609,4.098537445068359,747.748718 +1150,Multiclass classification,AdaBoost,ImageSegments,0.7789382071366405,0.7789382071366405,0.7750313949659527,4.098442077636719,811.629001 +1196,Multiclass classification,AdaBoost,ImageSegments,0.7849372384937239,0.7849372384937239,0.7820003890472508,4.098487854003906,878.060098 +1242,Multiclass classification,AdaBoost,ImageSegments,0.7856567284448026,0.7856567284448026,0.7827470902102026,4.098438262939453,947.241797 +1288,Multiclass classification,AdaBoost,ImageSegments,0.7894327894327894,0.7894327894327894,0.785982924599392,4.0983428955078125,1018.793017 +1334,Multiclass classification,AdaBoost,ImageSegments,0.7906976744186046,0.7906976744186046,0.7876424482584368,4.098438262939453,1093.0782689999999 +1380,Multiclass classification,AdaBoost,ImageSegments,0.7933284989122552,0.7933284989122552,0.7906471924204205,4.098392486572266,1169.7117919999998 +1426,Multiclass classification,AdaBoost,ImageSegments,0.7978947368421052,0.7978947368421052,0.7945020166797493,4.098480224609375,1248.577134 +1472,Multiclass classification,AdaBoost,ImageSegments,0.8028552005438477,0.8028552005438477,0.7982243751921434,4.098472595214844,1329.680821 +1518,Multiclass classification,AdaBoost,ImageSegments,0.8035596572181938,0.8035596572181938,0.7981876534181912,4.098491668701172,1413.4983189999998 +1564,Multiclass classification,AdaBoost,ImageSegments,0.8035828534868842,0.8035828534868842,0.798634974540431,4.098518371582031,1499.9052179999999 +1610,Multiclass classification,AdaBoost,ImageSegments,0.8048477315102548,0.8048477315102549,0.7997380784882049,4.098381042480469,1588.8368169999999 +1656,Multiclass classification,AdaBoost,ImageSegments,0.8066465256797583,0.8066465256797583,0.80161945439383,4.098377227783203,1680.2495139999999 +1702,Multiclass classification,AdaBoost,ImageSegments,0.8059964726631393,0.8059964726631393,0.8024858564723997,4.098514556884766,1774.345382 +1748,Multiclass classification,AdaBoost,ImageSegments,0.8070978820835718,0.8070978820835718,0.8029124203507955,4.098423004150391,1871.065136 +1794,Multiclass classification,AdaBoost,ImageSegments,0.8081427774679308,0.8081427774679307,0.8029834045630979,4.098461151123047,1970.0377899999999 +1840,Multiclass classification,AdaBoost,ImageSegments,0.8069603045133225,0.8069603045133223,0.801927622716254,4.098594665527344,2071.588776 +1886,Multiclass classification,AdaBoost,ImageSegments,0.8053050397877984,0.8053050397877984,0.8006727596367825,4.098400115966797,2175.772942 +1932,Multiclass classification,AdaBoost,ImageSegments,0.8047643707923355,0.8047643707923355,0.7995493059800365,4.098396301269531,2282.41088 +1978,Multiclass classification,AdaBoost,ImageSegments,0.8057663125948407,0.8057663125948407,0.8003960406612564,4.0984344482421875,2391.59611 +2024,Multiclass classification,AdaBoost,ImageSegments,0.8072170044488384,0.8072170044488384,0.8005625942078284,4.098430633544922,2503.16971 +2070,Multiclass classification,AdaBoost,ImageSegments,0.8066698888351861,0.8066698888351861,0.8002110568368,4.098316192626953,2617.3694960000003 +2116,Multiclass classification,AdaBoost,ImageSegments,0.807565011820331,0.807565011820331,0.8005131307885663,4.0983428955078125,2733.922308 +2162,Multiclass classification,AdaBoost,ImageSegments,0.8079592781119852,0.8079592781119852,0.8006755955605837,4.098320007324219,2852.747139 +2208,Multiclass classification,AdaBoost,ImageSegments,0.8087902129587675,0.8087902129587675,0.8009921695193862,4.098320007324219,2973.740681 +2254,Multiclass classification,AdaBoost,ImageSegments,0.8060363959165557,0.8060363959165557,0.7987732120640717,4.0983428955078125,3097.6149410000003 +2300,Multiclass classification,AdaBoost,ImageSegments,0.8051326663766856,0.8051326663766856,0.798077892809675,4.0983428955078125,3223.9293610000004 +2310,Multiclass classification,AdaBoost,ImageSegments,0.8046773495019489,0.8046773495019489,0.7977695866822911,4.098388671875,3350.8763620000004 +1056,Multiclass classification,AdaBoost,Insects,0.6360189573459716,0.6360189573459716,0.5992691812827112,6.474042892456055,88.969298 +2112,Multiclass classification,AdaBoost,Insects,0.6110847939365229,0.6110847939365229,0.5773210074897359,6.473905563354492,256.045675 +3168,Multiclass classification,AdaBoost,Insects,0.6043574360593622,0.6043574360593622,0.5704368753709179,6.473470687866211,489.83548 +4224,Multiclass classification,AdaBoost,Insects,0.6014681506038362,0.6014681506038362,0.5676969561642586,6.473196029663086,783.3519140000001 +5280,Multiclass classification,AdaBoost,Insects,0.6057965523773442,0.6057965523773442,0.5710016183775801,6.473196029663086,1130.801617 +6336,Multiclass classification,AdaBoost,Insects,0.5966850828729282,0.5966850828729282,0.5635903588556204,6.473356246948242,1527.884634 +7392,Multiclass classification,AdaBoost,Insects,0.5957245298335814,0.5957245298335814,0.5625002603439991,6.473814010620117,1971.450931 +8448,Multiclass classification,AdaBoost,Insects,0.5982005445720374,0.5982005445720374,0.5646892369665863,6.474157333374023,2461.351589 +9504,Multiclass classification,AdaBoost,Insects,0.596337998526781,0.596337998526781,0.5627085514562804,6.47450065612793,2997.75158 +10560,Multiclass classification,AdaBoost,Insects,0.5965527038545316,0.5965527038545316,0.5631320282838163,6.47468376159668,3580.189969 +11616,Multiclass classification,AdaBoost,Insects,0.5953508394317693,0.5953508394317693,0.562671447170627,6.47468376159668,4209.202801 +12672,Multiclass classification,AdaBoost,Insects,0.5979796385447084,0.5979796385447084,0.5680559575776837,6.474340438842773,4886.872526 +13728,Multiclass classification,AdaBoost,Insects,0.610767101333139,0.610767101333139,0.5941277335666079,6.473836898803711,5609.570035 +14784,Multiclass classification,AdaBoost,Insects,0.6019752418318338,0.6019752418318338,0.5851264744797859,6.473745346069336,6378.998584999999 +15840,Multiclass classification,AdaBoost,Insects,0.5705536965717533,0.5705536965717533,0.5545059657048704,6.473974227905273,7193.860588999999 +16896,Multiclass classification,AdaBoost,Insects,0.548091151228174,0.548091151228174,0.5320735507355622,6.474386215209961,8051.816493999999 +17952,Multiclass classification,AdaBoost,Insects,0.5307225224221492,0.5307225224221492,0.5138536287616571,6.474637985229492,8952.059017 +19008,Multiclass classification,AdaBoost,Insects,0.5182827379386542,0.5182827379386542,0.4990809738484312,6.47486686706543,9893.706361 +20064,Multiclass classification,AdaBoost,Insects,0.5182176145142801,0.5182176145142801,0.497867701567998,8.642622947692871,10881.966771000001 +21120,Multiclass classification,AdaBoost,Insects,0.5272503432927695,0.5272503432927695,0.5067114684709674,15.437758445739746,11917.076256 +22176,Multiclass classification,AdaBoost,Insects,0.533032694475761,0.533032694475761,0.5127471323280748,16.81709384918213,12999.258268 +23232,Multiclass classification,AdaBoost,Insects,0.5410442942619775,0.5410442942619775,0.5207771198745245,17.041016578674316,14124.681759 +24288,Multiclass classification,AdaBoost,Insects,0.5459710956478775,0.5459710956478775,0.5251711652768184,17.038064002990723,15290.808705 +25344,Multiclass classification,AdaBoost,Insects,0.5532099593576135,0.5532099593576135,0.5314216535856217,17.036622047424316,16491.42669 +26400,Multiclass classification,AdaBoost,Insects,0.5607788173794462,0.5607788173794462,0.5375130024626694,17.14900016784668,17723.115047 +27456,Multiclass classification,AdaBoost,Insects,0.5667091604443635,0.5667091604443635,0.5418496825562071,17.261820793151855,18986.675500999998 +28512,Multiclass classification,AdaBoost,Insects,0.5692890463329943,0.5692890463329943,0.5455529487931667,17.26294231414795,20283.404340999998 +29568,Multiclass classification,AdaBoost,Insects,0.5688436432509216,0.5688436432509216,0.5481992899375988,17.26337718963623,21617.011828 +30624,Multiclass classification,AdaBoost,Insects,0.5687228553701467,0.5687228553701467,0.5505043481720591,17.26330852508545,22980.237824 +31680,Multiclass classification,AdaBoost,Insects,0.5691467533697402,0.5691467533697402,0.5529220328647554,17.262804985046387,24378.053405 +32736,Multiclass classification,AdaBoost,Insects,0.5703986558729189,0.5703986558729189,0.5556828084411201,17.262507438659668,25809.039082 +33792,Multiclass classification,AdaBoost,Insects,0.5650025154626972,0.5650025154626972,0.5507695387439543,17.487704277038574,27274.413313999998 +34848,Multiclass classification,AdaBoost,Insects,0.5587281545039745,0.5587281545039745,0.5445349362041821,17.929275512695312,28775.770082 +35904,Multiclass classification,AdaBoost,Insects,0.5541876723393588,0.5541876723393588,0.5396635045593164,18.030207633972168,30311.158156999998 +36960,Multiclass classification,AdaBoost,Insects,0.549122000054114,0.549122000054114,0.5343517375956978,19.681435585021973,31880.342243 +38016,Multiclass classification,AdaBoost,Insects,0.5473628830724714,0.5473628830724714,0.5321033552605493,21.718143463134766,33482.866823 +39072,Multiclass classification,AdaBoost,Insects,0.5426787131120269,0.5426787131120269,0.52803892360078,22.487850189208984,35116.535538 +40128,Multiclass classification,AdaBoost,Insects,0.5419293742367982,0.5419293742367982,0.5284857300708793,23.76238250732422,36778.160188999995 +41184,Multiclass classification,AdaBoost,Insects,0.5417769467984362,0.5417769467984362,0.5296361895775551,23.860816955566406,38464.674338 +42240,Multiclass classification,AdaBoost,Insects,0.5422240109851085,0.5422240109851085,0.5313111510734391,24.196860313415527,40175.576412999995 +43296,Multiclass classification,AdaBoost,Insects,0.5444970550871925,0.5444970550871925,0.5344195798463859,24.296037673950195,41906.99288399999 +44352,Multiclass classification,AdaBoost,Insects,0.5463461928705102,0.5463461928705102,0.5369578677381479,24.84640598297119,43659.477484999996 +45408,Multiclass classification,AdaBoost,Insects,0.5482855066399454,0.5482855066399454,0.5392181145139481,25.28636360168457,45430.571796 +46464,Multiclass classification,AdaBoost,Insects,0.5506532079288896,0.5506532079288896,0.5419048601727473,25.498303413391113,47220.970484 +47520,Multiclass classification,AdaBoost,Insects,0.5514846692901787,0.5514846692901787,0.5429796926051395,25.823355674743652,49033.125825999996 +48576,Multiclass classification,AdaBoost,Insects,0.5515388574369532,0.5515388574369532,0.543031483592694,25.821112632751465,50867.580247 +49632,Multiclass classification,AdaBoost,Insects,0.551953416211642,0.551953416211642,0.5433574148660688,26.098894119262695,52723.913942 +50688,Multiclass classification,AdaBoost,Insects,0.5563359441276856,0.5563359441276856,0.5472854805195191,26.366034507751465,54600.511786999996 +51744,Multiclass classification,AdaBoost,Insects,0.5623562607502464,0.5623562607502464,0.552981536157949,27.10032081604004,56496.789585 +52800,Multiclass classification,AdaBoost,Insects,0.5634576412432054,0.5634576412432054,0.5545218292020726,27.943178176879883,58415.671716 +52848,Multiclass classification,AdaBoost,Insects,0.5635324616345299,0.5635324616345299,0.5546220283668154,27.942995071411133,60335.727695999994 +408,Multiclass classification,AdaBoost,Keystroke,0.9877149877149877,0.9877149877149877,0.7696139476961394,2.1207275390625,4.211814 +816,Multiclass classification,AdaBoost,Keystroke,0.9889570552147239,0.9889570552147239,0.9592655637573824,2.9369373321533203,23.739993000000002 +1224,Multiclass classification,AdaBoost,Keystroke,0.9836467702371219,0.9836467702371219,0.9326470331192014,4.590028762817383,86.527265 +1632,Multiclass classification,AdaBoost,Keystroke,0.9828326180257511,0.9828326180257511,0.9594506659780556,5.819695472717285,184.232691 +2040,Multiclass classification,AdaBoost,Keystroke,0.9705738106915155,0.9705738106915155,0.9304838721924584,8.549582481384277,323.308574 +2448,Multiclass classification,AdaBoost,Keystroke,0.9607682876992235,0.9607682876992235,0.9455756842664337,10.061903953552246,491.40663400000005 +2856,Multiclass classification,AdaBoost,Keystroke,0.9541155866900175,0.9541155866900175,0.9254688528922778,12.678574562072754,687.150288 +3264,Multiclass classification,AdaBoost,Keystroke,0.9436101746858719,0.9436101746858719,0.9191430707434157,16.086813926696777,906.458431 +3672,Multiclass classification,AdaBoost,Keystroke,0.9403432307273223,0.9403432307273223,0.9284235615798526,18.255277633666992,1151.002639 +4080,Multiclass classification,AdaBoost,Keystroke,0.9338073057121844,0.9338073057121844,0.9182429705382059,21.65336036682129,1427.8669570000002 +4488,Multiclass classification,AdaBoost,Keystroke,0.9318029864051705,0.9318029864051705,0.9319119487505448,22.768765449523926,1733.8095280000002 +4896,Multiclass classification,AdaBoost,Keystroke,0.9317671092951992,0.9317671092951992,0.9296889978700974,24.966033935546875,2062.8329900000003 +5304,Multiclass classification,AdaBoost,Keystroke,0.9281538751650009,0.9281538751650009,0.9197653564039141,29.062508583068848,2423.7318920000002 +5712,Multiclass classification,AdaBoost,Keystroke,0.9227805988443355,0.9227805988443355,0.9201475418022375,32.12655830383301,2815.063536 +6120,Multiclass classification,AdaBoost,Keystroke,0.9177970256577872,0.9177970256577872,0.9072843264203106,37.27707767486572,3238.118927 +6528,Multiclass classification,AdaBoost,Keystroke,0.9115979776313774,0.9115979776313774,0.909931232789514,41.43412208557129,3706.915394 +6936,Multiclass classification,AdaBoost,Keystroke,0.9129055515501081,0.9129055515501081,0.9153430596364791,44.48411560058594,4207.758914 +7344,Multiclass classification,AdaBoost,Keystroke,0.9135230832084978,0.9135230832084978,0.9124682676754273,47.44067192077637,4740.722969 +7752,Multiclass classification,AdaBoost,Keystroke,0.9121403689846471,0.9121403689846471,0.9121831707972875,51.03960132598877,5307.755902000001 +8160,Multiclass classification,AdaBoost,Keystroke,0.9086897904154921,0.9086897904154921,0.9062633734460517,56.064818382263184,5918.019803000001 +8568,Multiclass classification,AdaBoost,Keystroke,0.9059180576631259,0.9059180576631259,0.9058259471519292,61.820496559143066,6575.962782000001 +8976,Multiclass classification,AdaBoost,Keystroke,0.9042896935933148,0.9042896935933148,0.9043251050138335,64.74030494689941,7280.842530000002 +9384,Multiclass classification,AdaBoost,Keystroke,0.9018437599914739,0.9018437599914739,0.9009662752730246,68.81300067901611,8035.1031440000015 +9792,Multiclass classification,AdaBoost,Keystroke,0.8971504442855683,0.8971504442855683,0.8956423708961025,74.25286674499512,8855.073479000002 +10200,Multiclass classification,AdaBoost,Keystroke,0.8926365329934307,0.8926365329934307,0.8903074227158838,79.6785535812378,9744.976722000003 +10608,Multiclass classification,AdaBoost,Keystroke,0.8846987838220043,0.8846987838220043,0.8819820059100918,85.28873825073242,10726.537155000004 +11016,Multiclass classification,AdaBoost,Keystroke,0.8791647753064004,0.8791647753064004,0.8795835231396919,89.59383392333984,11788.438997000003 +11424,Multiclass classification,AdaBoost,Keystroke,0.8759520266129738,0.8759520266129738,0.8744149508862001,94.86630344390869,12917.703791000004 +11832,Multiclass classification,AdaBoost,Keystroke,0.872200152142676,0.872200152142676,0.8717012117300328,100.15169906616211,14117.771334000003 +12240,Multiclass classification,AdaBoost,Keystroke,0.8736824903995425,0.8736824903995425,0.8749440738646468,101.93578433990479,15365.994884000003 +12648,Multiclass classification,AdaBoost,Keystroke,0.8717482406894915,0.8717482406894915,0.8710221211412438,107.46908473968506,16670.526324000002 +13056,Multiclass classification,AdaBoost,Keystroke,0.8661815396399847,0.8661815396399847,0.8651994621744733,112.71690273284912,18043.485216 +13464,Multiclass classification,AdaBoost,Keystroke,0.8642204560647702,0.8642204560647702,0.8645487273027374,116.56386756896973,19480.298866 +13872,Multiclass classification,AdaBoost,Keystroke,0.8619421815298104,0.8619421815298104,0.862314869215492,121.52821636199951,20980.471725 +14280,Multiclass classification,AdaBoost,Keystroke,0.859163806989285,0.859163806989285,0.8592780138529494,125.80194187164307,22534.622977 +14688,Multiclass classification,AdaBoost,Keystroke,0.8591952066453326,0.8591952066453326,0.8604793833246808,129.6016607284546,24135.768583999998 +15096,Multiclass classification,AdaBoost,Keystroke,0.8607485922490891,0.8607485922490891,0.8621609789956539,132.68816757202148,25779.863983 +15504,Multiclass classification,AdaBoost,Keystroke,0.8604141133974069,0.8604141133974069,0.8613237595899307,136.05841445922852,27480.057181 +15912,Multiclass classification,AdaBoost,Keystroke,0.8536861290930803,0.8536861290930803,0.853192144751886,141.70578575134277,29254.423593 +16320,Multiclass classification,AdaBoost,Keystroke,0.8493167473497151,0.849316747349715,0.8496464102754333,147.49746799468994,31089.50572 +16728,Multiclass classification,AdaBoost,Keystroke,0.846296407006636,0.846296407006636,0.8470383589757107,153.1935510635376,32973.577156 +17136,Multiclass classification,AdaBoost,Keystroke,0.8411438576014006,0.8411438576014006,0.8410396667771575,156.28132915496826,34948.88082 +17544,Multiclass classification,AdaBoost,Keystroke,0.8365729920766117,0.8365729920766117,0.8367907010021001,161.03080940246582,36967.519165 +17952,Multiclass classification,AdaBoost,Keystroke,0.8355523369171634,0.8355523369171634,0.8362918425397341,166.63249397277832,39016.229588999995 +18360,Multiclass classification,AdaBoost,Keystroke,0.837572852551882,0.8375728525518821,0.8385662484273668,171.47760772705078,41092.028592999995 +18768,Multiclass classification,AdaBoost,Keystroke,0.8390259498055097,0.8390259498055097,0.8401126675526959,175.70373821258545,43194.947863999994 +19176,Multiclass classification,AdaBoost,Keystroke,0.8376531942633637,0.8376531942633637,0.838676297522501,180.87701034545898,45330.650987999994 +19584,Multiclass classification,AdaBoost,Keystroke,0.8390951335341879,0.8390951335341879,0.8403338496937821,185.05438709259033,47482.84587799999 +19992,Multiclass classification,AdaBoost,Keystroke,0.8372767745485469,0.8372767745485468,0.8385640183306876,190.1383810043335,49661.235199999996 +20400,Multiclass classification,AdaBoost,Keystroke,0.8347958233246727,0.8347958233246727,0.8360623278174891,194.794171333313,51861.27850099999 +46,Multiclass classification,Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.24576497265572897,4.149084091186523,2.196675 +92,Multiclass classification,Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.4934752395581889,4.152299880981445,7.023639 +138,Multiclass classification,Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646678,4.15202522277832,15.046926 +184,Multiclass classification,Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.6141431648908949,4.152608871459961,26.297795 +230,Multiclass classification,Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,4.151983261108398,40.50873 +276,Multiclass classification,Bagging,ImageSegments,0.64,0.64,0.6559607038460421,4.152521133422852,57.698206 +322,Multiclass classification,Bagging,ImageSegments,0.6666666666666666,0.6666666666666666,0.6673617488913626,4.152231216430664,77.585785 +368,Multiclass classification,Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6911959597548878,4.152448654174805,100.185488 +414,Multiclass classification,Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.7079630503641953,4.152788162231445,125.71728800000001 +460,Multiclass classification,Bagging,ImageSegments,0.7124183006535948,0.7124183006535948,0.7065500352371009,4.152704238891602,154.000542 +506,Multiclass classification,Bagging,ImageSegments,0.7207920792079208,0.7207920792079208,0.7127593158348896,4.152563095092773,184.883226 +552,Multiclass classification,Bagging,ImageSegments,0.7259528130671506,0.7259528130671506,0.7192025503807162,4.152528762817383,218.482328 +598,Multiclass classification,Bagging,ImageSegments,0.7319932998324958,0.7319932998324957,0.7251188986558661,4.152769088745117,254.840787 +644,Multiclass classification,Bagging,ImageSegments,0.7309486780715396,0.7309486780715396,0.7259740406437201,4.152563095092773,294.12903800000004 +690,Multiclass classification,Bagging,ImageSegments,0.7358490566037735,0.7358490566037735,0.7304359912942561,4.152692794799805,336.073433 +736,Multiclass classification,Bagging,ImageSegments,0.7374149659863946,0.7374149659863947,0.733149934717071,4.152753829956055,380.701162 +782,Multiclass classification,Bagging,ImageSegments,0.7426376440460948,0.7426376440460948,0.7385597120510639,4.152643203735352,428.175969 +828,Multiclass classification,Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7412375783772316,4.152631759643555,478.460063 +874,Multiclass classification,Bagging,ImageSegments,0.7491408934707904,0.7491408934707904,0.7454343548790067,4.153181076049805,531.417765 +920,Multiclass classification,Bagging,ImageSegments,0.7486398258977149,0.7486398258977149,0.7441307384051415,4.153326034545898,587.1362770000001 +966,Multiclass classification,Bagging,ImageSegments,0.7492227979274612,0.749222797927461,0.7439306216964366,4.153120040893555,645.6842 +1012,Multiclass classification,Bagging,ImageSegments,0.7487636003956478,0.7487636003956478,0.7437900284473965,4.153234481811523,707.105172 +1058,Multiclass classification,Bagging,ImageSegments,0.750236518448439,0.7502365184484389,0.7448138061687654,4.153268814086914,771.2868930000001 +1104,Multiclass classification,Bagging,ImageSegments,0.7524932003626473,0.7524932003626473,0.7468314646869904,4.153234481811523,838.222518 +1150,Multiclass classification,Bagging,ImageSegments,0.7554395126196692,0.7554395126196692,0.7493227137357602,4.153413772583008,907.556087 +1196,Multiclass classification,Bagging,ImageSegments,0.7581589958158996,0.7581589958158996,0.7527652773681007,4.153318405151367,979.5797180000001 +1242,Multiclass classification,Bagging,ImageSegments,0.7574536663980661,0.7574536663980661,0.7525915384194216,4.153432846069336,1054.216781 +1288,Multiclass classification,Bagging,ImageSegments,0.7622377622377622,0.7622377622377621,0.7563448085202398,4.153615951538086,1131.5718310000002 +1334,Multiclass classification,Bagging,ImageSegments,0.7621905476369092,0.7621905476369092,0.7566636999776912,4.153776168823242,1211.5912470000003 +1380,Multiclass classification,Bagging,ImageSegments,0.7635968092820885,0.7635968092820886,0.7587252257765656,4.153825759887695,1294.4019940000003 +1426,Multiclass classification,Bagging,ImageSegments,0.7663157894736842,0.7663157894736842,0.7609139797315134,4.153848648071289,1379.8910190000004 +1472,Multiclass classification,Bagging,ImageSegments,0.7709041468388851,0.7709041468388851,0.7637689949207689,4.153989791870117,1467.9946540000003 +1518,Multiclass classification,Bagging,ImageSegments,0.7719182597231378,0.7719182597231378,0.7639714255563932,4.154367446899414,1558.8129900000004 +1564,Multiclass classification,Bagging,ImageSegments,0.7722328854766475,0.7722328854766475,0.7650721335080709,4.154550552368164,1652.2028900000003 +1610,Multiclass classification,Bagging,ImageSegments,0.7725295214418894,0.7725295214418892,0.764505787280341,4.154642105102539,1748.3782850000002 +1656,Multiclass classification,Bagging,ImageSegments,0.7716012084592145,0.7716012084592145,0.7634170612719108,4.15452766418457,1847.3163560000003 +1702,Multiclass classification,Bagging,ImageSegments,0.7713109935332157,0.7713109935332157,0.7652815676598499,4.154825210571289,1948.7028940000002 +1748,Multiclass classification,Bagging,ImageSegments,0.77389811104751,0.77389811104751,0.7674409436090757,4.155008316040039,2052.533374 +1794,Multiclass classification,Bagging,ImageSegments,0.7752370329057445,0.7752370329057446,0.7674318582149376,4.155046463012695,2159.053176 +1840,Multiclass classification,Bagging,ImageSegments,0.7765089722675367,0.7765089722675368,0.7688731808749575,4.154977798461914,2268.233507 +1886,Multiclass classification,Bagging,ImageSegments,0.7750663129973475,0.7750663129973475,0.7678921362145585,4.154905319213867,2379.8377889999997 +1932,Multiclass classification,Bagging,ImageSegments,0.7752459865354738,0.7752459865354739,0.7671636716269125,4.155000686645508,2494.085284 +1978,Multiclass classification,Bagging,ImageSegments,0.7759231158320687,0.7759231158320687,0.7670573130332384,4.154901504516602,2611.0522539999997 +2024,Multiclass classification,Bagging,ImageSegments,0.7775580820563519,0.7775580820563519,0.7671264358471986,4.154878616333008,2730.5624909999997 +2070,Multiclass classification,Bagging,ImageSegments,0.77670372160464,0.7767037216046399,0.7665050383810529,4.15495491027832,2852.4395529999997 +2116,Multiclass classification,Bagging,ImageSegments,0.7773049645390071,0.7773049645390071,0.766340416614934,4.15495491027832,2976.9921299999996 +2162,Multiclass classification,Bagging,ImageSegments,0.7783433595557612,0.7783433595557612,0.766965714748886,4.155027389526367,3104.012504 +2208,Multiclass classification,Bagging,ImageSegments,0.780244676030811,0.780244676030811,0.7678552364681828,4.155023574829102,3233.6609839999996 +2254,Multiclass classification,Bagging,ImageSegments,0.7776298268974701,0.7776298268974701,0.7652407320979201,4.154973983764648,3365.6406429999997 +2300,Multiclass classification,Bagging,ImageSegments,0.7768595041322314,0.7768595041322314,0.764461061100325,4.15504264831543,3499.962334 +2310,Multiclass classification,Bagging,ImageSegments,0.7769597228237333,0.7769597228237333,0.7645642360301897,4.155065536499023,3634.8810719999997 +1056,Multiclass classification,Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762561,6.533428192138672,93.097088 +2112,Multiclass classification,Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.533924102783203,264.682132 +3168,Multiclass classification,Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244892,6.534633636474609,504.28420900000003 +4224,Multiclass classification,Bagging,Insects,0.6107032914989344,0.6107032914989344,0.5748501357120321,6.535015106201172,804.5259470000001 +5280,Multiclass classification,Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225517,6.535823822021484,1159.582019 +6336,Multiclass classification,Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789625,6.535648345947266,1564.000203 +7392,Multiclass classification,Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867537,6.535068511962891,2016.3102330000002 +8448,Multiclass classification,Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.534946441650391,2516.339397 +9504,Multiclass classification,Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297489,6.535068511962891,3064.243813 +10560,Multiclass classification,Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.535427093505859,3659.768381 +11616,Multiclass classification,Bagging,Insects,0.6005165733964701,0.6005165733964701,0.56512832395729,6.535404205322266,4303.8464189999995 +12672,Multiclass classification,Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306639,6.535358428955078,4997.310473 +13728,Multiclass classification,Bagging,Insects,0.6147009543235958,0.6147009543235958,0.5955104002005771,6.534030914306641,5738.022631999999 +14784,Multiclass classification,Bagging,Insects,0.6051545694378678,0.6051545694378678,0.586271708420286,6.533008575439453,6524.316427 +15840,Multiclass classification,Bagging,Insects,0.5703642906749163,0.5703642906749163,0.5530031721301686,6.534244537353516,7355.370967999999 +16896,Multiclass classification,Bagging,Insects,0.5440662918023084,0.5440662918023084,0.5274181049148582,6.532741546630859,8230.882624 +17952,Multiclass classification,Bagging,Insects,0.524650437301543,0.524650437301543,0.5077439094080566,6.533657073974609,9149.482306 +19008,Multiclass classification,Bagging,Insects,0.5142842110801283,0.5142842110801283,0.4945495171544722,5.423342704772949,10110.1367 +20064,Multiclass classification,Bagging,Insects,0.5202611772915317,0.5202611772915317,0.499632175624185,13.463048934936523,11121.939621 +21120,Multiclass classification,Bagging,Insects,0.5284814621904447,0.5284814621904447,0.5082299437323158,14.233846664428711,12202.117709999999 +22176,Multiclass classification,Bagging,Insects,0.5344757609921083,0.5344757609921083,0.5148729059414189,14.772774696350098,13344.394484999999 +23232,Multiclass classification,Bagging,Insects,0.5430674529723215,0.5430674529723215,0.5233933209280776,14.684733390808105,14542.607494 +24288,Multiclass classification,Bagging,Insects,0.5502120475974801,0.5502120475974801,0.5298443248135049,16.20911407470703,15791.070918 +25344,Multiclass classification,Bagging,Insects,0.5564061081955569,0.5564061081955569,0.5355525016331893,16.199478149414062,17093.843057 +26400,Multiclass classification,Bagging,Insects,0.561460661388689,0.561460661388689,0.5398397773012414,16.192718505859375,18441.382026 +27456,Multiclass classification,Bagging,Insects,0.564742305590967,0.564742305590967,0.5421523628031605,15.229331016540527,19838.570208 +28512,Multiclass classification,Bagging,Insects,0.5680614499666795,0.5680614499666795,0.5472893783055924,13.71937370300293,21280.868604 +29568,Multiclass classification,Bagging,Insects,0.5701288598775662,0.5701288598775662,0.55295508639855,11.343052864074707,22768.358846 +30624,Multiclass classification,Bagging,Insects,0.5724128922705156,0.5724128922705156,0.5585792537754973,9.387857437133789,24294.822849 +31680,Multiclass classification,Bagging,Insects,0.5749865841724802,0.5749865841724802,0.5636037623129485,9.38664436340332,25857.719223 +32736,Multiclass classification,Bagging,Insects,0.5781884832747823,0.5781884832747823,0.5684564968293649,9.385660171508789,27456.12944 +33792,Multiclass classification,Bagging,Insects,0.575656239827173,0.575656239827173,0.5663415557568727,7.860757827758789,29092.739018 +34848,Multiclass classification,Bagging,Insects,0.5754584325766924,0.5754584325766924,0.565994999425249,7.205549240112305,30762.023764 +35904,Multiclass classification,Bagging,Insects,0.5763863743976827,0.5763863743976827,0.5665127709334143,6.54947566986084,32461.363070000003 +36960,Multiclass classification,Bagging,Insects,0.5758813820720258,0.5758813820720258,0.56571927622701,6.547377586364746,34189.07998 +38016,Multiclass classification,Bagging,Insects,0.5767460213073786,0.5767460213073786,0.5661110063916132,6.546515464782715,35945.284935 +39072,Multiclass classification,Bagging,Insects,0.5764633615725219,0.5764633615725219,0.5659285794545608,6.543356895446777,37730.818682000005 +40128,Multiclass classification,Bagging,Insects,0.573454282652578,0.573454282652578,0.5636611811263741,8.510072708129883,39542.33547700001 +41184,Multiclass classification,Bagging,Insects,0.5726391957846685,0.5726391957846685,0.5633960246210544,8.712862014770508,41378.34519600001 +42240,Multiclass classification,Bagging,Insects,0.5723146854802433,0.5723146854802433,0.5635786987292998,10.13754653930664,43237.00688100001 +43296,Multiclass classification,Bagging,Insects,0.5717981291142165,0.5717981291142165,0.5635967907133216,10.13637924194336,45117.76335600001 +44352,Multiclass classification,Bagging,Insects,0.571103244571712,0.571103244571712,0.5633625241299441,10.135028839111328,47020.66134100001 +45408,Multiclass classification,Bagging,Insects,0.5712335102517233,0.5712335102517233,0.563808836162261,11.334146499633789,48947.97157600001 +46464,Multiclass classification,Bagging,Insects,0.5728213847577642,0.5728213847577642,0.5658781423773395,12.350201606750488,50897.74209700001 +47520,Multiclass classification,Bagging,Insects,0.576863991245607,0.576863991245607,0.5703778478941884,16.125893592834473,52890.49897200001 +48576,Multiclass classification,Bagging,Insects,0.5828512609366958,0.5828512609366958,0.5764029561430954,15.266244888305664,54904.91240000001 +49632,Multiclass classification,Bagging,Insects,0.5890270194031956,0.5890270194031956,0.5823661991476956,14.839654922485352,56940.07330000001 +50688,Multiclass classification,Bagging,Insects,0.5947087024286306,0.5947087024286306,0.5876086024291545,12.465810775756836,58994.29364000001 +51744,Multiclass classification,Bagging,Insects,0.600718937827339,0.600718937827339,0.5930357853224563,11.884730339050293,61065.77177200001 +52800,Multiclass classification,Bagging,Insects,0.6060342051932802,0.6060342051932802,0.5982060206393416,3.691446304321289,63151.215841000005 +52848,Multiclass classification,Bagging,Insects,0.6063920373909588,0.6063920373909588,0.5985419438128344,3.691621780395508,65236.99615100001 +408,Multiclass classification,Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.1448841094970703,5.867596 +816,Multiclass classification,Bagging,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,3.0916757583618164,25.808269 +1224,Multiclass classification,Bagging,Keystroke,0.8912510220768601,0.8912510220768601,0.8617021305177773,4.035944938659668,63.939426 +1632,Multiclass classification,Bagging,Keystroke,0.9031269160024524,0.9031269160024524,0.8868998230762758,4.988290786743164,125.34339 +2040,Multiclass classification,Bagging,Keystroke,0.898970083374203,0.898970083374203,0.888705938214812,6.037667274475098,214.307845 +2448,Multiclass classification,Bagging,Keystroke,0.8594196975888844,0.8594196975888844,0.8547805855679916,6.993380546569824,335.016386 +2856,Multiclass classification,Bagging,Keystroke,0.8651488616462347,0.8651488616462347,0.8483773016417727,7.939821243286133,488.12155800000005 +3264,Multiclass classification,Bagging,Keystroke,0.8553478394115844,0.8553478394115844,0.8302147847543373,8.885003089904785,675.394352 +3672,Multiclass classification,Bagging,Keystroke,0.8452737673658404,0.8452737673658404,0.8411086163638233,9.830622673034668,899.024814 +4080,Multiclass classification,Bagging,Keystroke,0.8374601618043638,0.8374601618043638,0.8238000521910981,11.003908157348633,1161.3046 +4488,Multiclass classification,Bagging,Keystroke,0.8250501448629374,0.8250501448629373,0.8343531144302688,11.974610328674316,1461.738376 +4896,Multiclass classification,Bagging,Keystroke,0.8232890704800817,0.8232890704800817,0.8292209535545839,12.919659614562988,1801.820426 +5304,Multiclass classification,Bagging,Keystroke,0.8199132566471808,0.819913256647181,0.8044565992905442,13.86521053314209,2181.861898 +5712,Multiclass classification,Bagging,Keystroke,0.7998599194536858,0.7998599194536857,0.8029484507582976,14.811628341674805,2601.779179 +6120,Multiclass classification,Bagging,Keystroke,0.7970256577872201,0.7970256577872201,0.7783451709211457,15.75713062286377,3063.5971010000003 +6528,Multiclass classification,Bagging,Keystroke,0.7720239007200858,0.7720239007200858,0.767005590841987,16.704151153564453,3570.6766780000003 +6936,Multiclass classification,Bagging,Keystroke,0.7645277577505407,0.7645277577505407,0.766187831914561,17.649503707885742,4126.519897 +7344,Multiclass classification,Bagging,Keystroke,0.773389622769985,0.7733896227699851,0.770832075885354,18.61162567138672,4733.5217410000005 +7752,Multiclass classification,Bagging,Keystroke,0.7737066185008385,0.7737066185008385,0.7718493223486268,19.557814598083496,5395.069721000001 +8160,Multiclass classification,Bagging,Keystroke,0.7765657556073048,0.7765657556073047,0.7724710929560354,20.503721237182617,6113.943535 +8568,Multiclass classification,Bagging,Keystroke,0.7730827594257033,0.7730827594257033,0.7727491763630034,21.882675170898438,6890.839823 +8976,Multiclass classification,Bagging,Keystroke,0.7714763231197772,0.7714763231197772,0.7717207236627096,22.87528133392334,7728.212391 +9384,Multiclass classification,Bagging,Keystroke,0.7702227432590856,0.7702227432590856,0.7694267539223918,23.822596549987793,8626.275614 +9792,Multiclass classification,Bagging,Keystroke,0.7656010621999796,0.7656010621999795,0.7644081311179032,24.768078804016113,9586.24664 +10200,Multiclass classification,Bagging,Keystroke,0.757623296401608,0.757623296401608,0.749720417225094,25.71299648284912,10618.940127 +10608,Multiclass classification,Bagging,Keystroke,0.737154709154332,0.737154709154332,0.7245707699101513,26.660439491271973,11726.561153 +11016,Multiclass classification,Bagging,Keystroke,0.729822968679074,0.7298229686790739,0.7256689004292383,27.605186462402344,12907.41343 +11424,Multiclass classification,Bagging,Keystroke,0.7229274271207213,0.7229274271207213,0.7092514304350318,28.551199913024902,14153.769988 +11832,Multiclass classification,Bagging,Keystroke,0.7133801031189249,0.7133801031189249,0.7054771135814562,29.4963436126709,15465.906612 +12240,Multiclass classification,Bagging,Keystroke,0.7177874009314487,0.7177874009314487,0.7138351093258007,30.441871643066406,16835.329364 +12648,Multiclass classification,Bagging,Keystroke,0.7147149521625682,0.7147149521625682,0.7065885995198201,31.388431549072266,18265.757575 +13056,Multiclass classification,Bagging,Keystroke,0.7031788586748372,0.7031788586748372,0.6954173783902821,32.33424186706543,19760.458564 +13464,Multiclass classification,Bagging,Keystroke,0.7011067369828419,0.7011067369828419,0.6966368809795416,33.27959156036377,21319.158047 +13872,Multiclass classification,Bagging,Keystroke,0.7007425564126595,0.7007425564126595,0.6971102154727419,34.22630214691162,22941.129728 +14280,Multiclass classification,Bagging,Keystroke,0.6961972126899643,0.6961972126899643,0.691133802747568,35.17108726501465,24623.129677 +14688,Multiclass classification,Bagging,Keystroke,0.698781235105876,0.698781235105876,0.696592906911097,36.11711597442627,26362.470984 +15096,Multiclass classification,Bagging,Keystroke,0.7048029148724744,0.7048029148724744,0.702773358939844,37.0643196105957,28156.052692 +15504,Multiclass classification,Bagging,Keystroke,0.7047668193252918,0.7047668193252918,0.7013012225519919,38.00920104980469,30004.434818 +15912,Multiclass classification,Bagging,Keystroke,0.6956822324178241,0.6956822324178241,0.6887843659114408,38.955204010009766,31904.325566000003 +16320,Multiclass classification,Bagging,Keystroke,0.6869906244255163,0.6869906244255163,0.6817298949676788,39.901418685913086,33880.147234000004 +16728,Multiclass classification,Bagging,Keystroke,0.6840437615830693,0.6840437615830693,0.6809878840610977,40.84670162200928,35894.19480500001 +17136,Multiclass classification,Bagging,Keystroke,0.6798949518529326,0.6798949518529326,0.6760668667678135,42.678324699401855,37945.35177200001 +17544,Multiclass classification,Bagging,Keystroke,0.6725759562218548,0.6725759562218548,0.6693298574086026,43.72208595275879,40033.19473500001 +17952,Multiclass classification,Bagging,Keystroke,0.6715503314578575,0.6715503314578575,0.6700615486077944,44.668694496154785,42156.41544100001 +18360,Multiclass classification,Bagging,Keystroke,0.6768887194291628,0.6768887194291628,0.6760264883444682,45.61451721191406,44306.462280000014 +18768,Multiclass classification,Bagging,Keystroke,0.6818884211648105,0.6818884211648105,0.6814185274246665,46.561092376708984,46484.07667300002 +19176,Multiclass classification,Bagging,Keystroke,0.6739504563233377,0.6739504563233377,0.6724064481498903,47.50611400604248,48682.185033000016 +19584,Multiclass classification,Bagging,Keystroke,0.677883878874534,0.677883878874534,0.6774885006147249,48.451809883117676,50904.928535000014 +19992,Multiclass classification,Bagging,Keystroke,0.6733530088539843,0.6733530088539843,0.6729949515014169,49.39821243286133,53145.742009000016 +20400,Multiclass classification,Bagging,Keystroke,0.6697387126819943,0.6697387126819943,0.6699810213452306,50.34487438201904,55411.38251600001 +46,Multiclass classification,Leveraging Bagging,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,4.0974016189575195,6.997987 +92,Multiclass classification,Leveraging Bagging,ImageSegments,0.5164835164835165,0.5164835164835165,0.5316649744849407,4.0979814529418945,22.017115 +138,Multiclass classification,Leveraging Bagging,ImageSegments,0.5547445255474452,0.5547445255474452,0.5804654781117263,4.0981035232543945,44.610383999999996 +184,Multiclass classification,Leveraging Bagging,ImageSegments,0.6174863387978142,0.6174863387978142,0.6394923756219437,4.0987138748168945,74.61421299999999 +230,Multiclass classification,Leveraging Bagging,ImageSegments,0.6506550218340611,0.6506550218340611,0.66859135700569,4.0987138748168945,111.65332099999999 +276,Multiclass classification,Leveraging Bagging,ImageSegments,0.6618181818181819,0.6618181818181819,0.6795855359270878,4.098832130432129,156.076644 +322,Multiclass classification,Leveraging Bagging,ImageSegments,0.6853582554517134,0.6853582554517134,0.6872635633687633,4.099373817443848,207.57491499999998 +368,Multiclass classification,Leveraging Bagging,ImageSegments,0.7111716621253406,0.7111716621253404,0.7098417316927395,4.099347114562988,266.34173899999996 +414,Multiclass classification,Leveraging Bagging,ImageSegments,0.7215496368038741,0.7215496368038742,0.7201557312728714,4.09926700592041,332.356571 +460,Multiclass classification,Leveraging Bagging,ImageSegments,0.7211328976034859,0.721132897603486,0.7175330036146421,4.099320411682129,405.380301 +506,Multiclass classification,Leveraging Bagging,ImageSegments,0.7287128712871287,0.7287128712871287,0.7233455022590812,4.099320411682129,485.520305 +552,Multiclass classification,Leveraging Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7255599965917697,4.099240303039551,572.983507 +598,Multiclass classification,Leveraging Bagging,ImageSegments,0.7353433835845896,0.7353433835845896,0.7308494254186014,4.0992631912231445,667.526521 +644,Multiclass classification,Leveraging Bagging,ImageSegments,0.7340590979782271,0.7340590979782271,0.7314183982762247,4.099823951721191,768.914228 +690,Multiclass classification,Leveraging Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7343909641298695,4.099823951721191,877.069835 +736,Multiclass classification,Leveraging Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7369557659594496,4.099850654602051,992.1901310000001 +782,Multiclass classification,Leveraging Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7419020281650245,4.099850654602051,1114.103609 +828,Multiclass classification,Leveraging Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7432199627682998,4.099850654602051,1242.576589 +874,Multiclass classification,Leveraging Bagging,ImageSegments,0.7502863688430699,0.7502863688430699,0.7482089866208982,4.099850654602051,1377.530874 +920,Multiclass classification,Leveraging Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7477650187313974,4.099823951721191,1518.374517 +966,Multiclass classification,Leveraging Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.747322646811651,4.099823951721191,1664.8953589999999 +1012,Multiclass classification,Leveraging Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.7469783619055548,4.099823951721191,1817.1987969999998 +1058,Multiclass classification,Leveraging Bagging,ImageSegments,0.7530747398297067,0.7530747398297066,0.7482363934596314,4.099823951721191,1975.1124209999998 +1104,Multiclass classification,Leveraging Bagging,ImageSegments,0.7552130553037172,0.7552130553037172,0.750118495060715,4.0998735427856445,2138.658016 +1150,Multiclass classification,Leveraging Bagging,ImageSegments,0.7571801566579635,0.7571801566579635,0.7516199800653577,4.0998735427856445,2307.825702 +1196,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598326359832636,0.7598326359832636,0.7548841797367702,4.0998735427856445,2482.820129 +1242,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598710717163578,0.7598710717163577,0.7553301531902636,4.0998735427856445,2663.4478950000002 +1288,Multiclass classification,Leveraging Bagging,ImageSegments,0.7645687645687645,0.7645687645687647,0.7590078532621816,4.1004838943481445,2849.419913 +1334,Multiclass classification,Leveraging Bagging,ImageSegments,0.7644411102775694,0.7644411102775694,0.7591993978414527,4.100506782531738,3040.9965970000003 +1380,Multiclass classification,Leveraging Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7601575050520947,4.100506782531738,3238.5768190000003 +1426,Multiclass classification,Leveraging Bagging,ImageSegments,0.7670175438596492,0.7670175438596492,0.7613339877221927,4.100506782531738,3441.4807240000005 +1472,Multiclass classification,Leveraging Bagging,ImageSegments,0.7715839564921821,0.7715839564921821,0.7641396475218201,4.100552558898926,3649.5015090000006 +1518,Multiclass classification,Leveraging Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.7648275341801108,4.100552558898926,3862.6942700000004 +1564,Multiclass classification,Leveraging Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7657569341108763,4.100552558898926,4080.8910560000004 +1610,Multiclass classification,Leveraging Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7651494083475014,4.1005754470825195,4304.577590000001 +1656,Multiclass classification,Leveraging Bagging,ImageSegments,0.7740181268882175,0.7740181268882175,0.7654813489818475,4.100529670715332,4533.710142000001 +1702,Multiclass classification,Leveraging Bagging,ImageSegments,0.7730746619635509,0.7730746619635509,0.766493027961906,4.100529670715332,4767.793233000001 +1748,Multiclass classification,Leveraging Bagging,ImageSegments,0.7756153405838581,0.7756153405838581,0.7686072256536652,4.100529670715332,5007.029776000001 +1794,Multiclass classification,Leveraging Bagging,ImageSegments,0.7769102063580591,0.7769102063580591,0.7685414235990152,4.100502967834473,5251.440116000002 +1840,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781402936378466,0.7781402936378466,0.7699957723931323,4.100502967834473,5500.964415000001 +1886,Multiclass classification,Leveraging Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7684985598909853,4.100502967834473,5755.503987000001 +1932,Multiclass classification,Leveraging Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7677434418046419,4.100502967834473,6014.862306000001 +1978,Multiclass classification,Leveraging Bagging,ImageSegments,0.7774405665149215,0.7774405665149215,0.7684788817649146,4.100502967834473,6279.121569000001 +2024,Multiclass classification,Leveraging Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7689103339153599,4.100502967834473,6548.278113000001 +2070,Multiclass classification,Leveraging Bagging,ImageSegments,0.7786370227162881,0.7786370227162881,0.7686288077529282,4.100502967834473,6822.363214000001 +2116,Multiclass classification,Leveraging Bagging,ImageSegments,0.7791962174940898,0.7791962174940898,0.768391950800897,4.100502967834473,7101.096348000001 +2162,Multiclass classification,Leveraging Bagging,ImageSegments,0.7801943544655252,0.7801943544655253,0.768962628827985,4.100525856018066,7384.333285000001 +2208,Multiclass classification,Leveraging Bagging,ImageSegments,0.7820570910738559,0.7820570910738559,0.7698068761587117,4.100499153137207,7672.298476000001 +2254,Multiclass classification,Leveraging Bagging,ImageSegments,0.7789613848202397,0.7789613848202397,0.7667173742344939,4.100499153137207,7965.117559000001 +2300,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781644193127447,0.7781644193127447,0.7659138381656089,4.100499153137207,8262.647904000001 +2310,Multiclass classification,Leveraging Bagging,ImageSegments,0.7782589865742746,0.7782589865742745,0.7660163657276376,4.100499153137207,8561.303246000001 +1056,Multiclass classification,Leveraging Bagging,Insects,0.6218009478672986,0.6218009478672986,0.5857016652718549,6.471495628356934,220.837673 +2112,Multiclass classification,Leveraging Bagging,Insects,0.6196115585030791,0.6196115585030791,0.5856756432415233,10.302834510803223,598.297395 +3168,Multiclass classification,Leveraging Bagging,Insects,0.628986422481844,0.628986422481844,0.5949930595607559,19.024110794067383,1103.516793 +4224,Multiclass classification,Leveraging Bagging,Insects,0.6294103717736207,0.6294103717736207,0.5952675443708706,19.52926254272461,1735.893967 +5280,Multiclass classification,Leveraging Bagging,Insects,0.6364841826103429,0.6364841826103429,0.5994911272790603,18.82306957244873,2497.807238 +6336,Multiclass classification,Leveraging Bagging,Insects,0.6352012628255722,0.6352012628255722,0.5993891820807258,20.00343894958496,3379.788115 +7392,Multiclass classification,Leveraging Bagging,Insects,0.638749830875389,0.638749830875389,0.6030343276880051,20.9547061920166,4385.582643 +8448,Multiclass classification,Leveraging Bagging,Insects,0.6405824553095774,0.6405824553095774,0.6028521616895871,23.98197650909424,5520.259032 +9504,Multiclass classification,Leveraging Bagging,Insects,0.6449542249815847,0.6449542249815847,0.6055705492028415,24.687146186828613,6764.141036999999 +10560,Multiclass classification,Leveraging Bagging,Insects,0.6485462638507434,0.6485462638507434,0.6081614166360887,28.76917839050293,8102.806145 +11616,Multiclass classification,Leveraging Bagging,Insects,0.6490744726646578,0.6490744726646578,0.6078786452761632,30.803756713867188,9530.02909 +12672,Multiclass classification,Leveraging Bagging,Insects,0.6514876489621971,0.6514876489621971,0.6111938480023122,35.14385414123535,11044.697830000001 +13728,Multiclass classification,Leveraging Bagging,Insects,0.6707947840023312,0.6707947840023312,0.6607574394823457,17.51351547241211,12617.098737 +14784,Multiclass classification,Leveraging Bagging,Insects,0.6821348846648176,0.6821348846648176,0.6733632096765088,9.275564193725586,14250.678949000001 +15840,Multiclass classification,Leveraging Bagging,Insects,0.6778205694803965,0.6778205694803965,0.670556396248407,11.964457511901855,15956.730999000001 +16896,Multiclass classification,Leveraging Bagging,Insects,0.6754661142349808,0.6754661142349808,0.6690281338426608,12.60369873046875,17732.973803 +17952,Multiclass classification,Leveraging Bagging,Insects,0.6721631106902123,0.6721631106902123,0.6660357480506892,12.93508529663086,19577.321708 +19008,Multiclass classification,Leveraging Bagging,Insects,0.6856947440416689,0.6856947440416689,0.6751812770122833,14.563780784606934,21465.395048 +20064,Multiclass classification,Leveraging Bagging,Insects,0.6926680954991776,0.6926680954991776,0.6785701715539604,23.61655616760254,23398.659989 +21120,Multiclass classification,Leveraging Bagging,Insects,0.6942090061082438,0.6942090061082438,0.6784920731228882,30.020954132080078,25401.280766 +22176,Multiclass classification,Leveraging Bagging,Insects,0.6958737316798196,0.6958737316798196,0.6784853924286285,31.293453216552734,27443.688764 +23232,Multiclass classification,Leveraging Bagging,Insects,0.6989798114588266,0.6989798114588266,0.6799590657327791,29.59604263305664,29526.276676999998 +24288,Multiclass classification,Leveraging Bagging,Insects,0.7011981718614897,0.7011981718614897,0.680282364066019,32.615909576416016,31645.669427999997 +25344,Multiclass classification,Leveraging Bagging,Insects,0.7031527443475516,0.7031527443475516,0.6805566439417602,33.91432285308838,33792.819539 +26400,Multiclass classification,Leveraging Bagging,Insects,0.7051782264479716,0.7051782264479716,0.6809495737401271,35.12977695465088,35966.301701 +27456,Multiclass classification,Leveraging Bagging,Insects,0.7065743944636678,0.7065743944636678,0.6805936316849747,38.84447956085205,38159.78466 +28512,Multiclass classification,Leveraging Bagging,Insects,0.7054820946301428,0.7054820946301428,0.681225779493031,34.570815086364746,40377.715598999996 +29568,Multiclass classification,Leveraging Bagging,Insects,0.7045692833226231,0.7045692833226231,0.6849598194839713,20.382534980773926,42611.23284999999 +30624,Multiclass classification,Leveraging Bagging,Insects,0.7031316330862424,0.7031316330862424,0.6877640955933652,22.55568027496338,44864.71640799999 +31680,Multiclass classification,Leveraging Bagging,Insects,0.7032418952618454,0.7032418952618454,0.6917227552448634,26.177990913391113,47133.482559 +32736,Multiclass classification,Leveraging Bagging,Insects,0.7037421719871697,0.7037421719871697,0.6952024388211077,25.761178016662598,49415.922784999995 +33792,Multiclass classification,Leveraging Bagging,Insects,0.7002160338551685,0.7002160338551685,0.6931280234945141,25.958494186401367,51714.47139399999 +34848,Multiclass classification,Leveraging Bagging,Insects,0.6973627571957414,0.6973627571957414,0.6902163957562899,18.894118309020996,54032.337051999995 +35904,Multiclass classification,Leveraging Bagging,Insects,0.6951786758766677,0.6951786758766677,0.6877287571005829,18.049145698547363,56371.370632 +36960,Multiclass classification,Leveraging Bagging,Insects,0.6919830081982737,0.6919830081982737,0.6843647347906762,22.045016288757324,58731.919307 +38016,Multiclass classification,Leveraging Bagging,Insects,0.6900697093252663,0.6900697093252663,0.68217396069655,25.079078674316406,61114.705623999995 +39072,Multiclass classification,Leveraging Bagging,Insects,0.688720534411712,0.688720534411712,0.6808510434728485,19.794261932373047,63520.517782999996 +40128,Multiclass classification,Leveraging Bagging,Insects,0.6867695068158597,0.6867695068158597,0.6796002866264578,10.854747772216797,65948.78476699999 +41184,Multiclass classification,Leveraging Bagging,Insects,0.6843843333414273,0.6843843333414273,0.6779529807793833,10.474969863891602,68395.63477799999 +42240,Multiclass classification,Leveraging Bagging,Insects,0.6822131205757712,0.6822131205757712,0.6764872431583758,14.707494735717773,70864.05938699999 +43296,Multiclass classification,Leveraging Bagging,Insects,0.6795472918350849,0.6795472918350849,0.674587653669649,12.672552108764648,73351.02096199998 +44352,Multiclass classification,Leveraging Bagging,Insects,0.6769633153705666,0.6769633153705666,0.6725984110786069,13.144417762756348,75857.66838799998 +45408,Multiclass classification,Leveraging Bagging,Insects,0.6748959411544475,0.6748959411544475,0.6710316194917795,14.719610214233398,78383.45415699997 +46464,Multiclass classification,Leveraging Bagging,Insects,0.6743215031315241,0.6743215031315241,0.670959098678123,15.027325630187988,80927.72302099997 +47520,Multiclass classification,Leveraging Bagging,Insects,0.6765293882447021,0.6765293882447021,0.6733002712216741,17.283148765563965,83488.02074099997 +48576,Multiclass classification,Leveraging Bagging,Insects,0.6805970149253732,0.6805970149253732,0.6770692638556323,17.906007766723633,86063.52222099998 +49632,Multiclass classification,Leveraging Bagging,Insects,0.6848340754770205,0.6848340754770205,0.6808344811077705,18.8202543258667,88653.18323199998 +50688,Multiclass classification,Leveraging Bagging,Insects,0.6890524197525992,0.6890524197525992,0.6843657264244208,21.507144927978516,91255.74433499998 +51744,Multiclass classification,Leveraging Bagging,Insects,0.6932531936687089,0.6932531936687089,0.6877873898777546,23.154582023620605,93870.63731899999 +52800,Multiclass classification,Leveraging Bagging,Insects,0.6956002954601412,0.6956002954601412,0.6902433463100389,14.128369331359863,96495.21656399999 +52848,Multiclass classification,Leveraging Bagging,Insects,0.6958578538043787,0.6958578538043787,0.6905081705907102,13.831001281738281,99120.19143899999 +408,Multiclass classification,Leveraging Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.0028390884399414,23.27864 +816,Multiclass classification,Leveraging Bagging,Keystroke,0.9521472392638037,0.9521472392638037,0.8408896590786493,4.076430320739746,76.761208 +1224,Multiclass classification,Leveraging Bagging,Keystroke,0.9533932951757972,0.9533932951757972,0.9542235338779169,5.6716413497924805,164.877928 +1632,Multiclass classification,Leveraging Bagging,Keystroke,0.9589209074187615,0.9589209074187615,0.9361222534860761,8.122180938720703,291.606081 +2040,Multiclass classification,Leveraging Bagging,Keystroke,0.9573320255026974,0.9573320255026974,0.9445755787125868,10.5212984085083,455.912148 +2448,Multiclass classification,Leveraging Bagging,Keystroke,0.9607682876992235,0.9607682876992235,0.9588299190873342,9.065413475036621,649.921126 +2856,Multiclass classification,Leveraging Bagging,Keystroke,0.9618213660245184,0.9618213660245184,0.9516555143941907,13.188368797302246,870.236672 +3264,Multiclass classification,Leveraging Bagging,Keystroke,0.9589334967821024,0.9589334967821024,0.9492703335352553,13.21088695526123,1122.958204 +3672,Multiclass classification,Leveraging Bagging,Keystroke,0.9585943884500137,0.9585943884500137,0.9531276848185062,16.65507411956787,1406.732623 +4080,Multiclass classification,Leveraging Bagging,Keystroke,0.9541554302525129,0.9541554302525129,0.9416377826660955,17.091320037841797,1722.764314 +4488,Multiclass classification,Leveraging Bagging,Keystroke,0.9529752618676176,0.9529752618676176,0.9549694463549354,10.336687088012695,2070.686487 +4896,Multiclass classification,Leveraging Bagging,Keystroke,0.9550561797752809,0.9550561797752809,0.95517907029875,11.520882606506348,2449.358224 +5304,Multiclass classification,Leveraging Bagging,Keystroke,0.9568168960965491,0.9568168960965491,0.9575833276239932,13.737529754638672,2856.6015070000003 +5712,Multiclass classification,Leveraging Bagging,Keystroke,0.9574505340570828,0.9574505340570828,0.9570632809827344,15.842782020568848,3290.691514 +6120,Multiclass classification,Leveraging Bagging,Keystroke,0.9557117176009152,0.9557117176009152,0.9522483041543378,20.042810440063477,3760.43818 +6528,Multiclass classification,Leveraging Bagging,Keystroke,0.9566416424084572,0.9566416424084572,0.9568246790885271,11.687369346618652,4258.095146 +6936,Multiclass classification,Leveraging Bagging,Keystroke,0.9574621485219899,0.9574621485219899,0.9579855320572277,12.487288475036621,4780.363237 +7344,Multiclass classification,Leveraging Bagging,Keystroke,0.9568296336647147,0.9568296336647147,0.9563404233689646,15.327423095703125,5334.336646 +7752,Multiclass classification,Leveraging Bagging,Keystroke,0.9565217391304348,0.9565217391304348,0.9563017119581124,18.70553493499756,5920.99825 +8160,Multiclass classification,Leveraging Bagging,Keystroke,0.9545287412673121,0.9545287412673121,0.9527980459948603,24.086796760559082,6539.621381999999 +8568,Multiclass classification,Leveraging Bagging,Keystroke,0.9545932064900199,0.9545932064900199,0.9549210113442089,21.21516990661621,7187.28418 +8976,Multiclass classification,Leveraging Bagging,Keystroke,0.9550974930362117,0.9550974930362117,0.9553627160759579,17.566545486450195,7867.8027329999995 +9384,Multiclass classification,Leveraging Bagging,Keystroke,0.9555579239049344,0.9555579239049344,0.9558253322166266,15.710055351257324,8578.363562999999 +9792,Multiclass classification,Leveraging Bagging,Keystroke,0.9552650393218262,0.9552650393218262,0.9553715117788079,18.717252731323242,9321.146735999999 +10200,Multiclass classification,Leveraging Bagging,Keystroke,0.9533287577213452,0.9533287577213452,0.9523119157834915,15.605277061462402,10099.276789999998 +10608,Multiclass classification,Leveraging Bagging,Keystroke,0.9521070990855096,0.9521070990855096,0.9515822083565743,11.186952590942383,10903.979313999998 +11016,Multiclass classification,Leveraging Bagging,Keystroke,0.953427144802542,0.953427144802542,0.9541201209142027,7.581887245178223,11728.435273 +11424,Multiclass classification,Leveraging Bagging,Keystroke,0.953689923837871,0.953689923837871,0.9538275342826803,10.15964126586914,12573.844722999998 +11832,Multiclass classification,Leveraging Bagging,Keystroke,0.9535964838137098,0.9535964838137098,0.9538502960885477,11.061944961547852,13441.174568999999 +12240,Multiclass classification,Leveraging Bagging,Keystroke,0.9541629218073372,0.9541629218073372,0.9544632162431566,11.249642372131348,14331.138910999998 +12648,Multiclass classification,Leveraging Bagging,Keystroke,0.9548509527951293,0.9548509527951293,0.9551609875055331,13.203255653381348,15243.410521999998 +13056,Multiclass classification,Leveraging Bagging,Keystroke,0.9551895825354271,0.955189582535427,0.9553883557595891,9.36058521270752,16176.429406999998 +13464,Multiclass classification,Leveraging Bagging,Keystroke,0.955953353635891,0.955953353635891,0.9562606797905644,11.575583457946777,17130.275872 +13872,Multiclass classification,Leveraging Bagging,Keystroke,0.9561675437964098,0.9561675437964098,0.9563487774281333,11.42638874053955,18106.072389999998 +14280,Multiclass classification,Leveraging Bagging,Keystroke,0.9549688353526157,0.9549688353526157,0.9548529395574759,10.249165534973145,19109.380900999997 +14688,Multiclass classification,Leveraging Bagging,Keystroke,0.9552665622659495,0.9552665622659495,0.955472434271787,8.168793678283691,20137.264238999996 +15096,Multiclass classification,Leveraging Bagging,Keystroke,0.9560781715799934,0.9560781715799934,0.9563263247313607,9.020037651062012,21191.151532999997 +15504,Multiclass classification,Leveraging Bagging,Keystroke,0.9563955363478036,0.9563955363478036,0.9565429512012837,8.031278610229492,22273.408126999995 +15912,Multiclass classification,Leveraging Bagging,Keystroke,0.9566337753755264,0.9566337753755264,0.9567672375037608,10.967172622680664,23379.117397999995 +16320,Multiclass classification,Leveraging Bagging,Keystroke,0.9563085973405233,0.9563085973405233,0.9563585840602682,11.29026985168457,24508.785403999995 +16728,Multiclass classification,Leveraging Bagging,Keystroke,0.955580797513003,0.955580797513003,0.9555776398983683,9.525394439697266,25660.924918999994 +17136,Multiclass classification,Leveraging Bagging,Keystroke,0.9564050189670266,0.9564050189670267,0.9565585833577668,10.421767234802246,26839.493165999993 +17544,Multiclass classification,Leveraging Bagging,Keystroke,0.9566778772159836,0.9566778772159836,0.9567660151847867,11.633780479431152,28038.177978999993 +17952,Multiclass classification,Leveraging Bagging,Keystroke,0.9564369672998718,0.9564369672998718,0.9564736297242662,8.448995590209961,29257.620389999993 +18360,Multiclass classification,Leveraging Bagging,Keystroke,0.9567514570510376,0.9567514570510375,0.9568227044222711,7.821832656860352,30499.29393699999 +18768,Multiclass classification,Leveraging Bagging,Keystroke,0.9568924175414291,0.9568924175414291,0.9569505378685396,9.859258651733398,31763.565401999993 +19176,Multiclass classification,Leveraging Bagging,Keystroke,0.9567144719687093,0.9567144719687093,0.956766336746882,11.256629943847656,33053.804573999994 +19584,Multiclass classification,Leveraging Bagging,Keystroke,0.9568503293673084,0.9568503293673084,0.9569026376832065,11.690522193908691,34367.36625199999 +19992,Multiclass classification,Leveraging Bagging,Keystroke,0.9564303936771548,0.9564303936771548,0.9564653381379459,12.451186180114746,35701.899461999994 +20400,Multiclass classification,Leveraging Bagging,Keystroke,0.9566155203686455,0.9566155203686455,0.9566498206969933,7.4099931716918945,37049.10208799999 +46,Multiclass classification,Stacking,ImageSegments,0.4,0.4000000000000001,0.3289160825620571,1.89190673828125,1.901401 +92,Multiclass classification,Stacking,ImageSegments,0.5494505494505495,0.5494505494505495,0.5607526488856412,2.084074020385742,6.467373 +138,Multiclass classification,Stacking,ImageSegments,0.5693430656934306,0.5693430656934306,0.5872103411959265,2.357966423034668,13.822826 +184,Multiclass classification,Stacking,ImageSegments,0.6174863387978142,0.6174863387978142,0.6372989403156369,2.7369613647460938,24.259991 +230,Multiclass classification,Stacking,ImageSegments,0.6375545851528385,0.6375545851528385,0.6548159763148107,2.862431526184082,37.817904999999996 +276,Multiclass classification,Stacking,ImageSegments,0.6618181818181819,0.6618181818181819,0.6802187985971371,2.982741355895996,54.565380999999995 +322,Multiclass classification,Stacking,ImageSegments,0.6915887850467289,0.6915887850467289,0.6955507555363084,3.080752372741699,74.633343 +368,Multiclass classification,Stacking,ImageSegments,0.7111716621253406,0.7111716621253404,0.7105739026832886,3.232259750366211,98.20470399999999 +414,Multiclass classification,Stacking,ImageSegments,0.7263922518159807,0.7263922518159807,0.7261041400072307,3.505929946899414,125.52754499999999 +460,Multiclass classification,Stacking,ImageSegments,0.7276688453159041,0.7276688453159043,0.72519869331257,3.7872886657714844,156.78717 +506,Multiclass classification,Stacking,ImageSegments,0.7425742574257426,0.7425742574257425,0.7379486431795568,6.240692138671875,210.52347600000002 +552,Multiclass classification,Stacking,ImageSegments,0.7422867513611615,0.7422867513611615,0.7388440561615693,6.313092231750488,268.009607 +598,Multiclass classification,Stacking,ImageSegments,0.7520938023450586,0.7520938023450586,0.749839509127547,6.682056427001953,329.26112900000004 +644,Multiclass classification,Stacking,ImageSegments,0.7573872472783826,0.7573872472783826,0.7582793237949303,7.269444465637207,394.37227100000007 +690,Multiclass classification,Stacking,ImageSegments,0.7634252539912917,0.7634252539912917,0.7648953830992049,7.531791687011719,463.2777280000001 +736,Multiclass classification,Stacking,ImageSegments,0.7673469387755102,0.7673469387755102,0.7694390547687558,7.987269401550293,536.1609010000001 +782,Multiclass classification,Stacking,ImageSegments,0.7772087067861716,0.7772087067861717,0.7788980835102386,8.317158699035645,613.0067590000001 +828,Multiclass classification,Stacking,ImageSegments,0.7823458282950423,0.7823458282950423,0.7854763667551727,8.613452911376953,693.8523060000001 +874,Multiclass classification,Stacking,ImageSegments,0.7915234822451317,0.7915234822451317,0.7933203073280156,8.694649696350098,778.7710210000001 +920,Multiclass classification,Stacking,ImageSegments,0.7986942328618063,0.7986942328618062,0.7996826842527437,8.824880599975586,867.8856220000001 +966,Multiclass classification,Stacking,ImageSegments,0.8041450777202073,0.8041450777202073,0.8044659150084363,9.089361190795898,961.2082950000001 +1012,Multiclass classification,Stacking,ImageSegments,0.8100890207715133,0.8100890207715133,0.8093994872208631,9.280214309692383,1058.9817440000002 +1058,Multiclass classification,Stacking,ImageSegments,0.8145695364238411,0.814569536423841,0.8133421993203876,9.165953636169434,1161.0697040000002 +1104,Multiclass classification,Stacking,ImageSegments,0.8213961922030825,0.8213961922030824,0.8206569542548617,8.760258674621582,1267.2341280000003 +1150,Multiclass classification,Stacking,ImageSegments,0.824194952132289,0.824194952132289,0.8228781271733864,8.742037773132324,1377.3471480000003 +1196,Multiclass classification,Stacking,ImageSegments,0.8292887029288702,0.8292887029288704,0.8281638601893785,8.87535572052002,1491.4919770000004 +1242,Multiclass classification,Stacking,ImageSegments,0.8340048348106366,0.8340048348106366,0.833490204478907,8.332135200500488,1609.3898390000004 +1288,Multiclass classification,Stacking,ImageSegments,0.8360528360528361,0.8360528360528361,0.8353480055004047,8.416248321533203,1730.8650650000004 +1334,Multiclass classification,Stacking,ImageSegments,0.8394598649662416,0.8394598649662416,0.8389194005130135,8.469959259033203,1855.8596220000004 +1380,Multiclass classification,Stacking,ImageSegments,0.8419144307469181,0.8419144307469181,0.8414934007209077,8.578604698181152,1984.2269550000003 +1426,Multiclass classification,Stacking,ImageSegments,0.8449122807017544,0.8449122807017544,0.8435602800871403,8.689190864562988,2115.814455 +1472,Multiclass classification,Stacking,ImageSegments,0.8484024473147519,0.8484024473147518,0.8459519552383536,8.800261497497559,2250.6136890000002 +1518,Multiclass classification,Stacking,ImageSegments,0.8503625576796309,0.8503625576796308,0.8475723684173131,9.025433540344238,2388.852428 +1564,Multiclass classification,Stacking,ImageSegments,0.8522072936660269,0.8522072936660269,0.8497128793769615,8.811847686767578,2530.498155 +1610,Multiclass classification,Stacking,ImageSegments,0.8527035425730267,0.8527035425730267,0.8503048238231962,8.729784965515137,2675.5358680000004 +1656,Multiclass classification,Stacking,ImageSegments,0.8531722054380665,0.8531722054380665,0.8508343416398155,8.761359214782715,2823.7565440000003 +1702,Multiclass classification,Stacking,ImageSegments,0.8571428571428571,0.8571428571428571,0.8561317791292776,8.798370361328125,2975.2442650000003 +1748,Multiclass classification,Stacking,ImageSegments,0.8580423583285632,0.8580423583285632,0.8567712479140972,8.86152172088623,3129.874549 +1794,Multiclass classification,Stacking,ImageSegments,0.8611266034578918,0.8611266034578918,0.8591986188286931,8.932531356811523,3287.541657 +1840,Multiclass classification,Stacking,ImageSegments,0.8618814573137574,0.8618814573137574,0.8601172531559075,8.819746017456055,3448.3205970000004 +1886,Multiclass classification,Stacking,ImageSegments,0.8636604774535809,0.8636604774535809,0.8623243992773615,9.007128715515137,3612.1367020000002 +1932,Multiclass classification,Stacking,ImageSegments,0.8648368720870016,0.8648368720870016,0.8630569076841595,9.368453979492188,3779.147863 +1978,Multiclass classification,Stacking,ImageSegments,0.8649468892261002,0.8649468892261002,0.8631362872103546,8.952109336853027,3949.363777 +2024,Multiclass classification,Stacking,ImageSegments,0.8665348492338112,0.8665348492338112,0.8639071890295129,9.146061897277832,4122.536804 +2070,Multiclass classification,Stacking,ImageSegments,0.8680521991300145,0.8680521991300145,0.8658036637930728,8.80567455291748,4298.84894 +2116,Multiclass classification,Stacking,ImageSegments,0.8695035460992908,0.8695035460992909,0.8667661913422944,8.892473220825195,4478.129032 +2162,Multiclass classification,Stacking,ImageSegments,0.8690421101341971,0.869042110134197,0.8663186552920692,8.910783767700195,4660.4030729999995 +2208,Multiclass classification,Stacking,ImageSegments,0.8699592206615315,0.8699592206615315,0.8669965232275297,8.99278450012207,4845.573407999999 +2254,Multiclass classification,Stacking,ImageSegments,0.869063470927652,0.8690634709276521,0.8666022158227548,9.09610366821289,5033.674223999999 +2300,Multiclass classification,Stacking,ImageSegments,0.8686385384949978,0.8686385384949978,0.8662053097556822,9.110825538635254,5224.6921839999995 +2310,Multiclass classification,Stacking,ImageSegments,0.8679081853616284,0.8679081853616284,0.8656034675726049,9.181622505187988,5416.881651 +1056,Multiclass classification,Stacking,Insects,0.6511848341232227,0.6511848341232227,0.5864257754346489,12.51792049407959,137.265242 +2112,Multiclass classification,Stacking,Insects,0.6873519658929418,0.6873519658929418,0.6004104483953082,15.371862411499023,366.367491 +3168,Multiclass classification,Stacking,Insects,0.6978212819703189,0.6978212819703189,0.602242348585179,17.772335052490234,671.574116 +4224,Multiclass classification,Stacking,Insects,0.7054226852948141,0.7054226852948141,0.6059831617919115,20.14197826385498,1043.757912 +5280,Multiclass classification,Stacking,Insects,0.7080886531540065,0.7080886531540066,0.6082411118035554,23.246225357055664,1476.569185 +6336,Multiclass classification,Stacking,Insects,0.708602999210734,0.708602999210734,0.6091818949546898,28.13547992706299,1970.1501170000001 +7392,Multiclass classification,Stacking,Insects,0.7104586659450683,0.7104586659450683,0.6104104212994758,30.164710998535156,2526.789716 +8448,Multiclass classification,Stacking,Insects,0.7130342133301764,0.7130342133301764,0.6119778058667307,27.698996543884277,3146.1270590000004 +9504,Multiclass classification,Stacking,Insects,0.717773334736399,0.717773334736399,0.6149023583636667,27.04288387298584,3829.1831980000006 +10560,Multiclass classification,Stacking,Insects,0.7215645420967894,0.7215645420967894,0.617635708330779,23.96706485748291,4572.729772000001 +11616,Multiclass classification,Stacking,Insects,0.7213086526043909,0.721308652604391,0.6182075626749539,26.15617847442627,5374.612830000001 +12672,Multiclass classification,Stacking,Insects,0.7240943887617394,0.7240943887617394,0.6351065980046956,25.051542282104492,6233.453892000001 +13728,Multiclass classification,Stacking,Insects,0.7432796678079697,0.7432796678079697,0.7402334392509421,15.30208683013916,7142.743199000001 +14784,Multiclass classification,Stacking,Insects,0.7491713454643848,0.7491713454643848,0.7487081677599373,11.128735542297363,8102.097506000001 +15840,Multiclass classification,Stacking,Insects,0.7424079803017867,0.7424079803017867,0.7445532404968841,16.950417518615723,9128.379042 +16896,Multiclass classification,Stacking,Insects,0.7382657591003255,0.7382657591003255,0.7427378731329454,18.26229953765869,10214.572621000001 +17952,Multiclass classification,Stacking,Insects,0.7309342097933262,0.7309342097933262,0.7368436311738037,23.776363372802734,11358.099531000002 +19008,Multiclass classification,Stacking,Insects,0.7429368127531962,0.7429368127531962,0.7441354243297112,12.958039283752441,12553.803014000001 +20064,Multiclass classification,Stacking,Insects,0.7475950755121368,0.7475950755121367,0.7439196968116685,12.612845420837402,13796.415893000001 +21120,Multiclass classification,Stacking,Insects,0.7492305506889531,0.7492305506889531,0.7418613509588597,16.95127773284912,15088.238885 +22176,Multiclass classification,Stacking,Insects,0.7509808342728298,0.7509808342728299,0.7400929587109365,17.926865577697754,16424.025269 +23232,Multiclass classification,Stacking,Insects,0.7532176832680471,0.7532176832680472,0.7391930166872092,20.939698219299316,17798.240955 +24288,Multiclass classification,Stacking,Insects,0.7550129699015935,0.7550129699015935,0.7379653286035112,25.43882942199707,19212.969178 +25344,Multiclass classification,Stacking,Insects,0.7569743124334136,0.7569743124334136,0.7375346698329149,29.94521999359131,20668.368585 +26400,Multiclass classification,Stacking,Insects,0.7580590173870222,0.7580590173870221,0.7363169253318035,34.1699275970459,22166.950006 +27456,Multiclass classification,Stacking,Insects,0.7593880896011656,0.7593880896011656,0.7352131419868576,32.93678665161133,23706.536377 +28512,Multiclass classification,Stacking,Insects,0.7573217354705202,0.7573217354705202,0.7350502568377754,21.273219108581543,25286.984696 +29568,Multiclass classification,Stacking,Insects,0.7555382690161329,0.7555382690161329,0.7386915112539557,20.747055053710938,26906.631088 +30624,Multiclass classification,Stacking,Insects,0.7544982529471312,0.7544982529471312,0.7426503125712552,24.910794258117676,28562.795387 +31680,Multiclass classification,Stacking,Insects,0.7531487736355315,0.7531487736355315,0.7453200395899969,32.13512706756592,30253.076649 +32736,Multiclass classification,Stacking,Insects,0.7530471971895525,0.7530471971895525,0.7484606399297139,36.17057991027832,31977.334616 +33792,Multiclass classification,Stacking,Insects,0.7480986061377289,0.748098606137729,0.7448942365218528,13.298456192016602,33736.870240000004 +34848,Multiclass classification,Stacking,Insects,0.7436795133010016,0.7436795133010016,0.7403442775964885,15.221885681152344,35530.857132000005 +35904,Multiclass classification,Stacking,Insects,0.7404952232403977,0.7404952232403977,0.7368033013057004,16.932289123535156,37356.72126300001 +36960,Multiclass classification,Stacking,Insects,0.7371411564165697,0.7371411564165696,0.7332530467261859,22.237309455871582,39213.91358200001 +38016,Multiclass classification,Stacking,Insects,0.7341049585689859,0.7341049585689859,0.7299460315219516,22.86026954650879,41100.10013700001 +39072,Multiclass classification,Stacking,Insects,0.7343042154027284,0.7343042154027284,0.7301016033872143,21.91624164581299,43017.482867000006 +40128,Multiclass classification,Stacking,Insects,0.7327734443143021,0.7327734443143021,0.728948208474553,20.388718605041504,44961.93393100001 +41184,Multiclass classification,Stacking,Insects,0.7327538061821626,0.7327538061821626,0.7292630064673854,15.630711555480957,46951.12268600001 +42240,Multiclass classification,Stacking,Insects,0.7331849712351145,0.7331849712351144,0.7301128191332076,20.110919952392578,48959.16072000001 +43296,Multiclass classification,Stacking,Insects,0.7337567848481349,0.7337567848481349,0.7309969621648841,24.057676315307617,50985.068017000005 +44352,Multiclass classification,Stacking,Insects,0.7342111790038556,0.7342111790038556,0.731637560144403,28.529647827148438,53028.942305000004 +45408,Multiclass classification,Stacking,Insects,0.7351289448763406,0.7351289448763407,0.7324911060941295,28.861422538757324,55091.119898000004 +46464,Multiclass classification,Stacking,Insects,0.7357682457008803,0.7357682457008803,0.7329742877599967,33.076725006103516,57170.52325500001 +47520,Multiclass classification,Stacking,Insects,0.7366947957659041,0.736694795765904,0.7341498113226347,21.352835655212402,59267.07909500001 +48576,Multiclass classification,Stacking,Insects,0.7403602676273804,0.7403602676273804,0.7381372580344014,19.381468772888184,61379.50627500001 +49632,Multiclass classification,Stacking,Insects,0.7442122866756664,0.7442122866756663,0.742109373234967,21.8067569732666,63507.849119000006 +50688,Multiclass classification,Stacking,Insects,0.7475289521968157,0.7475289521968157,0.7453466445950636,21.65154266357422,65647.81315 +51744,Multiclass classification,Stacking,Insects,0.7510581141410433,0.7510581141410433,0.7487124138061083,22.870601654052734,67797.610737 +52800,Multiclass classification,Stacking,Insects,0.7545218659444307,0.7545218659444307,0.752582163258218,10.554459571838379,69956.07865499999 +52848,Multiclass classification,Stacking,Insects,0.7547448294132117,0.7547448294132117,0.7528178949021433,10.58643913269043,72115.038215 +408,Multiclass classification,Stacking,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.786503791809082,20.578742 +816,Multiclass classification,Stacking,Keystroke,0.9815950920245399,0.98159509202454,0.9278568842209168,6.9002227783203125,101.28008299999999 +1224,Multiclass classification,Stacking,Keystroke,0.9803761242845462,0.9803761242845462,0.9574942570636209,9.112634658813477,223.840162 +1632,Multiclass classification,Stacking,Keystroke,0.9779276517473943,0.9779276517473943,0.9432755457272627,10.40715503692627,381.844655 +2040,Multiclass classification,Stacking,Keystroke,0.973516429622364,0.973516429622364,0.9361356188587967,12.656171798706055,575.269036 +2448,Multiclass classification,Stacking,Keystroke,0.9726195341234164,0.9726195341234164,0.9612590316809274,8.745987892150879,802.257021 +2856,Multiclass classification,Stacking,Keystroke,0.9754816112084063,0.9754816112084063,0.9751469891413959,9.931495666503906,1061.688609 +3264,Multiclass classification,Stacking,Keystroke,0.9754826846460313,0.9754826846460313,0.9697604489278108,10.511832237243652,1352.298412 +3672,Multiclass classification,Stacking,Keystroke,0.9733042767638246,0.9733042767638246,0.9642745555297418,11.800049781799316,1675.3338330000001 +4080,Multiclass classification,Stacking,Keystroke,0.9722971316499142,0.9722971316499142,0.9666413905932107,12.42660903930664,2030.1772580000002 +4488,Multiclass classification,Stacking,Keystroke,0.9734789391575663,0.9734789391575663,0.9728883985144964,9.746350288391113,2413.735444 +4896,Multiclass classification,Stacking,Keystroke,0.9740551583248213,0.9740551583248213,0.9730015599884005,10.666529655456543,2823.5055469999998 +5304,Multiclass classification,Stacking,Keystroke,0.9741655666603809,0.9741655666603809,0.9728266773902405,11.775634765625,3261.739577 +5712,Multiclass classification,Stacking,Keystroke,0.9747855016634565,0.9747855016634565,0.9744326987999562,12.58005428314209,3727.994683 +6120,Multiclass classification,Stacking,Keystroke,0.9751593397613989,0.9751593397613989,0.9747223863351728,13.55466365814209,4223.159482999999 +6528,Multiclass classification,Stacking,Keystroke,0.9751800214493642,0.9751800214493642,0.9745255481694279,11.360074043273926,4747.988555 +6936,Multiclass classification,Stacking,Keystroke,0.9763518385003604,0.9763518385003604,0.9769458779347455,11.155635833740234,5300.577354999999 +7344,Multiclass classification,Stacking,Keystroke,0.9765763311997822,0.9765763311997822,0.9763923596721359,12.33658504486084,5881.2072339999995 +7752,Multiclass classification,Stacking,Keystroke,0.9771642368726616,0.9771642368726616,0.9773496343719735,13.116165161132812,6492.775159 +8160,Multiclass classification,Stacking,Keystroke,0.976590268415247,0.976590268415247,0.9759275084076021,13.303799629211426,7137.299991 +8568,Multiclass classification,Stacking,Keystroke,0.9768880588303958,0.9768880588303958,0.9769304999907085,13.133574485778809,7814.540539 +8976,Multiclass classification,Stacking,Keystroke,0.9774930362116991,0.9774930362116991,0.9777587646121523,13.50635814666748,8523.072866 +9384,Multiclass classification,Stacking,Keystroke,0.9767664925929873,0.9767664925929873,0.9763135719034829,15.166536331176758,9263.702674 +9792,Multiclass classification,Stacking,Keystroke,0.9765090389132877,0.9765090389132877,0.9763153416047449,16.169885635375977,10037.443626 +10200,Multiclass classification,Stacking,Keystroke,0.9758799882341406,0.9758799882341406,0.9755246287395946,14.205968856811523,10844.068015 +10608,Multiclass classification,Stacking,Keystroke,0.9755821627227302,0.9755821627227302,0.9754319444516873,12.997503280639648,11685.064117 +11016,Multiclass classification,Stacking,Keystroke,0.9759418974126192,0.9759418974126192,0.9761027289556774,12.962043762207031,12559.39796 +11424,Multiclass classification,Stacking,Keystroke,0.9760133064869124,0.9760133064869124,0.9760613734021468,14.090433120727539,13467.395857 +11832,Multiclass classification,Stacking,Keystroke,0.9754881244188995,0.9754881244188995,0.9753195915858492,14.295487403869629,14408.853786 +12240,Multiclass classification,Stacking,Keystroke,0.9759784296102623,0.9759784296102623,0.9761779987511395,15.044499397277832,15385.688043 +12648,Multiclass classification,Stacking,Keystroke,0.9762789594370206,0.9762789594370206,0.9764127823145236,15.120206832885742,16404.149055 +13056,Multiclass classification,Stacking,Keystroke,0.9758713136729222,0.9758713136729221,0.975797420384815,15.049361228942871,17460.942559000003 +13464,Multiclass classification,Stacking,Keystroke,0.9757112084973631,0.9757112084973631,0.9757165619520196,15.162266731262207,18558.798501 +13872,Multiclass classification,Stacking,Keystroke,0.9759930790858626,0.9759930790858626,0.9761084708221816,15.711796760559082,19695.838422 +14280,Multiclass classification,Stacking,Keystroke,0.9754884795854052,0.9754884795854052,0.975424480421301,16.988737106323242,20872.643227 +14688,Multiclass classification,Stacking,Keystroke,0.975624702117519,0.975624702117519,0.9757017096421697,17.869779586791992,22084.930025 +15096,Multiclass classification,Stacking,Keystroke,0.9757535607817158,0.9757535607817158,0.9758249143111629,17.579912185668945,23330.568569000003 +15504,Multiclass classification,Stacking,Keystroke,0.9755531187512094,0.9755531187512094,0.9755669148190674,16.59157657623291,24610.336028 +15912,Multiclass classification,Stacking,Keystroke,0.9756772044497517,0.9756772044497517,0.9757389077528199,16.193113327026367,25925.188427 +16320,Multiclass classification,Stacking,Keystroke,0.9759176420123782,0.9759176420123782,0.9759886766110538,16.353660583496094,27266.573062 +16728,Multiclass classification,Stacking,Keystroke,0.9756680815448078,0.9756680815448078,0.9756766431570708,17.00908374786377,28641.859399 +17136,Multiclass classification,Stacking,Keystroke,0.9758389261744966,0.9758389261744966,0.975891563489883,18.364989280700684,30047.550521 +17544,Multiclass classification,Stacking,Keystroke,0.9753747933648749,0.9753747933648749,0.975363882573194,17.298136711120605,31485.782589000002 +17952,Multiclass classification,Stacking,Keystroke,0.9753217090969862,0.9753217090969862,0.9753429667022142,16.72727108001709,32956.927282000004 +18360,Multiclass classification,Stacking,Keystroke,0.9754888610490767,0.9754888610490767,0.9755190387029732,17.51059913635254,34461.639008000006 +18768,Multiclass classification,Stacking,Keystroke,0.9757553151809026,0.9757553151809026,0.9757835195290103,18.871691703796387,35998.873267 +19176,Multiclass classification,Stacking,Keystroke,0.9754367666232073,0.9754367666232073,0.9754369138844643,17.42948341369629,37568.082084 +19584,Multiclass classification,Stacking,Keystroke,0.9754889444926722,0.9754889444926722,0.9754964783302286,17.978480339050293,39170.395427 +19992,Multiclass classification,Stacking,Keystroke,0.9756390375669051,0.9756390375669051,0.975642520227376,19.26256561279297,40805.125646 +20400,Multiclass classification,Stacking,Keystroke,0.9754889945585568,0.9754889945585568,0.9754863274548964,18.711057662963867,42471.761869 +46,Multiclass classification,Voting,ImageSegments,0.4666666666666667,0.4666666666666667,0.3890768588137009,0.9137420654296875,0.663852 +92,Multiclass classification,Voting,ImageSegments,0.6153846153846154,0.6153846153846154,0.617040786788686,0.9906883239746094,2.032737 +138,Multiclass classification,Voting,ImageSegments,0.6715328467153284,0.6715328467153284,0.6884491245817251,1.0679149627685547,4.226265 +184,Multiclass classification,Voting,ImageSegments,0.7049180327868853,0.7049180327868853,0.7194266051408907,1.1443958282470703,7.386208 +230,Multiclass classification,Voting,ImageSegments,0.7292576419213974,0.7292576419213974,0.7448338459304749,1.2214689254760742,11.723904000000001 +276,Multiclass classification,Voting,ImageSegments,0.7381818181818182,0.7381818181818182,0.7559766728000937,1.2995519638061523,17.331033 +322,Multiclass classification,Voting,ImageSegments,0.7538940809968847,0.7538940809968847,0.7616248500949714,1.3766565322875977,24.26159 +368,Multiclass classification,Voting,ImageSegments,0.773841961852861,0.7738419618528611,0.7772939373537765,1.4532833099365234,32.770568000000004 +414,Multiclass classification,Voting,ImageSegments,0.7820823244552058,0.7820823244552059,0.7854200812154107,1.5304145812988281,42.983195 +460,Multiclass classification,Voting,ImageSegments,0.7777777777777778,0.7777777777777778,0.7796254955467015,1.6075658798217773,54.886431 +506,Multiclass classification,Voting,ImageSegments,0.7861386138613862,0.7861386138613862,0.7886239053396241,3.8640270233154297,87.00222099999999 +552,Multiclass classification,Voting,ImageSegments,0.7858439201451906,0.7858439201451906,0.7889431335032357,4.088808059692383,121.00394599999998 +598,Multiclass classification,Voting,ImageSegments,0.7906197654941374,0.7906197654941374,0.7944387660679091,4.304059028625488,157.00397999999998 +644,Multiclass classification,Voting,ImageSegments,0.7853810264385692,0.7853810264385692,0.7901251252871709,4.532710075378418,195.073691 +690,Multiclass classification,Voting,ImageSegments,0.7895500725689405,0.7895500725689405,0.7935315861788143,4.759090423583984,235.272046 +736,Multiclass classification,Voting,ImageSegments,0.7863945578231293,0.7863945578231294,0.7911065855691086,4.991429328918457,277.59961999999996 +782,Multiclass classification,Voting,ImageSegments,0.7887323943661971,0.7887323943661971,0.792926322670609,5.219735145568848,322.07131499999997 +828,Multiclass classification,Voting,ImageSegments,0.7896009673518742,0.7896009673518742,0.7950712422059908,5.452417373657227,368.82718 +874,Multiclass classification,Voting,ImageSegments,0.7938144329896907,0.7938144329896907,0.7979586706142276,5.699496269226074,417.885664 +920,Multiclass classification,Voting,ImageSegments,0.794341675734494,0.794341675734494,0.7973145688626199,5.9376373291015625,469.16999000000004 +966,Multiclass classification,Voting,ImageSegments,0.7937823834196891,0.7937823834196891,0.7958827691316667,6.182188987731934,522.9385980000001 +1012,Multiclass classification,Voting,ImageSegments,0.7912957467853611,0.7912957467853611,0.7931630938612351,6.34267520904541,579.2700850000001 +1058,Multiclass classification,Voting,ImageSegments,0.793755912961211,0.7937559129612108,0.7947921362588558,6.295009613037109,638.2805060000001 +1104,Multiclass classification,Voting,ImageSegments,0.7941976427923844,0.7941976427923844,0.7951664828862093,6.2213640213012695,699.725726 +1150,Multiclass classification,Voting,ImageSegments,0.7954743255004352,0.7954743255004351,0.7958304956922065,6.151959419250488,763.5071 +1196,Multiclass classification,Voting,ImageSegments,0.796652719665272,0.796652719665272,0.7972397572733622,6.087224006652832,829.5043310000001 +1242,Multiclass classification,Voting,ImageSegments,0.7953263497179693,0.7953263497179693,0.795947547023496,6.001987457275391,897.6078460000001 +1288,Multiclass classification,Voting,ImageSegments,0.7995337995337995,0.7995337995337995,0.799082939294124,5.924266815185547,967.7224320000001 +1334,Multiclass classification,Voting,ImageSegments,0.7981995498874719,0.7981995498874719,0.7978549794399667,5.872907638549805,1039.926656 +1380,Multiclass classification,Voting,ImageSegments,0.7991298042059464,0.7991298042059464,0.799072028035076,5.784454345703125,1114.0085680000002 +1426,Multiclass classification,Voting,ImageSegments,0.8007017543859649,0.8007017543859649,0.799801266098334,5.781437873840332,1190.125915 +1472,Multiclass classification,Voting,ImageSegments,0.8042148198504419,0.8042148198504419,0.8016037490391381,5.805401802062988,1268.322504 +1518,Multiclass classification,Voting,ImageSegments,0.8048780487804879,0.8048780487804877,0.8013581039030082,5.915700912475586,1348.933518 +1564,Multiclass classification,Voting,ImageSegments,0.8048624440179143,0.8048624440179143,0.8017038254481382,6.0695037841796875,1431.999326 +1610,Multiclass classification,Voting,ImageSegments,0.8048477315102548,0.8048477315102549,0.8009666848419111,6.138180732727051,1517.316045 +1656,Multiclass classification,Voting,ImageSegments,0.804833836858006,0.804833836858006,0.8009346118743482,6.1542863845825195,1604.689641 +1702,Multiclass classification,Voting,ImageSegments,0.8048206937095826,0.8048206937095828,0.802987300619633,6.14796257019043,1694.105126 +1748,Multiclass classification,Voting,ImageSegments,0.8065254722381225,0.8065254722381225,0.8041280306488863,6.185528755187988,1785.6451299999999 +1794,Multiclass classification,Voting,ImageSegments,0.8070273284997211,0.8070273284997211,0.8033862119520573,6.18717098236084,1879.2213629999999 +1840,Multiclass classification,Voting,ImageSegments,0.8085916258836324,0.8085916258836324,0.8051706679397826,6.228180885314941,1974.8859899999998 +1886,Multiclass classification,Voting,ImageSegments,0.8074270557029177,0.8074270557029178,0.8044133208197751,6.244633674621582,2072.712055 +1932,Multiclass classification,Voting,ImageSegments,0.8073537027446919,0.8073537027446919,0.8036280810428232,6.232837677001953,2172.610836 +1978,Multiclass classification,Voting,ImageSegments,0.808295397066262,0.808295397066262,0.8041943782356388,6.225313186645508,2274.5024089999997 +2024,Multiclass classification,Voting,ImageSegments,0.8096885813148789,0.809688581314879,0.8043903689108628,6.209332466125488,2378.336668 +2070,Multiclass classification,Voting,ImageSegments,0.8086031899468342,0.8086031899468342,0.8034099584264852,6.192641258239746,2484.108554 +2116,Multiclass classification,Voting,ImageSegments,0.808983451536643,0.808983451536643,0.8029929757635029,6.163993835449219,2591.83622 +2162,Multiclass classification,Voting,ImageSegments,0.8093475242943082,0.8093475242943081,0.8028985652670257,6.160528182983398,2701.493184 +2208,Multiclass classification,Voting,ImageSegments,0.8110557317625736,0.8110557317625736,0.8037088502350873,6.127141952514648,2812.975729 +2254,Multiclass classification,Voting,ImageSegments,0.8078118064802485,0.8078118064802485,0.8004652010359966,6.094814300537109,2926.3842619999996 +2300,Multiclass classification,Voting,ImageSegments,0.8064375815571988,0.8064375815571988,0.7990276111502428,6.073050498962402,3041.734776 +2310,Multiclass classification,Voting,ImageSegments,0.8064097011693374,0.8064097011693374,0.7989986920740723,6.073922157287598,3157.9431529999997 +1056,Multiclass classification,Voting,Insects,0.6293838862559241,0.6293838862559241,0.5938169901557457,7.681754112243652,78.197886 +2112,Multiclass classification,Voting,Insects,0.6290857413548081,0.6290857413548081,0.5936238360694311,7.563845634460449,217.436369 +3168,Multiclass classification,Voting,Insects,0.625197347647616,0.625197347647616,0.5890732389154221,7.54627799987793,406.781755 +4224,Multiclass classification,Voting,Insects,0.624437603599337,0.624437603599337,0.5890978975177876,7.509035110473633,643.136123 +5280,Multiclass classification,Voting,Insects,0.6309907179390036,0.6309907179390036,0.5943307513870396,7.529419898986816,922.055301 +6336,Multiclass classification,Voting,Insects,0.6249408050513023,0.6249408050513023,0.5899587518293812,7.541637420654297,1240.879558 +7392,Multiclass classification,Voting,Insects,0.6242727641726424,0.6242727641726424,0.589208790087756,7.5199432373046875,1598.2590730000002 +8448,Multiclass classification,Voting,Insects,0.6266129986977625,0.6266129986977625,0.5910042020201396,7.600367546081543,1990.9287910000003 +9504,Multiclass classification,Voting,Insects,0.6255919183415763,0.6255919183415763,0.5892477749449755,7.551809310913086,2416.671036 +10560,Multiclass classification,Voting,Insects,0.6269533099725353,0.6269533099725353,0.5906555376897765,7.57810115814209,2875.240995 +11616,Multiclass classification,Voting,Insects,0.6254842875591907,0.6254842875591907,0.5899069142128334,7.574300765991211,3366.8452850000003 +12672,Multiclass classification,Voting,Insects,0.6276536974193039,0.6276536974193039,0.5948280902959312,7.593076705932617,3891.533291 +13728,Multiclass classification,Voting,Insects,0.6419465287389816,0.6419465287389816,0.6240594787506325,7.568525314331055,4449.097087 +14784,Multiclass classification,Voting,Insects,0.6349861327200162,0.6349861327200162,0.6168664949740267,7.497129440307617,5038.3500540000005 +15840,Multiclass classification,Voting,Insects,0.6042048109097796,0.6042048109097796,0.5876183517420878,7.622871398925781,5663.9066330000005 +16896,Multiclass classification,Voting,Insects,0.5831311038768866,0.5831311038768866,0.5677288238088704,7.5406084060668945,6323.428796 +17952,Multiclass classification,Voting,Insects,0.5683805916104953,0.5683805916104953,0.5530005563922373,7.511743545532227,7015.247243 +19008,Multiclass classification,Voting,Insects,0.5655811016993739,0.5655811016993739,0.5465928919365096,7.569133758544922,7739.601247 +20064,Multiclass classification,Voting,Insects,0.5718985196630614,0.5718985196630614,0.5506497035356593,8.179316520690918,8496.204598999999 +21120,Multiclass classification,Voting,Insects,0.5817510298783086,0.5817510298783086,0.55937505855693,8.13927173614502,9285.092110999998 +22176,Multiclass classification,Voting,Insects,0.5905298759864712,0.5905298759864712,0.5668099949242361,8.13715648651123,10104.551325999999 +23232,Multiclass classification,Voting,Insects,0.6004907236020834,0.6004907236020834,0.5756153967719769,8.254791259765625,10955.282647999999 +24288,Multiclass classification,Voting,Insects,0.6088854119487792,0.6088854119487792,0.5822871692574689,8.217899322509766,11836.441737999998 +25344,Multiclass classification,Voting,Insects,0.617014560233595,0.617014560233595,0.5890646667396601,8.13050651550293,12747.590801999997 +26400,Multiclass classification,Voting,Insects,0.6237357475661957,0.6237357475661957,0.5942060376379845,8.178851127624512,13688.250944999996 +27456,Multiclass classification,Voting,Insects,0.6299763248952832,0.6299763248952832,0.5983574644866619,8.215079307556152,14661.447404999995 +28512,Multiclass classification,Voting,Insects,0.6312651257409421,0.6312651257409421,0.6016879522351425,8.160200119018555,15669.084531999995 +29568,Multiclass classification,Voting,Insects,0.6310751851726587,0.6310751851726587,0.6062390002054064,8.153844833374023,16709.899933999994 +30624,Multiclass classification,Voting,Insects,0.6313228619011854,0.6313228619011854,0.610710416812842,8.221953392028809,17785.196262999994 +31680,Multiclass classification,Voting,Insects,0.6320590927743931,0.6320590927743931,0.614817700164209,8.237210273742676,18894.010558999995 +32736,Multiclass classification,Voting,Insects,0.6331144035436077,0.6331144035436077,0.6184679282473909,8.208189964294434,20033.816622999995 +33792,Multiclass classification,Voting,Insects,0.6291616110798733,0.6291616110798733,0.6151628967287334,8.149331092834473,21206.789184999994 +34848,Multiclass classification,Voting,Insects,0.6245587855482538,0.6245587855482538,0.6103108800280445,8.270771980285645,22409.569843999994 +35904,Multiclass classification,Voting,Insects,0.6211737180736986,0.6211737180736986,0.6063163580543118,8.246885299682617,23639.112908999996 +36960,Multiclass classification,Voting,Insects,0.6171433209772992,0.6171433209772992,0.6018416894357856,8.222872734069824,24895.212450999996 +38016,Multiclass classification,Voting,Insects,0.6153360515585953,0.6153360515585953,0.5996210858832133,8.711487770080566,26177.407049999994 +39072,Multiclass classification,Voting,Insects,0.613472908295155,0.613472908295155,0.5980758777202522,8.84398365020752,27486.887242999994 +40128,Multiclass classification,Voting,Insects,0.6139008647544048,0.6139008647544048,0.5993833357378361,9.00393295288086,28821.579146999993 +41184,Multiclass classification,Voting,Insects,0.6157395041643396,0.6157395041643396,0.6018873090815099,8.895415306091309,30174.675792999995 +42240,Multiclass classification,Voting,Insects,0.6179833802883591,0.6179833802883591,0.6047393094362844,8.820836067199707,31551.592344999994 +43296,Multiclass classification,Voting,Insects,0.6202101859337106,0.6202101859337106,0.60743097275183,8.80302619934082,32950.21258099999 +44352,Multiclass classification,Voting,Insects,0.6221054767648982,0.6221054767648982,0.6097047537791253,8.807188034057617,34370.71930599999 +45408,Multiclass classification,Voting,Insects,0.623736428304006,0.623736428304006,0.6112415003179203,8.906554222106934,35814.22252799999 +46464,Multiclass classification,Voting,Insects,0.6259389191399608,0.6259389191399608,0.6133867892257391,8.822076797485352,37279.498286999995 +47520,Multiclass classification,Voting,Insects,0.6274542814453166,0.6274542814453166,0.6153714367024555,8.875716209411621,38770.246245999995 +48576,Multiclass classification,Voting,Insects,0.6317858980957283,0.6317858980957283,0.6202967225132047,8.86828327178955,40284.403256 +49632,Multiclass classification,Voting,Insects,0.6360137817090125,0.6360137817090125,0.6247992459885968,8.835649490356445,41820.805007999996 +50688,Multiclass classification,Voting,Insects,0.6403811628228145,0.6403811628228145,0.6293790828873279,8.924153327941895,43378.957976 +51744,Multiclass classification,Voting,Insects,0.6455559206076185,0.6455559206076185,0.6346828420183047,9.218049049377441,44959.107880999996 +52800,Multiclass classification,Voting,Insects,0.648269853595712,0.648269853595712,0.6377385869395499,9.400546073913574,46560.782 +52848,Multiclass classification,Voting,Insects,0.6485325562472799,0.6485325562472799,0.637999701607352,9.406517028808594,48163.738895 +408,Multiclass classification,Voting,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,1.4587059020996094,10.139614 +816,Multiclass classification,Voting,Keystroke,0.9496932515337423,0.9496932515337423,0.7435135353411919,6.019382476806641,66.737739 +1224,Multiclass classification,Voting,Keystroke,0.9149632052330335,0.9149632052330335,0.9012024099743488,7.076447486877441,151.07716299999998 +1632,Multiclass classification,Voting,Keystroke,0.9258123850398529,0.9258123850398529,0.913338738884437,7.232892990112305,261.540164 +2040,Multiclass classification,Voting,Keystroke,0.9230014713094654,0.9230014713094654,0.9086113906821328,7.553393363952637,397.83621500000004 +2448,Multiclass classification,Voting,Keystroke,0.8961994278708623,0.8961994278708623,0.8992132713257572,7.640434265136719,558.733108 +2856,Multiclass classification,Voting,Keystroke,0.9001751313485113,0.9001751313485113,0.8860451027148403,7.9326982498168945,743.600486 +3264,Multiclass classification,Voting,Keystroke,0.8924302788844621,0.8924302788844621,0.8761196773917237,8.074724197387695,952.077233 +3672,Multiclass classification,Voting,Keystroke,0.8874965949332607,0.8874965949332607,0.8846937712308092,8.20841121673584,1184.393658 +4080,Multiclass classification,Voting,Keystroke,0.8815886246629075,0.8815886246629075,0.868452721773406,8.525882720947266,1441.2089369999999 +4488,Multiclass classification,Voting,Keystroke,0.8760864720303098,0.8760864720303098,0.8834419600614621,8.681946754455566,1719.7568239999998 +4896,Multiclass classification,Voting,Keystroke,0.8737487231869254,0.8737487231869254,0.8797220914000274,8.834684371948242,2018.9742069999998 +5304,Multiclass classification,Voting,Keystroke,0.8693192532528757,0.8693192532528757,0.8538682361373632,9.067034721374512,2339.6996679999997 +5712,Multiclass classification,Voting,Keystroke,0.8607949571003327,0.8607949571003327,0.8654889627515672,9.271133422851562,2680.904224 +6120,Multiclass classification,Voting,Keystroke,0.8561856512502043,0.8561856512502043,0.84095068957581,9.378315925598145,3042.663698 +6528,Multiclass classification,Voting,Keystroke,0.8434196414891987,0.8434196414891987,0.8427350578509161,9.608606338500977,3424.478417 +6936,Multiclass classification,Voting,Keystroke,0.8392213410237923,0.8392213410237923,0.8447429510460126,9.751982688903809,3824.86879 +7344,Multiclass classification,Voting,Keystroke,0.8454310227427482,0.8454310227427482,0.847842289102327,9.957889556884766,4243.00141 +7752,Multiclass classification,Voting,Keystroke,0.8456973293768546,0.8456973293768547,0.8480563212460421,10.19985294342041,4680.993142 +8160,Multiclass classification,Voting,Keystroke,0.8469175144012747,0.8469175144012746,0.8472851046009279,10.418806076049805,5138.9878340000005 +8568,Multiclass classification,Voting,Keystroke,0.8469709349830746,0.8469709349830746,0.8501227536717817,10.607142448425293,5616.664707000001 +8976,Multiclass classification,Voting,Keystroke,0.8475766016713092,0.8475766016713092,0.8507851780426926,10.772598266601562,6113.940894000001 +9384,Multiclass classification,Voting,Keystroke,0.8459980816370031,0.8459980816370031,0.8471668648040658,10.97368335723877,6631.342845000001 +9792,Multiclass classification,Voting,Keystroke,0.8418956184250843,0.8418956184250843,0.8426049398612477,11.192140579223633,7169.901201000001 +10200,Multiclass classification,Voting,Keystroke,0.8344935778017453,0.8344935778017454,0.8308153568434791,11.354521751403809,7729.92345 +10608,Multiclass classification,Voting,Keystroke,0.817384745922504,0.817384745922504,0.8105787344487394,11.59365177154541,8312.440227000001 +11016,Multiclass classification,Voting,Keystroke,0.8127099409895597,0.8127099409895597,0.8142119266109252,11.793928146362305,8918.030696000002 +11424,Multiclass classification,Voting,Keystroke,0.8079313665411888,0.8079313665411888,0.8037472320719128,11.945178031921387,9547.170938000001 +11832,Multiclass classification,Voting,Keystroke,0.8040740427689967,0.8040740427689967,0.8039730126613296,12.203582763671875,10200.281645000001 +12240,Multiclass classification,Voting,Keystroke,0.8072554947299616,0.8072554947299616,0.8097160881214022,12.414502143859863,10877.318664 +12648,Multiclass classification,Voting,Keystroke,0.8043014153554202,0.8043014153554202,0.8038043720799647,12.561456680297852,11578.515438 +13056,Multiclass classification,Voting,Keystroke,0.7996936039831483,0.7996936039831483,0.8010057260657798,12.889472007751465,12304.325005 +13464,Multiclass classification,Voting,Keystroke,0.7974448488449826,0.7974448488449826,0.7996515087686575,12.99599838256836,13054.609905000001 +13872,Multiclass classification,Voting,Keystroke,0.7978516329031793,0.7978516329031793,0.8006715750629478,13.20394229888916,13829.291085 +14280,Multiclass classification,Voting,Keystroke,0.797674907206387,0.7976749072063871,0.8002875748518964,13.364522933959961,14628.347686000001 +14688,Multiclass classification,Voting,Keystroke,0.8007761966364813,0.8007761966364813,0.8043248634763072,13.53370189666748,15451.756014 +15096,Multiclass classification,Voting,Keystroke,0.8051010268300762,0.8051010268300763,0.8085780284871096,13.774932861328125,16299.960754 +15504,Multiclass classification,Voting,Keystroke,0.8052634973876024,0.8052634973876024,0.8077470357827514,13.933537483215332,17172.988913 +15912,Multiclass classification,Voting,Keystroke,0.7978756834894098,0.7978756834894098,0.7983136026998061,14.138628005981445,18070.675966000003 +16320,Multiclass classification,Voting,Keystroke,0.793369691770329,0.7933696917703291,0.7956625263629296,14.30509090423584,18993.333450000002 +16728,Multiclass classification,Voting,Keystroke,0.7901596221677527,0.7901596221677527,0.7932579365729884,14.447582244873047,19941.842904 +17136,Multiclass classification,Voting,Keystroke,0.7861686606361249,0.7861686606361248,0.7888822346867281,14.767212867736816,20916.572711 +17544,Multiclass classification,Voting,Keystroke,0.780425240836801,0.780425240836801,0.7838193866310822,14.989240646362305,21922.215184 +17952,Multiclass classification,Voting,Keystroke,0.7802907915993538,0.7802907915993537,0.7845235361146662,15.200251579284668,22957.213951 +18360,Multiclass classification,Voting,Keystroke,0.783975162045863,0.783975162045863,0.7883700169311393,15.375930786132812,24020.765336 +18768,Multiclass classification,Voting,Keystroke,0.7869664837214259,0.7869664837214259,0.7913854757843782,15.5132417678833,25114.453204 +19176,Multiclass classification,Voting,Keystroke,0.7816427640156454,0.7816427640156454,0.7858184292134073,15.77665901184082,26236.293864000003 +19584,Multiclass classification,Voting,Keystroke,0.7846090997293571,0.7846090997293571,0.7893723685613512,15.996115684509277,27388.205854000003 +19992,Multiclass classification,Voting,Keystroke,0.7807013155920164,0.7807013155920164,0.785620728786203,16.12063980102539,28569.915626 +20400,Multiclass classification,Voting,Keystroke,0.7791068189617139,0.7791068189617139,0.7841355172773921,16.39253330230713,29779.243894000003 +46,Multiclass classification,[baseline] Last Class,ImageSegments,0.17777777777777778,0.17777777777777778,0.15260266049739735,0.0013666152954101562,0.110776 +92,Multiclass classification,[baseline] Last Class,ImageSegments,0.13186813186813187,0.13186813186813187,0.1213108980966124,0.0013637542724609375,0.225611 +138,Multiclass classification,[baseline] Last Class,ImageSegments,0.12408759124087591,0.12408759124087591,0.11874455065544491,0.001369476318359375,0.34363900000000003 +184,Multiclass classification,[baseline] Last Class,ImageSegments,0.12568306010928962,0.12568306010928962,0.12262983423071581,0.0013647079467773438,0.48452400000000007 +230,Multiclass classification,[baseline] Last Class,ImageSegments,0.12663755458515283,0.12663755458515283,0.12503852041208066,0.0013637542724609375,0.6292090000000001 +276,Multiclass classification,[baseline] Last Class,ImageSegments,0.12727272727272726,0.12727272727272726,0.12427907918144998,0.0013666152954101562,0.7861950000000001 +322,Multiclass classification,[baseline] Last Class,ImageSegments,0.13395638629283488,0.13395638629283488,0.13210036596246022,0.0013666152954101562,1.0166240000000002 +368,Multiclass classification,[baseline] Last Class,ImageSegments,0.13896457765667575,0.13896457765667575,0.13745011462972964,0.0013675689697265625,1.2507780000000002 +414,Multiclass classification,[baseline] Last Class,ImageSegments,0.14043583535108958,0.14043583535108958,0.14035813096947544,0.0013666152954101562,1.5223060000000002 +460,Multiclass classification,[baseline] Last Class,ImageSegments,0.14596949891067537,0.14596949891067537,0.14563148710727947,0.00136566162109375,1.7974560000000002 +506,Multiclass classification,[baseline] Last Class,ImageSegments,0.13861386138613863,0.13861386138613863,0.13833816102314941,0.0013666152954101562,2.0756200000000002 +552,Multiclass classification,[baseline] Last Class,ImageSegments,0.1397459165154265,0.1397459165154265,0.13938652491777898,0.0013666152954101562,2.402759 +598,Multiclass classification,[baseline] Last Class,ImageSegments,0.1373534338358459,0.1373534338358459,0.13727981043458612,0.0013675689697265625,2.771723 +644,Multiclass classification,[baseline] Last Class,ImageSegments,0.13996889580093314,0.13996889580093314,0.14017571709017965,0.0013666152954101562,3.149556 +690,Multiclass classification,[baseline] Last Class,ImageSegments,0.1378809869375907,0.1378809869375907,0.13801517784553327,0.001369476318359375,3.580436 +736,Multiclass classification,[baseline] Last Class,ImageSegments,0.1401360544217687,0.1401360544217687,0.14031088927958282,0.0013675689697265625,4.0152470000000005 +782,Multiclass classification,[baseline] Last Class,ImageSegments,0.14212548015364918,0.14212548015364918,0.1420930265541123,0.0013647079467773438,4.453992 +828,Multiclass classification,[baseline] Last Class,ImageSegments,0.14268440145102781,0.14268440145102781,0.14229874553046912,0.0013666152954101562,4.959761 +874,Multiclass classification,[baseline] Last Class,ImageSegments,0.13860252004581902,0.13860252004581902,0.13845352694595275,0.0013647079467773438,5.469480000000001 +920,Multiclass classification,[baseline] Last Class,ImageSegments,0.13492927094668117,0.13492927094668117,0.1348083913046733,0.0013666152954101562,6.0005820000000005 +966,Multiclass classification,[baseline] Last Class,ImageSegments,0.13367875647668392,0.13367875647668392,0.13349177774445276,0.0013637542724609375,6.5350530000000004 +1012,Multiclass classification,[baseline] Last Class,ImageSegments,0.13254203758654798,0.13254203758654798,0.1324936677659038,0.0013675689697265625,7.07275 +1058,Multiclass classification,[baseline] Last Class,ImageSegments,0.13339640491958374,0.13339640491958374,0.1331834965440007,0.00136566162109375,7.6454260000000005 +1104,Multiclass classification,[baseline] Last Class,ImageSegments,0.13417951042611062,0.13417951042611062,0.13402826529501538,0.0013666152954101562,8.221471000000001 +1150,Multiclass classification,[baseline] Last Class,ImageSegments,0.134029590948651,0.134029590948651,0.13406391150519123,0.0013637542724609375,8.800858000000002 +1196,Multiclass classification,[baseline] Last Class,ImageSegments,0.13640167364016736,0.13640167364016736,0.1363948420172951,0.001369476318359375,9.430169000000001 +1242,Multiclass classification,[baseline] Last Class,ImageSegments,0.13940370668815472,0.13940370668815472,0.13919772383892226,0.0013637542724609375,10.062783000000001 +1288,Multiclass classification,[baseline] Last Class,ImageSegments,0.1414141414141414,0.1414141414141414,0.14118715023210152,0.0013666152954101562,10.698372 +1334,Multiclass classification,[baseline] Last Class,ImageSegments,0.14328582145536384,0.14328582145536384,0.14302553278156666,0.0013637542724609375,11.387531000000001 +1380,Multiclass classification,[baseline] Last Class,ImageSegments,0.14358230601885424,0.14358230601885424,0.1433209000486506,0.001369476318359375,12.080639000000001 +1426,Multiclass classification,[baseline] Last Class,ImageSegments,0.14175438596491227,0.14175438596491227,0.14145466559291123,0.001369476318359375,12.777602000000002 +1472,Multiclass classification,[baseline] Last Class,ImageSegments,0.13936097892590074,0.13936097892590074,0.13907629713942624,0.0013647079467773438,13.546128000000001 +1518,Multiclass classification,[baseline] Last Class,ImageSegments,0.13974950560316415,0.13974950560316415,0.13951366685898453,0.0013666152954101562,14.318195000000001 +1564,Multiclass classification,[baseline] Last Class,ImageSegments,0.13691618682021753,0.13691618682021753,0.13664170474395118,0.0013666152954101562,15.093811 +1610,Multiclass classification,[baseline] Last Class,ImageSegments,0.13610938471100062,0.13610938471100062,0.13597683881903072,0.0013637542724609375,15.942934000000001 +1656,Multiclass classification,[baseline] Last Class,ImageSegments,0.1365558912386707,0.1365558912386707,0.13633224623774592,0.001369476318359375,16.795246000000002 +1702,Multiclass classification,[baseline] Last Class,ImageSegments,0.13932980599647266,0.13932980599647266,0.13901296274399097,0.0013675689697265625,17.650687 +1748,Multiclass classification,[baseline] Last Class,ImageSegments,0.14195764167143674,0.14195764167143674,0.14147197312723642,0.00136566162109375,18.510738 +1794,Multiclass classification,[baseline] Last Class,ImageSegments,0.14221974344673732,0.14221974344673732,0.14194103966110075,0.0013647079467773438,19.374685 +1840,Multiclass classification,[baseline] Last Class,ImageSegments,0.14138118542686243,0.14138118542686243,0.14114329766598663,0.0013675689697265625,20.242449999999998 +1886,Multiclass classification,[baseline] Last Class,ImageSegments,0.140053050397878,0.140053050397878,0.13973258713820755,0.0013666152954101562,21.182872999999997 +1932,Multiclass classification,[baseline] Last Class,ImageSegments,0.14293112377006734,0.14293112377006734,0.14275229229825853,0.0013666152954101562,22.126859999999997 +1978,Multiclass classification,[baseline] Last Class,ImageSegments,0.14618108244815378,0.14618108244815378,0.14597158151605963,0.001369476318359375,23.074112999999997 +2024,Multiclass classification,[baseline] Last Class,ImageSegments,0.14434008897676717,0.14434008897676717,0.14416625237761066,0.001369476318359375,24.067370999999998 +2070,Multiclass classification,[baseline] Last Class,ImageSegments,0.14403093281778637,0.14403093281778637,0.14385543497127623,0.0013666152954101562,25.063920999999997 +2116,Multiclass classification,[baseline] Last Class,ImageSegments,0.14468085106382977,0.14468085106382977,0.14460362317776573,0.0013637542724609375,26.063629999999996 +2162,Multiclass classification,[baseline] Last Class,ImageSegments,0.14530310041647385,0.14530310041647385,0.14520465913821792,0.001369476318359375,27.083891999999995 +2208,Multiclass classification,[baseline] Last Class,ImageSegments,0.14499320344358857,0.14499320344358857,0.14491109851991696,0.001369476318359375,28.107943999999996 +2254,Multiclass classification,[baseline] Last Class,ImageSegments,0.14647137150466044,0.14647137150466044,0.14640425534129609,0.0013666152954101562,29.207950999999998 +2300,Multiclass classification,[baseline] Last Class,ImageSegments,0.14789038712483688,0.14789038712483688,0.14788688524810298,0.0013675689697265625,30.311507 +2310,Multiclass classification,[baseline] Last Class,ImageSegments,0.14811606756171503,0.14811606756171503,0.14811566784252675,0.001369476318359375,31.415920999999997 +1056,Multiclass classification,[baseline] Last Class,Insects,0.15829383886255924,0.15829383886255924,0.1376212379233521,0.0013856887817382812,0.57267 +2112,Multiclass classification,[baseline] Last Class,Insects,0.16579819990525818,0.16579819990525818,0.15110451064118433,0.0013856887817382812,1.690872 +3168,Multiclass classification,[baseline] Last Class,Insects,0.17019261130407326,0.17019261130407326,0.15681512355039637,0.0013885498046875,3.2981429999999996 +4224,Multiclass classification,[baseline] Last Class,Insects,0.16599573762727918,0.16599573762727918,0.15254433156050665,0.0013856887817382812,5.4736839999999995 +5280,Multiclass classification,[baseline] Last Class,Insects,0.17086569426027656,0.17086569426027656,0.15676679113993588,0.0013837814331054688,8.202311 +6336,Multiclass classification,[baseline] Last Class,Insects,0.17379636937647988,0.17379636937647988,0.16137568195972998,0.0013837814331054688,11.448991 +7392,Multiclass classification,[baseline] Last Class,Insects,0.1752130970098769,0.1752130970098769,0.16189407904134778,0.0013837814331054688,15.242684 +8448,Multiclass classification,[baseline] Last Class,Insects,0.17722268260921037,0.17722268260921037,0.163740045170864,0.0013818740844726562,19.537217000000002 +9504,Multiclass classification,[baseline] Last Class,Insects,0.17731242765442493,0.17731242765442493,0.1637492974453096,0.0013885498046875,24.318802 +10560,Multiclass classification,[baseline] Last Class,Insects,0.17908892887584052,0.17908892887584052,0.16564210767474952,0.0013837814331054688,29.683683000000002 +11616,Multiclass classification,[baseline] Last Class,Insects,0.17899268187688333,0.17899268187688333,0.16559253835337612,0.0013856887817382812,35.598037000000005 +12672,Multiclass classification,[baseline] Last Class,Insects,0.18530502722752742,0.1853050272275274,0.18269809988409802,0.0013866424560546875,41.981502000000006 +13728,Multiclass classification,[baseline] Last Class,Insects,0.24797843665768193,0.24797843665768193,0.26603936845528803,0.0013866424560546875,48.94863000000001 +14784,Multiclass classification,[baseline] Last Class,Insects,0.2795778935263478,0.2795778935263478,0.28229742751715126,0.0013818740844726562,56.43945000000001 +15840,Multiclass classification,[baseline] Last Class,Insects,0.27615379758823155,0.27615379758823155,0.2847375853365436,0.0013818740844726562,64.48233200000001 +16896,Multiclass classification,[baseline] Last Class,Insects,0.2723290914471737,0.2723290914471737,0.2859139704285301,0.0013856887817382812,73.03679300000002 +17952,Multiclass classification,[baseline] Last Class,Insects,0.2720739791655061,0.2720739791655061,0.2880143206503878,0.0013866424560546875,82.10379000000002 +19008,Multiclass classification,[baseline] Last Class,Insects,0.28252748987215237,0.28252748987215237,0.2877504429321087,0.0013866424560546875,91.70347300000002 +20064,Multiclass classification,[baseline] Last Class,Insects,0.28724517769027563,0.28724517769027563,0.28667392366619265,0.0013818740844726562,101.81113500000002 +21120,Multiclass classification,[baseline] Last Class,Insects,0.28306264501160094,0.28306264501160094,0.28164766024255256,0.0013837814331054688,112.42818900000002 +22176,Multiclass classification,[baseline] Last Class,Insects,0.2805411499436302,0.2805411499436302,0.27862960725280095,0.0013866424560546875,123.55266000000002 +23232,Multiclass classification,[baseline] Last Class,Insects,0.2797124531875511,0.2797124531875511,0.27719419757933417,0.0013856887817382812,135.22034100000002 +24288,Multiclass classification,[baseline] Last Class,Insects,0.2777205912628155,0.2777205912628155,0.2745878480946635,0.0013866424560546875,147.32084400000002 +25344,Multiclass classification,[baseline] Last Class,Insects,0.2756579726157124,0.2756579726157124,0.27233803052028965,0.0013818740844726562,159.88729300000003 +26400,Multiclass classification,[baseline] Last Class,Insects,0.27394977082465244,0.27394977082465244,0.26996904425699914,0.0013837814331054688,172.95537600000003 +27456,Multiclass classification,[baseline] Last Class,Insects,0.27189947186304864,0.27189947186304864,0.26719485323886244,0.0013866424560546875,186.52082400000003 +28512,Multiclass classification,[baseline] Last Class,Insects,0.2723860965942969,0.2723860965942969,0.2686965366571337,0.0013885498046875,200.59564800000004 +29568,Multiclass classification,[baseline] Last Class,Insects,0.2738187844556431,0.2738187844556431,0.2720266804437783,0.0013885498046875,215.16150500000003 +30624,Multiclass classification,[baseline] Last Class,Insects,0.27538124938771513,0.27538124938771513,0.27486986638103517,0.0013885498046875,230.19075300000003 +31680,Multiclass classification,[baseline] Last Class,Insects,0.2780390795163989,0.2780390795163989,0.2784141751235631,0.0013856887817382812,245.71900300000004 +32736,Multiclass classification,[baseline] Last Class,Insects,0.279670077898274,0.279670077898274,0.28021922512452757,0.0013837814331054688,261.76959600000004 +33792,Multiclass classification,[baseline] Last Class,Insects,0.2808440117190968,0.2808440117190968,0.28119627453717067,0.0013856887817382812,278.22772000000003 +34848,Multiclass classification,[baseline] Last Class,Insects,0.2772405085086234,0.2772405085086234,0.27819051828647573,0.0013837814331054688,295.19763900000004 +35904,Multiclass classification,[baseline] Last Class,Insects,0.2739325404562293,0.2739325404562293,0.2754200456137155,0.0013856887817382812,312.64260700000005 +36960,Multiclass classification,[baseline] Last Class,Insects,0.271246516410076,0.271246516410076,0.27333283767820205,0.0013818740844726562,330.5037730000001 +38016,Multiclass classification,[baseline] Last Class,Insects,0.26855188741286334,0.26855188741286334,0.2710722002891223,0.0013856887817382812,348.8496650000001 +39072,Multiclass classification,[baseline] Last Class,Insects,0.277034117376059,0.277034117376059,0.2770619820799866,0.0013866424560546875,367.6207990000001 +40128,Multiclass classification,[baseline] Last Class,Insects,0.27617315024796274,0.27617315024796274,0.2760769006623072,0.0013837814331054688,386.8573710000001 +41184,Multiclass classification,[baseline] Last Class,Insects,0.27567200058276475,0.27567200058276475,0.27543526329721163,0.0013837814331054688,406.52795400000014 +42240,Multiclass classification,[baseline] Last Class,Insects,0.27401216884869434,0.27401216884869434,0.27359461935885426,0.0013885498046875,426.69962200000015 +43296,Multiclass classification,[baseline] Last Class,Insects,0.2738422450629403,0.2738422450629403,0.27319488690835786,0.0013856887817382812,447.37129900000014 +44352,Multiclass classification,[baseline] Last Class,Insects,0.2729588960790061,0.2729588960790061,0.27209116538690487,0.0013866424560546875,468.49129600000015 +45408,Multiclass classification,[baseline] Last Class,Insects,0.27205056489087587,0.27205056489087587,0.2708084959373003,0.0013866424560546875,490.06234300000017 +46464,Multiclass classification,[baseline] Last Class,Insects,0.27137722488862104,0.27137722488862104,0.2698631410415437,0.0013837814331054688,512.0778290000002 +47520,Multiclass classification,[baseline] Last Class,Insects,0.27235421620825356,0.27235421620825356,0.27170627983222856,0.0013837814331054688,534.5781510000002 +48576,Multiclass classification,[baseline] Last Class,Insects,0.2741327843540916,0.2741327843540916,0.27449463409742436,0.0013818740844726562,557.5265480000002 +49632,Multiclass classification,[baseline] Last Class,Insects,0.27535209848683284,0.27535209848683284,0.27650368764304034,0.0013818740844726562,580.9705880000001 +50688,Multiclass classification,[baseline] Last Class,Insects,0.2768362696549411,0.2768362696549411,0.27863440912734966,0.0013837814331054688,604.9012140000001 +51744,Multiclass classification,[baseline] Last Class,Insects,0.27827918752294994,0.27827918752294994,0.2805971515128955,0.0013885498046875,629.3033230000001 +52800,Multiclass classification,[baseline] Last Class,Insects,0.28911532415386654,0.28911532415386654,0.28929532027297566,0.0013866424560546875,654.1512880000001 +52848,Multiclass classification,[baseline] Last Class,Insects,0.2897610081934642,0.2897610081934642,0.28976272570313216,0.0013866424560546875,679.0036960000001 +408,Multiclass classification,[baseline] Last Class,Keystroke,0.9975429975429976,0.9975429975429976,0.9660408844388819,0.0006122589111328125,0.255536 +816,Multiclass classification,[baseline] Last Class,Keystroke,0.9975460122699387,0.9975460122699387,0.9879967903427672,0.0006628036499023438,0.794196 +1224,Multiclass classification,[baseline] Last Class,Keystroke,0.9975470155355682,0.9975470155355682,0.9931179599499375,0.000713348388671875,1.53447 +1632,Multiclass classification,[baseline] Last Class,Keystroke,0.9975475168608215,0.9975475168608215,0.9950750839342831,0.0012521743774414062,2.469131 +2040,Multiclass classification,[baseline] Last Class,Keystroke,0.9975478175576263,0.9975478175576263,0.9960150346160551,0.0013027191162109375,3.675833 +2448,Multiclass classification,[baseline] Last Class,Keystroke,0.9975480179812015,0.9975480179812015,0.9965317313935653,0.0013532638549804688,5.030286 +2856,Multiclass classification,[baseline] Last Class,Keystroke,0.9975481611208407,0.9975481611208407,0.9968424283169279,0.00140380859375,6.586031 +3264,Multiclass classification,[baseline] Last Class,Keystroke,0.9975482684646031,0.9975482684646031,0.9970416021996,0.0014543533325195312,8.377109 +3672,Multiclass classification,[baseline] Last Class,Keystroke,0.9975483519476982,0.9975483519476982,0.9971755428551425,0.0015048980712890625,10.331252000000001 +4080,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484187300809,0.9975484187300809,0.9972690115789393,0.0015554428100585938,12.525489 +4488,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484733675062,0.9975484733675062,0.9973361791525123,0.001605987548828125,14.940819000000001 +4896,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485188968335,0.9975485188968335,0.9973856025730918,0.0016565322875976562,17.495259 +5304,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485574203281,0.9975485574203281,0.9974226798335742,0.0017070770263671875,20.336762 +5712,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485904395027,0.9975485904395027,0.99745094204078,0.0017576217651367188,23.402208 +6120,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486190554013,0.9975486190554013,0.9974727709453766,0.00180816650390625,26.661861000000002 +6528,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486440937643,0.9975486440937643,0.997489815700999,0.0018587112426757812,30.164710000000003 +6936,Multiclass classification,[baseline] Last Class,Keystroke,0.997548666186013,0.997548666186013,0.9975032443691146,0.0019092559814453125,33.838397 +7344,Multiclass classification,[baseline] Last Class,Keystroke,0.997548685823233,0.997548685823233,0.9975139007887865,0.0034246444702148438,37.738436 +7752,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487033931105,0.9975487033931105,0.9975224052755716,0.003475189208984375,41.800015 +8160,Multiclass classification,[baseline] Last Class,Keystroke,0.997548719205785,0.997548719205785,0.9975292209193424,0.0035257339477539062,46.105028000000004 +8568,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487335123147,0.9975487335123147,0.9975346982235258,0.0035762786865234375,50.63279300000001 +8976,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487465181059,0.9975487465181059,0.9975391057693664,0.0036268234252929688,55.447067000000004 +9384,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487583928381,0.9975487583928381,0.997542651662671,0.0036773681640625,60.387128000000004 +9792,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487692779083,0.9975487692779083,0.9975454987794795,0.0037279129028320312,65.547582 +10200,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487792920874,0.9975487792920874,0.9975477757646256,0.0037784576416015625,70.981052 +10608,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487885358726,0.9975487885358726,0.9975495850737114,0.0038290023803710938,76.594226 +11016,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487970948707,0.9975487970948707,0.9975510089260562,0.003879547119140625,82.44596800000001 +11424,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488050424582,0.9975488050424582,0.9975521137613483,0.003930091857910156,88.533094 +11832,Multiclass classification,[baseline] Last Class,Keystroke,0.99754881244189,0.99754881244189,0.9975529536110199,0.0039806365966796875,94.81874400000001 +12240,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488193479859,0.9975488193479859,0.9975535726732964,0.004031181335449219,101.331754 +12648,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488258084921,0.9975488258084921,0.9975540072976319,0.00408172607421875,108.05167800000001 +13056,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488318651857,0.9975488318651857,0.997554287526727,0.004132270812988281,114.99668100000001 +13464,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488375547797,0.9975488375547797,0.9975544383040469,0.0041828155517578125,122.1119 +13872,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488429096676,0.9975488429096676,0.9975544804262362,0.004233360290527344,129.47010500000002 +14280,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488479585405,0.9975488479585405,0.9975544312994101,0.004283905029296875,136.988051 +14688,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488527269013,0.9975488527269013,0.9975543055435039,0.004334449768066406,144.742896 +15096,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488572374959,0.9975488572374959,0.9975541154780816,0.0043849945068359375,152.648866 +15504,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488615106753,0.9975488615106753,0.9975538715150367,0.004435539245605469,160.767465 +15912,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488655647037,0.9975488655647037,0.9975535824776959,0.004486083984375,169.09858 +16320,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488694160182,0.9975488694160182,0.9975532558614028,0.004536628723144531,177.653336 +16728,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488730794524,0.9975488730794524,0.997552898047314,0.0045871734619140625,186.438203 +17136,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488765684272,0.9975488765684272,0.9975525144785747,0.004637718200683594,195.44716799999998 +17544,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488798951149,0.9975488798951149,0.9975521098061079,0.004688262939453125,204.56363499999998 +17952,Multiclass classification,[baseline] Last Class,Keystroke,0.997548883070581,0.997548883070581,0.9975516880097278,0.004738807678222656,213.933058 +18360,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488861049077,0.9975488861049077,0.997551252499137,0.0047893524169921875,223.513668 +18768,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488890073001,0.9975488890073001,0.9975508061984416,0.004839897155761719,233.322943 +19176,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488917861799,0.9975488917861799,0.9975503516171184,0.00489044189453125,243.357771 +19584,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488944492672,0.9975488944492672,0.9975498909097889,0.004940986633300781,253.567103 +19992,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488970036517,0.9975488970036517,0.9975494259267257,0.0049915313720703125,264.004285 +20400,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488994558557,0.9975488994558557,0.9975489582566448,0.005042076110839844,274.675054 diff --git a/benchmarks/regression.csv b/benchmarks/regression.csv index 87b6f471b7..6d0b88b0a6 100644 --- a/benchmarks/regression.csv +++ b/benchmarks/regression.csv @@ -1,1633 +1,1769 @@ step,track,model,dataset,MAE,RMSE,R2,Memory in Mb,Time in s -11,Regression,Linear Regression,ChickWeights,32.364564569910335,32.97872020361878,-1398.9905780691188,0.004130363464355469,0.003051 -22,Regression,Linear Regression,ChickWeights,22.977933628105813,25.38362603225939,-681.3960169454474,0.004130363464355469,0.0083 -33,Regression,Linear Regression,ChickWeights,16.216942910977988,20.82463881551788,-300.18738429635704,0.004130363464355469,0.014943 -44,Regression,Linear Regression,ChickWeights,12.450847696587651,18.04722398474583,-255.42929659358055,0.004130363464355469,0.023007 -55,Regression,Linear Regression,ChickWeights,11.888340701788199,18.699705575978978,-67.26141846932143,0.004130363464355469,0.032524 -66,Regression,Linear Regression,ChickWeights,11.481406471145082,17.562600262725994,-24.955493215822358,0.004130363464355469,0.043462 -77,Regression,Linear Regression,ChickWeights,10.781108661026625,16.493572764286025,-14.34295652053857,0.004130363464355469,0.055844000000000005 -88,Regression,Linear Regression,ChickWeights,9.717703273355898,15.46585610846664,-11.231382330967593,0.004130363464355469,0.06965700000000001 -99,Regression,Linear Regression,ChickWeights,8.826979124235404,14.601347274688614,-8.118374730562003,0.004130363464355469,0.084902 -110,Regression,Linear Regression,ChickWeights,8.34720326953035,13.931298318002055,-4.796525071049026,0.004130363464355469,0.101965 -121,Regression,Linear Regression,ChickWeights,8.037877082888846,13.410806382891339,-3.136902586442697,0.004130363464355469,0.120474 -132,Regression,Linear Regression,ChickWeights,7.656433924417384,12.898278689410903,-2.1275837609073576,0.004130363464355469,0.140407 -143,Regression,Linear Regression,ChickWeights,7.307942088554156,12.437137940834392,-1.3553409371460328,0.004130363464355469,0.161783 -154,Regression,Linear Regression,ChickWeights,7.037714222368383,12.042115748312936,-0.8765797740197239,0.004130363464355469,0.184594 -165,Regression,Linear Regression,ChickWeights,7.129031762481882,11.913307711374014,-0.47642580101504595,0.004130363464355469,0.208855 -176,Regression,Linear Regression,ChickWeights,7.184514897799321,11.77636646389892,-0.1632359842489146,0.004130363464355469,0.23453000000000002 -187,Regression,Linear Regression,ChickWeights,7.115123062484693,11.572523949602724,0.08026778192309036,0.004130363464355469,0.261536 -198,Regression,Linear Regression,ChickWeights,7.006474290899419,11.366304822809298,0.2942397460202306,0.004130363464355469,0.289991 -209,Regression,Linear Regression,ChickWeights,7.129008217053805,11.440940870898142,0.41052686032506414,0.004130363464355469,0.319876 -220,Regression,Linear Regression,ChickWeights,7.62421928608864,12.045617517752783,0.42792292503665363,0.004130363464355469,0.351191 -231,Regression,Linear Regression,ChickWeights,7.729844807682863,12.068921171072352,0.5087708730950672,0.004130363464355469,0.383941 -242,Regression,Linear Regression,ChickWeights,7.873703200353374,12.233427305577539,0.5938836560989084,0.004130363464355469,0.41811699999999996 -253,Regression,Linear Regression,ChickWeights,7.894340397324045,12.218207932001457,0.6481596201604607,0.004130363464355469,0.45372499999999993 -264,Regression,Linear Regression,ChickWeights,8.479294890454037,13.126132095776898,0.6289858847198173,0.004210472106933594,0.49076999999999993 -275,Regression,Linear Regression,ChickWeights,8.914096443559163,13.971715828104035,0.6301108693673194,0.004210472106933594,0.529253 -286,Regression,Linear Regression,ChickWeights,9.123963222012373,14.305597328390173,0.6641373552910966,0.004210472106933594,0.569186 -297,Regression,Linear Regression,ChickWeights,9.083791720841957,14.24670706195338,0.7111028643570333,0.004210472106933594,0.610599 -308,Regression,Linear Regression,ChickWeights,9.589205789771716,14.956254664628933,0.716435491318643,0.004210472106933594,0.653398 -319,Regression,Linear Regression,ChickWeights,10.6480954226875,17.335456678654833,0.654294294845865,0.004210472106933594,0.697581 -330,Regression,Linear Regression,ChickWeights,11.061417554605157,17.89416376383148,0.6847745473646168,0.004210472106933594,0.743241 -341,Regression,Linear Regression,ChickWeights,11.240970714084437,17.96809449059472,0.7153933828209167,0.004210472106933594,0.7902760000000001 -352,Regression,Linear Regression,ChickWeights,11.393007763406809,18.07679096199219,0.7381404893604309,0.004210472106933594,0.8386990000000001 -363,Regression,Linear Regression,ChickWeights,12.251680566634816,19.3891577397662,0.7074601934283691,0.004210472106933594,0.8885620000000001 -374,Regression,Linear Regression,ChickWeights,12.75183556333798,20.473547618215626,0.7001526953506461,0.004210472106933594,0.9398400000000001 -385,Regression,Linear Regression,ChickWeights,13.120977867369845,21.06680160073653,0.7191139726408686,0.004210472106933594,0.9925610000000001 -396,Regression,Linear Regression,ChickWeights,13.243904830041805,21.04850718241465,0.7385587649833809,0.004210472106933594,1.046701 -407,Regression,Linear Regression,ChickWeights,14.114140715648693,22.50284796635845,0.7222415724766076,0.004210472106933594,1.102244 -418,Regression,Linear Regression,ChickWeights,14.877176135328032,23.91912678123439,0.7054123344015044,0.004210472106933594,1.159239 -429,Regression,Linear Regression,ChickWeights,15.420211528669606,24.826921056607986,0.71797392321154,0.004210472106933594,1.2176479999999998 -440,Regression,Linear Regression,ChickWeights,15.588380621816588,24.899467271207534,0.7364018543257719,0.004210472106933594,1.277456 -451,Regression,Linear Regression,ChickWeights,16.102138383202178,25.5012042182244,0.73526123694725,0.004210472106933594,1.3387229999999999 -462,Regression,Linear Regression,ChickWeights,17.19666374070754,27.602141070792264,0.7086782414730581,0.004210472106933594,1.4014049999999998 -473,Regression,Linear Regression,ChickWeights,17.97145397683086,28.90516312323801,0.7179019616037816,0.004210472106933594,1.4654839999999998 -484,Regression,Linear Regression,ChickWeights,18.29792978215437,29.184271659667466,0.728186505594778,0.004210472106933594,1.5310409999999999 -495,Regression,Linear Regression,ChickWeights,18.74962346435519,29.709578411858935,0.7350194821983969,0.004210472106933594,1.5979919999999999 -506,Regression,Linear Regression,ChickWeights,19.63242676502778,31.145843529930993,0.717244444772776,0.004210472106933594,1.666343 -517,Regression,Linear Regression,ChickWeights,20.352340621675207,32.13418072986834,0.7159654376794024,0.004210472106933594,1.736162 -528,Regression,Linear Regression,ChickWeights,21.13777985928475,33.324214910779105,0.7253645356808669,0.004210472106933594,1.807369 -539,Regression,Linear Regression,ChickWeights,21.30552841968683,33.32197733500869,0.7367405849979859,0.004210472106933594,1.880014 -550,Regression,Linear Regression,ChickWeights,22.288420935356612,34.93191609140748,0.7196038878445231,0.004210472106933594,1.954091 -561,Regression,Linear Regression,ChickWeights,22.987903852135958,35.84862508987654,0.7176082524890277,0.004210472106933594,2.029559 -572,Regression,Linear Regression,ChickWeights,23.835304128485923,37.028707868367256,0.7213067136137974,0.004210472106933594,2.106474 -20,Regression,Linear Regression,TrumpApproval,23.20376765378399,26.086393589237737,-1595.1823041445402,0.004813194274902344,0.006002 -40,Regression,Linear Regression,TrumpApproval,14.037845165976737,19.010285970857193,-144.292318589198,0.004813194274902344,0.015135000000000001 -60,Regression,Linear Regression,TrumpApproval,11.507970876430278,16.25440462414082,-142.20309184289852,0.004813194274902344,0.026737 -80,Regression,Linear Regression,TrumpApproval,9.557907896578074,14.248619966820991,-109.38231735939875,0.004813194274902344,0.04106 -100,Regression,Linear Regression,TrumpApproval,8.00119890237694,12.784639272000032,-54.757167221204185,0.004813194274902344,0.058228 -120,Regression,Linear Regression,TrumpApproval,6.9642139928158,11.706689332840265,-38.660847151370525,0.004813194274902344,0.078171 -140,Regression,Linear Regression,TrumpApproval,6.158017211594616,10.855926078196223,-34.244125473921144,0.004813194274902344,0.100823 -160,Regression,Linear Regression,TrumpApproval,5.477712824897756,10.159717829752895,-26.222218487939156,0.004813194274902344,0.126265 -180,Regression,Linear Regression,TrumpApproval,5.024407839120485,9.597258286357787,-20.334227408789637,0.004813194274902344,0.154466 -200,Regression,Linear Regression,TrumpApproval,4.585662202332267,9.108145701438088,-18.272229834363905,0.004813194274902344,0.185533 -220,Regression,Linear Regression,TrumpApproval,4.26060909213789,8.692057179629266,-17.933082537971817,0.004813194274902344,0.219335 -240,Regression,Linear Regression,TrumpApproval,3.9717866152166024,8.326248244302885,-16.503720237291063,0.004813194274902344,0.255864 -260,Regression,Linear Regression,TrumpApproval,3.713770572650404,8.00217875002923,-15.385557669694744,0.004973411560058594,0.29514799999999997 -280,Regression,Linear Regression,TrumpApproval,3.519033617242816,7.718418241237259,-14.960370233444369,0.004973411560058594,0.33718899999999996 -300,Regression,Linear Regression,TrumpApproval,3.3459125962612686,7.4642342223287805,-13.679347912302557,0.004973411560058594,0.382005 -320,Regression,Linear Regression,TrumpApproval,3.2142611116185447,7.238080925352425,-13.486769876410833,0.004973411560058594,0.429554 -340,Regression,Linear Regression,TrumpApproval,3.0579195410777067,7.023783188903098,-13.415885727361019,0.004973411560058594,0.479826 -360,Regression,Linear Regression,TrumpApproval,2.945682332324278,6.834004497968132,-12.75946181118139,0.004973411560058594,0.532818 -380,Regression,Linear Regression,TrumpApproval,2.8346230504950753,6.655478314361804,-12.501407484394289,0.004973411560058594,0.588545 -400,Regression,Linear Regression,TrumpApproval,2.750580257859316,6.492898516140861,-12.2130072039923,0.004973411560058594,0.647075 -420,Regression,Linear Regression,TrumpApproval,2.6430441633874873,6.337629923196658,-12.005235448499764,0.004973411560058594,0.708317 -440,Regression,Linear Regression,TrumpApproval,2.55209658354648,6.194505226365406,-11.19965041203295,0.004973411560058594,0.7723099999999999 -460,Regression,Linear Regression,TrumpApproval,2.456089002686458,6.059000096335146,-10.068304379054995,0.004973411560058594,0.8388639999999999 -480,Regression,Linear Regression,TrumpApproval,2.366054305985814,5.93188196569365,-9.364683952709628,0.004973411560058594,0.9079419999999999 -500,Regression,Linear Regression,TrumpApproval,2.2878529832492873,5.812913918334153,-8.7442989221461,0.004973411560058594,0.9796759999999999 -520,Regression,Linear Regression,TrumpApproval,2.2263077878678064,5.701877933590318,-8.391958889485423,0.004973411560058594,1.054282 -540,Regression,Linear Regression,TrumpApproval,2.159275760054389,5.596308740310266,-8.01424271274666,0.004973411560058594,1.13157 -560,Regression,Linear Regression,TrumpApproval,2.121286703179314,5.500929056902255,-7.917124287747498,0.004973411560058594,1.211538 -580,Regression,Linear Regression,TrumpApproval,2.058130800812745,5.4056516105350205,-7.823875188349783,0.004973411560058594,1.294212 -600,Regression,Linear Regression,TrumpApproval,2.010772210317983,5.316298216027806,-7.440165070210115,0.004973411560058594,1.379521 -620,Regression,Linear Regression,TrumpApproval,1.9712240547218982,5.232121316388296,-7.05039272692726,0.004973411560058594,1.467481 -640,Regression,Linear Regression,TrumpApproval,1.9189061166281676,5.150155111235484,-6.654334565315682,0.004973411560058594,1.558 -660,Regression,Linear Regression,TrumpApproval,1.87787066207154,5.072802363597129,-6.372735616761029,0.004973411560058594,1.651027 -680,Regression,Linear Regression,TrumpApproval,1.8268195769848845,4.997758130035794,-6.2693000939147145,0.004973411560058594,1.746732 -700,Regression,Linear Regression,TrumpApproval,1.786028440025988,4.9266674679383895,-6.249499636750513,0.004973411560058594,1.84509 -720,Regression,Linear Regression,TrumpApproval,1.7407901109286728,4.857855812572241,-6.20325679847918,0.004973411560058594,1.9462679999999999 -740,Regression,Linear Regression,TrumpApproval,1.6980813320245582,4.791872643159282,-6.00468916158508,0.004973411560058594,2.050093 -760,Regression,Linear Regression,TrumpApproval,1.6641755726043945,4.729168490426344,-5.896482494172518,0.004973411560058594,2.156434 -780,Regression,Linear Regression,TrumpApproval,1.6304504193200038,4.6685949390255965,-5.751055148180852,0.004973411560058594,2.265329 -800,Regression,Linear Regression,TrumpApproval,1.6024144632936517,4.610765602340218,-5.644394777378336,0.004973411560058594,2.376883 -820,Regression,Linear Regression,TrumpApproval,1.5772046524793362,4.5553425632171916,-5.556843815680599,0.004973411560058594,2.490967 -840,Regression,Linear Regression,TrumpApproval,1.5504230286371175,4.501494961913348,-5.462190804899245,0.004973411560058594,2.6076259999999998 -860,Regression,Linear Regression,TrumpApproval,1.5213504760602443,4.449264316210896,-5.302224210455831,0.004973411560058594,2.7268899999999996 -880,Regression,Linear Regression,TrumpApproval,1.4920620594434295,4.398585386750051,-5.128866413959423,0.004973411560058594,2.8486969999999996 -900,Regression,Linear Regression,TrumpApproval,1.4631535468073946,4.3495699730724,-5.018290064232882,0.004973411560058594,2.973079 -920,Regression,Linear Regression,TrumpApproval,1.4376774845864433,4.302379067062498,-4.985157602999735,0.004973411560058594,3.100144 -940,Regression,Linear Regression,TrumpApproval,1.415413283450155,4.2569033587476754,-4.909007968017405,0.004973411560058594,3.229628 -960,Regression,Linear Regression,TrumpApproval,1.3929189597034646,4.212790914002432,-4.847686152244137,0.004973411560058594,3.361574 -980,Regression,Linear Regression,TrumpApproval,1.3689716642677325,4.1697584324400925,-4.840094251784054,0.004973411560058594,3.49595 -1000,Regression,Linear Regression,TrumpApproval,1.348598310616665,4.128277744647548,-4.8208398605179,0.004973411560058594,3.6327000000000003 -11,Regression,Linear Regression with l1 regularization,ChickWeights,32.42675747760146,33.032143455333795,-1403.5300282096139,0.004361152648925781,0.002172 -22,Regression,Linear Regression with l1 regularization,ChickWeights,23.116711205346814,25.467535638550565,-685.9150105173057,0.004361152648925781,0.005938 -33,Regression,Linear Regression with l1 regularization,ChickWeights,16.40645850052153,20.90890407573329,-302.6297778360383,0.004361152648925781,0.010657 -44,Regression,Linear Regression with l1 regularization,ChickWeights,12.633013937743208,18.123648450153386,-257.60569409037487,0.004361152648925781,0.016312 -55,Regression,Linear Regression with l1 regularization,ChickWeights,12.09340740686418,18.755320878466158,-67.66805855020911,0.004361152648925781,0.022822000000000002 -66,Regression,Linear Regression with l1 regularization,ChickWeights,11.723217014070245,17.64468538999345,-25.198684873207917,0.004361152648925781,0.030046000000000003 -77,Regression,Linear Regression with l1 regularization,ChickWeights,10.995180265837302,16.56912334002292,-14.483838555564008,0.004361152648925781,0.037972000000000006 -88,Regression,Linear Regression with l1 regularization,ChickWeights,9.883907021821408,15.530287677810513,-11.333507781967656,0.004361152648925781,0.04659800000000001 -99,Regression,Linear Regression with l1 regularization,ChickWeights,8.972176235897166,14.66146594146288,-8.193616152032533,0.004361152648925781,0.055927000000000004 -110,Regression,Linear Regression with l1 regularization,ChickWeights,8.50780758363674,13.99831063395296,-4.852424068253989,0.004361152648925781,0.06618700000000001 -121,Regression,Linear Regression with l1 regularization,ChickWeights,8.189643323772653,13.479618530659062,-3.1794651999709123,0.004361152648925781,0.07716500000000001 -132,Regression,Linear Regression with l1 regularization,ChickWeights,7.801679323915957,12.961634417305982,-2.158384304610562,0.004361152648925781,0.08884500000000001 -143,Regression,Linear Regression with l1 regularization,ChickWeights,7.451861020480359,12.498785048420814,-1.3787482214976663,0.004361152648925781,0.101232 -154,Regression,Linear Regression with l1 regularization,ChickWeights,7.149646459280303,12.093459492377487,-0.8926161646086501,0.004361152648925781,0.114319 -165,Regression,Linear Regression with l1 regularization,ChickWeights,7.229782522017506,11.96532542528415,-0.4893471433346175,0.004361152648925781,0.128105 -176,Regression,Linear Regression with l1 regularization,ChickWeights,7.2725124267989045,11.818048782353436,-0.17148507897118015,0.004361152648925781,0.14260699999999998 -187,Regression,Linear Regression with l1 regularization,ChickWeights,7.196746625780547,11.610365671998538,0.07424296733128555,0.004361152648925781,0.15781399999999998 -198,Regression,Linear Regression with l1 regularization,ChickWeights,7.1064479168500405,11.42347112116664,0.28712271717281546,0.004361152648925781,0.173728 -209,Regression,Linear Regression with l1 regularization,ChickWeights,7.187961051781539,11.470757896418931,0.4074503232991815,0.004361152648925781,0.19033999999999998 -220,Regression,Linear Regression with l1 regularization,ChickWeights,7.669011107337858,12.056664202246258,0.426873173509426,0.004361152648925781,0.20765199999999998 -231,Regression,Linear Regression with l1 regularization,ChickWeights,7.786810512364482,12.097059810994589,0.5064776054701067,0.004361152648925781,0.22566199999999997 -242,Regression,Linear Regression with l1 regularization,ChickWeights,7.967587416991401,12.312376354870244,0.5886249568088757,0.004361152648925781,0.24437199999999998 -253,Regression,Linear Regression with l1 regularization,ChickWeights,7.942191618805254,12.251768500136135,0.6462241186586895,0.004361152648925781,0.26377399999999995 -264,Regression,Linear Regression with l1 regularization,ChickWeights,8.532657015260138,13.159069559279287,0.6271215737447722,0.004441261291503906,0.28388899999999995 -275,Regression,Linear Regression with l1 regularization,ChickWeights,8.974527826218258,14.016709692996269,0.6277246858047626,0.004441261291503906,0.30470499999999995 -286,Regression,Linear Regression with l1 regularization,ChickWeights,9.187875132430849,14.367497338174372,0.6612245262436964,0.004441261291503906,0.32623499999999994 -297,Regression,Linear Regression with l1 regularization,ChickWeights,9.146460078204477,14.316362398212213,0.7082709930315267,0.004441261291503906,0.3484709999999999 -308,Regression,Linear Regression with l1 regularization,ChickWeights,9.641370323857412,15.001693346690402,0.7147098761119641,0.004441261291503906,0.3714179999999999 -319,Regression,Linear Regression with l1 regularization,ChickWeights,10.700825117113602,17.38383679543193,0.6523619983610591,0.004441261291503906,0.3950729999999999 -330,Regression,Linear Regression with l1 regularization,ChickWeights,11.121905143066762,17.96551370253039,0.6822557197544588,0.004441261291503906,0.4194319999999999 -341,Regression,Linear Regression with l1 regularization,ChickWeights,11.300130820443067,18.038310133249194,0.7131646675929553,0.004441261291503906,0.4444929999999999 -352,Regression,Linear Regression with l1 regularization,ChickWeights,11.446939695127732,18.134419536696374,0.7364682185789984,0.004441261291503906,0.4702589999999999 -363,Regression,Linear Regression with l1 regularization,ChickWeights,12.306713664516929,19.446501901626007,0.7057272396553849,0.004441261291503906,0.4967349999999999 -374,Regression,Linear Regression with l1 regularization,ChickWeights,12.804714538172899,20.530886427306594,0.6984708214392588,0.004441261291503906,0.523921 -385,Regression,Linear Regression with l1 regularization,ChickWeights,13.174415073298738,21.133761140382827,0.7173255769005842,0.004441261291503906,0.551805 -396,Regression,Linear Regression with l1 regularization,ChickWeights,13.2883642577537,21.10340115690396,0.7371933225224319,0.004441261291503906,0.580392 -407,Regression,Linear Regression with l1 regularization,ChickWeights,14.156717848187574,22.549679209142333,0.7210842693854467,0.004441261291503906,0.6096860000000001 -418,Regression,Linear Regression with l1 regularization,ChickWeights,14.91944953335544,23.967687063528587,0.7042149845564116,0.004441261291503906,0.6396860000000001 -429,Regression,Linear Regression with l1 regularization,ChickWeights,15.467166242517186,24.886955839016704,0.7166083213097005,0.004441261291503906,0.6704000000000001 -440,Regression,Linear Regression with l1 regularization,ChickWeights,15.631989433801651,24.954278611820005,0.735240056765428,0.004441261291503906,0.7018220000000001 -451,Regression,Linear Regression with l1 regularization,ChickWeights,16.140858755557055,25.549476814516595,0.7342580119275103,0.004441261291503906,0.7339490000000001 -462,Regression,Linear Regression with l1 regularization,ChickWeights,17.234115417053438,27.64913352119068,0.7076854506057617,0.004441261291503906,0.766781 -473,Regression,Linear Regression with l1 regularization,ChickWeights,18.0152369206823,28.967470484053976,0.7166844816496443,0.004441261291503906,0.800319 -484,Regression,Linear Regression with l1 regularization,ChickWeights,18.337846632158772,29.233263620963758,0.727273146935064,0.004441261291503906,0.834558 -495,Regression,Linear Regression with l1 regularization,ChickWeights,18.785497932712666,29.755293652524703,0.7342033839127224,0.004441261291503906,0.86951 -506,Regression,Linear Regression with l1 regularization,ChickWeights,19.668627686193567,31.194713202801612,0.7163564282233008,0.004441261291503906,0.90517 -517,Regression,Linear Regression with l1 regularization,ChickWeights,20.38947136061499,32.18441644636668,0.7150766748346216,0.004441261291503906,0.941543 -528,Regression,Linear Regression with l1 regularization,ChickWeights,21.174247281712567,33.375214332088184,0.7245232875146275,0.004441261291503906,0.978621 -539,Regression,Linear Regression with l1 regularization,ChickWeights,21.335113723003413,33.369412847265615,0.7359905254430201,0.004441261291503906,1.016405 -550,Regression,Linear Regression with l1 regularization,ChickWeights,22.319908918673338,34.98038586656285,0.7188252208291055,0.004441261291503906,1.054903 -561,Regression,Linear Regression with l1 regularization,ChickWeights,23.021029445869836,35.899425045778656,0.7168073485461736,0.004441261291503906,1.094104 -572,Regression,Linear Regression with l1 regularization,ChickWeights,23.867950458471242,37.077313876148,0.7205745757906983,0.004441261291503906,1.134008 -20,Regression,Linear Regression with l1 regularization,TrumpApproval,23.431235633428948,26.218144216470428,-1611.3462157506035,0.005043983459472656,0.004147 -40,Regression,Linear Regression with l1 regularization,TrumpApproval,14.103513545807008,19.087149489688155,-145.46960290724672,0.005043983459472656,0.010783000000000001 -60,Regression,Linear Regression with l1 regularization,TrumpApproval,11.461367198760033,16.23222714650599,-141.81258639462544,0.005043983459472656,0.018889000000000003 -80,Regression,Linear Regression with l1 regularization,TrumpApproval,9.44127715126052,14.201674320759095,-108.65615123709478,0.005043983459472656,0.028452000000000005 -100,Regression,Linear Regression with l1 regularization,TrumpApproval,7.797022024035496,12.721240349043525,-54.205539694816935,0.005043983459472656,0.039551 -120,Regression,Linear Regression with l1 regularization,TrumpApproval,6.691147023027369,11.629122321270428,-38.137013046560284,0.005043983459472656,0.052122 -140,Regression,Linear Regression with l1 regularization,TrumpApproval,5.831783229321132,10.770680162674168,-33.69279126993988,0.005043983459472656,0.066155 -160,Regression,Linear Regression with l1 regularization,TrumpApproval,5.153144449790517,10.076741978726949,-25.779378860246055,0.005043983459472656,0.08166000000000001 -180,Regression,Linear Regression with l1 regularization,TrumpApproval,4.653806702754108,9.504457154537077,-19.92363756907144,0.005043983459472656,0.09862900000000001 -200,Regression,Linear Regression with l1 regularization,TrumpApproval,4.222103651464603,9.017588926226491,-17.890910704295415,0.005043983459472656,0.117152 -220,Regression,Linear Regression with l1 regularization,TrumpApproval,3.8867321432249606,8.600346446062781,-17.535660715889627,0.005043983459472656,0.137154 -240,Regression,Linear Regression with l1 regularization,TrumpApproval,3.599340946609919,8.235553698215059,-16.124474758948196,0.005043983459472656,0.15861 -260,Regression,Linear Regression with l1 regularization,TrumpApproval,3.3420538385748753,7.912854669167724,-15.021792727856035,0.005204200744628906,0.181525 -280,Regression,Linear Regression with l1 regularization,TrumpApproval,3.1218767056970433,7.6253908516912965,-14.577959242562436,0.005204200744628906,0.205897 -300,Regression,Linear Regression with l1 regularization,TrumpApproval,2.9443386382725367,7.3683114276631025,-13.30448388815742,0.005204200744628906,0.231796 -320,Regression,Linear Regression with l1 regularization,TrumpApproval,2.815755936756583,7.138230019587049,-13.089830522149688,0.005204200744628906,0.259173 -340,Regression,Linear Regression with l1 regularization,TrumpApproval,2.6933833627330244,6.928597052658441,-13.027805837336182,0.005204200744628906,0.288012 -360,Regression,Linear Regression with l1 regularization,TrumpApproval,2.6051143953909115,6.739167122054826,-12.380223856378365,0.005204200744628906,0.318313 -380,Regression,Linear Regression with l1 regularization,TrumpApproval,2.490520088621087,6.560408055638074,-12.118440379947032,0.005204200744628906,0.350072 -400,Regression,Linear Regression with l1 regularization,TrumpApproval,2.3796000807759676,6.3947378153227445,-11.816514349639151,0.005204200744628906,0.383345 -420,Regression,Linear Regression with l1 regularization,TrumpApproval,2.2783211278818065,6.240996339547436,-11.61166202267384,0.005204200744628906,0.418092 -440,Regression,Linear Regression with l1 regularization,TrumpApproval,2.2145782025697205,6.101000859547544,-10.83412932108482,0.005204200744628906,0.45429800000000004 -460,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1464112051078263,5.968705291729145,-9.740869666058233,0.005204200744628906,0.49195900000000004 -480,Regression,Linear Regression with l1 regularization,TrumpApproval,2.072454283271577,5.843773480387036,-9.059069500744918,0.005204200744628906,0.5310900000000001 -500,Regression,Linear Regression with l1 regularization,TrumpApproval,1.9987939253418097,5.726023081885353,-8.45516263823347,0.005204200744628906,0.5716870000000001 -520,Regression,Linear Regression with l1 regularization,TrumpApproval,1.9284894530733134,5.614979238753044,-8.107866604370331,0.005204200744628906,0.613828 -540,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8708966196370942,5.510640598064868,-7.740375491210358,0.005204200744628906,0.657455 -560,Regression,Linear Regression with l1 regularization,TrumpApproval,1.824494929332068,5.412971505312132,-7.634241934578528,0.005204200744628906,0.702504 -580,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7819511272383441,5.3203622791847724,-7.547628985954546,0.005204200744628906,0.748994 -600,Regression,Linear Regression with l1 regularization,TrumpApproval,1.749732390051898,5.233421242207161,-7.179064952823026,0.005204200744628906,0.796981 -620,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7052484400139905,5.14921027958835,-6.797272491389374,0.005204200744628906,0.846385 -640,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6664817162996513,5.068963198399273,-6.414896594696412,0.005204200744628906,0.897251 -660,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6214900718153242,4.99171743637858,-6.138924064419633,0.005204200744628906,0.949568 -680,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5818233283584242,4.918048415051679,-6.0392717086910395,0.005204200744628906,1.003319 -700,Regression,Linear Regression with l1 regularization,TrumpApproval,1.555259840641907,4.848763195223404,-6.02204294951599,0.005204200744628906,1.0585930000000001 -720,Regression,Linear Regression with l1 regularization,TrumpApproval,1.532054525069809,4.783346185353484,-5.983984772166844,0.005204200744628906,1.1153050000000002 -740,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5146024654094865,4.7215191517857305,-5.800515662606876,0.005204200744628906,1.1734340000000003 -760,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4818289913711118,4.659306491272199,-5.694229888242945,0.005204200744628906,1.2330550000000002 -780,Regression,Linear Regression with l1 regularization,TrumpApproval,1.452402157072035,4.5995629070091395,-5.552882667907455,0.005204200744628906,1.2940880000000001 -800,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4293016341621463,4.542852809132106,-5.450103306475059,0.005204200744628906,1.356613 -820,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4031025761620077,4.487566758646917,-5.363185739412862,0.005204200744628906,1.420603 -840,Regression,Linear Regression with l1 regularization,TrumpApproval,1.381369306663701,4.43470615373713,-5.271853959037296,0.005204200744628906,1.48601 -860,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3592027324075535,4.3834303912536,-5.11710119981194,0.005204200744628906,1.552867 -880,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3365452172324814,4.3337346544721145,-4.949476248649982,0.005204200744628906,1.621167 -900,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3099328416755933,4.285399956971287,-4.842022075269781,0.005204200744628906,1.690888 -920,Regression,Linear Regression with l1 regularization,TrumpApproval,1.286195364366444,4.238743982476022,-4.809417914789597,0.005204200744628906,1.762146 -940,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2707541496507981,4.194574036632911,-4.737236102715975,0.005204200744628906,1.834847 -960,Regression,Linear Regression with l1 regularization,TrumpApproval,1.252007480522268,4.151199175197265,-4.677947706919012,0.005204200744628906,1.9089589999999999 -980,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2327553756112288,4.10900051997898,-4.671141158917876,0.005204200744628906,1.9845519999999999 -1000,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2158544056174447,4.0682125513101886,-4.652689174637866,0.005204200744628906,2.061558 -11,Regression,Linear Regression with l2 regularization,ChickWeights,32.49979765090093,33.085767570527814,-1408.0939351430807,0.004153251647949219,0.00221 -22,Regression,Linear Regression with l2 regularization,ChickWeights,23.548763427243948,25.711783397814365,-699.1539821884553,0.004153251647949219,0.0060149999999999995 -33,Regression,Linear Regression with l2 regularization,ChickWeights,16.994606748791693,21.16216382986949,-310.0297747454571,0.004153251647949219,0.010773 -44,Regression,Linear Regression with l2 regularization,ChickWeights,13.434663043069094,18.386175360023177,-265.1519301746234,0.004153251647949219,0.016452 -55,Regression,Linear Regression with l2 regularization,ChickWeights,12.685726760044922,18.64479618798502,-66.86112450289035,0.004153251647949219,0.023006000000000002 -66,Regression,Linear Regression with l2 regularization,ChickWeights,12.3059577367172,17.58876192176611,-25.03287862681572,0.004153251647949219,0.030252 -77,Regression,Linear Regression with l2 regularization,ChickWeights,11.844694328458633,16.6807536659431,-14.693178357367543,0.004153251647949219,0.03818 -88,Regression,Linear Regression with l2 regularization,ChickWeights,11.154725738202893,15.783555067193374,-11.739056703820452,0.004153251647949219,0.046793 -99,Regression,Linear Regression with l2 regularization,ChickWeights,10.383929359740238,14.970988963724652,-8.585892537614809,0.004153251647949219,0.056087 -110,Regression,Linear Regression with l2 regularization,ChickWeights,9.96579366260941,14.36848414897767,-5.166041463970149,0.004153251647949219,0.066275 -121,Regression,Linear Regression with l2 regularization,ChickWeights,9.69456937849415,13.920059192886765,-3.4570517192093604,0.004153251647949219,0.07716100000000001 -132,Regression,Linear Regression with l2 regularization,ChickWeights,9.550940690791585,13.540299798742515,-2.4466881997122822,0.004153251647949219,0.088733 -143,Regression,Linear Regression with l2 regularization,ChickWeights,9.359343163302276,13.17888693683795,-1.6446630191344274,0.004153251647949219,0.100994 -154,Regression,Linear Regression with l2 regularization,ChickWeights,9.050096583806178,12.809003240652471,-1.1232058766616237,0.004153251647949219,0.113939 -165,Regression,Linear Regression with l2 regularization,ChickWeights,9.017680561209701,12.690905771048246,-0.6754526017728211,0.004153251647949219,0.127575 -176,Regression,Linear Regression with l2 regularization,ChickWeights,9.128420828629457,12.675650490262221,-0.3476766887041909,0.004153251647949219,0.141896 -187,Regression,Linear Regression with l2 regularization,ChickWeights,9.200055067293626,12.61943948921252,-0.0936676208508318,0.004153251647949219,0.156913 -198,Regression,Linear Regression with l2 regularization,ChickWeights,9.131920516304557,12.48608852319409,0.14832986501041,0.004153251647949219,0.172621 -209,Regression,Linear Regression with l2 regularization,ChickWeights,9.262178084838375,12.632807163510389,0.2813122010719644,0.004153251647949219,0.18901 -220,Regression,Linear Regression with l2 regularization,ChickWeights,9.624585089266471,13.14522964439942,0.3187088278286061,0.004153251647949219,0.206079 -231,Regression,Linear Regression with l2 regularization,ChickWeights,9.871229105762628,13.33182219595452,0.4005868940419749,0.004153251647949219,0.22382400000000002 -242,Regression,Linear Regression with l2 regularization,ChickWeights,10.053594156641497,13.615837484576032,0.49691323204665605,0.004153251647949219,0.24225500000000003 -253,Regression,Linear Regression with l2 regularization,ChickWeights,10.103586276318884,13.654347763291469,0.5605873283913356,0.004153251647949219,0.261365 -264,Regression,Linear Regression with l2 regularization,ChickWeights,10.539757430756955,14.361033144769575,0.5558923432468688,0.004233360290527344,0.281177 -275,Regression,Linear Regression with l2 regularization,ChickWeights,11.000663746720075,15.253690514733572,0.5591184315358017,0.004233360290527344,0.301675 -286,Regression,Linear Regression with l2 regularization,ChickWeights,11.301045102393736,15.716738058687294,0.5946085911267809,0.004233360290527344,0.32286000000000004 -297,Regression,Linear Regression with l2 regularization,ChickWeights,11.275670942063872,15.722526759958118,0.648148884657232,0.004233360290527344,0.34473600000000004 -308,Regression,Linear Regression with l2 regularization,ChickWeights,11.737413962135747,16.425717512690383,0.6579773485389515,0.004233360290527344,0.367308 -319,Regression,Linear Regression with l2 regularization,ChickWeights,12.62956768598283,18.523981673419602,0.6052658886851463,0.004233360290527344,0.390571 -330,Regression,Linear Regression with l2 regularization,ChickWeights,13.20835169207171,19.400144953397177,0.6294827674482892,0.004233360290527344,0.41452 -341,Regression,Linear Regression with l2 regularization,ChickWeights,13.34580485099439,19.473222157632836,0.6657152345574865,0.004233360290527344,0.439156 -352,Regression,Linear Regression with l2 regularization,ChickWeights,13.572829398695534,19.644456145190084,0.6907528542453616,0.004233360290527344,0.464482 -363,Regression,Linear Regression with l2 regularization,ChickWeights,14.286348966120116,20.694687599962585,0.666738740088638,0.004233360290527344,0.490494 -374,Regression,Linear Regression with l2 regularization,ChickWeights,14.777113556436731,21.820651771093797,0.6593962849465681,0.004233360290527344,0.517211 -385,Regression,Linear Regression with l2 regularization,ChickWeights,15.215863000720542,22.583610768099227,0.6772102871974224,0.004233360290527344,0.544615 -396,Regression,Linear Regression with l2 regularization,ChickWeights,15.33144965807796,22.564695888148663,0.6995373757169706,0.004233360290527344,0.5727059999999999 -407,Regression,Linear Regression with l2 regularization,ChickWeights,16.156138146741625,23.924755047114473,0.6860306363812331,0.004233360290527344,0.601482 -418,Regression,Linear Regression with l2 regularization,ChickWeights,16.826141311926307,25.281544830782227,0.6708975363085852,0.004233360290527344,0.6309469999999999 -429,Regression,Linear Regression with l2 regularization,ChickWeights,17.40263926327312,26.38004441662919,0.6815842184892376,0.004233360290527344,0.6611069999999999 -440,Regression,Linear Regression with l2 regularization,ChickWeights,17.533400796677356,26.42712307382207,0.7030645738539452,0.004233360290527344,0.6919729999999998 -451,Regression,Linear Regression with l2 regularization,ChickWeights,18.01931805998843,26.98764790902567,0.7034989551644695,0.004233360290527344,0.7235269999999998 -462,Regression,Linear Regression with l2 regularization,ChickWeights,19.020059735828532,28.983219342716673,0.6787962347144068,0.004233360290527344,0.7557739999999998 -473,Regression,Linear Regression with l2 regularization,ChickWeights,19.888963224686226,30.578078926209333,0.6843036130043219,0.004233360290527344,0.7887069999999998 -484,Regression,Linear Regression with l2 regularization,ChickWeights,20.14556805064173,30.710181129007662,0.6990197135707891,0.004233360290527344,0.8223269999999998 -495,Regression,Linear Regression with l2 regularization,ChickWeights,20.606054171923702,31.270986299633186,0.7064351021760091,0.004233360290527344,0.8566339999999998 -506,Regression,Linear Regression with l2 regularization,ChickWeights,21.410220326067673,32.615082621422005,0.6899384474766328,0.004233360290527344,0.8916409999999998 -517,Regression,Linear Regression with l2 regularization,ChickWeights,22.149063292155795,33.66176418126127,0.6883188968774838,0.004233360290527344,0.9273449999999998 -528,Regression,Linear Regression with l2 regularization,ChickWeights,22.923596011881333,34.92960124509041,0.6982661596564212,0.004233360290527344,0.9637369999999997 -539,Regression,Linear Regression with l2 regularization,ChickWeights,23.042465823580866,34.93124976178739,0.7106985365247873,0.004233360290527344,1.0008159999999997 -550,Regression,Linear Regression with l2 regularization,ChickWeights,23.974366627279803,36.47485289150521,0.6942867450600009,0.004233360290527344,1.0385829999999998 -561,Regression,Linear Regression with l2 regularization,ChickWeights,24.688352372874245,37.45551228620605,0.6917248794696187,0.004233360290527344,1.077041 -572,Regression,Linear Regression with l2 regularization,ChickWeights,25.520405522785374,38.65530944983144,0.6962839796503111,0.004233360290527344,1.116183 -20,Regression,Linear Regression with l2 regularization,TrumpApproval,23.51586240468561,26.237375459551668,-1613.712423965852,0.004836082458496094,0.004083 -40,Regression,Linear Regression with l2 regularization,TrumpApproval,14.404588626352647,19.156187724628055,-146.53108046561562,0.004836082458496094,0.01063 -60,Regression,Linear Regression with l2 regularization,TrumpApproval,12.231791081689295,16.474815193156783,-146.113106004694,0.004836082458496094,0.018597000000000002 -80,Regression,Linear Regression with l2 regularization,TrumpApproval,9.897294138330498,14.380930849374858,-111.44182781593443,0.004836082458496094,0.027966000000000005 -100,Regression,Linear Regression with l2 regularization,TrumpApproval,8.086918618304638,12.871841233853624,-55.52038254270653,0.004836082458496094,0.038819000000000006 -120,Regression,Linear Regression with l2 regularization,TrumpApproval,7.090525087262731,11.800543733353924,-39.29933105483862,0.004836082458496094,0.05107300000000001 -140,Regression,Linear Regression with l2 regularization,TrumpApproval,6.252895179240796,10.939748534807466,-34.79049149741283,0.004836082458496094,0.06473100000000001 -160,Regression,Linear Regression with l2 regularization,TrumpApproval,5.613172563674658,10.244365728872303,-26.67772385659461,0.004836082458496094,0.07979200000000002 -180,Regression,Linear Regression with l2 regularization,TrumpApproval,5.139122523864994,9.674573881172426,-20.679349421494624,0.004836082458496094,0.09624700000000001 -200,Regression,Linear Regression with l2 regularization,TrumpApproval,4.970287512907828,9.286148634805688,-19.03287536608377,0.004836082458496094,0.11416100000000001 -220,Regression,Linear Regression with l2 regularization,TrumpApproval,4.714509288646119,8.88589717245386,-18.78694495096694,0.004836082458496094,0.13348100000000002 -240,Regression,Linear Regression with l2 regularization,TrumpApproval,4.3785831664508095,8.51169894931726,-17.292125083299812,0.004836082458496094,0.15421200000000002 -260,Regression,Linear Regression with l2 regularization,TrumpApproval,4.192432902948977,8.203158141559566,-16.21895925323482,0.004996299743652344,0.17633200000000002 -280,Regression,Linear Regression with l2 regularization,TrumpApproval,4.032216676138626,7.92689648751503,-15.834209174335765,0.004996299743652344,0.19986500000000001 -300,Regression,Linear Regression with l2 regularization,TrumpApproval,3.888249411356283,7.680244711632193,-14.54126486396457,0.004996299743652344,0.22485700000000003 -320,Regression,Linear Regression with l2 regularization,TrumpApproval,3.851910342336546,7.48583024126048,-14.495465984230798,0.004996299743652344,0.251273 -340,Regression,Linear Regression with l2 regularization,TrumpApproval,3.7628843932429423,7.2990742240635536,-14.5680671641431,0.004996299743652344,0.27909500000000004 -360,Regression,Linear Regression with l2 regularization,TrumpApproval,3.7414983297472317,7.147194018854174,-14.049499953660261,0.004996299743652344,0.30831700000000006 -380,Regression,Linear Regression with l2 regularization,TrumpApproval,3.6331417160345065,6.972318267910069,-13.817498979914417,0.004996299743652344,0.3389480000000001 -400,Regression,Linear Regression with l2 regularization,TrumpApproval,3.505723677240456,6.801269751447825,-13.497878046830476,0.004996299743652344,0.37103900000000006 -420,Regression,Linear Regression with l2 regularization,TrumpApproval,3.3777899375444775,6.6406714986639335,-13.278693194916952,0.004996299743652344,0.4045350000000001 -440,Regression,Linear Regression with l2 regularization,TrumpApproval,3.2855655851554295,6.496417025168413,-12.417819031510927,0.004996299743652344,0.4394350000000001 -460,Regression,Linear Regression with l2 regularization,TrumpApproval,3.204438206990859,6.363151879091182,-11.207416254643826,0.004996299743652344,0.4757300000000001 -480,Regression,Linear Regression with l2 regularization,TrumpApproval,3.1147239220792944,6.234124280033156,-10.44779843680249,0.004996299743652344,0.5134280000000001 -500,Regression,Linear Regression with l2 regularization,TrumpApproval,3.0674610457420313,6.126558214637352,-9.824203383261038,0.004996299743652344,0.5525380000000001 -520,Regression,Linear Regression with l2 regularization,TrumpApproval,2.990407253638538,6.01302433311803,-9.444947809169491,0.004996299743652344,0.593122 -540,Regression,Linear Regression with l2 regularization,TrumpApproval,2.9353658306947947,5.909270916056388,-9.050640009643079,0.004996299743652344,0.635128 -560,Regression,Linear Regression with l2 regularization,TrumpApproval,2.8633890512526734,5.806679023039649,-8.935926541145857,0.004996299743652344,0.6785490000000001 -580,Regression,Linear Regression with l2 regularization,TrumpApproval,2.8074953384874966,5.7111093740410706,-8.849273711490637,0.004996299743652344,0.723357 -600,Regression,Linear Regression with l2 regularization,TrumpApproval,2.7371272959787114,5.616984296672238,-8.421904346026553,0.004996299743652344,0.769575 -620,Regression,Linear Regression with l2 regularization,TrumpApproval,2.6952794581589052,5.533794104458184,-8.005492094038127,0.004996299743652344,0.817237 -640,Regression,Linear Regression with l2 regularization,TrumpApproval,2.663427445960627,5.457208108806078,-7.594247452705627,0.004996299743652344,0.866263 -660,Regression,Linear Regression with l2 regularization,TrumpApproval,2.6161528712216042,5.378587544649896,-7.28837246231315,0.004996299743652344,0.916653 -680,Regression,Linear Regression with l2 regularization,TrumpApproval,2.5798661012588893,5.305972052989116,-7.193548818831784,0.004996299743652344,0.968481 -700,Regression,Linear Regression with l2 regularization,TrumpApproval,2.537320459903927,5.236098928386573,-7.188742583674767,0.004996299743652344,1.021719 -720,Regression,Linear Regression with l2 regularization,TrumpApproval,2.4875620374759775,5.165214649048708,-7.14359946989749,0.004996299743652344,1.076348 -740,Regression,Linear Regression with l2 regularization,TrumpApproval,2.4359344014471898,5.096521995605296,-6.923665614900413,0.004996299743652344,1.1323770000000002 -760,Regression,Linear Regression with l2 regularization,TrumpApproval,2.407655610747697,5.035258504842907,-6.818106944929671,0.004996299743652344,1.1897670000000002 -780,Regression,Linear Regression with l2 regularization,TrumpApproval,2.359216458068109,4.971257259303496,-6.65476288050581,0.004996299743652344,1.2485520000000003 -800,Regression,Linear Regression with l2 regularization,TrumpApproval,2.316070029510264,4.9101929612142525,-6.535402552442101,0.004996299743652344,1.3087800000000003 -820,Regression,Linear Regression with l2 regularization,TrumpApproval,2.2777366252623445,4.852446886619337,-6.440024000118236,0.004996299743652344,1.3703650000000003 -840,Regression,Linear Regression with l2 regularization,TrumpApproval,2.243368105900121,4.7970088928814505,-6.33849981001193,0.004996299743652344,1.4333480000000003 -860,Regression,Linear Regression with l2 regularization,TrumpApproval,2.208161873346368,4.742699334581194,-6.1609167229990645,0.004996299743652344,1.4977110000000002 -880,Regression,Linear Regression with l2 regularization,TrumpApproval,2.173524312605847,4.690026300839657,-5.967944096931786,0.004996299743652344,1.5634250000000003 -900,Regression,Linear Regression with l2 regularization,TrumpApproval,2.143113317062195,4.639957881226245,-5.848706397355668,0.004996299743652344,1.6305320000000003 -920,Regression,Linear Regression with l2 regularization,TrumpApproval,2.107662636673197,4.590154711256589,-5.812600067991807,0.004996299743652344,1.6990770000000004 -940,Regression,Linear Regression with l2 regularization,TrumpApproval,2.084055644614135,4.5438188766398575,-5.732386133187966,0.004996299743652344,1.7689810000000004 -960,Regression,Linear Regression with l2 regularization,TrumpApproval,2.0573225686306618,4.498049663013517,-5.666421016566231,0.004996299743652344,1.8402960000000004 -980,Regression,Linear Regression with l2 regularization,TrumpApproval,2.029372876222096,4.453478872426294,-5.661880798559547,0.004996299743652344,1.9129870000000004 -1000,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9991816433402003,4.409973662813209,-5.642320489885111,0.004996299743652344,1.9870360000000005 -11,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,29.34636433128918,30.877867366178624,-1226.3038921604407,0.0034055709838867188,0.002755 -22,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.78579478575624,22.219906445728544,-521.8939460594183,0.0034055709838867188,0.007689 -33,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.764406226748013,18.43476392899385,-235.02444355689545,0.0034055709838867188,0.013926999999999998 -44,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.514750373746141,16.140786164803156,-204.11441945614393,0.0034055709838867188,0.021043 -55,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.118868381910097,17.807152193193623,-60.900579144648304,0.0034055709838867188,0.029020999999999998 -66,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.068652098820438,16.444921319285292,-21.75701273895947,0.0034055709838867188,0.037852 -77,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.238561614797426,15.414518181428047,-12.401070176830181,0.0034055709838867188,0.047528999999999995 -88,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.397438800335843,14.475112340817045,-9.714489831884093,0.0034055709838867188,0.05806599999999999 -99,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.833048644024143,13.715858800506394,-7.045954767890709,0.0034055709838867188,0.069447 -110,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.655503029296072,13.224205829308971,-4.223044606682061,0.0034055709838867188,0.08190399999999999 -121,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.616633860178707,12.86945366769748,-2.809655727072758,0.0034055709838867188,0.09521999999999999 -132,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.226514673025618,12.359535435581538,-1.871770505407636,0.0034055709838867188,0.10940099999999998 -143,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.028644030968596,11.955222747545205,-1.1763474084194208,0.0034055709838867188,0.12445599999999998 -154,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.986848640780892,11.69340351574852,-0.7694704308339801,0.0034055709838867188,0.14037099999999997 -165,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.055944609610511,11.647997480703344,-0.41139782315780704,0.0034055709838867188,0.15710899999999997 -176,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.165300854035116,11.693346051910419,-0.14689275551772507,0.0034055709838867188,0.17464399999999997 -187,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.086580322778478,11.479257625599036,0.09503282026869064,0.0034055709838867188,0.19298799999999997 -198,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.0079421524022685,11.279628541389027,0.3049625679809165,0.0034055709838867188,0.21213599999999996 -209,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.136281210496879,11.437970564343967,0.4108328996032877,0.0034055709838867188,0.23209399999999997 -220,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.591733813835971,12.23821647621677,0.40948264143079904,0.0034055709838867188,0.252837 -231,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.6364242427237805,12.197368986664097,0.4982590675647103,0.0034055709838867188,0.274371 -242,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.775639220351206,12.334191292520584,0.5871659255406134,0.0034055709838867188,0.296703 -253,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.787415018822619,12.26821713761009,0.6452735558950271,0.0034055709838867188,0.31983 -264,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.387229506824328,13.12794439290609,0.6288834273883384,0.0034589767456054688,0.343762 -275,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.893165599265544,14.22060275652947,0.6168153604937621,0.0034589767456054688,0.368493 -286,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.145334455404228,14.488680433063887,0.6554856011642076,0.0034589767456054688,0.394025 -297,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.138984650870034,14.40937364029996,0.7044680409221951,0.0034589767456054688,0.420371 -308,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.653721426872565,15.186897141025446,0.7076222812215553,0.0034589767456054688,0.447515 -319,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.688547371510406,17.568442046558065,0.6449394075517852,0.0034589767456054688,0.475457 -330,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.136888804773205,18.130051576409294,0.6764089203981734,0.0034589767456054688,0.5041950000000001 -341,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.389709627918275,18.31814497212097,0.7041960757214223,0.0034589767456054688,0.53373 -352,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.506556624125665,18.357319157972537,0.7299499902857889,0.0034589767456054688,0.56408 -363,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.507799925411486,19.941572040394526,0.6905532928077196,0.0034589767456054688,0.5952430000000001 -374,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.006439137322559,20.915910426515573,0.6870553798396234,0.0034589767456054688,0.6272240000000001 -385,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.44126440090726,21.59107831138786,0.7049595304644938,0.0034589767456054688,0.6600050000000001 -396,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.452600873244053,21.4799043653453,0.7277322681469997,0.0034589767456054688,0.6935880000000001 -407,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,14.30954893105116,22.795153034451378,0.7149787127692513,0.0034589767456054688,0.7279810000000001 -418,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.058465273046647,24.128248961177892,0.7002387244037227,0.0034589767456054688,0.7631730000000001 -429,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.61316127520364,25.007094384238126,0.7138656442877266,0.0034589767456054688,0.799166 -440,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.833192306896644,25.159785721055627,0.7308613212218023,0.0034589767456054688,0.8359580000000001 -451,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.36714766376461,25.770608582556893,0.729638089688956,0.0034589767456054688,0.87355 -462,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,17.39003054773241,27.773207338784317,0.7050560764454472,0.0034589767456054688,0.911945 -473,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.288428268963266,29.396681708172505,0.7082265052542642,0.0034589767456054688,0.951137 -484,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.65703381705754,29.64739580601693,0.7194912595024185,0.0034589767456054688,0.991127 -495,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.170202167844984,30.22319045197901,0.7257784495510498,0.0034589767456054688,1.031919 -506,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,20.001643634540255,31.52072905752619,0.7103967314785831,0.0034589767456054688,1.073505 -517,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,20.7279777099298,32.51187613530653,0.7092492864364832,0.0034589767456054688,1.115928 -528,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.566830336201956,33.84128388534863,0.7167757554755416,0.0034589767456054688,1.159144 -539,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.76383497583081,33.92033428284125,0.727201090633804,0.0034589767456054688,1.203152 -550,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,22.69913149057359,35.42417858076478,0.7116454901231712,0.0034589767456054688,1.247925 -561,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,23.377380034069706,36.327056125710044,0.7100204288247138,0.0034589767456054688,1.293462 -572,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.23392939046311,37.557568322570944,0.7132890208408607,0.0034589767456054688,1.339772 -20,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,19.88769754963664,24.32970381980572,-1387.4429851591376,0.004302024841308594,0.004188 -40,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,13.670966252574736,18.651500155088886,-138.85979610511808,0.004302024841308594,0.011118 -60,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,10.928349961418421,15.667746469337834,-132.052589810652,0.004302024841308594,0.019635 -80,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.678171261463671,14.124417656525663,-107.46634425227307,0.004302024841308594,0.029706999999999997 -100,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.352313294103828,13.38210191485773,-60.090321390572015,0.004302024841308594,0.041437 -120,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.620142702346234,12.447697479286916,-43.84064485890953,0.004302024841308594,0.05473 -140,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.069393725677067,11.747669450243144,-40.272086023690846,0.004302024841308594,0.069574 -160,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.915997483818508,11.323556682786094,-32.81628847194888,0.004302024841308594,0.085987 -180,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.901057285338727,11.048721851620664,-27.27526182903414,0.004302024841308594,0.103947 -200,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.54356570823564,10.615481381947994,-25.17890116869019,0.004302024841308594,0.12354799999999999 -220,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.241521693861677,10.231227443733495,-25.232015334984826,0.004302024841308594,0.14471199999999998 -240,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.0081924215580145,9.935910620042465,-23.925679436807183,0.004302024841308594,0.16741999999999999 -260,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.801990869552961,9.666795781283113,-22.91166502123009,0.004435539245605469,0.191681 -280,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.624313646675292,9.422664085622767,-22.786676328633025,0.004435539245605469,0.217492 -300,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.459069309379183,9.212027904845302,-21.358709628275353,0.004435539245605469,0.244937 -320,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.271769455500056,8.978276576497144,-21.290029949269993,0.004435539245605469,0.27394799999999997 -340,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.106653016686588,8.760106381933458,-21.42424847489592,0.004435539245605469,0.304509 -360,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.987363514041657,8.573066448421043,-20.653260781415113,0.004435539245605469,0.336642 -380,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.8616356212919145,8.393806584529749,-20.475259224624704,0.004435539245605469,0.370328 -400,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.709779552257037,8.216191215364908,-20.15755692495403,0.004435539245605469,0.40563899999999997 -420,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.611842526965273,8.072589482864162,-20.100376445858966,0.004435539245605469,0.442509 -440,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.621266668644241,8.061169250733263,-19.659995268253144,0.004435539245605469,0.480923 -460,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.589374981160082,7.977922595195435,-18.1892858176961,0.004435539245605469,0.520896 -480,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.601949193869223,7.939757215258646,-17.568871361729258,0.004435539245605469,0.5624170000000001 -500,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.5323381947357735,7.8271184711570925,-16.667155417363553,0.004435539245605469,0.6055100000000001 -520,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.482642323931563,7.735647602994712,-16.286764124140948,0.004435539245605469,0.6502260000000001 -540,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.40625682094428,7.63238246035087,-15.7666448554263,0.004435539245605469,0.696447 -560,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.339029679572209,7.532223621796607,-15.718570546376949,0.004435539245605469,0.744222 -580,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.278136676956568,7.444054065699633,-15.733326658615429,0.004435539245605469,0.7935180000000001 -600,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.267448053107655,7.406286860836784,-15.380732931295814,0.004435539245605469,0.8443630000000001 -620,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.236749685364014,7.349650271122191,-14.885277446643839,0.004435539245605469,0.8968030000000001 -640,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.212060707085285,7.303465599867649,-14.393054201831113,0.004435539245605469,0.9507400000000001 -660,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.169763730821092,7.239859498572405,-14.017341318649356,0.004435539245605469,1.0062160000000002 -680,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.126488999027155,7.167741867944588,-13.952260083042017,0.004435539245605469,1.0632270000000001 -700,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.093088048073085,7.110044108134988,-14.098928285386927,0.004435539245605469,1.1217890000000001 -720,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.0907403672935505,7.074302078930798,-14.275901824522656,0.004435539245605469,1.181923 -740,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.094807885255693,7.045283722404726,-14.141723406350016,0.004435539245605469,1.243554 -760,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.043787658869922,6.9756465425414875,-14.00468895273832,0.004435539245605469,1.306712 -780,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.064043270517397,6.970645877797938,-14.050305184338798,0.004435539245605469,1.3714110000000002 -800,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.100755566375936,7.010389089224324,-14.360083784619212,0.004435539245605469,1.4376520000000002 -820,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.088064827359337,6.979627415872227,-14.392786134873418,0.004435539245605469,1.5054450000000001 -840,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.068856460674791,6.93736545783203,-14.348127111309871,0.004435539245605469,1.5747550000000001 -860,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.054699520351681,6.9002481606255905,-14.158172906563095,0.004435539245605469,1.645553 -880,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.051365258335469,6.881479694679562,-14.000915574481752,0.004435539245605469,1.717914 -900,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.025346349320704,6.842894221251935,-13.895672840614656,0.004435539245605469,1.791764 -920,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.9962011230931616,6.800765764747934,-13.95456831471299,0.004435539245605469,1.867216 -940,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.964612353038952,6.764345134580912,-13.920332812605437,0.004435539245605469,1.944198 -960,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.9296416909632255,6.717192667284049,-13.866880687662514,0.004435539245605469,2.02269 -980,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.910978624057615,6.682715130965835,-14.000438748691009,0.004435539245605469,2.102738 -1000,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.906394390750375,6.665596501187553,-14.174901311798424,0.004435539245605469,2.184247 -11,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.361343182089286,50.93510711941157,-3338.580868182736,0.0034055709838867188,0.003338 -22,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,33.77268754890631,41.67984599422324,-1838.8455756180547,0.0034055709838867188,0.00919 -33,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.39258875507137,38.96806999674433,-1053.6287703611629,0.0034055709838867188,0.016390000000000002 -44,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,30.948810318548677,36.76152485506615,-1062.9809670274265,0.0034055709838867188,0.024921000000000002 -55,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,33.955857035779495,41.369655851763525,-333.094700988151,0.0034055709838867188,0.034827000000000004 -66,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.010493836145145,40.92418807176112,-139.93270784533922,0.0034055709838867188,0.046059 -77,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.064338631511326,40.5595538563462,-91.78246602216655,0.0034055709838867188,0.058629 -88,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.363478110253816,40.47408671194747,-82.76869054449229,0.0034055709838867188,0.072516 -99,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.108671766629826,39.953033914579606,-67.2701887367714,0.0034055709838867188,0.087732 -110,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,35.5272246808861,41.294149289689244,-49.9285816695273,0.0034055709838867188,0.10461200000000001 -121,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,35.85052464333277,41.487247497278275,-38.59084342971371,0.0034055709838867188,0.12281900000000001 -132,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.414238795248615,42.10793271457587,-32.332913655998325,0.0034055709838867188,0.14236400000000002 -143,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.270929183098715,43.0841670883325,-27.264953879536876,0.0034055709838867188,0.163229 -154,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.68834060456834,43.351809236536255,-23.320689906510196,0.0034055709838867188,0.18538000000000002 -165,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.63751931524077,43.32469674855668,-18.52621065458175,0.0034055709838867188,0.208847 -176,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,38.77878602757167,44.749537188256824,-15.796635622291838,0.0034055709838867188,0.233634 -187,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,39.47954967522975,45.39032172466195,-13.149195443978995,0.0034055709838867188,0.259707 -198,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.91548075261064,46.964281697881674,-11.049082215962626,0.0034055709838867188,0.287109 -209,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,41.53723737741547,47.716579905431935,-9.253665706385002,0.0034055709838867188,0.31584 -220,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,41.999353809226264,48.63121942098776,-8.324525171754294,0.0034055709838867188,0.345843 -231,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,42.76794868726854,49.65880643089243,-7.316484115890946,0.0034055709838867188,0.377141 -242,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,44.368299619960766,51.88245138837915,-6.304578357179455,0.0034055709838867188,0.409767 -253,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,45.426835880148886,53.192808855117775,-5.668628208432534,0.0034055709838867188,0.443674 -264,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,46.93737170570451,55.9021194350506,-5.729354993233594,0.0034589767456054688,0.478844 -275,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,48.507353400069945,58.8434937271261,-5.560984055006233,0.0034589767456054688,0.515365 -286,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,49.83788736782816,60.74084767697289,-5.054961799673954,0.0034589767456054688,0.5532039999999999 -297,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,51.62361105051165,63.27455882125991,-4.698656745997159,0.0034589767456054688,0.5923289999999999 -308,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,53.1847087657747,65.32139627595005,-4.409001340116382,0.0034589767456054688,0.6328059999999999 -319,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,55.18568520771326,70.36874449488667,-4.696335728503607,0.0034589767456054688,0.6746009999999999 -330,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,56.89081675494835,72.51519786174504,-4.176742159590063,0.0034589767456054688,0.7176629999999999 -341,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,58.741890997125225,75.13624143509449,-3.976681879584251,0.0034589767456054688,0.7620199999999999 -352,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,60.2425190521958,76.8261590755005,-3.729812475420129,0.0034589767456054688,0.8076819999999999 -363,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,62.22918871806441,80.45649530418282,-4.037201263859445,0.0034589767456054688,0.854609 -374,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,64.15805403621584,84.29062360683722,-4.082442538013045,0.0034589767456054688,0.9028449999999999 -385,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,65.35464556519459,85.58979700811152,-3.6363575203718748,0.0034589767456054688,0.9523969999999999 -396,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,67.08652533638559,87.70251677464411,-3.538952097004609,0.0034589767456054688,1.00322 -407,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,68.34565949899115,89.3540667816312,-3.3794635851122994,0.0034589767456054688,1.055319 -418,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,70.95506872236689,94.70758550832085,-3.618420231648276,0.0034589767456054688,1.108722 -429,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,72.57879663609631,96.82281941707701,-3.2894241164712765,0.0034589767456054688,1.163438 -440,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,74.40667828471298,99.12463362784464,-3.1775863007897582,0.0034589767456054688,1.219419 -451,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,75.93202948942836,101.47969042740627,-3.1923199945959375,0.0034589767456054688,1.276691 -462,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,78.59712471455953,106.68213481552291,-3.3518185198786687,0.0034589767456054688,1.335258 -473,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,81.70573920737151,112.24508574603003,-3.253866822086197,0.0034589767456054688,1.395089 -484,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,82.72251416230137,113.16810597159807,-3.0871576545332315,0.0034589767456054688,1.456215 -495,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,84.44211178292737,115.99711612480067,-3.039385949207989,0.0034589767456054688,1.518639 -506,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,86.55149997089892,119.94151559804615,-3.1932428575971077,0.0034589767456054688,1.582321 -517,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,87.81070722823603,121.26627191062052,-3.0449837234699952,0.0034589767456054688,1.64731 -528,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,90.44199567451936,126.238673662019,-2.9411377147279807,0.0034589767456054688,1.713605 -539,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,91.59067370330054,127.24192286613216,-2.8386881284150203,0.0034589767456054688,1.781138 -550,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,93.78609063040562,130.999704877259,-2.943372518937519,0.0034589767456054688,1.8499700000000001 -561,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,96.49865979675555,135.93192637304293,-3.060223463970532,0.0034589767456054688,1.920139 -572,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,99.56805853722271,141.40025114882988,-3.0639630782357843,0.0034589767456054688,1.991547 -20,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,38.256966119949794,53.46437671117289,-6703.762875072117,0.004302024841308594,0.004258 -40,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,36.86518407094958,46.91757933405302,-883.9863015306486,0.004302024841308594,0.011255999999999999 -60,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,34.815387267092916,43.77024226536395,-1037.4083280498467,0.004302024841308594,0.019887 -80,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.82099099523828,40.95636211937148,-911.003802678922,0.004302024841308594,0.030078999999999998 -100,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.41507697560151,39.67328525303196,-535.9329715822871,0.004302024841308594,0.041942 -120,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.126533810299065,38.74392424963554,-433.4111998192701,0.004302024841308594,0.055368 -140,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.578696449953306,37.792227545537656,-426.12792465890055,0.004302024841308594,0.070349 -160,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.74326128631808,36.75147996394125,-355.2131008927076,0.004302024841308594,0.086898 -180,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.01457804514465,35.792387341570375,-295.7316609698654,0.004302024841308594,0.10499800000000001 -200,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.69358993814741,35.287452593511624,-288.27615949420783,0.004302024841308594,0.124741 -220,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.653103799764548,34.981694493871686,-305.6605175079266,0.004302024841308594,0.146064 -240,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.438535707776744,34.575414655319626,-300.8328104705327,0.004302024841308594,0.168934 -260,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.47891951475184,34.56744094622709,-304.7589578451177,0.004435539245605469,0.193372 -280,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.263090992629436,34.21824031120625,-312.6907330516863,0.004435539245605469,0.21937099999999998 -300,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.22587791049338,34.04470159277256,-304.37628668920115,0.004435539245605469,0.246997 -320,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.39018713755992,34.027497016659055,-319.1729973057711,0.004435539245605469,0.276184 -340,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.42027037377748,33.93053373578466,-335.4190027425263,0.004435539245605469,0.306921 -360,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.664307062669348,34.045635299267715,-340.48671423689984,0.004435539245605469,0.339233 -380,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.83498723746747,34.11808971579651,-353.80514851973993,0.004435539245605469,0.373101 -400,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.001082895685222,34.17628998525774,-365.0785427385626,0.004435539245605469,0.408596 -420,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.14554243966483,34.20268208319664,-377.77804236740525,0.004435539245605469,0.445652 -440,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.318997649837996,34.29163258825692,-372.8612574130533,0.004435539245605469,0.484259 -460,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.44816166194883,34.34474274542465,-354.6310813895727,0.004435539245605469,0.524426 -480,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.547707035634,34.3460988289287,-346.47686362533597,0.004435539245605469,0.5661419999999999 -500,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.677713278160862,34.40102042286509,-340.27577766039445,0.004435539245605469,0.6094289999999999 -520,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.774567489967993,34.41616981796676,-341.1727526506248,0.004435539245605469,0.6543389999999999 -540,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.8774276068303,34.45885995564912,-340.7651110108783,0.004435539245605469,0.7007559999999999 -560,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.809160821418953,34.35467967149731,-346.7959646189386,0.004435539245605469,0.7487349999999999 -580,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.794626934327955,34.31319945532044,-354.5377185045104,0.004435539245605469,0.798249 -600,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.95165244409411,34.41180694760753,-352.62847406429205,0.004435539245605469,0.849322 -620,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.05924245979316,34.47559777081781,-348.53049085374005,0.004435539245605469,0.9019860000000001 -640,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.174863467025094,34.55495775749415,-343.57800745174796,0.004435539245605469,0.9561540000000001 -660,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.18138785276698,34.53217565702543,-340.6493968041527,0.004435539245605469,1.011873 -680,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.262098564397714,34.57860649898553,-346.98225985471817,0.004435539245605469,1.06915 -700,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.21351117376332,34.56642775102796,-355.8704038677769,0.004435539245605469,1.127995 -720,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.187412408778744,34.52480046664081,-362.83293670855346,0.004435539245605469,1.188423 -740,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.11951946368673,34.44075758421072,-360.84595808308967,0.004435539245605469,1.250358 -760,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.06286774404838,34.392033812715184,-363.73192746261515,0.004435539245605469,1.3138290000000001 -780,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.122129081397333,34.4147077867913,-365.8490836115921,0.004435539245605469,1.378848 -800,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.106214253085106,34.39160753515401,-368.6700713862459,0.004435539245605469,1.445416 -820,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.221689813214,34.48240321448139,-374.7057202811433,0.004435539245605469,1.513551 -840,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.279425902447173,34.50697415799086,-378.7344488771949,0.004435539245605469,1.583215 -860,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.342876956175303,34.54160049479585,-378.8414458535583,0.004435539245605469,1.65438 -880,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.26946109805924,34.463329541799936,-375.2431199026635,0.004435539245605469,1.727121 -900,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.252963502360984,34.437602544585566,-376.2648040183398,0.004435539245605469,1.801358 -920,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.171400346182477,34.35879469425013,-380.710483609337,0.004435539245605469,1.877212 -940,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.028682196255257,34.23221645186374,-381.1175926718204,0.004435539245605469,1.9546070000000002 -960,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.091829199349828,34.349474902309865,-387.76257537598985,0.004435539245605469,2.033516 -980,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.00094350988743,34.303507421677764,-394.25294990859175,0.004435539245605469,2.113994 -1000,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.128814077399102,34.425663215951964,-403.7738674316034,0.004435539245605469,2.195941 -11,Regression,k-Nearest Neighbors,ChickWeights,4.6439393939393945,12.708027567111456,-206.8805289598106,0.0077915191650390625,0.002301 -22,Regression,k-Nearest Neighbors,ChickWeights,2.7674242424242426,9.021574170013263,-85.19732920009746,0.011640548706054688,0.0070350000000000005 -33,Regression,k-Nearest Neighbors,ChickWeights,2.3661616161616172,7.437810062008745,-37.421294111394644,0.016172409057617188,0.013843000000000001 -44,Regression,k-Nearest Neighbors,ChickWeights,2.0155303030303036,6.463663489621867,-31.893061768560024,0.020200729370117188,0.022662 -55,Regression,k-Nearest Neighbors,ChickWeights,2.2124242424242424,6.080421054665558,-6.217272109648366,0.024396896362304688,0.033503000000000005 -66,Regression,k-Nearest Neighbors,ChickWeights,2.2800505050505055,5.694858940322259,-1.7290883479828647,0.028783798217773438,0.046733000000000004 -77,Regression,k-Nearest Neighbors,ChickWeights,2.61926406926407,5.7079502667942235,-0.8375532519268223,0.03319740295410156,0.062616 -88,Regression,k-Nearest Neighbors,ChickWeights,2.530492424242425,5.412982721609634,-0.4983072905775765,0.03750419616699219,0.08146600000000001 -99,Regression,k-Nearest Neighbors,ChickWeights,2.4755892255892267,5.17010990945742,-0.14322345740966957,0.04228782653808594,0.10359300000000002 -110,Regression,k-Nearest Neighbors,ChickWeights,2.7716666666666683,5.296236752390676,0.16224058779712935,0.04331779479980469,0.129424 -121,Regression,k-Nearest Neighbors,ChickWeights,3.180853994490359,5.621206607854847,0.2731837882445769,0.04387855529785156,0.158775 -132,Regression,k-Nearest Neighbors,ChickWeights,3.3642676767676765,5.706770043255583,0.38775368143556643,0.04362678527832031,0.191664 -143,Regression,k-Nearest Neighbors,ChickWeights,3.6467365967365963,5.919243012407738,0.46648673933101714,0.04393196105957031,0.228099 -154,Regression,k-Nearest Neighbors,ChickWeights,3.7550865800865787,5.97572666401829,0.537892640768072,0.04406929016113281,0.268063 -165,Regression,k-Nearest Neighbors,ChickWeights,4.093838383838381,6.488494998076776,0.562039588096868,0.04465293884277344,0.311593 -176,Regression,k-Nearest Neighbors,ChickWeights,4.458428030303029,6.947478945595657,0.5951448357515823,0.04467964172363281,0.358632 -187,Regression,k-Nearest Neighbors,ChickWeights,4.792959001782529,7.272258331212408,0.6368016898131145,0.04475975036621094,0.40948 -198,Regression,k-Nearest Neighbors,ChickWeights,5.229713804713803,7.766788141562423,0.6704650236153215,0.04428291320800781,0.46388 -209,Regression,k-Nearest Neighbors,ChickWeights,5.61188197767145,8.429860803311705,0.6799768871245477,0.04433631896972656,0.521779 -220,Regression,k-Nearest Neighbors,ChickWeights,6.048560606060604,9.536044923225655,0.6414638231876792,0.04430961608886719,0.583178 -231,Regression,k-Nearest Neighbors,ChickWeights,6.582178932178929,10.203249124116919,0.648905367768132,0.04489326477050781,0.6480619999999999 -242,Regression,k-Nearest Neighbors,ChickWeights,7.071418732782365,10.928542055135825,0.6759002976153703,0.04494667053222656,0.7164429999999999 -253,Regression,k-Nearest Neighbors,ChickWeights,7.4777997364953865,11.323352624926212,0.6978095597045382,0.04505348205566406,0.7883119999999999 -264,Regression,k-Nearest Neighbors,ChickWeights,7.970770202020199,12.283351878677939,0.6750992767833781,0.04463005065917969,0.8636689999999999 -275,Regression,k-Nearest Neighbors,ChickWeights,8.55812121212121,13.382565810664547,0.6606476529151027,0.04468345642089844,0.9425549999999999 -286,Regression,k-Nearest Neighbors,ChickWeights,9.054137529137526,14.013384412631826,0.6777181990167639,0.04484367370605469,1.024953 -297,Regression,k-Nearest Neighbors,ChickWeights,9.468967452300786,14.435360812541292,0.7034011013652389,0.04540061950683594,1.110877 -308,Regression,k-Nearest Neighbors,ChickWeights,9.90871212121212,15.173853281638724,0.7081243055691319,0.04542732238769531,1.200187 -319,Regression,k-Nearest Neighbors,ChickWeights,10.713740856844305,17.013635837866804,0.6670107307192514,0.04553413391113281,1.2928449999999998 -330,Regression,k-Nearest Neighbors,ChickWeights,11.460252525252525,18.125243873896302,0.6765805165314649,0.04508399963378906,1.388826 -341,Regression,k-Nearest Neighbors,ChickWeights,11.901710654936462,18.576691605351197,0.6957870549438744,0.04519081115722656,1.488176 -352,Regression,k-Nearest Neighbors,ChickWeights,12.310464015151513,18.922178666477887,0.7130752857476492,0.04516410827636719,1.591164 -363,Regression,k-Nearest Neighbors,ChickWeights,12.780394857667583,19.823234941774256,0.694215027528111,0.04561424255371094,1.697605 -374,Regression,k-Nearest Neighbors,ChickWeights,13.344073083778964,20.889730456192645,0.6878383009059359,0.04561424255371094,1.807438 -385,Regression,k-Nearest Neighbors,ChickWeights,13.830865800865798,21.557316750546793,0.7058815074667231,0.04569435119628906,1.920665 -396,Regression,k-Nearest Neighbors,ChickWeights,14.08051346801346,21.615344438143325,0.7242879119502419,0.04521751403808594,2.037277 -407,Regression,k-Nearest Neighbors,ChickWeights,14.665069615069608,22.797561920331077,0.714918470135155,0.04524421691894531,2.157231 -418,Regression,k-Nearest Neighbors,ChickWeights,15.362400318979262,24.076729101709564,0.7015174886253861,0.04574775695800781,2.280525 -429,Regression,k-Nearest Neighbors,ChickWeights,15.914413364413358,24.924104546372128,0.7157616535295273,0.04582786560058594,2.4071919999999998 -440,Regression,k-Nearest Neighbors,ChickWeights,16.21655303030303,25.17906749713446,0.730448642009748,0.04585456848144531,2.5372429999999997 -451,Regression,k-Nearest Neighbors,ChickWeights,16.571359940872135,25.529131814454708,0.7346810631079229,0.04588127136230469,2.6706619999999996 -462,Regression,k-Nearest Neighbors,ChickWeights,17.517063492063492,27.458379114103476,0.7117049574257082,0.04537773132324219,2.8074329999999996 -473,Regression,k-Nearest Neighbors,ChickWeights,18.23357998590557,28.586680380220994,0.7240841374900429,0.04535102844238281,2.9476329999999997 -484,Regression,k-Nearest Neighbors,ChickWeights,18.61876721763086,29.038858036362505,0.7308884346272555,0.04588127136230469,3.0911699999999995 -495,Regression,k-Nearest Neighbors,ChickWeights,19.16138047138048,29.754410032566323,0.7342191699916846,0.04588127136230469,3.2380789999999995 -506,Regression,k-Nearest Neighbors,ChickWeights,19.693445322793156,30.658970587616192,0.7260154404176653,0.04585456848144531,3.3883479999999997 -517,Regression,k-Nearest Neighbors,ChickWeights,20.317762733720187,31.53258587823862,0.7265009007981393,0.04537773132324219,3.5419949999999996 -528,Regression,k-Nearest Neighbors,ChickWeights,21.03841540404041,32.63466371480821,0.7366125677104822,0.04543113708496094,3.699025 -539,Regression,k-Nearest Neighbors,ChickWeights,21.282900432900444,32.839173901100196,0.7443140702924032,0.04540443420410156,3.859333 -550,Regression,k-Nearest Neighbors,ChickWeights,21.858333333333338,33.61129662942374,0.7404041745313898,0.04601478576660156,4.022901 -561,Regression,k-Nearest Neighbors,ChickWeights,22.363071895424838,34.18934989679706,0.7431446126310046,0.04606819152832031,4.189756 -572,Regression,k-Nearest Neighbors,ChickWeights,22.904341491841496,34.79445522931405,0.7539238777546076,0.04612159729003906,4.359905 -20,Regression,k-Nearest Neighbors,TrumpApproval,2.5579731333333355,9.79490509533104,-224.03748800996974,0.016119003295898438,0.00492 -40,Regression,k-Nearest Neighbors,TrumpApproval,1.814472516666667,6.975921914401759,-18.564491994995524,0.028676986694335938,0.014556 -60,Regression,k-Nearest Neighbors,TrumpApproval,1.3860128111111119,5.7089304150813796,-16.665248891500116,0.04073143005371094,0.029158 -80,Regression,k-Nearest Neighbors,TrumpApproval,1.1425351583333334,4.950945892995169,-12.326934431680348,0.05278587341308594,0.04994 -100,Regression,k-Nearest Neighbors,TrumpApproval,1.0609460066666667,4.443635225860514,-5.735976224554387,0.06531715393066406,0.078368 -120,Regression,k-Nearest Neighbors,TrumpApproval,1.0173975888888886,4.080152828774464,-3.8177766328983793,0.06587028503417969,0.115087 -140,Regression,k-Nearest Neighbors,TrumpApproval,0.9499007047619044,3.7850734619410638,-3.284514427728187,0.06536674499511719,0.160142 -160,Regression,k-Nearest Neighbors,TrumpApproval,0.8997031916666666,3.548063392436267,-2.320037309218333,0.06536674499511719,0.213621 -180,Regression,k-Nearest Neighbors,TrumpApproval,0.8894699925925924,3.3651237174821174,-1.6229174672077478,0.06587028503417969,0.275231 -200,Regression,k-Nearest Neighbors,TrumpApproval,0.8368248133333331,3.1964619940401167,-1.3736195901717156,0.06536674499511719,0.34510799999999997 -220,Regression,k-Nearest Neighbors,TrumpApproval,0.7973693939393937,3.051737973437425,-1.333837761501293,0.06536674499511719,0.42318599999999995 -240,Regression,k-Nearest Neighbors,TrumpApproval,0.7882918027777774,2.9302063484469683,-1.1678441811700537,0.06587028503417969,0.5093219999999999 -260,Regression,k-Nearest Neighbors,TrumpApproval,0.756480287179487,2.818118800540444,-1.032185390259123,0.06550025939941406,0.603278 -280,Regression,k-Nearest Neighbors,TrumpApproval,0.7152656523809521,2.716213897230066,-0.9765794643185606,0.06550025939941406,0.705058 -300,Regression,k-Nearest Neighbors,TrumpApproval,0.6924420288888888,2.6266701457407935,-0.8178051192110904,0.06600379943847656,0.8148869999999999 -320,Regression,k-Nearest Neighbors,TrumpApproval,0.6685162833333335,2.544831183351663,-0.7907817018280727,0.06600379943847656,0.9324959999999999 -340,Regression,k-Nearest Neighbors,TrumpApproval,0.6729953196078432,2.478015515638401,-0.7943500832815562,0.06550025939941406,1.057867 -360,Regression,k-Nearest Neighbors,TrumpApproval,0.6574798574074076,2.410620514027796,-0.7120191674014065,0.06600379943847656,1.1910109999999998 -380,Regression,k-Nearest Neighbors,TrumpApproval,0.6458375333333335,2.3511270035956984,-0.6848943678054311,0.06600379943847656,1.3319119999999998 -400,Regression,k-Nearest Neighbors,TrumpApproval,0.6474776666666668,2.299895164719867,-0.6578320154352482,0.06550025939941406,1.4805999999999997 -420,Regression,k-Nearest Neighbors,TrumpApproval,0.6263061492063494,2.245732257498697,-0.6329783328857519,0.06600379943847656,1.6370479999999996 -440,Regression,k-Nearest Neighbors,TrumpApproval,0.6161101106060607,2.196509834675512,-0.5339119919932027,0.06600379943847656,1.8010919999999997 -460,Regression,k-Nearest Neighbors,TrumpApproval,0.6114796710144929,2.151899880839346,-0.39612176608758154,0.06550025939941406,1.9727339999999998 -480,Regression,k-Nearest Neighbors,TrumpApproval,0.595679659722222,2.1075134371992843,-0.3083133320099125,0.06550025939941406,2.1519839999999997 -500,Regression,k-Nearest Neighbors,TrumpApproval,0.5871453373333329,2.0674213824343695,-0.23259619346371974,0.06600379943847656,2.338834 -520,Regression,k-Nearest Neighbors,TrumpApproval,0.5787837589743586,2.029002666882104,-0.1892840306210153,0.06550025939941406,2.5333539999999997 -540,Regression,k-Nearest Neighbors,TrumpApproval,0.5648130308641971,1.991660903442925,-0.14171238475006254,0.06550025939941406,2.7354719999999997 -560,Regression,k-Nearest Neighbors,TrumpApproval,0.5728799083333329,1.9649652423985717,-0.1377909554693113,0.06600379943847656,2.9451739999999997 -580,Regression,k-Nearest Neighbors,TrumpApproval,0.5729694735632179,1.9355227317321977,-0.1312531571152491,0.06550025939941406,3.1626749999999997 -600,Regression,k-Nearest Neighbors,TrumpApproval,0.5633090777777773,1.904172510525728,-0.08279153968794839,0.06550025939941406,3.387869 -620,Regression,k-Nearest Neighbors,TrumpApproval,0.5627685010752685,1.877938378401011,-0.037108351625399605,0.06600379943847656,3.620659 -640,Regression,k-Nearest Neighbors,TrumpApproval,0.5527265229166664,1.8490211996046175,0.013378435498877628,0.06600379943847656,3.861011 -660,Regression,k-Nearest Neighbors,TrumpApproval,0.5494711494949491,1.8235613734746332,0.04726187505437851,0.06550025939941406,4.108899 -680,Regression,k-Nearest Neighbors,TrumpApproval,0.5450294392156858,1.7983422134533726,0.05878940572589009,0.06600379943847656,4.364357 -700,Regression,k-Nearest Neighbors,TrumpApproval,0.5469558666666661,1.7765896178823126,0.057295083327486895,0.06600379943847656,4.627459 -720,Regression,k-Nearest Neighbors,TrumpApproval,0.543456514814814,1.7539567687409483,0.06097441190332087,0.06550025939941406,4.898124 -740,Regression,k-Nearest Neighbors,TrumpApproval,0.5378193549549544,1.7312282531924004,0.08570362891572103,0.06600379943847656,5.1763650000000005 -760,Regression,k-Nearest Neighbors,TrumpApproval,0.5339037666666662,1.709797998783685,0.0985374850173486,0.06600379943847656,5.462206 -780,Regression,k-Nearest Neighbors,TrumpApproval,0.5278528008547004,1.688590710651063,0.11682237578267707,0.06550025939941406,5.755616 -800,Regression,k-Nearest Neighbors,TrumpApproval,0.5283612208333329,1.6702821330140922,0.12805516368066394,0.06550025939941406,6.056664 -820,Regression,k-Nearest Neighbors,TrumpApproval,0.5218793715447149,1.650734458460968,0.13899199371192528,0.06600379943847656,6.365303 -840,Regression,k-Nearest Neighbors,TrumpApproval,0.5164909984126979,1.6320321449108954,0.15057770250639546,0.06550025939941406,6.681529 -860,Regression,k-Nearest Neighbors,TrumpApproval,0.5203083496124026,1.617111920842916,0.16747440563774907,0.06550025939941406,7.005502 -880,Regression,k-Nearest Neighbors,TrumpApproval,0.5169651212121207,1.6004898524254525,0.18855338810104805,0.06600379943847656,7.3370109999999995 -900,Regression,k-Nearest Neighbors,TrumpApproval,0.5126542296296291,1.583708561642625,0.20213208493959922,0.06550025939941406,7.6761029999999995 -920,Regression,k-Nearest Neighbors,TrumpApproval,0.5104052702898545,1.5679886127118026,0.20504223486761397,0.06550025939941406,8.022855999999999 -940,Regression,k-Nearest Neighbors,TrumpApproval,0.5051605113475173,1.551953998854848,0.21461123840440588,0.06600379943847656,8.377156 -960,Regression,k-Nearest Neighbors,TrumpApproval,0.5025567965277773,1.5374441378201587,0.22116952746321128,0.06600379943847656,8.739002999999999 -980,Regression,k-Nearest Neighbors,TrumpApproval,0.49825265639455735,1.522650494486571,0.22124917342604244,0.06550025939941406,9.108430999999998 -1000,Regression,k-Nearest Neighbors,TrumpApproval,0.4939745494666663,1.5080707141004983,0.22323214061337782,0.06600379943847656,9.485458999999997 -11,Regression,Hoeffding Tree,ChickWeights,8.042756132756132,17.336048579080593,-385.86349170941764,0.016574859619140625,0.003293 -22,Regression,Hoeffding Tree,ChickWeights,4.456785613727984,12.282422261556867,-158.770726389092,0.018154144287109375,0.009578 -33,Regression,Hoeffding Tree,ChickWeights,3.4353973358733074,10.070376517434479,-69.4325218162971,0.023418426513671875,0.01815 -44,Regression,Hoeffding Tree,ChickWeights,2.736909422894262,8.732393473100391,-59.03623058514604,0.024471282958984375,0.029162 -55,Regression,Hoeffding Tree,ChickWeights,2.788577579622257,8.074088551816661,-11.726025456653014,0.031314849853515625,0.042723 -66,Regression,Hoeffding Tree,ChickWeights,3.3958800855981375,7.878422021930021,-4.223121571879303,0.040790557861328125,0.059358999999999995 -77,Regression,Hoeffding Tree,ChickWeights,3.8895265016210883,7.800910386370324,-2.432180745921895,0.047107696533203125,0.07961599999999999 -88,Regression,Hoeffding Tree,ChickWeights,4.072650698433535,7.572197783925699,-1.9320509270116553,0.052898406982421875,0.103875 -99,Regression,Hoeffding Tree,ChickWeights,4.410984939713907,7.55185413515251,-1.439151418709002,0.053951263427734375,0.132276 -110,Regression,Hoeffding Tree,ChickWeights,4.441558524813062,7.364764038532391,-0.6199522309877294,0.055530548095703125,0.164898 -121,Regression,Hoeffding Tree,ChickWeights,4.487290951327676,7.260940155844585,-0.21269398713682386,0.055530548095703125,0.201538 -132,Regression,Hoeffding Tree,ChickWeights,4.401729970486312,7.0591187066650845,0.06320102490380497,0.055530548095703125,0.242248 -143,Regression,Hoeffding Tree,ChickWeights,4.303599977233167,6.863829202938119,0.28262569921695146,0.056056976318359375,0.28717899999999996 -154,Regression,Hoeffding Tree,ChickWeights,4.2479679761417515,6.717580819449276,0.41603443739821244,0.056056976318359375,0.33616199999999996 -165,Regression,Hoeffding Tree,ChickWeights,4.525268599337025,6.978492074792776,0.49339428301547505,0.056056976318359375,0.38909699999999997 -176,Regression,Hoeffding Tree,ChickWeights,4.7434869323510185,7.161143757518859,0.5698598432460567,0.056583404541015625,0.44603699999999996 -187,Regression,Hoeffding Tree,ChickWeights,4.817684977356876,7.1877471099050325,0.6451941261958376,0.056583404541015625,0.5072089999999999 -198,Regression,Hoeffding Tree,ChickWeights,4.83667165494537,7.176577259975889,0.7186458480933114,0.056583404541015625,0.5725709999999999 -209,Regression,Hoeffding Tree,ChickWeights,5.073405719834179,7.569308518085582,0.7419802486075263,0.02174663543701172,0.645323 -220,Regression,Hoeffding Tree,ChickWeights,5.671396913729996,8.67042326781336,0.7036008152378226,0.02806377410888672,0.719465 -231,Regression,Hoeffding Tree,ChickWeights,5.870013976865108,8.892004937785565,0.7333469233470653,0.03332805633544922,0.7950900000000001 -242,Regression,Hoeffding Tree,ChickWeights,6.098410134572789,9.27860472778795,0.7663748869623117,0.03806591033935547,0.8723510000000001 -253,Regression,Hoeffding Tree,ChickWeights,6.1962339865774,9.406595007903094,0.7914570321252903,0.04175090789794922,0.9513590000000001 -264,Regression,Hoeffding Tree,ChickWeights,6.851942913488504,10.678395276366356,0.7544562538164442,0.041831016540527344,1.031981 -275,Regression,Hoeffding Tree,ChickWeights,7.351838545672251,11.801369148896674,0.7361015298851068,0.041831016540527344,1.114342 -286,Regression,Hoeffding Tree,ChickWeights,7.621792166351879,12.282711040561285,0.7524071035845484,0.042357444763183594,1.1984219999999999 -297,Regression,Hoeffding Tree,ChickWeights,7.6372566302059255,12.295347873811286,0.7848229800793282,0.042357444763183594,1.2842529999999999 -308,Regression,Hoeffding Tree,ChickWeights,8.1943326666584,13.18128308543095,0.7797471460356308,0.042357444763183594,1.3717719999999998 -319,Regression,Hoeffding Tree,ChickWeights,9.301321372784987,15.883856969554804,0.7097662534619076,0.042357444763183594,1.4609289999999997 -330,Regression,Hoeffding Tree,ChickWeights,9.759535973032726,16.540475274696632,0.7306639846914664,0.042357444763183594,1.5518979999999998 -341,Regression,Hoeffding Tree,ChickWeights,9.98108531256273,16.6656027575944,0.7551596457293974,0.042357444763183594,1.6445489999999998 -352,Regression,Hoeffding Tree,ChickWeights,10.172493780682656,16.824682995393008,0.773160093080841,0.042357444763183594,1.7389499999999998 -363,Regression,Hoeffding Tree,ChickWeights,11.068151856114426,18.263714825485387,0.7404354867888504,0.042357444763183594,1.8350029999999997 -374,Regression,Hoeffding Tree,ChickWeights,11.603116520774071,19.443156920913136,0.7295745843003554,0.042357444763183594,1.9329859999999996 -385,Regression,Hoeffding Tree,ChickWeights,12.00507936887919,20.0961554988217,0.744401153020958,0.042357444763183594,2.0327189999999997 -396,Regression,Hoeffding Tree,ChickWeights,12.159003512064782,20.104597547074984,0.7614813985176707,0.042357444763183594,2.1341089999999996 -407,Regression,Hoeffding Tree,ChickWeights,13.058561054914291,21.64678300128301,0.7429728504217219,0.039313316345214844,2.2395339999999995 -418,Regression,Hoeffding Tree,ChickWeights,13.849374886718222,23.13707582414104,0.7243608784812086,0.039839744567871094,2.3466389999999997 -429,Regression,Hoeffding Tree,ChickWeights,14.418764158229274,24.093967285200236,0.7343803671174814,0.040892601013183594,2.4553529999999997 -440,Regression,Hoeffding Tree,ChickWeights,14.611969435637262,24.1872515908579,0.7512659254336986,0.041419029235839844,2.5657939999999995 -451,Regression,Hoeffding Tree,ChickWeights,15.149542104002839,24.823452151261105,0.7491462990276416,0.042998313903808594,2.6778409999999995 -462,Regression,Hoeffding Tree,ChickWeights,16.266748325298664,26.992269976456935,0.7214095667925529,0.043524742126464844,2.7915709999999994 -473,Regression,Hoeffding Tree,ChickWeights,17.063164501315207,28.337029081432476,0.7288823142196059,0.043524742126464844,2.9070249999999995 -484,Regression,Hoeffding Tree,ChickWeights,17.41028324926407,28.63458736095403,0.7383292654808864,0.043524742126464844,3.0241479999999994 -495,Regression,Hoeffding Tree,ChickWeights,17.881702409973563,29.18189849457619,0.7443486730566713,0.043524742126464844,3.1430089999999993 -506,Regression,Hoeffding Tree,ChickWeights,18.783373559654446,30.65392766804094,0.7261055653266129,0.043524742126464844,3.263499999999999 -517,Regression,Hoeffding Tree,ChickWeights,19.52135237811833,31.66784012367412,0.7241496029986892,0.043524742126464844,3.3857279999999994 -528,Regression,Hoeffding Tree,ChickWeights,20.32410387080197,32.88418602247989,0.7325694870351915,0.043524742126464844,3.509655999999999 -539,Regression,Hoeffding Tree,ChickWeights,20.508458063622076,32.89095814819447,0.7435070498169007,0.043524742126464844,3.635277999999999 -550,Regression,Hoeffding Tree,ChickWeights,21.507291986413147,34.52927015042095,0.7260306568753008,0.043524742126464844,3.762600999999999 -561,Regression,Hoeffding Tree,ChickWeights,22.222091157093345,35.46412346985515,0.7236334646353288,0.043524742126464844,3.891587999999999 -572,Regression,Hoeffding Tree,ChickWeights,23.084218600655667,36.66377836765904,0.7267728639741885,0.044051170349121094,4.022359999999999 -20,Regression,Hoeffding Tree,TrumpApproval,4.834704431652337,13.708514217962266,-439.7934984576362,0.05081939697265625,0.006949 -40,Regression,Hoeffding Tree,TrumpApproval,3.4692310697037447,9.813795721313518,-37.72035957928713,0.07398223876953125,0.020525 -60,Regression,Hoeffding Tree,TrumpApproval,2.530247618203559,8.024836796214231,-33.90460110966681,0.08661651611328125,0.041034 -80,Regression,Hoeffding Tree,TrumpApproval,2.1398752670733447,6.982837000856316,-25.510487239912003,0.09661865234375,0.06908 -100,Regression,Hoeffding Tree,TrumpApproval,2.2521629689485394,6.362737158647257,-12.810573390910955,0.1060943603515625,0.105159 -120,Regression,Hoeffding Tree,TrumpApproval,2.2753311831165886,5.895687482983747,-9.059182991303912,0.1103057861328125,0.149228 -140,Regression,Hoeffding Tree,TrumpApproval,2.181766409647037,5.493495699082884,-8.025069637302263,0.1124114990234375,0.201546 -160,Regression,Hoeffding Tree,TrumpApproval,2.0641207293789914,5.165105496730293,-6.03588310696345,0.1166229248046875,0.262282 -180,Regression,Hoeffding Tree,TrumpApproval,1.9901037542149176,4.906162642056599,-4.575276834209563,0.1187286376953125,0.33158200000000004 -200,Regression,Hoeffding Tree,TrumpApproval,1.854788525917255,4.661016718308231,-4.0470005140641305,0.015085220336914062,0.42238300000000006 -220,Regression,Hoeffding Tree,TrumpApproval,1.777996659033335,4.4592908674997,-3.98319384245183,0.031249046325683594,0.517121 -240,Regression,Hoeffding Tree,TrumpApproval,1.696058551537428,4.2776990038095555,-3.6201076872383524,0.037039756774902344,0.616341 -260,Regression,Hoeffding Tree,TrumpApproval,1.6130985138696277,4.114896288193841,-3.3327388943406255,0.044569969177246094,0.720441 -280,Regression,Hoeffding Tree,TrumpApproval,1.5797289220452373,3.9859591491501196,-3.256494063385272,0.05755901336669922,0.829564 -300,Regression,Hoeffding Tree,TrumpApproval,1.5530598651848762,3.8727363700469204,-2.9515921533937095,0.06756114959716797,0.9443549999999999 -320,Regression,Hoeffding Tree,TrumpApproval,1.487708679701559,3.7532986907452788,-2.895390019001044,0.07545757293701172,1.064913 -340,Regression,Hoeffding Tree,TrumpApproval,1.447577018485449,3.651821310152781,-2.8968902417113367,0.08072185516357422,1.191641 -360,Regression,Hoeffding Tree,TrumpApproval,1.4679964351503354,3.577735862687295,-2.771095017816483,0.08861827850341797,1.3247849999999999 -380,Regression,Hoeffding Tree,TrumpApproval,1.4337467115212041,3.490600620499155,-2.7138197592485107,0.09388256072998047,1.4646529999999998 -400,Regression,Hoeffding Tree,TrumpApproval,1.394545215153479,3.4083536810761967,-2.6409419214114362,0.10125255584716797,1.6116329999999999 -420,Regression,Hoeffding Tree,TrumpApproval,1.3582303513295786,3.3304244373469776,-2.5913988321456767,0.10546398162841797,1.7657539999999998 -440,Regression,Hoeffding Tree,TrumpApproval,1.3237600646089562,3.2576165394889824,-2.3739144098898306,0.11125469207763672,1.9272029999999998 -460,Regression,Hoeffding Tree,TrumpApproval,1.313374021763953,3.1966490326870396,-2.080839603370824,0.11967754364013672,2.096272 -480,Regression,Hoeffding Tree,TrumpApproval,1.2871329178228548,3.1331240379472574,-1.891520259695389,0.12757396697998047,2.277254 -500,Regression,Hoeffding Tree,TrumpApproval,1.257405891185914,3.0730152095835095,-1.7232796098685204,0.13231182098388672,2.471873 -520,Regression,Hoeffding Tree,TrumpApproval,1.236691492049667,3.017934049641527,-1.6311150670478525,0.13757610321044922,2.675336 -540,Regression,Hoeffding Tree,TrumpApproval,1.2120036461347103,2.9640451937301795,-1.5286898077621993,0.13968181610107422,2.8858550000000003 -560,Regression,Hoeffding Tree,TrumpApproval,1.1973910792850353,2.9159627023633448,-1.5056283897938432,0.14441967010498047,3.1010690000000003 -580,Regression,Hoeffding Tree,TrumpApproval,1.1729976236433703,2.867868059269123,-1.4835996265310456,0.14705181121826172,3.321073 -600,Regression,Hoeffding Tree,TrumpApproval,1.164383909889498,2.8255860627341494,-1.384236602114556,0.14148998260498047,3.5505340000000003 -620,Regression,Hoeffding Tree,TrumpApproval,1.1607847453289375,2.7880287925830083,-1.2858933574415188,0.14412212371826172,3.784549 -640,Regression,Hoeffding Tree,TrumpApproval,1.1488689392891114,2.748475985188827,-1.179970998158682,0.14622783660888672,4.023051000000001 -660,Regression,Hoeffding Tree,TrumpApproval,1.1353328006378431,2.711500109160092,-1.1064542348150135,0.09333324432373047,4.270219000000001 -680,Regression,Hoeffding Tree,TrumpApproval,1.1237673891934872,2.67586665558629,-1.083872122585975,0.10280895233154297,4.520950000000001 -700,Regression,Hoeffding Tree,TrumpApproval,1.1197870211993892,2.6450513004918434,-1.0896316212520398,0.11070537567138672,4.77542 -720,Regression,Hoeffding Tree,TrumpApproval,1.0988128320741317,2.6090976903580367,-1.0778794022938847,0.11702251434326172,5.033773 -740,Regression,Hoeffding Tree,TrumpApproval,1.0821696401958585,2.5758102479785587,-1.0239793040320149,0.12070751190185547,5.296116 -760,Regression,Hoeffding Tree,TrumpApproval,1.0646778366488154,2.542912274197232,-0.9939800998628658,0.12386608123779297,5.562507999999999 -780,Regression,Hoeffding Tree,TrumpApproval,1.0458773095514022,2.5110610332394967,-0.9530519853191719,0.13018321990966797,5.833025999999999 -800,Regression,Hoeffding Tree,TrumpApproval,1.037522387437363,2.4829468635274075,-0.9268335078372076,0.14051342010498047,6.107975999999999 -820,Regression,Hoeffding Tree,TrumpApproval,1.0344825476074344,2.459713101244265,-0.9117084698305153,0.14683055877685547,6.387556999999999 -840,Regression,Hoeffding Tree,TrumpApproval,1.0265033672562451,2.4338535651506326,-0.8891007921837151,0.15051555633544922,6.671677999999999 -860,Regression,Hoeffding Tree,TrumpApproval,1.0098876748057108,2.406249644034785,-0.8433117943327111,0.15209484100341797,6.960477999999999 -880,Regression,Hoeffding Tree,TrumpApproval,0.9987418826957182,2.3807044173500915,-0.795415858642577,0.15525341033935547,7.253952999999999 -900,Regression,Hoeffding Tree,TrumpApproval,0.9899512872354768,2.3562979624350358,-0.7662040947107138,0.15735912322998047,7.552186999999999 -920,Regression,Hoeffding Tree,TrumpApproval,0.9763510674237786,2.3315103197053233,-0.7576521562914318,0.12545299530029297,7.860606999999999 -940,Regression,Hoeffding Tree,TrumpApproval,0.9721072130212217,2.310027291919755,-0.7400479483856388,0.13442516326904297,8.173459999999999 -960,Regression,Hoeffding Tree,TrumpApproval,0.9655343485152449,2.289186508543074,-0.7266590967915565,0.14021587371826172,8.490537999999999 -980,Regression,Hoeffding Tree,TrumpApproval,0.9583856890611192,2.2686834337047155,-0.7288044208040367,0.14442729949951172,8.812014999999999 -1000,Regression,Hoeffding Tree,TrumpApproval,0.9497447679766952,2.248146643879841,-0.7262238291744263,0.14863872528076172,9.137958999999999 -11,Regression,Hoeffding Adaptive Tree,ChickWeights,8.051220648038832,17.336198122120386,-385.87016600913427,0.023288726806640625,0.00403 -22,Regression,Hoeffding Adaptive Tree,ChickWeights,4.498502947359929,12.285286375364281,-158.84524831763767,0.024929046630859375,0.011609 -33,Regression,Hoeffding Adaptive Tree,ChickWeights,3.4668695042339137,10.074636808082968,-69.49212762837747,0.030193328857421875,0.022078 -44,Regression,Hoeffding Adaptive Tree,ChickWeights,2.7637805804889553,8.735764655686483,-59.08259408516962,0.031307220458984375,0.035307 -55,Regression,Hoeffding Adaptive Tree,ChickWeights,2.814517498310432,8.074396776941786,-11.726997097138026,0.038150787353515625,0.051286 -66,Regression,Hoeffding Adaptive Tree,ChickWeights,3.396900059747575,7.862006773633152,-4.201378762014764,0.047626495361328125,0.070551 -77,Regression,Hoeffding Adaptive Tree,ChickWeights,3.8844336568547537,7.782255505653143,-2.415785129732385,0.054004669189453125,0.093692 -88,Regression,Hoeffding Adaptive Tree,ChickWeights,4.068768385552718,7.555909217267645,-1.9194502155140074,0.059795379638671875,0.121065 -99,Regression,Hoeffding Adaptive Tree,ChickWeights,4.311602452908636,7.487314706483316,-1.3976387620786475,0.060848236083984375,0.152902 -110,Regression,Hoeffding Adaptive Tree,ChickWeights,4.261918758035323,7.240982145259267,-0.5659557565320237,0.062427520751953125,0.189312 -121,Regression,Hoeffding Adaptive Tree,ChickWeights,4.32509570871032,7.149348278127394,-0.17570514229398082,0.062427520751953125,0.22997900000000002 -132,Regression,Hoeffding Adaptive Tree,ChickWeights,4.243770455182887,6.949556168474376,0.0920549285716371,0.062427520751953125,0.275055 -143,Regression,Hoeffding Adaptive Tree,ChickWeights,4.119311205048765,6.7400830594316625,0.3082592266545521,0.024164199829101562,0.330922 -154,Regression,Hoeffding Adaptive Tree,ChickWeights,4.094718549433554,6.618738421062464,0.43309293168511476,0.034926414489746094,0.389383 -165,Regression,Hoeffding Adaptive Tree,ChickWeights,4.3535914858207265,6.858418841195889,0.5106778054828556,0.041365623474121094,0.450937 -176,Regression,Hoeffding Adaptive Tree,ChickWeights,4.494676115333661,6.99651956882687,0.5894091082089881,0.047156333923339844,0.515937 -187,Regression,Hoeffding Adaptive Tree,ChickWeights,4.531460188122701,6.982633238942946,0.6651551017011323,0.052016258239746094,0.584675 -198,Regression,Hoeffding Adaptive Tree,ChickWeights,4.550856564096301,6.9489545654121585,0.736210476532317,0.054648399353027344,0.6572239999999999 -209,Regression,Hoeffding Adaptive Tree,ChickWeights,4.745146525796211,7.286245359964537,0.7609173164436883,0.054648399353027344,0.733675 -220,Regression,Hoeffding Adaptive Tree,ChickWeights,5.330595718754616,8.515887777891804,0.7140722769094752,0.055296897888183594,0.814194 -231,Regression,Hoeffding Adaptive Tree,ChickWeights,5.50563091305374,8.701126762111178,0.7446721431039482,0.055296897888183594,0.898563 -242,Regression,Hoeffding Adaptive Tree,ChickWeights,5.723610994733609,9.068119167211083,0.7768542529953268,0.055296897888183594,0.986859 -253,Regression,Hoeffding Adaptive Tree,ChickWeights,5.834317193911452,9.203767847944404,0.8003533769470821,0.055296897888183594,1.07894 -264,Regression,Hoeffding Adaptive Tree,ChickWeights,6.561326226922799,10.595386608942691,0.7582588922727441,0.05537700653076172,1.17475 -275,Regression,Hoeffding Adaptive Tree,ChickWeights,7.055343281410319,11.793355798881397,0.7364597921881992,0.05537700653076172,1.274735 -286,Regression,Hoeffding Adaptive Tree,ChickWeights,7.331998951413002,12.245186296589463,0.7539176280650666,0.05391216278076172,1.3832309999999999 -297,Regression,Hoeffding Adaptive Tree,ChickWeights,7.416966166983629,12.289761227218738,0.7850184759487971,0.05443859100341797,1.495361 -308,Regression,Hoeffding Adaptive Tree,ChickWeights,7.99448083149163,13.217085318753208,0.7785490451915651,0.05456066131591797,1.6112419999999998 -319,Regression,Hoeffding Adaptive Tree,ChickWeights,9.157233410060112,16.13339057164046,0.7005755447430102,0.05613994598388672,1.73087 -330,Regression,Hoeffding Adaptive Tree,ChickWeights,9.47509121278654,16.446724789755304,0.7337084949314072,0.05613994598388672,1.854306 -341,Regression,Hoeffding Adaptive Tree,ChickWeights,9.757592549477597,16.701217161288277,0.7541120796137563,0.05613994598388672,1.981549 -352,Regression,Hoeffding Adaptive Tree,ChickWeights,9.93501095513177,16.87017564150386,0.7719317193583981,0.05613994598388672,2.112498 -363,Regression,Hoeffding Adaptive Tree,ChickWeights,10.85670423687554,18.405576814105093,0.7363875320655082,0.05613994598388672,2.247206 -374,Regression,Hoeffding Adaptive Tree,ChickWeights,11.59956477803306,20.214372545093333,0.7076961913412723,0.0658864974975586,2.386792 -385,Regression,Hoeffding Adaptive Tree,ChickWeights,12.011712885443343,20.838356414394227,0.7251727140820715,0.0713338851928711,2.5311649999999997 -396,Regression,Hoeffding Adaptive Tree,ChickWeights,12.02179706392092,20.699504894468422,0.7471567277697432,0.07817745208740234,2.6805389999999996 -407,Regression,Hoeffding Adaptive Tree,ChickWeights,12.867904342374958,22.04935022682606,0.733324041761514,0.08257198333740234,2.8351709999999994 -418,Regression,Hoeffding Adaptive Tree,ChickWeights,13.7726214629037,23.76360253855293,0.7092307493255587,0.08467769622802734,2.9951159999999994 -429,Regression,Hoeffding Adaptive Tree,ChickWeights,14.320664029675132,24.677208926329655,0.7213650338700139,0.06568050384521484,3.1646219999999996 -440,Regression,Hoeffding Adaptive Tree,ChickWeights,14.560745017781516,24.854467305977835,0.7373537773889289,0.0706624984741211,3.3387759999999997 -451,Regression,Hoeffding Adaptive Tree,ChickWeights,15.052718157581781,25.416929688531035,0.7370081245929037,0.07874202728271484,3.5178329999999995 -462,Regression,Hoeffding Adaptive Tree,ChickWeights,16.182360709465044,27.521690548068367,0.7103739673524856,0.0857076644897461,3.7020849999999994 -473,Regression,Hoeffding Adaptive Tree,ChickWeights,17.028395162723214,29.038217655150593,0.7152989103492285,0.0888662338256836,3.891670999999999 -484,Regression,Hoeffding Adaptive Tree,ChickWeights,17.39410374569335,29.368989764604034,0.7247347991767862,0.0889272689819336,4.084988999999999 -495,Regression,Hoeffding Adaptive Tree,ChickWeights,17.903720470221085,29.931789274576047,0.7310408495158558,0.0889272689819336,4.281388999999999 -506,Regression,Hoeffding Adaptive Tree,ChickWeights,18.828904303353827,31.360943457558232,0.7133254165496098,0.08951473236083984,4.480870999999999 -517,Regression,Hoeffding Adaptive Tree,ChickWeights,19.65231385288099,32.53535290181733,0.7088292337107098,0.0743856430053711,4.686204999999998 -528,Regression,Hoeffding Adaptive Tree,ChickWeights,20.485248099981963,33.800991401884744,0.7174497851371298,0.0787191390991211,4.894319999999999 -539,Regression,Hoeffding Adaptive Tree,ChickWeights,20.62693587606907,33.76714158926765,0.7296595823775684,0.0872030258178711,5.105511999999998 -550,Regression,Hoeffding Adaptive Tree,ChickWeights,21.611004652382654,35.363345192969206,0.7126350131259431,0.0935201644897461,5.319765999999999 -561,Regression,Hoeffding Adaptive Tree,ChickWeights,22.391562087266266,36.4058649659661,0.708760885936744,0.09343624114990234,5.537231999999999 -572,Regression,Hoeffding Adaptive Tree,ChickWeights,23.25574522992599,37.57896806973795,0.7129622004044582,0.09461116790771484,5.757816999999998 -20,Regression,Hoeffding Adaptive Tree,TrumpApproval,4.828377634536296,13.70786256219322,-439.7515918302183,0.05759429931640625,0.005178 -40,Regression,Hoeffding Adaptive Tree,TrumpApproval,3.453811275213839,9.811073218407973,-37.69887927291551,0.08081817626953125,0.01451 -60,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.5116544078850294,8.021960641037959,-33.879585508404254,0.09345245361328125,0.027873000000000002 -80,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.1224425015381523,6.9797990571526345,-25.487425023640153,0.103515625,0.045557 -100,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.246653919301699,6.363694444016854,-12.814729355257526,0.1129913330078125,0.06794 -120,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.270681160376927,5.896666779393501,-9.062525006956841,0.1172027587890625,0.095003 -140,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.1411489845018936,5.486121567062232,-8.000856498367144,0.1193084716796875,0.126859 -160,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.9595309296437795,5.145701533389061,-5.983118424699933,0.048110008239746094,0.170894 -180,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.8606850760789415,4.874784956472401,-4.504190782470528,0.06455135345458984,0.218268 -200,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.753768292507887,4.635064394721464,-3.990954055000616,0.07511425018310547,0.26933799999999997 -220,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.6442100676088158,4.426753978888705,-3.9107401204837533,0.0809926986694336,0.324221 -240,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.5586094458315778,4.243291414416048,-3.546083102970604,0.0831594467163086,0.383071 -260,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4757349309283123,4.079934399827006,-3.259426129696055,0.08697795867919922,0.445946 -280,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.429182741517093,3.94384178403937,-3.1670173911200505,0.09651470184326172,0.513038 -300,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.395554812880875,3.827170050036498,-2.859150937529973,0.10505962371826172,0.584571 -320,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3420726443092519,3.7085248950294267,-2.8030066952692625,0.11137676239013672,0.660678 -340,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2981450416106572,3.6023493777501,-2.7920215666096264,0.09692668914794922,0.7468359999999999 -360,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3017629599133824,3.52722030011446,-2.665355451896766,0.10482311248779297,0.837109 -380,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2791887566109008,3.4421979658375847,-2.6115379824259644,0.11014842987060547,0.931726 -400,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2309239845123983,3.35653802988408,-2.5310802374824855,0.11751842498779297,1.0309650000000001 -420,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2044881660092321,3.279580566778272,-2.4825797995498564,0.12172985076904297,1.13497 -440,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1805943216757935,3.2099310427881282,-2.2758615914195226,0.12752056121826172,1.2436960000000001 -460,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1747748413206256,3.150463961675437,-1.9924589881120154,0.13547801971435547,1.3572460000000002 -480,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1643181646721343,3.0907225956471227,-1.8137863387673288,0.14337444305419922,1.47581 -500,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1467427258000205,3.033773569773335,-1.6541724792729693,0.13623332977294922,1.6042770000000002 -520,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1310531754619513,2.979836071514613,-1.5651047078316291,0.14155864715576172,1.7377410000000002 -540,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1142715036195536,2.927551584361061,-1.466806182052764,0.14366436004638672,1.8762310000000002 -560,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1052485405576076,2.88137538641146,-1.4465405352520455,0.14840221405029297,2.0198690000000004 -580,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0859142446379713,2.834429565013283,-1.4260211929714153,0.15103435516357422,2.168629 -600,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0769717813954383,2.7922355683541964,-1.3282862925750138,0.15471935272216797,2.322692 -620,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0766885404316004,2.7563858053447157,-1.2342999001530521,0.15787792205810547,2.481953 -640,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.066276571170311,2.7176435544384367,-1.1313354614936588,0.15998363494873047,2.6464339999999997 -660,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0564634149761172,2.6824145051502803,-1.061505763110988,0.16366863250732422,2.8162789999999998 -680,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0488950225738152,2.6482621618515996,-1.0410990478472515,0.16630077362060547,2.9915459999999996 -700,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0494335446356584,2.619695292588707,-1.0497603683353351,0.15471935272216797,3.177748 -720,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0311572549278785,2.584165722213007,-1.0383576153991259,0.15945720672607422,3.3691549999999997 -740,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0166711191684197,2.551525288520744,-0.9859947129346351,0.16320323944091797,3.5658709999999996 -760,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9986122460589341,2.518502326974768,-0.9558825700512557,0.16478252410888672,3.7680209999999996 -780,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9842422617410425,2.4876653972852822,-0.9168282284512221,0.16958141326904297,3.9756869999999997 -800,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9822601758268574,2.462050972175237,-0.8945384291924348,0.16168498992919922,4.194112 -820,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9867607749501652,2.442599600947298,-0.8851995145588711,0.16431713104248047,4.4181 -840,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9793985197413392,2.417271328475225,-0.8634469862274994,0.16800212860107422,4.647504 -860,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9647458553132983,2.3903176435066817,-0.8189831286734526,0.17010784149169922,4.882465 -880,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9550000119490066,2.3652388018081414,-0.7721647401587992,0.17273998260498047,5.123038 -900,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9450286973597519,2.34076619049233,-0.7429966155689833,0.11153697967529297,5.374164 -920,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9340382087332013,2.316653968016665,-0.7353240501222194,0.11639690399169922,5.629799 -940,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9315944774387301,2.295899595899596,-0.7188294143518761,0.12230968475341797,5.8899680000000005 -960,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9266882702688716,2.2758824318695603,-0.7066477496763941,0.12967967987060547,6.154803 -980,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9199947893356224,2.255690063945526,-0.7090584581073034,0.13494396209716797,6.424549 -1000,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.910675347240908,2.2342958873570486,-0.7050189374098543,0.13822460174560547,6.699174 -11,Regression,Stochastic Gradient Tree,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.009614944458007812,0.001833 -22,Regression,Stochastic Gradient Tree,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012609481811523438,0.00539 -33,Regression,Stochastic Gradient Tree,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.015787124633789062,0.009921 -44,Regression,Stochastic Gradient Tree,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.018873214721679688,0.015497 -55,Regression,Stochastic Gradient Tree,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.021825790405273438,0.022338 -66,Regression,Stochastic Gradient Tree,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.024618148803710938,0.030239000000000002 -77,Regression,Stochastic Gradient Tree,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.027502059936523438,0.039152000000000006 -88,Regression,Stochastic Gradient Tree,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.030019760131835938,0.04912300000000001 -99,Regression,Stochastic Gradient Tree,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.03290367126464844,0.060256000000000004 -110,Regression,Stochastic Gradient Tree,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.26967811584472656,0.074892 -121,Regression,Stochastic Gradient Tree,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.26967811584472656,0.095588 -132,Regression,Stochastic Gradient Tree,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.26967811584472656,0.12212100000000001 -143,Regression,Stochastic Gradient Tree,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.26967811584472656,0.15448800000000001 -154,Regression,Stochastic Gradient Tree,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.26967811584472656,0.19265400000000002 -165,Regression,Stochastic Gradient Tree,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.26967811584472656,0.23664700000000002 -176,Regression,Stochastic Gradient Tree,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.26967811584472656,0.28637300000000004 -187,Regression,Stochastic Gradient Tree,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.26967811584472656,0.34182100000000004 -198,Regression,Stochastic Gradient Tree,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.26967811584472656,0.40300900000000006 -209,Regression,Stochastic Gradient Tree,ChickWeights,55.16354936929098,57.0482200725598,-13.656313160472004,0.6838865280151367,0.4890500000000001 -220,Regression,Stochastic Gradient Tree,ChickWeights,53.62203856749311,56.03531795068661,-11.37998411824978,0.6869077682495117,0.5845720000000001 -231,Regression,Stochastic Gradient Tree,ChickWeights,52.77279286370195,55.29408706815337,-9.311090357596036,0.6899290084838867,0.6897040000000001 -242,Regression,Stochastic Gradient Tree,ChickWeights,52.49661908339594,55.007104536867395,-7.210918602421254,0.6929502487182617,0.8041680000000001 -253,Regression,Stochastic Gradient Tree,ChickWeights,52.25631812193077,54.713446605156875,-6.055353919833875,0.6947126388549805,0.9281400000000001 -264,Regression,Stochastic Gradient Tree,ChickWeights,51.62511478420569,54.312843786153664,-5.352168023774992,0.6947126388549805,1.061635 -275,Regression,Stochastic Gradient Tree,ChickWeights,51.4425344352617,54.29364548356293,-4.585603291722447,0.6947126388549805,1.204612 -286,Regression,Stochastic Gradient Tree,ChickWeights,51.75651621106165,54.635705044608144,-3.8989478253777694,0.6947126388549805,1.357118 -297,Regression,Stochastic Gradient Tree,ChickWeights,52.373839404142416,55.25476711535166,-3.3456400671942,0.6947126388549805,1.518976 -308,Regression,Stochastic Gradient Tree,ChickWeights,52.87239275875638,55.86677247417265,-2.9565197175813713,0.6947126388549805,1.6900950000000001 -319,Regression,Stochastic Gradient Tree,ChickWeights,52.69554478958866,56.2770501442128,-2.6433309475704183,0.6947126388549805,1.870449 -330,Regression,Stochastic Gradient Tree,ChickWeights,53.85316804407712,57.75044402630399,-2.2832890424968193,0.6947126388549805,2.06005 -341,Regression,Stochastic Gradient Tree,ChickWeights,54.90678041411178,59.01114057562677,-2.0697921090482247,0.6947126388549805,2.258925 -352,Regression,Stochastic Gradient Tree,ChickWeights,56.00533746556472,60.302245208561004,-1.9140207825503284,0.6947126388549805,2.467073 -363,Regression,Stochastic Gradient Tree,ChickWeights,55.99599298772852,60.54917173074773,-1.852879941931207,0.6947126388549805,2.684461 -374,Regression,Stochastic Gradient Tree,ChickWeights,56.87222492302705,61.81275171085535,-1.7331917323651345,0.6947126388549805,2.911073 -385,Regression,Stochastic Gradient Tree,ChickWeights,58.41786698150333,63.95254893573906,-1.5885028214279253,0.6947126388549805,3.146928 -396,Regression,Stochastic Gradient Tree,ChickWeights,59.7033976124885,65.46926983257002,-1.5293357430909813,0.6947126388549805,3.392032 -407,Regression,Stochastic Gradient Tree,ChickWeights,60.057805647389294,66.17359973042984,-1.4019380007417155,1.1097631454467773,3.672033 -418,Regression,Stochastic Gradient Tree,ChickWeights,59.7070864579051,66.11592086962122,-1.2507954049688483,1.1127843856811523,3.9657679999999997 -429,Regression,Stochastic Gradient Tree,ChickWeights,60.122823673891816,66.73609937588846,-1.0378169857688957,1.1158056259155273,4.272702 -440,Regression,Stochastic Gradient Tree,ChickWeights,60.39504675635191,66.96100690444877,-0.906365593827489,1.1188268661499023,4.593074 -451,Regression,Stochastic Gradient Tree,ChickWeights,60.27126048587789,66.93502892662679,-0.8239085862185902,1.120589256286621,4.926735 -462,Regression,Stochastic Gradient Tree,ChickWeights,60.340686610373176,67.43825007380137,-0.7390015352251049,1.120589256286621,5.273856 -473,Regression,Stochastic Gradient Tree,ChickWeights,61.40703262301831,69.11306667757516,-0.6127592621572406,1.120589256286621,5.634397 -484,Regression,Stochastic Gradient Tree,ChickWeights,61.95796621360106,69.71422620021941,-0.5510154280248158,1.120589256286621,6.008344 -495,Regression,Stochastic Gradient Tree,ChickWeights,62.59018166487368,70.55352405729404,-0.4943708535906215,1.120589256286621,6.395653 -506,Regression,Stochastic Gradient Tree,ChickWeights,62.49664579133251,70.88193125644693,-0.46447524520130457,1.120589256286621,6.796304 -517,Regression,Stochastic Gradient Tree,ChickWeights,63.25224079915844,71.92080214464903,-0.4228062717918979,1.120589256286621,7.21023 -528,Regression,Stochastic Gradient Tree,ChickWeights,64.80783657170488,74.3681944005728,-0.36776422230083305,1.120589256286621,7.637592 -539,Regression,Stochastic Gradient Tree,ChickWeights,65.59959781369417,75.30113885843834,-0.3443906138479853,1.120589256286621,8.078512 -550,Regression,Stochastic Gradient Tree,ChickWeights,65.79684627343133,76.01328745307667,-0.32771909731089166,1.120589256286621,8.532886 -561,Regression,Stochastic Gradient Tree,ChickWeights,66.6512855136148,77.20436469287773,-0.30975691666695093,1.120589256286621,9.000807 -572,Regression,Stochastic Gradient Tree,ChickWeights,68.11975592628174,79.56492566870935,-0.2867456678376987,1.120589256286621,9.482145 -20,Regression,Stochastic Gradient Tree,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.019941329956054688,0.002755 -40,Regression,Stochastic Gradient Tree,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.03173637390136719,0.008058000000000001 -60,Regression,Stochastic Gradient Tree,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.04389762878417969,0.015549 -80,Regression,Stochastic Gradient Tree,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.05624198913574219,0.025294 -100,Regression,Stochastic Gradient Tree,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.5381031036376953,0.041246000000000005 -120,Regression,Stochastic Gradient Tree,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.5386066436767578,0.070023 -140,Regression,Stochastic Gradient Tree,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.5391101837158203,0.110787 -160,Regression,Stochastic Gradient Tree,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.5393619537353516,0.163463 -180,Regression,Stochastic Gradient Tree,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.5396137237548828,0.227995 -200,Regression,Stochastic Gradient Tree,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.5077581405639648,0.320041 -220,Regression,Stochastic Gradient Tree,TrumpApproval,36.497516001377406,38.019453444701035,-361.2329206514933,1.3602590560913086,0.441408 -240,Regression,Stochastic Gradient Tree,TrumpApproval,33.64243104419191,36.40668421494773,-333.65237138497804,1.360762596130371,0.581306 -260,Regression,Stochastic Gradient Tree,TrumpApproval,31.222114965034955,34.98371838354962,-312.16748668977897,1.3610143661499023,0.739627 -280,Regression,Stochastic Gradient Tree,TrumpApproval,29.182059468614717,33.71869814960704,-303.5986275675674,1.361769676208496,0.915776 -300,Regression,Stochastic Gradient Tree,TrumpApproval,27.34275770505051,32.57805191350732,-278.63174197976707,1.3620214462280273,1.109879 -320,Regression,Stochastic Gradient Tree,TrumpApproval,25.81388747443183,31.5521424826706,-274.2849072221064,1.3630285263061523,1.321838 -340,Regression,Stochastic Gradient Tree,TrumpApproval,24.51835124153299,30.62414457186519,-273.0482727941538,1.3640356063842773,1.5516940000000001 -360,Regression,Stochastic Gradient Tree,TrumpApproval,23.451930423400693,29.787924926455332,-260.4155562259403,1.3660497665405273,1.799643 -380,Regression,Stochastic Gradient Tree,TrumpApproval,22.468440533492842,29.014219480552867,-255.59151052979877,1.3665533065795898,2.065559 -400,Regression,Stochastic Gradient Tree,TrumpApproval,21.594907007575774,28.301677882839346,-250.0434007116766,0.510127067565918,2.355987 -420,Regression,Stochastic Gradient Tree,TrumpApproval,20.62268781294523,27.62086591367872,-246.0239415518119,1.3623762130737305,2.6744339999999998 -440,Regression,Stochastic Gradient Tree,TrumpApproval,19.786863931462925,26.990398924900393,-230.60756767519214,1.3643903732299805,3.010878 -460,Regression,Stochastic Gradient Tree,TrumpApproval,19.05732899619648,26.404670160589287,-209.2038511633616,1.3666563034057617,3.365293 -480,Regression,Stochastic Gradient Tree,TrumpApproval,18.376512097202227,25.854792215140314,-195.90337768575387,1.3701810836791992,3.737716 -500,Regression,Stochastic Gradient Tree,TrumpApproval,17.755044410127518,25.338820973360427,-184.15507530651482,1.3716917037963867,4.128280999999999 -520,Regression,Stochastic Gradient Tree,TrumpApproval,17.16611419898163,24.851444862058347,-177.4118263333629,1.3737058639526367,4.537221 -540,Regression,Stochastic Gradient Tree,TrumpApproval,16.628565596068775,24.392285078947275,-170.25012213753183,1.3747129440307617,4.9643749999999995 -560,Regression,Stochastic Gradient Tree,TrumpApproval,16.091244232649693,23.955027361350904,-168.10096043791202,1.3752164840698242,5.410107999999999 -580,Regression,Stochastic Gradient Tree,TrumpApproval,15.590768135673304,23.54051091957351,-166.33817208986073,1.3764753341674805,5.874175999999999 -600,Regression,Stochastic Gradient Tree,TrumpApproval,15.168708628495342,23.15108754841241,-159.05714501634571,0.5124959945678711,6.365194 -620,Regression,Stochastic Gradient Tree,TrumpApproval,14.742446374247313,22.779539618023726,-151.59887848495535,3.0642080307006836,6.921285 -640,Regression,Stochastic Gradient Tree,TrumpApproval,14.319364852585176,22.42187566882095,-144.08105420081068,3.0679845809936523,7.51197 -660,Regression,Stochastic Gradient Tree,TrumpApproval,13.916412195872256,22.080274918425697,-138.68241285181185,3.0712575912475586,8.136981 -680,Regression,Stochastic Gradient Tree,TrumpApproval,13.515604789075645,21.753254558457893,-136.71797028279042,3.074782371520996,8.796442 -700,Regression,Stochastic Gradient Tree,TrumpApproval,13.16391092204058,21.44141764506316,-136.3120101768532,3.0773000717163086,9.490636 -720,Regression,Stochastic Gradient Tree,TrumpApproval,12.828283113852926,21.142484202016185,-135.44313416922282,3.078558921813965,10.219341 -740,Regression,Stochastic Gradient Tree,TrumpApproval,12.504466467012781,20.855361315179096,-131.6825380828392,3.0800695419311523,10.982324 -760,Regression,Stochastic Gradient Tree,TrumpApproval,12.187542748969033,20.57929219886472,-129.592708960364,3.0813283920288086,11.779656000000001 -780,Regression,Stochastic Gradient Tree,TrumpApproval,11.899403743710545,20.31464229706916,-126.82553676745258,3.08359432220459,12.611571000000001 -800,Regression,Stochastic Gradient Tree,TrumpApproval,11.634366305883285,20.06137952581079,-124.7856004590591,3.084601402282715,13.493909000000002 -820,Regression,Stochastic Gradient Tree,TrumpApproval,11.363415331478278,19.815492221289514,-123.0687724200615,3.08560848236084,14.412321000000002 -840,Regression,Stochastic Gradient Tree,TrumpApproval,11.106640469158773,19.57848368678801,-121.24309788996561,3.086615562438965,15.363464000000002 -860,Regression,Stochastic Gradient Tree,TrumpApproval,10.873909665943762,19.350226189127362,-118.20364312373843,3.0871191024780273,16.347229000000002 -880,Regression,Stochastic Gradient Tree,TrumpApproval,10.65545006969969,19.130035299019603,-114.92727947355435,3.0873708724975586,17.36361 -900,Regression,Stochastic Gradient Tree,TrumpApproval,10.439309697188909,18.916827199314994,-112.83532852765143,3.08762264251709,18.412326 -920,Regression,Stochastic Gradient Tree,TrumpApproval,10.21789524284777,18.710158789526105,-112.19133803320567,3.087874412536621,19.493438 -940,Regression,Stochastic Gradient Tree,TrumpApproval,10.012578535125467,18.510293787577226,-110.72583714230211,3.077906608581543,20.736809 -960,Regression,Stochastic Gradient Tree,TrumpApproval,9.811853150109151,18.316579311485903,-109.54344305213982,3.0804243087768555,22.013587 -980,Regression,Stochastic Gradient Tree,TrumpApproval,9.61909067795052,18.12881604876013,-109.39183420714343,3.080927848815918,23.322572 -1000,Regression,Stochastic Gradient Tree,TrumpApproval,9.438738635632271,17.946847607318464,-109.00797869183796,3.0824384689331055,24.663779 -11,Regression,Adaptive Random Forest,ChickWeights,7.837563210503649,16.830121687224917,-363.61289911513376,0.15062332153320312,0.018991 -22,Regression,Adaptive Random Forest,ChickWeights,4.3557641651310055,11.925612892987612,-149.62275175212707,0.17615127563476562,0.051966 -33,Regression,Adaptive Random Forest,ChickWeights,3.371112580593177,9.780386843070694,-65.43453306461763,0.21428680419921875,0.10284099999999999 -44,Regression,Adaptive Random Forest,ChickWeights,2.6950975095192975,8.482165721989492,-55.64483692929184,0.2312774658203125,0.17143399999999998 -55,Regression,Adaptive Random Forest,ChickWeights,2.750371500828058,7.825470627419848,-10.954370249441634,0.2869682312011719,0.25594999999999996 -66,Regression,Adaptive Random Forest,ChickWeights,2.874973614360605,7.312672972792191,-3.4999113348114523,0.33329010009765625,0.36036799999999997 -77,Regression,Adaptive Random Forest,ChickWeights,3.049190601733834,7.064366487423796,-1.814660484448317,0.3119354248046875,0.49218199999999995 -88,Regression,Adaptive Random Forest,ChickWeights,2.9760015514160614,6.690266116634344,-1.2888345310585096,0.3463325500488281,0.6436869999999999 -99,Regression,Adaptive Random Forest,ChickWeights,3.4458208345296213,6.801756467406213,-0.9786716534372655,0.3914375305175781,0.8148389999999999 -110,Regression,Adaptive Random Forest,ChickWeights,3.8036130411377513,6.901055458118455,-0.4223797783359673,0.4219093322753906,1.008913 -121,Regression,Adaptive Random Forest,ChickWeights,4.044286594485068,6.961321070951229,-0.1146764842737158,0.4422340393066406,1.227422 -132,Regression,Adaptive Random Forest,ChickWeights,4.278051046290026,6.992189163715883,0.08088093472531654,0.4695549011230469,1.467051 -143,Regression,Adaptive Random Forest,ChickWeights,4.514698307868387,7.09845673012605,0.23274318106229808,0.5039863586425781,1.72858 -154,Regression,Adaptive Random Forest,ChickWeights,4.7116828167337585,7.225211881769595,0.32444204647582153,0.5465545654296875,2.00923 -165,Regression,Adaptive Random Forest,ChickWeights,5.098965674365188,7.74448667501397,0.3760752970367085,0.5543212890625,2.317031 -176,Regression,Adaptive Random Forest,ChickWeights,5.613573109580669,8.405070008975855,0.4074460804745814,0.5778732299804688,2.650775 -187,Regression,Adaptive Random Forest,ChickWeights,6.0300184245211925,8.816070592615688,0.4662285490406689,0.597381591796875,3.008386 -198,Regression,Adaptive Random Forest,ChickWeights,6.135166503700917,8.873071763991604,0.5699028278126741,0.6156463623046875,3.389767 -209,Regression,Adaptive Random Forest,ChickWeights,6.663870316688535,9.680163486851024,0.5780063435312484,0.6372909545898438,3.797698 -220,Regression,Adaptive Random Forest,ChickWeights,7.114976257039628,10.70304519196575,0.548340525531499,0.6516532897949219,4.233731 -231,Regression,Adaptive Random Forest,ChickWeights,7.558935022860611,11.205896813348822,0.5765126454701623,0.6456108093261719,4.696369 -242,Regression,Adaptive Random Forest,ChickWeights,7.8784966144348445,11.546829616877769,0.6381907286214681,0.6543197631835938,5.1845419999999995 -253,Regression,Adaptive Random Forest,ChickWeights,8.045465222989945,11.7730800696534,0.6733287963504477,0.6542396545410156,5.698149999999999 -264,Regression,Adaptive Random Forest,ChickWeights,8.55861765945315,12.698578392308455,0.6527621165047097,0.7007179260253906,6.239901999999999 -275,Regression,Adaptive Random Forest,ChickWeights,9.012932654808607,13.883768164483953,0.6347528903285174,0.7182159423828125,6.810226999999999 -286,Regression,Adaptive Random Forest,ChickWeights,9.46045520422096,14.421722772543973,0.6586625054492083,0.7397651672363281,7.406784999999999 -297,Regression,Adaptive Random Forest,ChickWeights,9.467084537445258,14.393153360282469,0.7051330126585751,0.6962127685546875,8.032812 -308,Regression,Adaptive Random Forest,ChickWeights,9.993822911601686,15.306673373378164,0.7029922374440833,0.712249755859375,8.684709 -319,Regression,Adaptive Random Forest,ChickWeights,10.906399564516823,17.709412566907307,0.6392184800849823,0.7230567932128906,9.367602999999999 -330,Regression,Adaptive Random Forest,ChickWeights,11.354718196032138,18.25739128586918,0.6718473555535345,0.748199462890625,10.07931 -341,Regression,Adaptive Random Forest,ChickWeights,11.709200775641314,18.709755653596346,0.691413317744465,0.7508468627929688,10.818764 -352,Regression,Adaptive Random Forest,ChickWeights,12.007404346564588,19.024465799394036,0.7099648583835785,0.7794418334960938,11.583692 -363,Regression,Adaptive Random Forest,ChickWeights,12.612646669962949,20.078004824306742,0.6863045708062432,0.8219375610351562,12.374611 -374,Regression,Adaptive Random Forest,ChickWeights,13.305874945245664,21.69198202373745,0.6634013148524545,0.8368568420410156,13.19172 -385,Regression,Adaptive Random Forest,ChickWeights,13.874152914620149,22.398641632517744,0.6824761966895349,0.8399162292480469,14.039444 -396,Regression,Adaptive Random Forest,ChickWeights,14.02164789427361,22.45496751107478,0.7024524705388633,0.8451423645019531,14.916421999999999 -407,Regression,Adaptive Random Forest,ChickWeights,14.881080524519055,23.990711305579165,0.684297135752792,0.844085693359375,15.821423999999999 -418,Regression,Adaptive Random Forest,ChickWeights,15.672731015633323,25.84901428202966,0.6559576619146927,0.8621559143066406,16.75642 -429,Regression,Adaptive Random Forest,ChickWeights,16.52730145447373,27.035273550157452,0.6655701177301542,0.8826904296875,17.718062999999997 -440,Regression,Adaptive Random Forest,ChickWeights,16.835543678126854,27.29532071757395,0.6832339396752392,0.8854255676269531,18.709096 -451,Regression,Adaptive Random Forest,ChickWeights,17.2847401584601,27.83347178305896,0.6846223453976918,0.9156723022460938,19.725565 -462,Regression,Adaptive Random Forest,ChickWeights,18.263556988874104,29.983264015814683,0.6562480279042127,0.9476966857910156,20.769031 -473,Regression,Adaptive Random Forest,ChickWeights,19.168820167599023,31.233753579350584,0.6706197338145063,0.9631462097167969,21.841162999999998 -484,Regression,Adaptive Random Forest,ChickWeights,19.66691155441429,31.662924032965286,0.6800549958221269,0.9797592163085938,22.942597 -495,Regression,Adaptive Random Forest,ChickWeights,20.1980005296915,32.326142080110245,0.6862897411402615,1.0035591125488281,24.073218999999998 -506,Regression,Adaptive Random Forest,ChickWeights,21.038101965066165,33.861783151779306,0.6657814123530009,1.0402488708496094,25.232436 -517,Regression,Adaptive Random Forest,ChickWeights,21.79336863548733,34.905834748448235,0.6648549707242759,1.0591621398925781,26.421057 -528,Regression,Adaptive Random Forest,ChickWeights,22.71415913579816,36.0778798798642,0.6781016272298894,1.0802803039550781,27.640294 -539,Regression,Adaptive Random Forest,ChickWeights,22.973012146363907,36.13494365478664,0.690416970144236,1.1105842590332031,28.88878 -550,Regression,Adaptive Random Forest,ChickWeights,23.822482702164027,37.524990725820025,0.6764299185798421,1.15643310546875,30.168770000000002 -561,Regression,Adaptive Random Forest,ChickWeights,24.754282528765707,38.80763200620001,0.669066082535116,1.1719589233398438,31.481567000000002 -572,Regression,Adaptive Random Forest,ChickWeights,25.964764492717645,40.6034258569803,0.6648997528039327,1.186126708984375,32.828648 -20,Regression,Adaptive Random Forest,TrumpApproval,4.656631863584941,13.301513571178564,-414.0080590272913,0.20159912109375,0.060655 -40,Regression,Adaptive Random Forest,TrumpApproval,3.3092233454389866,9.514642226769206,-35.395716979334736,0.28952789306640625,0.186458 -60,Regression,Adaptive Random Forest,TrumpApproval,2.3702455482222518,7.779742234417967,-31.80504797847069,0.3228263854980469,0.354007 -80,Regression,Adaptive Random Forest,TrumpApproval,2.0011825545667588,6.767036502792006,-23.897224712465306,0.3692893981933594,0.55542 -100,Regression,Adaptive Random Forest,TrumpApproval,2.0568714899324476,6.139015712379544,-11.856454989718417,0.4076881408691406,0.79515 -120,Regression,Adaptive Random Forest,TrumpApproval,2.0030922669838476,5.651046465030358,-8.241693395555247,0.4241790771484375,1.072178 -140,Regression,Adaptive Random Forest,TrumpApproval,1.8854667406427696,5.2532308662388045,-7.2528881392181574,0.44397735595703125,1.385482 -160,Regression,Adaptive Random Forest,TrumpApproval,1.9340845560700954,4.984838031470717,-5.553334350331147,0.45578765869140625,1.738613 -180,Regression,Adaptive Random Forest,TrumpApproval,1.9596878280460812,4.757627458420989,-4.242801539132487,0.47260284423828125,2.128929 -200,Regression,Adaptive Random Forest,TrumpApproval,1.9000266951648126,4.53846274922039,-3.785084131964374,0.5018348693847656,2.555084 -220,Regression,Adaptive Random Forest,TrumpApproval,1.7756062902244436,4.331882031903988,-3.702506681895046,0.5390548706054688,3.014926 -240,Regression,Adaptive Random Forest,TrumpApproval,1.722064335127714,4.161337926832492,-3.3721758647478204,0.5583267211914062,3.510014 -260,Regression,Adaptive Random Forest,TrumpApproval,1.668741133448935,4.009095884933904,-3.112800247055813,0.5867843627929688,4.039729 -280,Regression,Adaptive Random Forest,TrumpApproval,1.596023168515899,3.86837254754146,-3.0090634555073343,0.6034698486328125,4.6068750000000005 -300,Regression,Adaptive Random Forest,TrumpApproval,1.5862781822088614,3.757725401796155,-2.72037165832895,0.6344451904296875,5.213001 -320,Regression,Adaptive Random Forest,TrumpApproval,1.5269819355711358,3.6440152095112572,-2.6718510406861706,0.6524238586425781,5.854158 -340,Regression,Adaptive Random Forest,TrumpApproval,1.4570659608871932,3.537119081966105,-2.6559352799702967,0.6771697998046875,6.534176 -360,Regression,Adaptive Random Forest,TrumpApproval,1.4064005847783463,3.441784227878547,-2.4899419787597266,0.7120590209960938,7.253771 -380,Regression,Adaptive Random Forest,TrumpApproval,1.3534816779602579,3.352627624678765,-2.426029812278527,0.7612266540527344,8.012458 -400,Regression,Adaptive Random Forest,TrumpApproval,1.3254238940946812,3.27422063813923,-2.360008106665105,0.7831077575683594,8.809453000000001 -420,Regression,Adaptive Random Forest,TrumpApproval,1.2822507927277378,3.197239276537114,-2.309899038967682,0.8153495788574219,9.645719000000001 -440,Regression,Adaptive Random Forest,TrumpApproval,1.2469389910195894,3.127027882921648,-2.108834809107039,0.8549957275390625,10.520540000000002 -460,Regression,Adaptive Random Forest,TrumpApproval,1.209007072666545,3.060196802253249,-1.8234356170996366,0.8641319274902344,11.437965000000002 -480,Regression,Adaptive Random Forest,TrumpApproval,1.173565091899577,2.996986036098964,-1.6456994146583188,0.9048995971679688,12.395296000000002 -500,Regression,Adaptive Random Forest,TrumpApproval,1.1545102154576106,2.9412312685847275,-1.494716293761022,0.9429206848144531,13.397268000000002 -520,Regression,Adaptive Random Forest,TrumpApproval,1.1235341152888954,2.885679937630878,-1.4055626483775598,0.9187583923339844,14.455292000000002 -540,Regression,Adaptive Random Forest,TrumpApproval,1.0931325909773704,2.8325152336083046,-1.3092471918321817,0.9785118103027344,15.562633000000002 -560,Regression,Adaptive Random Forest,TrumpApproval,1.080423433010604,2.7861157835692825,-1.2874470666919131,0.8416099548339844,16.721025 -580,Regression,Adaptive Random Forest,TrumpApproval,1.0538460644054608,2.73873459117484,-1.2649736085810765,0.9269638061523438,17.931443 -600,Regression,Adaptive Random Forest,TrumpApproval,1.037762850657022,2.6954140775252133,-1.1696179031883163,1.0152244567871094,19.191903 -620,Regression,Adaptive Random Forest,TrumpApproval,1.022747434499167,2.654976815796094,-1.0729218181826923,0.9687690734863281,20.501534 -640,Regression,Adaptive Random Forest,TrumpApproval,1.006861222675929,2.6151861949722415,-0.9736586966592728,0.803070068359375,21.860298999999998 -660,Regression,Adaptive Random Forest,TrumpApproval,0.9869414277387362,2.5762284524749504,-0.9015227260176624,0.7759437561035156,23.260671 -680,Regression,Adaptive Random Forest,TrumpApproval,0.966244541930286,2.538677410892474,-0.8756731719459285,0.8428535461425781,24.702724 -700,Regression,Adaptive Random Forest,TrumpApproval,0.9583039577617943,2.5060806369029174,-0.8758219540617476,0.9465827941894531,26.183847 -720,Regression,Adaptive Random Forest,TrumpApproval,0.9436352676783603,2.472672874668102,-0.8662636043278715,1.0379486083984375,27.712492 -740,Regression,Adaptive Random Forest,TrumpApproval,0.9279533445219875,2.44025252932111,-0.816552178803118,1.1120071411132812,29.286906000000002 -760,Regression,Adaptive Random Forest,TrumpApproval,0.9160723193407865,2.4102496967776017,-0.7913569678512289,1.17376708984375,30.912860000000002 -780,Regression,Adaptive Random Forest,TrumpApproval,0.8969957702252379,2.379401465215729,-0.753616871824967,1.2616004943847656,32.587144 -800,Regression,Adaptive Random Forest,TrumpApproval,0.8879951793456313,2.351379467833174,-0.7280439441517783,1.3552436828613281,34.314383 -820,Regression,Adaptive Random Forest,TrumpApproval,0.8778121160020841,2.323761508818731,-0.7062232791684944,1.4321250915527344,36.09551 -840,Regression,Adaptive Random Forest,TrumpApproval,0.8674818777939237,2.297554956673723,-0.683441607427212,1.4874954223632812,37.948423 -860,Regression,Adaptive Random Forest,TrumpApproval,0.8596582290799093,2.2724190258224723,-0.6439714465644337,1.5595359802246094,39.857894 -880,Regression,Adaptive Random Forest,TrumpApproval,0.8554226716886886,2.248810009475745,-0.601989379910626,1.6191024780273438,41.823765 -900,Regression,Adaptive Random Forest,TrumpApproval,0.8440517106498798,2.2244180426447486,-0.5740310312005918,1.1261253356933594,43.864200000000004 -920,Regression,Adaptive Random Forest,TrumpApproval,0.8347159483348575,2.2010585469199837,-0.5664676720057789,1.1678504943847656,45.946931000000006 -940,Regression,Adaptive Random Forest,TrumpApproval,0.8262269751007542,2.1790511985851073,-0.5483240613297584,1.119964599609375,48.07255500000001 -960,Regression,Adaptive Random Forest,TrumpApproval,0.8159800926650994,2.1571455676024542,-0.5332153251602936,1.1733894348144531,50.238479000000005 -980,Regression,Adaptive Random Forest,TrumpApproval,0.80675324923832,2.1360469188887925,-0.5325676025464574,1.2283477783203125,52.446400000000004 -1000,Regression,Adaptive Random Forest,TrumpApproval,0.801132918984696,2.1160276454820277,-0.5292923269414216,1.2836189270019531,54.694244000000005 -11,Regression,Adaptive Model Rules,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.019614219665527344,0.002799 -22,Regression,Adaptive Model Rules,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.021178245544433594,0.009348 -33,Regression,Adaptive Model Rules,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.02634716033935547,0.018276 -44,Regression,Adaptive Model Rules,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.027434349060058594,0.029790999999999998 -55,Regression,Adaptive Model Rules,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.034033775329589844,0.044022 -66,Regression,Adaptive Model Rules,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.043257713317871094,0.061336 -77,Regression,Adaptive Model Rules,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.04948711395263672,0.082289 -88,Regression,Adaptive Model Rules,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.05513286590576172,0.107232 -99,Regression,Adaptive Model Rules,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.05624675750732422,0.136317 -110,Regression,Adaptive Model Rules,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.057623863220214844,0.169599 -121,Regression,Adaptive Model Rules,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.05775737762451172,0.206842 -132,Regression,Adaptive Model Rules,ChickWeights,5.247611065864998,7.56430675955835,-0.07568150661018036,0.05781078338623047,0.24815199999999998 -143,Regression,Adaptive Model Rules,ChickWeights,5.0844132960442625,7.343803904848652,0.17878850148449155,0.058394432067871094,0.29376199999999997 -154,Regression,Adaptive Model Rules,ChickWeights,4.973008915037768,7.173430375731751,0.3340904988080935,0.058447837829589844,0.343499 -165,Regression,Adaptive Model Rules,ChickWeights,5.201973475639973,7.389818367745889,0.43191354366781964,0.058447837829589844,0.397279 -176,Regression,Adaptive Model Rules,ChickWeights,5.377897753885034,7.538080975572278,0.5233859928595415,0.059058189392089844,0.455134 -187,Regression,Adaptive Model Rules,ChickWeights,5.414777515271245,7.541781669769663,0.6093812059493195,0.059111595153808594,0.517245 -198,Regression,Adaptive Model Rules,ChickWeights,5.40059238519783,7.511878220104288,0.6917410630009373,0.059058189392089844,0.583523 -209,Regression,Adaptive Model Rules,ChickWeights,5.933708937482518,9.717098931216647,0.5747798982216288,0.025275230407714844,0.662886 -220,Regression,Adaptive Model Rules,ChickWeights,6.498767742677896,10.515698120348512,0.5640139167754625,0.031424522399902344,0.744596 -231,Regression,Adaptive Model Rules,ChickWeights,6.70504504336628,10.673746805737519,0.6157790851383267,0.03654003143310547,0.828906 -242,Regression,Adaptive Model Rules,ChickWeights,7.118759598962231,11.248237924166032,0.6566609789141779,0.04107189178466797,0.9161250000000001 -253,Regression,Adaptive Model Rules,ChickWeights,7.339750662382254,11.398711123846239,0.6937739359440155,0.04459667205810547,1.006337 -264,Regression,Adaptive Model Rules,ChickWeights,7.866457552558359,12.301057885719082,0.6741619364384577,0.04470348358154297,1.099545 -275,Regression,Adaptive Model Rules,ChickWeights,8.421243223038738,13.285884557144795,0.6655331879845522,0.04470348358154297,1.19574 -286,Regression,Adaptive Model Rules,ChickWeights,8.956033363560122,14.109625220244896,0.6732762786849746,0.04520702362060547,1.295357 -297,Regression,Adaptive Model Rules,ChickWeights,9.573413802719209,14.887232530340057,0.6845415314559343,0.045233726501464844,1.398184 -308,Regression,Adaptive Model Rules,ChickWeights,10.140823162094344,15.798657858475249,0.6835926538539661,0.04526042938232422,1.5040010000000001 -319,Regression,Adaptive Model Rules,ChickWeights,11.056460411651761,17.826108509419473,0.6344480842540857,0.04526042938232422,1.6128270000000002 -330,Regression,Adaptive Model Rules,ChickWeights,11.706749123156325,18.647901518188576,0.6576594092850414,0.045287132263183594,1.7247210000000002 -341,Regression,Adaptive Model Rules,ChickWeights,11.849547188265053,18.683751607733633,0.6922705096711406,0.045287132263183594,1.8398310000000002 -352,Regression,Adaptive Model Rules,ChickWeights,11.96088648820193,18.74329807265456,0.7184745225672177,0.045287132263183594,1.9580810000000002 -363,Regression,Adaptive Model Rules,ChickWeights,12.783089048199372,19.958388531582212,0.6900311672733117,0.04531383514404297,2.0793790000000003 -374,Regression,Adaptive Model Rules,ChickWeights,13.27307991721093,20.988857849066502,0.6848686892374445,0.045340538024902344,2.203704 -385,Regression,Adaptive Model Rules,ChickWeights,13.623649100869688,21.545378780740656,0.7062071700264252,0.045340538024902344,2.331151 -396,Regression,Adaptive Model Rules,ChickWeights,13.714864044781413,21.4916185882578,0.7274352207736796,0.045340538024902344,2.4617500000000003 -407,Regression,Adaptive Model Rules,ChickWeights,14.57407318940339,22.903346450438516,0.712266679293069,0.04568004608154297,2.5991990000000005 -418,Regression,Adaptive Model Rules,ChickWeights,15.311297276648313,24.25392212062312,0.6971079497894322,0.04573345184326172,2.7396560000000005 -429,Regression,Adaptive Model Rules,ChickWeights,15.833945440380871,25.12811892959106,0.7110893860103431,0.04573345184326172,2.8832220000000004 -440,Regression,Adaptive Model Rules,ChickWeights,15.995632485589844,25.20571130808328,0.7298778762133054,0.045653343200683594,3.0299210000000003 -451,Regression,Adaptive Model Rules,ChickWeights,16.482571154231422,25.77399383544894,0.7295670550023294,0.04618358612060547,3.179584 -462,Regression,Adaptive Model Rules,ChickWeights,17.556958821758087,27.82207110996992,0.7040173234911381,0.046927452087402344,3.3322830000000003 -473,Regression,Adaptive Model Rules,ChickWeights,18.31809908164516,29.103026344234387,0.7140266770057507,0.046980857849121094,3.488055 -484,Regression,Adaptive Model Rules,ChickWeights,18.645508344467764,29.39095020592674,0.7243229903014706,0.04695415496826172,3.6469690000000003 -495,Regression,Adaptive Model Rules,ChickWeights,19.076944683969508,29.900823849283483,0.7315970559213162,0.04690074920654297,3.8089630000000003 -506,Regression,Adaptive Model Rules,ChickWeights,19.9412049113122,31.299098765867257,0.7144549629170223,0.046980857849121094,3.9740900000000003 -517,Regression,Adaptive Model Rules,ChickWeights,20.652539482762663,32.28122969713156,0.7133599532452177,0.04700756072998047,4.142383000000001 -528,Regression,Adaptive Model Rules,ChickWeights,21.437431132207106,33.471760575953454,0.7229272102715563,0.04700756072998047,4.313758000000001 -539,Regression,Adaptive Model Rules,ChickWeights,21.589008276225865,33.459905509370415,0.7345566785536845,0.04700756072998047,4.488246000000001 -550,Regression,Adaptive Model Rules,ChickWeights,22.551700866868885,35.03737693702089,0.717908278090669,0.047034263610839844,4.665796000000001 -561,Regression,Adaptive Model Rules,ChickWeights,23.243872726229487,35.949191367533466,0.7160216409608307,0.04700756072998047,4.846464000000001 -572,Regression,Adaptive Model Rules,ChickWeights,24.092513885911806,37.13693189688246,0.7196752558485364,0.04695415496826172,5.030284000000001 -20,Regression,Adaptive Model Rules,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.053809165954589844,0.005953 -40,Regression,Adaptive Model Rules,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.07615184783935547,0.016156 -60,Regression,Adaptive Model Rules,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.08839702606201172,0.030248999999999998 -80,Regression,Adaptive Model Rules,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.09804439544677734,0.048479999999999995 -100,Regression,Adaptive Model Rules,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.10713481903076172,0.071134 -120,Regression,Adaptive Model Rules,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.11132335662841797,0.098322 -140,Regression,Adaptive Model Rules,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.11333751678466797,0.13009 -160,Regression,Adaptive Model Rules,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.1174459457397461,0.16650700000000002 -180,Regression,Adaptive Model Rules,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.1194601058959961,0.207686 -200,Regression,Adaptive Model Rules,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.017621994018554688,0.26294300000000004 -220,Regression,Adaptive Model Rules,TrumpApproval,2.0738811328897206,4.417664564856108,-3.890594467356201,0.035803794860839844,0.32065000000000005 -240,Regression,Adaptive Model Rules,TrumpApproval,1.9726100065438286,4.2375242409752385,-3.5337340888030546,0.041502952575683594,0.38109000000000004 -260,Regression,Adaptive Model Rules,TrumpApproval,1.8594315384151245,4.074751007989252,-3.248610147038553,0.04884624481201172,0.44441200000000003 -280,Regression,Adaptive Model Rules,TrumpApproval,1.7773205119132678,3.936654153117972,-3.1518424972300867,0.06379222869873047,0.510957 -300,Regression,Adaptive Model Rules,TrumpApproval,1.8265705896173514,3.8591002097544127,-2.923813511442849,0.0735006332397461,0.581026 -320,Regression,Adaptive Model Rules,TrumpApproval,1.7437620649419607,3.7394874649640353,-2.8667745903740336,0.08108043670654297,0.6546050000000001 -340,Regression,Adaptive Model Rules,TrumpApproval,1.7029951846067328,3.640753753244776,-2.8733054628571226,0.0861959457397461,0.7321480000000001 -360,Regression,Adaptive Model Rules,TrumpApproval,1.691125588823449,3.557868621003357,-2.729329365262769,0.09378337860107422,0.8137190000000001 -380,Regression,Adaptive Model Rules,TrumpApproval,1.641476039217788,3.4678199943963026,-2.665503107324644,0.09889888763427734,0.8994270000000001 -400,Regression,Adaptive Model Rules,TrumpApproval,1.6260112424669562,3.3952504336469187,-2.613000890937967,0.10611629486083984,0.9893730000000001 -420,Regression,Adaptive Model Rules,TrumpApproval,1.6289201270983786,3.3343146523246907,-2.599793842225358,0.11011791229248047,1.08339 -440,Regression,Adaptive Model Rules,TrumpApproval,1.6670601236468519,3.302206999442347,-2.466911261860751,0.11576366424560547,1.1816330000000002 -460,Regression,Adaptive Model Rules,TrumpApproval,1.69667104754334,3.2720484626442996,-2.2278892819413008,0.12384700775146484,1.2846220000000002 -480,Regression,Adaptive Model Rules,TrumpApproval,1.6506098779434177,3.2067821053781245,-2.029074572324324,0.13150691986083984,1.3924660000000002 -500,Regression,Adaptive Model Rules,TrumpApproval,1.6365240614669594,3.1603547309397073,-1.8802784791951508,0.07843685150146484,1.5052050000000001 -520,Regression,Adaptive Model Rules,TrumpApproval,1.6536721067944387,3.126560253923372,-1.823930193625598,0.08357906341552734,1.621512 -540,Regression,Adaptive Model Rules,TrumpApproval,1.6698160029512246,3.0946441969309766,-1.7564325082786318,0.0873861312866211,1.745676 -560,Regression,Adaptive Model Rules,TrumpApproval,1.6408841434358417,3.0465865813662636,-1.735141389893172,0.08855342864990234,1.8735 -580,Regression,Adaptive Model Rules,TrumpApproval,1.6127327645791831,2.9996113742580612,-1.7170225021123482,0.08905696868896484,2.004994 -600,Regression,Adaptive Model Rules,TrumpApproval,1.6269498006919803,2.9730823953265535,-1.6396488808638732,0.09091472625732422,2.1402289999999997 -620,Regression,Adaptive Model Rules,TrumpApproval,1.64112955570108,2.949075530135499,-1.5576036781852802,0.0923452377319336,2.2792199999999996 -640,Regression,Adaptive Model Rules,TrumpApproval,1.6562657927450177,2.9273758724267736,-1.4729982020585646,0.09443950653076172,2.4220089999999996 -660,Regression,Adaptive Model Rules,TrumpApproval,1.6610090165740414,2.900076441293188,-1.409637238697782,0.09603023529052734,2.5686609999999996 -680,Regression,Adaptive Model Rules,TrumpApproval,1.640070345532056,2.8623424740678667,-1.3844340745604549,0.09812450408935547,2.7190639999999995 -700,Regression,Adaptive Model Rules,TrumpApproval,1.6119603204138224,2.8240252200668348,-1.3819830911167421,0.10156917572021484,2.8733429999999993 -720,Regression,Adaptive Model Rules,TrumpApproval,1.589173412563986,2.7883164816052854,-1.3731423466582644,0.1027364730834961,3.0315409999999994 -740,Regression,Adaptive Model Rules,TrumpApproval,1.5872474989945902,2.762320631839069,-1.3276973292362433,0.1038503646850586,3.198810999999999 -760,Regression,Adaptive Model Rules,TrumpApproval,1.573860293324891,2.731605449949154,-1.3008801881813965,0.1038503646850586,3.3700799999999993 -780,Regression,Adaptive Model Rules,TrumpApproval,1.5672492734296428,2.7047187411026274,-1.2659143323804294,0.10642147064208984,3.5453349999999992 -800,Regression,Adaptive Model Rules,TrumpApproval,1.5527653312522924,2.676901954415756,-1.2396196471003753,0.10703182220458984,3.7246999999999995 -820,Regression,Adaptive Model Rules,TrumpApproval,1.5366430278321572,2.648967131435787,-1.2172052322327516,0.1085958480834961,3.9080949999999994 -840,Regression,Adaptive Model Rules,TrumpApproval,1.5234128351461855,2.622526466573217,-1.1933402061449065,0.11015987396240234,4.095543999999999 -860,Regression,Adaptive Model Rules,TrumpApproval,1.520997799745444,2.601206078568585,-1.1541054380753062,0.11077022552490234,4.287108999999999 -880,Regression,Adaptive Model Rules,TrumpApproval,1.4988763963538276,2.573388458013685,-1.0978034673380694,0.11124706268310547,4.482816999999999 -900,Regression,Adaptive Model Rules,TrumpApproval,1.4758663089418147,2.54594713286987,-1.061955243276925,0.11164379119873047,4.682644999999999 -920,Regression,Adaptive Model Rules,TrumpApproval,1.4629376967309233,2.523979038782575,-1.059822201667401,0.11167049407958984,4.886850999999999 -940,Regression,Adaptive Model Rules,TrumpApproval,1.4561394845136584,2.50519739840106,-1.0464959899330828,0.11270427703857422,5.100838999999999 -960,Regression,Adaptive Model Rules,TrumpApproval,1.4393535919618172,2.483254475687026,-1.0318268701719249,0.11320781707763672,5.318975999999998 -980,Regression,Adaptive Model Rules,TrumpApproval,1.4209594300543067,2.4596960058574417,-1.0321742156649796,0.11381816864013672,5.541285999999999 -1000,Regression,Adaptive Model Rules,TrumpApproval,1.4020445673510784,2.4364355463770164,-1.027485181852556,0.11442852020263672,5.767785999999998 -11,Regression,Streaming Random Patches,ChickWeights,4.674710287324511,12.709622005759085,-206.93269654300337,0.14386653900146484,0.043578 -22,Regression,Streaming Random Patches,ChickWeights,2.741934273684416,9.017856101646904,-85.12629469646626,0.16807842254638672,0.114891 -33,Regression,Streaming Random Patches,ChickWeights,2.321314094852809,7.424021720293775,-37.27897402435965,0.20960521697998047,0.217395 -44,Regression,Streaming Random Patches,ChickWeights,1.9425031371298072,6.446443185481759,-31.718029761567877,0.24174785614013672,0.352059 -55,Regression,Streaming Random Patches,ChickWeights,2.220127898780405,6.120501061993398,-6.312733162160137,0.30608272552490234,0.516621 -66,Regression,Streaming Random Patches,ChickWeights,2.3297521261863876,5.733717860182345,-1.7664593315707076,0.35672664642333984,0.719974 -77,Regression,Streaming Random Patches,ChickWeights,2.702798931003846,5.8295610878248265,-0.9166874006339529,0.3732900619506836,0.96062 -88,Regression,Streaming Random Patches,ChickWeights,2.6099619817757915,5.526618942218035,-0.5618763668879856,0.41286373138427734,1.239929 -99,Regression,Streaming Random Patches,ChickWeights,2.746820956366501,5.433915350818854,-0.2628661224764999,0.4623746871948242,1.563224 -110,Regression,Streaming Random Patches,ChickWeights,2.8880448046427323,5.393209741308231,0.131281330475993,0.5318593978881836,1.933351 -121,Regression,Streaming Random Patches,ChickWeights,3.102793618966865,5.4667968998241765,0.31256539815016504,0.5604543685913086,2.356966 -132,Regression,Streaming Random Patches,ChickWeights,3.301393875206229,5.777687225912497,0.3724425463943938,0.1946859359741211,2.8345469999999997 -143,Regression,Streaming Random Patches,ChickWeights,3.2818761969220303,5.651296511520541,0.5136946280960023,0.22883892059326172,3.343438 -154,Regression,Streaming Random Patches,ChickWeights,3.2515588511167266,5.5381909018976145,0.6030852117170243,0.2577199935913086,3.884102 -165,Regression,Streaming Random Patches,ChickWeights,3.5490619378471253,5.901472315915889,0.6377005660204649,0.26273250579833984,4.460716 -176,Regression,Streaming Random Patches,ChickWeights,3.7480992047402877,6.114989482363781,0.6863562530103127,0.29411983489990234,5.073099 -187,Regression,Streaming Random Patches,ChickWeights,3.8553217511154356,6.172883744230479,0.7383134393857906,0.3320951461791992,5.716163 -198,Regression,Streaming Random Patches,ChickWeights,4.064611732985858,6.634154210053631,0.7595694090278171,0.25274181365966797,6.400463 -209,Regression,Streaming Random Patches,ChickWeights,4.2854283677061895,7.034301545827039,0.7771654635449772,0.32021617889404297,7.116785 -220,Regression,Streaming Random Patches,ChickWeights,4.87812017832838,8.464168223538273,0.7175347810295998,0.35108089447021484,7.873189 -231,Regression,Streaming Random Patches,ChickWeights,5.139510271881338,8.709209118107985,0.7441975817879227,0.32001399993896484,8.671972 -242,Regression,Streaming Random Patches,ChickWeights,5.512568989147682,9.214318878962654,0.7696009669773758,0.3710927963256836,9.508629 -253,Regression,Streaming Random Patches,ChickWeights,5.61462831814645,9.300369065456374,0.7961404678287027,0.41926097869873047,10.381303 -264,Regression,Streaming Random Patches,ChickWeights,6.307820522062941,10.632794713133398,0.7565488951511344,0.3416013717651367,11.288641 -275,Regression,Streaming Random Patches,ChickWeights,6.788015101176612,11.834244259749067,0.7346291964126817,0.3569021224975586,12.231102 -286,Regression,Streaming Random Patches,ChickWeights,7.083325494446596,12.279756566760542,0.7525262008661281,0.39655208587646484,13.20812 -297,Regression,Streaming Random Patches,ChickWeights,7.191320030958258,12.343414248948324,0.7831373025283351,0.4258260726928711,14.218150999999999 -308,Regression,Streaming Random Patches,ChickWeights,7.797209174725968,13.278742843330225,0.7764780945249113,0.45011425018310547,15.260696 -319,Regression,Streaming Random Patches,ChickWeights,8.817827198046418,15.969694059781501,0.7066209046531168,0.4718656539916992,16.339578 -330,Regression,Streaming Random Patches,ChickWeights,9.357743221083844,16.830542617885925,0.7211345584533546,0.3236379623413086,17.459623999999998 -341,Regression,Streaming Random Patches,ChickWeights,9.597227793355115,16.978620563243194,0.7458759585671038,0.3676939010620117,18.612920999999996 -352,Regression,Streaming Random Patches,ChickWeights,9.737351596839199,17.046818432657442,0.7671306392320572,0.36295223236083984,19.795535999999995 -363,Regression,Streaming Random Patches,ChickWeights,10.474784553852622,18.420082712462506,0.7359718490826586,0.3571195602416992,21.012741999999996 -374,Regression,Streaming Random Patches,ChickWeights,11.136965297216438,20.001694167276444,0.7138145773791849,0.39987850189208984,22.263944999999996 -385,Regression,Streaming Random Patches,ChickWeights,11.663755213802826,20.723109459955122,0.7282041838356077,0.3288450241088867,23.553921999999996 -396,Regression,Streaming Random Patches,ChickWeights,11.794892571100942,20.71536814309967,0.7467690419180344,0.4024953842163086,24.874664999999997 -407,Regression,Streaming Random Patches,ChickWeights,12.7361976076631,22.396840643020628,0.7248523596518419,0.4475545883178711,26.248018999999996 -418,Regression,Streaming Random Patches,ChickWeights,13.669165628181593,24.17119440887557,0.6991706950778176,0.4968290328979492,27.657995999999997 -429,Regression,Streaming Random Patches,ChickWeights,14.21091951782044,24.907688885339496,0.7161359437094021,0.5376424789428711,29.100111 -440,Regression,Streaming Random Patches,ChickWeights,14.453337266265764,25.002813963201465,0.7342091543116155,0.5657072067260742,30.577019 -451,Regression,Streaming Random Patches,ChickWeights,14.795462221424788,25.396591749368834,0.7374288341656263,0.25832653045654297,32.092868 -462,Regression,Streaming Random Patches,ChickWeights,16.121556592117344,28.147488646444906,0.6970529793362255,0.3047628402709961,33.634783000000006 -473,Regression,Streaming Random Patches,ChickWeights,16.919755395139642,29.31210430009691,0.7099030173412568,0.3544912338256836,35.20760200000001 -484,Regression,Streaming Random Patches,ChickWeights,17.222944705236912,29.519121223412064,0.7219133474565596,0.39832592010498047,36.808400000000006 -495,Regression,Streaming Random Patches,ChickWeights,17.875632407347712,30.34841895302658,0.7235012910446976,0.43242549896240234,38.43633400000001 -506,Regression,Streaming Random Patches,ChickWeights,18.888217233083253,31.835585198521954,0.7045822227904736,0.46784114837646484,40.09290200000001 -517,Regression,Streaming Random Patches,ChickWeights,19.824673890348247,33.232962754926376,0.6962090391160322,0.49755001068115234,41.78457100000001 -528,Regression,Streaming Random Patches,ChickWeights,20.55396214529221,34.09704280994323,0.712478586733055,0.5351285934448242,43.50851800000001 -539,Regression,Streaming Random Patches,ChickWeights,20.794926737672743,34.209729391662115,0.7225264054992676,0.576685905456543,45.26814300000001 -550,Regression,Streaming Random Patches,ChickWeights,21.840113545070352,36.13121679398973,0.7000199686285788,0.4753904342651367,47.07007900000001 -561,Regression,Streaming Random Patches,ChickWeights,22.59682566601873,37.083244303938294,0.6978222813826447,0.5189352035522461,48.90757000000001 -572,Regression,Streaming Random Patches,ChickWeights,23.516206925607502,38.207190389030345,0.703284935102584,0.5585355758666992,50.782863000000006 -20,Regression,Streaming Random Patches,TrumpApproval,2.677140920600926,9.804891856735377,-224.4966127051096,0.2373647689819336,0.10297 -40,Regression,Streaming Random Patches,TrumpApproval,2.2277778132201114,7.083306817310631,-19.171465983096805,0.3270711898803711,0.289755 -60,Regression,Streaming Random Patches,TrumpApproval,1.615301860635012,5.7908261762165685,-17.175707266102673,0.34931087493896484,0.561259 -80,Regression,Streaming Random Patches,TrumpApproval,1.3541232617236425,5.026566774725167,-12.73715546617699,0.3968191146850586,0.918423 -100,Regression,Streaming Random Patches,TrumpApproval,1.2177336486572592,4.516233376106315,-5.957872973758095,0.41078853607177734,1.362708 -120,Regression,Streaming Random Patches,TrumpApproval,1.1154353306650457,4.135716354504269,-3.949887064372274,0.4562673568725586,1.881933 -140,Regression,Streaming Random Patches,TrumpApproval,1.2694693870086102,4.857381406328817,-6.05598089151511,0.20268917083740234,2.479905 -160,Regression,Streaming Random Patches,TrumpApproval,1.1977296704805873,4.553187715474859,-4.4675320024162675,0.32157421112060547,3.138645 -180,Regression,Streaming Random Patches,TrumpApproval,1.1382354235034091,4.3021116541255315,-3.2869252353070264,0.4017667770385742,3.869636 -200,Regression,Streaming Random Patches,TrumpApproval,1.0619299314905082,4.083716699099621,-2.8742107303306135,0.47729015350341797,4.674132999999999 -220,Regression,Streaming Random Patches,TrumpApproval,1.0098981716710287,3.898715068147509,-2.8090719143731833,0.5200605392456055,5.552957999999999 -240,Regression,Streaming Random Patches,TrumpApproval,0.9646445055554522,3.736185930434801,-2.5244277699140394,0.590418815612793,6.501358999999999 -260,Regression,Streaming Random Patches,TrumpApproval,0.9172332240820483,3.5913301496738597,-2.300314999970906,0.677699089050293,7.522147999999999 -280,Regression,Streaming Random Patches,TrumpApproval,0.8713372213927624,3.4617932319016353,-2.2106176891844487,0.7459287643432617,8.615362 -300,Regression,Streaming Random Patches,TrumpApproval,0.8589405406556339,3.350216865329824,-1.9572094631572514,0.8300580978393555,9.791068 -320,Regression,Streaming Random Patches,TrumpApproval,0.8332253596869799,3.2462964384589745,-1.9140771338193199,0.8134641647338867,11.051060999999999 -340,Regression,Streaming Random Patches,TrumpApproval,0.8452804825423809,3.1781241500612394,-1.9514868559348706,0.7938528060913086,12.398121 -360,Regression,Streaming Random Patches,TrumpApproval,0.8633895166787324,3.1040171399305283,-1.8385669899509445,0.8660383224487305,13.839894 -380,Regression,Streaming Random Patches,TrumpApproval,0.8513159306308952,3.0258511220091995,-1.790715802132521,0.9304952621459961,15.372487 -400,Regression,Streaming Random Patches,TrumpApproval,0.8510161564157176,2.9641796059686003,-1.7538068241726803,0.9046812057495117,17.004842 -420,Regression,Streaming Random Patches,TrumpApproval,0.8518447067444684,2.91289276041272,-1.747346648183163,0.2984609603881836,18.737382 -440,Regression,Streaming Random Patches,TrumpApproval,0.8395925714721912,2.8496340177457244,-1.5817388594042834,0.3453207015991211,20.534513 -460,Regression,Streaming Random Patches,TrumpApproval,0.8276812048623222,2.790693875069675,-1.3480296319058032,0.3807516098022461,22.405168 -480,Regression,Streaming Random Patches,TrumpApproval,0.8089244758990292,2.7333778802045163,-1.2007484949677454,0.44824886322021484,24.346508 -500,Regression,Streaming Random Patches,TrumpApproval,0.7928829832001318,2.679994202833863,-1.0712404739849797,0.49751949310302734,26.364916 -520,Regression,Streaming Random Patches,TrumpApproval,0.7968627360462042,2.655906961990266,-1.037727311679443,0.37538814544677734,28.465481 -540,Regression,Streaming Random Patches,TrumpApproval,0.7742648164612971,2.606576025481956,-0.9555400217068246,0.40636730194091797,30.635854000000002 -560,Regression,Streaming Random Patches,TrumpApproval,0.7686196547282114,2.5632583130611786,-0.9361432250468023,0.43744945526123047,32.873421 -580,Regression,Streaming Random Patches,TrumpApproval,0.7604339230100455,2.522208925126442,-0.9209911676265596,0.49677181243896484,35.174013 -600,Regression,Streaming Random Patches,TrumpApproval,0.7505205215384793,2.4816305596178174,-0.839105014250833,0.569575309753418,37.541938 -620,Regression,Streaming Random Patches,TrumpApproval,0.7415649894603582,2.4434454055931187,-0.7557664113175795,0.6584272384643555,39.976025 -640,Regression,Streaming Random Patches,TrumpApproval,0.7318408439896782,2.4068861956825156,-0.6717754169622376,0.7120962142944336,42.479937 -660,Regression,Streaming Random Patches,TrumpApproval,0.7165688958291918,2.37061113672817,-0.6101020988109156,0.8089780807495117,45.058017 -680,Regression,Streaming Random Patches,TrumpApproval,0.7019759043195819,2.336022363731008,-0.5881668313132071,0.8398160934448242,47.720249 -700,Regression,Streaming Random Patches,TrumpApproval,0.7046161229979929,2.307765546298159,-0.5906876272832315,0.9275884628295898,50.471651 -720,Regression,Streaming Random Patches,TrumpApproval,0.699791115281057,2.2802792890719745,-0.5871418479715558,0.9087285995483398,53.310919 -740,Regression,Streaming Random Patches,TrumpApproval,0.6993154625867034,2.2532668970074776,-0.5488294820654547,0.8719320297241211,56.236366 -760,Regression,Streaming Random Patches,TrumpApproval,0.6904813287783395,2.224703478329614,-0.5261679577762028,0.9295186996459961,59.248630999999996 -780,Regression,Streaming Random Patches,TrumpApproval,0.6809571474431378,2.1968956754751456,-0.4949206250779803,1.0446271896362305,62.34821899999999 -800,Regression,Streaming Random Patches,TrumpApproval,0.6801944660295238,2.1726014141558028,-0.4752630155166446,1.1031560897827148,65.54039399999999 -820,Regression,Streaming Random Patches,TrumpApproval,0.6800251543540311,2.1538267014914845,-0.46579848690850234,1.0328702926635742,68.82165799999999 -840,Regression,Streaming Random Patches,TrumpApproval,0.6756860923792902,2.1342936501586,-0.4526954783446735,0.7475957870483398,72.18837399999998 -860,Regression,Streaming Random Patches,TrumpApproval,0.6733560632024954,2.111426905397114,-0.41928475998502446,0.8119535446166992,75.62699399999998 -880,Regression,Streaming Random Patches,TrumpApproval,0.6699617254268444,2.0889651464935617,-0.38234513818638316,0.8655576705932617,79.14156799999998 -900,Regression,Streaming Random Patches,TrumpApproval,0.6641763033522209,2.070781028281848,-0.3641082228169974,0.8467855453491211,82.73512999999998 -920,Regression,Streaming Random Patches,TrumpApproval,0.6581948551182502,2.0492186837607402,-0.35779705060721523,0.9400205612182617,86.40810799999998 -940,Regression,Streaming Random Patches,TrumpApproval,0.6518145768911946,2.0285221107553344,-0.3417959776408932,0.8168668746948242,90.15720199999998 -960,Regression,Streaming Random Patches,TrumpApproval,0.6459384626205039,2.0083339154310043,-0.328972794111009,0.9120321273803711,93.98330699999998 -980,Regression,Streaming Random Patches,TrumpApproval,0.6434185155449816,1.9895342463135757,-0.3295384550375804,0.9782476425170898,97.88845799999999 -1000,Regression,Streaming Random Patches,TrumpApproval,0.6405613133684143,1.9713432953350207,-0.32730996532857737,1.0593442916870117,101.87257699999999 -11,Regression,Bagging,ChickWeights,10.961585696594295,17.742218537059934,-404.203665453531,0.1782550811767578,0.00837 -22,Regression,Bagging,ChickWeights,5.889263372306967,12.56756036562654,-166.2750319368382,0.1910533905029297,0.024461000000000004 -33,Regression,Bagging,ChickWeights,4.385855416063054,10.300574999811516,-72.68935558725912,0.22809410095214844,0.048530000000000004 -44,Regression,Bagging,ChickWeights,3.447994945519213,8.931729546653537,-61.80843214992921,0.2438068389892578,0.081553 -55,Regression,Bagging,ChickWeights,3.35253400433443,8.24824849102831,-12.280953122644297,0.2916851043701172,0.123768 -66,Regression,Bagging,ChickWeights,3.890626377385135,8.046318086318358,-4.448112248710658,0.3612346649169922,0.176552 -77,Regression,Bagging,ChickWeights,4.3386080839549335,7.968508799771546,-2.5812421382870157,0.4169635772705078,0.24268299999999998 -88,Regression,Bagging,ChickWeights,4.490657725713863,7.741140850532905,-2.064344233357061,0.4634113311767578,0.322916 -99,Regression,Bagging,ChickWeights,4.7848487383098295,7.7069639973194795,-1.5403773740733393,0.4844684600830078,0.418203 -110,Regression,Bagging,ChickWeights,4.819508170706054,7.5258766681538125,-0.6916040970280823,0.5139484405517578,0.528794 -121,Regression,Bagging,ChickWeights,4.831868630589585,7.4067264415397105,-0.26188018024053505,0.5271091461181641,0.654758 -132,Regression,Bagging,ChickWeights,4.689813959013141,7.185628463196342,0.029322522663730477,0.5355319976806641,0.796727 -143,Regression,Bagging,ChickWeights,4.575735548989308,6.988996035323699,0.25622347620189123,0.5445156097412109,0.954849 -154,Regression,Bagging,ChickWeights,4.5047654468276,6.840884466051986,0.3943998861813385,0.5490932464599609,1.128949 -165,Regression,Bagging,ChickWeights,4.788922390491115,7.112527411058041,0.4737467226899117,0.5519008636474609,1.318602 -176,Regression,Bagging,ChickWeights,4.970710142849908,7.252656159084941,0.5587960603089545,0.5291376113891602,1.527133 -187,Regression,Bagging,ChickWeights,5.027942129925975,7.276897995624471,0.6363381045701173,0.5012483596801758,1.7549830000000002 -198,Regression,Bagging,ChickWeights,5.049038224539579,7.28413559977408,0.7101491068659838,0.3717927932739258,2.012416 -209,Regression,Bagging,ChickWeights,5.250417000807892,7.635375364934655,0.7374564682191251,0.3473329544067383,2.287127 -220,Regression,Bagging,ChickWeights,5.8380227010176045,8.722095838276593,0.7000574251839753,0.3545808792114258,2.5755529999999998 -231,Regression,Bagging,ChickWeights,6.044258003678201,8.960072203538655,0.7292489015049135,0.39151477813720703,2.875072 -242,Regression,Bagging,ChickWeights,6.276524828158362,9.354066864400572,0.7625593259413487,0.4183511734008789,3.187566 -253,Regression,Bagging,ChickWeights,6.3739207187481774,9.482128758285446,0.7880944389945109,0.43993473052978516,3.5127859999999997 -264,Regression,Bagging,ChickWeights,6.979732945669537,10.70081988088543,0.7534238884125567,0.45258426666259766,3.851311 -275,Regression,Bagging,ChickWeights,7.440978636214291,11.778455343325094,0.7371253175053303,0.4610300064086914,4.203753 -286,Regression,Bagging,ChickWeights,7.722695321089189,12.251182899165162,0.7536765505554787,0.47004032135009766,4.570129 -297,Regression,Bagging,ChickWeights,7.706764271589622,12.242915074252833,0.7866542868859879,0.47332096099853516,4.950063 -308,Regression,Bagging,ChickWeights,8.246398153738868,13.148346181255997,0.7808464902384371,0.4770059585571289,5.342659 -319,Regression,Bagging,ChickWeights,9.3474227240372,15.837966045238385,0.7114408913609181,0.4802255630493164,5.749268000000001 -330,Regression,Bagging,ChickWeights,9.817205251980441,16.4984558080814,0.732030690326682,0.4911470413208008,6.171356000000001 -341,Regression,Bagging,ChickWeights,10.029686613445753,16.618597428793866,0.7565388422982208,0.49631214141845703,6.607662000000001 -352,Regression,Bagging,ChickWeights,10.183424974922595,16.72954607175143,0.7757182197717174,0.5041093826293945,7.058783000000001 -363,Regression,Bagging,ChickWeights,11.087226456936445,18.183293641227465,0.7427163510168195,0.5065813064575195,7.524030000000001 -374,Regression,Bagging,ChickWeights,11.611208342142504,19.353368352464965,0.7320664683002076,0.4931306838989258,8.007171000000001 -385,Regression,Bagging,ChickWeights,12.01727910935522,20.0211446068419,0.7463056879958478,0.4681062698364258,8.510702000000002 -396,Regression,Bagging,ChickWeights,12.181393533243165,20.04691533881536,0.7628481050767555,0.4353647232055664,9.035917000000001 -407,Regression,Bagging,ChickWeights,13.09061742270854,21.647454224788547,0.7429569103775002,0.42530155181884766,9.583169000000002 -418,Regression,Bagging,ChickWeights,13.857583790923206,23.134723844753264,0.7244169153448246,0.44881534576416016,10.147004 -429,Regression,Bagging,ChickWeights,14.415812868423222,24.060867034272455,0.7351096813998451,0.4820413589477539,10.724926 -440,Regression,Bagging,ChickWeights,14.633210713365676,24.146050913573614,0.7521125932778163,0.5092172622680664,11.318068 -451,Regression,Bagging,ChickWeights,15.133020029766048,24.73830229788057,0.7508643131645836,0.5367746353149414,11.926425 -462,Regression,Bagging,ChickWeights,16.308165732303497,27.095114472384648,0.7192825818530082,0.5905351638793945,12.55391 -473,Regression,Bagging,ChickWeights,17.097647362788752,28.427463452620437,0.7271490710401121,0.6096200942993164,13.198999 -484,Regression,Bagging,ChickWeights,17.403233741388874,28.60630060223112,0.7388459944901757,0.6277017593383789,13.862263 -495,Regression,Bagging,ChickWeights,17.845914876737442,29.12415998011611,0.7453593218363636,0.6518182754516602,14.544130000000001 -506,Regression,Bagging,ChickWeights,18.749535538879957,30.62874049978988,0.7265554777237875,0.6593713760375977,15.245298000000002 -517,Regression,Bagging,ChickWeights,19.47487310339954,31.618292560451803,0.7250121198400961,0.6093225479125977,15.967903000000002 -528,Regression,Bagging,ChickWeights,20.253849759201476,32.7838221306768,0.7341994138392889,0.6206216812133789,16.707851 -539,Regression,Bagging,ChickWeights,20.434012229678878,32.81136182644564,0.7447469767293837,0.6340532302856445,17.46759 -550,Regression,Bagging,ChickWeights,21.48271816680656,34.486522216355176,0.7267085961280999,0.6409807205200195,18.246304000000002 -561,Regression,Bagging,ChickWeights,22.214948014440754,35.44362766111904,0.7239528137855742,0.6374177932739258,19.046772 -572,Regression,Bagging,ChickWeights,23.05953013824058,36.58622235196899,0.7279275728793118,0.6435747146606445,19.865794 -20,Regression,Bagging,TrumpApproval,6.585839365171216,13.881516159864546,-450.9893611410228,0.4141368865966797,0.017776 -40,Regression,Bagging,TrumpApproval,4.363712565198605,9.938671227284793,-38.712022235644,0.5953998565673828,0.05778 -60,Regression,Bagging,TrumpApproval,3.1246200328296143,8.126568674182735,-34.79519075888522,0.7211894989013672,0.123528 -80,Regression,Bagging,TrumpApproval,2.5854221987839483,7.070456887280416,-26.179962781345687,0.8254451751708984,0.217362 -100,Regression,Bagging,TrumpApproval,2.612795341566278,6.441452396131832,-13.15439617190778,0.9287128448486328,0.340609 -120,Regression,Bagging,TrumpApproval,2.5650159676967723,5.964319967024998,-9.294746752112971,0.9687213897705078,0.493962 -140,Regression,Bagging,TrumpApproval,2.4246087892523565,5.555151474129083,-8.228790661275799,0.9892597198486328,0.677921 -160,Regression,Bagging,TrumpApproval,2.2372944440918734,5.2113826313001965,-6.162524907472281,1.0282306671142578,0.8947890000000001 -180,Regression,Bagging,TrumpApproval,2.1466345389477697,4.9537003451084844,-4.683842281929501,0.9880685806274414,1.151076 -200,Regression,Bagging,TrumpApproval,1.9892950861944354,4.704683592711833,-4.14200943628533,0.5611734390258789,1.4770729999999999 -220,Regression,Bagging,TrumpApproval,1.8962186057938935,4.4991342088580515,-4.072640393025983,0.39672183990478516,1.8417389999999998 -240,Regression,Bagging,TrumpApproval,1.803503053095204,4.314740779926486,-3.700467691841136,0.45048999786376953,2.2257529999999996 -260,Regression,Bagging,TrumpApproval,1.7080100607704198,4.14999374436193,-3.406965132392963,0.5133523941040039,2.6310909999999996 -280,Regression,Bagging,TrumpApproval,1.6655932342441322,4.017842470146439,-3.324861014485008,0.6190156936645508,3.0590589999999995 -300,Regression,Bagging,TrumpApproval,1.6385421968346507,3.904974440220909,-3.01765496812087,0.7099161148071289,3.5109909999999998 -320,Regression,Bagging,TrumpApproval,1.5504197008133211,3.781681667850968,-2.954527764504537,0.777043342590332,3.9877279999999997 -340,Regression,Bagging,TrumpApproval,1.5047182142648992,3.6774767295200856,-2.9518368180177696,0.8162164688110352,4.4912399999999995 -360,Regression,Bagging,TrumpApproval,1.5167434089128324,3.599404249538014,-2.8169122586309756,0.8943338394165039,5.022537 -380,Regression,Bagging,TrumpApproval,1.47690532831993,3.5095784857397434,-2.754312484791544,0.9456682205200195,5.5820669999999994 -400,Regression,Bagging,TrumpApproval,1.4473785178172314,3.4281100357630656,-2.683273334840131,1.0103578567504883,6.1727229999999995 -420,Regression,Bagging,TrumpApproval,1.40955398667131,3.3497394826483737,-2.633176800681623,0.9896020889282227,6.801780999999999 -440,Regression,Bagging,TrumpApproval,1.3766290707405415,3.2780180874471507,-2.4163065189839363,1.0508508682250977,7.462708999999999 -460,Regression,Bagging,TrumpApproval,1.3473843223583597,3.2105206765417766,-2.1076358108912725,1.1290063858032227,8.158304 -480,Regression,Bagging,TrumpApproval,1.3146601146346069,3.145698486393554,-1.914776430829721,1.2007226943969727,8.887782 -500,Regression,Bagging,TrumpApproval,1.2829933656636,3.0848353870921095,-1.7442697801193798,1.2468271255493164,9.6533 -520,Regression,Bagging,TrumpApproval,1.253253159364902,3.027195393916086,-1.647288417049146,1.2976083755493164,10.456286 -540,Regression,Bagging,TrumpApproval,1.221748892460797,2.9719574458877616,-1.5422080467663055,1.2989130020141602,11.302161 -560,Regression,Bagging,TrumpApproval,1.2084725532442826,2.924408360153415,-1.5201637785773734,1.2362565994262695,12.194852 -580,Regression,Bagging,TrumpApproval,1.1798260089354877,2.8751438072223436,-1.496217338554359,1.0948266983032227,13.14623 -600,Regression,Bagging,TrumpApproval,1.1697523000107772,2.832108990371036,-1.3952574337441268,0.9711389541625977,14.161434999999999 -620,Regression,Bagging,TrumpApproval,1.1550049446613664,2.791430677298195,-1.29147514335963,0.9871377944946289,15.218796 -640,Regression,Bagging,TrumpApproval,1.1314798119108302,2.748614104980716,-1.180190104825623,0.955540657043457,16.316332 -660,Regression,Bagging,TrumpApproval,1.1121913646065007,2.708986615305391,-1.102550782410015,0.9793291091918945,17.449759 -680,Regression,Bagging,TrumpApproval,1.096472283083769,2.6715401075955145,-1.0771388394715324,1.0325212478637695,18.614745 -700,Regression,Bagging,TrumpApproval,1.0884657542544798,2.639348967980305,-1.080631470653255,1.1148195266723633,19.813024 -720,Regression,Bagging,TrumpApproval,1.0667544669913536,2.603003384812668,-1.0681837571806945,1.1698732376098633,21.046602999999998 -740,Regression,Bagging,TrumpApproval,1.0459321867530782,2.5682868281355016,-1.0121733037883445,1.2092561721801758,22.315315 -760,Regression,Bagging,TrumpApproval,1.0276781933457848,2.5353029000371494,-0.9820644398878615,1.2420778274536133,23.623046 -780,Regression,Bagging,TrumpApproval,1.0094276125302497,2.50338787783782,-0.9411341749539492,1.3017473220825195,24.968597 -800,Regression,Bagging,TrumpApproval,1.0023537746213038,2.475512975868657,-0.9153129864924485,1.3318758010864258,26.357820999999998 -820,Regression,Bagging,TrumpApproval,0.9977235614586971,2.450813157375169,-0.8978992844128302,1.3786516189575195,27.784761999999997 -840,Regression,Bagging,TrumpApproval,0.9889333901170725,2.4246168260512984,-0.8747893145143344,1.416356086730957,29.251495999999996 -860,Regression,Bagging,TrumpApproval,0.9712423333697714,2.3967734443047704,-0.8288218633529623,1.4480867385864258,30.757563999999995 -880,Regression,Bagging,TrumpApproval,0.9593965092028914,2.3708401769846086,-0.7805683851951253,1.4688615798950195,32.305296999999996 -900,Regression,Bagging,TrumpApproval,0.9500350341868696,2.3460297253016638,-0.7508441641708208,1.3691072463989258,33.916340999999996 -920,Regression,Bagging,TrumpApproval,0.9362562616709212,2.321054413049932,-0.7419227486050888,1.2137422561645508,35.592853999999996 -940,Regression,Bagging,TrumpApproval,0.9293426247327622,2.2986373047272863,-0.7229310397545319,1.2394838333129883,37.31016399999999 -960,Regression,Bagging,TrumpApproval,0.9223382441623138,2.2770208564096435,-0.7083555493245846,1.2827730178833008,39.06327099999999 -980,Regression,Bagging,TrumpApproval,0.9155630176835056,2.2562776680559558,-0.7099489887647161,1.2986268997192383,40.85878199999999 -1000,Regression,Bagging,TrumpApproval,0.9044154317407225,2.234829366963466,-0.7058332444386559,1.3350114822387695,42.69044199999999 -11,Regression,Exponentially Weighted Average,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.05248260498046875,0.006254 -22,Regression,Exponentially Weighted Average,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.059535980224609375,0.016967 -33,Regression,Exponentially Weighted Average,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.07399749755859375,0.032538 -44,Regression,Exponentially Weighted Average,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.08022689819335938,0.05351499999999999 -55,Regression,Exponentially Weighted Average,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.09786605834960938,0.08048899999999999 -66,Regression,Exponentially Weighted Average,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.121429443359375,0.11473799999999999 -77,Regression,Exponentially Weighted Average,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.13842391967773438,0.15747899999999998 -88,Regression,Exponentially Weighted Average,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.15406036376953125,0.209356 -99,Regression,Exponentially Weighted Average,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.1602935791015625,0.271082 -110,Regression,Exponentially Weighted Average,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.164306640625,0.34316199999999997 -121,Regression,Exponentially Weighted Average,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.16500091552734375,0.42516599999999993 -132,Regression,Exponentially Weighted Average,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.165863037109375,0.5168919999999999 -143,Regression,Exponentially Weighted Average,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.13225841522216797,0.622087 -154,Regression,Exponentially Weighted Average,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.14046764373779297,0.736412 -165,Regression,Exponentially Weighted Average,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.1464834213256836,0.8597999999999999 -176,Regression,Exponentially Weighted Average,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.15288448333740234,0.9928599999999999 -187,Regression,Exponentially Weighted Average,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.15793132781982422,1.135584 -198,Regression,Exponentially Weighted Average,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.16001415252685547,1.287864 -209,Regression,Exponentially Weighted Average,ChickWeights,57.5645933014354,59.46206220864915,-14.922837840066967,0.12554073333740234,1.4549239999999999 -220,Regression,Exponentially Weighted Average,ChickWeights,58.69090909090908,60.81327606250582,-13.581197962556498,0.13231182098388672,1.6307399999999999 -231,Regression,Exponentially Weighted Average,ChickWeights,60.25541125541125,62.66764529032318,-12.244451024360147,0.13700389862060547,1.8153519999999999 -242,Regression,Exponentially Weighted Average,ChickWeights,62.17355371900826,65.06963847478845,-10.489760184397113,0.14158916473388672,2.009009 -253,Regression,Exponentially Weighted Average,ChickWeights,63.936758893280626,67.17295239601157,-9.634560128382748,0.14522075653076172,2.211595 -264,Regression,Exponentially Weighted Average,ChickWeights,65.10606060606062,68.57980310513724,-9.127665748505592,0.14588451385498047,2.423132 -275,Regression,Exponentially Weighted Average,ChickWeights,66.61454545454548,70.46451073219248,-8.408339126213217,0.14593791961669922,2.643805 -286,Regression,Exponentially Weighted Average,ChickWeights,68.48951048951052,72.8020594498525,-7.6983532427125105,0.1282644271850586,2.877713 -297,Regression,Exponentially Weighted Average,ChickWeights,70.55218855218858,75.3669362796119,-7.08492451355157,0.13004207611083984,3.120317 -308,Regression,Exponentially Weighted Average,ChickWeights,72.39285714285718,77.65033596401675,-6.643510181414674,0.1343069076538086,3.37158 -319,Regression,Exponentially Weighted Average,ChickWeights,73.45454545454551,79.15086186624424,-6.206879640065647,0.14070415496826172,3.631653 -330,Regression,Exponentially Weighted Average,ChickWeights,75.77878787878792,82.20832738177494,-5.653192449779911,0.14494609832763672,3.900744 -341,Regression,Exponentially Weighted Average,ChickWeights,77.92375366568919,84.89106353805269,-5.352795814687307,0.1466054916381836,4.1790140000000005 -352,Regression,Exponentially Weighted Average,ChickWeights,80.04545454545458,87.49376601169416,-5.134510311668016,0.14665889739990234,4.46626 -363,Regression,Exponentially Weighted Average,ChickWeights,80.99724517906337,88.57562798692558,-5.105139086016474,0.14615535736083984,4.762509 -374,Regression,Exponentially Weighted Average,ChickWeights,82.77807486631018,90.83029071422122,-4.901675845817959,0.15692806243896484,5.06867 -385,Regression,Exponentially Weighted Average,ChickWeights,85.1766233766234,93.99517810235533,-4.591702735915359,0.16295909881591797,5.384646 -396,Regression,Exponentially Weighted Average,ChickWeights,87.26767676767678,96.48964983485283,-4.494054297851511,0.16985607147216797,5.710516 -407,Regression,Exponentially Weighted Average,ChickWeights,89.00737100737103,98.71879502607636,-4.345544683073043,0.1741170883178711,6.048375 -418,Regression,Exponentially Weighted Average,ChickWeights,90.57416267942587,100.72635724110245,-4.224084264201084,0.17566967010498047,6.396311 -429,Regression,Exponentially Weighted Average,ChickWeights,93.12121212121215,104.19735398794236,-3.9677178403495805,0.17532634735107422,6.754458 -440,Regression,Exponentially Weighted Average,ChickWeights,95.41818181818185,107.03565676064125,-3.8710119659250104,0.15803813934326172,7.124987 -451,Regression,Exponentially Weighted Average,ChickWeights,97.16629711751665,109.07665280092142,-3.843505105397095,0.16715145111083984,7.505263 -462,Regression,Exponentially Weighted Average,ChickWeights,98.71645021645023,111.17636431671961,-3.72620239405422,0.17374706268310547,7.89547 -473,Regression,Exponentially Weighted Average,ChickWeights,101.54122621564484,115.20584573786859,-3.4812404756668593,0.17859935760498047,8.295795 -484,Regression,Exponentially Weighted Average,ChickWeights,103.77066115702482,117.90601559037043,-3.4365483842712585,0.18236827850341797,8.706368 -495,Regression,Exponentially Weighted Average,ChickWeights,106.02424242424244,120.71525892518193,-3.37467008920777,0.1661062240600586,9.13027 -506,Regression,Exponentially Weighted Average,ChickWeights,107.31620553359684,122.26004165941237,-3.356924458603192,0.17095088958740234,9.564033 -517,Regression,Exponentially Weighted Average,ChickWeights,109.39651837524178,124.91233289427785,-3.291877964737682,0.17259502410888672,10.007821 -528,Regression,Exponentially Weighted Average,ChickWeights,112.36553030303028,129.1106745698386,-3.1225038051323804,0.17818355560302734,10.461943999999999 -539,Regression,Exponentially Weighted Average,ChickWeights,114.52504638218922,131.65752925403248,-3.109734667916423,0.18294811248779297,10.926196 -550,Regression,Exponentially Weighted Average,ChickWeights,115.89999999999996,133.35909826820617,-3.0866973064470367,0.18257808685302734,11.400602999999998 -561,Regression,Exponentially Weighted Average,ChickWeights,117.86452762923346,135.8046463151548,-3.0526234314410727,0.18224620819091797,11.885435 -572,Regression,Exponentially Weighted Average,ChickWeights,120.54020979020974,139.4624607986965,-2.953338846956928,0.18338680267333984,12.380554 -20,Regression,Exponentially Weighted Average,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.1279449462890625,0.015909 -40,Regression,Exponentially Weighted Average,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.18556594848632812,0.053644 -60,Regression,Exponentially Weighted Average,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.22249984741210938,0.118601 -80,Regression,Exponentially Weighted Average,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.25476837158203125,0.215522 -100,Regression,Exponentially Weighted Average,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.28536224365234375,0.349036 -120,Regression,Exponentially Weighted Average,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.29428863525390625,0.520816 -140,Regression,Exponentially Weighted Average,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.2989387512207031,0.713599 -160,Regression,Exponentially Weighted Average,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.23287487030029297,0.934381 -180,Regression,Exponentially Weighted Average,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.25080013275146484,1.175315 -200,Regression,Exponentially Weighted Average,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.1596212387084961,1.445583 -220,Regression,Exponentially Weighted Average,TrumpApproval,39.727043136363626,39.77723500009918,-395.50198072931875,0.18315410614013672,1.734898 -240,Regression,Exponentially Weighted Average,TrumpApproval,39.56323079166665,39.61325406766278,-395.19837684116754,0.19051647186279297,2.043551 -260,Regression,Exponentially Weighted Average,TrumpApproval,39.42014538461535,39.46968290441584,-397.63185900832246,0.20207500457763672,2.3716209999999998 -280,Regression,Exponentially Weighted Average,TrumpApproval,39.33200189285712,39.37942345737111,-414.45601593500356,0.22603130340576172,2.7200119999999997 -300,Regression,Exponentially Weighted Average,TrumpApproval,39.18435719999999,39.23275803924839,-404.5402138221895,0.24378108978271484,3.0886229999999997 -320,Regression,Exponentially Weighted Average,TrumpApproval,39.13568690624999,39.1818628962716,-423.5167725219512,0.2581815719604492,3.4776649999999996 -340,Regression,Exponentially Weighted Average,TrumpApproval,39.14620944117645,39.18989510023786,-447.7943063391533,0.2493734359741211,3.8925269999999994 -360,Regression,Exponentially Weighted Average,TrumpApproval,39.24072974999997,39.28395553300239,-453.6543473793619,0.26435375213623047,4.328145999999999 -380,Regression,Exponentially Weighted Average,TrumpApproval,39.29597665789471,39.337699215460226,-470.6701690846498,0.2747945785522461,4.784845 -400,Regression,Exponentially Weighted Average,TrumpApproval,39.35730624999997,39.39781946688104,-485.4842825426507,0.2898855209350586,5.262664 -420,Regression,Exponentially Weighted Average,TrumpApproval,39.40549083333331,39.44465897881697,-502.77995042269276,0.2975950241088867,5.761624 -440,Regression,Exponentially Weighted Average,TrumpApproval,39.49730674999998,39.53710368662846,-495.9856416828035,0.30903148651123047,6.281948 -460,Regression,Exponentially Weighted Average,TrumpApproval,39.61474728260867,39.65658853240579,-473.14358309219216,0.3255758285522461,6.824272 -480,Regression,Exponentially Weighted Average,TrumpApproval,39.71032456249997,39.753047582709755,-464.4916761787406,0.34113216400146484,7.389190999999999 -500,Regression,Exponentially Weighted Average,TrumpApproval,39.80313951999997,39.84667590965187,-456.87508245086684,0.27970027923583984,7.9812069999999995 -520,Regression,Exponentially Weighted Average,TrumpApproval,39.873547134615364,39.916931033645376,-459.2932847271911,0.29067134857177734,8.594204 -540,Regression,Exponentially Weighted Average,TrumpApproval,39.94649651851849,39.98996046818772,-459.28610565666287,0.2965841293334961,9.232432 -560,Regression,Exponentially Weighted Average,TrumpApproval,39.976066142857114,40.018487723609816,-470.92618770667195,0.3019857406616211,9.891907999999999 -580,Regression,Exponentially Weighted Average,TrumpApproval,40.00338510344825,40.044755101652726,-483.2331705341176,0.30562496185302734,10.572818 -600,Regression,Exponentially Weighted Average,TrumpApproval,40.07393431666663,40.11569326301364,-479.5746686678817,0.3106412887573242,11.275212999999999 -620,Regression,Exponentially Weighted Average,TrumpApproval,40.1459417741935,40.18827077358568,-473.96334667177865,0.3147268295288086,11.999135999999998 -640,Regression,Exponentially Weighted Average,TrumpApproval,40.219438156249964,40.26249426545423,-466.8085709746123,0.3194303512573242,12.744586999999997 -660,Regression,Exponentially Weighted Average,TrumpApproval,40.28296777272724,40.32626722721455,-464.9172853497744,0.3247060775756836,13.511768999999997 -680,Regression,Exponentially Weighted Average,TrumpApproval,40.31998279411761,40.36256991107017,-473.1325264408024,0.32892894744873047,14.300919999999998 -700,Regression,Exponentially Weighted Average,TrumpApproval,40.31359012857138,40.35509446667054,-485.40526703956544,0.27628612518310547,15.116748999999999 -720,Regression,Exponentially Weighted Average,TrumpApproval,40.31730695833329,40.357915759594896,-496.1610725544049,0.2866239547729492,15.953536999999999 -740,Regression,Exponentially Weighted Average,TrumpApproval,40.36653568918915,40.407119416424955,-497.07428037101636,0.22740459442138672,16.820776 -760,Regression,Exponentially Weighted Average,TrumpApproval,40.403143671052604,40.443256311482514,-503.3712175162706,0.23808956146240234,17.708028 -780,Regression,Exponentially Weighted Average,TrumpApproval,40.44545064102563,40.48534274444009,-506.6856716110208,0.25345516204833984,18.615627999999997 -800,Regression,Exponentially Weighted Average,TrumpApproval,40.478548249999996,40.518050685964006,-512.1052117095793,0.26721858978271484,19.543924999999998 -820,Regression,Exponentially Weighted Average,TrumpApproval,40.50894034146341,40.5479845946661,-518.5068774177179,0.27524471282958984,20.493024 -840,Regression,Exponentially Weighted Average,TrumpApproval,40.5406558690476,40.579310897365986,-524.140575335229,0.28415584564208984,21.463054 -860,Regression,Exponentially Weighted Average,TrumpApproval,40.58371181395347,40.62239247493601,-524.3496319016275,0.29000377655029297,22.454245 -880,Regression,Exponentially Weighted Average,TrumpApproval,40.62855514772725,40.66738601007716,-522.897851512946,0.27073192596435547,23.471721 -900,Regression,Exponentially Weighted Average,TrumpApproval,40.664104233333326,40.702738445808535,-526.020768835918,0.2802000045776367,24.510012 -920,Regression,Exponentially Weighted Average,TrumpApproval,40.68274704347825,40.72073961991632,-535.1540147256861,0.29017162322998047,25.569536 -940,Regression,Exponentially Weighted Average,TrumpApproval,40.70972619148935,40.74737437775791,-540.4099749760601,0.29757213592529297,26.655616 -960,Regression,Exponentially Weighted Average,TrumpApproval,40.734006364583315,40.771242977826994,-546.7118652484228,0.30805492401123047,27.763053999999997 -980,Regression,Exponentially Weighted Average,TrumpApproval,40.74031829795916,40.776840159239676,-557.5026042066913,0.3134031295776367,28.892280999999997 -1000,Regression,Exponentially Weighted Average,TrumpApproval,40.75359492299998,40.78950075300399,-567.2567645513548,0.31664180755615234,30.043213999999995 -11,Regression,River MLP,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.012152671813964844,0.028772 -22,Regression,River MLP,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012152671813964844,0.079044 -33,Regression,River MLP,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.012152671813964844,0.14985700000000002 -44,Regression,River MLP,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.012152671813964844,0.237747 -55,Regression,River MLP,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.012152671813964844,0.33842500000000003 -66,Regression,River MLP,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.012152671813964844,0.452041 -77,Regression,River MLP,ChickWeights,43.46441536424646,43.6630525281676,-106.5245816691109,0.012152671813964844,0.578307 -88,Regression,River MLP,ChickWeights,43.36686847531974,43.58986852756654,-96.16251088961317,0.012152671813964844,0.71715 -99,Regression,River MLP,ChickWeights,39.3777319762956,41.29293125499619,-71.92610223487543,0.012152671813964844,0.8686729999999999 -110,Regression,River MLP,ChickWeights,36.38865389064824,39.46494228501369,-45.51654802750055,0.012152671813964844,1.032976 -121,Regression,River MLP,ChickWeights,33.47571311046551,37.663819251639204,-31.629785850276598,0.012152671813964844,1.209623 -132,Regression,River MLP,ChickWeights,30.829458151969483,36.06523958622657,-23.452489437409387,0.012152671813964844,1.398231 -143,Regression,River MLP,ChickWeights,28.694004741896396,34.66464981164902,-17.297279097398025,0.012152671813964844,1.59888 -154,Regression,River MLP,ChickWeights,26.901670019158168,33.42439644535526,-13.457346308406352,0.012152671813964844,1.811498 -165,Regression,River MLP,ChickWeights,25.73601455188179,32.42918896219868,-9.940044331027545,0.012152671813964844,2.036514 -176,Regression,River MLP,ChickWeights,24.444168946165895,31.465121114844866,-7.304318935113031,0.012152671813964844,2.273516 -187,Regression,River MLP,ChickWeights,23.31106527863985,30.560727675640003,-5.414053903472148,0.012152671813964844,2.522216 -198,Regression,River MLP,ChickWeights,22.2497659881735,29.729269659264478,-3.828220321961205,0.012152671813964844,2.782587 -209,Regression,River MLP,ChickWeights,21.58385771370322,29.10991604615937,-2.8161237021822814,0.012152671813964844,3.05463 -220,Regression,River MLP,ChickWeights,21.489125733317938,28.89967848441583,-2.2929294700983545,0.012152671813964844,3.338483 -231,Regression,River MLP,ChickWeights,20.960775268964717,28.36167354440183,-1.7127571457874735,0.012152671813964844,3.633999 -242,Regression,River MLP,ChickWeights,20.684837308385994,27.973225917526175,-1.123436519018493,0.012152671813964844,3.9411810000000003 -253,Regression,River MLP,ChickWeights,20.10836609318901,27.433189641281587,-0.7737126718723288,0.012152671813964844,4.259926 -264,Regression,River MLP,ChickWeights,20.477468707260805,27.77140109557264,-0.6607814047377214,0.012312889099121094,4.590346 -275,Regression,River MLP,ChickWeights,20.61134029899391,28.056502395486245,-0.49155454936660403,0.012312889099121094,4.932228 -286,Regression,River MLP,ChickWeights,20.562403371381592,27.861367163045813,-0.27395637869960554,0.012312889099121094,5.285550000000001 -297,Regression,River MLP,ChickWeights,20.280215853243796,27.63742758295081,-0.08720009619051883,0.012312889099121094,5.650314000000001 -308,Regression,River MLP,ChickWeights,20.449355350398,27.920149137510645,0.011807308141553507,0.012312889099121094,6.026518000000001 -319,Regression,River MLP,ChickWeights,21.501341692909694,30.009678165732634,-0.03599735953192962,0.012312889099121094,6.414170000000001 -330,Regression,River MLP,ChickWeights,21.657358056011915,30.050226639054536,0.11101595735800762,0.012312889099121094,6.813245000000001 -341,Regression,River MLP,ChickWeights,21.886687938329302,30.333453058468624,0.18888085688667844,0.012312889099121094,7.223789000000001 -352,Regression,River MLP,ChickWeights,21.886047000767114,30.34044931799507,0.2623171341711791,0.012312889099121094,7.645758000000001 -363,Regression,River MLP,ChickWeights,22.895757506339034,32.0447958992957,0.2009350122171253,0.012312889099121094,8.07951 -374,Regression,River MLP,ChickWeights,23.752495639064158,33.838912051163845,0.18088151494061133,0.012312889099121094,8.524693000000001 -385,Regression,River MLP,ChickWeights,24.560900349589296,34.753482086388026,0.2355843412503995,0.012312889099121094,8.981311000000002 -396,Regression,River MLP,ChickWeights,24.352453583923488,34.473609026370774,0.2986981406442135,0.012312889099121094,9.449373000000001 -407,Regression,River MLP,ChickWeights,25.719275464870663,37.04357441923364,0.2473067090757558,0.012312889099121094,9.928858000000002 -418,Regression,River MLP,ChickWeights,27.096550121003176,39.902565812758986,0.1801666848023049,0.012312889099121094,10.419752000000003 -429,Regression,River MLP,ChickWeights,28.343681876579897,42.0624752869508,0.19046954409903982,0.012312889099121094,10.922067000000002 -440,Regression,River MLP,ChickWeights,29.341897763641,43.65194027092424,0.18984270930397695,0.012312889099121094,11.435807000000002 -451,Regression,River MLP,ChickWeights,30.67273802938304,45.6370688200433,0.15212536617682837,0.012312889099121094,11.960959000000003 -462,Regression,River MLP,ChickWeights,32.56224145473062,49.915337115223885,0.047301683757009605,0.012312889099121094,12.497734000000003 -473,Regression,River MLP,ChickWeights,34.43415680949522,53.78695495416363,0.023205662187861797,0.012312889099121094,13.045956000000004 -484,Regression,River MLP,ChickWeights,37.59302308486845,59.431785855457115,-0.12722563369819695,0.012312889099121094,13.605608000000004 -495,Regression,River MLP,ChickWeights,39.81029832190851,63.2563754686467,-0.20123941420174463,0.012312889099121094,14.176690000000004 -506,Regression,River MLP,ChickWeights,40.81849028215024,64.7117163856453,-0.2206096222811289,0.012312889099121094,14.759208000000005 -517,Regression,River MLP,ChickWeights,42.30053541895924,66.69347450894223,-0.22349841903640066,0.012312889099121094,15.353157000000005 -528,Regression,River MLP,ChickWeights,43.87998520264807,69.22658128427689,-0.18517514283849468,0.012312889099121094,15.958328000000005 -539,Regression,River MLP,ChickWeights,45.090482136166365,70.91807128614899,-0.1924391171188431,0.012312889099121094,16.574693000000003 -550,Regression,River MLP,ChickWeights,46.166033810637174,72.79977252028388,-0.2178315347358042,0.012312889099121094,17.202244000000004 -561,Regression,River MLP,ChickWeights,47.99895001321041,75.49864048778444,-0.2525216922711382,0.012312889099121094,17.841166000000005 -572,Regression,River MLP,ChickWeights,49.57826542612398,77.90262570405233,-0.23354098460271144,0.012312889099121094,18.491295000000004 -20,Regression,River MLP,TrumpApproval,30.45103241731707,33.23585723529438,-2590.0045530336465,0.013110160827636719,0.03648 -40,Regression,River MLP,TrumpApproval,18.3406114455606,24.162855811212598,-233.72636807636488,0.013110160827636719,0.100456 -60,Regression,River MLP,TrumpApproval,14.012302795940927,20.27429916426714,-221.7932161673039,0.013110160827636719,0.19099100000000002 -80,Regression,River MLP,TrumpApproval,11.49107264681655,17.720640796103456,-169.73114207921216,0.013110160827636719,0.30723900000000004 -100,Regression,River MLP,TrumpApproval,9.726608796116105,15.913172800750537,-85.38479390162912,0.013110160827636719,0.4491740000000001 -120,Regression,River MLP,TrumpApproval,8.642897473622819,14.610205599232696,-60.77410396737057,0.013110160827636719,0.6162740000000001 -140,Regression,River MLP,TrumpApproval,7.627861449957184,13.547130531753504,-53.88423494401591,0.013110160827636719,0.8076440000000001 -160,Regression,River MLP,TrumpApproval,6.825616478903957,12.68640242718137,-41.446041076168875,0.013110160827636719,1.023068 -180,Regression,River MLP,TrumpApproval,6.298325880816026,11.99455373608459,-32.32351025206219,0.013110160827636719,1.262663 -200,Regression,River MLP,TrumpApproval,5.791221405645649,11.389261045241726,-29.134439780883504,0.013110160827636719,1.526002 -220,Regression,River MLP,TrumpApproval,5.429217208457164,10.877504011291329,-28.650681734644422,0.013110160827636719,1.812923 -240,Regression,River MLP,TrumpApproval,5.060396678997669,10.421476408956162,-26.4214333861576,0.013110160827636719,2.123256 -260,Regression,River MLP,TrumpApproval,4.7577579662422185,10.01970369652003,-24.68943115642662,0.013350486755371094,2.456738 -280,Regression,River MLP,TrumpApproval,4.47898479527949,9.659781964354623,-23.99890538466576,0.013350486755371094,2.813161 -300,Regression,River MLP,TrumpApproval,4.231628573868652,9.33546881214548,-21.961936464609707,0.013350486755371094,3.192823 -320,Regression,River MLP,TrumpApproval,4.032288217093912,9.046394584789834,-21.629541054113563,0.013350486755371094,3.5954460000000004 -340,Regression,River MLP,TrumpApproval,3.8351551756309497,8.779241117397504,-21.522318216666985,0.013350486755371094,4.021045 -360,Regression,River MLP,TrumpApproval,3.660607443401045,8.536652519215579,-20.469707840891584,0.013350486755371094,4.469678 -380,Regression,River MLP,TrumpApproval,3.5052706051205775,8.311598150636895,-20.056664379019765,0.013350486755371094,4.941134 -400,Regression,River MLP,TrumpApproval,3.360453882251005,8.10307441673556,-19.578991923208985,0.013350486755371094,5.435716 -420,Regression,River MLP,TrumpApproval,3.230603986740656,7.909485894846834,-19.256340082684435,0.013350486755371094,5.953156 -440,Regression,River MLP,TrumpApproval,3.1198347345540465,7.731010347257149,-18.002320884204543,0.013350486755371094,6.493473 -460,Regression,River MLP,TrumpApproval,3.010353198494384,7.5628771106824955,-16.244605442867506,0.013350486755371094,7.056611999999999 -480,Regression,River MLP,TrumpApproval,2.9023580604284582,7.404341650720478,-15.148937801753288,0.013350486755371094,7.642589999999999 -500,Regression,River MLP,TrumpApproval,2.8120368518109427,7.256642134427339,-14.185679409391113,0.013350486755371094,8.251631 -520,Regression,River MLP,TrumpApproval,2.722217494708863,7.11705765703134,-13.632593906904136,0.013350486755371094,8.883484 -540,Regression,River MLP,TrumpApproval,2.6379600337769933,6.984812005374008,-13.042206620144155,0.013350486755371094,9.537690999999999 -560,Regression,River MLP,TrumpApproval,2.5735320859360358,6.8629311186902635,-12.879442173523902,0.013350486755371094,10.214296 -580,Regression,River MLP,TrumpApproval,2.508294944253365,6.745457437445754,-12.739978855164816,0.013350486755371094,10.913295999999999 -600,Regression,River MLP,TrumpApproval,2.4431260057234407,6.633435680824833,-12.140422111767565,0.013350486755371094,11.634953999999999 -620,Regression,River MLP,TrumpApproval,2.386700775516181,6.528113194197335,-11.53247390884172,0.013350486755371094,12.379039999999998 -640,Regression,River MLP,TrumpApproval,2.3242014075410804,6.426013651213371,-10.916538218303476,0.013350486755371094,13.145560999999997 -660,Regression,River MLP,TrumpApproval,2.2685968162113417,6.328883008296775,-10.475904112331508,0.013350486755371094,13.934486999999997 -680,Regression,River MLP,TrumpApproval,2.2077966601853354,6.2353065982608955,-10.31508330333358,0.013350486755371094,14.745848999999998 -700,Regression,River MLP,TrumpApproval,2.1581493026656715,6.146460408814674,-10.283704638807178,0.013350486755371094,15.579853999999997 -720,Regression,River MLP,TrumpApproval,2.1027567473920863,6.060643545524585,-10.211846444382044,0.013350486755371094,16.436290999999997 -740,Regression,River MLP,TrumpApproval,2.051121816682194,5.978350933609188,-9.90287777753909,0.013350486755371094,17.315177999999996 -760,Regression,River MLP,TrumpApproval,2.003155134641025,5.899358540561457,-9.731678337792127,0.013350486755371094,18.216513999999997 -780,Regression,River MLP,TrumpApproval,1.9581628436412764,5.823548668044576,-9.504483044737295,0.013350486755371094,19.140318999999998 -800,Regression,River MLP,TrumpApproval,1.920522972774301,5.751090425279772,-9.337362153707343,0.013350486755371094,20.086782999999997 -820,Regression,River MLP,TrumpApproval,1.884378546616626,5.681435320930898,-9.199265318679966,0.013350486755371094,21.055681999999997 -840,Regression,River MLP,TrumpApproval,1.8462172344878474,5.613649762186833,-9.049787223051387,0.013350486755371094,22.047026999999996 -860,Regression,River MLP,TrumpApproval,1.8114873001009923,5.548504609880171,-8.800976301633217,0.013350486755371094,23.060785999999997 -880,Regression,River MLP,TrumpApproval,1.7737427907909609,5.485176730961097,-8.530931568707373,0.013350486755371094,24.096957999999997 -900,Regression,River MLP,TrumpApproval,1.7378742216236305,5.423968591721732,-8.358684442608821,0.013350486755371094,25.155794999999998 -920,Regression,River MLP,TrumpApproval,1.7058202733924563,5.364926267836746,-8.30648673172487,0.013350486755371094,26.237104 -940,Regression,River MLP,TrumpApproval,1.6771033983092818,5.307938337650396,-8.187106100071555,0.013350486755371094,27.340904 -960,Regression,River MLP,TrumpApproval,1.6470899020587193,5.252615019014566,-8.09065943244505,0.013350486755371094,28.467202999999998 -980,Regression,River MLP,TrumpApproval,1.6179664800066769,5.198922142498095,-8.078721464420347,0.013350486755371094,29.615951999999997 -1000,Regression,River MLP,TrumpApproval,1.591389315944533,5.1470018023734205,-8.048080908594342,0.013350486755371094,30.787286999999996 -11,Regression,[baseline] Mean predictor,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.0004901885986328125,0.002154 -22,Regression,[baseline] Mean predictor,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.0004901885986328125,0.005944 -33,Regression,[baseline] Mean predictor,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.0004901885986328125,0.010037 -44,Regression,[baseline] Mean predictor,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.0004901885986328125,0.014469000000000001 -55,Regression,[baseline] Mean predictor,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.0004901885986328125,0.019181 -66,Regression,[baseline] Mean predictor,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.0004901885986328125,0.024182000000000002 -77,Regression,[baseline] Mean predictor,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.0004901885986328125,0.029453 -88,Regression,[baseline] Mean predictor,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.0004901885986328125,0.034986 -99,Regression,[baseline] Mean predictor,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.0004901885986328125,0.040781000000000005 -110,Regression,[baseline] Mean predictor,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.0004901885986328125,0.046845000000000005 -121,Regression,[baseline] Mean predictor,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.0004901885986328125,0.053171 -132,Regression,[baseline] Mean predictor,ChickWeights,5.875758254207378,8.194624755054596,-0.2624191661321591,0.0004901885986328125,0.05976 -143,Regression,[baseline] Mean predictor,ChickWeights,6.530760796045927,8.870097879563003,-0.19803554240449484,0.0004901885986328125,0.066612 -154,Regression,[baseline] Mean predictor,ChickWeights,7.121466111912466,9.458403141043558,-0.15770278521517955,0.0004901885986328125,0.073726 -165,Regression,[baseline] Mean predictor,ChickWeights,7.772438504082036,10.375670403553157,-0.11989999304508925,0.0004901885986328125,0.081109 -176,Regression,[baseline] Mean predictor,ChickWeights,8.565827130563894,11.410434180005831,-0.09206765686265328,0.0004901885986328125,0.088752 -187,Regression,[baseline] Mean predictor,ChickWeights,9.429958588641576,12.495061319237752,-0.07221531716282037,0.0004901885986328125,0.096665 -198,Regression,[baseline] Mean predictor,ChickWeights,10.47731537859646,13.900491647656429,-0.05555027037575888,0.0004901885986328125,0.104851 -209,Regression,[baseline] Mean predictor,ChickWeights,11.43172675762076,15.229123619635446,-0.04445651287163721,0.0004901885986328125,0.1133 -220,Regression,[baseline] Mean predictor,ChickWeights,11.974320980081139,16.22368260926648,-0.03775608698471111,0.0004901885986328125,0.122025 -231,Regression,[baseline] Mean predictor,ChickWeights,12.9382196746461,17.489503190785292,-0.03157819728271183,0.0004901885986328125,0.131017 -242,Regression,[baseline] Mean predictor,ChickWeights,14.229204186206863,19.43725798629848,-0.02523677186741935,0.0004901885986328125,0.140272 -253,Regression,[baseline] Mean predictor,ChickWeights,15.339413196393396,20.820238312545918,-0.021649789303838762,0.0004901885986328125,0.149788 -264,Regression,[baseline] Mean predictor,ChickWeights,15.948617107030818,21.75817315507082,-0.019440185124094622,0.0004901885986328125,0.159572 -275,Regression,[baseline] Mean predictor,ChickWeights,16.794155127707494,23.16724301729152,-0.016999619323781356,0.0004901885986328125,0.169617 -286,Regression,[baseline] Mean predictor,ChickWeights,17.990009992534457,24.865985915258104,-0.014754713395529917,0.0004901885986328125,0.17992 -297,Regression,[baseline] Mean predictor,ChickWeights,19.34919450213405,26.676209297603677,-0.012890456560007202,0.0004901885986328125,0.190491 -308,Regression,[baseline] Mean predictor,ChickWeights,20.46881241431745,28.248013022827834,-0.011537481517321035,0.0004901885986328125,0.201321 -319,Regression,[baseline] Mean predictor,ChickWeights,20.993702124162965,29.638141143499492,-0.010503673119392376,0.0004901885986328125,0.212414 -330,Regression,[baseline] Mean predictor,ChickWeights,22.586872779548433,32.01796640002603,-0.009220237952050514,0.0004901885986328125,0.223765 -341,Regression,[baseline] Mean predictor,ChickWeights,23.973458872107372,33.821533603903084,-0.008387701903732392,0.0004901885986328125,0.235368 -352,Regression,[baseline] Mean predictor,ChickWeights,25.315991788770976,35.461698606860665,-0.007731302158646702,0.0004901885986328125,0.247225 -363,Regression,[baseline] Mean predictor,ChickWeights,25.615062978866305,35.981300981590465,-0.007443749031205149,0.0004901885986328125,0.259337 -374,Regression,[baseline] Mean predictor,ChickWeights,26.673321526932543,37.51836715700961,-0.006935846124255907,0.0004901885986328125,0.271713 -385,Regression,[baseline] Mean predictor,ChickWeights,28.27694482780972,39.8753298933956,-0.006332510983879436,0.0004901885986328125,0.28435499999999997 -396,Regression,[baseline] Mean predictor,ChickWeights,29.55612496209691,41.288487059450155,-0.005980181891907188,0.0004901885986328125,0.29720599999999997 -407,Regression,[baseline] Mean predictor,ChickWeights,30.561677112682855,42.81802042618151,-0.005646723150046551,0.0004901885986328125,0.31025099999999994 -418,Regression,[baseline] Mean predictor,ChickWeights,31.39346669137945,44.18765357092498,-0.0053697143301307815,0.0004901885986328125,0.3234889999999999 -429,Regression,[baseline] Mean predictor,ChickWeights,33.10612890637694,46.865579751152914,-0.004966366070605188,0.0004901885986328125,0.33691999999999994 -440,Regression,[baseline] Mean predictor,ChickWeights,34.54914638861108,48.61167278858254,-0.004716123854972665,0.0004901885986328125,0.35054899999999994 -451,Regression,[baseline] Mean predictor,ChickWeights,35.43263419295921,49.67507127970072,-0.004553693807187953,0.0004901885986328125,0.36437299999999995 -462,Regression,[baseline] Mean predictor,ChickWeights,36.308550382896186,51.2507761435036,-0.004357377489546899,0.0004901885986328125,0.3783909999999999 -473,Regression,[baseline] Mean predictor,ChickWeights,38.26330298063241,54.532250497281034,-0.004051661204895529,0.0004901885986328125,0.3926029999999999 -484,Regression,[baseline] Mean predictor,ChickWeights,39.598662348008276,56.08659790201894,-0.0039023944795495424,0.0004901885986328125,0.4070059999999999 -495,Regression,[baseline] Mean predictor,ChickWeights,40.94697327298068,57.823326559810994,-0.0037535911132069444,0.0004901885986328125,0.4215999999999999 -506,Regression,[baseline] Mean predictor,ChickWeights,41.42384714758024,58.679845942015916,-0.003665234721119459,0.0004901885986328125,0.4363849999999999 -517,Regression,[baseline] Mean predictor,ChickWeights,42.72663002099646,60.40151056768402,-0.0035345422299792872,0.0004901885986328125,0.45136399999999993 -528,Regression,[baseline] Mean predictor,ChickWeights,44.77321528369677,63.69509749878913,-0.0033415055563215112,0.0004901885986328125,0.4665329999999999 -539,Regression,[baseline] Mean predictor,ChickWeights,45.99579764939489,65.0494992510053,-0.0032526095626370655,0.0004901885986328125,0.4818929999999999 -550,Regression,[baseline] Mean predictor,ChickWeights,46.57020777663759,66.07332710234044,-0.0031815200825582313,0.0004901885986328125,0.4974459999999999 -561,Regression,[baseline] Mean predictor,ChickWeights,47.758257606406204,67.5643396193493,-0.0030950009187136196,0.0004901885986328125,0.5131919999999999 -572,Regression,[baseline] Mean predictor,ChickWeights,49.49138874897682,70.24569214117749,-0.002971942406188699,0.0004901885986328125,0.5291269999999999 -20,Regression,[baseline] Mean predictor,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.0004901885986328125,0.003379 -40,Regression,[baseline] Mean predictor,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.0004901885986328125,0.008178999999999999 -60,Regression,[baseline] Mean predictor,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.0004901885986328125,0.013590999999999999 -80,Regression,[baseline] Mean predictor,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.0004901885986328125,0.019584999999999998 -100,Regression,[baseline] Mean predictor,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.0004901885986328125,0.026254999999999997 -120,Regression,[baseline] Mean predictor,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.0004901885986328125,0.033502 -140,Regression,[baseline] Mean predictor,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.0004901885986328125,0.041316 -160,Regression,[baseline] Mean predictor,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.0004901885986328125,0.049694999999999996 -180,Regression,[baseline] Mean predictor,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.0004901885986328125,0.058637999999999996 -200,Regression,[baseline] Mean predictor,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.0004901885986328125,0.06823299999999999 -220,Regression,[baseline] Mean predictor,TrumpApproval,1.9409519346841397,3.56902990398404,-2.19210047340805,0.0004901885986328125,0.078397 -240,Regression,[baseline] Mean predictor,TrumpApproval,1.9366756524315063,3.4612902974772624,-2.024876884626847,0.0004901885986328125,0.089124 -260,Regression,[baseline] Mean predictor,TrumpApproval,1.9250039777458068,3.363327951159923,-1.8945640461454523,0.0004901885986328125,0.10041399999999999 -280,Regression,[baseline] Mean predictor,TrumpApproval,1.8726934920539138,3.257010428159885,-1.8420037280027222,0.0004901885986328125,0.11227 -300,Regression,[baseline] Mean predictor,TrumpApproval,1.8907476896224937,3.1958821895815714,-1.6910252267675165,0.0004901885986328125,0.124747 -320,Regression,[baseline] Mean predictor,TrumpApproval,1.819623890420079,3.103812605138666,-1.663886258690169,0.0004901885986328125,0.137792 -340,Regression,[baseline] Mean predictor,TrumpApproval,1.7396293145937214,3.014220627768389,-1.654906383755708,0.0004901885986328125,0.151284 -360,Regression,[baseline] Mean predictor,TrumpApproval,1.7350691203787965,2.9569384317632506,-1.5759385016835008,0.0004901885986328125,0.16520200000000002 -380,Regression,[baseline] Mean predictor,TrumpApproval,1.6987131960417108,2.8893997308323693,-1.5446951110541192,0.0004901885986328125,0.179547 -400,Regression,[baseline] Mean predictor,TrumpApproval,1.673610627740774,2.82935583501861,-1.5089937655143242,0.0004901885986328125,0.194363 -420,Regression,[baseline] Mean predictor,TrumpApproval,1.6410137122925974,2.7701802079251965,-1.484737486096575,0.0004901885986328125,0.209602 -440,Regression,[baseline] Mean predictor,TrumpApproval,1.6565972573555454,2.7427790467379385,-1.391750010744973,0.0004901885986328125,0.22526800000000002 -460,Regression,[baseline] Mean predictor,TrumpApproval,1.699464840115161,2.7394674040138396,-1.2626191030939884,0.0004901885986328125,0.24135600000000001 -480,Regression,[baseline] Mean predictor,TrumpApproval,1.7224824441896143,2.7219018737730583,-1.182307732575659,0.0004901885986328125,0.25786400000000004 -500,Regression,[baseline] Mean predictor,TrumpApproval,1.7446092142173422,2.7058035442295596,-1.1113262021905803,0.0004901885986328125,0.27478900000000006 -520,Regression,[baseline] Mean predictor,TrumpApproval,1.7464998751860934,2.677192702589883,-1.0705208906620065,0.0004901885986328125,0.29219700000000004 -540,Regression,[baseline] Mean predictor,TrumpApproval,1.7535492786865425,2.653885630983747,-1.0271707062792519,0.0004901885986328125,0.31002500000000005 -560,Regression,[baseline] Mean predictor,TrumpApproval,1.7201019899937544,2.614359234374483,-1.0141103337708768,0.0004901885986328125,0.32827100000000003 -580,Regression,[baseline] Mean predictor,TrumpApproval,1.6887559504032665,2.5757257291728384,-1.0033760803823184,0.0004901885986328125,0.34693300000000005 -600,Regression,[baseline] Mean predictor,TrumpApproval,1.701917368353294,2.5614247637328695,-0.9592753712060649,0.0004901885986328125,0.36605800000000005 -620,Regression,[baseline] Mean predictor,TrumpApproval,1.7178157166185173,2.5513468959681562,-0.9142580419512063,0.0004901885986328125,0.38561000000000006 -640,Regression,[baseline] Mean predictor,TrumpApproval,1.7365901196485038,2.545046385321895,-0.8692105635365064,0.0004901885986328125,0.40558200000000005 -660,Regression,[baseline] Mean predictor,TrumpApproval,1.7465677425181807,2.532051562790666,-0.8368676529707118,0.0004901885986328125,0.42596800000000007 -680,Regression,[baseline] Mean predictor,TrumpApproval,1.731617734826669,2.5042261861708606,-0.8251107974736909,0.0004901885986328125,0.44678300000000004 -700,Regression,[baseline] Mean predictor,TrumpApproval,1.6973720107412233,2.4702678919797196,-0.8225927549994396,0.0004901885986328125,0.468073 -720,Regression,[baseline] Mean predictor,TrumpApproval,1.6698372433333928,2.4400355004771073,-0.81732226470892,0.0004901885986328125,0.489788 -740,Regression,[baseline] Mean predictor,TrumpApproval,1.6732482399922957,2.425592833263792,-0.7947920429290933,0.0004901885986328125,0.511921 -760,Regression,[baseline] Mean predictor,TrumpApproval,1.6653913599894004,2.404136439714782,-0.7822814452716051,0.0004901885986328125,0.53447 -780,Regression,[baseline] Mean predictor,TrumpApproval,1.6644612180457288,2.387561393188575,-0.7656652158374817,0.0004901885986328125,0.557436 -800,Regression,[baseline] Mean predictor,TrumpApproval,1.6556359332933146,2.368497267913513,-0.7532954885990883,0.0004901885986328125,0.580851 -820,Regression,[baseline] Mean predictor,TrumpApproval,1.6452077788467467,2.348678653798561,-0.7430103139622937,0.0004901885986328125,0.604682 -840,Regression,[baseline] Mean predictor,TrumpApproval,1.6374623223784903,2.3305035344735936,-0.7320713255917544,0.0004901885986328125,0.6289220000000001 -860,Regression,[baseline] Mean predictor,TrumpApproval,1.6419505315856449,2.3202080137162757,-0.7138439732116804,0.0004901885986328125,0.653572 -880,Regression,[baseline] Mean predictor,TrumpApproval,1.6490002164922652,2.3126155324510744,-0.6941855677649247,0.0004901885986328125,0.6786340000000001 -900,Regression,[baseline] Mean predictor,TrumpApproval,1.6474991175923384,2.299197536504521,-0.6816400531907807,0.0004901885986328125,0.704114 -920,Regression,[baseline] Mean predictor,TrumpApproval,1.6301006788336792,2.2779225390149764,-0.6777843948800273,0.0004901885986328125,0.730046 -940,Regression,[baseline] Mean predictor,TrumpApproval,1.6221876471839873,2.2623787372500574,-0.6690049120995847,0.0004901885986328125,0.7563869999999999 -960,Regression,[baseline] Mean predictor,TrumpApproval,1.6124120493571745,2.245866476718547,-0.6619276404267609,0.0004901885986328125,0.7831509999999999 -980,Regression,[baseline] Mean predictor,TrumpApproval,1.5867001120604314,2.223758235975506,-0.661013659831075,0.0004901885986328125,0.810322 -1000,Regression,[baseline] Mean predictor,TrumpApproval,1.5681359363812417,2.2037391763141216,-0.6587014308970958,0.0004901885986328125,0.8379 +11,Regression,Linear Regression,ChickWeights,30.432219699626994,31.267456151778337,-1257.4692714745631,0.004130363464355469,0.000963 +22,Regression,Linear Regression,ChickWeights,20.75760844034268,23.632210645041404,-590.4769976066937,0.004130363464355469,0.002374 +33,Regression,Linear Regression,ChickWeights,14.555240079240876,19.349294933329695,-259.0232069515881,0.004130363464355469,0.004113 +44,Regression,Linear Regression,ChickWeights,11.143633659136759,16.767243978820222,-220.34524244378574,0.004130363464355469,0.006175 +55,Regression,Linear Regression,ChickWeights,10.841164000616114,17.714902804136145,-60.2608923989398,0.004130363464355469,0.008581 +66,Regression,Linear Regression,ChickWeights,10.32598508406065,16.527353468164844,-21.985729074745297,0.004130363464355469,0.01133 +77,Regression,Linear Regression,ChickWeights,9.718401993814265,15.521096390186141,-12.587024696233003,0.004130363464355469,0.014424 +88,Regression,Linear Regression,ChickWeights,8.767755200283737,14.552446235427842,-9.829280875288257,0.004130363464355469,0.017858 +99,Regression,Linear Regression,ChickWeights,7.977130626229444,13.740429605807138,-7.074807888709797,0.004130363464355469,0.021634999999999998 +110,Regression,Linear Regression,ChickWeights,7.506893871110683,13.098273311725844,-4.124041411671393,0.004130363464355469,0.025751999999999997 +121,Regression,Linear Regression,ChickWeights,7.252833276832352,12.607637144454216,-2.6562249812820733,0.004130363464355469,0.030208999999999996 +132,Regression,Linear Regression,ChickWeights,6.896359231575217,12.121970224209305,-1.7624336939368233,0.004130363464355469,0.03500399999999999 +143,Regression,Linear Regression,ChickWeights,6.581914741629191,11.688367143429069,-1.080274127204615,0.004130363464355469,0.040138999999999994 +154,Regression,Linear Regression,ChickWeights,6.347682986169337,11.314945909537578,-0.6567859420078188,0.004130363464355469,0.045612999999999994 +165,Regression,Linear Regression,ChickWeights,6.47676439389405,11.21748999353191,-0.30899590760610374,0.004130363464355469,0.051426999999999994 +176,Regression,Linear Regression,ChickWeights,6.552290709218319,11.100632967129414,-0.03357189497448321,0.004130363464355469,0.05758099999999999 +187,Regression,Linear Regression,ChickWeights,6.503097179992549,10.915357728148932,0.18175912588502985,0.004130363464355469,0.06407299999999999 +198,Regression,Linear Regression,ChickWeights,6.420443618722296,10.727647067877953,0.3713230272376924,0.004130363464355469,0.070904 +209,Regression,Linear Regression,ChickWeights,6.54715053669462,10.814712106795348,0.47329133398018763,0.004130363464355469,0.07807199999999999 +220,Regression,Linear Regression,ChickWeights,7.075852889975692,11.488147441481185,0.479648982578291,0.004130363464355469,0.08557599999999999 +231,Regression,Linear Regression,ChickWeights,7.197265349840174,11.527376107145999,0.5518657524511614,0.004130363464355469,0.09341599999999999 +242,Regression,Linear Regression,ChickWeights,7.359957454348683,11.71365363090123,0.6276606533313056,0.004130363464355469,0.10159099999999999 +253,Regression,Linear Regression,ChickWeights,7.389343614466645,11.704104182671559,0.6771453727427903,0.004130363464355469,0.11010199999999999 +264,Regression,Linear Regression,ChickWeights,8.007684680730522,12.681713023454451,0.6536838584261326,0.004210472106933594,0.11894999999999999 +275,Regression,Linear Regression,ChickWeights,8.456356064016727,13.562457362384485,0.6514630282957669,0.004210472106933594,0.128137 +286,Regression,Linear Regression,ChickWeights,8.682222588679535,13.91372755183948,0.6822857451181047,0.004210472106933594,0.137663 +297,Regression,Linear Regression,ChickWeights,8.656490376145301,13.862729792291397,0.7264657185265005,0.004210472106933594,0.14752700000000002 +308,Regression,Linear Regression,ChickWeights,9.17087534181789,14.586626878398466,0.730278281446047,0.004210472106933594,0.15773800000000002 +319,Regression,Linear Regression,ChickWeights,10.253235573939358,17.040182474587255,0.6659707835095393,0.004210472106933594,0.270641 +330,Regression,Linear Regression,ChickWeights,10.67218268870669,17.597898989920818,0.6951262006904333,0.004210472106933594,0.38498 +341,Regression,Linear Regression,ChickWeights,10.865878827617594,17.684075493652397,0.7243197409220903,0.004210472106933594,0.500381 +352,Regression,Linear Regression,ChickWeights,11.014541487264223,17.788847456042067,0.7464163188501894,0.004210472106933594,0.6168239999999999 +363,Regression,Linear Regression,ChickWeights,11.893125923244742,19.14640328452056,0.7147396000186461,0.004210472106933594,0.7343709999999999 +374,Regression,Linear Regression,ChickWeights,12.40252640363099,20.24468752454989,0.7068188127948265,0.004210472106933594,0.8529599999999999 +385,Regression,Linear Regression,ChickWeights,12.78041264925886,20.84297745742841,0.7250508110390363,0.004210472106933594,0.972583 +396,Regression,Linear Regression,ChickWeights,12.908163646252072,20.82655299121286,0.7440434321899679,0.004210472106933594,1.093238 +407,Regression,Linear Regression,ChickWeights,13.78624220521945,22.297725224665914,0.7272822586077066,0.004210472106933594,1.2149269999999999 +418,Regression,Linear Regression,ChickWeights,14.56231380927385,23.732773749874315,0.7099846963904786,0.004210472106933594,1.3375199999999998 +429,Regression,Linear Regression,ChickWeights,15.109717404902197,24.642068489898374,0.7221580232945248,0.004210472106933594,1.4604629999999998 +440,Regression,Linear Regression,ChickWeights,15.287005413554732,24.721522560240437,0.7401560140604169,0.004210472106933594,1.583729 +451,Regression,Linear Regression,ChickWeights,15.806865735774078,25.331119330890413,0.7387809061287051,0.004210472106933594,1.707315 +462,Regression,Linear Regression,ChickWeights,16.912347710111163,27.450327347193873,0.7118740092210123,0.004210472106933594,1.831218 +473,Regression,Linear Regression,ChickWeights,17.68786801080465,28.748046923071918,0.7209603573249957,0.004210472106933594,1.955435 +484,Regression,Linear Regression,ChickWeights,18.02230431978895,29.040370094251127,0.7308604085348502,0.004210472106933594,2.079964 +495,Regression,Linear Regression,ChickWeights,18.476434617297652,29.565622398548214,0.7375811559076941,0.004210472106933594,2.204806 +506,Regression,Linear Regression,ChickWeights,19.368862660258834,31.016595939650866,0.7195863076124669,0.004210472106933594,2.32996 +517,Regression,Linear Regression,ChickWeights,20.093492725340727,32.00802507821089,0.7181912437784894,0.004210472106933594,2.455434 +528,Regression,Linear Regression,ChickWeights,20.883641447975457,33.20140091570763,0.727385103943677,0.004210472106933594,2.581219 +539,Regression,Linear Regression,ChickWeights,21.055940734584826,33.19901872731025,0.7386798629639011,0.004210472106933594,2.707313 +550,Regression,Linear Regression,ChickWeights,22.046658398851132,34.818142407426606,0.7214274205964286,0.004210472106933594,2.833717 +561,Regression,Linear Regression,ChickWeights,22.750150790689958,35.737018888500465,0.7193638350430389,0.004210472106933594,2.960429 +572,Regression,Linear Regression,ChickWeights,23.60149518688988,36.92142939550449,0.722919218201958,0.004210472106933594,3.087448 +578,Regression,Linear Regression,ChickWeights,23.758656678867762,37.03767126301035,0.7279537206511313,0.004210472106933594,3.2147080000000003 +20,Regression,Linear Regression,TrumpApproval,20.715375599336316,24.276120972986362,-1381.3340079163324,0.004813194274902344,0.003774 +40,Regression,Linear Regression,TrumpApproval,12.956746822999646,17.85530816845139,-127.17403450091604,0.004813194274902344,0.008234 +60,Regression,Linear Regression,TrumpApproval,10.540337295823328,15.264267507077205,-125.28803290438402,0.004813194274902344,0.013346 +80,Regression,Linear Regression,TrumpApproval,8.92648259034571,13.436420463778147,-97.15695382305036,0.004813194274902344,0.019104 +100,Regression,Linear Regression,TrumpApproval,7.5495393499287236,12.076339439187347,-48.75014684916543,0.004813194274902344,0.025552 +120,Regression,Linear Regression,TrumpApproval,6.5712666531069654,11.058195411086313,-34.388513465790076,0.004813194274902344,0.032651 +140,Regression,Linear Regression,TrumpApproval,5.868178209177549,10.265658199354172,-30.515672886293014,0.004813194274902344,0.040397 +160,Regression,Linear Regression,TrumpApproval,5.226493262391851,9.609365926739029,-23.352843972650145,0.004813194274902344,0.048786 +180,Regression,Linear Regression,TrumpApproval,4.806672346419344,9.079121174210673,-18.092824435696784,0.004813194274902344,0.057857000000000006 +200,Regression,Linear Regression,TrumpApproval,4.400421129740624,8.617551092451054,-16.252012396913173,0.004813194274902344,0.06766 +220,Regression,Linear Regression,TrumpApproval,4.083414123099576,8.223437931584808,-15.946617088642817,0.004813194274902344,0.07815899999999999 +240,Regression,Linear Regression,TrumpApproval,3.8235343884157706,7.87966547036827,-14.67643164713968,0.004813194274902344,0.08935399999999999 +260,Regression,Linear Regression,TrumpApproval,3.5733429968046226,7.572887494545769,-13.674649599158814,0.004973411560058594,0.10124299999999999 +280,Regression,Linear Regression,TrumpApproval,3.399764262602937,7.307305033384193,-13.305426773388605,0.004973411560058594,0.11382999999999999 +300,Regression,Linear Regression,TrumpApproval,3.2435269592384794,7.069212717011484,-12.166742621467943,0.004973411560058594,0.21774699999999997 +320,Regression,Linear Regression,TrumpApproval,3.1105754847518408,6.854541649824586,-11.99216513034567,0.004973411560058594,0.416803 +340,Regression,Linear Regression,TrumpApproval,2.9569354047226284,6.651479799277566,-11.928129373171446,0.004973411560058594,0.618037 +360,Regression,Linear Regression,TrumpApproval,2.8537856094930785,6.474036710445056,-11.348131391644102,0.004973411560058594,0.8214119999999999 +380,Regression,Linear Regression,TrumpApproval,2.750449728962714,6.305826559379086,-11.120053648606476,0.004973411560058594,1.026924 +400,Regression,Linear Regression,TrumpApproval,2.6634141155755278,6.151161672136967,-10.858744866397979,0.004973411560058594,1.2341469999999999 +420,Regression,Linear Regression,TrumpApproval,2.556259025339157,6.003825249623929,-10.671335514866263,0.004973411560058594,1.4420449999999998 +440,Regression,Linear Regression,TrumpApproval,2.471571610669061,5.868919367302693,-9.950915405915524,0.004973411560058594,1.6506019999999997 +460,Regression,Linear Regression,TrumpApproval,2.3796807630395826,5.740715994508566,-8.935993501779443,0.004973411560058594,1.8600329999999996 +480,Regression,Linear Regression,TrumpApproval,2.2935423272146473,5.620383847029998,-8.304713733239236,0.004973411560058594,2.0701579999999997 +500,Regression,Linear Regression,TrumpApproval,2.2170719472274363,5.50775327209046,-7.748060324415055,0.004973411560058594,2.2809679999999997 +520,Regression,Linear Regression,TrumpApproval,2.1605380581247338,5.4030519069184955,-7.433320998258445,0.004973411560058594,2.492507 +540,Regression,Linear Regression,TrumpApproval,2.093930365363914,5.302901387269021,-7.093810234661742,0.004973411560058594,2.7047299999999996 +560,Regression,Linear Regression,TrumpApproval,2.0590245226095627,5.213512799867119,-7.009651494197669,0.004973411560058594,2.9176339999999996 +580,Regression,Linear Regression,TrumpApproval,1.9976476082662873,5.1231852511763165,-6.925804791819894,0.004973411560058594,3.1312199999999994 +600,Regression,Linear Regression,TrumpApproval,1.950641059884997,5.038426259116397,-6.58092298894084,0.004973411560058594,3.3455269999999993 +620,Regression,Linear Regression,TrumpApproval,1.9139787950639096,4.959092402037442,-6.2321238970256,0.004973411560058594,3.560656999999999 +640,Regression,Linear Regression,TrumpApproval,1.8644177203659011,4.8815607080230725,-5.87676544995844,0.004973411560058594,3.776474999999999 +660,Regression,Linear Regression,TrumpApproval,1.8242147858959745,4.808190620674182,-5.62363098938706,0.004973411560058594,3.9929739999999994 +680,Regression,Linear Regression,TrumpApproval,1.7745110240786572,4.737042423784333,-5.530654039159668,0.004973411560058594,4.2677439999999995 +700,Regression,Linear Regression,TrumpApproval,1.73663030353679,4.669916427921507,-5.51357997146441,0.004973411560058594,4.544746 +720,Regression,Linear Regression,TrumpApproval,1.692679144669073,4.604703216269991,-5.472066122280332,0.004973411560058594,4.823995 +740,Regression,Linear Regression,TrumpApproval,1.6517738073879173,4.542197076044804,-5.293761488897717,0.004973411560058594,5.105493 +760,Regression,Linear Regression,TrumpApproval,1.6176019850850996,4.482676429973872,-5.196305855003581,0.004973411560058594,5.389141 +780,Regression,Linear Regression,TrumpApproval,1.5865007641193463,4.425455260516019,-5.066178353973196,0.004973411560058594,5.673523 +800,Regression,Linear Regression,TrumpApproval,1.5595678531598225,4.370690133148669,-4.970481738375755,0.004973411560058594,5.9679660000000005 +820,Regression,Linear Regression,TrumpApproval,1.5359483450738913,4.318357063182573,-4.892367885242343,0.004973411560058594,6.2645230000000005 +840,Regression,Linear Regression,TrumpApproval,1.5094802852221159,4.267276732370252,-4.807214337073276,0.004973411560058594,6.563154000000001 +860,Regression,Linear Regression,TrumpApproval,1.4815681878661566,4.217864876321593,-4.663732871777943,0.004973411560058594,6.863868000000001 +880,Regression,Linear Regression,TrumpApproval,1.4526831778170481,4.169823323470254,-4.507942651608292,0.004973411560058594,7.276079000000001 +900,Regression,Linear Regression,TrumpApproval,1.425504815240136,4.123417367951589,-4.4087270270070205,0.004973411560058594,7.880966000000001 +920,Regression,Linear Regression,TrumpApproval,1.401135420694234,4.078757160785335,-4.379153600942964,0.004973411560058594,8.488067000000001 +940,Regression,Linear Regression,TrumpApproval,1.3798894262867005,4.035722473386745,-4.310917809364017,0.004973411560058594,9.096757 +960,Regression,Linear Regression,TrumpApproval,1.3578157698337674,3.993911445090692,-4.255827563021541,0.004973411560058594,9.706153 +980,Regression,Linear Regression,TrumpApproval,1.3349554985290681,3.953168904153961,-4.2491478554421755,0.004973411560058594,10.316233 +1000,Regression,Linear Regression,TrumpApproval,1.3157385915327033,3.9139344489617316,-4.232086679588724,0.004973411560058594,10.926998000000001 +1001,Regression,Linear Regression,TrumpApproval,1.3145482000473083,3.9119809164882438,-4.230354806784151,0.004973411560058594,11.537908000000002 +11,Regression,Linear Regression with l1 regularization,ChickWeights,30.519429760441792,31.341724959881887,-1263.4547929656035,0.004361152648925781,0.001889 +22,Regression,Linear Regression with l1 regularization,ChickWeights,20.93274945698016,23.730069634788823,-595.3856524245364,0.004361152648925781,0.005264 +33,Regression,Linear Regression with l1 regularization,ChickWeights,14.671976905269485,19.432784890847977,-261.2719879213097,0.004361152648925781,0.045495999999999995 +44,Regression,Linear Regression with l1 regularization,ChickWeights,11.206218788565426,16.83704009498573,-222.1918420065333,0.004361152648925781,0.086209 +55,Regression,Linear Regression with l1 regularization,ChickWeights,10.7873677371092,17.69725945175844,-60.138926246201024,0.004361152648925781,0.127302 +66,Regression,Linear Regression with l1 regularization,ChickWeights,10.358479420064798,16.54420972880916,-22.032639310332936,0.004361152648925781,0.168766 +77,Regression,Linear Regression with l1 regularization,ChickWeights,9.753598876381378,15.536347024393615,-12.613738343052718,0.004361152648925781,0.21060099999999998 +88,Regression,Linear Regression with l1 regularization,ChickWeights,8.774706713989955,14.560860647391403,-9.841807755380493,0.004361152648925781,0.252804 +99,Regression,Linear Regression with l1 regularization,ChickWeights,7.976543403311107,13.74760854733656,-7.083247758311314,0.004361152648925781,0.295373 +110,Regression,Linear Regression with l1 regularization,ChickWeights,7.5284067705618165,13.110785837893241,-4.133835882207287,0.004361152648925781,0.338308 +121,Regression,Linear Regression with l1 regularization,ChickWeights,7.271666718491515,12.6229442838289,-2.665108536473531,0.004361152648925781,0.38161 +132,Regression,Linear Regression with l1 regularization,ChickWeights,6.91845605456336,12.134014714075713,-1.7679259750984961,0.004361152648925781,0.425278 +143,Regression,Linear Regression with l1 regularization,ChickWeights,6.610383809165891,11.700505099139125,-1.084596952740374,0.004361152648925781,0.469311 +154,Regression,Linear Regression with l1 regularization,ChickWeights,6.3485668448406924,11.31852948419668,-0.6578355548574832,0.004361152648925781,0.51371 +165,Regression,Linear Regression with l1 regularization,ChickWeights,6.473998962981321,11.222073845492618,-0.3100659276219817,0.004361152648925781,0.558476 +176,Regression,Linear Regression with l1 regularization,ChickWeights,6.543521830550948,11.096254270292283,-0.032756661210885385,0.004361152648925781,0.603607 +187,Regression,Linear Regression with l1 regularization,ChickWeights,6.493894355635018,10.908553918682982,0.18277886707380187,0.004361152648925781,0.649102 +198,Regression,Linear Regression with l1 regularization,ChickWeights,6.432058292739276,10.739983052449066,0.36987633376979445,0.004361152648925781,0.6949599999999999 +209,Regression,Linear Regression with l1 regularization,ChickWeights,6.530905166315106,10.805387069826965,0.47419925648761396,0.004361152648925781,0.74118 +220,Regression,Linear Regression with l1 regularization,ChickWeights,7.049069109840064,11.46222613381468,0.4819945238144716,0.004361152648925781,0.7877609999999999 +231,Regression,Linear Regression with l1 regularization,ChickWeights,7.185364391622807,11.520615160379734,0.5523912707049028,0.004361152648925781,0.834703 +242,Regression,Linear Regression with l1 regularization,ChickWeights,7.384443509591489,11.759466507882767,0.6247424700583044,0.004361152648925781,0.882006 +253,Regression,Linear Regression with l1 regularization,ChickWeights,7.370825288025247,11.706644644448966,0.6770052015955412,0.004361152648925781,0.929669 +264,Regression,Linear Regression with l1 regularization,ChickWeights,7.997212264968545,12.688148058774217,0.6533323093865229,0.004441261291503906,0.977694 +275,Regression,Linear Regression with l1 regularization,ChickWeights,8.45564901988644,13.583827871673952,0.6503637760490552,0.004441261291503906,1.026082 +286,Regression,Linear Regression with l1 regularization,ChickWeights,8.687395226209604,13.953064893865328,0.6804867014487179,0.004441261291503906,1.074833 +297,Regression,Linear Regression with l1 regularization,ChickWeights,8.660171229881424,13.910099225377925,0.7245931722706233,0.004441261291503906,1.1239519999999998 +308,Regression,Linear Regression with l1 regularization,ChickWeights,9.16625719191718,14.612234985526298,0.7293304097140514,0.004441261291503906,1.1734349999999998 +319,Regression,Linear Regression with l1 regularization,ChickWeights,10.250950211093048,17.0718306278326,0.664728869016383,0.004441261291503906,1.2232849999999997 +330,Regression,Linear Regression with l1 regularization,ChickWeights,10.679670450254022,17.65395670255975,0.6931807697512926,0.004441261291503906,1.3407639999999996 +341,Regression,Linear Regression with l1 regularization,ChickWeights,10.873729384474112,17.73873175202587,0.7226130148559202,0.004441261291503906,1.6983979999999996 +352,Regression,Linear Regression with l1 regularization,ChickWeights,11.018541118771262,17.831871437600412,0.745188204067577,0.004441261291503906,2.0571989999999998 +363,Regression,Linear Regression with l1 regularization,ChickWeights,11.899574150448762,19.190338217602402,0.7134289333715201,0.004441261291503906,2.417118 +374,Regression,Linear Regression with l1 regularization,ChickWeights,12.408282768986876,20.289550367060546,0.7055179762102581,0.004441261291503906,2.778154 +385,Regression,Linear Regression with l1 regularization,ChickWeights,12.788104615245373,20.897902847676004,0.7235998101431352,0.004441261291503906,3.1404569999999996 +396,Regression,Linear Regression with l1 regularization,ChickWeights,12.908222014164421,20.86950621812891,0.7429865604297317,0.004441261291503906,3.5031369999999997 +407,Regression,Linear Regression with l1 regularization,ChickWeights,13.785647364051679,22.333927174809716,0.726395986248676,0.004441261291503906,3.8661689999999997 +418,Regression,Linear Regression with l1 regularization,ChickWeights,14.562464823979756,23.771461386342615,0.709038397249883,0.004441261291503906,4.229544 +429,Regression,Linear Regression with l1 regularization,ChickWeights,15.115712915071189,24.692790084324347,0.7210130632693055,0.004441261291503906,4.59326 +440,Regression,Linear Regression with l1 regularization,ChickWeights,15.290646451171162,24.766775019882367,0.7392038606135755,0.004441261291503906,4.9573149999999995 +451,Regression,Linear Regression with l1 regularization,ChickWeights,15.806610158983217,25.370563596297366,0.7379667599208486,0.004441261291503906,5.321708999999999 +462,Regression,Linear Regression with l1 regularization,ChickWeights,16.91167446753811,27.489289014578034,0.711055524573946,0.004441261291503906,5.686440999999999 +473,Regression,Linear Regression with l1 regularization,ChickWeights,17.69453441784174,28.803034656505247,0.7198918720890418,0.004441261291503906,6.051508999999999 +484,Regression,Linear Regression with l1 regularization,ChickWeights,18.025914293879836,29.08166628667707,0.7300944167213836,0.004441261291503906,6.416912999999999 +495,Regression,Linear Regression with l1 regularization,ChickWeights,18.47687089345869,29.604201733284565,0.7368958634072684,0.004441261291503906,6.782651999999999 +506,Regression,Linear Regression with l1 regularization,ChickWeights,19.37032815671457,31.058772984483273,0.7188231637639817,0.004441261291503906,7.148725999999999 +517,Regression,Linear Regression with l1 regularization,ChickWeights,20.096649322747314,32.051830787895724,0.717419357352562,0.004441261291503906,7.515149999999999 +528,Regression,Linear Regression with l1 regularization,ChickWeights,20.88685610593147,33.24610520798377,0.7266504806846955,0.004441261291503906,7.882236999999999 +539,Regression,Linear Regression with l1 regularization,ChickWeights,21.052957054073875,33.24035912136826,0.7380286507287471,0.004441261291503906,8.25334 +550,Regression,Linear Regression with l1 regularization,ChickWeights,22.046178761536364,34.86098206113683,0.7207414968982613,0.004441261291503906,8.62558 +561,Regression,Linear Regression with l1 regularization,ChickWeights,22.751953045975853,35.78242297978339,0.7186502822700677,0.004441261291503906,8.998921 +572,Regression,Linear Regression with l1 regularization,ChickWeights,23.603432973098663,36.96472548228527,0.7222689970347711,0.004441261291503906,9.373355 +578,Regression,Linear Regression with l1 regularization,ChickWeights,23.757667537133976,37.078025255419426,0.7273605875689941,0.004441261291503906,9.748496 +20,Regression,Linear Regression with l1 regularization,TrumpApproval,20.96628233331211,24.387937149248955,-1394.0974368768457,0.005043983459472656,0.003367 +40,Regression,Linear Regression with l1 regularization,TrumpApproval,12.95809265443779,17.886947111698607,-127.62867621055315,0.005043983459472656,0.060679000000000004 +60,Regression,Linear Regression with l1 regularization,TrumpApproval,10.43403375286247,15.198987179765494,-124.2101566950438,0.005043983459472656,0.118829 +80,Regression,Linear Regression with l1 regularization,TrumpApproval,8.76952679896777,13.348146279436204,-95.87145335263979,0.005043983459472656,0.177742 +100,Regression,Linear Regression with l1 regularization,TrumpApproval,7.318348711169017,11.969856517585775,-47.87667264392048,0.005043983459472656,0.237441 +120,Regression,Linear Regression with l1 regularization,TrumpApproval,6.2853039116310185,10.94189036106609,-33.648027646243705,0.005043983459472656,0.297866 +140,Regression,Linear Regression with l1 regularization,TrumpApproval,5.5208355911538485,10.138862242229527,-29.74195117722151,0.005043983459472656,0.35901700000000003 +160,Regression,Linear Regression with l1 regularization,TrumpApproval,4.9080595636493145,9.487746704217276,-22.740310036230184,0.005043983459472656,0.420891 +180,Regression,Linear Regression with l1 regularization,TrumpApproval,4.437342628193194,8.948953859899,-17.549281500204398,0.005043983459472656,0.483487 +200,Regression,Linear Regression with l1 regularization,TrumpApproval,4.020740144728086,8.490404067975657,-15.746680942149272,0.005043983459472656,0.546844 +220,Regression,Linear Regression with l1 regularization,TrumpApproval,3.702540763677515,8.09713522450445,-15.430052960036054,0.005043983459472656,0.610978 +240,Regression,Linear Regression with l1 regularization,TrumpApproval,3.449057445346116,7.7551931287900455,-14.185073150160106,0.005043983459472656,0.675884 +260,Regression,Linear Regression with l1 regularization,TrumpApproval,3.201640426877581,7.451485247160068,-13.20791735379428,0.005204200744628906,0.741568 +280,Regression,Linear Regression with l1 regularization,TrumpApproval,2.9861522146348123,7.180696949733205,-12.814002869999907,0.005204200744628906,0.808037 +300,Regression,Linear Regression with l1 regularization,TrumpApproval,2.8260389726991693,6.939608203297966,-11.688379207589731,0.005204200744628906,0.926618 +320,Regression,Linear Regression with l1 regularization,TrumpApproval,2.694730270614988,6.722171113188908,-11.495217468089896,0.005204200744628906,1.2155880000000001 +340,Regression,Linear Regression with l1 regularization,TrumpApproval,2.572442774284147,6.524300196624447,-11.438471282384336,0.005204200744628906,1.5070640000000002 +360,Regression,Linear Regression with l1 regularization,TrumpApproval,2.4832798669216825,6.3452949037256134,-10.86190793294698,0.005204200744628906,1.8008990000000002 +380,Regression,Linear Regression with l1 regularization,TrumpApproval,2.371542642654472,6.177015076243767,-10.629949316856383,0.005204200744628906,2.0970690000000003 +400,Regression,Linear Regression with l1 regularization,TrumpApproval,2.263251524870982,6.020874949010495,-10.361708857360679,0.005204200744628906,2.4016 +420,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1669018257777095,5.8760767656227735,-10.179937857653263,0.005204200744628906,2.706944 +440,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1025509089011916,5.743886676480224,-9.489284487381322,0.005204200744628906,3.013076 +460,Regression,Linear Regression with l1 regularization,TrumpApproval,2.0360304025506277,5.61908468135586,-8.519416508014515,0.005204200744628906,3.319991 +480,Regression,Linear Regression with l1 regularization,TrumpApproval,1.9657178079674962,5.50138701729293,-7.914879120336785,0.005204200744628906,3.627685 +500,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8948913466896102,5.390446783732167,-7.379388774419297,0.005204200744628906,3.9361800000000002 +520,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8304411336225566,5.286008256480869,-7.071904701569496,0.005204200744628906,4.245555 +540,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7733791235095338,5.187623645241403,-6.74573862520947,0.005204200744628906,4.555757000000001 +560,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7328732375480083,5.096231477200102,-6.653340289034931,0.005204200744628906,4.866786000000001 +580,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6922671720641331,5.009032279128942,-6.5765398617523605,0.005204200744628906,5.1996410000000015 +600,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6600221636451293,4.9270067527590165,-6.249341959517198,0.005204200744628906,5.545038000000002 +620,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6169171465584515,4.847662648980224,-5.910766757861972,0.005204200744628906,5.892753000000002 +640,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5787668849144931,4.771995268006674,-5.5715350899413965,0.005204200744628906,6.242777000000002 +660,Regression,Linear Regression with l1 regularization,TrumpApproval,1.535700232104731,4.69925054984221,-5.326885534626132,0.005204200744628906,6.599796000000002 +680,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5003699975160405,4.630081239411466,-5.239062722957792,0.005204200744628906,6.957619000000002 +700,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4782734303433982,4.565354365023557,-5.225160013321354,0.005204200744628906,7.316272000000002 +720,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4563696019956498,4.503833132228122,-5.19161922746511,0.005204200744628906,7.675697000000002 +740,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4392280778003554,4.445645440595998,-5.02903742417401,0.005204200744628906,8.035893000000002 +760,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4073407178561264,4.387021097703184,-4.9346827726614455,0.005204200744628906,8.396854000000001 +780,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3782504190107006,4.330701361336262,-4.809192109617374,0.005204200744628906,8.758623000000002 +800,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3571814777264213,4.277370073659861,-4.718248073230613,0.005204200744628906,9.121240000000002 +820,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3328025450945626,4.2253925636381995,-4.641399853721709,0.005204200744628906,9.484674000000002 +840,Regression,Linear Regression with l1 regularization,TrumpApproval,1.311715211433691,4.175582527272098,-4.560327645533724,0.005204200744628906,9.848922000000002 +860,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2897432923325247,4.127236925138345,-4.422957957045758,0.005204200744628906,10.213983000000002 +880,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2672991203860131,4.080383024210964,-4.274192362897992,0.005204200744628906,10.579853000000002 +900,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2421842209255052,4.03488734209176,-4.178968845613636,0.005204200744628906,10.965988000000001 +920,Regression,Linear Regression with l1 regularization,TrumpApproval,1.220808929344255,3.991045752926761,-4.15028972045945,0.005204200744628906,11.354575 +940,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2057181063421543,3.9494511154557617,-4.0862825167589865,0.005204200744628906,11.745471 +960,Regression,Linear Regression with l1 regularization,TrumpApproval,1.188437369603739,3.9086583836793856,-4.0338431039290805,0.005204200744628906,12.138778 +980,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1710173649101312,3.8690392813429555,-4.028105053503917,0.005204200744628906,12.545048 +1000,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1544521877618488,3.8306020851942315,-4.0116636492940465,0.005204200744628906,12.952187 +1001,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1537672749321948,3.8287168981917103,-4.010074752320696,0.005204200744628906,13.359495 +11,Regression,Linear Regression with l2 regularization,ChickWeights,30.6062254572366,31.39938120772091,-1268.1112549740517,0.004153251647949219,0.000711 +22,Regression,Linear Regression with l2 regularization,ChickWeights,21.412737763681047,23.97862157826266,-607.9443275975191,0.004153251647949219,0.001889 +33,Regression,Linear Regression with l2 regularization,ChickWeights,15.119104680903606,19.655410372524667,-267.315679768846,0.004153251647949219,0.003406 +44,Regression,Linear Regression with l2 regularization,ChickWeights,11.691588950452092,17.042779535378298,-227.6797328948204,0.004153251647949219,0.00525 +55,Regression,Linear Regression with l2 regularization,ChickWeights,11.128477598777668,17.570968714531574,-59.26944361385635,0.004153251647949219,0.0074210000000000005 +66,Regression,Linear Regression with l2 regularization,ChickWeights,10.755656716101159,16.483156797846284,-21.862958739409084,0.004153251647949219,0.009919 +77,Regression,Linear Regression with l2 regularization,ChickWeights,10.454334080303978,15.644372833730271,-12.803711937026078,0.004153251647949219,0.012745000000000001 +88,Regression,Linear Regression with l2 regularization,ChickWeights,9.893519322025275,14.807378680481822,-10.212022929829027,0.004153251647949219,0.015896 +99,Regression,Linear Regression with l2 regularization,ChickWeights,9.219705201317108,14.044546137802199,-7.436202462041329,0.004153251647949219,0.019372 +110,Regression,Linear Regression with l2 regularization,ChickWeights,8.828389618716818,13.455080798744472,-4.4070097733575375,0.004153251647949219,0.023173 +121,Regression,Linear Regression with l2 regularization,ChickWeights,8.61456960864212,13.037583740326507,-2.9098467157738415,0.004153251647949219,0.027299 +132,Regression,Linear Regression with l2 regularization,ChickWeights,8.52880743945525,12.690080989153241,-2.0274307958032884,0.004153251647949219,0.031782 +143,Regression,Linear Regression with l2 regularization,ChickWeights,8.39143583855712,12.359614263508039,-1.3260696348061907,0.004153251647949219,0.036638 +154,Regression,Linear Regression with l2 regularization,ChickWeights,8.12180315101294,12.009375103170282,-0.866389387173786,0.004153251647949219,0.041874999999999996 +165,Regression,Linear Regression with l2 regularization,ChickWeights,8.136940986261356,11.920551719153746,-0.47822185831879493,0.004153251647949219,0.04749199999999999 +176,Regression,Linear Regression with l2 regularization,ChickWeights,8.284290032332207,11.93362687305613,-0.19451089204457617,0.004153251647949219,0.05348299999999999 +187,Regression,Linear Regression with l2 regularization,ChickWeights,8.390309464431912,11.903488345267945,0.026908395403585694,0.004153251647949219,0.05984699999999999 +198,Regression,Linear Regression with l2 regularization,ChickWeights,8.350219958465262,11.791481226840993,0.2404518209934976,0.004153251647949219,0.06659 +209,Regression,Linear Regression with l2 regularization,ChickWeights,8.499019855105985,11.958125495095471,0.3560283448388185,0.004153251647949219,0.073713 +220,Regression,Linear Regression with l2 regularization,ChickWeights,8.90272187978296,12.527163169679886,0.3812690011074207,0.004153251647949219,0.081218 +231,Regression,Linear Regression with l2 regularization,ChickWeights,9.171291167504231,12.73748746029564,0.45283948771259386,0.004153251647949219,0.08971 +242,Regression,Linear Regression with l2 regularization,ChickWeights,9.37629466014084,13.047657656056804,0.538024139715424,0.004153251647949219,0.099295 +253,Regression,Linear Regression with l2 regularization,ChickWeights,9.440817816219349,13.0964165059942,0.5957634168273553,0.004153251647949219,0.110134 +264,Regression,Linear Regression with l2 regularization,ChickWeights,9.906487060964153,13.855497684527965,0.5866088718530376,0.004233360290527344,0.12224199999999999 +275,Regression,Linear Regression with l2 regularization,ChickWeights,10.387009537918406,14.786939232799543,0.5856869069436603,0.004233360290527344,0.13552899999999998 +286,Regression,Linear Regression with l2 regularization,ChickWeights,10.701469010841246,15.270898285463774,0.6172820078624095,0.004233360290527344,0.14989199999999997 +297,Regression,Linear Regression with l2 regularization,ChickWeights,10.689852199892528,15.284847538688991,0.6674656839655615,0.004233360290527344,0.17720199999999997 +308,Regression,Linear Regression with l2 regularization,ChickWeights,11.168487287417783,16.008183102465477,0.6751444757196481,0.004233360290527344,0.20585199999999998 +319,Regression,Linear Regression with l2 regularization,ChickWeights,12.085867087734242,18.170753499240714,0.6201764868699093,0.004233360290527344,0.23488799999999999 +330,Regression,Linear Regression with l2 regularization,ChickWeights,12.672501856506585,19.05837058612535,0.6424226539377311,0.004233360290527344,0.26428199999999996 +341,Regression,Linear Regression with l2 regularization,ChickWeights,12.822446828447035,19.13937756684808,0.6770787925994421,0.004233360290527344,0.29402799999999996 +352,Regression,Linear Regression with l2 regularization,ChickWeights,13.055746883990931,19.312136445778254,0.7011272480618885,0.004233360290527344,0.32412699999999994 +363,Regression,Linear Regression with l2 regularization,ChickWeights,13.79008745873622,20.396105048894267,0.6762859401979866,0.004233360290527344,0.35457799999999995 +374,Regression,Linear Regression with l2 regularization,ChickWeights,14.293199062265238,21.539399675842862,0.6681199603719434,0.004233360290527344,0.38537999999999994 +385,Regression,Linear Regression with l2 regularization,ChickWeights,14.740320816630273,22.311026164960477,0.6849554171717112,0.004233360290527344,0.42616299999999996 +396,Regression,Linear Regression with l2 regularization,ChickWeights,14.862968645899144,22.294096988116678,0.7067005430463744,0.004233360290527344,0.467315 +407,Regression,Linear Regression with l2 regularization,ChickWeights,15.699705023283963,23.67314903355933,0.6925996644733732,0.004233360290527344,0.508826 +418,Regression,Linear Regression with l2 regularization,ChickWeights,16.38213993729544,25.048095107979137,0.6769473375050636,0.004233360290527344,0.55069 +429,Regression,Linear Regression with l2 regularization,ChickWeights,16.967894830794286,26.153201890569886,0.6870368010887093,0.004233360290527344,0.592905 +440,Regression,Linear Regression with l2 regularization,ChickWeights,17.10728249235129,26.204092785638924,0.7080553660644732,0.004233360290527344,0.6354690000000001 +451,Regression,Linear Regression with l2 regularization,ChickWeights,17.603016925007317,26.772391386711114,0.7082099437723521,0.004233360290527344,0.6783830000000001 +462,Regression,Linear Regression with l2 regularization,ChickWeights,18.614531201761594,28.786744962703725,0.6831362914484524,0.004233360290527344,0.7216460000000001 +473,Regression,Linear Regression with l2 regularization,ChickWeights,19.488293352005442,30.38515335394973,0.6882746780375071,0.004233360290527344,0.7652570000000001 +484,Regression,Linear Regression with l2 regularization,ChickWeights,19.755002868307955,30.52390276571354,0.7026599444855313,0.004233360290527344,0.8092140000000001 +495,Regression,Linear Regression with l2 regularization,ChickWeights,20.22217092676305,31.08727194033441,0.7098743070293987,0.004233360290527344,0.8535240000000001 +506,Regression,Linear Regression with l2 regularization,ChickWeights,21.03670858216615,32.44431034253017,0.6931769059461363,0.004233360290527344,0.898204 +517,Regression,Linear Regression with l2 regularization,ChickWeights,21.78200415465676,33.496021791915204,0.6913806254796178,0.004233360290527344,0.943264 +528,Regression,Linear Regression with l2 regularization,ChickWeights,22.56258004106143,34.768391171729405,0.7010449079513538,0.004233360290527344,0.9886969999999999 +539,Regression,Linear Regression with l2 regularization,ChickWeights,22.68725373887437,34.77075336357408,0.7133508993505916,0.004233360290527344,1.0345 +550,Regression,Linear Regression with l2 regularization,ChickWeights,23.627725892037507,36.324416048782524,0.6968033114915981,0.004233360290527344,1.080674 +561,Regression,Linear Regression with l2 regularization,ChickWeights,24.347376192466918,37.30920796407717,0.6941284720923248,0.004233360290527344,1.127216 +572,Regression,Linear Regression with l2 regularization,ChickWeights,25.18573737545828,38.51358935872805,0.698506895988072,0.004233360290527344,1.174127 +578,Regression,Linear Regression with l2 regularization,ChickWeights,25.27380465992389,38.58852748240754,0.7046942807227952,0.004233360290527344,1.2212839999999998 +20,Regression,Linear Regression with l2 regularization,TrumpApproval,20.994354275814885,24.339467027537435,-1388.5575385664913,0.004836082458496094,0.002841 +40,Regression,Linear Regression with l2 regularization,TrumpApproval,12.808927193108108,17.83271591943186,-126.84988353201342,0.004836082458496094,0.0066630000000000005 +60,Regression,Linear Regression with l2 regularization,TrumpApproval,10.864002308096953,15.320672400398038,-126.22308256175273,0.004836082458496094,0.011298 +80,Regression,Linear Regression with l2 regularization,TrumpApproval,8.882777304938948,13.38981065066765,-96.4771385394691,0.004836082458496094,0.01675 +100,Regression,Linear Regression with l2 regularization,TrumpApproval,7.231639558854497,11.98203471414171,-47.97617801736401,0.004836082458496094,0.023066000000000003 +120,Regression,Linear Regression with l2 regularization,TrumpApproval,6.334108393931037,10.98237795329033,-33.904913895880355,0.004836082458496094,0.030179000000000004 +140,Regression,Linear Regression with l2 regularization,TrumpApproval,5.563493982833803,10.178707085968126,-29.98405233271513,0.004836082458496094,0.038033000000000004 +160,Regression,Linear Regression with l2 regularization,TrumpApproval,5.002122045077101,9.533278572445496,-22.968717144675633,0.004836082458496094,0.04831800000000001 +180,Regression,Linear Regression with l2 regularization,TrumpApproval,4.587842803317817,9.003737317880292,-17.777085610739057,0.004836082458496094,0.07294600000000001 +200,Regression,Linear Regression with l2 regularization,TrumpApproval,4.458683971614509,8.652080760634158,-16.390543570087573,0.004836082458496094,0.09946700000000001 +220,Regression,Linear Regression with l2 regularization,TrumpApproval,4.239995800771734,8.280452519944822,-16.182419642449048,0.004836082458496094,0.129303 +240,Regression,Linear Regression with l2 regularization,TrumpApproval,3.943592784264584,7.932077353220182,-14.885669934585447,0.004836082458496094,0.159907 +260,Regression,Linear Regression with l2 regularization,TrumpApproval,3.7846302486799286,7.646201644169009,-13.960159512708739,0.004996299743652344,0.191262 +280,Regression,Linear Regression with l2 regularization,TrumpApproval,3.6468171672887713,7.389977926170562,-13.630953412847402,0.004996299743652344,0.22336299999999998 +300,Regression,Linear Regression with l2 regularization,TrumpApproval,3.5261123680869226,7.1621871014808685,-12.51535851099468,0.004996299743652344,0.25877399999999995 +320,Regression,Linear Regression with l2 regularization,TrumpApproval,3.5074300839639245,6.985469271455791,-12.493228210862723,0.004996299743652344,0.29652399999999995 +340,Regression,Linear Regression with l2 regularization,TrumpApproval,3.434140699763514,6.814822943627961,-12.570888751300782,0.004996299743652344,0.33652199999999993 +360,Regression,Linear Regression with l2 regularization,TrumpApproval,3.4272200155971797,6.678288393486038,-12.13957341395007,0.004996299743652344,0.3787709999999999 +380,Regression,Linear Regression with l2 regularization,TrumpApproval,3.332029752839207,6.516115548498917,-11.941900403072644,0.004996299743652344,0.4232519999999999 +400,Regression,Linear Regression with l2 regularization,TrumpApproval,3.217390968362987,6.356555790563252,-11.66392028459017,0.004996299743652344,0.4696459999999999 +420,Regression,Linear Regression with l2 regularization,TrumpApproval,3.100825681509746,6.206562691759863,-11.472880484909139,0.004996299743652344,0.516851 +440,Regression,Linear Regression with l2 regularization,TrumpApproval,3.0187726323631243,6.072312098448126,-10.723095644711893,0.004996299743652344,0.5652379999999999 +460,Regression,Linear Regression with l2 regularization,TrumpApproval,2.947022825868371,5.94849802587685,-9.668265577306911,0.004996299743652344,0.6161209999999999 +480,Regression,Linear Regression with l2 regularization,TrumpApproval,2.867282537241402,5.828292237410032,-9.005843404687633,0.004996299743652344,0.6743809999999999 +500,Regression,Linear Regression with l2 regularization,TrumpApproval,2.8281006485905213,5.729646774374514,-8.467133754251039,0.004996299743652344,0.7334769999999999 +520,Regression,Linear Regression with l2 regularization,TrumpApproval,2.759113137285707,5.623931694381955,-8.136932704030892,0.004996299743652344,0.79343 +540,Regression,Linear Regression with l2 regularization,TrumpApproval,2.7113951403332286,5.52770300084093,-7.7945843187225226,0.004996299743652344,0.8541829999999999 +560,Regression,Linear Regression with l2 regularization,TrumpApproval,2.646739535451309,5.4320905955210534,-7.695343452205655,0.004996299743652344,0.9420199999999999 +580,Regression,Linear Regression with l2 regularization,TrumpApproval,2.5972398336076634,5.343168086286508,-7.621065103502998,0.004996299743652344,1.0306859999999998 +600,Regression,Linear Regression with l2 regularization,TrumpApproval,2.533455116608919,5.255265792942869,-7.247487071141652,0.004996299743652344,1.1201999999999999 +620,Regression,Linear Regression with l2 regularization,TrumpApproval,2.497138699914293,5.178243230235351,-6.88544757519055,0.004996299743652344,1.2104949999999999 +640,Regression,Linear Regression with l2 regularization,TrumpApproval,2.4712145738198297,5.107804033669319,-6.528964961790648,0.004996299743652344,1.3015299999999999 +660,Regression,Linear Regression with l2 regularization,TrumpApproval,2.429247896498525,5.0347117637840935,-6.262430681498119,0.004996299743652344,1.3932969999999998 +680,Regression,Linear Regression with l2 regularization,TrumpApproval,2.3980901245026116,4.967521902410674,-6.18160824182094,0.004996299743652344,1.4857959999999997 +700,Regression,Linear Regression with l2 regularization,TrumpApproval,2.360396901712673,4.90286744871834,-6.1796262474179136,0.004996299743652344,1.5790619999999997 +720,Regression,Linear Regression with l2 regularization,TrumpApproval,2.3150393936015323,4.836721469702358,-6.140716883970916,0.004996299743652344,1.6730619999999998 +740,Regression,Linear Regression with l2 regularization,TrumpApproval,2.267679208737699,4.77254376860168,-5.948293801048677,0.004996299743652344,1.7677929999999997 +760,Regression,Linear Regression with l2 regularization,TrumpApproval,2.2434173929652075,4.715867112708654,-5.857742612566196,0.004996299743652344,1.8632789999999997 +780,Regression,Linear Regression with l2 regularization,TrumpApproval,2.199009654391343,4.656030786420359,-5.714767077599112,0.004996299743652344,1.9595409999999998 +800,Regression,Linear Regression with l2 regularization,TrumpApproval,2.1596596811720175,4.59898830049537,-5.610494506343661,0.004996299743652344,2.0566079999999998 +820,Regression,Linear Regression with l2 regularization,TrumpApproval,2.1249482408574707,4.545176313025559,-5.527610390446187,0.004996299743652344,2.175652 +840,Regression,Linear Regression with l2 regularization,TrumpApproval,2.094058354623314,4.493551443258636,-5.439404045425388,0.004996299743652344,2.37025 +860,Regression,Linear Regression with l2 regularization,TrumpApproval,2.062104039794744,4.442864622918497,-5.284107374622643,0.004996299743652344,2.565666 +880,Regression,Linear Regression with l2 regularization,TrumpApproval,2.0307065941401414,4.393695684791879,-5.115247638705276,0.004996299743652344,2.7618669999999996 +900,Regression,Linear Regression with l2 regularization,TrumpApproval,2.003263565299311,4.347049681772325,-5.011317673951863,0.004996299743652344,2.9588469999999996 +920,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9706878923964866,4.300488305941944,-4.979898179552831,0.004996299743652344,3.1566579999999997 +940,Regression,Linear Regression with l2 regularization,TrumpApproval,1.949819924248383,4.257394252920471,-4.91037086709413,0.004996299743652344,3.355253 +960,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9258186229947107,4.214725843085415,-4.85305905428677,0.004996299743652344,3.5546249999999997 +980,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9004260103609922,4.173138113177231,-4.849565137575967,0.004996299743652344,3.754774 +1000,Regression,Linear Regression with l2 regularization,TrumpApproval,1.872733130377695,4.13253797119814,-4.832859832721421,0.004996299743652344,3.955698 +1001,Regression,Linear Regression with l2 regularization,TrumpApproval,1.871510887330926,4.130524228438989,-4.8310671605777085,0.004996299743652344,4.156775 +11,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,26.624124996337724,28.77138517975663,-1064.5628215382144,0.0034055709838867188,0.000572 +22,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.0510878175865,20.931739283093208,-463.02330712701985,0.0034055709838867188,0.001645 +33,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.49930786476168,17.564629142555763,-213.26922094451623,0.0034055709838867188,0.003059 +44,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.378514545021682,15.405121473747096,-185.84310618709696,0.0034055709838867188,0.004809 +55,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.844108697295251,17.128215293517524,-56.27037115396167,0.0034055709838867188,0.0068920000000000006 +66,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.889488781892217,15.88743125142584,-20.240220516271876,0.0034055709838867188,0.009306 +77,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.103343480706034,14.91594241381016,-11.548186613409534,0.0034055709838867188,0.012052 +88,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.288900850158633,14.011374344891147,-9.038968322803399,0.0034055709838867188,0.015129 +99,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.736865157066078,13.281093172283262,-6.5439573170464564,0.0034055709838867188,0.018712 +110,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.618125386224052,12.858171267844924,-3.9379074608874927,0.0034055709838867188,0.022717 +121,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.580936033253089,12.515247629942861,-2.6028352541815996,0.0034055709838867188,0.027127000000000002 +132,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.191573127926202,12.024287681643044,-1.7180920054032294,0.0034055709838867188,0.031928 +143,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.001452140019149,11.63905100750295,-1.062756769701151,0.0034055709838867188,0.037113999999999994 +154,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.959260067984971,11.397763679955697,-0.6811278108981134,0.0034055709838867188,0.04268899999999999 +165,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.036161429677985,11.359538570018055,-0.3423577921849861,0.0034055709838867188,0.04865399999999999 +176,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.141200516910354,11.407680550849575,-0.09154063283497149,0.0034055709838867188,0.05501099999999999 +187,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.061965626679777,11.211626858308708,0.1367382583661435,0.0034055709838867188,0.06175399999999999 +198,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.988600359846859,11.023879576943443,0.33612315722527375,0.0034055709838867188,0.06888499999999999 +209,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.115468527113427,11.18859440458875,0.43624345152336885,0.0034055709838867188,0.07639599999999999 +220,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.571784360381598,11.998798941058181,0.4323613497222297,0.0034055709838867188,0.08428799999999999 +231,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.610536559977233,11.962244483436113,0.5174164020157423,0.0034055709838867188,0.09375399999999999 +242,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.753677752144043,12.109970858596688,0.6020391290764042,0.0034055709838867188,0.10451099999999999 +253,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.763402728464486,12.046916639776326,0.6579556132088519,0.0034055709838867188,0.123112 +264,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.37232599699494,12.938281421109101,0.6395292100633578,0.0034589767456054688,0.147917 +275,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.870502401884236,14.03783628218945,0.6266016212673495,0.0034589767456054688,0.173118 +286,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.125553299295866,14.312481045438886,0.6638140497188074,0.0034589767456054688,0.198688 +297,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.11642729851449,14.234872044017683,0.7115826499817814,0.0034589767456054688,0.225283 +308,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.63053955101658,15.01159987060024,0.7143329631149452,0.0034589767456054688,0.252265 +319,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.671899739762464,17.42953249336733,0.650531972004655,0.0034589767456054688,0.27961600000000003 +330,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.113559839827301,17.980470366868552,0.6817264420663893,0.0034589767456054688,0.307329 +341,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.368994570730054,18.183536514460908,0.7085274545262112,0.0034589767456054688,0.335405 +352,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.47998520043724,18.216810890558104,0.7340681346165732,0.0034589767456054688,0.363842 +363,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.490995837872445,19.84181186896939,0.693641639088806,0.0034589767456054688,0.392636 +374,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.988870134156189,20.81926805033374,0.6899406322869175,0.0034589767456054688,0.42178699999999997 +385,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.420579982415202,21.48960215237335,0.7077263415053474,0.0034589767456054688,0.45129199999999997 +396,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.424816444492956,21.37796604773129,0.7303103668220552,0.0034589767456054688,0.481151 +407,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,14.284688005634004,22.701575115822557,0.7173140296578691,0.0034589767456054688,0.514769 +418,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.038658536726118,24.042516108283174,0.7023651722582169,0.0034589767456054688,0.548784 +429,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.590290098257741,24.916858152232297,0.7159269075042054,0.0034589767456054688,0.583167 +440,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.812702077031824,25.072500493300815,0.7327254930224041,0.0034589767456054688,0.617914 +451,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.346042839206106,25.68091484988461,0.7315167856134144,0.0034589767456054688,0.653021 +462,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,17.370765923434053,27.689388199834635,0.7068336630361484,0.0034589767456054688,0.688487 +473,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.264179516209435,29.30099868065636,0.7101227966068765,0.0034589767456054688,0.724309 +484,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.63502154656571,29.559619400414906,0.7211497930314269,0.0034589767456054688,0.760485 +495,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.145243718121584,30.130361606680754,0.7274603750306702,0.0034589767456054688,0.7970149999999999 +506,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.98075812634153,31.43770898148617,0.7119202509985216,0.0034589767456054688,0.8338999999999999 +517,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,20.7046141289421,32.42665929478992,0.7107714616434232,0.0034589767456054688,0.8711439999999999 +528,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.54126059149082,33.75343345950398,0.7182443212146572,0.0034589767456054688,0.908739 +539,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.736037457517718,33.829762552174344,0.7286559632490104,0.0034589767456054688,0.946687 +550,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,22.674609740448528,35.33904665998618,0.7130297805712756,0.0034589767456054688,0.984985 +561,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,23.350956760305525,36.24046007710213,0.7114012814424304,0.0034589767456054688,1.023633 +572,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.20743030595361,37.47019278346573,0.7146215025224946,0.0034589767456054688,1.06263 +578,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.342328163686027,37.59599019491026,0.7196900586014492,0.0034589767456054688,1.1018649999999999 +20,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,20.806898309502586,26.56763494383828,-1654.6182189603317,0.004302024841308594,0.003003 +40,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,14.866074912822512,20.957300378156614,-175.5777711351631,0.004302024841308594,0.009504 +60,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,11.772648582583251,17.555009093750932,-166.03688377592212,0.004302024841308594,0.016813 +80,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,10.372925375947808,15.758572852966298,-134.01675577859288,0.004302024841308594,0.024890000000000002 +100,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.950999863257042,14.807263848606526,-73.79513907078027,0.004302024841308594,0.033777 +120,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.131163180965077,13.743973626529105,-53.66614209724606,0.004302024841308594,0.043434 +140,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.532294463666167,12.935885124148239,-49.04322437944699,0.004302024841308594,0.05944 +160,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.33219708929472,12.40854626547306,-39.60710527883104,0.004302024841308594,0.077842 +180,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.281092452540433,12.043698542516514,-32.597139849683785,0.004302024841308594,0.09862299999999999 +200,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.889313429527772,11.548268653424005,-29.981738551789036,0.004302024841308594,0.121857 +220,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.555718436766954,11.115454500430198,-29.962115725262894,0.004302024841308594,0.149384 +240,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.300584612865839,10.768588372618428,-28.278525817685868,0.004302024841308594,0.177703 +260,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.073956995660685,10.455941089275187,-26.975057358486694,0.004435539245605469,0.206834 +280,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.879100927439736,10.179149173565092,-26.75935065850941,0.004435539245605469,0.236782 +300,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.698392466938299,9.935855831167725,-25.01038668400382,0.004435539245605469,0.26759 +320,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.496977203333427,9.674599820332077,-24.881575653507443,0.004435539245605469,0.299212 +340,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.319501534649956,9.433800456219284,-25.005937123592886,0.004435539245605469,0.33164299999999997 +360,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.189316591643737,9.224778235838508,-24.070488458586468,0.004435539245605469,0.36488499999999996 +380,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.05373584315195,9.026036673488779,-23.83217081250324,0.004435539245605469,0.39894499999999994 +400,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.893196935767096,8.831009888188627,-23.44247524737401,0.004435539245605469,0.43386499999999995 +420,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.787168115685009,8.669033337133486,-23.33356914323267,0.004435539245605469,0.46960899999999994 +440,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.789860410241021,8.633597516354541,-22.69832962851382,0.004435539245605469,0.5061589999999999 +460,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.751464501173282,8.532971696368575,-20.952287666855003,0.004435539245605469,0.5516559999999999 +480,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.758413491181221,8.476961067588123,-20.166616413985547,0.004435539245605469,0.599555 +500,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.682950272504451,8.3510488559106,-19.111518541810455,0.004435539245605469,0.6519079999999999 +520,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.627995468360723,8.245754355787446,-18.64179334000355,0.004435539245605469,0.705132 +540,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.546541731300828,8.130789587119862,-18.02792190581397,0.004435539245605469,0.7591289999999999 +560,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.474658569482086,8.019262742277965,-17.95054121046633,0.004435539245605469,0.813877 +580,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.409420416004319,7.920158789530457,-17.94222667017848,0.004435539245605469,0.869386 +600,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.394854582323811,7.870548110777217,-17.498743363524373,0.004435539245605469,0.941553 +620,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.360408122735632,7.801849933723111,-16.900148820132806,0.004435539245605469,1.016153 +640,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.332182524169608,7.745335706289596,-16.312002846243146,0.004435539245605469,1.093294 +660,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.286484086266954,7.672164501241343,-15.864310422998045,0.004435539245605469,1.1728 +680,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.240017672508232,7.591734569529257,-15.773518040276521,0.004435539245605469,1.259908 +700,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.203631741702394,7.526058935068808,-15.917522479350382,0.004435539245605469,1.347915 +720,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.1985518333986755,7.481861117849272,-16.086729414362967,0.004435539245605469,1.436748 +740,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.200051628353664,7.4433149034441595,-15.900950117878587,0.004435539245605469,1.526395 +760,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.146415466772512,7.367313347205083,-15.736951087672441,0.004435539245605469,1.61685 +780,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.164438314106662,7.352756459959702,-15.745558187055657,0.004435539245605469,1.708114 +800,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.199091748701669,7.381485816300255,-16.029304780133675,0.004435539245605469,1.8001699999999998 +820,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.184244405270293,7.343677512392477,-16.040406471643664,0.004435539245605469,1.892982 +840,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.162940711797175,7.2951968772545595,-15.972283354719572,0.004435539245605469,1.986541 +860,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.146772746928229,7.251973114148485,-15.742866229030533,0.004435539245605469,2.080851 +880,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.141562534384022,7.225910341165371,-15.54014218136778,0.004435539245605469,2.175912 +900,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.113671043317916,7.181653170625269,-15.40700562565575,0.004435539245605469,2.271722 +920,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.0827257569327715,7.134180367835326,-15.456838882747107,0.004435539245605469,2.368328 +940,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.049460198345376,7.092641752853287,-15.403746251323405,0.004435539245605469,2.491683 +960,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.012955702794688,7.041188655025779,-15.335641983730405,0.004435539245605469,2.616037 +980,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.992587411597517,7.002009756347646,-15.468099715303381,0.004435539245605469,2.7412199999999998 +1000,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.986581819477306,6.97972894589718,-15.638912943338369,0.004435539245605469,2.867218 +1001,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.984033991902679,6.9766666383395455,-15.63541499061877,0.004435539245605469,2.993378 +11,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,39.19936706045659,55.118879370280126,-3909.733983269086,0.0034055709838867188,0.001533 +22,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.495026158423794,43.23165104261441,-1978.3965328342838,0.0034055709838867188,0.004589 +33,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,30.680053698816124,39.985066603327745,-1109.3949268723327,0.0034055709838867188,0.008788 +44,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,29.375885022911746,37.29886968855784,-1094.3128086885838,0.0034055709838867188,0.014141 +55,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.707444751978134,40.753235251415205,-323.21264874535376,0.0034055709838867188,0.020635 +66,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.96097441162184,40.14945868859866,-134.64726490280094,0.0034055709838867188,0.028271 +77,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.25989567011213,39.82501544894248,-88.45229320906665,0.0034055709838867188,0.041195 +88,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.76307262878121,39.802536485586,-80.01195436020778,0.0034055709838867188,0.054526000000000005 +99,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.66411513705659,39.325402336106926,-65.1420916497486,0.0034055709838867188,0.06822700000000001 +110,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.19940912800194,40.704130728492046,-48.48362457590105,0.0034055709838867188,0.082293 +121,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.629161705635866,40.92880988729008,-37.532194399087835,0.0034055709838867188,0.096719 +132,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,35.29035427006805,41.59178542812187,-31.520750879588867,0.0034055709838867188,0.111503 +143,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.23638449140802,42.62018794050648,-26.659453761649477,0.0034055709838867188,0.126644 +154,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.725010132899186,42.91835139661213,-22.836775105518452,0.0034055709838867188,0.14214100000000002 +165,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.731745662210095,42.91744223234227,-18.16084111691443,0.0034055709838867188,0.15799600000000003 +176,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.94402632003076,44.39720610255875,-15.533183531813599,0.0034055709838867188,0.17420800000000003 +187,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,38.698580833397834,45.06856008203835,-12.949305609255877,0.0034055709838867188,0.19077600000000003 +198,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.18624064352699,46.68267333461602,-10.905017439998023,0.0034055709838867188,0.20770000000000002 +209,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.854323276826534,47.463811090322665,-9.145320047375163,0.0034055709838867188,0.224978 +220,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,41.36451127701117,48.41262940233051,-8.240888884363313,0.0034055709838867188,0.24261000000000002 +231,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,42.17342712408468,49.46918668675267,-7.253093192412523,0.0034055709838867188,0.260594 +242,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,43.81461612103895,51.73551020684679,-6.2632609796369465,0.0034055709838867188,0.27893 +253,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,44.90819615603068,53.07334253773936,-5.6387075541588505,0.0034055709838867188,0.297618 +264,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,46.45334973048907,55.82244674340302,-5.710187070138379,0.0034589767456054688,0.31665899999999997 +275,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,48.05643802527038,58.804796990371536,-5.552357606253864,0.0034589767456054688,0.33605399999999996 +286,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,49.41721923566732,60.72765972830183,-5.052332799264802,0.0034589767456054688,0.35580199999999995 +297,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,51.23299901747073,63.29154255446438,-4.701716347098143,0.0034589767456054688,0.37590499999999993 +308,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,52.82583967659276,65.36972550348784,-4.417008197806662,0.0034589767456054688,0.39636799999999994 +319,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,54.851023886215806,70.45860717413167,-4.71089374971376,0.0034589767456054688,0.41718399999999994 +330,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,56.58220488738844,72.62689780553444,-4.192702597603254,0.0034589767456054688,0.43835299999999994 +341,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,58.456862484765374,75.26810540469758,-3.994165343488624,0.0034589767456054688,0.45987999999999996 +352,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,59.98229295122657,76.97767263775137,-3.748486775975887,0.0034589767456054688,0.48176199999999997 +363,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,61.989108820835376,80.62951920841103,-4.0588898392459045,0.0034589767456054688,0.503996 +374,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,63.93796840595574,84.48840832488506,-4.106322034628877,0.0034589767456054688,0.526584 +385,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,65.15236861414519,85.79755918852514,-3.6588935912158114,0.0034589767456054688,0.551564 +396,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,66.90365663892747,87.9249329113371,-3.562003118430529,0.0034589767456054688,0.5777180000000001 +407,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,68.17917622540308,89.58756462611774,-3.402382103640547,0.0034589767456054688,0.6050150000000001 +418,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,70.80702754948452,94.96753809429286,-3.643808228470034,0.0034589767456054688,0.6334650000000001 +429,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,72.44730173566225,97.09455233033468,-3.313534410167211,0.0034589767456054688,0.663057 +440,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,74.29167351363806,99.40774027870643,-3.2014833153265316,0.0034589767456054688,0.693783 +451,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,75.83174494284101,101.77506329990584,-3.216760347648722,0.0034589767456054688,0.725638 +462,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,78.5111288104629,106.99570126481906,-3.3774383617967887,0.0034589767456054688,0.758621 +473,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,81.63741116996734,112.58139375423264,-3.2793958269429844,0.0034589767456054688,0.79391 +484,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,82.66628549198501,113.50934761838603,-3.1118432523868984,0.0034589767456054688,0.829617 +495,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,84.40016304476833,116.34990208847,-3.0639935553557365,0.0034589767456054688,0.865723 +506,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,86.52132256561038,120.30772815943004,-3.2188880650982723,0.0034589767456054688,0.902224 +517,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,87.79244029751037,121.63869088166206,-3.0698668478095374,0.0034589767456054688,0.9391200000000001 +528,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,90.43735682351402,126.62066541565774,-2.9650251625135784,0.0034589767456054688,0.9763980000000001 +539,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,91.59763342322412,127.62959496409519,-2.862114672867245,0.0034589767456054688,1.014034 +550,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,93.80067010965053,131.39026699356185,-2.9669210878459156,0.0034589767456054688,1.0520230000000002 +561,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,96.52355815418714,136.33091173427522,-3.0840934586689466,0.0034589767456054688,1.0903630000000002 +572,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,99.60515399822415,141.80943605664237,-3.0875177525301325,0.0034589767456054688,1.1290550000000001 +578,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,100.62422612381133,143.06646930774232,-3.0591132110693486,0.0034589767456054688,1.167982 +20,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,48.24517612267716,65.52170729560882,-10068.892101934754,0.004302024841308594,0.0014 +40,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,41.96170708962665,54.398737007050464,-1188.7151382109587,0.004302024841308594,0.0037089999999999996 +60,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,37.75687919715097,48.78450375470138,-1288.953469480389,0.004302024841308594,0.0068 +80,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,34.906129137913965,44.99379649673769,-1099.675197364534,0.004302024841308594,0.010662 +100,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,33.91700787894482,42.88559259598606,-626.4029768570122,0.004302024841308594,0.015342999999999999 +120,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,33.25318798467783,41.41783833748641,-495.44216046034904,0.004302024841308594,0.020797999999999997 +140,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.454169303664,40.065346416262614,-479.0547686921885,0.004302024841308594,0.027020999999999996 +160,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.456143135335843,38.757475924320815,-395.16052146995384,0.004302024841308594,0.034013999999999996 +180,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.609503890456164,37.605439707525925,-326.55474603918685,0.004302024841308594,0.04177499999999999 +200,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.18524212396377,36.915721425331306,-315.5882175367347,0.004302024841308594,0.05032999999999999 +220,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.065472528043387,36.44442035805252,-331.8421153382835,0.004302024841308594,0.05964399999999999 +240,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.78865598017145,35.91292614465666,-324.6366197799479,0.004302024841308594,0.06971499999999999 +260,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.781114432924582,35.79236523689611,-326.81251307944893,0.004435539245605469,0.08530999999999998 +280,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.533238425747374,35.35890478021234,-333.95306350592915,0.004435539245605469,0.10330299999999998 +300,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.457831453745214,35.09485674586195,-323.50634592736793,0.004435539245605469,0.12379499999999997 +320,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.587592426740265,34.99099947403571,-337.56135797788454,0.004435539245605469,0.14665799999999998 +340,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.592186767063264,34.82748458593961,-353.4405109424598,0.004435539245605469,0.178415 +360,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.811871336212132,34.87255971663822,-357.27671195740294,0.004435539245605469,0.21101599999999998 +380,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.96085998978186,34.8837386376585,-369.9082961036221,0.004435539245605469,0.24444299999999997 +400,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.10861760053803,34.89146879370085,-380.5600928496674,0.004435539245605469,0.27875099999999997 +420,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.237056214581205,34.87113676993804,-392.72834237954817,0.004435539245605469,0.31389799999999995 +440,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.396870134836657,34.919939008119975,-386.68686883790514,0.004435539245605469,0.34987699999999994 +460,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.514209015244397,34.936230377424984,-366.98596965456716,0.004435539245605469,0.38662699999999994 +480,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.60304766371323,34.90556469597589,-357.88920799289923,0.004435539245605469,0.42413599999999996 +500,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.723498435612367,34.929338036322235,-350.83863344189655,0.004435539245605469,0.462404 +520,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.811640107301315,34.91688659850233,-351.20164208687333,0.004435539245605469,0.5014689999999999 +540,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.90684609870959,34.93305250730057,-350.23597283973027,0.004435539245605469,0.541291 +560,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.83222834631613,34.80844611918478,-356.0442181025925,0.004435539245605469,0.58187 +580,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.81214247339674,34.7464796172207,-363.5733105101423,0.004435539245605469,0.629234 +600,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.96266506693229,34.82326914665847,-361.13570824853826,0.004435539245605469,0.6774979999999999 +620,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.066025409450507,34.86865150113252,-356.54586556020155,0.004435539245605469,0.743837 +640,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.17687176552783,34.929241693507976,-351.0830657253844,0.004435539245605469,0.810979 +660,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.17965741293356,34.892251240403844,-347.8114699445998,0.004435539245605469,0.87889 +680,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.2564554130016,34.924087575336145,-353.970503405387,0.004435539245605469,0.947564 +700,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.205643809070587,34.8991435368638,-362.77351000506667,0.004435539245605469,1.017054 +720,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.176512353694502,34.84497410939031,-369.61240318757564,0.004435539245605469,1.087302 +740,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.10578554229227,34.74995813099662,-367.37224871607793,0.004435539245605469,1.158309 +760,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.047274834607858,34.691812272848594,-370.11801689051305,0.004435539245605469,1.23401 +780,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.10380007346799,34.703384372728905,-372.0292841604823,0.004435539245605469,1.319073 +800,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.08555480002386,34.670374973731704,-374.68721566223496,0.004435539245605469,1.406663 +820,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.19885148971359,34.750222550469864,-380.56447731408525,0.004435539245605469,1.496625 +840,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.254637155655836,34.7657556157579,-384.4513633760299,0.004435539245605469,1.5889890000000002 +860,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.316317159155098,34.79186578621207,-384.3655387384781,0.004435539245605469,1.693075 +880,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.241979864666273,34.706915019978055,-380.5804591262153,0.004435539245605469,2.065505 +900,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.224226006229813,34.673719949901624,-381.45588348920205,0.004435539245605469,2.440347 +920,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.14134426690263,34.58836614994504,-385.8283921715467,0.004435539245605469,2.817664 +940,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.997921544748234,34.45657797481166,-386.1428842704396,0.004435539245605469,3.197344 +960,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.060400411885407,34.566740304864304,-392.69608794190344,0.004435539245605469,3.57811 +980,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.96911325529305,34.515642414657464,-399.1566023636026,0.004435539245605469,3.959721 +1000,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.09609109084082,34.63142513356851,-408.62698817771025,0.004435539245605469,4.342153 +1001,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.093334457100017,34.62570772928248,-408.7651443035993,0.004435539245605469,4.724752 +11,Regression,k-Nearest Neighbors,ChickWeights,4.6439393939393945,12.708027567111456,-206.8805289598106,0.0208587646484375,0.001745 +22,Regression,k-Nearest Neighbors,ChickWeights,2.7674242424242426,9.021574170013263,-85.19732920009746,0.030094146728515625,0.005817 +33,Regression,k-Nearest Neighbors,ChickWeights,2.3601010101010105,7.4346315168437105,-37.38846247874159,0.0395355224609375,0.012524 +44,Regression,k-Nearest Neighbors,ChickWeights,1.9882575757575767,6.459864921032004,-31.8544119108943,0.0488433837890625,0.023287000000000002 +55,Regression,k-Nearest Neighbors,ChickWeights,2.201515151515152,6.079045396219125,-6.214006750846093,0.05837249755859375,0.035467 +66,Regression,k-Nearest Neighbors,ChickWeights,2.2709595959595963,5.693634951086079,-1.7279153546475992,0.06856918334960938,0.049542 +77,Regression,k-Nearest Neighbors,ChickWeights,2.6114718614718617,5.706903555891601,-0.8368793810695487,0.0782623291015625,0.06880900000000001 +88,Regression,k-Nearest Neighbors,ChickWeights,2.5236742424242427,5.412016943708686,-0.4977726858852578,0.08795547485351562,0.09929800000000001 +99,Regression,k-Nearest Neighbors,ChickWeights,2.4695286195286204,5.169211114529652,-0.1428260058474422,0.09764862060546875,0.13234700000000002 +110,Regression,k-Nearest Neighbors,ChickWeights,2.7553030303030313,5.269495069058163,0.1706792355598563,0.10734176635742188,0.16795900000000002 +121,Regression,k-Nearest Neighbors,ChickWeights,3.1511019283746564,5.580125306741311,0.2837685080447375,0.117034912109375,0.20637100000000003 +132,Regression,k-Nearest Neighbors,ChickWeights,3.3157828282828294,5.649452649212155,0.3999904226030885,0.12723159790039062,0.24803200000000003 +143,Regression,k-Nearest Neighbors,ChickWeights,3.6019813519813537,5.868270501527574,0.47563568627460706,0.13692474365234375,0.31342200000000003 +154,Regression,k-Nearest Neighbors,ChickWeights,3.7459956709956725,5.964828521670115,0.5395766265984425,0.14661788940429688,0.409887 +165,Regression,k-Nearest Neighbors,ChickWeights,4.050202020202021,6.4542180762994805,0.5666546129487657,0.15631103515625,0.575073 +176,Regression,k-Nearest Neighbors,ChickWeights,4.420928030303032,6.954884488253524,0.5942812793055753,0.16600418090820312,0.746583 +187,Regression,k-Nearest Neighbors,ChickWeights,4.757664884135474,7.278917476631412,0.6361362300357987,0.17569732666015625,0.9226570000000001 +198,Regression,k-Nearest Neighbors,ChickWeights,5.192340067340069,7.767087259749381,0.6704396407154757,0.18589401245117188,1.103506 +209,Regression,k-Nearest Neighbors,ChickWeights,5.571690590111645,8.414476478500024,0.6811438926382001,0.195587158203125,1.2899880000000001 +220,Regression,k-Nearest Neighbors,ChickWeights,6.017651515151518,9.535434778453542,0.641509702161033,0.20528030395507812,1.4817580000000001 +231,Regression,k-Nearest Neighbors,ChickWeights,6.514646464646468,10.15268578355149,0.652376522878304,0.21497344970703125,1.688886 +242,Regression,k-Nearest Neighbors,ChickWeights,7.006955922865016,10.883499074839365,0.6785664047839641,0.22466659545898438,1.9058080000000002 +253,Regression,k-Nearest Neighbors,ChickWeights,7.401119894598158,11.259257694820905,0.7012209269570091,0.2343597412109375,2.129833 +264,Regression,k-Nearest Neighbors,ChickWeights,7.873800505050509,12.237701558545494,0.6775097363055258,0.24460983276367188,2.3598440000000003 +275,Regression,k-Nearest Neighbors,ChickWeights,8.501393939393942,13.456617650281162,0.6568816796501455,0.254302978515625,2.605392 +286,Regression,k-Nearest Neighbors,ChickWeights,8.999592074592076,14.081405883193678,0.6745818706784585,0.2639961242675781,2.8696780000000004 +297,Regression,k-Nearest Neighbors,ChickWeights,9.403647586980924,14.487230370517851,0.7012657763253116,0.27368927001953125,3.1592620000000005 +308,Regression,k-Nearest Neighbors,ChickWeights,9.825595238095241,15.247017337775036,0.7053028346163965,0.2833824157714844,3.4714450000000006 +319,Regression,k-Nearest Neighbors,ChickWeights,10.570794148380358,17.082267622288043,0.6643188025566307,0.2930755615234375,3.8957660000000005 +330,Regression,k-Nearest Neighbors,ChickWeights,11.342676767676771,18.20491056057454,0.6737311884314376,0.3032722473144531,4.333389 +341,Regression,k-Nearest Neighbors,ChickWeights,11.756256109481921,18.5968301788559,0.6951271166039881,0.31296539306640625,4.790776 +352,Regression,k-Nearest Neighbors,ChickWeights,12.16955492424243,18.94133239132977,0.7124941202708752,0.3226585388183594,5.260338 +363,Regression,k-Nearest Neighbors,ChickWeights,12.609595959595964,19.7022738973151,0.6979354313341102,0.3323516845703125,5.750168 +374,Regression,k-Nearest Neighbors,ChickWeights,13.251024955436726,20.7851367099449,0.6909564285254863,0.3420448303222656,6.251716 +385,Regression,k-Nearest Neighbors,ChickWeights,13.78255411255412,21.481025974379733,0.7079595790244884,0.35224151611328125,6.769761 +396,Regression,k-Nearest Neighbors,ChickWeights,14.010311447811455,21.53574862211497,0.7263147242326703,0.3619346618652344,7.297159 +407,Regression,k-Nearest Neighbors,ChickWeights,14.576126126126132,22.56379182999173,0.720735043690873,0.3716278076171875,7.841892 +418,Regression,k-Nearest Neighbors,ChickWeights,15.256658692185015,23.708044463333223,0.710588766956741,0.38134765625,8.4256 +429,Regression,k-Nearest Neighbors,ChickWeights,15.863597513597522,24.650993900023582,0.7219567169230845,0.3910675048828125,9.118775999999999 +440,Regression,k-Nearest Neighbors,ChickWeights,16.15655303030304,24.89490243600041,0.7364984966983625,0.4007606506347656,9.834439999999999 +451,Regression,k-Nearest Neighbors,ChickWeights,16.474242424242437,25.235361878916873,0.7407521096740679,0.41095733642578125,10.564262999999999 +462,Regression,k-Nearest Neighbors,ChickWeights,17.206240981241,26.51959634874256,0.731081178462164,0.42067718505859375,11.311639 +473,Regression,k-Nearest Neighbors,ChickWeights,18.061486962649766,27.919441407022266,0.7368140706560946,0.430450439453125,12.077289 +484,Regression,k-Nearest Neighbors,ChickWeights,18.444800275482105,28.396609389438456,0.742660608098584,0.4401702880859375,12.86575 +495,Regression,k-Nearest Neighbors,ChickWeights,18.85067340067341,28.917019336286593,0.7489686179689856,0.4499168395996094,13.665871000000001 +506,Regression,k-Nearest Neighbors,ChickWeights,19.397397891963116,29.705616030262235,0.7427898649120724,2.5872955322265625,16.820609 +517,Regression,k-Nearest Neighbors,ChickWeights,20.115441650548043,30.735303248634356,0.7401565757784102,2.6296463012695312,20.027458000000003 +528,Regression,k-Nearest Neighbors,ChickWeights,20.836142676767683,31.986233829047414,0.7469752640852343,2.6746597290039062,23.267231000000002 +539,Regression,k-Nearest Neighbors,ChickWeights,21.017594310451457,32.125858524254696,0.7553011842320496,2.717792510986328,26.555547000000004 +550,Regression,k-Nearest Neighbors,ChickWeights,21.677242424242426,32.83678407493398,0.7522301799631583,2.769092559814453,29.885669000000004 +561,Regression,k-Nearest Neighbors,ChickWeights,22.80977421271539,35.198755082788004,0.727753941720713,2.8112449645996094,33.249092000000005 +572,Regression,k-Nearest Neighbors,ChickWeights,24.195600233100233,38.25560047694445,0.7025325582791198,2.857513427734375,36.653026000000004 +578,Regression,k-Nearest Neighbors,ChickWeights,24.840628604382932,39.201635479156685,0.6952358931227007,2.8852157592773438,40.087818000000006 +20,Regression,k-Nearest Neighbors,TrumpApproval,2.554585433333335,9.794739803036965,-224.02989290855143,0.033588409423828125,0.001545 +40,Regression,k-Nearest Neighbors,TrumpApproval,1.7993247666666672,6.973235588114817,-18.54942689237887,0.055484771728515625,0.0054 +60,Regression,k-Nearest Neighbors,TrumpApproval,1.366773144444445,5.705236645726316,-16.642396889136542,0.07735443115234375,0.012332 +80,Regression,k-Nearest Neighbors,TrumpApproval,1.1277757833333335,4.947712433075743,-12.30953248968821,0.09975433349609375,0.028826 +100,Regression,k-Nearest Neighbors,TrumpApproval,1.046201766666667,4.4398629296748915,-5.724544452799038,0.12165069580078125,0.050318 +120,Regression,k-Nearest Neighbors,TrumpApproval,1.000865705555556,4.0744555355418335,-3.804331488196434,0.14354705810546875,0.086896 +140,Regression,k-Nearest Neighbors,TrumpApproval,0.9447764619047624,3.7809361134406263,-3.275153002458012,0.16594696044921875,0.149837 +160,Regression,k-Nearest Neighbors,TrumpApproval,0.9352969166666671,3.5531790499707645,-2.3296179824080356,0.18784332275390625,0.23044599999999998 +180,Regression,k-Nearest Neighbors,TrumpApproval,0.9445764925925928,3.380979243961517,-1.647692611170827,0.20973968505859375,0.432465 +200,Regression,k-Nearest Neighbors,TrumpApproval,0.9456943733333335,3.2327893391999836,-1.427877878808435,0.23213958740234375,0.648003 +220,Regression,k-Nearest Neighbors,TrumpApproval,0.9124697575757575,3.0919339165015143,-1.3957229068060464,0.25403594970703125,0.888162 +240,Regression,k-Nearest Neighbors,TrumpApproval,0.9329223611111109,2.985727855147271,-1.2507750530936188,0.27593231201171875,1.171554 +260,Regression,k-Nearest Neighbors,TrumpApproval,0.9025974717948716,2.873740673763463,-1.11319648675526,0.2984657287597656,1.484213 +280,Regression,k-Nearest Neighbors,TrumpApproval,0.8654126523809523,2.773524640439575,-1.0608690746642817,0.3203620910644531,1.81098 +300,Regression,k-Nearest Neighbors,TrumpApproval,0.8525042622222223,2.688069339615046,-0.9037818439458585,0.3422584533691406,2.170241 +320,Regression,k-Nearest Neighbors,TrumpApproval,0.8265282395833334,2.6077957497476296,-0.880493509713772,0.3646583557128906,2.5581009999999997 +340,Regression,k-Nearest Neighbors,TrumpApproval,0.8137511019607846,2.539210136300266,-0.8840673465916704,0.3865547180175781,3.1375499999999996 +360,Regression,k-Nearest Neighbors,TrumpApproval,0.7887328240740744,2.4696835584739105,-0.7969398815662787,0.4084510803222656,3.7440789999999997 +380,Regression,k-Nearest Neighbors,TrumpApproval,0.7710879228070179,2.4087271831437693,-0.7684619785143365,0.4303474426269531,4.380375 +400,Regression,k-Nearest Neighbors,TrumpApproval,0.756179386666667,2.351105641867075,-0.7324819925835522,0.4527473449707031,5.04744 +420,Regression,k-Nearest Neighbors,TrumpApproval,0.7300392539682541,2.295700426816902,-0.7064552265553199,0.4746437072753906,5.735437 +440,Regression,k-Nearest Neighbors,TrumpApproval,0.7180258560606063,2.24592493832078,-0.6037054809307543,0.4965400695800781,6.669957 +460,Regression,k-Nearest Neighbors,TrumpApproval,0.7103659666666668,2.200554873752302,-0.45996881871915263,0.5189399719238281,7.633919000000001 +480,Regression,k-Nearest Neighbors,TrumpApproval,0.6905233472222223,2.1551860359584523,-0.36817166319202155,0.5408363342285156,8.635162000000001 +500,Regression,k-Nearest Neighbors,TrumpApproval,0.6835753693333335,2.1161668272230596,-0.2914054626850582,2.7066993713378906,12.111573000000002 +520,Regression,k-Nearest Neighbors,TrumpApproval,0.6741869282051286,2.0775236231845557,-0.24684449790743135,2.7946739196777344,15.640183000000002 +540,Regression,k-Nearest Neighbors,TrumpApproval,0.6635047197530868,2.0412653603832833,-0.19929175315985925,2.8836631774902344,19.224183000000004 +560,Regression,k-Nearest Neighbors,TrumpApproval,0.6666769047619049,2.01181749557566,-0.19269635778937388,2.973125457763672,22.863484000000003 +580,Regression,k-Nearest Neighbors,TrumpApproval,0.662313208045977,1.9804661409620818,-0.18439917312598686,3.0619163513183594,26.560120000000005 +600,Regression,k-Nearest Neighbors,TrumpApproval,0.6595208444444446,1.9515625148224913,-0.13735805248393262,3.158283233642578,30.314082000000006 +620,Regression,k-Nearest Neighbors,TrumpApproval,0.6603871010752689,1.924909501402362,-0.08963762017358134,3.248737335205078,34.12595900000001 +640,Regression,k-Nearest Neighbors,TrumpApproval,0.6518434010416667,1.8967107462711992,-0.038171332083393406,3.341320037841797,37.99311800000001 +660,Regression,k-Nearest Neighbors,TrumpApproval,0.6481796161616163,1.873162681009878,-0.005272423030620033,3.435527801513672,41.91854100000001 +680,Regression,k-Nearest Neighbors,TrumpApproval,0.6594073715686274,1.8574009428793898,-0.004045635504021261,3.5269508361816406,45.90599500000001 +700,Regression,k-Nearest Neighbors,TrumpApproval,0.6619153695238096,1.8376987056605067,-0.008672432190871993,3.615283966064453,49.957909000000015 +720,Regression,k-Nearest Neighbors,TrumpApproval,0.6538050537037038,1.8142062090777376,-0.004645759004504146,3.7059364318847656,54.07172000000001 +740,Regression,k-Nearest Neighbors,TrumpApproval,0.6437102684684685,1.7904191974020043,0.02211499739805689,3.8005104064941406,58.24412800000001 +760,Regression,k-Nearest Neighbors,TrumpApproval,0.6465423666666668,1.7722456151874884,0.03148604083873929,3.8912620544433594,62.47501600000001 +780,Regression,k-Nearest Neighbors,TrumpApproval,0.6423591829059828,1.752432393946061,0.0487781645727875,3.987659454345703,66.76475200000002 +800,Regression,k-Nearest Neighbors,TrumpApproval,0.6415445258333332,1.7335108155585357,0.060790557140155244,4.087154388427734,71.11932700000001 +820,Regression,k-Nearest Neighbors,TrumpApproval,0.641812437398374,1.7198679523833968,0.06536301290969171,4.179523468017578,75.53533700000001 +840,Regression,k-Nearest Neighbors,TrumpApproval,0.6391550126984127,1.7023246638821516,0.07583179507597027,4.276576995849609,80.015814 +860,Regression,k-Nearest Neighbors,TrumpApproval,0.6397551612403103,1.6865214638981003,0.0944734629735503,4.372867584228516,84.56990800000001 +880,Regression,k-Nearest Neighbors,TrumpApproval,0.6401663234848486,1.6719359262678322,0.11449022182092672,4.465221405029297,89.18993400000001 +900,Regression,k-Nearest Neighbors,TrumpApproval,0.6373928251851855,1.6559913256631793,0.1276383063389357,4.558887481689453,93.87754600000001 +920,Regression,k-Nearest Neighbors,TrumpApproval,0.6333341724637681,1.6410816825275085,0.12919955333528133,4.652858734130859,98.62624600000001 +940,Regression,k-Nearest Neighbors,TrumpApproval,0.637460545390071,1.6307722122541641,0.13281132177791266,4.746517181396484,103.437785 +960,Regression,k-Nearest Neighbors,TrumpApproval,0.6446958777777775,1.6213030711335545,0.13389079092896516,4.844425201416016,108.312072 +980,Regression,k-Nearest Neighbors,TrumpApproval,0.643768610068027,1.6085965270907718,0.1308548353743899,4.935100555419922,113.251739 +1000,Regression,k-Nearest Neighbors,TrumpApproval,0.6420156240666665,1.59493855356346,0.13116812210504825,5.030651092529297,118.255967 +1001,Regression,k-Nearest Neighbors,TrumpApproval,0.6416785025641023,1.5941707450098015,0.1314249186277071,5.032634735107422,123.301096 +11,Regression,Hoeffding Tree,ChickWeights,8.042756132756132,17.336048579080593,-385.86349170941764,0.016208648681640625,0.002632 +22,Regression,Hoeffding Tree,ChickWeights,4.456785613727984,12.282422261556867,-158.770726389092,0.017787933349609375,0.007319 +33,Regression,Hoeffding Tree,ChickWeights,3.4353973358733074,10.070376517434479,-69.4325218162971,0.023052215576171875,0.013907 +44,Regression,Hoeffding Tree,ChickWeights,2.736909422894262,8.732393473100391,-59.03623058514604,0.024105072021484375,0.021700999999999998 +55,Regression,Hoeffding Tree,ChickWeights,2.788577579622257,8.074088551816661,-11.726025456653014,0.030948638916015625,0.030334999999999997 +66,Regression,Hoeffding Tree,ChickWeights,3.3958800855981375,7.878422021930021,-4.223121571879303,0.040424346923828125,0.040093 +77,Regression,Hoeffding Tree,ChickWeights,3.8895265016210883,7.800910386370324,-2.432180745921895,0.046741485595703125,0.05116999999999999 +88,Regression,Hoeffding Tree,ChickWeights,4.072650698433535,7.572197783925699,-1.9320509270116553,0.052532196044921875,0.06356099999999999 +99,Regression,Hoeffding Tree,ChickWeights,4.410984939713907,7.55185413515251,-1.439151418709002,0.053585052490234375,0.07724199999999999 +110,Regression,Hoeffding Tree,ChickWeights,4.370948473977548,7.327634340090197,-0.6036593212329582,0.055164337158203125,0.09210199999999999 +121,Regression,Hoeffding Tree,ChickWeights,4.401973824893138,7.197046558152955,-0.19144536988389782,0.055164337158203125,0.10816699999999999 +132,Regression,Hoeffding Tree,ChickWeights,4.283071400630936,6.979735895990854,0.08415196835499827,0.055164337158203125,0.13239599999999999 +143,Regression,Hoeffding Tree,ChickWeights,4.169649051526778,6.77851615807502,0.3003478880703081,0.055690765380859375,0.16024499999999997 +154,Regression,Hoeffding Tree,ChickWeights,4.107721988217097,6.620782354691122,0.4327427443050297,0.055690765380859375,0.19148199999999999 +165,Regression,Hoeffding Tree,ChickWeights,4.3861341291386235,6.8739888422895685,0.5084535624523276,0.055690765380859375,0.23199899999999998 +176,Regression,Hoeffding Tree,ChickWeights,4.592324836010107,7.0395287886899816,0.5843455987500039,0.056217193603515625,0.273788 +187,Regression,Hoeffding Tree,ChickWeights,4.658423416973056,7.057579140031887,0.6579286220132116,0.056217193603515625,0.316974 +198,Regression,Hoeffding Tree,ChickWeights,4.6782517314261085,7.042640058036562,0.7290497323677609,0.056217193603515625,0.361531 +209,Regression,Hoeffding Tree,ChickWeights,4.8966529592561265,7.410861778989444,0.7526693351807108,0.02174663543701172,0.409543 +220,Regression,Hoeffding Tree,ChickWeights,5.507880191409123,8.546476599974424,0.7120144996082314,0.02806377410888672,0.458317 +231,Regression,Hoeffding Tree,ChickWeights,5.703958017872014,8.760797449465004,0.7411581545051223,0.03332805633544922,0.507954 +242,Regression,Hoeffding Tree,ChickWeights,5.934527728379076,9.145062262320872,0.7730513990797492,0.03806591033935547,0.576578 +253,Regression,Hoeffding Tree,ChickWeights,6.025889093973978,9.259481324724224,0.7979290061199974,0.04175090789794922,0.647861 +264,Regression,Hoeffding Tree,ChickWeights,6.701040765258382,10.569442782845146,0.7594412957229723,0.041831016540527344,0.7217790000000001 +275,Regression,Hoeffding Tree,ChickWeights,7.201977905163474,11.695812678726385,0.740801257827299,0.041831016540527344,0.7983520000000001 +286,Regression,Hoeffding Tree,ChickWeights,7.4760897436283305,12.176082777300051,0.7566872347890514,0.042357444763183594,0.889757 +297,Regression,Hoeffding Tree,ChickWeights,7.495029117947843,12.186858586615225,0.7886035011133373,0.042357444763183594,0.982264 +308,Regression,Hoeffding Tree,ChickWeights,8.05089484284177,13.06419009031293,0.7836428997387894,0.042357444763183594,1.075782 +319,Regression,Hoeffding Tree,ChickWeights,9.171875092169309,15.802620207207104,0.7127274179827436,0.042357444763183594,1.1703270000000001 +330,Regression,Hoeffding Tree,ChickWeights,9.626867556328977,16.443718231711543,0.7338058453397931,0.042357444763183594,1.26591 +341,Regression,Hoeffding Tree,ChickWeights,9.854283538219805,16.574189924013226,0.7578382368534643,0.042357444763183594,1.362543 +352,Regression,Hoeffding Tree,ChickWeights,10.034558550660114,16.72149964752778,0.7759339138910493,0.042357444763183594,1.460131 +363,Regression,Hoeffding Tree,ChickWeights,10.942839439265006,18.18973374364872,0.7425340708967089,0.042357444763183594,1.65219 +374,Regression,Hoeffding Tree,ChickWeights,11.480189522121245,19.36955258798825,0.7316181626186655,0.042357444763183594,1.847066 +385,Regression,Hoeffding Tree,ChickWeights,11.884428250077962,20.018801475409063,0.7463650656532205,0.042357444763183594,2.044712 +396,Regression,Hoeffding Tree,ChickWeights,12.037067702603977,20.025071614924446,0.7633646392298079,0.042357444763183594,2.245044 +407,Regression,Hoeffding Tree,ChickWeights,12.938689395183468,21.571547182252875,0.7447563988620904,0.039313316345214844,2.459951 +418,Regression,Hoeffding Tree,ChickWeights,13.737065020554605,23.070023559587742,0.7259561921053947,0.039839744567871094,2.675857 +429,Regression,Hoeffding Tree,ChickWeights,14.305628841534727,24.020997573013894,0.7359868139097058,0.040892601013183594,2.892761 +440,Regression,Hoeffding Tree,ChickWeights,14.503019064271445,24.118168317988548,0.7526847575357923,0.041419029235839844,3.110678 +451,Regression,Hoeffding Tree,ChickWeights,15.042001004765993,24.757154413851225,0.7504844548860922,0.042998313903808594,3.329579 +462,Regression,Hoeffding Tree,ChickWeights,16.165694044127083,26.934291479182736,0.7226050873941003,0.043524742126464844,3.549505 +473,Regression,Hoeffding Tree,ChickWeights,16.958578383564387,28.26726815061745,0.7302155620528221,0.043524742126464844,3.77047 +484,Regression,Hoeffding Tree,ChickWeights,17.309589456804158,28.5754148947933,0.7394096166099926,0.043524742126464844,4.010874 +495,Regression,Hoeffding Tree,ChickWeights,17.77955786237919,29.119281838039548,0.7454446166142166,0.043524742126464844,4.254034 +506,Regression,Hoeffding Tree,ChickWeights,18.687135400012505,30.600738447390604,0.7270552375925041,0.043524742126464844,4.499866 +517,Regression,Hoeffding Tree,ChickWeights,19.426270300418786,31.613839238226678,0.7250895764829616,0.043524742126464844,4.748399 +528,Regression,Hoeffding Tree,ChickWeights,20.230319490239392,32.829508990096734,0.7334580691909136,0.043524742126464844,5.00325 +539,Regression,Hoeffding Tree,ChickWeights,20.415951878027045,32.83473210597698,0.7443832332812113,0.043524742126464844,5.259172 +550,Regression,Hoeffding Tree,ChickWeights,21.41946931942451,34.477948502753435,0.726844465494657,0.043524742126464844,5.516121 +561,Regression,Hoeffding Tree,ChickWeights,22.135259536350134,35.412182207518484,0.7244424125617825,0.043524742126464844,5.774111 +572,Regression,Hoeffding Tree,ChickWeights,22.998428764364284,36.61317436816486,0.7275265693889857,0.044051170349121094,6.033148000000001 +578,Regression,Hoeffding Tree,ChickWeights,23.16185046142029,36.73359474841229,0.7324023432169282,0.044051170349121094,6.293050000000001 +20,Regression,Hoeffding Tree,TrumpApproval,4.834704431652337,13.708514217962266,-439.7934984576362,0.05008697509765625,0.001817 +40,Regression,Hoeffding Tree,TrumpApproval,3.4692310697037447,9.813795721313518,-37.72035957928713,0.07324981689453125,0.005553000000000001 +60,Regression,Hoeffding Tree,TrumpApproval,2.530247618203559,8.024836796214231,-33.90460110966681,0.08588409423828125,0.011369 +80,Regression,Hoeffding Tree,TrumpApproval,2.1398752670733447,6.982837000856316,-25.510487239912003,0.09588623046875,0.023421 +100,Regression,Hoeffding Tree,TrumpApproval,2.2521629689485394,6.362737158647257,-12.810573390910955,0.1053619384765625,0.037893 +120,Regression,Hoeffding Tree,TrumpApproval,2.2753311831165886,5.895687482983747,-9.059182991303912,0.1095733642578125,0.054815 +140,Regression,Hoeffding Tree,TrumpApproval,2.181766409647037,5.493495699082884,-8.025069637302263,0.1116790771484375,0.07421900000000001 +160,Regression,Hoeffding Tree,TrumpApproval,2.0635226048812747,5.165876255053421,-6.037983110569301,0.1158905029296875,0.09851900000000001 +180,Regression,Hoeffding Tree,TrumpApproval,1.9951428730766114,4.906287161641783,-4.575559841528811,0.1179962158203125,0.13018800000000003 +200,Regression,Hoeffding Tree,TrumpApproval,1.8700446037321659,4.662539866408188,-4.050299616280768,0.015085220336914062,0.16948700000000003 +220,Regression,Hoeffding Tree,TrumpApproval,1.7830718267282506,4.458344141345012,-3.981078161152351,0.031249046325683594,0.21017900000000003 +240,Regression,Hoeffding Tree,TrumpApproval,1.714887283408722,4.280191261764102,-3.6254927572925757,0.037039756774902344,0.27354600000000007 +260,Regression,Hoeffding Tree,TrumpApproval,1.6268995152596541,4.116599014627653,-3.336325373761703,0.044569969177246094,0.33844300000000005 +280,Regression,Hoeffding Tree,TrumpApproval,1.6037708656255951,3.992199218884993,-3.269831686495559,0.05755901336669922,0.40512500000000007 +300,Regression,Hoeffding Tree,TrumpApproval,1.5808413297038584,3.882244388071726,-2.9710192082752114,0.06756114959716797,0.4768960000000001 +320,Regression,Hoeffding Tree,TrumpApproval,1.5112246352788372,3.7620340381312185,-2.9135432145577016,0.07545757293701172,0.5560110000000001 +340,Regression,Hoeffding Tree,TrumpApproval,1.464954049061847,3.6574443601858126,-2.9089002921657214,0.08072185516357422,0.6372390000000001 +360,Regression,Hoeffding Tree,TrumpApproval,1.4845626481571885,3.5832345434246853,-2.782695640732784,0.08861827850341797,0.7206760000000001 +380,Regression,Hoeffding Tree,TrumpApproval,1.4519403327978173,3.4965427251184518,-2.72647470962537,0.09388256072998047,0.8063740000000001 +400,Regression,Hoeffding Tree,TrumpApproval,1.4093274160891025,3.4133346926199284,-2.6515915354000197,0.10125255584716797,0.8943350000000001 +420,Regression,Hoeffding Tree,TrumpApproval,1.3677964737960675,3.3343173536823296,-2.5997996751089016,0.10546398162841797,1.079576 +440,Regression,Hoeffding Tree,TrumpApproval,1.3357172246731819,3.2621145597551164,-2.3832380441779537,0.11125469207763672,1.2716070000000002 +460,Regression,Hoeffding Tree,TrumpApproval,1.3223220949397412,3.20054856097613,-2.088360697350681,0.11967754364013672,1.466366 +480,Regression,Hoeffding Tree,TrumpApproval,1.2961820395725512,3.1370925842333546,-1.8988499404168713,0.12757396697998047,1.663894 +500,Regression,Hoeffding Tree,TrumpApproval,1.2652762767168435,3.076750388249757,-1.7299037995212605,0.13231182098388672,1.864298 +520,Regression,Hoeffding Tree,TrumpApproval,1.2471740635308572,3.0222901376128295,-1.6387160551274738,0.13757610321044922,2.0709020000000002 +540,Regression,Hoeffding Tree,TrumpApproval,1.222508472081129,2.9683885282447466,-1.5361060189709668,0.13968181610107422,2.286544 +560,Regression,Hoeffding Tree,TrumpApproval,1.2073384071706728,2.920065266046622,-1.5126838513129575,0.14441967010498047,2.5053300000000003 +580,Regression,Hoeffding Tree,TrumpApproval,1.1845779132924192,2.8723790540044147,-1.4914188956527816,0.14705181121826172,2.7271330000000003 +600,Regression,Hoeffding Tree,TrumpApproval,1.1745692976588702,2.8296294830278073,-1.3910651808346999,0.14148998260498047,2.96944 +620,Regression,Hoeffding Tree,TrumpApproval,1.1708259630571383,2.7920061348512903,-1.2924200227078337,0.14412212371826172,3.214893 +640,Regression,Hoeffding Tree,TrumpApproval,1.1599967464968943,2.7528504813508814,-1.186915838733254,0.14622783660888672,3.481443 +660,Regression,Hoeffding Tree,TrumpApproval,1.1455993461288598,2.715465758170179,-1.112620243595547,0.09333324432373047,3.768351 +680,Regression,Hoeffding Tree,TrumpApproval,1.1331386715536063,2.679518493749607,-1.0895638535289454,0.10280895233154297,4.057625 +700,Regression,Hoeffding Tree,TrumpApproval,1.1287919059851137,2.648832972736431,-1.0956110522943683,0.11070537567138672,4.349483 +720,Regression,Hoeffding Tree,TrumpApproval,1.1090542602054634,2.6130484736329,-1.0841769561048746,0.11702251434326172,4.655905000000001 +740,Regression,Hoeffding Tree,TrumpApproval,1.0919225542546631,2.579731998640208,-1.0301471378292058,0.12070751190185547,4.969391000000001 +760,Regression,Hoeffding Tree,TrumpApproval,1.0729346607841277,2.546521266569091,-0.9996439724530697,0.12386608123779297,5.409378000000001 +780,Regression,Hoeffding Tree,TrumpApproval,1.0548522699101792,2.514796200212546,-0.958866579835745,0.13018321990966797,5.856313000000001 +800,Regression,Hoeffding Tree,TrumpApproval,1.0458975693179249,2.4863814517835756,-0.9321678603320387,0.14051342010498047,6.306401000000001 +820,Regression,Hoeffding Tree,TrumpApproval,1.042667475968943,2.463395040447954,-0.9174360179218257,0.14683055877685547,6.759550000000001 +840,Regression,Hoeffding Tree,TrumpApproval,1.0338402028724885,2.4371652901742165,-0.8942452584110789,0.15051555633544922,7.215791000000001 +860,Regression,Hoeffding Tree,TrumpApproval,1.0182769822752689,2.409744604248102,-0.8486703239118398,0.15209484100341797,7.680462000000001 +880,Regression,Hoeffding Tree,TrumpApproval,1.0072949101764561,2.3841216724611445,-0.8005738256179296,0.15525341033935547,8.149683000000001 +900,Regression,Hoeffding Tree,TrumpApproval,0.9984699415812968,2.359722022526475,-0.7713409518355698,0.15735912322998047,8.622145000000002 +920,Regression,Hoeffding Tree,TrumpApproval,0.9848390746890626,2.3349754381173082,-0.7628805257854674,0.12545299530029297,9.108636 +940,Regression,Hoeffding Tree,TrumpApproval,0.9804934467335737,2.3136297350671566,-0.7454793227879806,0.13442516326904297,9.599086 +960,Regression,Hoeffding Tree,TrumpApproval,0.9715993160407668,2.291923159938466,-0.7307898991199615,0.14021587371826172,10.092417 +980,Regression,Hoeffding Tree,TrumpApproval,0.96479276321034,2.271398262551761,-0.7329444574748756,0.14442729949951172,10.588738 +1000,Regression,Hoeffding Tree,TrumpApproval,0.9567764270416781,2.250974677037298,-0.7305695321170174,0.14863872528076172,11.174487 +1001,Regression,Hoeffding Tree,TrumpApproval,0.9561028857812052,2.249867758958838,-0.7300222157865335,0.14863872528076172,11.765557999999999 +11,Regression,Hoeffding Adaptive Tree,ChickWeights,8.051220648038832,17.336198122120386,-385.87016600913427,0.022922515869140625,0.002862 +22,Regression,Hoeffding Adaptive Tree,ChickWeights,4.498502947359929,12.285286375364281,-158.84524831763767,0.024562835693359375,0.008031 +33,Regression,Hoeffding Adaptive Tree,ChickWeights,3.4668695042339137,10.074636808082968,-69.49212762837747,0.029827117919921875,0.0152 +44,Regression,Hoeffding Adaptive Tree,ChickWeights,2.7637805804889553,8.735764655686483,-59.08259408516962,0.030941009521484375,0.027573 +55,Regression,Hoeffding Adaptive Tree,ChickWeights,2.814517498310432,8.074396776941786,-11.726997097138026,0.037784576416015625,0.040817 +66,Regression,Hoeffding Adaptive Tree,ChickWeights,3.396900059747575,7.862006773633152,-4.201378762014764,0.047260284423828125,0.055065 +77,Regression,Hoeffding Adaptive Tree,ChickWeights,3.8844336568547537,7.782255505653143,-2.415785129732385,0.053638458251953125,0.07050300000000001 +88,Regression,Hoeffding Adaptive Tree,ChickWeights,4.068768385552718,7.555909217267645,-1.9194502155140074,0.059429168701171875,0.08723500000000001 +99,Regression,Hoeffding Adaptive Tree,ChickWeights,4.319029347030655,7.489629607912237,-1.3991215781815165,0.060482025146484375,0.105314 +110,Regression,Hoeffding Adaptive Tree,ChickWeights,4.231978704025333,7.230698639905546,-0.5615110336669555,0.062061309814453125,0.124657 +121,Regression,Hoeffding Adaptive Tree,ChickWeights,4.279767976439616,7.114292598648662,-0.1642036472993016,0.062061309814453125,0.145348 +132,Regression,Hoeffding Adaptive Tree,ChickWeights,4.161677712403324,6.8979209349412445,0.1054968774084013,0.062061309814453125,0.183683 +143,Regression,Hoeffding Adaptive Tree,ChickWeights,4.036201943040193,6.686446116179646,0.3192250351622916,0.024164199829101562,0.23334700000000003 +154,Regression,Hoeffding Adaptive Tree,ChickWeights,4.002163310161137,6.555243218534794,0.4439177197734564,0.034926414489746094,0.28395200000000004 +165,Regression,Hoeffding Adaptive Tree,ChickWeights,4.269310553181931,6.794169336453219,0.5198027804498322,0.041365623474121094,0.33552800000000005 +176,Regression,Hoeffding Adaptive Tree,ChickWeights,4.394431170074558,6.916563516446891,0.5987399306940604,0.047156333923339844,0.38817000000000007 +187,Regression,Hoeffding Adaptive Tree,ChickWeights,4.429782113532627,6.896434310822903,0.6733712331422652,0.052016258239746094,0.44195900000000005 +198,Regression,Hoeffding Adaptive Tree,ChickWeights,4.448580123995543,6.86078369215091,0.7428621234581485,0.054648399353027344,0.49692500000000006 +209,Regression,Hoeffding Adaptive Tree,ChickWeights,4.634718338792146,7.17917659207716,0.7678921596594357,0.054648399353027344,0.5531490000000001 +220,Regression,Hoeffding Adaptive Tree,ChickWeights,5.229854791420841,8.435313620968111,0.7194573631198581,0.055296897888183594,0.6106500000000001 +231,Regression,Hoeffding Adaptive Tree,ChickWeights,5.4006373247873825,8.615072190659467,0.7496975788166091,0.055296897888183594,0.6850710000000001 +242,Regression,Hoeffding Adaptive Tree,ChickWeights,5.6226073005416035,8.982158345389516,0.781064800957145,0.055296897888183594,0.7630730000000001 +253,Regression,Hoeffding Adaptive Tree,ChickWeights,5.728895576419993,9.10264619767678,0.8047163053551843,0.055296897888183594,0.8439190000000001 +264,Regression,Hoeffding Adaptive Tree,ChickWeights,6.468790531655633,10.532848432020362,0.7611041743489119,0.05537700653076172,0.926058 +275,Regression,Hoeffding Adaptive Tree,ChickWeights,6.961259791220884,11.725202267966395,0.7394969764024641,0.05537700653076172,1.0095260000000001 +286,Regression,Hoeffding Adaptive Tree,ChickWeights,7.243017687832032,12.175095097400797,0.7567267064951951,0.05391216278076172,1.109836 +297,Regression,Hoeffding Adaptive Tree,ChickWeights,7.333189926829036,12.221129948725446,0.7874128689691341,0.05443859100341797,1.3004930000000001 +308,Regression,Hoeffding Adaptive Tree,ChickWeights,7.907494608974745,13.13418786953933,0.7813182108747583,0.05456066131591797,1.4945650000000001 +319,Regression,Hoeffding Adaptive Tree,ChickWeights,9.086203691627809,16.084282058543664,0.7023956098414756,0.05613994598388672,1.6919570000000002 +330,Regression,Hoeffding Adaptive Tree,ChickWeights,9.398286710797228,16.38837159928856,0.7355947540985646,0.05613994598388672,1.900794 +341,Regression,Hoeffding Adaptive Tree,ChickWeights,9.688169379844998,16.65705092991554,0.7554108572015372,0.05613994598388672,2.110987 +352,Regression,Hoeffding Adaptive Tree,ChickWeights,9.856066264187849,16.815734957180027,0.7734013139584004,0.05613994598388672,2.322457 +363,Regression,Hoeffding Adaptive Tree,ChickWeights,10.788654210226415,18.368645129880047,0.7374443731514406,0.05613994598388672,2.535213 +374,Regression,Hoeffding Adaptive Tree,ChickWeights,11.535989444086796,20.177763325541775,0.7087539856658172,0.0658864974975586,2.749718 +385,Regression,Hoeffding Adaptive Tree,ChickWeights,11.949331836981814,20.800028245688587,0.7261827687212361,0.0713338851928711,2.965855 +396,Regression,Hoeffding Adaptive Tree,ChickWeights,11.958714190964645,20.660643879084812,0.748105206776327,0.07817745208740234,3.1836569999999997 +407,Regression,Hoeffding Adaptive Tree,ChickWeights,12.807531574997112,22.01468171576837,0.7341619793955468,0.08257198333740234,3.4189649999999996 +418,Regression,Hoeffding Adaptive Tree,ChickWeights,13.71794187476778,23.73901232910809,0.7098322050491193,0.08467769622802734,3.6591389999999997 +429,Regression,Hoeffding Adaptive Tree,ChickWeights,14.269314924317156,24.652748132937095,0.7219171428567855,0.06568050384521484,3.9122609999999995 +440,Regression,Hoeffding Adaptive Tree,ChickWeights,14.511771919641937,24.834167752766053,0.7377826277560943,0.0706624984741211,4.16702 +451,Regression,Hoeffding Adaptive Tree,ChickWeights,15.00667707818897,25.401748915029017,0.7373221851710817,0.07874202728271484,4.423509 +462,Regression,Hoeffding Adaptive Tree,ChickWeights,16.106263610815663,27.4394567629727,0.7121021651653525,0.0857076644897461,4.681795 +473,Regression,Hoeffding Adaptive Tree,ChickWeights,16.950411373417108,28.951900473786843,0.7169889638801871,0.0888662338256836,4.941903 +484,Regression,Hoeffding Adaptive Tree,ChickWeights,17.321905164714362,29.29627092175635,0.7260962478080234,0.0889272689819336,5.203768 +495,Regression,Hoeffding Adaptive Tree,ChickWeights,17.829552469069228,29.855361574147427,0.732412614196017,0.0889272689819336,5.467412 +506,Regression,Hoeffding Adaptive Tree,ChickWeights,18.715769054600834,31.21095148117224,0.7160610523989874,0.08951473236083984,5.743327000000001 +517,Regression,Hoeffding Adaptive Tree,ChickWeights,19.54236471467993,32.39367117342827,0.7113596352744775,0.0743856430053711,6.026441000000001 +528,Regression,Hoeffding Adaptive Tree,ChickWeights,20.379374275832948,33.670378810622296,0.7196292071862618,0.0787191390991211,6.311317000000001 +539,Regression,Hoeffding Adaptive Tree,ChickWeights,20.522458105265056,33.639909372937744,0.7316929916628531,0.0872030258178711,6.597982000000001 +550,Regression,Hoeffding Adaptive Tree,ChickWeights,21.5114661084191,35.24478084224406,0.714558707096332,0.0935201644897461,6.886526000000001 +561,Regression,Hoeffding Adaptive Tree,ChickWeights,22.293418976341684,36.29050935662323,0.7106036021726428,0.09343624114990234,7.177067000000001 +572,Regression,Hoeffding Adaptive Tree,ChickWeights,23.158877831353536,37.47206255417766,0.7145930209145848,0.09461116790771484,7.581349000000001 +578,Regression,Hoeffding Adaptive Tree,ChickWeights,23.373902189510932,37.6579284312523,0.7187656938003131,0.09473323822021484,7.990285000000001 +20,Regression,Hoeffding Adaptive Tree,TrumpApproval,4.828377634536296,13.70786256219322,-439.7515918302183,0.05686187744140625,0.005477 +40,Regression,Hoeffding Adaptive Tree,TrumpApproval,3.453811275213839,9.811073218407973,-37.69887927291551,0.08008575439453125,0.014203 +60,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.5116544078850294,8.021960641037959,-33.879585508404254,0.09272003173828125,0.025216000000000002 +80,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.1224425015381523,6.9797990571526345,-25.487425023640153,0.102783203125,0.038581000000000004 +100,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.246653919301699,6.363694444016854,-12.814729355257526,0.1122589111328125,0.054574000000000004 +120,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.270681160376927,5.896666779393501,-9.062525006956841,0.1164703369140625,0.096737 +140,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.162967815650222,5.491011289549727,-8.016908386196121,0.1185760498046875,0.14519300000000002 +160,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.9648637778298337,5.1475477542568076,-5.988130255135697,0.048110008239746094,0.20161800000000002 +180,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.8665278782891501,4.8758843309507505,-4.506673701927233,0.06455135345458984,0.259848 +200,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.773434994745299,4.638841370319518,-3.9990913279754245,0.07511425018310547,0.334681 +220,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.6594682627798778,4.42936028038101,-3.916524330360767,0.0809926986694336,0.415547 +240,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.5811297097344512,4.24689633509078,-3.553810703437006,0.0831594467163086,0.499019 +260,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4918706813368772,4.083314206963185,-3.2664860479391056,0.08697795867919922,0.584777 +280,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4582505621214346,3.950619643811522,-3.181352514384196,0.09651470184326172,0.672987 +300,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4293807431017047,3.836527362327468,-2.8780450161882043,0.10505962371826172,0.763791 +320,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3766835460490845,3.7183907131031066,-2.8232679475596494,0.11137676239013672,0.862886 +340,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3285707966483495,3.6114631285578054,-2.8112330604866624,0.09692668914794922,1.077289 +360,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3305028688272291,3.538102571280229,-2.6880072396238157,0.10482311248779297,1.294249 +380,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3086678355415842,3.4529556765760527,-2.6341471363086995,0.11014842987060547,1.513868 +400,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.256053624567095,3.3666460142322228,-2.552379472359031,0.11751842498779297,1.736306 +420,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2254239545780012,3.2887455105144454,-2.5020714662192383,0.12172985076904297,1.9788649999999999 +440,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.204020924712129,3.2198773978896,-2.2961943419959137,0.12752056121826172,2.244474 +460,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1975328241312166,3.1601130927415366,-2.010817456858815,0.13547801971435547,2.513147 +480,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.186148143661266,3.1001176815841753,-1.8309188655239268,0.14337444305419922,2.784897 +500,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1667856894749518,3.0429667282148514,-1.6702825792738007,0.13623332977294922,3.07197 +520,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.153194144927427,2.98944402729251,-1.5816728306403074,0.14155864715576172,3.362254 +540,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1356058423088553,2.9370365647466374,-1.4828164968540292,0.14366436004638672,3.6556480000000002 +560,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.125648357086568,2.890393580385493,-1.4618789770567937,0.14840221405029297,3.952243 +580,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1072323197222282,2.84377722554966,-1.4420491211959612,0.15103435516357422,4.252035 +600,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0962221602561253,2.8010574809052513,-1.343021715112041,0.15471935272216797,4.557891000000001 +620,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0955492072151647,2.7650292224496735,-1.2483344123018605,0.15787792205810547,4.883158000000001 +640,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.085957414095071,2.726589883354214,-1.1453910301968575,0.15998363494873047,5.354946000000001 +660,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0751762466913892,2.6908702968299423,-1.074523242859362,0.16366863250732422,5.834119000000001 +680,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0667684392102676,2.656475453821568,-1.0537791659469917,0.16630077362060547,6.316591000000001 +700,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0667188907522647,2.6278494556992995,-1.0625405514881172,0.15471935272216797,6.8062700000000005 +720,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0487756272096453,2.5923957614440996,-1.0513617944147002,0.15945720672607422,7.299083 +740,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0336933816342644,2.5596915816453274,-0.9987276211091367,0.16320323944091797,7.795134000000001 +760,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0143808347523189,2.5263993770636084,-0.9681675843117681,0.16478252410888672,8.298925 +780,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0004245938094416,2.495691505058861,-0.9292169429583497,0.16958141326904297,8.809149000000001 +800,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9976736219043986,2.469777786083391,-0.9064485942635294,0.16168498992919922,9.32665 +820,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0020392091388555,2.450590646975973,-0.8975546778436754,0.16431713104248047,9.853038000000002 +840,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9936292081382507,2.424886643827349,-0.8752066007627983,0.16800212860107422,10.484318000000002 +860,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9794930742877991,2.3980423354299125,-0.8307587924463844,0.17010784149169922,11.125036000000001 +880,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9694853941742789,2.372794343098121,-0.7835048635250907,0.17273998260498047,11.769143000000001 +900,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9594920424525858,2.348266033222206,-0.7541836724323567,0.11153697967529297,12.432199 +920,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9482726802907966,2.324135545417226,-0.7465505219679065,0.11639690399169922,13.105410000000001 +940,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9455376055826031,2.30345366329758,-0.7301587545146957,0.12230968475341797,13.781537 +960,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9379298129457146,2.2821811442731286,-0.7161074287562055,0.12967967987060547,14.460614 +980,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.930996996530802,2.261860474984104,-0.7184214614837348,0.13494396209716797,15.148323 +1000,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9214575102921838,2.2404008018877137,-0.714349138962711,0.13822460174560547,15.950631 +1001,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9213134079227226,2.239416179559339,-0.7139861919037982,0.13822460174560547,16.757639 +11,Regression,Stochastic Gradient Tree,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.009614944458007812,0.001328 +22,Regression,Stochastic Gradient Tree,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012609481811523438,0.003944 +33,Regression,Stochastic Gradient Tree,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.015787124633789062,0.007623 +44,Regression,Stochastic Gradient Tree,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.018873214721679688,0.012489 +55,Regression,Stochastic Gradient Tree,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.021825790405273438,0.019505 +66,Regression,Stochastic Gradient Tree,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.024618148803710938,0.027128 +77,Regression,Stochastic Gradient Tree,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.027502059936523438,0.035372 +88,Regression,Stochastic Gradient Tree,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.030019760131835938,0.047911 +99,Regression,Stochastic Gradient Tree,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.03290367126464844,0.072727 +110,Regression,Stochastic Gradient Tree,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.26967811584472656,0.103163 +121,Regression,Stochastic Gradient Tree,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.26967811584472656,0.146595 +132,Regression,Stochastic Gradient Tree,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.26967811584472656,0.196283 +143,Regression,Stochastic Gradient Tree,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.26967811584472656,0.25852200000000003 +154,Regression,Stochastic Gradient Tree,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.26967811584472656,0.329566 +165,Regression,Stochastic Gradient Tree,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.26967811584472656,0.40393 +176,Regression,Stochastic Gradient Tree,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.26967811584472656,0.481694 +187,Regression,Stochastic Gradient Tree,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.26967811584472656,0.681251 +198,Regression,Stochastic Gradient Tree,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.26967811584472656,0.884966 +209,Regression,Stochastic Gradient Tree,ChickWeights,55.16354936929098,57.0482200725598,-13.656313160472004,0.6838865280151367,1.1316950000000001 +220,Regression,Stochastic Gradient Tree,ChickWeights,53.62203856749311,56.03531795068661,-11.37998411824978,0.6869077682495117,1.3969520000000002 +231,Regression,Stochastic Gradient Tree,ChickWeights,52.77279286370195,55.29408706815337,-9.311090357596036,0.6899290084838867,1.6754760000000002 +242,Regression,Stochastic Gradient Tree,ChickWeights,52.49661908339594,55.007104536867395,-7.210918602421254,0.6929502487182617,1.9600240000000002 +253,Regression,Stochastic Gradient Tree,ChickWeights,52.25631812193077,54.713446605156875,-6.055353919833875,0.6947126388549805,2.2702780000000002 +264,Regression,Stochastic Gradient Tree,ChickWeights,51.62511478420569,54.312843786153664,-5.352168023774992,0.6947126388549805,2.586688 +275,Regression,Stochastic Gradient Tree,ChickWeights,51.4425344352617,54.29364548356293,-4.585603291722447,0.6947126388549805,2.915419 +286,Regression,Stochastic Gradient Tree,ChickWeights,51.75651621106165,54.635705044608144,-3.8989478253777694,0.6947126388549805,3.266148 +297,Regression,Stochastic Gradient Tree,ChickWeights,52.373839404142416,55.25476711535166,-3.3456400671942,0.6947126388549805,3.622985 +308,Regression,Stochastic Gradient Tree,ChickWeights,52.87239275875638,55.86677247417265,-2.9565197175813713,0.6947126388549805,3.98691 +319,Regression,Stochastic Gradient Tree,ChickWeights,52.69554478958866,56.2770501442128,-2.6433309475704183,0.6947126388549805,4.356941 +330,Regression,Stochastic Gradient Tree,ChickWeights,53.85316804407712,57.75044402630399,-2.2832890424968193,0.6947126388549805,4.733992 +341,Regression,Stochastic Gradient Tree,ChickWeights,54.90678041411178,59.01114057562677,-2.0697921090482247,0.6947126388549805,5.128946 +352,Regression,Stochastic Gradient Tree,ChickWeights,56.00533746556472,60.302245208561004,-1.9140207825503284,0.6947126388549805,5.54848 +363,Regression,Stochastic Gradient Tree,ChickWeights,55.99599298772852,60.54917173074773,-1.852879941931207,0.6947126388549805,6.1724879999999995 +374,Regression,Stochastic Gradient Tree,ChickWeights,56.87222492302705,61.81275171085535,-1.7331917323651345,0.6947126388549805,6.808446 +385,Regression,Stochastic Gradient Tree,ChickWeights,58.41786698150333,63.95254893573906,-1.5885028214279253,0.6947126388549805,7.450193 +396,Regression,Stochastic Gradient Tree,ChickWeights,59.7033976124885,65.46926983257002,-1.5293357430909813,0.6947126388549805,8.100657 +407,Regression,Stochastic Gradient Tree,ChickWeights,60.057805647389294,66.17359973042984,-1.4019380007417155,1.1097631454467773,8.796904 +418,Regression,Stochastic Gradient Tree,ChickWeights,59.7070864579051,66.11592086962122,-1.2507954049688483,1.1127843856811523,9.50192 +429,Regression,Stochastic Gradient Tree,ChickWeights,60.122823673891816,66.73609937588846,-1.0378169857688957,1.1158056259155273,10.222461000000001 +440,Regression,Stochastic Gradient Tree,ChickWeights,60.39504675635191,66.96100690444877,-0.906365593827489,1.1188268661499023,10.951743 +451,Regression,Stochastic Gradient Tree,ChickWeights,60.27126048587789,66.93502892662679,-0.8239085862185902,1.120589256286621,11.696828 +462,Regression,Stochastic Gradient Tree,ChickWeights,60.340686610373176,67.43825007380137,-0.7390015352251049,1.120589256286621,12.465469 +473,Regression,Stochastic Gradient Tree,ChickWeights,61.40703262301831,69.11306667757516,-0.6127592621572406,1.120589256286621,13.248766 +484,Regression,Stochastic Gradient Tree,ChickWeights,61.95796621360106,69.71422620021941,-0.5510154280248158,1.120589256286621,14.047315 +495,Regression,Stochastic Gradient Tree,ChickWeights,62.59018166487368,70.55352405729404,-0.4943708535906215,1.120589256286621,14.854826 +506,Regression,Stochastic Gradient Tree,ChickWeights,62.49664579133251,70.88193125644693,-0.46447524520130457,1.120589256286621,15.674674999999999 +517,Regression,Stochastic Gradient Tree,ChickWeights,63.25224079915844,71.92080214464903,-0.4228062717918979,1.120589256286621,16.51129 +528,Regression,Stochastic Gradient Tree,ChickWeights,64.80783657170488,74.3681944005728,-0.36776422230083305,1.120589256286621,17.364023 +539,Regression,Stochastic Gradient Tree,ChickWeights,65.59959781369417,75.30113885843834,-0.3443906138479853,1.120589256286621,18.342071999999998 +550,Regression,Stochastic Gradient Tree,ChickWeights,65.79684627343133,76.01328745307667,-0.32771909731089166,1.120589256286621,19.334775999999998 +561,Regression,Stochastic Gradient Tree,ChickWeights,66.6512855136148,77.20436469287773,-0.30975691666695093,1.120589256286621,20.336346 +572,Regression,Stochastic Gradient Tree,ChickWeights,68.11975592628174,79.56492566870935,-0.2867456678376987,1.120589256286621,21.353617 +578,Regression,Stochastic Gradient Tree,ChickWeights,68.75877313437184,80.35800679505147,-0.2806007657015741,1.120589256286621,22.38029 +20,Regression,Stochastic Gradient Tree,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.019941329956054688,0.002168 +40,Regression,Stochastic Gradient Tree,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.03173637390136719,0.006794 +60,Regression,Stochastic Gradient Tree,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.04389762878417969,0.018434 +80,Regression,Stochastic Gradient Tree,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.05624198913574219,0.031286 +100,Regression,Stochastic Gradient Tree,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.5381031036376953,0.048039 +120,Regression,Stochastic Gradient Tree,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.5386066436767578,0.080711 +140,Regression,Stochastic Gradient Tree,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.5391101837158203,0.16679100000000002 +160,Regression,Stochastic Gradient Tree,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.5393619537353516,0.262676 +180,Regression,Stochastic Gradient Tree,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.5396137237548828,0.43318 +200,Regression,Stochastic Gradient Tree,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.5077581405639648,0.638958 +220,Regression,Stochastic Gradient Tree,TrumpApproval,36.497516001377406,38.019453444701035,-361.2329206514933,1.3602590560913086,0.9135530000000001 +240,Regression,Stochastic Gradient Tree,TrumpApproval,33.64243104419191,36.40668421494773,-333.65237138497804,1.360762596130371,1.221179 +260,Regression,Stochastic Gradient Tree,TrumpApproval,31.222114965034955,34.98371838354962,-312.16748668977897,1.3610143661499023,1.570709 +280,Regression,Stochastic Gradient Tree,TrumpApproval,29.182059468614717,33.71869814960704,-303.5986275675674,1.361769676208496,1.939253 +300,Regression,Stochastic Gradient Tree,TrumpApproval,27.34275770505051,32.57805191350732,-278.63174197976707,1.3620214462280273,2.3245549999999997 +320,Regression,Stochastic Gradient Tree,TrumpApproval,25.81388747443183,31.5521424826706,-274.2849072221064,1.3630285263061523,2.8771169999999997 +340,Regression,Stochastic Gradient Tree,TrumpApproval,24.51835124153299,30.62414457186519,-273.0482727941538,1.3640356063842773,3.4469999999999996 +360,Regression,Stochastic Gradient Tree,TrumpApproval,23.451930423400693,29.787924926455332,-260.4155562259403,1.3660497665405273,4.029196 +380,Regression,Stochastic Gradient Tree,TrumpApproval,22.468440533492842,29.014219480552867,-255.59151052979877,1.3665533065795898,4.629964999999999 +400,Regression,Stochastic Gradient Tree,TrumpApproval,21.594907007575774,28.301677882839346,-250.0434007116766,0.510127067565918,5.253793 +420,Regression,Stochastic Gradient Tree,TrumpApproval,20.62268781294523,27.62086591367872,-246.0239415518119,1.3623762130737305,5.968102 +440,Regression,Stochastic Gradient Tree,TrumpApproval,19.786863931462925,26.990398924900393,-230.60756767519214,1.3643903732299805,6.700306 +460,Regression,Stochastic Gradient Tree,TrumpApproval,19.05732899619648,26.404670160589287,-209.2038511633616,1.3666563034057617,7.451319000000001 +480,Regression,Stochastic Gradient Tree,TrumpApproval,18.376512097202227,25.854792215140314,-195.90337768575387,1.3701810836791992,8.221931000000001 +500,Regression,Stochastic Gradient Tree,TrumpApproval,17.755044410127518,25.338820973360427,-184.15507530651482,1.3716917037963867,9.124580000000002 +520,Regression,Stochastic Gradient Tree,TrumpApproval,17.16611419898163,24.851444862058347,-177.4118263333629,1.3737058639526367,10.044684000000002 +540,Regression,Stochastic Gradient Tree,TrumpApproval,16.628565596068775,24.392285078947275,-170.25012213753183,1.3747129440307617,10.981068000000002 +560,Regression,Stochastic Gradient Tree,TrumpApproval,16.091244232649693,23.955027361350904,-168.10096043791202,1.3752164840698242,11.990243000000003 +580,Regression,Stochastic Gradient Tree,TrumpApproval,15.590768135673304,23.54051091957351,-166.33817208986073,1.3764753341674805,13.016881000000003 +600,Regression,Stochastic Gradient Tree,TrumpApproval,15.168708628495342,23.15108754841241,-159.05714501634571,0.5124959945678711,14.090212000000003 +620,Regression,Stochastic Gradient Tree,TrumpApproval,14.742446374247313,22.779539618023726,-151.59887848495535,3.0642080307006836,15.285325000000004 +640,Regression,Stochastic Gradient Tree,TrumpApproval,14.319364852585176,22.42187566882095,-144.08105420081068,3.0679845809936523,16.529242000000004 +660,Regression,Stochastic Gradient Tree,TrumpApproval,13.916412195872256,22.080274918425697,-138.68241285181185,3.0712575912475586,17.842975000000003 +680,Regression,Stochastic Gradient Tree,TrumpApproval,13.515604789075645,21.753254558457893,-136.71797028279042,3.074782371520996,19.280557 +700,Regression,Stochastic Gradient Tree,TrumpApproval,13.16391092204058,21.44141764506316,-136.3120101768532,3.0773000717163086,20.753146 +720,Regression,Stochastic Gradient Tree,TrumpApproval,12.828283113852926,21.142484202016185,-135.44313416922282,3.078558921813965,22.284495 +740,Regression,Stochastic Gradient Tree,TrumpApproval,12.504466467012781,20.855361315179096,-131.6825380828392,3.0800695419311523,23.930702 +760,Regression,Stochastic Gradient Tree,TrumpApproval,12.187542748969033,20.57929219886472,-129.592708960364,3.0813283920288086,25.608717 +780,Regression,Stochastic Gradient Tree,TrumpApproval,11.899403743710545,20.31464229706916,-126.82553676745258,3.08359432220459,27.347365999999997 +800,Regression,Stochastic Gradient Tree,TrumpApproval,11.634366305883285,20.06137952581079,-124.7856004590591,3.084601402282715,29.130084999999998 +820,Regression,Stochastic Gradient Tree,TrumpApproval,11.363415331478278,19.815492221289514,-123.0687724200615,3.08560848236084,30.98707 +840,Regression,Stochastic Gradient Tree,TrumpApproval,11.106640469158773,19.57848368678801,-121.24309788996561,3.086615562438965,32.880055 +860,Regression,Stochastic Gradient Tree,TrumpApproval,10.873909665943762,19.350226189127362,-118.20364312373843,3.0871191024780273,34.808534 +880,Regression,Stochastic Gradient Tree,TrumpApproval,10.65545006969969,19.130035299019603,-114.92727947355435,3.0873708724975586,36.791638 +900,Regression,Stochastic Gradient Tree,TrumpApproval,10.439309697188909,18.916827199314994,-112.83532852765143,3.08762264251709,38.832751 +920,Regression,Stochastic Gradient Tree,TrumpApproval,10.21789524284777,18.710158789526105,-112.19133803320567,3.087874412536621,40.951802 +940,Regression,Stochastic Gradient Tree,TrumpApproval,10.012578535125467,18.510293787577226,-110.72583714230211,3.077906608581543,43.146806 +960,Regression,Stochastic Gradient Tree,TrumpApproval,9.811853150109151,18.316579311485903,-109.54344305213982,3.0804243087768555,45.38444 +980,Regression,Stochastic Gradient Tree,TrumpApproval,9.61909067795052,18.12881604876013,-109.39183420714343,3.080927848815918,47.662490999999996 +1000,Regression,Stochastic Gradient Tree,TrumpApproval,9.438738635632271,17.946847607318464,-109.00797869183796,3.0824384689331055,50.039238 +1001,Regression,Stochastic Gradient Tree,TrumpApproval,9.429746533156267,17.937886241411594,-108.97151968967047,3.0824384689331055,52.450717 +11,Regression,Adaptive Random Forest,ChickWeights,7.837563210503649,16.830121687224917,-363.61289911513376,0.1506052017211914,0.01357 +22,Regression,Adaptive Random Forest,ChickWeights,4.3557641651310055,11.925612892987612,-149.62275175212707,0.1761331558227539,0.033876 +33,Regression,Adaptive Random Forest,ChickWeights,3.3711466349197527,9.780434627556833,-65.4351822307151,0.21426868438720703,0.06570500000000001 +44,Regression,Adaptive Random Forest,ChickWeights,2.6922077728217833,8.482083592242564,-55.643739991610765,0.23125934600830078,0.11059100000000002 +55,Regression,Adaptive Random Forest,ChickWeights,2.74736475641488,7.825318026963682,-10.953904022002215,0.28695011138916016,0.16601600000000002 +66,Regression,Adaptive Random Forest,ChickWeights,2.8724679940162905,7.312536888278379,-3.4997438549991955,0.33327198028564453,0.24397800000000003 +77,Regression,Adaptive Random Forest,ChickWeights,3.0470429271529937,7.064245743713448,-1.8145642692685127,0.3119173049926758,0.346862 +88,Regression,Adaptive Random Forest,ChickWeights,2.9741223361578246,6.690154558226259,-1.288758200280824,0.3463144302368164,0.647937 +99,Regression,Adaptive Random Forest,ChickWeights,3.5306191185317584,6.892431773474538,-1.031779280787943,0.3914194107055664,0.960086 +110,Regression,Adaptive Random Forest,ChickWeights,3.8799314967396747,6.981555605673833,-0.45575716788658527,0.4218912124633789,1.292146 +121,Regression,Adaptive Random Forest,ChickWeights,4.113667008668635,7.033914104811044,-0.13804551272932075,0.4422159194946289,1.662194 +132,Regression,Adaptive Random Forest,ChickWeights,4.34164975929163,7.0584702899254435,0.06337311670854429,0.46953678131103516,2.044753 +143,Regression,Adaptive Random Forest,ChickWeights,4.57586761829926,7.15786747745719,0.2198462773680444,0.5039682388305664,2.459644 +154,Regression,Adaptive Random Forest,ChickWeights,4.72768375743327,7.245199860946492,0.3206991207112422,0.5465364456176758,2.897301 +165,Regression,Adaptive Random Forest,ChickWeights,5.104360720447454,7.731417459148682,0.3781793296599212,0.5543031692504883,3.478625 +176,Regression,Adaptive Random Forest,ChickWeights,5.614563299993537,8.384781892618234,0.41030323544665537,0.577855110168457,4.083596 +187,Regression,Adaptive Random Forest,ChickWeights,6.030281219875818,8.796345271037008,0.46861442712072365,0.5973634719848633,4.712472 +198,Regression,Adaptive Random Forest,ChickWeights,6.128233569544692,8.84845009665535,0.572286448100142,0.6156282424926758,5.362321 +209,Regression,Adaptive Random Forest,ChickWeights,6.65587905711115,9.652732357425101,0.5803946009681837,0.637272834777832,6.0687109999999995 +220,Regression,Adaptive Random Forest,ChickWeights,7.106977341119842,10.677274056234571,0.550512947323095,0.6516351699829102,6.792503 +231,Regression,Adaptive Random Forest,ChickWeights,7.51605472684967,11.121858780588035,0.582840670024279,0.6455926895141602,7.540065 +242,Regression,Adaptive Random Forest,ChickWeights,7.8763674823035235,11.54794620868086,0.6381207504866175,0.654301643371582,8.314509000000001 +253,Regression,Adaptive Random Forest,ChickWeights,8.048654689630025,11.785882981718466,0.6726179175042853,0.6542215347290039,9.228486 +264,Regression,Adaptive Random Forest,ChickWeights,8.558470564817128,12.694815113306078,0.6529678969258632,0.7006998062133789,10.165772 +275,Regression,Adaptive Random Forest,ChickWeights,9.011287699636803,13.865710758190522,0.6357023625954032,0.7181978225708008,11.12726 +286,Regression,Adaptive Random Forest,ChickWeights,9.454493871269733,14.39909947248495,0.6597325750664246,0.7397470474243164,12.112744 +297,Regression,Adaptive Random Forest,ChickWeights,9.455634964453314,14.370566123736594,0.7060577585099084,0.6961946487426758,13.208544 +308,Regression,Adaptive Random Forest,ChickWeights,9.98259297559382,15.278989711680778,0.7040656028742478,0.7122316360473633,14.327932 +319,Regression,Adaptive Random Forest,ChickWeights,10.896304985778038,17.680267148091307,0.6404050215106214,0.7230386734008789,15.472301 +330,Regression,Adaptive Random Forest,ChickWeights,11.34830207391465,18.238325787402868,0.6725323523293205,0.7481813430786133,16.699404 +341,Regression,Adaptive Random Forest,ChickWeights,11.700671911575691,18.698639858183288,0.6917798823884449,0.750828742980957,17.953966 +352,Regression,Adaptive Random Forest,ChickWeights,12.012928806619971,19.028065448277466,0.7098550919958697,0.779423713684082,19.24314 +363,Regression,Adaptive Random Forest,ChickWeights,12.590729727774809,20.061815233276363,0.6868102538385266,0.8219194412231445,20.584825000000002 +374,Regression,Adaptive Random Forest,ChickWeights,13.29572445199132,21.688967498502105,0.6634948622954009,0.8368387222290039,21.949532 +385,Regression,Adaptive Random Forest,ChickWeights,13.850252347511734,22.377982941031114,0.6830616430184708,0.8398981094360352,23.337733 +396,Regression,Adaptive Random Forest,ChickWeights,13.995508749414423,22.434927630401365,0.7029833246789492,0.8451242446899414,24.808931 +407,Regression,Adaptive Random Forest,ChickWeights,14.855647843034443,23.972462409994428,0.6847772413527866,0.8440675735473633,26.305221 +418,Regression,Adaptive Random Forest,ChickWeights,15.648428200057216,25.832735423225586,0.6563908585574095,0.8621377944946289,27.821711999999998 +429,Regression,Adaptive Random Forest,ChickWeights,16.477960681723363,27.016517310630082,0.6660339910533338,0.8826723098754883,29.421276999999996 +440,Regression,Adaptive Random Forest,ChickWeights,16.794784005292485,27.277386650758192,0.6836500576952018,0.8854074478149414,31.044845999999996 +451,Regression,Adaptive Random Forest,ChickWeights,17.2443539228967,27.815314781786782,0.6850336806962379,0.915654182434082,32.692344999999996 +462,Regression,Adaptive Random Forest,ChickWeights,18.21783864235053,29.965283642676138,0.6566601868655235,0.9476785659790039,34.453267999999994 +473,Regression,Adaptive Random Forest,ChickWeights,19.154558799374207,31.279498058996012,0.6696542166515442,0.9631280899047852,36.23863899999999 +484,Regression,Adaptive Random Forest,ChickWeights,19.653022199172934,31.710924929172922,0.6790841892096586,0.979741096496582,38.04845199999999 +495,Regression,Adaptive Random Forest,ChickWeights,20.17748759588543,32.35841629000369,0.6856630158751376,1.0035409927368164,39.89288999999999 +506,Regression,Adaptive Random Forest,ChickWeights,20.994447812000203,33.88452895368057,0.6653322556738073,1.0402307510375977,41.77183299999999 +517,Regression,Adaptive Random Forest,ChickWeights,21.74940325928189,34.92971521251369,0.6643962418834424,1.0591440200805664,43.682228999999985 +528,Regression,Adaptive Random Forest,ChickWeights,22.718068194641532,36.272080231437364,0.6746268651566016,1.0802621841430664,45.622796999999984 +539,Regression,Adaptive Random Forest,ChickWeights,22.976084812890594,36.32299861842887,0.6871862958215178,1.1105661392211914,47.62064799999998 +550,Regression,Adaptive Random Forest,ChickWeights,23.812560792713985,37.68037385984369,0.6737446986071818,1.1564149856567383,49.63871399999998 +561,Regression,Adaptive Random Forest,ChickWeights,24.744158926088524,38.956389615090316,0.6665241448790927,1.171940803527832,51.68634399999998 +572,Regression,Adaptive Random Forest,ChickWeights,25.965548256363952,40.779089345824126,0.6619939776632806,1.1861085891723633,53.83172099999997 +578,Regression,Adaptive Random Forest,ChickWeights,26.10164191353107,40.80941552099692,0.669724624616493,1.1904268264770508,56.00600499999997 +20,Regression,Adaptive Random Forest,TrumpApproval,4.656196028844478,13.301506400077992,-414.0076115498352,0.20158100128173828,0.057323 +40,Regression,Adaptive Random Forest,TrumpApproval,3.307191630717303,9.514843640593101,-35.39725790498291,0.28950977325439453,0.159522 +60,Regression,Adaptive Random Forest,TrumpApproval,2.3916587233350866,7.783560456255013,-31.83725667748105,0.32280826568603516,0.43784 +80,Regression,Adaptive Random Forest,TrumpApproval,2.0172424359013847,6.770328731809264,-23.921456088954436,0.36927127838134766,0.749169 +100,Regression,Adaptive Random Forest,TrumpApproval,2.069330341220504,6.141775226189047,-11.868015650386665,0.4076700210571289,1.082433 +120,Regression,Adaptive Random Forest,TrumpApproval,2.013474643057227,5.653544639730099,-8.249866206703038,0.4241609573364258,1.485703 +140,Regression,Adaptive Random Forest,TrumpApproval,1.8943659201342373,5.255534318342925,-7.260127227254786,0.44395923614501953,2.052392 +160,Regression,Adaptive Random Forest,TrumpApproval,1.9423634360618716,4.987168106592344,-5.559462264629689,0.45576953887939453,2.6505280000000004 +180,Regression,Adaptive Random Forest,TrumpApproval,1.9639788846395134,4.758402061618727,-4.244508869717663,0.47258472442626953,3.3297560000000006 +200,Regression,Adaptive Random Forest,TrumpApproval,1.9045329443413326,4.539431452034987,-3.787127034775958,0.5018167495727539,4.037089000000001 +220,Regression,Adaptive Random Forest,TrumpApproval,1.7801675790175082,4.332908187325825,-3.7047348470369075,0.539036750793457,4.871381000000001 +240,Regression,Adaptive Random Forest,TrumpApproval,1.7262455165213564,4.162317120423255,-3.3742337174670682,0.5583086013793945,5.726343000000002 +260,Regression,Adaptive Random Forest,TrumpApproval,1.6726006855046047,4.010034080286883,-3.1147254000977194,0.586766242980957,6.608691000000002 +280,Regression,Adaptive Random Forest,TrumpApproval,1.6001254820213158,3.8693841240389197,-3.01116046378164,0.6034517288208008,7.537396000000002 +300,Regression,Adaptive Random Forest,TrumpApproval,1.5903246290151523,3.758572865099384,-2.72204992570884,0.6344270706176758,8.563678000000001 +320,Regression,Adaptive Random Forest,TrumpApproval,1.5306703522535514,3.644833568467773,-2.673500446315358,0.6524057388305664,9.653495000000001 +340,Regression,Adaptive Random Forest,TrumpApproval,1.462120415173825,3.538151879462345,-2.658070572544154,0.6771516799926758,10.778313 +360,Regression,Adaptive Random Forest,TrumpApproval,1.4104873891633294,3.442715407420023,-2.491830651593505,0.712040901184082,12.006044000000001 +380,Regression,Adaptive Random Forest,TrumpApproval,1.3577274631021345,3.3535534396577877,-2.4279222429470106,0.7612085342407227,13.264975000000002 +400,Regression,Adaptive Random Forest,TrumpApproval,1.328889471148693,3.2750276755937473,-2.3616646758926834,0.7830896377563477,14.608014 +420,Regression,Adaptive Random Forest,TrumpApproval,1.2856838141339133,3.198005596242657,-2.3114858734875385,0.8153314590454102,15.987435000000001 +440,Regression,Adaptive Random Forest,TrumpApproval,1.2502461578606217,3.1277634460074983,-2.1102975482726696,0.8549776077270508,17.476653000000002 +460,Regression,Adaptive Random Forest,TrumpApproval,1.2118787702501406,3.0607885313580625,-1.8245276191275441,0.8641138076782227,19.005275 +480,Regression,Adaptive Random Forest,TrumpApproval,1.1755519926992437,2.997482691409013,-1.6465763687209671,0.904881477355957,20.582478000000002 +500,Regression,Adaptive Random Forest,TrumpApproval,1.1542746800420942,2.9412002898427465,-1.494663742459657,0.9429025650024414,22.213321 +520,Regression,Adaptive Random Forest,TrumpApproval,1.1232655769227813,2.8856256311301474,-1.4054721071787495,0.9187402725219727,23.93785 +540,Regression,Adaptive Random Forest,TrumpApproval,1.0927628011224122,2.8324064719208977,-1.3090698562992058,0.9784936904907227,25.72016 +560,Regression,Adaptive Random Forest,TrumpApproval,1.0798076211233283,2.7860066009246953,-1.2872677886963872,0.8415918350219727,27.559893 +580,Regression,Adaptive Random Forest,TrumpApproval,1.0533259806656756,2.7386650773118006,-1.2648586320750757,0.926945686340332,29.430927 +600,Regression,Adaptive Random Forest,TrumpApproval,1.0370277841126194,2.695306817676886,-1.1694452334238137,1.0152063369750977,31.394822 +620,Regression,Adaptive Random Forest,TrumpApproval,1.0220360797787769,2.6548714349996483,-1.0727572654712625,0.9687509536743164,33.420245 +640,Regression,Adaptive Random Forest,TrumpApproval,1.006223169156282,2.6150891537993277,-0.9735122270872276,0.8030519485473633,35.538976 +660,Regression,Adaptive Random Forest,TrumpApproval,0.9862189251721106,2.576116595691222,-0.901357605879683,0.7759256362915039,37.763356 +680,Regression,Adaptive Random Forest,TrumpApproval,0.9658028732124053,2.5385905860617046,-0.8755448750460146,0.8428354263305664,40.028227 +700,Regression,Adaptive Random Forest,TrumpApproval,0.9580702867531661,2.506070409170758,-0.8758066430098239,0.9465646743774414,42.353807 +720,Regression,Adaptive Random Forest,TrumpApproval,0.9436099236768006,2.472715624364642,-0.8663281360281503,1.0379304885864258,44.723793 +740,Regression,Adaptive Random Forest,TrumpApproval,0.9279645732871133,2.4402852992549255,-0.8166009677654511,1.1119890213012695,47.149898 +760,Regression,Adaptive Random Forest,TrumpApproval,0.9159099470417099,2.410261116608071,-0.7913739428902633,1.1737489700317383,49.62861 +780,Regression,Adaptive Random Forest,TrumpApproval,0.8968362370347621,2.379411736746614,-0.7536320120768303,1.261582374572754,52.179919 +800,Regression,Adaptive Random Forest,TrumpApproval,0.8878342141912964,2.3513921402434717,-0.7280625702741705,1.3552255630493164,54.784302 +820,Regression,Adaptive Random Forest,TrumpApproval,0.8775558321142263,2.3237456817971016,-0.7062000372474271,1.4321069717407227,57.452517 +840,Regression,Adaptive Random Forest,TrumpApproval,0.8672496542857573,2.297532908897418,-0.6834092983276716,1.4874773025512695,60.173386 +860,Regression,Adaptive Random Forest,TrumpApproval,0.8593706057522699,2.272389423762812,-0.6439286158863597,1.5595178604125977,62.976048 +880,Regression,Adaptive Random Forest,TrumpApproval,0.8551106332542915,2.2487703297155224,-0.6019328469057044,1.619084358215332,65.856537 +900,Regression,Adaptive Random Forest,TrumpApproval,0.8437512715146732,2.224375873084905,-0.5739713521606553,1.1261072158813477,68.798174 +920,Regression,Adaptive Random Forest,TrumpApproval,0.8344220404851989,2.2010168562801016,-0.5664083310843888,1.167832374572754,71.779904 +940,Regression,Adaptive Random Forest,TrumpApproval,0.825939320609599,2.179009982884269,-0.5482654902802699,1.1199464797973633,74.829165 +960,Regression,Adaptive Random Forest,TrumpApproval,0.8156984309758435,2.1571048007400404,-0.5331573747015668,1.1733713150024414,77.929016 +980,Regression,Adaptive Random Forest,TrumpApproval,0.806477335746804,2.1360065895495888,-0.5325097322303367,1.2283296585083008,81.05698100000001 +1000,Regression,Adaptive Random Forest,TrumpApproval,0.8008625237630099,2.1159877488140326,-0.5292346593649373,1.2836008071899414,84.241983 +1001,Regression,Adaptive Random Forest,TrumpApproval,0.800378499538596,2.1149541843634605,-0.5287610996295022,1.2846193313598633,87.445729 +11,Regression,Aggregated Mondrian Forest,ChickWeights,1.0878895070954884,1.3778002085324723,-1.2599207317049026,0.17917156219482422,0.003809 +22,Regression,Aggregated Mondrian Forest,ChickWeights,1.15171477394762,1.5218208011368886,-1.3974856828423898,0.33136653900146484,0.017797 +33,Regression,Aggregated Mondrian Forest,ChickWeights,1.2596040860169628,1.630698561429495,-0.8214033882315572,0.48356151580810547,0.056943 +44,Regression,Aggregated Mondrian Forest,ChickWeights,1.1470025325021567,1.5136945038262,-0.7860708992998826,0.6357030868530273,0.120895 +55,Regression,Aggregated Mondrian Forest,ChickWeights,1.7448650745312246,2.8901942810902064,-0.6023944619968462,0.795161247253418,0.34128800000000004 +66,Regression,Aggregated Mondrian Forest,ChickWeights,1.9741736434582027,3.1122799656868354,0.1967701507194095,0.949946403503418,0.602886 +77,Regression,Aggregated Mondrian Forest,ChickWeights,2.3465039451978784,3.868783481489585,0.16539043694478717,1.1044378280639648,0.885897 +88,Regression,Aggregated Mondrian Forest,ChickWeights,2.3152944739841907,3.751470845606434,0.286453015670338,1.2595434188842773,1.213122 +99,Regression,Aggregated Mondrian Forest,ChickWeights,2.4851266884813286,3.8753788781661265,0.3615628965518305,1.4176397323608398,1.708637 +110,Regression,Aggregated Mondrian Forest,ChickWeights,2.679180085056696,4.098463178184459,0.5005082908479199,1.580409049987793,2.233256 +121,Regression,Aggregated Mondrian Forest,ChickWeights,2.993112128155013,4.501608187312601,0.5353065115430311,1.7385053634643555,2.789253 +132,Regression,Aggregated Mondrian Forest,ChickWeights,3.049130101089184,4.474860576824222,0.624267329970883,1.8972959518432617,3.530617 +143,Regression,Aggregated Mondrian Forest,ChickWeights,3.129389359320645,4.535626207267123,0.6870855629914132,2.0540571212768555,4.307795 +154,Regression,Aggregated Mondrian Forest,ChickWeights,3.2350921629171503,4.614317779917637,0.7245583098520811,2.21335506439209,5.238077 +165,Regression,Aggregated Mondrian Forest,ChickWeights,3.615407192454655,5.434402308521257,0.6928112980835472,2.370730400085449,6.2153469999999995 +176,Regression,Aggregated Mondrian Forest,ChickWeights,3.842899644735678,5.8926781106586255,0.7087106044563829,2.5251951217651367,7.308114 +187,Regression,Aggregated Mondrian Forest,ChickWeights,3.939333513046091,5.936527515565436,0.7578865873655871,2.680434226989746,8.444739 +198,Regression,Aggregated Mondrian Forest,ChickWeights,4.1526220464224926,6.160116941975886,0.7926170753106898,2.8339643478393555,9.747256 +209,Regression,Aggregated Mondrian Forest,ChickWeights,4.486090256229248,6.857164593682279,0.7881489392686998,2.9901113510131836,11.107311 +220,Regression,Aggregated Mondrian Forest,ChickWeights,5.095083445923365,8.268326900050806,0.7303274183124314,3.147219657897949,12.601650999999999 +231,Regression,Aggregated Mondrian Forest,ChickWeights,5.345901760482457,8.651953805757511,0.7474084291359289,3.301150321960449,14.172197999999998 +242,Regression,Aggregated Mondrian Forest,ChickWeights,5.775936882313693,9.234098241635358,0.7685060952534608,3.4575910568237305,15.876551999999998 +253,Regression,Aggregated Mondrian Forest,ChickWeights,6.050411841877211,9.480574702158652,0.7880472652798773,3.6158742904663086,17.675130999999997 +264,Regression,Aggregated Mondrian Forest,ChickWeights,6.7396819662512994,10.861908099063555,0.7457953067175733,3.77274227142334,19.599012999999996 +275,Regression,Aggregated Mondrian Forest,ChickWeights,7.418933110619537,12.596893007879746,0.699156722346497,3.926619529724121,21.625010999999997 +286,Regression,Aggregated Mondrian Forest,ChickWeights,7.830180870941,13.02165358749325,0.7215679622698357,4.082179069519043,23.771257999999996 +297,Regression,Aggregated Mondrian Forest,ChickWeights,8.059624975297776,13.201631143135529,0.7517949935656911,4.237311363220215,26.095146999999997 +308,Regression,Aggregated Mondrian Forest,ChickWeights,8.517266870602596,14.029786197157003,0.750336177123377,4.390841484069824,28.501656999999998 +319,Regression,Aggregated Mondrian Forest,ChickWeights,9.872910663629112,17.67011178426297,0.6406578335650022,4.544772148132324,31.026265 +330,Regression,Aggregated Mondrian Forest,ChickWeights,10.355957081475973,18.251720539867826,0.671924837978655,4.698409080505371,33.677707999999996 +341,Regression,Aggregated Mondrian Forest,ChickWeights,10.779061369126929,18.644503325392748,0.6934349036095702,4.852313041687012,36.483968999999995 +352,Regression,Aggregated Mondrian Forest,ChickWeights,10.97013178962945,18.690294927177735,0.7199342471488321,5.009421348571777,39.412921 +363,Regression,Aggregated Mondrian Forest,ChickWeights,11.836385670325258,20.411474322578705,0.6756306209292051,5.165541648864746,42.477176 +374,Regression,Aggregated Mondrian Forest,ChickWeights,12.650208752532226,22.152599191433616,0.6487731216482631,5.323611259460449,45.685202 +385,Regression,Aggregated Mondrian Forest,ChickWeights,13.26433375884341,22.74111870549559,0.672536034672935,5.478930473327637,49.011466999999996 +396,Regression,Aggregated Mondrian Forest,ChickWeights,13.285084454056173,22.62858691877232,0.6976709876564118,5.63400936126709,52.447627 +407,Regression,Aggregated Mondrian Forest,ChickWeights,14.297859574888522,24.603705237609702,0.6677818184200794,5.789168357849121,55.986709 +418,Regression,Aggregated Mondrian Forest,ChickWeights,15.277775247208368,26.91758918665374,0.6267299649165277,5.945448875427246,59.629264 +429,Regression,Aggregated Mondrian Forest,ChickWeights,16.148002577856595,27.91235298687263,0.643353503146777,6.099112510681152,63.379205999999996 +440,Regression,Aggregated Mondrian Forest,ChickWeights,16.450833155107055,28.053185003016473,0.6652347923635655,6.252856254577637,67.23493599999999 +451,Regression,Aggregated Mondrian Forest,ChickWeights,16.938736394119786,28.680885446607185,0.6649461832952053,6.4079084396362305,71.205463 +462,Regression,Aggregated Mondrian Forest,ChickWeights,18.465286457846624,32.222162406640614,0.6027938253530374,6.562827110290527,75.286683 +473,Regression,Aggregated Mondrian Forest,ChickWeights,19.368786292726078,33.403615991184594,0.6231055060350865,6.717398643493652,79.474485 +484,Regression,Aggregated Mondrian Forest,ChickWeights,19.88015130963188,33.764210229402664,0.6360141173956066,6.872824668884277,83.769797 +495,Regression,Aggregated Mondrian Forest,ChickWeights,20.57744796303998,34.830627929035586,0.6356250399764956,7.029131889343262,88.176271 +506,Regression,Aggregated Mondrian Forest,ChickWeights,21.43571571603741,36.40788480688662,0.6134430891768862,7.184878349304199,92.694929 +517,Regression,Aggregated Mondrian Forest,ChickWeights,22.34914238062968,37.807266067412606,0.6066317565796657,7.340197563171387,97.332285 +528,Regression,Aggregated Mondrian Forest,ChickWeights,23.191315994328228,38.81894260965106,0.6271697641288401,7.495863914489746,102.073572 +539,Regression,Aggregated Mondrian Forest,ChickWeights,23.34075784343543,38.827434948624926,0.6423969913213963,7.652411460876465,106.92036399999999 +550,Regression,Aggregated Mondrian Forest,ChickWeights,24.125457325549842,39.99965605849559,0.6321733437483394,7.811335563659668,111.876093 +561,Regression,Aggregated Mondrian Forest,ChickWeights,24.859407485948264,40.9180834433101,0.6319179521646502,7.9698591232299805,116.942977 +572,Regression,Aggregated Mondrian Forest,ChickWeights,25.582967433016186,41.65667948828452,0.6471310448579161,8.12806224822998,122.12293 +578,Regression,Aggregated Mondrian Forest,ChickWeights,25.674172622955844,41.71227980537356,0.65479005999511,8.21412181854248,127.41509599999999 +20,Regression,Aggregated Mondrian Forest,TrumpApproval,0.38127899900663437,0.4856864156914124,0.4734504440676397,0.3661947250366211,0.012803 +40,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3903932807396207,0.4802129236445582,0.908098150441852,0.7077703475952148,0.066318 +60,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3553562094560649,0.44754485397583466,0.8908737885344612,1.0465993881225586,0.161186 +80,Regression,Aggregated Mondrian Forest,TrumpApproval,0.37850782280668976,0.48181042919820394,0.8725877843301283,1.386988639831543,0.310355 +100,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3456451525369771,0.45047631187257403,0.9301027529077078,1.7262754440307617,0.520575 +120,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3369671927041528,0.4421642502671299,0.9427860880043346,2.0684385299682617,0.828496 +140,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3170957614007029,0.42172730009164255,0.9461011323760549,2.404311180114746,1.349971 +160,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3307037984070857,0.4315519243898653,0.9502341257934384,2.7412595748901367,1.938101 +180,Regression,Aggregated Mondrian Forest,TrumpApproval,0.32391175568062514,0.4198186275021798,0.9586532343987925,3.0777502059936523,2.6707590000000003 +200,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3216990324082377,0.4152317221827294,0.959406710806771,3.4146718978881836,3.5252660000000002 +220,Regression,Aggregated Mondrian Forest,TrumpApproval,0.31954266191014236,0.40981538467826617,0.9573113010689299,3.7566747665405273,4.592894 +240,Regression,Aggregated Mondrian Forest,TrumpApproval,0.32058338930030683,0.40973424053171104,0.9569909127297425,4.096526145935059,5.7511 +260,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30963502752669864,0.3977121378847216,0.958918388812615,4.436213493347168,7.098098 +280,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3021514917324106,0.38814569807877647,0.9590118642619369,4.772730827331543,8.655558000000001 +300,Regression,Aggregated Mondrian Forest,TrumpApproval,0.302378432925199,0.38793022611752936,0.9597410141114792,5.1073408126831055,10.324296 +320,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30426355112773323,0.38808206022274244,0.9577019017103428,5.440821647644043,12.208908000000001 +340,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30611168054734034,0.391104192749731,0.9546026616524738,5.773791313171387,14.314788 +360,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3092553456162419,0.39535170942611675,0.9532980843409116,6.1096906661987305,16.588556 +380,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3076082401740039,0.39602056260851237,0.9515479192781826,6.445483207702637,19.05639 +400,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3014609513967445,0.3890658925747077,0.951945723428325,6.780200004577637,21.697867000000002 +420,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29711382936926317,0.3834166944817816,0.9518088379060109,7.1165571212768555,24.511965000000004 +440,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29823263394549426,0.3845174635941775,0.952475485177767,7.451247215270996,27.506667000000004 +460,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2946274143925286,0.38012238021570716,0.9560358463571957,7.7864484786987305,30.697063000000004 +480,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29339680191338824,0.3767220961039539,0.9578595845961764,8.120524406433105,34.045785 +500,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2879691623817681,0.37098658104945786,0.9600287871527097,8.45413875579834,37.543006000000005 +520,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2858512762877924,0.36803361403114826,0.9606162857565064,8.791060447692871,41.194255000000005 +540,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28337361966731006,0.3643610952909248,0.9615619008486129,9.128493309020996,45.008334000000005 +560,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28829960115327347,0.3724151060029248,0.9588932716362207,9.463343620300293,48.989560000000004 +580,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28931551719826604,0.37295833877986023,0.957758997678865,9.801314353942871,53.13082800000001 +600,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28804453093670845,0.3714720231862385,0.9585805866298193,10.13992977142334,57.44083900000001 +620,Regression,Aggregated Mondrian Forest,TrumpApproval,0.285913070387579,0.3694806072400069,0.9596689430521099,10.477011680603027,61.917696000000014 +640,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2840899599351511,0.36696425721615267,0.9609793991220156,10.80936336517334,66.56801000000002 +660,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2804371592513609,0.3629082548929102,0.9621252180674722,11.1486234664917,71.37699000000002 +680,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2832721366095781,0.36464822523085283,0.9611623224331389,11.485033988952637,76.34970200000002 +700,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28537572628090535,0.3664704272728199,0.959741096022156,11.82703685760498,81.48889300000002 +720,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2846995678315971,0.3663721647933076,0.9588788359612517,12.1649808883667,86.79821800000002 +740,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28438969208914106,0.36562346066591106,0.9590812376103318,12.5026273727417,92.27027700000002 +760,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2812524955317089,0.36227858565351356,0.9593969665626949,12.84118938446045,97.89633500000002 +780,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2784736419799919,0.35904953945649953,0.9599459478528628,13.181014060974121,103.67983800000002 +800,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28122680710979,0.3614117991183927,0.9590552347518728,13.522452354431152,109.62063100000002 +820,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2798414038154103,0.35991057058708614,0.9589530251045719,13.858756065368652,115.71824100000002 +840,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2792299366421054,0.358810295818463,0.9588293518961978,14.199065208435059,121.97670500000002 +860,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2757234931419036,0.35572944295397174,0.9596100235239657,14.53821849822998,128.39866500000002 +880,Regression,Aggregated Mondrian Forest,TrumpApproval,0.27250879183678145,0.3526976163964828,0.9604999212537026,14.877989768981934,134.987331 +900,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2707423985398595,0.35053221584455113,0.9608236269181287,15.214400291442871,141.746125 +920,Regression,Aggregated Mondrian Forest,TrumpApproval,0.27149682778681117,0.35071953709177356,0.960138783514385,15.551268577575684,148.677278 +940,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2704179121014184,0.350599255928843,0.9598313134304426,15.892088890075684,155.784091 +960,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2707082259086565,0.3516525290937312,0.9591696518431827,16.229090690612793,163.071856 +980,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2709225398475326,0.3517696828828596,0.9583487676398438,16.574454307556152,170.541967 +1000,Regression,Aggregated Mondrian Forest,TrumpApproval,0.26869541998932756,0.349579763566054,0.9581740736545136,16.91306972503662,178.198313 +1001,Regression,Aggregated Mondrian Forest,TrumpApproval,0.268533139095725,0.34942113435685235,0.9581842100200093,16.932257652282715,186.03359 +11,Regression,Adaptive Model Rules,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.019614219665527344,0.000715 +22,Regression,Adaptive Model Rules,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.021178245544433594,0.002248 +33,Regression,Adaptive Model Rules,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.02634716033935547,0.0045000000000000005 +44,Regression,Adaptive Model Rules,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.027434349060058594,0.007522000000000001 +55,Regression,Adaptive Model Rules,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.034033775329589844,0.011341 +66,Regression,Adaptive Model Rules,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.043257713317871094,0.016081 +77,Regression,Adaptive Model Rules,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.04948711395263672,0.021988 +88,Regression,Adaptive Model Rules,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.05513286590576172,0.050945000000000004 +99,Regression,Adaptive Model Rules,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.05624675750732422,0.083616 +110,Regression,Adaptive Model Rules,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.057623863220214844,0.119642 +121,Regression,Adaptive Model Rules,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.05775737762451172,0.157993 +132,Regression,Adaptive Model Rules,ChickWeights,5.216037157212015,7.551012267266295,-0.0719037453282565,0.05781078338623047,0.197604 +143,Regression,Adaptive Model Rules,ChickWeights,5.030848211447775,7.321940337412501,0.18367095381254994,0.058394432067871094,0.238591 +154,Regression,Adaptive Model Rules,ChickWeights,4.907406922429448,7.137924382310331,0.3406662269828342,0.058447837829589844,0.28091 +165,Regression,Adaptive Model Rules,ChickWeights,5.132506734403487,7.341156657504303,0.439370571581684,0.058447837829589844,0.32452 +176,Regression,Adaptive Model Rules,ChickWeights,5.292049153445915,7.468652514259996,0.5321251372519638,0.059058189392089844,0.369448 +187,Regression,Adaptive Model Rules,ChickWeights,5.31698748044205,7.461166418014795,0.6176873420824156,0.059111595153808594,0.415771 +198,Regression,Adaptive Model Rules,ChickWeights,5.300228902480157,7.425148329077998,0.6988181014109027,0.059058189392089844,0.474994 +209,Regression,Adaptive Model Rules,ChickWeights,5.830499581707958,9.648698249017793,0.5807452540036802,0.02527141571044922,0.54427 +220,Regression,Adaptive Model Rules,ChickWeights,6.400718854692065,10.45569246424029,0.5689754490886993,0.03142070770263672,0.614353 +231,Regression,Adaptive Model Rules,ChickWeights,6.611665150046439,10.617456980307361,0.6198209084753062,0.036536216735839844,0.6853290000000001 +242,Regression,Adaptive Model Rules,ChickWeights,7.029624246247838,11.197269958950692,0.6597654020329642,0.041068077087402344,0.7572760000000001 +253,Regression,Adaptive Model Rules,ChickWeights,7.254490759785878,11.350610231674398,0.6963529412438163,0.044592857360839844,0.830219 +264,Regression,Adaptive Model Rules,ChickWeights,7.784750145903498,12.258358647532567,0.6764200982742594,0.044699668884277344,0.9041760000000001 +275,Regression,Adaptive Model Rules,ChickWeights,8.342804112650073,13.247943494163705,0.6674407623884211,0.044699668884277344,0.986871 +286,Regression,Adaptive Model Rules,ChickWeights,8.88061114203256,14.075280539927816,0.6748649197186086,0.045203208923339844,1.0705630000000002 +297,Regression,Adaptive Model Rules,ChickWeights,9.500784996803779,14.855892526018593,0.6858683144490312,0.04522991180419922,1.1552540000000002 +308,Regression,Adaptive Model Rules,ChickWeights,10.070788242104461,15.77018489177999,0.6847321098344714,0.045256614685058594,1.2409280000000003 +319,Regression,Adaptive Model Rules,ChickWeights,10.988840488902909,17.80174938329892,0.6354464447499208,0.045256614685058594,1.3276340000000002 +330,Regression,Adaptive Model Rules,ChickWeights,11.635092222175304,18.61329763011445,0.6589287557789436,0.04528331756591797,1.4153970000000002 +341,Regression,Adaptive Model Rules,ChickWeights,11.7817306308102,18.651657721342477,0.6933268021215234,0.04528331756591797,1.5042310000000003 +352,Regression,Adaptive Model Rules,ChickWeights,11.878812775671825,18.699040402285984,0.7198024587207095,0.04528331756591797,1.5941090000000002 +363,Regression,Adaptive Model Rules,ChickWeights,12.712200605470676,19.934033697107445,0.690787203614232,0.045310020446777344,1.6850070000000001 +374,Regression,Adaptive Model Rules,ChickWeights,13.202927457133043,20.9603625224819,0.6857237785454591,0.04533672332763672,1.7769460000000001 +385,Regression,Adaptive Model Rules,ChickWeights,13.5542070698499,21.51079994203591,0.707149447507495,0.04533672332763672,1.8699420000000002 +396,Regression,Adaptive Model Rules,ChickWeights,13.642433072457155,21.454130101613703,0.7283852775805406,0.04533672332763672,1.9639870000000001 +407,Regression,Adaptive Model Rules,ChickWeights,14.50232093628697,22.86556238504221,0.7132152539462153,0.045676231384277344,2.060696 +418,Regression,Adaptive Model Rules,ChickWeights,15.245933470432924,24.220098655355127,0.6979521608965717,0.045729637145996094,2.158386 +429,Regression,Adaptive Model Rules,ChickWeights,15.766409258920858,25.08619072251902,0.7120527209837302,0.045729637145996094,2.2570650000000003 +440,Regression,Adaptive Model Rules,ChickWeights,15.931210335947624,25.166941851240068,0.7307081986676882,0.04564952850341797,2.4439120000000005 +451,Regression,Adaptive Model Rules,ChickWeights,16.418312975299003,25.736739737917958,0.7303482652633313,0.046179771423339844,2.6336030000000004 +462,Regression,Adaptive Model Rules,ChickWeights,17.4982370763817,27.78944281741256,0.7047111429028308,0.04692363739013672,2.8262370000000003 +473,Regression,Adaptive Model Rules,ChickWeights,18.254684132762545,29.056725346353634,0.7149358826261665,0.04697704315185547,3.0200670000000005 +484,Regression,Adaptive Model Rules,ChickWeights,18.58513038702809,29.354635254956722,0.7250038129485413,0.046950340270996094,3.2149970000000003 +495,Regression,Adaptive Model Rules,ChickWeights,19.01404260598322,29.86038018890717,0.7323226450377984,0.046896934509277344,3.430483 +506,Regression,Adaptive Model Rules,ChickWeights,19.88342353555136,31.26600741511644,0.7150584356224581,0.04697704315185547,3.648791 +517,Regression,Adaptive Model Rules,ChickWeights,20.595063111922975,32.24616798680886,0.713982273554131,0.047003746032714844,3.869932 +528,Regression,Adaptive Model Rules,ChickWeights,21.380474467010046,33.43504054753495,0.7235347994633756,0.047003746032714844,4.098743 +539,Regression,Adaptive Model Rules,ChickWeights,21.53249764026729,33.42135235584957,0.735168024057878,0.047003746032714844,4.328606 +550,Regression,Adaptive Model Rules,ChickWeights,22.499187843294454,35.002118414433774,0.7184757368310433,0.04703044891357422,4.559521999999999 +561,Regression,Adaptive Model Rules,ChickWeights,23.191634121895575,35.912468657285935,0.7166015220750928,0.047003746032714844,4.791517 +572,Regression,Adaptive Model Rules,ChickWeights,24.040656821383894,37.100860859043735,0.7202195492645626,0.046950340270996094,5.02459 +578,Regression,Adaptive Model Rules,ChickWeights,24.194319129377014,37.21658551958108,0.7253190778127725,0.04697704315185547,5.258551 +20,Regression,Adaptive Model Rules,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.053809165954589844,0.004347 +40,Regression,Adaptive Model Rules,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.07615184783935547,0.011776 +60,Regression,Adaptive Model Rules,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.08839702606201172,0.021496 +80,Regression,Adaptive Model Rules,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.09804439544677734,0.033628000000000005 +100,Regression,Adaptive Model Rules,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.10713481903076172,0.04833900000000001 +120,Regression,Adaptive Model Rules,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.11132335662841797,0.06604700000000001 +140,Regression,Adaptive Model Rules,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.11333751678466797,0.086317 +160,Regression,Adaptive Model Rules,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.1174459457397461,0.109348 +180,Regression,Adaptive Model Rules,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.1194601058959961,0.13519 +200,Regression,Adaptive Model Rules,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.017618179321289062,0.169486 +220,Regression,Adaptive Model Rules,TrumpApproval,2.0738811328897206,4.417664564856108,-3.890594467356201,0.03579998016357422,0.205189 +240,Regression,Adaptive Model Rules,TrumpApproval,1.9726100065438286,4.2375242409752385,-3.5337340888030546,0.04149913787841797,0.24247600000000002 +260,Regression,Adaptive Model Rules,TrumpApproval,1.8594315384151245,4.074751007989252,-3.248610147038553,0.048842430114746094,0.28140600000000004 +280,Regression,Adaptive Model Rules,TrumpApproval,1.7773205119132678,3.936654153117972,-3.1518424972300867,0.06378841400146484,0.322149 +300,Regression,Adaptive Model Rules,TrumpApproval,1.8265705896173514,3.8591002097544127,-2.923813511442849,0.07349681854248047,0.364943 +320,Regression,Adaptive Model Rules,TrumpApproval,1.7442837931334845,3.739506488697679,-2.866813933026025,0.08107662200927734,0.40978200000000004 +340,Regression,Adaptive Model Rules,TrumpApproval,1.6994316865849048,3.6380049904847285,-2.8674589929341425,0.08619213104248047,0.45684600000000003 +360,Regression,Adaptive Model Rules,TrumpApproval,1.6868885299887,3.5545855692388106,-2.7224500036418355,0.0937795639038086,0.506202 +380,Regression,Adaptive Model Rules,TrumpApproval,1.637461983479605,3.464628975063406,-2.658760364179245,0.09889507293701172,0.560423 +400,Regression,Adaptive Model Rules,TrumpApproval,1.622197889515682,3.392154183911459,-2.6064142473473755,0.10611248016357422,0.624493 +420,Regression,Adaptive Model Rules,TrumpApproval,1.6252883623828789,3.3313119696358306,-2.5933132470830746,0.11011409759521484,0.691125 +440,Regression,Adaptive Model Rules,TrumpApproval,1.663593439145693,3.2993129689970107,-2.4608371725208844,0.11575984954833984,0.760369 +460,Regression,Adaptive Model Rules,TrumpApproval,1.6928806013876205,3.26900202016339,-2.221881423949668,0.12384319305419922,0.832438 +480,Regression,Adaptive Model Rules,TrumpApproval,1.6463369530072471,3.2036213976345094,-2.0231064080329655,0.13150310516357422,0.907505 +500,Regression,Adaptive Model Rules,TrumpApproval,1.6312675040418116,3.1569789450171624,-1.8741285299844175,0.07843685150146484,0.99002 +520,Regression,Adaptive Model Rules,TrumpApproval,1.6486177246548734,3.1232792518100463,-1.81800645719813,0.08357906341552734,1.082168 +540,Regression,Adaptive Model Rules,TrumpApproval,1.664948820150162,3.091452157271598,-1.7507490735781142,0.0873861312866211,1.179534 +560,Regression,Adaptive Model Rules,TrumpApproval,1.6361907885919602,3.043459997537018,-1.7295303491345493,0.08855342864990234,1.466782 +580,Regression,Adaptive Model Rules,TrumpApproval,1.6082012495575047,2.9965453347231947,-1.7114709556760634,0.08905696868896484,1.757406 +600,Regression,Adaptive Model Rules,TrumpApproval,1.622569336171024,2.97009213510141,-1.6343417506962359,0.09091472625732422,2.050417 +620,Regression,Adaptive Model Rules,TrumpApproval,1.6368903964872519,2.946158197159977,-1.5525460315178896,0.0923452377319336,2.345765 +640,Regression,Adaptive Model Rules,TrumpApproval,1.652159107256621,2.9245287804119107,-1.4681901897894076,0.09443950653076172,2.643466 +660,Regression,Adaptive Model Rules,TrumpApproval,1.6570267761004454,2.8972896524900835,-1.4050084478390592,0.09603023529052734,2.943569 +680,Regression,Adaptive Model Rules,TrumpApproval,1.6362052297782712,2.859601997032609,-1.379870428705038,0.09812450408935547,3.2460560000000003 +700,Regression,Adaptive Model Rules,TrumpApproval,1.608205636538717,2.8213269237454877,-1.377433396876134,0.10156917572021484,3.5543920000000004 +720,Regression,Adaptive Model Rules,TrumpApproval,1.5855230254631891,2.785659545407005,-1.3686218528413674,0.1027364730834961,3.8755710000000003 +740,Regression,Adaptive Model Rules,TrumpApproval,1.583695771004626,2.7597111871599203,-1.3233016566851918,0.1038503646850586,4.202987 +760,Regression,Adaptive Model Rules,TrumpApproval,1.5704020318609786,2.7290361106702816,-1.2965538228485634,0.1038503646850586,4.532954 +780,Regression,Adaptive Model Rules,TrumpApproval,1.5638796853366008,2.702190403614744,-1.2616800152467116,0.10642147064208984,4.876142 +800,Regression,Adaptive Model Rules,TrumpApproval,1.5494799828615766,2.674411214594314,-1.2354538504080876,0.10703182220458984,5.2219169999999995 +820,Regression,Adaptive Model Rules,TrumpApproval,1.533437809889996,2.6465115200139584,-1.2130964074464639,0.1085958480834961,5.570086999999999 +840,Regression,Adaptive Model Rules,TrumpApproval,1.5202839319169328,2.6201051582792827,-1.1892919715417847,0.11015987396240234,5.920738999999999 +860,Regression,Adaptive Model Rules,TrumpApproval,1.5178574341866524,2.5988091386120904,-1.1501373691585313,0.11077022552490234,6.280333 +880,Regression,Adaptive Model Rules,TrumpApproval,1.4962844530295305,2.571223801389781,-1.0942757338776041,0.11124706268310547,6.652339 +900,Regression,Adaptive Model Rules,TrumpApproval,1.4724252749133646,2.5436398469986066,-1.0582196084183888,0.11164379119873047,7.031402 +920,Regression,Adaptive Model Rules,TrumpApproval,1.4596881679466962,2.5220256913044325,-1.056635177134157,0.11167049407958984,7.413564 +940,Regression,Adaptive Model Rules,TrumpApproval,1.452139596196528,2.5028075284250018,-1.0425932823285438,0.11270427703857422,7.802334 +960,Regression,Adaptive Model Rules,TrumpApproval,1.4364147887122178,2.481230554777158,-1.0285162299402342,0.11320781707763672,8.193766 +980,Regression,Adaptive Model Rules,TrumpApproval,1.4186260884044517,2.45780687839372,-1.0290538610685447,0.11381816864013672,8.587828 +1000,Regression,Adaptive Model Rules,TrumpApproval,1.3997779646996387,2.434572696055838,-1.024386017127401,0.11442852020263672,8.984547 +1001,Regression,Adaptive Model Rules,TrumpApproval,1.3984653255896196,2.433357833975862,-1.0237164038272608,0.11442852020263672,9.38293 +11,Regression,Streaming Random Patches,ChickWeights,4.674710287324511,12.709622005759085,-206.93269654300337,0.14386653900146484,0.053261 +22,Regression,Streaming Random Patches,ChickWeights,2.741934273684416,9.017856101646904,-85.12629469646626,0.16807842254638672,0.14276 +33,Regression,Streaming Random Patches,ChickWeights,2.3543476002974106,7.430504888974863,-37.34585890537725,0.20960521697998047,0.266148 +44,Regression,Streaming Random Patches,ChickWeights,1.9327820011330463,6.452362261246447,-31.77814024428305,0.24174785614013672,0.646479 +55,Regression,Streaming Random Patches,ChickWeights,2.2606373648191784,6.1461368420669364,-6.374120366305681,0.30608272552490234,1.057843 +66,Regression,Streaming Random Patches,ChickWeights,2.3521495161457713,5.750947689984691,-1.7831107407377038,0.35672664642333984,1.521792 +77,Regression,Streaming Random Patches,ChickWeights,2.707478618787897,5.832856917221716,-0.9188552689556648,0.3732900619506836,2.259814 +88,Regression,Streaming Random Patches,ChickWeights,2.60389034076398,5.525482549715508,-0.5612341217350767,0.41286373138427734,3.03269 +99,Regression,Streaming Random Patches,ChickWeights,2.7646559934763433,5.466320467144536,-0.27797322073999386,0.4623746871948242,3.8541800000000004 +110,Regression,Streaming Random Patches,ChickWeights,2.880719733615897,5.407041915578862,0.12681954319141486,0.5318593978881836,4.717509000000001 +121,Regression,Streaming Random Patches,ChickWeights,3.0896780011355176,5.466874267225462,0.31254594053869156,0.5604543685913086,5.631386000000001 +132,Regression,Streaming Random Patches,ChickWeights,3.270943191870578,5.7618521847151385,0.37587775495273845,0.1946859359741211,6.649151000000001 +143,Regression,Streaming Random Patches,ChickWeights,3.2470159770350198,5.633009027852055,0.5168368848346436,0.22883892059326172,7.703019000000001 +154,Regression,Streaming Random Patches,ChickWeights,3.2192007860807728,5.520141144427338,0.6056681999848637,0.2577199935913086,8.869892000000002 +165,Regression,Streaming Random Patches,ChickWeights,3.5165819956804767,5.874797514643079,0.6409683688760216,0.26273250579833984,10.065309000000003 +176,Regression,Streaming Random Patches,ChickWeights,3.700430602978386,6.068185859760413,0.6911390854727877,0.29411983489990234,11.324773000000002 +187,Regression,Streaming Random Patches,ChickWeights,3.8037309027428843,6.1218084380222,0.7426259865968339,0.3320951461791992,12.629055000000003 +198,Regression,Streaming Random Patches,ChickWeights,4.006649900662983,6.578339511639692,0.7635979888713487,0.25274181365966797,13.981910000000003 +209,Regression,Streaming Random Patches,ChickWeights,4.229383118423565,6.982583803939909,0.780430075854037,0.32021617889404297,15.391493000000002 +220,Regression,Streaming Random Patches,ChickWeights,4.825249759558611,8.423350501384354,0.720252540483964,0.35108089447021484,16.880127 +231,Regression,Streaming Random Patches,ChickWeights,5.088028806665401,8.669832171958772,0.7465054715218886,0.32001399993896484,18.399834000000002 +242,Regression,Streaming Random Patches,ChickWeights,5.462442991686406,9.175585237136575,0.7715339230013022,0.3710927963256836,20.020863000000002 +253,Regression,Streaming Random Patches,ChickWeights,5.5636194675564115,9.260101660572655,0.797901929856006,0.41926097869873047,21.677784000000003 +264,Regression,Streaming Random Patches,ChickWeights,6.261116867150435,10.599777327157994,0.7580584961853923,0.3416013717651367,23.387217000000003 +275,Regression,Streaming Random Patches,ChickWeights,6.742618468073929,11.802240597789721,0.7360625543191792,0.3569021224975586,25.127202000000004 +286,Regression,Streaming Random Patches,ChickWeights,7.039594962415952,12.249488193444416,0.7537446936710837,0.39655208587646484,26.911059000000005 +297,Regression,Streaming Random Patches,ChickWeights,7.148800229885712,12.311677740983953,0.7842510327710687,0.4258260726928711,28.739538000000007 +308,Regression,Streaming Random Patches,ChickWeights,7.753683144422786,13.244190950829555,0.7776398094561477,0.45011425018310547,30.593856000000006 +319,Regression,Streaming Random Patches,ChickWeights,8.773143666519827,15.939753659782179,0.7077199430319765,0.4718656539916992,32.508295000000004 +330,Regression,Streaming Random Patches,ChickWeights,9.312574124937234,16.796832021919023,0.7222505421616592,0.3236379623413086,34.514388000000004 +341,Regression,Streaming Random Patches,ChickWeights,9.544591695703465,16.94083213248977,0.7470058810264122,0.3676939010620117,36.550995 +352,Regression,Streaming Random Patches,ChickWeights,9.680039805071173,17.006622056031052,0.7682275557667291,0.36295223236083984,38.648436000000004 +363,Regression,Streaming Random Patches,ChickWeights,10.417098847501563,18.381838377902795,0.7370670774420947,0.3571195602416992,40.771216 +374,Regression,Streaming Random Patches,ChickWeights,11.080869197293334,19.965812195666537,0.7148404591067161,0.39987850189208984,42.937715000000004 +385,Regression,Streaming Random Patches,ChickWeights,11.60940210623338,20.687378926969966,0.7291406299077132,0.3288450241088867,45.213373000000004 +396,Regression,Streaming Random Patches,ChickWeights,11.737814918904208,20.678726756260627,0.7476640805491588,0.4024953842163086,47.506899000000004 +407,Regression,Streaming Random Patches,ChickWeights,12.682108791666492,22.361726245252385,0.7257144517605874,0.4475545883178711,49.84079500000001 +418,Regression,Streaming Random Patches,ChickWeights,13.622708961705229,24.146094170569185,0.6997951546356598,0.4968290328979492,52.212878 +429,Regression,Streaming Random Patches,ChickWeights,14.165217959113354,24.88032134675199,0.7167593971826183,0.5376424789428711,54.630802 +440,Regression,Streaming Random Patches,ChickWeights,14.411006646174876,24.97835315387625,0.7347289581181171,0.5657072067260742,57.077112 +451,Regression,Streaming Random Patches,ChickWeights,14.766578325445964,25.376772271610328,0.7378384948653763,0.25832653045654297,59.570857 +462,Regression,Streaming Random Patches,ChickWeights,16.09445226127713,28.12961105819035,0.6974376845026461,0.3047628402709961,62.101037999999996 +473,Regression,Streaming Random Patches,ChickWeights,16.916275460891086,29.341089843915018,0.7093290035354769,0.3544912338256836,64.67061 +484,Regression,Streaming Random Patches,ChickWeights,17.222566694739786,29.549549967606488,0.7213397403469026,0.39832592010498047,67.26974899999999 +495,Regression,Streaming Random Patches,ChickWeights,17.854950072386483,30.34354672604944,0.7235900637963901,0.43242549896240234,69.89740799999998 +506,Regression,Streaming Random Patches,ChickWeights,18.84874733203415,31.79966974813451,0.7052484004379906,0.46784114837646484,72.66954099999998 +517,Regression,Streaming Random Patches,ChickWeights,19.785853660032195,33.20181112471305,0.6967783021275082,0.49755001068115234,75.49558399999998 +528,Regression,Streaming Random Patches,ChickWeights,20.52664258005787,34.100310164439925,0.7124234805234935,0.5351285934448242,78.36005299999998 +539,Regression,Streaming Random Patches,ChickWeights,20.766026265849113,34.21097619783695,0.7225061795517687,0.576685905456543,81.26779099999997 +550,Regression,Streaming Random Patches,ChickWeights,21.840503815170695,36.158966072689324,0.6995590139862528,0.4753904342651367,84.25568299999998 +561,Regression,Streaming Random Patches,ChickWeights,22.596906243133255,37.108967777985264,0.6974029137241997,0.5189352035522461,87.27382099999997 +572,Regression,Streaming Random Patches,ChickWeights,23.534320250737128,38.28067851879141,0.7021424273708647,0.5585355758666992,90.32555299999997 +578,Regression,Streaming Random Patches,ChickWeights,23.709683411591413,38.441629018276466,0.7069383356385298,0.3551816940307617,93.40141099999997 +20,Regression,Streaming Random Patches,TrumpApproval,2.677140920600926,9.804891856735377,-224.4966127051096,0.2373647689819336,0.078317 +40,Regression,Streaming Random Patches,TrumpApproval,2.42676306487335,7.1506632844470275,-19.556918338481843,0.3270711898803711,0.26006399999999996 +60,Regression,Streaming Random Patches,TrumpApproval,1.8116457742622056,5.852230884873156,-17.563213740222555,0.34931087493896484,0.628767 +80,Regression,Streaming Random Patches,TrumpApproval,1.5261658032230543,5.084894428469453,-13.057813649460385,0.3968191146850586,1.163603 +100,Regression,Streaming Random Patches,TrumpApproval,1.4048851039170587,4.580518627305071,-6.157363117938322,0.41078853607177734,1.7740749999999998 +120,Regression,Streaming Random Patches,TrumpApproval,1.2872329076385731,4.198963935277897,-4.1024421406573515,0.4562673568725586,2.4559889999999998 +140,Regression,Streaming Random Patches,TrumpApproval,1.4191481295394186,4.9019146331166,-6.185954638838571,0.20268917083740234,3.2282499999999996 +160,Regression,Streaming Random Patches,TrumpApproval,1.3292908695512107,4.594852312450113,-4.568052693162012,0.32157421112060547,4.121231 +180,Regression,Streaming Random Patches,TrumpApproval,1.2559271503392595,4.341680890984575,-3.3661470093978725,0.4017667770385742,5.147797 +200,Regression,Streaming Random Patches,TrumpApproval,1.169313410896163,4.12134361195162,-2.94593273053925,0.47729015350341797,6.3380719999999995 +220,Regression,Streaming Random Patches,TrumpApproval,1.1066399346497042,3.9344660517514094,-2.8792501333638807,0.5200605392456055,7.615136 +240,Regression,Streaming Random Patches,TrumpApproval,1.0535228972416337,3.7704097053754206,-2.5892915995637975,0.590418815612793,8.98983 +260,Regression,Streaming Random Patches,TrumpApproval,1.0002808672832586,3.6243313765079748,-2.3612477765650133,0.677699089050293,10.49221 +280,Regression,Streaming Random Patches,TrumpApproval,0.9484528900796008,3.4935839886686573,-2.269856713922535,0.7459287643432617,12.09809 +300,Regression,Streaming Random Patches,TrumpApproval,0.9319658343242508,3.3810597344709987,-2.011909605217061,0.8300580978393555,13.828035999999999 +320,Regression,Streaming Random Patches,TrumpApproval,0.9015525068993323,3.2761126415748776,-1.9678527090537403,0.8134641647338867,15.697273999999998 +340,Regression,Streaming Random Patches,TrumpApproval,0.9086073156973856,3.2065165500712434,-2.0044577988421626,0.7938528060913086,17.678026 +360,Regression,Streaming Random Patches,TrumpApproval,0.9209686764414103,3.130698586248577,-1.8875761151685357,0.8660383224487305,19.789752 +380,Regression,Streaming Random Patches,TrumpApproval,0.9054594388814018,3.0518145886207013,-1.8388130234405762,0.9304952621459961,22.025823 +400,Regression,Streaming Random Patches,TrumpApproval,0.9021459083449618,2.9892737243691805,-1.8006304820861794,0.9046812057495117,24.377516 +420,Regression,Streaming Random Patches,TrumpApproval,0.9000900027115483,2.937103674242639,-1.7932063526136273,0.2984609603881836,26.835964 +440,Regression,Streaming Random Patches,TrumpApproval,0.884833385913356,2.873027664171451,-1.6243016536608597,0.3453207015991211,29.357596 +460,Regression,Streaming Random Patches,TrumpApproval,0.8690754265879537,2.8131168800056585,-1.3859136859847618,0.3807516098022461,31.984181 +480,Regression,Streaming Random Patches,TrumpApproval,0.8473225380080763,2.7551095271995596,-1.235881587238783,0.44824886322021484,34.68911 +500,Regression,Streaming Random Patches,TrumpApproval,0.8286186581223807,2.701061117260836,-1.1039316995995572,0.49751949310302734,37.472375 +520,Regression,Streaming Random Patches,TrumpApproval,0.8308331247066605,2.6760879938297655,-1.0688124961584973,0.37538814544677734,40.349897 +540,Regression,Streaming Random Patches,TrumpApproval,0.8063786739892863,2.6263308571208617,-0.9852938090834253,0.40636730194091797,43.316687 +560,Regression,Streaming Random Patches,TrumpApproval,0.7997929461413226,2.582727501713279,-0.9656668153374994,0.43744945526123047,46.3679 +580,Regression,Streaming Random Patches,TrumpApproval,0.7908850979728871,2.5414571653673215,-0.950423138286411,0.49677181243896484,49.479346 +600,Regression,Streaming Random Patches,TrumpApproval,0.7789627943481009,2.500361162030882,-0.8669718089652279,0.569575309753418,52.654549 +620,Regression,Streaming Random Patches,TrumpApproval,0.7682254429218135,2.4616773328611172,-0.7820656947310429,0.6584272384643555,55.885269 +640,Regression,Streaming Random Patches,TrumpApproval,0.756836908225871,2.4246570785119217,-0.6965531574296129,0.7120962142944336,59.181412 +660,Regression,Streaming Random Patches,TrumpApproval,0.7406340846119412,2.388088765643962,-0.6339309775627988,0.8089780807495117,62.54643 +680,Regression,Streaming Random Patches,TrumpApproval,0.7257657440750075,2.3532857176647086,-0.6117268738432657,0.8398160934448242,65.979515 +700,Regression,Streaming Random Patches,TrumpApproval,0.7284794894639326,2.3250297797266612,-0.6145762958857721,0.9275884628295898,69.48963400000001 +720,Regression,Streaming Random Patches,TrumpApproval,0.7231955460113827,2.297270435922827,-0.6108826519065647,0.9087285995483398,73.071921 +740,Regression,Streaming Random Patches,TrumpApproval,0.7217944839950929,2.2699024953355416,-0.5717835473178023,0.8719320297241211,76.723943 +760,Regression,Streaming Random Patches,TrumpApproval,0.7121024512438853,2.241058668519108,-0.5486900768629306,0.9295186996459961,80.452493 +780,Regression,Streaming Random Patches,TrumpApproval,0.7019422114012909,2.213016497735788,-0.5169405784918113,1.0446271896362305,84.252351 +800,Regression,Streaming Random Patches,TrumpApproval,0.7005931807120314,2.1884283322336215,-0.49683523063919743,1.1031560897827148,88.13395700000001 +820,Regression,Streaming Random Patches,TrumpApproval,0.6997484436891046,2.169363820936814,-0.48702250637871436,1.0328702926635742,92.08914500000002 +840,Regression,Streaming Random Patches,TrumpApproval,0.6949195885567419,2.14957176369946,-0.4735678496288336,0.7475957870483398,96.10836100000002 +860,Regression,Streaming Random Patches,TrumpApproval,0.6920590805093112,2.1264870362465933,-0.439603572136809,0.8119535446166992,100.18224400000001 +880,Regression,Streaming Random Patches,TrumpApproval,0.6882027439938415,2.1038322601427426,-0.40209138862407245,0.8655576705932617,104.313167 +900,Regression,Streaming Random Patches,TrumpApproval,0.6818594219129391,2.085410616994055,-0.38345052454273,0.8467855453491211,108.50343900000001 +920,Regression,Streaming Random Patches,TrumpApproval,0.6756248333192205,2.0637081851469703,-0.37706620583821104,0.9400205612182617,112.75318600000001 +940,Regression,Streaming Random Patches,TrumpApproval,0.6689624136970388,2.0428592411141833,-0.3608300191067024,0.8168668746948242,117.06153900000001 +960,Regression,Streaming Random Patches,TrumpApproval,0.6627773066160889,2.022520414368002,-0.3478143420231987,0.9120321273803711,121.43268700000002 +980,Regression,Streaming Random Patches,TrumpApproval,0.6600305544016135,2.003593726688123,-0.34839580077130505,0.9782476425170898,125.86239700000002 +1000,Regression,Streaming Random Patches,TrumpApproval,0.657029407021691,1.9853014454830338,-0.3461726175729922,1.0593442916870117,130.37428000000003 +1001,Regression,Streaming Random Patches,TrumpApproval,0.6566965628025029,1.9843359394661053,-0.34576147632314735,1.0646085739135742,134.90266400000002 +11,Regression,Bagging,ChickWeights,10.955590565999659,17.7409835250609,-404.147256051216,0.1553668975830078,0.005094 +22,Regression,Bagging,ChickWeights,5.88626580700965,12.566688603347808,-166.25182631838038,0.1681652069091797,0.018278 +33,Regression,Bagging,ChickWeights,4.383857039198176,10.299865918219764,-72.67921052893462,0.20520591735839844,0.039075 +44,Regression,Bagging,ChickWeights,3.446496162870555,8.931116231999566,-61.79980671874969,0.2209186553955078,0.065167 +55,Regression,Bagging,ChickWeights,3.3513349782155033,8.247717183177938,-12.279242202465667,0.2687969207763672,0.09656600000000001 +66,Regression,Bagging,ChickWeights,3.889627188952696,8.0458642201752,-4.4474976461238604,0.3383464813232422,0.144605 +77,Regression,Bagging,ChickWeights,4.337751636727128,7.9681159743419645,-2.5808890563388096,0.3940753936767578,0.201306 +88,Regression,Bagging,ChickWeights,4.489908334389532,7.740787033322287,-2.0640641214272355,0.4405231475830078,0.274421 +99,Regression,Bagging,ChickWeights,4.7831270806190425,7.705843596650206,-1.5396388125269618,0.4615802764892578,0.37284300000000004 +110,Regression,Bagging,ChickWeights,4.73395080514245,7.47334250555501,-0.6680701376440403,0.4910602569580078,0.5845670000000001 +121,Regression,Bagging,ChickWeights,4.733710015085173,7.331306378435282,-0.23631246502535208,0.5042209625244141,0.806575 +132,Regression,Bagging,ChickWeights,4.565752852065114,7.0976416640915465,0.05294854472396571,0.5126438140869141,1.039517 +143,Regression,Bagging,ChickWeights,4.439022558662509,6.895745596080793,0.2759386934515202,0.5216274261474609,1.284562 +154,Regression,Bagging,ChickWeights,4.362170284876481,6.736533340066285,0.4127346685162743,0.5262050628662109,1.543379 +165,Regression,Bagging,ChickWeights,4.647894929983432,7.008861526290804,0.48897532974091285,0.5290126800537109,1.82575 +176,Regression,Bagging,ChickWeights,4.817744211127824,7.136288548419971,0.5728405597677722,0.5066156387329102,2.145522 +187,Regression,Bagging,ChickWeights,4.867036081233358,7.152118590173611,0.6487028411390494,0.4790925979614258,2.491984 +198,Regression,Bagging,ChickWeights,4.887061675652274,7.15502256260352,0.720333392468735,0.3514680862426758,2.944156 +209,Regression,Bagging,ChickWeights,5.070260383978678,7.484020932149266,0.7477619935177958,0.3277406692504883,3.412471 +220,Regression,Bagging,ChickWeights,5.671227628293087,8.602358503958763,0.7082361492582977,0.3353548049926758,3.897275 +231,Regression,Bagging,ChickWeights,5.872335352103937,8.83175934179542,0.7369479677711775,0.37228870391845703,4.389179 +242,Regression,Bagging,ChickWeights,6.107145463120707,9.222510821361377,0.769191114739663,0.3991250991821289,4.889463 +253,Regression,Bagging,ChickWeights,6.19844823305091,9.33633324769997,0.7945607849348244,0.42070865631103516,5.506109 +264,Regression,Bagging,ChickWeights,6.823605404288741,10.586090935492885,0.758682880699561,0.43335819244384766,6.131382 +275,Regression,Bagging,ChickWeights,7.289576170484155,11.670233638164651,0.7419337665758028,0.4418039321899414,6.777342 +286,Regression,Bagging,ChickWeights,7.579012857443305,12.145524073459754,0.75790700185782,0.45081424713134766,7.439278 +297,Regression,Bagging,ChickWeights,7.564986803201262,12.135208564512551,0.7903915740986345,0.45409488677978516,8.115354 +308,Regression,Bagging,ChickWeights,8.103353916061925,13.02855032884451,0.7848217554522041,0.4577798843383789,8.913327 +319,Regression,Bagging,ChickWeights,9.2182891996096,15.75502466975724,0.7144552709875674,0.4609994888305664,9.721339 +330,Regression,Bagging,ChickWeights,9.685083231372472,16.400765556025647,0.7351946818510116,0.4719209671020508,10.546217 +341,Regression,Bagging,ChickWeights,9.903299441393282,16.527032528363478,0.759214288686034,0.47708606719970703,11.387382 +352,Regression,Bagging,ChickWeights,10.047801743751695,16.62843798311862,0.778421004008311,0.48488330841064453,12.279877 +363,Regression,Bagging,ChickWeights,10.963674059851892,18.110346084278056,0.7447765461541069,0.48735523223876953,13.191450000000001 +374,Regression,Bagging,ChickWeights,11.492835005144466,19.28081121430548,0.7340717056357994,0.4739046096801758,14.120497000000002 +385,Regression,Bagging,ChickWeights,11.898657720927194,19.943213385356135,0.7482768269892894,0.4488801956176758,15.069160000000002 +396,Regression,Bagging,ChickWeights,12.0617729851989,19.965773188137263,0.7647640178574115,0.4161386489868164,16.114715 +407,Regression,Bagging,ChickWeights,12.97304553348899,21.57136186484404,0.7447607843499935,0.40607547760009766,17.182106 +418,Regression,Bagging,ChickWeights,13.747411939847145,23.06575414024212,0.7260576137332548,0.42958927154541016,18.259955 +429,Regression,Bagging,ChickWeights,14.305030376306712,23.986335540211613,0.7367482003695625,0.4628152847290039,19.347052 +440,Regression,Bagging,ChickWeights,14.526268288674354,24.074522605416067,0.7535790611891788,0.4899911880493164,20.478651000000003 +451,Regression,Bagging,ChickWeights,15.027594657922048,24.671918598232004,0.7521995995009789,0.5175485610961914,21.623166 +462,Regression,Bagging,ChickWeights,16.208274238827823,27.03842360672758,0.7204560380476568,0.5713090896606445,22.784126 +473,Regression,Bagging,ChickWeights,16.99589357541462,28.364120175265192,0.7283636722963454,0.5903940200805664,23.973479 +484,Regression,Bagging,ChickWeights,17.304815327407063,28.547476213614512,0.739918935022724,0.6084756851196289,25.225522 +495,Regression,Bagging,ChickWeights,17.747173803776352,29.064129392830434,0.7464079684248853,0.6325922012329102,26.517497000000002 +506,Regression,Bagging,ChickWeights,18.655435380807354,30.57773482627066,0.7274654471662664,0.6401453018188477,27.826703000000002 +517,Regression,Bagging,ChickWeights,19.38212758204995,31.566942752755278,0.7259045847606268,0.5900964736938477,29.161388000000002 +528,Regression,Bagging,ChickWeights,20.162555380752366,32.73116726334994,0.7350525453098611,0.6013956069946289,30.577640000000002 +539,Regression,Bagging,ChickWeights,20.34377517847412,32.75647044736101,0.7456003073902285,0.6148271560668945,32.006392000000005 +550,Regression,Bagging,ChickWeights,21.397093652240404,34.43808497807088,0.7274757471078548,0.6217546463012695,33.45442800000001 +561,Regression,Bagging,ChickWeights,22.130535392790673,35.39551310036421,0.7247017706467436,0.6181917190551758,34.916661000000005 +572,Regression,Bagging,ChickWeights,22.976096797270415,36.539264510866154,0.7286255260499503,0.6243486404418945,36.463466000000004 +578,Regression,Bagging,ChickWeights,23.114298050830314,36.631109645590286,0.7338934315030725,0.6280336380004883,38.020312000000004 +20,Regression,Bagging,TrumpApproval,6.57361785669815,13.877675781396096,-450.73930630825186,0.3875865936279297,0.030628 +40,Regression,Bagging,TrumpApproval,4.357601810962072,9.93598927447802,-38.690592530050864,0.5688495635986328,0.09352200000000001 +60,Regression,Bagging,TrumpApproval,3.120546196671925,8.124382016407804,-34.775930157070896,0.6946392059326172,0.180013 +80,Regression,Bagging,TrumpApproval,2.5823668216656817,7.068571931029129,-26.165472568815836,0.7988948822021484,0.365225 +100,Regression,Bagging,TrumpApproval,2.6103510398716643,6.439797187103485,-13.147122820254193,0.9021625518798828,0.589181 +120,Regression,Bagging,TrumpApproval,2.5653436103516496,5.96335184363353,-9.29140495411716,0.9421710968017578,0.842127 +140,Regression,Bagging,TrumpApproval,2.4314692166818666,5.556159680491977,-8.232140838080387,0.9627094268798828,1.43468 +160,Regression,Bagging,TrumpApproval,2.270493582871441,5.217534738727647,-6.179445803611509,1.0016803741455078,2.055929 +180,Regression,Bagging,TrumpApproval,2.1841879014169865,4.9594120506005,-4.6969569828406526,0.9622507095336914,2.757735 +200,Regression,Bagging,TrumpApproval,2.030794616399332,4.7110231793054895,-4.155876544063708,0.5397500991821289,3.551711 +220,Regression,Bagging,TrumpApproval,1.922882727301643,4.50300441964265,-4.081371242371108,0.37749576568603516,4.407055 +240,Regression,Bagging,TrumpApproval,1.8390508968191754,4.321014818317665,-3.7141474735663893,0.43126392364501953,5.281937999999999 +260,Regression,Bagging,TrumpApproval,1.7379678526387645,4.155226166492631,-3.4180849750109363,0.4941263198852539,6.197258999999999 +280,Regression,Bagging,TrumpApproval,1.7042826877160742,4.0269186303191065,-3.3444224917120184,0.5997896194458008,7.197467999999999 +300,Regression,Bagging,TrumpApproval,1.6796571065333832,3.9174008876388,-3.043265693703045,0.6906900405883789,8.219126999999999 +320,Regression,Bagging,TrumpApproval,1.5891460162001483,3.7936804881645676,-2.979662055693274,0.757817268371582,9.288978999999998 +340,Regression,Bagging,TrumpApproval,1.5335884019062007,3.685003453386448,-2.968029890458662,0.7969903945922852,10.383406999999998 +360,Regression,Bagging,TrumpApproval,1.54418408246079,3.606838798974545,-2.832696164635261,0.8751077651977539,11.515104999999998 +380,Regression,Bagging,TrumpApproval,1.5054402320411853,3.5177984273762375,-2.771919367898169,0.9264421463012695,12.700466999999998 +400,Regression,Bagging,TrumpApproval,1.4723491084260332,3.435525460733128,-2.699225318590951,0.9911317825317383,13.924277999999997 +420,Regression,Bagging,TrumpApproval,1.429579158986208,3.3562143901072865,-2.6472359354971333,0.9703760147094727,15.190464999999998 +440,Regression,Bagging,TrumpApproval,1.3992424504019558,3.2853835752695946,-2.4316761923151153,1.0316247940063477,16.527614999999997 +460,Regression,Bagging,TrumpApproval,1.365864594828797,3.217129802398014,-2.120443637805541,1.1097803115844727,17.906522 +480,Regression,Bagging,TrumpApproval,1.3301487592579586,3.151695763852486,-1.9259010739386908,1.1814966201782227,19.341224 +500,Regression,Bagging,TrumpApproval,1.2968821746115176,3.0904885141585767,-1.7543370405557224,1.2276010513305664,20.84359 +520,Regression,Bagging,TrumpApproval,1.2678501702074907,3.0331483120333496,-1.6577103317872361,1.2783823013305664,22.421222999999998 +540,Regression,Bagging,TrumpApproval,1.2343399552126226,2.9775564396478966,-1.551795811786203,1.2796869277954102,24.086657999999996 +560,Regression,Bagging,TrumpApproval,1.220715255826944,2.9300367331798807,-1.5298738258017646,1.2170305252075195,25.790580999999996 +580,Regression,Bagging,TrumpApproval,1.1924146311245054,2.8809984439523286,-1.5063937515255619,1.0756006240844727,27.649708999999994 +600,Regression,Bagging,TrumpApproval,1.1793821598427467,2.837096711415006,-1.4037015974174163,0.9519128799438477,29.574726999999996 +620,Regression,Bagging,TrumpApproval,1.1645342598465298,2.7961228756221597,-1.2991852345603885,0.9679117202758789,31.579397999999998 +640,Regression,Bagging,TrumpApproval,1.1398425529628198,2.753175690936408,-1.1874325743915652,0.936314582824707,33.628169 +660,Regression,Bagging,TrumpApproval,1.1198821044801988,2.7133180185933967,-1.1092797015680484,0.9601030349731445,35.724068 +680,Regression,Bagging,TrumpApproval,1.103543375947734,2.675681585437618,-1.0835838801311364,1.0132951736450195,37.850435000000004 +700,Regression,Bagging,TrumpApproval,1.0951788603095205,2.6436744635360436,-1.0874567422761272,1.0955934524536133,40.041881000000004 +720,Regression,Bagging,TrumpApproval,1.073603439060177,2.607328103868497,-1.0750617689055861,1.1506471633911133,42.267731000000005 +740,Regression,Bagging,TrumpApproval,1.052216298294681,2.5725220480150233,-1.0188151031858665,1.1900300979614258,44.534549000000005 +760,Regression,Bagging,TrumpApproval,1.0329729065757678,2.539265324444459,-0.9882648176410449,1.2228517532348633,46.85075200000001 +780,Regression,Bagging,TrumpApproval,1.0152010354431573,2.5074934380267417,-0.9475063222432678,1.2825212478637695,49.22632000000001 +800,Regression,Bagging,TrumpApproval,1.007374170791078,2.4793027589713232,-0.9211818120686948,1.3126497268676758,51.66223500000001 +820,Regression,Bagging,TrumpApproval,1.002386830132377,2.4547936379778226,-0.9040692252875042,1.3594255447387695,54.13772100000001 +840,Regression,Bagging,TrumpApproval,0.9929186057762197,2.428247138481691,-0.8804076589484491,1.397130012512207,56.66204300000001 +860,Regression,Bagging,TrumpApproval,0.9756374748391698,2.400488599154532,-0.8344958433267429,1.4288606643676758,59.23948900000001 +880,Regression,Bagging,TrumpApproval,0.964181960731443,2.37450147105602,-0.7860721035239808,1.4496355056762695,61.87688900000001 +900,Regression,Bagging,TrumpApproval,0.9549728240782616,2.3497625798451978,-0.7564202624419067,1.3498811721801758,64.59788000000002 +920,Regression,Bagging,TrumpApproval,0.9412862327577896,2.3247867434211136,-0.747529388757789,1.1945161819458008,67.39612200000002 +940,Regression,Bagging,TrumpApproval,0.934469347520636,2.302477656767798,-0.7286928787152502,1.2202577590942383,70.24630900000002 +960,Regression,Bagging,TrumpApproval,0.9259837543593707,2.280476047701142,-0.7135440601163805,1.2635469436645508,73.13750900000002 +980,Regression,Bagging,TrumpApproval,0.9196316545824527,2.2597103614150886,-0.715155968132227,1.2794008255004883,76.07904100000002 +1000,Regression,Bagging,TrumpApproval,0.9087747756519627,2.2382775608394114,-0.7111012811273396,1.3157854080200195,79.06112300000002 +1001,Regression,Bagging,TrumpApproval,0.9082029688272106,2.2371845268363217,-0.710571805505718,1.3157854080200195,82.06893700000002 +11,Regression,Exponentially Weighted Average,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.06525707244873047,0.004749 +22,Regression,Exponentially Weighted Average,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.07764339447021484,0.02229 +33,Regression,Exponentially Weighted Average,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.09733104705810547,0.042479 +44,Regression,Exponentially Weighted Average,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.10875988006591797,0.065964 +55,Regression,Exponentially Weighted Average,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.13167858123779297,0.11433299999999999 +66,Regression,Exponentially Weighted Average,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.1604146957397461,0.17057299999999997 +77,Regression,Exponentially Weighted Average,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.18252849578857422,0.23188899999999998 +88,Regression,Exponentially Weighted Average,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.2035512924194336,0.308411 +99,Regression,Exponentially Weighted Average,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.21530437469482422,0.393152 +110,Regression,Exponentially Weighted Average,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.2280874252319336,0.484559 +121,Regression,Exponentially Weighted Average,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.23778057098388672,0.583103 +132,Regression,Exponentially Weighted Average,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.24797725677490234,0.792791 +143,Regression,Exponentially Weighted Average,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.22072410583496094,1.018133 +154,Regression,Exponentially Weighted Average,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.2406787872314453,1.2542229999999999 +165,Regression,Exponentially Weighted Average,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.25681114196777344,1.5160939999999998 +176,Regression,Exponentially Weighted Average,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.2727985382080078,1.7876089999999998 +187,Regression,Exponentially Weighted Average,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.2873516082763672,2.0795459999999997 +198,Regression,Exponentially Weighted Average,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.30018043518066406,2.3857599999999994 +209,Regression,Exponentially Weighted Average,ChickWeights,57.5645933014354,59.46206220864915,-14.922837840066967,0.27625083923339844,2.7086299999999994 +220,Regression,Exponentially Weighted Average,ChickWeights,58.69090909090908,60.81327606250582,-13.581197962556498,0.2926349639892578,3.0480419999999993 +231,Regression,Exponentially Weighted Average,ChickWeights,60.25541125541125,62.66764529032318,-12.244451024360147,0.30736351013183594,3.437715999999999 +242,Regression,Exponentially Weighted Average,ChickWeights,62.17355371900826,65.06963847478845,-10.489760184397113,0.32158851623535156,3.8493299999999993 +253,Regression,Exponentially Weighted Average,ChickWeights,63.936758893280626,67.17295239601157,-9.634560128382748,0.3348064422607422,4.393024 +264,Regression,Exponentially Weighted Average,ChickWeights,65.10606060606062,68.57980310513724,-9.127665748505592,0.3451099395751953,4.977281 +275,Regression,Exponentially Weighted Average,ChickWeights,66.61454545454548,70.46451073219248,-8.408339126213217,0.35480308532714844,5.586244 +286,Regression,Exponentially Weighted Average,ChickWeights,68.48951048951052,72.8020594498525,-7.6983532427125105,0.3655261993408203,6.228505 +297,Regression,Exponentially Weighted Average,ChickWeights,70.55218855218858,75.3669362796119,-7.08492451355157,0.3711223602294922,6.899176000000001 +308,Regression,Exponentially Weighted Average,ChickWeights,72.39285714285718,77.65033596401675,-6.643510181414674,0.3809375762939453,7.597348 +319,Regression,Exponentially Weighted Average,ChickWeights,73.45454545454551,79.15086186624424,-6.206879640065647,0.39273643493652344,8.345726 +330,Regression,Exponentially Weighted Average,ChickWeights,75.77878787878792,82.20832738177494,-5.653192449779911,0.40398597717285156,9.225203 +341,Regression,Exponentially Weighted Average,ChickWeights,77.92375366568919,84.89106353805269,-5.352795814687307,0.4136791229248047,10.124705 +352,Regression,Exponentially Weighted Average,ChickWeights,80.04545454545458,87.49376601169416,-5.134510311668016,0.4233722686767578,11.048248000000001 +363,Regression,Exponentially Weighted Average,ChickWeights,80.99724517906337,88.57562798692558,-5.105139086016474,0.43306541442871094,11.989622 +374,Regression,Exponentially Weighted Average,ChickWeights,82.77807486631018,90.83029071422122,-4.901675845817959,0.45247840881347656,12.967698 +385,Regression,Exponentially Weighted Average,ChickWeights,85.1766233766234,93.99517810235533,-4.591702735915359,0.4681224822998047,14.032748 +396,Regression,Exponentially Weighted Average,ChickWeights,87.26767676767678,96.48964983485283,-4.494054297851511,0.48465919494628906,15.12168 +407,Regression,Exponentially Weighted Average,ChickWeights,89.00737100737103,98.71879502607636,-4.345544683073043,0.4991130828857422,16.236767999999998 +418,Regression,Exponentially Weighted Average,ChickWeights,90.57416267942587,100.72635724110245,-4.224084264201084,0.5109386444091797,17.416859999999996 +429,Regression,Exponentially Weighted Average,ChickWeights,93.12121212121215,104.19735398794236,-3.9677178403495805,0.5206584930419922,18.643557999999995 +440,Regression,Exponentially Weighted Average,ChickWeights,95.41818181818185,107.03565676064125,-3.8710119659250104,0.5303516387939453,19.910713999999995 +451,Regression,Exponentially Weighted Average,ChickWeights,97.16629711751665,109.07665280092142,-3.843505105397095,0.5280742645263672,21.225085999999994 +462,Regression,Exponentially Weighted Average,ChickWeights,98.71645021645023,111.17636431671961,-3.72620239405422,0.5443019866943359,22.607732999999993 +473,Regression,Exponentially Weighted Average,ChickWeights,101.54122621564484,115.20584573786859,-3.4812404756668593,0.5577869415283203,24.015635999999994 +484,Regression,Exponentially Weighted Average,ChickWeights,103.77066115702482,117.90601559037043,-3.4365483842712585,0.5712184906005859,25.474227999999993 +495,Regression,Exponentially Weighted Average,ChickWeights,106.02424242424244,120.71525892518193,-3.37467008920777,0.5825443267822266,26.96779499999999 +506,Regression,Exponentially Weighted Average,ChickWeights,107.31620553359684,122.26004165941237,-3.356924458603192,2.7805843353271484,30.43480299999999 +517,Regression,Exponentially Weighted Average,ChickWeights,109.39651837524178,124.91233289427785,-3.291877964737682,2.825040817260742,33.93932499999999 +528,Regression,Exponentially Weighted Average,ChickWeights,112.36553030303028,129.1106745698386,-3.1225038051323804,2.876035690307617,37.48648499999999 +539,Regression,Exponentially Weighted Average,ChickWeights,114.52504638218922,131.65752925403248,-3.109734667916423,2.927671432495117,41.07985699999999 +550,Regression,Exponentially Weighted Average,ChickWeights,115.89999999999996,133.35909826820617,-3.0866973064470367,2.9773387908935547,44.71417799999999 +561,Regression,Exponentially Weighted Average,ChickWeights,117.86452762923346,135.8046463151548,-3.0526234314410727,3.0208263397216797,48.39640499999999 +572,Regression,Exponentially Weighted Average,ChickWeights,120.54020979020974,139.4624607986965,-2.953338846956928,3.065652847290039,52.120071999999986 +578,Regression,Exponentially Weighted Average,ChickWeights,121.81833910034597,141.00422703423635,-2.942935834251463,3.092409133911133,55.885136999999986 +20,Regression,Exponentially Weighted Average,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.1445150375366211,0.017697 +40,Regression,Exponentially Weighted Average,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.21173763275146484,0.059669 +60,Regression,Exponentially Weighted Average,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.25832653045654297,0.116093 +80,Regression,Exponentially Weighted Average,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.3003568649291992,0.19454100000000002 +100,Regression,Exponentially Weighted Average,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.3407926559448242,0.29552300000000004 +120,Regression,Exponentially Weighted Average,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.3709287643432617,0.510712 +140,Regression,Exponentially Weighted Average,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.3974485397338867,0.760252 +160,Regression,Exponentially Weighted Average,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.3402233123779297,1.044132 +180,Regression,Exponentially Weighted Average,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.3811016082763672,1.367502 +200,Regression,Exponentially Weighted Average,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.3127880096435547,1.794637 +220,Regression,Exponentially Weighted Average,TrumpApproval,39.727043136363626,39.77723500009918,-395.50198072931875,0.3578624725341797,2.266575 +240,Regression,Exponentially Weighted Average,TrumpApproval,39.56323079166665,39.61325406766278,-395.19837684116754,0.3875255584716797,2.775551 +260,Regression,Exponentially Weighted Average,TrumpApproval,39.42014538461535,39.46968290441584,-397.63185900832246,0.42084693908691406,3.358614 +280,Regression,Exponentially Weighted Average,TrumpApproval,39.33200189285712,39.37942345737111,-414.45601593500356,0.47014808654785156,4.097303 +300,Regression,Exponentially Weighted Average,TrumpApproval,39.18435719999999,39.23275803924839,-404.5402138221895,0.5117359161376953,4.882924 +320,Regression,Exponentially Weighted Average,TrumpApproval,39.13568690624999,39.1818628962716,-423.5167725219512,0.5480670928955078,5.740529 +340,Regression,Exponentially Weighted Average,TrumpApproval,39.14620944117645,39.18989510023786,-447.7943063391533,0.5615406036376953,6.72918 +360,Regression,Exponentially Weighted Average,TrumpApproval,39.24072974999997,39.28395553300239,-453.6543473793619,0.5990085601806641,7.76074 +380,Regression,Exponentially Weighted Average,TrumpApproval,39.29597665789471,39.337699215460226,-470.6701690846498,0.6312580108642578,8.924786000000001 +400,Regression,Exponentially Weighted Average,TrumpApproval,39.35730624999997,39.39781946688104,-485.4842825426507,0.6682605743408203,10.154856 +420,Regression,Exponentially Weighted Average,TrumpApproval,39.40549083333331,39.44465897881697,-502.77995042269276,0.6983966827392578,11.469727 +440,Regression,Exponentially Weighted Average,TrumpApproval,39.49730674999998,39.53710368662846,-495.9856416828035,0.7316226959228516,12.901862000000001 +460,Regression,Exponentially Weighted Average,TrumpApproval,39.61474728260867,39.65658853240579,-473.14358309219216,0.7705020904541016,14.448849000000001 +480,Regression,Exponentially Weighted Average,TrumpApproval,39.71032456249997,39.753047582709755,-464.4916761787406,0.8079357147216797,16.094285 +500,Regression,Exponentially Weighted Average,TrumpApproval,39.80313951999997,39.84667590965187,-456.87508245086684,2.9298267364501953,19.825027 +520,Regression,Exponentially Weighted Average,TrumpApproval,39.873547134615364,39.916931033645376,-459.2932847271911,3.0076160430908203,23.629967 +540,Regression,Exponentially Weighted Average,TrumpApproval,39.94649651851849,39.98996046818772,-459.28610565666287,3.1059207916259766,27.509295 +560,Regression,Exponentially Weighted Average,TrumpApproval,39.976066142857114,40.018487723609816,-470.92618770667195,3.203706741333008,31.453258 +580,Regression,Exponentially Weighted Average,TrumpApproval,40.00338510344825,40.044755101652726,-483.2331705341176,3.296670913696289,35.460995000000004 +600,Regression,Exponentially Weighted Average,TrumpApproval,40.07393431666663,40.11569326301364,-479.5746686678817,3.391347885131836,39.544454 +620,Regression,Exponentially Weighted Average,TrumpApproval,40.1459417741935,40.18827077358568,-473.96334667177865,3.4906063079833984,43.697410000000005 +640,Regression,Exponentially Weighted Average,TrumpApproval,40.219438156249964,40.26249426545423,-466.8085709746123,3.5870800018310547,47.924640000000004 +660,Regression,Exponentially Weighted Average,TrumpApproval,40.28296777272724,40.32626722721455,-464.9172853497744,3.6864986419677734,52.218165000000006 +680,Regression,Exponentially Weighted Average,TrumpApproval,40.31998279411761,40.36256991107017,-473.1325264408024,3.782560348510742,56.58359200000001 +700,Regression,Exponentially Weighted Average,TrumpApproval,40.31359012857138,40.35509446667054,-485.40526703956544,3.816682815551758,61.02307200000001 +720,Regression,Exponentially Weighted Average,TrumpApproval,40.31730695833329,40.357915759594896,-496.1610725544049,3.917215347290039,65.52718500000002 +740,Regression,Exponentially Weighted Average,TrumpApproval,40.36653568918915,40.407119416424955,-497.07428037101636,4.021100997924805,70.10250300000001 +760,Regression,Exponentially Weighted Average,TrumpApproval,40.403143671052604,40.443256311482514,-503.3712175162706,4.113973617553711,74.743136 +780,Regression,Exponentially Weighted Average,TrumpApproval,40.44545064102563,40.48534274444009,-506.6856716110208,4.221334457397461,79.45160200000001 +800,Regression,Exponentially Weighted Average,TrumpApproval,40.478548249999996,40.518050685964006,-512.1052117095793,4.33137321472168,84.22367000000001 +820,Regression,Exponentially Weighted Average,TrumpApproval,40.50894034146341,40.5479845946661,-518.5068774177179,4.393171310424805,89.06994900000001 +840,Regression,Exponentially Weighted Average,TrumpApproval,40.5406558690476,40.579310897365986,-524.140575335229,4.501546859741211,93.98653900000001 +860,Regression,Exponentially Weighted Average,TrumpApproval,40.58371181395347,40.62239247493601,-524.3496319016275,4.600507736206055,98.97766000000001 +880,Regression,Exponentially Weighted Average,TrumpApproval,40.62855514772725,40.66738601007716,-522.897851512946,4.697576522827148,104.03937600000002 +900,Regression,Exponentially Weighted Average,TrumpApproval,40.664104233333326,40.702738445808535,-526.020768835918,4.774164199829102,109.17863200000002 +920,Regression,Exponentially Weighted Average,TrumpApproval,40.68274704347825,40.72073961991632,-535.1540147256861,4.872934341430664,114.38942200000002 +940,Regression,Exponentially Weighted Average,TrumpApproval,40.70972619148935,40.74737437775791,-540.4099749760601,4.975519180297852,119.67171900000002 +960,Regression,Exponentially Weighted Average,TrumpApproval,40.734006364583315,40.771242977826994,-546.7118652484228,5.07771110534668,125.02115000000002 +980,Regression,Exponentially Weighted Average,TrumpApproval,40.74031829795916,40.776840159239676,-557.5026042066913,5.174932479858398,130.44017000000002 +1000,Regression,Exponentially Weighted Average,TrumpApproval,40.75359492299998,40.78950075300399,-567.2567645513548,5.274145126342773,135.92720000000003 +1001,Regression,Exponentially Weighted Average,TrumpApproval,40.75458054545452,40.7904615623717,-567.6629514867817,5.276128768920898,141.45243100000002 +11,Regression,River MLP,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.012152671813964844,0.004659 +22,Regression,River MLP,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012152671813964844,0.035685 +33,Regression,River MLP,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.012152671813964844,0.08499899999999999 +44,Regression,River MLP,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.012152671813964844,0.154054 +55,Regression,River MLP,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.012152671813964844,0.236038 +66,Regression,River MLP,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.012152671813964844,0.334153 +77,Regression,River MLP,ChickWeights,43.46421300395698,43.662828265715675,-106.52347713504811,0.012152671813964844,0.44537099999999996 +88,Regression,River MLP,ChickWeights,43.359772412267546,43.583709810639945,-96.13505707304522,0.012152671813964844,0.810611 +99,Regression,River MLP,ChickWeights,39.34760833403674,41.28110871337288,-71.88434940071843,0.012152671813964844,1.1795 +110,Regression,River MLP,ChickWeights,36.27694842893514,39.43109665219568,-45.43679588251114,0.012152671813964844,1.5519370000000001 +121,Regression,River MLP,ChickWeights,33.384495302116036,37.629851771248454,-31.570957412576163,0.012152671813964844,1.9276810000000002 +132,Regression,River MLP,ChickWeights,30.760956861305427,36.03410144813446,-23.410283906032372,0.012152671813964844,2.306782 +143,Regression,River MLP,ChickWeights,28.636527077105512,34.63559483719005,-17.266619380249022,0.012152671813964844,2.6893580000000004 +154,Regression,River MLP,ChickWeights,26.848366395937333,33.39569685051843,-13.432529594168455,0.012152671813964844,3.0876050000000004 +165,Regression,River MLP,ChickWeights,25.68994399106157,32.40192925941153,-9.921659843170621,0.012152671813964844,3.5625080000000002 +176,Regression,River MLP,ChickWeights,24.410830110997512,31.436328147747602,-7.289127728255313,0.012152671813964844,4.041374 +187,Regression,River MLP,ChickWeights,23.27797268834062,30.533192270092133,-5.402500905628177,0.012152671813964844,4.523515000000001 +198,Regression,River MLP,ChickWeights,22.21718064008457,29.702466677215767,-3.8195183008370286,0.012152671813964844,5.01074 +209,Regression,River MLP,ChickWeights,21.553196218218126,29.080353233595055,-2.8083766468986493,0.012152671813964844,5.506907 +220,Regression,River MLP,ChickWeights,21.477175147376318,28.875533005215566,-2.287429329651187,0.012152671813964844,6.006767 +231,Regression,River MLP,ChickWeights,20.952511346795177,28.337840419816686,-1.7081998467380504,0.012152671813964844,6.546588 +242,Regression,River MLP,ChickWeights,20.676711476832995,27.952207916118613,-1.120246781438643,0.012152671813964844,7.091828 +253,Regression,River MLP,ChickWeights,20.0995501155626,27.40917062551413,-0.77060809797316,0.012152671813964844,7.64039 +264,Regression,River MLP,ChickWeights,20.480542447806847,27.75611161124588,-0.6589532289622051,0.012312889099121094,8.194426 +275,Regression,River MLP,ChickWeights,20.614496901205563,28.041519628611827,-0.48996193124700227,0.012312889099121094,8.751968 +286,Regression,River MLP,ChickWeights,20.565278129073285,27.847512109279503,-0.27268965368266684,0.012312889099121094,9.312993 +297,Regression,River MLP,ChickWeights,20.274958424672484,27.625687239183637,-0.08627660996898667,0.012312889099121094,9.883614000000001 +308,Regression,River MLP,ChickWeights,20.45327995927562,27.912230969749725,0.012367732509979579,0.012312889099121094,10.459328000000001 +319,Regression,River MLP,ChickWeights,21.510251079645446,30.009813508826145,-0.03600670421086383,0.012312889099121094,11.038549000000001 +330,Regression,River MLP,ChickWeights,21.665590739767385,30.044848619277115,0.11133412773326878,0.012312889099121094,11.621241000000001 +341,Regression,River MLP,ChickWeights,21.89048226594194,30.334415208142868,0.18882940014056426,0.012312889099121094,12.207835000000001 +352,Regression,River MLP,ChickWeights,21.893952955210157,30.339570241246864,0.2623598804373043,0.012312889099121094,12.797939000000001 +363,Regression,River MLP,ChickWeights,22.90533819708786,32.054250800509514,0.20046341020600145,0.012312889099121094,13.393067000000002 +374,Regression,River MLP,ChickWeights,23.763742364378043,33.85198840163,0.1802483296948254,0.012312889099121094,13.997035000000002 +385,Regression,River MLP,ChickWeights,24.571542788077334,34.76894700117602,0.23490387687324887,0.012312889099121094,14.642537000000003 +396,Regression,River MLP,ChickWeights,24.36284577450351,34.48838301267373,0.2980969129506975,0.012312889099121094,15.292230000000002 +407,Regression,River MLP,ChickWeights,25.734101326034633,37.062399123466015,0.24654151138402014,0.012312889099121094,15.945417000000003 +418,Regression,River MLP,ChickWeights,27.11119186006235,39.928390080533305,0.17910517682254135,0.012312889099121094,16.606412000000002 +429,Regression,River MLP,ChickWeights,28.3590406143643,42.09339312084405,0.18927902325132573,0.012312889099121094,17.270793 +440,Regression,River MLP,ChickWeights,29.357663164641018,43.68705243766196,0.18853885804982917,0.012312889099121094,17.938627 +451,Regression,River MLP,ChickWeights,30.691095011752385,45.674890437608916,0.15071943546696187,0.012312889099121094,18.61722 +462,Regression,River MLP,ChickWeights,32.58469239048663,49.95891321104316,0.04563754616882043,0.012312889099121094,19.304175 +473,Regression,River MLP,ChickWeights,34.457204466549335,53.836031572602984,0.02142234380208996,0.012312889099121094,19.9947 +484,Regression,River MLP,ChickWeights,37.61656772325176,59.47905507802133,-0.1290194303344141,0.012312889099121094,20.688692000000003 +495,Regression,River MLP,ChickWeights,39.835053793820734,63.30264151550531,-0.20299724250356888,0.012312889099121094,21.389042000000003 +506,Regression,River MLP,ChickWeights,40.84375246428709,64.75828138125749,-0.2223668969879733,0.012312889099121094,22.096762000000002 +517,Regression,River MLP,ChickWeights,42.3259867290128,66.7403629812066,-0.22521937114525392,0.012312889099121094,22.959028000000004 +528,Regression,River MLP,ChickWeights,43.903763311459905,69.27444073484561,-0.18681443911135398,0.012312889099121094,23.825387000000003 +539,Regression,River MLP,ChickWeights,45.11725117254523,70.97054614693485,-0.19420442814828975,0.012312889099121094,24.695185000000002 +550,Regression,River MLP,ChickWeights,46.19146939493793,72.84904016514736,-0.2194804408254527,0.012312889099121094,25.570022 +561,Regression,River MLP,ChickWeights,48.0255382358928,75.55118081102547,-0.25426558573377567,0.012312889099121094,26.453531 +572,Regression,River MLP,ChickWeights,49.60861685612801,77.95895414232838,-0.23532548305844236,0.012312889099121094,27.340524000000002 +578,Regression,River MLP,ChickWeights,51.40782550111089,80.92025038917566,-0.298583502299673,0.012312889099121094,28.229463000000003 +20,Regression,River MLP,TrumpApproval,28.203089584036217,31.678254793976468,-2352.839799462937,0.013110160827636719,0.018592 +40,Regression,River MLP,TrumpApproval,17.631407237579232,23.536801219235826,-221.7205207554288,0.013110160827636719,0.053933999999999996 +60,Regression,River MLP,TrumpApproval,13.441671937224772,19.739075566761823,-210.18539534147195,0.013110160827636719,0.098078 +80,Regression,River MLP,TrumpApproval,11.196749290061339,17.292913087737123,-161.5886474703317,0.013110160827636719,0.159515 +100,Regression,River MLP,TrumpApproval,9.529407951935296,15.54264880746251,-81.40884208187767,0.013110160827636719,0.228076 +120,Regression,River MLP,TrumpApproval,8.478754286735066,14.272499783288554,-57.95136830581733,0.013110160827636719,0.332328 +140,Regression,River MLP,TrumpApproval,7.525552058981039,13.242333407520347,-51.44233495767236,0.013110160827636719,0.527456 +160,Regression,River MLP,TrumpApproval,6.729532853932534,12.401843141618142,-39.56324503441056,0.013110160827636719,0.7296090000000001 +180,Regression,River MLP,TrumpApproval,6.20494414148211,11.727398222866162,-30.855608065765253,0.013110160827636719,0.938725 +200,Regression,River MLP,TrumpApproval,5.707613016041334,11.135875265707485,-27.808506433676282,0.013110160827636719,1.173453 +220,Regression,River MLP,TrumpApproval,5.35235544657082,10.636236352263047,-27.34993958822678,0.013110160827636719,1.423387 +240,Regression,River MLP,TrumpApproval,4.997211310189409,10.191758203807838,-25.225868327846438,0.013110160827636719,1.696442 +260,Regression,River MLP,TrumpApproval,4.698339965696975,9.799142308635478,-23.570888426665658,0.013350486755371094,2.044166 +280,Regression,River MLP,TrumpApproval,4.429952698677103,9.448184269747657,-22.91569767610472,0.013350486755371094,2.398587 +300,Regression,River MLP,TrumpApproval,4.185436867704573,9.131292683908228,-20.968518634865898,0.013350486755371094,2.759636 +320,Regression,River MLP,TrumpApproval,3.989857840855361,8.848493522992882,-20.65027251080777,0.013350486755371094,3.127609 +340,Regression,River MLP,TrumpApproval,3.793510888989401,8.58729864958044,-20.548263158423392,0.013350486755371094,3.5072970000000003 +360,Regression,River MLP,TrumpApproval,3.624008920532304,8.350732680595982,-19.544713512132038,0.013350486755371094,3.994133 +380,Regression,River MLP,TrumpApproval,3.4723941591948395,8.130732570333006,-19.15022282865096,0.013350486755371094,4.488831 +400,Regression,River MLP,TrumpApproval,3.327129028584169,7.926491124989248,-18.691844486765735,0.013350486755371094,4.996594 +420,Regression,River MLP,TrumpApproval,3.1988914312464622,7.737168953530663,-18.383340677896445,0.013350486755371094,5.512642 +440,Regression,River MLP,TrumpApproval,3.0905418652204037,7.562941397323993,-17.185096454486196,0.013350486755371094,6.041213 +460,Regression,River MLP,TrumpApproval,2.9841087219087,7.398658950448711,-15.50384711789857,0.013350486755371094,6.671723 +480,Regression,River MLP,TrumpApproval,2.878027938067315,7.243616567021465,-14.455461195017083,0.013350486755371094,7.311147 +500,Regression,River MLP,TrumpApproval,2.790174040420949,7.099353406661882,-13.534510391092628,0.013350486755371094,7.962598 +520,Regression,River MLP,TrumpApproval,2.702735665583147,6.962851553400364,-13.005371203657658,0.013350486755371094,8.621042 +540,Regression,River MLP,TrumpApproval,2.619923274637493,6.8334980874356335,-12.440396167539378,0.013350486755371094,9.286045999999999 +560,Regression,River MLP,TrumpApproval,2.556848015725479,6.714676061099242,-12.286263553450382,0.013350486755371094,9.968405999999998 +580,Regression,River MLP,TrumpApproval,2.4920466556079988,6.599727625727584,-12.1527109643015,0.013350486755371094,10.662936999999998 +600,Regression,River MLP,TrumpApproval,2.4260558633236777,6.490118235625791,-11.578750092830703,0.013350486755371094,11.381531999999998 +620,Regression,River MLP,TrumpApproval,2.3708694907142864,6.387338726311831,-10.997792654674662,0.013350486755371094,12.131809999999998 +640,Regression,River MLP,TrumpApproval,2.309077397643504,6.287531812954472,-10.40846497658843,0.013350486755371094,12.902857999999998 +660,Regression,River MLP,TrumpApproval,2.253417256192923,6.192441467899733,-9.986430076809746,0.013350486755371094,13.765244 +680,Regression,River MLP,TrumpApproval,2.1933714736526424,6.100884478116631,-9.832475924452435,0.013350486755371094,14.638727 +700,Regression,River MLP,TrumpApproval,2.1444840100167193,6.014053532220149,-9.80279442231031,0.013350486755371094,15.530555 +720,Regression,River MLP,TrumpApproval,2.0889149793500317,5.930058413028094,-9.733901359629304,0.013350486755371094,16.434104 +740,Regression,River MLP,TrumpApproval,2.038014375475162,5.849577408393744,-9.43824097776182,0.013350486755371094,17.347853 +760,Regression,River MLP,TrumpApproval,1.990139846596363,5.772270602035659,-9.274280741061778,0.013350486755371094,18.296023 +780,Regression,River MLP,TrumpApproval,1.946515411702069,5.69815370877383,-9.05697999731458,0.013350486755371094,19.263123 +800,Regression,River MLP,TrumpApproval,1.9088971171085878,5.627293726045093,-8.897112526821198,0.013350486755371094,20.241821 +820,Regression,River MLP,TrumpApproval,1.8732261968689674,5.559226632327132,-8.765208356441784,0.013350486755371094,21.227083 +840,Regression,River MLP,TrumpApproval,1.8347271749400444,5.492864392938861,-8.621969919695442,0.013350486755371094,22.223141000000002 +860,Regression,River MLP,TrumpApproval,1.8001515928803729,5.4291765082898245,-8.383942958793503,0.013350486755371094,23.230427000000002 +880,Regression,River MLP,TrumpApproval,1.762610565031098,5.367211780988228,-8.125392758933815,0.013350486755371094,24.244373000000003 +900,Regression,River MLP,TrumpApproval,1.7278213800286457,5.307357454879676,-7.9606012045493255,0.013350486755371094,25.346781000000004 +920,Regression,River MLP,TrumpApproval,1.6959197142820022,5.249600105314111,-7.910676780558388,0.013350486755371094,26.455778000000002 +940,Regression,River MLP,TrumpApproval,1.6672680101890094,5.193857129216991,-7.796440957593809,0.013350486755371094,27.578615000000003 +960,Regression,River MLP,TrumpApproval,1.6372087427382507,5.139738169534271,-7.704147396017831,0.013350486755371094,28.708325000000002 +980,Regression,River MLP,TrumpApproval,1.6082702309133736,5.087224346398139,-7.692803163516414,0.013350486755371094,29.852185000000002 +1000,Regression,River MLP,TrumpApproval,1.582128560540168,5.036439630005545,-7.663534315499042,0.013350486755371094,31.047385000000002 +1001,Regression,River MLP,TrumpApproval,1.5805783932319006,5.033923389051291,-7.660658200179739,0.013350486755371094,32.243154000000004 +11,Regression,[baseline] Mean predictor,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.0004901885986328125,0.000258 +22,Regression,[baseline] Mean predictor,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.0004901885986328125,0.000737 +33,Regression,[baseline] Mean predictor,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.0004901885986328125,0.00134 +44,Regression,[baseline] Mean predictor,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.0004901885986328125,0.002066 +55,Regression,[baseline] Mean predictor,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.0004901885986328125,0.0029100000000000003 +66,Regression,[baseline] Mean predictor,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.0004901885986328125,0.0038720000000000004 +77,Regression,[baseline] Mean predictor,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.0004901885986328125,0.004952000000000001 +88,Regression,[baseline] Mean predictor,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.0004901885986328125,0.006149000000000001 +99,Regression,[baseline] Mean predictor,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.0004901885986328125,0.007464000000000001 +110,Regression,[baseline] Mean predictor,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.0004901885986328125,0.008896000000000001 +121,Regression,[baseline] Mean predictor,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.0004901885986328125,0.010446 +132,Regression,[baseline] Mean predictor,ChickWeights,5.875758254207378,8.194624755054596,-0.2624191661321591,0.0004901885986328125,0.012113 +143,Regression,[baseline] Mean predictor,ChickWeights,6.530760796045927,8.870097879563003,-0.19803554240449484,0.0004901885986328125,0.013898 +154,Regression,[baseline] Mean predictor,ChickWeights,7.121466111912466,9.458403141043558,-0.15770278521517955,0.0004901885986328125,0.015801 +165,Regression,[baseline] Mean predictor,ChickWeights,7.772438504082036,10.375670403553157,-0.11989999304508925,0.0004901885986328125,0.017821999999999998 +176,Regression,[baseline] Mean predictor,ChickWeights,8.565827130563894,11.410434180005831,-0.09206765686265328,0.0004901885986328125,0.019960999999999996 +187,Regression,[baseline] Mean predictor,ChickWeights,9.429958588641576,12.495061319237752,-0.07221531716282037,0.0004901885986328125,0.022216999999999997 +198,Regression,[baseline] Mean predictor,ChickWeights,10.47731537859646,13.900491647656429,-0.05555027037575888,0.0004901885986328125,0.024589999999999997 +209,Regression,[baseline] Mean predictor,ChickWeights,11.43172675762076,15.229123619635446,-0.04445651287163721,0.0004901885986328125,0.027079 +220,Regression,[baseline] Mean predictor,ChickWeights,11.974320980081139,16.22368260926648,-0.03775608698471111,0.0004901885986328125,0.029685 +231,Regression,[baseline] Mean predictor,ChickWeights,12.9382196746461,17.489503190785292,-0.03157819728271183,0.0004901885986328125,0.032406 +242,Regression,[baseline] Mean predictor,ChickWeights,14.229204186206863,19.43725798629848,-0.02523677186741935,0.0004901885986328125,0.035243 +253,Regression,[baseline] Mean predictor,ChickWeights,15.339413196393396,20.820238312545918,-0.021649789303838762,0.0004901885986328125,0.041904 +264,Regression,[baseline] Mean predictor,ChickWeights,15.948617107030818,21.75817315507082,-0.019440185124094622,0.0004901885986328125,0.048726 +275,Regression,[baseline] Mean predictor,ChickWeights,16.794155127707494,23.16724301729152,-0.016999619323781356,0.0004901885986328125,0.055688 +286,Regression,[baseline] Mean predictor,ChickWeights,17.990009992534457,24.865985915258104,-0.014754713395529917,0.0004901885986328125,0.062787 +297,Regression,[baseline] Mean predictor,ChickWeights,19.34919450213405,26.676209297603677,-0.012890456560007202,0.0004901885986328125,0.070018 +308,Regression,[baseline] Mean predictor,ChickWeights,20.46881241431745,28.248013022827834,-0.011537481517321035,0.0004901885986328125,0.077383 +319,Regression,[baseline] Mean predictor,ChickWeights,20.993702124162965,29.638141143499492,-0.010503673119392376,0.0004901885986328125,0.08488399999999999 +330,Regression,[baseline] Mean predictor,ChickWeights,22.586872779548433,32.01796640002603,-0.009220237952050514,0.0004901885986328125,0.09251699999999999 +341,Regression,[baseline] Mean predictor,ChickWeights,23.973458872107372,33.821533603903084,-0.008387701903732392,0.0004901885986328125,0.10028199999999998 +352,Regression,[baseline] Mean predictor,ChickWeights,25.315991788770976,35.461698606860665,-0.007731302158646702,0.0004901885986328125,0.10817799999999998 +363,Regression,[baseline] Mean predictor,ChickWeights,25.615062978866305,35.981300981590465,-0.007443749031205149,0.0004901885986328125,0.11620599999999998 +374,Regression,[baseline] Mean predictor,ChickWeights,26.673321526932543,37.51836715700961,-0.006935846124255907,0.0004901885986328125,0.12436199999999997 +385,Regression,[baseline] Mean predictor,ChickWeights,28.27694482780972,39.8753298933956,-0.006332510983879436,0.0004901885986328125,0.13263999999999998 +396,Regression,[baseline] Mean predictor,ChickWeights,29.55612496209691,41.288487059450155,-0.005980181891907188,0.0004901885986328125,0.14104099999999997 +407,Regression,[baseline] Mean predictor,ChickWeights,30.561677112682855,42.81802042618151,-0.005646723150046551,0.0004901885986328125,0.14956599999999998 +418,Regression,[baseline] Mean predictor,ChickWeights,31.39346669137945,44.18765357092498,-0.0053697143301307815,0.0004901885986328125,0.15821399999999997 +429,Regression,[baseline] Mean predictor,ChickWeights,33.10612890637694,46.865579751152914,-0.004966366070605188,0.0004901885986328125,0.16698499999999997 +440,Regression,[baseline] Mean predictor,ChickWeights,34.54914638861108,48.61167278858254,-0.004716123854972665,0.0004901885986328125,0.17588299999999996 +451,Regression,[baseline] Mean predictor,ChickWeights,35.43263419295921,49.67507127970072,-0.004553693807187953,0.0004901885986328125,0.18490599999999996 +462,Regression,[baseline] Mean predictor,ChickWeights,36.308550382896186,51.2507761435036,-0.004357377489546899,0.0004901885986328125,0.19405499999999995 +473,Regression,[baseline] Mean predictor,ChickWeights,38.26330298063241,54.532250497281034,-0.004051661204895529,0.0004901885986328125,0.20332799999999995 +484,Regression,[baseline] Mean predictor,ChickWeights,39.598662348008276,56.08659790201894,-0.0039023944795495424,0.0004901885986328125,0.21272499999999994 +495,Regression,[baseline] Mean predictor,ChickWeights,40.94697327298068,57.823326559810994,-0.0037535911132069444,0.0004901885986328125,0.22224499999999994 +506,Regression,[baseline] Mean predictor,ChickWeights,41.42384714758024,58.679845942015916,-0.003665234721119459,0.0004901885986328125,0.23188899999999996 +517,Regression,[baseline] Mean predictor,ChickWeights,42.72663002099646,60.40151056768402,-0.0035345422299792872,0.0004901885986328125,0.24165999999999996 +528,Regression,[baseline] Mean predictor,ChickWeights,44.77321528369677,63.69509749878913,-0.0033415055563215112,0.0004901885986328125,0.25155399999999994 +539,Regression,[baseline] Mean predictor,ChickWeights,45.99579764939489,65.0494992510053,-0.0032526095626370655,0.0004901885986328125,0.26157099999999994 +550,Regression,[baseline] Mean predictor,ChickWeights,46.57020777663759,66.07332710234044,-0.0031815200825582313,0.0004901885986328125,0.2717109999999999 +561,Regression,[baseline] Mean predictor,ChickWeights,47.758257606406204,67.5643396193493,-0.0030950009187136196,0.0004901885986328125,0.28197199999999994 +572,Regression,[baseline] Mean predictor,ChickWeights,49.49138874897682,70.24569214117749,-0.002971942406188699,0.0004901885986328125,0.29235599999999995 +578,Regression,[baseline] Mean predictor,ChickWeights,50.250899455914585,71.11438743304103,-0.002929468639104371,0.0004901885986328125,0.30283499999999997 +20,Regression,[baseline] Mean predictor,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.0004901885986328125,0.001338 +40,Regression,[baseline] Mean predictor,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.0004901885986328125,0.0038250000000000003 +60,Regression,[baseline] Mean predictor,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.0004901885986328125,0.00717 +80,Regression,[baseline] Mean predictor,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.0004901885986328125,0.011356999999999999 +100,Regression,[baseline] Mean predictor,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.0004901885986328125,0.020929 +120,Regression,[baseline] Mean predictor,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.0004901885986328125,0.030834 +140,Regression,[baseline] Mean predictor,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.0004901885986328125,0.041039 +160,Regression,[baseline] Mean predictor,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.0004901885986328125,0.051538 +180,Regression,[baseline] Mean predictor,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.0004901885986328125,0.062312 +200,Regression,[baseline] Mean predictor,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.0004901885986328125,0.073408 +220,Regression,[baseline] Mean predictor,TrumpApproval,1.9409519346841397,3.56902990398404,-2.19210047340805,0.0004901885986328125,0.084777 +240,Regression,[baseline] Mean predictor,TrumpApproval,1.9366756524315063,3.4612902974772624,-2.024876884626847,0.0004901885986328125,0.096419 +260,Regression,[baseline] Mean predictor,TrumpApproval,1.9250039777458068,3.363327951159923,-1.8945640461454523,0.0004901885986328125,0.108333 +280,Regression,[baseline] Mean predictor,TrumpApproval,1.8726934920539138,3.257010428159885,-1.8420037280027222,0.0004901885986328125,0.120517 +300,Regression,[baseline] Mean predictor,TrumpApproval,1.8907476896224937,3.1958821895815714,-1.6910252267675165,0.0004901885986328125,0.133002 +320,Regression,[baseline] Mean predictor,TrumpApproval,1.819623890420079,3.103812605138666,-1.663886258690169,0.0004901885986328125,0.145758 +340,Regression,[baseline] Mean predictor,TrumpApproval,1.7396293145937214,3.014220627768389,-1.654906383755708,0.0004901885986328125,0.158784 +360,Regression,[baseline] Mean predictor,TrumpApproval,1.7350691203787965,2.9569384317632506,-1.5759385016835008,0.0004901885986328125,0.172076 +380,Regression,[baseline] Mean predictor,TrumpApproval,1.6987131960417108,2.8893997308323693,-1.5446951110541192,0.0004901885986328125,0.185636 +400,Regression,[baseline] Mean predictor,TrumpApproval,1.673610627740774,2.82935583501861,-1.5089937655143242,0.0004901885986328125,0.199488 +420,Regression,[baseline] Mean predictor,TrumpApproval,1.6410137122925974,2.7701802079251965,-1.484737486096575,0.0004901885986328125,0.213608 +440,Regression,[baseline] Mean predictor,TrumpApproval,1.6565972573555454,2.7427790467379385,-1.391750010744973,0.0004901885986328125,0.227993 +460,Regression,[baseline] Mean predictor,TrumpApproval,1.699464840115161,2.7394674040138396,-1.2626191030939884,0.0004901885986328125,0.242643 +480,Regression,[baseline] Mean predictor,TrumpApproval,1.7224824441896143,2.7219018737730583,-1.182307732575659,0.0004901885986328125,0.25756 +500,Regression,[baseline] Mean predictor,TrumpApproval,1.7446092142173422,2.7058035442295596,-1.1113262021905803,0.0004901885986328125,0.272747 +520,Regression,[baseline] Mean predictor,TrumpApproval,1.7464998751860934,2.677192702589883,-1.0705208906620065,0.0004901885986328125,0.288233 +540,Regression,[baseline] Mean predictor,TrumpApproval,1.7535492786865425,2.653885630983747,-1.0271707062792519,0.0004901885986328125,0.303987 +560,Regression,[baseline] Mean predictor,TrumpApproval,1.7201019899937544,2.614359234374483,-1.0141103337708768,0.0004901885986328125,0.320009 +580,Regression,[baseline] Mean predictor,TrumpApproval,1.6887559504032665,2.5757257291728384,-1.0033760803823184,0.0004901885986328125,0.336298 +600,Regression,[baseline] Mean predictor,TrumpApproval,1.701917368353294,2.5614247637328695,-0.9592753712060649,0.0004901885986328125,0.35287999999999997 +620,Regression,[baseline] Mean predictor,TrumpApproval,1.7178157166185173,2.5513468959681562,-0.9142580419512063,0.0004901885986328125,0.369731 +640,Regression,[baseline] Mean predictor,TrumpApproval,1.7365901196485038,2.545046385321895,-0.8692105635365064,0.0004901885986328125,0.386852 +660,Regression,[baseline] Mean predictor,TrumpApproval,1.7465677425181807,2.532051562790666,-0.8368676529707118,0.0004901885986328125,0.40424 +680,Regression,[baseline] Mean predictor,TrumpApproval,1.731617734826669,2.5042261861708606,-0.8251107974736909,0.0004901885986328125,0.42189499999999996 +700,Regression,[baseline] Mean predictor,TrumpApproval,1.6973720107412233,2.4702678919797196,-0.8225927549994396,0.0004901885986328125,0.439849 +720,Regression,[baseline] Mean predictor,TrumpApproval,1.6698372433333928,2.4400355004771073,-0.81732226470892,0.0004901885986328125,0.458072 +740,Regression,[baseline] Mean predictor,TrumpApproval,1.6732482399922957,2.425592833263792,-0.7947920429290933,0.0004901885986328125,0.47656299999999996 +760,Regression,[baseline] Mean predictor,TrumpApproval,1.6653913599894004,2.404136439714782,-0.7822814452716051,0.0004901885986328125,0.49532099999999996 +780,Regression,[baseline] Mean predictor,TrumpApproval,1.6644612180457288,2.387561393188575,-0.7656652158374817,0.0004901885986328125,0.514347 +800,Regression,[baseline] Mean predictor,TrumpApproval,1.6556359332933146,2.368497267913513,-0.7532954885990883,0.0004901885986328125,0.533661 +820,Regression,[baseline] Mean predictor,TrumpApproval,1.6452077788467467,2.348678653798561,-0.7430103139622937,0.0004901885986328125,0.5532450000000001 +840,Regression,[baseline] Mean predictor,TrumpApproval,1.6374623223784903,2.3305035344735936,-0.7320713255917544,0.0004901885986328125,0.5730930000000001 +860,Regression,[baseline] Mean predictor,TrumpApproval,1.6419505315856449,2.3202080137162757,-0.7138439732116804,0.0004901885986328125,0.6284980000000001 +880,Regression,[baseline] Mean predictor,TrumpApproval,1.6490002164922652,2.3126155324510744,-0.6941855677649247,0.0004901885986328125,0.6842080000000001 +900,Regression,[baseline] Mean predictor,TrumpApproval,1.6474991175923384,2.299197536504521,-0.6816400531907807,0.0004901885986328125,0.7401880000000002 +920,Regression,[baseline] Mean predictor,TrumpApproval,1.6301006788336792,2.2779225390149764,-0.6777843948800273,0.0004901885986328125,0.7964830000000002 +940,Regression,[baseline] Mean predictor,TrumpApproval,1.6221876471839873,2.2623787372500574,-0.6690049120995847,0.0004901885986328125,0.8530460000000002 +960,Regression,[baseline] Mean predictor,TrumpApproval,1.6124120493571745,2.245866476718547,-0.6619276404267609,0.0004901885986328125,0.9098760000000002 +980,Regression,[baseline] Mean predictor,TrumpApproval,1.5867001120604314,2.223758235975506,-0.661013659831075,0.0004901885986328125,0.9669740000000002 +1000,Regression,[baseline] Mean predictor,TrumpApproval,1.5681359363812417,2.2037391763141216,-0.6587014308970958,0.0004901885986328125,1.0243380000000002 +1001,Regression,[baseline] Mean predictor,TrumpApproval,1.567554989468773,2.202858861923226,-0.6584830635688459,0.0004901885986328125,1.081765 diff --git a/benchmarks/render.py b/benchmarks/render.py index 9e4bea987c..0a3b2143b2 100644 --- a/benchmarks/render.py +++ b/benchmarks/render.py @@ -1,8 +1,9 @@ +from __future__ import annotations + import json import shutil import textwrap from pathlib import Path -from typing import List import pandas as pd from dominate.tags import pre @@ -77,7 +78,9 @@ def render_df(df_path: Path) -> dict: track_dir = Path(f"../docs/benchmarks/{track_name}") track_dir.mkdir(exist_ok=True) with open(f"../docs/benchmarks/{track_name}/index.md", "w", encoding="utf-8") as f: - print_ = lambda x: print(x, file=f, end="\n\n") + + def print_(x): + return print(x, file=f, end="\n\n") print_(f"# {track_name}") @@ -119,14 +122,14 @@ def render_df(df_path: Path) -> dict: for dataset_name, dataset_details in track_details["Dataset"].items(): print_(f'???- abstract "{dataset_name}"') print_(textwrap.indent(dataset_details, " ")) - print_(f"") + print_("") print_("## Models") for model_name, model_details in track_details["Model"].items(): print_(f'???- example "{model_name}"') print_( f" 
{textwrap.indent(model_details, '    ').replace('    ', '', 1)}
" ) - print_(f"") + print_("") print_("## Environment") print_( diff --git a/benchmarks/run.py b/benchmarks/run.py index c4df0be893..0d3eca7a53 100644 --- a/benchmarks/run.py +++ b/benchmarks/run.py @@ -1,19 +1,19 @@ +from __future__ import annotations + import copy import itertools import json import logging import multiprocessing -from datetime import timedelta -from typing import List import pandas as pd from config import MODELS, N_CHECKPOINTS, TRACKS +from tqdm import tqdm from river import metrics logging.basicConfig(level=logging.WARN) logger = logging.getLogger(__name__) -from tqdm import tqdm def run_dataset(model_str, no_dataset, no_track): @@ -48,7 +48,7 @@ def run_dataset(model_str, no_dataset, no_track): return results -def run_track(models: List[str], no_track: int, n_workers: int = 50): +def run_track(models: list[str], no_track: int, n_workers: int = 50): pool = multiprocessing.Pool(processes=n_workers) track = TRACKS[no_track] runs = list(itertools.product(models, range(len(track.datasets)), [no_track])) @@ -61,7 +61,6 @@ def run_track(models: List[str], no_track: int, n_workers: int = 50): if __name__ == "__main__": - MODELS["Binary classification"].update(MODELS["Multiclass classification"]) details = {} diff --git a/setup.py b/setup.py index dbf4ecd524..18b302366a 100644 --- a/setup.py +++ b/setup.py @@ -95,15 +95,12 @@ "torch": ["river_torch"], "benchmark": base_packages + [ - "scikit-learn==1.1.2", - "scipy==1.9.3", - "torch==1.13.0", - "vowpalwabbit==9.6.0", - "torch==1.13.0", - "dominate==2.7.0", - "slugify==0.0.1", - "watermark==2.3.1", - "river-torch==0.1.2", + "dominate==2.8.0", + "scikit-learn==1.3.1", + "scipy==1.11.3", + "tabulate==0.9.0", + "vowpalwabbit==9.9.0", + "watermark==2.4.3", ], ":python_version == '3.6'": ["dataclasses"], }, From 746866574845c746e9e61f071c1bd2f46ebcb20d Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Fri, 6 Oct 2023 12:53:17 +0200 Subject: [PATCH 139/347] update benchmark files --- .gitignore | 2 - .../binary_classification.csv | 3637 ++ .../benchmarks/Binary classification/index.md | 37950 ++++++++++++++++ .../Multiclass classification/index.md | 24908 ++++++++++ .../multiclass_classification.csv | 2129 + docs/benchmarks/Regression/index.md | 20664 +++++++++ docs/benchmarks/Regression/regression.csv | 1769 + 7 files changed, 91057 insertions(+), 2 deletions(-) create mode 100644 docs/benchmarks/Binary classification/binary_classification.csv create mode 100644 docs/benchmarks/Binary classification/index.md create mode 100644 docs/benchmarks/Multiclass classification/index.md create mode 100644 docs/benchmarks/Multiclass classification/multiclass_classification.csv create mode 100644 docs/benchmarks/Regression/index.md create mode 100644 docs/benchmarks/Regression/regression.csv diff --git a/.gitignore b/.gitignore index 28ddefaa7f..7940257d13 100644 --- a/.gitignore +++ b/.gitignore @@ -7,8 +7,6 @@ docs/recipes/*_files/ docs/examples/*.md docs/examples/*/*.md docs/examples/*_files/ -docs/benchmarks/*/*.md -docs/benchmarks/*/*.csv docs/api/ site docs/linkified diff --git a/docs/benchmarks/Binary classification/binary_classification.csv b/docs/benchmarks/Binary classification/binary_classification.csv new file mode 100644 index 0000000000..4b38617bef --- /dev/null +++ b/docs/benchmarks/Binary classification/binary_classification.csv @@ -0,0 +1,3637 @@ +step,track,model,dataset,Accuracy,F1,Memory in Mb,Time in s +106,Binary classification,Logistic regression,Bananas,0.49056603773584906,0.3414634146341463,0.004187583923339844,0.00989 +212,Binary classification,Logistic regression,Bananas,0.5141509433962265,0.3832335329341317,0.004187583923339844,0.123413 +318,Binary classification,Logistic regression,Bananas,0.5220125786163522,0.42424242424242425,0.004240989685058594,0.315017 +424,Binary classification,Logistic regression,Bananas,0.5165094339622641,0.40233236151603496,0.004240989685058594,0.5849610000000001 +530,Binary classification,Logistic 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classification,Logistic regression,Phishing,0.8844444444444445,0.8617021276595744,0.005564689636230469,15.105192999999998 +925,Binary classification,Logistic regression,Phishing,0.8864864864864865,0.8655569782330347,0.005564689636230469,15.990264999999997 +950,Binary classification,Logistic regression,Phishing,0.8852631578947369,0.8655980271270037,0.005564689636230469,16.878196999999997 +975,Binary classification,Logistic regression,Phishing,0.8861538461538462,0.8664259927797834,0.005564689636230469,17.769031 +1000,Binary classification,Logistic regression,Phishing,0.887,0.8675263774912075,0.005564689636230469,18.72316 +1025,Binary classification,Logistic regression,Phishing,0.8868292682926829,0.8678815489749431,0.005564689636230469,19.680949 +1050,Binary classification,Logistic regression,Phishing,0.8885714285714286,0.8704318936877077,0.005564689636230469,20.642059 +1075,Binary classification,Logistic regression,Phishing,0.8874418604651163,0.8703108252947481,0.005564689636230469,21.642509 +1100,Binary classification,Logistic regression,Phishing,0.889090909090909,0.8723849372384936,0.005564689636230469,22.64645 +1125,Binary classification,Logistic regression,Phishing,0.8897777777777778,0.8742393509127788,0.005564689636230469,23.715816 +1150,Binary classification,Logistic regression,Phishing,0.8895652173913043,0.8738828202581926,0.005564689636230469,24.78868 +1175,Binary classification,Logistic regression,Phishing,0.8885106382978724,0.872444011684518,0.005564689636230469,25.864657 +1200,Binary classification,Logistic regression,Phishing,0.8891666666666667,0.8729703915950333,0.005564689636230469,26.968066 +1225,Binary classification,Logistic regression,Phishing,0.889795918367347,0.8737137511693172,0.005564689636230469,28.075126 +1250,Binary classification,Logistic regression,Phishing,0.8872,0.8712328767123287,0.005564689636230469,29.206647 +1903,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,1.174944 +3806,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,3.465965 +5709,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,6.937403 +7612,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,11.610183 +9515,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,17.462392 +11418,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,24.519273000000002 +13321,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,32.784706 +15224,Binary classification,Logistic regression,SMTP,0.9996715712033631,0.7058823529411764,0.004383087158203125,42.234241 +17127,Binary classification,Logistic regression,SMTP,0.9997080632918783,0.761904761904762,0.004383087158203125,52.882453 +19030,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.761904761904762,0.004383087158203125,64.622668 +20933,Binary classification,Logistic regression,SMTP,0.999761142693355,0.761904761904762,0.004383087158203125,77.568109 +22836,Binary classification,Logistic regression,SMTP,0.9997810474689087,0.761904761904762,0.004383087158203125,91.771967 +24739,Binary classification,Logistic regression,SMTP,0.9997978899713004,0.761904761904762,0.004383087158203125,107.109486 +26642,Binary classification,Logistic regression,SMTP,0.9997747916823061,0.7272727272727273,0.004383087158203125,123.68183400000001 +28545,Binary classification,Logistic regression,SMTP,0.9997898055701524,0.7272727272727273,0.004383087158203125,141.369945 +30448,Binary classification,Logistic regression,SMTP,0.9998029427220179,0.7272727272727273,0.004383087158203125,160.23044 +32351,Binary classification,Logistic regression,SMTP,0.999814534326605,0.7272727272727273,0.004383087158203125,180.23963199999997 +34254,Binary classification,Logistic regression,SMTP,0.999824837975127,0.7272727272727273,0.004383087158203125,201.31894799999998 +36157,Binary classification,Logistic regression,SMTP,0.9998340570290677,0.7272727272727273,0.004383087158203125,223.51927299999997 +38060,Binary classification,Logistic regression,SMTP,0.9998423541776142,0.7272727272727273,0.004383087158203125,246.97671399999996 +39963,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.7272727272727273,0.004383087158203125,271.56812399999995 +41866,Binary classification,Logistic regression,SMTP,0.999856685616013,0.7272727272727273,0.004383087158203125,297.29584399999993 +43769,Binary classification,Logistic regression,SMTP,0.9998629166761863,0.7272727272727273,0.004383087158203125,324.2115329999999 +45672,Binary classification,Logistic regression,SMTP,0.9998686284813453,0.7272727272727273,0.004383087158203125,352.27523699999995 +47575,Binary classification,Logistic regression,SMTP,0.9998738833420915,0.7272727272727273,0.004383087158203125,381.59710399999994 +49478,Binary classification,Logistic regression,SMTP,0.9998787339827803,0.7272727272727273,0.004383087158203125,412.11662699999994 +51381,Binary classification,Logistic regression,SMTP,0.9998443004223351,0.6666666666666666,0.004383087158203125,443.86742899999996 +53284,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.6666666666666666,0.004383087158203125,476.83879799999994 +55187,Binary classification,Logistic regression,SMTP,0.999855038324243,0.6666666666666666,0.004383087158203125,510.9819989999999 +57090,Binary classification,Logistic regression,SMTP,0.9997022245577158,0.48484848484848486,0.004383087158203125,546.274013 +58993,Binary classification,Logistic regression,SMTP,0.9997118302171444,0.48484848484848486,0.004383087158203125,582.6678519999999 +60896,Binary classification,Logistic regression,SMTP,0.9997208355228586,0.48484848484848486,0.004383087158203125,620.2082039999999 +62799,Binary classification,Logistic regression,SMTP,0.999697447411583,0.45714285714285713,0.004383087158203125,658.8625569999999 +64702,Binary classification,Logistic regression,SMTP,0.9997063460171247,0.45714285714285713,0.004383087158203125,698.5852799999999 +66605,Binary classification,Logistic regression,SMTP,0.9997147361309211,0.45714285714285713,0.004383087158203125,739.3620329999999 +68508,Binary classification,Logistic regression,SMTP,0.9996934664564723,0.4324324324324324,0.004383087158203125,781.2563779999999 +70411,Binary classification,Logistic regression,SMTP,0.9997017511468379,0.4324324324324324,0.004383087158203125,824.198222 +72314,Binary classification,Logistic regression,SMTP,0.9997095998008685,0.4324324324324324,0.004383087158203125,868.202086 +74217,Binary classification,Logistic regression,SMTP,0.9997170459598205,0.4324324324324324,0.004383087158203125,913.268811 +76120,Binary classification,Logistic regression,SMTP,0.999724119810825,0.4324324324324324,0.004383087158203125,959.4161730000001 +78023,Binary classification,Logistic regression,SMTP,0.9997308485959269,0.4324324324324324,0.004383087158203125,1006.608919 +79926,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.4324324324324324,0.004383087158203125,1054.8516300000001 +81829,Binary classification,Logistic regression,SMTP,0.9997433672658838,0.4324324324324324,0.004383087158203125,1104.06085 +83732,Binary classification,Logistic regression,SMTP,0.9997491998280228,0.4324324324324324,0.004383087158203125,1154.258062 +85635,Binary classification,Logistic regression,SMTP,0.9997547731651778,0.4324324324324324,0.004383087158203125,1205.3715320000001 +87538,Binary classification,Logistic regression,SMTP,0.9997601041833261,0.4324324324324324,0.004383087158203125,1257.4462130000002 +89441,Binary classification,Logistic regression,SMTP,0.9997540277948592,0.4210526315789474,0.004383087158203125,1310.5048250000002 +91344,Binary classification,Logistic regression,SMTP,0.9997591522157996,0.4210526315789474,0.004383087158203125,1364.5437910000003 +93247,Binary classification,Logistic regression,SMTP,0.9997640674767017,0.4210526315789474,0.004383087158203125,1419.4942320000002 +95150,Binary classification,Logistic regression,SMTP,0.9997687861271676,0.4210526315789474,0.004383087158203125,1475.4318390000003 +95156,Binary classification,Logistic regression,SMTP,0.9997688007062088,0.4210526315789474,0.004383087158203125,1531.3705140000004 +106,Binary classification,Aggregated Mondrian Forest,Bananas,0.7047619047619048,0.6990291262135924,0.8133068084716797,0.833499 +212,Binary classification,Aggregated Mondrian Forest,Bananas,0.7867298578199052,0.7668393782383419,1.3378009796142578,2.8663 +318,Binary classification,Aggregated Mondrian Forest,Bananas,0.8233438485804416,0.806896551724138,1.855398178100586,6.250927 +424,Binary classification,Aggregated Mondrian Forest,Bananas,0.8392434988179669,0.8229166666666667,2.3226680755615234,11.143336 +530,Binary classification,Aggregated Mondrian Forest,Bananas,0.8412098298676749,0.8181818181818182,2.776212692260742,17.797124 +636,Binary classification,Aggregated Mondrian Forest,Bananas,0.8488188976377953,0.8267148014440434,3.173288345336914,26.396562 +742,Binary classification,Aggregated Mondrian Forest,Bananas,0.8596491228070176,0.8359621451104102,3.5500621795654297,36.969223 +848,Binary classification,Aggregated Mondrian Forest,Bananas,0.8677685950413223,0.8461538461538461,3.917997360229492,49.692848 +954,Binary classification,Aggregated Mondrian Forest,Bananas,0.8730325288562435,0.8515337423312884,4.238534927368164,64.631677 +1060,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772426817752597,0.8549107142857144,4.491437911987305,81.765253 +1166,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772532188841202,0.8557013118062564,4.809717178344727,101.295253 +1272,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772619984264359,0.8566176470588236,5.171953201293945,123.161687 +1378,Binary classification,Aggregated Mondrian Forest,Bananas,0.8779956427015251,0.8561643835616438,5.501619338989258,147.513883 +1484,Binary classification,Aggregated Mondrian Forest,Bananas,0.8813216453135536,0.860759493670886,5.80189323425293,174.53874199999998 +1590,Binary classification,Aggregated Mondrian Forest,Bananas,0.8785399622404028,0.8579838116261957,6.17225456237793,204.250002 +1696,Binary classification,Aggregated Mondrian Forest,Bananas,0.8790560471976401,0.8585231193926847,6.45002555847168,237.091398 +1802,Binary classification,Aggregated Mondrian Forest,Bananas,0.8806218767351471,0.8613797549967763,6.703157424926758,272.83416 +1908,Binary classification,Aggregated Mondrian Forest,Bananas,0.8783429470372313,0.8602409638554217,7.075212478637695,311.419605 +2014,Binary classification,Aggregated Mondrian Forest,Bananas,0.8777943368107303,0.8607021517553795,7.409914016723633,352.79492 +2120,Binary classification,Aggregated Mondrian Forest,Bananas,0.8791882963662104,0.8636847710330138,7.730207443237305,397.065386 +2226,Binary classification,Aggregated Mondrian Forest,Bananas,0.8782022471910113,0.8626457171819564,8.068941116333008,444.302777 +2332,Binary classification,Aggregated Mondrian Forest,Bananas,0.8777348777348777,0.8621190130624092,8.392999649047852,494.454577 +2438,Binary classification,Aggregated Mondrian Forest,Bananas,0.8781288469429627,0.8624363131079205,8.738908767700195,547.433225 +2544,Binary classification,Aggregated Mondrian Forest,Bananas,0.8784899724734565,0.8635761589403974,9.069158554077148,603.367304 +2650,Binary classification,Aggregated Mondrian Forest,Bananas,0.8799546998867497,0.8654822335025381,9.380228042602539,661.971994 +2756,Binary classification,Aggregated Mondrian Forest,Bananas,0.8820326678765881,0.8676171079429736,9.675683975219727,723.088894 +2862,Binary classification,Aggregated Mondrian Forest,Bananas,0.8836071303739951,0.86905230043256,10.005556106567383,786.780009 +2968,Binary classification,Aggregated Mondrian Forest,Bananas,0.8840579710144928,0.8691019786910198,10.283010482788086,853.0146269999999 +3074,Binary classification,Aggregated Mondrian Forest,Bananas,0.8831760494630654,0.8683535020168683,10.632661819458008,921.6671329999999 +3180,Binary classification,Aggregated Mondrian Forest,Bananas,0.8858131487889274,0.8707725169099323,10.90281867980957,992.810764 +3286,Binary classification,Aggregated Mondrian Forest,Bananas,0.8852359208523592,0.8696854476322157,11.200468063354492,1066.389204 +3392,Binary classification,Aggregated Mondrian Forest,Bananas,0.8849896785608965,0.87017310252996,11.512235641479492,1142.442462 +3498,Binary classification,Aggregated Mondrian Forest,Bananas,0.8864741206748642,0.8712293220888745,11.797895431518555,1221.036812 +3604,Binary classification,Aggregated Mondrian Forest,Bananas,0.8878712184290869,0.8721518987341771,12.102933883666992,1302.125963 +3710,Binary classification,Aggregated Mondrian Forest,Bananas,0.8878403882448099,0.8725490196078431,12.41331672668457,1385.838182 +3816,Binary classification,Aggregated Mondrian Forest,Bananas,0.889646133682831,0.8746650788925276,12.665735244750977,1472.135343 +3922,Binary classification,Aggregated Mondrian Forest,Bananas,0.8885488395817394,0.8730758059831543,13.002767562866211,1561.047711 +4028,Binary classification,Aggregated Mondrian Forest,Bananas,0.8872609883287808,0.8714609286523215,13.407987594604492,1652.580672 +4134,Binary classification,Aggregated Mondrian Forest,Bananas,0.8874909266876361,0.8717241379310345,13.751871109008789,1746.8148660000002 +4240,Binary classification,Aggregated Mondrian Forest,Bananas,0.8886529841943854,0.8731864588930682,13.96497917175293,1843.750561 +4346,Binary classification,Aggregated Mondrian Forest,Bananas,0.8895281933256617,0.8742138364779874,14.240518569946289,1943.4032140000002 +4452,Binary classification,Aggregated Mondrian Forest,Bananas,0.8890137047854415,0.8735926305015352,14.605810165405273,2045.776976 +4558,Binary classification,Aggregated Mondrian Forest,Bananas,0.8894009216589862,0.874439461883408,14.917993545532227,2150.6554650000003 +4664,Binary classification,Aggregated Mondrian Forest,Bananas,0.8893416255629423,0.8748180494905387,15.239774703979492,2258.064088 +4770,Binary classification,Aggregated Mondrian Forest,Bananas,0.8880268400083875,0.8729176582579724,15.676980972290039,2367.913167 +4876,Binary classification,Aggregated Mondrian Forest,Bananas,0.8888205128205128,0.8733644859813083,15.964864730834961,2480.267593 +4982,Binary classification,Aggregated Mondrian Forest,Bananas,0.889580405541056,0.8746010031919745,16.210702896118164,2595.134509 +5088,Binary classification,Aggregated Mondrian Forest,Bananas,0.8891291527422842,0.8740509155873157,16.543100357055664,2712.434229 +5194,Binary classification,Aggregated Mondrian Forest,Bananas,0.8894665896398999,0.8743982494529539,16.87101936340332,2832.294496 +5300,Binary classification,Aggregated Mondrian Forest,Bananas,0.889413096810719,0.8742489270386266,17.23769187927246,2954.746773 +906,Binary classification,Aggregated Mondrian Forest,Elec2,0.8662983425414365,0.8638920134983127,5.093213081359863,9.961559 +1812,Binary classification,Aggregated Mondrian Forest,Elec2,0.8895637769188294,0.863013698630137,9.274415016174316,34.997891 +2718,Binary classification,Aggregated Mondrian Forest,Elec2,0.8737578211262422,0.8433074463225217,14.81954288482666,77.180768 +3624,Binary classification,Aggregated Mondrian Forest,Elec2,0.8746894838531604,0.8451568894952252,20.35789203643799,135.799753 +4530,Binary classification,Aggregated Mondrian Forest,Elec2,0.869728416869066,0.8295782784517621,25.320820808410645,209.04868100000002 +5436,Binary classification,Aggregated Mondrian Forest,Elec2,0.8658693652253909,0.8254728273880776,30.942105293273926,297.509476 +6342,Binary classification,Aggregated Mondrian Forest,Elec2,0.8613783314934553,0.8220287507592631,36.922226905822754,401.254404 +7248,Binary classification,Aggregated Mondrian Forest,Elec2,0.8563543535255967,0.8144715736945286,42.8322229385376,518.853069 +8154,Binary classification,Aggregated Mondrian Forest,Elec2,0.8547773825585674,0.8211480362537765,49.13461780548096,650.61595 +9060,Binary classification,Aggregated Mondrian Forest,Elec2,0.8564963020200905,0.8276776246023331,54.274807929992676,797.031608 +9966,Binary classification,Aggregated Mondrian Forest,Elec2,0.8559959859508279,0.830478440637921,59.58850955963135,957.298151 +10872,Binary classification,Aggregated Mondrian Forest,Elec2,0.858522675006899,0.8360690684289065,64.43849277496338,1132.655012 +11778,Binary classification,Aggregated Mondrian Forest,Elec2,0.8588774730406725,0.8365138697619515,69.77676105499268,1321.3849659999998 +12684,Binary classification,Aggregated Mondrian Forest,Elec2,0.8572892848695104,0.8352148579752368,75.08023929595947,1522.7749099999999 +13590,Binary classification,Aggregated Mondrian Forest,Elec2,0.8577525940098609,0.8380665158750105,79.94311618804932,1737.8701859999999 +14496,Binary classification,Aggregated Mondrian Forest,Elec2,0.8584339427388755,0.8393863494051347,84.43613529205322,1968.1055499999998 +15402,Binary classification,Aggregated Mondrian Forest,Elec2,0.8584507499513019,0.8387335404645658,89.24470615386963,2211.4324739999997 +16308,Binary classification,Aggregated Mondrian Forest,Elec2,0.8561354019746121,0.8352296670880741,95.65516376495361,2468.910492 +17214,Binary classification,Aggregated Mondrian Forest,Elec2,0.8563295183872655,0.8333445649976414,100.85075855255127,2740.76049 +18120,Binary classification,Aggregated Mondrian Forest,Elec2,0.8570009382416248,0.834176,106.8406229019165,3026.823297 +19026,Binary classification,Aggregated Mondrian Forest,Elec2,0.858712220762155,0.8348082595870207,111.74584293365479,3325.548438 +19932,Binary classification,Aggregated Mondrian Forest,Elec2,0.8587125583262255,0.8363361618040218,117.02025699615479,3636.553219 +20838,Binary classification,Aggregated Mondrian Forest,Elec2,0.8564572635216202,0.8339348176114596,123.37252902984619,3960.554229 +21744,Binary classification,Aggregated Mondrian Forest,Elec2,0.8540219840868325,0.8286917098445596,130.42929553985596,4298.210438 +22650,Binary classification,Aggregated Mondrian Forest,Elec2,0.8531944015188309,0.8264160793526494,136.64212131500244,4650.500753 +23556,Binary classification,Aggregated Mondrian Forest,Elec2,0.8528550201655699,0.8255134917438581,142.6701021194458,5016.675492 +24462,Binary classification,Aggregated Mondrian Forest,Elec2,0.8532766444544376,0.8247130647130647,148.4442949295044,5397.142957 +25368,Binary classification,Aggregated Mondrian Forest,Elec2,0.8514605589939686,0.8225487425826504,154.72937488555908,5792.939295 +26274,Binary classification,Aggregated Mondrian Forest,Elec2,0.8521676245575306,0.8231490756761678,160.280930519104,6204.791143 +27180,Binary classification,Aggregated Mondrian Forest,Elec2,0.8530851024688179,0.8247069669432372,165.12001132965088,6630.671498000001 +28086,Binary classification,Aggregated Mondrian Forest,Elec2,0.8528752002848495,0.8239904583404327,171.1938066482544,7068.974646000001 +28992,Binary classification,Aggregated Mondrian Forest,Elec2,0.8532303128557138,0.8236415633937083,176.66365909576416,7519.88705 +29898,Binary classification,Aggregated Mondrian Forest,Elec2,0.8538649362812323,0.8241355713883187,181.78493976593018,7981.8746790000005 +30804,Binary classification,Aggregated Mondrian Forest,Elec2,0.8542349771126189,0.8238110186783865,187.08849048614502,8454.454599 +31710,Binary classification,Aggregated Mondrian Forest,Elec2,0.8525655176763695,0.8216125462662648,193.5201120376587,8938.242097 +32616,Binary classification,Aggregated Mondrian Forest,Elec2,0.852245899126169,0.821432541594101,199.6366205215454,9433.534304 +33522,Binary classification,Aggregated Mondrian Forest,Elec2,0.852003221860923,0.8214247147330909,205.81115818023682,9940.639789 +34428,Binary classification,Aggregated Mondrian Forest,Elec2,0.851715223516426,0.8209965286300361,212.10033893585205,10459.964952 +35334,Binary classification,Aggregated Mondrian Forest,Elec2,0.8513287861206238,0.8197137660019906,218.64550113677979,10993.026606 +36240,Binary classification,Aggregated Mondrian Forest,Elec2,0.8508788873865173,0.8179735920237133,225.19258975982666,11538.003928999999 +37146,Binary classification,Aggregated Mondrian Forest,Elec2,0.8496432898102032,0.8159741671883752,232.33557987213135,12096.169426999999 +38052,Binary classification,Aggregated Mondrian Forest,Elec2,0.8497279966360937,0.8155126798735239,238.56606006622314,12664.877691 +38958,Binary classification,Aggregated Mondrian Forest,Elec2,0.8494493929203994,0.8154906093686098,244.89648151397705,13243.508414 +39864,Binary classification,Aggregated Mondrian Forest,Elec2,0.8492336251661942,0.8164773421277635,251.12543201446533,13830.859859 +40770,Binary classification,Aggregated Mondrian Forest,Elec2,0.8486104638328141,0.8170174918470204,257.83575916290283,14427.278119 +41676,Binary classification,Aggregated Mondrian Forest,Elec2,0.8490941811637672,0.8186928820595613,264.1331262588501,15032.883602 +42582,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493929217256523,0.8194385787087872,270.1314744949341,15648.679676 +43488,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493802745648125,0.8194995590828924,276.04683017730713,16273.986894 +44394,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493681436262474,0.8189620164063134,282.1419038772583,16909.074578 +45300,Binary classification,Aggregated Mondrian Forest,Elec2,0.8499083864985982,0.8197651300267741,287.208477973938,17554.066457 +45312,Binary classification,Aggregated Mondrian Forest,Elec2,0.8499039968219637,0.8197312269727252,287.3145227432251,18206.640571 +25,Binary classification,Aggregated Mondrian Forest,Phishing,0.6666666666666666,0.6923076923076924,0.2663440704345703,0.180038 +50,Binary classification,Aggregated Mondrian Forest,Phishing,0.7755102040816326,0.7555555555555555,0.40291404724121094,0.591649 +75,Binary classification,Aggregated Mondrian Forest,Phishing,0.7972972972972973,0.7945205479452055,0.5196552276611328,1.2897159999999999 +100,Binary classification,Aggregated Mondrian Forest,Phishing,0.8181818181818182,0.8125,0.6383838653564453,2.331468 +125,Binary classification,Aggregated Mondrian Forest,Phishing,0.8225806451612904,0.819672131147541,0.7669887542724609,3.7241540000000004 +150,Binary classification,Aggregated Mondrian Forest,Phishing,0.8456375838926175,0.847682119205298,0.9175167083740234,5.520175 +175,Binary classification,Aggregated Mondrian Forest,Phishing,0.867816091954023,0.8606060606060606,1.0086803436279297,7.7498439999999995 +200,Binary classification,Aggregated Mondrian Forest,Phishing,0.864321608040201,0.8571428571428572,1.1245098114013672,10.53336 +225,Binary classification,Aggregated Mondrian Forest,Phishing,0.8660714285714286,0.8557692307692308,1.2114391326904297,13.795268 +250,Binary classification,Aggregated Mondrian Forest,Phishing,0.8554216867469879,0.8448275862068965,1.322244644165039,17.57486 +275,Binary classification,Aggregated Mondrian Forest,Phishing,0.8540145985401459,0.84251968503937,1.3987751007080078,21.876977 +300,Binary classification,Aggregated Mondrian Forest,Phishing,0.8561872909698997,0.8413284132841329,1.489828109741211,26.743447 +325,Binary classification,Aggregated Mondrian Forest,Phishing,0.8672839506172839,0.8501742160278746,1.5769939422607422,32.2729 +350,Binary classification,Aggregated Mondrian Forest,Phishing,0.8681948424068768,0.8486842105263156,1.638784408569336,38.477964 +375,Binary classification,Aggregated Mondrian Forest,Phishing,0.8689839572192514,0.8482972136222912,1.7178211212158203,45.357054 +400,Binary classification,Aggregated Mondrian Forest,Phishing,0.8671679197994987,0.8436578171091446,1.7941875457763672,52.888585 +425,Binary classification,Aggregated Mondrian Forest,Phishing,0.8702830188679245,0.8433048433048433,1.8353633880615234,61.095765 +450,Binary classification,Aggregated Mondrian Forest,Phishing,0.8730512249443207,0.8455284552845528,1.9096240997314453,70.024579 +475,Binary classification,Aggregated Mondrian Forest,Phishing,0.8755274261603375,0.8506329113924052,1.988790512084961,79.720297 +500,Binary classification,Aggregated Mondrian Forest,Phishing,0.875751503006012,0.8530805687203792,2.063833236694336,90.07863400000001 +525,Binary classification,Aggregated Mondrian Forest,Phishing,0.8778625954198473,0.8525345622119817,2.144712448120117,101.25810100000001 +550,Binary classification,Aggregated Mondrian Forest,Phishing,0.8779599271402551,0.8533916849015317,2.1996402740478516,113.25181900000001 +575,Binary classification,Aggregated Mondrian Forest,Phishing,0.8780487804878049,0.8535564853556484,2.2528209686279297,125.93584100000001 +600,Binary classification,Aggregated Mondrian Forest,Phishing,0.8797996661101837,0.8536585365853657,2.283121109008789,139.44840100000002 +625,Binary classification,Aggregated Mondrian Forest,Phishing,0.8814102564102564,0.852589641434263,2.343900680541992,153.77905700000002 +650,Binary classification,Aggregated Mondrian Forest,Phishing,0.884437596302003,0.8587570621468926,2.418844223022461,168.92061400000003 +675,Binary classification,Aggregated Mondrian Forest,Phishing,0.884272997032641,0.8617021276595745,2.468423843383789,184.94000100000002 +700,Binary classification,Aggregated Mondrian Forest,Phishing,0.8884120171673819,0.8650519031141869,2.478273391723633,201.76583000000002 +725,Binary classification,Aggregated Mondrian Forest,Phishing,0.8895027624309392,0.8684210526315789,2.5243663787841797,219.457713 +750,Binary classification,Aggregated Mondrian Forest,Phishing,0.8918558077436582,0.8716323296354993,2.5813236236572266,238.014124 +775,Binary classification,Aggregated Mondrian Forest,Phishing,0.8914728682170543,0.8707692307692307,2.6200389862060547,257.461391 +800,Binary classification,Aggregated Mondrian Forest,Phishing,0.8898623279098874,0.8702064896755163,2.657014846801758,277.779634 +825,Binary classification,Aggregated Mondrian Forest,Phishing,0.8907766990291263,0.872159090909091,2.706361770629883,298.980548 +850,Binary classification,Aggregated Mondrian Forest,Phishing,0.8928150765606596,0.8741355463347164,2.730466842651367,321.097396 +875,Binary classification,Aggregated Mondrian Forest,Phishing,0.8958810068649885,0.8771929824561403,2.7533512115478516,344.186724 +900,Binary classification,Aggregated Mondrian Forest,Phishing,0.8976640711902113,0.8786279683377309,2.807779312133789,368.101507 +925,Binary classification,Aggregated Mondrian Forest,Phishing,0.9004329004329005,0.8829516539440204,2.8523120880126953,392.98062400000003 +950,Binary classification,Aggregated Mondrian Forest,Phishing,0.9009483667017913,0.8850855745721271,2.913583755493164,418.83123200000006 +975,Binary classification,Aggregated Mondrian Forest,Phishing,0.9024640657084189,0.8867699642431467,2.943540573120117,445.63277700000003 +1000,Binary classification,Aggregated Mondrian Forest,Phishing,0.9009009009009009,0.8850174216027874,2.9903697967529297,473.39902800000004 +1025,Binary classification,Aggregated Mondrian Forest,Phishing,0.8994140625,0.8836158192090395,3.035707473754883,502.22467600000004 +1050,Binary classification,Aggregated Mondrian Forest,Phishing,0.9008579599618685,0.8857142857142858,3.069150924682617,532.049603 +1075,Binary classification,Aggregated Mondrian Forest,Phishing,0.9013035381750466,0.8869936034115138,3.114839553833008,562.838704 +1100,Binary classification,Aggregated Mondrian Forest,Phishing,0.9035486806187443,0.8898128898128899,3.132375717163086,594.67778 +1125,Binary classification,Aggregated Mondrian Forest,Phishing,0.905693950177936,0.8933601609657947,3.1889095306396484,627.518257 +1150,Binary classification,Aggregated Mondrian Forest,Phishing,0.9060052219321149,0.893491124260355,3.220029830932617,661.4048929999999 +1175,Binary classification,Aggregated Mondrian Forest,Phishing,0.9045996592844975,0.8916827852998066,3.270620346069336,696.4079739999999 +1200,Binary classification,Aggregated Mondrian Forest,Phishing,0.9040867389491243,0.8909952606635072,3.311410903930664,732.4743999999998 +1225,Binary classification,Aggregated Mondrian Forest,Phishing,0.9044117647058824,0.8911627906976743,3.344022750854492,769.4892029999999 +1250,Binary classification,Aggregated Mondrian Forest,Phishing,0.9047237790232185,0.8921124206708976,3.391061782836914,807.5726659999999 +1903,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,2.745403 +3806,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,8.183125 +5709,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,16.539666 +7612,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,27.755785000000003 +9515,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,41.777067 +11418,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,58.637769000000006 +13321,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,78.268206 +15224,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998686198515404,0.9090909090909091,0.09231853485107422,101.443914 +17127,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998832184981898,0.9230769230769231,0.09723186492919922,131.805417 +19030,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998948972620737,0.9230769230769231,0.09728145599365234,169.246217 +20933,Binary classification,Aggregated Mondrian Forest,SMTP,0.999904452512899,0.9230769230769231,0.09728145599365234,213.148727 +22836,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999124151521787,0.9230769230769231,0.09728145599365234,263.357684 +24739,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999191527205109,0.9230769230769231,0.09730815887451172,319.49775 +26642,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998873916144289,0.888888888888889,0.10914134979248047,381.401703 +28545,Binary classification,Aggregated Mondrian Forest,SMTP,0.999894899103139,0.888888888888889,0.10916423797607422,448.60874 +30448,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999014681249384,0.888888888888889,0.10916423797607422,520.91477 +32351,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999072642967543,0.888888888888889,0.10966777801513672,598.09858 +34254,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999124164306776,0.888888888888889,0.11131954193115234,680.064697 +36157,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999170262197146,0.888888888888889,0.11127376556396484,766.82968 +38060,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999211750177356,0.888888888888889,0.11127376556396484,858.2478070000001 +39963,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999249286822481,0.888888888888889,0.11127376556396484,954.233503 +41866,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999283410963812,0.888888888888889,0.11127376556396484,1054.7914 +43769,Binary classification,Aggregated Mondrian Forest,SMTP,0.999931456772071,0.888888888888889,0.11127376556396484,1159.6703400000001 +45672,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999343128024348,0.888888888888889,0.11127376556396484,1268.4432900000002 +47575,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999369403455669,0.888888888888889,0.1298818588256836,1381.2685860000001 +49478,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999393657659115,0.888888888888889,0.1299276351928711,1498.4984390000002 +51381,Binary classification,Aggregated Mondrian Forest,SMTP,0.999941611521993,0.9032258064516129,0.14348888397216797,1620.0599740000002 +53284,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999436968639153,0.9032258064516129,0.14348888397216797,1745.8256190000002 +55187,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999456383865473,0.9032258064516129,0.14403820037841797,1875.6542580000003 +57090,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998248349068998,0.7619047619047621,0.1476888656616211,2010.2723030000002 +58993,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998304854895579,0.7619047619047621,0.15108394622802734,2149.2802330000004 +60896,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998357829050004,0.7619047619047621,0.1510610580444336,2292.3763450000006 +62799,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998089111118188,0.7272727272727272,0.15114116668701172,2439.4913830000005 +64702,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998145314601011,0.7272727272727272,0.15341472625732422,2590.5360980000005 +66605,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998198306408024,0.7272727272727272,0.1576833724975586,2745.6113380000006 +68508,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998248354182784,0.75,0.1762075424194336,2905.2725530000007 +70411,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998295696634001,0.75,0.1762075424194336,3069.514522000001 +72314,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998340547342801,0.75,0.1762075424194336,3238.428366000001 +74217,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998383097984263,0.75,0.17613887786865234,3411.935267000001 +76120,Binary classification,Aggregated Mondrian Forest,SMTP,0.99984235210657,0.75,0.1760702133178711,3590.1136440000014 +78023,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998461972264233,0.75,0.1782979965209961,3773.0849660000013 +79926,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998498592430404,0.75,0.1782979965209961,3960.4867140000015 +81829,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998533509312216,0.75,0.1782979965209961,4152.338698000001 +83732,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998566839044082,0.75,0.17832088470458984,4348.642178000001 +85635,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998598687437232,0.75,0.17832088470458984,4549.410423000001 +87538,Binary classification,Aggregated Mondrian Forest,SMTP,0.999862915110182,0.75,0.17832088470458984,4754.622131000001 +89441,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998546511627907,0.7346938775510204,0.17834758758544922,4964.315109000001 +91344,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998576792967168,0.7346938775510204,0.19727230072021484,5178.776489000001 +93247,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998605838320143,0.7346938775510204,0.21181774139404297,5398.511461000001 +95150,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998633721846788,0.7346938775510204,0.21174907684326172,5623.669278000001 +95156,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998633807997478,0.7346938775510204,0.21174907684326172,5848.865968000001 +106,Binary classification,ALMA,Bananas,0.5377358490566038,0.5242718446601942,0.0028944015502929688,0.039715 +212,Binary classification,ALMA,Bananas,0.5330188679245284,0.5217391304347825,0.0028944015502929688,0.180531 +318,Binary classification,ALMA,Bananas,0.5188679245283019,0.5173501577287066,0.0029211044311523438,0.33386499999999997 +424,Binary classification,ALMA,Bananas,0.5330188679245284,0.5330188679245282,0.0029211044311523438,0.49377399999999994 +530,Binary classification,ALMA,Bananas,0.5207547169811321,0.5115384615384615,0.0029211044311523438,0.7446539999999999 +636,Binary classification,ALMA,Bananas,0.5377358490566038,0.5303514376996804,0.0029211044311523438,1.03169 +742,Binary classification,ALMA,Bananas,0.522911051212938,0.512396694214876,0.0029211044311523438,1.379859 +848,Binary classification,ALMA,Bananas,0.5235849056603774,0.5061124694376529,0.0029211044311523438,1.737155 +954,Binary classification,ALMA,Bananas,0.5157232704402516,0.5,0.0029211044311523438,2.173505 +1060,Binary classification,ALMA,Bananas,0.5160377358490567,0.4975514201762978,0.0029211044311523438,2.70321 +1166,Binary classification,ALMA,Bananas,0.5154373927958834,0.49598572702943805,0.0029211044311523438,3.270309 +1272,Binary classification,ALMA,Bananas,0.5165094339622641,0.4979591836734694,0.0029211044311523438,3.844268 +1378,Binary classification,ALMA,Bananas,0.5195936139332366,0.4977238239757208,0.0029211044311523438,4.501151 +1484,Binary classification,ALMA,Bananas,0.5195417789757413,0.4968242766407903,0.0029211044311523438,5.229491 +1590,Binary classification,ALMA,Bananas,0.5226415094339623,0.4983476536682089,0.0029211044311523438,6.030342 +1696,Binary classification,ALMA,Bananas,0.5194575471698113,0.49473031618102914,0.0029211044311523438,6.8837410000000006 +1802,Binary classification,ALMA,Bananas,0.5205327413984462,0.4965034965034965,0.0029211044311523438,7.813207 +1908,Binary classification,ALMA,Bananas,0.5193920335429769,0.4964305326743548,0.0029211044311523438,8.751116 +2014,Binary classification,ALMA,Bananas,0.519364448857994,0.4989648033126293,0.0029211044311523438,9.762632 +2120,Binary classification,ALMA,Bananas,0.5174528301886793,0.4997555012224939,0.0029211044311523438,10.806008 +2226,Binary classification,ALMA,Bananas,0.5197663971248877,0.5002337540906966,0.0029211044311523438,11.968014 +2332,Binary classification,ALMA,Bananas,0.5175814751286449,0.4975435462259938,0.0029211044311523438,13.16512 +2438,Binary classification,ALMA,Bananas,0.5176374077112387,0.4957118353344769,0.0029211044311523438,14.408045 +2544,Binary classification,ALMA,Bananas,0.5196540880503144,0.5008169934640523,0.0029211044311523438,15.661105 +2650,Binary classification,ALMA,Bananas,0.520377358490566,0.5037094884810621,0.0029211044311523438,17.014893999999998 +2756,Binary classification,ALMA,Bananas,0.521044992743106,0.5041322314049587,0.0029211044311523438,18.454389 +2862,Binary classification,ALMA,Bananas,0.5213137665967854,0.5032632342277013,0.0029211044311523438,19.942263 +2968,Binary classification,ALMA,Bananas,0.5175202156334232,0.49859943977591037,0.0029211044311523438,21.473074 +3074,Binary classification,ALMA,Bananas,0.5152895250487963,0.49696151249155973,0.0029211044311523438,23.106855 +3180,Binary classification,ALMA,Bananas,0.5132075471698113,0.4931237721021611,0.0029211044311523438,24.747764 +3286,Binary classification,ALMA,Bananas,0.5130858186244674,0.4927076727964489,0.0029211044311523438,26.464385 +3392,Binary classification,ALMA,Bananas,0.5103183962264151,0.49095923996322405,0.0029211044311523438,28.215584 +3498,Binary classification,ALMA,Bananas,0.5091480846197828,0.48914013686402846,0.0029211044311523438,30.068049 +3604,Binary classification,ALMA,Bananas,0.5097114317425083,0.4876775877065816,0.0029211044311523438,31.96012 +3710,Binary classification,ALMA,Bananas,0.5118598382749326,0.49086308687095864,0.0029211044311523438,33.864206 +3816,Binary classification,ALMA,Bananas,0.510482180293501,0.4893384363039912,0.0029211044311523438,35.803291 +3922,Binary classification,ALMA,Bananas,0.50790413054564,0.48588172615876407,0.0029211044311523438,37.844614 +4028,Binary classification,ALMA,Bananas,0.506454816285998,0.48443983402489627,0.0029211044311523438,39.968017 +4134,Binary classification,ALMA,Bananas,0.5050798258345428,0.48281092012133464,0.0029211044311523438,42.128298 +4240,Binary classification,ALMA,Bananas,0.5068396226415094,0.48484848484848486,0.0029211044311523438,44.30306 +4346,Binary classification,ALMA,Bananas,0.5080533824206167,0.4858104858104858,0.0029211044311523438,46.485881 +4452,Binary classification,ALMA,Bananas,0.5080862533692723,0.4847058823529412,0.0029211044311523438,48.746465 +4558,Binary classification,ALMA,Bananas,0.5063624396665204,0.48370812299219823,0.0029211044311523438,51.058035000000004 +4664,Binary classification,ALMA,Bananas,0.5051457975986278,0.4829749103942652,0.0029211044311523438,53.475871000000005 +4770,Binary classification,ALMA,Bananas,0.5048218029350104,0.48201754385964907,0.0029211044311523438,55.900409 +4876,Binary classification,ALMA,Bananas,0.5036915504511895,0.4802405498281787,0.0029211044311523438,58.409701000000005 +4982,Binary classification,ALMA,Bananas,0.5038137294259334,0.4811083123425693,0.0029211044311523438,60.970617000000004 +5088,Binary classification,ALMA,Bananas,0.5029481132075472,0.47995064774830354,0.0029211044311523438,63.61249900000001 +5194,Binary classification,ALMA,Bananas,0.5040431266846361,0.4810636583400483,0.0029211044311523438,66.28843800000001 +5300,Binary classification,ALMA,Bananas,0.5064150943396226,0.4825949367088608,0.0029211044311523438,68.97313600000001 +906,Binary classification,ALMA,Elec2,0.9072847682119205,0.9056179775280899,0.0043582916259765625,0.679052 +1812,Binary classification,ALMA,Elec2,0.9166666666666666,0.8967874231032126,0.0043582916259765625,1.978643 +2718,Binary classification,ALMA,Elec2,0.9175864606328182,0.898458748866727,0.0043582916259765625,3.929769 +3624,Binary classification,ALMA,Elec2,0.9268763796909493,0.9098945936756205,0.0043582916259765625,6.478699 +4530,Binary classification,ALMA,Elec2,0.9271523178807947,0.9076664801343034,0.0043582916259765625,9.702945 +5436,Binary classification,ALMA,Elec2,0.9269683590875644,0.9074376311494521,0.0043582916259765625,13.508006 +6342,Binary classification,ALMA,Elec2,0.9274676758120467,0.9089108910891088,0.0043582916259765625,17.915655 +7248,Binary classification,ALMA,Elec2,0.9254966887417219,0.9066390041493776,0.0043582916259765625,22.910275000000002 +8154,Binary classification,ALMA,Elec2,0.9251900907530046,0.9100294985250738,0.0043582916259765625,28.53571 +9060,Binary classification,ALMA,Elec2,0.9266004415011038,0.9135128105085185,0.0043582916259765625,34.833569000000004 +9966,Binary classification,ALMA,Elec2,0.9293598233995585,0.9182535996284256,0.0043582916259765625,41.779447000000005 +10872,Binary classification,ALMA,Elec2,0.931383370125092,0.9217208814270723,0.0043582916259765625,49.40882500000001 +11778,Binary classification,ALMA,Elec2,0.9313975208014943,0.9218568665377176,0.0043582916259765625,57.61408300000001 +12684,Binary classification,ALMA,Elec2,0.9290444654683065,0.9191665169750315,0.0043582916259765625,66.44160400000001 +13590,Binary classification,ALMA,Elec2,0.9298013245033112,0.9209872453205235,0.0043582916259765625,75.85703300000002 +14496,Binary classification,ALMA,Elec2,0.9305325607064018,0.9222213640225535,0.0043582916259765625,85.90938600000001 +15402,Binary classification,ALMA,Elec2,0.9308531359563693,0.922279792746114,0.0043582916259765625,96.62160000000002 +16308,Binary classification,ALMA,Elec2,0.9293598233995585,0.9203319502074688,0.0043582916259765625,107.96559300000001 +17214,Binary classification,ALMA,Elec2,0.9279656093877077,0.9176298658163943,0.0043582916259765625,119.98412300000001 +18120,Binary classification,ALMA,Elec2,0.9266004415011038,0.9160141449861076,0.0043582916259765625,132.63841200000002 +19026,Binary classification,ALMA,Elec2,0.9265741616734994,0.915143048047136,0.0043582916259765625,146.00166900000002 +19932,Binary classification,ALMA,Elec2,0.9262994180212724,0.9155892662184681,0.0043582916259765625,159.938555 +20838,Binary classification,ALMA,Elec2,0.9232171993473461,0.9122710823555213,0.0043582916259765625,174.54734100000002 +21744,Binary classification,ALMA,Elec2,0.9225073583517293,0.9101955977189149,0.0043582916259765625,189.73533200000003 +22650,Binary classification,ALMA,Elec2,0.9217218543046357,0.9087540528022233,0.0043582916259765625,205.61954500000002 +23556,Binary classification,ALMA,Elec2,0.9186619120393955,0.9050639183430779,0.0043582916259765625,222.16184900000002 +24462,Binary classification,ALMA,Elec2,0.9173003025100155,0.902885123133791,0.0043582916259765625,239.39901500000002 +25368,Binary classification,ALMA,Elec2,0.9144985808893094,0.8997643144322751,0.0043582916259765625,257.26822000000004 +26274,Binary classification,ALMA,Elec2,0.9142498287280201,0.8992622401073105,0.0043582916259765625,275.765482 +27180,Binary classification,ALMA,Elec2,0.9138337012509198,0.89909521757863,0.0043582916259765625,294.848784 +28086,Binary classification,ALMA,Elec2,0.9110232856227302,0.8955049132343716,0.0043582916259765625,314.617946 +28992,Binary classification,ALMA,Elec2,0.9101476269315674,0.8940927755417328,0.0043582916259765625,335.076303 +29898,Binary classification,ALMA,Elec2,0.9094922737306843,0.8931701539676272,0.0043582916259765625,356.063392 +30804,Binary classification,ALMA,Elec2,0.9083235943383976,0.8913093680240166,0.0043582916259765625,377.56682800000004 +31710,Binary classification,ALMA,Elec2,0.9062125512456638,0.8888722815933038,0.0043582916259765625,399.67833300000007 +32616,Binary classification,ALMA,Elec2,0.9052918812852587,0.8879294706671989,0.0043582916259765625,422.4669390000001 +33522,Binary classification,ALMA,Elec2,0.9050474315374978,0.8877050626212737,0.0043582916259765625,445.7904010000001 +34428,Binary classification,ALMA,Elec2,0.9050772626931567,0.8877901387172092,0.0043582916259765625,469.7481780000001 +35334,Binary classification,ALMA,Elec2,0.9045395369898681,0.8866104144955793,0.0043582916259765625,494.21097500000013 +36240,Binary classification,ALMA,Elec2,0.9048013245033113,0.8860483551327785,0.0043582916259765625,519.3392310000002 +37146,Binary classification,ALMA,Elec2,0.9045119259139611,0.8854217139903738,0.0043582916259765625,545.0643520000001 +38052,Binary classification,ALMA,Elec2,0.9042625880374224,0.8846092933388237,0.0043582916259765625,571.4397520000001 +38958,Binary classification,ALMA,Elec2,0.904409877303763,0.88502624266749,0.0043582916259765625,598.3711440000001 +39864,Binary classification,ALMA,Elec2,0.904926750953241,0.8863636363636365,0.0043582916259765625,626.0397580000001 +40770,Binary classification,ALMA,Elec2,0.9055187637969095,0.8878667908709827,0.0043582916259765625,654.3634580000002 +41676,Binary classification,ALMA,Elec2,0.9061090315769268,0.8892285916489738,0.0043582916259765625,683.2521370000002 +42582,Binary classification,ALMA,Elec2,0.9063923723639097,0.889810361032786,0.0043582916259765625,712.7682970000002 +43488,Binary classification,ALMA,Elec2,0.9067098969830758,0.8902534693104661,0.0043582916259765625,742.9022130000002 +44394,Binary classification,ALMA,Elec2,0.9062711177186106,0.8894732648019762,0.0043582916259765625,773.7300850000001 +45300,Binary classification,ALMA,Elec2,0.9064238410596026,0.8897844569823977,0.0043582916259765625,805.1132940000001 +45312,Binary classification,ALMA,Elec2,0.9064265536723164,0.8897670549084858,0.0043582916259765625,836.4982200000001 +25,Binary classification,ALMA,Phishing,0.56,0.5217391304347826,0.004366874694824219,0.003459 +50,Binary classification,ALMA,Phishing,0.7,0.6341463414634146,0.004366874694824219,0.050212 +75,Binary classification,ALMA,Phishing,0.7066666666666667,0.676470588235294,0.004366874694824219,0.100001 +100,Binary classification,ALMA,Phishing,0.72,0.702127659574468,0.004366874694824219,0.15312800000000001 +125,Binary classification,ALMA,Phishing,0.72,0.7058823529411765,0.004366874694824219,0.22806300000000002 +150,Binary classification,ALMA,Phishing,0.7133333333333334,0.7189542483660132,0.004366874694824219,0.334445 +175,Binary classification,ALMA,Phishing,0.7314285714285714,0.718562874251497,0.004366874694824219,0.511774 +200,Binary classification,ALMA,Phishing,0.735,0.7225130890052356,0.004366874694824219,0.692607 +225,Binary classification,ALMA,Phishing,0.7244444444444444,0.701923076923077,0.004366874694824219,0.876779 +250,Binary classification,ALMA,Phishing,0.724,0.7038626609442059,0.004366874694824219,1.156005 +275,Binary classification,ALMA,Phishing,0.7345454545454545,0.7137254901960783,0.004580497741699219,1.438242 +300,Binary classification,ALMA,Phishing,0.7366666666666667,0.7127272727272725,0.004580497741699219,1.723391 +325,Binary classification,ALMA,Phishing,0.7476923076923077,0.7172413793103447,0.004580497741699219,2.078902 +350,Binary classification,ALMA,Phishing,0.7542857142857143,0.7225806451612904,0.004580497741699219,2.4379969999999997 +375,Binary classification,ALMA,Phishing,0.7573333333333333,0.723404255319149,0.004580497741699219,2.8003189999999996 +400,Binary classification,ALMA,Phishing,0.76,0.7257142857142856,0.004580497741699219,3.1655669999999994 +425,Binary classification,ALMA,Phishing,0.76,0.7197802197802199,0.004580497741699219,3.585508999999999 +450,Binary classification,ALMA,Phishing,0.7622222222222222,0.7206266318537858,0.004580497741699219,4.009149999999999 +475,Binary classification,ALMA,Phishing,0.7663157894736842,0.7272727272727272,0.004580497741699219,4.435539999999999 +500,Binary classification,ALMA,Phishing,0.768,0.7327188940092165,0.004580497741699219,4.959426999999999 +525,Binary classification,ALMA,Phishing,0.7714285714285715,0.7321428571428573,0.004580497741699219,5.485955999999999 +550,Binary classification,ALMA,Phishing,0.7709090909090909,0.7341772151898734,0.004580497741699219,6.028689999999999 +575,Binary classification,ALMA,Phishing,0.7739130434782608,0.7379032258064516,0.004580497741699219,6.595545999999999 +600,Binary classification,ALMA,Phishing,0.78,0.7401574803149605,0.004580497741699219,7.165257999999999 +625,Binary classification,ALMA,Phishing,0.7744,0.7314285714285715,0.004580497741699219,7.741693999999999 +650,Binary classification,ALMA,Phishing,0.7815384615384615,0.7427536231884059,0.004580497741699219,8.363988999999998 +675,Binary classification,ALMA,Phishing,0.7837037037037037,0.75,0.004580497741699219,9.010548999999997 +700,Binary classification,ALMA,Phishing,0.79,0.7545909849749582,0.004580497741699219,9.660288999999997 +725,Binary classification,ALMA,Phishing,0.7917241379310345,0.7606973058637084,0.004580497741699219,10.349524999999996 +750,Binary classification,ALMA,Phishing,0.792,0.7621951219512195,0.004580497741699219,11.041959999999996 +775,Binary classification,ALMA,Phishing,0.792258064516129,0.7614814814814814,0.004580497741699219,11.737615999999996 +800,Binary classification,ALMA,Phishing,0.795,0.7670454545454546,0.004580497741699219,12.526707999999996 +825,Binary classification,ALMA,Phishing,0.793939393939394,0.7671232876712327,0.004580497741699219,13.319008999999996 +850,Binary classification,ALMA,Phishing,0.7976470588235294,0.7706666666666667,0.004580497741699219,14.117527999999997 +875,Binary classification,ALMA,Phishing,0.8022857142857143,0.7744458930899608,0.004580497741699219,14.959668999999996 +900,Binary classification,ALMA,Phishing,0.8011111111111111,0.7737041719342603,0.004580497741699219,15.804425999999996 +925,Binary classification,ALMA,Phishing,0.8054054054054054,0.7804878048780488,0.004580497741699219,16.651646999999997 +950,Binary classification,ALMA,Phishing,0.8073684210526316,0.7849588719153936,0.004580497741699219,17.502014999999997 +975,Binary classification,ALMA,Phishing,0.8102564102564103,0.7880870561282932,0.004580497741699219,18.418237999999995 +1000,Binary classification,ALMA,Phishing,0.811,0.7892976588628764,0.004580497741699219,19.337672999999995 +1025,Binary classification,ALMA,Phishing,0.8146341463414634,0.7943722943722944,0.004580497741699219,20.260238999999995 +1050,Binary classification,ALMA,Phishing,0.8161904761904762,0.7970557308096741,0.004580497741699219,21.242111999999995 +1075,Binary classification,ALMA,Phishing,0.815813953488372,0.7983706720977597,0.004580497741699219,22.243638999999995 +1100,Binary classification,ALMA,Phishing,0.8190909090909091,0.8023833167825224,0.004580497741699219,23.247954999999994 +1125,Binary classification,ALMA,Phishing,0.8213333333333334,0.8061716489874637,0.004580497741699219,24.273946999999993 +1150,Binary classification,ALMA,Phishing,0.8226086956521739,0.8071833648393195,0.004580497741699219,25.351544999999994 +1175,Binary classification,ALMA,Phishing,0.8212765957446808,0.8059149722735675,0.004580497741699219,26.431981999999994 +1200,Binary classification,ALMA,Phishing,0.8233333333333334,0.8076225045372051,0.004580497741699219,27.515220999999993 +1225,Binary classification,ALMA,Phishing,0.8244897959183674,0.8088888888888888,0.004580497741699219,28.636604999999992 +1250,Binary classification,ALMA,Phishing,0.8256,0.810763888888889,0.004580497741699219,29.761263999999994 +1903,Binary classification,ALMA,SMTP,0.720966894377299,0.0,0.003093719482421875,1.027868 +3806,Binary classification,ALMA,SMTP,0.7769311613242249,0.0,0.003093719482421875,3.106358 +5709,Binary classification,ALMA,SMTP,0.7509196006305833,0.0,0.003093719482421875,6.233245 +7612,Binary classification,ALMA,SMTP,0.7900683131897005,0.0,0.003093719482421875,10.302873 +9515,Binary classification,ALMA,SMTP,0.7826589595375723,0.0,0.003093719482421875,15.393504 +11418,Binary classification,ALMA,SMTP,0.7699246803293046,0.0,0.003093719482421875,21.578682 +13321,Binary classification,ALMA,SMTP,0.7722393213722694,0.0,0.003093719482421875,28.779608 +15224,Binary classification,ALMA,SMTP,0.7791644771413557,0.004146919431279621,0.003093719482421875,37.113003 +17127,Binary classification,ALMA,SMTP,0.783207800548841,0.004824443848834093,0.003093719482421875,46.3898 +19030,Binary classification,ALMA,SMTP,0.7891224382553862,0.004465393202679235,0.003093719482421875,56.715322 +20933,Binary classification,ALMA,SMTP,0.7832131084889887,0.003950834064969272,0.003093719482421875,68.217717 +22836,Binary classification,ALMA,SMTP,0.7821422315641969,0.0036050470658922497,0.003093719482421875,80.77427399999999 +24739,Binary classification,ALMA,SMTP,0.7877440478596548,0.0034162080091098878,0.003093719482421875,94.43257899999999 +26642,Binary classification,ALMA,SMTP,0.78188574431349,0.003429943405933802,0.003093719482421875,109.06303 +28545,Binary classification,ALMA,SMTP,0.7857418111753371,0.003259452411994785,0.003093719482421875,124.719605 +30448,Binary classification,ALMA,SMTP,0.7871452968996322,0.0030764497769573914,0.003093719482421875,141.369597 +32351,Binary classification,ALMA,SMTP,0.7866835646502427,0.0028897558156335793,0.003093719482421875,159.093537 +34254,Binary classification,ALMA,SMTP,0.7860979739592456,0.0027221995372260785,0.003093719482421875,177.829964 +36157,Binary classification,ALMA,SMTP,0.7771939043615345,0.0024764735017335313,0.003093719482421875,197.576045 +38060,Binary classification,ALMA,SMTP,0.7831581713084603,0.00241750271969056,0.003093719482421875,218.31617 +39963,Binary classification,ALMA,SMTP,0.779496033831294,0.002264492753623189,0.003093719482421875,240.067789 +41866,Binary classification,ALMA,SMTP,0.7831175655663307,0.0021978021978021974,0.003093719482421875,262.83473100000003 +43769,Binary classification,ALMA,SMTP,0.7791130708949257,0.002064409578860446,0.003093719482421875,286.65613700000006 +45672,Binary classification,ALMA,SMTP,0.7808066211245402,0.001993819160602133,0.003093719482421875,311.45044200000007 +47575,Binary classification,ALMA,SMTP,0.7799684708355229,0.001906941266209001,0.003093719482421875,337.28748800000005 +49478,Binary classification,ALMA,SMTP,0.7778810784591131,0.00181653042688465,0.003093719482421875,364.14950600000003 +51381,Binary classification,ALMA,SMTP,0.7807944570950351,0.0021263400372109505,0.003093719482421875,392.065506 +53284,Binary classification,ALMA,SMTP,0.7777193904361535,0.0020222446916076846,0.003093719482421875,421.04388200000005 +55187,Binary classification,ALMA,SMTP,0.7785891604906953,0.0019603038470963,0.003093719482421875,451.02283200000005 +57090,Binary classification,ALMA,SMTP,0.7758801891749869,0.002650245537454205,0.003093719482421875,482.06238400000007 +58993,Binary classification,ALMA,SMTP,0.774159646059702,0.002545481769858501,0.003093719482421875,514.138052 +60896,Binary classification,ALMA,SMTP,0.7746157383079348,0.002471109819027546,0.003093719482421875,547.227498 +62799,Binary classification,ALMA,SMTP,0.7704899759550311,0.0023534297778085413,0.003093719482421875,581.3499069999999 +64702,Binary classification,ALMA,SMTP,0.771274458285679,0.0022921863412660956,0.003093719482421875,616.580127 +66605,Binary classification,ALMA,SMTP,0.7721942797087306,0.002235812454790557,0.003093719482421875,652.800029 +68508,Binary classification,ALMA,SMTP,0.7705085537455479,0.0024111675126903555,0.003093719482421875,690.043858 +70411,Binary classification,ALMA,SMTP,0.7685872945988553,0.0023267205486162137,0.003093719482421875,728.276014 +72314,Binary classification,ALMA,SMTP,0.7687999557485411,0.002267709017127171,0.003093719482421875,767.526171 +74217,Binary classification,ALMA,SMTP,0.7657140547313958,0.0021806496040399402,0.003093719482421875,807.759105 +76120,Binary classification,ALMA,SMTP,0.7665002627430373,0.0021333932180552435,0.003093719482421875,848.897716 +78023,Binary classification,ALMA,SMTP,0.7657101111210797,0.002074462277541216,0.003093719482421875,890.953712 +79926,Binary classification,ALMA,SMTP,0.7636313590070816,0.002007395668251453,0.003093719482421875,933.942273 +81829,Binary classification,ALMA,SMTP,0.7647777682728617,0.001970341180130665,0.003093719482421875,977.888636 +83732,Binary classification,ALMA,SMTP,0.7652868676252806,0.0019298156518206286,0.003093719482421875,1022.739679 +85635,Binary classification,ALMA,SMTP,0.7642552694575816,0.0018787699001285476,0.003093719482421875,1068.5586680000001 +87538,Binary classification,ALMA,SMTP,0.7644680024674998,0.0018396591789310612,0.003093719482421875,1115.337297 +89441,Binary classification,ALMA,SMTP,0.7635312664214399,0.0018876828692779614,0.003093719482421875,1162.988807 +91344,Binary classification,ALMA,SMTP,0.7650091960063058,0.0018600325505696352,0.003093719482421875,1211.563326 +93247,Binary classification,ALMA,SMTP,0.7647859984771628,0.0018204159650480134,0.003093719482421875,1260.984755 +95150,Binary classification,ALMA,SMTP,0.7649710982658959,0.0017854751595768425,0.003093719482421875,1311.295723 +95156,Binary classification,ALMA,SMTP,0.7649859178611963,0.0017854751595768425,0.003093719482421875,1361.607838 +106,Binary classification,sklearn SGDClassifier,Bananas,0.5283018867924528,0.4680851063829788,0.005551338195800781,0.50714 +212,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.4673913043478261,0.005551338195800781,1.5449950000000001 +318,Binary classification,sklearn SGDClassifier,Bananas,0.5345911949685535,0.4861111111111111,0.005578041076660156,3.028568 +424,Binary classification,sklearn SGDClassifier,Bananas,0.5188679245283019,0.46596858638743455,0.005578041076660156,4.996646 +530,Binary classification,sklearn SGDClassifier,Bananas,0.5264150943396226,0.42562929061784893,0.005578041076660156,7.439068000000001 +636,Binary classification,sklearn SGDClassifier,Bananas,0.5235849056603774,0.3878787878787879,0.005578041076660156,10.329429000000001 +742,Binary classification,sklearn SGDClassifier,Bananas,0.5363881401617251,0.36296296296296293,0.005578041076660156,13.751562000000002 +848,Binary classification,sklearn SGDClassifier,Bananas,0.5400943396226415,0.33898305084745767,0.005578041076660156,17.710995 +954,Binary classification,sklearn SGDClassifier,Bananas,0.5440251572327044,0.31496062992125984,0.005578041076660156,22.189814 +1060,Binary classification,sklearn SGDClassifier,Bananas,0.5518867924528302,0.2962962962962963,0.005578041076660156,27.154881999999997 +1166,Binary classification,sklearn SGDClassifier,Bananas,0.5523156089193825,0.27900552486187846,0.005578041076660156,32.629664 +1272,Binary classification,sklearn SGDClassifier,Bananas,0.5542452830188679,0.27586206896551724,0.005578041076660156,38.64774 +1378,Binary classification,sklearn SGDClassifier,Bananas,0.5566037735849056,0.2611850060459492,0.005578041076660156,45.182197 +1484,Binary classification,sklearn SGDClassifier,Bananas,0.557277628032345,0.24742268041237112,0.005578041076660156,52.237503000000004 +1590,Binary classification,sklearn SGDClassifier,Bananas,0.5578616352201258,0.23503808487486397,0.005578041076660156,59.74126700000001 +1696,Binary classification,sklearn SGDClassifier,Bananas,0.5595518867924528,0.22590673575129533,0.005578041076660156,67.72058500000001 +1802,Binary classification,sklearn SGDClassifier,Bananas,0.5566037735849056,0.21589793915603536,0.005578041076660156,76.17010700000002 +1908,Binary classification,sklearn SGDClassifier,Bananas,0.5545073375262054,0.21150278293135436,0.005578041076660156,85.08376800000002 +2014,Binary classification,sklearn SGDClassifier,Bananas,0.5496524329692155,0.20088105726872246,0.005578041076660156,94.42289500000003 +2120,Binary classification,sklearn SGDClassifier,Bananas,0.5466981132075471,0.19446772841575857,0.005578041076660156,104.22299500000003 +2226,Binary classification,sklearn SGDClassifier,Bananas,0.550314465408805,0.2036595067621321,0.005578041076660156,114.52672400000003 +2332,Binary classification,sklearn SGDClassifier,Bananas,0.5493138936535163,0.21036814425244177,0.005578041076660156,125.29025300000004 +2438,Binary classification,sklearn SGDClassifier,Bananas,0.5479901558654635,0.21173104434907009,0.005578041076660156,136.49837800000003 +2544,Binary classification,sklearn SGDClassifier,Bananas,0.5483490566037735,0.22626262626262625,0.005578041076660156,148.22051100000004 +2650,Binary classification,sklearn SGDClassifier,Bananas,0.5460377358490566,0.2322910019144863,0.005578041076660156,160.35879600000004 +2756,Binary classification,sklearn SGDClassifier,Bananas,0.5395500725689405,0.23044269254093389,0.005578041076660156,173.00042100000005 +2862,Binary classification,sklearn SGDClassifier,Bananas,0.5394828791055206,0.2310385064177363,0.005578041076660156,186.18470300000004 +2968,Binary classification,sklearn SGDClassifier,Bananas,0.5411051212938005,0.23050847457627116,0.005578041076660156,199.85251500000004 +3074,Binary classification,sklearn SGDClassifier,Bananas,0.5396877033181522,0.227198252321136,0.005578041076660156,214.04388100000003 +3180,Binary classification,sklearn SGDClassifier,Bananas,0.5430817610062894,0.22835900159320233,0.005578041076660156,228.72034700000003 +3286,Binary classification,sklearn SGDClassifier,Bananas,0.5444309190505173,0.22475401346452614,0.005578041076660156,243.83191300000004 +3392,Binary classification,sklearn SGDClassifier,Bananas,0.5445165094339622,0.22478675363773204,0.005578041076660156,259.480064 +3498,Binary classification,sklearn SGDClassifier,Bananas,0.5463121783876501,0.22014742014742014,0.005578041076660156,275.54602900000003 +3604,Binary classification,sklearn SGDClassifier,Bananas,0.548834628190899,0.21676300578034682,0.005578041076660156,292.10262700000004 +3710,Binary classification,sklearn SGDClassifier,Bananas,0.547978436657682,0.21230624706434945,0.005578041076660156,309.14156900000006 +3816,Binary classification,sklearn SGDClassifier,Bananas,0.5474318658280922,0.20743460302891234,0.005578041076660156,326.6693460000001 +3922,Binary classification,sklearn SGDClassifier,Bananas,0.5484446710861806,0.20332883490778228,0.005578041076660156,344.5959390000001 +4028,Binary classification,sklearn SGDClassifier,Bananas,0.5489076464746773,0.1992066989863376,0.005578041076660156,363.0622570000001 +4134,Binary classification,sklearn SGDClassifier,Bananas,0.5491049830672472,0.19516407599309155,0.005578041076660156,382.0101650000001 +4240,Binary classification,sklearn SGDClassifier,Bananas,0.5483490566037735,0.19095901985635824,0.005578041076660156,401.4423870000001 +4346,Binary classification,sklearn SGDClassifier,Bananas,0.548550391164289,0.18858560794044665,0.005578041076660156,421.3360060000001 +4452,Binary classification,sklearn SGDClassifier,Bananas,0.550763701707098,0.1935483870967742,0.005578041076660156,441.6814400000001 +4558,Binary classification,sklearn SGDClassifier,Bananas,0.5482667836770513,0.19349784567175873,0.005578041076660156,462.3969810000001 +4664,Binary classification,sklearn SGDClassifier,Bananas,0.5490994854202401,0.19763449065242272,0.005578041076660156,483.6474930000001 +4770,Binary classification,sklearn SGDClassifier,Bananas,0.550104821802935,0.19985085756897839,0.005578041076660156,505.4107300000001 +4876,Binary classification,sklearn SGDClassifier,Bananas,0.5504511894995898,0.2,0.005578041076660156,527.6413840000001 +4982,Binary classification,sklearn SGDClassifier,Bananas,0.5503813729425934,0.2062367115520907,0.005578041076660156,550.3366250000001 +5088,Binary classification,sklearn SGDClassifier,Bananas,0.5479559748427673,0.20415224913494812,0.005578041076660156,573.5279610000001 +5194,Binary classification,sklearn SGDClassifier,Bananas,0.5462071621101271,0.20236886632825718,0.005578041076660156,597.2511250000001 +5300,Binary classification,sklearn SGDClassifier,Bananas,0.5464150943396227,0.205026455026455,0.005578041076660156,621.4261850000001 +906,Binary classification,sklearn SGDClassifier,Elec2,0.8002207505518764,0.7868080094228505,0.006801605224609375,4.395754 +1812,Binary classification,sklearn SGDClassifier,Elec2,0.8140176600441501,0.7501853224610822,0.006801605224609375,13.314942 +2718,Binary classification,sklearn SGDClassifier,Elec2,0.8005886681383371,0.7262626262626262,0.006801605224609375,26.594138 +3624,Binary classification,sklearn SGDClassifier,Elec2,0.8189845474613686,0.7586460632818247,0.006801605224609375,44.068779 +4530,Binary classification,sklearn SGDClassifier,Elec2,0.8278145695364238,0.7588126159554731,0.006801605224609375,65.924464 +5436,Binary classification,sklearn SGDClassifier,Elec2,0.8211920529801324,0.7498713329902212,0.006801605224609375,92.07692 +6342,Binary classification,sklearn SGDClassifier,Elec2,0.8222958057395143,0.7575822757582275,0.006801605224609375,122.546282 +7248,Binary classification,sklearn SGDClassifier,Elec2,0.8253311258278145,0.7598634294385433,0.006801605224609375,157.279906 +8154,Binary classification,sklearn SGDClassifier,Elec2,0.8303899926416483,0.780789348549691,0.006801605224609375,196.124663 +9060,Binary classification,sklearn SGDClassifier,Elec2,0.8364238410596027,0.7958677685950413,0.006801605224609375,238.938569 +9966,Binary classification,sklearn SGDClassifier,Elec2,0.8371462974111981,0.8011273128293102,0.006801605224609375,285.711115 +10872,Binary classification,sklearn SGDClassifier,Elec2,0.8393119941133186,0.8079586676926458,0.006801605224609375,336.134661 +11778,Binary classification,sklearn SGDClassifier,Elec2,0.8422482594668025,0.8114088509947219,0.006801605224609375,390.124895 +12684,Binary classification,sklearn SGDClassifier,Elec2,0.8409019236833807,0.810445237647943,0.006801605224609375,447.475874 +13590,Binary classification,sklearn SGDClassifier,Elec2,0.8427520235467255,0.8154098643862832,0.006801605224609375,507.886031 +14496,Binary classification,sklearn SGDClassifier,Elec2,0.8438189845474614,0.8177720540888602,0.006801605224609375,571.200551 +15402,Binary classification,sklearn SGDClassifier,Elec2,0.845214907154915,0.8184587267742918,0.006801605224609375,637.3575030000001 +16308,Binary classification,sklearn SGDClassifier,Elec2,0.8397105714986509,0.8108264582428716,0.006801605224609375,706.2352450000001 +17214,Binary classification,sklearn SGDClassifier,Elec2,0.8384454513767864,0.8053202660133008,0.006801605224609375,777.6642180000001 +18120,Binary classification,sklearn SGDClassifier,Elec2,0.840728476821192,0.8082646824342281,0.006801605224609375,851.7523750000001 +19026,Binary classification,sklearn SGDClassifier,Elec2,0.843950383685483,0.8100326316462987,0.006801605224609375,928.4036970000002 +19932,Binary classification,sklearn SGDClassifier,Elec2,0.8412101143889223,0.8075636894266431,0.006801605224609375,1007.5500400000002 +20838,Binary classification,sklearn SGDClassifier,Elec2,0.8373644303675977,0.8028848950154133,0.006801605224609375,1089.2694250000002 +21744,Binary classification,sklearn SGDClassifier,Elec2,0.8382542310522443,0.8008155405788072,0.006801605224609375,1173.5296240000002 +22650,Binary classification,sklearn SGDClassifier,Elec2,0.8376600441501104,0.7982441700960219,0.006801605224609375,1260.4177460000003 +23556,Binary classification,sklearn SGDClassifier,Elec2,0.8337578536254033,0.7924748277689453,0.006801605224609375,1349.7468710000003 +24462,Binary classification,sklearn SGDClassifier,Elec2,0.8313302264737144,0.7887569117345894,0.006801605224609375,1441.5955620000002 +25368,Binary classification,sklearn SGDClassifier,Elec2,0.8278539892778304,0.7842711060613546,0.006801605224609375,1535.8662290000002 +26274,Binary classification,sklearn SGDClassifier,Elec2,0.8282712948161681,0.784486052732136,0.006801605224609375,1632.5910050000002 +27180,Binary classification,sklearn SGDClassifier,Elec2,0.8285504047093452,0.785431439359057,0.006801605224609375,1731.7068200000003 +28086,Binary classification,sklearn SGDClassifier,Elec2,0.825357829523606,0.7809192013935414,0.006801605224609375,1833.2277320000003 +28992,Binary classification,sklearn SGDClassifier,Elec2,0.8246412803532008,0.7785135488368041,0.006801605224609375,1936.9708820000003 +29898,Binary classification,sklearn SGDClassifier,Elec2,0.8228644056458626,0.7766343315056937,0.006801605224609375,2042.9068730000004 +30804,Binary classification,sklearn SGDClassifier,Elec2,0.8227827554863005,0.775599128540305,0.006801605224609375,2150.889688 +31710,Binary classification,sklearn SGDClassifier,Elec2,0.8180384736676127,0.7686632988533396,0.006801605224609375,2260.9522580000003 +32616,Binary classification,sklearn SGDClassifier,Elec2,0.8156119695854795,0.765426320305796,0.006801605224609375,2373.012802 +33522,Binary classification,sklearn SGDClassifier,Elec2,0.8136746017540719,0.7636955205811137,0.006801605224609375,2487.1515040000004 +34428,Binary classification,sklearn SGDClassifier,Elec2,0.8108516323922389,0.7597934341571375,0.006801605224609375,2603.2748090000005 +35334,Binary classification,sklearn SGDClassifier,Elec2,0.811031867323258,0.7582811425261557,0.006801605224609375,2721.3856460000006 +36240,Binary classification,sklearn SGDClassifier,Elec2,0.8123344370860928,0.7585643792821898,0.006801605224609375,2841.498866000001 +37146,Binary classification,sklearn SGDClassifier,Elec2,0.8119312981209282,0.7567887480852249,0.006801605224609375,2963.634415000001 +38052,Binary classification,sklearn SGDClassifier,Elec2,0.8118364343529907,0.7562636165577343,0.006801605224609375,3087.755988000001 +38958,Binary classification,sklearn SGDClassifier,Elec2,0.8128497356127111,0.7583601232890332,0.006801605224609375,3213.7594090000007 +39864,Binary classification,sklearn SGDClassifier,Elec2,0.8136162954043749,0.7616297722168751,0.006801605224609375,3341.6530200000007 +40770,Binary classification,sklearn SGDClassifier,Elec2,0.8154034829531518,0.7662732919254659,0.006801605224609375,3471.3422760000008 +41676,Binary classification,sklearn SGDClassifier,Elec2,0.8169929935694404,0.7702641645832705,0.006801605224609375,3602.9648230000007 +42582,Binary classification,sklearn SGDClassifier,Elec2,0.8180216993095675,0.7720681236579698,0.006801605224609375,3737.0799900000006 +43488,Binary classification,sklearn SGDClassifier,Elec2,0.8185936350257542,0.7730894238789657,0.006801605224609375,3873.4157740000005 +44394,Binary classification,sklearn SGDClassifier,Elec2,0.8179708969680587,0.7710051290770494,0.006801605224609375,4011.6127160000005 +45300,Binary classification,sklearn SGDClassifier,Elec2,0.8190949227373069,0.7729350807680585,0.006801605224609375,4151.679177000001 +45312,Binary classification,sklearn SGDClassifier,Elec2,0.8190986935028248,0.7728922505749037,0.006801605224609375,4291.771713000001 +25,Binary classification,sklearn SGDClassifier,Phishing,0.68,0.6923076923076923,0.006802558898925781,0.149754 +50,Binary classification,sklearn SGDClassifier,Phishing,0.8,0.782608695652174,0.006802558898925781,0.457736 +75,Binary classification,sklearn SGDClassifier,Phishing,0.8266666666666667,0.8219178082191781,0.006802558898925781,0.892069 +100,Binary classification,sklearn SGDClassifier,Phishing,0.83,0.8210526315789473,0.006802558898925781,1.4190939999999999 +125,Binary classification,sklearn SGDClassifier,Phishing,0.816,0.8067226890756303,0.006802558898925781,2.091236 +150,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8187919463087249,0.006802558898925781,2.916232 +175,Binary classification,sklearn SGDClassifier,Phishing,0.8285714285714286,0.8170731707317075,0.006802558898925781,3.840025 +200,Binary classification,sklearn SGDClassifier,Phishing,0.825,0.8128342245989306,0.006802558898925781,4.9100459999999995 +225,Binary classification,sklearn SGDClassifier,Phishing,0.8222222222222222,0.8058252427184465,0.006802558898925781,6.121922 +250,Binary classification,sklearn SGDClassifier,Phishing,0.824,0.8103448275862069,0.006802558898925781,7.4794909999999994 +275,Binary classification,sklearn SGDClassifier,Phishing,0.8254545454545454,0.8110236220472441,0.007016181945800781,8.920382 +300,Binary classification,sklearn SGDClassifier,Phishing,0.8366666666666667,0.8191881918819188,0.007016181945800781,10.509974 +325,Binary classification,sklearn SGDClassifier,Phishing,0.8461538461538461,0.8251748251748252,0.007016181945800781,12.191811999999999 +350,Binary classification,sklearn SGDClassifier,Phishing,0.8514285714285714,0.8289473684210525,0.007016181945800781,13.999137 +375,Binary classification,sklearn SGDClassifier,Phishing,0.8506666666666667,0.8271604938271606,0.007016181945800781,15.959285 +400,Binary classification,sklearn SGDClassifier,Phishing,0.8525,0.8269794721407624,0.007016181945800781,18.058664 +425,Binary classification,sklearn SGDClassifier,Phishing,0.8564705882352941,0.828169014084507,0.007016181945800781,20.312993 +450,Binary classification,sklearn SGDClassifier,Phishing,0.86,0.8301886792452831,0.007016181945800781,22.675489 +475,Binary classification,sklearn SGDClassifier,Phishing,0.8589473684210527,0.830379746835443,0.007016181945800781,25.19503 +500,Binary classification,sklearn SGDClassifier,Phishing,0.858,0.8329411764705883,0.007016181945800781,27.784240999999998 +525,Binary classification,sklearn SGDClassifier,Phishing,0.8571428571428571,0.8283752860411898,0.007016181945800781,30.514065 +550,Binary classification,sklearn SGDClassifier,Phishing,0.8618181818181818,0.8354978354978354,0.007016181945800781,33.400870999999995 +575,Binary classification,sklearn SGDClassifier,Phishing,0.8626086956521739,0.8364389233954452,0.007016181945800781,36.397645999999995 +600,Binary classification,sklearn SGDClassifier,Phishing,0.8666666666666667,0.8387096774193549,0.007016181945800781,39.57675499999999 +625,Binary classification,sklearn SGDClassifier,Phishing,0.8672,0.8362919132149901,0.007016181945800781,42.828695999999994 +650,Binary classification,sklearn SGDClassifier,Phishing,0.8707692307692307,0.8432835820895522,0.007016181945800781,46.253181999999995 +675,Binary classification,sklearn SGDClassifier,Phishing,0.8725925925925926,0.8485915492957746,0.007016181945800781,49.816151 +700,Binary classification,sklearn SGDClassifier,Phishing,0.8771428571428571,0.8522336769759451,0.007016181945800781,53.516545 +725,Binary classification,sklearn SGDClassifier,Phishing,0.8786206896551724,0.8566775244299674,0.007016181945800781,57.358180000000004 +750,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8589341692789968,0.007016181945800781,61.281034000000005 +775,Binary classification,sklearn SGDClassifier,Phishing,0.8812903225806452,0.8597560975609757,0.007016181945800781,65.347537 +800,Binary classification,sklearn SGDClassifier,Phishing,0.88125,0.8613138686131386,0.007016181945800781,69.566336 +825,Binary classification,sklearn SGDClassifier,Phishing,0.8812121212121212,0.8623595505617978,0.007016181945800781,73.91498000000001 +850,Binary classification,sklearn SGDClassifier,Phishing,0.8823529411764706,0.8630136986301369,0.007016181945800781,78.39968800000001 +875,Binary classification,sklearn SGDClassifier,Phishing,0.8857142857142857,0.8663101604278075,0.007016181945800781,83.02084100000002 +900,Binary classification,sklearn SGDClassifier,Phishing,0.8844444444444445,0.8645833333333334,0.007016181945800781,87.71921500000002 +925,Binary classification,sklearn SGDClassifier,Phishing,0.8864864864864865,0.8682559598494354,0.007016181945800781,92.55798800000002 +950,Binary classification,sklearn SGDClassifier,Phishing,0.8863157894736842,0.8695652173913043,0.007016181945800781,97.51738800000003 +975,Binary classification,sklearn SGDClassifier,Phishing,0.8871794871794871,0.8702830188679245,0.007016181945800781,102.59954100000003 +1000,Binary classification,sklearn SGDClassifier,Phishing,0.888,0.871264367816092,0.007016181945800781,107.87282600000003 +1025,Binary classification,sklearn SGDClassifier,Phishing,0.8878048780487805,0.8715083798882682,0.007016181945800781,113.28564700000003 +1050,Binary classification,sklearn SGDClassifier,Phishing,0.8895238095238095,0.8739130434782609,0.007016181945800781,118.79277100000003 +1075,Binary classification,sklearn SGDClassifier,Phishing,0.8883720930232558,0.8736842105263158,0.007016181945800781,124.46348200000003 +1100,Binary classification,sklearn SGDClassifier,Phishing,0.89,0.8756423432682425,0.007016181945800781,130.26843700000003 +1125,Binary classification,sklearn SGDClassifier,Phishing,0.8915555555555555,0.8784860557768924,0.007016181945800781,136.21796400000002 +1150,Binary classification,sklearn SGDClassifier,Phishing,0.8913043478260869,0.878048780487805,0.007016181945800781,142.31432400000003 +1175,Binary classification,sklearn SGDClassifier,Phishing,0.8902127659574468,0.876555023923445,0.007016181945800781,148.52290000000002 +1200,Binary classification,sklearn SGDClassifier,Phishing,0.8908333333333334,0.8769953051643193,0.007016181945800781,154.887447 +1225,Binary classification,sklearn SGDClassifier,Phishing,0.8914285714285715,0.8776448942042319,0.007016181945800781,161.410896 +1250,Binary classification,sklearn SGDClassifier,Phishing,0.8896,0.8761220825852785,0.007016181945800781,167.984219 +1903,Binary classification,sklearn SGDClassifier,SMTP,0.9968470835522859,0.0,0.0057430267333984375,9.012274 +3806,Binary classification,sklearn SGDClassifier,SMTP,0.9984235417761429,0.0,0.0057430267333984375,26.992092 +5709,Binary classification,sklearn SGDClassifier,SMTP,0.998949027850762,0.0,0.0057430267333984375,53.749217 +7612,Binary classification,sklearn SGDClassifier,SMTP,0.9992117708880714,0.0,0.0057430267333984375,89.545782 +9515,Binary classification,sklearn SGDClassifier,SMTP,0.9993694167104572,0.0,0.0057430267333984375,133.365466 +11418,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.0,0.0057430267333984375,185.06742500000001 +13321,Binary classification,sklearn SGDClassifier,SMTP,0.9995495833646123,0.0,0.0057430267333984375,243.739666 +15224,Binary classification,sklearn SGDClassifier,SMTP,0.9992774566473989,0.5217391304347826,0.0057430267333984375,308.57406100000003 +17127,Binary classification,sklearn SGDClassifier,SMTP,0.9993577392421323,0.5925925925925927,0.0057430267333984375,378.971453 +19030,Binary classification,sklearn SGDClassifier,SMTP,0.999421965317919,0.5925925925925927,0.0057430267333984375,454.798992 +20933,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.5925925925925927,0.0057430267333984375,535.937031 +22836,Binary classification,sklearn SGDClassifier,SMTP,0.9995183044315993,0.5925925925925927,0.0057430267333984375,622.51799 +24739,Binary classification,sklearn SGDClassifier,SMTP,0.9995553579368608,0.5925925925925927,0.0057430267333984375,714.221122 +26642,Binary classification,sklearn SGDClassifier,SMTP,0.9995495833646123,0.5714285714285714,0.0057430267333984375,811.098386 +28545,Binary classification,sklearn SGDClassifier,SMTP,0.9995796111403048,0.5714285714285714,0.0057430267333984375,912.878884 +30448,Binary classification,sklearn SGDClassifier,SMTP,0.9996058854440357,0.5714285714285714,0.0057430267333984375,1019.269091 +32351,Binary classification,sklearn SGDClassifier,SMTP,0.9996290686532101,0.5714285714285714,0.0057430267333984375,1129.962426 +34254,Binary classification,sklearn SGDClassifier,SMTP,0.999649675950254,0.5714285714285714,0.0057430267333984375,1244.872652 +36157,Binary classification,sklearn SGDClassifier,SMTP,0.9996681140581354,0.5714285714285714,0.0057430267333984375,1363.9176400000001 +38060,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.5714285714285714,0.0057430267333984375,1487.072194 +39963,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.5714285714285714,0.0057430267333984375,1614.171257 +41866,Binary classification,sklearn SGDClassifier,SMTP,0.999713371232026,0.5714285714285714,0.0057430267333984375,1745.093316 +43769,Binary classification,sklearn SGDClassifier,SMTP,0.9997258333523726,0.5714285714285714,0.0057430267333984375,1880.714485 +45672,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.5714285714285714,0.0057430267333984375,2020.289668 +47575,Binary classification,sklearn SGDClassifier,SMTP,0.9997477666841829,0.5714285714285714,0.0057430267333984375,2163.67936 +49478,Binary classification,sklearn SGDClassifier,SMTP,0.9997574679655604,0.5714285714285714,0.0057430267333984375,2310.817497 +51381,Binary classification,sklearn SGDClassifier,SMTP,0.9997275257390864,0.5333333333333333,0.0057430267333984375,2461.472155 +53284,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.5333333333333333,0.0057430267333984375,2615.677493 +55187,Binary classification,sklearn SGDClassifier,SMTP,0.9997463170674253,0.5333333333333333,0.0057430267333984375,2773.449537 +57090,Binary classification,sklearn SGDClassifier,SMTP,0.999597127342792,0.41025641025641024,0.0057430267333984375,2934.793173 +58993,Binary classification,sklearn SGDClassifier,SMTP,0.9996101232349601,0.41025641025641024,0.0057430267333984375,3099.6490870000002 +60896,Binary classification,sklearn SGDClassifier,SMTP,0.9996223068838676,0.41025641025641024,0.0057430267333984375,3268.0999800000004 +62799,Binary classification,sklearn SGDClassifier,SMTP,0.9996019044889249,0.3902439024390244,0.0057430267333984375,3440.0722140000003 +64702,Binary classification,sklearn SGDClassifier,SMTP,0.9996136131804272,0.3902439024390244,0.0057430267333984375,3615.5032180000003 +66605,Binary classification,sklearn SGDClassifier,SMTP,0.9996246528038436,0.3902439024390244,0.0057430267333984375,3794.4378890000003 +68508,Binary classification,sklearn SGDClassifier,SMTP,0.9996058854440357,0.37209302325581395,0.0057430267333984375,3977.0945010000005 +70411,Binary classification,sklearn SGDClassifier,SMTP,0.9996165371887915,0.37209302325581395,0.0057430267333984375,4163.174128000001 +72314,Binary classification,sklearn SGDClassifier,SMTP,0.9996266283154023,0.37209302325581395,0.0057430267333984375,4352.606393000001 +74217,Binary classification,sklearn SGDClassifier,SMTP,0.9996362019483407,0.37209302325581395,0.0057430267333984375,4545.416162000001 +76120,Binary classification,sklearn SGDClassifier,SMTP,0.9996452968996321,0.37209302325581395,0.0057430267333984375,4741.562007000001 +78023,Binary classification,sklearn SGDClassifier,SMTP,0.999653948194763,0.37209302325581395,0.0057430267333984375,4941.079189000001 +79926,Binary classification,sklearn SGDClassifier,SMTP,0.9996621875234591,0.37209302325581395,0.0057430267333984375,5143.951781000001 +81829,Binary classification,sklearn SGDClassifier,SMTP,0.9996700436275648,0.37209302325581395,0.0057430267333984375,5350.231536 +83732,Binary classification,sklearn SGDClassifier,SMTP,0.9996775426360293,0.37209302325581395,0.0057430267333984375,5559.929647 +85635,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.37209302325581395,0.0057430267333984375,5773.003953 +87538,Binary classification,sklearn SGDClassifier,SMTP,0.9996915625214192,0.37209302325581395,0.0057430267333984375,5989.467769000001 +89441,Binary classification,sklearn SGDClassifier,SMTP,0.9996869444661844,0.36363636363636365,0.0057430267333984375,6209.264779000001 +91344,Binary classification,sklearn SGDClassifier,SMTP,0.9996934664564723,0.36363636363636365,0.0057430267333984375,6432.452666000001 +93247,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.36363636363636365,0.0057430267333984375,6658.918178000001 +95150,Binary classification,sklearn SGDClassifier,SMTP,0.9997057277982133,0.36363636363636365,0.0057430267333984375,6888.546542000001 +95156,Binary classification,sklearn SGDClassifier,SMTP,0.9997057463533566,0.36363636363636365,0.0057430267333984375,7118.179378000001 +106,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5,0.0,0.0006465911865234375,0.16057 +212,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5283018867924528,0.0,0.0006465911865234375,0.37729 +318,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5314465408805031,0.0,0.0006465911865234375,0.7064710000000001 +424,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5400943396226415,0.0,0.0006465911865234375,1.0774430000000002 +530,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5547169811320755,0.0,0.0006465911865234375,1.4923790000000001 +636,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5550314465408805,0.0,0.0006465911865234375,1.9966470000000003 +742,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5660377358490566,0.0,0.0006465911865234375,2.539797 +848,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5636792452830188,0.0,0.0006465911865234375,3.1757850000000003 +954,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5649895178197065,0.0,0.0006465911865234375,3.8551140000000004 +1060,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5707547169811321,0.0,0.0006465911865234375,4.635951 +1166,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5686106346483705,0.0,0.0006465911865234375,5.458947 +1272,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5644654088050315,0.0,0.0006465911865234375,6.34328 +1378,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5682148040638607,0.0,0.0006465911865234375,7.308669 +1484,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5680592991913747,0.0,0.0006465911865234375,8.359952 +1590,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5679245283018868,0.0,0.0006465911865234375,9.451883 +1696,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5683962264150944,0.0,0.0006465911865234375,10.590847 +1802,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5643729189789123,0.0,0.0006465911865234375,11.83715 +1908,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.560272536687631,0.0,0.0006465911865234375,13.126961999999999 +2014,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5551142005958292,0.0,0.0006465911865234375,14.497203999999998 +2120,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,15.938437999999998 +2226,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5512129380053908,0.0,0.0006465911865234375,17.424999 +2332,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5506003430531733,0.0,0.0006465911865234375,19.022886 +2438,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.551681706316653,0.0,0.0006465911865234375,20.666828 +2544,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487421383647799,0.0,0.0006465911865234375,22.355415999999998 +2650,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5467924528301886,0.0,0.0006465911865234375,24.051772 +2756,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5471698113207547,0.0,0.0006465911865234375,25.858309 +2862,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5489168413696716,0.0,0.0006465911865234375,27.751458999999997 +2968,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505390835579514,0.0,0.0006465911865234375,29.665551999999998 +3074,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487963565387117,0.0,0.0006465911865234375,31.686176 +3180,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,33.740652 +3286,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5517346317711503,0.0,0.0006465911865234375,35.89104 +3392,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5498231132075472,0.0,0.0006465911865234375,38.079414 +3498,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514579759862779,0.0,0.0006465911865234375,40.353903 +3604,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5535516093229744,0.0,0.0006465911865234375,42.668922 +3710,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5522911051212938,0.0,0.0006465911865234375,45.086801 +3816,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5516247379454927,0.0,0.0006465911865234375,47.540759 +3922,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5525242223355431,0.0,0.0006465911865234375,50.094246 +4028,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5528798411122146,0.0,0.0006465911865234375,52.697210999999996 +4134,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5529753265602322,0.0,0.0006465911865234375,55.369586999999996 +4240,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5523584905660377,0.0,0.0006465911865234375,58.109435999999995 +4346,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5526921306948919,0.0,0.0006465911865234375,60.894093 +4452,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5530098831985625,0.0,0.0006465911865234375,63.717346 +4558,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5508995173321632,0.0,0.0006465911865234375,66.643891 +4664,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5497427101200686,0.0,0.0006465911865234375,69.6601 +4770,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505241090146751,0.0,0.0006465911865234375,72.725555 +4876,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5518867924528302,0.0,0.0006465911865234375,75.798736 +4982,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509835407466881,0.0,0.0006465911865234375,78.970205 +5088,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5511006289308176,0.0,0.0006465911865234375,82.150165 +5194,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514054678475163,0.0,0.0006465911865234375,85.416127 +5300,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5513207547169812,0.0,0.0006465911865234375,88.72481 +906,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6799116997792495,0.5482866043613708,0.0006465911865234375,0.820242 +1812,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7190949227373068,0.4904904904904904,0.0006465911865234375,2.329863 +2718,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.43243243243243246,0.0006465911865234375,4.585071 +3624,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7047461368653422,0.4478844169246646,0.0006465911865234375,7.424633 +4530,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7024282560706402,0.4118673647469459,0.0006465911865234375,10.992865 +5436,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7041942604856513,0.4165457184325108,0.0006465911865234375,15.263433 +6342,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.40485829959514175,0.0006465911865234375,20.287067999999998 +7248,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695364238410596,0.3953997809419496,0.0006465911865234375,26.004013999999998 +8154,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6873926907039489,0.4084474355999072,0.0006465911865234375,32.433811999999996 +9060,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6864238410596026,0.42408270829110073,0.0006465911865234375,39.59982599999999 +9966,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.687537627934979,0.4433321415802646,0.0006465911865234375,47.447314999999996 +10872,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6938925680647535,0.4717460317460317,0.0006465911865234375,55.964904999999995 +11778,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6932416369502462,0.47155185022670765,0.0006465911865234375,65.217817 +12684,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944970040996531,0.47557179591284343,0.0006465911865234375,75.258293 +13590,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6942604856512141,0.48429936701005344,0.0006465911865234375,85.993354 +14496,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6935016556291391,0.48606130711393875,0.0006465911865234375,97.389046 +15402,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6929619529931178,0.48095708484249805,0.0006465911865234375,109.49806 +16308,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6904586705911209,0.47130289065772935,0.0006465911865234375,122.273049 +17214,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6921691646334379,0.4645852278468223,0.0006465911865234375,135.723897 +18120,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.694205298013245,0.46859115757168884,0.0006465911865234375,150.008391 +19026,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6967307894460212,0.467515688445921,0.0006465911865234375,164.94785199999998 +19932,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6958157736303432,0.4737435986459509,0.0006465911865234375,180.578389 +20838,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.4696604963891426,0.0006465911865234375,196.972492 +21744,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6968359087564385,0.4670978172999191,0.0006465911865234375,214.03759399999998 +22650,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6977041942604857,0.4643667370726746,0.0006465911865234375,231.758696 +23556,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952368823229751,0.4573285962657797,0.0006465911865234375,250.24763199999998 +24462,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6978170223203336,0.4597281099254495,0.0006465911865234375,269.425119 +25368,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6976505834121728,0.46122506322000567,0.0006465911865234375,289.286378 +26274,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6983329527289336,0.4614757439869547,0.0006465911865234375,309.833587 +27180,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6959896983075791,0.4576304561864129,0.0006465911865234375,331.075152 +28086,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695649077832372,0.4559572301425662,0.0006465911865234375,353.085242 +28992,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952262693156733,0.4515207945375543,0.0006465911865234375,375.713328 +29898,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6939260151180681,0.4465678863017841,0.0006465911865234375,399.011341 +30804,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6941630957018569,0.44330201500915917,0.0006465911865234375,423.007885 +31710,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6917376222011984,0.4368266405484819,0.0006465911865234375,447.82068599999997 +32616,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6893549178317391,0.4316805025802109,0.0006465911865234375,473.25918699999994 +33522,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.688353916830738,0.42909448603748845,0.0006465911865234375,499.4094079999999 +34428,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6863599395840595,0.4245363461948412,0.0006465911865234375,526.1781749999999 +35334,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6869304352748061,0.4212013394725827,0.0006465911865234375,553.6489789999999 +36240,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6911147902869758,0.4267718148299877,0.0006465911865234375,581.8684239999999 +37146,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6919722177354224,0.42698317307692313,0.0006465911865234375,610.7708879999999 +38052,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944181646168401,0.43117111828588206,0.0006465911865234375,640.2659799999999 +38958,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6937727809435803,0.43082061068702293,0.0006465911865234375,670.4759659999999 +39864,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6930814770218744,0.4344288818009522,0.0006465911865234375,701.3646249999998 +40770,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6924208977189109,0.4391771019677997,0.0006465911865234375,733.0001779999998 +41676,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.44722270288977334,0.0006465911865234375,765.3741459999998 +42582,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6956225635244939,0.45507672903090185,0.0006465911865234375,798.4329459999998 +43488,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6962150478292862,0.4576097220511558,0.0006465911865234375,832.1587539999998 +44394,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6963103122043519,0.45575649927337314,0.0006465911865234375,866.6068249999998 +45300,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.697439293598234,0.4596278189560006,0.0006465911865234375,901.8081399999999 +45312,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6974752824858758,0.45959157927935035,0.0006465911865234375,937.0113409999999 +25,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.52,0.33333333333333337,0.0006465911865234375,0.00395 +50,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.56,0.21428571428571427,0.0006465911865234375,0.07842199999999999 +75,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.5866666666666667,0.3404255319148936,0.0006465911865234375,0.15624899999999997 +100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6,0.375,0.0006465911865234375,0.23731799999999997 +125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.64,0.4705882352941176,0.0006465911865234375,0.41813199999999995 +150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.62,0.44660194174757284,0.0006465911865234375,0.6021909999999999 +175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6342857142857142,0.41818181818181815,0.0006465911865234375,0.7890869999999999 +200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.63,0.4126984126984127,0.0006465911865234375,1.0120959999999999 +225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6488888888888888,0.4316546762589928,0.0006465911865234375,1.2378889999999998 +250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.648,0.4358974358974359,0.0006465911865234375,1.4692909999999997 +275,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6618181818181819,0.4561403508771929,0.0006465911865234375,1.7531039999999996 +300,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6733333333333333,0.46153846153846156,0.0006465911865234375,2.0408739999999996 +325,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.683076923076923,0.46632124352331616,0.0006465911865234375,2.33165 +350,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6942857142857143,0.47804878048780486,0.0006465911865234375,2.715045 +375,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7013333333333334,0.4909090909090909,0.0006465911865234375,3.1015189999999997 +400,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.705,0.4913793103448276,0.0006465911865234375,3.4910129999999997 +425,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7105882352941176,0.4896265560165975,0.0006465911865234375,3.9231499999999997 +450,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7222222222222222,0.5098039215686275,0.0006465911865234375,4.358171 +475,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7157894736842105,0.5054945054945055,0.0006465911865234375,4.7960329999999995 +500,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.718,0.5252525252525252,0.0006465911865234375,5.248043999999999 +525,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7257142857142858,0.5294117647058824,0.0006465911865234375,5.702586999999999 +550,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7218181818181818,0.5233644859813085,0.0006465911865234375,6.1599829999999995 +575,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7217391304347827,0.5209580838323353,0.0006465911865234375,6.620335 +600,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7283333333333334,0.5275362318840581,0.0006465911865234375,7.1135079999999995 +625,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7376,0.5340909090909091,0.0006465911865234375,7.613357 +650,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7369230769230769,0.5415549597855228,0.0006465911865234375,8.116107 +675,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7333333333333333,0.5477386934673367,0.0006465911865234375,8.713363 +700,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74,0.5560975609756097,0.0006465911865234375,9.313647 +725,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.743448275862069,0.5753424657534246,0.0006465911865234375,9.917033 +750,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7453333333333333,0.5820568927789934,0.0006465911865234375,10.613639 +775,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7470967741935484,0.5847457627118644,0.0006465911865234375,11.313362999999999 +800,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74625,0.5915492957746479,0.0006465911865234375,12.015877 +825,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7490909090909091,0.602687140115163,0.0006465911865234375,12.766805 +850,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7541176470588236,0.6122448979591837,0.0006465911865234375,13.520246 +875,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7554285714285714,0.6123188405797102,0.0006465911865234375,14.2887 +900,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7566666666666667,0.6123893805309735,0.0006465911865234375,15.059985000000001 +925,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.76,0.6237288135593221,0.0006465911865234375,15.877357000000002 +950,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7589473684210526,0.6288492706645057,0.0006465911865234375,16.697524 +975,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7610256410256411,0.631911532385466,0.0006465911865234375,17.562951 +1000,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.761,0.6328725038402457,0.0006465911865234375,18.43148 +1025,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7609756097560976,0.635958395245171,0.0006465911865234375,19.325245 +1050,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7638095238095238,0.6436781609195402,0.0006465911865234375,20.222005 +1075,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7665116279069767,0.651872399445215,0.0006465911865234375,21.121639 +1100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.77,0.6594885598923284,0.0006465911865234375,22.071389 +1125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.768,0.6597131681877444,0.0006465911865234375,23.024294 +1150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7695652173913043,0.6615581098339719,0.0006465911865234375,23.980116000000002 +1175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7702127659574468,0.6633416458852868,0.0006465911865234375,24.939110000000003 +1200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7741666666666667,0.6691086691086692,0.0006465911865234375,25.901192 +1225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7771428571428571,0.6746126340882003,0.0006465911865234375,26.865956 +1250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7736,0.6697782963827306,0.0006465911865234375,27.833412 +1903,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,1.287853 +3806,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,3.7805989999999996 +5709,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,7.576109 +7612,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,12.534125 +9515,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,18.771881 +11418,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,26.322128 +13321,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,35.214625 +15224,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9992774566473989,0.0,0.0006465911865234375,45.441497999999996 +17127,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999299351900508,0.14285714285714288,0.0006465911865234375,56.927386 +19030,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9993694167104572,0.14285714285714288,0.0006465911865234375,69.74153799999999 +20933,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9994267424640519,0.14285714285714288,0.0006465911865234375,83.765543 +22836,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999474513925381,0.14285714285714288,0.0006465911865234375,99.06549199999999 +24739,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999514935931121,0.14285714285714288,0.0006465911865234375,115.581943 +26642,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995120486449967,0.13333333333333333,0.0006465911865234375,133.361343 +28545,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995445787353302,0.13333333333333333,0.0006465911865234375,152.36548100000002 +30448,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995730425643721,0.13333333333333333,0.0006465911865234375,172.57866 +32351,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,193.96982500000001 +34254,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996204822794418,0.13333333333333333,0.0006465911865234375,216.600052 +36157,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996404568963133,0.13333333333333333,0.0006465911865234375,240.511883 +38060,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996584340514977,0.13333333333333333,0.0006465911865234375,265.709607 +39963,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996746990966644,0.13333333333333333,0.0006465911865234375,292.142637 +41866,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996894855013615,0.13333333333333333,0.0006465911865234375,319.87834699999996 +43769,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999702986131737,0.13333333333333333,0.0006465911865234375,348.79444399999994 +45672,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997153617095814,0.13333333333333333,0.0006465911865234375,378.9697039999999 +47575,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997267472411981,0.13333333333333333,0.0006465911865234375,410.4053809999999 +49478,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997372569626904,0.13333333333333333,0.0006465911865234375,443.05761399999994 +51381,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997080632918783,0.11764705882352941,0.0006465911865234375,477.02532299999996 +53284,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997184896028827,0.11764705882352941,0.0006465911865234375,512.247669 +55187,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997281968579557,0.11764705882352941,0.0006465911865234375,548.612896 +57090,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995796111403048,0.14285714285714285,0.0006465911865234375,586.233337 +58993,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995931720712626,0.14285714285714285,0.0006465911865234375,625.057298 +60896,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996058854440357,0.14285714285714285,0.0006465911865234375,665.0820319999999 +62799,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999585980668482,0.13333333333333333,0.0006465911865234375,706.1803269999999 +64702,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,748.3509649999999 +66605,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996096389159973,0.13333333333333333,0.0006465911865234375,791.6670189999999 +68508,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995912886086297,0.125,0.0006465911865234375,836.0877649999999 +70411,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996023348624504,0.125,0.0006465911865234375,881.7116259999999 +72314,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996127997344912,0.125,0.0006465911865234375,928.538605 +74217,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996227279464274,0.125,0.0006465911865234375,976.4092069999999 +76120,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996321597477666,0.125,0.0006465911865234375,1025.3623619999998 +78023,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996411314612358,0.125,0.0006465911865234375,1075.4142359999998 +79926,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999649675950254,0.125,0.0006465911865234375,1126.58116 +81829,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996578230211783,0.125,0.0006465911865234375,1178.778759 +83732,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999665599770697,0.125,0.0006465911865234375,1231.992839 +85635,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996730308869037,0.125,0.0006465911865234375,1286.303482 +87538,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996801389111014,0.125,0.0006465911865234375,1341.617815 +89441,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996757639114053,0.1212121212121212,0.0006465911865234375,1397.839199 +91344,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996825188299177,0.1212121212121212,0.0006465911865234375,1455.075933 +93247,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996889980374704,0.1212121212121212,0.0006465911865234375,1513.261041 +95150,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999695218076721,0.1212121212121212,0.0006465911865234375,1572.312523 +95156,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996952372945479,0.1212121212121212,0.0006465911865234375,1631.3703540000001 +106,Binary classification,Naive Bayes,Bananas,0.5333333333333333,0.46153846153846156,0.014024734497070312,0.089081 +212,Binary classification,Naive Bayes,Bananas,0.5592417061611374,0.5026737967914437,0.014024734497070312,0.244558 +318,Binary classification,Naive Bayes,Bananas,0.555205047318612,0.5154639175257733,0.014024734497070312,0.45393700000000003 +424,Binary classification,Naive Bayes,Bananas,0.5626477541371159,0.5066666666666667,0.014024734497070312,0.76271 +530,Binary classification,Naive Bayes,Bananas,0.5689981096408318,0.48181818181818187,0.014024734497070312,1.109688 +636,Binary classification,Naive Bayes,Bananas,0.5716535433070866,0.4645669291338582,0.014024734497070312,1.6198730000000001 +742,Binary classification,Naive Bayes,Bananas,0.5870445344129555,0.4555160142348755,0.014024734497070312,2.197835 +848,Binary classification,Naive Bayes,Bananas,0.5962219598583235,0.4554140127388535,0.014024734497070312,2.834188 +954,Binary classification,Naive Bayes,Bananas,0.6002098635886673,0.4454148471615721,0.014024734497070312,3.5547570000000004 +1060,Binary classification,Naive Bayes,Bananas,0.6090651558073654,0.44054054054054054,0.014024734497070312,4.339157 +1166,Binary classification,Naive Bayes,Bananas,0.6068669527896996,0.42606516290726815,0.014024734497070312,5.220598 +1272,Binary classification,Naive Bayes,Bananas,0.6136900078678206,0.433679354094579,0.014024734497070312,6.1398969999999995 +1378,Binary classification,Naive Bayes,Bananas,0.6143790849673203,0.419672131147541,0.014024734497070312,7.157157999999999 +1484,Binary classification,Naive Bayes,Bananas,0.6142953472690492,0.4127310061601643,0.014024734497070312,8.301379999999998 +1590,Binary classification,Naive Bayes,Bananas,0.6135934550031467,0.40618955512572535,0.014024734497070312,9.487101 +1696,Binary classification,Naive Bayes,Bananas,0.6141592920353982,0.4010989010989011,0.014024734497070312,10.730798 +1802,Binary classification,Naive Bayes,Bananas,0.614658523042754,0.40378006872852235,0.014024734497070312,12.063669 +1908,Binary classification,Naive Bayes,Bananas,0.6151022548505506,0.4080645161290322,0.014024734497070312,13.448557000000001 +2014,Binary classification,Naive Bayes,Bananas,0.6100347739692003,0.40485216072782415,0.014024734497070312,14.939018 +2120,Binary classification,Naive Bayes,Bananas,0.608305804624823,0.4071428571428571,0.014024734497070312,16.439687 +2226,Binary classification,Naive Bayes,Bananas,0.6089887640449438,0.4089673913043478,0.014024734497070312,18.077419 +2332,Binary classification,Naive Bayes,Bananas,0.6096096096096096,0.4098573281452659,0.014024734497070312,19.752885 +2438,Binary classification,Naive Bayes,Bananas,0.6101764464505539,0.40846824408468246,0.014024734497070312,21.52049 +2544,Binary classification,Naive Bayes,Bananas,0.6114825009830909,0.41538461538461535,0.014024734497070312,23.348549 +2650,Binary classification,Naive Bayes,Bananas,0.6100415251038127,0.41273450824332003,0.014024734497070312,25.279207 +2756,Binary classification,Naive Bayes,Bananas,0.6076225045372051,0.4070213933077345,0.014024734497070312,27.31295 +2862,Binary classification,Naive Bayes,Bananas,0.6085284865431667,0.4092827004219409,0.014024734497070312,29.37924 +2968,Binary classification,Naive Bayes,Bananas,0.6083586113919784,0.4065372829417773,0.014024734497070312,31.545651 +3074,Binary classification,Naive Bayes,Bananas,0.60624796615685,0.40628066732090284,0.014024734497070312,33.733230999999996 +3180,Binary classification,Naive Bayes,Bananas,0.6071091538219566,0.4077761972498815,0.014024734497070312,36.007059 +3286,Binary classification,Naive Bayes,Bananas,0.6063926940639269,0.4049700874367234,0.014024734497070312,38.312687 +3392,Binary classification,Naive Bayes,Bananas,0.6048363314656443,0.40602836879432624,0.014024734497070312,40.720333999999994 +3498,Binary classification,Naive Bayes,Bananas,0.6065198741778668,0.40535868625756266,0.014024734497070312,43.213055999999995 +3604,Binary classification,Naive Bayes,Bananas,0.6086594504579517,0.40905280804694044,0.014024734497070312,45.745915999999994 +3710,Binary classification,Naive Bayes,Bananas,0.6085198166621731,0.4078303425774878,0.014024734497070312,48.41046399999999 +3816,Binary classification,Naive Bayes,Bananas,0.6070773263433814,0.40492258832870187,0.014024734497070312,51.18183799999999 +3922,Binary classification,Naive Bayes,Bananas,0.6067329762815609,0.4027885360185902,0.014024734497070312,54.01538599999999 +4028,Binary classification,Naive Bayes,Bananas,0.6088899925502855,0.405436013590034,0.014024734497070312,56.94241999999999 +4134,Binary classification,Naive Bayes,Bananas,0.6106944108395839,0.40780272359219727,0.014024734497070312,59.88324999999999 +4240,Binary classification,Naive Bayes,Bananas,0.611936777541873,0.41186986056489094,0.014024734497070312,62.92792699999999 +4346,Binary classification,Naive Bayes,Bananas,0.6131185270425776,0.4128536500174642,0.014024734497070312,66.00451999999999 +4452,Binary classification,Naive Bayes,Bananas,0.6137946528869916,0.413510747185261,0.014024734497070312,69.16846599999998 +4558,Binary classification,Naive Bayes,Bananas,0.6122448979591837,0.4115884115884116,0.014024734497070312,72.34300699999999 +4664,Binary classification,Naive Bayes,Bananas,0.6126956894702981,0.41249186727391024,0.014024734497070312,75.66876099999999 +4770,Binary classification,Naive Bayes,Bananas,0.6143845669951772,0.41302266198531756,0.014024734497070312,79.08375899999999 +4876,Binary classification,Naive Bayes,Bananas,0.6153846153846154,0.4131455399061033,0.014024734497070312,82.54849799999998 +4982,Binary classification,Naive Bayes,Bananas,0.6163420999799237,0.41684467500762895,0.014024734497070312,86.09394899999998 +5088,Binary classification,Naive Bayes,Bananas,0.6150973068606251,0.41412327947336924,0.014024734497070312,89.70897599999998 +5194,Binary classification,Naive Bayes,Bananas,0.6146735990756788,0.4133685136323659,0.014024734497070312,93.40231399999998 +5300,Binary classification,Naive Bayes,Bananas,0.6152104170598226,0.4139120436907157,0.014024734497070312,97.15397799999998 +906,Binary classification,Naive Bayes,Elec2,0.8187845303867404,0.8284518828451883,0.05103778839111328,0.90253 +1812,Binary classification,Naive Bayes,Elec2,0.8023191606847045,0.7475317348377998,0.05103778839111328,2.6687279999999998 +2718,Binary classification,Naive Bayes,Elec2,0.784688995215311,0.706177800100452,0.05103778839111328,5.38565 +3624,Binary classification,Naive Bayes,Elec2,0.8032017664918576,0.7356321839080461,0.05103778839111328,8.965856 +4530,Binary classification,Naive Bayes,Elec2,0.7979686465003312,0.7073872721458268,0.05103778839111328,13.460125000000001 +5436,Binary classification,Naive Bayes,Elec2,0.7937442502299908,0.6972724817715366,0.05103778839111328,18.947959 +6342,Binary classification,Naive Bayes,Elec2,0.7982967986122063,0.7065840789171829,0.05103778839111328,25.368016 +7248,Binary classification,Naive Bayes,Elec2,0.790396025941769,0.6875128574367414,0.05103778839111328,32.74734 +8154,Binary classification,Naive Bayes,Elec2,0.7841285416411137,0.6888260254596887,0.05103778839111328,41.092102 +9060,Binary classification,Naive Bayes,Elec2,0.7897118887294403,0.7086710506193606,0.05103778839111328,50.330248999999995 +9966,Binary classification,Naive Bayes,Elec2,0.793176116407426,0.7240594457089301,0.05103778839111328,60.487047999999994 +10872,Binary classification,Naive Bayes,Elec2,0.7960629196946003,0.7361656551231703,0.05103778839111328,71.57583 +11778,Binary classification,Naive Bayes,Elec2,0.792137216608644,0.7295027624309391,0.05103778839111328,83.57396899999999 +12684,Binary classification,Naive Bayes,Elec2,0.7820704880548766,0.7260111022997621,0.05103778839111328,96.50585899999999 +13590,Binary classification,Naive Bayes,Elec2,0.7858562072264331,0.7383564107174968,0.05103778839111328,110.37856099999999 +14496,Binary classification,Naive Bayes,Elec2,0.7866850638151086,0.7435727317963178,0.05103778839111328,125.16282799999999 +15402,Binary classification,Naive Bayes,Elec2,0.785728199467567,0.738593155893536,0.05103778839111328,140.864825 +16308,Binary classification,Naive Bayes,Elec2,0.7806463481940271,0.7274666666666666,0.05103778839111328,157.49451299999998 +17214,Binary classification,Naive Bayes,Elec2,0.7788880497298554,0.7181158346911569,0.05103778839111328,175.04662 +18120,Binary classification,Naive Bayes,Elec2,0.7728903361112645,0.7138983522213725,0.05103778839111328,193.53438899999998 +19026,Binary classification,Naive Bayes,Elec2,0.7701445466491459,0.7094931242941608,0.05103778839111328,212.92156899999998 +19932,Binary classification,Naive Bayes,Elec2,0.7628317696051378,0.702236220472441,0.05103778839111328,233.287368 +20838,Binary classification,Naive Bayes,Elec2,0.7537553390603254,0.6903626817934946,0.05103778839111328,254.638983 +21744,Binary classification,Naive Bayes,Elec2,0.7508163546888654,0.6836389115964032,0.05103778839111328,276.932282 +22650,Binary classification,Naive Bayes,Elec2,0.7509823833281822,0.6798001589644601,0.05103778839111328,300.18967299999997 +23556,Binary classification,Naive Bayes,Elec2,0.7457015495648482,0.668217569513681,0.05103778839111328,324.33763999999996 +24462,Binary classification,Naive Bayes,Elec2,0.7466170638976329,0.665839982747466,0.05103778839111328,349.45202499999994 +25368,Binary classification,Naive Bayes,Elec2,0.7447865336854969,0.6611180904522613,0.05103778839111328,375.51598899999993 +26274,Binary classification,Naive Bayes,Elec2,0.7448711605069843,0.6581322996888865,0.05103778839111328,402.57675099999994 +27180,Binary classification,Naive Bayes,Elec2,0.741123661650539,0.650402464473815,0.05103778839111328,430.58128099999993 +28086,Binary classification,Naive Bayes,Elec2,0.7390065871461634,0.6440019426906265,0.05103778839111328,459.5402439999999 +28992,Binary classification,Naive Bayes,Elec2,0.7358145631402849,0.6343280019097637,0.05103778839111328,489.4018749999999 +29898,Binary classification,Naive Bayes,Elec2,0.7320466936481921,0.6243023964732918,0.05103778839111328,520.249024 +30804,Binary classification,Naive Bayes,Elec2,0.7297990455475116,0.6158319870759289,0.05103778839111328,552.052505 +31710,Binary classification,Naive Bayes,Elec2,0.7256930209088902,0.6059617649723658,0.05103778839111328,584.838155 +32616,Binary classification,Naive Bayes,Elec2,0.7215391690939752,0.596427301813011,0.05103778839111328,618.5052350000001 +33522,Binary classification,Naive Bayes,Elec2,0.7176695205990274,0.5867248908296943,0.05103778839111328,653.1230730000001 +34428,Binary classification,Naive Bayes,Elec2,0.7142359194818021,0.5779493779493778,0.05103778839111328,688.6949970000001 +35334,Binary classification,Naive Bayes,Elec2,0.7138369229898395,0.5724554949469323,0.05103778839111328,725.19325 +36240,Binary classification,Naive Bayes,Elec2,0.7174866856149452,0.5752924583091347,0.05103778839111328,762.649856 +37146,Binary classification,Naive Bayes,Elec2,0.7169740207295733,0.5716148486206756,0.05103778839111328,801.028112 +38052,Binary classification,Naive Bayes,Elec2,0.7183516858952459,0.573859795618116,0.05103778839111328,840.263393 +38958,Binary classification,Naive Bayes,Elec2,0.7206407064198989,0.5799529121154812,0.05103778839111328,880.2889349999999 +39864,Binary classification,Naive Bayes,Elec2,0.7217720693374808,0.5866964784795975,0.05103778839111328,921.221106 +40770,Binary classification,Naive Bayes,Elec2,0.7228776766660944,0.5923065819861432,0.05103778839111328,962.947955 +41676,Binary classification,Naive Bayes,Elec2,0.724127174565087,0.5973170817134251,0.05103778839111328,1005.542302 +42582,Binary classification,Naive Bayes,Elec2,0.7260280406754186,0.6013259517462921,0.05103778839111328,1049.006993 +43488,Binary classification,Naive Bayes,Elec2,0.7277117299422816,0.6045222270465248,0.05103778839111328,1093.33419 +44394,Binary classification,Naive Bayes,Elec2,0.7273894532921857,0.6015933631814591,0.05103778839111328,1138.520645 +45300,Binary classification,Naive Bayes,Elec2,0.7287136581381487,0.6038234630387828,0.05103778839111328,1184.586595 +45312,Binary classification,Naive Bayes,Elec2,0.7287413652314008,0.6037845330582509,0.05103778839111328,1230.65543 +25,Binary classification,Naive Bayes,Phishing,0.5833333333333334,0.7058823529411764,0.05722999572753906,0.005899 +50,Binary classification,Naive Bayes,Phishing,0.7346938775510204,0.7636363636363637,0.05722999572753906,0.034194 +75,Binary classification,Naive Bayes,Phishing,0.7837837837837838,0.8048780487804877,0.05722999572753906,0.070237 +100,Binary classification,Naive Bayes,Phishing,0.8080808080808081,0.819047619047619,0.05722999572753906,0.11917699999999999 +125,Binary classification,Naive Bayes,Phishing,0.8145161290322581,0.8217054263565893,0.05722999572753906,0.172458 +150,Binary classification,Naive Bayes,Phishing,0.8187919463087249,0.830188679245283,0.05722999572753906,0.294112 +175,Binary classification,Naive Bayes,Phishing,0.8333333333333334,0.8323699421965318,0.05722999572753906,0.432822 +200,Binary classification,Naive Bayes,Phishing,0.8341708542713567,0.83248730964467,0.05722999572753906,0.620751 +225,Binary classification,Naive Bayes,Phishing,0.8303571428571429,0.8240740740740741,0.05722999572753906,0.8126760000000001 +250,Binary classification,Naive Bayes,Phishing,0.8313253012048193,0.825,0.05722999572753906,1.097418 +275,Binary classification,Naive Bayes,Phishing,0.8321167883211679,0.8244274809160306,0.05722999572753906,1.3867479999999999 +300,Binary classification,Naive Bayes,Phishing,0.8394648829431438,0.8285714285714285,0.05722999572753906,1.7081089999999999 +325,Binary classification,Naive Bayes,Phishing,0.845679012345679,0.8299319727891157,0.05722999572753906,2.0343679999999997 +350,Binary classification,Naive Bayes,Phishing,0.8510028653295129,0.8322580645161292,0.05722999572753906,2.472974 +375,Binary classification,Naive Bayes,Phishing,0.8529411764705882,0.8318042813455658,0.05722999572753906,2.916035 +400,Binary classification,Naive Bayes,Phishing,0.8546365914786967,0.8313953488372093,0.05722999572753906,3.458949 +425,Binary classification,Naive Bayes,Phishing,0.8561320754716981,0.8291316526610645,0.05722999572753906,4.00711 +450,Binary classification,Naive Bayes,Phishing,0.8596881959910914,0.8310991957104559,0.05722999572753906,4.560221 +475,Binary classification,Naive Bayes,Phishing,0.8565400843881856,0.8291457286432161,0.05722999572753906,5.117465 +500,Binary classification,Naive Bayes,Phishing,0.8577154308617234,0.8337236533957845,0.05722999572753906,5.6788810000000005 +525,Binary classification,Naive Bayes,Phishing,0.8587786259541985,0.8310502283105022,0.05722999572753906,6.3168120000000005 +550,Binary classification,Naive Bayes,Phishing,0.8579234972677595,0.8311688311688311,0.05722999572753906,6.9590250000000005 +575,Binary classification,Naive Bayes,Phishing,0.8606271777003485,0.8340248962655602,0.05722999572753906,7.6702010000000005 +600,Binary classification,Naive Bayes,Phishing,0.8647746243739566,0.8363636363636363,0.05722999572753906,8.386169 +625,Binary classification,Naive Bayes,Phishing,0.8669871794871795,0.8356435643564357,0.05722999572753906,9.138945000000001 +650,Binary classification,Naive Bayes,Phishing,0.8705701078582434,0.8426966292134833,0.05722999572753906,9.901064000000002 +675,Binary classification,Naive Bayes,Phishing,0.870919881305638,0.8465608465608465,0.05722999572753906,10.713223000000001 +700,Binary classification,Naive Bayes,Phishing,0.8755364806866953,0.8502581755593803,0.05722999572753906,11.569231 +725,Binary classification,Naive Bayes,Phishing,0.8784530386740331,0.8562091503267973,0.05722999572753906,12.458796 +750,Binary classification,Naive Bayes,Phishing,0.8798397863818425,0.8584905660377359,0.05722999572753906,13.352328 +775,Binary classification,Naive Bayes,Phishing,0.8798449612403101,0.8580152671755725,0.05722999572753906,14.337352 +800,Binary classification,Naive Bayes,Phishing,0.8798498122653317,0.8596491228070174,0.05722999572753906,15.326948 +825,Binary classification,Naive Bayes,Phishing,0.8798543689320388,0.860759493670886,0.05722999572753906,16.325159 +850,Binary classification,Naive Bayes,Phishing,0.8798586572438163,0.8602739726027396,0.05722999572753906,17.375421 +875,Binary classification,Naive Bayes,Phishing,0.8832951945080092,0.8636363636363635,0.05722999572753906,18.429913 +900,Binary classification,Naive Bayes,Phishing,0.8809788654060067,0.8608582574772432,0.05722999572753906,19.528876999999998 +925,Binary classification,Naive Bayes,Phishing,0.8820346320346321,0.8635794743429286,0.05722999572753906,20.632713999999996 +950,Binary classification,Naive Bayes,Phishing,0.8819810326659642,0.8650602409638554,0.05722999572753906,21.817704999999997 +975,Binary classification,Naive Bayes,Phishing,0.8829568788501027,0.8661971830985915,0.05722999572753906,23.016962999999997 +1000,Binary classification,Naive Bayes,Phishing,0.8808808808808809,0.8643101482326111,0.05722999572753906,24.246232999999997 +1025,Binary classification,Naive Bayes,Phishing,0.880859375,0.8647450110864746,0.05722999572753906,25.480750999999998 +1050,Binary classification,Naive Bayes,Phishing,0.882745471877979,0.8673139158576052,0.05722999572753906,26.819710999999998 +1075,Binary classification,Naive Bayes,Phishing,0.8817504655493482,0.8672936259143157,0.05722999572753906,28.162913999999997 +1100,Binary classification,Naive Bayes,Phishing,0.8835304822565969,0.8693877551020409,0.05722999572753906,29.538843999999997 +1125,Binary classification,Naive Bayes,Phishing,0.8861209964412812,0.8735177865612648,0.05722999572753906,30.932978999999996 +1150,Binary classification,Naive Bayes,Phishing,0.8859878154917319,0.8731848983543079,0.05722999572753906,32.425236999999996 +1175,Binary classification,Naive Bayes,Phishing,0.8850085178875639,0.8717948717948718,0.05722999572753906,33.921729 +1200,Binary classification,Naive Bayes,Phishing,0.8865721434528774,0.8731343283582089,0.05722999572753906,35.452877 +1225,Binary classification,Naive Bayes,Phishing,0.886437908496732,0.8728270814272644,0.05722999572753906,36.988201000000004 +1250,Binary classification,Naive Bayes,Phishing,0.8847077662129704,0.8714285714285714,0.05722999572753906,38.528021 +1903,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,1.286863 +3806,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,3.863138 +5709,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,7.731956 +7612,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,13.024672 +9515,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,19.659339000000003 +11418,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,27.654251000000002 +13321,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,36.976608 +15224,Binary classification,Naive Bayes,SMTP,0.9997372397030808,0.7777777777777778,0.020140647888183594,47.719054 +17127,Binary classification,Naive Bayes,SMTP,0.9997664369963798,0.8181818181818181,0.020140647888183594,59.951688 +19030,Binary classification,Naive Bayes,SMTP,0.9997897945241474,0.8181818181818181,0.020140647888183594,73.68853899999999 +20933,Binary classification,Naive Bayes,SMTP,0.9998089050257978,0.8181818181818181,0.020140647888183594,88.86509 +22836,Binary classification,Naive Bayes,SMTP,0.9998248303043573,0.8181818181818181,0.020140647888183594,105.535247 +24739,Binary classification,Naive Bayes,SMTP,0.9998383054410219,0.8181818181818181,0.020140647888183594,123.661746 +26642,Binary classification,Naive Bayes,SMTP,0.9998498554859052,0.8333333333333333,0.020140647888183594,143.18039199999998 +28545,Binary classification,Naive Bayes,SMTP,0.999859865470852,0.8333333333333333,0.020140647888183594,164.12799199999998 +30448,Binary classification,Naive Bayes,SMTP,0.9998686241665845,0.8333333333333333,0.020140647888183594,186.62736299999997 +32351,Binary classification,Naive Bayes,SMTP,0.9998763523956723,0.8333333333333333,0.020140647888183594,210.51749199999998 +34254,Binary classification,Naive Bayes,SMTP,0.9998832219075702,0.8333333333333333,0.020140647888183594,235.83223599999997 +36157,Binary classification,Naive Bayes,SMTP,0.9998893682929527,0.8333333333333333,0.020140647888183594,262.657063 +38060,Binary classification,Naive Bayes,SMTP,0.9998949000236474,0.8333333333333333,0.020140647888183594,290.942762 +39963,Binary classification,Naive Bayes,SMTP,0.9998999049096642,0.8333333333333333,0.020140647888183594,320.716469 +41866,Binary classification,Naive Bayes,SMTP,0.999904454795175,0.8333333333333333,0.020140647888183594,352.027934 +43769,Binary classification,Naive Bayes,SMTP,0.9999086090294279,0.8333333333333333,0.020140647888183594,384.764181 +45672,Binary classification,Naive Bayes,SMTP,0.9999124170699131,0.8333333333333333,0.020140647888183594,419.010574 +47575,Binary classification,Naive Bayes,SMTP,0.9999159204607558,0.8333333333333333,0.020140647888183594,454.738206 +49478,Binary classification,Naive Bayes,SMTP,0.9999191543545486,0.8333333333333333,0.020140647888183594,491.833824 +51381,Binary classification,Naive Bayes,SMTP,0.9999026858699883,0.8275862068965517,0.020140647888183594,530.367488 +53284,Binary classification,Naive Bayes,SMTP,0.9999061614398589,0.8275862068965517,0.020140647888183594,570.267553 +55187,Binary classification,Naive Bayes,SMTP,0.9998912767730946,0.7999999999999999,0.020140647888183594,611.572363 +57090,Binary classification,Naive Bayes,SMTP,0.9993869221741492,0.4444444444444444,0.020140647888183594,654.167087 +58993,Binary classification,Naive Bayes,SMTP,0.9988473013289938,0.29166666666666663,0.020140647888183594,698.17064 +60896,Binary classification,Naive Bayes,SMTP,0.9986369981115034,0.2522522522522523,0.020140647888183594,743.490298 +62799,Binary classification,Naive Bayes,SMTP,0.9979139463040224,0.1761006289308176,0.020140647888183594,790.115891 +64702,Binary classification,Naive Bayes,SMTP,0.9979443903494536,0.17391304347826086,0.020140647888183594,838.025404 +66605,Binary classification,Naive Bayes,SMTP,0.9977478830100295,0.15730337078651685,0.020140647888183594,887.160342 +68508,Binary classification,Naive Bayes,SMTP,0.9967302611411972,0.12500000000000003,0.020140647888183594,937.511304 +70411,Binary classification,Naive Bayes,SMTP,0.9964777730436017,0.1142857142857143,0.020140647888183594,989.136144 +72314,Binary classification,Naive Bayes,SMTP,0.9964045192427364,0.10958904109589042,0.020140647888183594,1041.952402 +74217,Binary classification,Naive Bayes,SMTP,0.9958230031260106,0.0935672514619883,0.020140647888183594,1095.894331 +76120,Binary classification,Naive Bayes,SMTP,0.9956515456062218,0.08815426997245178,0.020140647888183594,1151.054816 +78023,Binary classification,Naive Bayes,SMTP,0.9951936633257287,0.07862407862407862,0.020140647888183594,1207.299045 +79926,Binary classification,Naive Bayes,SMTP,0.9946700031279324,0.06986899563318777,0.020140647888183594,1264.68116 +81829,Binary classification,Naive Bayes,SMTP,0.9945862052109302,0.06736842105263158,0.020140647888183594,1323.182614 +83732,Binary classification,Naive Bayes,SMTP,0.9945539883675102,0.06557377049180328,0.020140647888183594,1382.690018 +85635,Binary classification,Naive Bayes,SMTP,0.9939860335847911,0.05850091407678244,0.020140647888183594,1443.2513940000001 +87538,Binary classification,Naive Bayes,SMTP,0.9938540274398254,0.05614035087719298,0.020140647888183594,1504.7859910000002 +89441,Binary classification,Naive Bayes,SMTP,0.9938618067978533,0.05507745266781411,0.020140647888183594,1567.3680330000002 +91344,Binary classification,Naive Bayes,SMTP,0.9939677917300723,0.0548885077186964,0.020140647888183594,1630.9013820000002 +93247,Binary classification,Naive Bayes,SMTP,0.993543958990198,0.050473186119873815,0.020140647888183594,1695.4283070000001 +95150,Binary classification,Naive Bayes,SMTP,0.993483904192372,0.049079754601226995,0.020140647888183594,1760.949483 +95156,Binary classification,Naive Bayes,SMTP,0.9934843150648941,0.049079754601226995,0.020140647888183594,1826.472109 +106,Binary classification,Hoeffding Tree,Bananas,0.49523809523809526,0.208955223880597,0.019225120544433594,0.143993 +212,Binary classification,Hoeffding Tree,Bananas,0.5213270142180095,0.3129251700680272,0.019248008728027344,0.331364 +318,Binary classification,Hoeffding Tree,Bananas,0.5299684542586751,0.40637450199203184,0.019248008728027344,0.6339969999999999 +424,Binary classification,Hoeffding Tree,Bananas,0.5437352245862884,0.42388059701492536,0.019248008728027344,1.026482 +530,Binary classification,Hoeffding Tree,Bananas,0.553875236294896,0.4099999999999999,0.019248008728027344,1.502748 +636,Binary classification,Hoeffding Tree,Bananas,0.5590551181102362,0.4017094017094017,0.019248008728027344,2.038539 +742,Binary classification,Hoeffding Tree,Bananas,0.5762483130904184,0.3984674329501916,0.019248008728027344,2.585217 +848,Binary classification,Hoeffding Tree,Bananas,0.5867768595041323,0.40476190476190477,0.019248008728027344,3.2443470000000003 +954,Binary classification,Hoeffding Tree,Bananas,0.5918153200419727,0.3987635239567234,0.019248008728027344,4.029044000000001 +1060,Binary classification,Hoeffding Tree,Bananas,0.6015108593012276,0.39714285714285713,0.019248008728027344,4.857172 +1166,Binary classification,Hoeffding Tree,Bananas,0.6,0.38522427440633245,0.019248008728027344,5.757943 +1272,Binary classification,Hoeffding Tree,Bananas,0.6073957513768686,0.3966142684401451,0.019248008728027344,6.7312840000000005 +1378,Binary classification,Hoeffding Tree,Bananas,0.6085693536673928,0.384,0.019248008728027344,7.793338 +1484,Binary classification,Hoeffding Tree,Bananas,0.6089008766014835,0.3790149892933619,0.019248008728027344,8.86628 +1590,Binary classification,Hoeffding Tree,Bananas,0.6085588420390182,0.37424547283702214,0.019248008728027344,10.05345 +1696,Binary classification,Hoeffding Tree,Bananas,0.6094395280235988,0.37072243346007605,0.019248008728027344,11.283728 +1802,Binary classification,Hoeffding Tree,Bananas,0.6102165463631316,0.37544483985765126,0.019248008728027344,12.570083 +1908,Binary classification,Hoeffding Tree,Bananas,0.610907184058731,0.3816666666666667,0.019248008728027344,13.875162 +2014,Binary classification,Hoeffding Tree,Bananas,0.6060606060606061,0.3799843627834245,0.019248008728027344,15.24589 +2120,Binary classification,Hoeffding Tree,Bananas,0.6045304388862671,0.38382352941176473,0.019248008728027344,16.723857 +2226,Binary classification,Hoeffding Tree,Bananas,0.6053932584269663,0.38687150837988826,0.019248008728027344,18.213202 +2332,Binary classification,Hoeffding Tree,Bananas,0.6061776061776062,0.38881491344873503,0.019248008728027344,19.838043 +2438,Binary classification,Hoeffding Tree,Bananas,0.606893721789085,0.388250319284802,0.019248008728027344,21.528239 +2544,Binary classification,Hoeffding Tree,Bananas,0.608336610302792,0.39636363636363636,0.019248008728027344,23.295102 +2650,Binary classification,Hoeffding Tree,Bananas,0.6070215175537939,0.3944153577661431,0.019248008728027344,25.108421 +2756,Binary classification,Hoeffding Tree,Bananas,0.6047186932849364,0.3892316320807628,0.019248008728027344,26.99552 +2862,Binary classification,Hoeffding Tree,Bananas,0.6057322614470465,0.3922413793103448,0.019248008728027344,28.904989 +2968,Binary classification,Hoeffding Tree,Bananas,0.6056622851365016,0.3899895724713243,0.019248008728027344,30.87684 +3074,Binary classification,Hoeffding Tree,Bananas,0.6036446469248291,0.3903903903903904,0.019248008728027344,32.946938 +3180,Binary classification,Hoeffding Tree,Bananas,0.6045926391947153,0.3924601256645723,0.019248008728027344,35.112766 +3286,Binary classification,Hoeffding Tree,Bananas,0.6039573820395738,0.39006094702297234,0.019248008728027344,37.308874 +3392,Binary classification,Hoeffding Tree,Bananas,0.6024771453848422,0.39169675090252704,0.019248008728027344,39.588614 +3498,Binary classification,Hoeffding Tree,Bananas,0.6030883614526737,0.39335664335664333,0.03483390808105469,41.900558 +3604,Binary classification,Hoeffding Tree,Bananas,0.6069941715237303,0.40353833192923344,0.03483390808105469,44.304379999999995 +3710,Binary classification,Hoeffding Tree,Bananas,0.6079805877595039,0.40798045602605865,0.03483390808105469,46.776579 +3816,Binary classification,Hoeffding Tree,Bananas,0.6107470511140236,0.4146629877808436,0.03483390808105469,49.299973 +3922,Binary classification,Hoeffding Tree,Bananas,0.6123437898495282,0.4180704441041348,0.04409217834472656,51.9923 +4028,Binary classification,Hoeffding Tree,Bananas,0.6143531164638689,0.4246017043349389,0.05025672912597656,54.748055 +4134,Binary classification,Hoeffding Tree,Bananas,0.617227195741592,0.43216080402010054,0.05025672912597656,57.554127 +4240,Binary classification,Hoeffding Tree,Bananas,0.6218447747110167,0.4439819632327437,0.05025672912597656,60.441345 +4346,Binary classification,Hoeffding Tree,Bananas,0.6239355581127733,0.45130960376091334,0.05025672912597656,63.387968 +4452,Binary classification,Hoeffding Tree,Bananas,0.6259267580319029,0.45676998368678623,0.05025672912597656,66.368103 +4558,Binary classification,Hoeffding Tree,Bananas,0.6276058810621022,0.46382306477093216,0.05025672912597656,69.45485500000001 +4664,Binary classification,Hoeffding Tree,Bananas,0.6283508470941453,0.4695439240893787,0.05025672912597656,72.676145 +4770,Binary classification,Hoeffding Tree,Bananas,0.6288530090165653,0.47164179104477605,0.05941963195800781,75.94439200000001 +4876,Binary classification,Hoeffding Tree,Bananas,0.6311794871794871,0.47580174927113705,0.05946540832519531,79.31100500000001 +4982,Binary classification,Hoeffding Tree,Bananas,0.6336077092953222,0.484026010743568,0.05946540832519531,82.69585900000001 +5088,Binary classification,Hoeffding Tree,Bananas,0.6361313151169649,0.49050371593724196,0.05946540832519531,86.19871000000002 +5194,Binary classification,Hoeffding Tree,Bananas,0.6383593298671288,0.495703544575725,0.05946540832519531,89.82165300000003 +5300,Binary classification,Hoeffding Tree,Bananas,0.6421966408756369,0.5034049240440022,0.05946540832519531,93.53024900000003 +906,Binary classification,Hoeffding Tree,Elec2,0.8530386740331491,0.8513966480446927,0.1757516860961914,1.081486 +1812,Binary classification,Hoeffding Tree,Elec2,0.8663721700717836,0.8393094289508632,0.2084512710571289,3.2087309999999998 +2718,Binary classification,Hoeffding Tree,Elec2,0.8365844681634156,0.809278350515464,0.23302173614501953,6.394793 +3624,Binary classification,Hoeffding Tree,Elec2,0.8459839911675407,0.8210391276459269,0.23302173614501953,10.694791 +4530,Binary classification,Hoeffding Tree,Elec2,0.8511812762199161,0.8157463094587206,0.23296833038330078,16.02834 +5436,Binary classification,Hoeffding Tree,Elec2,0.8404783808647655,0.8020095912308747,0.23296833038330078,22.571918 +6342,Binary classification,Hoeffding Tree,Elec2,0.8334647531935025,0.7966884867154409,0.23296833038330078,30.238204 +7248,Binary classification,Hoeffding Tree,Elec2,0.8330343590451221,0.7912353347135956,0.23296833038330078,38.961308 +8154,Binary classification,Hoeffding Tree,Elec2,0.8344167790997179,0.8013537374926426,0.23296833038330078,48.732242 +9060,Binary classification,Hoeffding Tree,Elec2,0.8403797328623468,0.8129849974133472,0.2980508804321289,59.636444 +9966,Binary classification,Hoeffding Tree,Elec2,0.8398394380331159,0.8171402383134739,0.29816532135009766,71.55266999999999 +10872,Binary classification,Hoeffding Tree,Elec2,0.840493054916751,0.8200124558854057,0.29816532135009766,84.613385 +11778,Binary classification,Hoeffding Tree,Elec2,0.8404517279442982,0.8184014690248381,0.3811311721801758,98.771271 +12684,Binary classification,Hoeffding Tree,Elec2,0.8397066939998423,0.8184983483617534,0.3811311721801758,114.088656 +13590,Binary classification,Hoeffding Tree,Elec2,0.8422253293104717,0.8228684732319893,0.3811311721801758,130.484857 +14496,Binary classification,Hoeffding Tree,Elec2,0.8440841669541221,0.8259128023417038,0.38237476348876953,148.034702 +15402,Binary classification,Hoeffding Tree,Elec2,0.8445555483410169,0.8246153846153847,0.3824014663696289,166.630313 +16308,Binary classification,Hoeffding Tree,Elec2,0.8382903047770895,0.8146221441124781,0.40816211700439453,186.33286 +17214,Binary classification,Hoeffding Tree,Elec2,0.8345436588624876,0.8052516411378555,0.40816211700439453,207.14980100000002 +18120,Binary classification,Hoeffding Tree,Elec2,0.8332689442022186,0.8030253635000325,0.40884876251220703,229.10312700000003 +19026,Binary classification,Hoeffding Tree,Elec2,0.8340604467805519,0.8008327550312283,0.4101419448852539,252.19556200000002 +19932,Binary classification,Hoeffding Tree,Elec2,0.8288595655009784,0.7951228302000121,0.4740419387817383,276.349221 +20838,Binary classification,Hoeffding Tree,Elec2,0.8238230071507414,0.787570163763671,0.4986543655395508,301.796373 +21744,Binary classification,Hoeffding Tree,Elec2,0.8251391252357081,0.7858028169014086,0.49881458282470703,328.42960500000004 +22650,Binary classification,Hoeffding Tree,Elec2,0.8245838668373879,0.7828843106180666,0.4754457473754883,356.18046400000003 +23556,Binary classification,Hoeffding Tree,Elec2,0.81761834005519,0.7712703652433182,0.5000581741333008,385.033694 +24462,Binary classification,Hoeffding Tree,Elec2,0.8151342954090184,0.7656509121061359,0.5002222061157227,415.059878 +25368,Binary classification,Hoeffding Tree,Elec2,0.8133401663578665,0.7649540828989824,0.5574884414672852,446.38466700000004 +26274,Binary classification,Hoeffding Tree,Elec2,0.8142199215925094,0.7659329592864336,0.5574884414672852,478.875266 +27180,Binary classification,Hoeffding Tree,Elec2,0.8130909893667906,0.7650758416574177,0.5574884414672852,512.642228 +28086,Binary classification,Hoeffding Tree,Elec2,0.810646252447926,0.7611605137878379,0.5575571060180664,547.6069200000001 +28992,Binary classification,Hoeffding Tree,Elec2,0.8084233037839329,0.755846667838931,0.5575571060180664,583.7938770000001 +29898,Binary classification,Hoeffding Tree,Elec2,0.8039602635715958,0.7488322262695523,0.5575571060180664,621.109455 +30804,Binary classification,Hoeffding Tree,Elec2,0.8052787066194851,0.7498540328634582,0.6720895767211914,659.6567610000001 +31710,Binary classification,Hoeffding Tree,Elec2,0.802863540319783,0.7460285215130216,0.6720895767211914,699.5069490000001 +32616,Binary classification,Hoeffding Tree,Elec2,0.8010731258623333,0.7451889089623752,0.6838197708129883,740.492735 +33522,Binary classification,Hoeffding Tree,Elec2,0.8010500880045345,0.7469934367768125,0.7644319534301758,782.6537000000001 +34428,Binary classification,Hoeffding Tree,Elec2,0.799663055160194,0.7444893120438633,0.7656755447387695,825.979857 +35334,Binary classification,Hoeffding Tree,Elec2,0.7997056576005434,0.7438746335637508,0.796971321105957,870.4262610000001 +36240,Binary classification,Hoeffding Tree,Elec2,0.798283617097602,0.7418420680887131,0.8215837478637695,916.0524170000001 +37146,Binary classification,Hoeffding Tree,Elec2,0.7980347287656482,0.741577678263865,0.8528566360473633,962.7298460000001 +38052,Binary classification,Hoeffding Tree,Elec2,0.7942761031247536,0.7384913476314559,0.8296480178833008,1010.431184 +38958,Binary classification,Hoeffding Tree,Elec2,0.791975768154632,0.7385131646876614,0.8296480178833008,1059.228611 +39864,Binary classification,Hoeffding Tree,Elec2,0.7917617841105787,0.7414904549842734,0.8308916091918945,1109.092208 +40770,Binary classification,Hoeffding Tree,Elec2,0.7937158134857367,0.7465187775031646,0.8308916091918945,1159.9359670000001 +41676,Binary classification,Hoeffding Tree,Elec2,0.7945770845830834,0.749744219357479,0.8553438186645508,1211.819823 +42582,Binary classification,Hoeffding Tree,Elec2,0.7952373124163359,0.7509355271802782,0.8799333572387695,1264.739744 +43488,Binary classification,Hoeffding Tree,Elec2,0.7953871271874353,0.7515912897822445,0.881199836730957,1318.691004 +44394,Binary classification,Hoeffding Tree,Elec2,0.7949676750839096,0.7499038303016982,0.881199836730957,1373.745004 +45300,Binary classification,Hoeffding Tree,Elec2,0.7956246274752202,0.7508745492707605,0.9384660720825195,1429.8385990000002 +45312,Binary classification,Hoeffding Tree,Elec2,0.7956346141113637,0.7508341405661393,0.9384660720825195,1485.976427 +25,Binary classification,Hoeffding Tree,Phishing,0.5833333333333334,0.6428571428571429,0.06842708587646484,0.007366 +50,Binary classification,Hoeffding Tree,Phishing,0.7346938775510204,0.7346938775510203,0.06842708587646484,0.021904 +75,Binary classification,Hoeffding Tree,Phishing,0.7837837837837838,0.7894736842105262,0.06842708587646484,0.108104 +100,Binary classification,Hoeffding Tree,Phishing,0.8080808080808081,0.8080808080808081,0.06842708587646484,0.26246400000000003 +125,Binary classification,Hoeffding Tree,Phishing,0.8145161290322581,0.8130081300813008,0.06842708587646484,0.42699600000000004 +150,Binary classification,Hoeffding Tree,Phishing,0.8187919463087249,0.8235294117647058,0.06842708587646484,0.670297 +175,Binary classification,Hoeffding Tree,Phishing,0.8333333333333334,0.8263473053892215,0.06842708587646484,0.944361 +200,Binary classification,Hoeffding Tree,Phishing,0.8341708542713567,0.8272251308900525,0.0684499740600586,1.225091 +225,Binary classification,Hoeffding Tree,Phishing,0.8303571428571429,0.8190476190476189,0.0684499740600586,1.620606 +250,Binary classification,Hoeffding Tree,Phishing,0.8313253012048193,0.8205128205128206,0.0684499740600586,2.072395 +275,Binary classification,Hoeffding Tree,Phishing,0.8321167883211679,0.8203125000000001,0.0684499740600586,2.536963 +300,Binary classification,Hoeffding Tree,Phishing,0.8394648829431438,0.8248175182481753,0.0684499740600586,3.035956 +325,Binary classification,Hoeffding Tree,Phishing,0.845679012345679,0.8263888888888888,0.0684499740600586,3.5418380000000003 +350,Binary classification,Hoeffding Tree,Phishing,0.8510028653295129,0.8289473684210527,0.0684499740600586,4.130076000000001 +375,Binary classification,Hoeffding Tree,Phishing,0.8529411764705882,0.8286604361370716,0.0684499740600586,4.770647 +400,Binary classification,Hoeffding Tree,Phishing,0.8546365914786967,0.8284023668639053,0.0684499740600586,5.418701 +425,Binary classification,Hoeffding Tree,Phishing,0.8561320754716981,0.8262108262108262,0.0684499740600586,6.103302 +450,Binary classification,Hoeffding Tree,Phishing,0.8596881959910914,0.8283378746594006,0.0684499740600586,6.878701 +475,Binary classification,Hoeffding Tree,Phishing,0.8565400843881856,0.826530612244898,0.0684499740600586,7.659851000000001 +500,Binary classification,Hoeffding Tree,Phishing,0.8577154308617234,0.8313539192399049,0.0684499740600586,8.471725000000001 +525,Binary classification,Hoeffding Tree,Phishing,0.8587786259541985,0.8287037037037036,0.0684499740600586,9.290161000000001 +550,Binary classification,Hoeffding Tree,Phishing,0.8579234972677595,0.8289473684210527,0.0684499740600586,10.200081 +575,Binary classification,Hoeffding Tree,Phishing,0.8606271777003485,0.8319327731092437,0.0684499740600586,11.144835 +600,Binary classification,Hoeffding Tree,Phishing,0.8647746243739566,0.834355828220859,0.0684499740600586,12.149797 +625,Binary classification,Hoeffding Tree,Phishing,0.8669871794871795,0.8336673346693387,0.0684499740600586,13.191129 +650,Binary classification,Hoeffding Tree,Phishing,0.8705701078582434,0.8409090909090909,0.0684499740600586,14.297317 +675,Binary classification,Hoeffding Tree,Phishing,0.870919881305638,0.8449197860962566,0.0684499740600586,15.408972 +700,Binary classification,Hoeffding Tree,Phishing,0.8755364806866953,0.8486956521739131,0.0684499740600586,16.598807 +725,Binary classification,Hoeffding Tree,Phishing,0.8784530386740331,0.8547854785478548,0.0684499740600586,17.82593 +750,Binary classification,Hoeffding Tree,Phishing,0.8798397863818425,0.8571428571428571,0.0684499740600586,19.123649 +775,Binary classification,Hoeffding Tree,Phishing,0.8798449612403101,0.8567026194144837,0.0684499740600586,20.439518 +800,Binary classification,Hoeffding Tree,Phishing,0.8798498122653317,0.8584070796460177,0.006070137023925781,21.870793 +825,Binary classification,Hoeffding Tree,Phishing,0.8786407766990292,0.8575498575498576,0.1326732635498047,23.308925 +850,Binary classification,Hoeffding Tree,Phishing,0.8798586572438163,0.8579387186629527,0.13269615173339844,24.793982 +875,Binary classification,Hoeffding Tree,Phishing,0.8810068649885584,0.8583106267029972,0.13269615173339844,26.327422 +900,Binary classification,Hoeffding Tree,Phishing,0.882091212458287,0.8590425531914893,0.1327190399169922,27.867454 +925,Binary classification,Hoeffding Tree,Phishing,0.8831168831168831,0.8611825192802056,0.1327190399169922,29.49699 +950,Binary classification,Hoeffding Tree,Phishing,0.880927291886196,0.8599752168525404,0.1327190399169922,31.132602 +975,Binary classification,Hoeffding Tree,Phishing,0.8819301848049281,0.8609431680773881,0.1327190399169922,32.858381 +1000,Binary classification,Hoeffding Tree,Phishing,0.8828828828828829,0.8621908127208481,0.1327190399169922,34.600804000000004 +1025,Binary classification,Hoeffding Tree,Phishing,0.8818359375,0.8613974799541809,0.1327190399169922,36.37479200000001 +1050,Binary classification,Hoeffding Tree,Phishing,0.8836987607244995,0.8641425389755011,0.1327190399169922,38.236126000000006 +1075,Binary classification,Hoeffding Tree,Phishing,0.8845437616387337,0.8658008658008659,0.1327190399169922,40.114172 +1100,Binary classification,Hoeffding Tree,Phishing,0.8844404003639672,0.8656084656084656,0.1327190399169922,41.998405000000005 +1125,Binary classification,Hoeffding Tree,Phishing,0.8816725978647687,0.8630278063851698,0.1327190399169922,43.96255500000001 +1150,Binary classification,Hoeffding Tree,Phishing,0.8807658833768495,0.8614762386248735,0.1327190399169922,45.93298000000001 +1175,Binary classification,Hoeffding Tree,Phishing,0.879045996592845,0.8594059405940594,0.1327190399169922,47.92952100000001 +1200,Binary classification,Hoeffding Tree,Phishing,0.8807339449541285,0.8610301263362489,0.1327190399169922,50.02063300000001 +1225,Binary classification,Hoeffding Tree,Phishing,0.880718954248366,0.8609523809523809,0.1327190399169922,52.11693600000001 +1250,Binary classification,Hoeffding Tree,Phishing,0.8799039231385108,0.8605947955390334,0.1327190399169922,54.27575100000001 +1903,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,1.086226 +3806,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,3.167363 +5709,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,6.335451 +7612,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,10.587829 +9515,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,15.968691 +11418,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,22.515101 +13321,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,30.177580000000003 +15224,Binary classification,Hoeffding Tree,SMTP,0.9992774091834724,0.0,0.02622222900390625,38.907943 +17127,Binary classification,Hoeffding Tree,SMTP,0.9992409202382343,0.0,0.0170440673828125,48.927066 +19030,Binary classification,Hoeffding Tree,SMTP,0.9993168322034789,0.0,0.0170440673828125,60.172540000000005 +20933,Binary classification,Hoeffding Tree,SMTP,0.999378941333843,0.0,0.0170440673828125,72.66801500000001 +22836,Binary classification,Hoeffding Tree,SMTP,0.9994306984891613,0.0,0.0170440673828125,86.48422800000002 +24739,Binary classification,Hoeffding Tree,SMTP,0.9994744926833212,0.0,0.0170440673828125,101.51872100000001 +26642,Binary classification,Hoeffding Tree,SMTP,0.9994744942006681,0.0,0.0170440673828125,117.73184500000002 +28545,Binary classification,Hoeffding Tree,SMTP,0.9995095291479821,0.0,0.0170440673828125,135.114956 +30448,Binary classification,Hoeffding Tree,SMTP,0.9995401845830459,0.0,0.0170440673828125,153.67141 +32351,Binary classification,Hoeffding Tree,SMTP,0.9995672333848532,0.0,0.0170440673828125,173.49434000000002 +34254,Binary classification,Hoeffding Tree,SMTP,0.9995912766764955,0.0,0.0170440673828125,194.59387200000003 +36157,Binary classification,Hoeffding Tree,SMTP,0.9996127890253347,0.0,0.0170440673828125,216.90021000000004 +38060,Binary classification,Hoeffding Tree,SMTP,0.9996321500827662,0.0,0.0170440673828125,240.42103000000003 +39963,Binary classification,Hoeffding Tree,SMTP,0.9996496671838246,0.0,0.0170440673828125,265.208452 +41866,Binary classification,Hoeffding Tree,SMTP,0.9996655917831124,0.0,0.0170440673828125,291.209442 +43769,Binary classification,Hoeffding Tree,SMTP,0.9996801316029976,0.0,0.0170440673828125,318.43843100000004 +45672,Binary classification,Hoeffding Tree,SMTP,0.9996934597446958,0.0,0.0170440673828125,346.89099000000004 +47575,Binary classification,Hoeffding Tree,SMTP,0.9997057216126456,0.0,0.0170440673828125,376.52940800000005 +49478,Binary classification,Hoeffding Tree,SMTP,0.99971704024092,0.0,0.0170440673828125,407.49804200000005 +51381,Binary classification,Hoeffding Tree,SMTP,0.9996885947839627,0.0,0.0170440673828125,439.6062170000001 +53284,Binary classification,Hoeffding Tree,SMTP,0.9996997166075484,0.0,0.0170440673828125,472.95335100000005 +55187,Binary classification,Hoeffding Tree,SMTP,0.999710071394919,0.0,0.0170440673828125,507.47402400000004 +57090,Binary classification,Hoeffding Tree,SMTP,0.9995620872672494,0.0,0.0170440673828125,543.2100710000001 +58993,Binary classification,Hoeffding Tree,SMTP,0.9995762137238947,0.0,0.0170440673828125,580.098547 +60896,Binary classification,Hoeffding Tree,SMTP,0.999589457262501,0.0,0.0170440673828125,618.1800870000001 +62799,Binary classification,Hoeffding Tree,SMTP,0.9995700500015924,0.0,0.0170440673828125,657.2504170000001 +64702,Binary classification,Hoeffding Tree,SMTP,0.9995826957852274,0.0,0.0170440673828125,697.447209 +66605,Binary classification,Hoeffding Tree,SMTP,0.9995946189418053,0.0,0.0170440673828125,738.7766780000001 +68508,Binary classification,Hoeffding Tree,SMTP,0.9995766855941729,0.0,0.0170440673828125,781.207926 +70411,Binary classification,Hoeffding Tree,SMTP,0.9995881266865502,0.0,0.0170440673828125,824.7260210000001 +72314,Binary classification,Hoeffding Tree,SMTP,0.9995989656078437,0.0,0.0170440673828125,869.3947840000001 +74217,Binary classification,Hoeffding Tree,SMTP,0.99960924867953,0.0,0.0170440673828125,915.176721 +76120,Binary classification,Hoeffding Tree,SMTP,0.9996190175908775,0.0,0.0170440673828125,961.985028 +78023,Binary classification,Hoeffding Tree,SMTP,0.9996283099638563,0.0,0.0170440673828125,1009.890756 +79926,Binary classification,Hoeffding Tree,SMTP,0.9996371598373475,0.0,0.0170440673828125,1058.848202 +81829,Binary classification,Hoeffding Tree,SMTP,0.9996455980837855,0.0,0.0170440673828125,1108.7919539999998 +83732,Binary classification,Hoeffding Tree,SMTP,0.9996536527689864,0.0,0.0170440673828125,1159.7893379999998 +85635,Binary classification,Hoeffding Tree,SMTP,0.999661349463998,0.0,0.0170440673828125,1211.840415 +87538,Binary classification,Hoeffding Tree,SMTP,0.9996687115162731,0.0,0.0170440673828125,1264.8087919999998 +89441,Binary classification,Hoeffding Tree,SMTP,0.9996645796064401,0.0,0.0170440673828125,1318.8158339999998 +91344,Binary classification,Hoeffding Tree,SMTP,0.999671567607808,0.0,0.0170440673828125,1373.8298589999997 +93247,Binary classification,Hoeffding Tree,SMTP,0.9996782703815713,0.0,0.0170440673828125,1429.7685149999998 +95150,Binary classification,Hoeffding Tree,SMTP,0.9996847050415664,0.0,0.0170440673828125,1486.6476859999998 +95156,Binary classification,Hoeffding Tree,SMTP,0.9996847249224948,0.0,0.0170440673828125,1543.5553739999998 +106,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5714285714285714,0.628099173553719,0.025684356689453125,0.216494 +212,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5592417061611374,0.5903083700440529,0.025768280029296875,0.463954 +318,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5615141955835962,0.5947521865889213,0.025829315185546875,0.862573 +424,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5555555555555556,0.5822222222222222,0.025829315185546875,1.3898329999999999 +530,Binary classification,Hoeffding Adaptive Tree,Bananas,0.555765595463138,0.5506692160611854,0.025829315185546875,1.9641119999999999 +636,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5543307086614173,0.5291181364392679,0.025890350341796875,2.6939569999999997 +742,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5708502024291497,0.5167173252279634,0.025890350341796875,3.4801279999999997 +848,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5761511216056671,0.510231923601637,0.025890350341796875,4.453125999999999 +954,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5844700944386149,0.505,0.025890350341796875,5.580188 +1060,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5920679886685553,0.49532710280373826,0.025890350341796875,6.755147 +1166,Binary classification,Hoeffding Adaptive Tree,Bananas,0.590557939914163,0.478688524590164,0.025890350341796875,8.08575 +1272,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971675845790716,0.48073022312373226,0.025890350341796875,9.558451000000002 +1378,Binary classification,Hoeffding Adaptive Tree,Bananas,0.599128540305011,0.4661508704061895,0.025951385498046875,11.087295000000001 +1484,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5994605529332434,0.458029197080292,0.025951385498046875,12.740385000000002 +1590,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5997482693517936,0.4517241379310345,0.025951385498046875,14.490633000000003 +1696,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6011799410029498,0.4459016393442623,0.025951385498046875,16.383274000000004 +1802,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6018878400888396,0.44547563805104406,0.025951385498046875,18.381747000000004 +1908,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6030414263240692,0.44704163623082543,0.025951385498046875,20.420329000000002 +2014,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5986090412319921,0.44352617079889806,0.025951385498046875,22.615668000000003 +2120,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5960358659745163,0.4427083333333333,0.025951385498046875,24.891681000000002 +2226,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5968539325842697,0.4425108763206961,0.025951385498046875,27.295309000000003 +2332,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5975975975975976,0.44233055885850175,0.025951385498046875,29.792211 +2438,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5982765695527288,0.4396107613050944,0.025951385498046875,32.414577 +2544,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5973259929217459,0.4398249452954048,0.03029155731201172,35.17394 +2650,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5956964892412231,0.44363636363636366,0.056708335876464844,38.067898 +2756,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5985480943738657,0.44975124378109455,0.056952476501464844,41.158639 +2862,Binary classification,Hoeffding Adaptive Tree,Bananas,0.600139811254806,0.4536771728748806,0.057196617126464844,44.452629 +2968,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5979103471520054,0.45250114731528224,0.057303428649902344,47.888765 +3074,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971363488447771,0.4497777777777778,0.057425498962402344,51.476908 +3180,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6008178672538534,0.44993498049414826,0.057486534118652344,55.178717 +3286,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6024353120243531,0.4470787468247248,0.057608604431152344,59.035765 +3392,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6012975523444412,0.444991789819376,0.057669639587402344,63.084021 +3498,Binary classification,Hoeffding Adaptive Tree,Bananas,0.603946239633972,0.44310414153598715,0.057730674743652344,67.283017 +3604,Binary classification,Hoeffding Adaptive Tree,Bananas,0.607826810990841,0.4452296819787986,0.057730674743652344,71.628079 +3710,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6071717444055001,0.441976254308694,0.057730674743652344,76.17092 +3816,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6062909567496724,0.43787425149700593,0.057791709899902344,80.84267399999999 +3922,Binary classification,Hoeffding Adaptive Tree,Bananas,0.606988013261923,0.4353242946134115,0.057852745056152344,85.696272 +4028,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6088899925502855,0.4360902255639098,0.057852745056152344,90.65857299999999 +4134,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6082748608758771,0.4341139461726669,0.057913780212402344,95.89501499999999 +4240,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6105213493748526,0.4370951244459598,0.057913780212402344,101.21739399999998 +4346,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6119677790563867,0.43724966622162886,0.057974815368652344,106.75825799999998 +4452,Binary classification,Hoeffding Adaptive Tree,Bananas,0.614243990114581,0.4387054593004249,0.057974815368652344,112.41943399999998 +4558,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6126837831906956,0.4355612408058842,0.057974815368652344,118.26167899999999 +4664,Binary classification,Hoeffding Adaptive Tree,Bananas,0.613339052112374,0.4360337816703159,0.057974815368652344,124.26691199999999 +4770,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6148039421262319,0.4352905010759299,0.0641164779663086,130.389994 +4876,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6157948717948718,0.4332829046898639,0.0641164779663086,136.677955 +4982,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6167436257779563,0.43470535978679303,0.0641164779663086,143.134937 +5088,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6158836249262827,0.4313154831199068,0.0641775131225586,149.736273 +5194,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6160215674947044,0.42963386727688785,0.0642385482788086,156.525598 +5300,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6165314210228345,0.42824985931344967,0.06184673309326172,163.516222 +906,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8386740331491712,0.8370535714285713,0.15532493591308594,2.212895 +1812,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8823854224185533,0.857334226389819,0.2904033660888672,6.521798 +2718,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8715495031284505,0.8438478747203579,0.1283740997314453,13.845606 +3624,Binary classification,Hoeffding Adaptive Tree,Elec2,0.875241512558653,0.8472972972972973,0.2500133514404297,22.913432999999998 +4530,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8737028041510267,0.8396860986547084,0.3712940216064453,34.075607999999995 +5436,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8656853725850966,0.8300744878957169,0.4407672882080078,47.709683999999996 +6342,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8646901119697209,0.8296943231441047,0.26204872131347656,63.50523799999999 +7248,Binary classification,Hoeffding Adaptive Tree,Elec2,0.864771629639851,0.8289703315881326,0.2866535186767578,81.25362299999999 +8154,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8572304673126456,0.8312064965197217,0.28668785095214844,101.144105 +9060,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8580417264598742,0.8370501773948302,0.2865924835205078,123.033084 +9966,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8544907175112895,0.8369320737741789,0.3109416961669922,147.021637 +10872,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8583386992916935,0.8434322895485971,0.37159156799316406,172.950216 +11778,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8529336842999066,0.8357982555934774,0.46280479431152344,201.490822 +12684,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8533469999211543,0.8362099330750264,0.1895275115966797,232.642586 +13590,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8551769813820002,0.8395303326810176,0.19393348693847656,265.763258 +14496,Binary classification,Hoeffding Adaptive Tree,Elec2,0.855122456019317,0.8397435897435896,0.1697406768798828,300.834382 +15402,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8537757288487761,0.8365984617617181,0.1694965362548828,338.213883 +16308,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8506776231066413,0.8316626339440029,0.16408348083496094,377.804328 +17214,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8495904258409341,0.8278704873346187,0.1691112518310547,419.462025 +18120,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8498261493459904,0.827752104830031,0.19867897033691406,463.148728 +19026,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8522996057818659,0.8287211995611362,0.25722312927246094,508.959572 +19932,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8470222266820531,0.8238895627563102,0.31537818908691406,557.675148 +20838,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8434035609732687,0.8200121352529097,0.32396507263183594,609.8923100000001 +21744,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8450535804626776,0.8196563353139554,0.3222179412841797,664.4541280000001 +22650,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8452911828336792,0.8184455958549223,0.4409503936767578,721.910691 +23556,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8424962852897474,0.8143700590413289,0.44409751892089844,782.4177400000001 +24462,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8403990024937655,0.8111089607122121,0.5018138885498047,845.9758730000001 +25368,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8367169945204399,0.8072053621299572,0.5608501434326172,913.0025360000001 +26274,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8375137974346287,0.8080226649278229,0.31543540954589844,983.5552650000001 +27180,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8378527539644579,0.8096081565645656,0.31577491760253906,1056.640488 +28086,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8349652839594089,0.8051456678017405,0.18702125549316406,1131.521461 +28992,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8328791693973991,0.8007566722868775,0.2230243682861328,1208.700577 +29898,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8303843194969395,0.7968267959453503,0.10404396057128906,1288.2399990000001 +30804,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8300490211992338,0.7958984755740965,0.22612571716308594,1369.046902 +31710,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8277460657857391,0.7934815486993346,0.37830543518066406,1451.951614 +32616,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8227502682814656,0.7867025790502896,0.1292285919189453,1536.962592 +33522,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8198442767220548,0.7850354180756772,0.1289234161376953,1623.2849270000002 +34428,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8166264850262875,0.7809127190699289,0.19405555725097656,1710.9311020000002 +35334,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8170265757224124,0.7804530172852923,0.34708213806152344,1800.2335220000002 +36240,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8175446342338365,0.7795559111822364,0.4078502655029297,1890.9592170000003 +37146,Binary classification,Hoeffding Adaptive Tree,Elec2,0.816610580158837,0.7771817349208426,0.4166545867919922,1983.2562510000002 +38052,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8155107618722242,0.7753456221198157,0.1291065216064453,2077.01254 +38958,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8146674538593834,0.7751479289940829,0.20058250427246094,2172.116472 +39864,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8157188370167825,0.7778516995282448,0.16962623596191406,2268.811937 +40770,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8171650028207706,0.7812151452891106,0.2508678436279297,2366.696648 +41676,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8187402519496101,0.7846022241231821,0.2554492950439453,2465.8461540000003 +42582,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8196848359597003,0.7859015113490603,0.3113727569580078,2566.2285070000003 +43488,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8203141168625107,0.7864093592827466,0.2909717559814453,2667.8448940000003 +44394,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8199265649989863,0.7853959731543624,0.43657493591308594,2771.03508 +45300,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8212543323252169,0.7873854475750336,0.43532752990722656,2875.842657 +45312,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8212575312837942,0.7873440987265327,0.43532752990722656,2980.68937 +25,Binary classification,Hoeffding Adaptive Tree,Phishing,0.5833333333333334,0.6428571428571429,0.07476425170898438,0.008848 +50,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7346938775510204,0.7346938775510203,0.07482528686523438,0.123836 +75,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7837837837837838,0.7894736842105262,0.07482528686523438,0.332733 +100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8080808080808081,0.8080808080808081,0.07488632202148438,0.575353 +125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8225806451612904,0.819672131147541,0.07488632202148438,0.909775 +150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.825503355704698,0.8289473684210527,0.07490921020507812,1.284417 +175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8333333333333334,0.8242424242424242,0.07497024536132812,1.765942 +200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8291457286432161,0.8191489361702128,0.07497024536132812,2.25515 +225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8303571428571429,0.8155339805825242,0.07497024536132812,2.805996 +250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8313253012048193,0.817391304347826,0.07497024536132812,3.45663 +275,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8321167883211679,0.8174603174603176,0.07497024536132812,4.129749 +300,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8361204013377926,0.8178438661710038,0.07497024536132812,4.871246 +325,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8425925925925926,0.8197879858657244,0.07503128051757812,5.6743250000000005 +350,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8481375358166189,0.822742474916388,0.07503128051757812,6.528728000000001 +375,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8502673796791443,0.8227848101265823,0.07503128051757812,7.4517880000000005 +400,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8521303258145363,0.8228228228228228,0.07503128051757812,8.382915 +425,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8537735849056604,0.8208092485549133,0.07503128051757812,9.397859 +450,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8574610244988864,0.8232044198895027,0.07503128051757812,10.451359 +475,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8565400843881856,0.8238341968911918,0.07503128051757812,11.619284 +500,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8557114228456913,0.8260869565217391,0.07503128051757812,12.854081 +525,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8568702290076335,0.823529411764706,0.07503128051757812,14.155744 +550,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8561020036429873,0.8240534521158129,0.07503128051757812,15.508673 +575,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8554006968641115,0.8230277185501066,0.11409282684326172,16.956902 +600,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8547579298831386,0.8176100628930818,0.14198589324951172,18.498414999999998 +625,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8573717948717948,0.8172484599589321,0.14222240447998047,20.046891 +650,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8597842835130971,0.8233009708737864,0.14239025115966797,21.659211 +675,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8590504451038575,0.8263254113345521,0.14245128631591797,23.289464 +700,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8640915593705293,0.8306595365418894,0.14251232147216797,25.025232 +725,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8646408839779005,0.8344594594594595,0.14257335662841797,26.77059 +750,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8664886515353805,0.8371335504885993,0.14263439178466797,28.602532 +775,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8643410852713178,0.8330683624801273,0.14265727996826172,30.506622 +800,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8635794743429287,0.8340943683409437,0.14265727996826172,32.425334 +825,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8628640776699029,0.8345534407027819,0.14265727996826172,34.352118 +850,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8645465253239105,0.8364153627311521,0.14271831512451172,36.371837 +875,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8672768878718535,0.838888888888889,0.14271831512451172,38.48177 +900,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8665183537263627,0.8378378378378378,0.14277935028076172,40.637242 +925,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8668831168831169,0.8400520156046815,0.14277935028076172,42.878637 +950,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8661749209694415,0.8410513141426783,0.14284038543701172,45.126805999999995 +975,Binary classification,Hoeffding Adaptive Tree,Phishing,0.86652977412731,0.8414634146341464,0.14284038543701172,47.480371 +1000,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8638638638638638,0.8392434988179669,0.14284038543701172,49.860963999999996 +1025,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8623046875,0.8377445339470656,0.14284038543701172,52.359728 +1050,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8636796949475691,0.8402234636871508,0.14284038543701172,54.917308999999996 +1075,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8649906890130353,0.8429035752979415,0.14284038543701172,57.530035 +1100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8671519563239308,0.8456659619450316,0.14284038543701172,60.237019 +1125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8701067615658363,0.8507157464212679,0.14284038543701172,62.977514 +1150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8720626631853786,0.852852852852853,0.14290142059326172,65.816825 +1175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8713798977853492,0.8521057786483839,0.14290142059326172,68.70086099999999 +1200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.872393661384487,0.8530259365994236,0.14290142059326172,71.69850399999999 +1225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8733660130718954,0.8541862652869238,0.14296245574951172,74.74610599999998 +1250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8742994395516414,0.8560953253895509,0.14296245574951172,77.86495099999998 +1903,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02372455596923828,1.538486 +3806,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02378559112548828,4.672632 +5709,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,9.280899 +7612,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,15.518128 +9515,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,23.359252 +11418,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02390766143798828,32.724381 +13321,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02390766143798828,43.566998 +15224,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992774091834724,0.0,0.03315448760986328,56.059492 +17127,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992409202382343,0.0,0.02393054962158203,70.315874 +19030,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9993168322034789,0.0,0.02393054962158203,86.215201 +20933,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999378941333843,0.0,0.02399158477783203,103.810178 +22836,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994306984891613,0.0,0.02399158477783203,123.100461 +24739,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744926833212,0.0,0.02399158477783203,144.232391 +26642,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744942006681,0.0,0.02399158477783203,167.059054 +28545,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995095291479821,0.0,0.02399158477783203,191.625058 +30448,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995401845830459,0.0,0.02399158477783203,217.971038 +32351,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995672333848532,0.0,0.02399158477783203,246.1385 +34254,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995912766764955,0.0,0.02399158477783203,276.028821 +36157,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996127890253347,0.0,0.02399158477783203,307.727155 +38060,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996321500827662,0.0,0.02399158477783203,341.14046099999996 +39963,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996496671838246,0.0,0.02399158477783203,376.24706999999995 +41866,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996655917831124,0.0,0.02405261993408203,412.98223499999995 +43769,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996801316029976,0.0,0.02405261993408203,451.37683599999997 +45672,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996934597446958,0.0,0.02405261993408203,491.34241199999997 +47575,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9997057216126456,0.0,0.02405261993408203,532.9409959999999 +49478,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99971704024092,0.0,0.02405261993408203,576.05932 +51381,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996885947839627,0.0,0.02405261993408203,620.875381 +53284,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996997166075484,0.0,0.02405261993408203,667.2134759999999 +55187,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999710071394919,0.0,0.02405261993408203,715.0678459999999 +57090,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995620872672494,0.0,0.02405261993408203,764.41064 +58993,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995762137238947,0.0,0.02405261993408203,815.214443 +60896,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999589457262501,0.0,0.02405261993408203,867.5634849999999 +62799,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995700500015924,0.0,0.02405261993408203,921.3075979999999 +64702,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995826957852274,0.0,0.02405261993408203,976.5012499999999 +66605,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995946189418053,0.0,0.02405261993408203,1033.066349 +68508,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995766855941729,0.0,0.02405261993408203,1090.838953 +70411,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995881266865502,0.0,0.02405261993408203,1149.858677 +72314,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995989656078437,0.0,0.02405261993408203,1210.0750699999999 +74217,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99960924867953,0.0,0.02405261993408203,1271.4412419999999 +76120,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996190175908775,0.0,0.02405261993408203,1333.994434 +78023,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996283099638563,0.0,0.02405261993408203,1397.762471 +79926,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996371598373475,0.0,0.02405261993408203,1462.67416 +81829,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996455980837855,0.0,0.02405261993408203,1528.680001 +83732,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996536527689864,0.0,0.02411365509033203,1595.853878 +85635,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999661349463998,0.0,0.02411365509033203,1664.0432529999998 +87538,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996687115162731,0.0,0.02411365509033203,1733.3243249999998 +89441,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996645796064401,0.0,0.02411365509033203,1803.716354 +91344,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999671567607808,0.0,0.02411365509033203,1875.203937 +93247,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996782703815713,0.0,0.02411365509033203,1947.740223 +95150,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996847050415664,0.0,0.02411365509033203,2021.343945 +95156,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996847249224948,0.0,0.02411365509033203,2094.94949 +106,Binary classification,Adaptive Random Forest,Bananas,0.638095238095238,0.5777777777777778,0.6023197174072266,1.25138 +212,Binary classification,Adaptive Random Forest,Bananas,0.7535545023696683,0.711111111111111,1.0872974395751953,3.920593 +318,Binary classification,Adaptive Random Forest,Bananas,0.7760252365930599,0.7380073800738007,1.471883773803711,8.005582 +424,Binary classification,Adaptive Random Forest,Bananas,0.8085106382978723,0.7768595041322315,1.8271961212158203,13.704046000000002 +530,Binary classification,Adaptive Random Forest,Bananas,0.8204158790170132,0.7845804988662132,2.2761096954345703,21.017212 +636,Binary classification,Adaptive Random Forest,Bananas,0.8362204724409449,0.8052434456928838,2.6539440155029297,30.07026 +742,Binary classification,Adaptive Random Forest,Bananas,0.8434547908232118,0.8110749185667754,3.0667247772216797,40.88011 +848,Binary classification,Adaptive Random Forest,Bananas,0.8512396694214877,0.8220338983050847,3.4897289276123047,53.580909000000005 +954,Binary classification,Adaptive Random Forest,Bananas,0.8583420776495279,0.8301886792452831,3.9347667694091797,68.252368 +1060,Binary classification,Adaptive Random Forest,Bananas,0.8659112370160529,0.8378995433789953,4.283300399780273,84.83204500000001 +1166,Binary classification,Adaptive Random Forest,Bananas,0.8695278969957082,0.8429752066115702,4.800313949584961,103.574646 +1272,Binary classification,Adaptive Random Forest,Bananas,0.8693941778127459,0.8442776735459662,5.391313552856445,124.497156 +1378,Binary classification,Adaptive Random Forest,Bananas,0.8714596949891068,0.8454148471615721,5.846994400024414,147.830368 +1484,Binary classification,Adaptive Random Forest,Bananas,0.8759271746459879,0.8518518518518519,6.193078994750977,173.44391299999998 +1590,Binary classification,Adaptive Random Forest,Bananas,0.8753933291378225,0.8520179372197308,6.296388626098633,201.55742899999998 +1696,Binary classification,Adaptive Random Forest,Bananas,0.8755162241887906,0.8523442967109867,6.211141586303711,232.060182 +1802,Binary classification,Adaptive Random Forest,Bananas,0.8767351471404775,0.8550913838120104,6.65928840637207,264.912691 +1908,Binary classification,Adaptive Random Forest,Bananas,0.8730991085474568,0.8522588522588523,6.686662673950195,300.275827 +2014,Binary classification,Adaptive Random Forest,Bananas,0.8708395429706905,0.8507462686567164,7.210599899291992,338.470819 +2120,Binary classification,Adaptive Random Forest,Bananas,0.8725814063237376,0.8540540540540541,7.48176383972168,378.991608 +2226,Binary classification,Adaptive Random Forest,Bananas,0.8723595505617977,0.8539094650205761,7.915548324584961,421.86917 +2332,Binary classification,Adaptive Random Forest,Bananas,0.8751608751608752,0.8574228319451249,8.423246383666992,467.302026 +2438,Binary classification,Adaptive Random Forest,Bananas,0.8740254411161263,0.8560712611345522,8.870996475219727,515.200386 +2544,Binary classification,Adaptive Random Forest,Bananas,0.874557609123083,0.857779759251003,9.376256942749023,565.505493 +2650,Binary classification,Adaptive Random Forest,Bananas,0.8761796904492262,0.8600682593856656,9.769472122192383,618.233402 +2756,Binary classification,Adaptive Random Forest,Bananas,0.8780399274047187,0.8621821164889254,10.359186172485352,673.368122 +2862,Binary classification,Adaptive Random Forest,Bananas,0.8797623208668298,0.8638163103721298,10.741575241088867,730.824809 +2968,Binary classification,Adaptive Random Forest,Bananas,0.8800134816312774,0.8637059724349159,11.09235954284668,790.5343869999999 +3074,Binary classification,Adaptive Random Forest,Bananas,0.8805727302310445,0.8648250460405157,11.562868118286133,852.458144 +3180,Binary classification,Adaptive Random Forest,Bananas,0.8826675055048757,0.8667381207574134,10.152639389038086,916.4431709999999 +3286,Binary classification,Adaptive Random Forest,Bananas,0.882496194824962,0.8663434903047091,10.670488357543945,982.3914759999999 +3392,Binary classification,Adaptive Random Forest,Bananas,0.8826304924800944,0.867244829886591,11.057397842407227,1050.1851049999998 +3498,Binary classification,Adaptive Random Forest,Bananas,0.8839004861309694,0.8680961663417803,10.334085464477539,1119.7824649999998 +3604,Binary classification,Adaptive Random Forest,Bananas,0.8850957535387177,0.8689043698543382,10.692270278930664,1191.1920959999998 +3710,Binary classification,Adaptive Random Forest,Bananas,0.8846050148287948,0.8687116564417178,11.112970352172852,1264.4750769999998 +3816,Binary classification,Adaptive Random Forest,Bananas,0.8859764089121888,0.870420017873101,11.59941291809082,1339.6069449999998 +3922,Binary classification,Adaptive Random Forest,Bananas,0.884723284876307,0.8687572590011614,12.03856086730957,1416.6409169999997 +4028,Binary classification,Adaptive Random Forest,Bananas,0.8840327787434815,0.867892503536068,12.434591293334961,1495.7064749999997 +4134,Binary classification,Adaptive Random Forest,Bananas,0.884587466731188,0.868558831634059,12.796384811401367,1576.7285749999996 +4240,Binary classification,Adaptive Random Forest,Bananas,0.8858221278603444,0.8701019860440149,13.120096206665039,1659.7498909999997 +4346,Binary classification,Adaptive Random Forest,Bananas,0.8872266973532796,0.8716605552645365,13.362188339233398,1744.8641249999996 +4452,Binary classification,Adaptive Random Forest,Bananas,0.8869916872612896,0.8713225888974162,13.906320571899414,1831.9815919999996 +4558,Binary classification,Adaptive Random Forest,Bananas,0.8872064955014264,0.8719481813652217,14.321008682250977,1921.0718599999996 +4664,Binary classification,Adaptive Random Forest,Bananas,0.8876259918507399,0.8728155339805825,14.677774429321289,2012.2265639999996 +4770,Binary classification,Adaptive Random Forest,Bananas,0.8867687146152233,0.8716119828815977,15.041936874389648,2105.5104569999994 +4876,Binary classification,Adaptive Random Forest,Bananas,0.886974358974359,0.8715318256003731,15.36302375793457,2200.9404059999993 +4982,Binary classification,Adaptive Random Forest,Bananas,0.8877735394499097,0.8726941471191073,14.241693496704102,2298.464636999999 +5088,Binary classification,Adaptive Random Forest,Bananas,0.886966778061726,0.8716804284757868,14.559698104858398,2397.961828999999 +5194,Binary classification,Adaptive Random Forest,Bananas,0.8869632197188523,0.8716939890710382,15.019205093383789,2499.520506999999 +5300,Binary classification,Adaptive Random Forest,Bananas,0.886959803736554,0.8717070036410367,15.355104446411133,2603.0162549999986 +906,Binary classification,Adaptive Random Forest,Elec2,0.8674033149171271,0.8669623059866962,3.022599220275879,14.706798 +1812,Binary classification,Adaptive Random Forest,Elec2,0.8956377691882937,0.8737474949899798,3.453568458557129,43.639849999999996 +2718,Binary classification,Adaptive Random Forest,Elec2,0.889216047110784,0.8638625056535504,5.134407997131348,89.85880599999999 +3624,Binary classification,Adaptive Random Forest,Elec2,0.8901462876069556,0.8665325285043594,5.045891761779785,149.36791399999998 +4530,Binary classification,Adaptive Random Forest,Elec2,0.8924707440936189,0.8628555336524922,6.377499580383301,220.195834 +5436,Binary classification,Adaptive Random Forest,Elec2,0.8870285188592456,0.8556652562294312,8.556572914123535,302.53910099999996 +6342,Binary classification,Adaptive Random Forest,Elec2,0.884245387162908,0.8540175019888624,10.355942726135254,396.16016899999994 +7248,Binary classification,Adaptive Random Forest,Elec2,0.8835380157306472,0.8516174402250353,10.061070442199707,501.0892999999999 +8154,Binary classification,Adaptive Random Forest,Elec2,0.8847050165583221,0.8605341246290802,12.516213417053223,615.7123809999999 +9060,Binary classification,Adaptive Random Forest,Elec2,0.8869632409758251,0.8668400520156047,14.3945894241333,740.068354 +9966,Binary classification,Adaptive Random Forest,Elec2,0.8839939789262419,0.8666974169741698,15.028592109680176,874.678214 +10872,Binary classification,Adaptive Random Forest,Elec2,0.886119032287738,0.8712830110210023,18.58602237701416,1018.7273250000001 +11778,Binary classification,Adaptive Random Forest,Elec2,0.8851150547677676,0.869464544138929,18.284192085266113,1172.329585 +12684,Binary classification,Adaptive Random Forest,Elec2,0.8825987542379563,0.8672550592850139,18.562626838684082,1335.5183499999998 +13590,Binary classification,Adaptive Random Forest,Elec2,0.8835087202884686,0.8699794661190965,22.36763858795166,1507.9316749999998 +14496,Binary classification,Adaptive Random Forest,Elec2,0.883270093135564,0.870085995085995,23.97218418121338,1690.157865 +15402,Binary classification,Adaptive Random Forest,Elec2,0.8826050256476852,0.8679713743245216,24.89116382598877,1881.8741799999998 +16308,Binary classification,Adaptive Random Forest,Elec2,0.8806034218433801,0.8649885583524027,9.630642890930176,2082.7138459999996 +17214,Binary classification,Adaptive Random Forest,Elec2,0.880787776680416,0.862742474916388,9.825531959533691,2290.8714669999995 +18120,Binary classification,Adaptive Random Forest,Elec2,0.881505601854407,0.8635178946030132,13.432568550109863,2506.1422499999994 +19026,Binary classification,Adaptive Random Forest,Elec2,0.8835742444152431,0.864335150364427,11.236374855041504,2728.1698299999994 +19932,Binary classification,Adaptive Random Forest,Elec2,0.8847022226682053,0.8666744024135531,10.915810585021973,2956.9763619999994 +20838,Binary classification,Adaptive Random Forest,Elec2,0.8845803138647598,0.866618601297765,6.771607398986816,3192.2184919999995 +21744,Binary classification,Adaptive Random Forest,Elec2,0.8842845973416732,0.8643665768194071,9.905537605285645,3433.7445519999997 +22650,Binary classification,Adaptive Random Forest,Elec2,0.8832178021104684,0.8619303648796786,11.609391212463379,3682.1047309999994 +23556,Binary classification,Adaptive Random Forest,Elec2,0.8819783485459562,0.8599637316139432,7.878331184387207,3937.9632889999993 +24462,Binary classification,Adaptive Random Forest,Elec2,0.8805854216916724,0.8573800107416631,10.653840065002441,4201.066475999999 +25368,Binary classification,Adaptive Random Forest,Elec2,0.8791343083533725,0.8556497175141242,11.591797828674316,4470.752341999999 +26274,Binary classification,Adaptive Random Forest,Elec2,0.8801431127012522,0.8566616596112704,13.86082935333252,4746.937765999999 +27180,Binary classification,Adaptive Random Forest,Elec2,0.881195040288458,0.8584082438061829,14.463074684143066,5029.888654999999 +28086,Binary classification,Adaptive Random Forest,Elec2,0.8798646964571836,0.8561807331628303,15.367924690246582,5319.9108369999985 +28992,Binary classification,Adaptive Random Forest,Elec2,0.8796523058880342,0.8551019560612982,16.377129554748535,5616.458601999999 +29898,Binary classification,Adaptive Random Forest,Elec2,0.879285547044854,0.8544934080554772,16.05477237701416,5919.470834999999 +30804,Binary classification,Adaptive Random Forest,Elec2,0.8791351491737818,0.8535347574648885,17.57622241973877,6228.255318999999 +31710,Binary classification,Adaptive Random Forest,Elec2,0.8775111167176511,0.851267519338286,17.546963691711426,6543.393541999999 +32616,Binary classification,Adaptive Random Forest,Elec2,0.8771117583933773,0.8510701545778836,17.15481662750244,6864.899302999999 +33522,Binary classification,Adaptive Random Forest,Elec2,0.8770621401509502,0.8512864927285193,13.577618598937988,7192.851994 +34428,Binary classification,Adaptive Random Forest,Elec2,0.8757080198681267,0.849643346568748,12.363858222961426,7526.9490559999995 +35334,Binary classification,Adaptive Random Forest,Elec2,0.8754139189992358,0.848624484181568,12.552750587463379,7866.609101999999 +36240,Binary classification,Adaptive Random Forest,Elec2,0.875134523579569,0.8474427699672971,12.88097858428955,8211.574848999999 +37146,Binary classification,Adaptive Random Forest,Elec2,0.8743034055727554,0.8459838363846282,15.634392738342285,8562.247513999999 +38052,Binary classification,Adaptive Random Forest,Elec2,0.8741163175737826,0.8451642099818981,17.75814151763916,8918.936239999999 +38958,Binary classification,Adaptive Random Forest,Elec2,0.8743743101368175,0.8458873913591133,18.55082416534424,9281.578228999999 +39864,Binary classification,Adaptive Random Forest,Elec2,0.8744951458746206,0.847381104908331,20.39273166656494,9650.394165999998 +40770,Binary classification,Adaptive Random Forest,Elec2,0.8750521229365449,0.8493434283686265,20.04684543609619,10025.208226999997 +41676,Binary classification,Adaptive Random Forest,Elec2,0.8757768446310737,0.8512911843276937,22.40410327911377,10405.883388999997 +42582,Binary classification,Adaptive Random Forest,Elec2,0.8760010333247223,0.8517603458925264,17.905674934387207,10792.424187999997 +43488,Binary classification,Adaptive Random Forest,Elec2,0.8758249591832041,0.8516320474777449,17.979458808898926,11185.171687999997 +44394,Binary classification,Adaptive Random Forest,Elec2,0.8758362804946725,0.8511557571829769,19.483532905578613,11584.749531999996 +45300,Binary classification,Adaptive Random Forest,Elec2,0.8765977173889048,0.8524053440354862,22.381768226623535,11990.855977999996 +45312,Binary classification,Adaptive Random Forest,Elec2,0.8766083291033082,0.8523906328378699,22.39494037628174,12397.578789999996 +25,Binary classification,Adaptive Random Forest,Phishing,0.625,0.7096774193548387,0.41788291931152344,0.504078 +50,Binary classification,Adaptive Random Forest,Phishing,0.7346938775510204,0.7450980392156864,0.6195468902587891,1.506996 +75,Binary classification,Adaptive Random Forest,Phishing,0.7837837837837838,0.7999999999999999,0.8261966705322266,2.945496 +100,Binary classification,Adaptive Random Forest,Phishing,0.797979797979798,0.8039215686274509,0.9074077606201172,4.810448 +125,Binary classification,Adaptive Random Forest,Phishing,0.7903225806451613,0.7968749999999999,1.0524044036865234,7.208508 +150,Binary classification,Adaptive Random Forest,Phishing,0.8120805369127517,0.8227848101265823,1.155344009399414,10.051324000000001 +175,Binary classification,Adaptive Random Forest,Phishing,0.8390804597701149,0.8372093023255814,1.2272701263427734,13.451327000000001 +200,Binary classification,Adaptive Random Forest,Phishing,0.8442211055276382,0.8426395939086295,1.3437442779541016,17.363237 +225,Binary classification,Adaptive Random Forest,Phishing,0.8526785714285714,0.8465116279069769,1.4417095184326172,21.777532 +250,Binary classification,Adaptive Random Forest,Phishing,0.8554216867469879,0.85,1.652822494506836,26.610844 +275,Binary classification,Adaptive Random Forest,Phishing,0.8540145985401459,0.8473282442748092,1.7137775421142578,32.103705 +300,Binary classification,Adaptive Random Forest,Phishing,0.8595317725752508,0.85,1.6836071014404297,38.177642 +325,Binary classification,Adaptive Random Forest,Phishing,0.8672839506172839,0.8542372881355932,1.8154468536376953,44.724647 +350,Binary classification,Adaptive Random Forest,Phishing,0.8681948424068768,0.8525641025641026,1.9355945587158203,51.783981999999995 +375,Binary classification,Adaptive Random Forest,Phishing,0.8663101604278075,0.8484848484848485,2.1126270294189453,59.453596999999995 +400,Binary classification,Adaptive Random Forest,Phishing,0.8696741854636592,0.8505747126436781,2.2513599395751953,67.701448 +425,Binary classification,Adaptive Random Forest,Phishing,0.8702830188679245,0.8467966573816157,2.4080867767333984,76.525873 +450,Binary classification,Adaptive Random Forest,Phishing,0.8775055679287305,0.8533333333333333,2.413846969604492,85.91210000000001 +475,Binary classification,Adaptive Random Forest,Phishing,0.879746835443038,0.85785536159601,2.540945053100586,95.91125100000001 +500,Binary classification,Adaptive Random Forest,Phishing,0.8817635270541082,0.8624708624708626,2.727457046508789,106.551347 +525,Binary classification,Adaptive Random Forest,Phishing,0.8835877862595419,0.8623024830699774,2.780088424682617,117.81370000000001 +550,Binary classification,Adaptive Random Forest,Phishing,0.8816029143897997,0.8602150537634409,2.8441905975341797,129.668358 +575,Binary classification,Adaptive Random Forest,Phishing,0.8832752613240418,0.8618556701030927,2.9667911529541016,142.149346 +600,Binary classification,Adaptive Random Forest,Phishing,0.8864774624373957,0.8634538152610441,2.9313793182373047,155.146824 +625,Binary classification,Adaptive Random Forest,Phishing,0.8878205128205128,0.8622047244094488,3.1180286407470703,168.863765 +650,Binary classification,Adaptive Random Forest,Phishing,0.8906009244992296,0.8672897196261682,3.1772937774658203,183.176428 +675,Binary classification,Adaptive Random Forest,Phishing,0.8931750741839762,0.8732394366197184,3.270914077758789,198.116157 +700,Binary classification,Adaptive Random Forest,Phishing,0.8969957081545065,0.8762886597938143,3.2819652557373047,213.702702 +725,Binary classification,Adaptive Random Forest,Phishing,0.8950276243093923,0.8762214983713356,3.465627670288086,229.894704 +750,Binary classification,Adaptive Random Forest,Phishing,0.897196261682243,0.8791208791208791,3.637697219848633,246.71919699999998 +775,Binary classification,Adaptive Random Forest,Phishing,0.8979328165374677,0.8793893129770992,3.6838626861572266,264.27439799999996 +800,Binary classification,Adaptive Random Forest,Phishing,0.8961201501877347,0.8784773060029282,3.7807750701904297,282.50010699999996 +825,Binary classification,Adaptive Random Forest,Phishing,0.8968446601941747,0.8801128349788435,3.913633346557617,301.46455399999996 +850,Binary classification,Adaptive Random Forest,Phishing,0.8987043580683156,0.8818681318681318,4.011789321899414,321.06457099999994 +875,Binary classification,Adaptive Random Forest,Phishing,0.9016018306636155,0.8847184986595175,4.15968132019043,341.44966299999993 +900,Binary classification,Adaptive Random Forest,Phishing,0.9010011123470523,0.8836601307189543,3.946676254272461,362.64137199999993 +925,Binary classification,Adaptive Random Forest,Phishing,0.9036796536796536,0.8877679697351829,4.049928665161133,384.6765929999999 +950,Binary classification,Adaptive Random Forest,Phishing,0.9030558482613277,0.8883495145631068,3.6841602325439453,407.5276099999999 +975,Binary classification,Adaptive Random Forest,Phishing,0.9045174537987679,0.8899408284023669,3.787748336791992,431.1052179999999 +1000,Binary classification,Adaptive Random Forest,Phishing,0.9049049049049049,0.8904267589388698,4.052656173706055,455.43358799999993 +1025,Binary classification,Adaptive Random Forest,Phishing,0.9033203125,0.888888888888889,4.062379837036133,480.5827999999999 +1050,Binary classification,Adaptive Random Forest,Phishing,0.9046711153479504,0.8908296943231442,4.190084457397461,506.4991779999999 +1075,Binary classification,Adaptive Random Forest,Phishing,0.9059590316573557,0.8928950159066809,4.285711288452148,533.2628339999999 +1100,Binary classification,Adaptive Random Forest,Phishing,0.9062784349408554,0.8934850051706308,4.370790481567383,560.8485399999998 +1125,Binary classification,Adaptive Random Forest,Phishing,0.9065836298932385,0.8948948948948948,3.9348621368408203,589.2233909999999 +1150,Binary classification,Adaptive Random Forest,Phishing,0.9077458659704091,0.896078431372549,4.19316291809082,618.4066789999998 +1175,Binary classification,Adaptive Random Forest,Phishing,0.9063032367972743,0.8942307692307692,4.349401473999023,648.4320089999999 +1200,Binary classification,Adaptive Random Forest,Phishing,0.9065888240200167,0.8943396226415095,4.34752082824707,679.2709969999999 +1225,Binary classification,Adaptive Random Forest,Phishing,0.9068627450980392,0.8944444444444444,4.031515121459961,710.9105619999998 +1250,Binary classification,Adaptive Random Forest,Phishing,0.9079263410728583,0.896115627822945,4.102910995483398,743.3769359999998 +1903,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17035293579101562,12.381202 +3806,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17157363891601562,37.073822 +5709,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,74.106824 +7612,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,122.30955 +9515,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,180.539768 +11418,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17401504516601562,247.17494200000002 +13321,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17401504516601562,321.611977 +15224,Binary classification,Adaptive Random Forest,SMTP,0.9992774091834724,0.0,0.23138427734375,404.612477 +17127,Binary classification,Adaptive Random Forest,SMTP,0.9992409202382343,0.0,0.17718124389648438,498.19936 +19030,Binary classification,Adaptive Random Forest,SMTP,0.9993168322034789,0.0,0.16917037963867188,601.949625 +20933,Binary classification,Adaptive Random Forest,SMTP,0.999378941333843,0.0,0.17043685913085938,714.510104 +22836,Binary classification,Adaptive Random Forest,SMTP,0.9994306984891613,0.0,0.17826461791992188,835.601872 +24739,Binary classification,Adaptive Random Forest,SMTP,0.9994744926833212,0.0,0.16260147094726562,964.8451829999999 +26642,Binary classification,Adaptive Random Forest,SMTP,0.9994744942006681,0.0,0.170440673828125,1103.1275799999999 +28545,Binary classification,Adaptive Random Forest,SMTP,0.9995095291479821,0.0,0.17826461791992188,1249.157139 +30448,Binary classification,Adaptive Random Forest,SMTP,0.9995401845830459,0.0,0.17816925048828125,1402.52966 +32351,Binary classification,Adaptive Random Forest,SMTP,0.9995672333848532,0.0,0.16262054443359375,1563.348194 +34254,Binary classification,Adaptive Random Forest,SMTP,0.9995912766764955,0.0,0.17043685913085938,1731.617157 +36157,Binary classification,Adaptive Random Forest,SMTP,0.9996127890253347,0.0,0.17043304443359375,1907.1505949999998 +38060,Binary classification,Adaptive Random Forest,SMTP,0.9996321500827662,0.0,0.1781158447265625,2090.2143509999996 +39963,Binary classification,Adaptive Random Forest,SMTP,0.9996496671838246,0.0,0.1704559326171875,2280.326256 +41866,Binary classification,Adaptive Random Forest,SMTP,0.9996655917831124,0.0,0.163818359375,2477.410916 +43769,Binary classification,Adaptive Random Forest,SMTP,0.9996801316029976,0.0,0.17158126831054688,2681.481315 +45672,Binary classification,Adaptive Random Forest,SMTP,0.9996934597446958,0.0,0.171630859375,2892.603814 +47575,Binary classification,Adaptive Random Forest,SMTP,0.9997057216126456,0.0,0.17947769165039062,3110.724932 +49478,Binary classification,Adaptive Random Forest,SMTP,0.99971704024092,0.0,0.32259368896484375,3336.815763 +51381,Binary classification,Adaptive Random Forest,SMTP,0.9996885947839627,0.0,0.3238716125488281,3571.848368 +53284,Binary classification,Adaptive Random Forest,SMTP,0.9996997166075484,0.0,0.2926750183105469,3815.616702 +55187,Binary classification,Adaptive Random Forest,SMTP,0.999710071394919,0.0,0.3244895935058594,4068.2038989999996 +57090,Binary classification,Adaptive Random Forest,SMTP,0.9995620872672494,0.0,0.2933502197265625,4329.847041999999 +58993,Binary classification,Adaptive Random Forest,SMTP,0.9995762137238947,0.0,0.27820587158203125,4599.769264 +60896,Binary classification,Adaptive Random Forest,SMTP,0.999589457262501,0.0,0.3094024658203125,4878.106527 +62799,Binary classification,Adaptive Random Forest,SMTP,0.9995700500015924,0.0,0.30953216552734375,5164.770925 +64702,Binary classification,Adaptive Random Forest,SMTP,0.9995826957852274,0.0,0.29401397705078125,5459.39874 +66605,Binary classification,Adaptive Random Forest,SMTP,0.9995946189418053,0.0,0.293975830078125,5761.313045999999 +68508,Binary classification,Adaptive Random Forest,SMTP,0.9995766855941729,0.0,0.32515716552734375,6070.487373999999 +70411,Binary classification,Adaptive Random Forest,SMTP,0.9995881266865502,0.0,0.3101234436035156,6386.879354 +72314,Binary classification,Adaptive Random Forest,SMTP,0.9995989656078437,0.0,0.3101387023925781,6710.520715 +74217,Binary classification,Adaptive Random Forest,SMTP,0.99960924867953,0.0,0.3101615905761719,7041.446151 +76120,Binary classification,Adaptive Random Forest,SMTP,0.9996190175908775,0.0,0.3101768493652344,7379.414547 +78023,Binary classification,Adaptive Random Forest,SMTP,0.9996283099638563,0.0,0.3100128173828125,7724.224697000001 +79926,Binary classification,Adaptive Random Forest,SMTP,0.9996371598373475,0.0,0.31014251708984375,8075.760258 +81829,Binary classification,Adaptive Random Forest,SMTP,0.9996455980837855,0.0,0.3100852966308594,8434.038373 +83732,Binary classification,Adaptive Random Forest,SMTP,0.9996536527689864,0.0,0.31072235107421875,8799.103777999999 +85635,Binary classification,Adaptive Random Forest,SMTP,0.999661349463998,0.0,0.310821533203125,9170.869249 +87538,Binary classification,Adaptive Random Forest,SMTP,0.9996687115162731,0.0,0.2950706481933594,9549.506615999999 +89441,Binary classification,Adaptive Random Forest,SMTP,0.9996645796064401,0.0,0.2951812744140625,9935.081315999998 +91344,Binary classification,Adaptive Random Forest,SMTP,0.999671567607808,0.0,0.2957954406738281,10327.647623999997 +93247,Binary classification,Adaptive Random Forest,SMTP,0.9996782703815713,0.0,0.3115119934082031,10728.119291999998 +95150,Binary classification,Adaptive Random Forest,SMTP,0.9996847050415664,0.0,0.3115577697753906,11135.737891999997 +95156,Binary classification,Adaptive Random Forest,SMTP,0.9996847249224948,0.0,0.32709503173828125,11543.384409999997 +106,Binary classification,Streaming Random Patches,Bananas,0.5428571428571428,0.4,0.2255392074584961,2.569769 +212,Binary classification,Streaming Random Patches,Bananas,0.5592417061611374,0.4685714285714286,0.6304416656494141,8.011061999999999 +318,Binary classification,Streaming Random Patches,Bananas,0.637223974763407,0.5724907063197027,0.9710559844970703,16.523663 +424,Binary classification,Streaming Random Patches,Bananas,0.6926713947990544,0.6448087431693988,1.2628002166748047,28.175313 +530,Binary classification,Streaming Random Patches,Bananas,0.7145557655954632,0.6621923937360179,1.5703105926513672,43.218913 +636,Binary classification,Streaming Random Patches,Bananas,0.7448818897637796,0.7000000000000001,1.467294692993164,61.573557 +742,Binary classification,Streaming Random Patches,Bananas,0.7624831309041835,0.7170418006430868,1.877767562866211,83.22394800000001 +848,Binary classification,Streaming Random Patches,Bananas,0.7827626918536009,0.7430167597765364,2.3253536224365234,108.413447 +954,Binary classification,Streaming Random Patches,Bananas,0.7964323189926548,0.7599009900990098,1.7426891326904297,137.183902 +1060,Binary classification,Streaming Random Patches,Bananas,0.8054768649669499,0.7674943566591422,1.7942829132080078,169.50541299999998 +1166,Binary classification,Streaming Random Patches,Bananas,0.8103004291845494,0.7747196738022425,1.8575687408447266,205.46256099999997 +1272,Binary classification,Streaming Random Patches,Bananas,0.8151062155782848,0.7822057460611677,1.917165756225586,244.58779499999997 +1378,Binary classification,Streaming Random Patches,Bananas,0.8191721132897604,0.7851596203623814,2.1873340606689453,286.663235 +1484,Binary classification,Streaming Random Patches,Bananas,0.8240053944706676,0.7916999201915402,2.2810306549072266,331.700902 +1590,Binary classification,Streaming Random Patches,Bananas,0.8231592196349906,0.7916975537435137,2.585817337036133,379.560946 +1696,Binary classification,Streaming Random Patches,Bananas,0.8271386430678466,0.7961029923451634,2.8855953216552734,430.279509 +1802,Binary classification,Streaming Random Patches,Bananas,0.8334258745141588,0.8046875,2.8240184783935547,483.724395 +1908,Binary classification,Streaming Random Patches,Bananas,0.8332459360251704,0.8060975609756097,3.138376235961914,539.732848 +2014,Binary classification,Streaming Random Patches,Bananas,0.8340784898161947,0.8084862385321101,3.5751514434814453,598.378334 +2120,Binary classification,Streaming Random Patches,Bananas,0.8367154318074563,0.813778256189451,3.890401840209961,659.469374 +2226,Binary classification,Streaming Random Patches,Bananas,0.8382022471910112,0.8157625383828044,4.414094924926758,723.240852 +2332,Binary classification,Streaming Random Patches,Bananas,0.8404118404118404,0.8185365853658537,4.828973770141602,789.4489060000001 +2438,Binary classification,Streaming Random Patches,Bananas,0.8432498974148543,0.8216619981325864,4.724649429321289,858.0992190000001 +2544,Binary classification,Streaming Random Patches,Bananas,0.8450648839952811,0.8247330960854093,4.20762825012207,929.1630060000001 +2650,Binary classification,Streaming Random Patches,Bananas,0.846734616836542,0.8270868824531515,4.517709732055664,1002.5522950000001 +2756,Binary classification,Streaming Random Patches,Bananas,0.8500907441016334,0.8306683066830667,4.757001876831055,1078.240465 +2862,Binary classification,Streaming Random Patches,Bananas,0.8521495980426425,0.8324752475247525,4.690572738647461,1156.1945970000002 +2968,Binary classification,Streaming Random Patches,Bananas,0.854061341422312,0.8339087073264287,4.873067855834961,1236.185934 +3074,Binary classification,Streaming Random Patches,Bananas,0.8538887081028311,0.8340110905730129,5.244169235229492,1318.3478 +3180,Binary classification,Streaming Random Patches,Bananas,0.8565586662472475,0.836441893830703,5.473237991333008,1402.707418 +3286,Binary classification,Streaming Random Patches,Bananas,0.8575342465753425,0.8371607515657619,5.716192245483398,1489.296647 +3392,Binary classification,Streaming Random Patches,Bananas,0.8593335299321734,0.8400938652363393,6.05610466003418,1578.3688909999998 +3498,Binary classification,Streaming Random Patches,Bananas,0.8615956534172148,0.8419333768778575,6.433168411254883,1669.799251 +3604,Binary classification,Streaming Random Patches,Bananas,0.8631695809048016,0.8430436166825852,6.670698165893555,1763.5519829999998 +3710,Binary classification,Streaming Random Patches,Bananas,0.8638447020760313,0.8444718201416692,7.050989151000977,1859.6268179999997 +3816,Binary classification,Streaming Random Patches,Bananas,0.8657929226736566,0.84688995215311,7.316404342651367,1958.0125029999997 +3922,Binary classification,Streaming Random Patches,Bananas,0.8653404743687835,0.846064139941691,7.61528205871582,2058.6755279999998 +4028,Binary classification,Streaming Random Patches,Bananas,0.8644151974174323,0.8449744463373083,7.967977523803711,2161.5792619999997 +4134,Binary classification,Streaming Random Patches,Bananas,0.8654730220179047,0.8462389380530975,7.394952774047852,2266.5958729999998 +4240,Binary classification,Streaming Random Patches,Bananas,0.8674215616890776,0.8486806677436726,7.571531295776367,2373.458397 +4346,Binary classification,Streaming Random Patches,Bananas,0.8688147295742232,0.8503937007874016,7.877435684204102,2482.305033 +4452,Binary classification,Streaming Random Patches,Bananas,0.8683441923163334,0.8496664956387892,8.180627822875977,2593.165336 +4558,Binary classification,Streaming Random Patches,Bananas,0.8689927583936801,0.8508618536097925,8.39448356628418,2705.945127 +4664,Binary classification,Streaming Random Patches,Bananas,0.8691829294445635,0.8516536964980544,8.710580825805664,2820.674173 +4770,Binary classification,Streaming Random Patches,Bananas,0.8689452715453974,0.8510131108462455,9.014997482299805,2937.453009 +4876,Binary classification,Streaming Random Patches,Bananas,0.8703589743589744,0.8523364485981308,9.167715072631836,3056.177406 +4982,Binary classification,Streaming Random Patches,Bananas,0.8713109817305762,0.8538864827900615,9.482858657836914,3176.936292 +5088,Binary classification,Streaming Random Patches,Bananas,0.8714369962649892,0.8539526574363555,9.87147331237793,3299.754349 +5194,Binary classification,Streaming Random Patches,Bananas,0.8717504332755632,0.8543944031482291,10.204122543334961,3424.594329 +5300,Binary classification,Streaming Random Patches,Bananas,0.8716739007359879,0.8542648949849978,10.538087844848633,3551.414099 +906,Binary classification,Streaming Random Patches,Elec2,0.8828729281767956,0.8811659192825113,5.258722305297852,37.408806 +1812,Binary classification,Streaming Random Patches,Elec2,0.9039204859193816,0.8804945054945055,8.443174362182617,104.985856 +2718,Binary classification,Streaming Random Patches,Elec2,0.8873757821126242,0.8602739726027397,12.445928573608398,198.51440200000002 +3624,Binary classification,Streaming Random Patches,Elec2,0.884902014904775,0.8576305906452714,16.533422470092773,314.268209 +4530,Binary classification,Streaming Random Patches,Elec2,0.8812099801280636,0.8452243958573072,19.266294479370117,451.77035 +5436,Binary classification,Streaming Random Patches,Elec2,0.8756209751609936,0.8372652864708715,24.12981605529785,609.66041 +6342,Binary classification,Streaming Random Patches,Elec2,0.8719444882510645,0.8340825500612996,28.348302841186523,788.9253299999999 +7248,Binary classification,Streaming Random Patches,Elec2,0.8691872498965089,0.8308351177730193,31.664392471313477,988.709629 +8154,Binary classification,Streaming Random Patches,Elec2,0.8690052741322213,0.8387681159420289,35.27585411071777,1209.125777 +9060,Binary classification,Streaming Random Patches,Elec2,0.869742797218236,0.844162704701532,38.39363670349121,1448.169528 +9966,Binary classification,Streaming Random Patches,Elec2,0.8681384846964375,0.8455934195064629,42.49019813537598,1706.33653 +10872,Binary classification,Streaming Random Patches,Elec2,0.8687333272008095,0.849265870920038,46.91076469421387,1983.228319 +11778,Binary classification,Streaming Random Patches,Elec2,0.8694064702386006,0.8495402073958129,41.518564224243164,2278.772125 +12684,Binary classification,Streaming Random Patches,Elec2,0.8672238429393676,0.8479320931912588,46.98099327087402,2591.81768 +13590,Binary classification,Streaming Random Patches,Elec2,0.8682758113179778,0.8513289036544851,50.757638931274414,2922.748705 +14496,Binary classification,Streaming Random Patches,Elec2,0.8687823387374957,0.8527863777089782,43.74130058288574,3269.777293 +15402,Binary classification,Streaming Random Patches,Elec2,0.8686448931887539,0.8518708354689903,49.06788444519043,3632.343799 +16308,Binary classification,Streaming Random Patches,Elec2,0.8649659655362728,0.8467427616926504,54.357858657836914,4011.129855 +17214,Binary classification,Streaming Random Patches,Elec2,0.865392435949573,0.8447987139125192,52.38222694396973,4407.893822 +18120,Binary classification,Streaming Random Patches,Elec2,0.8652795408135107,0.8448286822198208,59.36540412902832,4822.718697 +19026,Binary classification,Streaming Random Patches,Elec2,0.867700394218134,0.8459136822773186,57.10729789733887,5251.994155 +19932,Binary classification,Streaming Random Patches,Elec2,0.8692489087351363,0.84882236918436,51.34463310241699,5694.600867 +20838,Binary classification,Streaming Random Patches,Elec2,0.8691750251955656,0.8490085299656586,53.35045051574707,6149.875029 +21744,Binary classification,Streaming Random Patches,Elec2,0.8700271351699398,0.8478682170542635,59.89077186584473,6616.7506189999995 +22650,Binary classification,Streaming Random Patches,Elec2,0.8694865115457636,0.8459614382490881,65.61615180969238,7094.9355989999995 +23556,Binary classification,Streaming Random Patches,Elec2,0.868860114625345,0.8444690599667689,72.22853660583496,7585.744803 +24462,Binary classification,Streaming Random Patches,Elec2,0.8682392379706472,0.8428648042513772,80.47726249694824,8090.441156999999 +25368,Binary classification,Streaming Random Patches,Elec2,0.8672290771474751,0.8417888012025554,73.03231239318848,8608.413273 +26274,Binary classification,Streaming Random Patches,Elec2,0.8684581128915617,0.8430802760624773,79.16955757141113,9138.877569 +27180,Binary classification,Streaming Random Patches,Elec2,0.8698259685786821,0.8451234459814394,84.2670955657959,9681.232951 +28086,Binary classification,Streaming Random Patches,Elec2,0.8689335944454335,0.8434616202423985,93.53372383117676,10236.036265 +28992,Binary classification,Streaming Random Patches,Elec2,0.8688558518160808,0.8423322551215061,92.75358009338379,10801.143387 +29898,Binary classification,Streaming Random Patches,Elec2,0.8689166137070609,0.8421603769785332,90.79977607727051,11375.205452 +30804,Binary classification,Streaming Random Patches,Elec2,0.8684868356978216,0.8408063818917749,97.95510292053223,11957.401901000001 +31710,Binary classification,Streaming Random Patches,Elec2,0.8668832192752847,0.8384553561177235,105.25788688659668,12547.475367000001 +32616,Binary classification,Streaming Random Patches,Elec2,0.8664724819868159,0.8381943154374885,94.53887367248535,13145.300695000002 +33522,Binary classification,Streaming Random Patches,Elec2,0.8661436114674383,0.8380553650701988,102.01883506774902,13750.733881000002 +34428,Binary classification,Streaming Random Patches,Elec2,0.8648444534812793,0.8364441632394811,100.42782783508301,14363.910035000003 +35334,Binary classification,Streaming Random Patches,Elec2,0.8647723091727281,0.8357849876271651,107.87262153625488,14984.863075000003 +36240,Binary classification,Streaming Random Patches,Elec2,0.8642346643119292,0.83420946219167,112.83228874206543,15613.594311000003 +37146,Binary classification,Streaming Random Patches,Elec2,0.8632655808318751,0.8326689289361843,120.66686058044434,16250.422859000002 +38052,Binary classification,Streaming Random Patches,Elec2,0.8627631336889964,0.8316135689410551,126.15458106994629,16895.166705000003 +38958,Binary classification,Streaming Random Patches,Elec2,0.8634391765279668,0.8328620797989319,107.22049903869629,17548.303432000004 +39864,Binary classification,Streaming Random Patches,Elec2,0.8644356922459423,0.8353041570157259,103.5422191619873,18208.490072000004 +40770,Binary classification,Streaming Random Patches,Elec2,0.865436974171552,0.8378745788758201,97.78764533996582,18874.504490000003 +41676,Binary classification,Streaming Random Patches,Elec2,0.8666586682663467,0.8403940603728063,102.76390266418457,19545.753018000003 +42582,Binary classification,Streaming Random Patches,Elec2,0.8673821657546793,0.8415055151702265,107.51249122619629,20221.607860000004 +43488,Binary classification,Streaming Random Patches,Elec2,0.8677075907742544,0.841885392332005,102.9560489654541,20902.251232000002 +44394,Binary classification,Streaming Random Patches,Elec2,0.8679071024711104,0.8415905775568644,103.86480903625488,21587.715975000003 +45300,Binary classification,Streaming Random Patches,Elec2,0.8688933530541513,0.843053830501308,107.29028511047363,22278.011723000003 +45312,Binary classification,Streaming Random Patches,Elec2,0.8688839354682086,0.8430092751631743,107.32242012023926,22968.976341 +25,Binary classification,Streaming Random Patches,Phishing,0.8333333333333334,0.8333333333333334,0.7029104232788086,1.141902 +50,Binary classification,Streaming Random Patches,Phishing,0.8571428571428571,0.8372093023255814,0.9397382736206055,3.355867 +75,Binary classification,Streaming Random Patches,Phishing,0.8783783783783784,0.8695652173913043,0.9708013534545898,6.532426 +100,Binary classification,Streaming Random Patches,Phishing,0.8888888888888888,0.8817204301075269,1.056624412536621,10.815831 +125,Binary classification,Streaming Random Patches,Phishing,0.8790322580645161,0.8739495798319329,1.3782567977905273,16.293882 +150,Binary classification,Streaming Random Patches,Phishing,0.8791946308724832,0.8783783783783784,1.379134178161621,22.890072 +175,Binary classification,Streaming Random Patches,Phishing,0.896551724137931,0.888888888888889,1.4786596298217773,30.523139999999998 +200,Binary classification,Streaming Random Patches,Phishing,0.8944723618090452,0.8864864864864866,1.6607275009155273,39.247513 +225,Binary classification,Streaming Random Patches,Phishing,0.8973214285714286,0.8866995073891626,1.686568260192871,49.014512999999994 +250,Binary classification,Streaming Random Patches,Phishing,0.891566265060241,0.88,1.9668035507202148,59.910523 +275,Binary classification,Streaming Random Patches,Phishing,0.8905109489051095,0.8780487804878049,2.071291923522949,71.88595 +300,Binary classification,Streaming Random Patches,Phishing,0.8896321070234113,0.8754716981132077,2.2423620223999023,85.00905599999999 +325,Binary classification,Streaming Random Patches,Phishing,0.8888888888888888,0.8723404255319148,2.4750547409057617,99.14632499999999 +350,Binary classification,Streaming Random Patches,Phishing,0.8853868194842407,0.8666666666666667,2.5328550338745117,114.35437499999999 +375,Binary classification,Streaming Random Patches,Phishing,0.8850267379679144,0.8652037617554859,2.8150205612182617,130.79065599999998 +400,Binary classification,Streaming Random Patches,Phishing,0.8822055137844611,0.8613569321533923,2.795191764831543,148.41625799999997 +425,Binary classification,Streaming Random Patches,Phishing,0.8844339622641509,0.8611898016997167,2.962000846862793,167.06847699999997 +450,Binary classification,Streaming Random Patches,Phishing,0.888641425389755,0.8648648648648649,3.03415584564209,186.853211 +475,Binary classification,Streaming Random Patches,Phishing,0.890295358649789,0.8686868686868687,3.071761131286621,207.82899999999998 +500,Binary classification,Streaming Random Patches,Phishing,0.8917835671342685,0.8726415094339622,3.1551198959350586,229.951047 +525,Binary classification,Streaming Random Patches,Phishing,0.8950381679389313,0.8741418764302059,3.1928510665893555,253.214946 +550,Binary classification,Streaming Random Patches,Phishing,0.8943533697632058,0.8739130434782608,3.2878904342651367,277.566695 +575,Binary classification,Streaming Random Patches,Phishing,0.8937282229965157,0.8726513569937369,3.4417715072631836,303.140017 +600,Binary classification,Streaming Random Patches,Phishing,0.8964941569282137,0.8739837398373984,3.515273094177246,329.715755 +625,Binary classification,Streaming Random Patches,Phishing,0.8958333333333334,0.8707753479125249,3.5807180404663086,357.461609 +650,Binary classification,Streaming Random Patches,Phishing,0.8983050847457628,0.8754716981132076,3.695376396179199,386.398038 +675,Binary classification,Streaming Random Patches,Phishing,0.8961424332344213,0.8754448398576512,3.7550153732299805,416.46849299999997 +700,Binary classification,Streaming Random Patches,Phishing,0.899856938483548,0.8784722222222222,3.7909955978393555,447.643877 +725,Binary classification,Streaming Random Patches,Phishing,0.899171270718232,0.8797364085667215,3.9393529891967773,479.929216 +750,Binary classification,Streaming Random Patches,Phishing,0.9012016021361816,0.8825396825396825,3.942519187927246,513.493128 +775,Binary classification,Streaming Random Patches,Phishing,0.9018087855297158,0.8827160493827161,4.2751874923706055,548.2965389999999 +800,Binary classification,Streaming Random Patches,Phishing,0.899874843554443,0.8816568047337278,4.513812065124512,584.3583229999999 +825,Binary classification,Streaming Random Patches,Phishing,0.8992718446601942,0.8819345661450925,4.773520469665527,621.611368 +850,Binary classification,Streaming Random Patches,Phishing,0.901060070671378,0.8836565096952909,4.8153791427612305,660.1796979999999 +875,Binary classification,Streaming Random Patches,Phishing,0.902745995423341,0.884979702300406,4.980830192565918,699.8371419999999 +900,Binary classification,Streaming Random Patches,Phishing,0.9043381535038932,0.8862433862433862,5.134486198425293,740.7850809999999 +925,Binary classification,Streaming Random Patches,Phishing,0.9069264069264069,0.8903061224489796,5.209948539733887,782.8443949999998 +950,Binary classification,Streaming Random Patches,Phishing,0.9083245521601686,0.8932515337423312,5.338950157165527,826.1813729999999 +975,Binary classification,Streaming Random Patches,Phishing,0.9106776180698152,0.895808383233533,5.382990837097168,870.7373769999999 +1000,Binary classification,Streaming Random Patches,Phishing,0.9109109109109109,0.896149358226371,5.44773006439209,916.477949 +1025,Binary classification,Streaming Random Patches,Phishing,0.9111328125,0.896942242355606,5.5915327072143555,963.445117 +1050,Binary classification,Streaming Random Patches,Phishing,0.9113441372735939,0.8976897689768977,5.678961753845215,1011.481541 +1075,Binary classification,Streaming Random Patches,Phishing,0.9115456238361266,0.8986125933831376,5.788058280944824,1060.652982 +1100,Binary classification,Streaming Random Patches,Phishing,0.9117379435850773,0.8990634755463061,5.880267143249512,1110.965738 +1125,Binary classification,Streaming Random Patches,Phishing,0.9119217081850534,0.9003021148036253,6.120665550231934,1162.442095 +1150,Binary classification,Streaming Random Patches,Phishing,0.9129677980852916,0.9013806706114399,6.185591697692871,1215.0055750000001 +1175,Binary classification,Streaming Random Patches,Phishing,0.9114139693356048,0.8996138996138997,6.431841850280762,1268.8167280000002 +1200,Binary classification,Streaming Random Patches,Phishing,0.9124270225187656,0.9004739336492891,6.484606742858887,1323.7082160000002 +1225,Binary classification,Streaming Random Patches,Phishing,0.9133986928104575,0.9014869888475836,6.481654167175293,1379.6419000000003 +1250,Binary classification,Streaming Random Patches,Phishing,0.9135308246597278,0.9019963702359347,6.595587730407715,1436.6903440000003 +1903,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1670236587524414,31.246172 +3806,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1682443618774414,90.057064 +5709,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,168.92668600000002 +7612,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,266.339332 +9515,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,379.70068100000003 +11418,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1706857681274414,507.50093200000003 +13321,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1706857681274414,650.046105 +15224,Binary classification,Streaming Random Patches,SMTP,0.9992774091834724,0.0,0.2171335220336914,806.74928 +17127,Binary classification,Streaming Random Patches,SMTP,0.9992409202382343,0.0,0.1745767593383789,979.905317 +19030,Binary classification,Streaming Random Patches,SMTP,0.9993168322034789,0.0,0.1744394302368164,1169.37771 +20933,Binary classification,Streaming Random Patches,SMTP,0.999378941333843,0.0,0.17577457427978516,1374.513378 +22836,Binary classification,Streaming Random Patches,SMTP,0.9994306984891613,0.0,0.17572879791259766,1595.365052 +24739,Binary classification,Streaming Random Patches,SMTP,0.9994744926833212,0.0,0.1757516860961914,1830.5281120000002 +26642,Binary classification,Streaming Random Patches,SMTP,0.9994744942006681,0.0,0.17572879791259766,2079.072293 +28545,Binary classification,Streaming Random Patches,SMTP,0.9995095291479821,0.0,0.17572879791259766,2341.3308700000002 +30448,Binary classification,Streaming Random Patches,SMTP,0.9995401845830459,0.0,0.17563724517822266,2616.3107910000003 +32351,Binary classification,Streaming Random Patches,SMTP,0.9995672333848532,0.0,0.17577457427978516,2903.8369350000003 +34254,Binary classification,Streaming Random Patches,SMTP,0.9995912766764955,0.0,0.1756601333618164,3203.0985050000004 +36157,Binary classification,Streaming Random Patches,SMTP,0.9996127890253347,0.0,0.17568302154541016,3513.3936680000006 +38060,Binary classification,Streaming Random Patches,SMTP,0.9996321500827662,0.0,0.1757059097290039,3834.7595300000007 +39963,Binary classification,Streaming Random Patches,SMTP,0.9996496671838246,0.0,0.17577457427978516,4167.168481000001 +41866,Binary classification,Streaming Random Patches,SMTP,0.9996655917831124,0.0,0.1769266128540039,4510.027218000001 +43769,Binary classification,Streaming Random Patches,SMTP,0.9996801316029976,0.0,0.1769266128540039,4863.234695000001 +45672,Binary classification,Streaming Random Patches,SMTP,0.9996934597446958,0.0,0.1769266128540039,5226.731618000001 +47575,Binary classification,Streaming Random Patches,SMTP,0.9997057216126456,0.0,0.1770639419555664,5600.511358000001 +49478,Binary classification,Streaming Random Patches,SMTP,0.99971704024092,0.0,0.17690372467041016,5984.651066 +51381,Binary classification,Streaming Random Patches,SMTP,0.9996885947839627,0.0,0.16916751861572266,6379.477192 +53284,Binary classification,Streaming Random Patches,SMTP,0.9996997166075484,0.0,0.1770639419555664,6785.903036000001 +55187,Binary classification,Streaming Random Patches,SMTP,0.999710071394919,0.0,0.17704105377197266,7201.6518080000005 +57090,Binary classification,Streaming Random Patches,SMTP,0.9995620872672494,0.0,0.1769266128540039,7626.427031 +58993,Binary classification,Streaming Random Patches,SMTP,0.9995762137238947,0.0,0.1769266128540039,8059.133738 +60896,Binary classification,Streaming Random Patches,SMTP,0.999589457262501,0.0,0.1769723892211914,8499.283325 +62799,Binary classification,Streaming Random Patches,SMTP,0.9995700500015924,0.0,0.1769266128540039,8946.957028 +64702,Binary classification,Streaming Random Patches,SMTP,0.9995826957852274,0.0,0.17699527740478516,9401.959764000001 +66605,Binary classification,Streaming Random Patches,SMTP,0.9995946189418053,0.0,0.1769723892211914,9864.111715000001 +68508,Binary classification,Streaming Random Patches,SMTP,0.9995766855941729,0.0,0.1691446304321289,10332.853073 +70411,Binary classification,Streaming Random Patches,SMTP,0.9995881266865502,0.0,0.17690372467041016,10808.168746 +72314,Binary classification,Streaming Random Patches,SMTP,0.9995989656078437,0.0,0.16912174224853516,11290.14581 +74217,Binary classification,Streaming Random Patches,SMTP,0.99960924867953,0.0,0.1769723892211914,11778.656001 +76120,Binary classification,Streaming Random Patches,SMTP,0.9996190175908775,0.0,0.1769723892211914,12273.787996 +78023,Binary classification,Streaming Random Patches,SMTP,0.9996283099638563,0.0,0.17699527740478516,12775.472063 +79926,Binary classification,Streaming Random Patches,SMTP,0.9996371598373475,0.0,0.1769723892211914,13283.764207999999 +81829,Binary classification,Streaming Random Patches,SMTP,0.9996455980837855,0.0,0.1770181655883789,13798.661938 +83732,Binary classification,Streaming Random Patches,SMTP,0.9996536527689864,0.0,0.17826175689697266,14320.191281 +85635,Binary classification,Streaming Random Patches,SMTP,0.999661349463998,0.0,0.1703653335571289,14848.294436 +87538,Binary classification,Streaming Random Patches,SMTP,0.9996687115162731,0.0,0.17029666900634766,15383.005183 +89441,Binary classification,Streaming Random Patches,SMTP,0.9996645796064401,0.0,0.1781930923461914,15923.647685 +91344,Binary classification,Streaming Random Patches,SMTP,0.999671567607808,0.0,0.1781473159790039,16470.415157 +93247,Binary classification,Streaming Random Patches,SMTP,0.9996782703815713,0.0,0.17821598052978516,17023.687732 +95150,Binary classification,Streaming Random Patches,SMTP,0.9996847050415664,0.0,0.17821598052978516,17582.958979 +95156,Binary classification,Streaming Random Patches,SMTP,0.9996847249224948,0.0,0.17817020416259766,18142.251024999998 +106,Binary classification,k-Nearest Neighbors,Bananas,0.7238095238095238,0.6881720430107527,0.10328006744384766,0.213787 +212,Binary classification,k-Nearest Neighbors,Bananas,0.8056872037914692,0.7807486631016043,0.1952676773071289,0.888466 +318,Binary classification,k-Nearest Neighbors,Bananas,0.807570977917981,0.7859649122807018,0.28677845001220703,2.29757 +424,Binary classification,k-Nearest Neighbors,Bananas,0.8297872340425532,0.8115183246073298,0.3787660598754883,4.640547 +530,Binary classification,k-Nearest Neighbors,Bananas,0.831758034026465,0.8061002178649236,2.6361207962036133,29.527472000000003 +636,Binary classification,k-Nearest Neighbors,Bananas,0.8472440944881889,0.8245931283905967,3.060887336730957,56.29478 +742,Binary classification,k-Nearest Neighbors,Bananas,0.8529014844804319,0.8278041074249604,3.5180253982543945,85.033958 +848,Binary classification,k-Nearest Neighbors,Bananas,0.8559622195985832,0.8328767123287671,3.9749040603637695,116.00953899999999 +954,Binary classification,k-Nearest Neighbors,Bananas,0.8604407135362014,0.8372093023255813,4.4283952713012695,149.38786199999998 +1060,Binary classification,k-Nearest Neighbors,Bananas,0.8706326723323891,0.8476084538375974,4.5923662185668945,185.28018899999998 +1166,Binary classification,k-Nearest Neighbors,Bananas,0.871244635193133,0.8484848484848485,4.394963264465332,223.46140799999998 +1272,Binary classification,k-Nearest Neighbors,Bananas,0.8693941778127459,0.8477064220183486,4.242337226867676,263.59538599999996 +1378,Binary classification,k-Nearest Neighbors,Bananas,0.8714596949891068,0.8488471391972673,4.1376237869262695,305.59304199999997 +1484,Binary classification,k-Nearest Neighbors,Bananas,0.8759271746459879,0.8548895899053628,4.233838081359863,349.62285599999996 +1590,Binary classification,k-Nearest Neighbors,Bananas,0.8735053492762744,0.8527472527472527,4.485638618469238,396.123591 +1696,Binary classification,k-Nearest Neighbors,Bananas,0.8755162241887906,0.854982817869416,4.566784858703613,444.689218 +1802,Binary classification,k-Nearest Neighbors,Bananas,0.8778456413103831,0.858611825192802,4.580937385559082,495.109217 +1908,Binary classification,k-Nearest Neighbors,Bananas,0.8778185631882538,0.8598917618761276,4.5537919998168945,547.400313 +2014,Binary classification,k-Nearest Neighbors,Bananas,0.877297565822156,0.8605307735742519,4.4779558181762695,601.303554 +2120,Binary classification,k-Nearest Neighbors,Bananas,0.8787163756488909,0.8635156664896441,4.453892707824707,656.840699 +2226,Binary classification,k-Nearest Neighbors,Bananas,0.8782022471910113,0.8630621526023244,4.4562273025512695,714.0315049999999 +2332,Binary classification,k-Nearest Neighbors,Bananas,0.8777348777348777,0.862782859894078,4.439526557922363,772.745195 +2438,Binary classification,k-Nearest Neighbors,Bananas,0.8785391875256463,0.8635944700460828,4.450131416320801,833.024947 +2544,Binary classification,k-Nearest Neighbors,Bananas,0.8788832088084939,0.864793678665496,4.448788642883301,894.808032 +2650,Binary classification,k-Nearest Neighbors,Bananas,0.8784446961117403,0.8647058823529411,4.491581916809082,958.170481 +2756,Binary classification,k-Nearest Neighbors,Bananas,0.879491833030853,0.8659127625201939,4.482541084289551,1022.970177 +2862,Binary classification,k-Nearest Neighbors,Bananas,0.8808109052778749,0.867056530214425,4.4542436599731445,1089.131713 +2968,Binary classification,k-Nearest Neighbors,Bananas,0.8813616447590158,0.8673700075357951,4.489590644836426,1156.687087 +3074,Binary classification,k-Nearest Neighbors,Bananas,0.8805727302310445,0.8665939658306071,4.4426774978637695,1225.526022 +3180,Binary classification,k-Nearest Neighbors,Bananas,0.8820383768480654,0.8677248677248678,4.4409685134887695,1295.666774 +3286,Binary classification,k-Nearest Neighbors,Bananas,0.882496194824962,0.8678082191780822,4.441540718078613,1367.284144 +3392,Binary classification,k-Nearest Neighbors,Bananas,0.8832202890002949,0.8693931398416888,4.4570817947387695,1440.244405 +3498,Binary classification,k-Nearest Neighbors,Bananas,0.8850443237060337,0.8709055876685934,4.465977668762207,1514.504066 +3604,Binary classification,k-Nearest Neighbors,Bananas,0.8856508465167916,0.8710888610763454,4.4596757888793945,1590.040367 +3710,Binary classification,k-Nearest Neighbors,Bananas,0.8864923159881369,0.8724628900333233,4.477154731750488,1666.899992 +3816,Binary classification,k-Nearest Neighbors,Bananas,0.8875491480996068,0.8737120989108037,4.4705095291137695,1745.0344980000002 +3922,Binary classification,k-Nearest Neighbors,Bananas,0.8867635807192042,0.8724870763928776,4.4566545486450195,1824.4605280000003 +4028,Binary classification,k-Nearest Neighbors,Bananas,0.8852743978147505,0.8706606942889138,4.454602241516113,1905.1708120000003 +4134,Binary classification,k-Nearest Neighbors,Bananas,0.8857972417130414,0.8712493180578287,4.461682319641113,1987.1975480000003 +4240,Binary classification,k-Nearest Neighbors,Bananas,0.886765746638358,0.8724760892667376,4.4584245681762695,2070.530861 +4346,Binary classification,k-Nearest Neighbors,Bananas,0.8876869965477561,0.8735751295336789,4.494175910949707,2155.1880220000003 +4452,Binary classification,k-Nearest Neighbors,Bananas,0.8869916872612896,0.8725614390676463,4.517621040344238,2241.198533 +4558,Binary classification,k-Nearest Neighbors,Bananas,0.8869870528856704,0.8729336294103133,4.495129585266113,2328.452784 +4664,Binary classification,k-Nearest Neighbors,Bananas,0.886982629208664,0.873286847799952,4.453595161437988,2416.918556 +4770,Binary classification,k-Nearest Neighbors,Bananas,0.8857202767875865,0.8715531463587085,4.469174385070801,2506.628209 +4876,Binary classification,k-Nearest Neighbors,Bananas,0.8861538461538462,0.871616932685635,4.478787422180176,2597.661861 +4982,Binary classification,k-Nearest Neighbors,Bananas,0.8869704878538446,0.8728832693610296,4.4154863357543945,2689.8978660000002 +5088,Binary classification,k-Nearest Neighbors,Bananas,0.885983880479654,0.8716814159292035,4.439602851867676,2783.355406 +5194,Binary classification,k-Nearest Neighbors,Bananas,0.885422684382823,0.8711842390127733,4.5029191970825195,2878.2182510000002 +5300,Binary classification,k-Nearest Neighbors,Bananas,0.8850726552179656,0.8708377518557794,4.509961128234863,2974.330637 +906,Binary classification,k-Nearest Neighbors,Elec2,0.8784530386740331,0.8711943793911008,4.434150695800781,37.114054 +1812,Binary classification,k-Nearest Neighbors,Elec2,0.8801766979569299,0.8453314326443336,4.643096923828125,93.709907 +2718,Binary classification,k-Nearest Neighbors,Elec2,0.8568273831431726,0.8160756501182034,4.6672821044921875,164.56349699999998 +3624,Binary classification,k-Nearest Neighbors,Elec2,0.8746894838531604,0.8411476557032889,4.594398498535156,248.37817199999998 +4530,Binary classification,k-Nearest Neighbors,Elec2,0.8783395893133142,0.8399651466744118,4.710762023925781,344.82085099999995 +5436,Binary classification,k-Nearest Neighbors,Elec2,0.8745170193192272,0.8360576923076923,4.698677062988281,452.38148599999994 +6342,Binary classification,k-Nearest Neighbors,Elec2,0.8747831572307208,0.8384865744507731,4.6694183349609375,569.8523869999999 +7248,Binary classification,k-Nearest Neighbors,Elec2,0.8723609769559818,0.8348509194786646,4.666007995605469,697.1091419999999 +8154,Binary classification,k-Nearest Neighbors,Elec2,0.8718263215994113,0.8430695299594534,4.7265625,834.9178869999998 +9060,Binary classification,k-Nearest Neighbors,Elec2,0.8738271332376643,0.8493475682087781,4.708610534667969,981.8103579999998 +9966,Binary classification,k-Nearest Neighbors,Elec2,0.8720521826392373,0.8501234277653698,4.6251678466796875,1137.002296 +10872,Binary classification,k-Nearest Neighbors,Elec2,0.8740686229417717,0.8545628386274301,4.637184143066406,1300.798705 +11778,Binary classification,k-Nearest Neighbors,Elec2,0.8742464124989386,0.8546756942400157,4.6933135986328125,1473.412993 +12684,Binary classification,k-Nearest Neighbors,Elec2,0.872664196168099,0.8527937289217027,4.810676574707031,1655.5819629999999 +13590,Binary classification,k-Nearest Neighbors,Elec2,0.8748252262859666,0.8573824096587573,4.703468322753906,1846.5010799999998 +14496,Binary classification,k-Nearest Neighbors,Elec2,0.8750603656433252,0.85826093762229,4.7199859619140625,2046.3736259999998 +15402,Binary classification,k-Nearest Neighbors,Elec2,0.8755924939938965,0.8581371242410781,4.7149505615234375,2254.7741159999996 +16308,Binary classification,k-Nearest Neighbors,Elec2,0.872079475072055,0.8535112359550563,4.6830902099609375,2471.4717159999996 +17214,Binary classification,k-Nearest Neighbors,Elec2,0.8723058153721025,0.8517669274345832,4.657257080078125,2696.3467009999995 +18120,Binary classification,k-Nearest Neighbors,Elec2,0.87234394834152,0.8515118443859535,4.7351837158203125,2929.4346569999993 +19026,Binary classification,k-Nearest Neighbors,Elec2,0.8734822601839685,0.8509505232522138,4.8458709716796875,3171.2751989999992 +19932,Binary classification,k-Nearest Neighbors,Elec2,0.8722091214690683,0.8505193966782089,4.8552703857421875,3421.524358999999 +20838,Binary classification,k-Nearest Neighbors,Elec2,0.8678312616979411,0.8451765234989881,4.8942718505859375,3679.924087999999 +21744,Binary classification,k-Nearest Neighbors,Elec2,0.8677735363105368,0.8427672955974842,4.7196807861328125,3945.793201999999 +22650,Binary classification,k-Nearest Neighbors,Elec2,0.8669256920835356,0.840444679724722,4.8090057373046875,4218.904371999999 +23556,Binary classification,k-Nearest Neighbors,Elec2,0.8647845468053492,0.8373257061136934,4.794342041015625,4499.001777999999 +24462,Binary classification,k-Nearest Neighbors,Elec2,0.8644372674870201,0.8359715077166601,4.7589569091796875,4786.067247999999 +25368,Binary classification,k-Nearest Neighbors,Elec2,0.8619860448614342,0.8330710914032329,4.846771240234375,5079.937268999999 +26274,Binary classification,k-Nearest Neighbors,Elec2,0.8623301488219846,0.8333410127632125,4.699310302734375,5380.101847999999 +27180,Binary classification,k-Nearest Neighbors,Elec2,0.8632767945840538,0.8350350705851016,4.794769287109375,5686.6129089999995 +28086,Binary classification,k-Nearest Neighbors,Elec2,0.862061598718177,0.8333620096352374,4.6817474365234375,5999.306036 +28992,Binary classification,k-Nearest Neighbors,Elec2,0.8618191852643924,0.8323989624299222,4.8116455078125,6318.1198079999995 +29898,Binary classification,k-Nearest Neighbors,Elec2,0.8607218115529987,0.8308417289567761,4.769432067871094,6643.155825 +30804,Binary classification,k-Nearest Neighbors,Elec2,0.8599162419244879,0.8291562735083342,4.83782958984375,6975.21929 +31710,Binary classification,k-Nearest Neighbors,Elec2,0.8578006244283958,0.8263832736513803,4.7655487060546875,7313.109579 +32616,Binary classification,k-Nearest Neighbors,Elec2,0.8558332055802544,0.8246307623452186,4.726959228515625,7656.831982 +33522,Binary classification,k-Nearest Neighbors,Elec2,0.8543897855075923,0.8232354325861008,4.798057556152344,8006.181245 +34428,Binary classification,k-Nearest Neighbors,Elec2,0.8533128068086095,0.8218066337332393,4.773887634277344,8361.39601 +35334,Binary classification,k-Nearest Neighbors,Elec2,0.8518099227351201,0.8192737815822173,4.808341979980469,8722.632877 +36240,Binary classification,k-Nearest Neighbors,Elec2,0.8522310218273131,0.8186651315566692,4.722572326660156,9089.688296 +37146,Binary classification,k-Nearest Neighbors,Elec2,0.8505586216179836,0.8161859664227292,4.720252990722656,9462.293988 +38052,Binary classification,k-Nearest Neighbors,Elec2,0.8507792173661665,0.81590039556449,4.766929626464844,9840.72504 +38958,Binary classification,k-Nearest Neighbors,Elec2,0.8507841979618553,0.8163523204751524,4.769111633300781,10225.058019 +39864,Binary classification,k-Nearest Neighbors,Elec2,0.850889295838246,0.8178809976101478,4.736076354980469,10615.291715 +40770,Binary classification,k-Nearest Neighbors,Elec2,0.8509161372611543,0.8193329766363474,4.725471496582031,11011.540551999999 +41676,Binary classification,k-Nearest Neighbors,Elec2,0.8518536292741452,0.8217770336585647,4.700096130371094,11414.574327999999 +42582,Binary classification,k-Nearest Neighbors,Elec2,0.8529156196425636,0.8235028885444553,4.746559143066406,11823.240958999999 +43488,Binary classification,k-Nearest Neighbors,Elec2,0.8525536367190195,0.8231074817920989,4.826316833496094,12236.954316 +44394,Binary classification,k-Nearest Neighbors,Elec2,0.8525217939765278,0.8226754421602882,4.775764465332031,12655.735001 +45300,Binary classification,k-Nearest Neighbors,Elec2,0.853131415704541,0.8236541468974475,4.7673492431640625,13079.486139999999 +45312,Binary classification,k-Nearest Neighbors,Elec2,0.8531482421487057,0.8236416644579911,4.7660369873046875,13503.439196 +25,Binary classification,k-Nearest Neighbors,Phishing,0.5833333333333334,0.7058823529411764,0.041108131408691406,0.04635 +50,Binary classification,k-Nearest Neighbors,Phishing,0.7551020408163265,0.7777777777777778,0.0695962905883789,0.16308 +75,Binary classification,k-Nearest Neighbors,Phishing,0.7972972972972973,0.8235294117647058,0.09861469268798828,0.336872 +100,Binary classification,k-Nearest Neighbors,Phishing,0.797979797979798,0.8148148148148148,0.12712955474853516,0.6777850000000001 +125,Binary classification,k-Nearest Neighbors,Phishing,0.8064516129032258,0.8208955223880596,0.15564441680908203,1.226658 +150,Binary classification,k-Nearest Neighbors,Phishing,0.8187919463087249,0.834355828220859,0.1846628189086914,1.947513 +175,Binary classification,k-Nearest Neighbors,Phishing,0.8390804597701149,0.8426966292134832,0.21317768096923828,2.9471350000000003 +200,Binary classification,k-Nearest Neighbors,Phishing,0.8391959798994975,0.8415841584158417,0.24219608306884766,4.26255 +225,Binary classification,k-Nearest Neighbors,Phishing,0.8392857142857143,0.8363636363636364,0.27071094512939453,5.830504 +250,Binary classification,k-Nearest Neighbors,Phishing,0.8232931726907631,0.8225806451612903,0.2992258071899414,7.819846 +275,Binary classification,k-Nearest Neighbors,Phishing,0.8248175182481752,0.8208955223880596,0.3284578323364258,10.199689 +300,Binary classification,k-Nearest Neighbors,Phishing,0.8260869565217391,0.8181818181818181,0.35697269439697266,12.972731999999999 +325,Binary classification,k-Nearest Neighbors,Phishing,0.8364197530864198,0.8250825082508251,0.38599109649658203,16.225203999999998 +350,Binary classification,k-Nearest Neighbors,Phishing,0.8452722063037249,0.83125,0.4145059585571289,19.962148 +375,Binary classification,k-Nearest Neighbors,Phishing,0.839572192513369,0.8235294117647058,0.4430208206176758,24.257676 +400,Binary classification,k-Nearest Neighbors,Phishing,0.8421052631578947,0.8225352112676055,0.47203922271728516,29.175886 +425,Binary classification,k-Nearest Neighbors,Phishing,0.8443396226415094,0.819672131147541,0.500554084777832,34.714831 +450,Binary classification,k-Nearest Neighbors,Phishing,0.8463251670378619,0.8198433420365536,0.5295724868774414,40.875927999999995 +475,Binary classification,k-Nearest Neighbors,Phishing,0.8438818565400844,0.8177339901477833,0.5580873489379883,47.66810099999999 +500,Binary classification,k-Nearest Neighbors,Phishing,0.845691382765531,0.8229885057471266,2.6757898330688477,79.03492 +525,Binary classification,k-Nearest Neighbors,Phishing,0.8454198473282443,0.8187919463087249,2.7769289016723633,111.488727 +550,Binary classification,k-Nearest Neighbors,Phishing,0.848816029143898,0.8237791932059448,2.8829355239868164,145.039549 +575,Binary classification,k-Nearest Neighbors,Phishing,0.8519163763066202,0.8268839103869654,2.989964485168457,179.782132 +600,Binary classification,k-Nearest Neighbors,Phishing,0.8514190317195326,0.8230616302186878,3.098984718322754,215.642079 +625,Binary classification,k-Nearest Neighbors,Phishing,0.8525641025641025,0.8210116731517509,3.2059221267700195,252.521109 +650,Binary classification,k-Nearest Neighbors,Phishing,0.8582434514637904,0.8302583025830258,3.3169260025024414,290.529559 +675,Binary classification,k-Nearest Neighbors,Phishing,0.8620178041543026,0.8382608695652174,3.4291276931762695,329.62297 +700,Binary classification,k-Nearest Neighbors,Phishing,0.8669527896995708,0.8421052631578948,3.5458459854125977,369.665809 +725,Binary classification,k-Nearest Neighbors,Phishing,0.8674033149171271,0.8456591639871384,3.6591615676879883,410.97711300000003 +750,Binary classification,k-Nearest Neighbors,Phishing,0.8678237650200267,0.8465116279069768,3.769242286682129,453.55010200000004 +775,Binary classification,k-Nearest Neighbors,Phishing,0.8669250645994832,0.8446455505279035,3.881718635559082,497.265773 +800,Binary classification,k-Nearest Neighbors,Phishing,0.8648310387984981,0.8434782608695652,3.9942636489868164,542.189116 +825,Binary classification,k-Nearest Neighbors,Phishing,0.8628640776699029,0.8423988842398884,4.110844612121582,588.325742 +850,Binary classification,k-Nearest Neighbors,Phishing,0.8657243816254417,0.8451086956521738,4.225159645080566,635.696811 +875,Binary classification,k-Nearest Neighbors,Phishing,0.868421052631579,0.847277556440903,4.342709541320801,684.216091 +900,Binary classification,k-Nearest Neighbors,Phishing,0.8698553948832035,0.8482490272373541,4.455658912658691,733.952375 +925,Binary classification,k-Nearest Neighbors,Phishing,0.8712121212121212,0.8514357053682896,4.573666572570801,785.013527 +950,Binary classification,k-Nearest Neighbors,Phishing,0.8735511064278187,0.8561151079136691,4.697152137756348,837.27657 +975,Binary classification,k-Nearest Neighbors,Phishing,0.8757700205338809,0.8581477139507622,4.820996284484863,890.836474 +1000,Binary classification,k-Nearest Neighbors,Phishing,0.8758758758758759,0.858447488584475,4.93715763092041,945.663339 +1025,Binary classification,k-Nearest Neighbors,Phishing,0.8759765625,0.8590455049944505,4.905686378479004,1001.6659109999999 +1050,Binary classification,k-Nearest Neighbors,Phishing,0.8779790276453765,0.8617710583153347,4.881028175354004,1058.8496129999999 +1075,Binary classification,k-Nearest Neighbors,Phishing,0.8780260707635009,0.86282722513089,4.857575416564941,1117.1299479999998 +1100,Binary classification,k-Nearest Neighbors,Phishing,0.8789808917197452,0.8641470888661901,4.821175575256348,1176.4409139999998 +1125,Binary classification,k-Nearest Neighbors,Phishing,0.8798932384341637,0.8662041625371655,4.749619483947754,1236.7626339999997 +1150,Binary classification,k-Nearest Neighbors,Phishing,0.8807658833768495,0.8668610301263362,4.722535133361816,1298.0588499999997 +1175,Binary classification,k-Nearest Neighbors,Phishing,0.879045996592845,0.8645038167938931,4.706612586975098,1360.3228199999996 +1200,Binary classification,k-Nearest Neighbors,Phishing,0.8807339449541285,0.865979381443299,4.686341285705566,1423.5043029999997 +1225,Binary classification,k-Nearest Neighbors,Phishing,0.8815359477124183,0.8666053357865686,4.653275489807129,1487.6393369999996 +1250,Binary classification,k-Nearest Neighbors,Phishing,0.8815052041633307,0.867145421903052,4.596428871154785,1552.6498929999996 +1903,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.559709548950195,49.463009 +3806,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.594751358032227,126.299444 +5709,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.435243606567383,223.803561 +7612,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.493677139282227,340.763146 +9515,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.534708023071289,475.19475 +11418,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.455095291137695,625.35715 +13321,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.479013442993164,790.6914730000001 +15224,Binary classification,k-Nearest Neighbors,SMTP,0.9998686198515404,0.9,4.445444107055664,971.1285220000001 +17127,Binary classification,k-Nearest Neighbors,SMTP,0.9998832184981898,0.9166666666666666,4.544534683227539,1166.447296 +19030,Binary classification,k-Nearest Neighbors,SMTP,0.9998948972620737,0.9166666666666666,4.52708625793457,1376.022994 +20933,Binary classification,k-Nearest Neighbors,SMTP,0.999904452512899,0.9166666666666666,4.493997573852539,1599.513782 +22836,Binary classification,k-Nearest Neighbors,SMTP,0.9999124151521787,0.9166666666666666,4.490983963012695,1835.5325109999999 +24739,Binary classification,k-Nearest Neighbors,SMTP,0.9999191527205109,0.9166666666666666,4.531465530395508,2083.498651 +26642,Binary classification,k-Nearest Neighbors,SMTP,0.9998873916144289,0.88,4.54191780090332,2343.113276 +28545,Binary classification,k-Nearest Neighbors,SMTP,0.999894899103139,0.88,4.488824844360352,2613.833594 +30448,Binary classification,k-Nearest Neighbors,SMTP,0.9999014681249384,0.88,4.459695816040039,2894.8346570000003 +32351,Binary classification,k-Nearest Neighbors,SMTP,0.9999072642967543,0.88,4.475152969360352,3186.5040240000003 +34254,Binary classification,k-Nearest Neighbors,SMTP,0.9999124164306776,0.88,4.543954849243164,3487.9588300000005 +36157,Binary classification,k-Nearest Neighbors,SMTP,0.9999170262197146,0.88,4.482622146606445,3798.7831540000006 +38060,Binary classification,k-Nearest Neighbors,SMTP,0.9999211750177356,0.88,4.496248245239258,4119.269013000001 +39963,Binary classification,k-Nearest Neighbors,SMTP,0.9999249286822481,0.88,4.471353530883789,4448.958874000001 +41866,Binary classification,k-Nearest Neighbors,SMTP,0.9999283410963812,0.88,4.53770637512207,4788.142489000001 +43769,Binary classification,k-Nearest Neighbors,SMTP,0.999931456772071,0.88,4.51286506652832,5135.940338 +45672,Binary classification,k-Nearest Neighbors,SMTP,0.9999343128024348,0.88,4.49894905090332,5492.9415070000005 +47575,Binary classification,k-Nearest Neighbors,SMTP,0.9999369403455669,0.88,4.555765151977539,5859.118968000001 +49478,Binary classification,k-Nearest Neighbors,SMTP,0.9999393657659115,0.88,4.430139541625977,6234.600581000001 +51381,Binary classification,k-Nearest Neighbors,SMTP,0.9999221486959906,0.8571428571428571,4.466188430786133,6619.789592000001 +53284,Binary classification,k-Nearest Neighbors,SMTP,0.9999249291518871,0.8571428571428571,4.526651382446289,7013.956607000001 +55187,Binary classification,k-Nearest Neighbors,SMTP,0.9999275178487298,0.8571428571428571,4.485139846801758,7418.775951000001 +57090,Binary classification,k-Nearest Neighbors,SMTP,0.9997898018882797,0.7391304347826089,4.452577590942383,7831.9045620000015 +58993,Binary classification,k-Nearest Neighbors,SMTP,0.9997965825874695,0.7391304347826089,4.485406875610352,8252.946705000002 +60896,Binary classification,k-Nearest Neighbors,SMTP,0.9998029394860005,0.7391304347826089,4.502649307250977,8681.511861000003 +62799,Binary classification,k-Nearest Neighbors,SMTP,0.9997770629637887,0.7083333333333334,4.495584487915039,9116.499033000002 +64702,Binary classification,k-Nearest Neighbors,SMTP,0.9997836200367846,0.7083333333333334,4.500345230102539,9557.922914000002 +66605,Binary classification,k-Nearest Neighbors,SMTP,0.9997898024142694,0.7083333333333334,4.572656631469727,10005.892137000003 +68508,Binary classification,k-Nearest Neighbors,SMTP,0.9997664472243712,0.68,4.537866592407227,10460.064213000003 +70411,Binary classification,k-Nearest Neighbors,SMTP,0.9997727595512002,0.68,4.469621658325195,10920.643886000003 +72314,Binary classification,k-Nearest Neighbors,SMTP,0.9997787396457068,0.68,4.537904739379883,11387.046035000003 +74217,Binary classification,k-Nearest Neighbors,SMTP,0.9997844130645683,0.68,4.493074417114258,11859.048710000003 +76120,Binary classification,k-Nearest Neighbors,SMTP,0.99978980280876,0.68,4.520692825317383,12336.641580000003 +78023,Binary classification,k-Nearest Neighbors,SMTP,0.9997949296352311,0.68,4.566102981567383,12820.170836000003 +79926,Binary classification,k-Nearest Neighbors,SMTP,0.9997998123240538,0.68,4.500688552856445,13309.287446000002 +81829,Binary classification,k-Nearest Neighbors,SMTP,0.9998044679082955,0.68,4.506959915161133,13804.219559000003 +83732,Binary classification,k-Nearest Neighbors,SMTP,0.9998089118725442,0.68,4.503435134887695,14304.793113000003 +85635,Binary classification,k-Nearest Neighbors,SMTP,0.9998131583249644,0.68,4.498682022094727,14810.923265000003 +87538,Binary classification,k-Nearest Neighbors,SMTP,0.9998172201469093,0.68,4.491861343383789,15322.783751000003 +89441,Binary classification,k-Nearest Neighbors,SMTP,0.9998099284436494,0.6666666666666666,4.496858596801758,15840.351229000004 +91344,Binary classification,k-Nearest Neighbors,SMTP,0.9998138883110912,0.6666666666666666,4.480546951293945,16363.581709000004 +93247,Binary classification,k-Nearest Neighbors,SMTP,0.999817686549557,0.6666666666666666,4.533571243286133,16892.331870000005 +95150,Binary classification,k-Nearest Neighbors,SMTP,0.9998213328568876,0.6666666666666666,4.517786026000977,17426.665113000006 +95156,Binary classification,k-Nearest Neighbors,SMTP,0.9998213441227471,0.6666666666666666,4.518220901489258,17961.110841000005 +106,Binary classification,ADWIN Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.1797952651977539,0.693272 +212,Binary classification,ADWIN Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.1805887222290039,2.027128 +318,Binary classification,ADWIN Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.18126773834228516,4.089008 +424,Binary classification,ADWIN Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.18131351470947266,6.9179189999999995 +530,Binary classification,ADWIN Bagging,Bananas,0.55765595463138,0.45581395348837206,0.1813364028930664,10.429995 +636,Binary classification,ADWIN Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.1819925308227539,14.687229 +742,Binary classification,ADWIN Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.1820383071899414,19.647457 +848,Binary classification,ADWIN Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.18196964263916016,25.366910999999998 +954,Binary classification,ADWIN Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.1819467544555664,31.800241999999997 +1060,Binary classification,ADWIN Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.1819467544555664,39.029576 +1166,Binary classification,ADWIN Bagging,Bananas,0.590557939914163,0.4015056461731493,0.18192386627197266,46.984262 +1272,Binary classification,ADWIN Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.18192386627197266,55.672123 +1378,Binary classification,ADWIN Bagging,Bananas,0.599128540305011,0.3973799126637554,0.18253421783447266,65.02100899999999 +1484,Binary classification,ADWIN Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.18248844146728516,75.177128 +1590,Binary classification,ADWIN Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.1824655532836914,86.053176 +1696,Binary classification,ADWIN Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.1824655532836914,97.727606 +1802,Binary classification,ADWIN Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.18248844146728516,110.067211 +1908,Binary classification,ADWIN Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.18248844146728516,123.213825 +2014,Binary classification,ADWIN Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.18248844146728516,137.051202 +2120,Binary classification,ADWIN Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.18248844146728516,151.568436 +2226,Binary classification,ADWIN Bagging,Bananas,0.597752808988764,0.40133779264214053,0.18244266510009766,166.814875 +2332,Binary classification,ADWIN Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.18244266510009766,182.823981 +2438,Binary classification,ADWIN Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.1824655532836914,199.616425 +2544,Binary classification,ADWIN Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.1824655532836914,217.084375 +2650,Binary classification,ADWIN Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.18309879302978516,235.279245 +2756,Binary classification,ADWIN Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.18309879302978516,254.250965 +2862,Binary classification,ADWIN Bagging,Bananas,0.599790283117791,0.4051948051948052,0.18309879302978516,273.857236 +2968,Binary classification,ADWIN Bagging,Bananas,0.599932591843613,0.40261701056869653,0.1831216812133789,294.204326 +3074,Binary classification,ADWIN Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.1831216812133789,315.311898 +3180,Binary classification,ADWIN Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.1831216812133789,337.189575 +3286,Binary classification,ADWIN Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.17859649658203125,359.75124 +3392,Binary classification,ADWIN Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.2364349365234375,383.144231 +3498,Binary classification,ADWIN Bagging,Bananas,0.597369173577352,0.40237691001697795,0.2806434631347656,407.32428699999997 +3604,Binary classification,ADWIN Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3000526428222656,432.314941 +3710,Binary classification,ADWIN Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.3464546203613281,458.107983 +3816,Binary classification,ADWIN Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.3760719299316406,484.645149 +3922,Binary classification,ADWIN Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.4113121032714844,512.014837 +4028,Binary classification,ADWIN Bagging,Bananas,0.608393344921778,0.4195804195804196,0.4392280578613281,540.239956 +4134,Binary classification,ADWIN Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.4532661437988281,569.3761920000001 +4240,Binary classification,ADWIN Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.4546051025390625,599.333749 +4346,Binary classification,ADWIN Bagging,Bananas,0.6186421173762946,0.4384954252795662,0.4373931884765625,630.119 +4452,Binary classification,ADWIN Bagging,Bananas,0.6212087171422153,0.44209133024487096,0.43770599365234375,661.732786 +4558,Binary classification,ADWIN Bagging,Bananas,0.6214614878209348,0.44372782973234437,0.42758941650390625,694.0925080000001 +4664,Binary classification,ADWIN Bagging,Bananas,0.6219172206733863,0.44542308902170497,0.3975372314453125,727.2725200000001 +4770,Binary classification,ADWIN Bagging,Bananas,0.6227720696162717,0.4449244060475162,0.42584228515625,761.2761580000001 +4876,Binary classification,ADWIN Bagging,Bananas,0.6235897435897436,0.4444444444444444,0.393829345703125,796.1318860000001 +4982,Binary classification,ADWIN Bagging,Bananas,0.6251756675366392,0.44910002950722927,0.39398193359375,831.6856570000001 +5088,Binary classification,ADWIN Bagging,Bananas,0.624139964615687,0.44675925925925924,0.39410400390625,867.9313910000001 +5194,Binary classification,ADWIN Bagging,Bananas,0.6248796456768727,0.44690516751845544,0.394500732421875,904.9575060000001 +5300,Binary classification,ADWIN Bagging,Bananas,0.6259671636157765,0.44821826280623617,0.40065765380859375,942.730038 +906,Binary classification,ADWIN Bagging,Elec2,0.8651933701657458,0.8685344827586208,1.5650663375854492,11.845084 +1812,Binary classification,ADWIN Bagging,Elec2,0.8895637769188294,0.8684210526315789,1.8734617233276367,34.886970000000005 +2718,Binary classification,ADWIN Bagging,Elec2,0.8778064041221936,0.8547681539807523,1.7035398483276367,70.08374500000001 +3624,Binary classification,ADWIN Bagging,Elec2,0.8835219431410434,0.8607260726072606,1.6641263961791992,115.953507 +4530,Binary classification,ADWIN Bagging,Elec2,0.8878339589313314,0.8599007170435742,2.0698423385620117,171.720075 +5436,Binary classification,ADWIN Bagging,Elec2,0.886292548298068,0.8580615525953147,2.326838493347168,235.88433300000003 +6342,Binary classification,ADWIN Bagging,Elec2,0.8845607948273143,0.8556782334384859,1.8882322311401367,307.801578 +7248,Binary classification,ADWIN Bagging,Elec2,0.8835380157306472,0.8526021655606008,1.7675046920776367,386.842642 +8154,Binary classification,ADWIN Bagging,Elec2,0.8854409419845456,0.8617115783239561,1.8750486373901367,473.251889 +9060,Binary classification,ADWIN Bagging,Elec2,0.8863009162159179,0.8664765361680064,1.8668012619018555,566.6143930000001 +9966,Binary classification,ADWIN Bagging,Elec2,0.883492222779729,0.8657337805019083,1.624751091003418,666.8526760000001 +10872,Binary classification,ADWIN Bagging,Elec2,0.8851071658541072,0.8689539397754694,1.8364439010620117,773.0944300000001 +11778,Binary classification,ADWIN Bagging,Elec2,0.8819733378619343,0.8645224171539961,1.461909294128418,884.893217 +12684,Binary classification,ADWIN Bagging,Elec2,0.8788930063865016,0.8610709117221419,1.412806510925293,1002.145707 +13590,Binary classification,ADWIN Bagging,Elec2,0.880197218338362,0.863970588235294,1.521845817565918,1125.032122 +14496,Binary classification,ADWIN Bagging,Elec2,0.8799586064160055,0.8644437519476471,1.922499656677246,1253.0070850000002 +15402,Binary classification,ADWIN Bagging,Elec2,0.8805272384910071,0.8643667993513195,1.924330711364746,1386.9590420000002 +16308,Binary classification,ADWIN Bagging,Elec2,0.8794382780401054,0.8622670589883704,1.6739492416381836,1526.6043270000002 +17214,Binary classification,ADWIN Bagging,Elec2,0.8781153779120432,0.8583389601620527,2.050276756286621,1671.5850450000003 +18120,Binary classification,ADWIN Bagging,Elec2,0.8772559192008389,0.8570510348373829,2.0607213973999023,1821.6195730000002 +19026,Binary classification,ADWIN Bagging,Elec2,0.8782128777923784,0.8564702967230379,1.4914274215698242,1976.6826170000002 +19932,Binary classification,ADWIN Bagging,Elec2,0.8703025437760273,0.8482357776081723,0.7451009750366211,2137.4066430000003 +20838,Binary classification,ADWIN Bagging,Elec2,0.8626001823679033,0.8387314820030418,0.7786626815795898,2303.79277 +21744,Binary classification,ADWIN Bagging,Elec2,0.8638642321666743,0.8378259916721456,0.8927946090698242,2475.479733 +22650,Binary classification,ADWIN Bagging,Elec2,0.8620689655172413,0.8337590464027246,1.0149259567260742,2652.48421 +23556,Binary classification,ADWIN Bagging,Elec2,0.8552748885586924,0.8239789332369494,0.7698392868041992,2835.224884 +24462,Binary classification,ADWIN Bagging,Elec2,0.8513143371080496,0.8171902488062327,0.8274068832397461,3023.118045 +25368,Binary classification,ADWIN Bagging,Elec2,0.8471242165017543,0.8121670057153928,0.9264287948608398,3216.225907 +26274,Binary classification,ADWIN Bagging,Elec2,0.8469912077037263,0.8116213683223992,1.0318632125854492,3414.151818 +27180,Binary classification,ADWIN Bagging,Elec2,0.8476397218440708,0.8134600657687283,0.828364372253418,3617.194246 +28086,Binary classification,ADWIN Bagging,Elec2,0.8442585009791703,0.8083260297984224,0.9404935836791992,3825.52125 +28992,Binary classification,ADWIN Bagging,Elec2,0.8416405091235211,0.8035935828877006,0.932948112487793,4039.2676589999996 +29898,Binary classification,ADWIN Bagging,Elec2,0.8389804997156906,0.7997670742866649,0.8770322799682617,4258.3083369999995 +30804,Binary classification,ADWIN Bagging,Elec2,0.8374508976398403,0.7964054812344976,1.024897575378418,4482.681616999999 +31710,Binary classification,ADWIN Bagging,Elec2,0.8326973414488,0.7888725275599952,0.9494352340698242,4711.951208999999 +32616,Binary classification,ADWIN Bagging,Elec2,0.827318718381113,0.7808219178082191,0.861109733581543,4945.765051999999 +33522,Binary classification,ADWIN Bagging,Elec2,0.8271531278899794,0.7806964420893262,1.093031883239746,5184.141968999998 +34428,Binary classification,ADWIN Bagging,Elec2,0.8255439044935661,0.7779174678302028,1.133570671081543,5427.007607999998 +35334,Binary classification,ADWIN Bagging,Elec2,0.8253191067840263,0.7766196163590301,1.1872129440307617,5674.462564999998 +36240,Binary classification,ADWIN Bagging,Elec2,0.8264576837109192,0.7764070110569915,1.431788444519043,5926.318084999998 +37146,Binary classification,ADWIN Bagging,Elec2,0.8245255081437609,0.7721616331096196,1.357222557067871,6182.753490999998 +38052,Binary classification,ADWIN Bagging,Elec2,0.8241833328953247,0.7704974271012006,1.1449995040893555,6443.434381999998 +38958,Binary classification,ADWIN Bagging,Elec2,0.8233950252842878,0.7698534823041413,1.147334098815918,6708.424011999998 +39864,Binary classification,ADWIN Bagging,Elec2,0.8222160901086221,0.7699250073044834,0.7468709945678711,6977.570913999998 +40770,Binary classification,ADWIN Bagging,Elec2,0.8227329588658049,0.7725140860587366,0.9336042404174805,7251.011506999998 +41676,Binary classification,ADWIN Bagging,Elec2,0.8235872825434913,0.7755388654820785,1.097620964050293,7528.4976689999985 +42582,Binary classification,ADWIN Bagging,Elec2,0.8245696437378174,0.7776785714285713,1.5453977584838867,7809.843107999998 +43488,Binary classification,ADWIN Bagging,Elec2,0.8251431462276082,0.7787605469886529,0.8941831588745117,8094.859004999998 +44394,Binary classification,ADWIN Bagging,Elec2,0.8243867276372401,0.7766701042740918,0.744959831237793,8383.886548999999 +45300,Binary classification,ADWIN Bagging,Elec2,0.823770944170953,0.7766305716444221,0.5983161926269531,8676.996437 +45312,Binary classification,ADWIN Bagging,Elec2,0.8237734766392267,0.7765871128395959,0.5984382629394531,8970.151376 +25,Binary classification,ADWIN Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.6633157730102539,0.427424 +50,Binary classification,ADWIN Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.6639947891235352,1.324595 +75,Binary classification,ADWIN Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.6639490127563477,2.554164 +100,Binary classification,ADWIN Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.6645593643188477,4.28613 +125,Binary classification,ADWIN Bagging,Phishing,0.8548387096774194,0.85,0.6645593643188477,6.454494 +150,Binary classification,ADWIN Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.6645593643188477,8.992416 +175,Binary classification,ADWIN Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.6651926040649414,11.944220000000001 +200,Binary classification,ADWIN Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.6653299331665039,15.477702 +225,Binary classification,ADWIN Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7024993896484375,19.458772 +250,Binary classification,ADWIN Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.730194091796875,23.970287 +275,Binary classification,ADWIN Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7302398681640625,28.914006 +300,Binary classification,ADWIN Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7302398681640625,34.304573 +325,Binary classification,ADWIN Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7308502197265625,40.225778999999996 +350,Binary classification,ADWIN Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7308731079101562,46.580822 +375,Binary classification,ADWIN Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.7674179077148438,53.398646 +400,Binary classification,ADWIN Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.804534912109375,60.683253 +425,Binary classification,ADWIN Bagging,Phishing,0.8679245283018868,0.839080459770115,0.8596954345703125,68.459501 +450,Binary classification,ADWIN Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.8597640991210938,76.689807 +475,Binary classification,ADWIN Bagging,Phishing,0.869198312236287,0.8402061855670103,0.859832763671875,85.340536 +500,Binary classification,ADWIN Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.8598556518554688,94.431883 +525,Binary classification,ADWIN Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.8598556518554688,103.968058 +550,Binary classification,ADWIN Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.8599014282226562,113.947374 +575,Binary classification,ADWIN Bagging,Phishing,0.867595818815331,0.8382978723404255,0.8599014282226562,124.33795699999999 +600,Binary classification,ADWIN Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.8599014282226562,135.24069899999998 +625,Binary classification,ADWIN Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.8966064453125,146.56138699999997 +650,Binary classification,ADWIN Bagging,Phishing,0.8767334360554699,0.846153846153846,0.897308349609375,158.32837399999997 +675,Binary classification,ADWIN Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.92486572265625,170.52005599999995 +700,Binary classification,ADWIN Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.8633918762207031,183.10001399999996 +725,Binary classification,ADWIN Bagging,Phishing,0.8825966850828729,0.8576214405360134,0.9612770080566406,196.14419299999997 +750,Binary classification,ADWIN Bagging,Phishing,0.8865153538050734,0.8631239935587761,0.9975471496582031,209.59830599999998 +775,Binary classification,ADWIN Bagging,Phishing,0.8875968992248062,0.863849765258216,1.0525703430175781,223.56014999999996 +800,Binary classification,ADWIN Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.1531257629394531,237.94997899999996 +825,Binary classification,ADWIN Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.1537437438964844,252.78040299999995 +850,Binary classification,ADWIN Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.1632118225097656,267.983484 +875,Binary classification,ADWIN Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.1906776428222656,283.60495299999997 +900,Binary classification,ADWIN Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.2457008361816406,299.65263899999997 +925,Binary classification,ADWIN Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.2457923889160156,316.05876399999994 +950,Binary classification,ADWIN Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.2458381652832031,332.97263699999996 +975,Binary classification,ADWIN Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.2458839416503906,350.27117 +1000,Binary classification,ADWIN Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.2458610534667969,368.008026 +1025,Binary classification,ADWIN Bagging,Phishing,0.8876953125,0.8700564971751412,1.2459068298339844,386.172169 +1050,Binary classification,ADWIN Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.2458839416503906,404.74234 +1075,Binary classification,ADWIN Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.2458839416503906,423.71995200000003 +1100,Binary classification,ADWIN Bagging,Phishing,0.89171974522293,0.8761706555671176,1.2458839416503906,443.13918 +1125,Binary classification,ADWIN Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.2458839416503906,462.95518100000004 +1150,Binary classification,ADWIN Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.2458839416503906,483.090208 +1175,Binary classification,ADWIN Bagging,Phishing,0.8926746166950597,0.877906976744186,1.2458839416503906,503.74833900000004 +1200,Binary classification,ADWIN Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.2550315856933594,524.8299360000001 +1225,Binary classification,ADWIN Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3099861145019531,546.3067460000001 +1250,Binary classification,ADWIN Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3100776672363281,568.2182720000001 +1903,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.15993690490722656,9.565839 +3806,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16054725646972656,28.660555 +5709,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.1610889434814453,57.169533 +7612,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16111183166503906,95.02592100000001 +9515,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16111183166503906,141.315828 +11418,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16172218322753906,195.517174 +13321,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.1617450714111328,256.578558 +15224,Binary classification,ADWIN Bagging,SMTP,0.9992774091834724,0.0,0.2173633575439453,324.084886 +17127,Binary classification,ADWIN Bagging,SMTP,0.9992409202382343,0.0,0.16245460510253906,398.342181 +19030,Binary classification,ADWIN Bagging,SMTP,0.9993168322034789,0.0,0.16227149963378906,479.30544 +20933,Binary classification,ADWIN Bagging,SMTP,0.999378941333843,0.0,0.1629047393798828,566.848605 +22836,Binary classification,ADWIN Bagging,SMTP,0.9994306984891613,0.0,0.1629962921142578,660.867353 +24739,Binary classification,ADWIN Bagging,SMTP,0.9994744926833212,0.0,0.1631336212158203,761.190417 +26642,Binary classification,ADWIN Bagging,SMTP,0.9994744942006681,0.0,0.1628131866455078,867.1613560000001 +28545,Binary classification,ADWIN Bagging,SMTP,0.9995095291479821,0.0,0.1630420684814453,978.3809960000001 +30448,Binary classification,ADWIN Bagging,SMTP,0.9995401845830459,0.0,0.16292762756347656,1094.7710920000002 +32351,Binary classification,ADWIN Bagging,SMTP,0.9995672333848532,0.0,0.16306495666503906,1216.3368180000002 +34254,Binary classification,ADWIN Bagging,SMTP,0.9995912766764955,0.0,0.16297340393066406,1342.8439390000003 +36157,Binary classification,ADWIN Bagging,SMTP,0.9996127890253347,0.0,0.16297340393066406,1474.4175280000004 +38060,Binary classification,ADWIN Bagging,SMTP,0.9996321500827662,0.0,0.1629962921142578,1611.5765880000004 +39963,Binary classification,ADWIN Bagging,SMTP,0.9996496671838246,0.0,0.1628589630126953,1753.4950250000004 +41866,Binary classification,ADWIN Bagging,SMTP,0.9996655917831124,0.0,0.1635608673095703,1900.0594340000005 +43769,Binary classification,ADWIN Bagging,SMTP,0.9996801316029976,0.0,0.1636524200439453,2051.0940990000004 +45672,Binary classification,ADWIN Bagging,SMTP,0.9996934597446958,0.0,0.16362953186035156,2206.5822620000004 +47575,Binary classification,ADWIN Bagging,SMTP,0.9997057216126456,0.0,0.1635608673095703,2366.582792 +49478,Binary classification,ADWIN Bagging,SMTP,0.99971704024092,0.0,0.1516590118408203,2531.0740060000003 +51381,Binary classification,ADWIN Bagging,SMTP,0.9996885947839627,0.0,0.16358375549316406,2700.0024150000004 +53284,Binary classification,ADWIN Bagging,SMTP,0.9996997166075484,0.0,0.16358375549316406,2873.4051700000005 +55187,Binary classification,ADWIN Bagging,SMTP,0.999710071394919,0.0,0.16353797912597656,3051.2280980000005 +57090,Binary classification,ADWIN Bagging,SMTP,0.9995620872672494,0.0,0.1635608673095703,3233.6982610000005 +58993,Binary classification,ADWIN Bagging,SMTP,0.9995762137238947,0.0,0.1634693145751953,3420.4241430000006 +60896,Binary classification,ADWIN Bagging,SMTP,0.999589457262501,0.0,0.16340065002441406,3611.3532100000007 +62799,Binary classification,ADWIN Bagging,SMTP,0.9995700500015924,0.0,0.1635608673095703,3806.5756970000007 +64702,Binary classification,ADWIN Bagging,SMTP,0.9995826957852274,0.0,0.1636524200439453,4005.990633000001 +66605,Binary classification,ADWIN Bagging,SMTP,0.9995946189418053,0.0,0.1635608673095703,4209.692892000001 +68508,Binary classification,ADWIN Bagging,SMTP,0.9995766855941729,0.0,0.1636066436767578,4417.671552000001 +70411,Binary classification,ADWIN Bagging,SMTP,0.9995881266865502,0.0,0.16344642639160156,4629.924287000001 +72314,Binary classification,ADWIN Bagging,SMTP,0.9995989656078437,0.0,0.1636066436767578,4846.389066000001 +74217,Binary classification,ADWIN Bagging,SMTP,0.99960924867953,0.0,0.1636066436767578,5067.145737000001 +76120,Binary classification,ADWIN Bagging,SMTP,0.9996190175908775,0.0,0.1636524200439453,5292.119512000001 +78023,Binary classification,ADWIN Bagging,SMTP,0.9996283099638563,0.0,0.1636524200439453,5520.9977020000015 +79926,Binary classification,ADWIN Bagging,SMTP,0.9996371598373475,0.0,0.1633319854736328,5753.467598000001 +81829,Binary classification,ADWIN Bagging,SMTP,0.9996455980837855,0.0,0.1635608673095703,5989.673013000001 +83732,Binary classification,ADWIN Bagging,SMTP,0.9996536527689864,0.0,0.1642627716064453,6229.466851000001 +85635,Binary classification,ADWIN Bagging,SMTP,0.999661349463998,0.0,0.16428565979003906,6472.919364000001 +87538,Binary classification,ADWIN Bagging,SMTP,0.9996687115162731,0.0,0.16410255432128906,6719.889077000002 +89441,Binary classification,ADWIN Bagging,SMTP,0.9996645796064401,0.0,0.16414833068847656,6970.567347000002 +91344,Binary classification,ADWIN Bagging,SMTP,0.999671567607808,0.0,0.16405677795410156,7224.925889000002 +93247,Binary classification,ADWIN Bagging,SMTP,0.9996782703815713,0.0,0.1523609161376953,7483.091298000002 +95150,Binary classification,ADWIN Bagging,SMTP,0.9996847050415664,0.0,0.16419410705566406,7744.930013000002 +95156,Binary classification,ADWIN Bagging,SMTP,0.9996847249224948,0.0,0.1642169952392578,8006.777295000002 +106,Binary classification,AdaBoost,Bananas,0.5523809523809524,0.5252525252525252,0.16639232635498047,0.661448 +212,Binary classification,AdaBoost,Bananas,0.5829383886255924,0.5555555555555555,0.16659832000732422,2.064295 +318,Binary classification,AdaBoost,Bananas,0.6025236593059937,0.5827814569536425,0.16664409637451172,4.087538 +424,Binary classification,AdaBoost,Bananas,0.6099290780141844,0.5758354755784061,0.16664409637451172,6.767182 +530,Binary classification,AdaBoost,Bananas,0.5841209829867675,0.5089285714285714,0.16659832000732422,10.090322 +636,Binary classification,AdaBoost,Bananas,0.5748031496062992,0.4981412639405205,0.16664409637451172,14.036758 +742,Binary classification,AdaBoost,Bananas,0.582995951417004,0.48925619834710743,0.16657543182373047,18.750104 +848,Binary classification,AdaBoost,Bananas,0.5749704840613932,0.4812680115273775,0.16652965545654297,24.116907 +954,Binary classification,AdaBoost,Bananas,0.5760755508919203,0.482051282051282,0.16652965545654297,30.255333 +1060,Binary classification,AdaBoost,Bananas,0.5873465533522191,0.48284023668639053,0.16652965545654297,37.077733 +1166,Binary classification,AdaBoost,Bananas,0.5931330472103005,0.49250535331905776,0.16657543182373047,44.554975 +1272,Binary classification,AdaBoost,Bananas,0.5979543666404405,0.5034013605442177,0.16657543182373047,52.671883 +1378,Binary classification,AdaBoost,Bananas,0.6005809731299927,0.4990892531876139,0.16657543182373047,61.596702 +1484,Binary classification,AdaBoost,Bananas,0.6089008766014835,0.5117845117845117,0.16657543182373047,71.17423 +1590,Binary classification,AdaBoost,Bananas,0.6091881686595343,0.5121759622937941,0.16657543182373047,81.390284 +1696,Binary classification,AdaBoost,Bananas,0.6135693215339233,0.5194424064563462,0.16657543182373047,92.36515499999999 +1802,Binary classification,AdaBoost,Bananas,0.6185452526374237,0.5354969574036511,0.16657543182373047,104.03539999999998 +1908,Binary classification,AdaBoost,Bananas,0.6208704771893025,0.5467084639498432,0.16659832000732422,116.33010199999998 +2014,Binary classification,AdaBoost,Bananas,0.620963735717834,0.5561372891215823,0.16662120819091797,129.40978099999998 +2120,Binary classification,AdaBoost,Bananas,0.6252949504483247,0.56941431670282,0.16662120819091797,143.16628799999998 +2226,Binary classification,AdaBoost,Bananas,0.6242696629213483,0.5721596724667348,0.16664409637451172,157.57341499999998 +2332,Binary classification,AdaBoost,Bananas,0.6229086229086229,0.5763855421686748,0.16664409637451172,172.619666 +2438,Binary classification,AdaBoost,Bananas,0.62330734509643,0.5796703296703297,0.16664409637451172,188.342899 +2544,Binary classification,AdaBoost,Bananas,0.6244593000393236,0.5860424794104898,0.16664409637451172,204.77139699999998 +2650,Binary classification,AdaBoost,Bananas,0.6266515666289165,0.591828312009905,0.16668987274169922,221.90157299999998 +2756,Binary classification,AdaBoost,Bananas,0.6250453720508167,0.5921831819976313,0.16668987274169922,239.67413799999997 +2862,Binary classification,AdaBoost,Bananas,0.6249563089828731,0.5927893738140417,0.16668987274169922,258.142834 +2968,Binary classification,AdaBoost,Bananas,0.6248736097067745,0.5924569754668619,0.16668987274169922,277.299077 +3074,Binary classification,AdaBoost,Bananas,0.6260982753010088,0.5958494548012664,0.16668987274169922,297.096451 +3180,Binary classification,AdaBoost,Bananas,0.62378106322743,0.5934738273283481,0.15410423278808594,317.58276 +3286,Binary classification,AdaBoost,Bananas,0.6246575342465753,0.5937397034596376,0.1971263885498047,338.83455200000003 +3392,Binary classification,AdaBoost,Bananas,0.6234149218519611,0.5931825422108953,0.2317180633544922,360.725819 +3498,Binary classification,AdaBoost,Bananas,0.6211038032599371,0.5894019212891229,0.2662029266357422,383.343908 +3604,Binary classification,AdaBoost,Bananas,0.6194837635303914,0.5866747060596926,0.31232261657714844,406.679113 +3710,Binary classification,AdaBoost,Bananas,0.6238878403882449,0.5915080527086384,0.32015037536621094,430.74398699999995 +3816,Binary classification,AdaBoost,Bananas,0.6277850589777195,0.5970488081725313,0.32617759704589844,455.49479399999996 +3922,Binary classification,AdaBoost,Bananas,0.6322366743177761,0.6009961261759823,0.35390281677246094,480.941019 +4028,Binary classification,AdaBoost,Bananas,0.6354606406754407,0.6034575904916262,0.3679637908935547,507.154231 +4134,Binary classification,AdaBoost,Bananas,0.6399709654004355,0.6073878627968339,0.3758831024169922,534.089651 +4240,Binary classification,AdaBoost,Bananas,0.644963434772352,0.6130110568269478,0.37590599060058594,561.67777 +4346,Binary classification,AdaBoost,Bananas,0.6508630609896433,0.6185567010309279,0.3759288787841797,589.949243 +4452,Binary classification,AdaBoost,Bananas,0.6535609975286453,0.620384047267356,0.3758831024169922,618.987429 +4558,Binary classification,AdaBoost,Bananas,0.6570111915734036,0.6243691420331651,0.3760662078857422,648.691271 +4664,Binary classification,AdaBoost,Bananas,0.6607334334119666,0.6288127639605818,0.3761119842529297,679.16585 +4770,Binary classification,AdaBoost,Bananas,0.6630320821975257,0.6303197607545433,0.43157005310058594,710.3587739999999 +4876,Binary classification,AdaBoost,Bananas,0.6670769230769231,0.6330544879041374,0.43948936462402344,742.192598 +4982,Binary classification,AdaBoost,Bananas,0.6707488456133307,0.6378091872791519,0.44574546813964844,774.746403 +5088,Binary classification,AdaBoost,Bananas,0.6734814232356988,0.6407094959982694,0.45186424255371094,807.9427459999999 +5194,Binary classification,AdaBoost,Bananas,0.674369343346813,0.6412051771695311,0.4518413543701172,841.965614 +5300,Binary classification,AdaBoost,Bananas,0.6778637478769579,0.64504054897068,0.4531536102294922,876.7139659999999 +906,Binary classification,AdaBoost,Elec2,0.9337016574585635,0.933184855233853,1.423478126525879,13.145088 +1812,Binary classification,AdaBoost,Elec2,0.9491993373826615,0.9378378378378379,2.051041603088379,36.742593 +2718,Binary classification,AdaBoost,Elec2,0.9385351490614648,0.9243316719528772,2.3655481338500977,75.07794200000001 +3624,Binary classification,AdaBoost,Elec2,0.9359646701628485,0.9209809264305179,2.6522607803344727,124.64144900000001 +4530,Binary classification,AdaBoost,Elec2,0.9361890041951866,0.9185226952354102,3.339066505432129,184.39942100000002 +5436,Binary classification,AdaBoost,Elec2,0.9332106715731371,0.9144876325088339,3.582810401916504,253.628197 +6342,Binary classification,AdaBoost,Elec2,0.9309257214950323,0.9124350259896042,3.74349308013916,332.298993 +7248,Binary classification,AdaBoost,Elec2,0.9232785980405686,0.9024903542616626,3.9959611892700195,420.603134 +8154,Binary classification,AdaBoost,Elec2,0.9207653624432725,0.9042962962962964,4.062603950500488,517.241068 +9060,Binary classification,AdaBoost,Elec2,0.9214041284910034,0.9072191816523326,4.2443437576293945,621.271616 +9966,Binary classification,AdaBoost,Elec2,0.9173105870546914,0.9037158214536105,4.387467384338379,732.039898 +10872,Binary classification,AdaBoost,Elec2,0.916842976727072,0.9044195390145908,4.416756629943848,849.200167 +11778,Binary classification,AdaBoost,Elec2,0.9150887322747728,0.9024580569644947,4.712822914123535,973.870605 +12684,Binary classification,AdaBoost,Elec2,0.9128755026413309,0.9002077124537162,5.243111610412598,1105.403187 +13590,Binary classification,AdaBoost,Elec2,0.9123555817205092,0.900890405259216,5.419106483459473,1243.898383 +14496,Binary classification,AdaBoost,Elec2,0.9112107623318386,0.9002402914502752,5.619416236877441,1388.7625189999999 +15402,Binary classification,AdaBoost,Elec2,0.9125381468735796,0.9014414282578475,5.888123512268066,1539.2398159999998 +16308,Binary classification,AdaBoost,Elec2,0.9096093702091127,0.8977808599167822,6.072480201721191,1695.9498239999998 +17214,Binary classification,AdaBoost,Elec2,0.9093708243769244,0.8958611481975968,6.119706153869629,1858.6477019999998 +18120,Binary classification,AdaBoost,Elec2,0.9071140791434406,0.892972972972973,6.420571327209473,2027.8856049999997 +19026,Binary classification,AdaBoost,Elec2,0.907910643889619,0.8927784577723377,6.732544898986816,2202.4394989999996 +19932,Binary classification,AdaBoost,Elec2,0.9079323666649942,0.8936540133294696,6.836274147033691,2383.1617499999998 +20838,Binary classification,AdaBoost,Elec2,0.9073283102174018,0.8931673582295988,7.145352363586426,2570.030805 +21744,Binary classification,AdaBoost,Elec2,0.9069585613760751,0.8912424063222407,7.368103981018066,2762.4680289999997 +22650,Binary classification,AdaBoost,Elec2,0.9053379840169544,0.8884611382790553,7.513260841369629,2961.0147009999996 +23556,Binary classification,AdaBoost,Elec2,0.9031203566121843,0.885441767068273,7.7879228591918945,3165.9004179999997 +24462,Binary classification,AdaBoost,Elec2,0.9015984628592453,0.8830361047669955,7.954785346984863,3377.5787039999996 +25368,Binary classification,AdaBoost,Elec2,0.8990026412267907,0.8799775133514476,8.00295352935791,3596.2655339999997 +26274,Binary classification,AdaBoost,Elec2,0.8993263045712329,0.8800942925789926,8.124005317687988,3821.215918 +27180,Binary classification,AdaBoost,Elec2,0.8986717686449097,0.8798324461122262,8.133870124816895,4052.049016 +28086,Binary classification,AdaBoost,Elec2,0.8958874844222895,0.8761436801084379,8.60555362701416,4289.013778 +28992,Binary classification,AdaBoost,Elec2,0.8951398709944466,0.8747011787981206,8.944867134094238,4531.544758 +29898,Binary classification,AdaBoost,Elec2,0.8927986085560424,0.8719485396939551,9.235833168029785,4780.469894 +30804,Binary classification,AdaBoost,Elec2,0.8921533616855502,0.8705882352941176,9.317421913146973,5034.96976 +31710,Binary classification,AdaBoost,Elec2,0.8903465892964143,0.8684499262229957,9.565300941467285,5295.565511 +32616,Binary classification,AdaBoost,Elec2,0.8890387858347386,0.867226767435888,9.898663520812988,5561.886477999999 +33522,Binary classification,AdaBoost,Elec2,0.8882789892902956,0.8666547979348406,10.141366004943848,5833.648399 +34428,Binary classification,AdaBoost,Elec2,0.8878496528887211,0.8660444783679699,10.462204933166504,6110.893153 +35334,Binary classification,AdaBoost,Elec2,0.8864800611326522,0.8639185750636134,10.841256141662598,6393.894783 +36240,Binary classification,AdaBoost,Elec2,0.8857584370429648,0.8622387861040862,11.138581275939941,6682.129445 +37146,Binary classification,AdaBoost,Elec2,0.8846412706959214,0.8604643589827087,11.587563514709473,6975.5213029999995 +38052,Binary classification,AdaBoost,Elec2,0.883682426217445,0.8588377878420617,12.028901100158691,7273.546291 +38958,Binary classification,AdaBoost,Elec2,0.8819210924865878,0.8569117830036083,12.1774263381958,7576.403824 +39864,Binary classification,AdaBoost,Elec2,0.880741539773725,0.8567122792211707,12.330445289611816,7883.760394 +40770,Binary classification,AdaBoost,Elec2,0.880423851455763,0.8574603081781235,12.583298683166504,8195.49812 +41676,Binary classification,AdaBoost,Elec2,0.8811517696460708,0.8591977712710009,12.884881019592285,8511.394385 +42582,Binary classification,AdaBoost,Elec2,0.8815199267278834,0.8597403319525146,13.200516700744629,8831.520838 +43488,Binary classification,AdaBoost,Elec2,0.8809069377055212,0.8591399896646449,13.322876930236816,9156.403803000001 +44394,Binary classification,AdaBoost,Elec2,0.880476651724371,0.8583404527979496,13.499638557434082,9485.877502000001 +45300,Binary classification,AdaBoost,Elec2,0.8805713150400671,0.8587024655244463,13.542492866516113,9819.626928000001 +45312,Binary classification,AdaBoost,Elec2,0.8805808744013595,0.8586874200203704,13.542401313781738,10153.705154000001 +25,Binary classification,AdaBoost,Phishing,0.6666666666666666,0.7142857142857143,0.6517477035522461,0.344782 +50,Binary classification,AdaBoost,Phishing,0.7551020408163265,0.7391304347826088,0.6519079208374023,1.052047 +75,Binary classification,AdaBoost,Phishing,0.7972972972972973,0.7945205479452055,0.6519308090209961,2.04852 +100,Binary classification,AdaBoost,Phishing,0.8080808080808081,0.7999999999999999,0.6519536972045898,3.481699 +125,Binary classification,AdaBoost,Phishing,0.8064516129032258,0.8000000000000002,0.6519804000854492,5.345952 +150,Binary classification,AdaBoost,Phishing,0.8187919463087249,0.8211920529801323,0.6519804000854492,7.607799 +175,Binary classification,AdaBoost,Phishing,0.8390804597701149,0.8313253012048192,0.6519804000854492,10.226605 +200,Binary classification,AdaBoost,Phishing,0.8341708542713567,0.8253968253968254,0.6889629364013672,13.384675 +225,Binary classification,AdaBoost,Phishing,0.8303571428571429,0.8173076923076923,0.6891918182373047,16.995274 +250,Binary classification,AdaBoost,Phishing,0.8273092369477911,0.8154506437768241,0.6892147064208984,21.029961999999998 +275,Binary classification,AdaBoost,Phishing,0.8321167883211679,0.8188976377952757,0.6892833709716797,25.500590999999996 +300,Binary classification,AdaBoost,Phishing,0.8394648829431438,0.823529411764706,0.6893062591552734,30.459704999999996 +325,Binary classification,AdaBoost,Phishing,0.845679012345679,0.8263888888888888,0.6893062591552734,35.865097 +350,Binary classification,AdaBoost,Phishing,0.8510028653295129,0.8289473684210527,0.6893062591552734,41.618204999999996 +375,Binary classification,AdaBoost,Phishing,0.8502673796791443,0.8260869565217391,0.6892795562744141,47.877466 +400,Binary classification,AdaBoost,Phishing,0.849624060150376,0.8235294117647061,0.6893062591552734,54.483198 +425,Binary classification,AdaBoost,Phishing,0.8561320754716981,0.8271954674220963,0.6893062591552734,61.544972 +450,Binary classification,AdaBoost,Phishing,0.8530066815144766,0.8225806451612903,0.6893062591552734,69.002264 +475,Binary classification,AdaBoost,Phishing,0.8523206751054853,0.8241206030150755,0.6893062591552734,77.051638 +500,Binary classification,AdaBoost,Phishing,0.8557114228456913,0.8317757009345793,0.6893062591552734,85.50066 +525,Binary classification,AdaBoost,Phishing,0.8530534351145038,0.8253968253968255,0.6893062591552734,94.362743 +550,Binary classification,AdaBoost,Phishing,0.8579234972677595,0.832618025751073,0.6893062591552734,103.688841 +575,Binary classification,AdaBoost,Phishing,0.8588850174216028,0.8336755646817249,0.6893062591552734,113.60832099999999 +600,Binary classification,AdaBoost,Phishing,0.8631051752921536,0.8360000000000001,0.6893062591552734,123.90541499999999 +625,Binary classification,AdaBoost,Phishing,0.8621794871794872,0.83203125,0.6893062591552734,134.725616 +650,Binary classification,AdaBoost,Phishing,0.8659476117103235,0.8391866913123845,0.6893291473388672,146.008333 +675,Binary classification,AdaBoost,Phishing,0.8679525222551929,0.8446771378708552,0.6893291473388672,157.728693 +700,Binary classification,AdaBoost,Phishing,0.8726752503576538,0.848381601362862,0.6893291473388672,169.816185 +725,Binary classification,AdaBoost,Phishing,0.8756906077348067,0.8543689320388349,0.6893291473388672,182.22931499999999 +750,Binary classification,AdaBoost,Phishing,0.87716955941255,0.8566978193146417,0.6893291473388672,195.113196 +775,Binary classification,AdaBoost,Phishing,0.8785529715762274,0.8575757575757577,0.6893291473388672,208.50176599999998 +800,Binary classification,AdaBoost,Phishing,0.8785982478097623,0.8592162554426704,0.7275295257568359,222.49654599999997 +825,Binary classification,AdaBoost,Phishing,0.8798543689320388,0.8619246861924686,0.7627391815185547,236.94804099999996 +850,Binary classification,AdaBoost,Phishing,0.8798586572438163,0.8614130434782608,0.7627849578857422,251.81301299999996 +875,Binary classification,AdaBoost,Phishing,0.8787185354691075,0.8594164456233422,0.7628536224365234,267.105354 +900,Binary classification,AdaBoost,Phishing,0.8787541713014461,0.8589909443725743,0.7628765106201172,282.96574799999996 +925,Binary classification,AdaBoost,Phishing,0.8809523809523809,0.8628428927680798,0.7628765106201172,299.16754399999996 +950,Binary classification,AdaBoost,Phishing,0.8798735511064278,0.8629807692307693,0.7628765106201172,315.934336 +975,Binary classification,AdaBoost,Phishing,0.8819301848049281,0.8651817116060961,0.7628765106201172,333.021735 +1000,Binary classification,AdaBoost,Phishing,0.8828828828828829,0.8662857142857143,0.7645549774169922,350.749086 +1025,Binary classification,AdaBoost,Phishing,0.8828125,0.8666666666666666,0.8362636566162109,368.847305 +1050,Binary classification,AdaBoost,Phishing,0.8846520495710201,0.8691891891891892,0.8363094329833984,387.397742 +1075,Binary classification,AdaBoost,Phishing,0.8836126629422719,0.8691099476439791,0.8378963470458984,406.476047 +1100,Binary classification,AdaBoost,Phishing,0.8844404003639672,0.8702757916241062,0.8380107879638672,425.926155 +1125,Binary classification,AdaBoost,Phishing,0.8861209964412812,0.8732673267326733,0.8731288909912109,445.763528 +1150,Binary classification,AdaBoost,Phishing,0.8842471714534378,0.8707482993197277,0.8731517791748047,466.112685 +1175,Binary classification,AdaBoost,Phishing,0.8816013628620102,0.8677450047573739,0.8732662200927734,487.01292 +1200,Binary classification,AdaBoost,Phishing,0.8798999165971643,0.8654205607476635,0.8732662200927734,508.389566 +1225,Binary classification,AdaBoost,Phishing,0.880718954248366,0.8660550458715598,0.8732891082763672,530.180451 +1250,Binary classification,AdaBoost,Phishing,0.8783026421136909,0.8635547576301617,0.8733119964599609,552.608585 +1903,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14459228515625,4.671696 +3806,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14466094970703125,14.150102 +5709,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14461517333984375,28.360088 +7612,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1446380615234375,47.155736000000005 +9515,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1446380615234375,70.60316700000001 +11418,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14461517333984375,98.66041500000001 +13321,Binary classification,AdaBoost,SMTP,1.0,0.0,0.144683837890625,131.464682 +15224,Binary classification,AdaBoost,SMTP,0.9996715496288511,0.761904761904762,0.3174581527709961,185.699411 +17127,Binary classification,AdaBoost,SMTP,0.9997080462454747,0.8,0.3083944320678711,248.358611 +19030,Binary classification,AdaBoost,SMTP,0.9997372431551842,0.8,0.3005514144897461,315.58373 +20933,Binary classification,AdaBoost,SMTP,0.9997611312822473,0.8,0.29268550872802734,387.343573 +22836,Binary classification,AdaBoost,SMTP,0.9997810378804467,0.8,0.29268550872802734,463.52644699999996 +24739,Binary classification,AdaBoost,SMTP,0.9997978818012774,0.8,0.29268550872802734,544.0157879999999 +26642,Binary classification,AdaBoost,SMTP,0.9998123193573815,0.8148148148148148,0.35791683197021484,629.1407149999999 +28545,Binary classification,AdaBoost,SMTP,0.9998248318385651,0.8148148148148148,0.35791683197021484,718.5008859999999 +30448,Binary classification,AdaBoost,SMTP,0.9998357802082307,0.8148148148148148,0.35791683197021484,812.0251739999999 +32351,Binary classification,AdaBoost,SMTP,0.9998454404945905,0.8148148148148148,0.35796260833740234,909.6873759999999 +34254,Binary classification,AdaBoost,SMTP,0.9998540273844627,0.8148148148148148,0.3579854965209961,1011.4173349999999 +36157,Binary classification,AdaBoost,SMTP,0.999861710366191,0.8148148148148148,0.35800838470458984,1116.7962549999997 +38060,Binary classification,AdaBoost,SMTP,0.9998686250295594,0.8148148148148148,0.35800838470458984,1225.6397989999998 +39963,Binary classification,AdaBoost,SMTP,0.9998748811370802,0.8148148148148148,0.35800838470458984,1337.9609139999998 +41866,Binary classification,AdaBoost,SMTP,0.9998805684939687,0.8148148148148148,0.35800838470458984,1453.6581889999998 +43769,Binary classification,AdaBoost,SMTP,0.9998857612867849,0.8148148148148148,0.35800838470458984,1572.7472669999997 +45672,Binary classification,AdaBoost,SMTP,0.9998905213373913,0.8148148148148148,0.35800838470458984,1695.2891979999997 +47575,Binary classification,AdaBoost,SMTP,0.9998738806911338,0.7857142857142857,0.39035701751708984,1822.1171059999997 +49478,Binary classification,AdaBoost,SMTP,0.9998787315318228,0.7857142857142857,0.39284420013427734,1952.4393319999997 +51381,Binary classification,AdaBoost,SMTP,0.9998637602179836,0.787878787878788,0.48020076751708984,2088.5562569999997 +53284,Binary classification,AdaBoost,SMTP,0.9998686260158024,0.787878787878788,0.48024654388427734,2228.050331 +55187,Binary classification,AdaBoost,SMTP,0.9998550356974595,0.7647058823529411,0.5258626937866211,2371.062704 +57090,Binary classification,AdaBoost,SMTP,0.999281823118289,0.4383561643835616,0.8453359603881836,2524.551865 +58993,Binary classification,AdaBoost,SMTP,0.9993049905071875,0.4383561643835616,0.8887395858764648,2681.9732639999997 +60896,Binary classification,AdaBoost,SMTP,0.9993267099105017,0.4383561643835616,0.8967199325561523,2843.1145869999996 +62799,Binary classification,AdaBoost,SMTP,0.9993152648173509,0.4266666666666667,1.0689306259155273,3009.1891499999997 +64702,Binary classification,AdaBoost,SMTP,0.9993354043986955,0.4266666666666667,1.0783147811889648,3178.9568209999998 +66605,Binary classification,AdaBoost,SMTP,0.9993393790162753,0.42105263157894735,1.0915288925170898,3352.4236079999996 +68508,Binary classification,AdaBoost,SMTP,0.9993577298670209,0.45000000000000007,1.0735387802124023,3530.7159619999998 +70411,Binary classification,AdaBoost,SMTP,0.9993750887658003,0.45000000000000007,1.0788640975952148,3712.739099 +72314,Binary classification,AdaBoost,SMTP,0.9993915340256938,0.45000000000000007,1.0906057357788086,3898.148799 +74217,Binary classification,AdaBoost,SMTP,0.9994071359275628,0.45000000000000007,1.0906057357788086,4086.957815 +76120,Binary classification,AdaBoost,SMTP,0.9994219577240899,0.45000000000000007,1.090651512145996,4279.139143 +78023,Binary classification,AdaBoost,SMTP,0.9994232395990874,0.4444444444444444,1.1481237411499023,4474.636732 +79926,Binary classification,AdaBoost,SMTP,0.9994369721614013,0.4444444444444444,1.1493444442749023,4673.509203 +81829,Binary classification,AdaBoost,SMTP,0.999450065992081,0.4444444444444444,1.1612462997436523,4875.758715 +83732,Binary classification,AdaBoost,SMTP,0.9994625646415306,0.4444444444444444,1.161269187927246,5081.435613 +85635,Binary classification,AdaBoost,SMTP,0.9994745077889623,0.4444444444444444,1.1612234115600586,5290.523743 +87538,Binary classification,AdaBoost,SMTP,0.9994859316631824,0.4444444444444444,1.1584348678588867,5502.994331999999 +89441,Binary classification,AdaBoost,SMTP,0.9994633273703041,0.42857142857142855,1.2966947555541992,5719.6431489999995 +91344,Binary classification,AdaBoost,SMTP,0.9994745081724927,0.42857142857142855,1.3124494552612305,5939.715090999999 +93247,Binary classification,AdaBoost,SMTP,0.9994316110074427,0.40449438202247195,1.3362340927124023,6163.415711999999 +95150,Binary classification,AdaBoost,SMTP,0.9994429789067673,0.40449438202247195,1.3363256454467773,6390.433574999999 +95156,Binary classification,AdaBoost,SMTP,0.9994430140297409,0.40449438202247195,1.3363256454467773,6617.502892999999 +106,Binary classification,Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.22373199462890625,0.813651 +212,Binary classification,Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.22452545166015625,2.392298 +318,Binary classification,Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.2251434326171875,4.879886 +424,Binary classification,Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.225250244140625,8.257922 +530,Binary classification,Bagging,Bananas,0.55765595463138,0.45581395348837206,0.22527313232421875,12.416081000000002 +636,Binary classification,Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.22574615478515625,17.551695000000002 +742,Binary classification,Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.22597503662109375,23.418389 +848,Binary classification,Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.2259063720703125,30.181971 +954,Binary classification,Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.22588348388671875,37.806045 +1060,Binary classification,Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.22563934326171875,46.336236 +1166,Binary classification,Bagging,Bananas,0.590557939914163,0.4015056461731493,0.225738525390625,55.794626 +1272,Binary classification,Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.226043701171875,66.093431 +1378,Binary classification,Bagging,Bananas,0.599128540305011,0.3973799126637554,0.226348876953125,77.304266 +1484,Binary classification,Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.2263031005859375,89.41731899999999 +1590,Binary classification,Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.22628021240234375,102.38846199999999 +1696,Binary classification,Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.22634124755859375,116.26624899999999 +1802,Binary classification,Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.2263641357421875,130.90050499999998 +1908,Binary classification,Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.2263641357421875,146.406164 +2014,Binary classification,Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.2263641357421875,162.81699799999998 +2120,Binary classification,Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.2263641357421875,180.02605599999998 +2226,Binary classification,Bagging,Bananas,0.597752808988764,0.40133779264214053,0.226318359375,198.114263 +2332,Binary classification,Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.22637939453125,217.043548 +2438,Binary classification,Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.22640228271484375,236.778245 +2544,Binary classification,Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.22676849365234375,257.426783 +2650,Binary classification,Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.2269744873046875,278.871455 +2756,Binary classification,Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.2269744873046875,301.18545300000005 +2862,Binary classification,Bagging,Bananas,0.599790283117791,0.4051948051948052,0.2269744873046875,324.33878000000004 +2968,Binary classification,Bagging,Bananas,0.599932591843613,0.40261701056869653,0.22699737548828125,348.42370100000005 +3074,Binary classification,Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.22699737548828125,373.34600700000004 +3180,Binary classification,Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.22699737548828125,399.17600200000004 +3286,Binary classification,Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.22489070892333984,425.805579 +3392,Binary classification,Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.2988729476928711,453.430877 +3498,Binary classification,Bagging,Bananas,0.597369173577352,0.40237691001697795,0.3531064987182617,482.040674 +3604,Binary classification,Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3826017379760742,511.80088500000005 +3710,Binary classification,Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.4367246627807617,542.835536 +3816,Binary classification,Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.4704160690307617,575.071901 +3922,Binary classification,Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.5176496505737305,608.725741 +4028,Binary classification,Bagging,Bananas,0.608393344921778,0.4195804195804196,0.5480222702026367,643.745138 +4134,Binary classification,Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.5632429122924805,680.30634 +4240,Binary classification,Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.5676107406616211,718.216367 +4346,Binary classification,Bagging,Bananas,0.6193325661680092,0.438560760353021,0.5822668075561523,757.397991 +4452,Binary classification,Bagging,Bananas,0.6218827229835991,0.4421610871726881,0.5884695053100586,797.943115 +4558,Binary classification,Bagging,Bananas,0.6219003730524468,0.44293566117038474,0.6275625228881836,839.803567 +4664,Binary classification,Bagging,Bananas,0.623203945957538,0.4455664247396655,0.6328649520874023,883.024854 +4770,Binary classification,Bagging,Bananas,0.6250786328370728,0.446096654275093,0.6821584701538086,927.682473 +4876,Binary classification,Bagging,Bananas,0.6266666666666667,0.44680851063829785,0.6950826644897461,973.7206689999999 +4982,Binary classification,Bagging,Bananas,0.629592451314997,0.4530091906314853,0.7119512557983398,1021.0804549999999 +5088,Binary classification,Bagging,Bananas,0.6298407705917043,0.4527753560011624,0.6960439682006836,1069.7402539999998 +5194,Binary classification,Bagging,Bananas,0.6321971885230118,0.456459874786568,0.6964941024780273,1119.6924109999998 +5300,Binary classification,Bagging,Bananas,0.6340819022457067,0.4594368553108447,0.7031240463256836,1170.8531239999998 +906,Binary classification,Bagging,Elec2,0.8629834254143647,0.8663793103448276,1.7490100860595703,16.056337 +1812,Binary classification,Bagging,Elec2,0.8890115958034235,0.8680236375574525,2.496591567993164,50.652304 +2718,Binary classification,Bagging,Elec2,0.87523003312477,0.8521587440034889,1.8562908172607422,106.697102 +3624,Binary classification,Bagging,Elec2,0.8868341153739995,0.8653972422849641,2.5584278106689453,176.150463 +4530,Binary classification,Bagging,Elec2,0.8880547582247736,0.8593619972260749,3.1707210540771484,258.677392 +5436,Binary classification,Bagging,Elec2,0.8829806807727691,0.8518863530507685,2.113290786743164,353.604927 +6342,Binary classification,Bagging,Elec2,0.8814067181832519,0.8497802636835796,2.4726314544677734,459.993548 +7248,Binary classification,Bagging,Elec2,0.883262039464606,0.8516310066643283,2.354246139526367,576.649537 +8154,Binary classification,Bagging,Elec2,0.8828652029927634,0.8585394756332394,2.1453304290771484,702.348431 +9060,Binary classification,Bagging,Elec2,0.8839827795562424,0.8639129871811472,2.1982364654541016,836.637311 +9966,Binary classification,Bagging,Elec2,0.880983442047165,0.8635840809753854,2.4484920501708984,979.832141 +10872,Binary classification,Bagging,Elec2,0.881151687977187,0.8654446990210373,2.578580856323242,1131.438926 +11778,Binary classification,Bagging,Elec2,0.8799354674365288,0.8634344214796214,2.730459213256836,1291.261447 +12684,Binary classification,Bagging,Elec2,0.8768430182133564,0.8601361031518624,2.090116500854492,1459.3451100000002 +13590,Binary classification,Bagging,Elec2,0.8789462064905438,0.8639483913654784,1.8772754669189453,1635.065473 +14496,Binary classification,Bagging,Elec2,0.878854777509486,0.86444341516134,2.105062484741211,1818.351758 +15402,Binary classification,Bagging,Elec2,0.8775404194532822,0.86187197890728,2.440736770629883,2009.388651 +16308,Binary classification,Bagging,Elec2,0.8765560802109523,0.8599262403451395,2.627225875854492,2209.910977 +17214,Binary classification,Bagging,Elec2,0.8758496485214663,0.8567214213878646,2.5119991302490234,2419.733571 +18120,Binary classification,Bagging,Elec2,0.8760969148407749,0.8567600331780769,2.7164859771728516,2638.279319 +19026,Binary classification,Bagging,Elec2,0.8772141918528252,0.8562284588872477,3.019651412963867,2865.7383750000004 +19932,Binary classification,Bagging,Elec2,0.8739651798705534,0.8535106134826219,2.721925735473633,3103.6766270000003 +20838,Binary classification,Bagging,Elec2,0.8716225944233815,0.8503663925714606,2.4018421173095703,3351.3122810000004 +21744,Binary classification,Bagging,Elec2,0.872556684910086,0.8492300995701616,2.248655319213867,3607.2754260000006 +22650,Binary classification,Bagging,Elec2,0.870722769217184,0.845275840202917,2.6111698150634766,3871.072358000001 +23556,Binary classification,Bagging,Elec2,0.8645722776480578,0.8365611230658879,1.8957767486572266,4144.250301000001 +24462,Binary classification,Bagging,Elec2,0.8614120436613385,0.8315276811450154,1.5607776641845703,4424.237452000001 +25368,Binary classification,Bagging,Elec2,0.8560334292584855,0.8249113050148624,1.3715801239013672,4711.1951020000015 +26274,Binary classification,Bagging,Elec2,0.8558596277547292,0.824277295717136,1.6112499237060547,5004.571280000002 +27180,Binary classification,Bagging,Elec2,0.8564332756907906,0.8258035714285713,2.025979995727539,5304.465246000002 +28086,Binary classification,Bagging,Elec2,0.8535517179989318,0.8215385950449082,1.8488483428955078,5611.580754000001 +28992,Binary classification,Bagging,Elec2,0.8515746266082578,0.8178624338624338,2.0671520233154297,5927.3319900000015 +29898,Binary classification,Bagging,Elec2,0.849048399504967,0.8140885684860969,1.3224430084228516,6250.652578000001 +30804,Binary classification,Bagging,Elec2,0.8473849949680226,0.8106344410876132,1.549489974975586,6580.126329000001 +31710,Binary classification,Bagging,Elec2,0.8429783342268756,0.8039377830281552,1.5209712982177734,6916.182134000001 +32616,Binary classification,Bagging,Elec2,0.8411773723746743,0.8020785572367416,1.9952220916748047,7258.669481000001 +33522,Binary classification,Bagging,Elec2,0.8415023418155783,0.8033751526590429,1.8286800384521484,7608.320925000001 +34428,Binary classification,Bagging,Elec2,0.839689778371627,0.8006357692446627,2.2426509857177734,7966.087012000001 +35334,Binary classification,Bagging,Elec2,0.8395550901423598,0.7993487417265422,2.1107349395751953,8331.986249000001 +36240,Binary classification,Bagging,Elec2,0.8400618118601507,0.7984280447937677,1.8943347930908203,8703.460619000001 +37146,Binary classification,Bagging,Elec2,0.839278503163279,0.796356938190749,1.3389415740966797,9080.992051000001 +38052,Binary classification,Bagging,Elec2,0.8389267036345956,0.7946940006029546,1.6071338653564453,9463.837927 +38958,Binary classification,Bagging,Elec2,0.8382832353620658,0.7942655607079877,1.8687000274658203,9853.704286 +39864,Binary classification,Bagging,Elec2,0.8387477109098663,0.7967495098969203,1.466756820678711,10249.501094 +40770,Binary classification,Bagging,Elec2,0.8400009811376291,0.8001225677953119,2.0175647735595703,10651.197686 +41676,Binary classification,Bagging,Elec2,0.8407918416316736,0.8026413635146792,2.1117191314697266,11058.632461 +42582,Binary classification,Bagging,Elec2,0.8411732932528593,0.8035781708344224,2.033967971801758,11470.822586999999 +43488,Binary classification,Bagging,Elec2,0.8416538275806563,0.804308286915994,1.7070560455322266,11887.700533 +44394,Binary classification,Bagging,Elec2,0.8406280269411844,0.8019483246087955,2.2816905975341797,12309.209906 +45300,Binary classification,Bagging,Elec2,0.8404379787633282,0.802124397722295,2.2888126373291016,12736.77001 +45312,Binary classification,Bagging,Elec2,0.8404360971949416,0.8020804817957842,2.2889575958251953,13164.474026 +25,Binary classification,Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.7072525024414062,0.45657 +50,Binary classification,Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.7079315185546875,1.426682 +75,Binary classification,Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.708251953125,2.8732379999999997 +100,Binary classification,Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.70849609375,4.790442 +125,Binary classification,Bagging,Phishing,0.8548387096774194,0.85,0.70849609375,7.239611999999999 +150,Binary classification,Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.708740234375,10.202642999999998 +175,Binary classification,Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.7091293334960938,13.595279999999999 +200,Binary classification,Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.7092666625976562,17.527801 +225,Binary classification,Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7491827011108398,22.029145 +250,Binary classification,Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.7771825790405273,27.026806999999998 +275,Binary classification,Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7774114608764648,32.501577 +300,Binary classification,Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7775945663452148,38.42215899999999 +325,Binary classification,Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7779607772827148,44.92146699999999 +350,Binary classification,Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7781057357788086,51.897794999999995 +375,Binary classification,Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.8172750473022461,59.363139999999994 +400,Binary classification,Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.8571996688842773,67.416022 +425,Binary classification,Bagging,Phishing,0.8679245283018868,0.839080459770115,0.9128484725952148,76.017673 +450,Binary classification,Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.9131002426147461,85.092057 +475,Binary classification,Bagging,Phishing,0.869198312236287,0.8402061855670103,0.9133520126342773,94.603797 +500,Binary classification,Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.9135580062866211,104.609638 +525,Binary classification,Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.9136190414428711,115.080656 +550,Binary classification,Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.9137258529663086,126.050962 +575,Binary classification,Bagging,Phishing,0.867595818815331,0.8382978723404255,0.9137868881225586,137.397676 +600,Binary classification,Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.9139089584350586,149.31562 +625,Binary classification,Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.9536046981811523,161.695664 +650,Binary classification,Bagging,Phishing,0.8767334360554699,0.846153846153846,0.9540624618530273,174.565593 +675,Binary classification,Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.9818639755249023,187.898512 +700,Binary classification,Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.9230222702026367,201.73587500000002 +725,Binary classification,Bagging,Phishing,0.8825966850828729,0.8576214405360134,1.021204948425293,216.08538800000002 +750,Binary classification,Bagging,Phishing,0.8865153538050734,0.8631239935587761,1.0604047775268555,230.90612000000002 +775,Binary classification,Bagging,Phishing,0.8875968992248062,0.863849765258216,1.1157331466674805,246.307883 +800,Binary classification,Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.2215375900268555,262.241262 +825,Binary classification,Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.2229490280151367,278.553882 +850,Binary classification,Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.235407829284668,295.406724 +875,Binary classification,Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.263422966003418,312.749904 +900,Binary classification,Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.318751335144043,330.632511 +925,Binary classification,Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.318964958190918,348.945693 +950,Binary classification,Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.3194990158081055,367.70198800000003 +975,Binary classification,Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.319605827331543,386.96933700000005 +1000,Binary classification,Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.3197660446166992,406.75034200000005 +1025,Binary classification,Bagging,Phishing,0.8876953125,0.8700564971751412,1.3200559616088867,426.99481600000007 +1050,Binary classification,Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.320155143737793,447.8295280000001 +1075,Binary classification,Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.320277214050293,469.1965740000001 +1100,Binary classification,Bagging,Phishing,0.89171974522293,0.8761706555671176,1.320643424987793,491.1150510000001 +1125,Binary classification,Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.320704460144043,513.5552500000001 +1150,Binary classification,Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.320704460144043,536.4428780000001 +1175,Binary classification,Bagging,Phishing,0.8926746166950597,0.877906976744186,1.320765495300293,559.8515450000001 +1200,Binary classification,Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.3328428268432617,583.8125340000001 +1225,Binary classification,Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3880414962768555,608.2234330000001 +1250,Binary classification,Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3882551193237305,633.1359570000001 +1903,Binary classification,Bagging,SMTP,1.0,0.0,0.2038736343383789,10.878823 +3806,Binary classification,Bagging,SMTP,1.0,0.0,0.2044839859008789,32.501535000000004 +5709,Binary classification,Bagging,SMTP,1.0,0.0,0.20502567291259766,64.818606 +7612,Binary classification,Bagging,SMTP,1.0,0.0,0.2050485610961914,107.076722 +9515,Binary classification,Bagging,SMTP,1.0,0.0,0.2050485610961914,158.807432 +11418,Binary classification,Bagging,SMTP,1.0,0.0,0.2056589126586914,218.4459 +13321,Binary classification,Bagging,SMTP,1.0,0.0,0.20568180084228516,285.19327499999997 +15224,Binary classification,Bagging,SMTP,0.9992774091834724,0.0,0.26130008697509766,359.03964299999996 +17127,Binary classification,Bagging,SMTP,0.9992409202382343,0.0,0.2063913345336914,440.61769699999996 +19030,Binary classification,Bagging,SMTP,0.9993168322034789,0.0,0.2062082290649414,529.79805 +20933,Binary classification,Bagging,SMTP,0.999378941333843,0.0,0.20684146881103516,626.4074929999999 +22836,Binary classification,Bagging,SMTP,0.9994306984891613,0.0,0.20693302154541016,729.8228539999999 +24739,Binary classification,Bagging,SMTP,0.9994744926833212,0.0,0.20707035064697266,839.3276679999999 +26642,Binary classification,Bagging,SMTP,0.9994744942006681,0.0,0.20674991607666016,954.8656409999999 +28545,Binary classification,Bagging,SMTP,0.9995095291479821,0.0,0.20697879791259766,1076.4115539999998 +30448,Binary classification,Bagging,SMTP,0.9995401845830459,0.0,0.2068643569946289,1203.4939569999997 +32351,Binary classification,Bagging,SMTP,0.9995672333848532,0.0,0.2070016860961914,1337.1471509999997 +34254,Binary classification,Bagging,SMTP,0.9995912766764955,0.0,0.2069101333618164,1476.3596139999997 +36157,Binary classification,Bagging,SMTP,0.9996127890253347,0.0,0.2069101333618164,1621.0863639999998 +38060,Binary classification,Bagging,SMTP,0.9996321500827662,0.0,0.20693302154541016,1771.0710339999998 +39963,Binary classification,Bagging,SMTP,0.9996496671838246,0.0,0.20679569244384766,1926.3060879999998 +41866,Binary classification,Bagging,SMTP,0.9996655917831124,0.0,0.20749759674072266,2086.761849 +43769,Binary classification,Bagging,SMTP,0.9996801316029976,0.0,0.20697879791259766,2252.424258 +45672,Binary classification,Bagging,SMTP,0.9996934597446958,0.0,0.2072610855102539,2423.176177 +47575,Binary classification,Bagging,SMTP,0.9997057216126456,0.0,0.20725345611572266,2599.130792 +49478,Binary classification,Bagging,SMTP,0.99971704024092,0.0,0.19541263580322266,2780.435567 +51381,Binary classification,Bagging,SMTP,0.9996885947839627,0.0,0.2073373794555664,2966.651736 +53284,Binary classification,Bagging,SMTP,0.9996997166075484,0.0,0.2073373794555664,3157.874809 +55187,Binary classification,Bagging,SMTP,0.999710071394919,0.0,0.2073526382446289,3353.943675 +57090,Binary classification,Bagging,SMTP,0.9995620872672494,0.0,0.20707035064697266,3555.07983 +58993,Binary classification,Bagging,SMTP,0.9995762137238947,0.0,0.20703983306884766,3761.219357 +60896,Binary classification,Bagging,SMTP,0.999589457262501,0.0,0.2070322036743164,3972.252634 +62799,Binary classification,Bagging,SMTP,0.9995700500015924,0.0,0.20719242095947266,4188.261246 +64702,Binary classification,Bagging,SMTP,0.9995826957852274,0.0,0.20734500885009766,4409.191759 +66605,Binary classification,Bagging,SMTP,0.9995946189418053,0.0,0.20731449127197266,4634.878548000001 +68508,Binary classification,Bagging,SMTP,0.9995766855941729,0.0,0.20736026763916016,4864.934707 +70411,Binary classification,Bagging,SMTP,0.9995881266865502,0.0,0.2072000503540039,5099.2875650000005 +72314,Binary classification,Bagging,SMTP,0.9995989656078437,0.0,0.20736026763916016,5337.886074000001 +74217,Binary classification,Bagging,SMTP,0.99960924867953,0.0,0.20736026763916016,5580.709613000001 +76120,Binary classification,Bagging,SMTP,0.9996190175908775,0.0,0.20746707916259766,5827.7179830000005 +78023,Binary classification,Bagging,SMTP,0.9996283099638563,0.0,0.20746707916259766,6079.015463000001 +79926,Binary classification,Bagging,SMTP,0.9996371598373475,0.0,0.20714664459228516,6334.716663000001 +81829,Binary classification,Bagging,SMTP,0.9996455980837855,0.0,0.20755863189697266,6594.692589000001 +83732,Binary classification,Bagging,SMTP,0.9996536527689864,0.0,0.20795536041259766,6858.666542000001 +85635,Binary classification,Bagging,SMTP,0.999661349463998,0.0,0.2080392837524414,7126.604303000001 +87538,Binary classification,Bagging,SMTP,0.9996687115162731,0.0,0.2078561782836914,7398.595214000001 +89441,Binary classification,Bagging,SMTP,0.9996645796064401,0.0,0.2079019546508789,7674.616155000001 +91344,Binary classification,Bagging,SMTP,0.999671567607808,0.0,0.2078104019165039,7954.656032000001 +93247,Binary classification,Bagging,SMTP,0.9996782703815713,0.0,0.19611454010009766,8238.690655 +95150,Binary classification,Bagging,SMTP,0.9996847050415664,0.0,0.2079477310180664,8526.751857000001 +95156,Binary classification,Bagging,SMTP,0.9996847249224948,0.0,0.20797061920166016,8814.843001000001 +106,Binary classification,Leveraging Bagging,Bananas,0.5142857142857142,0.45161290322580644,0.1802501678466797,1.958268 +212,Binary classification,Leveraging Bagging,Bananas,0.5402843601895735,0.4756756756756757,0.1808605194091797,6.1304110000000005 +318,Binary classification,Leveraging Bagging,Bananas,0.5394321766561514,0.4930555555555555,0.18149375915527344,12.559627 +424,Binary classification,Leveraging Bagging,Bananas,0.5531914893617021,0.4932975871313673,0.1814708709716797,21.158430000000003 +530,Binary classification,Leveraging Bagging,Bananas,0.5614366729678639,0.4703196347031963,0.1814708709716797,31.954501 +636,Binary classification,Leveraging Bagging,Bananas,0.5763779527559055,0.4836852207293666,0.41109561920166016,45.100937 +742,Binary classification,Leveraging Bagging,Bananas,0.5991902834008097,0.4940374787052811,0.5197267532348633,60.647662000000004 +848,Binary classification,Leveraging Bagging,Bananas,0.6210153482880756,0.5201793721973094,0.6145830154418945,78.646382 +954,Binary classification,Leveraging Bagging,Bananas,0.6411332633788038,0.5464190981432361,0.681065559387207,99.167462 +1060,Binary classification,Leveraging Bagging,Bananas,0.6515580736543909,0.555956678700361,0.7228097915649414,122.156435 +1166,Binary classification,Leveraging Bagging,Bananas,0.6626609442060086,0.5732899022801302,0.8111352920532227,147.677601 +1272,Binary classification,Leveraging Bagging,Bananas,0.6766325727773407,0.5958702064896755,0.8519144058227539,175.66982000000002 +1378,Binary classification,Leveraging Bagging,Bananas,0.6877269426289034,0.6062271062271062,0.9361848831176758,206.11220300000002 +1484,Binary classification,Leveraging Bagging,Bananas,0.6999325691166555,0.6238377007607777,0.978398323059082,239.08437400000003 +1590,Binary classification,Leveraging Bagging,Bananas,0.7073631214600378,0.6375681995323461,1.0816278457641602,274.51056400000004 +1696,Binary classification,Leveraging Bagging,Bananas,0.7162241887905605,0.6496722505462491,1.146012306213379,312.20930000000004 +1802,Binary classification,Leveraging Bagging,Bananas,0.7262631871182677,0.6662153012863914,1.231095314025879,352.32891700000005 +1908,Binary classification,Leveraging Bagging,Bananas,0.7320398531725223,0.677602523659306,1.3021745681762695,394.808348 +2014,Binary classification,Leveraging Bagging,Bananas,0.7391952309985097,0.6902654867256638,1.3571443557739258,439.684188 +2120,Binary classification,Leveraging Bagging,Bananas,0.7456347333647947,0.7020453289110005,1.439896583557129,486.825513 +2226,Binary classification,Leveraging Bagging,Bananas,0.750561797752809,0.7080483955812729,1.4615755081176758,536.150148 +2332,Binary classification,Leveraging Bagging,Bananas,0.7554697554697555,0.715,1.4801912307739258,587.578093 +2438,Binary classification,Leveraging Bagging,Bananas,0.7599507591300779,0.7202295552367289,1.5264062881469727,641.095023 +2544,Binary classification,Leveraging Bagging,Bananas,0.7624852536374361,0.7257039055404179,1.5866899490356445,696.665992 +2650,Binary classification,Leveraging Bagging,Bananas,0.7678369195922989,0.7331887201735358,1.6338167190551758,754.18395 +2756,Binary classification,Leveraging Bagging,Bananas,0.7731397459165155,0.7396917950853811,1.718327522277832,813.521765 +2862,Binary classification,Leveraging Bagging,Bananas,0.777350576721426,0.7440739252711932,1.7761125564575195,874.768076 +2968,Binary classification,Leveraging Bagging,Bananas,0.7812605325244355,0.7479611650485437,1.876938819885254,937.8386529999999 +3074,Binary classification,Leveraging Bagging,Bananas,0.7845753335502766,0.7526158445440957,1.974156379699707,1002.618853 +3180,Binary classification,Leveraging Bagging,Bananas,0.7892418999685435,0.7572463768115942,2.0079431533813477,1069.033932 +3286,Binary classification,Leveraging Bagging,Bananas,0.7923896499238965,0.7605337078651686,2.0704050064086914,1137.092928 +3392,Binary classification,Leveraging Bagging,Bananas,0.7938661161899144,0.7636117686844774,2.141594886779785,1206.880705 +3498,Binary classification,Leveraging Bagging,Bananas,0.7966828710323134,0.7657331136738056,2.2472352981567383,1278.374701 +3604,Binary classification,Leveraging Bagging,Bananas,0.7998889814043852,0.7685393258426965,2.2915468215942383,1351.505796 +3710,Binary classification,Leveraging Bagging,Bananas,0.8021029927204099,0.7717661691542288,2.3504953384399414,1426.3168569999998 +3816,Binary classification,Leveraging Bagging,Bananas,0.8055045871559633,0.7761013880506941,2.3972253799438477,1502.8248519999997 +3922,Binary classification,Leveraging Bagging,Bananas,0.8071920428462127,0.7776470588235294,2.4447336196899414,1580.9903339999996 +4028,Binary classification,Leveraging Bagging,Bananas,0.8085423392103303,0.7788930312589618,2.513848304748535,1660.8236829999996 +4134,Binary classification,Leveraging Bagging,Bananas,0.8107911928381321,0.7816862088218872,2.6076173782348633,1742.3313809999995 +4240,Binary classification,Leveraging Bagging,Bananas,0.8136352913422977,0.7852093529091897,2.653599739074707,1825.5059619999995 +4346,Binary classification,Leveraging Bagging,Bananas,0.8161104718066743,0.7881198621055423,2.7111101150512695,1910.3837179999996 +4452,Binary classification,Leveraging Bagging,Bananas,0.8173444169849472,0.7894327894327894,2.7411813735961914,1996.8929369999996 +4558,Binary classification,Leveraging Bagging,Bananas,0.8183015141540487,0.7910146390711761,2.7629594802856445,2085.0686209999994 +4664,Binary classification,Leveraging Bagging,Bananas,0.8205018228608192,0.7941971969510695,2.818455696105957,2174.8872669999996 +4770,Binary classification,Leveraging Bagging,Bananas,0.8209268190396309,0.7942168674698795,2.852097511291504,2266.3018959999995 +4876,Binary classification,Leveraging Bagging,Bananas,0.822974358974359,0.795932844644124,2.940415382385254,2359.3812719999996 +4982,Binary classification,Leveraging Bagging,Bananas,0.825135514956836,0.7990772779700116,2.986912727355957,2454.1200449999997 +5088,Binary classification,Leveraging Bagging,Bananas,0.825437389424022,0.7995485327313769,3.072648048400879,2550.4404699999996 +5194,Binary classification,Leveraging Bagging,Bananas,0.8266897746967071,0.8008849557522125,3.1882104873657227,2648.3615419999996 +5300,Binary classification,Leveraging Bagging,Bananas,0.8282694848084544,0.8026886383347789,3.2357072830200195,2747.9506659999997 +906,Binary classification,Leveraging Bagging,Elec2,0.8895027624309392,0.8873873873873873,2.5379486083984375,35.61841 +1812,Binary classification,Leveraging Bagging,Elec2,0.9127553837658752,0.8941018766756033,3.2672500610351562,98.66906499999999 +2718,Binary classification,Leveraging Bagging,Elec2,0.9013617960986382,0.8815207780725023,2.908538818359375,185.051304 +3624,Binary classification,Leveraging Bagging,Elec2,0.905051062655258,0.8859416445623343,4.239933013916016,291.140366 +4530,Binary classification,Leveraging Bagging,Elec2,0.9059395009935968,0.8829026937877955,4.5028228759765625,414.05054599999994 +5436,Binary classification,Leveraging Bagging,Elec2,0.904691812327507,0.8806451612903227,5.411556243896484,555.0527619999999 +6342,Binary classification,Leveraging Bagging,Elec2,0.904746885349314,0.8810086682427108,3.64324951171875,712.2276919999999 +7248,Binary classification,Leveraging Bagging,Elec2,0.9038222712846695,0.8793908980792524,4.176555633544922,885.4512659999999 +8154,Binary classification,Leveraging Bagging,Elec2,0.9062921623942107,0.8879107981220656,4.873016357421875,1073.942042 +9060,Binary classification,Leveraging Bagging,Elec2,0.9073849210729661,0.8915600361897376,6.068294525146484,1277.3056459999998 +9966,Binary classification,Leveraging Bagging,Elec2,0.9066733567486202,0.8924855491329481,5.883171081542969,1496.0059789999998 +10872,Binary classification,Leveraging Bagging,Elec2,0.9090240088308343,0.8966238110170377,7.123630523681641,1728.5474849999998 +11778,Binary classification,Leveraging Bagging,Elec2,0.9088052984631061,0.8963720571208027,4.904956817626953,1974.076492 +12684,Binary classification,Leveraging Bagging,Elec2,0.9071197666167311,0.8948214285714287,4.745685577392578,2233.08693 +13590,Binary classification,Leveraging Bagging,Elec2,0.908234601515932,0.8972732515034187,5.919612884521484,2504.250556 +14496,Binary classification,Leveraging Bagging,Elec2,0.9082442221455674,0.8976293103448276,4.272552490234375,2787.365554 +15402,Binary classification,Leveraging Bagging,Elec2,0.9089669501980391,0.8979027090008739,4.651363372802734,3081.637323 +16308,Binary classification,Leveraging Bagging,Elec2,0.9085668731219722,0.8971653217463273,5.967304229736328,3386.795808 +17214,Binary classification,Leveraging Bagging,Elec2,0.9075698599895428,0.8943488943488943,5.553913116455078,3703.0592819999997 +18120,Binary classification,Leveraging Bagging,Elec2,0.9077211766653789,0.8943911066195048,7.001399993896484,4030.5410249999995 +19026,Binary classification,Leveraging Bagging,Elec2,0.9078580814717477,0.8932854446947099,7.953182220458984,4368.505934999999 +19932,Binary classification,Leveraging Bagging,Elec2,0.9081832321509207,0.8944636678200691,8.54180908203125,4717.761576999999 +20838,Binary classification,Leveraging Bagging,Elec2,0.9064644622546432,0.8926111631494847,7.284095764160156,5078.966551999999 +21744,Binary classification,Leveraging Bagging,Elec2,0.9064066596145886,0.8909840895698291,8.78485107421875,5451.1446879999985 +22650,Binary classification,Leveraging Bagging,Elec2,0.9054704401960352,0.8890386110391294,9.895774841308594,5834.8071089999985 +23556,Binary classification,Leveraging Bagging,Elec2,0.9032052642751008,0.8860113988601139,9.921958923339844,6231.782156999999 +24462,Binary classification,Leveraging Bagging,Elec2,0.9009443604104493,0.8825098191339766,6.414276123046875,6640.471452999998 +25368,Binary classification,Leveraging Bagging,Elec2,0.8975046320022076,0.87847059923343,7.025360107421875,7059.615553999998 +26274,Binary classification,Leveraging Bagging,Elec2,0.8978418909146272,0.878705712219812,8.249675750732422,7487.941528999998 +27180,Binary classification,Leveraging Bagging,Elec2,0.8983038375216159,0.879888753693725,7.590415954589844,7924.652190999998 +28086,Binary classification,Leveraging Bagging,Elec2,0.8965640021363718,0.8772448763997466,7.862815856933594,8369.790676999999 +28992,Binary classification,Leveraging Bagging,Elec2,0.8963126487530613,0.8762962962962964,9.08489990234375,8823.391086 +29898,Binary classification,Leveraging Bagging,Elec2,0.8952403251162324,0.8748901493968203,2.6490402221679688,9284.744376999999 +30804,Binary classification,Leveraging Bagging,Elec2,0.8947505113138331,0.8736357966947302,3.2276954650878906,9752.904185 +31710,Binary classification,Leveraging Bagging,Elec2,0.8935948784256835,0.8721291594027135,3.8703384399414062,10228.075712 +32616,Binary classification,Leveraging Bagging,Elec2,0.8929940211559099,0.8717194736455194,4.073085784912109,10710.354293 +33522,Binary classification,Leveraging Bagging,Elec2,0.8932311088571343,0.872338148742643,4.776435852050781,11199.826533 +34428,Binary classification,Leveraging Bagging,Elec2,0.8924390739826299,0.8711865585974188,4.868198394775391,11696.46579 +35334,Binary classification,Leveraging Bagging,Elec2,0.8922537005066086,0.8703735231025913,5.445720672607422,12200.310087 +36240,Binary classification,Leveraging Bagging,Elec2,0.8919948122188802,0.8690619563762879,4.9837493896484375,12711.187581 +37146,Binary classification,Leveraging Bagging,Elec2,0.8920985327769552,0.8688996467355751,5.313899993896484,13229.101200000001 +38052,Binary classification,Leveraging Bagging,Elec2,0.8916979842842501,0.8676919125437441,5.129566192626953,13754.199791000001 +38958,Binary classification,Leveraging Bagging,Elec2,0.8912647277767796,0.8674924924924926,5.3661651611328125,14286.524658 +39864,Binary classification,Leveraging Bagging,Elec2,0.8915535709806086,0.8687813021702837,5.776020050048828,14825.184636 +40770,Binary classification,Leveraging Bagging,Elec2,0.8920012754789178,0.8703512852978417,6.964508056640625,15370.438083000001 +41676,Binary classification,Leveraging Bagging,Elec2,0.89250149970006,0.8717728547713092,8.029548645019531,15922.831901000001 +42582,Binary classification,Leveraging Bagging,Elec2,0.8927925600619995,0.8723184068469779,8.723072052001953,16481.133162000002 +43488,Binary classification,Leveraging Bagging,Elec2,0.8927495573389749,0.8722821622213702,8.793426513671875,17045.039295000002 +44394,Binary classification,Leveraging Bagging,Elec2,0.8920775797986169,0.8710467526175545,6.634971618652344,17614.470389000002 +45300,Binary classification,Leveraging Bagging,Elec2,0.8926687123336056,0.8720122143834896,7.5638427734375,18188.843118 +45312,Binary classification,Leveraging Bagging,Elec2,0.8926529981682152,0.8719663069228745,7.565349578857422,18763.342135 +25,Binary classification,Leveraging Bagging,Phishing,0.75,0.75,0.6626491546630859,1.23946 +50,Binary classification,Leveraging Bagging,Phishing,0.8163265306122449,0.8,0.6635112762451172,3.9379920000000004 +75,Binary classification,Leveraging Bagging,Phishing,0.8378378378378378,0.8333333333333334,0.6635112762451172,8.007437 +100,Binary classification,Leveraging Bagging,Phishing,0.8484848484848485,0.8421052631578947,0.6476030349731445,13.398751 +125,Binary classification,Leveraging Bagging,Phishing,0.8467741935483871,0.8403361344537815,0.9203081130981445,19.997869 +150,Binary classification,Leveraging Bagging,Phishing,0.8456375838926175,0.8456375838926175,0.9203310012817383,27.874049 +175,Binary classification,Leveraging Bagging,Phishing,0.867816091954023,0.8588957055214724,1.0861825942993164,37.283539 +200,Binary classification,Leveraging Bagging,Phishing,0.8693467336683417,0.8617021276595744,1.2813997268676758,47.998757 +225,Binary classification,Leveraging Bagging,Phishing,0.8660714285714286,0.8557692307692308,1.3089113235473633,59.952352999999995 +250,Binary classification,Leveraging Bagging,Phishing,0.8554216867469879,0.8434782608695653,1.3089799880981445,73.11976999999999 +275,Binary classification,Leveraging Bagging,Phishing,0.8576642335766423,0.844621513944223,1.2476167678833008,87.72028699999998 +300,Binary classification,Leveraging Bagging,Phishing,0.862876254180602,0.8464419475655431,1.4594087600708008,103.60422699999998 +325,Binary classification,Leveraging Bagging,Phishing,0.8703703703703703,0.851063829787234,1.4950456619262695,120.70292199999997 +350,Binary classification,Leveraging Bagging,Phishing,0.8710601719197708,0.8494983277591974,1.5330171585083008,138.98074599999998 +375,Binary classification,Leveraging Bagging,Phishing,0.8716577540106952,0.8481012658227849,1.809849739074707,158.64485 +400,Binary classification,Leveraging Bagging,Phishing,0.8696741854636592,0.8433734939759037,2.068051338195801,179.64681099999999 +425,Binary classification,Leveraging Bagging,Phishing,0.8702830188679245,0.8405797101449276,2.104710578918457,201.85095299999998 +450,Binary classification,Leveraging Bagging,Phishing,0.8752783964365256,0.845303867403315,2.104527473449707,225.23996699999998 +475,Binary classification,Leveraging Bagging,Phishing,0.8776371308016878,0.8505154639175259,2.132199287414551,249.91758899999996 +500,Binary classification,Leveraging Bagging,Phishing,0.875751503006012,0.8502415458937198,2.1503801345825195,275.75512899999995 +525,Binary classification,Leveraging Bagging,Phishing,0.8778625954198473,0.8497652582159624,2.187130928039551,302.922289 +550,Binary classification,Leveraging Bagging,Phishing,0.8743169398907104,0.8463251670378619,2.2971315383911133,331.41425 +575,Binary classification,Leveraging Bagging,Phishing,0.8763066202090593,0.8479657387580299,2.4067888259887695,361.219435 +600,Binary classification,Leveraging Bagging,Phishing,0.8764607679465777,0.8451882845188285,2.406834602355957,392.26266999999996 +625,Binary classification,Leveraging Bagging,Phishing,0.8782051282051282,0.8442622950819672,2.352017402648926,424.555734 +650,Binary classification,Leveraging Bagging,Phishing,0.8813559322033898,0.850485436893204,2.279099464416504,458.24235899999996 +675,Binary classification,Leveraging Bagging,Phishing,0.8798219584569733,0.8513761467889909,2.54854679107666,493.20967599999994 +700,Binary classification,Leveraging Bagging,Phishing,0.8841201716738197,0.8550983899821109,2.565995216369629,529.352808 +725,Binary classification,Leveraging Bagging,Phishing,0.8812154696132597,0.8537414965986394,2.870518684387207,566.738792 +750,Binary classification,Leveraging Bagging,Phishing,0.8825100133511349,0.8557377049180328,2.941006660461426,605.3090109999999 +775,Binary classification,Leveraging Bagging,Phishing,0.8837209302325582,0.856687898089172,3.0508241653442383,645.053635 +800,Binary classification,Leveraging Bagging,Phishing,0.8836045056320401,0.8584474885844748,3.1606874465942383,686.031259 +825,Binary classification,Leveraging Bagging,Phishing,0.8810679611650486,0.8567251461988304,3.270321846008301,728.1933819999999 +850,Binary classification,Leveraging Bagging,Phishing,0.8833922261484098,0.8591749644381224,3.2882280349731445,771.57935 +875,Binary classification,Leveraging Bagging,Phishing,0.8844393592677345,0.8595271210013908,3.2632036209106445,816.1995999999999 +900,Binary classification,Leveraging Bagging,Phishing,0.8832035595105673,0.8575305291723202,3.380833625793457,861.997768 +925,Binary classification,Leveraging Bagging,Phishing,0.8841991341991342,0.8597640891218873,3.4813432693481445,908.9730649999999 +950,Binary classification,Leveraging Bagging,Phishing,0.8851422550052687,0.8628930817610063,3.5117311477661133,957.1207809999999 +975,Binary classification,Leveraging Bagging,Phishing,0.8870636550308009,0.8651960784313726,3.5666399002075195,1006.3541349999998 +1000,Binary classification,Leveraging Bagging,Phishing,0.8878878878878879,0.8663484486873507,3.645543098449707,1056.728559 +1025,Binary classification,Leveraging Bagging,Phishing,0.8876953125,0.8667439165701043,3.735753059387207,1108.282035 +1050,Binary classification,Leveraging Bagging,Phishing,0.8894184938036225,0.8693693693693694,3.808384895324707,1160.9493009999999 +1075,Binary classification,Leveraging Bagging,Phishing,0.8901303538175046,0.87117903930131,3.9453020095825195,1214.7027389999998 +1100,Binary classification,Leveraging Bagging,Phishing,0.89171974522293,0.873269435569755,3.945347785949707,1269.5439849999998 +1125,Binary classification,Leveraging Bagging,Phishing,0.8905693950177936,0.8730650154798762,3.972836494445801,1325.4243339999998 +1150,Binary classification,Leveraging Bagging,Phishing,0.8920800696257616,0.8747474747474747,3.945645332336426,1382.2374609999997 +1175,Binary classification,Leveraging Bagging,Phishing,0.8909710391822828,0.8732673267326733,3.9732484817504883,1440.0925429999998 +1200,Binary classification,Leveraging Bagging,Phishing,0.8924103419516264,0.8746355685131195,3.9472780227661133,1498.9929549999997 +1225,Binary classification,Leveraging Bagging,Phishing,0.8937908496732027,0.8761904761904762,3.982327461242676,1558.8210809999996 +1250,Binary classification,Leveraging Bagging,Phishing,0.8943154523618895,0.8773234200743495,4.0114030838012695,1619.6535709999996 +1903,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16020870208740234,31.58816 +3806,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16081905364990234,89.620857 +5709,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,167.42750999999998 +7612,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,263.688726 +9515,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,375.65509199999997 +11418,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16203975677490234,502.19041899999996 +13321,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16203975677490234,643.105063 +15224,Binary classification,Leveraging Bagging,SMTP,0.9992117191092426,0.0,0.2446889877319336,797.8947989999999 +17127,Binary classification,Leveraging Bagging,SMTP,0.9991825294873292,0.0,0.1627492904663086,968.1431969999999 +19030,Binary classification,Leveraging Bagging,SMTP,0.9992642808345158,0.0,0.16258907318115234,1153.4864309999998 +20933,Binary classification,Leveraging Bagging,SMTP,0.9993311675902924,0.0,0.16315364837646484,1353.5382149999998 +22836,Binary classification,Leveraging Bagging,SMTP,0.9993869060652507,0.0,0.1632680892944336,1568.3644279999999 +24739,Binary classification,Leveraging Bagging,SMTP,0.9994340690435767,0.0,0.16329097747802734,1796.9469139999999 +26642,Binary classification,Leveraging Bagging,SMTP,0.9994369580721444,0.0,0.1630849838256836,2038.09448 +28545,Binary classification,Leveraging Bagging,SMTP,0.9994744955156951,0.0,0.1630849838256836,2291.954796 +30448,Binary classification,Leveraging Bagging,SMTP,0.999507340624692,0.0,0.16315364837646484,2557.874515 +32351,Binary classification,Leveraging Bagging,SMTP,0.9995363214837713,0.0,0.1631765365600586,2835.65121 +34254,Binary classification,Leveraging Bagging,SMTP,0.999562082153388,0.0,0.16315364837646484,3124.961925 +36157,Binary classification,Leveraging Bagging,SMTP,0.9995851310985728,0.0,0.16310787200927734,3424.787068 +38060,Binary classification,Leveraging Bagging,SMTP,0.9996058750886782,0.0,0.1631307601928711,3734.997034 +39963,Binary classification,Leveraging Bagging,SMTP,0.9996246434112407,0.0,0.16319942474365234,4055.587677 +41866,Binary classification,Leveraging Bagging,SMTP,0.9996417054819061,0.0,0.1638784408569336,4386.458848 +43769,Binary classification,Leveraging Bagging,SMTP,0.9996572838603546,0.0,0.1636495590209961,4726.906731 +45672,Binary classification,Leveraging Bagging,SMTP,0.999671564012174,0.0,0.16385555267333984,5077.03597 +47575,Binary classification,Leveraging Bagging,SMTP,0.9996847017278345,0.0,0.16394710540771484,5436.8723119999995 +49478,Binary classification,Leveraging Bagging,SMTP,0.9996968288295571,0.0,0.16371822357177734,5806.3009919999995 +51381,Binary classification,Leveraging Bagging,SMTP,0.9996691319579603,0.0,0.16394710540771484,6185.544045999999 +53284,Binary classification,Leveraging Bagging,SMTP,0.9996809488955202,0.0,0.1641073226928711,6574.848143999999 +55187,Binary classification,Leveraging Bagging,SMTP,0.9996919508571014,0.0,0.16376399993896484,6975.121680999999 +57090,Binary classification,Leveraging Bagging,SMTP,0.9995445707579393,0.0,0.1638784408569336,7384.584095999999 +58993,Binary classification,Leveraging Bagging,SMTP,0.9995592622728505,0.0,0.16390132904052734,7802.5953629999985 +60896,Binary classification,Leveraging Bagging,SMTP,0.999573035553001,0.0,0.16371822357177734,8228.594131999998 +62799,Binary classification,Leveraging Bagging,SMTP,0.9995541259275773,0.0,0.16390132904052734,8661.618187999999 +64702,Binary classification,Leveraging Bagging,SMTP,0.9995672400735692,0.0,0.16399288177490234,9101.660232999999 +66605,Binary classification,Leveraging Bagging,SMTP,0.9995796048285388,0.0,0.1638326644897461,9548.697329999999 +68508,Binary classification,Leveraging Bagging,SMTP,0.9995620885456961,0.0,0.16380977630615234,10002.655235999999 +70411,Binary classification,Leveraging Bagging,SMTP,0.9995739241585002,0.0,0.16371822357177734,10463.105480999999 +72314,Binary classification,Leveraging Bagging,SMTP,0.9995851368357004,0.0,0.16376399993896484,10929.695224 +74217,Binary classification,Leveraging Bagging,SMTP,0.9995957744960655,0.0,0.16380977630615234,11402.447204 +76120,Binary classification,Leveraging Bagging,SMTP,0.9996058802664249,0.0,0.1638784408569336,11881.476782 +78023,Binary classification,Leveraging Bagging,SMTP,0.9996154930660582,0.0,0.1637411117553711,12366.666901 +79926,Binary classification,Leveraging Bagging,SMTP,0.9996246481076009,0.0,0.16371822357177734,12858.057531 +81829,Binary classification,Leveraging Bagging,SMTP,0.999633377328054,0.0,0.1521596908569336,13355.582794 +83732,Binary classification,Leveraging Bagging,SMTP,0.9996417097610204,0.0,0.15288448333740234,13859.323941 +85635,Binary classification,Leveraging Bagging,SMTP,0.9996496718593082,0.0,0.16432857513427734,14369.189455 +87538,Binary classification,Leveraging Bagging,SMTP,0.9996572877754549,0.0,0.16460323333740234,14885.224126 +89441,Binary classification,Leveraging Bagging,SMTP,0.9996533989266547,0.0,0.16451168060302734,15407.418989999998 +91344,Binary classification,Leveraging Bagging,SMTP,0.9996606198614015,0.0,0.16432857513427734,15935.791255999999 +93247,Binary classification,Leveraging Bagging,SMTP,0.9996675460609571,0.0,0.16451168060302734,16470.041814999997 +95150,Binary classification,Leveraging Bagging,SMTP,0.9996741952096186,0.0,0.1645345687866211,17009.813748999997 +95156,Binary classification,Leveraging Bagging,SMTP,0.9996742157532447,0.0,0.16460323333740234,17549.605714999998 +106,Binary classification,Stacking,Bananas,0.6095238095238096,0.577319587628866,0.7777948379516602,2.119535 +212,Binary classification,Stacking,Bananas,0.7109004739336493,0.6702702702702703,1.3802881240844727,6.931057 +318,Binary classification,Stacking,Bananas,0.7602523659305994,0.7361111111111112,1.8119163513183594,15.160032000000001 +424,Binary classification,Stacking,Bananas,0.7943262411347518,0.772845953002611,2.401026725769043,27.407145 +530,Binary classification,Stacking,Bananas,0.8052930056710775,0.7775377969762419,5.0262651443481445,65.121823 +636,Binary classification,Stacking,Bananas,0.8236220472440945,0.7992831541218638,5.88111686706543,107.288216 +742,Binary classification,Stacking,Bananas,0.8299595141700404,0.8025078369905957,6.734616279602051,153.798119 +848,Binary classification,Stacking,Bananas,0.8347107438016529,0.8087431693989071,7.555168151855469,204.76644700000003 +954,Binary classification,Stacking,Bananas,0.8426023084994754,0.8166259168704157,8.384669303894043,260.019764 +1060,Binary classification,Stacking,Bananas,0.8536355051935789,0.8275862068965517,8.926264762878418,319.31365700000003 +1166,Binary classification,Stacking,Bananas,0.8532188841201717,0.8274470232088799,9.188977241516113,382.58132300000005 +1272,Binary classification,Stacking,Bananas,0.8536585365853658,0.8290441176470588,9.45701789855957,449.39877900000005 +1378,Binary classification,Stacking,Bananas,0.8576615831517792,0.8321917808219177,9.84501838684082,519.6596460000001 +1484,Binary classification,Stacking,Bananas,0.8590694538098449,0.8345209817893903,10.364198684692383,593.3590610000001 +1590,Binary classification,Stacking,Bananas,0.8565135305223411,0.8321060382916053,10.468925476074219,670.9077340000001 +1696,Binary classification,Stacking,Bananas,0.8595870206489675,0.8354080221300139,10.966409683227539,752.0270200000001 +1802,Binary classification,Stacking,Bananas,0.8634092171016102,0.8410852713178295,10.118447303771973,836.636461 +1908,Binary classification,Stacking,Bananas,0.8626114315679078,0.8417874396135265,10.3862943649292,924.5557000000001 +2014,Binary classification,Stacking,Bananas,0.8614008941877794,0.8415672913117546,10.646858215332031,1015.8872450000001 +2120,Binary classification,Stacking,Bananas,0.8640868334119868,0.8459893048128343,10.9229736328125,1110.420791 +2226,Binary classification,Stacking,Bananas,0.8642696629213483,0.8462321792260691,11.325839042663574,1208.24484 +2332,Binary classification,Stacking,Bananas,0.864006864006864,0.8461911693352742,11.659860610961914,1308.9666100000002 +2438,Binary classification,Stacking,Bananas,0.8641772671317193,0.8465461288827074,11.19693660736084,1412.476276 +2544,Binary classification,Stacking,Bananas,0.8651199370821864,0.8484312859036677,11.452000617980957,1518.7765900000002 +2650,Binary classification,Stacking,Bananas,0.864477161192903,0.8480744815911976,11.787381172180176,1627.8671150000002 +2756,Binary classification,Stacking,Bananas,0.8653357531760436,0.849125660837739,12.11353874206543,1739.6750610000001 +2862,Binary classification,Stacking,Bananas,0.8678783642083188,0.8515318146111548,12.359809875488281,1854.1318460000002 +2968,Binary classification,Stacking,Bananas,0.8695652173913043,0.8529076396807297,12.722334861755371,1971.3857770000002 +3074,Binary classification,Stacking,Bananas,0.8682069638789457,0.8515939904727006,13.0479736328125,2091.3191060000004 +3180,Binary classification,Stacking,Bananas,0.8700849323686694,0.8530771967271433,13.308364868164062,2213.9114950000003 +3286,Binary classification,Stacking,Bananas,0.8700152207001522,0.8523002421307506,13.608009338378906,2339.1031470000003 +3392,Binary classification,Stacking,Bananas,0.871129460336184,0.8544788544788545,13.707662582397461,2466.937686 +3498,Binary classification,Stacking,Bananas,0.872748069774092,0.8557536466774717,14.051713943481445,2598.213051 +3604,Binary classification,Stacking,Bananas,0.8742714404662781,0.856872037914692,14.268294334411621,2732.2090540000004 +3710,Binary classification,Stacking,Bananas,0.8751685090320841,0.8583664729275007,14.518733978271484,2868.9613940000004 +3816,Binary classification,Stacking,Bananas,0.8762778505897771,0.8596908442330559,14.742842674255371,3008.2640120000005 +3922,Binary classification,Stacking,Bananas,0.8747768426421831,0.8578048074138431,15.053866386413574,3150.2484420000005 +4028,Binary classification,Stacking,Bananas,0.8733548547305686,0.8560135516657256,15.453622817993164,3294.8873180000005 +4134,Binary classification,Stacking,Bananas,0.874183401887249,0.8569069895432032,15.755488395690918,3442.0965550000005 +4240,Binary classification,Stacking,Bananas,0.8749705119131871,0.8579849946409432,15.976973533630371,3591.8798750000005 +4346,Binary classification,Stacking,Bananas,0.8759493670886076,0.8590849673202615,16.313834190368652,3744.3343260000006 +4452,Binary classification,Stacking,Bananas,0.8755335879577623,0.8585291113381002,16.729196548461914,3899.4355060000007 +4558,Binary classification,Stacking,Bananas,0.8757954794821154,0.8592039800995025,17.032727241516113,4057.1530940000007 +4664,Binary classification,Stacking,Bananas,0.8756165558653227,0.8594961240310078,17.45319175720215,4217.5274930000005 +4770,Binary classification,Stacking,Bananas,0.8754455860767456,0.8590412909349787,17.6948184967041,4380.594611 +4876,Binary classification,Stacking,Bananas,0.8756923076923077,0.8588070829450141,17.917430877685547,4546.383704000001 +4982,Binary classification,Stacking,Bananas,0.8761292913069665,0.8596770525358198,18.09793186187744,4714.847995000001 +5088,Binary classification,Stacking,Bananas,0.8757617456261058,0.8591800356506238,18.51348114013672,4886.014522000001 +5194,Binary classification,Stacking,Bananas,0.876372039283651,0.8598865124399825,18.892748832702637,5059.990176000001 +5300,Binary classification,Stacking,Bananas,0.8762030571806001,0.8596491228070176,19.194592475891113,5236.837813000001 +906,Binary classification,Stacking,Elec2,0.9116022099447514,0.908256880733945,7.041282653808594,59.400144 +1812,Binary classification,Stacking,Elec2,0.906129210381005,0.8855989232839839,9.07800579071045,148.928403 +2718,Binary classification,Stacking,Elec2,0.9002576370997424,0.8772088808337108,9.477606773376465,264.671315 +3624,Binary classification,Stacking,Elec2,0.9064311344189898,0.8849677638276212,10.383838653564453,404.188666 +4530,Binary classification,Stacking,Elec2,0.90527710311327,0.8788477831121152,11.437847137451172,565.129871 +5436,Binary classification,Stacking,Elec2,0.9000919963201472,0.8723854289071681,14.209432601928711,747.817107 +6342,Binary classification,Stacking,Elec2,0.897019397571361,0.8704622098789923,15.688876152038574,951.5612199999999 +7248,Binary classification,Stacking,Elec2,0.8965089002345799,0.8694744169857292,19.837779998779297,1176.633371 +8154,Binary classification,Stacking,Elec2,0.8980743284680486,0.8778839088905216,22.41482448577881,1420.714573 +9060,Binary classification,Stacking,Elec2,0.9000993487139861,0.8828478964401294,25.97023296356201,1683.800751 +9966,Binary classification,Stacking,Elec2,0.8982438534872053,0.8827203331020125,28.783666610717773,1965.440637 +10872,Binary classification,Stacking,Elec2,0.9006531137889798,0.8870765370138016,30.882869720458984,2263.780185 +11778,Binary classification,Stacking,Elec2,0.9021822195805383,0.8888030888030888,33.277831077575684,2578.868179 +12684,Binary classification,Stacking,Elec2,0.9012851848931641,0.8879541793449078,35.50911808013916,2911.085292 +13590,Binary classification,Stacking,Elec2,0.901905953344617,0.8899529431189631,38.6168327331543,3259.512573 +14496,Binary classification,Stacking,Elec2,0.9030010348395998,0.8917128773875539,41.26064682006836,3624.142031 +15402,Binary classification,Stacking,Elec2,0.9039023440036361,0.8922382408620941,43.65532207489014,4004.812957 +16308,Binary classification,Stacking,Elec2,0.9002882197829153,0.8879239040529363,45.1539192199707,4403.507418 +17214,Binary classification,Stacking,Elec2,0.8997850461860222,0.8856176646111001,37.54582214355469,4816.829749 +18120,Binary classification,Stacking,Elec2,0.9001048622992439,0.8858908082209053,41.9152717590332,5243.603799 +19026,Binary classification,Stacking,Elec2,0.9014454664914586,0.886177381169186,44.45838737487793,5683.033974000001 +19932,Binary classification,Stacking,Elec2,0.9011088254477949,0.8867826986041704,48.213175773620605,6135.158415000001 +20838,Binary classification,Stacking,Elec2,0.8992657292316553,0.8848411696933121,47.78572177886963,6598.876461000001 +21744,Binary classification,Stacking,Elec2,0.8986800349537782,0.8824376967821121,49.356743812561035,7073.276106000001 +22650,Binary classification,Stacking,Elec2,0.898361958585368,0.8812912541254125,47.91073036193848,7558.681153000001 +23556,Binary classification,Stacking,Elec2,0.8961579282530249,0.8784656663022956,53.37149906158447,8055.088432000001 +24462,Binary classification,Stacking,Elec2,0.8945259801316381,0.8760211436809228,52.06687641143799,8562.621137000002 +25368,Binary classification,Stacking,Elec2,0.8922221784207829,0.8734610756271406,20.63737678527832,9080.309208000002 +26274,Binary classification,Stacking,Elec2,0.8928177216153466,0.873801201039706,17.83812713623047,9607.290172000003 +27180,Binary classification,Stacking,Elec2,0.8932263880201626,0.874632797649905,19.305461883544922,10142.799192000002 +28086,Binary classification,Stacking,Elec2,0.8915435285739719,0.8721564677243349,18.23539447784424,10686.882271000002 +28992,Binary classification,Stacking,Elec2,0.891345590010693,0.8712498978173793,21.229859352111816,11240.330868000003 +29898,Binary classification,Stacking,Elec2,0.8905575810281968,0.8700246285850481,25.24380111694336,11801.274420000003 +30804,Binary classification,Stacking,Elec2,0.890335356945752,0.8690697674418605,28.836254119873047,12369.623812000003 +31710,Binary classification,Stacking,Elec2,0.8895266328171813,0.8679458664756663,27.00519371032715,12944.308752000003 +32616,Binary classification,Stacking,Elec2,0.8879963207113292,0.86634224872855,30.17805576324463,13524.938061000003 +33522,Binary classification,Stacking,Elec2,0.8870260433757943,0.8654563541407612,32.107930183410645,14111.446119000002 +34428,Binary classification,Stacking,Elec2,0.8857582711244082,0.8638770636486346,32.29031944274902,14703.865141000002 +35334,Binary classification,Stacking,Elec2,0.8850083491353692,0.8622665175090681,36.271653175354004,15302.291934000003 +36240,Binary classification,Stacking,Elec2,0.8850133833715058,0.8612988050461006,35.55355262756348,15906.592759000003 +37146,Binary classification,Stacking,Elec2,0.8836182527931081,0.859061715515274,38.756625175476074,16517.021041000004 +38052,Binary classification,Stacking,Elec2,0.8830516937794013,0.8577547628180539,41.063425064086914,17133.624997000003 +38958,Binary classification,Stacking,Elec2,0.8831788895448828,0.8582198822393221,42.255154609680176,17756.348401000003 +39864,Binary classification,Stacking,Elec2,0.8836765923287259,0.8598797328740216,43.789937019348145,18385.013164000004 +40770,Binary classification,Stacking,Elec2,0.8840295322426354,0.8614708467623791,40.85312080383301,19019.639174000004 +41676,Binary classification,Stacking,Elec2,0.8847030593881223,0.8632106356933413,39.29591369628906,19660.085711000003 +42582,Binary classification,Stacking,Elec2,0.8851130786031328,0.8639675212724542,42.33915042877197,20306.976876000004 +43488,Binary classification,Stacking,Elec2,0.8848391473313864,0.8636091290375292,42.20731544494629,20960.039327000006 +44394,Binary classification,Stacking,Elec2,0.8848467100669024,0.8632350580555408,44.13865566253662,21618.107174000004 +45300,Binary classification,Stacking,Elec2,0.8854720854765006,0.8642027012878233,40.63082981109619,22281.084676000006 +45312,Binary classification,Stacking,Elec2,0.8854582772395224,0.8641574621787154,40.75471591949463,22944.429270000004 +25,Binary classification,Stacking,Phishing,0.6666666666666666,0.7142857142857143,0.6122617721557617,0.640752 +50,Binary classification,Stacking,Phishing,0.7755102040816326,0.7659574468085107,0.7524843215942383,1.992597 +75,Binary classification,Stacking,Phishing,0.8243243243243243,0.8266666666666667,0.9228668212890625,4.151733 +100,Binary classification,Stacking,Phishing,0.8282828282828283,0.8282828282828283,1.193608283996582,7.194986 +125,Binary classification,Stacking,Phishing,0.8306451612903226,0.8292682926829269,1.3295679092407227,11.208746999999999 +150,Binary classification,Stacking,Phishing,0.8389261744966443,0.8441558441558442,1.3798675537109375,16.195196 +175,Binary classification,Stacking,Phishing,0.8563218390804598,0.8520710059171597,1.4546594619750977,22.422242999999998 +200,Binary classification,Stacking,Phishing,0.8542713567839196,0.8497409326424871,1.6083984375,29.888727999999997 +225,Binary classification,Stacking,Phishing,0.8526785714285714,0.8436018957345972,1.7997064590454102,38.482186 +250,Binary classification,Stacking,Phishing,0.8433734939759037,0.8354430379746836,1.9343080520629883,48.311454 +275,Binary classification,Stacking,Phishing,0.8467153284671532,0.8372093023255813,2.053934097290039,59.483297 +300,Binary classification,Stacking,Phishing,0.8461538461538461,0.8333333333333334,2.12460994720459,72.133375 +325,Binary classification,Stacking,Phishing,0.8518518518518519,0.8356164383561644,2.201033592224121,86.30699200000001 +350,Binary classification,Stacking,Phishing,0.8595988538681948,0.8414239482200646,2.2356014251708984,102.05020300000001 +375,Binary classification,Stacking,Phishing,0.8556149732620321,0.8353658536585366,2.328523635864258,119.41383300000001 +400,Binary classification,Stacking,Phishing,0.8571428571428571,0.8347826086956521,2.316814422607422,138.615081 +425,Binary classification,Stacking,Phishing,0.8608490566037735,0.8356545961002786,2.353947639465332,159.65345200000002 +450,Binary classification,Stacking,Phishing,0.8619153674832962,0.8351063829787234,2.4291276931762695,182.47163200000003 +475,Binary classification,Stacking,Phishing,0.8649789029535865,0.8407960199004976,2.5799179077148438,207.23725000000002 +500,Binary classification,Stacking,Phishing,0.8637274549098196,0.841860465116279,4.818408012390137,255.437855 +525,Binary classification,Stacking,Phishing,0.8645038167938931,0.8397291196388261,5.000759124755859,305.327991 +550,Binary classification,Stacking,Phishing,0.8652094717668488,0.8418803418803419,5.177936553955078,356.788916 +575,Binary classification,Stacking,Phishing,0.8658536585365854,0.8425357873210634,5.324765205383301,409.72389599999997 +600,Binary classification,Stacking,Phishing,0.8697829716193656,0.8446215139442231,5.557343482971191,464.11454999999995 +625,Binary classification,Stacking,Phishing,0.8685897435897436,0.8404669260700389,5.701066970825195,520.0238599999999 +650,Binary classification,Stacking,Phishing,0.8721109399075501,0.847145488029466,5.875107765197754,577.5076929999999 +675,Binary classification,Stacking,Phishing,0.8753709198813057,0.8541666666666667,5.993474006652832,636.4901669999999 +700,Binary classification,Stacking,Phishing,0.8798283261802575,0.8576271186440678,6.097118377685547,696.8595059999999 +725,Binary classification,Stacking,Phishing,0.8798342541436464,0.8603531300160514,6.2616376876831055,758.6517739999999 +750,Binary classification,Stacking,Phishing,0.8811748998664887,0.8624420401854715,6.510566711425781,822.0041369999999 +775,Binary classification,Stacking,Phishing,0.8824289405684754,0.8631578947368422,6.659415245056152,886.863173 +800,Binary classification,Stacking,Phishing,0.8823529411764706,0.8645533141210374,6.793304443359375,953.2293709999999 +825,Binary classification,Stacking,Phishing,0.8822815533980582,0.8654646324549237,7.087222099304199,1021.1070179999999 +850,Binary classification,Stacking,Phishing,0.8833922261484098,0.8660351826792964,7.324291229248047,1090.559446 +875,Binary classification,Stacking,Phishing,0.88558352402746,0.8677248677248677,7.470724105834961,1161.497406 +900,Binary classification,Stacking,Phishing,0.8854282536151279,0.8670967741935484,7.810632705688477,1233.983859 +925,Binary classification,Stacking,Phishing,0.8874458874458875,0.8706467661691542,7.971014976501465,1307.96073 +950,Binary classification,Stacking,Phishing,0.8872497365648051,0.8718562874251498,8.080266952514648,1383.548543 +975,Binary classification,Stacking,Phishing,0.8891170431211499,0.8738317757009346,8.205357551574707,1460.7186840000002 +1000,Binary classification,Stacking,Phishing,0.8888888888888888,0.8737201365187712,8.39128303527832,1539.5016600000001 +1025,Binary classification,Stacking,Phishing,0.888671875,0.8738938053097345,8.406519889831543,1619.9124450000002 +1050,Binary classification,Stacking,Phishing,0.8903717826501429,0.8762109795479011,8.400672912597656,1701.87919 +1075,Binary classification,Stacking,Phishing,0.8910614525139665,0.877742946708464,8.453279495239258,1785.406003 +1100,Binary classification,Stacking,Phishing,0.8926296633303002,0.8795918367346939,8.421560287475586,1870.444315 +1125,Binary classification,Stacking,Phishing,0.8932384341637011,0.8811881188118813,8.383057594299316,1956.980231 +1150,Binary classification,Stacking,Phishing,0.8929503916449086,0.8806983511154219,8.433841705322266,2045.031177 +1175,Binary classification,Stacking,Phishing,0.8909710391822828,0.8783269961977185,8.58321475982666,2134.506504 +1200,Binary classification,Stacking,Phishing,0.8932443703085905,0.8803738317757008,8.605175971984863,2225.421876 +1225,Binary classification,Stacking,Phishing,0.8946078431372549,0.8817598533455545,8.70528507232666,2317.752916 +1250,Binary classification,Stacking,Phishing,0.8951160928742994,0.88272157564906,8.721240997314453,2411.4111159999998 +1903,Binary classification,Stacking,SMTP,1.0,0.0,4.7766571044921875,62.937451 +3806,Binary classification,Stacking,SMTP,1.0,0.0,4.703582763671875,162.906939 +5709,Binary classification,Stacking,SMTP,1.0,0.0,4.683967590332031,291.99956099999997 +7612,Binary classification,Stacking,SMTP,1.0,0.0,4.6548919677734375,449.905783 +9515,Binary classification,Stacking,SMTP,1.0,0.0,4.674896240234375,634.264648 +11418,Binary classification,Stacking,SMTP,1.0,0.0,4.68939208984375,844.6132379999999 +13321,Binary classification,Stacking,SMTP,1.0,0.0,4.6727142333984375,1077.972428 +15224,Binary classification,Stacking,SMTP,0.9992774091834724,0.0,4.755153656005859,1334.293209 +17127,Binary classification,Stacking,SMTP,0.9992409202382343,0.0,4.668712615966797,1613.416001 +19030,Binary classification,Stacking,SMTP,0.9993168322034789,0.0,4.707424163818359,1913.210948 +20933,Binary classification,Stacking,SMTP,0.999378941333843,0.0,4.680248260498047,2233.020054 +22836,Binary classification,Stacking,SMTP,0.9994306984891613,0.0,4.695384979248047,2572.124593 +24739,Binary classification,Stacking,SMTP,0.9994744926833212,0.0,4.721019744873047,2930.571035 +26642,Binary classification,Stacking,SMTP,0.9994744942006681,0.0,4.747562408447266,3309.032834 +28545,Binary classification,Stacking,SMTP,0.9995095291479821,0.0,4.741054534912109,3705.221814 +30448,Binary classification,Stacking,SMTP,0.9995401845830459,0.0,4.678241729736328,4115.910057 +32351,Binary classification,Stacking,SMTP,0.9995672333848532,0.0,4.619670867919922,4539.977119 +34254,Binary classification,Stacking,SMTP,0.9995912766764955,0.0,4.749675750732422,4977.188868 +36157,Binary classification,Stacking,SMTP,0.9996127890253347,0.0,4.678524017333984,5426.058059 +38060,Binary classification,Stacking,SMTP,0.9996321500827662,0.0,4.705173492431641,5886.859448 +39963,Binary classification,Stacking,SMTP,0.9996496671838246,0.0,4.729236602783203,6359.258672 +41866,Binary classification,Stacking,SMTP,0.9996655917831124,0.0,4.729305267333984,6843.735511 +43769,Binary classification,Stacking,SMTP,0.9996801316029976,0.0,4.741458892822266,7339.76837 +45672,Binary classification,Stacking,SMTP,0.9996934597446958,0.0,4.677211761474609,7847.750223999999 +47575,Binary classification,Stacking,SMTP,0.9997057216126456,0.0,4.833148956298828,8367.044639 +49478,Binary classification,Stacking,SMTP,0.99971704024092,0.0,4.807292938232422,8898.416265 +51381,Binary classification,Stacking,SMTP,0.9996885947839627,0.0,4.893611907958984,9441.161399999999 +53284,Binary classification,Stacking,SMTP,0.9996997166075484,0.0,4.877178192138672,9993.506038 +55187,Binary classification,Stacking,SMTP,0.999710071394919,0.0,4.888896942138672,10554.524537 +57090,Binary classification,Stacking,SMTP,0.9995620872672494,0.0,4.783634185791016,11123.611551 +58993,Binary classification,Stacking,SMTP,0.9995762137238947,0.0,4.831531524658203,11701.029088 +60896,Binary classification,Stacking,SMTP,0.999589457262501,0.0,4.854015350341797,12286.755545999999 +62799,Binary classification,Stacking,SMTP,0.9995700500015924,0.0,4.858226776123047,12880.441842999999 +64702,Binary classification,Stacking,SMTP,0.9995826957852274,0.0,4.846561431884766,13482.274685999999 +66605,Binary classification,Stacking,SMTP,0.9995946189418053,0.0,4.872089385986328,14092.292626999999 +68508,Binary classification,Stacking,SMTP,0.9995766855941729,0.0,4.843868255615234,14710.448755 +70411,Binary classification,Stacking,SMTP,0.9995881266865502,0.0,4.835132598876953,15336.827121999999 +72314,Binary classification,Stacking,SMTP,0.9995989656078437,0.0,4.892154693603516,15971.964918999998 +74217,Binary classification,Stacking,SMTP,0.99960924867953,0.0,4.812671661376953,16613.982513 +76120,Binary classification,Stacking,SMTP,0.9996190175908775,0.0,4.880641937255859,17262.391145999998 +78023,Binary classification,Stacking,SMTP,0.9996283099638563,0.0,4.831180572509766,17916.095854 +79926,Binary classification,Stacking,SMTP,0.9996371598373475,0.0,4.851375579833984,18574.918078 +81829,Binary classification,Stacking,SMTP,0.9996455980837855,0.0,4.851016998291016,19239.025055 +83732,Binary classification,Stacking,SMTP,0.9996536527689864,0.0,4.869503021240234,19908.351771999998 +85635,Binary classification,Stacking,SMTP,0.999661349463998,0.0,4.886287689208984,20582.843625999998 +87538,Binary classification,Stacking,SMTP,0.9996687115162731,0.0,4.888690948486328,21262.535161 +89441,Binary classification,Stacking,SMTP,0.9996645796064401,0.0,4.888484954833984,21947.343421999998 +91344,Binary classification,Stacking,SMTP,0.999671567607808,0.0,4.876293182373047,22637.362347 +93247,Binary classification,Stacking,SMTP,0.9996782703815713,0.0,4.905620574951172,23332.514825 +95150,Binary classification,Stacking,SMTP,0.9996847050415664,0.0,4.880191802978516,24032.848442 +95156,Binary classification,Stacking,SMTP,0.9996847249224948,0.0,4.888683319091797,24733.238040999997 +106,Binary classification,Voting,Bananas,0.6761904761904762,0.6136363636363638,0.14342212677001953,0.374142 +212,Binary classification,Voting,Bananas,0.7772511848341233,0.7374301675977653,0.23540592193603516,1.3677169999999998 +318,Binary classification,Voting,Bananas,0.7886435331230284,0.7527675276752769,0.3270235061645508,3.238746 +424,Binary classification,Voting,Bananas,0.7990543735224587,0.7658402203856748,0.41901111602783203,6.2520690000000005 +530,Binary classification,Voting,Bananas,0.8015122873345936,0.7575057736720554,2.719620704650879,30.609493999999998 +636,Binary classification,Voting,Bananas,0.8173228346456692,0.7777777777777779,3.159085273742676,56.745796 +742,Binary classification,Voting,Bananas,0.8259109311740891,0.7839195979899498,3.6036806106567383,84.9251 +848,Binary classification,Voting,Bananas,0.8299881936245572,0.7913043478260869,4.0666093826293945,115.11768599999999 +954,Binary classification,Voting,Bananas,0.8352570828961176,0.7963683527885861,4.521588325500488,147.5017 +1060,Binary classification,Voting,Bananas,0.8470254957507082,0.8094117647058824,4.660099983215332,182.008963 +1166,Binary classification,Voting,Bananas,0.8497854077253219,0.8132337246531482,4.474972724914551,218.357961 +1272,Binary classification,Voting,Bananas,0.8489378442171518,0.8135922330097087,4.3258256912231445,256.411001 +1378,Binary classification,Voting,Bananas,0.8482207697893972,0.8112014453477868,4.1847429275512695,296.011707 +1484,Binary classification,Voting,Bananas,0.8530006743088334,0.8180300500834724,4.276310920715332,337.212047 +1590,Binary classification,Voting,Bananas,0.8539962240402769,0.8198757763975156,4.522702217102051,380.36472399999997 +1696,Binary classification,Voting,Bananas,0.8584070796460177,0.8250728862973761,4.5992326736450195,425.08911199999994 +1802,Binary classification,Voting,Bananas,0.8622987229317046,0.8315217391304348,4.6097002029418945,471.42038399999996 +1908,Binary classification,Voting,Bananas,0.8610382800209754,0.8319594166138238,4.583279609680176,519.2335119999999 +2014,Binary classification,Voting,Bananas,0.8584202682563339,0.8302561048243002,4.509037971496582,568.4848139999999 +2120,Binary classification,Voting,Bananas,0.8612553091080698,0.8355704697986577,4.487088203430176,619.150273 +2226,Binary classification,Voting,Bananas,0.8624719101123596,0.8370607028753994,4.479489326477051,671.27983 +2332,Binary classification,Voting,Bananas,0.8614328614328615,0.8357905439755974,4.476758003234863,724.8282939999999 +2438,Binary classification,Voting,Bananas,0.8621255642183012,0.8364167478091528,4.495999336242676,779.843051 +2544,Binary classification,Voting,Bananas,0.8623672827369249,0.8375116063138347,4.492741584777832,836.3752579999999 +2650,Binary classification,Voting,Bananas,0.8618346545866364,0.8374777975133214,4.535428047180176,894.373804 +2756,Binary classification,Voting,Bananas,0.8627949183303085,0.8384615384615384,4.529454231262207,953.727688 +2862,Binary classification,Voting,Bananas,0.8661307235232436,0.842061855670103,4.489285469055176,1014.5232589999999 +2968,Binary classification,Voting,Bananas,0.8678800134816312,0.8437001594896333,4.522076606750488,1076.832167 +3074,Binary classification,Voting,Bananas,0.8662544744549301,0.8419838523644751,4.4861345291137695,1140.498025 +3180,Binary classification,Voting,Bananas,0.8678829820698333,0.8432835820895522,4.490513801574707,1205.597432 +3286,Binary classification,Voting,Bananas,0.8684931506849315,0.8433647570703406,4.5036516189575195,1272.097546 +3392,Binary classification,Voting,Bananas,0.8690651725154822,0.8449720670391062,4.519848823547363,1340.030829 +3498,Binary classification,Voting,Bananas,0.8687446382613668,0.8439306358381503,4.534294128417969,1409.423343 +3604,Binary classification,Voting,Bananas,0.8701082431307244,0.8451356717405691,4.515525817871094,1480.1764939999998 +3710,Binary classification,Voting,Bananas,0.8705850633593961,0.8462524023062139,4.521697998046875,1552.3097799999998 +3816,Binary classification,Voting,Bananas,0.8718217562254259,0.847900466562986,4.52362060546875,1625.8426229999998 +3922,Binary classification,Voting,Bananas,0.8704412139760265,0.845873786407767,4.511474609375,1700.7302089999998 +4028,Binary classification,Voting,Bananas,0.8698783213310156,0.8450620934358367,4.530387878417969,1777.041184 +4134,Binary classification,Voting,Bananas,0.8707960319380595,0.8461981566820277,4.540306091308594,1854.755673 +4240,Binary classification,Voting,Bananas,0.8723755602736495,0.8485018202184262,4.5452880859375,1933.8281619999998 +4346,Binary classification,Voting,Bananas,0.8734177215189873,0.8498088476242489,4.5819854736328125,2014.1792419999997 +4452,Binary classification,Voting,Bananas,0.8732869018198157,0.8494394020288306,4.5782928466796875,2095.8385169999997 +4558,Binary classification,Voting,Bananas,0.8720649550142637,0.8482166102577455,4.539161682128906,2178.6750019999995 +4664,Binary classification,Voting,Bananas,0.8719708342268926,0.8485156051763512,4.509727478027344,2262.6244259999994 +4770,Binary classification,Voting,Bananas,0.8712518347661984,0.8472636815920398,4.5496673583984375,2347.8035639999994 +4876,Binary classification,Voting,Bananas,0.8717948717948718,0.8474493531852575,4.560760498046875,2434.1199849999994 +4982,Binary classification,Voting,Bananas,0.8725155591246737,0.8487735175041676,4.513671875,2521.5491019999995 +5088,Binary classification,Voting,Bananas,0.8718301552978179,0.8480186480186479,4.541267395019531,2610.1395069999994 +5194,Binary classification,Voting,Bananas,0.8725207009435779,0.848927430397079,4.5822906494140625,2699.9579359999993 +5300,Binary classification,Voting,Bananas,0.8726174749952821,0.8491620111731844,4.5840301513671875,2790.9651129999993 +906,Binary classification,Voting,Elec2,0.8795580110497238,0.880351262349067,4.715929985046387,35.551681 +1812,Binary classification,Voting,Elec2,0.8807288790723358,0.8536585365853658,4.9170331954956055,87.613259 +2718,Binary classification,Voting,Elec2,0.8689731321310269,0.8344186046511628,4.988085746765137,154.923184 +3624,Binary classification,Voting,Elec2,0.8793817278498481,0.8493622888659084,4.879870414733887,235.92708 +4530,Binary classification,Voting,Elec2,0.8792227864870833,0.8405712620227338,5.017077445983887,328.79801 +5436,Binary classification,Voting,Elec2,0.8689972401103956,0.8260869565217391,4.985064506530762,432.967134 +6342,Binary classification,Voting,Elec2,0.8680018924459865,0.8269588587967748,4.949084281921387,548.642546 +7248,Binary classification,Voting,Elec2,0.8643576652407893,0.8194010655888295,4.962946891784668,674.769885 +8154,Binary classification,Voting,Elec2,0.8671654605666625,0.8317016317016317,5.020190238952637,811.067026 +9060,Binary classification,Voting,Elec2,0.8711778341980351,0.8417627118644068,5.0752363204956055,957.595858 +9966,Binary classification,Voting,Elec2,0.8706472654290015,0.845165165165165,4.979113578796387,1113.746382 +10872,Binary classification,Voting,Elec2,0.8737006715113605,0.8516156922079325,4.9885969161987305,1279.290866 +11778,Binary classification,Voting,Elec2,0.8733972998216863,0.8507059176930009,5.106616020202637,1454.661274 +12684,Binary classification,Voting,Elec2,0.873294961759836,0.8513551012857274,5.2120466232299805,1640.606202 +13590,Binary classification,Voting,Elec2,0.8755611156082125,0.8560973534167304,5.144991874694824,1836.8358979999998 +14496,Binary classification,Voting,Elec2,0.8765781303897896,0.8583643416989946,5.178118705749512,2042.985996 +15402,Binary classification,Voting,Elec2,0.8766963184208818,0.8575500712624708,5.108157157897949,2257.366998 +16308,Binary classification,Voting,Elec2,0.8711596247010487,0.8493366798135532,5.1558027267456055,2480.0447249999997 +17214,Binary classification,Voting,Elec2,0.8687038865973392,0.8429683157309616,5.140070915222168,2710.925816 +18120,Binary classification,Voting,Elec2,0.8689773166289531,0.8433623647400369,5.172907829284668,2950.660411 +19026,Binary classification,Voting,Elec2,0.8696977660972405,0.8421320766732471,5.328249931335449,3199.829749 +19932,Binary classification,Voting,Elec2,0.8659876574180925,0.8380132209351688,5.355593681335449,3457.907085 +20838,Binary classification,Voting,Elec2,0.8617843259586313,0.8322851153039832,5.442904472351074,3724.5720149999997 +21744,Binary classification,Voting,Elec2,0.862668445016787,0.8307064293003741,5.347712516784668,3998.8413929999997 +22650,Binary classification,Voting,Elec2,0.8610093160845953,0.8268045774647886,5.363558769226074,4280.436632 +23556,Binary classification,Voting,Elec2,0.85434090426661,0.8163571160948456,5.3763532638549805,4569.013267 +24462,Binary classification,Voting,Elec2,0.8534401700666366,0.8138145936120489,5.330439567565918,4864.708005 +25368,Binary classification,Voting,Elec2,0.8518941932431899,0.8121030257564391,5.456484794616699,5167.517117 +26274,Binary classification,Voting,Elec2,0.8530811098846725,0.8130931628897928,5.321070671081543,5477.642376000001 +27180,Binary classification,Voting,Elec2,0.8524964126715479,0.8125672074430782,5.426630973815918,5794.594214000001 +28086,Binary classification,Voting,Elec2,0.8496350364963504,0.8078795323233702,5.3666486740112305,6118.251751000001 +28992,Binary classification,Voting,Elec2,0.8475043979165948,0.8032575319300432,5.4148359298706055,6448.591708000001 +29898,Binary classification,Voting,Elec2,0.8460046158477439,0.8002429711905589,5.3892927169799805,6785.693940000001 +30804,Binary classification,Voting,Elec2,0.8462162776352953,0.7992881657556884,5.532000541687012,7130.317230000001 +31710,Binary classification,Voting,Elec2,0.8429783342268756,0.7938387644403958,5.507189750671387,7481.445887000001 +32616,Binary classification,Voting,Elec2,0.8419745515866932,0.7926122646064703,5.485476493835449,7839.281994000001 +33522,Binary classification,Voting,Elec2,0.8421884788639957,0.7931492922499414,5.629275321960449,8203.623919000001 +34428,Binary classification,Voting,Elec2,0.8400092950300636,0.7894656371837016,5.587969779968262,8574.868737 +35334,Binary classification,Voting,Elec2,0.8398947159878867,0.7879287722586691,5.669405937194824,8954.118864 +36240,Binary classification,Voting,Elec2,0.8408896492728828,0.7879523389232127,5.650286674499512,9340.118817 +37146,Binary classification,Voting,Elec2,0.8397092475434109,0.7854569040069184,5.6525373458862305,9731.872155000001 +38052,Binary classification,Voting,Elec2,0.8398202412551575,0.7854402083993381,5.638819694519043,10129.488521000001 +38958,Binary classification,Voting,Elec2,0.8406704828400544,0.787685992816829,5.6699628829956055,10532.813227 +39864,Binary classification,Voting,Elec2,0.841381732433585,0.7910235647949235,5.623560905456543,10941.028498 +40770,Binary classification,Voting,Elec2,0.8422085408030612,0.7943480067772769,5.627467155456543,11353.937417 +41676,Binary classification,Voting,Elec2,0.8431673665266947,0.7973835947671896,5.641619682312012,11771.547133999999 +42582,Binary classification,Voting,Elec2,0.8438505436697118,0.7987529888919157,5.640711784362793,12193.871152999998 +43488,Binary classification,Voting,Elec2,0.843999356129418,0.7991592160577892,5.725451469421387,12620.815871999997 +44394,Binary classification,Voting,Elec2,0.8432635775910616,0.7972256221950225,5.7456769943237305,13052.460535999997 +45300,Binary classification,Voting,Elec2,0.8436830835117773,0.7980031379261162,5.7547407150268555,13488.904282999996 +45312,Binary classification,Voting,Elec2,0.8436803425216834,0.7979576118892089,5.757502555847168,13925.545040999996 +25,Binary classification,Voting,Phishing,0.5833333333333334,0.7058823529411764,0.17400836944580078,0.162813 +50,Binary classification,Voting,Phishing,0.7346938775510204,0.7636363636363637,0.20249652862548828,0.520257 +75,Binary classification,Voting,Phishing,0.7837837837837838,0.8048780487804877,0.23151493072509766,1.0500919999999998 +100,Binary classification,Voting,Phishing,0.8080808080808081,0.819047619047619,0.26002979278564453,1.7634529999999997 +125,Binary classification,Voting,Phishing,0.8145161290322581,0.8217054263565893,0.2885446548461914,2.7650449999999998 +150,Binary classification,Voting,Phishing,0.8187919463087249,0.830188679245283,0.3175630569458008,4.083864999999999 +175,Binary classification,Voting,Phishing,0.8390804597701149,0.8390804597701148,0.34607791900634766,5.803779 +200,Binary classification,Voting,Phishing,0.8391959798994975,0.8383838383838383,0.3750925064086914,7.866028 +225,Binary classification,Voting,Phishing,0.8348214285714286,0.8294930875576038,0.4036073684692383,10.317012 +250,Binary classification,Voting,Phishing,0.8313253012048193,0.8264462809917356,0.43212223052978516,13.231482 +275,Binary classification,Voting,Phishing,0.8357664233576643,0.8288973384030419,0.46164798736572266,16.680039999999998 +300,Binary classification,Voting,Phishing,0.842809364548495,0.8327402135231317,0.49016284942626953,20.639260999999998 +325,Binary classification,Voting,Phishing,0.8549382716049383,0.8417508417508418,0.5191812515258789,25.174847 +350,Binary classification,Voting,Phishing,0.8624641833810889,0.8471337579617835,0.5476961135864258,30.349829 +375,Binary classification,Voting,Phishing,0.8609625668449198,0.8433734939759037,0.5762109756469727,36.11991 +400,Binary classification,Voting,Phishing,0.8621553884711779,0.8424068767908309,0.605229377746582,42.598397999999996 +425,Binary classification,Voting,Phishing,0.8632075471698113,0.839779005524862,0.6337442398071289,49.849208 +450,Binary classification,Voting,Phishing,0.8663697104677061,0.8412698412698413,0.6627893447875977,57.823257999999996 +475,Binary classification,Voting,Phishing,0.8649789029535865,0.8407960199004976,0.6913042068481445,66.560459 +500,Binary classification,Voting,Phishing,0.8657314629258517,0.8445475638051043,2.87209415435791,96.81871699999999 +525,Binary classification,Voting,Phishing,0.8683206106870229,0.8442437923250564,2.980504035949707,127.96343099999999 +550,Binary classification,Voting,Phishing,0.8688524590163934,0.8461538461538463,3.08364200592041,159.97764899999999 +575,Binary classification,Voting,Phishing,0.8710801393728222,0.848360655737705,3.1883134841918945,192.90880399999998 +600,Binary classification,Voting,Phishing,0.8747913188647746,0.8502994011976048,3.300492286682129,226.71997 +625,Binary classification,Voting,Phishing,0.8733974358974359,0.8460038986354775,3.412938117980957,261.370264 +650,Binary classification,Voting,Phishing,0.8767334360554699,0.8523985239852399,3.522160530090332,296.927054 +675,Binary classification,Voting,Phishing,0.8783382789317508,0.8571428571428572,3.6335840225219727,333.391974 +700,Binary classification,Voting,Phishing,0.882689556509299,0.8605442176870748,3.7482118606567383,370.807982 +725,Binary classification,Voting,Phishing,0.8839779005524862,0.864516129032258,3.8615503311157227,409.129822 +750,Binary classification,Voting,Phishing,0.8851802403204272,0.8664596273291927,3.975522041320801,448.34317699999997 +775,Binary classification,Voting,Phishing,0.8863049095607235,0.8670694864048338,4.095002174377441,488.481904 +800,Binary classification,Voting,Phishing,0.886107634543179,0.8683068017366136,4.146827697753906,529.573688 +825,Binary classification,Voting,Phishing,0.8859223300970874,0.8690807799442897,4.390903472900391,571.6173799999999 +850,Binary classification,Voting,Phishing,0.8869257950530035,0.8695652173913044,4.504707336425781,614.6492939999999 +875,Binary classification,Voting,Phishing,0.8890160183066361,0.8711819389110226,4.624469757080078,658.6172529999999 +900,Binary classification,Voting,Phishing,0.8876529477196885,0.869340232858991,4.741554260253906,703.5317429999999 +925,Binary classification,Voting,Phishing,0.8896103896103896,0.8728179551122195,4.862430572509766,749.5245539999999 +950,Binary classification,Voting,Phishing,0.8904109589041096,0.8752997601918464,4.984291076660156,796.5006999999998 +975,Binary classification,Voting,Phishing,0.8921971252566735,0.8771929824561404,5.102375030517578,844.5149469999998 +1000,Binary classification,Voting,Phishing,0.8928928928928929,0.8779931584948689,5.219093322753906,893.5084249999998 +1025,Binary classification,Voting,Phishing,0.892578125,0.8780487804878048,5.178688049316406,943.5715689999997 +1050,Binary classification,Voting,Phishing,0.894184938036225,0.8802588996763754,5.151969909667969,994.6247309999998 +1075,Binary classification,Voting,Phishing,0.8929236499068901,0.8798328108672936,5.117225646972656,1046.6512989999997 +1100,Binary classification,Voting,Phishing,0.8944494995450409,0.8816326530612245,5.075950622558594,1099.6701919999996 +1125,Binary classification,Voting,Phishing,0.8959074733096085,0.884272997032641,5.007194519042969,1153.6019869999996 +1150,Binary classification,Voting,Phishing,0.896431679721497,0.8845780795344327,4.982025146484375,1208.4750359999996 +1175,Binary classification,Voting,Phishing,0.8952299829642248,0.8829686013320648,4.96966552734375,1264.1689839999997 +1200,Binary classification,Voting,Phishing,0.896580483736447,0.8841121495327102,4.9371490478515625,1320.7795169999997 +1225,Binary classification,Voting,Phishing,0.8970588235294118,0.8844036697247706,4.8813018798828125,1378.3046599999998 +1250,Binary classification,Voting,Phishing,0.8967173738991193,0.8845120859444942,4.820304870605469,1436.7224909999998 +1903,Binary classification,Voting,SMTP,1.0,0.0,4.661611557006836,43.527964 +3806,Binary classification,Voting,SMTP,1.0,0.0,4.557134628295898,113.03314999999999 +5709,Binary classification,Voting,SMTP,1.0,0.0,4.496244430541992,201.747221 +7612,Binary classification,Voting,SMTP,1.0,0.0,4.508665084838867,310.754596 +9515,Binary classification,Voting,SMTP,1.0,0.0,4.565656661987305,436.825284 +11418,Binary classification,Voting,SMTP,1.0,0.0,4.554738998413086,579.964386 +13321,Binary classification,Voting,SMTP,1.0,0.0,4.492513656616211,739.485002 +15224,Binary classification,Voting,SMTP,0.9997372397030808,0.7777777777777778,4.532373428344727,915.25293 +17127,Binary classification,Voting,SMTP,0.9997664369963798,0.8181818181818181,4.528841018676758,1107.42481 +19030,Binary classification,Voting,SMTP,0.9997897945241474,0.8181818181818181,4.520586013793945,1314.036215 +20933,Binary classification,Voting,SMTP,0.9998089050257978,0.8181818181818181,4.519166946411133,1534.1862760000001 +22836,Binary classification,Voting,SMTP,0.9998248303043573,0.8181818181818181,4.512857437133789,1768.1243410000002 +24739,Binary classification,Voting,SMTP,0.9998383054410219,0.8181818181818181,4.568696975708008,2014.7470960000003 +26642,Binary classification,Voting,SMTP,0.9998123193573815,0.782608695652174,4.55253791809082,2273.6909760000003 +28545,Binary classification,Voting,SMTP,0.9998248318385651,0.782608695652174,4.554193496704102,2543.9053730000005 +30448,Binary classification,Voting,SMTP,0.9998357802082307,0.782608695652174,4.487833023071289,2824.6447140000005 +32351,Binary classification,Voting,SMTP,0.9998454404945905,0.782608695652174,4.52525520324707,3116.2234200000003 +34254,Binary classification,Voting,SMTP,0.9998540273844627,0.782608695652174,4.608850479125977,3418.6303260000004 +36157,Binary classification,Voting,SMTP,0.999861710366191,0.782608695652174,4.462549209594727,3731.2161630000005 +38060,Binary classification,Voting,SMTP,0.9998686250295594,0.782608695652174,4.517663955688477,4053.8854360000005 +39963,Binary classification,Voting,SMTP,0.9998748811370802,0.782608695652174,4.596040725708008,4386.480402 +41866,Binary classification,Voting,SMTP,0.9998805684939687,0.782608695652174,4.581964492797852,4729.507439 +43769,Binary classification,Voting,SMTP,0.9998857612867849,0.782608695652174,4.56077766418457,5082.623411 +45672,Binary classification,Voting,SMTP,0.9998905213373913,0.782608695652174,4.554697036743164,5446.283888999999 +47575,Binary classification,Voting,SMTP,0.9998949005759449,0.782608695652174,4.589784622192383,5821.612630999999 +49478,Binary classification,Voting,SMTP,0.9998989429431857,0.782608695652174,4.514947891235352,6206.523862999999 +51381,Binary classification,Voting,SMTP,0.9998637602179836,0.72,4.571069717407227,6600.877267999999 +53284,Binary classification,Voting,SMTP,0.9998686260158024,0.72,4.544900894165039,7002.824473 +55187,Binary classification,Voting,SMTP,0.9998731562352771,0.72,4.490015029907227,7412.266105 +57090,Binary classification,Voting,SMTP,0.9997197358510396,0.5294117647058824,4.520219802856445,7829.0322129999995 +58993,Binary classification,Voting,SMTP,0.9997287767832926,0.5294117647058824,4.567926406860352,8253.169596 +60896,Binary classification,Voting,SMTP,0.9997372526480006,0.5294117647058824,4.602060317993164,8684.574786 +62799,Binary classification,Voting,SMTP,0.9997133666677283,0.5,4.518564224243164,9122.220249 +64702,Binary classification,Voting,SMTP,0.9997217971901516,0.5,4.562410354614258,9566.245368 +66605,Binary classification,Voting,SMTP,0.9997297459612036,0.5,4.587350845336914,10016.770375 +68508,Binary classification,Voting,SMTP,0.9997226560789408,0.5128205128205129,4.58268928527832,10473.685786 +70411,Binary classification,Voting,SMTP,0.9997301519670502,0.5128205128205129,4.552003860473633,10937.213874000001 +72314,Binary classification,Voting,SMTP,0.9997372533292769,0.5128205128205129,4.568490982055664,11407.187031000001 +74217,Binary classification,Voting,SMTP,0.9997439905141748,0.5128205128205129,4.501398086547852,11883.434676 +76120,Binary classification,Voting,SMTP,0.9997503908354025,0.5128205128205129,4.530572891235352,12366.124718000001 +78023,Binary classification,Voting,SMTP,0.999756478941837,0.5128205128205129,4.565195083618164,12855.350972 +79926,Binary classification,Voting,SMTP,0.9997622771348139,0.5128205128205129,4.555276870727539,13350.750206 +81829,Binary classification,Voting,SMTP,0.9997678056411008,0.5128205128205129,4.523683547973633,13852.638924 +83732,Binary classification,Voting,SMTP,0.9997730828486463,0.5128205128205129,4.51640510559082,14360.452684 +85635,Binary classification,Voting,SMTP,0.9997781255108952,0.5128205128205129,4.554742813110352,14874.156807 +87538,Binary classification,Voting,SMTP,0.9997829489244549,0.5128205128205129,4.55351448059082,15393.589182 +89441,Binary classification,Voting,SMTP,0.9997763864042933,0.5,4.576028823852539,15919.905447 +91344,Binary classification,Voting,SMTP,0.9997700973254655,0.4878048780487804,4.509759902954102,16451.368555 +93247,Binary classification,Voting,SMTP,0.9997747892671,0.4878048780487804,4.633722305297852,16987.639193000003 +95150,Binary classification,Voting,SMTP,0.9997792935290964,0.4878048780487804,4.606340408325195,17528.67073 +95156,Binary classification,Voting,SMTP,0.9997793074457464,0.4878048780487804,4.602045059204102,18069.786262 +106,Binary classification,[baseline] Last Class,Bananas,0.5333333333333333,0.5242718446601942,0.0005102157592773438,0.004468 +212,Binary classification,[baseline] Last Class,Bananas,0.5876777251184834,0.5538461538461539,0.0005102157592773438,0.067972 +318,Binary classification,[baseline] Last Class,Bananas,0.5457413249211357,0.5102040816326531,0.0005102157592773438,0.134988 +424,Binary classification,[baseline] Last Class,Bananas,0.5460992907801419,0.5025906735751295,0.0005102157592773438,0.20522 +530,Binary classification,[baseline] Last Class,Bananas,0.5671077504725898,0.5096359743040686,0.0005102157592773438,0.337716 +636,Binary classification,[baseline] Last Class,Bananas,0.5464566929133858,0.4875444839857651,0.0005102157592773438,0.474055 +742,Binary classification,[baseline] Last Class,Bananas,0.5573549257759784,0.4875,0.0005102157592773438,0.646583 +848,Binary classification,[baseline] Last Class,Bananas,0.5501770956316411,0.4816326530612245,0.0005102157592773438,0.822555 +954,Binary classification,[baseline] Last Class,Bananas,0.5487932843651626,0.4794188861985472,0.0005102157592773438,1.00209 +1060,Binary classification,[baseline] Last Class,Bananas,0.5448536355051936,0.46799116997792495,0.0005102157592773438,1.292978 +1166,Binary classification,[baseline] Last Class,Bananas,0.534763948497854,0.4590818363273453,0.0005102157592773438,1.5875979999999998 +1272,Binary classification,[baseline] Last Class,Bananas,0.5287175452399685,0.456935630099728,0.0005102157592773438,1.885535 +1378,Binary classification,[baseline] Last Class,Bananas,0.5286855482933914,0.45232067510548524,0.0005102157592773438,2.211477 +1484,Binary classification,[baseline] Last Class,Bananas,0.5252865812542145,0.44913928012519555,0.0005102157592773438,2.547239 +1590,Binary classification,[baseline] Last Class,Bananas,0.5204531151667715,0.4437956204379563,0.0005102157592773438,2.88734 +1696,Binary classification,[baseline] Last Class,Bananas,0.5227138643067847,0.4455106237148732,0.0005102157592773438,3.258534 +1802,Binary classification,[baseline] Last Class,Bananas,0.524153248195447,0.4523961661341854,0.0005102157592773438,3.633124 +1908,Binary classification,[baseline] Last Class,Bananas,0.5233350812794966,0.456664674237896,0.0005102157592773438,4.01125 +2014,Binary classification,[baseline] Last Class,Bananas,0.5171385991058122,0.4563758389261745,0.0005102157592773438,4.505139000000001 +2120,Binary classification,[baseline] Last Class,Bananas,0.5143935818782445,0.45813586097946285,0.0005102157592773438,5.002779 +2226,Binary classification,[baseline] Last Class,Bananas,0.5114606741573033,0.45459106874059213,0.0005102157592773438,5.503925000000001 +2332,Binary classification,[baseline] Last Class,Bananas,0.510939510939511,0.45506692160611856,0.0005102157592773438,6.074663000000001 +2438,Binary classification,[baseline] Last Class,Bananas,0.5104636848584325,0.4530032095369097,0.0005102157592773438,6.648598000000001 +2544,Binary classification,[baseline] Last Class,Bananas,0.5084545812033032,0.45462478184991273,0.0005102157592773438,7.226634000000001 +2650,Binary classification,[baseline] Last Class,Bananas,0.5096262740656852,0.458072590738423,0.0005102157592773438,7.8632990000000005 +2756,Binary classification,[baseline] Last Class,Bananas,0.5092558983666061,0.45746388443017655,0.0005102157592773438,8.503527 +2862,Binary classification,[baseline] Last Class,Bananas,0.5103110800419434,0.4563445867287544,0.0005102157592773438,9.147193 +2968,Binary classification,[baseline] Last Class,Bananas,0.5133131108864173,0.457957957957958,0.0005102157592773438,9.82546 +3074,Binary classification,[baseline] Last Class,Bananas,0.5099251545720794,0.4563176895306859,0.0005102157592773438,10.507099 +3180,Binary classification,[baseline] Last Class,Bananas,0.5102233406731677,0.45387583304103823,0.0005102157592773438,11.191893 +3286,Binary classification,[baseline] Last Class,Bananas,0.5095890410958904,0.45222713362801764,0.0005102157592773438,11.975438 +3392,Binary classification,[baseline] Last Class,Bananas,0.5107637864936597,0.4558871761233191,0.0005102157592773438,12.764918 +3498,Binary classification,[baseline] Last Class,Bananas,0.5124392336288247,0.45579316948611553,0.0005102157592773438,13.557573 +3604,Binary classification,[baseline] Last Class,Bananas,0.5134610047182903,0.45440398381574854,0.0005102157592773438,14.3795 +3710,Binary classification,[baseline] Last Class,Bananas,0.5122674575357239,0.4546276756104914,0.0005102157592773438,15.204998 +3816,Binary classification,[baseline] Last Class,Bananas,0.510615989515072,0.4536142815335089,0.0005102157592773438,16.116361 +3922,Binary classification,[baseline] Last Class,Bananas,0.5090538128028564,0.45078459343794575,0.0005102157592773438,17.035489000000002 +4028,Binary classification,[baseline] Last Class,Bananas,0.5108020859200397,0.45247359644246804,0.0005102157592773438,17.958008000000003 +4134,Binary classification,[baseline] Last Class,Bananas,0.5102830873457537,0.4517876489707476,0.0005102157592773438,18.927027000000002 +4240,Binary classification,[baseline] Last Class,Bananas,0.5102618542108988,0.4525316455696203,0.0005102157592773438,19.900154000000004 +4346,Binary classification,[baseline] Last Class,Bananas,0.5074798619102416,0.4490216271884655,0.0005102157592773438,20.876623000000006 +4452,Binary classification,[baseline] Last Class,Bananas,0.5099977533138621,0.45132075471698113,0.0005102157592773438,21.913356000000007 +4558,Binary classification,[baseline] Last Class,Bananas,0.5099846390168971,0.45390070921985815,0.0005102157592773438,22.953869000000008 +4664,Binary classification,[baseline] Last Class,Bananas,0.5099721209521767,0.4553039332538737,0.0005102157592773438,23.99911400000001 +4770,Binary classification,[baseline] Last Class,Bananas,0.5110085971901867,0.4556489262371615,0.0005102157592773438,25.08372100000001 +4876,Binary classification,[baseline] Last Class,Bananas,0.5109743589743589,0.4539624370132845,0.0005102157592773438,26.17171900000001 +4982,Binary classification,[baseline] Last Class,Bananas,0.5099377635013049,0.45379279480868207,0.0005102157592773438,27.26320500000001 +5088,Binary classification,[baseline] Last Class,Bananas,0.5099272655789266,0.45364891518737677,0.0005102157592773438,28.44143600000001 +5194,Binary classification,[baseline] Last Class,Bananas,0.5097246293086848,0.4531786941580756,0.0005102157592773438,29.62357400000001 +5300,Binary classification,[baseline] Last Class,Bananas,0.5095301000188714,0.4529572721532309,0.0005102157592773438,30.80903600000001 +906,Binary classification,[baseline] Last Class,Elec2,0.8530386740331491,0.8500563697857948,0.0005102157592773438,0.224121 +1812,Binary classification,[baseline] Last Class,Elec2,0.8619547211485368,0.8287671232876712,0.0005102157592773438,0.785464 +2718,Binary classification,[baseline] Last Class,Elec2,0.8450496871549503,0.80958842152872,0.0005102157592773438,1.64751 +3624,Binary classification,[baseline] Last Class,Elec2,0.8418437758763456,0.8056968463886063,0.0005102157592773438,2.8059529999999997 +4530,Binary classification,[baseline] Last Class,Elec2,0.8388165157871494,0.7960893854748604,0.0005102157592773438,4.158177 +5436,Binary classification,[baseline] Last Class,Elec2,0.8413983440662374,0.7995348837209302,0.0005102157592773438,5.857693 +6342,Binary classification,[baseline] Last Class,Elec2,0.8370919413341744,0.7958094485076103,0.0005102157592773438,7.811494 +7248,Binary classification,[baseline] Last Class,Elec2,0.8359321098385539,0.7948231233822259,0.0005102157592773438,10.005109 +8154,Binary classification,[baseline] Last Class,Elec2,0.8352753587636453,0.8021799970540581,0.0005102157592773438,12.510532 +9060,Binary classification,[baseline] Last Class,Elec2,0.8358538470029805,0.8069081937410726,0.0005102157592773438,15.278307999999999 +9966,Binary classification,[baseline] Last Class,Elec2,0.8372303060712494,0.8118765947575969,0.0005102157592773438,18.289258999999998 +10872,Binary classification,[baseline] Last Class,Elec2,0.8368135406126391,0.8140461215932915,0.0005102157592773438,21.565545999999998 +11778,Binary classification,[baseline] Last Class,Elec2,0.8374798335739153,0.8150724637681159,0.0005102157592773438,25.041396 +12684,Binary classification,[baseline] Last Class,Elec2,0.8384451628163684,0.8161177420802298,0.0005102157592773438,28.814916 +13590,Binary classification,[baseline] Last Class,Elec2,0.842004562513798,0.8223417459660736,0.0005102157592773438,32.85712 +14496,Binary classification,[baseline] Last Class,Elec2,0.8448430493273542,0.8264794383149447,0.0005102157592773438,37.134508000000004 +15402,Binary classification,[baseline] Last Class,Elec2,0.8460489578598792,0.8270983738058776,0.0005102157592773438,41.682175 +16308,Binary classification,[baseline] Last Class,Elec2,0.844851904090268,0.8251313243019076,0.0005102157592773438,46.494991 +17214,Binary classification,[baseline] Last Class,Elec2,0.8443618195549875,0.8222177981286084,0.0005102157592773438,51.515798 +18120,Binary classification,[baseline] Last Class,Elec2,0.8450797505381091,0.8227792158595871,0.0005102157592773438,56.800748 +19026,Binary classification,[baseline] Last Class,Elec2,0.8462023653088042,0.8224083515416363,0.0005102157592773438,62.372633 +19932,Binary classification,[baseline] Last Class,Elec2,0.847523957653906,0.8255753888538139,0.0005102157592773438,68.180409 +20838,Binary classification,[baseline] Last Class,Elec2,0.84661899505687,0.8249917862227577,0.0005102157592773438,74.270057 +21744,Binary classification,[baseline] Last Class,Elec2,0.8452835395299637,0.8209495422610177,0.0005102157592773438,80.612623 +22650,Binary classification,[baseline] Last Class,Elec2,0.8444081416398075,0.8188733552631579,0.0005102157592773438,87.17507 +23556,Binary classification,[baseline] Last Class,Elec2,0.8451284228401613,0.8194595664654062,0.0005102157592773438,93.968638 +24462,Binary classification,[baseline] Last Class,Elec2,0.8464903315481788,0.8198781599270878,0.0005102157592773438,100.983267 +25368,Binary classification,[baseline] Last Class,Elec2,0.8462963692986951,0.8199492034172247,0.0005102157592773438,108.278888 +26274,Binary classification,[baseline] Last Class,Elec2,0.8477524454763445,0.8213168944876262,0.0005102157592773438,115.769594 +27180,Binary classification,[baseline] Last Class,Elec2,0.8495529636851982,0.8240457851026293,0.0005102157592773438,123.465792 +28086,Binary classification,[baseline] Last Class,Elec2,0.8509880719245149,0.825107610012955,0.0005102157592773438,131.36678899999998 +28992,Binary classification,[baseline] Last Class,Elec2,0.8521265220240765,0.8258237516759436,0.0005102157592773438,139.55273799999998 +29898,Binary classification,[baseline] Last Class,Elec2,0.8531959728400843,0.8268160833366216,0.0005102157592773438,147.964309 +30804,Binary classification,[baseline] Last Class,Elec2,0.8537480115573158,0.8267107743201139,0.0005102157592773438,156.664426 +31710,Binary classification,[baseline] Last Class,Elec2,0.8530385694913116,0.8259895444361464,0.0005102157592773438,165.606267 +32616,Binary classification,[baseline] Last Class,Elec2,0.8536869538555879,0.8269760696156635,0.0005102157592773438,174.782391 +33522,Binary classification,[baseline] Last Class,Elec2,0.8541511291429253,0.8276032300151628,0.0005102157592773438,184.217189 +34428,Binary classification,[baseline] Last Class,Elec2,0.8549684840386905,0.8286724084685859,0.0005102157592773438,193.875362 +35334,Binary classification,[baseline] Last Class,Elec2,0.8555175048821215,0.8284321962695346,0.0005102157592773438,203.79136499999998 +36240,Binary classification,[baseline] Last Class,Elec2,0.8545213720025387,0.8259146744155329,0.0005102157592773438,213.957306 +37146,Binary classification,[baseline] Last Class,Elec2,0.854354556467896,0.8252696854208386,0.0005102157592773438,224.37720199999998 +38052,Binary classification,[baseline] Last Class,Elec2,0.8545636119944285,0.8247736052181622,0.0005102157592773438,234.998191 +38958,Binary classification,[baseline] Last Class,Elec2,0.8548142824139435,0.8254213223038459,0.0005102157592773438,245.89740899999998 +39864,Binary classification,[baseline] Last Class,Elec2,0.8546521837292728,0.8262981172802495,0.0005102157592773438,257.034489 +40770,Binary classification,[baseline] Last Class,Elec2,0.8540067207927592,0.8267652366261132,0.0005102157592773438,268.379106 +41676,Binary classification,[baseline] Last Class,Elec2,0.8537012597480504,0.8274320002264302,0.0005102157592773438,279.987419 +42582,Binary classification,[baseline] Last Class,Elec2,0.8536201592259458,0.8277177368086459,0.0005102157592773438,291.808183 +43488,Binary classification,[baseline] Last Class,Elec2,0.853473451836181,0.8276626818845675,0.0005102157592773438,303.899029 +44394,Binary classification,[baseline] Last Class,Elec2,0.8533777847858897,0.8271686890948196,0.0005102157592773438,316.239245 +45300,Binary classification,[baseline] Last Class,Elec2,0.8533521711296055,0.8273155007928462,0.0005102157592773438,328.81397599999997 +45312,Binary classification,[baseline] Last Class,Elec2,0.8533027300214076,0.8272294856132872,0.0005102157592773438,341.389555 +25,Binary classification,[baseline] Last Class,Phishing,0.625,0.64,0.0005102157592773438,0.001863 +50,Binary classification,[baseline] Last Class,Phishing,0.6530612244897959,0.6222222222222223,0.0005102157592773438,0.005016 +75,Binary classification,[baseline] Last Class,Phishing,0.5675675675675675,0.5555555555555556,0.0005102157592773438,0.009415 +100,Binary classification,[baseline] Last Class,Phishing,0.5555555555555556,0.5416666666666666,0.0005102157592773438,0.115037 +125,Binary classification,[baseline] Last Class,Phishing,0.5241935483870968,0.5123966942148761,0.0005102157592773438,0.22212700000000002 +150,Binary classification,[baseline] Last Class,Phishing,0.5234899328859061,0.5298013245033113,0.0005102157592773438,0.330326 +175,Binary classification,[baseline] Last Class,Phishing,0.5229885057471264,0.496969696969697,0.0005102157592773438,0.439628 +200,Binary classification,[baseline] Last Class,Phishing,0.507537688442211,0.47872340425531923,0.0005102157592773438,0.550035 +225,Binary classification,[baseline] Last Class,Phishing,0.5,0.45098039215686275,0.0005102157592773438,0.6616070000000001 +250,Binary classification,[baseline] Last Class,Phishing,0.5180722891566265,0.4782608695652174,0.0005102157592773438,0.774476 +275,Binary classification,[baseline] Last Class,Phishing,0.5218978102189781,0.4738955823293172,0.0005102157592773438,0.8884620000000001 +300,Binary classification,[baseline] Last Class,Phishing,0.5217391304347826,0.460377358490566,0.0005102157592773438,1.0035580000000002 +325,Binary classification,[baseline] Last Class,Phishing,0.5216049382716049,0.44839857651245546,0.0005102157592773438,1.151113 +350,Binary classification,[baseline] Last Class,Phishing,0.5329512893982808,0.4511784511784511,0.0005102157592773438,1.299965 +375,Binary classification,[baseline] Last Class,Phishing,0.5267379679144385,0.4380952380952381,0.0005102157592773438,1.4500600000000001 +400,Binary classification,[baseline] Last Class,Phishing,0.5263157894736842,0.43243243243243246,0.0005102157592773438,1.6013830000000002 +425,Binary classification,[baseline] Last Class,Phishing,0.5424528301886793,0.436046511627907,0.0005102157592773438,1.7539290000000003 +450,Binary classification,[baseline] Last Class,Phishing,0.5367483296213809,0.4222222222222222,0.0005102157592773438,1.9077010000000003 +475,Binary classification,[baseline] Last Class,Phishing,0.5358649789029536,0.43298969072164945,0.0005102157592773438,2.0627030000000004 +500,Binary classification,[baseline] Last Class,Phishing,0.5370741482965932,0.44604316546762596,0.0005102157592773438,2.2669650000000003 +525,Binary classification,[baseline] Last Class,Phishing,0.5400763358778626,0.43822843822843827,0.0005102157592773438,2.491531 +550,Binary classification,[baseline] Last Class,Phishing,0.5391621129326047,0.44150110375275936,0.0005102157592773438,2.717762 +575,Binary classification,[baseline] Last Class,Phishing,0.5418118466898955,0.4416135881104034,0.0005102157592773438,2.945135 +600,Binary classification,[baseline] Last Class,Phishing,0.5509181969949917,0.443064182194617,0.0005102157592773438,3.175983 +625,Binary classification,[baseline] Last Class,Phishing,0.5560897435897436,0.43584521384928715,0.0005102157592773438,3.407996 +650,Binary classification,[baseline] Last Class,Phishing,0.551617873651772,0.4393063583815029,0.0005102157592773438,3.641123 +675,Binary classification,[baseline] Last Class,Phishing,0.5459940652818991,0.44363636363636366,0.0005102157592773438,3.8753569999999997 +700,Binary classification,[baseline] Last Class,Phishing,0.5464949928469242,0.4389380530973452,0.0005102157592773438,4.110698999999999 +725,Binary classification,[baseline] Last Class,Phishing,0.5441988950276243,0.44630872483221484,0.0005102157592773438,4.380075 +750,Binary classification,[baseline] Last Class,Phishing,0.5367156208277704,0.44122383252818037,0.0005102157592773438,4.6504829999999995 +775,Binary classification,[baseline] Last Class,Phishing,0.5310077519379846,0.43369734789391573,0.0005102157592773438,4.940408 +800,Binary classification,[baseline] Last Class,Phishing,0.5294117647058824,0.4388059701492537,0.0005102157592773438,5.231503 +825,Binary classification,[baseline] Last Class,Phishing,0.5266990291262136,0.43965517241379315,0.0005102157592773438,5.523716 +850,Binary classification,[baseline] Last Class,Phishing,0.5241460541813898,0.4341736694677871,0.0005102157592773438,5.817038 +875,Binary classification,[baseline] Last Class,Phishing,0.522883295194508,0.4311050477489768,0.0005102157592773438,6.111637 +900,Binary classification,[baseline] Last Class,Phishing,0.5272525027808677,0.4340878828229028,0.0005102157592773438,6.407366 +925,Binary classification,[baseline] Last Class,Phishing,0.5227272727272727,0.43388960205391536,0.0005102157592773438,6.766113 +950,Binary classification,[baseline] Last Class,Phishing,0.5205479452054794,0.43896424167694204,0.0005102157592773438,7.1265789999999996 +975,Binary classification,[baseline] Last Class,Phishing,0.5174537987679672,0.43373493975903615,0.0005102157592773438,7.4884189999999995 +1000,Binary classification,[baseline] Last Class,Phishing,0.5185185185185185,0.4361078546307151,0.0005102157592773438,7.851253 +1025,Binary classification,[baseline] Last Class,Phishing,0.517578125,0.43863636363636366,0.0005102157592773438,8.215067 +1050,Binary classification,[baseline] Last Class,Phishing,0.5138226882745471,0.4370860927152318,0.0005102157592773438,8.579858 +1075,Binary classification,[baseline] Last Class,Phishing,0.5111731843575419,0.43729903536977494,0.0005102157592773438,8.945611 +1100,Binary classification,[baseline] Last Class,Phishing,0.5122838944494995,0.4393305439330544,0.0005102157592773438,9.312327999999999 +1125,Binary classification,[baseline] Last Class,Phishing,0.5124555160142349,0.44534412955465585,0.0005102157592773438,9.680001999999998 +1150,Binary classification,[baseline] Last Class,Phishing,0.5143603133159269,0.44642857142857145,0.0005102157592773438,10.125450999999998 +1175,Binary classification,[baseline] Last Class,Phishing,0.5187393526405452,0.4509232264334305,0.0005102157592773438,10.572147999999999 +1200,Binary classification,[baseline] Last Class,Phishing,0.5187656380316931,0.448901623686724,0.0005102157592773438,11.020091999999998 +1225,Binary classification,[baseline] Last Class,Phishing,0.5171568627450981,0.4471468662301216,0.0005102157592773438,11.469230999999999 +1250,Binary classification,[baseline] Last Class,Phishing,0.5156124899919936,0.4474885844748858,0.0005102157592773438,11.919638999999998 +1903,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.335236 +3806,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.143886 +5709,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,2.36402 +7612,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,4.028138 +9515,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,6.117771 +11418,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,8.657701 +13321,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,11.631 +15224,Binary classification,[baseline] Last Class,SMTP,0.9985548183669447,0.0,0.0005102157592773438,15.050370000000001 +17127,Binary classification,[baseline] Last Class,SMTP,0.9984818404764685,0.0,0.0005102157592773438,18.876176 +19030,Binary classification,[baseline] Last Class,SMTP,0.9986336644069578,0.0,0.0005102157592773438,23.061029 +20933,Binary classification,[baseline] Last Class,SMTP,0.9987578826676858,0.0,0.0005102157592773438,27.619004 +22836,Binary classification,[baseline] Last Class,SMTP,0.9988613969783228,0.0,0.0005102157592773438,32.587888 +24739,Binary classification,[baseline] Last Class,SMTP,0.9989489853666425,0.0,0.0005102157592773438,37.966612 +26642,Binary classification,[baseline] Last Class,SMTP,0.9989489884013363,0.0,0.0005102157592773438,43.747015999999995 +28545,Binary classification,[baseline] Last Class,SMTP,0.9990190582959642,0.0,0.0005102157592773438,49.923314999999995 +30448,Binary classification,[baseline] Last Class,SMTP,0.9990803691660919,0.0,0.0005102157592773438,56.476617 +32351,Binary classification,[baseline] Last Class,SMTP,0.9991344667697063,0.0,0.0005102157592773438,63.442318 +34254,Binary classification,[baseline] Last Class,SMTP,0.999182553352991,0.0,0.0005102157592773438,70.796392 +36157,Binary classification,[baseline] Last Class,SMTP,0.9992255780506694,0.0,0.0005102157592773438,78.554987 +38060,Binary classification,[baseline] Last Class,SMTP,0.9992643001655325,0.0,0.0005102157592773438,86.688101 +39963,Binary classification,[baseline] Last Class,SMTP,0.9992993343676493,0.0,0.0005102157592773438,95.30254000000001 +41866,Binary classification,[baseline] Last Class,SMTP,0.9993311835662247,0.0,0.0005102157592773438,104.31052400000002 +43769,Binary classification,[baseline] Last Class,SMTP,0.9993602632059952,0.0,0.0005102157592773438,113.72316700000002 +45672,Binary classification,[baseline] Last Class,SMTP,0.9993869194893915,0.0,0.0005102157592773438,123.54962900000001 +47575,Binary classification,[baseline] Last Class,SMTP,0.9994114432252911,0.0,0.0005102157592773438,133.72455100000002 +49478,Binary classification,[baseline] Last Class,SMTP,0.99943408048184,0.0,0.0005102157592773438,144.361296 +51381,Binary classification,[baseline] Last Class,SMTP,0.9994161152199299,0.0625,0.0005102157592773438,155.342577 +53284,Binary classification,[baseline] Last Class,SMTP,0.9994369686391532,0.0625,0.0005102157592773438,166.71976 +55187,Binary classification,[baseline] Last Class,SMTP,0.9994563838654731,0.0625,0.0005102157592773438,178.568204 +57090,Binary classification,[baseline] Last Class,SMTP,0.9994394717020793,0.36,0.0005102157592773438,190.760423 +58993,Binary classification,[baseline] Last Class,SMTP,0.9994575535665853,0.36,0.0005102157592773438,203.318295 +60896,Binary classification,[baseline] Last Class,SMTP,0.9994745052960013,0.36,0.0005102157592773438,216.324675 +62799,Binary classification,[baseline] Last Class,SMTP,0.9994585814834868,0.37037037037037035,0.0005102157592773438,229.75194900000002 +64702,Binary classification,[baseline] Last Class,SMTP,0.9994745058036197,0.37037037037037035,0.0005102157592773438,243.519605 +66605,Binary classification,[baseline] Last Class,SMTP,0.99948952014894,0.37037037037037035,0.0005102157592773438,257.632512 +68508,Binary classification,[baseline] Last Class,SMTP,0.9994745062548352,0.3793103448275862,0.0005102157592773438,272.164854 +70411,Binary classification,[baseline] Last Class,SMTP,0.9994887089902003,0.3793103448275862,0.0005102157592773438,287.040678 +72314,Binary classification,[baseline] Last Class,SMTP,0.9995021642028404,0.3793103448275862,0.0005102157592773438,302.255295 +74217,Binary classification,[baseline] Last Class,SMTP,0.9995149293952786,0.3793103448275862,0.0005102157592773438,317.85437 +76120,Binary classification,[baseline] Last Class,SMTP,0.99952705631971,0.3793103448275862,0.0005102157592773438,333.81575100000003 +78023,Binary classification,[baseline] Last Class,SMTP,0.99953859167927,0.3793103448275862,0.0005102157592773438,350.111597 +79926,Binary classification,[baseline] Last Class,SMTP,0.999549577729121,0.3793103448275862,0.0005102157592773438,366.757049 +81829,Binary classification,[baseline] Last Class,SMTP,0.9995600527936648,0.3793103448275862,0.0005102157592773438,383.769929 +83732,Binary classification,[baseline] Last Class,SMTP,0.9995700517132244,0.3793103448275862,0.0005102157592773438,401.12604899999997 +85635,Binary classification,[baseline] Last Class,SMTP,0.9995796062311698,0.3793103448275862,0.0005102157592773438,418.85540799999995 +87538,Binary classification,[baseline] Last Class,SMTP,0.999588745330546,0.3793103448275862,0.0005102157592773438,436.927356 +89441,Binary classification,[baseline] Last Class,SMTP,0.9995751341681575,0.36666666666666664,0.0005102157592773438,455.364423 +91344,Binary classification,[baseline] Last Class,SMTP,0.9995839856365567,0.36666666666666664,0.0005102157592773438,474.187698 +93247,Binary classification,[baseline] Last Class,SMTP,0.999592475816657,0.36666666666666664,0.0005102157592773438,493.377496 +95150,Binary classification,[baseline] Last Class,SMTP,0.9996006263859841,0.36666666666666664,0.0005102157592773438,512.867881 +95156,Binary classification,[baseline] Last Class,SMTP,0.9996006515684935,0.36666666666666664,0.0005102157592773438,532.358985 diff --git a/docs/benchmarks/Binary classification/index.md b/docs/benchmarks/Binary classification/index.md new file mode 100644 index 0000000000..3d8c8e14c0 --- /dev/null +++ b/docs/benchmarks/Binary classification/index.md @@ -0,0 +1,37950 @@ +# Binary classification + + + +=== "Table" + + | Model | Dataset | Accuracy | F1 | Memory in Mb | Time in s | + |:----------------------------------|:----------|-----------:|-----------:|---------------:|------------:| + | ADWIN Bagging | Bananas | 0.625967 | 0.448218 | 0.400658 | 942.73 | + | ADWIN Bagging | Elec2 | 0.823773 | 0.776587 | 0.598438 | 8970.15 | + | ADWIN Bagging | Phishing | 0.893515 | 0.879201 | 1.31008 | 568.218 | + | ADWIN Bagging | SMTP | 0.999685 | 0 | 0.164217 | 8006.78 | + | ALMA | Bananas | 0.506415 | 0.482595 | 0.0029211 | 68.9731 | + | ALMA | Elec2 | 0.906427 | 0.889767 | 0.00435829 | 836.498 | + | ALMA | Phishing | 0.8256 | 0.810764 | 0.0045805 | 29.7613 | + | ALMA | SMTP | 0.764986 | 0.00178548 | 0.00309372 | 1361.61 | + | AdaBoost | Bananas | 0.677864 | 0.645041 | 0.453154 | 876.714 | + | AdaBoost | Elec2 | 0.880581 | 0.858687 | 13.5424 | 10153.7 | + | AdaBoost | Phishing | 0.878303 | 0.863555 | 0.873312 | 552.609 | + | AdaBoost | SMTP | 0.999443 | 0.404494 | 1.33633 | 6617.5 | + | Adaptive Random Forest | Bananas | 0.88696 | 0.871707 | 15.3551 | 2603.02 | + | Adaptive Random Forest | Elec2 | 0.876608 | 0.852391 | 22.3949 | 12397.6 | + | Adaptive Random Forest | Phishing | 0.907926 | 0.896116 | 4.10291 | 743.377 | + | Adaptive Random Forest | SMTP | 0.999685 | 0 | 0.327095 | 11543.4 | + | Aggregated Mondrian Forest | Bananas | 0.889413 | 0.874249 | 17.2377 | 2954.75 | + | Aggregated Mondrian Forest | Elec2 | 0.849904 | 0.819731 | 287.315 | 18206.6 | + | Aggregated Mondrian Forest | Phishing | 0.904724 | 0.892112 | 3.39106 | 807.573 | + | Aggregated Mondrian Forest | SMTP | 0.999863 | 0.734694 | 0.211749 | 5848.87 | + | Bagging | Bananas | 0.634082 | 0.459437 | 0.703124 | 1170.85 | + | Bagging | Elec2 | 0.840436 | 0.80208 | 2.28896 | 13164.5 | + | Bagging | Phishing | 0.893515 | 0.879201 | 1.38826 | 633.136 | + | Bagging | SMTP | 0.999685 | 0 | 0.207971 | 8814.84 | + | Hoeffding Adaptive Tree | Bananas | 0.616531 | 0.42825 | 0.0618467 | 163.516 | + | Hoeffding Adaptive Tree | Elec2 | 0.821258 | 0.787344 | 0.435328 | 2980.69 | + | Hoeffding Adaptive Tree | Phishing | 0.874299 | 0.856095 | 0.142962 | 77.865 | + | Hoeffding Adaptive Tree | SMTP | 0.999685 | 0 | 0.0241137 | 2094.95 | + | Hoeffding Tree | Bananas | 0.642197 | 0.503405 | 0.0594654 | 93.5302 | + | Hoeffding Tree | Elec2 | 0.795635 | 0.750834 | 0.938466 | 1485.98 | + | Hoeffding Tree | Phishing | 0.879904 | 0.860595 | 0.132719 | 54.2758 | + | Hoeffding Tree | SMTP | 0.999685 | 0 | 0.0170441 | 1543.56 | + | Leveraging Bagging | Bananas | 0.828269 | 0.802689 | 3.23571 | 2747.95 | + | Leveraging Bagging | Elec2 | 0.892653 | 0.871966 | 7.56535 | 18763.3 | + | Leveraging Bagging | Phishing | 0.894315 | 0.877323 | 4.0114 | 1619.65 | + | Leveraging Bagging | SMTP | 0.999674 | 0 | 0.164603 | 17549.6 | + | Logistic regression | Bananas | 0.543208 | 0.197015 | 0.00424099 | 82.0689 | + | Logistic regression | Elec2 | 0.822144 | 0.777086 | 0.005373 | 953.54 | + | Logistic regression | Phishing | 0.8872 | 0.871233 | 0.00556469 | 29.2066 | + | Logistic regression | SMTP | 0.999769 | 0.421053 | 0.00438309 | 1531.37 | + | Naive Bayes | Bananas | 0.61521 | 0.413912 | 0.0140247 | 97.154 | + | Naive Bayes | Elec2 | 0.728741 | 0.603785 | 0.0510378 | 1230.66 | + | Naive Bayes | Phishing | 0.884708 | 0.871429 | 0.05723 | 38.528 | + | Naive Bayes | SMTP | 0.993484 | 0.0490798 | 0.0201406 | 1826.47 | + | Stacking | Bananas | 0.876203 | 0.859649 | 19.1946 | 5236.84 | + | Stacking | Elec2 | 0.885458 | 0.864157 | 40.7547 | 22944.4 | + | Stacking | Phishing | 0.895116 | 0.882722 | 8.72124 | 2411.41 | + | Stacking | SMTP | 0.999685 | 0 | 4.88868 | 24733.2 | + | Streaming Random Patches | Bananas | 0.871674 | 0.854265 | 10.5381 | 3551.41 | + | Streaming Random Patches | Elec2 | 0.868884 | 0.843009 | 107.322 | 22969 | + | Streaming Random Patches | Phishing | 0.913531 | 0.901996 | 6.59559 | 1436.69 | + | Streaming Random Patches | SMTP | 0.999685 | 0 | 0.17817 | 18142.3 | + | Voting | Bananas | 0.872617 | 0.849162 | 4.58403 | 2790.97 | + | Voting | Elec2 | 0.84368 | 0.797958 | 5.7575 | 13925.5 | + | Voting | Phishing | 0.896717 | 0.884512 | 4.8203 | 1436.72 | + | Voting | SMTP | 0.999779 | 0.487805 | 4.60205 | 18069.8 | + | Vowpal Wabbit logistic regression | Bananas | 0.551321 | 0 | 0.000646591 | 88.7248 | + | Vowpal Wabbit logistic regression | Elec2 | 0.697475 | 0.459592 | 0.000646591 | 937.011 | + | Vowpal Wabbit logistic regression | Phishing | 0.7736 | 0.669778 | 0.000646591 | 27.8334 | + | Vowpal Wabbit logistic regression | SMTP | 0.999695 | 0.121212 | 0.000646591 | 1631.37 | + | [baseline] Last Class | Bananas | 0.50953 | 0.452957 | 0.000510216 | 30.809 | + | [baseline] Last Class | Elec2 | 0.853303 | 0.827229 | 0.000510216 | 341.39 | + | [baseline] Last Class | Phishing | 0.515612 | 0.447489 | 0.000510216 | 11.9196 | + | [baseline] Last Class | SMTP | 0.999601 | 0.366667 | 0.000510216 | 532.359 | + | k-Nearest Neighbors | Bananas | 0.885073 | 0.870838 | 4.50996 | 2974.33 | + | k-Nearest Neighbors | Elec2 | 0.853148 | 0.823642 | 4.76604 | 13503.4 | + | k-Nearest Neighbors | Phishing | 0.881505 | 0.867145 | 4.59643 | 1552.65 | + | k-Nearest Neighbors | SMTP | 0.999821 | 0.666667 | 4.51822 | 17961.1 | + | sklearn SGDClassifier | Bananas | 0.546415 | 0.205026 | 0.00557804 | 621.426 | + | sklearn SGDClassifier | Elec2 | 0.819099 | 0.772892 | 0.00680161 | 4291.77 | + | sklearn SGDClassifier | Phishing | 0.8896 | 0.876122 | 0.00701618 | 167.984 | + | sklearn SGDClassifier | SMTP | 0.999706 | 0.363636 | 0.00574303 | 7118.18 | + +=== "Chart" + + *Try reloading the page if something is buggy* + + ```vegalite + { + "$schema": "https://vega.github.io/schema/vega-lite/v5.json", + "data": { + "values": [ + { + "step": 106, + "track": "Binary classification", + "model": "Logistic regression", + "dataset": "Bananas", + "Accuracy": 0.490566037735849, + "F1": 0.3414634146341463, + "Memory in Mb": 0.0041875839233398, + "Time in s": 0.00989 + }, + { + "step": 212, + "track": "Binary classification", + "model": "Logistic regression", + "dataset": "Bananas", + "Accuracy": 0.5141509433962265, + "F1": 0.3832335329341317, + "Memory in Mb": 0.0041875839233398, + "Time in s": 0.123413 + }, + { + "step": 318, + "track": "Binary classification", + "model": "Logistic regression", + "dataset": "Bananas", + "Accuracy": 0.5220125786163522, + "F1": 0.4242424242424242, + "Memory in Mb": 0.0042409896850585, + "Time in s": 0.315017 + }, + { + "step": 424, + "track": "Binary classification", + "model": "Logistic regression", + "dataset": "Bananas", + "Accuracy": 0.5165094339622641, + "F1": 0.4023323615160349, + "Memory in Mb": 0.0042409896850585, + "Time in s": 0.5849610000000001 + }, + { + "step": 530, + "track": "Binary classification", + "model": "Logistic regression", + "dataset": "Bananas", + "Accuracy": 0.5320754716981132, + "F1": 0.3641025641025641, + "Memory in Mb": 0.0042409896850585, + "Time in s": 0.937213 + }, + { + "step": 636, + "track": "Binary classification", + "model": "Logistic regression", + "dataset": "Bananas", + "Accuracy": 0.5377358490566038, + "F1": 0.3287671232876712, + "Memory in Mb": 0.0042409896850585, + "Time in s": 1.342505 + }, + { + "step": 742, + "track": "Binary classification", + "model": "Logistic regression", + "dataset": "Bananas", + "Accuracy": 0.5525606469002695, + "F1": 0.3054393305439331, + "Memory in Mb": 0.0042409896850585, + "Time in s": 1.895068 + }, + { + "step": 848, + "track": "Binary classification", + "model": "Logistic regression", + "dataset": "Bananas", + "Accuracy": 0.5530660377358491, + "F1": 0.2808349146110057, + 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classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9987578826676858, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 27.619004 + }, + { + "step": 22836, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9988613969783228, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 32.587888 + }, + { + "step": 24739, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9989489853666425, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 37.966612 + }, + { + "step": 26642, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9989489884013364, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 43.747016 + }, + { + "step": 28545, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9990190582959642, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 49.923315 + }, + { + "step": 30448, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999080369166092, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 56.476617 + }, + { + "step": 32351, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9991344667697064, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 63.442318 + }, + { + "step": 34254, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999182553352991, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 70.796392 + }, + { + "step": 36157, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9992255780506694, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 78.554987 + }, + { + "step": 38060, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9992643001655324, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 86.688101 + }, + { + "step": 39963, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9992993343676492, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 95.30254 + }, + { + "step": 41866, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9993311835662247, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 104.31052400000002 + }, + { + "step": 43769, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9993602632059952, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 113.72316700000002 + }, + { + "step": 45672, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9993869194893916, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 123.549629 + }, + { + "step": 47575, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994114432252912, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 133.72455100000002 + }, + { + "step": 49478, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.99943408048184, + "F1": 0.0, + "Memory in Mb": 0.0005102157592773, + "Time in s": 144.361296 + }, + { + "step": 51381, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.99941611521993, + "F1": 0.0625, + "Memory in Mb": 0.0005102157592773, + "Time in s": 155.342577 + }, + { + "step": 53284, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994369686391532, + "F1": 0.0625, + "Memory in Mb": 0.0005102157592773, + "Time in s": 166.71976 + }, + { + "step": 55187, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994563838654732, + "F1": 0.0625, + "Memory in Mb": 0.0005102157592773, + "Time in s": 178.568204 + }, + { + "step": 57090, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994394717020793, + "F1": 0.36, + "Memory in Mb": 0.0005102157592773, + "Time in s": 190.760423 + }, + { + "step": 58993, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994575535665852, + "F1": 0.36, + "Memory in Mb": 0.0005102157592773, + "Time in s": 203.318295 + }, + { + "step": 60896, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994745052960012, + "F1": 0.36, + "Memory in Mb": 0.0005102157592773, + "Time in s": 216.324675 + }, + { + "step": 62799, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994585814834868, + "F1": 0.3703703703703703, + "Memory in Mb": 0.0005102157592773, + "Time in s": 229.751949 + }, + { + "step": 64702, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994745058036196, + "F1": 0.3703703703703703, + "Memory in Mb": 0.0005102157592773, + "Time in s": 243.519605 + }, + { + "step": 66605, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.99948952014894, + "F1": 0.3703703703703703, + "Memory in Mb": 0.0005102157592773, + "Time in s": 257.632512 + }, + { + "step": 68508, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994745062548352, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 272.164854 + }, + { + "step": 70411, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994887089902004, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 287.040678 + }, + { + "step": 72314, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995021642028404, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 302.255295 + }, + { + "step": 74217, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995149293952786, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 317.85437 + }, + { + "step": 76120, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.99952705631971, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 333.81575100000003 + }, + { + "step": 78023, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.99953859167927, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 350.111597 + }, + { + "step": 79926, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999549577729121, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 366.757049 + }, + { + "step": 81829, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995600527936648, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 383.769929 + }, + { + "step": 83732, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995700517132244, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 401.126049 + }, + { + "step": 85635, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995796062311698, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 418.855408 + }, + { + "step": 87538, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999588745330546, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 436.927356 + }, + { + "step": 89441, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995751341681576, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 455.364423 + }, + { + "step": 91344, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995839856365568, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 474.187698 + }, + { + "step": 93247, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999592475816657, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 493.377496 + }, + { + "step": 95150, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999600626385984, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 512.867881 + }, + { + "step": 95156, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9996006515684936, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 532.358985 + } + ] + }, + "params": [ + { + "name": "models", + "select": { + "type": "point", + "fields": [ + "model" + ] + }, + "bind": "legend" + }, + { + "name": "Dataset", + "value": "Bananas", + "bind": { + "input": "select", + "options": [ + "Bananas", + "Elec2", + "Phishing", + "SMTP" + ] + } + }, + { + "name": "grid", + "select": "interval", + "bind": "scales" + } + ], + "transform": [ + { + "filter": { + "field": "dataset", + "equal": { + "expr": "Dataset" + } + } + } + ], + "repeat": { + "row": [ + "Accuracy", + "F1", + "Memory in Mb", + "Time in s" + ] + }, + "spec": { + "width": "container", + "mark": "line", + "encoding": { + "x": { + "field": "step", + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18, + "title": "Instance" + } + }, + "y": { + "field": { + "repeat": "row" + }, + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18 + } + }, + "color": { + "field": "model", + "type": "ordinal", + "scale": { + "scheme": "category20b" + }, + "title": "Models", + "legend": { + "titleFontSize": 18, + "labelFontSize": 18, + "labelLimit": 500 + } + }, + "opacity": { + "condition": { + "param": "models", + "value": 1 + }, + "value": 0.2 + } + } + } + } + ``` + + + +## Datasets + +???- abstract "Bananas" + + Bananas dataset. + + An artificial dataset where instances belongs to several clusters with a banana shape. + There are two attributes that correspond to the x and y axis, respectively. + + Name Bananas + Task Binary classification + Samples 5,300 + Features 2 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/banana.zip + + + +???- abstract "Elec2" + + Electricity prices in New South Wales. + + This is a binary classification task, where the goal is to predict if the price of electricity + will go up or down. + + This data was collected from the Australian New South Wales Electricity Market. In this market, + prices are not fixed and are affected by demand and supply of the market. They are set every + five minutes. Electricity transfers to/from the neighboring state of Victoria were done to + alleviate fluctuations. + + Name Elec2 + Task Binary classification + Samples 45,312 + Features 8 + Sparse False + Path /Users/mastelini/river_data/Elec2/electricity.csv + URL https://maxhalford.github.io/files/datasets/electricity.zip + Size 2.95 MB + Downloaded True + + + +???- abstract "Phishing" + + Phishing websites. + + This dataset contains features from web pages that are classified as phishing or not. + + Name Phishing + Task Binary classification + Samples 1,250 + Features 9 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/phishing.csv.gz + + + +???- abstract "SMTP" + + SMTP dataset from the KDD 1999 cup. + + The goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only + contains 2,211 (0.4%) positive labels. + + Name SMTP + Task Binary classification + Samples 95,156 + Features 3 + Sparse False + Path /Users/mastelini/river_data/SMTP/smtp.csv + URL https://maxhalford.github.io/files/datasets/smtp.zip + Size 5.23 MB + Downloaded True + + + +## Models + +???- example "Logistic regression" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      LogisticRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.005
+          )
+        )
+        loss=Log (
+          weight_pos=1.
+          weight_neg=1.
+        )
+        l2=0.
+        l1=0.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      )
+    )
+ + + +???- example "Aggregated Mondrian Forest" + + 
[]
+ + + +???- example "ALMA" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      ALMAClassifier (
+        p=2
+        alpha=0.9
+        B=1.111111
+        C=1.414214
+      )
+    )
+ + + +???- example "sklearn SGDClassifier" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SKL2RiverClassifier (
+        estimator=SGDClassifier(eta0=0.005, learning_rate='constant', loss='log_loss',
+                    penalty=None)
+        classes=[False, True]
+      )
+    )
+ + + +???- example "Vowpal Wabbit logistic regression" + + 
VW2RiverClassifier ()
+ + + +???- example "Naive Bayes" + + 
GaussianNB ()
+ + + +???- example "Hoeffding Tree" + + 
HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )
+ + + +???- example "Hoeffding Adaptive Tree" + + 
HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=True
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=42
+    )
+ + + +???- example "Adaptive Random Forest" + + 
[]
+ + + +???- example "Streaming Random Patches" + + 
SRPClassifier (
+      model=HoeffdingTreeClassifier (
+        grace_period=50
+        max_depth=inf
+        split_criterion="info_gain"
+        delta=0.01
+        tau=0.05
+        leaf_prediction="nba"
+        nb_threshold=0
+        nominal_attributes=None
+        splitter=GaussianSplitter (
+          n_splits=10
+        )
+        binary_split=False
+        min_branch_fraction=0.01
+        max_share_to_split=0.99
+        max_size=100.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+      )
+      n_models=10
+      subspace_size=0.6
+      training_method="patches"
+      lam=6
+      drift_detector=ADWIN (
+        delta=1e-05
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      warning_detector=ADWIN (
+        delta=0.0001
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      disable_detector="off"
+      disable_weighted_vote=False
+      seed=None
+      metric=Accuracy (
+        cm=ConfusionMatrix (
+          classes=[]
+        )
+      )
+    )
+ + + +???- example "k-Nearest Neighbors" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )
+ + + +???- example "ADWIN Bagging" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "AdaBoost" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "Bagging" + + 
[HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    )]
+ + + +???- example "Leveraging Bagging" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "Stacking" + + 
[Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SoftmaxRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=CrossEntropy (
+          class_weight={}
+        )
+        l2=0
+      )
+    ), GaussianNB (), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )]
+ + + +???- example "Voting" + + 
VotingClassifier (
+      models=[Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SoftmaxRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=CrossEntropy (
+          class_weight={}
+        )
+        l2=0
+      )
+    ), GaussianNB (), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )]
+      use_probabilities=True
+    )
+ + + +???- example "[baseline] Last Class" + + 
NoChangeClassifier ()
+ + + +## Environment + +
Python implementation: CPython
+Python version       : 3.10.13
+IPython version      : 8.16.1
+
+river       : 0.19.0
+numpy       : 1.25.2
+scikit-learn: 1.3.1
+pandas      : 2.1.1
+scipy       : 1.11.3
+
+Compiler    : Clang 14.0.6 
+OS          : Darwin
+Release     : 22.6.0
+Machine     : arm64
+Processor   : arm
+CPU cores   : 8
+Architecture: 64bit
+
+ diff --git a/docs/benchmarks/Multiclass classification/index.md b/docs/benchmarks/Multiclass classification/index.md new file mode 100644 index 0000000000..de3b47ecae --- /dev/null +++ b/docs/benchmarks/Multiclass classification/index.md @@ -0,0 +1,24908 @@ +# Multiclass classification + + + +=== "Table" + + | Model | Dataset | Accuracy | MicroF1 | MacroF1 | Memory in Mb | Time in s | + |:---------------------------|:--------------|-----------:|----------:|----------:|---------------:|------------:| + | ADWIN Bagging | ImageSegments | 0.777826 | 0.777826 | 0.765011 | 4.11628 | 3543.55 | + | ADWIN Bagging | Insects | 0.579465 | 0.579465 | 0.570198 | 15.3074 | 60279.4 | + | ADWIN Bagging | Keystroke | 0.81656 | 0.81656 | 0.815908 | 37.8558 | 41308 | + | AdaBoost | ImageSegments | 0.804677 | 0.804677 | 0.79777 | 4.09839 | 3350.88 | + | AdaBoost | Insects | 0.563532 | 0.563532 | 0.554622 | 27.943 | 60335.7 | + | AdaBoost | Keystroke | 0.834796 | 0.834796 | 0.836062 | 194.794 | 51861.3 | + | Adaptive Random Forest | ImageSegments | 0.818536 | 0.818536 | 0.814535 | 3.06348 | 1574.18 | + | Adaptive Random Forest | Insects | 0.745378 | 0.745378 | 0.743302 | 0.361794 | 25383.5 | + | Adaptive Random Forest | Keystroke | 0.969116 | 0.969116 | 0.969111 | 1.63546 | 7363.05 | + | Aggregated Mondrian Forest | ImageSegments | 0.901689 | 0.901689 | 0.900381 | 17.0502 | 2997.7 | + | Aggregated Mondrian Forest | Insects | 0.646981 | 0.646981 | 0.644352 | 1365.41 | 76295.7 | + | Aggregated Mondrian Forest | Keystroke | 0.881073 | 0.881073 | 0.879928 | 338.139 | 35528.4 | + | Bagging | ImageSegments | 0.77696 | 0.77696 | 0.764564 | 4.15507 | 3634.88 | + | Bagging | Insects | 0.606392 | 0.606392 | 0.598542 | 3.69162 | 65237 | + | Bagging | Keystroke | 0.669739 | 0.669739 | 0.669981 | 50.3449 | 55411.4 | + | Hoeffding Adaptive Tree | ImageSegments | 0.774361 | 0.774361 | 0.763362 | 0.423797 | 457.311 | + | Hoeffding Adaptive Tree | Insects | 0.613337 | 0.613337 | 0.604219 | 0.143826 | 11292.9 | + | Hoeffding Adaptive Tree | Keystroke | 0.723124 | 0.723124 | 0.721825 | 0.724475 | 8998.46 | + | Hoeffding Tree | ImageSegments | 0.776094 | 0.776094 | 0.763137 | 0.417154 | 328.067 | + | Hoeffding Tree | Insects | 0.537306 | 0.537306 | 0.527364 | 2.51923 | 7445.36 | + | Hoeffding Tree | Keystroke | 0.648218 | 0.648218 | 0.647249 | 5.09445 | 7138.73 | + | Leveraging Bagging | ImageSegments | 0.778259 | 0.778259 | 0.766016 | 4.1005 | 8561.3 | + | Leveraging Bagging | Insects | 0.695858 | 0.695858 | 0.690508 | 13.831 | 99120.2 | + | Leveraging Bagging | Keystroke | 0.956616 | 0.956616 | 0.95665 | 7.40999 | 37049.1 | + | Naive Bayes | ImageSegments | 0.731919 | 0.731919 | 0.730419 | 0.390004 | 248.959 | + | Naive Bayes | Insects | 0.506897 | 0.506897 | 0.493019 | 0.611693 | 4263.77 | + | Naive Bayes | Keystroke | 0.652532 | 0.652532 | 0.651577 | 4.86901 | 3544.69 | + | Stacking | ImageSegments | 0.867908 | 0.867908 | 0.865603 | 9.18162 | 5416.88 | + | Stacking | Insects | 0.754745 | 0.754745 | 0.752818 | 10.5864 | 72115 | + | Stacking | Keystroke | 0.975489 | 0.975489 | 0.975486 | 18.7111 | 42471.8 | + | Streaming Random Patches | ImageSegments | 0.766999 | 0.766999 | 0.764707 | 8.92653 | 6441.81 | + | Streaming Random Patches | Insects | 0.736163 | 0.736163 | 0.734622 | 9.632 | 90031.6 | + | Streaming Random Patches | Keystroke | 0.955929 | 0.955929 | 0.95592 | 39.636 | 31009.8 | + | Voting | ImageSegments | 0.80641 | 0.80641 | 0.798999 | 6.07392 | 3157.94 | + | Voting | Insects | 0.648533 | 0.648533 | 0.638 | 9.40652 | 48163.7 | + | Voting | Keystroke | 0.779107 | 0.779107 | 0.784136 | 16.3925 | 29779.2 | + | [baseline] Last Class | ImageSegments | 0.148116 | 0.148116 | 0.148116 | 0.00136948 | 31.4159 | + | [baseline] Last Class | Insects | 0.289761 | 0.289761 | 0.289763 | 0.00138664 | 679.004 | + | [baseline] Last Class | Keystroke | 0.997549 | 0.997549 | 0.997549 | 0.00504208 | 274.675 | + | k-Nearest Neighbors | ImageSegments | 0.873538 | 0.873538 | 0.872136 | 5.26871 | 2666.29 | + | k-Nearest Neighbors | Insects | 0.713115 | 0.713115 | 0.711381 | 6.27269 | 40639.9 | + | k-Nearest Neighbors | Keystroke | 0.910486 | 0.910486 | 0.910328 | 6.32511 | 21326.5 | + +=== "Chart" + + *Try reloading the page if something is buggy* + + ```vegalite + { + "$schema": "https://vega.github.io/schema/vega-lite/v5.json", + "data": { + "values": [ + { + "step": 46, + "track": "Multiclass classification", + "model": "Naive Bayes", + "dataset": "ImageSegments", + "Accuracy": 0.4666666666666667, + "MicroF1": 0.4666666666666667, + "MacroF1": 0.4009102009102009, + "Memory in Mb": 0.3899507522583008, + "Time in s": 0.450679 + }, + { + "step": 92, + "track": "Multiclass classification", + "model": "Naive Bayes", + "dataset": "ImageSegments", + "Accuracy": 0.5604395604395604, + "MicroF1": 0.5604395604395604, + "MacroF1": 0.5279334700387331, + "Memory in Mb": 0.3899507522583008, + "Time in s": 1.152847 + }, + { + "step": 138, + "track": "Multiclass 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0.9975488694160182, + "MacroF1": 0.9975532558614028, + "Memory in Mb": 0.0045366287231445, + "Time in s": 177.653336 + }, + { + "step": 16728, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.9975488730794524, + "MicroF1": 0.9975488730794524, + "MacroF1": 0.997552898047314, + "Memory in Mb": 0.004587173461914, + "Time in s": 186.438203 + }, + { + "step": 17136, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.9975488765684272, + "MicroF1": 0.9975488765684272, + "MacroF1": 0.9975525144785748, + "Memory in Mb": 0.0046377182006835, + "Time in s": 195.447168 + }, + { + "step": 17544, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.9975488798951148, + "MicroF1": 0.9975488798951148, + "MacroF1": 0.997552109806108, + "Memory in Mb": 0.0046882629394531, + "Time in s": 204.563635 + }, + { + "step": 17952, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.997548883070581, + "MicroF1": 0.997548883070581, + "MacroF1": 0.9975516880097278, + "Memory in Mb": 0.0047388076782226, + "Time in s": 213.933058 + }, + { + "step": 18360, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.9975488861049076, + "MicroF1": 0.9975488861049076, + "MacroF1": 0.997551252499137, + "Memory in Mb": 0.0047893524169921, + "Time in s": 223.513668 + }, + { + "step": 18768, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.9975488890073, + "MicroF1": 0.9975488890073, + "MacroF1": 0.9975508061984416, + "Memory in Mb": 0.0048398971557617, + "Time in s": 233.322943 + }, + { + "step": 19176, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.99754889178618, + "MicroF1": 0.99754889178618, + "MacroF1": 0.9975503516171184, + "Memory in Mb": 0.0048904418945312, + "Time in s": 243.357771 + }, + { + "step": 19584, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.9975488944492672, + "MicroF1": 0.9975488944492672, + "MacroF1": 0.9975498909097889, + "Memory in Mb": 0.0049409866333007, + "Time in s": 253.567103 + }, + { + "step": 19992, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.9975488970036516, + "MicroF1": 0.9975488970036516, + "MacroF1": 0.9975494259267256, + "Memory in Mb": 0.0049915313720703, + "Time in s": 264.004285 + }, + { + "step": 20400, + "track": "Multiclass classification", + "model": "[baseline] Last Class", + "dataset": "Keystroke", + "Accuracy": 0.9975488994558556, + "MicroF1": 0.9975488994558556, + "MacroF1": 0.9975489582566448, + "Memory in Mb": 0.0050420761108398, + "Time in s": 274.675054 + } + ] + }, + "params": [ + { + "name": "models", + "select": { + "type": "point", + "fields": [ + "model" + ] + }, + "bind": "legend" + }, + { + "name": "Dataset", + "value": "ImageSegments", + "bind": { + "input": "select", + "options": [ + "ImageSegments", + "Insects", + "Keystroke" + ] + } + }, + { + "name": "grid", + "select": "interval", + "bind": "scales" + } + ], + "transform": [ + { + "filter": { + "field": "dataset", + "equal": { + "expr": "Dataset" + } + } + } + ], + "repeat": { + "row": [ + "Accuracy", + "MicroF1", + "MacroF1", + "Memory in Mb", + "Time in s" + ] + }, + "spec": { + "width": "container", + "mark": "line", + "encoding": { + "x": { + "field": "step", + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18, + "title": "Instance" + } + }, + "y": { + "field": { + "repeat": "row" + }, + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18 + } + }, + "color": { + "field": "model", + "type": "ordinal", + "scale": { + "scheme": "category20b" + }, + "title": "Models", + "legend": { + "titleFontSize": 18, + "labelFontSize": 18, + "labelLimit": 500 + } + }, + "opacity": { + "condition": { + "param": "models", + "value": 1 + }, + "value": 0.2 + } + } + } + } + ``` + + + +## Datasets + +???- abstract "ImageSegments" + + Image segments classification. + + This dataset contains features that describe image segments into 7 classes: brickface, sky, + foliage, cement, window, path, and grass. + + Name ImageSegments + Task Multi-class classification + Samples 2,310 + Features 18 + Classes 7 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/segment.csv.zip + + + +???- abstract "Insects" + + Insects dataset. + + This dataset has different variants, which are: + + - abrupt_balanced + - abrupt_imbalanced + - gradual_balanced + - gradual_imbalanced + - incremental-abrupt_balanced + - incremental-abrupt_imbalanced + - incremental-reoccurring_balanced + - incremental-reoccurring_imbalanced + - incremental_balanced + - incremental_imbalanced + - out-of-control + + The number of samples and the difficulty change from one variant to another. The number of + classes is always the same (6), except for the last variant (24). + + Name Insects + Task Multi-class classification + Samples 52,848 + Features 33 + Classes 6 + Sparse False + Path /Users/mastelini/river_data/Insects/INSECTS-abrupt_balanced_norm.arff + URL http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-abrupt_balanced_norm.arff + Size 15.66 MB + Downloaded True + Variant abrupt_balanced + + Parameters + ---------- + variant + Indicates which variant of the dataset to load. + + + +???- abstract "Keystroke" + + CMU keystroke dataset. + + Users are tasked to type in a password. The task is to determine which user is typing in the + password. + + The only difference with the original dataset is that the "sessionIndex" and "rep" attributes + have been dropped. + + Name Keystroke + Task Multi-class classification + Samples 20,400 + Features 31 + Classes 51 + Sparse False + Path /Users/mastelini/river_data/Keystroke/DSL-StrongPasswordData.csv + URL http://www.cs.cmu.edu/~keystroke/DSL-StrongPasswordData.csv + Size 4.45 MB + Downloaded True + + + +## Models + +???- example "Naive Bayes" + + 
GaussianNB ()
+ + + +???- example "Hoeffding Tree" + + 
HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )
+ + + +???- example "Hoeffding Adaptive Tree" + + 
HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=True
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=42
+    )
+ + + +???- example "Adaptive Random Forest" + + 
[]
+ + + +???- example "Aggregated Mondrian Forest" + + 
[]
+ + + +???- example "Streaming Random Patches" + + 
SRPClassifier (
+      model=HoeffdingTreeClassifier (
+        grace_period=50
+        max_depth=inf
+        split_criterion="info_gain"
+        delta=0.01
+        tau=0.05
+        leaf_prediction="nba"
+        nb_threshold=0
+        nominal_attributes=None
+        splitter=GaussianSplitter (
+          n_splits=10
+        )
+        binary_split=False
+        min_branch_fraction=0.01
+        max_share_to_split=0.99
+        max_size=100.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+      )
+      n_models=10
+      subspace_size=0.6
+      training_method="patches"
+      lam=6
+      drift_detector=ADWIN (
+        delta=1e-05
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      warning_detector=ADWIN (
+        delta=0.0001
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      disable_detector="off"
+      disable_weighted_vote=False
+      seed=None
+      metric=Accuracy (
+        cm=ConfusionMatrix (
+          classes=[]
+        )
+      )
+    )
+ + + +???- example "k-Nearest Neighbors" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )
+ + + +???- example "ADWIN Bagging" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "AdaBoost" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "Bagging" + + 
[HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    )]
+ + + +???- example "Leveraging Bagging" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "Stacking" + + 
[Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SoftmaxRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=CrossEntropy (
+          class_weight={}
+        )
+        l2=0
+      )
+    ), GaussianNB (), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )]
+ + + +???- example "Voting" + + 
VotingClassifier (
+      models=[Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SoftmaxRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=CrossEntropy (
+          class_weight={}
+        )
+        l2=0
+      )
+    ), GaussianNB (), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )]
+      use_probabilities=True
+    )
+ + + +???- example "[baseline] Last Class" + + 
NoChangeClassifier ()
+ + + +## Environment + +
Python implementation: CPython
+Python version       : 3.10.13
+IPython version      : 8.16.1
+
+river       : 0.19.0
+numpy       : 1.25.2
+scikit-learn: 1.3.1
+pandas      : 2.1.1
+scipy       : 1.11.3
+
+Compiler    : Clang 14.0.6 
+OS          : Darwin
+Release     : 22.6.0
+Machine     : arm64
+Processor   : arm
+CPU cores   : 8
+Architecture: 64bit
+
+ diff --git a/docs/benchmarks/Multiclass classification/multiclass_classification.csv b/docs/benchmarks/Multiclass classification/multiclass_classification.csv new file mode 100644 index 0000000000..9394fc8dfd --- /dev/null +++ b/docs/benchmarks/Multiclass classification/multiclass_classification.csv @@ -0,0 +1,2129 @@ +step,track,model,dataset,Accuracy,MicroF1,MacroF1,Memory in Mb,Time in s +46,Multiclass classification,Naive Bayes,ImageSegments,0.4666666666666667,0.4666666666666667,0.4009102009102009,0.3899507522583008,0.450679 +92,Multiclass classification,Naive Bayes,ImageSegments,0.5604395604395604,0.5604395604395604,0.5279334700387331,0.3899507522583008,1.152847 +138,Multiclass classification,Naive Bayes,ImageSegments,0.5474452554744526,0.5474452554744526,0.5191892873237387,0.38997745513916016,2.278305 +184,Multiclass classification,Naive Bayes,ImageSegments,0.5573770491803278,0.5573770491803278,0.5225713529323662,0.3899507522583008,3.449742 +230,Multiclass classification,Naive Bayes,ImageSegments,0.5545851528384279,0.5545851528384279,0.5217226223148511,0.38997745513916016,4.939578 +276,Multiclass classification,Naive Bayes,ImageSegments,0.56,0.56,0.5450388711329709,0.38997745513916016,6.667081 +322,Multiclass classification,Naive Bayes,ImageSegments,0.5825545171339563,0.5825545171339563,0.5566705826058684,0.39000415802001953,8.548779999999999 +368,Multiclass classification,Naive Bayes,ImageSegments,0.5940054495912807,0.5940054495912807,0.5613773296963412,0.39000415802001953,10.607026 +414,Multiclass classification,Naive Bayes,ImageSegments,0.5980629539951574,0.5980629539951574,0.5624927052752284,0.39000415802001953,12.811145 +460,Multiclass classification,Naive Bayes,ImageSegments,0.599128540305011,0.599128540305011,0.5669821167583783,0.38997745513916016,15.144022 +506,Multiclass classification,Naive Bayes,ImageSegments,0.6099009900990099,0.6099009900990099,0.592228619098681,0.39000415802001953,17.683543 +552,Multiclass classification,Naive Bayes,ImageSegments,0.6116152450090744,0.6116152450090744,0.5983340184133136,0.3899507522583008,20.357047 +598,Multiclass classification,Naive Bayes,ImageSegments,0.6180904522613065,0.6180904522613065,0.611527101723203,0.38997745513916016,23.213992 +644,Multiclass classification,Naive Bayes,ImageSegments,0.6158631415241057,0.6158631415241057,0.6113311881078581,0.38997745513916016,26.205369 +690,Multiclass classification,Naive Bayes,ImageSegments,0.6182873730043541,0.6182873730043541,0.6150189987146761,0.38997745513916016,29.350024 +736,Multiclass classification,Naive Bayes,ImageSegments,0.617687074829932,0.617687074829932,0.6157912419016742,0.38997745513916016,32.567265 +782,Multiclass classification,Naive Bayes,ImageSegments,0.6274007682458387,0.6274007682458387,0.6216325704223051,0.38997745513916016,36.027093 +828,Multiclass classification,Naive Bayes,ImageSegments,0.6324062877871826,0.6324062877871826,0.6280704917469789,0.38997745513916016,39.646129 +874,Multiclass classification,Naive Bayes,ImageSegments,0.6426116838487973,0.6426116838487973,0.6349558095046656,0.38997745513916016,43.417442 +920,Multiclass classification,Naive Bayes,ImageSegments,0.6485310119695321,0.6485310119695321,0.6384515982514894,0.38997745513916016,47.360213 +966,Multiclass classification,Naive Bayes,ImageSegments,0.6507772020725389,0.6507772020725389,0.6399118827528387,0.38997745513916016,51.459671 +1012,Multiclass classification,Naive Bayes,ImageSegments,0.6508407517309595,0.6508407517309595,0.6387857120889422,0.38997745513916016,55.677121 +1058,Multiclass classification,Naive Bayes,ImageSegments,0.6537369914853358,0.6537369914853358,0.6398811322847953,0.38997745513916016,60.129657 +1104,Multiclass classification,Naive Bayes,ImageSegments,0.658204895738894,0.658204895738894,0.6463297068165035,0.38997745513916016,64.716333 +1150,Multiclass classification,Naive Bayes,ImageSegments,0.6640557006092254,0.6640557006092254,0.6508930463144657,0.39000415802001953,69.425449 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Bayes,ImageSegments,0.698844323589395,0.698844323589395,0.6985606658027719,0.38997745513916016,107.09626300000002 +1518,Multiclass classification,Naive Bayes,ImageSegments,0.7027027027027027,0.7027027027027027,0.7017787722939461,0.39000415802001953,113.17857300000003 +1564,Multiclass classification,Naive Bayes,ImageSegments,0.7056941778630839,0.7056941778630839,0.7062915374924865,0.38997745513916016,119.38367200000003 +1610,Multiclass classification,Naive Bayes,ImageSegments,0.7078931013051585,0.7078931013051585,0.7081385387673028,0.38997745513916016,125.72760100000004 +1656,Multiclass classification,Naive Bayes,ImageSegments,0.7093655589123867,0.7093655589123867,0.7109488618373111,0.3899507522583008,132.27559300000004 +1702,Multiclass classification,Naive Bayes,ImageSegments,0.7101704879482658,0.7101704879482658,0.7132092257742534,0.38997745513916016,138.94755600000005 +1748,Multiclass classification,Naive Bayes,ImageSegments,0.7143674871207785,0.7143674871207784,0.7178399485500211,0.3899507522583008,145.68584300000003 +1794,Multiclass classification,Naive Bayes,ImageSegments,0.7172336865588399,0.7172336865588399,0.7191260584555579,0.38997745513916016,152.67811600000005 +1840,Multiclass classification,Naive Bayes,ImageSegments,0.7199564980967917,0.7199564980967917,0.7217017555070446,0.39000415802001953,159.82058900000004 +1886,Multiclass classification,Naive Bayes,ImageSegments,0.7204244031830239,0.7204244031830238,0.7234495525792994,0.39000415802001953,167.13449700000004 +1932,Multiclass classification,Naive Bayes,ImageSegments,0.7219057483169342,0.7219057483169342,0.7238483512148008,0.39000415802001953,174.57489300000003 +1978,Multiclass classification,Naive Bayes,ImageSegments,0.723823975720789,0.723823975720789,0.7251399238639739,0.39000415802001953,182.21825900000005 +2024,Multiclass classification,Naive Bayes,ImageSegments,0.726643598615917,0.726643598615917,0.7268553573885639,0.39000415802001953,189.97396200000006 +2070,Multiclass classification,Naive Bayes,ImageSegments,0.7269212179797003,0.7269212179797003,0.7276782991451582,0.39000415802001953,197.92708900000005 +2116,Multiclass classification,Naive Bayes,ImageSegments,0.7286052009456265,0.7286052009456266,0.7283656039279267,0.39000415802001953,206.04766600000005 +2162,Multiclass classification,Naive Bayes,ImageSegments,0.7306802406293382,0.7306802406293383,0.7303992643507475,0.39000415802001953,214.36632800000004 +2208,Multiclass classification,Naive Bayes,ImageSegments,0.733574988672406,0.733574988672406,0.7322842940126589,0.39000415802001953,222.77231300000003 +2254,Multiclass classification,Naive Bayes,ImageSegments,0.7314691522414558,0.7314691522414558,0.7300322879925133,0.39000415802001953,231.40748800000003 +2300,Multiclass classification,Naive Bayes,ImageSegments,0.7316224445411048,0.7316224445411048,0.7300416811383057,0.39000415802001953,240.14309800000004 +2310,Multiclass classification,Naive Bayes,ImageSegments,0.7319185794716327,0.7319185794716329,0.7304188192194185,0.39000415802001953,248.95897400000004 +1056,Multiclass classification,Naive Bayes,Insects,0.623696682464455,0.623696682464455,0.5870724729616662,0.6116933822631836,4.116407 +2112,Multiclass classification,Naive Bayes,Insects,0.6148744670772146,0.6148744670772146,0.5800776869595596,0.6116933822631836,12.008893 +3168,Multiclass classification,Naive Bayes,Insects,0.6065677297126618,0.6065677297126618,0.5714781230184183,0.6116933822631836,23.636521000000002 +4224,Multiclass classification,Naive Bayes,Insects,0.6043097324177126,0.6043097324177126,0.5697541737710122,0.6116933822631836,38.735534 +5280,Multiclass classification,Naive Bayes,Insects,0.6088274294373934,0.6088274294373934,0.5727560614138387,0.6116933822631836,57.253764000000004 +6336,Multiclass classification,Naive Bayes,Insects,0.6023677979479084,0.6023677979479084,0.5679597008529512,0.6116933822631836,79.038555 +7392,Multiclass classification,Naive Bayes,Insects,0.5995129211202814,0.5995129211202814,0.5652603100832261,0.6116933822631836,104.109779 +8448,Multiclass classification,Naive Bayes,Insects,0.6019888717888008,0.6019888717888008,0.5673514925692325,0.6116933822631836,132.296427 +9504,Multiclass classification,Naive Bayes,Insects,0.5993896664211301,0.5993896664211301,0.5644951651039589,0.6116933822631836,163.68164199999998 +10560,Multiclass classification,Naive Bayes,Insects,0.5994885879344635,0.5994885879344635,0.564565538599863,0.6116933822631836,198.25211399999998 +11616,Multiclass classification,Naive Bayes,Insects,0.5972449418854929,0.5972449418854929,0.5631227877868952,0.6116933822631836,235.999104 +12672,Multiclass classification,Naive Bayes,Insects,0.6001894088864336,0.6001894088864336,0.5684733590606373,0.6116933822631836,276.973484 +13728,Multiclass classification,Naive Bayes,Insects,0.6120783856632913,0.6120783856632913,0.5935173038317552,0.6116933822631836,321.087465 +14784,Multiclass classification,Naive Bayes,Insects,0.6024487587093282,0.6024487587093282,0.5841270876002982,0.6116933822631836,368.414891 +15840,Multiclass classification,Naive Bayes,Insects,0.5676494728202538,0.5676494728202538,0.5507155080701159,0.6116933822631836,418.92674800000003 +16896,Multiclass classification,Naive Bayes,Insects,0.5418762947617638,0.5418762947617638,0.5256197352354142,0.6116933822631836,472.67283100000003 +17952,Multiclass classification,Naive Bayes,Insects,0.5232020500250683,0.5232020500250683,0.5066898143269706,0.6116933822631836,529.5973250000001 +19008,Multiclass classification,Naive Bayes,Insects,0.5118640500868101,0.5118640500868101,0.4926543583964285,0.6116933822631836,589.87103 +20064,Multiclass classification,Naive Bayes,Insects,0.5103922643672432,0.5103922643672432,0.4900586962359796,0.6116933822631836,653.257514 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Bayes,Insects,0.5301402294663996,0.5301402294663996,0.5051550433324518,0.6116933822631836,1183.302333 +28512,Multiclass classification,Naive Bayes,Insects,0.5277261407877661,0.5277261407877661,0.5036945145235058,0.6116933822631836,1271.323869 +29568,Multiclass classification,Naive Bayes,Insects,0.5204450908107011,0.5204450908107011,0.4989008712312768,0.6116933822631836,1362.446785 +30624,Multiclass classification,Naive Bayes,Insects,0.5147111648107632,0.5147111648107632,0.49582684007363204,0.6116933822631836,1456.7074690000002 +31680,Multiclass classification,Naive Bayes,Insects,0.5105590454244137,0.5105590454244137,0.4941101813344875,0.6116933822631836,1553.9918670000002 +32736,Multiclass classification,Naive Bayes,Insects,0.5075607148312204,0.5075607148312204,0.4931947798921405,0.6116933822631836,1654.3550870000001 +33792,Multiclass classification,Naive Bayes,Insects,0.5044538486579266,0.5044538486579266,0.4905626123916189,0.6116933822631836,1757.6376 +34848,Multiclass classification,Naive Bayes,Insects,0.5020231296811777,0.5020231296811777,0.48787984248812405,0.6116933822631836,1863.925375 +35904,Multiclass classification,Naive Bayes,Insects,0.49987466228448874,0.49987466228448874,0.48534350611524757,0.6116933822631836,1973.177917 +36960,Multiclass classification,Naive Bayes,Insects,0.49679374441949187,0.49679374441949187,0.4819418474093529,0.6116933822631836,2085.445724 +38016,Multiclass classification,Naive Bayes,Insects,0.49559384453505195,0.49559384453505195,0.4801892436835747,0.6116933822631836,2200.821931 +39072,Multiclass classification,Naive Bayes,Insects,0.49402370044278365,0.49402370044278365,0.47838078382052607,0.6116933822631836,2319.178703 +40128,Multiclass classification,Naive Bayes,Insects,0.493508111745209,0.493508111745209,0.47852138016706713,0.6116933822631836,2440.443075 +41184,Multiclass classification,Naive Bayes,Insects,0.49369885632421145,0.49369885632421145,0.47942014994272747,0.6116933822631836,2564.583087 +42240,Multiclass classification,Naive Bayes,Insects,0.4938800634484718,0.4938800634484718,0.48023774975329353,0.6116933822631836,2691.651665 +43296,Multiclass classification,Naive Bayes,Insects,0.49437579397159026,0.49437579397159026,0.4812132921167227,0.6116933822631836,2821.6013359999997 +44352,Multiclass classification,Naive Bayes,Insects,0.49403621113390905,0.49403621113390905,0.48123889196184183,0.6116933822631836,2954.3777659999996 +45408,Multiclass classification,Naive Bayes,Insects,0.4944832294580131,0.4944832294580131,0.4818441874360225,0.6116933822631836,3089.8310679999995 +46464,Multiclass classification,Naive Bayes,Insects,0.4945225232981082,0.4945225232981082,0.4820791268335544,0.6116933822631836,3227.9665449999993 +47520,Multiclass classification,Naive Bayes,Insects,0.4956333256171216,0.4956333256171216,0.48331686360214987,0.6116933822631836,3368.688097999999 +48576,Multiclass classification,Naive Bayes,Insects,0.4970869788986104,0.4970869788986104,0.48467037716343636,0.6116933822631836,3511.887438999999 +49632,Multiclass classification,Naive Bayes,Insects,0.4987608551107171,0.4987608551107171,0.4862426724473749,0.6116933822631836,3657.6494079999993 +50688,Multiclass classification,Naive Bayes,Insects,0.5009568528419516,0.5009568528419516,0.4881725476999718,0.6116933822631836,3806.0112589999994 +51744,Multiclass classification,Naive Bayes,Insects,0.5034497419940862,0.5034497419940862,0.4903712806540024,0.6116933822631836,3956.8935159999996 +52800,Multiclass classification,Naive Bayes,Insects,0.5068467205818292,0.5068467205818292,0.4930025316136313,0.6116933822631836,4110.278735 +52848,Multiclass classification,Naive Bayes,Insects,0.5068972694760346,0.5068972694760346,0.49301906278314944,0.6116933822631836,4263.766907 +408,Multiclass classification,Naive Bayes,Keystroke,0.9852579852579852,0.9852579852579852,0.6962686567164179,0.19356441497802734,0.780775 +816,Multiclass classification,Naive Bayes,Keystroke,0.947239263803681,0.947239263803681,0.7418606503288051,0.28890228271484375,2.463269 +1224,Multiclass classification,Naive Bayes,Keystroke,0.884709730171709,0.884709730171709,0.8705899666065842,0.38424015045166016,5.15507 +1632,Multiclass classification,Naive Bayes,Keystroke,0.8933169834457388,0.8933169834457388,0.8791291775937072,0.47957801818847656,8.960951 +2040,Multiclass classification,Naive Bayes,Keystroke,0.8921039725355566,0.8921039725355566,0.8831785360852743,0.575160026550293,14.051639 +2448,Multiclass classification,Naive Bayes,Keystroke,0.851655087862689,0.851655087862689,0.8581984289516641,0.6704978942871094,20.582881999999998 +2856,Multiclass classification,Naive Bayes,Keystroke,0.8598949211908932,0.8598949211908932,0.8469962214365346,0.7658357620239258,28.649143 +3264,Multiclass classification,Naive Bayes,Keystroke,0.8513637756665645,0.8513637756665645,0.8281280134770846,0.8611736297607422,38.532046 +3672,Multiclass classification,Naive Bayes,Keystroke,0.8422773086352493,0.8422773086352493,0.8409307955747314,0.9565114974975586,50.273206 +4080,Multiclass classification,Naive Bayes,Keystroke,0.8367246874233881,0.8367246874233881,0.8249418657104467,1.0523834228515625,63.882498 +4488,Multiclass classification,Naive Bayes,Keystroke,0.8203699576554491,0.8203699576554491,0.8300896799820437,1.147721290588379,79.531469 +4896,Multiclass classification,Naive Bayes,Keystroke,0.8192032686414709,0.8192032686414709,0.8269731591910484,1.2430591583251953,97.310117 +5304,Multiclass classification,Naive Bayes,Keystroke,0.8172732415613804,0.8172732415613804,0.8027823390848743,1.3383970260620117,117.35519000000001 +5712,Multiclass classification,Naive Bayes,Keystroke,0.7961828051129399,0.7961828051129399,0.8002006091139847,1.4337348937988281,139.817583 +6120,Multiclass classification,Naive Bayes,Keystroke,0.793920575257395,0.793920575257395,0.7746960355921345,1.5290727615356445,164.727582 +6528,Multiclass classification,Naive Bayes,Keystroke,0.7688064960931515,0.7688064960931515,0.7622487598340326,1.624410629272461,192.15170700000002 +6936,Multiclass classification,Naive Bayes,Keystroke,0.7568853640951694,0.7568853640951694,0.757813781660983,1.7197484970092773,222.24358600000002 +7344,Multiclass classification,Naive Bayes,Keystroke,0.7669889690862045,0.7669889690862046,0.7643943615019536,1.8150863647460938,255.230678 +7752,Multiclass classification,Naive Bayes,Keystroke,0.7676428847890595,0.7676428847890595,0.7655695901071293,1.9104242324829102,291.218411 +8160,Multiclass classification,Naive Bayes,Keystroke,0.7714180659394534,0.7714180659394533,0.7672011803374248,2.0057621002197266,330.398823 +8568,Multiclass classification,Naive Bayes,Keystroke,0.7702813120112058,0.7702813120112058,0.7699263138193525,2.1021223068237305,372.82766399999997 +8976,Multiclass classification,Naive Bayes,Keystroke,0.7680222841225627,0.7680222841225627,0.7682287234686136,2.197460174560547,418.63014999999996 +9384,Multiclass classification,Naive Bayes,Keystroke,0.7659597143770649,0.7659597143770649,0.7643546547243014,2.2927980422973633,468.01111099999997 +9792,Multiclass classification,Naive Bayes,Keystroke,0.7586559084873864,0.7586559084873864,0.7552148692020618,2.3881359100341797,521.0847249999999 +10200,Multiclass classification,Naive Bayes,Keystroke,0.7505637807628199,0.7505637807628199,0.7430512224080145,2.483473777770996,577.917349 +10608,Multiclass classification,Naive Bayes,Keystroke,0.7290468558499105,0.7290468558499106,0.715756093271779,2.5788116455078125,638.790947 +11016,Multiclass classification,Naive Bayes,Keystroke,0.7217430776214253,0.7217430776214253,0.7173640789896896,2.674149513244629,703.666983 +11424,Multiclass classification,Naive Bayes,Keystroke,0.7151361288628206,0.7151361288628206,0.7011862635194489,2.7694873809814453,772.6431349999999 +11832,Multiclass classification,Naive Bayes,Keystroke,0.705603921900093,0.705603921900093,0.6976881379682607,2.8648252487182617,845.8350979999999 +12240,Multiclass classification,Naive Bayes,Keystroke,0.7094533867146009,0.7094533867146009,0.7058405389403433,2.960163116455078,923.50335 +12648,Multiclass classification,Naive Bayes,Keystroke,0.7053846762077963,0.7053846762077963,0.6965736948063982,3.0555009841918945,1005.7536769999999 +13056,Multiclass classification,Naive Bayes,Keystroke,0.6927613941018766,0.6927613941018766,0.6842255816736498,3.150838851928711,1092.707972 +13464,Multiclass classification,Naive Bayes,Keystroke,0.6890737577063062,0.6890737577063062,0.6845669389392289,3.2461767196655273,1184.483965 +13872,Multiclass classification,Naive Bayes,Keystroke,0.6873332852714296,0.6873332852714296,0.68390545518227,3.3415145874023438,1281.216395 +14280,Multiclass classification,Naive Bayes,Keystroke,0.682960991666083,0.682960991666083,0.6781566371919944,3.43685245513916,1383.0399089999999 +14688,Multiclass classification,Naive Bayes,Keystroke,0.686185061619119,0.686185061619119,0.6843713776162116,3.5321903228759766,1489.909884 +15096,Multiclass classification,Naive Bayes,Keystroke,0.6928784365684001,0.6928784365684001,0.6911392400672977,3.627528190612793,1601.996709 +15504,Multiclass classification,Naive Bayes,Keystroke,0.6913500612784622,0.6913500612784622,0.687359772989117,3.7228660583496094,1719.445985 +15912,Multiclass classification,Naive Bayes,Keystroke,0.6819810194205267,0.6819810194205267,0.674915944935936,3.818203926086426,1842.197498 +16320,Multiclass classification,Naive Bayes,Keystroke,0.6726515105092223,0.6726515105092223,0.6670192172011686,3.913541793823242,1970.358299 +16728,Multiclass classification,Naive Bayes,Keystroke,0.6695163508100676,0.6695163508100676,0.6664051037977977,4.008879661560059,2103.939399 +17136,Multiclass classification,Naive Bayes,Keystroke,0.6650131310183834,0.6650131310183834,0.6608988619616458,4.1063079833984375,2242.845385 +17544,Multiclass classification,Naive Bayes,Keystroke,0.6568431853160804,0.6568431853160804,0.6531382897719189,4.201645851135254,2386.822189 +17952,Multiclass classification,Naive Bayes,Keystroke,0.6556180714166342,0.6556180714166342,0.6538448358590968,4.29698371887207,2536.044428 +18360,Multiclass classification,Naive Bayes,Keystroke,0.6614194672912468,0.6614194672912468,0.6603186829199909,4.392321586608887,2690.5476860000003 +18768,Multiclass classification,Naive Bayes,Keystroke,0.6669686151222891,0.6669686151222891,0.6662293616554571,4.487659454345703,2850.3140810000004 +19176,Multiclass classification,Naive Bayes,Keystroke,0.6579921773142112,0.6579921773142112,0.6554177118629491,4.5829973220825195,3015.4823350000006 +19584,Multiclass classification,Naive Bayes,Keystroke,0.6622580809886126,0.6622580809886126,0.6609360990360078,4.678335189819336,3186.2814100000005 +19992,Multiclass classification,Naive Bayes,Keystroke,0.6562453103896754,0.6562453103896754,0.6545704957554572,4.773673057556152,3362.6238980000007 +20400,Multiclass classification,Naive Bayes,Keystroke,0.6525319868621011,0.6525319868621011,0.6515767870317885,4.869010925292969,3544.6906370000006 +46,Multiclass classification,Hoeffding Tree,ImageSegments,0.35555555555555557,0.35555555555555557,0.25379424497071557,0.4170856475830078,0.290301 +92,Multiclass classification,Hoeffding Tree,ImageSegments,0.4945054945054945,0.4945054945054945,0.5043329927491418,0.4170818328857422,0.82046 +138,Multiclass classification,Hoeffding Tree,ImageSegments,0.5328467153284672,0.5328467153284672,0.5564033878668025,0.4171772003173828,1.6754229999999999 +184,Multiclass classification,Hoeffding Tree,ImageSegments,0.6010928961748634,0.6010928961748634,0.622766496539645,0.4171772003173828,2.801183 +230,Multiclass classification,Hoeffding Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6539827168809461,0.41720008850097656,4.271522 +276,Multiclass classification,Hoeffding Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.6671561759164943,0.4172496795654297,5.954744 +322,Multiclass classification,Hoeffding Tree,ImageSegments,0.67601246105919,0.67601246105919,0.6756614325426025,0.4172496795654297,7.864603 +368,Multiclass classification,Hoeffding Tree,ImageSegments,0.7029972752043597,0.7029972752043597,0.6993447851636564,0.4172229766845703,10.008665 +414,Multiclass classification,Hoeffding Tree,ImageSegments,0.7142857142857143,0.7142857142857143,0.7108606838045498,0.4171428680419922,12.399438 +460,Multiclass classification,Hoeffding Tree,ImageSegments,0.7145969498910676,0.7145969498910676,0.7090365931960759,0.4172191619873047,15.01004 +506,Multiclass classification,Hoeffding Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7126631585949761,0.4172191619873047,17.873655 +552,Multiclass classification,Hoeffding Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7157730164623107,0.4171123504638672,20.946970999999998 +598,Multiclass classification,Hoeffding Tree,ImageSegments,0.7286432160804021,0.7286432160804021,0.7216745323124732,0.41713523864746094,24.255884 +644,Multiclass classification,Hoeffding Tree,ImageSegments,0.7278382581648523,0.7278382581648523,0.7229105183087501,0.41710853576660156,27.838411999999998 +690,Multiclass classification,Hoeffding Tree,ImageSegments,0.7314949201741655,0.7314949201741654,0.7263583447448078,0.41710853576660156,31.647636 +736,Multiclass classification,Hoeffding Tree,ImageSegments,0.7333333333333333,0.7333333333333333,0.729431071218305,0.41713523864746094,35.743157 +782,Multiclass classification,Hoeffding Tree,ImageSegments,0.7387964148527529,0.7387964148527529,0.7349287389986899,0.41713523864746094,40.063089999999995 +828,Multiclass classification,Hoeffding Tree,ImageSegments,0.7376058041112454,0.7376058041112454,0.7356226390109741,0.41713523864746094,44.599844 +874,Multiclass classification,Hoeffding Tree,ImageSegments,0.7445589919816724,0.7445589919816724,0.7409366047432264,0.41713523864746094,49.398728999999996 +920,Multiclass classification,Hoeffding Tree,ImageSegments,0.7453754080522307,0.7453754080522307,0.7408438328939173,0.41710853576660156,54.404894 +966,Multiclass classification,Hoeffding Tree,ImageSegments,0.7471502590673575,0.7471502590673575,0.7416651838589269,0.41710853576660156,59.665949 +1012,Multiclass classification,Hoeffding Tree,ImageSegments,0.7467853610286844,0.7467853610286844,0.7416356251822,0.41710853576660156,65.211169 +1058,Multiclass classification,Hoeffding Tree,ImageSegments,0.7492904446546831,0.7492904446546831,0.7430778844390783,0.41710853576660156,70.961377 +1104,Multiclass classification,Hoeffding Tree,ImageSegments,0.7515865820489573,0.7515865820489573,0.7451256886686588,0.4171581268310547,76.969446 +1150,Multiclass classification,Hoeffding Tree,ImageSegments,0.7536988685813751,0.7536988685813751,0.7468312166689606,0.4171581268310547,83.201851 +1196,Multiclass classification,Hoeffding Tree,ImageSegments,0.7564853556485356,0.7564853556485356,0.7503479321738041,0.4171581268310547,89.604352 +1242,Multiclass classification,Hoeffding Tree,ImageSegments,0.7566478646253022,0.7566478646253022,0.7509717522131719,0.4171581268310547,96.30702600000001 +1288,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614607614607615,0.7614607614607615,0.7547643483779538,0.4171581268310547,103.26246200000001 +1334,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614403600900225,0.7614403600900225,0.7551060921605869,0.4171581268310547,110.41488900000002 +1380,Multiclass classification,Hoeffding Tree,ImageSegments,0.7621464829586657,0.7621464829586658,0.7562209880685912,0.4171581268310547,117.79988600000001 +1426,Multiclass classification,Hoeffding Tree,ImageSegments,0.7642105263157895,0.7642105263157895,0.7575332274919562,0.4171581268310547,125.46176800000002 +1472,Multiclass classification,Hoeffding Tree,ImageSegments,0.7688647178789939,0.768864717878994,0.760438686053582,0.4171581268310547,133.360363 +1518,Multiclass classification,Hoeffding Tree,ImageSegments,0.7705998681608438,0.7705998681608438,0.7612069012840872,0.4171581268310547,141.48549400000002 +1564,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709532949456174,0.7709532949456174,0.7622701654854867,0.4171581268310547,149.83563600000002 +1610,Multiclass classification,Hoeffding Tree,ImageSegments,0.7712865133623369,0.771286513362337,0.7617247271717752,0.41718101501464844,158.439217 +1656,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709969788519637,0.7709969788519637,0.7615629120572474,0.41718101501464844,167.22864700000002 +1702,Multiclass classification,Hoeffding Tree,ImageSegments,0.770135214579659,0.770135214579659,0.7627316365695143,0.41718101501464844,176.30742800000002 +1748,Multiclass classification,Hoeffding Tree,ImageSegments,0.7727532913566113,0.7727532913566113,0.7649467707214076,0.41718101501464844,185.609237 +1794,Multiclass classification,Hoeffding Tree,ImageSegments,0.7741215839375348,0.7741215839375348,0.7649332326562149,0.41715431213378906,195.10730800000002 +1840,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754214246873301,0.7754214246873301,0.7664700790631908,0.41715431213378906,204.88888000000003 +1886,Multiclass classification,Hoeffding Tree,ImageSegments,0.7740053050397878,0.7740053050397878,0.7655121135276625,0.41715431213378906,214.87796100000003 +1932,Multiclass classification,Hoeffding Tree,ImageSegments,0.7742102537545313,0.7742102537545313,0.7648034036287765,0.41715431213378906,225.10774000000004 +1978,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754172989377845,0.7754172989377845,0.7656013068970459,0.41715431213378906,235.56491900000003 +2024,Multiclass classification,Hoeffding Tree,ImageSegments,0.7770637666831438,0.7770637666831438,0.7660878232247856,0.41715431213378906,246.31694000000005 +2070,Multiclass classification,Hoeffding Tree,ImageSegments,0.7762203963267279,0.7762203963267279,0.7654829214385931,0.41715431213378906,257.28426500000006 +2116,Multiclass classification,Hoeffding Tree,ImageSegments,0.7768321513002364,0.7768321513002364,0.7653071619305024,0.41715431213378906,268.5154150000001 +2162,Multiclass classification,Hoeffding Tree,ImageSegments,0.7778806108283203,0.7778806108283203,0.7659351904174982,0.41715431213378906,279.94414300000005 +2208,Multiclass classification,Hoeffding Tree,ImageSegments,0.7797915722700498,0.7797915722700498,0.7668192864082087,0.41715431213378906,291.65328600000004 +2254,Multiclass classification,Hoeffding Tree,ImageSegments,0.7767421216156236,0.7767421216156236,0.7637794374955548,0.41715431213378906,303.618395 +2300,Multiclass classification,Hoeffding Tree,ImageSegments,0.7759895606785558,0.7759895606785558,0.763026662835187,0.41715431213378906,315.80512400000003 +2310,Multiclass classification,Hoeffding Tree,ImageSegments,0.776093546990039,0.776093546990039,0.7631372452021826,0.41715431213378906,328.06738900000005 +1056,Multiclass classification,Hoeffding Tree,Insects,0.6218009478672986,0.6218009478672986,0.5852663107194211,0.6579360961914062,7.68277 +2112,Multiclass classification,Hoeffding Tree,Insects,0.6153481762198011,0.6153481762198011,0.5806436317780949,0.6579360961914062,22.565114 +3168,Multiclass classification,Hoeffding Tree,Insects,0.6071992421850332,0.6071992421850332,0.572248584718361,0.6579360961914062,43.997682 +4224,Multiclass classification,Hoeffding Tree,Insects,0.6043097324177126,0.6043097324177126,0.5697573109597247,0.6579360961914062,71.858443 +5280,Multiclass classification,Hoeffding Tree,Insects,0.6088274294373934,0.6088274294373934,0.5727379077413696,0.6579360961914062,105.92483999999999 +6336,Multiclass classification,Hoeffding Tree,Insects,0.6026835043409629,0.6026835043409629,0.568251333238805,0.6579360961914062,146.287253 +7392,Multiclass classification,Hoeffding Tree,Insects,0.600189419564335,0.600189419564335,0.5659762112716077,0.6579360961914062,192.863981 +8448,Multiclass classification,Hoeffding Tree,Insects,0.60258079791642,0.60258079791642,0.5679781484640408,0.6579360961914062,245.806734 +9504,Multiclass classification,Hoeffding Tree,Insects,0.5998105861306956,0.5998105861306956,0.5649597336877693,0.6579360961914062,305.14044 +10560,Multiclass classification,Hoeffding Tree,Insects,0.5998674116867128,0.5998674116867128,0.5650173260529011,0.6579360961914062,370.68089100000003 +11616,Multiclass classification,Hoeffding Tree,Insects,0.5974171330176495,0.5974171330176495,0.5633067089377387,0.6579360961914062,442.33844300000004 +12672,Multiclass classification,Hoeffding Tree,Insects,0.6001894088864336,0.6001894088864336,0.5684760329567131,0.6579360961914062,520.121563 +13728,Multiclass classification,Hoeffding Tree,Insects,0.6120783856632913,0.6120783856632913,0.5935956771555828,0.6579360961914062,604.039429 +14784,Multiclass classification,Hoeffding Tree,Insects,0.6024487587093282,0.6024487587093282,0.5842148300149193,0.6579360961914062,694.113241 +15840,Multiclass classification,Hoeffding Tree,Insects,0.5677757434181451,0.5677757434181451,0.5509250187877572,0.6579360961914062,790.19156 +16896,Multiclass classification,Hoeffding Tree,Insects,0.5419354838709678,0.5419354838709678,0.5257359157219258,0.6579360961914062,892.361186 +17952,Multiclass classification,Hoeffding Tree,Insects,0.5233691716338923,0.5233691716338923,0.5068581838352059,0.6579360961914062,1000.4717479999999 +19008,Multiclass classification,Hoeffding Tree,Insects,0.5121271110643447,0.5121271110643447,0.49292899065094153,0.6579360961914062,1114.494528 +20064,Multiclass classification,Hoeffding Tree,Insects,0.5120370831879579,0.5120370831879579,0.4920970323041603,1.3099584579467773,1234.2056499999999 +21120,Multiclass classification,Hoeffding Tree,Insects,0.5173066906577016,0.5173066906577016,0.4973447169836249,1.310713768005371,1358.925583 +22176,Multiclass classification,Hoeffding Tree,Insects,0.5229312288613304,0.5229312288613304,0.5026343687424488,1.310713768005371,1488.370808 +23232,Multiclass classification,Hoeffding Tree,Insects,0.5301536739701261,0.5301536739701261,0.5095132087733324,1.310713768005371,1622.41448 +24288,Multiclass classification,Hoeffding Tree,Insects,0.5351422571746202,0.5351422571746202,0.5135975374357353,1.310713768005371,1760.8970379999998 +25344,Multiclass classification,Hoeffding Tree,Insects,0.5403069881229531,0.5403069881229531,0.5180803411538233,1.310713768005371,1903.5911449999999 +26400,Multiclass classification,Hoeffding Tree,Insects,0.5441493995984696,0.5441493995984696,0.5209012984387186,1.310713768005371,2050.469487 +27456,Multiclass classification,Hoeffding Tree,Insects,0.5475869604807867,0.5475869604807867,0.5230407124785976,1.310713768005371,2201.55681 +28512,Multiclass classification,Hoeffding Tree,Insects,0.5442460804601733,0.5442460804601733,0.5199893698637053,1.310713768005371,2356.711105 +29568,Multiclass classification,Hoeffding Tree,Insects,0.5439848479724017,0.5439848479724017,0.5225387960194383,1.310713768005371,2516.62263 +30624,Multiclass classification,Hoeffding Tree,Insects,0.5449825294713124,0.5449825294713124,0.5260472440529832,1.310713768005371,2681.5460789999997 +31680,Multiclass classification,Hoeffding Tree,Insects,0.5469238296663405,0.5469238296663405,0.5300194392617626,1.310713768005371,2851.622305 +32736,Multiclass classification,Hoeffding Tree,Insects,0.5492286543455017,0.5492286543455017,0.5337692045397758,1.310713768005371,3026.797274 +33792,Multiclass classification,Hoeffding Tree,Insects,0.5448196265277737,0.5448196265277737,0.5298516474077153,1.310713768005371,3207.119826 +34848,Multiclass classification,Hoeffding Tree,Insects,0.539357763939507,0.539357763939507,0.5246413689313029,1.310713768005371,3392.4010240000002 +35904,Multiclass classification,Hoeffding Tree,Insects,0.5352756037099964,0.5352756037099964,0.5204658240271913,1.310713768005371,3582.6817720000004 +36960,Multiclass classification,Hoeffding Tree,Insects,0.5307232338537298,0.5307232338537298,0.5158458403074864,1.310713768005371,3778.3092850000003 +38016,Multiclass classification,Hoeffding Tree,Insects,0.5287912666052874,0.5287912666052874,0.5138605376143625,1.8479537963867188,3978.8224330000003 +39072,Multiclass classification,Hoeffding Tree,Insects,0.5245322617798367,0.5245322617798367,0.5100329616180462,1.9625730514526367,4184.1075280000005 +40128,Multiclass classification,Hoeffding Tree,Insects,0.5244847608841927,0.5244847608841927,0.5114466799524962,1.9625730514526367,4393.646320000001 +41184,Multiclass classification,Hoeffding Tree,Insects,0.5269650098341548,0.5269650098341548,0.5145630920489553,1.9625730514526367,4606.675677000001 +42240,Multiclass classification,Hoeffding Tree,Insects,0.5290608205686688,0.5290608205686688,0.5171452370879218,1.9625730514526367,4823.052294000001 +43296,Multiclass classification,Hoeffding Tree,Insects,0.5316318281556762,0.5316318281556762,0.5200714653059241,1.9625730514526367,5042.794587000001 +44352,Multiclass classification,Hoeffding Tree,Insects,0.5332912448422809,0.5332912448422809,0.521951703681177,1.9633283615112305,5266.308108000001 +45408,Multiclass classification,Hoeffding Tree,Insects,0.5350937080185875,0.5350937080185875,0.5236272112757866,1.9633283615112305,5493.659660000001 +46464,Multiclass classification,Hoeffding Tree,Insects,0.5374168693368917,0.5374168693368917,0.5257977177437826,1.9633283615112305,5724.562244000002 +47520,Multiclass classification,Hoeffding Tree,Insects,0.5359540394368568,0.5359540394368568,0.5247049329892776,1.9633283615112305,5959.275286000002 +48576,Multiclass classification,Hoeffding Tree,Insects,0.5333196088522902,0.5333196088522902,0.5224640186909637,1.9633283615112305,6197.987866000002 +49632,Multiclass classification,Hoeffding Tree,Insects,0.5314017448771937,0.5314017448771937,0.5209076603734537,1.9633283615112305,6440.583835000002 +50688,Multiclass classification,Hoeffding Tree,Insects,0.5322271982954209,0.5322271982954209,0.5219695808096345,2.081958770751953,6687.224874000002 +51744,Multiclass classification,Hoeffding Tree,Insects,0.5377345727924551,0.5377345727924551,0.5274876060436412,2.3156700134277344,6937.746409000002 +52800,Multiclass classification,Hoeffding Tree,Insects,0.5370366863008769,0.5370366863008769,0.5270872650003847,2.519227981567383,7191.466386000002 +52848,Multiclass classification,Hoeffding Tree,Insects,0.5373058073305959,0.5373058073305959,0.5273644947479657,2.519227981567383,7445.3631460000015 +408,Multiclass classification,Hoeffding Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.2240447998046875,0.863228 +816,Multiclass classification,Hoeffding Tree,Keystroke,0.9423312883435583,0.9423312883435583,0.7661667470992702,0.3196687698364258,3.107641 +1224,Multiclass classification,Hoeffding Tree,Keystroke,0.8830744071954211,0.883074407195421,0.8761191747044462,0.41529273986816406,7.048775 +1632,Multiclass classification,Hoeffding Tree,Keystroke,0.8902513795217658,0.8902513795217658,0.8767853151263398,0.5114049911499023,13.087731999999999 +2040,Multiclass classification,Hoeffding Tree,Keystroke,0.8891613536047082,0.8891613536047082,0.8807858055314012,0.6185035705566406,21.551524999999998 +2448,Multiclass classification,Hoeffding Tree,Keystroke,0.848385778504291,0.848385778504291,0.8522513926518692,0.7141275405883789,32.816222999999994 +2856,Multiclass classification,Hoeffding Tree,Keystroke,0.8563922942206655,0.8563922942206655,0.8440193478447516,0.8097515106201172,47.080318999999996 +3264,Multiclass classification,Hoeffding Tree,Keystroke,0.8482991112473184,0.8482991112473184,0.8269786301577753,0.9053754806518555,64.636989 +3672,Multiclass classification,Hoeffding Tree,Keystroke,0.8392808499046581,0.8392808499046581,0.8374924160046072,1.0009994506835938,85.706576 +4080,Multiclass classification,Hoeffding Tree,Keystroke,0.8323118411375338,0.8323118411375338,0.8182261307945194,1.1217241287231445,110.70978199999999 +4488,Multiclass classification,Hoeffding Tree,Keystroke,0.8159126365054602,0.8159126365054602,0.8260965842218733,1.2173480987548828,139.812165 +4896,Multiclass classification,Hoeffding Tree,Keystroke,0.8149131767109296,0.8149131767109296,0.8221314665977922,1.312972068786621,173.369773 +5304,Multiclass classification,Hoeffding Tree,Keystroke,0.8125589289081652,0.8125589289081652,0.797613058026624,1.4085960388183594,211.780209 +5712,Multiclass classification,Hoeffding Tree,Keystroke,0.7907546839432674,0.7907546839432674,0.7936708037520236,1.5042200088500977,255.27399100000002 +6120,Multiclass classification,Hoeffding Tree,Keystroke,0.7886909625755842,0.7886909625755842,0.7694478218498494,1.599843978881836,304.294734 +6528,Multiclass classification,Hoeffding Tree,Keystroke,0.7635973647924008,0.7635973647924008,0.75687960152136,1.6954679489135742,359.144129 +6936,Multiclass classification,Hoeffding Tree,Keystroke,0.75155010814708,0.7515501081470799,0.7521509466338959,1.7910919189453125,420.22114200000004 +7344,Multiclass classification,Hoeffding Tree,Keystroke,0.7611330518861501,0.7611330518861501,0.7576671162861806,1.8881807327270508,487.76956500000006 +7752,Multiclass classification,Hoeffding Tree,Keystroke,0.7617081666881693,0.7617081666881692,0.7593340838982119,1.983804702758789,562.1432000000001 +8160,Multiclass classification,Hoeffding Tree,Keystroke,0.7655349920333374,0.7655349920333374,0.7610505848438686,2.0794286727905273,643.5514560000001 +8568,Multiclass classification,Hoeffding Tree,Keystroke,0.7644449632310026,0.7644449632310025,0.7639417799779614,2.223102569580078,732.3349550000001 +8976,Multiclass classification,Hoeffding Tree,Keystroke,0.7624512534818941,0.7624512534818941,0.7625605608371232,2.3187265396118164,828.9274100000001 +9384,Multiclass classification,Hoeffding Tree,Keystroke,0.7605243525524885,0.7605243525524885,0.7588384348689571,2.4143505096435547,933.4845880000001 +9792,Multiclass classification,Hoeffding Tree,Keystroke,0.753344908589521,0.753344908589521,0.7499438215834663,2.509974479675293,1046.19484 +10200,Multiclass classification,Hoeffding Tree,Keystroke,0.7450730463770958,0.7450730463770959,0.7369660419615974,2.6055984497070312,1167.344916 +10608,Multiclass classification,Hoeffding Tree,Keystroke,0.7240501555576506,0.7240501555576506,0.7111305646829175,2.7012224197387695,1296.919782 +11016,Multiclass classification,Hoeffding Tree,Keystroke,0.7166591012256015,0.7166591012256015,0.7122511515574346,2.796846389770508,1434.7760759999999 +11424,Multiclass classification,Hoeffding Tree,Keystroke,0.710146196270682,0.710146196270682,0.6963016796632095,2.892470359802246,1580.7280859999998 +11832,Multiclass classification,Hoeffding Tree,Keystroke,0.7005324993660722,0.7005324993660722,0.6925666211338901,2.9880943298339844,1735.0271709999997 +12240,Multiclass classification,Hoeffding Tree,Keystroke,0.7043876133671052,0.7043876133671052,0.7007845610449206,3.0837182998657227,1897.6526119999996 +12648,Multiclass classification,Hoeffding Tree,Keystroke,0.7004032576895707,0.7004032576895707,0.6915775762792657,3.179342269897461,2069.0860809999995 +13056,Multiclass classification,Hoeffding Tree,Keystroke,0.6877058598238223,0.6877058598238223,0.6789768292873962,3.274966239929199,2249.3891769999996 +13464,Multiclass classification,Hoeffding Tree,Keystroke,0.6838743222164451,0.6838743222164451,0.6791243465680947,3.3705902099609375,2438.6931489999997 +13872,Multiclass classification,Hoeffding Tree,Keystroke,0.6822146925239708,0.6822146925239708,0.6786558938530485,3.466214179992676,2637.6841019999997 +14280,Multiclass classification,Hoeffding Tree,Keystroke,0.6777085230058127,0.6777085230058127,0.6725285130045525,3.561838150024414,2845.8288079999998 +14688,Multiclass classification,Hoeffding Tree,Keystroke,0.6807380676788997,0.6807380676788997,0.6786761142186741,3.6574621200561523,3062.9942149999997 +15096,Multiclass classification,Hoeffding Tree,Keystroke,0.6873799271281882,0.6873799271281882,0.6854839306484398,3.7530860900878906,3290.055422 +15504,Multiclass classification,Hoeffding Tree,Keystroke,0.6858027478552539,0.6858027478552539,0.6816808496509055,3.848710060119629,3526.69202 +15912,Multiclass classification,Hoeffding Tree,Keystroke,0.6765759537426937,0.6765759537426937,0.6694713281964946,3.944334030151367,3772.997519 +16320,Multiclass classification,Hoeffding Tree,Keystroke,0.6673815797536614,0.6673815797536614,0.6617321933140904,4.0399580001831055,4029.133223 +16728,Multiclass classification,Hoeffding Tree,Keystroke,0.6643151790518323,0.6643151790518323,0.661178029358405,4.135581970214844,4295.086238 +17136,Multiclass classification,Hoeffding Tree,Keystroke,0.6598774438284214,0.6598774438284214,0.655734247886306,4.3294572830200195,4570.827071 +17544,Multiclass classification,Hoeffding Tree,Keystroke,0.6518269395200365,0.6518269395200365,0.6481085155228206,4.425081253051758,4856.254143 +17952,Multiclass classification,Hoeffding Tree,Keystroke,0.6507158375577963,0.6507158375577963,0.6489368995854258,4.520705223083496,5151.869359 +18360,Multiclass classification,Hoeffding Tree,Keystroke,0.6566806470940683,0.6566806470940683,0.6555764711123695,4.616329193115234,5457.498716 +18768,Multiclass classification,Hoeffding Tree,Keystroke,0.662279533223211,0.662279533223211,0.6615432060687808,4.711953163146973,5772.982264 +19176,Multiclass classification,Hoeffding Tree,Keystroke,0.6534028683181226,0.6534028683181226,0.6508089832432514,4.807577133178711,6098.679956 +19584,Multiclass classification,Hoeffding Tree,Keystroke,0.6577643874789358,0.6577643874789358,0.6564201177589184,4.903201103210449,6434.678037 +19992,Multiclass classification,Hoeffding Tree,Keystroke,0.6518433294982742,0.6518433294982742,0.6501496360982542,4.9988250732421875,6781.324361 +20400,Multiclass classification,Hoeffding Tree,Keystroke,0.6482180499044071,0.6482180499044071,0.6472493759146578,5.094449043273926,7138.730487 +46,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,0.42345714569091797,0.325579 +92,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5164835164835165,0.5164835164835165,0.5335477748411618,0.42351436614990234,1.056326 +138,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5474452554744526,0.5474452554744526,0.5743273066802479,0.42363643646240234,2.202996 +184,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6120218579234973,0.6120218579234973,0.6355989308336889,0.4237203598022461,3.699294 +230,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6557923943920432,0.4237203598022461,5.564336 +276,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.66910740948952,0.4237699508666992,7.749814 +322,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.67601246105919,0.67601246105919,0.678427291711157,0.4238309860229492,10.278631 +368,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7002724795640327,0.7002724795640327,0.6988359939675117,0.42380428314208984,13.125556000000001 +414,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.711864406779661,0.711864406779661,0.7104564330601258,0.4237241744995117,16.369918000000002 +460,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7124183006535948,0.7124183006535948,0.7087721216219991,0.4238004684448242,19.921878000000003 +506,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7145025942185106,0.4238004684448242,23.844357000000002 +552,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7174926871575792,0.4236936569213867,28.111685 +598,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7269681742043551,0.7269681742043551,0.7216367248754637,0.42371654510498047,32.752989 +644,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7262830482115086,0.7262830482115085,0.7230014848259525,0.4237508773803711,37.712808 +690,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7300435413642961,0.7300435413642961,0.7265684058467008,0.4237508773803711,43.006145000000004 +736,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7319727891156462,0.7319727891156461,0.7296570819427115,0.42377758026123047,48.68780100000001 +782,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.737516005121639,0.737516005121639,0.7350906419548328,0.42377758026123047,54.691720000000004 +828,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7363966142684402,0.7363966142684402,0.7359651798179677,0.42377758026123047,60.98272 +874,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7422680412371134,0.7422680412371134,0.7398886847335938,0.42377758026123047,67.641769 +920,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7421109902067464,0.7421109902067464,0.738912026501458,0.4237508773803711,74.649906 +966,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7419689119170985,0.7419689119170985,0.7379593683174607,0.4237508773803711,81.98079 +1012,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7418397626112759,0.741839762611276,0.7380802548116379,0.4237508773803711,89.699811 +1058,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7436140018921475,0.7436140018921475,0.7390703652035102,0.4237508773803711,97.73816099999999 +1104,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7461468721668177,0.7461468721668177,0.7413714574148674,0.4238004684448242,106.141078 +1150,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7476066144473456,0.7476066144473456,0.742441565911322,0.4238004684448242,114.87573499999999 +1196,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7506276150627615,0.7506276150627615,0.7460917536510117,0.4234342575073242,123.97312099999999 +1242,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7510072522159549,0.7510072522159549,0.7470578866974922,0.4235563278198242,133.391788 +1288,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.756021756021756,0.7560217560217559,0.7510482446555896,0.4236173629760742,143.113173 +1334,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7569392348087022,0.7569392348087022,0.7522366633133313,0.4236173629760742,153.228885 +1380,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7585206671501088,0.7585206671501088,0.7544196711061472,0.4236783981323242,163.64661999999998 +1426,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7614035087719299,0.7614035087719299,0.7567964121564391,0.4236783981323242,174.36664399999998 +1472,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7654656696125085,0.7654656696125085,0.7591802078998249,0.4236783981323242,185.463757 +1518,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7673038892551087,0.7673038892551087,0.7600352016074767,0.4237394332885742,196.90308 +1564,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7677543186180422,0.7677543186180422,0.7612494392404334,0.4237394332885742,208.647576 +1610,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7675574891236793,0.7675574891236793,0.7602773300593106,0.42376232147216797,220.786107 +1656,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.76797583081571,0.76797583081571,0.7607906010792568,0.42376232147216797,233.21939999999998 +1702,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7677836566725456,0.7677836566725456,0.7627036277641847,0.42376232147216797,245.952092 +1748,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7710360618202633,0.7710360618202633,0.7657334796773966,0.42376232147216797,259.02702999999997 +1794,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7724484104852203,0.7724484104852203,0.7657758298578787,0.4237356185913086,272.39410699999996 +1840,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7737901033170201,0.77379010331702,0.767302943564198,0.4237966537475586,286.06776199999996 +1886,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7724137931034483,0.7724137931034483,0.7666353585191567,0.4237966537475586,300.095471 +1932,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7731745209735889,0.7731745209735889,0.7666634536176192,0.4237966537475586,314.417396 +1978,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7738998482549317,0.7738998482549316,0.7665909326930368,0.4237966537475586,329.067854 +2024,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7750865051903114,0.7750865051903113,0.7662611838286661,0.4237966537475586,344.01511700000003 +2070,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7747704204929918,0.7747704204929918,0.7660645062500586,0.4237966537475586,359.290159 +2116,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7754137115839244,0.7754137115839244,0.7658988206988366,0.4237966537475586,374.882405 +2162,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7760296159185562,0.7760296159185563,0.7660708746783081,0.4237966537475586,390.75768 +2208,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.777979157227005,0.7779791572270048,0.7670029065892423,0.4237966537475586,407.002801 +2254,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7749667110519307,0.7749667110519308,0.7639707440456852,0.4237966537475586,423.546299 +2300,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7742496737712049,0.7742496737712049,0.7632394528829524,0.4237966537475586,440.39336399999996 +2310,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7743611953226505,0.7743611953226506,0.7633622232911937,0.4237966537475586,457.310729 +1056,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6161137440758294,0.6161137440758294,0.581384151333148,0.6645784378051758,11.249192 +2112,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6120322122216959,0.6120322122216959,0.5792161554760864,0.6646394729614258,32.358705 +3168,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6049889485317335,0.6049889485317335,0.5721633809277145,0.6647005081176758,62.851539 +4224,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.603125739995264,0.603125739995264,0.5703574432462961,0.6647005081176758,102.700179 +5280,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6061754120098504,0.6061754120098504,0.5722430970062696,0.6647615432739258,151.914202 +6336,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5995264404104184,0.5995264404104184,0.5671511237518188,0.6647615432739258,210.432187 +7392,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5972128264104992,0.5972128264104992,0.5650210504998666,0.6647615432739258,278.26775499999997 +8448,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5989108559251806,0.5989108559251806,0.566418690076869,0.6647615432739258,355.20493799999997 +9504,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5962327685993897,0.5962327685993897,0.5633780031885509,0.6647615432739258,441.186739 +10560,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5964579979164694,0.5964579979164694,0.5634236596216465,0.6648225784301758,536.283653 +11616,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.594317692638829,0.594317692638829,0.5620068495149612,0.6648225784301758,640.2689049999999 +12672,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5975061163286244,0.5975061163286244,0.567518061449456,0.6648225784301758,753.0441599999999 +13728,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6097472135207984,0.6097472135207984,0.5927729676671933,0.6648225784301758,874.528885 +14784,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001488195900697,0.6001488195900697,0.5832911478837771,0.6645174026489258,1004.5501099999999 +15840,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5673969316244712,0.5673969316244712,0.5522471754341495,0.8876123428344727,1142.6522839999998 +16896,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5712340929269014,0.5712340929269014,0.5590383236849579,1.4319400787353516,1288.8770269999998 +17952,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5741184335134533,0.5741184335134533,0.5632919959429028,1.8629226684570312,1445.4718369999998 +19008,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5867312042931552,0.5867312042931552,0.5723846445183198,0.4819307327270508,1609.073978 +20064,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5966704879629168,0.5966704879629168,0.5796820575913003,0.6649179458618164,1780.2710459999998 +21120,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5984658364505895,0.5984658364505895,0.5810209140208816,0.6650400161743164,1958.8195809999997 +22176,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001803833145434,0.6001803833145434,0.5822125955100945,1.2073478698730469,2144.7260309999997 +23232,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6020403770823468,0.6020403770823468,0.5837921358595156,1.3215751647949219,2339.5310459999996 +24288,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6047268085807221,0.6047268085807221,0.5859785990228289,1.3216361999511719,2543.839083 +25344,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6069131515605887,0.6069131515605887,0.587737290445056,1.3217582702636719,2757.2066809999997 +26400,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6094927838175689,0.6094927838175689,0.5895162861993263,1.3217582702636719,2979.334505 +27456,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6105991622655254,0.6105991622655254,0.5896134687358237,1.3219413757324219,3211.0823579999997 +28512,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6106064326049595,0.6106064326049595,0.5910741826972655,1.3219413757324219,3451.5448549999996 +29568,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6099029323231981,0.6099029323231981,0.5935355609859342,1.3219413757324219,3700.7129539999996 +30624,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6088887437546942,0.6088887437546942,0.5952474102625339,1.3214530944824219,3958.5322249999995 +31680,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6088891694813598,0.6088891694813598,0.5975058139751561,1.3216972351074219,4224.837575 +32736,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6095921796242554,0.6095921796242554,0.5998546240309938,1.3217582702636719,4499.473663 +33792,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6043917019324673,0.6043917019324673,0.595080118632132,0.6649713516235352,4783.331389999999 +34848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6034378856142566,0.6034378856142566,0.5941773754098104,0.6650934219360352,5073.360361999999 +35904,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6029022644347269,0.6029022644347269,0.5935512429191343,0.6651544570922852,5369.406481999999 +36960,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6013690846613815,0.6013690846613815,0.5919623858291095,0.6651544570922852,5671.388488999999 +38016,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6010259108246745,0.6010259108246745,0.5912597483191937,0.6651544570922852,5979.127636999999 +39072,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6003429653707353,0.6003429653707353,0.5902279082897147,0.6648492813110352,6292.481400999999 +40128,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5961322800109652,0.5961322800109652,0.5867765456240649,0.6648492813110352,6611.499413 +41184,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5939829541315591,0.5939829541315591,0.585290407267574,0.6650323867797852,6936.132393 +42240,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5925803167688629,0.5925803167688629,0.5844470095695741,0.6650934219360352,7266.407125 +43296,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5911306155445202,0.5911306155445202,0.5835517912214992,0.6651544570922852,7602.391688 +44352,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.58959211742689,0.58959211742689,0.58246410272577,1.1046571731567383,7943.862096 +45408,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5875746030347744,0.5875746030347744,0.5808874407233396,1.3207244873046875,8291.951918 +46464,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5862083808621914,0.5862083808621914,0.5791892600330408,1.3209075927734375,8644.890712 +47520,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5879332477535302,0.5879332477535302,0.5810233099134106,1.3210525512695312,9004.012781000001 +48576,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5928152341739578,0.5928152341739578,0.5858160887305829,1.3216018676757812,9370.107000000002 +49632,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5979327436481231,0.5979327436481231,0.5906079347867982,1.3215408325195312,9743.028377000002 +50688,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6027383747311934,0.6027383747311934,0.594893758427483,1.3217239379882812,10122.858893000002 +51744,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6077923583866417,0.6077923583866417,0.5993180348311721,1.3217239379882812,10509.572003000003 +52800,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.612985094414667,0.612985094414667,0.6039181082054342,0.14382553100585938,10901.200853000002 +52848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6133366132420005,0.6133366132420005,0.604218855594392,0.14382553100585938,11292.868844000002 +408,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.23062610626220703,0.871514 +816,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,0.3262500762939453,3.583779 +1224,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8863450531479967,0.8863450531479967,0.8786592421362933,0.4218740463256836,8.686347999999999 +1632,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.891477621091355,0.891477621091355,0.8818548670971931,0.5179252624511719,16.685395 +2040,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.889651790093183,0.889651790093183,0.8812768038030504,0.6251459121704102,28.245741 +2448,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8414384961176952,0.8414384961176952,0.8420581397672002,0.7206478118896484,43.571154 +2856,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8500875656742557,0.8500875656742557,0.8345582037188519,0.8163328170776367,63.099422000000004 +3264,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8406374501992032,0.8406374501992032,0.8151418555553325,0.911895751953125,87.33095300000001 +3672,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8321983110868973,0.8321983110868973,0.8307198315203921,1.0075807571411133,116.498805 +4080,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.826182887962736,0.826182887962736,0.8123767856033619,1.128366470336914,151.118073 +4488,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.809226654780477,0.809226654780477,0.8196273526663149,1.2239294052124023,191.82030500000002 +4896,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8081716036772216,0.8081716036772216,0.815232111826365,1.3194313049316406,239.06161600000001 +5304,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8057703186875353,0.8057703186875353,0.7903391475861199,1.415055274963379,293.29488000000003 +5712,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7860269655051655,0.7860269655051656,0.7895763142947654,1.5108013153076172,355.22640600000005 +6120,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.784441902271613,0.784441902271613,0.7657785418705475,1.6062421798706055,425.24061900000004 +6528,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7585414432357898,0.7585414432357898,0.751418836389106,1.7020492553710938,503.46722600000004 +6936,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7473684210526316,0.7473684210526316,0.7484284412750404,1.797490119934082,590.6999010000001 +7344,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7565027917744791,0.7565027917744791,0.7526701844923946,1.8947620391845703,687.248946 +7752,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7577086827506129,0.7577086827506129,0.755735065870518,1.9903860092163086,793.498598 +8160,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7617355068023042,0.7617355068023042,0.7576049653668414,2.085948944091797,909.902095 +8568,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7604762460604646,0.7604762460604646,0.7596175662696861,2.2296838760375977,1036.556796 +8976,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.756991643454039,0.7569916434540391,0.7575313939177277,2.325368881225586,1173.4366320000001 +9384,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7558350207822658,0.7558350207822658,0.7548436696787698,2.420870780944824,1320.145727 +9792,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.748340312531917,0.7483403125319169,0.744390859626019,2.5164337158203125,1476.9925130000001 +10200,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7393862143347387,0.7393862143347387,0.7315892779928432,2.612057685852051,1644.1937280000002 +10608,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7196191194494201,0.7196191194494201,0.7089541376321258,2.707803726196289,1822.1938220000002 +11016,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7123921924648207,0.7123921924648208,0.7092068316988943,2.8033666610717773,2011.0989090000003 +11424,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7062943184802591,0.7062943184802591,0.6946713230955313,2.8989906311035156,2211.8042590000005 +11832,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6967289324655566,0.6967289324655566,0.690232830798306,2.994553565979004,2423.5715250000003 +12240,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7007108423890841,0.7007108423890841,0.6983689907908355,3.090177536010742,2646.6754960000003 +12648,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6969241717403337,0.6969241717403337,0.6892508246262707,3.1858625411987305,2881.7592360000003 +13056,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6836461126005362,0.6836461126005362,0.6755391962059192,3.2815475463867188,3128.5577150000004 +13464,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6793433855752804,0.6793433855752804,0.6754035266161623,3.377110481262207,3387.3558160000002 +13872,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6769519140653161,0.6769519140653161,0.6742482232309566,3.4728565216064453,3658.0697 +14280,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6728762518383641,0.6728762518383641,0.6689356443053495,3.5684194564819336,3940.688111 +14688,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6762442976782188,0.6762442976782188,0.6753292472514647,3.663982391357422,4235.610853 +15096,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6830076184166942,0.6830076184166942,0.6822311287838643,3.75966739654541,4542.8267670000005 +15504,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6818035218989873,0.6818035218989873,0.6788656596145114,3.8552303314208984,4862.152597 +15912,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6816039218150964,0.6816039218150964,0.6801525397911032,0.2705574035644531,5190.397888 +16320,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6858263373981249,0.6858263373981249,0.685191280018575,0.46213340759277344,5522.880902000001 +16728,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6896634184253004,0.6896634184253004,0.6890226069872224,0.6535873413085938,5860.018685000001 +17136,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6925007295010213,0.6925007295010213,0.6918635442211969,0.9691534042358398,6202.345681000001 +17544,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6990252522373597,0.6990252522373597,0.6986638608261282,0.2649049758911133,6547.073149000001 +17952,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7038605091638349,0.7038605091638349,0.7032543903990934,0.579315185546875,6893.121988000001 +18360,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.710114930007081,0.7101149300070809,0.70950849929648,0.2349414825439453,7240.035665000001 +18768,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.715351414717323,0.715351414717323,0.7146010079934133,0.3305654525756836,7588.155090000001 +19176,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7179139504563233,0.7179139504563233,0.7169858006379833,0.4260063171386719,7937.751954000001 +19584,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7223612316805392,0.7223612316805392,0.7214649429496548,0.5217523574829102,8289.115139000001 +19992,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7219248661897854,0.7219248661897855,0.7206428236711905,0.6287288665771484,8642.591702000002 +20400,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7231236825334575,0.7231236825334575,0.7218249685926471,0.7244749069213867,8998.461289 +46,Multiclass classification,Adaptive Random Forest,ImageSegments,0.4222222222222222,0.4222222222222222,0.3590236094437775,0.9685115814208984,1.326052 +92,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5604395604395604,0.5604395604395604,0.5746538615446178,1.0556058883666992,4.053487 +138,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5766423357664233,0.5766423357664233,0.598257695340355,1.344954490661621,8.154789999999998 +184,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6229508196721312,0.6229508196721312,0.6451744040758778,1.4133405685424805,13.553012999999998 +230,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6506550218340611,0.6506550218340611,0.6680655280025949,1.5576086044311523,20.188933 +276,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6727272727272727,0.6727272727272727,0.6900672130049011,1.7550430297851562,28.051384 +322,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7040498442367601,0.7040498442367601,0.7087861936875776,1.832967758178711,37.153949999999995 +368,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7302452316076294,0.7302452316076294,0.7285991575377422,1.971024513244629,47.432601999999996 +414,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7457627118644068,0.7457627118644068,0.7430362907281778,1.991847038269043,58.97377399999999 +460,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7342047930283224,0.7342047930283224,0.7271744800226857,1.8101978302001953,71.823928 +506,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7405940594059406,0.7405940594059406,0.7304322149686578,1.7132930755615234,85.827474 +552,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7368421052631579,0.7368421052631579,0.7267508109083203,1.5079193115234375,101.049314 +598,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7403685092127303,0.7403685092127302,0.7318978254380314,1.6471452713012695,117.42015599999999 +644,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7325038880248833,0.7325038880248833,0.7248107612258207,1.7740907669067383,135.017443 +690,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7242380261248186,0.7242380261248187,0.7153272190465999,1.913142204284668,153.656893 +736,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7251700680272108,0.725170068027211,0.7148466398758337,2.0619029998779297,173.429455 +782,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7259923175416133,0.7259923175416134,0.7134712280209221,2.0208959579467773,194.315292 +828,Multiclass classification,Adaptive Random Forest,ImageSegments,0.727932285368803,0.727932285368803,0.7177600265828429,2.224555015563965,216.352158 +874,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7353951890034365,0.7353951890034366,0.7262567978322628,2.300021171569824,239.599524 +920,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7431991294885746,0.7431991294885745,0.7345004589126253,2.4412155151367188,263.99359400000003 +966,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7471502590673575,0.7471502590673575,0.7368855656689403,2.474191665649414,289.66420500000004 +1012,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7546983184965381,0.754698318496538,0.7446216664767904,2.5655078887939453,316.44421900000003 +1058,Multiclass classification,Adaptive Random Forest,ImageSegments,0.760643330179754,0.760643330179754,0.7502594177262459,2.798956871032715,344.45448600000003 +1104,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7624660018132366,0.7624660018132366,0.7523020427630668,2.48898983001709,373.71735 +1150,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7650130548302873,0.7650130548302874,0.7555087521342715,2.3284912109375,404.061966 +1196,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7690376569037657,0.7690376569037657,0.7603504370239861,2.0560731887817383,435.51003499999996 +1242,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7719580983078163,0.7719580983078163,0.7638249032322542,2.1193370819091797,467.96964099999997 +1288,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7746697746697747,0.7746697746697747,0.7668828628349821,2.277647018432617,501.44875399999995 +1334,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7771942985746436,0.7771942985746436,0.7696789046658701,2.3871631622314453,535.887669 +1380,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7817258883248731,0.7817258883248731,0.7754511149783997,2.3104944229125977,571.357393 +1426,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7866666666666666,0.7866666666666666,0.7797171864703156,2.4089183807373047,607.784244 +1472,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7912984364377974,0.7912984364377974,0.7836430453045393,2.5425024032592773,645.2286509999999 +1518,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7963085036255768,0.7963085036255768,0.7883976288226553,2.6389265060424805,683.7451019999999 +1564,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7978246960972489,0.7978246960972489,0.790949738475821,2.283763885498047,723.3862519999999 +1610,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798011187072716,0.7980111870727161,0.7914720525222512,2.519012451171875,764.1312649999999 +1656,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7981873111782477,0.7981873111782477,0.7919320984228655,2.307619094848633,806.0160599999999 +1702,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798941798941799,0.7989417989417988,0.7945012991620244,2.40640926361084,848.960292 +1748,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8019461934745278,0.8019461934745278,0.797056036319667,2.447686195373535,893.037184 +1794,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8047964305633017,0.8047964305633019,0.7993493873930555,2.5208606719970703,938.202728 +1840,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8069603045133225,0.8069603045133223,0.8019867749609348,2.8025121688842773,984.592034 +1886,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8084880636604774,0.8084880636604774,0.8043300839686539,2.9287471771240234,1032.221691 +1932,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8114966338684619,0.8114966338684619,0.8071482324590065,2.977842330932617,1081.048247 +1978,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8148710166919575,0.8148710166919576,0.8107088256390683,3.110445022583008,1130.9949649999999 +2024,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8161146811665843,0.8161146811665844,0.8110472160986095,3.3117494583129883,1182.226115 +2070,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8173030449492509,0.8173030449492509,0.8127793203399477,2.7790603637695312,1234.703432 +2116,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8193853427895981,0.8193853427895981,0.8144282151100146,2.8652515411376953,1288.356269 +2162,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8199907450254512,0.8199907450254512,0.8150157846003385,2.925917625427246,1343.2838700000002 +2208,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8205709107385591,0.8205709107385591,0.8153449009635614,2.785597801208496,1399.3252850000001 +2254,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8175765645805593,0.8175765645805593,0.813116129924445,2.868098258972168,1456.6402850000002 +2300,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8186167899086559,0.8186167899086559,0.8144518819207099,3.062863349914551,1515.2003170000003 +2310,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8185361628410567,0.8185361628410566,0.8145347387119569,3.063481330871582,1574.1800910000002 +1056,Multiclass classification,Adaptive Random Forest,Insects,0.6682464454976303,0.6682464454976303,0.6049011732627783,7.181946754455566,32.418226 +2112,Multiclass classification,Adaptive Random Forest,Insects,0.6944576030317385,0.6944576030317385,0.6288311688548281,9.897843360900879,94.87356399999999 +3168,Multiclass classification,Adaptive Random Forest,Insects,0.6984527944426903,0.6984527944426903,0.625371849015863,13.448436737060547,186.837042 +4224,Multiclass classification,Adaptive Random Forest,Insects,0.706369879232773,0.706369879232773,0.6266042661686886,17.43436622619629,307.272577 +5280,Multiclass classification,Adaptive Random Forest,Insects,0.7107406705815495,0.7107406705815495,0.6273487761971507,20.93905258178711,452.99825 +6336,Multiclass classification,Adaptive Random Forest,Insects,0.7108129439621153,0.7108129439621153,0.6274052515282983,25.022296905517578,622.602665 +7392,Multiclass classification,Adaptive Random Forest,Insects,0.7127587606548504,0.7127587606548504,0.6273117178459473,28.819257736206055,816.020547 +8448,Multiclass classification,Adaptive Random Forest,Insects,0.7164673848703682,0.7164673848703682,0.6293431255193823,32.802799224853516,1032.257355 +9504,Multiclass classification,Adaptive Random Forest,Insects,0.721666842049879,0.721666842049879,0.63170101976307,32.88048076629639,1271.699652 +10560,Multiclass classification,Adaptive Random Forest,Insects,0.724405720238659,0.724405720238659,0.6339052025360064,29.71586036682129,1533.827375 +11616,Multiclass classification,Adaptive Random Forest,Insects,0.7244080929832114,0.7244080929832114,0.6334336343217646,33.71169948577881,1818.162347 +12672,Multiclass classification,Adaptive Random Forest,Insects,0.7225949017441402,0.7225949017441402,0.6332595599893077,29.649346351623535,2125.062078 +13728,Multiclass classification,Adaptive Random Forest,Insects,0.7416769869600058,0.7416769869600057,0.7385871869253197,11.750191688537598,2443.2361889999997 +14784,Multiclass classification,Adaptive Random Forest,Insects,0.7472096326861936,0.7472096326861937,0.7473000008879964,7.712667465209961,2772.2292589999997 +15840,Multiclass classification,Adaptive Random Forest,Insects,0.7404507860344719,0.7404507860344719,0.7427443120881612,5.854048728942871,3118.2806729999998 +16896,Multiclass classification,Adaptive Random Forest,Insects,0.73666765315182,0.73666765315182,0.7407696345938622,9.543391227722168,3480.1200929999995 +17952,Multiclass classification,Adaptive Random Forest,Insects,0.7295972369227341,0.7295972369227341,0.7347001031972082,14.625198364257812,3856.6322749999995 +19008,Multiclass classification,Adaptive Random Forest,Insects,0.739780081022781,0.7397800810227809,0.7407912307996387,5.110816955566406,4245.133706999999 +20064,Multiclass classification,Adaptive Random Forest,Insects,0.7434581069630664,0.7434581069630664,0.7402037922066672,3.8148155212402344,4646.574114999999 +21120,Multiclass classification,Adaptive Random Forest,Insects,0.745111037454425,0.7451110374544251,0.7386209934273732,7.313493728637695,5063.578879 +22176,Multiclass classification,Adaptive Random Forest,Insects,0.7462006764374295,0.7462006764374295,0.7365944363606786,12.210733413696289,5495.973683 +23232,Multiclass classification,Adaptive Random Forest,Insects,0.7483965391072274,0.7483965391072274,0.7360584061499352,11.241872787475586,5944.2105440000005 +24288,Multiclass classification,Adaptive Random Forest,Insects,0.7495779635195784,0.7495779635195785,0.7345443205753824,12.262273788452148,6407.867088000001 +25344,Multiclass classification,Adaptive Random Forest,Insects,0.7508582251509293,0.7508582251509293,0.7336140903014292,15.815716743469238,6885.995097000001 +26400,Multiclass classification,Adaptive Random Forest,Insects,0.7510890564036516,0.7510890564036516,0.7317409587301968,20.072275161743164,7378.034229000001 +27456,Multiclass classification,Adaptive Random Forest,Insects,0.7520670187579676,0.7520670187579677,0.7304776676466566,23.249674797058105,7884.702304 +28512,Multiclass classification,Adaptive Random Forest,Insects,0.7487285609063169,0.7487285609063169,0.7285292321096271,2.7024307250976562,8406.670172 +29568,Multiclass classification,Adaptive Random Forest,Insects,0.7464741096492712,0.7464741096492712,0.7309964825863351,6.2935638427734375,8940.901067 +30624,Multiclass classification,Adaptive Random Forest,Insects,0.7457793162002416,0.7457793162002416,0.7347045068936117,9.350909233093262,9487.044090000001 +31680,Multiclass classification,Adaptive Random Forest,Insects,0.745036143817671,0.745036143817671,0.7375864352537521,14.599569320678711,10044.836672000001 +32736,Multiclass classification,Adaptive Random Forest,Insects,0.7451962731021842,0.7451962731021842,0.7406480970104784,19.125198364257812,10615.117300000002 +33792,Multiclass classification,Adaptive Random Forest,Insects,0.7402858749371134,0.7402858749371134,0.7370798749337869,6.808139801025391,11202.653786000003 +34848,Multiclass classification,Adaptive Random Forest,Insects,0.7366200820730623,0.7366200820730623,0.7333315604235389,5.8602495193481445,11807.700361000003 +35904,Multiclass classification,Adaptive Random Forest,Insects,0.733921956382475,0.7339219563824751,0.7303171015411175,9.36469554901123,12429.747970000002 +36960,Multiclass classification,Adaptive Random Forest,Insects,0.7304039611461349,0.7304039611461349,0.7265687877692525,14.848862648010254,13069.446785000002 +38016,Multiclass classification,Adaptive Random Forest,Insects,0.7276864395633302,0.7276864395633302,0.7236022807953257,19.807891845703125,13727.939023000003 +39072,Multiclass classification,Adaptive Random Forest,Insects,0.7250134370760922,0.7250134370760921,0.7209989950382084,16.71243381500244,14405.601845000003 +40128,Multiclass classification,Adaptive Random Forest,Insects,0.7235028783612032,0.7235028783612032,0.7198278735760195,8.331427574157715,15101.835691000002 +41184,Multiclass classification,Adaptive Random Forest,Insects,0.723623825364835,0.723623825364835,0.7203262236880287,6.9819841384887695,15814.868539000003 +42240,Multiclass classification,Adaptive Random Forest,Insects,0.7240464973129098,0.7240464973129098,0.7211005399097123,10.71219539642334,16543.112989 +43296,Multiclass classification,Adaptive Random Forest,Insects,0.7245409400623629,0.7245409400623629,0.721844297210525,10.330558776855469,17285.760894000003 +44352,Multiclass classification,Adaptive Random Forest,Insects,0.7248765529525828,0.7248765529525828,0.7223628081683402,13.299851417541504,18041.694028 +45408,Multiclass classification,Adaptive Random Forest,Insects,0.7254167859581122,0.7254167859581122,0.7228420559832612,15.662115097045898,18810.181113000002 +46464,Multiclass classification,Adaptive Random Forest,Insects,0.7263844349267159,0.7263844349267159,0.7236482152790997,19.25161361694336,19591.516438000002 +47520,Multiclass classification,Adaptive Random Forest,Insects,0.7265304404553968,0.7265304404553967,0.7240124567772878,14.065608024597168,20387.990038000004 +48576,Multiclass classification,Adaptive Random Forest,Insects,0.7304374678332476,0.7304374678332476,0.7281756207358935,7.354809761047363,21197.413376000004 +49632,Multiclass classification,Adaptive Random Forest,Insects,0.7344603171404969,0.7344603171404969,0.7322565876518081,7.006095886230469,22016.972025000003 +50688,Multiclass classification,Adaptive Random Forest,Insects,0.7380590684001815,0.7380590684001815,0.7356981427827818,10.14159107208252,22847.182754 +51744,Multiclass classification,Adaptive Random Forest,Insects,0.7420134124422627,0.7420134124422627,0.7394134340953542,13.563420295715332,23688.037606 +52800,Multiclass classification,Adaptive Random Forest,Insects,0.7451466883842497,0.7451466883842497,0.7430487162081567,0.3614501953125,24535.706056000003 +52848,Multiclass classification,Adaptive Random Forest,Insects,0.7453781671618067,0.7453781671618067,0.7433023109254195,0.36179351806640625,25383.518073000003 +408,Multiclass classification,Adaptive Random Forest,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.3354053497314453,3.23067 +816,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730061349693252,0.9730061349693252,0.8116978142719798,0.988037109375,11.21298 +1224,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730171708912511,0.9730171708912511,0.9579161898493525,2.195523262023926,25.427007 +1632,Multiclass classification,Adaptive Random Forest,Keystroke,0.9693439607602697,0.9693439607602697,0.9069773132409142,3.526730537414551,46.453053999999995 +2040,Multiclass classification,Adaptive Random Forest,Keystroke,0.9666503187837175,0.9666503187837175,0.9303026980117671,5.496582984924316,74.431187 +2448,Multiclass classification,Adaptive Random Forest,Keystroke,0.9660809154066203,0.9660809154066203,0.9555178664837441,2.29970645904541,107.969459 +2856,Multiclass classification,Adaptive Random Forest,Keystroke,0.9691768826619965,0.9691768826619965,0.9674134048328416,3.376467704772949,146.96126800000002 +3264,Multiclass classification,Adaptive Random Forest,Keystroke,0.9672080907140668,0.9672080907140668,0.9546197483047236,4.62060546875,192.073824 +3672,Multiclass classification,Adaptive Random Forest,Keystroke,0.9684009806592209,0.968400980659221,0.9654409635782653,3.119338035583496,243.354323 +4080,Multiclass classification,Adaptive Random Forest,Keystroke,0.9644520715861731,0.9644520715861731,0.95030552665756,4.705347061157227,301.433133 +4488,Multiclass classification,Adaptive Random Forest,Keystroke,0.9661243592600847,0.9661243592600847,0.9659906155964958,1.508072853088379,365.412759 +4896,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677221654749745,0.9677221654749745,0.96768641848376,2.487558364868164,434.672843 +5304,Multiclass classification,Adaptive Random Forest,Keystroke,0.9685083914765227,0.9685083914765227,0.9677400809149086,2.8771514892578125,509.85413800000003 +5712,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690071791279986,0.9690071791279986,0.9686984277929261,4.140267372131348,591.7133210000001 +6120,Multiclass classification,Adaptive Random Forest,Keystroke,0.9671514953423762,0.9671514953423762,0.9635575047511442,5.121949195861816,681.1937680000001 +6528,Multiclass classification,Adaptive Random Forest,Keystroke,0.9675195342423778,0.9675195342423778,0.9673223823066149,2.1385393142700195,777.2102420000001 +6936,Multiclass classification,Adaptive Random Forest,Keystroke,0.9685652487382841,0.9685652487382841,0.9688652926813892,2.7864933013916016,879.0640510000001 +7344,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686776521857552,0.9686776521857552,0.9682274153773371,3.314570426940918,987.0629210000001 +7752,Multiclass classification,Adaptive Random Forest,Keystroke,0.9682621597213262,0.9682621597213262,0.9674704101631952,4.690197944641113,1101.141854 +8160,Multiclass classification,Adaptive Random Forest,Keystroke,0.96727540139723,0.96727540139723,0.9662379529396136,5.223731994628906,1221.487909 +8568,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677833547332788,0.9677833547332788,0.9678822443058487,4.885932922363281,1347.980617 +8976,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686908077994429,0.9686908077994429,0.9690861219789195,6.402636528015137,1480.694289 +9384,Multiclass classification,Adaptive Random Forest,Keystroke,0.9683470105509965,0.9683470105509965,0.9680699356268633,6.928671836853027,1620.259773 +9792,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686446736799101,0.9686446736799101,0.9687197530276813,5.552419662475586,1766.078849 +10200,Multiclass classification,Adaptive Random Forest,Keystroke,0.9684282772820865,0.9684282772820866,0.9682582636163196,2.695918083190918,1917.758924 +10608,Multiclass classification,Adaptive Random Forest,Keystroke,0.9673800320543038,0.9673800320543038,0.9668238422002586,3.239151954650879,2074.190769 +11016,Multiclass classification,Adaptive Random Forest,Keystroke,0.9676804357694053,0.9676804357694053,0.9678040910458205,4.023995399475098,2235.420867 +11424,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677842948437363,0.9677842948437363,0.9678364439490078,4.695375442504883,2402.1641919999997 +11832,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677119432000676,0.9677119432000676,0.9677086079179034,5.258674621582031,2574.8706989999996 +12240,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687065936759539,0.9687065936759539,0.9690716756618885,6.001680374145508,2753.3671299999996 +12648,Multiclass classification,Adaptive Random Forest,Keystroke,0.9688463667272871,0.9688463667272871,0.9689334511448673,5.217698097229004,2937.7699639999996 +13056,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687476062811183,0.9687476062811183,0.968764477893114,5.266051292419434,3127.8024029999997 +13464,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687291094109782,0.9687291094109782,0.9687736841624996,6.279603958129883,3323.370395 +13872,Multiclass classification,Adaptive Random Forest,Keystroke,0.9695047220820416,0.9695047220820416,0.9697384724636318,4.041820526123047,3524.041026 +14280,Multiclass classification,Adaptive Random Forest,Keystroke,0.9682750892919673,0.9682750892919673,0.9680357071263168,2.1731691360473633,3729.110149 +14688,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686797848437394,0.9686797848437394,0.9688099431838716,2.4900379180908203,3938.3384300000002 +15096,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692613448161643,0.9692613448161643,0.9694122553904638,2.7789316177368164,4151.996270000001 +15504,Multiclass classification,Adaptive Random Forest,Keystroke,0.9694897761723538,0.9694897761723538,0.969571649124791,3.946505546569824,4370.227344000001 +15912,Multiclass classification,Adaptive Random Forest,Keystroke,0.9694550939601534,0.9694550939601534,0.9694916672888816,4.345325469970703,4594.050341000001 +16320,Multiclass classification,Adaptive Random Forest,Keystroke,0.9695447024940254,0.9695447024940254,0.9695954968773723,3.909954071044922,4823.361799000001 +16728,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692114545345848,0.9692114545345848,0.9692084456743588,1.764338493347168,5057.2303470000015 +17136,Multiclass classification,Adaptive Random Forest,Keystroke,0.9696527575138605,0.9696527575138605,0.9697329621491685,1.7167367935180664,5295.013901000001 +17544,Multiclass classification,Adaptive Random Forest,Keystroke,0.9696745140511885,0.9696745140511885,0.9697082565052514,2.8143720626831055,5537.319837000001 +17952,Multiclass classification,Adaptive Random Forest,Keystroke,0.968748259149908,0.968748259149908,0.968705960089485,2.951136589050293,5784.484584000001 +18360,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690070265264993,0.9690070265264993,0.9690448168177233,3.5441465377807617,6036.117893000001 +18768,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690946874833485,0.9690946874833485,0.9691164520527107,4.379698753356934,6292.729193000001 +19176,Multiclass classification,Adaptive Random Forest,Keystroke,0.968761408083442,0.968761408083442,0.9687617227117352,3.8120603561401367,6554.348831000001 +19584,Multiclass classification,Adaptive Random Forest,Keystroke,0.9689526630240515,0.9689526630240515,0.9689629146490384,2.019772529602051,6819.891372000001 +19992,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692861787804512,0.9692861787804512,0.9692901573177237,1.2564506530761719,7089.584863000001 +20400,Multiclass classification,Adaptive Random Forest,Keystroke,0.9691161331437815,0.9691161331437815,0.9691108096285476,1.6354646682739258,7363.046142000001 +46,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.5333333333333333,0.5333333333333333,0.5005728607232367,0.8510866165161133,0.941842 +92,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6153846153846154,0.6153846153846154,0.596131344383025,1.5052366256713867,2.918201 +138,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6496350364963503,0.6496350364963503,0.6567305057749026,2.146304130554199,6.147886 +184,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6994535519125683,0.6994535519125683,0.7070190759413217,2.7665939331054688,10.824064 +230,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7379912663755459,0.7379912663755459,0.7433871451842025,3.2484235763549805,16.931166 +276,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7490909090909091,0.7490909090909091,0.7566070103930901,3.776392936706543,24.729994 +322,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7694704049844237,0.7694704049844237,0.7681721604320974,4.142314910888672,34.173162000000005 +368,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.784741144414169,0.7847411444141691,0.7789718513534348,4.497910499572754,45.384105000000005 +414,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7990314769975787,0.7990314769975787,0.7943771701942021,4.869846343994141,58.265676000000006 +460,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7973856209150327,0.7973856209150327,0.7916511033189314,5.3911848068237305,73.08883800000001 +506,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.805940594059406,0.805940594059406,0.8010859843658406,5.806554794311523,89.87625100000001 +552,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8076225045372051,0.8076225045372051,0.8036838079612314,6.295863151550293,108.59930000000001 +598,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8174204355108877,0.8174204355108878,0.8156009215135775,6.727802276611328,129.48595400000002 +644,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8211508553654744,0.8211508553654744,0.8207645722848749,7.18087100982666,152.525841 +690,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8229317851959361,0.8229317851959362,0.8226135245892084,7.561182022094727,177.86541200000002 +736,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8231292517006803,0.8231292517006803,0.8228959515200417,7.975464820861816,205.49957600000002 +782,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8309859154929577,0.8309859154929577,0.8306123687436626,8.301925659179688,235.39408200000003 +828,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8343409915356711,0.834340991535671,0.835521648488366,8.722038269042969,267.718494 +874,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8407789232531501,0.8407789232531501,0.8414965916969209,9.057206153869629,302.46008700000004 +920,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8443960826985855,0.8443960826985855,0.8446110045111287,9.382828712463379,339.623661 +966,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8466321243523316,0.8466321243523316,0.8462590694093756,9.696897506713867,379.342347 +1012,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8516320474777448,0.8516320474777448,0.8504483916737715,9.949009895324707,421.625642 +1058,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8571428571428571,0.8571428571428571,0.8557487568785946,10.2299222946167,466.542637 +1104,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8603807796917498,0.8603807796917498,0.8594481550185353,10.524299621582031,514.218423 +1150,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8624891209747607,0.8624891209747607,0.8612253786789881,10.737759590148926,564.599929 +1196,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8652719665271966,0.8652719665271966,0.8642881992026393,11.010127067565918,617.836337 +1242,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8670427074939565,0.8670427074939565,0.8663181473795101,11.261144638061523,674.05967 +1288,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8694638694638694,0.8694638694638694,0.8687259920464652,11.505732536315918,733.385389 +1334,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8709677419354839,0.8709677419354839,0.870193396369452,11.826444625854492,796.067675 +1380,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8745467730239304,0.8745467730239304,0.874089581073643,12.086430549621582,861.584672 +1426,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8771929824561403,0.8771929824561403,0.8759011931352845,12.29430866241455,930.2045189999999 +1472,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8796736913664174,0.8796736913664174,0.877566397675441,12.500163078308105,1001.8141389999998 +1518,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8826631509558339,0.8826631509558339,0.8803270226288138,12.740474700927734,1076.4882149999999 +1564,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8841970569417786,0.8841970569417786,0.8822041640143002,12.987508773803711,1154.350794 +1610,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.886886264760721,0.886886264760721,0.8850836875294148,13.252826690673828,1235.463166 +1656,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.888821752265861,0.888821752265861,0.8870702351165313,13.500110626220703,1319.86391 +1702,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8912404467960023,0.8912404467960025,0.8905987472429445,13.767583847045898,1407.541035 +1748,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8929593589009731,0.892959358900973,0.8920318510221457,14.030475616455078,1498.676265 +1794,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.894032348020078,0.894032348020078,0.8925886559949978,14.271255493164062,1593.100327 +1840,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8945078847199565,0.8945078847199565,0.8931986390525462,14.574835777282715,1691.005218 +1886,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.896551724137931,0.896551724137931,0.8956464025201587,14.834091186523438,1792.408733 +1932,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8964267219057483,0.8964267219057483,0.8951782213786073,15.134613037109375,1897.156299 +1978,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8973191704602934,0.8973191704602934,0.8961901832930852,15.326050758361816,2005.31409 +2024,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8986653484923381,0.8986653484923381,0.8970310627029995,15.549851417541504,2116.877653 +2070,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8994683421942967,0.8994683421942967,0.8980105869909577,15.816215515136719,2232.114727 +2116,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.900709219858156,0.900709219858156,0.8989778942952686,15.957537651062012,2350.8625340000003 +2162,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9000462748727441,0.9000462748727441,0.8982611856050026,16.206623077392578,2473.2186990000005 +2208,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9012233801540552,0.9012233801540552,0.8993036839855942,16.400617599487305,2599.1257530000003 +2254,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9014647137150466,0.9014647137150466,0.8999821457114682,16.693093299865723,2728.6827460000004 +2300,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9016963897346673,0.9016963897346673,0.9003174232892135,16.988688468933105,2861.834306 +2310,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9016890428757037,0.9016890428757037,0.9003808534937335,17.050235748291016,2997.696193 +1056,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6511848341232227,0.6511848341232227,0.5805974192561721,27.882014274597168,41.422615 +2112,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6830885836096636,0.6830885836096636,0.6159001145696381,53.900901794433594,137.16292 +3168,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6889801073571203,0.6889801073571203,0.6135176771695448,79.45620250701904,291.10856 +4224,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6954771489462468,0.6954771489462468,0.6159765684907534,104.90542316436768,501.70617000000004 +5280,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7003220306876302,0.7003220306876302,0.6217575035584229,130.7021541595459,768.289754 +6336,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7021310181531176,0.7021310181531176,0.622391174421368,156.0168752670288,1090.319759 +7392,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7027465836828576,0.7027465836828576,0.6232948240709647,180.83974838256836,1466.44078 +8448,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7040369361903634,0.7040369361903634,0.6235946437988805,205.63252925872803,1896.370643 +9504,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7105124697463959,0.7105124697463959,0.6284709935917355,229.19151210784912,2381.431795 +10560,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7140827729898664,0.7140827729898664,0.6302854833117341,253.17632389068604,2925.98814 +11616,Multiclass classification,Aggregated Mondrian Forest,Insects,0.71562634524322,0.7156263452432199,0.6305326785921538,277.4567346572876,3530.8520359999998 +12672,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7145450240707126,0.7145450240707125,0.6284185449457835,301.7114896774292,4201.052974 +13728,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7057623661397247,0.7057623661397247,0.6885364031919957,327.2237205505371,4936.820951 +14784,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6967462625989312,0.6967462625989312,0.69194472505998,352.6018476486206,5738.465999 +15840,Multiclass classification,Aggregated Mondrian Forest,Insects,0.676684134099375,0.676684134099375,0.673854549025314,384.9730758666992,6613.285946 +16896,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6698431488606097,0.6698431488606097,0.668750254945471,415.2214603424072,7559.921071 +17952,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6646983454960727,0.6646983454960727,0.6646134205077884,444.32067584991455,8589.520858 +19008,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6620192560635555,0.6620192560635555,0.6605985532750915,472.75781440734863,9705.665905 +20064,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6597717190848826,0.6597717190848826,0.6570293922418718,499.48760890960693,10901.076562 +21120,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6539608882996354,0.6539608882996354,0.6496192149174075,528.8777961730957,12166.701144 +22176,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6547463359639233,0.6547463359639233,0.6484047117859243,557.1920728683472,13501.384366 +23232,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6583444535319185,0.6583444535319185,0.6499882024630633,584.0554361343384,14901.095396 +24288,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6611767612302878,0.6611767612302878,0.6506059068013808,610.3706150054932,16366.700533000001 +25344,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6659827171210986,0.6659827171210986,0.6532433614752314,635.6853046417236,17901.739193 +26400,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6702526610856472,0.6702526610856472,0.6554263220708306,660.4025926589966,19504.786084 +27456,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6745947914769623,0.6745947914769623,0.6575507550972549,684.36501121521,21172.086014 +28512,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6705482094630143,0.6705482094630143,0.6539581966383304,712.6770572662354,22902.746936 +29568,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6644231744850678,0.6644231744850678,0.6512239029866641,743.5559530258179,24691.477434 +30624,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6622799856317148,0.6622799856317148,0.6527566844616065,772.5478630065918,26538.585641 +31680,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6621736797247388,0.6621736797247388,0.6557760097374935,800.5439138412476,28440.416189000003 +32736,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6623797159004124,0.6623797159004124,0.6584479912704261,827.4998264312744,30418.714512000002 +33792,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6575123553608949,0.6575123553608949,0.6541419435809196,857.7161102294922,32439.386127 +34848,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6519069073377909,0.6519069073377909,0.6481893367707658,888.8327789306641,34499.720344 +35904,Multiclass classification,Aggregated Mondrian Forest,Insects,0.647550343982397,0.647550343982397,0.643407015045196,919.6311988830566,36599.09766 +36960,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6444438431775752,0.6444438431775752,0.6400224052225335,949.7819452285767,38735.650911 +38016,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6425358411153492,0.6425358411153492,0.6377821595167165,979.4456567764282,40896.763764999996 +39072,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6414476209976709,0.6414476209976709,0.6370415360917451,1009.0255756378174,43085.847267 +40128,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6409898572033793,0.6409898572033793,0.636858231937463,1037.841980934143,45303.144751 +41184,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6414782798727631,0.6414782798727631,0.637272014233453,1065.1163549423218,47540.369251 +42240,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6428419233409882,0.6428419233409882,0.6385110475108609,1091.8334274291992,49803.522006 +43296,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6441159487238711,0.6441159487238711,0.6396283228479406,1118.1560363769531,52086.93226 +44352,Multiclass classification,Aggregated Mondrian Forest,Insects,0.645058735992424,0.645058735992424,0.6403851797193834,1144.4119939804077,54391.61903 +45408,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6469266853128373,0.6469266853128373,0.6418265850265934,1169.9601306915283,56719.958571 +46464,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6487742935238792,0.6487742935238792,0.643191402092947,1194.6403436660767,59073.094705 +47520,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6459521454576065,0.6459521454576065,0.6406800374556137,1224.6073780059814,61451.967815 +48576,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6443643849716932,0.6443643849716932,0.6398250343320808,1254.4350862503052,63857.884093 +49632,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6446172754931394,0.6446172754931394,0.6406945505071863,1282.3891849517822,66293.298766 +50688,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6461222798745241,0.6461222798745241,0.6426238276925219,1309.4736614227295,68755.018108 +51744,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6489186943161394,0.6489186943161394,0.6457243405011626,1334.444143295288,71244.151045 +52800,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6470577094263149,0.6470577094263149,0.6443966707674731,1363.999231338501,73759.934152 +52848,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6469809071470471,0.6469809071470471,0.6443518314696601,1365.409776687622,76295.692169 +408,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9901719901719902,0.9901719901719902,0.8308395677472984,0.12276840209960938,1.485322 +816,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9914110429447853,0.9914110429447853,0.960934413925625,0.41584110260009766,6.729082 +1224,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9893704006541292,0.9893704006541292,0.9580466011674303,1.2467107772827148,20.148490000000002 +1632,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9889638258736971,0.9889638258736971,0.9786672150923964,2.28104305267334,50.264957 +2040,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.988719960765081,0.988719960765081,0.9803510904896324,3.352717399597168,91.64343099999999 +2448,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9885574172456069,0.9885574172456069,0.9830468792370581,4.983606338500977,148.278076 +2856,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9852889667250437,0.9852889667250437,0.9737767108051043,6.963967323303223,227.073424 +3264,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9825314128102973,0.9825314128102973,0.9734338986941852,9.8344087600708,324.702985 +3672,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9822936529555979,0.9822936529555979,0.9788760747631073,12.7888765335083,446.35643500000003 +4080,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9806325079676391,0.9806325079676391,0.9749453255203757,16.445659637451172,594.71846 +4488,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9801649208825496,0.9801649208825496,0.9779116862524243,20.943636894226074,768.8230350000001 +4896,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9801838610827375,0.9801838610827375,0.978782474664832,24.856953620910645,967.6114420000001 +5304,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9768055817461814,0.9768055817461814,0.9702080932270808,28.10527801513672,1191.0213970000002 +5712,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9746104009805638,0.9746104009805638,0.9718234131704067,32.14579772949219,1440.171835 +6120,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9697663016832816,0.9697663016832816,0.9621279568251032,36.40912055969238,1717.813161 +6528,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9656810173127011,0.9656810173127011,0.9634765255010708,42.20043754577637,2023.330442 +6936,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9653929343907715,0.9653929343907715,0.9646253117338193,46.972042083740234,2355.811343 +7344,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9635026555903582,0.9635026555903582,0.9611034281810401,50.8284969329834,2716.693574 +7752,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9610372855115469,0.9610372855115469,0.9585597537512924,55.062747955322266,3108.019641 +8160,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9593087388160314,0.9593087388160314,0.9577319445930262,59.75967216491699,3529.9614579999998 +8568,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9598459203922026,0.9598459203922026,0.9601713780248281,65.88526916503906,3981.5521329999997 +8976,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.959108635097493,0.959108635097493,0.9586518345557712,71.85272026062012,4465.222941 +9384,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9573697111797932,0.9573697111797932,0.9561353164275519,78.18439388275146,4984.801354 +9792,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9555714431620876,0.9555714431620876,0.9546392488298882,85.86389446258545,5537.4752260000005 +10200,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9486224139621532,0.9486224139621532,0.9433099305923253,94.35744285583496,6127.477763000001 +10608,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9431507495050439,0.9431507495050439,0.9403442056943527,104.1574821472168,6756.480909000001 +11016,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9408987743985474,0.9408987743985474,0.9399975161043574,113.0038013458252,7421.284759000001 +11424,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9380197846450145,0.9380197846450145,0.936341059397272,121.46645069122314,8125.715779000001 +11832,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9322965091708224,0.9322965091708224,0.9294143034054053,131.1031150817871,8872.899296000001 +12240,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9326742380913473,0.9326742380913473,0.9327603226303838,137.88959789276123,9652.703167000001 +12648,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.927571756147703,0.927571756147703,0.9249549620362734,145.5888376235962,10475.658214000001 +13056,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9247797778628878,0.9247797778628878,0.9237072084771099,154.53871536254883,11346.213643000001 +13464,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9238654088984625,0.9238654088984625,0.9233692422863465,161.3583574295044,12266.045404 +13872,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9202653017086007,0.9202653017086007,0.9191663953636944,170.12918186187744,13231.481182 +14280,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9163106660130261,0.9163106660130261,0.9150341930556871,179.0350112915039,14245.599713 +14688,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9161162933206237,0.9161162933206237,0.9160540991607554,184.834698677063,15311.95464 +15096,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9145412388208016,0.9145412388208016,0.91429667624259,191.58009719848633,16433.239395 +15504,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9105979487841063,0.9105979487841064,0.9097163708309961,200.08039951324463,17613.909715 +15912,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9068568914587393,0.9068568914587393,0.9060681758481206,210.5000762939453,18848.20155 +16320,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9031190636681169,0.9031190636681169,0.9023660107991418,221.55222129821777,20131.794746000003 +16728,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9005799007592515,0.9005799007592515,0.9001704241319546,231.28063201904297,21466.799965000002 +17136,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8989203384884739,0.8989203384884739,0.8987537815839572,248.11264038085938,22840.808564000003 +17544,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.893746793592886,0.8937467935928861,0.892807745348426,267.53482723236084,24265.711089000004 +17952,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8894212021614395,0.8894212021614395,0.8884694521151855,281.7739496231079,25739.620728000005 +18360,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8911705430579008,0.8911705430579007,0.8908032768807751,288.0978307723999,27256.627666000004 +18768,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8911387009111739,0.8911387009111739,0.8906428613252552,296.05272102355957,28820.747713000004 +19176,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8886049543676662,0.8886049543676662,0.8879368647002966,307.68266773223877,30435.231458000006 +19584,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8895470561201042,0.8895470561201042,0.889061241536932,313.4344787597656,32089.799369000008 +19992,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8862488119653844,0.8862488119653844,0.8855123768505595,324.9442596435547,33786.733744000005 +20400,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8810726015981175,0.8810726015981175,0.8799282628097613,338.1390075683594,35528.434162000005 +46,Multiclass classification,Streaming Random Patches,ImageSegments,0.35555555555555557,0.35555555555555557,0.24684873949579833,2.5926971435546875,5.672061 +92,Multiclass classification,Streaming Random Patches,ImageSegments,0.5274725274725275,0.5274725274725275,0.5392220990960486,2.5963096618652344,17.740863 +138,Multiclass classification,Streaming Random Patches,ImageSegments,0.5401459854014599,0.5401459854014599,0.5661177456005042,2.5979232788085938,35.937815 +184,Multiclass classification,Streaming Random Patches,ImageSegments,0.5956284153005464,0.5956284153005464,0.6144104879239446,2.6004638671875,59.975895 +230,Multiclass classification,Streaming Random Patches,ImageSegments,0.6200873362445415,0.6200873362445415,0.6319742698014011,2.6008224487304688,89.681265 +276,Multiclass classification,Streaming Random Patches,ImageSegments,0.6327272727272727,0.6327272727272727,0.6440706793955739,2.601276397705078,125.043898 +322,Multiclass classification,Streaming Random Patches,ImageSegments,0.6573208722741433,0.6573208722741433,0.6535377647060517,2.6028709411621094,166.036362 +368,Multiclass classification,Streaming Random Patches,ImageSegments,0.6784741144414169,0.6784741144414169,0.6717418242612484,2.6031723022460938,212.735146 +414,Multiclass classification,Streaming Random Patches,ImageSegments,0.6900726392251816,0.6900726392251816,0.6823551618652942,2.603717803955078,265.17548899999997 +460,Multiclass classification,Streaming Random Patches,ImageSegments,0.6971677559912854,0.6971677559912854,0.686858403065277,2.6037940979003906,323.486791 +506,Multiclass classification,Streaming Random Patches,ImageSegments,0.699009900990099,0.699009900990099,0.6869845800125663,2.604084014892578,387.600808 +552,Multiclass classification,Streaming Random Patches,ImageSegments,0.6987295825771325,0.6987295825771325,0.6895132041566728,2.6040496826171875,457.32381699999996 +598,Multiclass classification,Streaming Random Patches,ImageSegments,0.7035175879396985,0.7035175879396985,0.6939747146282641,2.6041183471679688,532.682096 +644,Multiclass classification,Streaming Random Patches,ImageSegments,0.6998444790046656,0.6998444790046656,0.6913714585468268,2.6053123474121094,613.490084 +690,Multiclass classification,Streaming Random Patches,ImageSegments,0.7024673439767779,0.7024673439767779,0.6944906634267102,2.6058921813964844,699.880084 +736,Multiclass classification,Streaming Random Patches,ImageSegments,0.7020408163265306,0.7020408163265306,0.69548275919944,2.605987548828125,791.65329 +782,Multiclass classification,Streaming Random Patches,ImageSegments,0.706786171574904,0.706786171574904,0.6991539785967766,2.6064224243164062,888.981823 +828,Multiclass classification,Streaming Random Patches,ImageSegments,0.7085852478839177,0.7085852478839177,0.70309750989463,2.6064682006835938,991.6474969999999 +874,Multiclass classification,Streaming Random Patches,ImageSegments,0.715922107674685,0.7159221076746849,0.7073525059690206,2.6064682006835938,1099.573439 +920,Multiclass classification,Streaming Random Patches,ImageSegments,0.7170837867247007,0.7170837867247007,0.707165908654469,2.6064682006835938,1212.915424 +966,Multiclass classification,Streaming Random Patches,ImageSegments,0.7160621761658031,0.716062176165803,0.7063689525089133,2.6064682006835938,1331.5593390000001 +1012,Multiclass classification,Streaming Random Patches,ImageSegments,0.7151335311572701,0.7151335311572701,0.7047830593764105,2.6064910888671875,1455.3800170000002 +1058,Multiclass classification,Streaming Random Patches,ImageSegments,0.7152317880794702,0.7152317880794702,0.7037726227430311,2.606658935546875,1584.3270810000001 +1104,Multiclass classification,Streaming Random Patches,ImageSegments,0.71441523118767,0.71441523118767,0.7026447500373862,2.6067771911621094,1718.2459470000001 +1150,Multiclass classification,Streaming Random Patches,ImageSegments,0.7162750217580505,0.7162750217580505,0.7030218527348165,2.6067771911621094,1857.0229310000002 +1196,Multiclass classification,Streaming Random Patches,ImageSegments,0.7179916317991631,0.7179916317991631,0.705575475090573,2.379610061645508,2000.273372 +1242,Multiclass classification,Streaming Random Patches,ImageSegments,0.7155519742143432,0.7155519742143431,0.7053749246401603,3.185004234313965,2147.034729 +1288,Multiclass classification,Streaming Random Patches,ImageSegments,0.7156177156177156,0.7156177156177156,0.7041730806550314,3.633350372314453,2296.192092 +1334,Multiclass classification,Streaming Random Patches,ImageSegments,0.7149287321830458,0.7149287321830458,0.7045092702498074,4.368736267089844,2447.850078 +1380,Multiclass classification,Streaming Random Patches,ImageSegments,0.7186366932559826,0.7186366932559827,0.7102131417787841,4.724300384521484,2601.871177 +1426,Multiclass classification,Streaming Random Patches,ImageSegments,0.7256140350877193,0.7256140350877193,0.7174099613082184,4.89253044128418,2758.036552 +1472,Multiclass classification,Streaming Random Patches,ImageSegments,0.7273963290278722,0.7273963290278722,0.7183919320082559,5.412370681762695,2916.512936 +1518,Multiclass classification,Streaming Random Patches,ImageSegments,0.7211601845748187,0.7211601845748187,0.7136134581802791,6.487729072570801,3077.492565 +1564,Multiclass classification,Streaming Random Patches,ImageSegments,0.7172104926423545,0.7172104926423546,0.7129536273040751,6.405126571655273,3241.109 +1610,Multiclass classification,Streaming Random Patches,ImageSegments,0.7209446861404599,0.7209446861404599,0.7163536024764182,6.857941627502441,3407.317179 +1656,Multiclass classification,Streaming Random Patches,ImageSegments,0.7238670694864048,0.7238670694864048,0.7196892738307762,7.034061431884766,3576.2587320000002 +1702,Multiclass classification,Streaming Random Patches,ImageSegments,0.7260435038212816,0.7260435038212816,0.7238533950478148,7.623349189758301,3747.8646120000003 +1748,Multiclass classification,Streaming Random Patches,ImageSegments,0.7309673726388094,0.7309673726388093,0.7286270619416129,8.106144905090332,3922.0639630000005 +1794,Multiclass classification,Streaming Random Patches,ImageSegments,0.7361963190184049,0.7361963190184049,0.7329274067865035,8.185744285583496,4098.8506050000005 +1840,Multiclass classification,Streaming Random Patches,ImageSegments,0.7389885807504079,0.7389885807504077,0.7360694376974826,8.929247856140137,4278.2789060000005 +1886,Multiclass classification,Streaming Random Patches,ImageSegments,0.7411140583554376,0.7411140583554376,0.7396669191579938,9.100563049316406,4460.600751000001 +1932,Multiclass classification,Streaming Random Patches,ImageSegments,0.7431382703262558,0.7431382703262558,0.7411378754700444,9.223885536193848,4645.683986000001 +1978,Multiclass classification,Streaming Random Patches,ImageSegments,0.7465857359635811,0.746585735963581,0.744200926808846,9.401692390441895,4833.458731000001 +2024,Multiclass classification,Streaming Random Patches,ImageSegments,0.7508650519031141,0.7508650519031143,0.7476945996538615,9.481804847717285,5024.230846 +2070,Multiclass classification,Streaming Random Patches,ImageSegments,0.7549540840985983,0.7549540840985983,0.7524477298078486,9.431160926818848,5217.969956000001 +2116,Multiclass classification,Streaming Random Patches,ImageSegments,0.7583924349881797,0.7583924349881797,0.7554386161495508,9.549637794494629,5414.604524000001 +2162,Multiclass classification,Streaming Random Patches,ImageSegments,0.7607589079130033,0.7607589079130033,0.7577216433051415,10.151451110839844,5614.378132000002 +2208,Multiclass classification,Streaming Random Patches,ImageSegments,0.7648391481649298,0.7648391481649298,0.7614528787516565,9.14443588256836,5817.274926000002 +2254,Multiclass classification,Streaming Random Patches,ImageSegments,0.7652019529516201,0.7652019529516201,0.762166830901651,8.801234245300293,6023.198429000002 +2300,Multiclass classification,Streaming Random Patches,ImageSegments,0.7672901261418008,0.7672901261418008,0.7647372124971393,8.857858657836914,6232.074878000002 +2310,Multiclass classification,Streaming Random Patches,ImageSegments,0.7669987007362494,0.7669987007362494,0.7647069285577738,8.926526069641113,6441.814766000002 +1056,Multiclass classification,Streaming Random Patches,Insects,0.6388625592417062,0.6388625592417062,0.6031100134310133,8.730474472045898,177.190345 +2112,Multiclass classification,Streaming Random Patches,Insects,0.659403126480341,0.659403126480341,0.6244477305834598,21.138185501098633,477.66892900000005 +3168,Multiclass classification,Streaming Random Patches,Insects,0.6722450268392801,0.6722450268392801,0.6321534006670183,28.433568000793457,884.814326 +4224,Multiclass classification,Streaming Random Patches,Insects,0.680322045938906,0.680322045938906,0.6340126191391743,39.2259521484375,1406.7405990000002 +5280,Multiclass classification,Streaming Random Patches,Insects,0.6878196628149271,0.6878196628149271,0.6395508722492685,32.51231288909912,2046.4837620000003 +6336,Multiclass classification,Streaming Random Patches,Insects,0.6876085240726125,0.6876085240726125,0.641396967542699,34.576416969299316,2792.1074670000003 +7392,Multiclass classification,Streaming Random Patches,Insects,0.6924638073332431,0.6924638073332431,0.6467777725107727,40.06835174560547,3634.422347 +8448,Multiclass classification,Streaming Random Patches,Insects,0.6949212738250267,0.6949212738250267,0.6476372139610082,43.01965808868408,4571.1184410000005 +9504,Multiclass classification,Streaming Random Patches,Insects,0.6992528675155214,0.6992528675155214,0.6494082466298291,45.71251583099365,5608.992399000001 +10560,Multiclass classification,Streaming Random Patches,Insects,0.7013921772895161,0.7013921772895161,0.6506452100316108,50.310431480407715,6744.395149000001 +11616,Multiclass classification,Streaming Random Patches,Insects,0.7043478260869566,0.7043478260869566,0.6524912605091605,59.279197692871094,7964.619750000001 +12672,Multiclass classification,Streaming Random Patches,Insects,0.7079946334148843,0.7079946334148843,0.6596828376773001,72.61201000213623,9269.589572 +13728,Multiclass classification,Streaming Random Patches,Insects,0.7208421359364756,0.7208421359364756,0.7145906055666686,38.78250694274902,10625.218377000001 +14784,Multiclass classification,Streaming Random Patches,Insects,0.7285395386592708,0.7285395386592708,0.7256542392368915,15.546477317810059,12027.231464 +15840,Multiclass classification,Streaming Random Patches,Insects,0.7206263021655408,0.7206263021655408,0.7196216319492748,14.88278579711914,13491.676191 +16896,Multiclass classification,Streaming Random Patches,Insects,0.7171352471145309,0.7171352471145309,0.7175260611854538,21.28149700164795,15025.812845 +17952,Multiclass classification,Streaming Random Patches,Insects,0.7121051751991533,0.7121051751991533,0.7136617513297842,29.336480140686035,16621.157643 +19008,Multiclass classification,Streaming Random Patches,Insects,0.7205240174672489,0.720524017467249,0.7180961996594418,20.976608276367188,18267.655244999998 +20064,Multiclass classification,Streaming Random Patches,Insects,0.7261625878482779,0.7261625878482779,0.7198561207408494,15.994047164916992,19960.279520999997 +21120,Multiclass classification,Streaming Random Patches,Insects,0.7272598134381363,0.7272598134381363,0.7183389579277755,17.52824878692627,21708.029386999995 +22176,Multiclass classification,Streaming Random Patches,Insects,0.7281623449830891,0.7281623449830891,0.7167723651435352,22.240838050842285,23503.558300999994 +23232,Multiclass classification,Streaming Random Patches,Insects,0.7307477078042272,0.7307477078042272,0.7170791531651185,26.114503860473633,25348.434525999994 +24288,Multiclass classification,Streaming Random Patches,Insects,0.7325318071396221,0.7325318071396222,0.7165563330554671,27.974491119384766,27240.683159999993 +25344,Multiclass classification,Streaming Random Patches,Insects,0.7353904431203883,0.7353904431203884,0.7174524973348954,37.12833023071289,29182.513668999993 +26400,Multiclass classification,Streaming Random Patches,Insects,0.7367703322095533,0.7367703322095533,0.7168965346030137,31.575971603393555,31184.058177999992 +27456,Multiclass classification,Streaming Random Patches,Insects,0.738371881260244,0.738371881260244,0.7164257197178175,35.22733116149902,33213.34443999999 +28512,Multiclass classification,Streaming Random Patches,Insects,0.7366279681526429,0.7366279681526429,0.7161250847684691,17.50509262084961,35271.15690999999 +29568,Multiclass classification,Streaming Random Patches,Insects,0.7354483038522678,0.7354483038522677,0.719616514898752,22.40646266937256,37354.25785299999 +30624,Multiclass classification,Streaming Random Patches,Insects,0.7348724814681775,0.7348724814681775,0.7237598149406717,31.226743698120117,39459.628930999985 +31680,Multiclass classification,Streaming Random Patches,Insects,0.7347769815966413,0.7347769815966413,0.7275990709197302,35.24118995666504,41587.294746999985 +32736,Multiclass classification,Streaming Random Patches,Insects,0.7351458683366427,0.7351458683366427,0.7308983066693725,48.40772724151611,43729.930447999985 +33792,Multiclass classification,Streaming Random Patches,Insects,0.7303423988636027,0.7303423988636027,0.7274356410957497,77.28174114227295,45887.55412199999 +34848,Multiclass classification,Streaming Random Patches,Insects,0.726805750853732,0.726805750853732,0.723911701718825,53.16175174713135,48068.300588999984 +35904,Multiclass classification,Streaming Random Patches,Insects,0.7248976408656658,0.7248976408656659,0.7218080521646734,41.53026580810547,50265.619736999986 +36960,Multiclass classification,Streaming Random Patches,Insects,0.7215833761735978,0.7215833761735979,0.7182506744185386,35.33352756500244,52485.185911999986 +38016,Multiclass classification,Streaming Random Patches,Insects,0.7196369854004998,0.7196369854004999,0.7160236415660819,44.00273513793945,54721.05808499999 +39072,Multiclass classification,Streaming Random Patches,Insects,0.7175142688950884,0.7175142688950884,0.713988650041017,46.12203025817871,56978.30571199999 +40128,Multiclass classification,Streaming Random Patches,Insects,0.7158023276098388,0.7158023276098388,0.7126852582249207,27.841010093688965,59249.54765999999 +41184,Multiclass classification,Streaming Random Patches,Insects,0.7157322196051769,0.715732219605177,0.7129296468122535,21.849401473999023,61534.70422899999 +42240,Multiclass classification,Streaming Random Patches,Insects,0.7156656170837378,0.7156656170837377,0.7131576552849198,28.021278381347656,63833.596648999985 +43296,Multiclass classification,Streaming Random Patches,Insects,0.715925626515764,0.715925626515764,0.7137513847694824,36.50454139709473,66146.66403399999 +44352,Multiclass classification,Streaming Random Patches,Insects,0.7161958016730176,0.7161958016730177,0.7143198962298327,46.888444900512695,68474.34057599999 +45408,Multiclass classification,Streaming Random Patches,Insects,0.7170260092056291,0.7170260092056291,0.7151715877390813,47.08374786376953,70816.527322 +46464,Multiclass classification,Streaming Random Patches,Insects,0.7181843617502098,0.7181843617502098,0.7162864260409335,43.18325901031494,73172.526917 +47520,Multiclass classification,Streaming Random Patches,Insects,0.7179023127591069,0.7179023127591069,0.716246618663062,54.80090522766113,75543.857611 +48576,Multiclass classification,Streaming Random Patches,Insects,0.7211528564076171,0.7211528564076171,0.719707905487922,60.4919376373291,77931.086228 +49632,Multiclass classification,Streaming Random Patches,Insects,0.7250710241582882,0.7250710241582882,0.7236001513027165,35.55128765106201,80332.853368 +50688,Multiclass classification,Streaming Random Patches,Insects,0.7288259316984631,0.7288259316984631,0.7271241427068512,21.017152786254883,82746.274567 +51744,Multiclass classification,Streaming Random Patches,Insects,0.7329107318864387,0.7329107318864386,0.7308784460773333,26.768343925476074,85169.877802 +52800,Multiclass classification,Streaming Random Patches,Insects,0.7359230288452433,0.7359230288452432,0.7343606492383059,9.57052230834961,87600.592492 +52848,Multiclass classification,Streaming Random Patches,Insects,0.7361628853104244,0.7361628853104244,0.7346220154259927,9.63199520111084,90031.55993999999 +408,Multiclass classification,Streaming Random Patches,Keystroke,0.9901719901719902,0.9901719901719902,0.8308395677472984,1.027322769165039,17.348128 +816,Multiclass classification,Streaming Random Patches,Keystroke,0.9877300613496932,0.9877300613496932,0.9320293882508496,2.6651391983032227,54.406226000000004 +1224,Multiclass classification,Streaming Random Patches,Keystroke,0.9828291087489779,0.9828291087489779,0.9464059415055075,5.679329872131348,114.007104 +1632,Multiclass classification,Streaming Random Patches,Keystroke,0.9828326180257511,0.9828326180257511,0.9632097550305241,8.335807800292969,201.582874 +2040,Multiclass classification,Streaming Random Patches,Keystroke,0.9749877390877881,0.9749877390877881,0.9373958892668122,12.631415367126465,318.920922 +2448,Multiclass classification,Streaming Random Patches,Keystroke,0.9701675521046179,0.9701675521046179,0.957381800109682,16.891732215881348,465.74626 +2856,Multiclass classification,Streaming Random Patches,Keystroke,0.9660245183887916,0.9660245183887916,0.9394754450400101,22.937668800354004,641.328845 +3264,Multiclass classification,Streaming Random Patches,Keystroke,0.9616916947594238,0.9616916947594238,0.9454054748805115,29.12161636352539,847.207258 +3672,Multiclass classification,Streaming Random Patches,Keystroke,0.9607736311631708,0.9607736311631708,0.953605417859829,28.669262886047363,1081.626023 +4080,Multiclass classification,Streaming Random Patches,Keystroke,0.9578328021573915,0.9578328021573915,0.9463612240153171,34.20732402801514,1345.493591 +4488,Multiclass classification,Streaming Random Patches,Keystroke,0.9589926454201025,0.9589926454201025,0.9613092683363473,18.652557373046875,1636.499529 +4896,Multiclass classification,Streaming Random Patches,Keystroke,0.9607763023493361,0.9607763023493361,0.9605208703626084,20.81053066253662,1952.783692 +5304,Multiclass classification,Streaming Random Patches,Keystroke,0.9615312087497643,0.9615312087497643,0.9603033149830379,27.91543483734131,2294.145458 +5712,Multiclass classification,Streaming Random Patches,Keystroke,0.9616529504465068,0.9616529504465068,0.9605387671994151,32.60424041748047,2660.691575 +6120,Multiclass classification,Streaming Random Patches,Keystroke,0.9597973525085798,0.9597973525085798,0.9561203427932812,39.11091995239258,3053.1932039999997 +6528,Multiclass classification,Streaming Random Patches,Keystroke,0.9589397885705531,0.9589397885705531,0.9571591040678328,29.255366325378418,3470.643733 +6936,Multiclass classification,Streaming Random Patches,Keystroke,0.959913482335977,0.959913482335977,0.9605956598361813,31.930577278137207,3910.602191 +7344,Multiclass classification,Streaming Random Patches,Keystroke,0.9602342366880022,0.9602342366880022,0.9598619882355601,26.562703132629395,4374.151898 +7752,Multiclass classification,Streaming Random Patches,Keystroke,0.9601341762353245,0.9601341762353245,0.9596510454605859,31.588034629821777,4858.190889 +8160,Multiclass classification,Streaming Random Patches,Keystroke,0.9584507905380562,0.9584507905380562,0.9567204261955369,39.12565612792969,5363.543193 +8568,Multiclass classification,Streaming Random Patches,Keystroke,0.9579782887825377,0.9579782887825377,0.957794146577291,44.816758155822754,5892.06228 +8976,Multiclass classification,Streaming Random Patches,Keystroke,0.9579944289693594,0.9579944289693594,0.9581242571113369,49.5586576461792,6445.036349 +9384,Multiclass classification,Streaming Random Patches,Keystroke,0.9570499840136417,0.9570499840136417,0.9565283447410108,50.185367584228516,7025.065903 +9792,Multiclass classification,Streaming Random Patches,Keystroke,0.9563885200694515,0.9563885200694515,0.9560487952418978,54.40623474121094,7630.795018999999 +10200,Multiclass classification,Streaming Random Patches,Keystroke,0.9532307088930287,0.9532307088930287,0.9512518567217172,66.82855319976807,8267.200675 +10608,Multiclass classification,Streaming Random Patches,Keystroke,0.9519185443574998,0.9519185443574998,0.9512557409849248,41.17615795135498,8934.408603 +11016,Multiclass classification,Streaming Random Patches,Keystroke,0.9528824330458465,0.9528824330458465,0.953398407731189,32.87209510803223,9625.903537 +11424,Multiclass classification,Streaming Random Patches,Keystroke,0.953689923837871,0.953689923837871,0.9540175301991308,28.078542709350586,10339.782646 +11832,Multiclass classification,Streaming Random Patches,Keystroke,0.9542726734849125,0.9542726734849125,0.9545119777330118,20.280012130737305,11070.86282 +12240,Multiclass classification,Streaming Random Patches,Keystroke,0.955470218155078,0.955470218155078,0.9559406438939211,21.54300308227539,11820.445116 +12648,Multiclass classification,Streaming Random Patches,Keystroke,0.9559579346880683,0.9559579346880683,0.9561632451269845,26.89114284515381,12588.394717000001 +13056,Multiclass classification,Streaming Random Patches,Keystroke,0.9558789735733435,0.9558789735733435,0.9559075747932771,21.382742881774902,13375.587705000002 +13464,Multiclass classification,Streaming Random Patches,Keystroke,0.9563247418851668,0.9563247418851668,0.9565051554876024,21.864919662475586,14180.689980000001 +13872,Multiclass classification,Streaming Random Patches,Keystroke,0.9569605652079879,0.9569605652079879,0.9571856017401091,25.72835636138916,15004.794290000002 +14280,Multiclass classification,Streaming Random Patches,Keystroke,0.9566496253239022,0.9566496253239022,0.9566382966080723,21.764866828918457,15848.339318000002 +14688,Multiclass classification,Streaming Random Patches,Keystroke,0.957241097569279,0.9572410975692791,0.957426459656079,25.14582061767578,16708.048820000004 +15096,Multiclass classification,Streaming Random Patches,Keystroke,0.9580655846306724,0.9580655846306724,0.9582773620158959,26.658535957336426,17588.164126000003 +15504,Multiclass classification,Streaming Random Patches,Keystroke,0.9584596529703928,0.9584596529703928,0.9585840009788793,30.767892837524414,18489.703328000003 +15912,Multiclass classification,Streaming Random Patches,Keystroke,0.9580793161963421,0.9580793161963421,0.9580713134265897,27.786094665527344,19412.324967000004 +16320,Multiclass classification,Streaming Random Patches,Keystroke,0.958514614866107,0.958514614866107,0.9586173296332885,25.79348850250244,20355.979039000005 +16728,Multiclass classification,Streaming Random Patches,Keystroke,0.9577330065164106,0.9577330065164106,0.9576699214368118,33.630208015441895,21321.132478000007 +17136,Multiclass classification,Streaming Random Patches,Keystroke,0.9576305806828129,0.9576305806828129,0.9576693803774444,33.920249938964844,22315.653729000005 +17544,Multiclass classification,Streaming Random Patches,Keystroke,0.956506868836573,0.956506868836573,0.9564470129227677,31.505155563354492,23335.132930000003 +17952,Multiclass classification,Streaming Random Patches,Keystroke,0.9563812600969306,0.9563812600969306,0.9564135249623555,19.79563045501709,24372.247222 +18360,Multiclass classification,Streaming Random Patches,Keystroke,0.9569148646440437,0.9569148646440437,0.9569804233582649,23.70892333984375,25428.663478000002 +18768,Multiclass classification,Streaming Random Patches,Keystroke,0.9574252677572335,0.9574252677572335,0.957475477736454,21.893744468688965,26504.246634000003 +19176,Multiclass classification,Streaming Random Patches,Keystroke,0.9568187744458931,0.9568187744458931,0.956806677474395,28.04871368408203,27598.635240000003 +19584,Multiclass classification,Streaming Random Patches,Keystroke,0.9567992646683348,0.9567992646683348,0.9568012672257533,32.11082458496094,28712.463090000005 +19992,Multiclass classification,Streaming Random Patches,Keystroke,0.9565304386974138,0.9565304386974138,0.9565268274864178,40.13526153564453,29849.822929000005 +20400,Multiclass classification,Streaming Random Patches,Keystroke,0.9559292122162851,0.9559292122162851,0.9559196349550496,39.63601016998291,31009.846621000004 +46,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.5111111111111111,0.5111111111111111,0.40938578329882686,0.09116363525390625,0.155273 +92,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6043956043956044,0.6043956043956044,0.5940974230447915,0.16827392578125,0.72683 +138,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6715328467153284,0.6715328467153284,0.6806196928151186,0.24543190002441406,1.742293 +184,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7049180327868853,0.7049180327868853,0.7184732466987995,0.32204627990722656,3.3407109999999998 +230,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.74235807860262,0.74235807860262,0.7523809662907407,0.39917659759521484,5.610709 +276,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7490909090909091,0.7490909090909091,0.7611097615339608,0.47675609588623047,8.7745 +322,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7663551401869159,0.766355140186916,0.7725898650917747,0.5538606643676758,12.918764 +368,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.784741144414169,0.7847411444141691,0.7844949397573193,0.6304874420166016,18.189003 +414,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7990314769975787,0.7990314769975787,0.7976353129150817,0.7076187133789062,24.731741 +460,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7952069716775599,0.7952069716775599,0.7930763833747545,0.7847471237182617,32.681045 +506,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7960396039603961,0.7960396039603961,0.7941234022368324,3.003793716430664,61.211909999999996 +552,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8021778584392014,0.8021778584392014,0.8007250644998717,3.2264842987060547,91.16202899999999 +598,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8090452261306532,0.8090452261306531,0.8095532779239047,3.4499826431274414,122.67972799999998 +644,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8164852255054432,0.8164852255054433,0.8176018556357175,3.6760778427124023,155.77468699999997 +690,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8214804063860668,0.8214804063860668,0.8221151176242331,3.8941650390625,190.53727699999996 +736,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8272108843537415,0.8272108843537415,0.8281233770721121,4.128121376037598,227.08550299999996 +782,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8361075544174136,0.8361075544174136,0.8364659566156888,4.367749214172363,265.419762 +828,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8403869407496977,0.8403869407496977,0.8412749002251585,4.601743698120117,305.543518 +874,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.845360824742268,0.845360824742268,0.8465057584066101,4.840575218200684,347.501906 +920,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8487486398258978,0.8487486398258978,0.8489576083149123,5.074535369873047,391.43023400000004 +966,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8538860103626943,0.8538860103626943,0.8530581393966605,5.3079938888549805,437.316456 +1012,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8585558852621167,0.8585558852621167,0.8570252804249208,5.479596138000488,485.23685 +1058,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8628192999053926,0.8628192999053927,0.8611045332429007,5.435150146484375,535.278485 +1104,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8631006346328196,0.8631006346328196,0.8616288881212748,5.355225563049316,587.436372 +1150,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8668407310704961,0.8668407310704961,0.8650902600877293,5.281754493713379,641.538536 +1196,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8719665271966527,0.8719665271966527,0.8702683106604537,5.235520362854004,697.554251 +1242,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8759065269943593,0.8759065269943593,0.8740479640614998,5.142333984375,755.471933 +1288,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8787878787878788,0.8787878787878788,0.8772603222806128,5.092559814453125,815.138635 +1334,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8777194298574643,0.8777194298574643,0.8760741143565023,5.055940628051758,876.491339 +1380,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8796229151559101,0.8796229151559101,0.8783130803325612,4.964084625244141,939.483975 +1426,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8785964912280702,0.8785964912280702,0.8768931648451159,4.951287269592285,1004.010152 +1472,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8769544527532291,0.8769544527532291,0.8748964905672628,4.969002723693848,1070.168576 +1518,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8727752142386289,0.8727752142386289,0.8705110235515202,5.101251602172852,1138.304608 +1564,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8688419705694178,0.8688419705694178,0.8667015278861958,5.262187957763672,1208.4659419999998 +1610,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8651336233685519,0.8651336233685519,0.8631350462642483,5.320252418518066,1280.5305069999997 +1656,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8640483383685801,0.8640483383685801,0.8620479268968886,5.35189151763916,1354.3819709999998 +1702,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8647854203409759,0.8647854203409759,0.8635043959538364,5.359102249145508,1430.0286029999997 +1748,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.866056096164854,0.866056096164854,0.864439618601765,5.402237892150879,1507.5136589999997 +1794,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8683770217512549,0.8683770217512549,0.8664209902402824,5.3993330001831055,1586.7199259999998 +1840,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8694942903752039,0.8694942903752039,0.867597342266498,5.4049272537231445,1667.6587319999999 +1886,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8710875331564987,0.8710875331564986,0.8694766742923737,5.4121294021606445,1750.3169159999998 +1932,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8705334023821854,0.8705334023821854,0.8686918451193435,5.405803680419922,1834.6099799999997 +1978,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8715225088517956,0.8715225088517956,0.8698703895904014,5.395906448364258,1920.5171279999997 +2024,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8729609490855166,0.8729609490855166,0.870902914954928,5.386837959289551,2008.1064549999996 +2070,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8733687771870469,0.8733687771870469,0.8714525187304558,5.375288963317871,2097.403794 +2116,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.875177304964539,0.875177304964539,0.8730645404016979,5.353263854980469,2188.326937 +2162,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8745950948634891,0.8745950948634891,0.872417325547954,5.322790145874023,2280.8823749999997 +2208,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8753964657906661,0.8753964657906661,0.8732500176589647,5.30323600769043,2374.9757969999996 +2254,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8748335552596538,0.8748335552596538,0.8732733602208504,5.278659820556641,2470.7327649999997 +2300,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8742931709438887,0.8742931709438887,0.8727466012343671,5.262259483337402,2568.119206 +2310,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8735383282806409,0.8735383282806409,0.8721361121313428,5.268708229064941,2666.293295 +1056,Multiclass classification,k-Nearest Neighbors,Insects,0.6597156398104266,0.6597156398104266,0.5853273709738578,6.371035575866699,65.756564 +2112,Multiclass classification,k-Nearest Neighbors,Insects,0.6807200378967314,0.6807200378967314,0.5992086579995298,6.2783002853393555,182.184591 +3168,Multiclass classification,k-Nearest Neighbors,Insects,0.6842437638143354,0.6842437638143354,0.6001715208792017,6.298460006713867,341.998975 +4224,Multiclass classification,k-Nearest Neighbors,Insects,0.6848212171442103,0.6848212171442103,0.6051604277089342,6.265153884887695,541.5068689999999 +5280,Multiclass classification,k-Nearest Neighbors,Insects,0.6872513733661678,0.6872513733661678,0.611100448555976,6.2555742263793945,777.52113 +6336,Multiclass classification,k-Nearest Neighbors,Insects,0.6842936069455406,0.6842936069455406,0.6118525331169307,6.3140106201171875,1048.588472 +7392,Multiclass classification,k-Nearest Neighbors,Insects,0.6852929238262752,0.6852929238262752,0.6157762907660722,6.288516998291016,1352.458798 +8448,Multiclass classification,k-Nearest Neighbors,Insects,0.6828459808215934,0.6828459808215934,0.6148503710479976,6.31680965423584,1687.096094 +9504,Multiclass classification,k-Nearest Neighbors,Insects,0.6851520572450805,0.6851520572450805,0.6155258331015067,6.223039627075195,2051.649807 +10560,Multiclass classification,k-Nearest Neighbors,Insects,0.6861445212614831,0.6861445212614831,0.6169474950376627,6.253497123718262,2444.130945 +11616,Multiclass classification,k-Nearest Neighbors,Insects,0.6873009040034438,0.6873009040034438,0.6200568175672779,6.251482009887695,2863.1289039999997 +12672,Multiclass classification,k-Nearest Neighbors,Insects,0.6866072133217583,0.6866072133217583,0.623883491026523,6.276742935180664,3309.0829679999997 +13728,Multiclass classification,k-Nearest Neighbors,Insects,0.7020470605376266,0.7020470605376266,0.6991473808978487,6.26933479309082,3781.2775089999996 +14784,Multiclass classification,k-Nearest Neighbors,Insects,0.7077724413177299,0.7077724413177299,0.7078402863830927,6.244691848754883,4278.402760999999 +15840,Multiclass classification,k-Nearest Neighbors,Insects,0.7016857124818486,0.7016857124818486,0.704840832390747,6.350223541259766,4805.403565999999 +16896,Multiclass classification,k-Nearest Neighbors,Insects,0.6992009470257473,0.6992009470257473,0.7048178275842342,6.243149757385254,5357.683726999999 +17952,Multiclass classification,k-Nearest Neighbors,Insects,0.6922734109520361,0.6922734109520361,0.6995766929659905,6.218992233276367,5935.240105999998 +19008,Multiclass classification,k-Nearest Neighbors,Insects,0.6974272636397116,0.6974272636397116,0.7006862112488368,6.24652099609375,6538.276384999998 +20064,Multiclass classification,k-Nearest Neighbors,Insects,0.699845486716842,0.699845486716842,0.6985118222305657,6.205791473388672,7167.459726999999 +21120,Multiclass classification,k-Nearest Neighbors,Insects,0.7016904209479615,0.7016904209479615,0.6971610909052677,6.218420028686523,7825.840650999999 +22176,Multiclass classification,k-Nearest Neighbors,Insects,0.7039909808342728,0.7039909808342728,0.6964197759629052,6.236072540283203,8511.079801999998 +23232,Multiclass classification,k-Nearest Neighbors,Insects,0.7076320433902974,0.7076320433902974,0.697368621848442,6.279313087463379,9222.986801999998 +24288,Multiclass classification,k-Nearest Neighbors,Insects,0.7098447729237863,0.7098447729237863,0.6967477548491564,6.2948198318481445,9960.927248999997 +25344,Multiclass classification,k-Nearest Neighbors,Insects,0.7127017322337529,0.712701732233753,0.6972185032799825,6.224791526794434,10724.419586999997 +26400,Multiclass classification,k-Nearest Neighbors,Insects,0.7145346414636918,0.7145346414636918,0.6967850611237018,6.263523101806641,11512.986172999998 +27456,Multiclass classification,k-Nearest Neighbors,Insects,0.7156437807321071,0.7156437807321071,0.6955595874776194,6.272575378417969,12326.628602999997 +28512,Multiclass classification,k-Nearest Neighbors,Insects,0.7130931921012942,0.7130931921012942,0.6943090782068162,6.22489070892334,13165.433758999998 +29568,Multiclass classification,k-Nearest Neighbors,Insects,0.7117732607298678,0.7117732607298677,0.6978751959025926,6.217726707458496,14028.837757999998 +30624,Multiclass classification,k-Nearest Neighbors,Insects,0.7122097769650263,0.7122097769650264,0.7026862643890369,6.243690490722656,14916.319238999999 +31680,Multiclass classification,k-Nearest Neighbors,Insects,0.7113545250797058,0.7113545250797058,0.7052714328980031,6.277059555053711,15827.904481999998 +32736,Multiclass classification,k-Nearest Neighbors,Insects,0.7111959676187567,0.7111959676187566,0.7078689284492299,6.295280456542969,16762.400180999997 +33792,Multiclass classification,k-Nearest Neighbors,Insects,0.7067562368678051,0.7067562368678051,0.704703743720216,6.183221817016602,17721.950532 +34848,Multiclass classification,k-Nearest Neighbors,Insects,0.7030734353028956,0.7030734353028956,0.7010614031639846,6.343389511108398,18710.094933 +35904,Multiclass classification,k-Nearest Neighbors,Insects,0.6998022449377489,0.6998022449377489,0.6976694331042329,6.273009300231934,19725.980479 +36960,Multiclass classification,k-Nearest Neighbors,Insects,0.6967179847939609,0.6967179847939609,0.6945045780432343,6.264690399169922,20767.047301000002 +38016,Multiclass classification,k-Nearest Neighbors,Insects,0.6941470472182033,0.6941470472182033,0.6917813776610243,6.265054702758789,21835.556239 +39072,Multiclass classification,k-Nearest Neighbors,Insects,0.691996621535154,0.691996621535154,0.6898060776768534,6.200959205627441,22932.108791000002 +40128,Multiclass classification,k-Nearest Neighbors,Insects,0.6904328756199068,0.6904328756199068,0.6882031611963276,6.413609504699707,24054.967875000002 +41184,Multiclass classification,k-Nearest Neighbors,Insects,0.6916446106403128,0.6916446106403128,0.6892941373261507,6.3101043701171875,25204.026441 +42240,Multiclass classification,k-Nearest Neighbors,Insects,0.692535334643339,0.692535334643339,0.6900712004452627,6.22797966003418,26378.286294 +43296,Multiclass classification,k-Nearest Neighbors,Insects,0.6935904838895947,0.6935904838895947,0.6909354899104013,6.220904350280762,27574.213319000002 +44352,Multiclass classification,k-Nearest Neighbors,Insects,0.6941895334941715,0.6941895334941715,0.691322114366645,6.22946834564209,28791.926615000004 +45408,Multiclass classification,k-Nearest Neighbors,Insects,0.6950690422181602,0.6950690422181602,0.6917362410920441,6.2850341796875,30030.670852000003 +46464,Multiclass classification,k-Nearest Neighbors,Insects,0.6964466349568473,0.6964466349568473,0.6926338572817136,6.2335004806518555,31290.345387 +47520,Multiclass classification,k-Nearest Neighbors,Insects,0.6963530377322755,0.6963530377322755,0.6929015597977773,6.2439117431640625,32571.46119 +48576,Multiclass classification,k-Nearest Neighbors,Insects,0.7006073082861555,0.7006073082861555,0.697843135408715,6.247167587280273,33874.398319 +49632,Multiclass classification,k-Nearest Neighbors,Insects,0.7046805424029337,0.7046805424029337,0.7023003034160373,6.246943473815918,35198.416197 +50688,Multiclass classification,k-Nearest Neighbors,Insects,0.7083867658373942,0.7083867658373942,0.7061355873839065,6.210485458374023,36536.506394 +51744,Multiclass classification,k-Nearest Neighbors,Insects,0.7126567844925884,0.7126567844925883,0.7104085577951368,6.270394325256348,37890.628371 +52800,Multiclass classification,k-Nearest Neighbors,Insects,0.7128544101214038,0.7128544101214038,0.7110869129037599,6.247260093688965,39264.666928 +52848,Multiclass classification,k-Nearest Neighbors,Insects,0.7131152194069673,0.7131152194069672,0.7113808258412672,6.272693634033203,40639.937472 +408,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.0294876098632812,8.610537 +816,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9251533742331288,0.9251533742331288,0.8588670451436246,5.3865966796875,60.879099000000004 +1224,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9247751430907605,0.9247751430907604,0.888226412135106,6.246823310852051,137.71097600000002 +1632,Multiclass classification,k-Nearest Neighbors,Keystroke,0.927038626609442,0.927038626609442,0.893336805209695,6.212030410766602,236.990146 +2040,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9298675821481118,0.9298675821481118,0.911424130088645,6.329436302185059,359.011082 +2448,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9239885574172456,0.9239885574172456,0.9121555472954921,6.208271026611328,503.2068 +2856,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9169877408056042,0.9169877408056042,0.8816257260944811,6.284844398498535,667.387655 +3264,Multiclass classification,k-Nearest Neighbors,Keystroke,0.908979466748391,0.908979466748391,0.9011431783951355,6.232160568237305,850.70223 +3672,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133751021520021,0.9133751021520021,0.9125908871445695,6.234919548034668,1054.346916 +4080,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9141946555528315,0.9141946555528315,0.9054789816810689,6.308258056640625,1277.35851 +4488,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9057276576777357,0.9057276576777357,0.9087691557812896,6.2749834060668945,1518.241674 +4896,Multiclass classification,k-Nearest Neighbors,Keystroke,0.908682328907048,0.908682328907048,0.9101970481905531,6.210176467895508,1774.8436390000002 +5304,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9092966245521403,0.9092966245521403,0.9045962329696907,6.311163902282715,2048.2991580000003 +5712,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9110488530905271,0.9110488530905271,0.9114244990736602,6.304790496826172,2337.0378760000003 +6120,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9089720542572316,0.9089720542572316,0.9032533666541098,6.217576026916504,2640.3521840000003 +6528,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9077677340278841,0.9077677340278841,0.9071900335968285,6.258184432983398,2958.617579 +6936,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9105984138428262,0.9105984138428262,0.9126270814361048,6.1720380783081055,3289.9253120000003 +7344,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9120250578782514,0.9120250578782514,0.9125633522308233,6.2164154052734375,3633.8320570000005 +7752,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133015094826474,0.9133015094826474,0.9136330015220733,6.260525703430176,3992.0508240000004 +8160,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9148179924010296,0.9148179924010296,0.9154195917586071,6.291139602661133,4363.477247000001 +8568,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9166569394186996,0.9166569394186995,0.9177086422960681,6.216147422790527,4747.269196000001 +8976,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9179944289693593,0.9179944289693593,0.9193727618453186,6.204837799072266,5142.613476000001 +9384,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9180432697431525,0.9180432697431525,0.9181590265081533,6.197464942932129,5549.1165660000015 +9792,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9166581554488816,0.9166581554488816,0.9168210748678531,6.232473373413086,5968.023607000002 +10200,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9154819099911756,0.9154819099911756,0.9145218669909496,6.197787284851074,6399.011787000002 +10608,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9126048835674555,0.9126048835674555,0.9111025938131312,6.2185258865356445,6842.990199000003 +11016,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9106672719019518,0.9106672719019518,0.911227786024665,6.261377334594727,7300.041981000002 +11424,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9103562986956141,0.9103562986956141,0.9101104800687125,6.227293968200684,7769.979179000002 +11832,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9092215366410278,0.9092215366410278,0.9094186121236619,6.287784576416016,8252.975566000001 +12240,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9110221423318898,0.9110221423318898,0.9118339797691071,6.31197452545166,8747.696417000001 +12648,Multiclass classification,k-Nearest Neighbors,Keystroke,0.912627500593026,0.912627500593026,0.9131272841889786,6.279851913452148,9255.008595000001 +13056,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9129069322098813,0.9129069322098813,0.913006147591119,6.346117973327637,9774.692327 +13464,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9136150932184506,0.9136150932184506,0.9138210444048112,6.238006591796875,10306.725428000002 +13872,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9137769447047798,0.9137769447047797,0.913931693448659,6.288792610168457,10851.686584000001 +14280,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9121787240002801,0.9121787240002801,0.9118234284090696,6.252389907836914,11409.551357 +14688,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133247089262613,0.9133247089262613,0.9136581918124823,6.268362998962402,11980.223294 +15096,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9134150380920835,0.9134150380920835,0.9134700562544149,6.304409027099609,12563.090191 +15504,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9127910726956073,0.9127910726956073,0.9127632118282708,6.222077369689941,13158.500651999999 +15912,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9127019043429074,0.9127019043429074,0.9127365496247325,6.263586044311523,13766.752283999998 +16320,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113303511244562,0.9113303511244562,0.9110956080213111,6.256609916687012,14388.121761999999 +16728,Multiclass classification,k-Nearest Neighbors,Keystroke,0.910384408441442,0.9103844084414419,0.9103324360258119,6.2080230712890625,15023.088924 +17136,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113510358914503,0.9113510358914503,0.9114963483082135,6.223179817199707,15670.063673 +17544,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113606566721769,0.9113606566721769,0.9113826667045093,6.23558235168457,16331.265293 +17952,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9114255473232689,0.911425547323269,0.9114384409485988,6.255133628845215,17006.553944 +18360,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9126858761370445,0.9126858761370445,0.9127656000580755,6.270404815673828,17693.792261 +18768,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9125059945649278,0.9125059945649276,0.9124701420883569,6.180520057678223,18393.849908 +19176,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9125945241199479,0.9125945241199479,0.9125632790621417,6.265439987182617,19106.227212 +19584,Multiclass classification,k-Nearest Neighbors,Keystroke,0.913547464637696,0.913547464637696,0.9135225066457016,6.303133964538574,19831.701162 +19992,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9111099994997749,0.9111099994997749,0.910917465793804,6.225028991699219,20571.760391 +20400,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9104858081278494,0.9104858081278494,0.9103279821226861,6.325108528137207,21326.45228 +46,Multiclass classification,ADWIN Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.245764972655729,4.105147361755371,2.153154 +92,Multiclass classification,ADWIN Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.4934752395581889,4.108363151550293,6.907408 +138,Multiclass classification,ADWIN Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646677,4.108027458190918,14.639156 +184,Multiclass classification,ADWIN Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.614143164890895,4.108977317810059,25.443956 +230,Multiclass classification,ADWIN Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,3.881842613220215,39.254234 +276,Multiclass classification,ADWIN Bagging,ImageSegments,0.64,0.64,0.6559607038460422,3.996514320373535,55.768073 +322,Multiclass classification,ADWIN Bagging,ImageSegments,0.6697819314641744,0.6697819314641744,0.6706320385346652,4.112936019897461,74.877199 +368,Multiclass classification,ADWIN Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6897433526546475,4.112924575805664,96.687005 +414,Multiclass classification,ADWIN Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.706570530482581,4.117301940917969,121.290167 +460,Multiclass classification,ADWIN Bagging,ImageSegments,0.7145969498910676,0.7145969498910676,0.7071122267088654,4.116390228271484,148.551711 +506,Multiclass classification,ADWIN Bagging,ImageSegments,0.7247524752475247,0.7247524752475247,0.7147973207987898,4.115703582763672,178.365171 +552,Multiclass classification,ADWIN Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7210771168277493,4.115436553955078,210.796119 +598,Multiclass classification,ADWIN Bagging,ImageSegments,0.7336683417085427,0.7336683417085426,0.7250288715672424,4.115207672119141,245.953277 +644,Multiclass classification,ADWIN Bagging,ImageSegments,0.7325038880248833,0.7325038880248833,0.7258924883659029,4.118658065795898,283.80303000000004 +690,Multiclass classification,ADWIN Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7302536378735861,4.118425369262695,324.296282 +736,Multiclass classification,ADWIN Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7329631379486719,4.118097305297852,367.43284 +782,Multiclass classification,ADWIN Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7387105187530085,4.117616653442383,413.28289 +828,Multiclass classification,ADWIN Bagging,ImageSegments,0.7460701330108828,0.7460701330108827,0.7425025596154723,4.117326736450195,461.953497 +874,Multiclass classification,ADWIN Bagging,ImageSegments,0.7514318442153494,0.7514318442153494,0.7467163857842193,4.117303848266602,513.2937440000001 +920,Multiclass classification,ADWIN Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7453933609147309,4.117105484008789,567.431634 +966,Multiclass classification,ADWIN Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.7451117895470661,4.116701126098633,624.2209760000001 +1012,Multiclass classification,ADWIN Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.744963080481548,4.116399765014648,683.8404210000001 +1058,Multiclass classification,ADWIN Bagging,ImageSegments,0.7511825922421949,0.7511825922421949,0.7446315489945475,4.117582321166992,746.2097300000001 +1104,Multiclass classification,ADWIN Bagging,ImageSegments,0.7533998186763372,0.7533998186763373,0.7466082689908061,4.117956161499023,811.1743250000002 +1150,Multiclass classification,ADWIN Bagging,ImageSegments,0.7563098346388164,0.7563098346388164,0.7491651771194966,4.117490768432617,878.6809830000002 +1196,Multiclass classification,ADWIN Bagging,ImageSegments,0.7589958158995815,0.7589958158995815,0.7526420027035883,4.117303848266602,948.8627130000002 +1242,Multiclass classification,ADWIN Bagging,ImageSegments,0.75825946817083,0.7582594681708301,0.7524016178277559,4.11713981628418,1021.5806660000002 +1288,Multiclass classification,ADWIN Bagging,ImageSegments,0.7637917637917638,0.7637917637917638,0.75666252908711,4.117353439331055,1096.9757510000002 +1334,Multiclass classification,ADWIN Bagging,ImageSegments,0.7636909227306826,0.7636909227306825,0.7569484848610158,4.11726188659668,1175.015685 +1380,Multiclass classification,ADWIN Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7590436403579585,4.11729621887207,1255.693831 +1426,Multiclass classification,ADWIN Bagging,ImageSegments,0.767719298245614,0.767719298245614,0.761211289695921,4.117136001586914,1338.965745 +1472,Multiclass classification,ADWIN Bagging,ImageSegments,0.7722637661454793,0.7722637661454793,0.764056696643358,4.117197036743164,1424.822234 +1518,Multiclass classification,ADWIN Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.764234133414765,4.117246627807617,1513.291691 +1564,Multiclass classification,ADWIN Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7653316001442944,4.117277145385742,1604.2857379999998 +1610,Multiclass classification,ADWIN Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7647353044337893,4.11713981628418,1697.9838939999997 +1656,Multiclass classification,ADWIN Bagging,ImageSegments,0.7734138972809668,0.7734138972809667,0.7645730180903108,4.116628646850586,1794.3974679999997 +1702,Multiclass classification,ADWIN Bagging,ImageSegments,0.7724867724867724,0.7724867724867724,0.7656182355666586,4.116819381713867,1893.3012909999998 +1748,Multiclass classification,ADWIN Bagging,ImageSegments,0.7750429307384087,0.7750429307384087,0.7677424040514297,4.116933822631836,1994.6627269999997 +1794,Multiclass classification,ADWIN Bagging,ImageSegments,0.7763524818739542,0.7763524818739542,0.7677176136548693,4.116861343383789,2098.663831 +1840,Multiclass classification,ADWIN Bagging,ImageSegments,0.7775965198477434,0.7775965198477434,0.7691578918725354,4.11646842956543,2205.2268019999997 +1886,Multiclass classification,ADWIN Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7681560201617949,4.116430282592773,2314.3219019999997 +1932,Multiclass classification,ADWIN Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7674170460709655,4.116365432739258,2425.9767019999995 +1978,Multiclass classification,ADWIN Bagging,ImageSegments,0.7769347496206374,0.7769347496206374,0.7672843880004774,4.116201400756836,2540.2532379999993 +2024,Multiclass classification,ADWIN Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7681802739952505,4.116155624389648,2657.1841359999994 +2070,Multiclass classification,ADWIN Bagging,ImageSegments,0.778153697438376,0.7781536974383759,0.767530439166732,4.116151809692383,2776.6077329999994 +2116,Multiclass classification,ADWIN Bagging,ImageSegments,0.7787234042553192,0.778723404255319,0.7673415220519754,4.116128921508789,2898.5867789999993 +2162,Multiclass classification,ADWIN Bagging,ImageSegments,0.7797316057380842,0.7797316057380842,0.7679341969633587,4.116201400756836,3023.0016299999993 +2208,Multiclass classification,ADWIN Bagging,ImageSegments,0.7816039873130947,0.7816039873130947,0.7687944234581563,4.11619758605957,3150.0382249999993 +2254,Multiclass classification,ADWIN Bagging,ImageSegments,0.7785175321793165,0.7785175321793165,0.7657018899401807,4.116170883178711,3279.4760369999995 +2300,Multiclass classification,ADWIN Bagging,ImageSegments,0.7777294475859069,0.7777294475859068,0.7649119672933203,4.116254806518555,3411.2017669999996 +2310,Multiclass classification,ADWIN Bagging,ImageSegments,0.7778258986574275,0.7778258986574276,0.765010539660814,4.116277694702148,3543.5486899999996 +1056,Multiclass classification,ADWIN Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762562,6.4894914627075195,90.340432 +2112,Multiclass classification,ADWIN Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.490170478820801,257.284509 +3168,Multiclass classification,ADWIN Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244894,6.491124153137207,490.807794 +4224,Multiclass classification,ADWIN Bagging,Insects,0.6107032914989344,0.6107032914989344,0.574850135712032,6.4912004470825195,783.3577379999999 +5280,Multiclass classification,ADWIN Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225517,6.491948127746582,1129.937274 +6336,Multiclass classification,ADWIN Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789626,6.490735054016113,1525.7763839999998 +7392,Multiclass classification,ADWIN Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867537,6.490704536437988,1967.9282259999998 +8448,Multiclass classification,ADWIN Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.490643501281738,2456.1055429999997 +9504,Multiclass classification,ADWIN Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297488,6.491009712219238,2990.761064 +10560,Multiclass classification,ADWIN Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.491185188293457,3571.5807019999997 +11616,Multiclass classification,ADWIN Bagging,Insects,0.6005165733964701,0.6005165733964701,0.56512832395729,6.491345405578613,4198.711386999999 +12672,Multiclass classification,ADWIN Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306638,6.491543769836426,4874.277407999999 +13728,Multiclass classification,ADWIN Bagging,Insects,0.6152108982297662,0.6152108982297662,0.5959760515786451,5.97607421875,5593.125681999999 +14784,Multiclass classification,ADWIN Bagging,Insects,0.6060339579246432,0.6060339579246432,0.5869142505177357,6.496403694152832,6355.050274999999 +15840,Multiclass classification,ADWIN Bagging,Insects,0.5713744554580465,0.5713744554580465,0.5537658591956377,6.4967546463012695,7160.569073999999 +16896,Multiclass classification,ADWIN Bagging,Insects,0.545546019532406,0.545546019532406,0.5286479939306438,6.381303787231445,8010.073855999999 +17952,Multiclass classification,ADWIN Bagging,Insects,0.526767311013314,0.526767311013314,0.509587529402725,6.497265815734863,8901.233847 +19008,Multiclass classification,ADWIN Bagging,Insects,0.517756615983585,0.517756615983585,0.4976462434137419,4.6858930587768555,9829.81162 +20064,Multiclass classification,ADWIN Bagging,Insects,0.5296815032647162,0.5296815032647162,0.5080882715573688,10.369908332824707,10791.950057 +21120,Multiclass classification,ADWIN Bagging,Insects,0.539750935176855,0.539750935176855,0.5184934777423561,10.92272663116455,11801.930673 +22176,Multiclass classification,ADWIN Bagging,Insects,0.5468771138669674,0.5468771138669674,0.525970977438283,10.920933723449707,12856.592968 +23232,Multiclass classification,ADWIN Bagging,Insects,0.5551633593043778,0.5551633593043778,0.5340735310276195,12.231526374816895,13957.460176 +24288,Multiclass classification,ADWIN Bagging,Insects,0.5615761518507844,0.5615761518507844,0.5396852076547556,12.87682819366455,15100.290122 +25344,Multiclass classification,ADWIN Bagging,Insects,0.5679280274632048,0.5679280274632048,0.5455634192548013,13.528642654418945,16285.362621 +26400,Multiclass classification,ADWIN Bagging,Insects,0.5727868479866661,0.5727868479866661,0.5496374434570932,13.632143020629883,17508.053461 +27456,Multiclass classification,ADWIN Bagging,Insects,0.5754143143325442,0.5754143143325442,0.5513680135969626,13.630533218383789,18766.96928 +28512,Multiclass classification,ADWIN Bagging,Insects,0.5772859598049875,0.5772859598049875,0.5551350356863173,13.627862930297852,20061.957755000003 +29568,Multiclass classification,ADWIN Bagging,Insects,0.577772516657084,0.577772516657084,0.5590861332292512,13.626611709594727,21394.440888000005 +30624,Multiclass classification,ADWIN Bagging,Insects,0.578225516768442,0.578225516768442,0.5625516131192055,12.769641876220703,22755.311286000004 +31680,Multiclass classification,ADWIN Bagging,Insects,0.5795637488557088,0.5795637488557088,0.5663363640160616,12.768932342529297,24150.336726000005 +32736,Multiclass classification,ADWIN Bagging,Insects,0.5811211241790133,0.5811211241790133,0.5696723582178381,12.768062591552734,25577.802283000005 +33792,Multiclass classification,ADWIN Bagging,Insects,0.575804208221124,0.575804208221124,0.5647934119551398,12.981294631958008,27039.455958000006 +34848,Multiclass classification,ADWIN Bagging,Insects,0.5701495107182828,0.5701495107182828,0.559068023359177,12.981256484985352,28537.493337000007 +35904,Multiclass classification,ADWIN Bagging,Insects,0.5657744478177311,0.5657744478177311,0.5542573482740075,12.983362197875977,30069.702466000006 +36960,Multiclass classification,ADWIN Bagging,Insects,0.5611894261208366,0.5611894261208366,0.5493152777162592,13.52482795715332,31635.746830000007 +38016,Multiclass classification,ADWIN Bagging,Insects,0.558779429172695,0.558779429172695,0.5463982360776033,13.526559829711914,33235.41425300001 +39072,Multiclass classification,ADWIN Bagging,Insects,0.5546825010877633,0.5546825010877633,0.5426283860139581,14.304903030395508,34865.73115700001 +40128,Multiclass classification,ADWIN Bagging,Insects,0.5542153662122761,0.5542153662122761,0.5429626632180721,15.152182579040527,36525.24862800001 +41184,Multiclass classification,ADWIN Bagging,Insects,0.5541364155112547,0.5541364155112547,0.5435420562964655,15.252725601196289,38211.297236000006 +42240,Multiclass classification,ADWIN Bagging,Insects,0.5542981604678141,0.5542981604678141,0.544391400018036,15.251314163208008,39923.86574200001 +43296,Multiclass classification,ADWIN Bagging,Insects,0.554151749624668,0.554151749624668,0.5448486588729107,13.424749374389648,41664.47056700001 +44352,Multiclass classification,ADWIN Bagging,Insects,0.5536290049829767,0.5536290049829767,0.5448029815059025,13.648665428161621,43429.160590000014 +45408,Multiclass classification,ADWIN Bagging,Insects,0.5541436342414165,0.5541436342414165,0.5454957405719211,14.18911075592041,45215.90786900002 +46464,Multiclass classification,ADWIN Bagging,Insects,0.5553020683124207,0.5553020683124207,0.546961663735647,15.156656265258789,47024.86244100002 +47520,Multiclass classification,ADWIN Bagging,Insects,0.5579662871693428,0.5579662871693428,0.5498636684303295,14.218542098999023,48856.78116300002 +48576,Multiclass classification,ADWIN Bagging,Insects,0.5627586206896552,0.5627586206896552,0.5545030394801858,14.845645904541016,50711.770017000024 +49632,Multiclass classification,ADWIN Bagging,Insects,0.5677701436602124,0.5677701436602124,0.5591808574875289,15.233248710632324,52589.43568900003 +50688,Multiclass classification,ADWIN Bagging,Insects,0.5730463432438297,0.5730463432438297,0.5639878919164368,15.890131950378418,54487.48381000003 +51744,Multiclass classification,ADWIN Bagging,Insects,0.5791894555785324,0.5791894555785324,0.5695807960578061,16.1916446685791,56406.77001200003 +52800,Multiclass classification,ADWIN Bagging,Insects,0.5794427924771303,0.5794427924771303,0.5701512686040561,15.307219505310059,58342.70756100003 +52848,Multiclass classification,ADWIN Bagging,Insects,0.5794652487369197,0.5794652487369197,0.5701984940722999,15.307356834411621,60279.413314000034 +408,Multiclass classification,ADWIN Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.100947380065918,5.705318 +816,Multiclass classification,ADWIN Bagging,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,3.048105239868164,25.352994000000002 +1224,Multiclass classification,ADWIN Bagging,Keystroke,0.8912510220768601,0.8912510220768601,0.8617021305177772,3.9921913146972656,63.032035 +1632,Multiclass classification,ADWIN Bagging,Keystroke,0.9031269160024524,0.9031269160024524,0.8868998230762756,4.944231986999512,123.60027500000001 +2040,Multiclass classification,ADWIN Bagging,Keystroke,0.898970083374203,0.898970083374203,0.888705938214812,5.993730545043945,211.611387 +2448,Multiclass classification,ADWIN Bagging,Keystroke,0.8598283612586841,0.8598283612586841,0.8569666636755086,6.37155818939209,330.620683 +2856,Multiclass classification,ADWIN Bagging,Keystroke,0.8669001751313485,0.8669001751313484,0.8547854134985733,7.318934440612793,479.663409 +3264,Multiclass classification,ADWIN Bagging,Keystroke,0.8581060373889059,0.8581060373889059,0.8327540420876277,8.264909744262695,660.474615 +3672,Multiclass classification,ADWIN Bagging,Keystroke,0.8490874421138654,0.8490874421138654,0.8463961237855363,8.926459312438965,875.371562 +4080,Multiclass classification,ADWIN Bagging,Keystroke,0.8421181662172101,0.84211816621721,0.8299816031455575,10.074895858764648,1125.854174 +4488,Multiclass classification,ADWIN Bagging,Keystroke,0.8301760641854246,0.8301760641854244,0.8400819204125556,11.044804573059082,1412.144879 +4896,Multiclass classification,ADWIN Bagging,Keystroke,0.8314606741573034,0.8314606741573034,0.8387821748480373,8.862092971801758,1728.261118 +5304,Multiclass classification,ADWIN Bagging,Keystroke,0.8333019045823119,0.8333019045823119,0.8299513887279447,9.715648651123047,2074.079546 +5712,Multiclass classification,ADWIN Bagging,Keystroke,0.8255997198389073,0.8255997198389075,0.831498100235552,10.513428688049316,2449.732459 +6120,Multiclass classification,ADWIN Bagging,Keystroke,0.8241542735741134,0.8241542735741134,0.813971923025991,11.555256843566895,2856.066254 +6528,Multiclass classification,ADWIN Bagging,Keystroke,0.8043511567335683,0.8043511567335683,0.8048077550156274,12.298343658447266,3295.046229 +6936,Multiclass classification,ADWIN Bagging,Keystroke,0.8002883922134102,0.8002883922134101,0.8062362865697692,11.726844787597656,3768.4733180000003 +7344,Multiclass classification,ADWIN Bagging,Keystroke,0.8093422306959008,0.8093422306959008,0.813125473572493,9.433514595031738,4267.304275 +7752,Multiclass classification,ADWIN Bagging,Keystroke,0.8157657076506257,0.8157657076506257,0.8184378776785012,10.539642333984375,4791.9677950000005 +8160,Multiclass classification,ADWIN Bagging,Keystroke,0.8199534256649099,0.81995342566491,0.8213128379144453,11.364830017089844,5345.1630700000005 +8568,Multiclass classification,ADWIN Bagging,Keystroke,0.8247928096183028,0.8247928096183028,0.8275146627418534,12.672901153564453,5929.400246 +8976,Multiclass classification,ADWIN Bagging,Keystroke,0.8295264623955432,0.8295264623955433,0.8318915513040454,13.833264350891113,6547.972923 +9384,Multiclass classification,ADWIN Bagging,Keystroke,0.8319300863263348,0.8319300863263348,0.8336463894938194,14.741169929504395,7204.877164 +9792,Multiclass classification,ADWIN Bagging,Keystroke,0.8342355224185476,0.8342355224185476,0.8362542817352725,16.02083969116211,7900.9214680000005 +10200,Multiclass classification,ADWIN Bagging,Keystroke,0.8343955289734287,0.8343955289734286,0.833886744496364,17.251243591308594,8638.28045 +10608,Multiclass classification,ADWIN Bagging,Keystroke,0.8258697086829452,0.8258697086829452,0.823298887298616,18.484009742736816,9418.818077 +11016,Multiclass classification,ADWIN Bagging,Keystroke,0.825692237857467,0.825692237857467,0.827229896548608,17.053231239318848,10241.512178 +11424,Multiclass classification,ADWIN Bagging,Keystroke,0.8263153287227524,0.8263153287227524,0.8251000136898328,18.097841262817383,11105.510711 +11832,Multiclass classification,ADWIN Bagging,Keystroke,0.8257966359563857,0.8257966359563859,0.8251092059206939,19.1904354095459,12012.672379000001 +12240,Multiclass classification,ADWIN Bagging,Keystroke,0.8289892965111528,0.8289892965111528,0.8300645161883343,17.2155818939209,12963.554855000002 +12648,Multiclass classification,ADWIN Bagging,Keystroke,0.8324503834901558,0.8324503834901558,0.8328446288662702,17.090572357177734,13955.688030000003 +13056,Multiclass classification,ADWIN Bagging,Keystroke,0.8295672156261968,0.8295672156261968,0.8279815503081916,18.284998893737793,14990.870602000003 +13464,Multiclass classification,ADWIN Bagging,Keystroke,0.828121518235163,0.828121518235163,0.8279872572314477,18.759904861450195,16071.989638000003 +13872,Multiclass classification,ADWIN Bagging,Keystroke,0.8300771393554899,0.8300771393554899,0.8300312724960401,19.937789916992188,17198.739284000003 +14280,Multiclass classification,ADWIN Bagging,Keystroke,0.8329714966034036,0.8329714966034036,0.8330653900337638,21.172300338745117,18373.548754000003 +14688,Multiclass classification,ADWIN Bagging,Keystroke,0.8360454823994008,0.8360454823994008,0.8362319050195895,22.12139320373535,19591.296889000005 +15096,Multiclass classification,ADWIN Bagging,Keystroke,0.8391520370983769,0.8391520370983769,0.8393677597260801,22.688467979431152,20852.364231000003 +15504,Multiclass classification,ADWIN Bagging,Keystroke,0.8400309617493389,0.8400309617493388,0.8398031059873,23.806550979614258,22157.639176000004 +15912,Multiclass classification,ADWIN Bagging,Keystroke,0.8335114072025642,0.8335114072025642,0.8310693286634668,25.06292724609375,23499.792247000005 +16320,Multiclass classification,ADWIN Bagging,Keystroke,0.8283595808566702,0.8283595808566702,0.826721014765785,26.060873985290527,24886.958001000006 +16728,Multiclass classification,ADWIN Bagging,Keystroke,0.826747175225683,0.8267471752256829,0.8259678903415486,27.245673179626465,26317.085564000008 +17136,Multiclass classification,ADWIN Bagging,Keystroke,0.821943390720747,0.821943390720747,0.8202405231953956,28.675668716430664,27793.820666000007 +17544,Multiclass classification,ADWIN Bagging,Keystroke,0.8182180926865417,0.8182180926865417,0.8170173651382093,29.74225902557373,29319.213378000008 +17952,Multiclass classification,ADWIN Bagging,Keystroke,0.81878446883182,0.81878446883182,0.8179349229322325,30.879840850830078,30892.431607000006 +18360,Multiclass classification,ADWIN Bagging,Keystroke,0.821123154855929,0.821123154855929,0.8204502524156659,32.019548416137695,32513.434007000007 +18768,Multiclass classification,ADWIN Bagging,Keystroke,0.8235200085256035,0.8235200085256035,0.8229965581236837,33.157379150390625,34181.929457000006 +19176,Multiclass classification,ADWIN Bagging,Keystroke,0.819973924380704,0.819973924380704,0.8189812465563673,34.295823097229004,35895.74071300001 +19584,Multiclass classification,ADWIN Bagging,Keystroke,0.821733135883164,0.821733135883164,0.8211010404575377,35.31475067138672,37655.039070000006 +19992,Multiclass classification,ADWIN Bagging,Keystroke,0.8188684908208694,0.8188684908208694,0.8180458262517715,36.57470226287842,39458.702899 +20400,Multiclass classification,ADWIN Bagging,Keystroke,0.816559635276239,0.816559635276239,0.8159075588016685,37.855767250061035,41308.014952000005 +46,Multiclass classification,AdaBoost,ImageSegments,0.1111111111111111,0.1111111111111111,0.0815018315018315,3.4160032272338867,1.208286 +92,Multiclass classification,AdaBoost,ImageSegments,0.23076923076923078,0.23076923076923078,0.2226391771283412,4.099128723144531,4.553382 +138,Multiclass classification,AdaBoost,ImageSegments,0.4233576642335766,0.4233576642335766,0.44635377186191566,4.099002838134766,10.351245 +184,Multiclass classification,AdaBoost,ImageSegments,0.5355191256830601,0.5355191256830601,0.5617062146473912,4.099178314208984,18.765494 +230,Multiclass classification,AdaBoost,ImageSegments,0.5938864628820961,0.5938864628820961,0.6236530662596055,4.0991668701171875,30.180838 +276,Multiclass classification,AdaBoost,ImageSegments,0.6290909090909091,0.6290909090909091,0.6558170665459355,4.099109649658203,44.412895 +322,Multiclass classification,AdaBoost,ImageSegments,0.660436137071651,0.660436137071651,0.678574720261515,4.098438262939453,61.214822 +368,Multiclass classification,AdaBoost,ImageSegments,0.6920980926430518,0.6920980926430518,0.7041680355881775,4.0984954833984375,80.427396 +414,Multiclass classification,AdaBoost,ImageSegments,0.7167070217917676,0.7167070217917676,0.7259075149442813,4.097980499267578,102.254145 +460,Multiclass classification,AdaBoost,ImageSegments,0.7254901960784313,0.7254901960784313,0.7325011710849479,4.098300933837891,127.09125599999999 +506,Multiclass classification,AdaBoost,ImageSegments,0.7386138613861386,0.7386138613861386,0.7428621938273078,4.098552703857422,154.35838199999998 +552,Multiclass classification,AdaBoost,ImageSegments,0.7422867513611615,0.7422867513611615,0.7453719085253248,4.098358154296875,184.30369299999998 +598,Multiclass classification,AdaBoost,ImageSegments,0.7487437185929648,0.7487437185929648,0.7504522188790486,4.098468780517578,216.734559 +644,Multiclass classification,AdaBoost,ImageSegments,0.7465007776049767,0.7465007776049767,0.7482323503576439,4.098541259765625,252.13907799999998 +690,Multiclass classification,AdaBoost,ImageSegments,0.7489114658925979,0.748911465892598,0.7488472102580618,4.098594665527344,290.044846 +736,Multiclass classification,AdaBoost,ImageSegments,0.7523809523809524,0.7523809523809524,0.7518283723099097,4.098430633544922,330.661237 +782,Multiclass classification,AdaBoost,ImageSegments,0.7541613316261203,0.7541613316261204,0.7531089046321314,4.098361968994141,373.819675 +828,Multiclass classification,AdaBoost,ImageSegments,0.7557436517533253,0.7557436517533253,0.7552013614952863,4.098308563232422,419.68676000000005 +874,Multiclass classification,AdaBoost,ImageSegments,0.7617411225658648,0.7617411225658649,0.7601066395856337,4.098381042480469,467.91378800000007 +920,Multiclass classification,AdaBoost,ImageSegments,0.763873775843308,0.763873775843308,0.7623480483274478,4.098400115966797,518.622959 +966,Multiclass classification,AdaBoost,ImageSegments,0.7678756476683938,0.7678756476683938,0.7646598072570266,4.098423004150391,571.868767 +1012,Multiclass classification,AdaBoost,ImageSegments,0.7705242334322453,0.7705242334322453,0.7668271197983111,4.098529815673828,627.754669 +1058,Multiclass classification,AdaBoost,ImageSegments,0.7757805108798487,0.7757805108798487,0.7714920336037777,4.098388671875,686.3790640000001 +1104,Multiclass classification,AdaBoost,ImageSegments,0.7760652765185857,0.7760652765185856,0.7719206139767609,4.098537445068359,747.748718 +1150,Multiclass classification,AdaBoost,ImageSegments,0.7789382071366405,0.7789382071366405,0.7750313949659527,4.098442077636719,811.629001 +1196,Multiclass classification,AdaBoost,ImageSegments,0.7849372384937239,0.7849372384937239,0.7820003890472508,4.098487854003906,878.060098 +1242,Multiclass classification,AdaBoost,ImageSegments,0.7856567284448026,0.7856567284448026,0.7827470902102026,4.098438262939453,947.241797 +1288,Multiclass classification,AdaBoost,ImageSegments,0.7894327894327894,0.7894327894327894,0.785982924599392,4.0983428955078125,1018.793017 +1334,Multiclass classification,AdaBoost,ImageSegments,0.7906976744186046,0.7906976744186046,0.7876424482584368,4.098438262939453,1093.0782689999999 +1380,Multiclass classification,AdaBoost,ImageSegments,0.7933284989122552,0.7933284989122552,0.7906471924204205,4.098392486572266,1169.7117919999998 +1426,Multiclass classification,AdaBoost,ImageSegments,0.7978947368421052,0.7978947368421052,0.7945020166797493,4.098480224609375,1248.577134 +1472,Multiclass classification,AdaBoost,ImageSegments,0.8028552005438477,0.8028552005438477,0.7982243751921434,4.098472595214844,1329.680821 +1518,Multiclass classification,AdaBoost,ImageSegments,0.8035596572181938,0.8035596572181938,0.7981876534181912,4.098491668701172,1413.4983189999998 +1564,Multiclass classification,AdaBoost,ImageSegments,0.8035828534868842,0.8035828534868842,0.798634974540431,4.098518371582031,1499.9052179999999 +1610,Multiclass classification,AdaBoost,ImageSegments,0.8048477315102548,0.8048477315102549,0.7997380784882049,4.098381042480469,1588.8368169999999 +1656,Multiclass classification,AdaBoost,ImageSegments,0.8066465256797583,0.8066465256797583,0.80161945439383,4.098377227783203,1680.2495139999999 +1702,Multiclass classification,AdaBoost,ImageSegments,0.8059964726631393,0.8059964726631393,0.8024858564723997,4.098514556884766,1774.345382 +1748,Multiclass classification,AdaBoost,ImageSegments,0.8070978820835718,0.8070978820835718,0.8029124203507955,4.098423004150391,1871.065136 +1794,Multiclass classification,AdaBoost,ImageSegments,0.8081427774679308,0.8081427774679307,0.8029834045630979,4.098461151123047,1970.0377899999999 +1840,Multiclass classification,AdaBoost,ImageSegments,0.8069603045133225,0.8069603045133223,0.801927622716254,4.098594665527344,2071.588776 +1886,Multiclass classification,AdaBoost,ImageSegments,0.8053050397877984,0.8053050397877984,0.8006727596367825,4.098400115966797,2175.772942 +1932,Multiclass classification,AdaBoost,ImageSegments,0.8047643707923355,0.8047643707923355,0.7995493059800365,4.098396301269531,2282.41088 +1978,Multiclass classification,AdaBoost,ImageSegments,0.8057663125948407,0.8057663125948407,0.8003960406612564,4.0984344482421875,2391.59611 +2024,Multiclass classification,AdaBoost,ImageSegments,0.8072170044488384,0.8072170044488384,0.8005625942078284,4.098430633544922,2503.16971 +2070,Multiclass classification,AdaBoost,ImageSegments,0.8066698888351861,0.8066698888351861,0.8002110568368,4.098316192626953,2617.3694960000003 +2116,Multiclass classification,AdaBoost,ImageSegments,0.807565011820331,0.807565011820331,0.8005131307885663,4.0983428955078125,2733.922308 +2162,Multiclass classification,AdaBoost,ImageSegments,0.8079592781119852,0.8079592781119852,0.8006755955605837,4.098320007324219,2852.747139 +2208,Multiclass classification,AdaBoost,ImageSegments,0.8087902129587675,0.8087902129587675,0.8009921695193862,4.098320007324219,2973.740681 +2254,Multiclass classification,AdaBoost,ImageSegments,0.8060363959165557,0.8060363959165557,0.7987732120640717,4.0983428955078125,3097.6149410000003 +2300,Multiclass classification,AdaBoost,ImageSegments,0.8051326663766856,0.8051326663766856,0.798077892809675,4.0983428955078125,3223.9293610000004 +2310,Multiclass classification,AdaBoost,ImageSegments,0.8046773495019489,0.8046773495019489,0.7977695866822911,4.098388671875,3350.8763620000004 +1056,Multiclass classification,AdaBoost,Insects,0.6360189573459716,0.6360189573459716,0.5992691812827112,6.474042892456055,88.969298 +2112,Multiclass classification,AdaBoost,Insects,0.6110847939365229,0.6110847939365229,0.5773210074897359,6.473905563354492,256.045675 +3168,Multiclass classification,AdaBoost,Insects,0.6043574360593622,0.6043574360593622,0.5704368753709179,6.473470687866211,489.83548 +4224,Multiclass classification,AdaBoost,Insects,0.6014681506038362,0.6014681506038362,0.5676969561642586,6.473196029663086,783.3519140000001 +5280,Multiclass classification,AdaBoost,Insects,0.6057965523773442,0.6057965523773442,0.5710016183775801,6.473196029663086,1130.801617 +6336,Multiclass classification,AdaBoost,Insects,0.5966850828729282,0.5966850828729282,0.5635903588556204,6.473356246948242,1527.884634 +7392,Multiclass classification,AdaBoost,Insects,0.5957245298335814,0.5957245298335814,0.5625002603439991,6.473814010620117,1971.450931 +8448,Multiclass classification,AdaBoost,Insects,0.5982005445720374,0.5982005445720374,0.5646892369665863,6.474157333374023,2461.351589 +9504,Multiclass classification,AdaBoost,Insects,0.596337998526781,0.596337998526781,0.5627085514562804,6.47450065612793,2997.75158 +10560,Multiclass classification,AdaBoost,Insects,0.5965527038545316,0.5965527038545316,0.5631320282838163,6.47468376159668,3580.189969 +11616,Multiclass classification,AdaBoost,Insects,0.5953508394317693,0.5953508394317693,0.562671447170627,6.47468376159668,4209.202801 +12672,Multiclass classification,AdaBoost,Insects,0.5979796385447084,0.5979796385447084,0.5680559575776837,6.474340438842773,4886.872526 +13728,Multiclass classification,AdaBoost,Insects,0.610767101333139,0.610767101333139,0.5941277335666079,6.473836898803711,5609.570035 +14784,Multiclass classification,AdaBoost,Insects,0.6019752418318338,0.6019752418318338,0.5851264744797859,6.473745346069336,6378.998584999999 +15840,Multiclass classification,AdaBoost,Insects,0.5705536965717533,0.5705536965717533,0.5545059657048704,6.473974227905273,7193.860588999999 +16896,Multiclass classification,AdaBoost,Insects,0.548091151228174,0.548091151228174,0.5320735507355622,6.474386215209961,8051.816493999999 +17952,Multiclass classification,AdaBoost,Insects,0.5307225224221492,0.5307225224221492,0.5138536287616571,6.474637985229492,8952.059017 +19008,Multiclass classification,AdaBoost,Insects,0.5182827379386542,0.5182827379386542,0.4990809738484312,6.47486686706543,9893.706361 +20064,Multiclass classification,AdaBoost,Insects,0.5182176145142801,0.5182176145142801,0.497867701567998,8.642622947692871,10881.966771000001 +21120,Multiclass classification,AdaBoost,Insects,0.5272503432927695,0.5272503432927695,0.5067114684709674,15.437758445739746,11917.076256 +22176,Multiclass classification,AdaBoost,Insects,0.533032694475761,0.533032694475761,0.5127471323280748,16.81709384918213,12999.258268 +23232,Multiclass classification,AdaBoost,Insects,0.5410442942619775,0.5410442942619775,0.5207771198745245,17.041016578674316,14124.681759 +24288,Multiclass classification,AdaBoost,Insects,0.5459710956478775,0.5459710956478775,0.5251711652768184,17.038064002990723,15290.808705 +25344,Multiclass classification,AdaBoost,Insects,0.5532099593576135,0.5532099593576135,0.5314216535856217,17.036622047424316,16491.42669 +26400,Multiclass classification,AdaBoost,Insects,0.5607788173794462,0.5607788173794462,0.5375130024626694,17.14900016784668,17723.115047 +27456,Multiclass classification,AdaBoost,Insects,0.5667091604443635,0.5667091604443635,0.5418496825562071,17.261820793151855,18986.675500999998 +28512,Multiclass classification,AdaBoost,Insects,0.5692890463329943,0.5692890463329943,0.5455529487931667,17.26294231414795,20283.404340999998 +29568,Multiclass classification,AdaBoost,Insects,0.5688436432509216,0.5688436432509216,0.5481992899375988,17.26337718963623,21617.011828 +30624,Multiclass classification,AdaBoost,Insects,0.5687228553701467,0.5687228553701467,0.5505043481720591,17.26330852508545,22980.237824 +31680,Multiclass classification,AdaBoost,Insects,0.5691467533697402,0.5691467533697402,0.5529220328647554,17.262804985046387,24378.053405 +32736,Multiclass classification,AdaBoost,Insects,0.5703986558729189,0.5703986558729189,0.5556828084411201,17.262507438659668,25809.039082 +33792,Multiclass classification,AdaBoost,Insects,0.5650025154626972,0.5650025154626972,0.5507695387439543,17.487704277038574,27274.413313999998 +34848,Multiclass classification,AdaBoost,Insects,0.5587281545039745,0.5587281545039745,0.5445349362041821,17.929275512695312,28775.770082 +35904,Multiclass classification,AdaBoost,Insects,0.5541876723393588,0.5541876723393588,0.5396635045593164,18.030207633972168,30311.158156999998 +36960,Multiclass classification,AdaBoost,Insects,0.549122000054114,0.549122000054114,0.5343517375956978,19.681435585021973,31880.342243 +38016,Multiclass classification,AdaBoost,Insects,0.5473628830724714,0.5473628830724714,0.5321033552605493,21.718143463134766,33482.866823 +39072,Multiclass classification,AdaBoost,Insects,0.5426787131120269,0.5426787131120269,0.52803892360078,22.487850189208984,35116.535538 +40128,Multiclass classification,AdaBoost,Insects,0.5419293742367982,0.5419293742367982,0.5284857300708793,23.76238250732422,36778.160188999995 +41184,Multiclass classification,AdaBoost,Insects,0.5417769467984362,0.5417769467984362,0.5296361895775551,23.860816955566406,38464.674338 +42240,Multiclass classification,AdaBoost,Insects,0.5422240109851085,0.5422240109851085,0.5313111510734391,24.196860313415527,40175.576412999995 +43296,Multiclass classification,AdaBoost,Insects,0.5444970550871925,0.5444970550871925,0.5344195798463859,24.296037673950195,41906.99288399999 +44352,Multiclass classification,AdaBoost,Insects,0.5463461928705102,0.5463461928705102,0.5369578677381479,24.84640598297119,43659.477484999996 +45408,Multiclass classification,AdaBoost,Insects,0.5482855066399454,0.5482855066399454,0.5392181145139481,25.28636360168457,45430.571796 +46464,Multiclass classification,AdaBoost,Insects,0.5506532079288896,0.5506532079288896,0.5419048601727473,25.498303413391113,47220.970484 +47520,Multiclass classification,AdaBoost,Insects,0.5514846692901787,0.5514846692901787,0.5429796926051395,25.823355674743652,49033.125825999996 +48576,Multiclass classification,AdaBoost,Insects,0.5515388574369532,0.5515388574369532,0.543031483592694,25.821112632751465,50867.580247 +49632,Multiclass classification,AdaBoost,Insects,0.551953416211642,0.551953416211642,0.5433574148660688,26.098894119262695,52723.913942 +50688,Multiclass classification,AdaBoost,Insects,0.5563359441276856,0.5563359441276856,0.5472854805195191,26.366034507751465,54600.511786999996 +51744,Multiclass classification,AdaBoost,Insects,0.5623562607502464,0.5623562607502464,0.552981536157949,27.10032081604004,56496.789585 +52800,Multiclass classification,AdaBoost,Insects,0.5634576412432054,0.5634576412432054,0.5545218292020726,27.943178176879883,58415.671716 +52848,Multiclass classification,AdaBoost,Insects,0.5635324616345299,0.5635324616345299,0.5546220283668154,27.942995071411133,60335.727695999994 +408,Multiclass classification,AdaBoost,Keystroke,0.9877149877149877,0.9877149877149877,0.7696139476961394,2.1207275390625,4.211814 +816,Multiclass classification,AdaBoost,Keystroke,0.9889570552147239,0.9889570552147239,0.9592655637573824,2.9369373321533203,23.739993000000002 +1224,Multiclass classification,AdaBoost,Keystroke,0.9836467702371219,0.9836467702371219,0.9326470331192014,4.590028762817383,86.527265 +1632,Multiclass classification,AdaBoost,Keystroke,0.9828326180257511,0.9828326180257511,0.9594506659780556,5.819695472717285,184.232691 +2040,Multiclass classification,AdaBoost,Keystroke,0.9705738106915155,0.9705738106915155,0.9304838721924584,8.549582481384277,323.308574 +2448,Multiclass classification,AdaBoost,Keystroke,0.9607682876992235,0.9607682876992235,0.9455756842664337,10.061903953552246,491.40663400000005 +2856,Multiclass classification,AdaBoost,Keystroke,0.9541155866900175,0.9541155866900175,0.9254688528922778,12.678574562072754,687.150288 +3264,Multiclass classification,AdaBoost,Keystroke,0.9436101746858719,0.9436101746858719,0.9191430707434157,16.086813926696777,906.458431 +3672,Multiclass classification,AdaBoost,Keystroke,0.9403432307273223,0.9403432307273223,0.9284235615798526,18.255277633666992,1151.002639 +4080,Multiclass classification,AdaBoost,Keystroke,0.9338073057121844,0.9338073057121844,0.9182429705382059,21.65336036682129,1427.8669570000002 +4488,Multiclass classification,AdaBoost,Keystroke,0.9318029864051705,0.9318029864051705,0.9319119487505448,22.768765449523926,1733.8095280000002 +4896,Multiclass classification,AdaBoost,Keystroke,0.9317671092951992,0.9317671092951992,0.9296889978700974,24.966033935546875,2062.8329900000003 +5304,Multiclass classification,AdaBoost,Keystroke,0.9281538751650009,0.9281538751650009,0.9197653564039141,29.062508583068848,2423.7318920000002 +5712,Multiclass classification,AdaBoost,Keystroke,0.9227805988443355,0.9227805988443355,0.9201475418022375,32.12655830383301,2815.063536 +6120,Multiclass classification,AdaBoost,Keystroke,0.9177970256577872,0.9177970256577872,0.9072843264203106,37.27707767486572,3238.118927 +6528,Multiclass classification,AdaBoost,Keystroke,0.9115979776313774,0.9115979776313774,0.909931232789514,41.43412208557129,3706.915394 +6936,Multiclass classification,AdaBoost,Keystroke,0.9129055515501081,0.9129055515501081,0.9153430596364791,44.48411560058594,4207.758914 +7344,Multiclass classification,AdaBoost,Keystroke,0.9135230832084978,0.9135230832084978,0.9124682676754273,47.44067192077637,4740.722969 +7752,Multiclass classification,AdaBoost,Keystroke,0.9121403689846471,0.9121403689846471,0.9121831707972875,51.03960132598877,5307.755902000001 +8160,Multiclass classification,AdaBoost,Keystroke,0.9086897904154921,0.9086897904154921,0.9062633734460517,56.064818382263184,5918.019803000001 +8568,Multiclass classification,AdaBoost,Keystroke,0.9059180576631259,0.9059180576631259,0.9058259471519292,61.820496559143066,6575.962782000001 +8976,Multiclass classification,AdaBoost,Keystroke,0.9042896935933148,0.9042896935933148,0.9043251050138335,64.74030494689941,7280.842530000002 +9384,Multiclass classification,AdaBoost,Keystroke,0.9018437599914739,0.9018437599914739,0.9009662752730246,68.81300067901611,8035.1031440000015 +9792,Multiclass classification,AdaBoost,Keystroke,0.8971504442855683,0.8971504442855683,0.8956423708961025,74.25286674499512,8855.073479000002 +10200,Multiclass classification,AdaBoost,Keystroke,0.8926365329934307,0.8926365329934307,0.8903074227158838,79.6785535812378,9744.976722000003 +10608,Multiclass classification,AdaBoost,Keystroke,0.8846987838220043,0.8846987838220043,0.8819820059100918,85.28873825073242,10726.537155000004 +11016,Multiclass classification,AdaBoost,Keystroke,0.8791647753064004,0.8791647753064004,0.8795835231396919,89.59383392333984,11788.438997000003 +11424,Multiclass classification,AdaBoost,Keystroke,0.8759520266129738,0.8759520266129738,0.8744149508862001,94.86630344390869,12917.703791000004 +11832,Multiclass classification,AdaBoost,Keystroke,0.872200152142676,0.872200152142676,0.8717012117300328,100.15169906616211,14117.771334000003 +12240,Multiclass classification,AdaBoost,Keystroke,0.8736824903995425,0.8736824903995425,0.8749440738646468,101.93578433990479,15365.994884000003 +12648,Multiclass classification,AdaBoost,Keystroke,0.8717482406894915,0.8717482406894915,0.8710221211412438,107.46908473968506,16670.526324000002 +13056,Multiclass classification,AdaBoost,Keystroke,0.8661815396399847,0.8661815396399847,0.8651994621744733,112.71690273284912,18043.485216 +13464,Multiclass classification,AdaBoost,Keystroke,0.8642204560647702,0.8642204560647702,0.8645487273027374,116.56386756896973,19480.298866 +13872,Multiclass classification,AdaBoost,Keystroke,0.8619421815298104,0.8619421815298104,0.862314869215492,121.52821636199951,20980.471725 +14280,Multiclass classification,AdaBoost,Keystroke,0.859163806989285,0.859163806989285,0.8592780138529494,125.80194187164307,22534.622977 +14688,Multiclass classification,AdaBoost,Keystroke,0.8591952066453326,0.8591952066453326,0.8604793833246808,129.6016607284546,24135.768583999998 +15096,Multiclass classification,AdaBoost,Keystroke,0.8607485922490891,0.8607485922490891,0.8621609789956539,132.68816757202148,25779.863983 +15504,Multiclass classification,AdaBoost,Keystroke,0.8604141133974069,0.8604141133974069,0.8613237595899307,136.05841445922852,27480.057181 +15912,Multiclass classification,AdaBoost,Keystroke,0.8536861290930803,0.8536861290930803,0.853192144751886,141.70578575134277,29254.423593 +16320,Multiclass classification,AdaBoost,Keystroke,0.8493167473497151,0.849316747349715,0.8496464102754333,147.49746799468994,31089.50572 +16728,Multiclass classification,AdaBoost,Keystroke,0.846296407006636,0.846296407006636,0.8470383589757107,153.1935510635376,32973.577156 +17136,Multiclass classification,AdaBoost,Keystroke,0.8411438576014006,0.8411438576014006,0.8410396667771575,156.28132915496826,34948.88082 +17544,Multiclass classification,AdaBoost,Keystroke,0.8365729920766117,0.8365729920766117,0.8367907010021001,161.03080940246582,36967.519165 +17952,Multiclass classification,AdaBoost,Keystroke,0.8355523369171634,0.8355523369171634,0.8362918425397341,166.63249397277832,39016.229588999995 +18360,Multiclass classification,AdaBoost,Keystroke,0.837572852551882,0.8375728525518821,0.8385662484273668,171.47760772705078,41092.028592999995 +18768,Multiclass classification,AdaBoost,Keystroke,0.8390259498055097,0.8390259498055097,0.8401126675526959,175.70373821258545,43194.947863999994 +19176,Multiclass classification,AdaBoost,Keystroke,0.8376531942633637,0.8376531942633637,0.838676297522501,180.87701034545898,45330.650987999994 +19584,Multiclass classification,AdaBoost,Keystroke,0.8390951335341879,0.8390951335341879,0.8403338496937821,185.05438709259033,47482.84587799999 +19992,Multiclass classification,AdaBoost,Keystroke,0.8372767745485469,0.8372767745485468,0.8385640183306876,190.1383810043335,49661.235199999996 +20400,Multiclass classification,AdaBoost,Keystroke,0.8347958233246727,0.8347958233246727,0.8360623278174891,194.794171333313,51861.27850099999 +46,Multiclass classification,Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.24576497265572897,4.149084091186523,2.196675 +92,Multiclass classification,Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.4934752395581889,4.152299880981445,7.023639 +138,Multiclass classification,Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646678,4.15202522277832,15.046926 +184,Multiclass classification,Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.6141431648908949,4.152608871459961,26.297795 +230,Multiclass classification,Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,4.151983261108398,40.50873 +276,Multiclass classification,Bagging,ImageSegments,0.64,0.64,0.6559607038460421,4.152521133422852,57.698206 +322,Multiclass classification,Bagging,ImageSegments,0.6666666666666666,0.6666666666666666,0.6673617488913626,4.152231216430664,77.585785 +368,Multiclass classification,Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6911959597548878,4.152448654174805,100.185488 +414,Multiclass classification,Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.7079630503641953,4.152788162231445,125.71728800000001 +460,Multiclass classification,Bagging,ImageSegments,0.7124183006535948,0.7124183006535948,0.7065500352371009,4.152704238891602,154.000542 +506,Multiclass classification,Bagging,ImageSegments,0.7207920792079208,0.7207920792079208,0.7127593158348896,4.152563095092773,184.883226 +552,Multiclass classification,Bagging,ImageSegments,0.7259528130671506,0.7259528130671506,0.7192025503807162,4.152528762817383,218.482328 +598,Multiclass classification,Bagging,ImageSegments,0.7319932998324958,0.7319932998324957,0.7251188986558661,4.152769088745117,254.840787 +644,Multiclass classification,Bagging,ImageSegments,0.7309486780715396,0.7309486780715396,0.7259740406437201,4.152563095092773,294.12903800000004 +690,Multiclass classification,Bagging,ImageSegments,0.7358490566037735,0.7358490566037735,0.7304359912942561,4.152692794799805,336.073433 +736,Multiclass classification,Bagging,ImageSegments,0.7374149659863946,0.7374149659863947,0.733149934717071,4.152753829956055,380.701162 +782,Multiclass classification,Bagging,ImageSegments,0.7426376440460948,0.7426376440460948,0.7385597120510639,4.152643203735352,428.175969 +828,Multiclass classification,Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7412375783772316,4.152631759643555,478.460063 +874,Multiclass classification,Bagging,ImageSegments,0.7491408934707904,0.7491408934707904,0.7454343548790067,4.153181076049805,531.417765 +920,Multiclass classification,Bagging,ImageSegments,0.7486398258977149,0.7486398258977149,0.7441307384051415,4.153326034545898,587.1362770000001 +966,Multiclass classification,Bagging,ImageSegments,0.7492227979274612,0.749222797927461,0.7439306216964366,4.153120040893555,645.6842 +1012,Multiclass classification,Bagging,ImageSegments,0.7487636003956478,0.7487636003956478,0.7437900284473965,4.153234481811523,707.105172 +1058,Multiclass classification,Bagging,ImageSegments,0.750236518448439,0.7502365184484389,0.7448138061687654,4.153268814086914,771.2868930000001 +1104,Multiclass classification,Bagging,ImageSegments,0.7524932003626473,0.7524932003626473,0.7468314646869904,4.153234481811523,838.222518 +1150,Multiclass classification,Bagging,ImageSegments,0.7554395126196692,0.7554395126196692,0.7493227137357602,4.153413772583008,907.556087 +1196,Multiclass classification,Bagging,ImageSegments,0.7581589958158996,0.7581589958158996,0.7527652773681007,4.153318405151367,979.5797180000001 +1242,Multiclass classification,Bagging,ImageSegments,0.7574536663980661,0.7574536663980661,0.7525915384194216,4.153432846069336,1054.216781 +1288,Multiclass classification,Bagging,ImageSegments,0.7622377622377622,0.7622377622377621,0.7563448085202398,4.153615951538086,1131.5718310000002 +1334,Multiclass classification,Bagging,ImageSegments,0.7621905476369092,0.7621905476369092,0.7566636999776912,4.153776168823242,1211.5912470000003 +1380,Multiclass classification,Bagging,ImageSegments,0.7635968092820885,0.7635968092820886,0.7587252257765656,4.153825759887695,1294.4019940000003 +1426,Multiclass classification,Bagging,ImageSegments,0.7663157894736842,0.7663157894736842,0.7609139797315134,4.153848648071289,1379.8910190000004 +1472,Multiclass classification,Bagging,ImageSegments,0.7709041468388851,0.7709041468388851,0.7637689949207689,4.153989791870117,1467.9946540000003 +1518,Multiclass classification,Bagging,ImageSegments,0.7719182597231378,0.7719182597231378,0.7639714255563932,4.154367446899414,1558.8129900000004 +1564,Multiclass classification,Bagging,ImageSegments,0.7722328854766475,0.7722328854766475,0.7650721335080709,4.154550552368164,1652.2028900000003 +1610,Multiclass classification,Bagging,ImageSegments,0.7725295214418894,0.7725295214418892,0.764505787280341,4.154642105102539,1748.3782850000002 +1656,Multiclass classification,Bagging,ImageSegments,0.7716012084592145,0.7716012084592145,0.7634170612719108,4.15452766418457,1847.3163560000003 +1702,Multiclass classification,Bagging,ImageSegments,0.7713109935332157,0.7713109935332157,0.7652815676598499,4.154825210571289,1948.7028940000002 +1748,Multiclass classification,Bagging,ImageSegments,0.77389811104751,0.77389811104751,0.7674409436090757,4.155008316040039,2052.533374 +1794,Multiclass classification,Bagging,ImageSegments,0.7752370329057445,0.7752370329057446,0.7674318582149376,4.155046463012695,2159.053176 +1840,Multiclass classification,Bagging,ImageSegments,0.7765089722675367,0.7765089722675368,0.7688731808749575,4.154977798461914,2268.233507 +1886,Multiclass classification,Bagging,ImageSegments,0.7750663129973475,0.7750663129973475,0.7678921362145585,4.154905319213867,2379.8377889999997 +1932,Multiclass classification,Bagging,ImageSegments,0.7752459865354738,0.7752459865354739,0.7671636716269125,4.155000686645508,2494.085284 +1978,Multiclass classification,Bagging,ImageSegments,0.7759231158320687,0.7759231158320687,0.7670573130332384,4.154901504516602,2611.0522539999997 +2024,Multiclass classification,Bagging,ImageSegments,0.7775580820563519,0.7775580820563519,0.7671264358471986,4.154878616333008,2730.5624909999997 +2070,Multiclass classification,Bagging,ImageSegments,0.77670372160464,0.7767037216046399,0.7665050383810529,4.15495491027832,2852.4395529999997 +2116,Multiclass classification,Bagging,ImageSegments,0.7773049645390071,0.7773049645390071,0.766340416614934,4.15495491027832,2976.9921299999996 +2162,Multiclass classification,Bagging,ImageSegments,0.7783433595557612,0.7783433595557612,0.766965714748886,4.155027389526367,3104.012504 +2208,Multiclass classification,Bagging,ImageSegments,0.780244676030811,0.780244676030811,0.7678552364681828,4.155023574829102,3233.6609839999996 +2254,Multiclass classification,Bagging,ImageSegments,0.7776298268974701,0.7776298268974701,0.7652407320979201,4.154973983764648,3365.6406429999997 +2300,Multiclass classification,Bagging,ImageSegments,0.7768595041322314,0.7768595041322314,0.764461061100325,4.15504264831543,3499.962334 +2310,Multiclass classification,Bagging,ImageSegments,0.7769597228237333,0.7769597228237333,0.7645642360301897,4.155065536499023,3634.8810719999997 +1056,Multiclass classification,Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762561,6.533428192138672,93.097088 +2112,Multiclass classification,Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.533924102783203,264.682132 +3168,Multiclass classification,Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244892,6.534633636474609,504.28420900000003 +4224,Multiclass classification,Bagging,Insects,0.6107032914989344,0.6107032914989344,0.5748501357120321,6.535015106201172,804.5259470000001 +5280,Multiclass classification,Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225517,6.535823822021484,1159.582019 +6336,Multiclass classification,Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789625,6.535648345947266,1564.000203 +7392,Multiclass classification,Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867537,6.535068511962891,2016.3102330000002 +8448,Multiclass classification,Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.534946441650391,2516.339397 +9504,Multiclass classification,Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297489,6.535068511962891,3064.243813 +10560,Multiclass classification,Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.535427093505859,3659.768381 +11616,Multiclass classification,Bagging,Insects,0.6005165733964701,0.6005165733964701,0.56512832395729,6.535404205322266,4303.8464189999995 +12672,Multiclass classification,Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306639,6.535358428955078,4997.310473 +13728,Multiclass classification,Bagging,Insects,0.6147009543235958,0.6147009543235958,0.5955104002005771,6.534030914306641,5738.022631999999 +14784,Multiclass classification,Bagging,Insects,0.6051545694378678,0.6051545694378678,0.586271708420286,6.533008575439453,6524.316427 +15840,Multiclass classification,Bagging,Insects,0.5703642906749163,0.5703642906749163,0.5530031721301686,6.534244537353516,7355.370967999999 +16896,Multiclass classification,Bagging,Insects,0.5440662918023084,0.5440662918023084,0.5274181049148582,6.532741546630859,8230.882624 +17952,Multiclass classification,Bagging,Insects,0.524650437301543,0.524650437301543,0.5077439094080566,6.533657073974609,9149.482306 +19008,Multiclass classification,Bagging,Insects,0.5142842110801283,0.5142842110801283,0.4945495171544722,5.423342704772949,10110.1367 +20064,Multiclass classification,Bagging,Insects,0.5202611772915317,0.5202611772915317,0.499632175624185,13.463048934936523,11121.939621 +21120,Multiclass classification,Bagging,Insects,0.5284814621904447,0.5284814621904447,0.5082299437323158,14.233846664428711,12202.117709999999 +22176,Multiclass classification,Bagging,Insects,0.5344757609921083,0.5344757609921083,0.5148729059414189,14.772774696350098,13344.394484999999 +23232,Multiclass classification,Bagging,Insects,0.5430674529723215,0.5430674529723215,0.5233933209280776,14.684733390808105,14542.607494 +24288,Multiclass classification,Bagging,Insects,0.5502120475974801,0.5502120475974801,0.5298443248135049,16.20911407470703,15791.070918 +25344,Multiclass classification,Bagging,Insects,0.5564061081955569,0.5564061081955569,0.5355525016331893,16.199478149414062,17093.843057 +26400,Multiclass classification,Bagging,Insects,0.561460661388689,0.561460661388689,0.5398397773012414,16.192718505859375,18441.382026 +27456,Multiclass classification,Bagging,Insects,0.564742305590967,0.564742305590967,0.5421523628031605,15.229331016540527,19838.570208 +28512,Multiclass classification,Bagging,Insects,0.5680614499666795,0.5680614499666795,0.5472893783055924,13.71937370300293,21280.868604 +29568,Multiclass classification,Bagging,Insects,0.5701288598775662,0.5701288598775662,0.55295508639855,11.343052864074707,22768.358846 +30624,Multiclass classification,Bagging,Insects,0.5724128922705156,0.5724128922705156,0.5585792537754973,9.387857437133789,24294.822849 +31680,Multiclass classification,Bagging,Insects,0.5749865841724802,0.5749865841724802,0.5636037623129485,9.38664436340332,25857.719223 +32736,Multiclass classification,Bagging,Insects,0.5781884832747823,0.5781884832747823,0.5684564968293649,9.385660171508789,27456.12944 +33792,Multiclass classification,Bagging,Insects,0.575656239827173,0.575656239827173,0.5663415557568727,7.860757827758789,29092.739018 +34848,Multiclass classification,Bagging,Insects,0.5754584325766924,0.5754584325766924,0.565994999425249,7.205549240112305,30762.023764 +35904,Multiclass classification,Bagging,Insects,0.5763863743976827,0.5763863743976827,0.5665127709334143,6.54947566986084,32461.363070000003 +36960,Multiclass classification,Bagging,Insects,0.5758813820720258,0.5758813820720258,0.56571927622701,6.547377586364746,34189.07998 +38016,Multiclass classification,Bagging,Insects,0.5767460213073786,0.5767460213073786,0.5661110063916132,6.546515464782715,35945.284935 +39072,Multiclass classification,Bagging,Insects,0.5764633615725219,0.5764633615725219,0.5659285794545608,6.543356895446777,37730.818682000005 +40128,Multiclass classification,Bagging,Insects,0.573454282652578,0.573454282652578,0.5636611811263741,8.510072708129883,39542.33547700001 +41184,Multiclass classification,Bagging,Insects,0.5726391957846685,0.5726391957846685,0.5633960246210544,8.712862014770508,41378.34519600001 +42240,Multiclass classification,Bagging,Insects,0.5723146854802433,0.5723146854802433,0.5635786987292998,10.13754653930664,43237.00688100001 +43296,Multiclass classification,Bagging,Insects,0.5717981291142165,0.5717981291142165,0.5635967907133216,10.13637924194336,45117.76335600001 +44352,Multiclass classification,Bagging,Insects,0.571103244571712,0.571103244571712,0.5633625241299441,10.135028839111328,47020.66134100001 +45408,Multiclass classification,Bagging,Insects,0.5712335102517233,0.5712335102517233,0.563808836162261,11.334146499633789,48947.97157600001 +46464,Multiclass classification,Bagging,Insects,0.5728213847577642,0.5728213847577642,0.5658781423773395,12.350201606750488,50897.74209700001 +47520,Multiclass classification,Bagging,Insects,0.576863991245607,0.576863991245607,0.5703778478941884,16.125893592834473,52890.49897200001 +48576,Multiclass classification,Bagging,Insects,0.5828512609366958,0.5828512609366958,0.5764029561430954,15.266244888305664,54904.91240000001 +49632,Multiclass classification,Bagging,Insects,0.5890270194031956,0.5890270194031956,0.5823661991476956,14.839654922485352,56940.07330000001 +50688,Multiclass classification,Bagging,Insects,0.5947087024286306,0.5947087024286306,0.5876086024291545,12.465810775756836,58994.29364000001 +51744,Multiclass classification,Bagging,Insects,0.600718937827339,0.600718937827339,0.5930357853224563,11.884730339050293,61065.77177200001 +52800,Multiclass classification,Bagging,Insects,0.6060342051932802,0.6060342051932802,0.5982060206393416,3.691446304321289,63151.215841000005 +52848,Multiclass classification,Bagging,Insects,0.6063920373909588,0.6063920373909588,0.5985419438128344,3.691621780395508,65236.99615100001 +408,Multiclass classification,Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.1448841094970703,5.867596 +816,Multiclass classification,Bagging,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,3.0916757583618164,25.808269 +1224,Multiclass classification,Bagging,Keystroke,0.8912510220768601,0.8912510220768601,0.8617021305177773,4.035944938659668,63.939426 +1632,Multiclass classification,Bagging,Keystroke,0.9031269160024524,0.9031269160024524,0.8868998230762758,4.988290786743164,125.34339 +2040,Multiclass classification,Bagging,Keystroke,0.898970083374203,0.898970083374203,0.888705938214812,6.037667274475098,214.307845 +2448,Multiclass classification,Bagging,Keystroke,0.8594196975888844,0.8594196975888844,0.8547805855679916,6.993380546569824,335.016386 +2856,Multiclass classification,Bagging,Keystroke,0.8651488616462347,0.8651488616462347,0.8483773016417727,7.939821243286133,488.12155800000005 +3264,Multiclass classification,Bagging,Keystroke,0.8553478394115844,0.8553478394115844,0.8302147847543373,8.885003089904785,675.394352 +3672,Multiclass classification,Bagging,Keystroke,0.8452737673658404,0.8452737673658404,0.8411086163638233,9.830622673034668,899.024814 +4080,Multiclass classification,Bagging,Keystroke,0.8374601618043638,0.8374601618043638,0.8238000521910981,11.003908157348633,1161.3046 +4488,Multiclass classification,Bagging,Keystroke,0.8250501448629374,0.8250501448629373,0.8343531144302688,11.974610328674316,1461.738376 +4896,Multiclass classification,Bagging,Keystroke,0.8232890704800817,0.8232890704800817,0.8292209535545839,12.919659614562988,1801.820426 +5304,Multiclass classification,Bagging,Keystroke,0.8199132566471808,0.819913256647181,0.8044565992905442,13.86521053314209,2181.861898 +5712,Multiclass classification,Bagging,Keystroke,0.7998599194536858,0.7998599194536857,0.8029484507582976,14.811628341674805,2601.779179 +6120,Multiclass classification,Bagging,Keystroke,0.7970256577872201,0.7970256577872201,0.7783451709211457,15.75713062286377,3063.5971010000003 +6528,Multiclass classification,Bagging,Keystroke,0.7720239007200858,0.7720239007200858,0.767005590841987,16.704151153564453,3570.6766780000003 +6936,Multiclass classification,Bagging,Keystroke,0.7645277577505407,0.7645277577505407,0.766187831914561,17.649503707885742,4126.519897 +7344,Multiclass classification,Bagging,Keystroke,0.773389622769985,0.7733896227699851,0.770832075885354,18.61162567138672,4733.5217410000005 +7752,Multiclass classification,Bagging,Keystroke,0.7737066185008385,0.7737066185008385,0.7718493223486268,19.557814598083496,5395.069721000001 +8160,Multiclass classification,Bagging,Keystroke,0.7765657556073048,0.7765657556073047,0.7724710929560354,20.503721237182617,6113.943535 +8568,Multiclass classification,Bagging,Keystroke,0.7730827594257033,0.7730827594257033,0.7727491763630034,21.882675170898438,6890.839823 +8976,Multiclass classification,Bagging,Keystroke,0.7714763231197772,0.7714763231197772,0.7717207236627096,22.87528133392334,7728.212391 +9384,Multiclass classification,Bagging,Keystroke,0.7702227432590856,0.7702227432590856,0.7694267539223918,23.822596549987793,8626.275614 +9792,Multiclass classification,Bagging,Keystroke,0.7656010621999796,0.7656010621999795,0.7644081311179032,24.768078804016113,9586.24664 +10200,Multiclass classification,Bagging,Keystroke,0.757623296401608,0.757623296401608,0.749720417225094,25.71299648284912,10618.940127 +10608,Multiclass classification,Bagging,Keystroke,0.737154709154332,0.737154709154332,0.7245707699101513,26.660439491271973,11726.561153 +11016,Multiclass classification,Bagging,Keystroke,0.729822968679074,0.7298229686790739,0.7256689004292383,27.605186462402344,12907.41343 +11424,Multiclass classification,Bagging,Keystroke,0.7229274271207213,0.7229274271207213,0.7092514304350318,28.551199913024902,14153.769988 +11832,Multiclass classification,Bagging,Keystroke,0.7133801031189249,0.7133801031189249,0.7054771135814562,29.4963436126709,15465.906612 +12240,Multiclass classification,Bagging,Keystroke,0.7177874009314487,0.7177874009314487,0.7138351093258007,30.441871643066406,16835.329364 +12648,Multiclass classification,Bagging,Keystroke,0.7147149521625682,0.7147149521625682,0.7065885995198201,31.388431549072266,18265.757575 +13056,Multiclass classification,Bagging,Keystroke,0.7031788586748372,0.7031788586748372,0.6954173783902821,32.33424186706543,19760.458564 +13464,Multiclass classification,Bagging,Keystroke,0.7011067369828419,0.7011067369828419,0.6966368809795416,33.27959156036377,21319.158047 +13872,Multiclass classification,Bagging,Keystroke,0.7007425564126595,0.7007425564126595,0.6971102154727419,34.22630214691162,22941.129728 +14280,Multiclass classification,Bagging,Keystroke,0.6961972126899643,0.6961972126899643,0.691133802747568,35.17108726501465,24623.129677 +14688,Multiclass classification,Bagging,Keystroke,0.698781235105876,0.698781235105876,0.696592906911097,36.11711597442627,26362.470984 +15096,Multiclass classification,Bagging,Keystroke,0.7048029148724744,0.7048029148724744,0.702773358939844,37.0643196105957,28156.052692 +15504,Multiclass classification,Bagging,Keystroke,0.7047668193252918,0.7047668193252918,0.7013012225519919,38.00920104980469,30004.434818 +15912,Multiclass classification,Bagging,Keystroke,0.6956822324178241,0.6956822324178241,0.6887843659114408,38.955204010009766,31904.325566000003 +16320,Multiclass classification,Bagging,Keystroke,0.6869906244255163,0.6869906244255163,0.6817298949676788,39.901418685913086,33880.147234000004 +16728,Multiclass classification,Bagging,Keystroke,0.6840437615830693,0.6840437615830693,0.6809878840610977,40.84670162200928,35894.19480500001 +17136,Multiclass classification,Bagging,Keystroke,0.6798949518529326,0.6798949518529326,0.6760668667678135,42.678324699401855,37945.35177200001 +17544,Multiclass classification,Bagging,Keystroke,0.6725759562218548,0.6725759562218548,0.6693298574086026,43.72208595275879,40033.19473500001 +17952,Multiclass classification,Bagging,Keystroke,0.6715503314578575,0.6715503314578575,0.6700615486077944,44.668694496154785,42156.41544100001 +18360,Multiclass classification,Bagging,Keystroke,0.6768887194291628,0.6768887194291628,0.6760264883444682,45.61451721191406,44306.462280000014 +18768,Multiclass classification,Bagging,Keystroke,0.6818884211648105,0.6818884211648105,0.6814185274246665,46.561092376708984,46484.07667300002 +19176,Multiclass classification,Bagging,Keystroke,0.6739504563233377,0.6739504563233377,0.6724064481498903,47.50611400604248,48682.185033000016 +19584,Multiclass classification,Bagging,Keystroke,0.677883878874534,0.677883878874534,0.6774885006147249,48.451809883117676,50904.928535000014 +19992,Multiclass classification,Bagging,Keystroke,0.6733530088539843,0.6733530088539843,0.6729949515014169,49.39821243286133,53145.742009000016 +20400,Multiclass classification,Bagging,Keystroke,0.6697387126819943,0.6697387126819943,0.6699810213452306,50.34487438201904,55411.38251600001 +46,Multiclass classification,Leveraging Bagging,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,4.0974016189575195,6.997987 +92,Multiclass classification,Leveraging Bagging,ImageSegments,0.5164835164835165,0.5164835164835165,0.5316649744849407,4.0979814529418945,22.017115 +138,Multiclass classification,Leveraging Bagging,ImageSegments,0.5547445255474452,0.5547445255474452,0.5804654781117263,4.0981035232543945,44.610383999999996 +184,Multiclass classification,Leveraging Bagging,ImageSegments,0.6174863387978142,0.6174863387978142,0.6394923756219437,4.0987138748168945,74.61421299999999 +230,Multiclass classification,Leveraging Bagging,ImageSegments,0.6506550218340611,0.6506550218340611,0.66859135700569,4.0987138748168945,111.65332099999999 +276,Multiclass classification,Leveraging Bagging,ImageSegments,0.6618181818181819,0.6618181818181819,0.6795855359270878,4.098832130432129,156.076644 +322,Multiclass classification,Leveraging Bagging,ImageSegments,0.6853582554517134,0.6853582554517134,0.6872635633687633,4.099373817443848,207.57491499999998 +368,Multiclass classification,Leveraging Bagging,ImageSegments,0.7111716621253406,0.7111716621253404,0.7098417316927395,4.099347114562988,266.34173899999996 +414,Multiclass classification,Leveraging Bagging,ImageSegments,0.7215496368038741,0.7215496368038742,0.7201557312728714,4.09926700592041,332.356571 +460,Multiclass classification,Leveraging Bagging,ImageSegments,0.7211328976034859,0.721132897603486,0.7175330036146421,4.099320411682129,405.380301 +506,Multiclass classification,Leveraging Bagging,ImageSegments,0.7287128712871287,0.7287128712871287,0.7233455022590812,4.099320411682129,485.520305 +552,Multiclass classification,Leveraging Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7255599965917697,4.099240303039551,572.983507 +598,Multiclass classification,Leveraging Bagging,ImageSegments,0.7353433835845896,0.7353433835845896,0.7308494254186014,4.0992631912231445,667.526521 +644,Multiclass classification,Leveraging Bagging,ImageSegments,0.7340590979782271,0.7340590979782271,0.7314183982762247,4.099823951721191,768.914228 +690,Multiclass classification,Leveraging Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7343909641298695,4.099823951721191,877.069835 +736,Multiclass classification,Leveraging Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7369557659594496,4.099850654602051,992.1901310000001 +782,Multiclass classification,Leveraging Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7419020281650245,4.099850654602051,1114.103609 +828,Multiclass classification,Leveraging Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7432199627682998,4.099850654602051,1242.576589 +874,Multiclass classification,Leveraging Bagging,ImageSegments,0.7502863688430699,0.7502863688430699,0.7482089866208982,4.099850654602051,1377.530874 +920,Multiclass classification,Leveraging Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7477650187313974,4.099823951721191,1518.374517 +966,Multiclass classification,Leveraging Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.747322646811651,4.099823951721191,1664.8953589999999 +1012,Multiclass classification,Leveraging Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.7469783619055548,4.099823951721191,1817.1987969999998 +1058,Multiclass classification,Leveraging Bagging,ImageSegments,0.7530747398297067,0.7530747398297066,0.7482363934596314,4.099823951721191,1975.1124209999998 +1104,Multiclass classification,Leveraging Bagging,ImageSegments,0.7552130553037172,0.7552130553037172,0.750118495060715,4.0998735427856445,2138.658016 +1150,Multiclass classification,Leveraging Bagging,ImageSegments,0.7571801566579635,0.7571801566579635,0.7516199800653577,4.0998735427856445,2307.825702 +1196,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598326359832636,0.7598326359832636,0.7548841797367702,4.0998735427856445,2482.820129 +1242,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598710717163578,0.7598710717163577,0.7553301531902636,4.0998735427856445,2663.4478950000002 +1288,Multiclass classification,Leveraging Bagging,ImageSegments,0.7645687645687645,0.7645687645687647,0.7590078532621816,4.1004838943481445,2849.419913 +1334,Multiclass classification,Leveraging Bagging,ImageSegments,0.7644411102775694,0.7644411102775694,0.7591993978414527,4.100506782531738,3040.9965970000003 +1380,Multiclass classification,Leveraging Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7601575050520947,4.100506782531738,3238.5768190000003 +1426,Multiclass classification,Leveraging Bagging,ImageSegments,0.7670175438596492,0.7670175438596492,0.7613339877221927,4.100506782531738,3441.4807240000005 +1472,Multiclass classification,Leveraging Bagging,ImageSegments,0.7715839564921821,0.7715839564921821,0.7641396475218201,4.100552558898926,3649.5015090000006 +1518,Multiclass classification,Leveraging Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.7648275341801108,4.100552558898926,3862.6942700000004 +1564,Multiclass classification,Leveraging Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7657569341108763,4.100552558898926,4080.8910560000004 +1610,Multiclass classification,Leveraging Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7651494083475014,4.1005754470825195,4304.577590000001 +1656,Multiclass classification,Leveraging Bagging,ImageSegments,0.7740181268882175,0.7740181268882175,0.7654813489818475,4.100529670715332,4533.710142000001 +1702,Multiclass classification,Leveraging Bagging,ImageSegments,0.7730746619635509,0.7730746619635509,0.766493027961906,4.100529670715332,4767.793233000001 +1748,Multiclass classification,Leveraging Bagging,ImageSegments,0.7756153405838581,0.7756153405838581,0.7686072256536652,4.100529670715332,5007.029776000001 +1794,Multiclass classification,Leveraging Bagging,ImageSegments,0.7769102063580591,0.7769102063580591,0.7685414235990152,4.100502967834473,5251.440116000002 +1840,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781402936378466,0.7781402936378466,0.7699957723931323,4.100502967834473,5500.964415000001 +1886,Multiclass classification,Leveraging Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7684985598909853,4.100502967834473,5755.503987000001 +1932,Multiclass classification,Leveraging Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7677434418046419,4.100502967834473,6014.862306000001 +1978,Multiclass classification,Leveraging Bagging,ImageSegments,0.7774405665149215,0.7774405665149215,0.7684788817649146,4.100502967834473,6279.121569000001 +2024,Multiclass classification,Leveraging Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7689103339153599,4.100502967834473,6548.278113000001 +2070,Multiclass classification,Leveraging Bagging,ImageSegments,0.7786370227162881,0.7786370227162881,0.7686288077529282,4.100502967834473,6822.363214000001 +2116,Multiclass classification,Leveraging Bagging,ImageSegments,0.7791962174940898,0.7791962174940898,0.768391950800897,4.100502967834473,7101.096348000001 +2162,Multiclass classification,Leveraging Bagging,ImageSegments,0.7801943544655252,0.7801943544655253,0.768962628827985,4.100525856018066,7384.333285000001 +2208,Multiclass classification,Leveraging Bagging,ImageSegments,0.7820570910738559,0.7820570910738559,0.7698068761587117,4.100499153137207,7672.298476000001 +2254,Multiclass classification,Leveraging Bagging,ImageSegments,0.7789613848202397,0.7789613848202397,0.7667173742344939,4.100499153137207,7965.117559000001 +2300,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781644193127447,0.7781644193127447,0.7659138381656089,4.100499153137207,8262.647904000001 +2310,Multiclass classification,Leveraging Bagging,ImageSegments,0.7782589865742746,0.7782589865742745,0.7660163657276376,4.100499153137207,8561.303246000001 +1056,Multiclass classification,Leveraging Bagging,Insects,0.6218009478672986,0.6218009478672986,0.5857016652718549,6.471495628356934,220.837673 +2112,Multiclass classification,Leveraging Bagging,Insects,0.6196115585030791,0.6196115585030791,0.5856756432415233,10.302834510803223,598.297395 +3168,Multiclass classification,Leveraging Bagging,Insects,0.628986422481844,0.628986422481844,0.5949930595607559,19.024110794067383,1103.516793 +4224,Multiclass classification,Leveraging Bagging,Insects,0.6294103717736207,0.6294103717736207,0.5952675443708706,19.52926254272461,1735.893967 +5280,Multiclass classification,Leveraging Bagging,Insects,0.6364841826103429,0.6364841826103429,0.5994911272790603,18.82306957244873,2497.807238 +6336,Multiclass classification,Leveraging Bagging,Insects,0.6352012628255722,0.6352012628255722,0.5993891820807258,20.00343894958496,3379.788115 +7392,Multiclass classification,Leveraging Bagging,Insects,0.638749830875389,0.638749830875389,0.6030343276880051,20.9547061920166,4385.582643 +8448,Multiclass classification,Leveraging Bagging,Insects,0.6405824553095774,0.6405824553095774,0.6028521616895871,23.98197650909424,5520.259032 +9504,Multiclass classification,Leveraging Bagging,Insects,0.6449542249815847,0.6449542249815847,0.6055705492028415,24.687146186828613,6764.141036999999 +10560,Multiclass classification,Leveraging Bagging,Insects,0.6485462638507434,0.6485462638507434,0.6081614166360887,28.76917839050293,8102.806145 +11616,Multiclass classification,Leveraging Bagging,Insects,0.6490744726646578,0.6490744726646578,0.6078786452761632,30.803756713867188,9530.02909 +12672,Multiclass classification,Leveraging Bagging,Insects,0.6514876489621971,0.6514876489621971,0.6111938480023122,35.14385414123535,11044.697830000001 +13728,Multiclass classification,Leveraging Bagging,Insects,0.6707947840023312,0.6707947840023312,0.6607574394823457,17.51351547241211,12617.098737 +14784,Multiclass classification,Leveraging Bagging,Insects,0.6821348846648176,0.6821348846648176,0.6733632096765088,9.275564193725586,14250.678949000001 +15840,Multiclass classification,Leveraging Bagging,Insects,0.6778205694803965,0.6778205694803965,0.670556396248407,11.964457511901855,15956.730999000001 +16896,Multiclass classification,Leveraging Bagging,Insects,0.6754661142349808,0.6754661142349808,0.6690281338426608,12.60369873046875,17732.973803 +17952,Multiclass classification,Leveraging Bagging,Insects,0.6721631106902123,0.6721631106902123,0.6660357480506892,12.93508529663086,19577.321708 +19008,Multiclass classification,Leveraging Bagging,Insects,0.6856947440416689,0.6856947440416689,0.6751812770122833,14.563780784606934,21465.395048 +20064,Multiclass classification,Leveraging Bagging,Insects,0.6926680954991776,0.6926680954991776,0.6785701715539604,23.61655616760254,23398.659989 +21120,Multiclass classification,Leveraging Bagging,Insects,0.6942090061082438,0.6942090061082438,0.6784920731228882,30.020954132080078,25401.280766 +22176,Multiclass classification,Leveraging Bagging,Insects,0.6958737316798196,0.6958737316798196,0.6784853924286285,31.293453216552734,27443.688764 +23232,Multiclass classification,Leveraging Bagging,Insects,0.6989798114588266,0.6989798114588266,0.6799590657327791,29.59604263305664,29526.276676999998 +24288,Multiclass classification,Leveraging Bagging,Insects,0.7011981718614897,0.7011981718614897,0.680282364066019,32.615909576416016,31645.669427999997 +25344,Multiclass classification,Leveraging Bagging,Insects,0.7031527443475516,0.7031527443475516,0.6805566439417602,33.91432285308838,33792.819539 +26400,Multiclass classification,Leveraging Bagging,Insects,0.7051782264479716,0.7051782264479716,0.6809495737401271,35.12977695465088,35966.301701 +27456,Multiclass classification,Leveraging Bagging,Insects,0.7065743944636678,0.7065743944636678,0.6805936316849747,38.84447956085205,38159.78466 +28512,Multiclass classification,Leveraging Bagging,Insects,0.7054820946301428,0.7054820946301428,0.681225779493031,34.570815086364746,40377.715598999996 +29568,Multiclass classification,Leveraging Bagging,Insects,0.7045692833226231,0.7045692833226231,0.6849598194839713,20.382534980773926,42611.23284999999 +30624,Multiclass classification,Leveraging Bagging,Insects,0.7031316330862424,0.7031316330862424,0.6877640955933652,22.55568027496338,44864.71640799999 +31680,Multiclass classification,Leveraging Bagging,Insects,0.7032418952618454,0.7032418952618454,0.6917227552448634,26.177990913391113,47133.482559 +32736,Multiclass classification,Leveraging Bagging,Insects,0.7037421719871697,0.7037421719871697,0.6952024388211077,25.761178016662598,49415.922784999995 +33792,Multiclass classification,Leveraging Bagging,Insects,0.7002160338551685,0.7002160338551685,0.6931280234945141,25.958494186401367,51714.47139399999 +34848,Multiclass classification,Leveraging Bagging,Insects,0.6973627571957414,0.6973627571957414,0.6902163957562899,18.894118309020996,54032.337051999995 +35904,Multiclass classification,Leveraging Bagging,Insects,0.6951786758766677,0.6951786758766677,0.6877287571005829,18.049145698547363,56371.370632 +36960,Multiclass classification,Leveraging Bagging,Insects,0.6919830081982737,0.6919830081982737,0.6843647347906762,22.045016288757324,58731.919307 +38016,Multiclass classification,Leveraging Bagging,Insects,0.6900697093252663,0.6900697093252663,0.68217396069655,25.079078674316406,61114.705623999995 +39072,Multiclass classification,Leveraging Bagging,Insects,0.688720534411712,0.688720534411712,0.6808510434728485,19.794261932373047,63520.517782999996 +40128,Multiclass classification,Leveraging Bagging,Insects,0.6867695068158597,0.6867695068158597,0.6796002866264578,10.854747772216797,65948.78476699999 +41184,Multiclass classification,Leveraging Bagging,Insects,0.6843843333414273,0.6843843333414273,0.6779529807793833,10.474969863891602,68395.63477799999 +42240,Multiclass classification,Leveraging Bagging,Insects,0.6822131205757712,0.6822131205757712,0.6764872431583758,14.707494735717773,70864.05938699999 +43296,Multiclass classification,Leveraging Bagging,Insects,0.6795472918350849,0.6795472918350849,0.674587653669649,12.672552108764648,73351.02096199998 +44352,Multiclass classification,Leveraging Bagging,Insects,0.6769633153705666,0.6769633153705666,0.6725984110786069,13.144417762756348,75857.66838799998 +45408,Multiclass classification,Leveraging Bagging,Insects,0.6748959411544475,0.6748959411544475,0.6710316194917795,14.719610214233398,78383.45415699997 +46464,Multiclass classification,Leveraging Bagging,Insects,0.6743215031315241,0.6743215031315241,0.670959098678123,15.027325630187988,80927.72302099997 +47520,Multiclass classification,Leveraging Bagging,Insects,0.6765293882447021,0.6765293882447021,0.6733002712216741,17.283148765563965,83488.02074099997 +48576,Multiclass classification,Leveraging Bagging,Insects,0.6805970149253732,0.6805970149253732,0.6770692638556323,17.906007766723633,86063.52222099998 +49632,Multiclass classification,Leveraging Bagging,Insects,0.6848340754770205,0.6848340754770205,0.6808344811077705,18.8202543258667,88653.18323199998 +50688,Multiclass classification,Leveraging Bagging,Insects,0.6890524197525992,0.6890524197525992,0.6843657264244208,21.507144927978516,91255.74433499998 +51744,Multiclass classification,Leveraging Bagging,Insects,0.6932531936687089,0.6932531936687089,0.6877873898777546,23.154582023620605,93870.63731899999 +52800,Multiclass classification,Leveraging Bagging,Insects,0.6956002954601412,0.6956002954601412,0.6902433463100389,14.128369331359863,96495.21656399999 +52848,Multiclass classification,Leveraging Bagging,Insects,0.6958578538043787,0.6958578538043787,0.6905081705907102,13.831001281738281,99120.19143899999 +408,Multiclass classification,Leveraging Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.0028390884399414,23.27864 +816,Multiclass classification,Leveraging Bagging,Keystroke,0.9521472392638037,0.9521472392638037,0.8408896590786493,4.076430320739746,76.761208 +1224,Multiclass classification,Leveraging Bagging,Keystroke,0.9533932951757972,0.9533932951757972,0.9542235338779169,5.6716413497924805,164.877928 +1632,Multiclass classification,Leveraging Bagging,Keystroke,0.9589209074187615,0.9589209074187615,0.9361222534860761,8.122180938720703,291.606081 +2040,Multiclass classification,Leveraging Bagging,Keystroke,0.9573320255026974,0.9573320255026974,0.9445755787125868,10.5212984085083,455.912148 +2448,Multiclass classification,Leveraging Bagging,Keystroke,0.9607682876992235,0.9607682876992235,0.9588299190873342,9.065413475036621,649.921126 +2856,Multiclass classification,Leveraging Bagging,Keystroke,0.9618213660245184,0.9618213660245184,0.9516555143941907,13.188368797302246,870.236672 +3264,Multiclass classification,Leveraging Bagging,Keystroke,0.9589334967821024,0.9589334967821024,0.9492703335352553,13.21088695526123,1122.958204 +3672,Multiclass classification,Leveraging Bagging,Keystroke,0.9585943884500137,0.9585943884500137,0.9531276848185062,16.65507411956787,1406.732623 +4080,Multiclass classification,Leveraging Bagging,Keystroke,0.9541554302525129,0.9541554302525129,0.9416377826660955,17.091320037841797,1722.764314 +4488,Multiclass classification,Leveraging Bagging,Keystroke,0.9529752618676176,0.9529752618676176,0.9549694463549354,10.336687088012695,2070.686487 +4896,Multiclass classification,Leveraging Bagging,Keystroke,0.9550561797752809,0.9550561797752809,0.95517907029875,11.520882606506348,2449.358224 +5304,Multiclass classification,Leveraging Bagging,Keystroke,0.9568168960965491,0.9568168960965491,0.9575833276239932,13.737529754638672,2856.6015070000003 +5712,Multiclass classification,Leveraging Bagging,Keystroke,0.9574505340570828,0.9574505340570828,0.9570632809827344,15.842782020568848,3290.691514 +6120,Multiclass classification,Leveraging Bagging,Keystroke,0.9557117176009152,0.9557117176009152,0.9522483041543378,20.042810440063477,3760.43818 +6528,Multiclass classification,Leveraging Bagging,Keystroke,0.9566416424084572,0.9566416424084572,0.9568246790885271,11.687369346618652,4258.095146 +6936,Multiclass classification,Leveraging Bagging,Keystroke,0.9574621485219899,0.9574621485219899,0.9579855320572277,12.487288475036621,4780.363237 +7344,Multiclass classification,Leveraging Bagging,Keystroke,0.9568296336647147,0.9568296336647147,0.9563404233689646,15.327423095703125,5334.336646 +7752,Multiclass classification,Leveraging Bagging,Keystroke,0.9565217391304348,0.9565217391304348,0.9563017119581124,18.70553493499756,5920.99825 +8160,Multiclass classification,Leveraging Bagging,Keystroke,0.9545287412673121,0.9545287412673121,0.9527980459948603,24.086796760559082,6539.621381999999 +8568,Multiclass classification,Leveraging Bagging,Keystroke,0.9545932064900199,0.9545932064900199,0.9549210113442089,21.21516990661621,7187.28418 +8976,Multiclass classification,Leveraging Bagging,Keystroke,0.9550974930362117,0.9550974930362117,0.9553627160759579,17.566545486450195,7867.8027329999995 +9384,Multiclass classification,Leveraging Bagging,Keystroke,0.9555579239049344,0.9555579239049344,0.9558253322166266,15.710055351257324,8578.363562999999 +9792,Multiclass classification,Leveraging Bagging,Keystroke,0.9552650393218262,0.9552650393218262,0.9553715117788079,18.717252731323242,9321.146735999999 +10200,Multiclass classification,Leveraging Bagging,Keystroke,0.9533287577213452,0.9533287577213452,0.9523119157834915,15.605277061462402,10099.276789999998 +10608,Multiclass classification,Leveraging Bagging,Keystroke,0.9521070990855096,0.9521070990855096,0.9515822083565743,11.186952590942383,10903.979313999998 +11016,Multiclass classification,Leveraging Bagging,Keystroke,0.953427144802542,0.953427144802542,0.9541201209142027,7.581887245178223,11728.435273 +11424,Multiclass classification,Leveraging Bagging,Keystroke,0.953689923837871,0.953689923837871,0.9538275342826803,10.15964126586914,12573.844722999998 +11832,Multiclass classification,Leveraging Bagging,Keystroke,0.9535964838137098,0.9535964838137098,0.9538502960885477,11.061944961547852,13441.174568999999 +12240,Multiclass classification,Leveraging Bagging,Keystroke,0.9541629218073372,0.9541629218073372,0.9544632162431566,11.249642372131348,14331.138910999998 +12648,Multiclass classification,Leveraging Bagging,Keystroke,0.9548509527951293,0.9548509527951293,0.9551609875055331,13.203255653381348,15243.410521999998 +13056,Multiclass classification,Leveraging Bagging,Keystroke,0.9551895825354271,0.955189582535427,0.9553883557595891,9.36058521270752,16176.429406999998 +13464,Multiclass classification,Leveraging Bagging,Keystroke,0.955953353635891,0.955953353635891,0.9562606797905644,11.575583457946777,17130.275872 +13872,Multiclass classification,Leveraging Bagging,Keystroke,0.9561675437964098,0.9561675437964098,0.9563487774281333,11.42638874053955,18106.072389999998 +14280,Multiclass classification,Leveraging Bagging,Keystroke,0.9549688353526157,0.9549688353526157,0.9548529395574759,10.249165534973145,19109.380900999997 +14688,Multiclass classification,Leveraging Bagging,Keystroke,0.9552665622659495,0.9552665622659495,0.955472434271787,8.168793678283691,20137.264238999996 +15096,Multiclass classification,Leveraging Bagging,Keystroke,0.9560781715799934,0.9560781715799934,0.9563263247313607,9.020037651062012,21191.151532999997 +15504,Multiclass classification,Leveraging Bagging,Keystroke,0.9563955363478036,0.9563955363478036,0.9565429512012837,8.031278610229492,22273.408126999995 +15912,Multiclass classification,Leveraging Bagging,Keystroke,0.9566337753755264,0.9566337753755264,0.9567672375037608,10.967172622680664,23379.117397999995 +16320,Multiclass classification,Leveraging Bagging,Keystroke,0.9563085973405233,0.9563085973405233,0.9563585840602682,11.29026985168457,24508.785403999995 +16728,Multiclass classification,Leveraging Bagging,Keystroke,0.955580797513003,0.955580797513003,0.9555776398983683,9.525394439697266,25660.924918999994 +17136,Multiclass classification,Leveraging Bagging,Keystroke,0.9564050189670266,0.9564050189670267,0.9565585833577668,10.421767234802246,26839.493165999993 +17544,Multiclass classification,Leveraging Bagging,Keystroke,0.9566778772159836,0.9566778772159836,0.9567660151847867,11.633780479431152,28038.177978999993 +17952,Multiclass classification,Leveraging Bagging,Keystroke,0.9564369672998718,0.9564369672998718,0.9564736297242662,8.448995590209961,29257.620389999993 +18360,Multiclass classification,Leveraging Bagging,Keystroke,0.9567514570510376,0.9567514570510375,0.9568227044222711,7.821832656860352,30499.29393699999 +18768,Multiclass classification,Leveraging Bagging,Keystroke,0.9568924175414291,0.9568924175414291,0.9569505378685396,9.859258651733398,31763.565401999993 +19176,Multiclass classification,Leveraging Bagging,Keystroke,0.9567144719687093,0.9567144719687093,0.956766336746882,11.256629943847656,33053.804573999994 +19584,Multiclass classification,Leveraging Bagging,Keystroke,0.9568503293673084,0.9568503293673084,0.9569026376832065,11.690522193908691,34367.36625199999 +19992,Multiclass classification,Leveraging Bagging,Keystroke,0.9564303936771548,0.9564303936771548,0.9564653381379459,12.451186180114746,35701.899461999994 +20400,Multiclass classification,Leveraging Bagging,Keystroke,0.9566155203686455,0.9566155203686455,0.9566498206969933,7.4099931716918945,37049.10208799999 +46,Multiclass classification,Stacking,ImageSegments,0.4,0.4000000000000001,0.3289160825620571,1.89190673828125,1.901401 +92,Multiclass classification,Stacking,ImageSegments,0.5494505494505495,0.5494505494505495,0.5607526488856412,2.084074020385742,6.467373 +138,Multiclass classification,Stacking,ImageSegments,0.5693430656934306,0.5693430656934306,0.5872103411959265,2.357966423034668,13.822826 +184,Multiclass classification,Stacking,ImageSegments,0.6174863387978142,0.6174863387978142,0.6372989403156369,2.7369613647460938,24.259991 +230,Multiclass classification,Stacking,ImageSegments,0.6375545851528385,0.6375545851528385,0.6548159763148107,2.862431526184082,37.817904999999996 +276,Multiclass classification,Stacking,ImageSegments,0.6618181818181819,0.6618181818181819,0.6802187985971371,2.982741355895996,54.565380999999995 +322,Multiclass classification,Stacking,ImageSegments,0.6915887850467289,0.6915887850467289,0.6955507555363084,3.080752372741699,74.633343 +368,Multiclass classification,Stacking,ImageSegments,0.7111716621253406,0.7111716621253404,0.7105739026832886,3.232259750366211,98.20470399999999 +414,Multiclass classification,Stacking,ImageSegments,0.7263922518159807,0.7263922518159807,0.7261041400072307,3.505929946899414,125.52754499999999 +460,Multiclass classification,Stacking,ImageSegments,0.7276688453159041,0.7276688453159043,0.72519869331257,3.7872886657714844,156.78717 +506,Multiclass classification,Stacking,ImageSegments,0.7425742574257426,0.7425742574257425,0.7379486431795568,6.240692138671875,210.52347600000002 +552,Multiclass classification,Stacking,ImageSegments,0.7422867513611615,0.7422867513611615,0.7388440561615693,6.313092231750488,268.009607 +598,Multiclass classification,Stacking,ImageSegments,0.7520938023450586,0.7520938023450586,0.749839509127547,6.682056427001953,329.26112900000004 +644,Multiclass classification,Stacking,ImageSegments,0.7573872472783826,0.7573872472783826,0.7582793237949303,7.269444465637207,394.37227100000007 +690,Multiclass classification,Stacking,ImageSegments,0.7634252539912917,0.7634252539912917,0.7648953830992049,7.531791687011719,463.2777280000001 +736,Multiclass classification,Stacking,ImageSegments,0.7673469387755102,0.7673469387755102,0.7694390547687558,7.987269401550293,536.1609010000001 +782,Multiclass classification,Stacking,ImageSegments,0.7772087067861716,0.7772087067861717,0.7788980835102386,8.317158699035645,613.0067590000001 +828,Multiclass classification,Stacking,ImageSegments,0.7823458282950423,0.7823458282950423,0.7854763667551727,8.613452911376953,693.8523060000001 +874,Multiclass classification,Stacking,ImageSegments,0.7915234822451317,0.7915234822451317,0.7933203073280156,8.694649696350098,778.7710210000001 +920,Multiclass classification,Stacking,ImageSegments,0.7986942328618063,0.7986942328618062,0.7996826842527437,8.824880599975586,867.8856220000001 +966,Multiclass classification,Stacking,ImageSegments,0.8041450777202073,0.8041450777202073,0.8044659150084363,9.089361190795898,961.2082950000001 +1012,Multiclass classification,Stacking,ImageSegments,0.8100890207715133,0.8100890207715133,0.8093994872208631,9.280214309692383,1058.9817440000002 +1058,Multiclass classification,Stacking,ImageSegments,0.8145695364238411,0.814569536423841,0.8133421993203876,9.165953636169434,1161.0697040000002 +1104,Multiclass classification,Stacking,ImageSegments,0.8213961922030825,0.8213961922030824,0.8206569542548617,8.760258674621582,1267.2341280000003 +1150,Multiclass classification,Stacking,ImageSegments,0.824194952132289,0.824194952132289,0.8228781271733864,8.742037773132324,1377.3471480000003 +1196,Multiclass classification,Stacking,ImageSegments,0.8292887029288702,0.8292887029288704,0.8281638601893785,8.87535572052002,1491.4919770000004 +1242,Multiclass classification,Stacking,ImageSegments,0.8340048348106366,0.8340048348106366,0.833490204478907,8.332135200500488,1609.3898390000004 +1288,Multiclass classification,Stacking,ImageSegments,0.8360528360528361,0.8360528360528361,0.8353480055004047,8.416248321533203,1730.8650650000004 +1334,Multiclass classification,Stacking,ImageSegments,0.8394598649662416,0.8394598649662416,0.8389194005130135,8.469959259033203,1855.8596220000004 +1380,Multiclass classification,Stacking,ImageSegments,0.8419144307469181,0.8419144307469181,0.8414934007209077,8.578604698181152,1984.2269550000003 +1426,Multiclass classification,Stacking,ImageSegments,0.8449122807017544,0.8449122807017544,0.8435602800871403,8.689190864562988,2115.814455 +1472,Multiclass classification,Stacking,ImageSegments,0.8484024473147519,0.8484024473147518,0.8459519552383536,8.800261497497559,2250.6136890000002 +1518,Multiclass classification,Stacking,ImageSegments,0.8503625576796309,0.8503625576796308,0.8475723684173131,9.025433540344238,2388.852428 +1564,Multiclass classification,Stacking,ImageSegments,0.8522072936660269,0.8522072936660269,0.8497128793769615,8.811847686767578,2530.498155 +1610,Multiclass classification,Stacking,ImageSegments,0.8527035425730267,0.8527035425730267,0.8503048238231962,8.729784965515137,2675.5358680000004 +1656,Multiclass classification,Stacking,ImageSegments,0.8531722054380665,0.8531722054380665,0.8508343416398155,8.761359214782715,2823.7565440000003 +1702,Multiclass classification,Stacking,ImageSegments,0.8571428571428571,0.8571428571428571,0.8561317791292776,8.798370361328125,2975.2442650000003 +1748,Multiclass classification,Stacking,ImageSegments,0.8580423583285632,0.8580423583285632,0.8567712479140972,8.86152172088623,3129.874549 +1794,Multiclass classification,Stacking,ImageSegments,0.8611266034578918,0.8611266034578918,0.8591986188286931,8.932531356811523,3287.541657 +1840,Multiclass classification,Stacking,ImageSegments,0.8618814573137574,0.8618814573137574,0.8601172531559075,8.819746017456055,3448.3205970000004 +1886,Multiclass classification,Stacking,ImageSegments,0.8636604774535809,0.8636604774535809,0.8623243992773615,9.007128715515137,3612.1367020000002 +1932,Multiclass classification,Stacking,ImageSegments,0.8648368720870016,0.8648368720870016,0.8630569076841595,9.368453979492188,3779.147863 +1978,Multiclass classification,Stacking,ImageSegments,0.8649468892261002,0.8649468892261002,0.8631362872103546,8.952109336853027,3949.363777 +2024,Multiclass classification,Stacking,ImageSegments,0.8665348492338112,0.8665348492338112,0.8639071890295129,9.146061897277832,4122.536804 +2070,Multiclass classification,Stacking,ImageSegments,0.8680521991300145,0.8680521991300145,0.8658036637930728,8.80567455291748,4298.84894 +2116,Multiclass classification,Stacking,ImageSegments,0.8695035460992908,0.8695035460992909,0.8667661913422944,8.892473220825195,4478.129032 +2162,Multiclass classification,Stacking,ImageSegments,0.8690421101341971,0.869042110134197,0.8663186552920692,8.910783767700195,4660.4030729999995 +2208,Multiclass classification,Stacking,ImageSegments,0.8699592206615315,0.8699592206615315,0.8669965232275297,8.99278450012207,4845.573407999999 +2254,Multiclass classification,Stacking,ImageSegments,0.869063470927652,0.8690634709276521,0.8666022158227548,9.09610366821289,5033.674223999999 +2300,Multiclass classification,Stacking,ImageSegments,0.8686385384949978,0.8686385384949978,0.8662053097556822,9.110825538635254,5224.6921839999995 +2310,Multiclass classification,Stacking,ImageSegments,0.8679081853616284,0.8679081853616284,0.8656034675726049,9.181622505187988,5416.881651 +1056,Multiclass classification,Stacking,Insects,0.6511848341232227,0.6511848341232227,0.5864257754346489,12.51792049407959,137.265242 +2112,Multiclass classification,Stacking,Insects,0.6873519658929418,0.6873519658929418,0.6004104483953082,15.371862411499023,366.367491 +3168,Multiclass classification,Stacking,Insects,0.6978212819703189,0.6978212819703189,0.602242348585179,17.772335052490234,671.574116 +4224,Multiclass classification,Stacking,Insects,0.7054226852948141,0.7054226852948141,0.6059831617919115,20.14197826385498,1043.757912 +5280,Multiclass classification,Stacking,Insects,0.7080886531540065,0.7080886531540066,0.6082411118035554,23.246225357055664,1476.569185 +6336,Multiclass classification,Stacking,Insects,0.708602999210734,0.708602999210734,0.6091818949546898,28.13547992706299,1970.1501170000001 +7392,Multiclass classification,Stacking,Insects,0.7104586659450683,0.7104586659450683,0.6104104212994758,30.164710998535156,2526.789716 +8448,Multiclass classification,Stacking,Insects,0.7130342133301764,0.7130342133301764,0.6119778058667307,27.698996543884277,3146.1270590000004 +9504,Multiclass classification,Stacking,Insects,0.717773334736399,0.717773334736399,0.6149023583636667,27.04288387298584,3829.1831980000006 +10560,Multiclass classification,Stacking,Insects,0.7215645420967894,0.7215645420967894,0.617635708330779,23.96706485748291,4572.729772000001 +11616,Multiclass classification,Stacking,Insects,0.7213086526043909,0.721308652604391,0.6182075626749539,26.15617847442627,5374.612830000001 +12672,Multiclass classification,Stacking,Insects,0.7240943887617394,0.7240943887617394,0.6351065980046956,25.051542282104492,6233.453892000001 +13728,Multiclass classification,Stacking,Insects,0.7432796678079697,0.7432796678079697,0.7402334392509421,15.30208683013916,7142.743199000001 +14784,Multiclass classification,Stacking,Insects,0.7491713454643848,0.7491713454643848,0.7487081677599373,11.128735542297363,8102.097506000001 +15840,Multiclass classification,Stacking,Insects,0.7424079803017867,0.7424079803017867,0.7445532404968841,16.950417518615723,9128.379042 +16896,Multiclass classification,Stacking,Insects,0.7382657591003255,0.7382657591003255,0.7427378731329454,18.26229953765869,10214.572621000001 +17952,Multiclass classification,Stacking,Insects,0.7309342097933262,0.7309342097933262,0.7368436311738037,23.776363372802734,11358.099531000002 +19008,Multiclass classification,Stacking,Insects,0.7429368127531962,0.7429368127531962,0.7441354243297112,12.958039283752441,12553.803014000001 +20064,Multiclass classification,Stacking,Insects,0.7475950755121368,0.7475950755121367,0.7439196968116685,12.612845420837402,13796.415893000001 +21120,Multiclass classification,Stacking,Insects,0.7492305506889531,0.7492305506889531,0.7418613509588597,16.95127773284912,15088.238885 +22176,Multiclass classification,Stacking,Insects,0.7509808342728298,0.7509808342728299,0.7400929587109365,17.926865577697754,16424.025269 +23232,Multiclass classification,Stacking,Insects,0.7532176832680471,0.7532176832680472,0.7391930166872092,20.939698219299316,17798.240955 +24288,Multiclass classification,Stacking,Insects,0.7550129699015935,0.7550129699015935,0.7379653286035112,25.43882942199707,19212.969178 +25344,Multiclass classification,Stacking,Insects,0.7569743124334136,0.7569743124334136,0.7375346698329149,29.94521999359131,20668.368585 +26400,Multiclass classification,Stacking,Insects,0.7580590173870222,0.7580590173870221,0.7363169253318035,34.1699275970459,22166.950006 +27456,Multiclass classification,Stacking,Insects,0.7593880896011656,0.7593880896011656,0.7352131419868576,32.93678665161133,23706.536377 +28512,Multiclass classification,Stacking,Insects,0.7573217354705202,0.7573217354705202,0.7350502568377754,21.273219108581543,25286.984696 +29568,Multiclass classification,Stacking,Insects,0.7555382690161329,0.7555382690161329,0.7386915112539557,20.747055053710938,26906.631088 +30624,Multiclass classification,Stacking,Insects,0.7544982529471312,0.7544982529471312,0.7426503125712552,24.910794258117676,28562.795387 +31680,Multiclass classification,Stacking,Insects,0.7531487736355315,0.7531487736355315,0.7453200395899969,32.13512706756592,30253.076649 +32736,Multiclass classification,Stacking,Insects,0.7530471971895525,0.7530471971895525,0.7484606399297139,36.17057991027832,31977.334616 +33792,Multiclass classification,Stacking,Insects,0.7480986061377289,0.748098606137729,0.7448942365218528,13.298456192016602,33736.870240000004 +34848,Multiclass classification,Stacking,Insects,0.7436795133010016,0.7436795133010016,0.7403442775964885,15.221885681152344,35530.857132000005 +35904,Multiclass classification,Stacking,Insects,0.7404952232403977,0.7404952232403977,0.7368033013057004,16.932289123535156,37356.72126300001 +36960,Multiclass classification,Stacking,Insects,0.7371411564165697,0.7371411564165696,0.7332530467261859,22.237309455871582,39213.91358200001 +38016,Multiclass classification,Stacking,Insects,0.7341049585689859,0.7341049585689859,0.7299460315219516,22.86026954650879,41100.10013700001 +39072,Multiclass classification,Stacking,Insects,0.7343042154027284,0.7343042154027284,0.7301016033872143,21.91624164581299,43017.482867000006 +40128,Multiclass classification,Stacking,Insects,0.7327734443143021,0.7327734443143021,0.728948208474553,20.388718605041504,44961.93393100001 +41184,Multiclass classification,Stacking,Insects,0.7327538061821626,0.7327538061821626,0.7292630064673854,15.630711555480957,46951.12268600001 +42240,Multiclass classification,Stacking,Insects,0.7331849712351145,0.7331849712351144,0.7301128191332076,20.110919952392578,48959.16072000001 +43296,Multiclass classification,Stacking,Insects,0.7337567848481349,0.7337567848481349,0.7309969621648841,24.057676315307617,50985.068017000005 +44352,Multiclass classification,Stacking,Insects,0.7342111790038556,0.7342111790038556,0.731637560144403,28.529647827148438,53028.942305000004 +45408,Multiclass classification,Stacking,Insects,0.7351289448763406,0.7351289448763407,0.7324911060941295,28.861422538757324,55091.119898000004 +46464,Multiclass classification,Stacking,Insects,0.7357682457008803,0.7357682457008803,0.7329742877599967,33.076725006103516,57170.52325500001 +47520,Multiclass classification,Stacking,Insects,0.7366947957659041,0.736694795765904,0.7341498113226347,21.352835655212402,59267.07909500001 +48576,Multiclass classification,Stacking,Insects,0.7403602676273804,0.7403602676273804,0.7381372580344014,19.381468772888184,61379.50627500001 +49632,Multiclass classification,Stacking,Insects,0.7442122866756664,0.7442122866756663,0.742109373234967,21.8067569732666,63507.849119000006 +50688,Multiclass classification,Stacking,Insects,0.7475289521968157,0.7475289521968157,0.7453466445950636,21.65154266357422,65647.81315 +51744,Multiclass classification,Stacking,Insects,0.7510581141410433,0.7510581141410433,0.7487124138061083,22.870601654052734,67797.610737 +52800,Multiclass classification,Stacking,Insects,0.7545218659444307,0.7545218659444307,0.752582163258218,10.554459571838379,69956.07865499999 +52848,Multiclass classification,Stacking,Insects,0.7547448294132117,0.7547448294132117,0.7528178949021433,10.58643913269043,72115.038215 +408,Multiclass classification,Stacking,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.786503791809082,20.578742 +816,Multiclass classification,Stacking,Keystroke,0.9815950920245399,0.98159509202454,0.9278568842209168,6.9002227783203125,101.28008299999999 +1224,Multiclass classification,Stacking,Keystroke,0.9803761242845462,0.9803761242845462,0.9574942570636209,9.112634658813477,223.840162 +1632,Multiclass classification,Stacking,Keystroke,0.9779276517473943,0.9779276517473943,0.9432755457272627,10.40715503692627,381.844655 +2040,Multiclass classification,Stacking,Keystroke,0.973516429622364,0.973516429622364,0.9361356188587967,12.656171798706055,575.269036 +2448,Multiclass classification,Stacking,Keystroke,0.9726195341234164,0.9726195341234164,0.9612590316809274,8.745987892150879,802.257021 +2856,Multiclass classification,Stacking,Keystroke,0.9754816112084063,0.9754816112084063,0.9751469891413959,9.931495666503906,1061.688609 +3264,Multiclass classification,Stacking,Keystroke,0.9754826846460313,0.9754826846460313,0.9697604489278108,10.511832237243652,1352.298412 +3672,Multiclass classification,Stacking,Keystroke,0.9733042767638246,0.9733042767638246,0.9642745555297418,11.800049781799316,1675.3338330000001 +4080,Multiclass classification,Stacking,Keystroke,0.9722971316499142,0.9722971316499142,0.9666413905932107,12.42660903930664,2030.1772580000002 +4488,Multiclass classification,Stacking,Keystroke,0.9734789391575663,0.9734789391575663,0.9728883985144964,9.746350288391113,2413.735444 +4896,Multiclass classification,Stacking,Keystroke,0.9740551583248213,0.9740551583248213,0.9730015599884005,10.666529655456543,2823.5055469999998 +5304,Multiclass classification,Stacking,Keystroke,0.9741655666603809,0.9741655666603809,0.9728266773902405,11.775634765625,3261.739577 +5712,Multiclass classification,Stacking,Keystroke,0.9747855016634565,0.9747855016634565,0.9744326987999562,12.58005428314209,3727.994683 +6120,Multiclass classification,Stacking,Keystroke,0.9751593397613989,0.9751593397613989,0.9747223863351728,13.55466365814209,4223.159482999999 +6528,Multiclass classification,Stacking,Keystroke,0.9751800214493642,0.9751800214493642,0.9745255481694279,11.360074043273926,4747.988555 +6936,Multiclass classification,Stacking,Keystroke,0.9763518385003604,0.9763518385003604,0.9769458779347455,11.155635833740234,5300.577354999999 +7344,Multiclass classification,Stacking,Keystroke,0.9765763311997822,0.9765763311997822,0.9763923596721359,12.33658504486084,5881.2072339999995 +7752,Multiclass classification,Stacking,Keystroke,0.9771642368726616,0.9771642368726616,0.9773496343719735,13.116165161132812,6492.775159 +8160,Multiclass classification,Stacking,Keystroke,0.976590268415247,0.976590268415247,0.9759275084076021,13.303799629211426,7137.299991 +8568,Multiclass classification,Stacking,Keystroke,0.9768880588303958,0.9768880588303958,0.9769304999907085,13.133574485778809,7814.540539 +8976,Multiclass classification,Stacking,Keystroke,0.9774930362116991,0.9774930362116991,0.9777587646121523,13.50635814666748,8523.072866 +9384,Multiclass classification,Stacking,Keystroke,0.9767664925929873,0.9767664925929873,0.9763135719034829,15.166536331176758,9263.702674 +9792,Multiclass classification,Stacking,Keystroke,0.9765090389132877,0.9765090389132877,0.9763153416047449,16.169885635375977,10037.443626 +10200,Multiclass classification,Stacking,Keystroke,0.9758799882341406,0.9758799882341406,0.9755246287395946,14.205968856811523,10844.068015 +10608,Multiclass classification,Stacking,Keystroke,0.9755821627227302,0.9755821627227302,0.9754319444516873,12.997503280639648,11685.064117 +11016,Multiclass classification,Stacking,Keystroke,0.9759418974126192,0.9759418974126192,0.9761027289556774,12.962043762207031,12559.39796 +11424,Multiclass classification,Stacking,Keystroke,0.9760133064869124,0.9760133064869124,0.9760613734021468,14.090433120727539,13467.395857 +11832,Multiclass classification,Stacking,Keystroke,0.9754881244188995,0.9754881244188995,0.9753195915858492,14.295487403869629,14408.853786 +12240,Multiclass classification,Stacking,Keystroke,0.9759784296102623,0.9759784296102623,0.9761779987511395,15.044499397277832,15385.688043 +12648,Multiclass classification,Stacking,Keystroke,0.9762789594370206,0.9762789594370206,0.9764127823145236,15.120206832885742,16404.149055 +13056,Multiclass classification,Stacking,Keystroke,0.9758713136729222,0.9758713136729221,0.975797420384815,15.049361228942871,17460.942559000003 +13464,Multiclass classification,Stacking,Keystroke,0.9757112084973631,0.9757112084973631,0.9757165619520196,15.162266731262207,18558.798501 +13872,Multiclass classification,Stacking,Keystroke,0.9759930790858626,0.9759930790858626,0.9761084708221816,15.711796760559082,19695.838422 +14280,Multiclass classification,Stacking,Keystroke,0.9754884795854052,0.9754884795854052,0.975424480421301,16.988737106323242,20872.643227 +14688,Multiclass classification,Stacking,Keystroke,0.975624702117519,0.975624702117519,0.9757017096421697,17.869779586791992,22084.930025 +15096,Multiclass classification,Stacking,Keystroke,0.9757535607817158,0.9757535607817158,0.9758249143111629,17.579912185668945,23330.568569000003 +15504,Multiclass classification,Stacking,Keystroke,0.9755531187512094,0.9755531187512094,0.9755669148190674,16.59157657623291,24610.336028 +15912,Multiclass classification,Stacking,Keystroke,0.9756772044497517,0.9756772044497517,0.9757389077528199,16.193113327026367,25925.188427 +16320,Multiclass classification,Stacking,Keystroke,0.9759176420123782,0.9759176420123782,0.9759886766110538,16.353660583496094,27266.573062 +16728,Multiclass classification,Stacking,Keystroke,0.9756680815448078,0.9756680815448078,0.9756766431570708,17.00908374786377,28641.859399 +17136,Multiclass classification,Stacking,Keystroke,0.9758389261744966,0.9758389261744966,0.975891563489883,18.364989280700684,30047.550521 +17544,Multiclass classification,Stacking,Keystroke,0.9753747933648749,0.9753747933648749,0.975363882573194,17.298136711120605,31485.782589000002 +17952,Multiclass classification,Stacking,Keystroke,0.9753217090969862,0.9753217090969862,0.9753429667022142,16.72727108001709,32956.927282000004 +18360,Multiclass classification,Stacking,Keystroke,0.9754888610490767,0.9754888610490767,0.9755190387029732,17.51059913635254,34461.639008000006 +18768,Multiclass classification,Stacking,Keystroke,0.9757553151809026,0.9757553151809026,0.9757835195290103,18.871691703796387,35998.873267 +19176,Multiclass classification,Stacking,Keystroke,0.9754367666232073,0.9754367666232073,0.9754369138844643,17.42948341369629,37568.082084 +19584,Multiclass classification,Stacking,Keystroke,0.9754889444926722,0.9754889444926722,0.9754964783302286,17.978480339050293,39170.395427 +19992,Multiclass classification,Stacking,Keystroke,0.9756390375669051,0.9756390375669051,0.975642520227376,19.26256561279297,40805.125646 +20400,Multiclass classification,Stacking,Keystroke,0.9754889945585568,0.9754889945585568,0.9754863274548964,18.711057662963867,42471.761869 +46,Multiclass classification,Voting,ImageSegments,0.4666666666666667,0.4666666666666667,0.3890768588137009,0.9137420654296875,0.663852 +92,Multiclass classification,Voting,ImageSegments,0.6153846153846154,0.6153846153846154,0.617040786788686,0.9906883239746094,2.032737 +138,Multiclass classification,Voting,ImageSegments,0.6715328467153284,0.6715328467153284,0.6884491245817251,1.0679149627685547,4.226265 +184,Multiclass classification,Voting,ImageSegments,0.7049180327868853,0.7049180327868853,0.7194266051408907,1.1443958282470703,7.386208 +230,Multiclass classification,Voting,ImageSegments,0.7292576419213974,0.7292576419213974,0.7448338459304749,1.2214689254760742,11.723904000000001 +276,Multiclass classification,Voting,ImageSegments,0.7381818181818182,0.7381818181818182,0.7559766728000937,1.2995519638061523,17.331033 +322,Multiclass classification,Voting,ImageSegments,0.7538940809968847,0.7538940809968847,0.7616248500949714,1.3766565322875977,24.26159 +368,Multiclass classification,Voting,ImageSegments,0.773841961852861,0.7738419618528611,0.7772939373537765,1.4532833099365234,32.770568000000004 +414,Multiclass classification,Voting,ImageSegments,0.7820823244552058,0.7820823244552059,0.7854200812154107,1.5304145812988281,42.983195 +460,Multiclass classification,Voting,ImageSegments,0.7777777777777778,0.7777777777777778,0.7796254955467015,1.6075658798217773,54.886431 +506,Multiclass classification,Voting,ImageSegments,0.7861386138613862,0.7861386138613862,0.7886239053396241,3.8640270233154297,87.00222099999999 +552,Multiclass classification,Voting,ImageSegments,0.7858439201451906,0.7858439201451906,0.7889431335032357,4.088808059692383,121.00394599999998 +598,Multiclass classification,Voting,ImageSegments,0.7906197654941374,0.7906197654941374,0.7944387660679091,4.304059028625488,157.00397999999998 +644,Multiclass classification,Voting,ImageSegments,0.7853810264385692,0.7853810264385692,0.7901251252871709,4.532710075378418,195.073691 +690,Multiclass classification,Voting,ImageSegments,0.7895500725689405,0.7895500725689405,0.7935315861788143,4.759090423583984,235.272046 +736,Multiclass classification,Voting,ImageSegments,0.7863945578231293,0.7863945578231294,0.7911065855691086,4.991429328918457,277.59961999999996 +782,Multiclass classification,Voting,ImageSegments,0.7887323943661971,0.7887323943661971,0.792926322670609,5.219735145568848,322.07131499999997 +828,Multiclass classification,Voting,ImageSegments,0.7896009673518742,0.7896009673518742,0.7950712422059908,5.452417373657227,368.82718 +874,Multiclass classification,Voting,ImageSegments,0.7938144329896907,0.7938144329896907,0.7979586706142276,5.699496269226074,417.885664 +920,Multiclass classification,Voting,ImageSegments,0.794341675734494,0.794341675734494,0.7973145688626199,5.9376373291015625,469.16999000000004 +966,Multiclass classification,Voting,ImageSegments,0.7937823834196891,0.7937823834196891,0.7958827691316667,6.182188987731934,522.9385980000001 +1012,Multiclass classification,Voting,ImageSegments,0.7912957467853611,0.7912957467853611,0.7931630938612351,6.34267520904541,579.2700850000001 +1058,Multiclass classification,Voting,ImageSegments,0.793755912961211,0.7937559129612108,0.7947921362588558,6.295009613037109,638.2805060000001 +1104,Multiclass classification,Voting,ImageSegments,0.7941976427923844,0.7941976427923844,0.7951664828862093,6.2213640213012695,699.725726 +1150,Multiclass classification,Voting,ImageSegments,0.7954743255004352,0.7954743255004351,0.7958304956922065,6.151959419250488,763.5071 +1196,Multiclass classification,Voting,ImageSegments,0.796652719665272,0.796652719665272,0.7972397572733622,6.087224006652832,829.5043310000001 +1242,Multiclass classification,Voting,ImageSegments,0.7953263497179693,0.7953263497179693,0.795947547023496,6.001987457275391,897.6078460000001 +1288,Multiclass classification,Voting,ImageSegments,0.7995337995337995,0.7995337995337995,0.799082939294124,5.924266815185547,967.7224320000001 +1334,Multiclass classification,Voting,ImageSegments,0.7981995498874719,0.7981995498874719,0.7978549794399667,5.872907638549805,1039.926656 +1380,Multiclass classification,Voting,ImageSegments,0.7991298042059464,0.7991298042059464,0.799072028035076,5.784454345703125,1114.0085680000002 +1426,Multiclass classification,Voting,ImageSegments,0.8007017543859649,0.8007017543859649,0.799801266098334,5.781437873840332,1190.125915 +1472,Multiclass classification,Voting,ImageSegments,0.8042148198504419,0.8042148198504419,0.8016037490391381,5.805401802062988,1268.322504 +1518,Multiclass classification,Voting,ImageSegments,0.8048780487804879,0.8048780487804877,0.8013581039030082,5.915700912475586,1348.933518 +1564,Multiclass classification,Voting,ImageSegments,0.8048624440179143,0.8048624440179143,0.8017038254481382,6.0695037841796875,1431.999326 +1610,Multiclass classification,Voting,ImageSegments,0.8048477315102548,0.8048477315102549,0.8009666848419111,6.138180732727051,1517.316045 +1656,Multiclass classification,Voting,ImageSegments,0.804833836858006,0.804833836858006,0.8009346118743482,6.1542863845825195,1604.689641 +1702,Multiclass classification,Voting,ImageSegments,0.8048206937095826,0.8048206937095828,0.802987300619633,6.14796257019043,1694.105126 +1748,Multiclass classification,Voting,ImageSegments,0.8065254722381225,0.8065254722381225,0.8041280306488863,6.185528755187988,1785.6451299999999 +1794,Multiclass classification,Voting,ImageSegments,0.8070273284997211,0.8070273284997211,0.8033862119520573,6.18717098236084,1879.2213629999999 +1840,Multiclass classification,Voting,ImageSegments,0.8085916258836324,0.8085916258836324,0.8051706679397826,6.228180885314941,1974.8859899999998 +1886,Multiclass classification,Voting,ImageSegments,0.8074270557029177,0.8074270557029178,0.8044133208197751,6.244633674621582,2072.712055 +1932,Multiclass classification,Voting,ImageSegments,0.8073537027446919,0.8073537027446919,0.8036280810428232,6.232837677001953,2172.610836 +1978,Multiclass classification,Voting,ImageSegments,0.808295397066262,0.808295397066262,0.8041943782356388,6.225313186645508,2274.5024089999997 +2024,Multiclass classification,Voting,ImageSegments,0.8096885813148789,0.809688581314879,0.8043903689108628,6.209332466125488,2378.336668 +2070,Multiclass classification,Voting,ImageSegments,0.8086031899468342,0.8086031899468342,0.8034099584264852,6.192641258239746,2484.108554 +2116,Multiclass classification,Voting,ImageSegments,0.808983451536643,0.808983451536643,0.8029929757635029,6.163993835449219,2591.83622 +2162,Multiclass classification,Voting,ImageSegments,0.8093475242943082,0.8093475242943081,0.8028985652670257,6.160528182983398,2701.493184 +2208,Multiclass classification,Voting,ImageSegments,0.8110557317625736,0.8110557317625736,0.8037088502350873,6.127141952514648,2812.975729 +2254,Multiclass classification,Voting,ImageSegments,0.8078118064802485,0.8078118064802485,0.8004652010359966,6.094814300537109,2926.3842619999996 +2300,Multiclass classification,Voting,ImageSegments,0.8064375815571988,0.8064375815571988,0.7990276111502428,6.073050498962402,3041.734776 +2310,Multiclass classification,Voting,ImageSegments,0.8064097011693374,0.8064097011693374,0.7989986920740723,6.073922157287598,3157.9431529999997 +1056,Multiclass classification,Voting,Insects,0.6293838862559241,0.6293838862559241,0.5938169901557457,7.681754112243652,78.197886 +2112,Multiclass classification,Voting,Insects,0.6290857413548081,0.6290857413548081,0.5936238360694311,7.563845634460449,217.436369 +3168,Multiclass classification,Voting,Insects,0.625197347647616,0.625197347647616,0.5890732389154221,7.54627799987793,406.781755 +4224,Multiclass classification,Voting,Insects,0.624437603599337,0.624437603599337,0.5890978975177876,7.509035110473633,643.136123 +5280,Multiclass classification,Voting,Insects,0.6309907179390036,0.6309907179390036,0.5943307513870396,7.529419898986816,922.055301 +6336,Multiclass classification,Voting,Insects,0.6249408050513023,0.6249408050513023,0.5899587518293812,7.541637420654297,1240.879558 +7392,Multiclass classification,Voting,Insects,0.6242727641726424,0.6242727641726424,0.589208790087756,7.5199432373046875,1598.2590730000002 +8448,Multiclass classification,Voting,Insects,0.6266129986977625,0.6266129986977625,0.5910042020201396,7.600367546081543,1990.9287910000003 +9504,Multiclass classification,Voting,Insects,0.6255919183415763,0.6255919183415763,0.5892477749449755,7.551809310913086,2416.671036 +10560,Multiclass classification,Voting,Insects,0.6269533099725353,0.6269533099725353,0.5906555376897765,7.57810115814209,2875.240995 +11616,Multiclass classification,Voting,Insects,0.6254842875591907,0.6254842875591907,0.5899069142128334,7.574300765991211,3366.8452850000003 +12672,Multiclass classification,Voting,Insects,0.6276536974193039,0.6276536974193039,0.5948280902959312,7.593076705932617,3891.533291 +13728,Multiclass classification,Voting,Insects,0.6419465287389816,0.6419465287389816,0.6240594787506325,7.568525314331055,4449.097087 +14784,Multiclass classification,Voting,Insects,0.6349861327200162,0.6349861327200162,0.6168664949740267,7.497129440307617,5038.3500540000005 +15840,Multiclass classification,Voting,Insects,0.6042048109097796,0.6042048109097796,0.5876183517420878,7.622871398925781,5663.9066330000005 +16896,Multiclass classification,Voting,Insects,0.5831311038768866,0.5831311038768866,0.5677288238088704,7.5406084060668945,6323.428796 +17952,Multiclass classification,Voting,Insects,0.5683805916104953,0.5683805916104953,0.5530005563922373,7.511743545532227,7015.247243 +19008,Multiclass classification,Voting,Insects,0.5655811016993739,0.5655811016993739,0.5465928919365096,7.569133758544922,7739.601247 +20064,Multiclass classification,Voting,Insects,0.5718985196630614,0.5718985196630614,0.5506497035356593,8.179316520690918,8496.204598999999 +21120,Multiclass classification,Voting,Insects,0.5817510298783086,0.5817510298783086,0.55937505855693,8.13927173614502,9285.092110999998 +22176,Multiclass classification,Voting,Insects,0.5905298759864712,0.5905298759864712,0.5668099949242361,8.13715648651123,10104.551325999999 +23232,Multiclass classification,Voting,Insects,0.6004907236020834,0.6004907236020834,0.5756153967719769,8.254791259765625,10955.282647999999 +24288,Multiclass classification,Voting,Insects,0.6088854119487792,0.6088854119487792,0.5822871692574689,8.217899322509766,11836.441737999998 +25344,Multiclass classification,Voting,Insects,0.617014560233595,0.617014560233595,0.5890646667396601,8.13050651550293,12747.590801999997 +26400,Multiclass classification,Voting,Insects,0.6237357475661957,0.6237357475661957,0.5942060376379845,8.178851127624512,13688.250944999996 +27456,Multiclass classification,Voting,Insects,0.6299763248952832,0.6299763248952832,0.5983574644866619,8.215079307556152,14661.447404999995 +28512,Multiclass classification,Voting,Insects,0.6312651257409421,0.6312651257409421,0.6016879522351425,8.160200119018555,15669.084531999995 +29568,Multiclass classification,Voting,Insects,0.6310751851726587,0.6310751851726587,0.6062390002054064,8.153844833374023,16709.899933999994 +30624,Multiclass classification,Voting,Insects,0.6313228619011854,0.6313228619011854,0.610710416812842,8.221953392028809,17785.196262999994 +31680,Multiclass classification,Voting,Insects,0.6320590927743931,0.6320590927743931,0.614817700164209,8.237210273742676,18894.010558999995 +32736,Multiclass classification,Voting,Insects,0.6331144035436077,0.6331144035436077,0.6184679282473909,8.208189964294434,20033.816622999995 +33792,Multiclass classification,Voting,Insects,0.6291616110798733,0.6291616110798733,0.6151628967287334,8.149331092834473,21206.789184999994 +34848,Multiclass classification,Voting,Insects,0.6245587855482538,0.6245587855482538,0.6103108800280445,8.270771980285645,22409.569843999994 +35904,Multiclass classification,Voting,Insects,0.6211737180736986,0.6211737180736986,0.6063163580543118,8.246885299682617,23639.112908999996 +36960,Multiclass classification,Voting,Insects,0.6171433209772992,0.6171433209772992,0.6018416894357856,8.222872734069824,24895.212450999996 +38016,Multiclass classification,Voting,Insects,0.6153360515585953,0.6153360515585953,0.5996210858832133,8.711487770080566,26177.407049999994 +39072,Multiclass classification,Voting,Insects,0.613472908295155,0.613472908295155,0.5980758777202522,8.84398365020752,27486.887242999994 +40128,Multiclass classification,Voting,Insects,0.6139008647544048,0.6139008647544048,0.5993833357378361,9.00393295288086,28821.579146999993 +41184,Multiclass classification,Voting,Insects,0.6157395041643396,0.6157395041643396,0.6018873090815099,8.895415306091309,30174.675792999995 +42240,Multiclass classification,Voting,Insects,0.6179833802883591,0.6179833802883591,0.6047393094362844,8.820836067199707,31551.592344999994 +43296,Multiclass classification,Voting,Insects,0.6202101859337106,0.6202101859337106,0.60743097275183,8.80302619934082,32950.21258099999 +44352,Multiclass classification,Voting,Insects,0.6221054767648982,0.6221054767648982,0.6097047537791253,8.807188034057617,34370.71930599999 +45408,Multiclass classification,Voting,Insects,0.623736428304006,0.623736428304006,0.6112415003179203,8.906554222106934,35814.22252799999 +46464,Multiclass classification,Voting,Insects,0.6259389191399608,0.6259389191399608,0.6133867892257391,8.822076797485352,37279.498286999995 +47520,Multiclass classification,Voting,Insects,0.6274542814453166,0.6274542814453166,0.6153714367024555,8.875716209411621,38770.246245999995 +48576,Multiclass classification,Voting,Insects,0.6317858980957283,0.6317858980957283,0.6202967225132047,8.86828327178955,40284.403256 +49632,Multiclass classification,Voting,Insects,0.6360137817090125,0.6360137817090125,0.6247992459885968,8.835649490356445,41820.805007999996 +50688,Multiclass classification,Voting,Insects,0.6403811628228145,0.6403811628228145,0.6293790828873279,8.924153327941895,43378.957976 +51744,Multiclass classification,Voting,Insects,0.6455559206076185,0.6455559206076185,0.6346828420183047,9.218049049377441,44959.107880999996 +52800,Multiclass classification,Voting,Insects,0.648269853595712,0.648269853595712,0.6377385869395499,9.400546073913574,46560.782 +52848,Multiclass classification,Voting,Insects,0.6485325562472799,0.6485325562472799,0.637999701607352,9.406517028808594,48163.738895 +408,Multiclass classification,Voting,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,1.4587059020996094,10.139614 +816,Multiclass classification,Voting,Keystroke,0.9496932515337423,0.9496932515337423,0.7435135353411919,6.019382476806641,66.737739 +1224,Multiclass classification,Voting,Keystroke,0.9149632052330335,0.9149632052330335,0.9012024099743488,7.076447486877441,151.07716299999998 +1632,Multiclass classification,Voting,Keystroke,0.9258123850398529,0.9258123850398529,0.913338738884437,7.232892990112305,261.540164 +2040,Multiclass classification,Voting,Keystroke,0.9230014713094654,0.9230014713094654,0.9086113906821328,7.553393363952637,397.83621500000004 +2448,Multiclass classification,Voting,Keystroke,0.8961994278708623,0.8961994278708623,0.8992132713257572,7.640434265136719,558.733108 +2856,Multiclass classification,Voting,Keystroke,0.9001751313485113,0.9001751313485113,0.8860451027148403,7.9326982498168945,743.600486 +3264,Multiclass classification,Voting,Keystroke,0.8924302788844621,0.8924302788844621,0.8761196773917237,8.074724197387695,952.077233 +3672,Multiclass classification,Voting,Keystroke,0.8874965949332607,0.8874965949332607,0.8846937712308092,8.20841121673584,1184.393658 +4080,Multiclass classification,Voting,Keystroke,0.8815886246629075,0.8815886246629075,0.868452721773406,8.525882720947266,1441.2089369999999 +4488,Multiclass classification,Voting,Keystroke,0.8760864720303098,0.8760864720303098,0.8834419600614621,8.681946754455566,1719.7568239999998 +4896,Multiclass classification,Voting,Keystroke,0.8737487231869254,0.8737487231869254,0.8797220914000274,8.834684371948242,2018.9742069999998 +5304,Multiclass classification,Voting,Keystroke,0.8693192532528757,0.8693192532528757,0.8538682361373632,9.067034721374512,2339.6996679999997 +5712,Multiclass classification,Voting,Keystroke,0.8607949571003327,0.8607949571003327,0.8654889627515672,9.271133422851562,2680.904224 +6120,Multiclass classification,Voting,Keystroke,0.8561856512502043,0.8561856512502043,0.84095068957581,9.378315925598145,3042.663698 +6528,Multiclass classification,Voting,Keystroke,0.8434196414891987,0.8434196414891987,0.8427350578509161,9.608606338500977,3424.478417 +6936,Multiclass classification,Voting,Keystroke,0.8392213410237923,0.8392213410237923,0.8447429510460126,9.751982688903809,3824.86879 +7344,Multiclass classification,Voting,Keystroke,0.8454310227427482,0.8454310227427482,0.847842289102327,9.957889556884766,4243.00141 +7752,Multiclass classification,Voting,Keystroke,0.8456973293768546,0.8456973293768547,0.8480563212460421,10.19985294342041,4680.993142 +8160,Multiclass classification,Voting,Keystroke,0.8469175144012747,0.8469175144012746,0.8472851046009279,10.418806076049805,5138.9878340000005 +8568,Multiclass classification,Voting,Keystroke,0.8469709349830746,0.8469709349830746,0.8501227536717817,10.607142448425293,5616.664707000001 +8976,Multiclass classification,Voting,Keystroke,0.8475766016713092,0.8475766016713092,0.8507851780426926,10.772598266601562,6113.940894000001 +9384,Multiclass classification,Voting,Keystroke,0.8459980816370031,0.8459980816370031,0.8471668648040658,10.97368335723877,6631.342845000001 +9792,Multiclass classification,Voting,Keystroke,0.8418956184250843,0.8418956184250843,0.8426049398612477,11.192140579223633,7169.901201000001 +10200,Multiclass classification,Voting,Keystroke,0.8344935778017453,0.8344935778017454,0.8308153568434791,11.354521751403809,7729.92345 +10608,Multiclass classification,Voting,Keystroke,0.817384745922504,0.817384745922504,0.8105787344487394,11.59365177154541,8312.440227000001 +11016,Multiclass classification,Voting,Keystroke,0.8127099409895597,0.8127099409895597,0.8142119266109252,11.793928146362305,8918.030696000002 +11424,Multiclass classification,Voting,Keystroke,0.8079313665411888,0.8079313665411888,0.8037472320719128,11.945178031921387,9547.170938000001 +11832,Multiclass classification,Voting,Keystroke,0.8040740427689967,0.8040740427689967,0.8039730126613296,12.203582763671875,10200.281645000001 +12240,Multiclass classification,Voting,Keystroke,0.8072554947299616,0.8072554947299616,0.8097160881214022,12.414502143859863,10877.318664 +12648,Multiclass classification,Voting,Keystroke,0.8043014153554202,0.8043014153554202,0.8038043720799647,12.561456680297852,11578.515438 +13056,Multiclass classification,Voting,Keystroke,0.7996936039831483,0.7996936039831483,0.8010057260657798,12.889472007751465,12304.325005 +13464,Multiclass classification,Voting,Keystroke,0.7974448488449826,0.7974448488449826,0.7996515087686575,12.99599838256836,13054.609905000001 +13872,Multiclass classification,Voting,Keystroke,0.7978516329031793,0.7978516329031793,0.8006715750629478,13.20394229888916,13829.291085 +14280,Multiclass classification,Voting,Keystroke,0.797674907206387,0.7976749072063871,0.8002875748518964,13.364522933959961,14628.347686000001 +14688,Multiclass classification,Voting,Keystroke,0.8007761966364813,0.8007761966364813,0.8043248634763072,13.53370189666748,15451.756014 +15096,Multiclass classification,Voting,Keystroke,0.8051010268300762,0.8051010268300763,0.8085780284871096,13.774932861328125,16299.960754 +15504,Multiclass classification,Voting,Keystroke,0.8052634973876024,0.8052634973876024,0.8077470357827514,13.933537483215332,17172.988913 +15912,Multiclass classification,Voting,Keystroke,0.7978756834894098,0.7978756834894098,0.7983136026998061,14.138628005981445,18070.675966000003 +16320,Multiclass classification,Voting,Keystroke,0.793369691770329,0.7933696917703291,0.7956625263629296,14.30509090423584,18993.333450000002 +16728,Multiclass classification,Voting,Keystroke,0.7901596221677527,0.7901596221677527,0.7932579365729884,14.447582244873047,19941.842904 +17136,Multiclass classification,Voting,Keystroke,0.7861686606361249,0.7861686606361248,0.7888822346867281,14.767212867736816,20916.572711 +17544,Multiclass classification,Voting,Keystroke,0.780425240836801,0.780425240836801,0.7838193866310822,14.989240646362305,21922.215184 +17952,Multiclass classification,Voting,Keystroke,0.7802907915993538,0.7802907915993537,0.7845235361146662,15.200251579284668,22957.213951 +18360,Multiclass classification,Voting,Keystroke,0.783975162045863,0.783975162045863,0.7883700169311393,15.375930786132812,24020.765336 +18768,Multiclass classification,Voting,Keystroke,0.7869664837214259,0.7869664837214259,0.7913854757843782,15.5132417678833,25114.453204 +19176,Multiclass classification,Voting,Keystroke,0.7816427640156454,0.7816427640156454,0.7858184292134073,15.77665901184082,26236.293864000003 +19584,Multiclass classification,Voting,Keystroke,0.7846090997293571,0.7846090997293571,0.7893723685613512,15.996115684509277,27388.205854000003 +19992,Multiclass classification,Voting,Keystroke,0.7807013155920164,0.7807013155920164,0.785620728786203,16.12063980102539,28569.915626 +20400,Multiclass classification,Voting,Keystroke,0.7791068189617139,0.7791068189617139,0.7841355172773921,16.39253330230713,29779.243894000003 +46,Multiclass classification,[baseline] Last Class,ImageSegments,0.17777777777777778,0.17777777777777778,0.15260266049739735,0.0013666152954101562,0.110776 +92,Multiclass classification,[baseline] Last Class,ImageSegments,0.13186813186813187,0.13186813186813187,0.1213108980966124,0.0013637542724609375,0.225611 +138,Multiclass classification,[baseline] Last Class,ImageSegments,0.12408759124087591,0.12408759124087591,0.11874455065544491,0.001369476318359375,0.34363900000000003 +184,Multiclass classification,[baseline] Last Class,ImageSegments,0.12568306010928962,0.12568306010928962,0.12262983423071581,0.0013647079467773438,0.48452400000000007 +230,Multiclass classification,[baseline] Last Class,ImageSegments,0.12663755458515283,0.12663755458515283,0.12503852041208066,0.0013637542724609375,0.6292090000000001 +276,Multiclass classification,[baseline] Last Class,ImageSegments,0.12727272727272726,0.12727272727272726,0.12427907918144998,0.0013666152954101562,0.7861950000000001 +322,Multiclass classification,[baseline] Last Class,ImageSegments,0.13395638629283488,0.13395638629283488,0.13210036596246022,0.0013666152954101562,1.0166240000000002 +368,Multiclass classification,[baseline] Last Class,ImageSegments,0.13896457765667575,0.13896457765667575,0.13745011462972964,0.0013675689697265625,1.2507780000000002 +414,Multiclass classification,[baseline] Last Class,ImageSegments,0.14043583535108958,0.14043583535108958,0.14035813096947544,0.0013666152954101562,1.5223060000000002 +460,Multiclass classification,[baseline] Last Class,ImageSegments,0.14596949891067537,0.14596949891067537,0.14563148710727947,0.00136566162109375,1.7974560000000002 +506,Multiclass classification,[baseline] Last Class,ImageSegments,0.13861386138613863,0.13861386138613863,0.13833816102314941,0.0013666152954101562,2.0756200000000002 +552,Multiclass classification,[baseline] Last Class,ImageSegments,0.1397459165154265,0.1397459165154265,0.13938652491777898,0.0013666152954101562,2.402759 +598,Multiclass classification,[baseline] Last Class,ImageSegments,0.1373534338358459,0.1373534338358459,0.13727981043458612,0.0013675689697265625,2.771723 +644,Multiclass classification,[baseline] Last Class,ImageSegments,0.13996889580093314,0.13996889580093314,0.14017571709017965,0.0013666152954101562,3.149556 +690,Multiclass classification,[baseline] Last Class,ImageSegments,0.1378809869375907,0.1378809869375907,0.13801517784553327,0.001369476318359375,3.580436 +736,Multiclass classification,[baseline] Last Class,ImageSegments,0.1401360544217687,0.1401360544217687,0.14031088927958282,0.0013675689697265625,4.0152470000000005 +782,Multiclass classification,[baseline] Last Class,ImageSegments,0.14212548015364918,0.14212548015364918,0.1420930265541123,0.0013647079467773438,4.453992 +828,Multiclass classification,[baseline] Last Class,ImageSegments,0.14268440145102781,0.14268440145102781,0.14229874553046912,0.0013666152954101562,4.959761 +874,Multiclass classification,[baseline] Last Class,ImageSegments,0.13860252004581902,0.13860252004581902,0.13845352694595275,0.0013647079467773438,5.469480000000001 +920,Multiclass classification,[baseline] Last Class,ImageSegments,0.13492927094668117,0.13492927094668117,0.1348083913046733,0.0013666152954101562,6.0005820000000005 +966,Multiclass classification,[baseline] Last Class,ImageSegments,0.13367875647668392,0.13367875647668392,0.13349177774445276,0.0013637542724609375,6.5350530000000004 +1012,Multiclass classification,[baseline] Last Class,ImageSegments,0.13254203758654798,0.13254203758654798,0.1324936677659038,0.0013675689697265625,7.07275 +1058,Multiclass classification,[baseline] Last Class,ImageSegments,0.13339640491958374,0.13339640491958374,0.1331834965440007,0.00136566162109375,7.6454260000000005 +1104,Multiclass classification,[baseline] Last Class,ImageSegments,0.13417951042611062,0.13417951042611062,0.13402826529501538,0.0013666152954101562,8.221471000000001 +1150,Multiclass classification,[baseline] Last Class,ImageSegments,0.134029590948651,0.134029590948651,0.13406391150519123,0.0013637542724609375,8.800858000000002 +1196,Multiclass classification,[baseline] Last Class,ImageSegments,0.13640167364016736,0.13640167364016736,0.1363948420172951,0.001369476318359375,9.430169000000001 +1242,Multiclass classification,[baseline] Last Class,ImageSegments,0.13940370668815472,0.13940370668815472,0.13919772383892226,0.0013637542724609375,10.062783000000001 +1288,Multiclass classification,[baseline] Last Class,ImageSegments,0.1414141414141414,0.1414141414141414,0.14118715023210152,0.0013666152954101562,10.698372 +1334,Multiclass classification,[baseline] Last Class,ImageSegments,0.14328582145536384,0.14328582145536384,0.14302553278156666,0.0013637542724609375,11.387531000000001 +1380,Multiclass classification,[baseline] Last Class,ImageSegments,0.14358230601885424,0.14358230601885424,0.1433209000486506,0.001369476318359375,12.080639000000001 +1426,Multiclass classification,[baseline] Last Class,ImageSegments,0.14175438596491227,0.14175438596491227,0.14145466559291123,0.001369476318359375,12.777602000000002 +1472,Multiclass classification,[baseline] Last Class,ImageSegments,0.13936097892590074,0.13936097892590074,0.13907629713942624,0.0013647079467773438,13.546128000000001 +1518,Multiclass classification,[baseline] Last Class,ImageSegments,0.13974950560316415,0.13974950560316415,0.13951366685898453,0.0013666152954101562,14.318195000000001 +1564,Multiclass classification,[baseline] Last Class,ImageSegments,0.13691618682021753,0.13691618682021753,0.13664170474395118,0.0013666152954101562,15.093811 +1610,Multiclass classification,[baseline] Last Class,ImageSegments,0.13610938471100062,0.13610938471100062,0.13597683881903072,0.0013637542724609375,15.942934000000001 +1656,Multiclass classification,[baseline] Last Class,ImageSegments,0.1365558912386707,0.1365558912386707,0.13633224623774592,0.001369476318359375,16.795246000000002 +1702,Multiclass classification,[baseline] Last Class,ImageSegments,0.13932980599647266,0.13932980599647266,0.13901296274399097,0.0013675689697265625,17.650687 +1748,Multiclass classification,[baseline] Last Class,ImageSegments,0.14195764167143674,0.14195764167143674,0.14147197312723642,0.00136566162109375,18.510738 +1794,Multiclass classification,[baseline] Last Class,ImageSegments,0.14221974344673732,0.14221974344673732,0.14194103966110075,0.0013647079467773438,19.374685 +1840,Multiclass classification,[baseline] Last Class,ImageSegments,0.14138118542686243,0.14138118542686243,0.14114329766598663,0.0013675689697265625,20.242449999999998 +1886,Multiclass classification,[baseline] Last Class,ImageSegments,0.140053050397878,0.140053050397878,0.13973258713820755,0.0013666152954101562,21.182872999999997 +1932,Multiclass classification,[baseline] Last Class,ImageSegments,0.14293112377006734,0.14293112377006734,0.14275229229825853,0.0013666152954101562,22.126859999999997 +1978,Multiclass classification,[baseline] Last Class,ImageSegments,0.14618108244815378,0.14618108244815378,0.14597158151605963,0.001369476318359375,23.074112999999997 +2024,Multiclass classification,[baseline] Last Class,ImageSegments,0.14434008897676717,0.14434008897676717,0.14416625237761066,0.001369476318359375,24.067370999999998 +2070,Multiclass classification,[baseline] Last Class,ImageSegments,0.14403093281778637,0.14403093281778637,0.14385543497127623,0.0013666152954101562,25.063920999999997 +2116,Multiclass classification,[baseline] Last Class,ImageSegments,0.14468085106382977,0.14468085106382977,0.14460362317776573,0.0013637542724609375,26.063629999999996 +2162,Multiclass classification,[baseline] Last Class,ImageSegments,0.14530310041647385,0.14530310041647385,0.14520465913821792,0.001369476318359375,27.083891999999995 +2208,Multiclass classification,[baseline] Last Class,ImageSegments,0.14499320344358857,0.14499320344358857,0.14491109851991696,0.001369476318359375,28.107943999999996 +2254,Multiclass classification,[baseline] Last Class,ImageSegments,0.14647137150466044,0.14647137150466044,0.14640425534129609,0.0013666152954101562,29.207950999999998 +2300,Multiclass classification,[baseline] Last Class,ImageSegments,0.14789038712483688,0.14789038712483688,0.14788688524810298,0.0013675689697265625,30.311507 +2310,Multiclass classification,[baseline] Last Class,ImageSegments,0.14811606756171503,0.14811606756171503,0.14811566784252675,0.001369476318359375,31.415920999999997 +1056,Multiclass classification,[baseline] Last Class,Insects,0.15829383886255924,0.15829383886255924,0.1376212379233521,0.0013856887817382812,0.57267 +2112,Multiclass classification,[baseline] Last Class,Insects,0.16579819990525818,0.16579819990525818,0.15110451064118433,0.0013856887817382812,1.690872 +3168,Multiclass classification,[baseline] Last Class,Insects,0.17019261130407326,0.17019261130407326,0.15681512355039637,0.0013885498046875,3.2981429999999996 +4224,Multiclass classification,[baseline] Last Class,Insects,0.16599573762727918,0.16599573762727918,0.15254433156050665,0.0013856887817382812,5.4736839999999995 +5280,Multiclass classification,[baseline] Last Class,Insects,0.17086569426027656,0.17086569426027656,0.15676679113993588,0.0013837814331054688,8.202311 +6336,Multiclass classification,[baseline] Last Class,Insects,0.17379636937647988,0.17379636937647988,0.16137568195972998,0.0013837814331054688,11.448991 +7392,Multiclass classification,[baseline] Last Class,Insects,0.1752130970098769,0.1752130970098769,0.16189407904134778,0.0013837814331054688,15.242684 +8448,Multiclass classification,[baseline] Last Class,Insects,0.17722268260921037,0.17722268260921037,0.163740045170864,0.0013818740844726562,19.537217000000002 +9504,Multiclass classification,[baseline] Last Class,Insects,0.17731242765442493,0.17731242765442493,0.1637492974453096,0.0013885498046875,24.318802 +10560,Multiclass classification,[baseline] Last Class,Insects,0.17908892887584052,0.17908892887584052,0.16564210767474952,0.0013837814331054688,29.683683000000002 +11616,Multiclass classification,[baseline] Last Class,Insects,0.17899268187688333,0.17899268187688333,0.16559253835337612,0.0013856887817382812,35.598037000000005 +12672,Multiclass classification,[baseline] Last Class,Insects,0.18530502722752742,0.1853050272275274,0.18269809988409802,0.0013866424560546875,41.981502000000006 +13728,Multiclass classification,[baseline] Last Class,Insects,0.24797843665768193,0.24797843665768193,0.26603936845528803,0.0013866424560546875,48.94863000000001 +14784,Multiclass classification,[baseline] Last Class,Insects,0.2795778935263478,0.2795778935263478,0.28229742751715126,0.0013818740844726562,56.43945000000001 +15840,Multiclass classification,[baseline] Last Class,Insects,0.27615379758823155,0.27615379758823155,0.2847375853365436,0.0013818740844726562,64.48233200000001 +16896,Multiclass classification,[baseline] Last Class,Insects,0.2723290914471737,0.2723290914471737,0.2859139704285301,0.0013856887817382812,73.03679300000002 +17952,Multiclass classification,[baseline] Last Class,Insects,0.2720739791655061,0.2720739791655061,0.2880143206503878,0.0013866424560546875,82.10379000000002 +19008,Multiclass classification,[baseline] Last Class,Insects,0.28252748987215237,0.28252748987215237,0.2877504429321087,0.0013866424560546875,91.70347300000002 +20064,Multiclass classification,[baseline] Last Class,Insects,0.28724517769027563,0.28724517769027563,0.28667392366619265,0.0013818740844726562,101.81113500000002 +21120,Multiclass classification,[baseline] Last Class,Insects,0.28306264501160094,0.28306264501160094,0.28164766024255256,0.0013837814331054688,112.42818900000002 +22176,Multiclass classification,[baseline] Last Class,Insects,0.2805411499436302,0.2805411499436302,0.27862960725280095,0.0013866424560546875,123.55266000000002 +23232,Multiclass classification,[baseline] Last Class,Insects,0.2797124531875511,0.2797124531875511,0.27719419757933417,0.0013856887817382812,135.22034100000002 +24288,Multiclass classification,[baseline] Last Class,Insects,0.2777205912628155,0.2777205912628155,0.2745878480946635,0.0013866424560546875,147.32084400000002 +25344,Multiclass classification,[baseline] Last Class,Insects,0.2756579726157124,0.2756579726157124,0.27233803052028965,0.0013818740844726562,159.88729300000003 +26400,Multiclass classification,[baseline] Last Class,Insects,0.27394977082465244,0.27394977082465244,0.26996904425699914,0.0013837814331054688,172.95537600000003 +27456,Multiclass classification,[baseline] Last Class,Insects,0.27189947186304864,0.27189947186304864,0.26719485323886244,0.0013866424560546875,186.52082400000003 +28512,Multiclass classification,[baseline] Last Class,Insects,0.2723860965942969,0.2723860965942969,0.2686965366571337,0.0013885498046875,200.59564800000004 +29568,Multiclass classification,[baseline] Last Class,Insects,0.2738187844556431,0.2738187844556431,0.2720266804437783,0.0013885498046875,215.16150500000003 +30624,Multiclass classification,[baseline] Last Class,Insects,0.27538124938771513,0.27538124938771513,0.27486986638103517,0.0013885498046875,230.19075300000003 +31680,Multiclass classification,[baseline] Last Class,Insects,0.2780390795163989,0.2780390795163989,0.2784141751235631,0.0013856887817382812,245.71900300000004 +32736,Multiclass classification,[baseline] Last Class,Insects,0.279670077898274,0.279670077898274,0.28021922512452757,0.0013837814331054688,261.76959600000004 +33792,Multiclass classification,[baseline] Last Class,Insects,0.2808440117190968,0.2808440117190968,0.28119627453717067,0.0013856887817382812,278.22772000000003 +34848,Multiclass classification,[baseline] Last Class,Insects,0.2772405085086234,0.2772405085086234,0.27819051828647573,0.0013837814331054688,295.19763900000004 +35904,Multiclass classification,[baseline] Last Class,Insects,0.2739325404562293,0.2739325404562293,0.2754200456137155,0.0013856887817382812,312.64260700000005 +36960,Multiclass classification,[baseline] Last Class,Insects,0.271246516410076,0.271246516410076,0.27333283767820205,0.0013818740844726562,330.5037730000001 +38016,Multiclass classification,[baseline] Last Class,Insects,0.26855188741286334,0.26855188741286334,0.2710722002891223,0.0013856887817382812,348.8496650000001 +39072,Multiclass classification,[baseline] Last Class,Insects,0.277034117376059,0.277034117376059,0.2770619820799866,0.0013866424560546875,367.6207990000001 +40128,Multiclass classification,[baseline] Last Class,Insects,0.27617315024796274,0.27617315024796274,0.2760769006623072,0.0013837814331054688,386.8573710000001 +41184,Multiclass classification,[baseline] Last Class,Insects,0.27567200058276475,0.27567200058276475,0.27543526329721163,0.0013837814331054688,406.52795400000014 +42240,Multiclass classification,[baseline] Last Class,Insects,0.27401216884869434,0.27401216884869434,0.27359461935885426,0.0013885498046875,426.69962200000015 +43296,Multiclass classification,[baseline] Last Class,Insects,0.2738422450629403,0.2738422450629403,0.27319488690835786,0.0013856887817382812,447.37129900000014 +44352,Multiclass classification,[baseline] Last Class,Insects,0.2729588960790061,0.2729588960790061,0.27209116538690487,0.0013866424560546875,468.49129600000015 +45408,Multiclass classification,[baseline] Last Class,Insects,0.27205056489087587,0.27205056489087587,0.2708084959373003,0.0013866424560546875,490.06234300000017 +46464,Multiclass classification,[baseline] Last Class,Insects,0.27137722488862104,0.27137722488862104,0.2698631410415437,0.0013837814331054688,512.0778290000002 +47520,Multiclass classification,[baseline] Last Class,Insects,0.27235421620825356,0.27235421620825356,0.27170627983222856,0.0013837814331054688,534.5781510000002 +48576,Multiclass classification,[baseline] Last Class,Insects,0.2741327843540916,0.2741327843540916,0.27449463409742436,0.0013818740844726562,557.5265480000002 +49632,Multiclass classification,[baseline] Last Class,Insects,0.27535209848683284,0.27535209848683284,0.27650368764304034,0.0013818740844726562,580.9705880000001 +50688,Multiclass classification,[baseline] Last Class,Insects,0.2768362696549411,0.2768362696549411,0.27863440912734966,0.0013837814331054688,604.9012140000001 +51744,Multiclass classification,[baseline] Last Class,Insects,0.27827918752294994,0.27827918752294994,0.2805971515128955,0.0013885498046875,629.3033230000001 +52800,Multiclass classification,[baseline] Last Class,Insects,0.28911532415386654,0.28911532415386654,0.28929532027297566,0.0013866424560546875,654.1512880000001 +52848,Multiclass classification,[baseline] Last Class,Insects,0.2897610081934642,0.2897610081934642,0.28976272570313216,0.0013866424560546875,679.0036960000001 +408,Multiclass classification,[baseline] Last Class,Keystroke,0.9975429975429976,0.9975429975429976,0.9660408844388819,0.0006122589111328125,0.255536 +816,Multiclass classification,[baseline] Last Class,Keystroke,0.9975460122699387,0.9975460122699387,0.9879967903427672,0.0006628036499023438,0.794196 +1224,Multiclass classification,[baseline] Last Class,Keystroke,0.9975470155355682,0.9975470155355682,0.9931179599499375,0.000713348388671875,1.53447 +1632,Multiclass classification,[baseline] Last Class,Keystroke,0.9975475168608215,0.9975475168608215,0.9950750839342831,0.0012521743774414062,2.469131 +2040,Multiclass classification,[baseline] Last Class,Keystroke,0.9975478175576263,0.9975478175576263,0.9960150346160551,0.0013027191162109375,3.675833 +2448,Multiclass classification,[baseline] Last Class,Keystroke,0.9975480179812015,0.9975480179812015,0.9965317313935653,0.0013532638549804688,5.030286 +2856,Multiclass classification,[baseline] Last Class,Keystroke,0.9975481611208407,0.9975481611208407,0.9968424283169279,0.00140380859375,6.586031 +3264,Multiclass classification,[baseline] Last Class,Keystroke,0.9975482684646031,0.9975482684646031,0.9970416021996,0.0014543533325195312,8.377109 +3672,Multiclass classification,[baseline] Last Class,Keystroke,0.9975483519476982,0.9975483519476982,0.9971755428551425,0.0015048980712890625,10.331252000000001 +4080,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484187300809,0.9975484187300809,0.9972690115789393,0.0015554428100585938,12.525489 +4488,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484733675062,0.9975484733675062,0.9973361791525123,0.001605987548828125,14.940819000000001 +4896,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485188968335,0.9975485188968335,0.9973856025730918,0.0016565322875976562,17.495259 +5304,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485574203281,0.9975485574203281,0.9974226798335742,0.0017070770263671875,20.336762 +5712,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485904395027,0.9975485904395027,0.99745094204078,0.0017576217651367188,23.402208 +6120,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486190554013,0.9975486190554013,0.9974727709453766,0.00180816650390625,26.661861000000002 +6528,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486440937643,0.9975486440937643,0.997489815700999,0.0018587112426757812,30.164710000000003 +6936,Multiclass classification,[baseline] Last Class,Keystroke,0.997548666186013,0.997548666186013,0.9975032443691146,0.0019092559814453125,33.838397 +7344,Multiclass classification,[baseline] Last Class,Keystroke,0.997548685823233,0.997548685823233,0.9975139007887865,0.0034246444702148438,37.738436 +7752,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487033931105,0.9975487033931105,0.9975224052755716,0.003475189208984375,41.800015 +8160,Multiclass classification,[baseline] Last Class,Keystroke,0.997548719205785,0.997548719205785,0.9975292209193424,0.0035257339477539062,46.105028000000004 +8568,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487335123147,0.9975487335123147,0.9975346982235258,0.0035762786865234375,50.63279300000001 +8976,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487465181059,0.9975487465181059,0.9975391057693664,0.0036268234252929688,55.447067000000004 +9384,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487583928381,0.9975487583928381,0.997542651662671,0.0036773681640625,60.387128000000004 +9792,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487692779083,0.9975487692779083,0.9975454987794795,0.0037279129028320312,65.547582 +10200,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487792920874,0.9975487792920874,0.9975477757646256,0.0037784576416015625,70.981052 +10608,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487885358726,0.9975487885358726,0.9975495850737114,0.0038290023803710938,76.594226 +11016,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487970948707,0.9975487970948707,0.9975510089260562,0.003879547119140625,82.44596800000001 +11424,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488050424582,0.9975488050424582,0.9975521137613483,0.003930091857910156,88.533094 +11832,Multiclass classification,[baseline] Last Class,Keystroke,0.99754881244189,0.99754881244189,0.9975529536110199,0.0039806365966796875,94.81874400000001 +12240,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488193479859,0.9975488193479859,0.9975535726732964,0.004031181335449219,101.331754 +12648,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488258084921,0.9975488258084921,0.9975540072976319,0.00408172607421875,108.05167800000001 +13056,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488318651857,0.9975488318651857,0.997554287526727,0.004132270812988281,114.99668100000001 +13464,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488375547797,0.9975488375547797,0.9975544383040469,0.0041828155517578125,122.1119 +13872,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488429096676,0.9975488429096676,0.9975544804262362,0.004233360290527344,129.47010500000002 +14280,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488479585405,0.9975488479585405,0.9975544312994101,0.004283905029296875,136.988051 +14688,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488527269013,0.9975488527269013,0.9975543055435039,0.004334449768066406,144.742896 +15096,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488572374959,0.9975488572374959,0.9975541154780816,0.0043849945068359375,152.648866 +15504,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488615106753,0.9975488615106753,0.9975538715150367,0.004435539245605469,160.767465 +15912,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488655647037,0.9975488655647037,0.9975535824776959,0.004486083984375,169.09858 +16320,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488694160182,0.9975488694160182,0.9975532558614028,0.004536628723144531,177.653336 +16728,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488730794524,0.9975488730794524,0.997552898047314,0.0045871734619140625,186.438203 +17136,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488765684272,0.9975488765684272,0.9975525144785747,0.004637718200683594,195.44716799999998 +17544,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488798951149,0.9975488798951149,0.9975521098061079,0.004688262939453125,204.56363499999998 +17952,Multiclass classification,[baseline] Last Class,Keystroke,0.997548883070581,0.997548883070581,0.9975516880097278,0.004738807678222656,213.933058 +18360,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488861049077,0.9975488861049077,0.997551252499137,0.0047893524169921875,223.513668 +18768,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488890073001,0.9975488890073001,0.9975508061984416,0.004839897155761719,233.322943 +19176,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488917861799,0.9975488917861799,0.9975503516171184,0.00489044189453125,243.357771 +19584,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488944492672,0.9975488944492672,0.9975498909097889,0.004940986633300781,253.567103 +19992,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488970036517,0.9975488970036517,0.9975494259267257,0.0049915313720703125,264.004285 +20400,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488994558557,0.9975488994558557,0.9975489582566448,0.005042076110839844,274.675054 diff --git a/docs/benchmarks/Regression/index.md b/docs/benchmarks/Regression/index.md new file mode 100644 index 0000000000..ac378dd8ac --- /dev/null +++ b/docs/benchmarks/Regression/index.md @@ -0,0 +1,20664 @@ +# Regression + + + +=== "Table" + + | Model | Dataset | MAE | RMSE | R2 | Memory in Mb | Time in s | + |:-----------------------------------------|:--------------|-----------:|-----------:|--------------:|---------------:|------------:| + | Adaptive Model Rules | ChickWeights | 24.1943 | 37.2166 | 0.725319 | 0.046977 | 5.25855 | + | Adaptive Model Rules | TrumpApproval | 1.39847 | 2.43336 | -1.02372 | 0.114429 | 9.38293 | + | Adaptive Random Forest | ChickWeights | 26.1016 | 40.8094 | 0.669725 | 1.19043 | 56.006 | + | Adaptive Random Forest | TrumpApproval | 0.800378 | 2.11495 | -0.528761 | 1.28462 | 87.4457 | + | Aggregated Mondrian Forest | ChickWeights | 25.6742 | 41.7123 | 0.65479 | 8.21412 | 127.415 | + | Aggregated Mondrian Forest | TrumpApproval | 0.268533 | 0.349421 | 0.958184 | 16.9323 | 186.034 | + | Bagging | ChickWeights | 23.1143 | 36.6311 | 0.733893 | 0.628034 | 38.0203 | + | Bagging | TrumpApproval | 0.908203 | 2.23718 | -0.710572 | 1.31579 | 82.0689 | + | Exponentially Weighted Average | ChickWeights | 121.818 | 141.004 | -2.94294 | 3.09241 | 55.8851 | + | Exponentially Weighted Average | TrumpApproval | 40.7546 | 40.7905 | -567.663 | 5.27613 | 141.452 | + | Hoeffding Adaptive Tree | ChickWeights | 23.3739 | 37.6579 | 0.718766 | 0.0947332 | 7.99029 | + | Hoeffding Adaptive Tree | TrumpApproval | 0.921313 | 2.23942 | -0.713986 | 0.138225 | 16.7576 | + | Hoeffding Tree | ChickWeights | 23.1619 | 36.7336 | 0.732402 | 0.0440512 | 6.29305 | + | Hoeffding Tree | TrumpApproval | 0.956103 | 2.24987 | -0.730022 | 0.148639 | 11.7656 | + | Linear Regression | ChickWeights | 23.7587 | 37.0377 | 0.727954 | 0.00421047 | 3.21471 | + | Linear Regression | TrumpApproval | 1.31455 | 3.91198 | -4.23035 | 0.00497341 | 11.5379 | + | Linear Regression with l1 regularization | ChickWeights | 23.7577 | 37.078 | 0.727361 | 0.00444126 | 9.7485 | + | Linear Regression with l1 regularization | TrumpApproval | 1.15377 | 3.82872 | -4.01007 | 0.0052042 | 13.3595 | + | Linear Regression with l2 regularization | ChickWeights | 25.2738 | 38.5885 | 0.704694 | 0.00423336 | 1.22128 | + | Linear Regression with l2 regularization | TrumpApproval | 1.87151 | 4.13052 | -4.83107 | 0.0049963 | 4.15677 | + | Passive-Aggressive Regressor, mode 1 | ChickWeights | 24.3423 | 37.596 | 0.71969 | 0.00345898 | 1.10187 | + | Passive-Aggressive Regressor, mode 1 | TrumpApproval | 4.98403 | 6.97667 | -15.6354 | 0.00443554 | 2.99338 | + | Passive-Aggressive Regressor, mode 2 | ChickWeights | 100.624 | 143.066 | -3.05911 | 0.00345898 | 1.16798 | + | Passive-Aggressive Regressor, mode 2 | TrumpApproval | 31.0933 | 34.6257 | -408.765 | 0.00443554 | 4.72475 | + | River MLP | ChickWeights | 51.4078 | 80.9203 | -0.298584 | 0.0123129 | 28.2295 | + | River MLP | TrumpApproval | 1.58058 | 5.03392 | -7.66066 | 0.0133505 | 32.2432 | + | Stochastic Gradient Tree | ChickWeights | 68.7588 | 80.358 | -0.280601 | 1.12059 | 22.3803 | + | Stochastic Gradient Tree | TrumpApproval | 9.42975 | 17.9379 | -108.972 | 3.08244 | 52.4507 | + | Streaming Random Patches | ChickWeights | 23.7097 | 38.4416 | 0.706938 | 0.355182 | 93.4014 | + | Streaming Random Patches | TrumpApproval | 0.656697 | 1.98434 | -0.345761 | 1.06461 | 134.903 | + | [baseline] Mean predictor | ChickWeights | 50.2509 | 71.1144 | -0.00292947 | 0.000490189 | 0.302835 | + | [baseline] Mean predictor | TrumpApproval | 1.56755 | 2.20286 | -0.658483 | 0.000490189 | 1.08177 | + | k-Nearest Neighbors | ChickWeights | 24.8406 | 39.2016 | 0.695236 | 2.88522 | 40.0878 | + | k-Nearest Neighbors | TrumpApproval | 0.641679 | 1.59417 | 0.131425 | 5.03263 | 123.301 | + +=== "Chart" + + *Try reloading the page if something is buggy* + + ```vegalite + { + "$schema": "https://vega.github.io/schema/vega-lite/v5.json", + "data": { + "values": [ + { + "step": 11, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 30.432219699626994, + "RMSE": 31.267456151778337, + "R2": -1257.4692714745631, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.000963 + }, + { + "step": 22, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 20.75760844034268, + "RMSE": 23.632210645041404, + "R2": -590.4769976066937, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.002374 + }, + { + "step": 33, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 14.555240079240876, + "RMSE": 19.34929493332969, + "R2": -259.0232069515881, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.004113 + }, + { + "step": 44, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 11.14363365913676, + "RMSE": 16.767243978820222, + "R2": -220.3452424437857, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.006175 + }, + { + "step": 55, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 10.841164000616114, + "RMSE": 17.714902804136145, + "R2": -60.2608923989398, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.008581 + }, + { + "step": 66, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 10.32598508406065, + "RMSE": 16.527353468164844, + "R2": -21.985729074745297, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.01133 + }, + { + "step": 77, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 9.718401993814265, + "RMSE": 15.52109639018614, + "R2": -12.587024696233003, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.014424 + }, + { + "step": 88, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 8.767755200283737, + "RMSE": 14.552446235427842, + "R2": -9.829280875288257, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.017858 + }, + { + "step": 99, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 7.977130626229444, + "RMSE": 13.740429605807138, + "R2": -7.074807888709797, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0216349999999999 + }, + { + "step": 110, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 7.506893871110683, + "RMSE": 13.098273311725844, + "R2": -4.124041411671393, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0257519999999999 + }, + { + "step": 121, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 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-0.6584830635688459, + "Memory in Mb": 0.0004901885986328, + "Time in s": 1.081765 + } + ] + }, + "params": [ + { + "name": "models", + "select": { + "type": "point", + "fields": [ + "model" + ] + }, + "bind": "legend" + }, + { + "name": "Dataset", + "value": "ChickWeights", + "bind": { + "input": "select", + "options": [ + "ChickWeights", + "TrumpApproval" + ] + } + }, + { + "name": "grid", + "select": "interval", + "bind": "scales" + } + ], + "transform": [ + { + "filter": { + "field": "dataset", + "equal": { + "expr": "Dataset" + } + } + } + ], + "repeat": { + "row": [ + "MAE", + "RMSE", + "R2", + "Memory in Mb", + "Time in s" + ] + }, + "spec": { + "width": "container", + "mark": "line", + "encoding": { + "x": { + "field": "step", + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18, + "title": "Instance" + } + }, + "y": { + "field": { + "repeat": "row" + }, + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18 + } + }, + "color": { + "field": "model", + "type": "ordinal", + "scale": { + "scheme": "category20b" + }, + "title": "Models", + "legend": { + "titleFontSize": 18, + "labelFontSize": 18, + "labelLimit": 500 + } + }, + "opacity": { + "condition": { + "param": "models", + "value": 1 + }, + "value": 0.2 + } + } + } + } + ``` + + + +## Datasets + +???- abstract "ChickWeights" + + Chick weights along time. + + The stream contains 578 items and 3 features. The goal is to predict the weight of each chick + along time, according to the diet the chick is on. The data is ordered by time and then by + chick. + + Name ChickWeights + Task Regression + Samples 578 + Features 3 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/chick-weights.csv + + + +???- abstract "TrumpApproval" + + Donald Trump approval ratings. + + This dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald + Trump's approval ratings. It contains 5 features, which are approval ratings collected by + 5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of + this task is to see if we can reproduce FiveThirtyEight's model. + + Name TrumpApproval + Task Regression + Samples 1,001 + Features 6 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/trump_approval.csv.gz + + + +## Models + +???- example "Linear Regression" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      LinearRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=Squared ()
+        l2=0.
+        l1=0.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      )
+    )
+ + + +???- example "Linear Regression with l1 regularization" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      LinearRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=Squared ()
+        l2=0.
+        l1=1.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      )
+    )
+ + + +???- example "Linear Regression with l2 regularization" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      LinearRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=Squared ()
+        l2=1.
+        l1=0.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      )
+    )
+ + + +???- example "Passive-Aggressive Regressor, mode 1" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      PARegressor (
+        C=1.
+        mode=1
+        eps=0.1
+        learn_intercept=True
+      )
+    )
+ + + +???- example "Passive-Aggressive Regressor, mode 2" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      PARegressor (
+        C=1.
+        mode=2
+        eps=0.1
+        learn_intercept=True
+      )
+    )
+ + + +???- example "k-Nearest Neighbors" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNRegressor (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        aggregation_method="mean"
+      )
+    )
+ + + +???- example "Hoeffding Tree" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      HoeffdingTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+      )
+    )
+ + + +???- example "Hoeffding Adaptive Tree" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=True
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=42
+      )
+    )
+ + + +???- example "Stochastic Gradient Tree" + + 
SGTRegressor (
+      delta=1e-07
+      grace_period=200
+      init_pred=0.
+      max_depth=inf
+      lambda_value=0.1
+      gamma=1.
+      nominal_attributes=[]
+      feature_quantizer=StaticQuantizer (
+        n_bins=64
+        warm_start=100
+        buckets=None
+      )
+    )
+ + + +???- example "Adaptive Random Forest" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      []
+    )
+ + + +???- example "Aggregated Mondrian Forest" + + 
[]
+ + + +???- example "Adaptive Model Rules" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      AMRules (
+        n_min=200
+        delta=1e-07
+        tau=0.05
+        pred_type="adaptive"
+        pred_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        fading_factor=0.99
+        anomaly_threshold=-0.75
+        m_min=30
+        ordered_rule_set=True
+        min_samples_split=5
+      )
+    )
+ + + +???- example "Streaming Random Patches" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SRPRegressor (
+        model=HoeffdingTreeRegressor (
+          grace_period=50
+          max_depth=inf
+          delta=0.01
+          tau=0.05
+          leaf_prediction="adaptive"
+          leaf_model=LinearRegression (
+            optimizer=SGD (
+              lr=Constant (
+                learning_rate=0.01
+              )
+            )
+            loss=Squared ()
+            l2=0.
+            l1=0.
+            intercept_init=0.
+            intercept_lr=Constant (
+              learning_rate=0.01
+            )
+            clip_gradient=1e+12
+            initializer=Zeros ()
+          )
+          model_selector_decay=0.95
+          nominal_attributes=None
+          splitter=TEBSTSplitter (
+            digits=1
+          )
+          min_samples_split=5
+          binary_split=False
+          max_size=500.
+          memory_estimate_period=1000000
+          stop_mem_management=False
+          remove_poor_attrs=False
+          merit_preprune=True
+        )
+        n_models=10
+        subspace_size=0.6
+        training_method="patches"
+        lam=6
+        drift_detector=ADWIN (
+          delta=1e-05
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        warning_detector=ADWIN (
+          delta=0.0001
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        disable_detector="off"
+        disable_weighted_vote=True
+        drift_detection_criteria="error"
+        aggregation_method="mean"
+        seed=42
+        metric=MAE ()
+      )
+    )
+ + + +???- example "Bagging" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      [HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      )]
+    )
+ + + +???- example "Exponentially Weighted Average" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      [LinearRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=Squared ()
+        l2=0.
+        l1=0.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=True
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), KNNRegressor (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        aggregation_method="mean"
+      ), AMRules (
+        n_min=200
+        delta=1e-07
+        tau=0.05
+        pred_type="adaptive"
+        pred_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        fading_factor=0.99
+        anomaly_threshold=-0.75
+        m_min=30
+        ordered_rule_set=True
+        min_samples_split=5
+      )]
+    )
+ + + +???- example "River MLP" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      MLPRegressor (
+        hidden_dims=(5,)
+        activations=(, , )
+        loss=Squared ()
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.001
+          )
+        )
+        seed=42
+      )
+    )
+ + + +???- example "[baseline] Mean predictor" + + 
StatisticRegressor (
+      statistic=Mean ()
+    )
+ + + +## Environment + +
Python implementation: CPython
+Python version       : 3.10.13
+IPython version      : 8.16.1
+
+river       : 0.19.0
+numpy       : 1.25.2
+scikit-learn: 1.3.1
+pandas      : 2.1.1
+scipy       : 1.11.3
+
+Compiler    : Clang 14.0.6 
+OS          : Darwin
+Release     : 22.6.0
+Machine     : arm64
+Processor   : arm
+CPU cores   : 8
+Architecture: 64bit
+
+ diff --git a/docs/benchmarks/Regression/regression.csv b/docs/benchmarks/Regression/regression.csv new file mode 100644 index 0000000000..6d0b88b0a6 --- /dev/null +++ b/docs/benchmarks/Regression/regression.csv @@ -0,0 +1,1769 @@ +step,track,model,dataset,MAE,RMSE,R2,Memory in Mb,Time in s +11,Regression,Linear Regression,ChickWeights,30.432219699626994,31.267456151778337,-1257.4692714745631,0.004130363464355469,0.000963 +22,Regression,Linear Regression,ChickWeights,20.75760844034268,23.632210645041404,-590.4769976066937,0.004130363464355469,0.002374 +33,Regression,Linear Regression,ChickWeights,14.555240079240876,19.349294933329695,-259.0232069515881,0.004130363464355469,0.004113 +44,Regression,Linear Regression,ChickWeights,11.143633659136759,16.767243978820222,-220.34524244378574,0.004130363464355469,0.006175 +55,Regression,Linear Regression,ChickWeights,10.841164000616114,17.714902804136145,-60.2608923989398,0.004130363464355469,0.008581 +66,Regression,Linear Regression,ChickWeights,10.32598508406065,16.527353468164844,-21.985729074745297,0.004130363464355469,0.01133 +77,Regression,Linear Regression,ChickWeights,9.718401993814265,15.521096390186141,-12.587024696233003,0.004130363464355469,0.014424 +88,Regression,Linear Regression,ChickWeights,8.767755200283737,14.552446235427842,-9.829280875288257,0.004130363464355469,0.017858 +99,Regression,Linear Regression,ChickWeights,7.977130626229444,13.740429605807138,-7.074807888709797,0.004130363464355469,0.021634999999999998 +110,Regression,Linear Regression,ChickWeights,7.506893871110683,13.098273311725844,-4.124041411671393,0.004130363464355469,0.025751999999999997 +121,Regression,Linear Regression,ChickWeights,7.252833276832352,12.607637144454216,-2.6562249812820733,0.004130363464355469,0.030208999999999996 +132,Regression,Linear Regression,ChickWeights,6.896359231575217,12.121970224209305,-1.7624336939368233,0.004130363464355469,0.03500399999999999 +143,Regression,Linear Regression,ChickWeights,6.581914741629191,11.688367143429069,-1.080274127204615,0.004130363464355469,0.040138999999999994 +154,Regression,Linear Regression,ChickWeights,6.347682986169337,11.314945909537578,-0.6567859420078188,0.004130363464355469,0.045612999999999994 +165,Regression,Linear Regression,ChickWeights,6.47676439389405,11.21748999353191,-0.30899590760610374,0.004130363464355469,0.051426999999999994 +176,Regression,Linear Regression,ChickWeights,6.552290709218319,11.100632967129414,-0.03357189497448321,0.004130363464355469,0.05758099999999999 +187,Regression,Linear Regression,ChickWeights,6.503097179992549,10.915357728148932,0.18175912588502985,0.004130363464355469,0.06407299999999999 +198,Regression,Linear Regression,ChickWeights,6.420443618722296,10.727647067877953,0.3713230272376924,0.004130363464355469,0.070904 +209,Regression,Linear Regression,ChickWeights,6.54715053669462,10.814712106795348,0.47329133398018763,0.004130363464355469,0.07807199999999999 +220,Regression,Linear Regression,ChickWeights,7.075852889975692,11.488147441481185,0.479648982578291,0.004130363464355469,0.08557599999999999 +231,Regression,Linear Regression,ChickWeights,7.197265349840174,11.527376107145999,0.5518657524511614,0.004130363464355469,0.09341599999999999 +242,Regression,Linear Regression,ChickWeights,7.359957454348683,11.71365363090123,0.6276606533313056,0.004130363464355469,0.10159099999999999 +253,Regression,Linear Regression,ChickWeights,7.389343614466645,11.704104182671559,0.6771453727427903,0.004130363464355469,0.11010199999999999 +264,Regression,Linear Regression,ChickWeights,8.007684680730522,12.681713023454451,0.6536838584261326,0.004210472106933594,0.11894999999999999 +275,Regression,Linear Regression,ChickWeights,8.456356064016727,13.562457362384485,0.6514630282957669,0.004210472106933594,0.128137 +286,Regression,Linear Regression,ChickWeights,8.682222588679535,13.91372755183948,0.6822857451181047,0.004210472106933594,0.137663 +297,Regression,Linear Regression,ChickWeights,8.656490376145301,13.862729792291397,0.7264657185265005,0.004210472106933594,0.14752700000000002 +308,Regression,Linear Regression,ChickWeights,9.17087534181789,14.586626878398466,0.730278281446047,0.004210472106933594,0.15773800000000002 +319,Regression,Linear Regression,ChickWeights,10.253235573939358,17.040182474587255,0.6659707835095393,0.004210472106933594,0.270641 +330,Regression,Linear Regression,ChickWeights,10.67218268870669,17.597898989920818,0.6951262006904333,0.004210472106933594,0.38498 +341,Regression,Linear Regression,ChickWeights,10.865878827617594,17.684075493652397,0.7243197409220903,0.004210472106933594,0.500381 +352,Regression,Linear Regression,ChickWeights,11.014541487264223,17.788847456042067,0.7464163188501894,0.004210472106933594,0.6168239999999999 +363,Regression,Linear Regression,ChickWeights,11.893125923244742,19.14640328452056,0.7147396000186461,0.004210472106933594,0.7343709999999999 +374,Regression,Linear Regression,ChickWeights,12.40252640363099,20.24468752454989,0.7068188127948265,0.004210472106933594,0.8529599999999999 +385,Regression,Linear Regression,ChickWeights,12.78041264925886,20.84297745742841,0.7250508110390363,0.004210472106933594,0.972583 +396,Regression,Linear Regression,ChickWeights,12.908163646252072,20.82655299121286,0.7440434321899679,0.004210472106933594,1.093238 +407,Regression,Linear Regression,ChickWeights,13.78624220521945,22.297725224665914,0.7272822586077066,0.004210472106933594,1.2149269999999999 +418,Regression,Linear Regression,ChickWeights,14.56231380927385,23.732773749874315,0.7099846963904786,0.004210472106933594,1.3375199999999998 +429,Regression,Linear Regression,ChickWeights,15.109717404902197,24.642068489898374,0.7221580232945248,0.004210472106933594,1.4604629999999998 +440,Regression,Linear Regression,ChickWeights,15.287005413554732,24.721522560240437,0.7401560140604169,0.004210472106933594,1.583729 +451,Regression,Linear Regression,ChickWeights,15.806865735774078,25.331119330890413,0.7387809061287051,0.004210472106933594,1.707315 +462,Regression,Linear Regression,ChickWeights,16.912347710111163,27.450327347193873,0.7118740092210123,0.004210472106933594,1.831218 +473,Regression,Linear Regression,ChickWeights,17.68786801080465,28.748046923071918,0.7209603573249957,0.004210472106933594,1.955435 +484,Regression,Linear Regression,ChickWeights,18.02230431978895,29.040370094251127,0.7308604085348502,0.004210472106933594,2.079964 +495,Regression,Linear Regression,ChickWeights,18.476434617297652,29.565622398548214,0.7375811559076941,0.004210472106933594,2.204806 +506,Regression,Linear Regression,ChickWeights,19.368862660258834,31.016595939650866,0.7195863076124669,0.004210472106933594,2.32996 +517,Regression,Linear Regression,ChickWeights,20.093492725340727,32.00802507821089,0.7181912437784894,0.004210472106933594,2.455434 +528,Regression,Linear Regression,ChickWeights,20.883641447975457,33.20140091570763,0.727385103943677,0.004210472106933594,2.581219 +539,Regression,Linear Regression,ChickWeights,21.055940734584826,33.19901872731025,0.7386798629639011,0.004210472106933594,2.707313 +550,Regression,Linear Regression,ChickWeights,22.046658398851132,34.818142407426606,0.7214274205964286,0.004210472106933594,2.833717 +561,Regression,Linear Regression,ChickWeights,22.750150790689958,35.737018888500465,0.7193638350430389,0.004210472106933594,2.960429 +572,Regression,Linear Regression,ChickWeights,23.60149518688988,36.92142939550449,0.722919218201958,0.004210472106933594,3.087448 +578,Regression,Linear Regression,ChickWeights,23.758656678867762,37.03767126301035,0.7279537206511313,0.004210472106933594,3.2147080000000003 +20,Regression,Linear Regression,TrumpApproval,20.715375599336316,24.276120972986362,-1381.3340079163324,0.004813194274902344,0.003774 +40,Regression,Linear Regression,TrumpApproval,12.956746822999646,17.85530816845139,-127.17403450091604,0.004813194274902344,0.008234 +60,Regression,Linear Regression,TrumpApproval,10.540337295823328,15.264267507077205,-125.28803290438402,0.004813194274902344,0.013346 +80,Regression,Linear Regression,TrumpApproval,8.92648259034571,13.436420463778147,-97.15695382305036,0.004813194274902344,0.019104 +100,Regression,Linear Regression,TrumpApproval,7.5495393499287236,12.076339439187347,-48.75014684916543,0.004813194274902344,0.025552 +120,Regression,Linear Regression,TrumpApproval,6.5712666531069654,11.058195411086313,-34.388513465790076,0.004813194274902344,0.032651 +140,Regression,Linear Regression,TrumpApproval,5.868178209177549,10.265658199354172,-30.515672886293014,0.004813194274902344,0.040397 +160,Regression,Linear Regression,TrumpApproval,5.226493262391851,9.609365926739029,-23.352843972650145,0.004813194274902344,0.048786 +180,Regression,Linear Regression,TrumpApproval,4.806672346419344,9.079121174210673,-18.092824435696784,0.004813194274902344,0.057857000000000006 +200,Regression,Linear Regression,TrumpApproval,4.400421129740624,8.617551092451054,-16.252012396913173,0.004813194274902344,0.06766 +220,Regression,Linear Regression,TrumpApproval,4.083414123099576,8.223437931584808,-15.946617088642817,0.004813194274902344,0.07815899999999999 +240,Regression,Linear Regression,TrumpApproval,3.8235343884157706,7.87966547036827,-14.67643164713968,0.004813194274902344,0.08935399999999999 +260,Regression,Linear Regression,TrumpApproval,3.5733429968046226,7.572887494545769,-13.674649599158814,0.004973411560058594,0.10124299999999999 +280,Regression,Linear Regression,TrumpApproval,3.399764262602937,7.307305033384193,-13.305426773388605,0.004973411560058594,0.11382999999999999 +300,Regression,Linear Regression,TrumpApproval,3.2435269592384794,7.069212717011484,-12.166742621467943,0.004973411560058594,0.21774699999999997 +320,Regression,Linear Regression,TrumpApproval,3.1105754847518408,6.854541649824586,-11.99216513034567,0.004973411560058594,0.416803 +340,Regression,Linear Regression,TrumpApproval,2.9569354047226284,6.651479799277566,-11.928129373171446,0.004973411560058594,0.618037 +360,Regression,Linear Regression,TrumpApproval,2.8537856094930785,6.474036710445056,-11.348131391644102,0.004973411560058594,0.8214119999999999 +380,Regression,Linear Regression,TrumpApproval,2.750449728962714,6.305826559379086,-11.120053648606476,0.004973411560058594,1.026924 +400,Regression,Linear Regression,TrumpApproval,2.6634141155755278,6.151161672136967,-10.858744866397979,0.004973411560058594,1.2341469999999999 +420,Regression,Linear Regression,TrumpApproval,2.556259025339157,6.003825249623929,-10.671335514866263,0.004973411560058594,1.4420449999999998 +440,Regression,Linear Regression,TrumpApproval,2.471571610669061,5.868919367302693,-9.950915405915524,0.004973411560058594,1.6506019999999997 +460,Regression,Linear Regression,TrumpApproval,2.3796807630395826,5.740715994508566,-8.935993501779443,0.004973411560058594,1.8600329999999996 +480,Regression,Linear Regression,TrumpApproval,2.2935423272146473,5.620383847029998,-8.304713733239236,0.004973411560058594,2.0701579999999997 +500,Regression,Linear Regression,TrumpApproval,2.2170719472274363,5.50775327209046,-7.748060324415055,0.004973411560058594,2.2809679999999997 +520,Regression,Linear Regression,TrumpApproval,2.1605380581247338,5.4030519069184955,-7.433320998258445,0.004973411560058594,2.492507 +540,Regression,Linear Regression,TrumpApproval,2.093930365363914,5.302901387269021,-7.093810234661742,0.004973411560058594,2.7047299999999996 +560,Regression,Linear Regression,TrumpApproval,2.0590245226095627,5.213512799867119,-7.009651494197669,0.004973411560058594,2.9176339999999996 +580,Regression,Linear Regression,TrumpApproval,1.9976476082662873,5.1231852511763165,-6.925804791819894,0.004973411560058594,3.1312199999999994 +600,Regression,Linear Regression,TrumpApproval,1.950641059884997,5.038426259116397,-6.58092298894084,0.004973411560058594,3.3455269999999993 +620,Regression,Linear Regression,TrumpApproval,1.9139787950639096,4.959092402037442,-6.2321238970256,0.004973411560058594,3.560656999999999 +640,Regression,Linear Regression,TrumpApproval,1.8644177203659011,4.8815607080230725,-5.87676544995844,0.004973411560058594,3.776474999999999 +660,Regression,Linear Regression,TrumpApproval,1.8242147858959745,4.808190620674182,-5.62363098938706,0.004973411560058594,3.9929739999999994 +680,Regression,Linear Regression,TrumpApproval,1.7745110240786572,4.737042423784333,-5.530654039159668,0.004973411560058594,4.2677439999999995 +700,Regression,Linear Regression,TrumpApproval,1.73663030353679,4.669916427921507,-5.51357997146441,0.004973411560058594,4.544746 +720,Regression,Linear Regression,TrumpApproval,1.692679144669073,4.604703216269991,-5.472066122280332,0.004973411560058594,4.823995 +740,Regression,Linear Regression,TrumpApproval,1.6517738073879173,4.542197076044804,-5.293761488897717,0.004973411560058594,5.105493 +760,Regression,Linear Regression,TrumpApproval,1.6176019850850996,4.482676429973872,-5.196305855003581,0.004973411560058594,5.389141 +780,Regression,Linear Regression,TrumpApproval,1.5865007641193463,4.425455260516019,-5.066178353973196,0.004973411560058594,5.673523 +800,Regression,Linear Regression,TrumpApproval,1.5595678531598225,4.370690133148669,-4.970481738375755,0.004973411560058594,5.9679660000000005 +820,Regression,Linear Regression,TrumpApproval,1.5359483450738913,4.318357063182573,-4.892367885242343,0.004973411560058594,6.2645230000000005 +840,Regression,Linear Regression,TrumpApproval,1.5094802852221159,4.267276732370252,-4.807214337073276,0.004973411560058594,6.563154000000001 +860,Regression,Linear Regression,TrumpApproval,1.4815681878661566,4.217864876321593,-4.663732871777943,0.004973411560058594,6.863868000000001 +880,Regression,Linear Regression,TrumpApproval,1.4526831778170481,4.169823323470254,-4.507942651608292,0.004973411560058594,7.276079000000001 +900,Regression,Linear Regression,TrumpApproval,1.425504815240136,4.123417367951589,-4.4087270270070205,0.004973411560058594,7.880966000000001 +920,Regression,Linear Regression,TrumpApproval,1.401135420694234,4.078757160785335,-4.379153600942964,0.004973411560058594,8.488067000000001 +940,Regression,Linear Regression,TrumpApproval,1.3798894262867005,4.035722473386745,-4.310917809364017,0.004973411560058594,9.096757 +960,Regression,Linear Regression,TrumpApproval,1.3578157698337674,3.993911445090692,-4.255827563021541,0.004973411560058594,9.706153 +980,Regression,Linear Regression,TrumpApproval,1.3349554985290681,3.953168904153961,-4.2491478554421755,0.004973411560058594,10.316233 +1000,Regression,Linear Regression,TrumpApproval,1.3157385915327033,3.9139344489617316,-4.232086679588724,0.004973411560058594,10.926998000000001 +1001,Regression,Linear Regression,TrumpApproval,1.3145482000473083,3.9119809164882438,-4.230354806784151,0.004973411560058594,11.537908000000002 +11,Regression,Linear Regression with l1 regularization,ChickWeights,30.519429760441792,31.341724959881887,-1263.4547929656035,0.004361152648925781,0.001889 +22,Regression,Linear Regression with l1 regularization,ChickWeights,20.93274945698016,23.730069634788823,-595.3856524245364,0.004361152648925781,0.005264 +33,Regression,Linear Regression with l1 regularization,ChickWeights,14.671976905269485,19.432784890847977,-261.2719879213097,0.004361152648925781,0.045495999999999995 +44,Regression,Linear Regression with l1 regularization,ChickWeights,11.206218788565426,16.83704009498573,-222.1918420065333,0.004361152648925781,0.086209 +55,Regression,Linear Regression with l1 regularization,ChickWeights,10.7873677371092,17.69725945175844,-60.138926246201024,0.004361152648925781,0.127302 +66,Regression,Linear Regression with l1 regularization,ChickWeights,10.358479420064798,16.54420972880916,-22.032639310332936,0.004361152648925781,0.168766 +77,Regression,Linear Regression with l1 regularization,ChickWeights,9.753598876381378,15.536347024393615,-12.613738343052718,0.004361152648925781,0.21060099999999998 +88,Regression,Linear Regression with l1 regularization,ChickWeights,8.774706713989955,14.560860647391403,-9.841807755380493,0.004361152648925781,0.252804 +99,Regression,Linear Regression with l1 regularization,ChickWeights,7.976543403311107,13.74760854733656,-7.083247758311314,0.004361152648925781,0.295373 +110,Regression,Linear Regression with l1 regularization,ChickWeights,7.5284067705618165,13.110785837893241,-4.133835882207287,0.004361152648925781,0.338308 +121,Regression,Linear Regression with l1 regularization,ChickWeights,7.271666718491515,12.6229442838289,-2.665108536473531,0.004361152648925781,0.38161 +132,Regression,Linear Regression with l1 regularization,ChickWeights,6.91845605456336,12.134014714075713,-1.7679259750984961,0.004361152648925781,0.425278 +143,Regression,Linear Regression with l1 regularization,ChickWeights,6.610383809165891,11.700505099139125,-1.084596952740374,0.004361152648925781,0.469311 +154,Regression,Linear Regression with l1 regularization,ChickWeights,6.3485668448406924,11.31852948419668,-0.6578355548574832,0.004361152648925781,0.51371 +165,Regression,Linear Regression with l1 regularization,ChickWeights,6.473998962981321,11.222073845492618,-0.3100659276219817,0.004361152648925781,0.558476 +176,Regression,Linear Regression with l1 regularization,ChickWeights,6.543521830550948,11.096254270292283,-0.032756661210885385,0.004361152648925781,0.603607 +187,Regression,Linear Regression with l1 regularization,ChickWeights,6.493894355635018,10.908553918682982,0.18277886707380187,0.004361152648925781,0.649102 +198,Regression,Linear Regression with l1 regularization,ChickWeights,6.432058292739276,10.739983052449066,0.36987633376979445,0.004361152648925781,0.6949599999999999 +209,Regression,Linear Regression with l1 regularization,ChickWeights,6.530905166315106,10.805387069826965,0.47419925648761396,0.004361152648925781,0.74118 +220,Regression,Linear Regression with l1 regularization,ChickWeights,7.049069109840064,11.46222613381468,0.4819945238144716,0.004361152648925781,0.7877609999999999 +231,Regression,Linear Regression with l1 regularization,ChickWeights,7.185364391622807,11.520615160379734,0.5523912707049028,0.004361152648925781,0.834703 +242,Regression,Linear Regression with l1 regularization,ChickWeights,7.384443509591489,11.759466507882767,0.6247424700583044,0.004361152648925781,0.882006 +253,Regression,Linear Regression with l1 regularization,ChickWeights,7.370825288025247,11.706644644448966,0.6770052015955412,0.004361152648925781,0.929669 +264,Regression,Linear Regression with l1 regularization,ChickWeights,7.997212264968545,12.688148058774217,0.6533323093865229,0.004441261291503906,0.977694 +275,Regression,Linear Regression with l1 regularization,ChickWeights,8.45564901988644,13.583827871673952,0.6503637760490552,0.004441261291503906,1.026082 +286,Regression,Linear Regression with l1 regularization,ChickWeights,8.687395226209604,13.953064893865328,0.6804867014487179,0.004441261291503906,1.074833 +297,Regression,Linear Regression with l1 regularization,ChickWeights,8.660171229881424,13.910099225377925,0.7245931722706233,0.004441261291503906,1.1239519999999998 +308,Regression,Linear Regression with l1 regularization,ChickWeights,9.16625719191718,14.612234985526298,0.7293304097140514,0.004441261291503906,1.1734349999999998 +319,Regression,Linear Regression with l1 regularization,ChickWeights,10.250950211093048,17.0718306278326,0.664728869016383,0.004441261291503906,1.2232849999999997 +330,Regression,Linear Regression with l1 regularization,ChickWeights,10.679670450254022,17.65395670255975,0.6931807697512926,0.004441261291503906,1.3407639999999996 +341,Regression,Linear Regression with l1 regularization,ChickWeights,10.873729384474112,17.73873175202587,0.7226130148559202,0.004441261291503906,1.6983979999999996 +352,Regression,Linear Regression with l1 regularization,ChickWeights,11.018541118771262,17.831871437600412,0.745188204067577,0.004441261291503906,2.0571989999999998 +363,Regression,Linear Regression with l1 regularization,ChickWeights,11.899574150448762,19.190338217602402,0.7134289333715201,0.004441261291503906,2.417118 +374,Regression,Linear Regression with l1 regularization,ChickWeights,12.408282768986876,20.289550367060546,0.7055179762102581,0.004441261291503906,2.778154 +385,Regression,Linear Regression with l1 regularization,ChickWeights,12.788104615245373,20.897902847676004,0.7235998101431352,0.004441261291503906,3.1404569999999996 +396,Regression,Linear Regression with l1 regularization,ChickWeights,12.908222014164421,20.86950621812891,0.7429865604297317,0.004441261291503906,3.5031369999999997 +407,Regression,Linear Regression with l1 regularization,ChickWeights,13.785647364051679,22.333927174809716,0.726395986248676,0.004441261291503906,3.8661689999999997 +418,Regression,Linear Regression with l1 regularization,ChickWeights,14.562464823979756,23.771461386342615,0.709038397249883,0.004441261291503906,4.229544 +429,Regression,Linear Regression with l1 regularization,ChickWeights,15.115712915071189,24.692790084324347,0.7210130632693055,0.004441261291503906,4.59326 +440,Regression,Linear Regression with l1 regularization,ChickWeights,15.290646451171162,24.766775019882367,0.7392038606135755,0.004441261291503906,4.9573149999999995 +451,Regression,Linear Regression with l1 regularization,ChickWeights,15.806610158983217,25.370563596297366,0.7379667599208486,0.004441261291503906,5.321708999999999 +462,Regression,Linear Regression with l1 regularization,ChickWeights,16.91167446753811,27.489289014578034,0.711055524573946,0.004441261291503906,5.686440999999999 +473,Regression,Linear Regression with l1 regularization,ChickWeights,17.69453441784174,28.803034656505247,0.7198918720890418,0.004441261291503906,6.051508999999999 +484,Regression,Linear Regression with l1 regularization,ChickWeights,18.025914293879836,29.08166628667707,0.7300944167213836,0.004441261291503906,6.416912999999999 +495,Regression,Linear Regression with l1 regularization,ChickWeights,18.47687089345869,29.604201733284565,0.7368958634072684,0.004441261291503906,6.782651999999999 +506,Regression,Linear Regression with l1 regularization,ChickWeights,19.37032815671457,31.058772984483273,0.7188231637639817,0.004441261291503906,7.148725999999999 +517,Regression,Linear Regression with l1 regularization,ChickWeights,20.096649322747314,32.051830787895724,0.717419357352562,0.004441261291503906,7.515149999999999 +528,Regression,Linear Regression with l1 regularization,ChickWeights,20.88685610593147,33.24610520798377,0.7266504806846955,0.004441261291503906,7.882236999999999 +539,Regression,Linear Regression with l1 regularization,ChickWeights,21.052957054073875,33.24035912136826,0.7380286507287471,0.004441261291503906,8.25334 +550,Regression,Linear Regression with l1 regularization,ChickWeights,22.046178761536364,34.86098206113683,0.7207414968982613,0.004441261291503906,8.62558 +561,Regression,Linear Regression with l1 regularization,ChickWeights,22.751953045975853,35.78242297978339,0.7186502822700677,0.004441261291503906,8.998921 +572,Regression,Linear Regression with l1 regularization,ChickWeights,23.603432973098663,36.96472548228527,0.7222689970347711,0.004441261291503906,9.373355 +578,Regression,Linear Regression with l1 regularization,ChickWeights,23.757667537133976,37.078025255419426,0.7273605875689941,0.004441261291503906,9.748496 +20,Regression,Linear Regression with l1 regularization,TrumpApproval,20.96628233331211,24.387937149248955,-1394.0974368768457,0.005043983459472656,0.003367 +40,Regression,Linear Regression with l1 regularization,TrumpApproval,12.95809265443779,17.886947111698607,-127.62867621055315,0.005043983459472656,0.060679000000000004 +60,Regression,Linear Regression with l1 regularization,TrumpApproval,10.43403375286247,15.198987179765494,-124.2101566950438,0.005043983459472656,0.118829 +80,Regression,Linear Regression with l1 regularization,TrumpApproval,8.76952679896777,13.348146279436204,-95.87145335263979,0.005043983459472656,0.177742 +100,Regression,Linear Regression with l1 regularization,TrumpApproval,7.318348711169017,11.969856517585775,-47.87667264392048,0.005043983459472656,0.237441 +120,Regression,Linear Regression with l1 regularization,TrumpApproval,6.2853039116310185,10.94189036106609,-33.648027646243705,0.005043983459472656,0.297866 +140,Regression,Linear Regression with l1 regularization,TrumpApproval,5.5208355911538485,10.138862242229527,-29.74195117722151,0.005043983459472656,0.35901700000000003 +160,Regression,Linear Regression with l1 regularization,TrumpApproval,4.9080595636493145,9.487746704217276,-22.740310036230184,0.005043983459472656,0.420891 +180,Regression,Linear Regression with l1 regularization,TrumpApproval,4.437342628193194,8.948953859899,-17.549281500204398,0.005043983459472656,0.483487 +200,Regression,Linear Regression with l1 regularization,TrumpApproval,4.020740144728086,8.490404067975657,-15.746680942149272,0.005043983459472656,0.546844 +220,Regression,Linear Regression with l1 regularization,TrumpApproval,3.702540763677515,8.09713522450445,-15.430052960036054,0.005043983459472656,0.610978 +240,Regression,Linear Regression with l1 regularization,TrumpApproval,3.449057445346116,7.7551931287900455,-14.185073150160106,0.005043983459472656,0.675884 +260,Regression,Linear Regression with l1 regularization,TrumpApproval,3.201640426877581,7.451485247160068,-13.20791735379428,0.005204200744628906,0.741568 +280,Regression,Linear Regression with l1 regularization,TrumpApproval,2.9861522146348123,7.180696949733205,-12.814002869999907,0.005204200744628906,0.808037 +300,Regression,Linear Regression with l1 regularization,TrumpApproval,2.8260389726991693,6.939608203297966,-11.688379207589731,0.005204200744628906,0.926618 +320,Regression,Linear Regression with l1 regularization,TrumpApproval,2.694730270614988,6.722171113188908,-11.495217468089896,0.005204200744628906,1.2155880000000001 +340,Regression,Linear Regression with l1 regularization,TrumpApproval,2.572442774284147,6.524300196624447,-11.438471282384336,0.005204200744628906,1.5070640000000002 +360,Regression,Linear Regression with l1 regularization,TrumpApproval,2.4832798669216825,6.3452949037256134,-10.86190793294698,0.005204200744628906,1.8008990000000002 +380,Regression,Linear Regression with l1 regularization,TrumpApproval,2.371542642654472,6.177015076243767,-10.629949316856383,0.005204200744628906,2.0970690000000003 +400,Regression,Linear Regression with l1 regularization,TrumpApproval,2.263251524870982,6.020874949010495,-10.361708857360679,0.005204200744628906,2.4016 +420,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1669018257777095,5.8760767656227735,-10.179937857653263,0.005204200744628906,2.706944 +440,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1025509089011916,5.743886676480224,-9.489284487381322,0.005204200744628906,3.013076 +460,Regression,Linear Regression with l1 regularization,TrumpApproval,2.0360304025506277,5.61908468135586,-8.519416508014515,0.005204200744628906,3.319991 +480,Regression,Linear Regression with l1 regularization,TrumpApproval,1.9657178079674962,5.50138701729293,-7.914879120336785,0.005204200744628906,3.627685 +500,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8948913466896102,5.390446783732167,-7.379388774419297,0.005204200744628906,3.9361800000000002 +520,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8304411336225566,5.286008256480869,-7.071904701569496,0.005204200744628906,4.245555 +540,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7733791235095338,5.187623645241403,-6.74573862520947,0.005204200744628906,4.555757000000001 +560,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7328732375480083,5.096231477200102,-6.653340289034931,0.005204200744628906,4.866786000000001 +580,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6922671720641331,5.009032279128942,-6.5765398617523605,0.005204200744628906,5.1996410000000015 +600,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6600221636451293,4.9270067527590165,-6.249341959517198,0.005204200744628906,5.545038000000002 +620,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6169171465584515,4.847662648980224,-5.910766757861972,0.005204200744628906,5.892753000000002 +640,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5787668849144931,4.771995268006674,-5.5715350899413965,0.005204200744628906,6.242777000000002 +660,Regression,Linear Regression with l1 regularization,TrumpApproval,1.535700232104731,4.69925054984221,-5.326885534626132,0.005204200744628906,6.599796000000002 +680,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5003699975160405,4.630081239411466,-5.239062722957792,0.005204200744628906,6.957619000000002 +700,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4782734303433982,4.565354365023557,-5.225160013321354,0.005204200744628906,7.316272000000002 +720,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4563696019956498,4.503833132228122,-5.19161922746511,0.005204200744628906,7.675697000000002 +740,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4392280778003554,4.445645440595998,-5.02903742417401,0.005204200744628906,8.035893000000002 +760,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4073407178561264,4.387021097703184,-4.9346827726614455,0.005204200744628906,8.396854000000001 +780,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3782504190107006,4.330701361336262,-4.809192109617374,0.005204200744628906,8.758623000000002 +800,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3571814777264213,4.277370073659861,-4.718248073230613,0.005204200744628906,9.121240000000002 +820,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3328025450945626,4.2253925636381995,-4.641399853721709,0.005204200744628906,9.484674000000002 +840,Regression,Linear Regression with l1 regularization,TrumpApproval,1.311715211433691,4.175582527272098,-4.560327645533724,0.005204200744628906,9.848922000000002 +860,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2897432923325247,4.127236925138345,-4.422957957045758,0.005204200744628906,10.213983000000002 +880,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2672991203860131,4.080383024210964,-4.274192362897992,0.005204200744628906,10.579853000000002 +900,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2421842209255052,4.03488734209176,-4.178968845613636,0.005204200744628906,10.965988000000001 +920,Regression,Linear Regression with l1 regularization,TrumpApproval,1.220808929344255,3.991045752926761,-4.15028972045945,0.005204200744628906,11.354575 +940,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2057181063421543,3.9494511154557617,-4.0862825167589865,0.005204200744628906,11.745471 +960,Regression,Linear Regression with l1 regularization,TrumpApproval,1.188437369603739,3.9086583836793856,-4.0338431039290805,0.005204200744628906,12.138778 +980,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1710173649101312,3.8690392813429555,-4.028105053503917,0.005204200744628906,12.545048 +1000,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1544521877618488,3.8306020851942315,-4.0116636492940465,0.005204200744628906,12.952187 +1001,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1537672749321948,3.8287168981917103,-4.010074752320696,0.005204200744628906,13.359495 +11,Regression,Linear Regression with l2 regularization,ChickWeights,30.6062254572366,31.39938120772091,-1268.1112549740517,0.004153251647949219,0.000711 +22,Regression,Linear Regression with l2 regularization,ChickWeights,21.412737763681047,23.97862157826266,-607.9443275975191,0.004153251647949219,0.001889 +33,Regression,Linear Regression with l2 regularization,ChickWeights,15.119104680903606,19.655410372524667,-267.315679768846,0.004153251647949219,0.003406 +44,Regression,Linear Regression with l2 regularization,ChickWeights,11.691588950452092,17.042779535378298,-227.6797328948204,0.004153251647949219,0.00525 +55,Regression,Linear Regression with l2 regularization,ChickWeights,11.128477598777668,17.570968714531574,-59.26944361385635,0.004153251647949219,0.0074210000000000005 +66,Regression,Linear Regression with l2 regularization,ChickWeights,10.755656716101159,16.483156797846284,-21.862958739409084,0.004153251647949219,0.009919 +77,Regression,Linear Regression with l2 regularization,ChickWeights,10.454334080303978,15.644372833730271,-12.803711937026078,0.004153251647949219,0.012745000000000001 +88,Regression,Linear Regression with l2 regularization,ChickWeights,9.893519322025275,14.807378680481822,-10.212022929829027,0.004153251647949219,0.015896 +99,Regression,Linear Regression with l2 regularization,ChickWeights,9.219705201317108,14.044546137802199,-7.436202462041329,0.004153251647949219,0.019372 +110,Regression,Linear Regression with l2 regularization,ChickWeights,8.828389618716818,13.455080798744472,-4.4070097733575375,0.004153251647949219,0.023173 +121,Regression,Linear Regression with l2 regularization,ChickWeights,8.61456960864212,13.037583740326507,-2.9098467157738415,0.004153251647949219,0.027299 +132,Regression,Linear Regression with l2 regularization,ChickWeights,8.52880743945525,12.690080989153241,-2.0274307958032884,0.004153251647949219,0.031782 +143,Regression,Linear Regression with l2 regularization,ChickWeights,8.39143583855712,12.359614263508039,-1.3260696348061907,0.004153251647949219,0.036638 +154,Regression,Linear Regression with l2 regularization,ChickWeights,8.12180315101294,12.009375103170282,-0.866389387173786,0.004153251647949219,0.041874999999999996 +165,Regression,Linear Regression with l2 regularization,ChickWeights,8.136940986261356,11.920551719153746,-0.47822185831879493,0.004153251647949219,0.04749199999999999 +176,Regression,Linear Regression with l2 regularization,ChickWeights,8.284290032332207,11.93362687305613,-0.19451089204457617,0.004153251647949219,0.05348299999999999 +187,Regression,Linear Regression with l2 regularization,ChickWeights,8.390309464431912,11.903488345267945,0.026908395403585694,0.004153251647949219,0.05984699999999999 +198,Regression,Linear Regression with l2 regularization,ChickWeights,8.350219958465262,11.791481226840993,0.2404518209934976,0.004153251647949219,0.06659 +209,Regression,Linear Regression with l2 regularization,ChickWeights,8.499019855105985,11.958125495095471,0.3560283448388185,0.004153251647949219,0.073713 +220,Regression,Linear Regression with l2 regularization,ChickWeights,8.90272187978296,12.527163169679886,0.3812690011074207,0.004153251647949219,0.081218 +231,Regression,Linear Regression with l2 regularization,ChickWeights,9.171291167504231,12.73748746029564,0.45283948771259386,0.004153251647949219,0.08971 +242,Regression,Linear Regression with l2 regularization,ChickWeights,9.37629466014084,13.047657656056804,0.538024139715424,0.004153251647949219,0.099295 +253,Regression,Linear Regression with l2 regularization,ChickWeights,9.440817816219349,13.0964165059942,0.5957634168273553,0.004153251647949219,0.110134 +264,Regression,Linear Regression with l2 regularization,ChickWeights,9.906487060964153,13.855497684527965,0.5866088718530376,0.004233360290527344,0.12224199999999999 +275,Regression,Linear Regression with l2 regularization,ChickWeights,10.387009537918406,14.786939232799543,0.5856869069436603,0.004233360290527344,0.13552899999999998 +286,Regression,Linear Regression with l2 regularization,ChickWeights,10.701469010841246,15.270898285463774,0.6172820078624095,0.004233360290527344,0.14989199999999997 +297,Regression,Linear Regression with l2 regularization,ChickWeights,10.689852199892528,15.284847538688991,0.6674656839655615,0.004233360290527344,0.17720199999999997 +308,Regression,Linear Regression with l2 regularization,ChickWeights,11.168487287417783,16.008183102465477,0.6751444757196481,0.004233360290527344,0.20585199999999998 +319,Regression,Linear Regression with l2 regularization,ChickWeights,12.085867087734242,18.170753499240714,0.6201764868699093,0.004233360290527344,0.23488799999999999 +330,Regression,Linear Regression with l2 regularization,ChickWeights,12.672501856506585,19.05837058612535,0.6424226539377311,0.004233360290527344,0.26428199999999996 +341,Regression,Linear Regression with l2 regularization,ChickWeights,12.822446828447035,19.13937756684808,0.6770787925994421,0.004233360290527344,0.29402799999999996 +352,Regression,Linear Regression with l2 regularization,ChickWeights,13.055746883990931,19.312136445778254,0.7011272480618885,0.004233360290527344,0.32412699999999994 +363,Regression,Linear Regression with l2 regularization,ChickWeights,13.79008745873622,20.396105048894267,0.6762859401979866,0.004233360290527344,0.35457799999999995 +374,Regression,Linear Regression with l2 regularization,ChickWeights,14.293199062265238,21.539399675842862,0.6681199603719434,0.004233360290527344,0.38537999999999994 +385,Regression,Linear Regression with l2 regularization,ChickWeights,14.740320816630273,22.311026164960477,0.6849554171717112,0.004233360290527344,0.42616299999999996 +396,Regression,Linear Regression with l2 regularization,ChickWeights,14.862968645899144,22.294096988116678,0.7067005430463744,0.004233360290527344,0.467315 +407,Regression,Linear Regression with l2 regularization,ChickWeights,15.699705023283963,23.67314903355933,0.6925996644733732,0.004233360290527344,0.508826 +418,Regression,Linear Regression with l2 regularization,ChickWeights,16.38213993729544,25.048095107979137,0.6769473375050636,0.004233360290527344,0.55069 +429,Regression,Linear Regression with l2 regularization,ChickWeights,16.967894830794286,26.153201890569886,0.6870368010887093,0.004233360290527344,0.592905 +440,Regression,Linear Regression with l2 regularization,ChickWeights,17.10728249235129,26.204092785638924,0.7080553660644732,0.004233360290527344,0.6354690000000001 +451,Regression,Linear Regression with l2 regularization,ChickWeights,17.603016925007317,26.772391386711114,0.7082099437723521,0.004233360290527344,0.6783830000000001 +462,Regression,Linear Regression with l2 regularization,ChickWeights,18.614531201761594,28.786744962703725,0.6831362914484524,0.004233360290527344,0.7216460000000001 +473,Regression,Linear Regression with l2 regularization,ChickWeights,19.488293352005442,30.38515335394973,0.6882746780375071,0.004233360290527344,0.7652570000000001 +484,Regression,Linear Regression with l2 regularization,ChickWeights,19.755002868307955,30.52390276571354,0.7026599444855313,0.004233360290527344,0.8092140000000001 +495,Regression,Linear Regression with l2 regularization,ChickWeights,20.22217092676305,31.08727194033441,0.7098743070293987,0.004233360290527344,0.8535240000000001 +506,Regression,Linear Regression with l2 regularization,ChickWeights,21.03670858216615,32.44431034253017,0.6931769059461363,0.004233360290527344,0.898204 +517,Regression,Linear Regression with l2 regularization,ChickWeights,21.78200415465676,33.496021791915204,0.6913806254796178,0.004233360290527344,0.943264 +528,Regression,Linear Regression with l2 regularization,ChickWeights,22.56258004106143,34.768391171729405,0.7010449079513538,0.004233360290527344,0.9886969999999999 +539,Regression,Linear Regression with l2 regularization,ChickWeights,22.68725373887437,34.77075336357408,0.7133508993505916,0.004233360290527344,1.0345 +550,Regression,Linear Regression with l2 regularization,ChickWeights,23.627725892037507,36.324416048782524,0.6968033114915981,0.004233360290527344,1.080674 +561,Regression,Linear Regression with l2 regularization,ChickWeights,24.347376192466918,37.30920796407717,0.6941284720923248,0.004233360290527344,1.127216 +572,Regression,Linear Regression with l2 regularization,ChickWeights,25.18573737545828,38.51358935872805,0.698506895988072,0.004233360290527344,1.174127 +578,Regression,Linear Regression with l2 regularization,ChickWeights,25.27380465992389,38.58852748240754,0.7046942807227952,0.004233360290527344,1.2212839999999998 +20,Regression,Linear Regression with l2 regularization,TrumpApproval,20.994354275814885,24.339467027537435,-1388.5575385664913,0.004836082458496094,0.002841 +40,Regression,Linear Regression with l2 regularization,TrumpApproval,12.808927193108108,17.83271591943186,-126.84988353201342,0.004836082458496094,0.0066630000000000005 +60,Regression,Linear Regression with l2 regularization,TrumpApproval,10.864002308096953,15.320672400398038,-126.22308256175273,0.004836082458496094,0.011298 +80,Regression,Linear Regression with l2 regularization,TrumpApproval,8.882777304938948,13.38981065066765,-96.4771385394691,0.004836082458496094,0.01675 +100,Regression,Linear Regression with l2 regularization,TrumpApproval,7.231639558854497,11.98203471414171,-47.97617801736401,0.004836082458496094,0.023066000000000003 +120,Regression,Linear Regression with l2 regularization,TrumpApproval,6.334108393931037,10.98237795329033,-33.904913895880355,0.004836082458496094,0.030179000000000004 +140,Regression,Linear Regression with l2 regularization,TrumpApproval,5.563493982833803,10.178707085968126,-29.98405233271513,0.004836082458496094,0.038033000000000004 +160,Regression,Linear Regression with l2 regularization,TrumpApproval,5.002122045077101,9.533278572445496,-22.968717144675633,0.004836082458496094,0.04831800000000001 +180,Regression,Linear Regression with l2 regularization,TrumpApproval,4.587842803317817,9.003737317880292,-17.777085610739057,0.004836082458496094,0.07294600000000001 +200,Regression,Linear Regression with l2 regularization,TrumpApproval,4.458683971614509,8.652080760634158,-16.390543570087573,0.004836082458496094,0.09946700000000001 +220,Regression,Linear Regression with l2 regularization,TrumpApproval,4.239995800771734,8.280452519944822,-16.182419642449048,0.004836082458496094,0.129303 +240,Regression,Linear Regression with l2 regularization,TrumpApproval,3.943592784264584,7.932077353220182,-14.885669934585447,0.004836082458496094,0.159907 +260,Regression,Linear Regression with l2 regularization,TrumpApproval,3.7846302486799286,7.646201644169009,-13.960159512708739,0.004996299743652344,0.191262 +280,Regression,Linear Regression with l2 regularization,TrumpApproval,3.6468171672887713,7.389977926170562,-13.630953412847402,0.004996299743652344,0.22336299999999998 +300,Regression,Linear Regression with l2 regularization,TrumpApproval,3.5261123680869226,7.1621871014808685,-12.51535851099468,0.004996299743652344,0.25877399999999995 +320,Regression,Linear Regression with l2 regularization,TrumpApproval,3.5074300839639245,6.985469271455791,-12.493228210862723,0.004996299743652344,0.29652399999999995 +340,Regression,Linear Regression with l2 regularization,TrumpApproval,3.434140699763514,6.814822943627961,-12.570888751300782,0.004996299743652344,0.33652199999999993 +360,Regression,Linear Regression with l2 regularization,TrumpApproval,3.4272200155971797,6.678288393486038,-12.13957341395007,0.004996299743652344,0.3787709999999999 +380,Regression,Linear Regression with l2 regularization,TrumpApproval,3.332029752839207,6.516115548498917,-11.941900403072644,0.004996299743652344,0.4232519999999999 +400,Regression,Linear Regression with l2 regularization,TrumpApproval,3.217390968362987,6.356555790563252,-11.66392028459017,0.004996299743652344,0.4696459999999999 +420,Regression,Linear Regression with l2 regularization,TrumpApproval,3.100825681509746,6.206562691759863,-11.472880484909139,0.004996299743652344,0.516851 +440,Regression,Linear Regression with l2 regularization,TrumpApproval,3.0187726323631243,6.072312098448126,-10.723095644711893,0.004996299743652344,0.5652379999999999 +460,Regression,Linear Regression with l2 regularization,TrumpApproval,2.947022825868371,5.94849802587685,-9.668265577306911,0.004996299743652344,0.6161209999999999 +480,Regression,Linear Regression with l2 regularization,TrumpApproval,2.867282537241402,5.828292237410032,-9.005843404687633,0.004996299743652344,0.6743809999999999 +500,Regression,Linear Regression with l2 regularization,TrumpApproval,2.8281006485905213,5.729646774374514,-8.467133754251039,0.004996299743652344,0.7334769999999999 +520,Regression,Linear Regression with l2 regularization,TrumpApproval,2.759113137285707,5.623931694381955,-8.136932704030892,0.004996299743652344,0.79343 +540,Regression,Linear Regression with l2 regularization,TrumpApproval,2.7113951403332286,5.52770300084093,-7.7945843187225226,0.004996299743652344,0.8541829999999999 +560,Regression,Linear Regression with l2 regularization,TrumpApproval,2.646739535451309,5.4320905955210534,-7.695343452205655,0.004996299743652344,0.9420199999999999 +580,Regression,Linear Regression with l2 regularization,TrumpApproval,2.5972398336076634,5.343168086286508,-7.621065103502998,0.004996299743652344,1.0306859999999998 +600,Regression,Linear Regression with l2 regularization,TrumpApproval,2.533455116608919,5.255265792942869,-7.247487071141652,0.004996299743652344,1.1201999999999999 +620,Regression,Linear Regression with l2 regularization,TrumpApproval,2.497138699914293,5.178243230235351,-6.88544757519055,0.004996299743652344,1.2104949999999999 +640,Regression,Linear Regression with l2 regularization,TrumpApproval,2.4712145738198297,5.107804033669319,-6.528964961790648,0.004996299743652344,1.3015299999999999 +660,Regression,Linear Regression with l2 regularization,TrumpApproval,2.429247896498525,5.0347117637840935,-6.262430681498119,0.004996299743652344,1.3932969999999998 +680,Regression,Linear Regression with l2 regularization,TrumpApproval,2.3980901245026116,4.967521902410674,-6.18160824182094,0.004996299743652344,1.4857959999999997 +700,Regression,Linear Regression with l2 regularization,TrumpApproval,2.360396901712673,4.90286744871834,-6.1796262474179136,0.004996299743652344,1.5790619999999997 +720,Regression,Linear Regression with l2 regularization,TrumpApproval,2.3150393936015323,4.836721469702358,-6.140716883970916,0.004996299743652344,1.6730619999999998 +740,Regression,Linear Regression with l2 regularization,TrumpApproval,2.267679208737699,4.77254376860168,-5.948293801048677,0.004996299743652344,1.7677929999999997 +760,Regression,Linear Regression with l2 regularization,TrumpApproval,2.2434173929652075,4.715867112708654,-5.857742612566196,0.004996299743652344,1.8632789999999997 +780,Regression,Linear Regression with l2 regularization,TrumpApproval,2.199009654391343,4.656030786420359,-5.714767077599112,0.004996299743652344,1.9595409999999998 +800,Regression,Linear Regression with l2 regularization,TrumpApproval,2.1596596811720175,4.59898830049537,-5.610494506343661,0.004996299743652344,2.0566079999999998 +820,Regression,Linear Regression with l2 regularization,TrumpApproval,2.1249482408574707,4.545176313025559,-5.527610390446187,0.004996299743652344,2.175652 +840,Regression,Linear Regression with l2 regularization,TrumpApproval,2.094058354623314,4.493551443258636,-5.439404045425388,0.004996299743652344,2.37025 +860,Regression,Linear Regression with l2 regularization,TrumpApproval,2.062104039794744,4.442864622918497,-5.284107374622643,0.004996299743652344,2.565666 +880,Regression,Linear Regression with l2 regularization,TrumpApproval,2.0307065941401414,4.393695684791879,-5.115247638705276,0.004996299743652344,2.7618669999999996 +900,Regression,Linear Regression with l2 regularization,TrumpApproval,2.003263565299311,4.347049681772325,-5.011317673951863,0.004996299743652344,2.9588469999999996 +920,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9706878923964866,4.300488305941944,-4.979898179552831,0.004996299743652344,3.1566579999999997 +940,Regression,Linear Regression with l2 regularization,TrumpApproval,1.949819924248383,4.257394252920471,-4.91037086709413,0.004996299743652344,3.355253 +960,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9258186229947107,4.214725843085415,-4.85305905428677,0.004996299743652344,3.5546249999999997 +980,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9004260103609922,4.173138113177231,-4.849565137575967,0.004996299743652344,3.754774 +1000,Regression,Linear Regression with l2 regularization,TrumpApproval,1.872733130377695,4.13253797119814,-4.832859832721421,0.004996299743652344,3.955698 +1001,Regression,Linear Regression with l2 regularization,TrumpApproval,1.871510887330926,4.130524228438989,-4.8310671605777085,0.004996299743652344,4.156775 +11,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,26.624124996337724,28.77138517975663,-1064.5628215382144,0.0034055709838867188,0.000572 +22,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.0510878175865,20.931739283093208,-463.02330712701985,0.0034055709838867188,0.001645 +33,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.49930786476168,17.564629142555763,-213.26922094451623,0.0034055709838867188,0.003059 +44,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.378514545021682,15.405121473747096,-185.84310618709696,0.0034055709838867188,0.004809 +55,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.844108697295251,17.128215293517524,-56.27037115396167,0.0034055709838867188,0.0068920000000000006 +66,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.889488781892217,15.88743125142584,-20.240220516271876,0.0034055709838867188,0.009306 +77,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.103343480706034,14.91594241381016,-11.548186613409534,0.0034055709838867188,0.012052 +88,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.288900850158633,14.011374344891147,-9.038968322803399,0.0034055709838867188,0.015129 +99,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.736865157066078,13.281093172283262,-6.5439573170464564,0.0034055709838867188,0.018712 +110,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.618125386224052,12.858171267844924,-3.9379074608874927,0.0034055709838867188,0.022717 +121,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.580936033253089,12.515247629942861,-2.6028352541815996,0.0034055709838867188,0.027127000000000002 +132,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.191573127926202,12.024287681643044,-1.7180920054032294,0.0034055709838867188,0.031928 +143,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.001452140019149,11.63905100750295,-1.062756769701151,0.0034055709838867188,0.037113999999999994 +154,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.959260067984971,11.397763679955697,-0.6811278108981134,0.0034055709838867188,0.04268899999999999 +165,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.036161429677985,11.359538570018055,-0.3423577921849861,0.0034055709838867188,0.04865399999999999 +176,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.141200516910354,11.407680550849575,-0.09154063283497149,0.0034055709838867188,0.05501099999999999 +187,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.061965626679777,11.211626858308708,0.1367382583661435,0.0034055709838867188,0.06175399999999999 +198,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.988600359846859,11.023879576943443,0.33612315722527375,0.0034055709838867188,0.06888499999999999 +209,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.115468527113427,11.18859440458875,0.43624345152336885,0.0034055709838867188,0.07639599999999999 +220,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.571784360381598,11.998798941058181,0.4323613497222297,0.0034055709838867188,0.08428799999999999 +231,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.610536559977233,11.962244483436113,0.5174164020157423,0.0034055709838867188,0.09375399999999999 +242,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.753677752144043,12.109970858596688,0.6020391290764042,0.0034055709838867188,0.10451099999999999 +253,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.763402728464486,12.046916639776326,0.6579556132088519,0.0034055709838867188,0.123112 +264,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.37232599699494,12.938281421109101,0.6395292100633578,0.0034589767456054688,0.147917 +275,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.870502401884236,14.03783628218945,0.6266016212673495,0.0034589767456054688,0.173118 +286,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.125553299295866,14.312481045438886,0.6638140497188074,0.0034589767456054688,0.198688 +297,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.11642729851449,14.234872044017683,0.7115826499817814,0.0034589767456054688,0.225283 +308,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.63053955101658,15.01159987060024,0.7143329631149452,0.0034589767456054688,0.252265 +319,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.671899739762464,17.42953249336733,0.650531972004655,0.0034589767456054688,0.27961600000000003 +330,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.113559839827301,17.980470366868552,0.6817264420663893,0.0034589767456054688,0.307329 +341,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.368994570730054,18.183536514460908,0.7085274545262112,0.0034589767456054688,0.335405 +352,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.47998520043724,18.216810890558104,0.7340681346165732,0.0034589767456054688,0.363842 +363,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.490995837872445,19.84181186896939,0.693641639088806,0.0034589767456054688,0.392636 +374,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.988870134156189,20.81926805033374,0.6899406322869175,0.0034589767456054688,0.42178699999999997 +385,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.420579982415202,21.48960215237335,0.7077263415053474,0.0034589767456054688,0.45129199999999997 +396,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.424816444492956,21.37796604773129,0.7303103668220552,0.0034589767456054688,0.481151 +407,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,14.284688005634004,22.701575115822557,0.7173140296578691,0.0034589767456054688,0.514769 +418,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.038658536726118,24.042516108283174,0.7023651722582169,0.0034589767456054688,0.548784 +429,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.590290098257741,24.916858152232297,0.7159269075042054,0.0034589767456054688,0.583167 +440,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.812702077031824,25.072500493300815,0.7327254930224041,0.0034589767456054688,0.617914 +451,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.346042839206106,25.68091484988461,0.7315167856134144,0.0034589767456054688,0.653021 +462,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,17.370765923434053,27.689388199834635,0.7068336630361484,0.0034589767456054688,0.688487 +473,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.264179516209435,29.30099868065636,0.7101227966068765,0.0034589767456054688,0.724309 +484,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.63502154656571,29.559619400414906,0.7211497930314269,0.0034589767456054688,0.760485 +495,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.145243718121584,30.130361606680754,0.7274603750306702,0.0034589767456054688,0.7970149999999999 +506,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.98075812634153,31.43770898148617,0.7119202509985216,0.0034589767456054688,0.8338999999999999 +517,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,20.7046141289421,32.42665929478992,0.7107714616434232,0.0034589767456054688,0.8711439999999999 +528,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.54126059149082,33.75343345950398,0.7182443212146572,0.0034589767456054688,0.908739 +539,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.736037457517718,33.829762552174344,0.7286559632490104,0.0034589767456054688,0.946687 +550,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,22.674609740448528,35.33904665998618,0.7130297805712756,0.0034589767456054688,0.984985 +561,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,23.350956760305525,36.24046007710213,0.7114012814424304,0.0034589767456054688,1.023633 +572,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.20743030595361,37.47019278346573,0.7146215025224946,0.0034589767456054688,1.06263 +578,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.342328163686027,37.59599019491026,0.7196900586014492,0.0034589767456054688,1.1018649999999999 +20,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,20.806898309502586,26.56763494383828,-1654.6182189603317,0.004302024841308594,0.003003 +40,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,14.866074912822512,20.957300378156614,-175.5777711351631,0.004302024841308594,0.009504 +60,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,11.772648582583251,17.555009093750932,-166.03688377592212,0.004302024841308594,0.016813 +80,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,10.372925375947808,15.758572852966298,-134.01675577859288,0.004302024841308594,0.024890000000000002 +100,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.950999863257042,14.807263848606526,-73.79513907078027,0.004302024841308594,0.033777 +120,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.131163180965077,13.743973626529105,-53.66614209724606,0.004302024841308594,0.043434 +140,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.532294463666167,12.935885124148239,-49.04322437944699,0.004302024841308594,0.05944 +160,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.33219708929472,12.40854626547306,-39.60710527883104,0.004302024841308594,0.077842 +180,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.281092452540433,12.043698542516514,-32.597139849683785,0.004302024841308594,0.09862299999999999 +200,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.889313429527772,11.548268653424005,-29.981738551789036,0.004302024841308594,0.121857 +220,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.555718436766954,11.115454500430198,-29.962115725262894,0.004302024841308594,0.149384 +240,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.300584612865839,10.768588372618428,-28.278525817685868,0.004302024841308594,0.177703 +260,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.073956995660685,10.455941089275187,-26.975057358486694,0.004435539245605469,0.206834 +280,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.879100927439736,10.179149173565092,-26.75935065850941,0.004435539245605469,0.236782 +300,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.698392466938299,9.935855831167725,-25.01038668400382,0.004435539245605469,0.26759 +320,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.496977203333427,9.674599820332077,-24.881575653507443,0.004435539245605469,0.299212 +340,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.319501534649956,9.433800456219284,-25.005937123592886,0.004435539245605469,0.33164299999999997 +360,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.189316591643737,9.224778235838508,-24.070488458586468,0.004435539245605469,0.36488499999999996 +380,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.05373584315195,9.026036673488779,-23.83217081250324,0.004435539245605469,0.39894499999999994 +400,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.893196935767096,8.831009888188627,-23.44247524737401,0.004435539245605469,0.43386499999999995 +420,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.787168115685009,8.669033337133486,-23.33356914323267,0.004435539245605469,0.46960899999999994 +440,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.789860410241021,8.633597516354541,-22.69832962851382,0.004435539245605469,0.5061589999999999 +460,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.751464501173282,8.532971696368575,-20.952287666855003,0.004435539245605469,0.5516559999999999 +480,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.758413491181221,8.476961067588123,-20.166616413985547,0.004435539245605469,0.599555 +500,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.682950272504451,8.3510488559106,-19.111518541810455,0.004435539245605469,0.6519079999999999 +520,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.627995468360723,8.245754355787446,-18.64179334000355,0.004435539245605469,0.705132 +540,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.546541731300828,8.130789587119862,-18.02792190581397,0.004435539245605469,0.7591289999999999 +560,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.474658569482086,8.019262742277965,-17.95054121046633,0.004435539245605469,0.813877 +580,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.409420416004319,7.920158789530457,-17.94222667017848,0.004435539245605469,0.869386 +600,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.394854582323811,7.870548110777217,-17.498743363524373,0.004435539245605469,0.941553 +620,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.360408122735632,7.801849933723111,-16.900148820132806,0.004435539245605469,1.016153 +640,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.332182524169608,7.745335706289596,-16.312002846243146,0.004435539245605469,1.093294 +660,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.286484086266954,7.672164501241343,-15.864310422998045,0.004435539245605469,1.1728 +680,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.240017672508232,7.591734569529257,-15.773518040276521,0.004435539245605469,1.259908 +700,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.203631741702394,7.526058935068808,-15.917522479350382,0.004435539245605469,1.347915 +720,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.1985518333986755,7.481861117849272,-16.086729414362967,0.004435539245605469,1.436748 +740,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.200051628353664,7.4433149034441595,-15.900950117878587,0.004435539245605469,1.526395 +760,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.146415466772512,7.367313347205083,-15.736951087672441,0.004435539245605469,1.61685 +780,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.164438314106662,7.352756459959702,-15.745558187055657,0.004435539245605469,1.708114 +800,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.199091748701669,7.381485816300255,-16.029304780133675,0.004435539245605469,1.8001699999999998 +820,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.184244405270293,7.343677512392477,-16.040406471643664,0.004435539245605469,1.892982 +840,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.162940711797175,7.2951968772545595,-15.972283354719572,0.004435539245605469,1.986541 +860,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.146772746928229,7.251973114148485,-15.742866229030533,0.004435539245605469,2.080851 +880,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.141562534384022,7.225910341165371,-15.54014218136778,0.004435539245605469,2.175912 +900,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.113671043317916,7.181653170625269,-15.40700562565575,0.004435539245605469,2.271722 +920,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.0827257569327715,7.134180367835326,-15.456838882747107,0.004435539245605469,2.368328 +940,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.049460198345376,7.092641752853287,-15.403746251323405,0.004435539245605469,2.491683 +960,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.012955702794688,7.041188655025779,-15.335641983730405,0.004435539245605469,2.616037 +980,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.992587411597517,7.002009756347646,-15.468099715303381,0.004435539245605469,2.7412199999999998 +1000,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.986581819477306,6.97972894589718,-15.638912943338369,0.004435539245605469,2.867218 +1001,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.984033991902679,6.9766666383395455,-15.63541499061877,0.004435539245605469,2.993378 +11,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,39.19936706045659,55.118879370280126,-3909.733983269086,0.0034055709838867188,0.001533 +22,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.495026158423794,43.23165104261441,-1978.3965328342838,0.0034055709838867188,0.004589 +33,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,30.680053698816124,39.985066603327745,-1109.3949268723327,0.0034055709838867188,0.008788 +44,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,29.375885022911746,37.29886968855784,-1094.3128086885838,0.0034055709838867188,0.014141 +55,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.707444751978134,40.753235251415205,-323.21264874535376,0.0034055709838867188,0.020635 +66,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.96097441162184,40.14945868859866,-134.64726490280094,0.0034055709838867188,0.028271 +77,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.25989567011213,39.82501544894248,-88.45229320906665,0.0034055709838867188,0.041195 +88,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.76307262878121,39.802536485586,-80.01195436020778,0.0034055709838867188,0.054526000000000005 +99,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.66411513705659,39.325402336106926,-65.1420916497486,0.0034055709838867188,0.06822700000000001 +110,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.19940912800194,40.704130728492046,-48.48362457590105,0.0034055709838867188,0.082293 +121,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.629161705635866,40.92880988729008,-37.532194399087835,0.0034055709838867188,0.096719 +132,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,35.29035427006805,41.59178542812187,-31.520750879588867,0.0034055709838867188,0.111503 +143,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.23638449140802,42.62018794050648,-26.659453761649477,0.0034055709838867188,0.126644 +154,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.725010132899186,42.91835139661213,-22.836775105518452,0.0034055709838867188,0.14214100000000002 +165,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.731745662210095,42.91744223234227,-18.16084111691443,0.0034055709838867188,0.15799600000000003 +176,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.94402632003076,44.39720610255875,-15.533183531813599,0.0034055709838867188,0.17420800000000003 +187,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,38.698580833397834,45.06856008203835,-12.949305609255877,0.0034055709838867188,0.19077600000000003 +198,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.18624064352699,46.68267333461602,-10.905017439998023,0.0034055709838867188,0.20770000000000002 +209,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.854323276826534,47.463811090322665,-9.145320047375163,0.0034055709838867188,0.224978 +220,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,41.36451127701117,48.41262940233051,-8.240888884363313,0.0034055709838867188,0.24261000000000002 +231,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,42.17342712408468,49.46918668675267,-7.253093192412523,0.0034055709838867188,0.260594 +242,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,43.81461612103895,51.73551020684679,-6.2632609796369465,0.0034055709838867188,0.27893 +253,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,44.90819615603068,53.07334253773936,-5.6387075541588505,0.0034055709838867188,0.297618 +264,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,46.45334973048907,55.82244674340302,-5.710187070138379,0.0034589767456054688,0.31665899999999997 +275,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,48.05643802527038,58.804796990371536,-5.552357606253864,0.0034589767456054688,0.33605399999999996 +286,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,49.41721923566732,60.72765972830183,-5.052332799264802,0.0034589767456054688,0.35580199999999995 +297,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,51.23299901747073,63.29154255446438,-4.701716347098143,0.0034589767456054688,0.37590499999999993 +308,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,52.82583967659276,65.36972550348784,-4.417008197806662,0.0034589767456054688,0.39636799999999994 +319,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,54.851023886215806,70.45860717413167,-4.71089374971376,0.0034589767456054688,0.41718399999999994 +330,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,56.58220488738844,72.62689780553444,-4.192702597603254,0.0034589767456054688,0.43835299999999994 +341,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,58.456862484765374,75.26810540469758,-3.994165343488624,0.0034589767456054688,0.45987999999999996 +352,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,59.98229295122657,76.97767263775137,-3.748486775975887,0.0034589767456054688,0.48176199999999997 +363,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,61.989108820835376,80.62951920841103,-4.0588898392459045,0.0034589767456054688,0.503996 +374,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,63.93796840595574,84.48840832488506,-4.106322034628877,0.0034589767456054688,0.526584 +385,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,65.15236861414519,85.79755918852514,-3.6588935912158114,0.0034589767456054688,0.551564 +396,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,66.90365663892747,87.9249329113371,-3.562003118430529,0.0034589767456054688,0.5777180000000001 +407,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,68.17917622540308,89.58756462611774,-3.402382103640547,0.0034589767456054688,0.6050150000000001 +418,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,70.80702754948452,94.96753809429286,-3.643808228470034,0.0034589767456054688,0.6334650000000001 +429,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,72.44730173566225,97.09455233033468,-3.313534410167211,0.0034589767456054688,0.663057 +440,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,74.29167351363806,99.40774027870643,-3.2014833153265316,0.0034589767456054688,0.693783 +451,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,75.83174494284101,101.77506329990584,-3.216760347648722,0.0034589767456054688,0.725638 +462,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,78.5111288104629,106.99570126481906,-3.3774383617967887,0.0034589767456054688,0.758621 +473,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,81.63741116996734,112.58139375423264,-3.2793958269429844,0.0034589767456054688,0.79391 +484,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,82.66628549198501,113.50934761838603,-3.1118432523868984,0.0034589767456054688,0.829617 +495,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,84.40016304476833,116.34990208847,-3.0639935553557365,0.0034589767456054688,0.865723 +506,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,86.52132256561038,120.30772815943004,-3.2188880650982723,0.0034589767456054688,0.902224 +517,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,87.79244029751037,121.63869088166206,-3.0698668478095374,0.0034589767456054688,0.9391200000000001 +528,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,90.43735682351402,126.62066541565774,-2.9650251625135784,0.0034589767456054688,0.9763980000000001 +539,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,91.59763342322412,127.62959496409519,-2.862114672867245,0.0034589767456054688,1.014034 +550,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,93.80067010965053,131.39026699356185,-2.9669210878459156,0.0034589767456054688,1.0520230000000002 +561,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,96.52355815418714,136.33091173427522,-3.0840934586689466,0.0034589767456054688,1.0903630000000002 +572,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,99.60515399822415,141.80943605664237,-3.0875177525301325,0.0034589767456054688,1.1290550000000001 +578,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,100.62422612381133,143.06646930774232,-3.0591132110693486,0.0034589767456054688,1.167982 +20,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,48.24517612267716,65.52170729560882,-10068.892101934754,0.004302024841308594,0.0014 +40,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,41.96170708962665,54.398737007050464,-1188.7151382109587,0.004302024841308594,0.0037089999999999996 +60,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,37.75687919715097,48.78450375470138,-1288.953469480389,0.004302024841308594,0.0068 +80,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,34.906129137913965,44.99379649673769,-1099.675197364534,0.004302024841308594,0.010662 +100,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,33.91700787894482,42.88559259598606,-626.4029768570122,0.004302024841308594,0.015342999999999999 +120,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,33.25318798467783,41.41783833748641,-495.44216046034904,0.004302024841308594,0.020797999999999997 +140,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.454169303664,40.065346416262614,-479.0547686921885,0.004302024841308594,0.027020999999999996 +160,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.456143135335843,38.757475924320815,-395.16052146995384,0.004302024841308594,0.034013999999999996 +180,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.609503890456164,37.605439707525925,-326.55474603918685,0.004302024841308594,0.04177499999999999 +200,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.18524212396377,36.915721425331306,-315.5882175367347,0.004302024841308594,0.05032999999999999 +220,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.065472528043387,36.44442035805252,-331.8421153382835,0.004302024841308594,0.05964399999999999 +240,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.78865598017145,35.91292614465666,-324.6366197799479,0.004302024841308594,0.06971499999999999 +260,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.781114432924582,35.79236523689611,-326.81251307944893,0.004435539245605469,0.08530999999999998 +280,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.533238425747374,35.35890478021234,-333.95306350592915,0.004435539245605469,0.10330299999999998 +300,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.457831453745214,35.09485674586195,-323.50634592736793,0.004435539245605469,0.12379499999999997 +320,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.587592426740265,34.99099947403571,-337.56135797788454,0.004435539245605469,0.14665799999999998 +340,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.592186767063264,34.82748458593961,-353.4405109424598,0.004435539245605469,0.178415 +360,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.811871336212132,34.87255971663822,-357.27671195740294,0.004435539245605469,0.21101599999999998 +380,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.96085998978186,34.8837386376585,-369.9082961036221,0.004435539245605469,0.24444299999999997 +400,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.10861760053803,34.89146879370085,-380.5600928496674,0.004435539245605469,0.27875099999999997 +420,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.237056214581205,34.87113676993804,-392.72834237954817,0.004435539245605469,0.31389799999999995 +440,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.396870134836657,34.919939008119975,-386.68686883790514,0.004435539245605469,0.34987699999999994 +460,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.514209015244397,34.936230377424984,-366.98596965456716,0.004435539245605469,0.38662699999999994 +480,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.60304766371323,34.90556469597589,-357.88920799289923,0.004435539245605469,0.42413599999999996 +500,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.723498435612367,34.929338036322235,-350.83863344189655,0.004435539245605469,0.462404 +520,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.811640107301315,34.91688659850233,-351.20164208687333,0.004435539245605469,0.5014689999999999 +540,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.90684609870959,34.93305250730057,-350.23597283973027,0.004435539245605469,0.541291 +560,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.83222834631613,34.80844611918478,-356.0442181025925,0.004435539245605469,0.58187 +580,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.81214247339674,34.7464796172207,-363.5733105101423,0.004435539245605469,0.629234 +600,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.96266506693229,34.82326914665847,-361.13570824853826,0.004435539245605469,0.6774979999999999 +620,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.066025409450507,34.86865150113252,-356.54586556020155,0.004435539245605469,0.743837 +640,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.17687176552783,34.929241693507976,-351.0830657253844,0.004435539245605469,0.810979 +660,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.17965741293356,34.892251240403844,-347.8114699445998,0.004435539245605469,0.87889 +680,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.2564554130016,34.924087575336145,-353.970503405387,0.004435539245605469,0.947564 +700,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.205643809070587,34.8991435368638,-362.77351000506667,0.004435539245605469,1.017054 +720,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.176512353694502,34.84497410939031,-369.61240318757564,0.004435539245605469,1.087302 +740,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.10578554229227,34.74995813099662,-367.37224871607793,0.004435539245605469,1.158309 +760,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.047274834607858,34.691812272848594,-370.11801689051305,0.004435539245605469,1.23401 +780,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.10380007346799,34.703384372728905,-372.0292841604823,0.004435539245605469,1.319073 +800,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.08555480002386,34.670374973731704,-374.68721566223496,0.004435539245605469,1.406663 +820,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.19885148971359,34.750222550469864,-380.56447731408525,0.004435539245605469,1.496625 +840,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.254637155655836,34.7657556157579,-384.4513633760299,0.004435539245605469,1.5889890000000002 +860,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.316317159155098,34.79186578621207,-384.3655387384781,0.004435539245605469,1.693075 +880,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.241979864666273,34.706915019978055,-380.5804591262153,0.004435539245605469,2.065505 +900,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.224226006229813,34.673719949901624,-381.45588348920205,0.004435539245605469,2.440347 +920,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.14134426690263,34.58836614994504,-385.8283921715467,0.004435539245605469,2.817664 +940,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.997921544748234,34.45657797481166,-386.1428842704396,0.004435539245605469,3.197344 +960,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.060400411885407,34.566740304864304,-392.69608794190344,0.004435539245605469,3.57811 +980,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.96911325529305,34.515642414657464,-399.1566023636026,0.004435539245605469,3.959721 +1000,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.09609109084082,34.63142513356851,-408.62698817771025,0.004435539245605469,4.342153 +1001,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.093334457100017,34.62570772928248,-408.7651443035993,0.004435539245605469,4.724752 +11,Regression,k-Nearest Neighbors,ChickWeights,4.6439393939393945,12.708027567111456,-206.8805289598106,0.0208587646484375,0.001745 +22,Regression,k-Nearest Neighbors,ChickWeights,2.7674242424242426,9.021574170013263,-85.19732920009746,0.030094146728515625,0.005817 +33,Regression,k-Nearest Neighbors,ChickWeights,2.3601010101010105,7.4346315168437105,-37.38846247874159,0.0395355224609375,0.012524 +44,Regression,k-Nearest Neighbors,ChickWeights,1.9882575757575767,6.459864921032004,-31.8544119108943,0.0488433837890625,0.023287000000000002 +55,Regression,k-Nearest Neighbors,ChickWeights,2.201515151515152,6.079045396219125,-6.214006750846093,0.05837249755859375,0.035467 +66,Regression,k-Nearest Neighbors,ChickWeights,2.2709595959595963,5.693634951086079,-1.7279153546475992,0.06856918334960938,0.049542 +77,Regression,k-Nearest Neighbors,ChickWeights,2.6114718614718617,5.706903555891601,-0.8368793810695487,0.0782623291015625,0.06880900000000001 +88,Regression,k-Nearest Neighbors,ChickWeights,2.5236742424242427,5.412016943708686,-0.4977726858852578,0.08795547485351562,0.09929800000000001 +99,Regression,k-Nearest Neighbors,ChickWeights,2.4695286195286204,5.169211114529652,-0.1428260058474422,0.09764862060546875,0.13234700000000002 +110,Regression,k-Nearest Neighbors,ChickWeights,2.7553030303030313,5.269495069058163,0.1706792355598563,0.10734176635742188,0.16795900000000002 +121,Regression,k-Nearest Neighbors,ChickWeights,3.1511019283746564,5.580125306741311,0.2837685080447375,0.117034912109375,0.20637100000000003 +132,Regression,k-Nearest Neighbors,ChickWeights,3.3157828282828294,5.649452649212155,0.3999904226030885,0.12723159790039062,0.24803200000000003 +143,Regression,k-Nearest Neighbors,ChickWeights,3.6019813519813537,5.868270501527574,0.47563568627460706,0.13692474365234375,0.31342200000000003 +154,Regression,k-Nearest Neighbors,ChickWeights,3.7459956709956725,5.964828521670115,0.5395766265984425,0.14661788940429688,0.409887 +165,Regression,k-Nearest Neighbors,ChickWeights,4.050202020202021,6.4542180762994805,0.5666546129487657,0.15631103515625,0.575073 +176,Regression,k-Nearest Neighbors,ChickWeights,4.420928030303032,6.954884488253524,0.5942812793055753,0.16600418090820312,0.746583 +187,Regression,k-Nearest Neighbors,ChickWeights,4.757664884135474,7.278917476631412,0.6361362300357987,0.17569732666015625,0.9226570000000001 +198,Regression,k-Nearest Neighbors,ChickWeights,5.192340067340069,7.767087259749381,0.6704396407154757,0.18589401245117188,1.103506 +209,Regression,k-Nearest Neighbors,ChickWeights,5.571690590111645,8.414476478500024,0.6811438926382001,0.195587158203125,1.2899880000000001 +220,Regression,k-Nearest Neighbors,ChickWeights,6.017651515151518,9.535434778453542,0.641509702161033,0.20528030395507812,1.4817580000000001 +231,Regression,k-Nearest Neighbors,ChickWeights,6.514646464646468,10.15268578355149,0.652376522878304,0.21497344970703125,1.688886 +242,Regression,k-Nearest Neighbors,ChickWeights,7.006955922865016,10.883499074839365,0.6785664047839641,0.22466659545898438,1.9058080000000002 +253,Regression,k-Nearest Neighbors,ChickWeights,7.401119894598158,11.259257694820905,0.7012209269570091,0.2343597412109375,2.129833 +264,Regression,k-Nearest Neighbors,ChickWeights,7.873800505050509,12.237701558545494,0.6775097363055258,0.24460983276367188,2.3598440000000003 +275,Regression,k-Nearest Neighbors,ChickWeights,8.501393939393942,13.456617650281162,0.6568816796501455,0.254302978515625,2.605392 +286,Regression,k-Nearest Neighbors,ChickWeights,8.999592074592076,14.081405883193678,0.6745818706784585,0.2639961242675781,2.8696780000000004 +297,Regression,k-Nearest Neighbors,ChickWeights,9.403647586980924,14.487230370517851,0.7012657763253116,0.27368927001953125,3.1592620000000005 +308,Regression,k-Nearest Neighbors,ChickWeights,9.825595238095241,15.247017337775036,0.7053028346163965,0.2833824157714844,3.4714450000000006 +319,Regression,k-Nearest Neighbors,ChickWeights,10.570794148380358,17.082267622288043,0.6643188025566307,0.2930755615234375,3.8957660000000005 +330,Regression,k-Nearest Neighbors,ChickWeights,11.342676767676771,18.20491056057454,0.6737311884314376,0.3032722473144531,4.333389 +341,Regression,k-Nearest Neighbors,ChickWeights,11.756256109481921,18.5968301788559,0.6951271166039881,0.31296539306640625,4.790776 +352,Regression,k-Nearest Neighbors,ChickWeights,12.16955492424243,18.94133239132977,0.7124941202708752,0.3226585388183594,5.260338 +363,Regression,k-Nearest Neighbors,ChickWeights,12.609595959595964,19.7022738973151,0.6979354313341102,0.3323516845703125,5.750168 +374,Regression,k-Nearest Neighbors,ChickWeights,13.251024955436726,20.7851367099449,0.6909564285254863,0.3420448303222656,6.251716 +385,Regression,k-Nearest Neighbors,ChickWeights,13.78255411255412,21.481025974379733,0.7079595790244884,0.35224151611328125,6.769761 +396,Regression,k-Nearest Neighbors,ChickWeights,14.010311447811455,21.53574862211497,0.7263147242326703,0.3619346618652344,7.297159 +407,Regression,k-Nearest Neighbors,ChickWeights,14.576126126126132,22.56379182999173,0.720735043690873,0.3716278076171875,7.841892 +418,Regression,k-Nearest Neighbors,ChickWeights,15.256658692185015,23.708044463333223,0.710588766956741,0.38134765625,8.4256 +429,Regression,k-Nearest Neighbors,ChickWeights,15.863597513597522,24.650993900023582,0.7219567169230845,0.3910675048828125,9.118775999999999 +440,Regression,k-Nearest Neighbors,ChickWeights,16.15655303030304,24.89490243600041,0.7364984966983625,0.4007606506347656,9.834439999999999 +451,Regression,k-Nearest Neighbors,ChickWeights,16.474242424242437,25.235361878916873,0.7407521096740679,0.41095733642578125,10.564262999999999 +462,Regression,k-Nearest Neighbors,ChickWeights,17.206240981241,26.51959634874256,0.731081178462164,0.42067718505859375,11.311639 +473,Regression,k-Nearest Neighbors,ChickWeights,18.061486962649766,27.919441407022266,0.7368140706560946,0.430450439453125,12.077289 +484,Regression,k-Nearest Neighbors,ChickWeights,18.444800275482105,28.396609389438456,0.742660608098584,0.4401702880859375,12.86575 +495,Regression,k-Nearest Neighbors,ChickWeights,18.85067340067341,28.917019336286593,0.7489686179689856,0.4499168395996094,13.665871000000001 +506,Regression,k-Nearest Neighbors,ChickWeights,19.397397891963116,29.705616030262235,0.7427898649120724,2.5872955322265625,16.820609 +517,Regression,k-Nearest Neighbors,ChickWeights,20.115441650548043,30.735303248634356,0.7401565757784102,2.6296463012695312,20.027458000000003 +528,Regression,k-Nearest Neighbors,ChickWeights,20.836142676767683,31.986233829047414,0.7469752640852343,2.6746597290039062,23.267231000000002 +539,Regression,k-Nearest Neighbors,ChickWeights,21.017594310451457,32.125858524254696,0.7553011842320496,2.717792510986328,26.555547000000004 +550,Regression,k-Nearest Neighbors,ChickWeights,21.677242424242426,32.83678407493398,0.7522301799631583,2.769092559814453,29.885669000000004 +561,Regression,k-Nearest Neighbors,ChickWeights,22.80977421271539,35.198755082788004,0.727753941720713,2.8112449645996094,33.249092000000005 +572,Regression,k-Nearest Neighbors,ChickWeights,24.195600233100233,38.25560047694445,0.7025325582791198,2.857513427734375,36.653026000000004 +578,Regression,k-Nearest Neighbors,ChickWeights,24.840628604382932,39.201635479156685,0.6952358931227007,2.8852157592773438,40.087818000000006 +20,Regression,k-Nearest Neighbors,TrumpApproval,2.554585433333335,9.794739803036965,-224.02989290855143,0.033588409423828125,0.001545 +40,Regression,k-Nearest Neighbors,TrumpApproval,1.7993247666666672,6.973235588114817,-18.54942689237887,0.055484771728515625,0.0054 +60,Regression,k-Nearest Neighbors,TrumpApproval,1.366773144444445,5.705236645726316,-16.642396889136542,0.07735443115234375,0.012332 +80,Regression,k-Nearest Neighbors,TrumpApproval,1.1277757833333335,4.947712433075743,-12.30953248968821,0.09975433349609375,0.028826 +100,Regression,k-Nearest Neighbors,TrumpApproval,1.046201766666667,4.4398629296748915,-5.724544452799038,0.12165069580078125,0.050318 +120,Regression,k-Nearest Neighbors,TrumpApproval,1.000865705555556,4.0744555355418335,-3.804331488196434,0.14354705810546875,0.086896 +140,Regression,k-Nearest Neighbors,TrumpApproval,0.9447764619047624,3.7809361134406263,-3.275153002458012,0.16594696044921875,0.149837 +160,Regression,k-Nearest Neighbors,TrumpApproval,0.9352969166666671,3.5531790499707645,-2.3296179824080356,0.18784332275390625,0.23044599999999998 +180,Regression,k-Nearest Neighbors,TrumpApproval,0.9445764925925928,3.380979243961517,-1.647692611170827,0.20973968505859375,0.432465 +200,Regression,k-Nearest Neighbors,TrumpApproval,0.9456943733333335,3.2327893391999836,-1.427877878808435,0.23213958740234375,0.648003 +220,Regression,k-Nearest Neighbors,TrumpApproval,0.9124697575757575,3.0919339165015143,-1.3957229068060464,0.25403594970703125,0.888162 +240,Regression,k-Nearest Neighbors,TrumpApproval,0.9329223611111109,2.985727855147271,-1.2507750530936188,0.27593231201171875,1.171554 +260,Regression,k-Nearest Neighbors,TrumpApproval,0.9025974717948716,2.873740673763463,-1.11319648675526,0.2984657287597656,1.484213 +280,Regression,k-Nearest Neighbors,TrumpApproval,0.8654126523809523,2.773524640439575,-1.0608690746642817,0.3203620910644531,1.81098 +300,Regression,k-Nearest Neighbors,TrumpApproval,0.8525042622222223,2.688069339615046,-0.9037818439458585,0.3422584533691406,2.170241 +320,Regression,k-Nearest Neighbors,TrumpApproval,0.8265282395833334,2.6077957497476296,-0.880493509713772,0.3646583557128906,2.5581009999999997 +340,Regression,k-Nearest Neighbors,TrumpApproval,0.8137511019607846,2.539210136300266,-0.8840673465916704,0.3865547180175781,3.1375499999999996 +360,Regression,k-Nearest Neighbors,TrumpApproval,0.7887328240740744,2.4696835584739105,-0.7969398815662787,0.4084510803222656,3.7440789999999997 +380,Regression,k-Nearest Neighbors,TrumpApproval,0.7710879228070179,2.4087271831437693,-0.7684619785143365,0.4303474426269531,4.380375 +400,Regression,k-Nearest Neighbors,TrumpApproval,0.756179386666667,2.351105641867075,-0.7324819925835522,0.4527473449707031,5.04744 +420,Regression,k-Nearest Neighbors,TrumpApproval,0.7300392539682541,2.295700426816902,-0.7064552265553199,0.4746437072753906,5.735437 +440,Regression,k-Nearest Neighbors,TrumpApproval,0.7180258560606063,2.24592493832078,-0.6037054809307543,0.4965400695800781,6.669957 +460,Regression,k-Nearest Neighbors,TrumpApproval,0.7103659666666668,2.200554873752302,-0.45996881871915263,0.5189399719238281,7.633919000000001 +480,Regression,k-Nearest Neighbors,TrumpApproval,0.6905233472222223,2.1551860359584523,-0.36817166319202155,0.5408363342285156,8.635162000000001 +500,Regression,k-Nearest Neighbors,TrumpApproval,0.6835753693333335,2.1161668272230596,-0.2914054626850582,2.7066993713378906,12.111573000000002 +520,Regression,k-Nearest Neighbors,TrumpApproval,0.6741869282051286,2.0775236231845557,-0.24684449790743135,2.7946739196777344,15.640183000000002 +540,Regression,k-Nearest Neighbors,TrumpApproval,0.6635047197530868,2.0412653603832833,-0.19929175315985925,2.8836631774902344,19.224183000000004 +560,Regression,k-Nearest Neighbors,TrumpApproval,0.6666769047619049,2.01181749557566,-0.19269635778937388,2.973125457763672,22.863484000000003 +580,Regression,k-Nearest Neighbors,TrumpApproval,0.662313208045977,1.9804661409620818,-0.18439917312598686,3.0619163513183594,26.560120000000005 +600,Regression,k-Nearest Neighbors,TrumpApproval,0.6595208444444446,1.9515625148224913,-0.13735805248393262,3.158283233642578,30.314082000000006 +620,Regression,k-Nearest Neighbors,TrumpApproval,0.6603871010752689,1.924909501402362,-0.08963762017358134,3.248737335205078,34.12595900000001 +640,Regression,k-Nearest Neighbors,TrumpApproval,0.6518434010416667,1.8967107462711992,-0.038171332083393406,3.341320037841797,37.99311800000001 +660,Regression,k-Nearest Neighbors,TrumpApproval,0.6481796161616163,1.873162681009878,-0.005272423030620033,3.435527801513672,41.91854100000001 +680,Regression,k-Nearest Neighbors,TrumpApproval,0.6594073715686274,1.8574009428793898,-0.004045635504021261,3.5269508361816406,45.90599500000001 +700,Regression,k-Nearest Neighbors,TrumpApproval,0.6619153695238096,1.8376987056605067,-0.008672432190871993,3.615283966064453,49.957909000000015 +720,Regression,k-Nearest Neighbors,TrumpApproval,0.6538050537037038,1.8142062090777376,-0.004645759004504146,3.7059364318847656,54.07172000000001 +740,Regression,k-Nearest Neighbors,TrumpApproval,0.6437102684684685,1.7904191974020043,0.02211499739805689,3.8005104064941406,58.24412800000001 +760,Regression,k-Nearest Neighbors,TrumpApproval,0.6465423666666668,1.7722456151874884,0.03148604083873929,3.8912620544433594,62.47501600000001 +780,Regression,k-Nearest Neighbors,TrumpApproval,0.6423591829059828,1.752432393946061,0.0487781645727875,3.987659454345703,66.76475200000002 +800,Regression,k-Nearest Neighbors,TrumpApproval,0.6415445258333332,1.7335108155585357,0.060790557140155244,4.087154388427734,71.11932700000001 +820,Regression,k-Nearest Neighbors,TrumpApproval,0.641812437398374,1.7198679523833968,0.06536301290969171,4.179523468017578,75.53533700000001 +840,Regression,k-Nearest Neighbors,TrumpApproval,0.6391550126984127,1.7023246638821516,0.07583179507597027,4.276576995849609,80.015814 +860,Regression,k-Nearest Neighbors,TrumpApproval,0.6397551612403103,1.6865214638981003,0.0944734629735503,4.372867584228516,84.56990800000001 +880,Regression,k-Nearest Neighbors,TrumpApproval,0.6401663234848486,1.6719359262678322,0.11449022182092672,4.465221405029297,89.18993400000001 +900,Regression,k-Nearest Neighbors,TrumpApproval,0.6373928251851855,1.6559913256631793,0.1276383063389357,4.558887481689453,93.87754600000001 +920,Regression,k-Nearest Neighbors,TrumpApproval,0.6333341724637681,1.6410816825275085,0.12919955333528133,4.652858734130859,98.62624600000001 +940,Regression,k-Nearest Neighbors,TrumpApproval,0.637460545390071,1.6307722122541641,0.13281132177791266,4.746517181396484,103.437785 +960,Regression,k-Nearest Neighbors,TrumpApproval,0.6446958777777775,1.6213030711335545,0.13389079092896516,4.844425201416016,108.312072 +980,Regression,k-Nearest Neighbors,TrumpApproval,0.643768610068027,1.6085965270907718,0.1308548353743899,4.935100555419922,113.251739 +1000,Regression,k-Nearest Neighbors,TrumpApproval,0.6420156240666665,1.59493855356346,0.13116812210504825,5.030651092529297,118.255967 +1001,Regression,k-Nearest Neighbors,TrumpApproval,0.6416785025641023,1.5941707450098015,0.1314249186277071,5.032634735107422,123.301096 +11,Regression,Hoeffding Tree,ChickWeights,8.042756132756132,17.336048579080593,-385.86349170941764,0.016208648681640625,0.002632 +22,Regression,Hoeffding Tree,ChickWeights,4.456785613727984,12.282422261556867,-158.770726389092,0.017787933349609375,0.007319 +33,Regression,Hoeffding Tree,ChickWeights,3.4353973358733074,10.070376517434479,-69.4325218162971,0.023052215576171875,0.013907 +44,Regression,Hoeffding Tree,ChickWeights,2.736909422894262,8.732393473100391,-59.03623058514604,0.024105072021484375,0.021700999999999998 +55,Regression,Hoeffding Tree,ChickWeights,2.788577579622257,8.074088551816661,-11.726025456653014,0.030948638916015625,0.030334999999999997 +66,Regression,Hoeffding Tree,ChickWeights,3.3958800855981375,7.878422021930021,-4.223121571879303,0.040424346923828125,0.040093 +77,Regression,Hoeffding Tree,ChickWeights,3.8895265016210883,7.800910386370324,-2.432180745921895,0.046741485595703125,0.05116999999999999 +88,Regression,Hoeffding Tree,ChickWeights,4.072650698433535,7.572197783925699,-1.9320509270116553,0.052532196044921875,0.06356099999999999 +99,Regression,Hoeffding Tree,ChickWeights,4.410984939713907,7.55185413515251,-1.439151418709002,0.053585052490234375,0.07724199999999999 +110,Regression,Hoeffding Tree,ChickWeights,4.370948473977548,7.327634340090197,-0.6036593212329582,0.055164337158203125,0.09210199999999999 +121,Regression,Hoeffding Tree,ChickWeights,4.401973824893138,7.197046558152955,-0.19144536988389782,0.055164337158203125,0.10816699999999999 +132,Regression,Hoeffding Tree,ChickWeights,4.283071400630936,6.979735895990854,0.08415196835499827,0.055164337158203125,0.13239599999999999 +143,Regression,Hoeffding Tree,ChickWeights,4.169649051526778,6.77851615807502,0.3003478880703081,0.055690765380859375,0.16024499999999997 +154,Regression,Hoeffding Tree,ChickWeights,4.107721988217097,6.620782354691122,0.4327427443050297,0.055690765380859375,0.19148199999999999 +165,Regression,Hoeffding Tree,ChickWeights,4.3861341291386235,6.8739888422895685,0.5084535624523276,0.055690765380859375,0.23199899999999998 +176,Regression,Hoeffding Tree,ChickWeights,4.592324836010107,7.0395287886899816,0.5843455987500039,0.056217193603515625,0.273788 +187,Regression,Hoeffding Tree,ChickWeights,4.658423416973056,7.057579140031887,0.6579286220132116,0.056217193603515625,0.316974 +198,Regression,Hoeffding Tree,ChickWeights,4.6782517314261085,7.042640058036562,0.7290497323677609,0.056217193603515625,0.361531 +209,Regression,Hoeffding Tree,ChickWeights,4.8966529592561265,7.410861778989444,0.7526693351807108,0.02174663543701172,0.409543 +220,Regression,Hoeffding Tree,ChickWeights,5.507880191409123,8.546476599974424,0.7120144996082314,0.02806377410888672,0.458317 +231,Regression,Hoeffding Tree,ChickWeights,5.703958017872014,8.760797449465004,0.7411581545051223,0.03332805633544922,0.507954 +242,Regression,Hoeffding Tree,ChickWeights,5.934527728379076,9.145062262320872,0.7730513990797492,0.03806591033935547,0.576578 +253,Regression,Hoeffding Tree,ChickWeights,6.025889093973978,9.259481324724224,0.7979290061199974,0.04175090789794922,0.647861 +264,Regression,Hoeffding Tree,ChickWeights,6.701040765258382,10.569442782845146,0.7594412957229723,0.041831016540527344,0.7217790000000001 +275,Regression,Hoeffding Tree,ChickWeights,7.201977905163474,11.695812678726385,0.740801257827299,0.041831016540527344,0.7983520000000001 +286,Regression,Hoeffding Tree,ChickWeights,7.4760897436283305,12.176082777300051,0.7566872347890514,0.042357444763183594,0.889757 +297,Regression,Hoeffding Tree,ChickWeights,7.495029117947843,12.186858586615225,0.7886035011133373,0.042357444763183594,0.982264 +308,Regression,Hoeffding Tree,ChickWeights,8.05089484284177,13.06419009031293,0.7836428997387894,0.042357444763183594,1.075782 +319,Regression,Hoeffding Tree,ChickWeights,9.171875092169309,15.802620207207104,0.7127274179827436,0.042357444763183594,1.1703270000000001 +330,Regression,Hoeffding Tree,ChickWeights,9.626867556328977,16.443718231711543,0.7338058453397931,0.042357444763183594,1.26591 +341,Regression,Hoeffding Tree,ChickWeights,9.854283538219805,16.574189924013226,0.7578382368534643,0.042357444763183594,1.362543 +352,Regression,Hoeffding Tree,ChickWeights,10.034558550660114,16.72149964752778,0.7759339138910493,0.042357444763183594,1.460131 +363,Regression,Hoeffding Tree,ChickWeights,10.942839439265006,18.18973374364872,0.7425340708967089,0.042357444763183594,1.65219 +374,Regression,Hoeffding Tree,ChickWeights,11.480189522121245,19.36955258798825,0.7316181626186655,0.042357444763183594,1.847066 +385,Regression,Hoeffding Tree,ChickWeights,11.884428250077962,20.018801475409063,0.7463650656532205,0.042357444763183594,2.044712 +396,Regression,Hoeffding Tree,ChickWeights,12.037067702603977,20.025071614924446,0.7633646392298079,0.042357444763183594,2.245044 +407,Regression,Hoeffding Tree,ChickWeights,12.938689395183468,21.571547182252875,0.7447563988620904,0.039313316345214844,2.459951 +418,Regression,Hoeffding Tree,ChickWeights,13.737065020554605,23.070023559587742,0.7259561921053947,0.039839744567871094,2.675857 +429,Regression,Hoeffding Tree,ChickWeights,14.305628841534727,24.020997573013894,0.7359868139097058,0.040892601013183594,2.892761 +440,Regression,Hoeffding Tree,ChickWeights,14.503019064271445,24.118168317988548,0.7526847575357923,0.041419029235839844,3.110678 +451,Regression,Hoeffding Tree,ChickWeights,15.042001004765993,24.757154413851225,0.7504844548860922,0.042998313903808594,3.329579 +462,Regression,Hoeffding Tree,ChickWeights,16.165694044127083,26.934291479182736,0.7226050873941003,0.043524742126464844,3.549505 +473,Regression,Hoeffding Tree,ChickWeights,16.958578383564387,28.26726815061745,0.7302155620528221,0.043524742126464844,3.77047 +484,Regression,Hoeffding Tree,ChickWeights,17.309589456804158,28.5754148947933,0.7394096166099926,0.043524742126464844,4.010874 +495,Regression,Hoeffding Tree,ChickWeights,17.77955786237919,29.119281838039548,0.7454446166142166,0.043524742126464844,4.254034 +506,Regression,Hoeffding Tree,ChickWeights,18.687135400012505,30.600738447390604,0.7270552375925041,0.043524742126464844,4.499866 +517,Regression,Hoeffding Tree,ChickWeights,19.426270300418786,31.613839238226678,0.7250895764829616,0.043524742126464844,4.748399 +528,Regression,Hoeffding Tree,ChickWeights,20.230319490239392,32.829508990096734,0.7334580691909136,0.043524742126464844,5.00325 +539,Regression,Hoeffding Tree,ChickWeights,20.415951878027045,32.83473210597698,0.7443832332812113,0.043524742126464844,5.259172 +550,Regression,Hoeffding Tree,ChickWeights,21.41946931942451,34.477948502753435,0.726844465494657,0.043524742126464844,5.516121 +561,Regression,Hoeffding Tree,ChickWeights,22.135259536350134,35.412182207518484,0.7244424125617825,0.043524742126464844,5.774111 +572,Regression,Hoeffding Tree,ChickWeights,22.998428764364284,36.61317436816486,0.7275265693889857,0.044051170349121094,6.033148000000001 +578,Regression,Hoeffding Tree,ChickWeights,23.16185046142029,36.73359474841229,0.7324023432169282,0.044051170349121094,6.293050000000001 +20,Regression,Hoeffding Tree,TrumpApproval,4.834704431652337,13.708514217962266,-439.7934984576362,0.05008697509765625,0.001817 +40,Regression,Hoeffding Tree,TrumpApproval,3.4692310697037447,9.813795721313518,-37.72035957928713,0.07324981689453125,0.005553000000000001 +60,Regression,Hoeffding Tree,TrumpApproval,2.530247618203559,8.024836796214231,-33.90460110966681,0.08588409423828125,0.011369 +80,Regression,Hoeffding Tree,TrumpApproval,2.1398752670733447,6.982837000856316,-25.510487239912003,0.09588623046875,0.023421 +100,Regression,Hoeffding Tree,TrumpApproval,2.2521629689485394,6.362737158647257,-12.810573390910955,0.1053619384765625,0.037893 +120,Regression,Hoeffding Tree,TrumpApproval,2.2753311831165886,5.895687482983747,-9.059182991303912,0.1095733642578125,0.054815 +140,Regression,Hoeffding Tree,TrumpApproval,2.181766409647037,5.493495699082884,-8.025069637302263,0.1116790771484375,0.07421900000000001 +160,Regression,Hoeffding Tree,TrumpApproval,2.0635226048812747,5.165876255053421,-6.037983110569301,0.1158905029296875,0.09851900000000001 +180,Regression,Hoeffding Tree,TrumpApproval,1.9951428730766114,4.906287161641783,-4.575559841528811,0.1179962158203125,0.13018800000000003 +200,Regression,Hoeffding Tree,TrumpApproval,1.8700446037321659,4.662539866408188,-4.050299616280768,0.015085220336914062,0.16948700000000003 +220,Regression,Hoeffding Tree,TrumpApproval,1.7830718267282506,4.458344141345012,-3.981078161152351,0.031249046325683594,0.21017900000000003 +240,Regression,Hoeffding Tree,TrumpApproval,1.714887283408722,4.280191261764102,-3.6254927572925757,0.037039756774902344,0.27354600000000007 +260,Regression,Hoeffding Tree,TrumpApproval,1.6268995152596541,4.116599014627653,-3.336325373761703,0.044569969177246094,0.33844300000000005 +280,Regression,Hoeffding Tree,TrumpApproval,1.6037708656255951,3.992199218884993,-3.269831686495559,0.05755901336669922,0.40512500000000007 +300,Regression,Hoeffding Tree,TrumpApproval,1.5808413297038584,3.882244388071726,-2.9710192082752114,0.06756114959716797,0.4768960000000001 +320,Regression,Hoeffding Tree,TrumpApproval,1.5112246352788372,3.7620340381312185,-2.9135432145577016,0.07545757293701172,0.5560110000000001 +340,Regression,Hoeffding Tree,TrumpApproval,1.464954049061847,3.6574443601858126,-2.9089002921657214,0.08072185516357422,0.6372390000000001 +360,Regression,Hoeffding Tree,TrumpApproval,1.4845626481571885,3.5832345434246853,-2.782695640732784,0.08861827850341797,0.7206760000000001 +380,Regression,Hoeffding Tree,TrumpApproval,1.4519403327978173,3.4965427251184518,-2.72647470962537,0.09388256072998047,0.8063740000000001 +400,Regression,Hoeffding Tree,TrumpApproval,1.4093274160891025,3.4133346926199284,-2.6515915354000197,0.10125255584716797,0.8943350000000001 +420,Regression,Hoeffding Tree,TrumpApproval,1.3677964737960675,3.3343173536823296,-2.5997996751089016,0.10546398162841797,1.079576 +440,Regression,Hoeffding Tree,TrumpApproval,1.3357172246731819,3.2621145597551164,-2.3832380441779537,0.11125469207763672,1.2716070000000002 +460,Regression,Hoeffding Tree,TrumpApproval,1.3223220949397412,3.20054856097613,-2.088360697350681,0.11967754364013672,1.466366 +480,Regression,Hoeffding Tree,TrumpApproval,1.2961820395725512,3.1370925842333546,-1.8988499404168713,0.12757396697998047,1.663894 +500,Regression,Hoeffding Tree,TrumpApproval,1.2652762767168435,3.076750388249757,-1.7299037995212605,0.13231182098388672,1.864298 +520,Regression,Hoeffding Tree,TrumpApproval,1.2471740635308572,3.0222901376128295,-1.6387160551274738,0.13757610321044922,2.0709020000000002 +540,Regression,Hoeffding Tree,TrumpApproval,1.222508472081129,2.9683885282447466,-1.5361060189709668,0.13968181610107422,2.286544 +560,Regression,Hoeffding Tree,TrumpApproval,1.2073384071706728,2.920065266046622,-1.5126838513129575,0.14441967010498047,2.5053300000000003 +580,Regression,Hoeffding Tree,TrumpApproval,1.1845779132924192,2.8723790540044147,-1.4914188956527816,0.14705181121826172,2.7271330000000003 +600,Regression,Hoeffding Tree,TrumpApproval,1.1745692976588702,2.8296294830278073,-1.3910651808346999,0.14148998260498047,2.96944 +620,Regression,Hoeffding Tree,TrumpApproval,1.1708259630571383,2.7920061348512903,-1.2924200227078337,0.14412212371826172,3.214893 +640,Regression,Hoeffding Tree,TrumpApproval,1.1599967464968943,2.7528504813508814,-1.186915838733254,0.14622783660888672,3.481443 +660,Regression,Hoeffding Tree,TrumpApproval,1.1455993461288598,2.715465758170179,-1.112620243595547,0.09333324432373047,3.768351 +680,Regression,Hoeffding Tree,TrumpApproval,1.1331386715536063,2.679518493749607,-1.0895638535289454,0.10280895233154297,4.057625 +700,Regression,Hoeffding Tree,TrumpApproval,1.1287919059851137,2.648832972736431,-1.0956110522943683,0.11070537567138672,4.349483 +720,Regression,Hoeffding Tree,TrumpApproval,1.1090542602054634,2.6130484736329,-1.0841769561048746,0.11702251434326172,4.655905000000001 +740,Regression,Hoeffding Tree,TrumpApproval,1.0919225542546631,2.579731998640208,-1.0301471378292058,0.12070751190185547,4.969391000000001 +760,Regression,Hoeffding Tree,TrumpApproval,1.0729346607841277,2.546521266569091,-0.9996439724530697,0.12386608123779297,5.409378000000001 +780,Regression,Hoeffding Tree,TrumpApproval,1.0548522699101792,2.514796200212546,-0.958866579835745,0.13018321990966797,5.856313000000001 +800,Regression,Hoeffding Tree,TrumpApproval,1.0458975693179249,2.4863814517835756,-0.9321678603320387,0.14051342010498047,6.306401000000001 +820,Regression,Hoeffding Tree,TrumpApproval,1.042667475968943,2.463395040447954,-0.9174360179218257,0.14683055877685547,6.759550000000001 +840,Regression,Hoeffding Tree,TrumpApproval,1.0338402028724885,2.4371652901742165,-0.8942452584110789,0.15051555633544922,7.215791000000001 +860,Regression,Hoeffding Tree,TrumpApproval,1.0182769822752689,2.409744604248102,-0.8486703239118398,0.15209484100341797,7.680462000000001 +880,Regression,Hoeffding Tree,TrumpApproval,1.0072949101764561,2.3841216724611445,-0.8005738256179296,0.15525341033935547,8.149683000000001 +900,Regression,Hoeffding Tree,TrumpApproval,0.9984699415812968,2.359722022526475,-0.7713409518355698,0.15735912322998047,8.622145000000002 +920,Regression,Hoeffding Tree,TrumpApproval,0.9848390746890626,2.3349754381173082,-0.7628805257854674,0.12545299530029297,9.108636 +940,Regression,Hoeffding Tree,TrumpApproval,0.9804934467335737,2.3136297350671566,-0.7454793227879806,0.13442516326904297,9.599086 +960,Regression,Hoeffding Tree,TrumpApproval,0.9715993160407668,2.291923159938466,-0.7307898991199615,0.14021587371826172,10.092417 +980,Regression,Hoeffding Tree,TrumpApproval,0.96479276321034,2.271398262551761,-0.7329444574748756,0.14442729949951172,10.588738 +1000,Regression,Hoeffding Tree,TrumpApproval,0.9567764270416781,2.250974677037298,-0.7305695321170174,0.14863872528076172,11.174487 +1001,Regression,Hoeffding Tree,TrumpApproval,0.9561028857812052,2.249867758958838,-0.7300222157865335,0.14863872528076172,11.765557999999999 +11,Regression,Hoeffding Adaptive Tree,ChickWeights,8.051220648038832,17.336198122120386,-385.87016600913427,0.022922515869140625,0.002862 +22,Regression,Hoeffding Adaptive Tree,ChickWeights,4.498502947359929,12.285286375364281,-158.84524831763767,0.024562835693359375,0.008031 +33,Regression,Hoeffding Adaptive Tree,ChickWeights,3.4668695042339137,10.074636808082968,-69.49212762837747,0.029827117919921875,0.0152 +44,Regression,Hoeffding Adaptive Tree,ChickWeights,2.7637805804889553,8.735764655686483,-59.08259408516962,0.030941009521484375,0.027573 +55,Regression,Hoeffding Adaptive Tree,ChickWeights,2.814517498310432,8.074396776941786,-11.726997097138026,0.037784576416015625,0.040817 +66,Regression,Hoeffding Adaptive Tree,ChickWeights,3.396900059747575,7.862006773633152,-4.201378762014764,0.047260284423828125,0.055065 +77,Regression,Hoeffding Adaptive Tree,ChickWeights,3.8844336568547537,7.782255505653143,-2.415785129732385,0.053638458251953125,0.07050300000000001 +88,Regression,Hoeffding Adaptive Tree,ChickWeights,4.068768385552718,7.555909217267645,-1.9194502155140074,0.059429168701171875,0.08723500000000001 +99,Regression,Hoeffding Adaptive Tree,ChickWeights,4.319029347030655,7.489629607912237,-1.3991215781815165,0.060482025146484375,0.105314 +110,Regression,Hoeffding Adaptive Tree,ChickWeights,4.231978704025333,7.230698639905546,-0.5615110336669555,0.062061309814453125,0.124657 +121,Regression,Hoeffding Adaptive Tree,ChickWeights,4.279767976439616,7.114292598648662,-0.1642036472993016,0.062061309814453125,0.145348 +132,Regression,Hoeffding Adaptive Tree,ChickWeights,4.161677712403324,6.8979209349412445,0.1054968774084013,0.062061309814453125,0.183683 +143,Regression,Hoeffding Adaptive Tree,ChickWeights,4.036201943040193,6.686446116179646,0.3192250351622916,0.024164199829101562,0.23334700000000003 +154,Regression,Hoeffding Adaptive Tree,ChickWeights,4.002163310161137,6.555243218534794,0.4439177197734564,0.034926414489746094,0.28395200000000004 +165,Regression,Hoeffding Adaptive Tree,ChickWeights,4.269310553181931,6.794169336453219,0.5198027804498322,0.041365623474121094,0.33552800000000005 +176,Regression,Hoeffding Adaptive Tree,ChickWeights,4.394431170074558,6.916563516446891,0.5987399306940604,0.047156333923339844,0.38817000000000007 +187,Regression,Hoeffding Adaptive Tree,ChickWeights,4.429782113532627,6.896434310822903,0.6733712331422652,0.052016258239746094,0.44195900000000005 +198,Regression,Hoeffding Adaptive Tree,ChickWeights,4.448580123995543,6.86078369215091,0.7428621234581485,0.054648399353027344,0.49692500000000006 +209,Regression,Hoeffding Adaptive Tree,ChickWeights,4.634718338792146,7.17917659207716,0.7678921596594357,0.054648399353027344,0.5531490000000001 +220,Regression,Hoeffding Adaptive Tree,ChickWeights,5.229854791420841,8.435313620968111,0.7194573631198581,0.055296897888183594,0.6106500000000001 +231,Regression,Hoeffding Adaptive Tree,ChickWeights,5.4006373247873825,8.615072190659467,0.7496975788166091,0.055296897888183594,0.6850710000000001 +242,Regression,Hoeffding Adaptive Tree,ChickWeights,5.6226073005416035,8.982158345389516,0.781064800957145,0.055296897888183594,0.7630730000000001 +253,Regression,Hoeffding Adaptive Tree,ChickWeights,5.728895576419993,9.10264619767678,0.8047163053551843,0.055296897888183594,0.8439190000000001 +264,Regression,Hoeffding Adaptive Tree,ChickWeights,6.468790531655633,10.532848432020362,0.7611041743489119,0.05537700653076172,0.926058 +275,Regression,Hoeffding Adaptive Tree,ChickWeights,6.961259791220884,11.725202267966395,0.7394969764024641,0.05537700653076172,1.0095260000000001 +286,Regression,Hoeffding Adaptive Tree,ChickWeights,7.243017687832032,12.175095097400797,0.7567267064951951,0.05391216278076172,1.109836 +297,Regression,Hoeffding Adaptive Tree,ChickWeights,7.333189926829036,12.221129948725446,0.7874128689691341,0.05443859100341797,1.3004930000000001 +308,Regression,Hoeffding Adaptive Tree,ChickWeights,7.907494608974745,13.13418786953933,0.7813182108747583,0.05456066131591797,1.4945650000000001 +319,Regression,Hoeffding Adaptive Tree,ChickWeights,9.086203691627809,16.084282058543664,0.7023956098414756,0.05613994598388672,1.6919570000000002 +330,Regression,Hoeffding Adaptive Tree,ChickWeights,9.398286710797228,16.38837159928856,0.7355947540985646,0.05613994598388672,1.900794 +341,Regression,Hoeffding Adaptive Tree,ChickWeights,9.688169379844998,16.65705092991554,0.7554108572015372,0.05613994598388672,2.110987 +352,Regression,Hoeffding Adaptive Tree,ChickWeights,9.856066264187849,16.815734957180027,0.7734013139584004,0.05613994598388672,2.322457 +363,Regression,Hoeffding Adaptive Tree,ChickWeights,10.788654210226415,18.368645129880047,0.7374443731514406,0.05613994598388672,2.535213 +374,Regression,Hoeffding Adaptive Tree,ChickWeights,11.535989444086796,20.177763325541775,0.7087539856658172,0.0658864974975586,2.749718 +385,Regression,Hoeffding Adaptive Tree,ChickWeights,11.949331836981814,20.800028245688587,0.7261827687212361,0.0713338851928711,2.965855 +396,Regression,Hoeffding Adaptive Tree,ChickWeights,11.958714190964645,20.660643879084812,0.748105206776327,0.07817745208740234,3.1836569999999997 +407,Regression,Hoeffding Adaptive Tree,ChickWeights,12.807531574997112,22.01468171576837,0.7341619793955468,0.08257198333740234,3.4189649999999996 +418,Regression,Hoeffding Adaptive Tree,ChickWeights,13.71794187476778,23.73901232910809,0.7098322050491193,0.08467769622802734,3.6591389999999997 +429,Regression,Hoeffding Adaptive Tree,ChickWeights,14.269314924317156,24.652748132937095,0.7219171428567855,0.06568050384521484,3.9122609999999995 +440,Regression,Hoeffding Adaptive Tree,ChickWeights,14.511771919641937,24.834167752766053,0.7377826277560943,0.0706624984741211,4.16702 +451,Regression,Hoeffding Adaptive Tree,ChickWeights,15.00667707818897,25.401748915029017,0.7373221851710817,0.07874202728271484,4.423509 +462,Regression,Hoeffding Adaptive Tree,ChickWeights,16.106263610815663,27.4394567629727,0.7121021651653525,0.0857076644897461,4.681795 +473,Regression,Hoeffding Adaptive Tree,ChickWeights,16.950411373417108,28.951900473786843,0.7169889638801871,0.0888662338256836,4.941903 +484,Regression,Hoeffding Adaptive Tree,ChickWeights,17.321905164714362,29.29627092175635,0.7260962478080234,0.0889272689819336,5.203768 +495,Regression,Hoeffding Adaptive Tree,ChickWeights,17.829552469069228,29.855361574147427,0.732412614196017,0.0889272689819336,5.467412 +506,Regression,Hoeffding Adaptive Tree,ChickWeights,18.715769054600834,31.21095148117224,0.7160610523989874,0.08951473236083984,5.743327000000001 +517,Regression,Hoeffding Adaptive Tree,ChickWeights,19.54236471467993,32.39367117342827,0.7113596352744775,0.0743856430053711,6.026441000000001 +528,Regression,Hoeffding Adaptive Tree,ChickWeights,20.379374275832948,33.670378810622296,0.7196292071862618,0.0787191390991211,6.311317000000001 +539,Regression,Hoeffding Adaptive Tree,ChickWeights,20.522458105265056,33.639909372937744,0.7316929916628531,0.0872030258178711,6.597982000000001 +550,Regression,Hoeffding Adaptive Tree,ChickWeights,21.5114661084191,35.24478084224406,0.714558707096332,0.0935201644897461,6.886526000000001 +561,Regression,Hoeffding Adaptive Tree,ChickWeights,22.293418976341684,36.29050935662323,0.7106036021726428,0.09343624114990234,7.177067000000001 +572,Regression,Hoeffding Adaptive Tree,ChickWeights,23.158877831353536,37.47206255417766,0.7145930209145848,0.09461116790771484,7.581349000000001 +578,Regression,Hoeffding Adaptive Tree,ChickWeights,23.373902189510932,37.6579284312523,0.7187656938003131,0.09473323822021484,7.990285000000001 +20,Regression,Hoeffding Adaptive Tree,TrumpApproval,4.828377634536296,13.70786256219322,-439.7515918302183,0.05686187744140625,0.005477 +40,Regression,Hoeffding Adaptive Tree,TrumpApproval,3.453811275213839,9.811073218407973,-37.69887927291551,0.08008575439453125,0.014203 +60,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.5116544078850294,8.021960641037959,-33.879585508404254,0.09272003173828125,0.025216000000000002 +80,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.1224425015381523,6.9797990571526345,-25.487425023640153,0.102783203125,0.038581000000000004 +100,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.246653919301699,6.363694444016854,-12.814729355257526,0.1122589111328125,0.054574000000000004 +120,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.270681160376927,5.896666779393501,-9.062525006956841,0.1164703369140625,0.096737 +140,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.162967815650222,5.491011289549727,-8.016908386196121,0.1185760498046875,0.14519300000000002 +160,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.9648637778298337,5.1475477542568076,-5.988130255135697,0.048110008239746094,0.20161800000000002 +180,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.8665278782891501,4.8758843309507505,-4.506673701927233,0.06455135345458984,0.259848 +200,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.773434994745299,4.638841370319518,-3.9990913279754245,0.07511425018310547,0.334681 +220,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.6594682627798778,4.42936028038101,-3.916524330360767,0.0809926986694336,0.415547 +240,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.5811297097344512,4.24689633509078,-3.553810703437006,0.0831594467163086,0.499019 +260,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4918706813368772,4.083314206963185,-3.2664860479391056,0.08697795867919922,0.584777 +280,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4582505621214346,3.950619643811522,-3.181352514384196,0.09651470184326172,0.672987 +300,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4293807431017047,3.836527362327468,-2.8780450161882043,0.10505962371826172,0.763791 +320,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3766835460490845,3.7183907131031066,-2.8232679475596494,0.11137676239013672,0.862886 +340,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3285707966483495,3.6114631285578054,-2.8112330604866624,0.09692668914794922,1.077289 +360,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3305028688272291,3.538102571280229,-2.6880072396238157,0.10482311248779297,1.294249 +380,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3086678355415842,3.4529556765760527,-2.6341471363086995,0.11014842987060547,1.513868 +400,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.256053624567095,3.3666460142322228,-2.552379472359031,0.11751842498779297,1.736306 +420,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2254239545780012,3.2887455105144454,-2.5020714662192383,0.12172985076904297,1.9788649999999999 +440,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.204020924712129,3.2198773978896,-2.2961943419959137,0.12752056121826172,2.244474 +460,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1975328241312166,3.1601130927415366,-2.010817456858815,0.13547801971435547,2.513147 +480,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.186148143661266,3.1001176815841753,-1.8309188655239268,0.14337444305419922,2.784897 +500,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1667856894749518,3.0429667282148514,-1.6702825792738007,0.13623332977294922,3.07197 +520,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.153194144927427,2.98944402729251,-1.5816728306403074,0.14155864715576172,3.362254 +540,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1356058423088553,2.9370365647466374,-1.4828164968540292,0.14366436004638672,3.6556480000000002 +560,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.125648357086568,2.890393580385493,-1.4618789770567937,0.14840221405029297,3.952243 +580,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1072323197222282,2.84377722554966,-1.4420491211959612,0.15103435516357422,4.252035 +600,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0962221602561253,2.8010574809052513,-1.343021715112041,0.15471935272216797,4.557891000000001 +620,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0955492072151647,2.7650292224496735,-1.2483344123018605,0.15787792205810547,4.883158000000001 +640,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.085957414095071,2.726589883354214,-1.1453910301968575,0.15998363494873047,5.354946000000001 +660,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0751762466913892,2.6908702968299423,-1.074523242859362,0.16366863250732422,5.834119000000001 +680,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0667684392102676,2.656475453821568,-1.0537791659469917,0.16630077362060547,6.316591000000001 +700,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0667188907522647,2.6278494556992995,-1.0625405514881172,0.15471935272216797,6.8062700000000005 +720,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0487756272096453,2.5923957614440996,-1.0513617944147002,0.15945720672607422,7.299083 +740,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0336933816342644,2.5596915816453274,-0.9987276211091367,0.16320323944091797,7.795134000000001 +760,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0143808347523189,2.5263993770636084,-0.9681675843117681,0.16478252410888672,8.298925 +780,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0004245938094416,2.495691505058861,-0.9292169429583497,0.16958141326904297,8.809149000000001 +800,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9976736219043986,2.469777786083391,-0.9064485942635294,0.16168498992919922,9.32665 +820,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0020392091388555,2.450590646975973,-0.8975546778436754,0.16431713104248047,9.853038000000002 +840,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9936292081382507,2.424886643827349,-0.8752066007627983,0.16800212860107422,10.484318000000002 +860,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9794930742877991,2.3980423354299125,-0.8307587924463844,0.17010784149169922,11.125036000000001 +880,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9694853941742789,2.372794343098121,-0.7835048635250907,0.17273998260498047,11.769143000000001 +900,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9594920424525858,2.348266033222206,-0.7541836724323567,0.11153697967529297,12.432199 +920,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9482726802907966,2.324135545417226,-0.7465505219679065,0.11639690399169922,13.105410000000001 +940,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9455376055826031,2.30345366329758,-0.7301587545146957,0.12230968475341797,13.781537 +960,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9379298129457146,2.2821811442731286,-0.7161074287562055,0.12967967987060547,14.460614 +980,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.930996996530802,2.261860474984104,-0.7184214614837348,0.13494396209716797,15.148323 +1000,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9214575102921838,2.2404008018877137,-0.714349138962711,0.13822460174560547,15.950631 +1001,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9213134079227226,2.239416179559339,-0.7139861919037982,0.13822460174560547,16.757639 +11,Regression,Stochastic Gradient Tree,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.009614944458007812,0.001328 +22,Regression,Stochastic Gradient Tree,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012609481811523438,0.003944 +33,Regression,Stochastic Gradient Tree,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.015787124633789062,0.007623 +44,Regression,Stochastic Gradient Tree,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.018873214721679688,0.012489 +55,Regression,Stochastic Gradient Tree,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.021825790405273438,0.019505 +66,Regression,Stochastic Gradient Tree,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.024618148803710938,0.027128 +77,Regression,Stochastic Gradient Tree,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.027502059936523438,0.035372 +88,Regression,Stochastic Gradient Tree,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.030019760131835938,0.047911 +99,Regression,Stochastic Gradient Tree,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.03290367126464844,0.072727 +110,Regression,Stochastic Gradient Tree,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.26967811584472656,0.103163 +121,Regression,Stochastic Gradient Tree,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.26967811584472656,0.146595 +132,Regression,Stochastic Gradient Tree,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.26967811584472656,0.196283 +143,Regression,Stochastic Gradient Tree,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.26967811584472656,0.25852200000000003 +154,Regression,Stochastic Gradient Tree,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.26967811584472656,0.329566 +165,Regression,Stochastic Gradient Tree,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.26967811584472656,0.40393 +176,Regression,Stochastic Gradient Tree,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.26967811584472656,0.481694 +187,Regression,Stochastic Gradient Tree,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.26967811584472656,0.681251 +198,Regression,Stochastic Gradient Tree,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.26967811584472656,0.884966 +209,Regression,Stochastic Gradient Tree,ChickWeights,55.16354936929098,57.0482200725598,-13.656313160472004,0.6838865280151367,1.1316950000000001 +220,Regression,Stochastic Gradient Tree,ChickWeights,53.62203856749311,56.03531795068661,-11.37998411824978,0.6869077682495117,1.3969520000000002 +231,Regression,Stochastic Gradient Tree,ChickWeights,52.77279286370195,55.29408706815337,-9.311090357596036,0.6899290084838867,1.6754760000000002 +242,Regression,Stochastic Gradient Tree,ChickWeights,52.49661908339594,55.007104536867395,-7.210918602421254,0.6929502487182617,1.9600240000000002 +253,Regression,Stochastic Gradient Tree,ChickWeights,52.25631812193077,54.713446605156875,-6.055353919833875,0.6947126388549805,2.2702780000000002 +264,Regression,Stochastic Gradient Tree,ChickWeights,51.62511478420569,54.312843786153664,-5.352168023774992,0.6947126388549805,2.586688 +275,Regression,Stochastic Gradient Tree,ChickWeights,51.4425344352617,54.29364548356293,-4.585603291722447,0.6947126388549805,2.915419 +286,Regression,Stochastic Gradient Tree,ChickWeights,51.75651621106165,54.635705044608144,-3.8989478253777694,0.6947126388549805,3.266148 +297,Regression,Stochastic Gradient Tree,ChickWeights,52.373839404142416,55.25476711535166,-3.3456400671942,0.6947126388549805,3.622985 +308,Regression,Stochastic Gradient Tree,ChickWeights,52.87239275875638,55.86677247417265,-2.9565197175813713,0.6947126388549805,3.98691 +319,Regression,Stochastic Gradient Tree,ChickWeights,52.69554478958866,56.2770501442128,-2.6433309475704183,0.6947126388549805,4.356941 +330,Regression,Stochastic Gradient Tree,ChickWeights,53.85316804407712,57.75044402630399,-2.2832890424968193,0.6947126388549805,4.733992 +341,Regression,Stochastic Gradient Tree,ChickWeights,54.90678041411178,59.01114057562677,-2.0697921090482247,0.6947126388549805,5.128946 +352,Regression,Stochastic Gradient Tree,ChickWeights,56.00533746556472,60.302245208561004,-1.9140207825503284,0.6947126388549805,5.54848 +363,Regression,Stochastic Gradient Tree,ChickWeights,55.99599298772852,60.54917173074773,-1.852879941931207,0.6947126388549805,6.1724879999999995 +374,Regression,Stochastic Gradient Tree,ChickWeights,56.87222492302705,61.81275171085535,-1.7331917323651345,0.6947126388549805,6.808446 +385,Regression,Stochastic Gradient Tree,ChickWeights,58.41786698150333,63.95254893573906,-1.5885028214279253,0.6947126388549805,7.450193 +396,Regression,Stochastic Gradient Tree,ChickWeights,59.7033976124885,65.46926983257002,-1.5293357430909813,0.6947126388549805,8.100657 +407,Regression,Stochastic Gradient Tree,ChickWeights,60.057805647389294,66.17359973042984,-1.4019380007417155,1.1097631454467773,8.796904 +418,Regression,Stochastic Gradient Tree,ChickWeights,59.7070864579051,66.11592086962122,-1.2507954049688483,1.1127843856811523,9.50192 +429,Regression,Stochastic Gradient Tree,ChickWeights,60.122823673891816,66.73609937588846,-1.0378169857688957,1.1158056259155273,10.222461000000001 +440,Regression,Stochastic Gradient Tree,ChickWeights,60.39504675635191,66.96100690444877,-0.906365593827489,1.1188268661499023,10.951743 +451,Regression,Stochastic Gradient Tree,ChickWeights,60.27126048587789,66.93502892662679,-0.8239085862185902,1.120589256286621,11.696828 +462,Regression,Stochastic Gradient Tree,ChickWeights,60.340686610373176,67.43825007380137,-0.7390015352251049,1.120589256286621,12.465469 +473,Regression,Stochastic Gradient Tree,ChickWeights,61.40703262301831,69.11306667757516,-0.6127592621572406,1.120589256286621,13.248766 +484,Regression,Stochastic Gradient Tree,ChickWeights,61.95796621360106,69.71422620021941,-0.5510154280248158,1.120589256286621,14.047315 +495,Regression,Stochastic Gradient Tree,ChickWeights,62.59018166487368,70.55352405729404,-0.4943708535906215,1.120589256286621,14.854826 +506,Regression,Stochastic Gradient Tree,ChickWeights,62.49664579133251,70.88193125644693,-0.46447524520130457,1.120589256286621,15.674674999999999 +517,Regression,Stochastic Gradient Tree,ChickWeights,63.25224079915844,71.92080214464903,-0.4228062717918979,1.120589256286621,16.51129 +528,Regression,Stochastic Gradient Tree,ChickWeights,64.80783657170488,74.3681944005728,-0.36776422230083305,1.120589256286621,17.364023 +539,Regression,Stochastic Gradient Tree,ChickWeights,65.59959781369417,75.30113885843834,-0.3443906138479853,1.120589256286621,18.342071999999998 +550,Regression,Stochastic Gradient Tree,ChickWeights,65.79684627343133,76.01328745307667,-0.32771909731089166,1.120589256286621,19.334775999999998 +561,Regression,Stochastic Gradient Tree,ChickWeights,66.6512855136148,77.20436469287773,-0.30975691666695093,1.120589256286621,20.336346 +572,Regression,Stochastic Gradient Tree,ChickWeights,68.11975592628174,79.56492566870935,-0.2867456678376987,1.120589256286621,21.353617 +578,Regression,Stochastic Gradient Tree,ChickWeights,68.75877313437184,80.35800679505147,-0.2806007657015741,1.120589256286621,22.38029 +20,Regression,Stochastic Gradient Tree,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.019941329956054688,0.002168 +40,Regression,Stochastic Gradient Tree,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.03173637390136719,0.006794 +60,Regression,Stochastic Gradient Tree,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.04389762878417969,0.018434 +80,Regression,Stochastic Gradient Tree,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.05624198913574219,0.031286 +100,Regression,Stochastic Gradient Tree,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.5381031036376953,0.048039 +120,Regression,Stochastic Gradient Tree,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.5386066436767578,0.080711 +140,Regression,Stochastic Gradient Tree,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.5391101837158203,0.16679100000000002 +160,Regression,Stochastic Gradient Tree,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.5393619537353516,0.262676 +180,Regression,Stochastic Gradient Tree,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.5396137237548828,0.43318 +200,Regression,Stochastic Gradient Tree,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.5077581405639648,0.638958 +220,Regression,Stochastic Gradient Tree,TrumpApproval,36.497516001377406,38.019453444701035,-361.2329206514933,1.3602590560913086,0.9135530000000001 +240,Regression,Stochastic Gradient Tree,TrumpApproval,33.64243104419191,36.40668421494773,-333.65237138497804,1.360762596130371,1.221179 +260,Regression,Stochastic Gradient Tree,TrumpApproval,31.222114965034955,34.98371838354962,-312.16748668977897,1.3610143661499023,1.570709 +280,Regression,Stochastic Gradient Tree,TrumpApproval,29.182059468614717,33.71869814960704,-303.5986275675674,1.361769676208496,1.939253 +300,Regression,Stochastic Gradient Tree,TrumpApproval,27.34275770505051,32.57805191350732,-278.63174197976707,1.3620214462280273,2.3245549999999997 +320,Regression,Stochastic Gradient Tree,TrumpApproval,25.81388747443183,31.5521424826706,-274.2849072221064,1.3630285263061523,2.8771169999999997 +340,Regression,Stochastic Gradient Tree,TrumpApproval,24.51835124153299,30.62414457186519,-273.0482727941538,1.3640356063842773,3.4469999999999996 +360,Regression,Stochastic Gradient Tree,TrumpApproval,23.451930423400693,29.787924926455332,-260.4155562259403,1.3660497665405273,4.029196 +380,Regression,Stochastic Gradient Tree,TrumpApproval,22.468440533492842,29.014219480552867,-255.59151052979877,1.3665533065795898,4.629964999999999 +400,Regression,Stochastic Gradient Tree,TrumpApproval,21.594907007575774,28.301677882839346,-250.0434007116766,0.510127067565918,5.253793 +420,Regression,Stochastic Gradient Tree,TrumpApproval,20.62268781294523,27.62086591367872,-246.0239415518119,1.3623762130737305,5.968102 +440,Regression,Stochastic Gradient Tree,TrumpApproval,19.786863931462925,26.990398924900393,-230.60756767519214,1.3643903732299805,6.700306 +460,Regression,Stochastic Gradient Tree,TrumpApproval,19.05732899619648,26.404670160589287,-209.2038511633616,1.3666563034057617,7.451319000000001 +480,Regression,Stochastic Gradient Tree,TrumpApproval,18.376512097202227,25.854792215140314,-195.90337768575387,1.3701810836791992,8.221931000000001 +500,Regression,Stochastic Gradient Tree,TrumpApproval,17.755044410127518,25.338820973360427,-184.15507530651482,1.3716917037963867,9.124580000000002 +520,Regression,Stochastic Gradient Tree,TrumpApproval,17.16611419898163,24.851444862058347,-177.4118263333629,1.3737058639526367,10.044684000000002 +540,Regression,Stochastic Gradient Tree,TrumpApproval,16.628565596068775,24.392285078947275,-170.25012213753183,1.3747129440307617,10.981068000000002 +560,Regression,Stochastic Gradient Tree,TrumpApproval,16.091244232649693,23.955027361350904,-168.10096043791202,1.3752164840698242,11.990243000000003 +580,Regression,Stochastic Gradient Tree,TrumpApproval,15.590768135673304,23.54051091957351,-166.33817208986073,1.3764753341674805,13.016881000000003 +600,Regression,Stochastic Gradient Tree,TrumpApproval,15.168708628495342,23.15108754841241,-159.05714501634571,0.5124959945678711,14.090212000000003 +620,Regression,Stochastic Gradient Tree,TrumpApproval,14.742446374247313,22.779539618023726,-151.59887848495535,3.0642080307006836,15.285325000000004 +640,Regression,Stochastic Gradient Tree,TrumpApproval,14.319364852585176,22.42187566882095,-144.08105420081068,3.0679845809936523,16.529242000000004 +660,Regression,Stochastic Gradient Tree,TrumpApproval,13.916412195872256,22.080274918425697,-138.68241285181185,3.0712575912475586,17.842975000000003 +680,Regression,Stochastic Gradient Tree,TrumpApproval,13.515604789075645,21.753254558457893,-136.71797028279042,3.074782371520996,19.280557 +700,Regression,Stochastic Gradient Tree,TrumpApproval,13.16391092204058,21.44141764506316,-136.3120101768532,3.0773000717163086,20.753146 +720,Regression,Stochastic Gradient Tree,TrumpApproval,12.828283113852926,21.142484202016185,-135.44313416922282,3.078558921813965,22.284495 +740,Regression,Stochastic Gradient Tree,TrumpApproval,12.504466467012781,20.855361315179096,-131.6825380828392,3.0800695419311523,23.930702 +760,Regression,Stochastic Gradient Tree,TrumpApproval,12.187542748969033,20.57929219886472,-129.592708960364,3.0813283920288086,25.608717 +780,Regression,Stochastic Gradient Tree,TrumpApproval,11.899403743710545,20.31464229706916,-126.82553676745258,3.08359432220459,27.347365999999997 +800,Regression,Stochastic Gradient Tree,TrumpApproval,11.634366305883285,20.06137952581079,-124.7856004590591,3.084601402282715,29.130084999999998 +820,Regression,Stochastic Gradient Tree,TrumpApproval,11.363415331478278,19.815492221289514,-123.0687724200615,3.08560848236084,30.98707 +840,Regression,Stochastic Gradient Tree,TrumpApproval,11.106640469158773,19.57848368678801,-121.24309788996561,3.086615562438965,32.880055 +860,Regression,Stochastic Gradient Tree,TrumpApproval,10.873909665943762,19.350226189127362,-118.20364312373843,3.0871191024780273,34.808534 +880,Regression,Stochastic Gradient Tree,TrumpApproval,10.65545006969969,19.130035299019603,-114.92727947355435,3.0873708724975586,36.791638 +900,Regression,Stochastic Gradient Tree,TrumpApproval,10.439309697188909,18.916827199314994,-112.83532852765143,3.08762264251709,38.832751 +920,Regression,Stochastic Gradient Tree,TrumpApproval,10.21789524284777,18.710158789526105,-112.19133803320567,3.087874412536621,40.951802 +940,Regression,Stochastic Gradient Tree,TrumpApproval,10.012578535125467,18.510293787577226,-110.72583714230211,3.077906608581543,43.146806 +960,Regression,Stochastic Gradient Tree,TrumpApproval,9.811853150109151,18.316579311485903,-109.54344305213982,3.0804243087768555,45.38444 +980,Regression,Stochastic Gradient Tree,TrumpApproval,9.61909067795052,18.12881604876013,-109.39183420714343,3.080927848815918,47.662490999999996 +1000,Regression,Stochastic Gradient Tree,TrumpApproval,9.438738635632271,17.946847607318464,-109.00797869183796,3.0824384689331055,50.039238 +1001,Regression,Stochastic Gradient Tree,TrumpApproval,9.429746533156267,17.937886241411594,-108.97151968967047,3.0824384689331055,52.450717 +11,Regression,Adaptive Random Forest,ChickWeights,7.837563210503649,16.830121687224917,-363.61289911513376,0.1506052017211914,0.01357 +22,Regression,Adaptive Random Forest,ChickWeights,4.3557641651310055,11.925612892987612,-149.62275175212707,0.1761331558227539,0.033876 +33,Regression,Adaptive Random Forest,ChickWeights,3.3711466349197527,9.780434627556833,-65.4351822307151,0.21426868438720703,0.06570500000000001 +44,Regression,Adaptive Random Forest,ChickWeights,2.6922077728217833,8.482083592242564,-55.643739991610765,0.23125934600830078,0.11059100000000002 +55,Regression,Adaptive Random Forest,ChickWeights,2.74736475641488,7.825318026963682,-10.953904022002215,0.28695011138916016,0.16601600000000002 +66,Regression,Adaptive Random Forest,ChickWeights,2.8724679940162905,7.312536888278379,-3.4997438549991955,0.33327198028564453,0.24397800000000003 +77,Regression,Adaptive Random Forest,ChickWeights,3.0470429271529937,7.064245743713448,-1.8145642692685127,0.3119173049926758,0.346862 +88,Regression,Adaptive Random Forest,ChickWeights,2.9741223361578246,6.690154558226259,-1.288758200280824,0.3463144302368164,0.647937 +99,Regression,Adaptive Random Forest,ChickWeights,3.5306191185317584,6.892431773474538,-1.031779280787943,0.3914194107055664,0.960086 +110,Regression,Adaptive Random Forest,ChickWeights,3.8799314967396747,6.981555605673833,-0.45575716788658527,0.4218912124633789,1.292146 +121,Regression,Adaptive Random Forest,ChickWeights,4.113667008668635,7.033914104811044,-0.13804551272932075,0.4422159194946289,1.662194 +132,Regression,Adaptive Random Forest,ChickWeights,4.34164975929163,7.0584702899254435,0.06337311670854429,0.46953678131103516,2.044753 +143,Regression,Adaptive Random Forest,ChickWeights,4.57586761829926,7.15786747745719,0.2198462773680444,0.5039682388305664,2.459644 +154,Regression,Adaptive Random Forest,ChickWeights,4.72768375743327,7.245199860946492,0.3206991207112422,0.5465364456176758,2.897301 +165,Regression,Adaptive Random Forest,ChickWeights,5.104360720447454,7.731417459148682,0.3781793296599212,0.5543031692504883,3.478625 +176,Regression,Adaptive Random Forest,ChickWeights,5.614563299993537,8.384781892618234,0.41030323544665537,0.577855110168457,4.083596 +187,Regression,Adaptive Random Forest,ChickWeights,6.030281219875818,8.796345271037008,0.46861442712072365,0.5973634719848633,4.712472 +198,Regression,Adaptive Random Forest,ChickWeights,6.128233569544692,8.84845009665535,0.572286448100142,0.6156282424926758,5.362321 +209,Regression,Adaptive Random Forest,ChickWeights,6.65587905711115,9.652732357425101,0.5803946009681837,0.637272834777832,6.0687109999999995 +220,Regression,Adaptive Random Forest,ChickWeights,7.106977341119842,10.677274056234571,0.550512947323095,0.6516351699829102,6.792503 +231,Regression,Adaptive Random Forest,ChickWeights,7.51605472684967,11.121858780588035,0.582840670024279,0.6455926895141602,7.540065 +242,Regression,Adaptive Random Forest,ChickWeights,7.8763674823035235,11.54794620868086,0.6381207504866175,0.654301643371582,8.314509000000001 +253,Regression,Adaptive Random Forest,ChickWeights,8.048654689630025,11.785882981718466,0.6726179175042853,0.6542215347290039,9.228486 +264,Regression,Adaptive Random Forest,ChickWeights,8.558470564817128,12.694815113306078,0.6529678969258632,0.7006998062133789,10.165772 +275,Regression,Adaptive Random Forest,ChickWeights,9.011287699636803,13.865710758190522,0.6357023625954032,0.7181978225708008,11.12726 +286,Regression,Adaptive Random Forest,ChickWeights,9.454493871269733,14.39909947248495,0.6597325750664246,0.7397470474243164,12.112744 +297,Regression,Adaptive Random Forest,ChickWeights,9.455634964453314,14.370566123736594,0.7060577585099084,0.6961946487426758,13.208544 +308,Regression,Adaptive Random Forest,ChickWeights,9.98259297559382,15.278989711680778,0.7040656028742478,0.7122316360473633,14.327932 +319,Regression,Adaptive Random Forest,ChickWeights,10.896304985778038,17.680267148091307,0.6404050215106214,0.7230386734008789,15.472301 +330,Regression,Adaptive Random Forest,ChickWeights,11.34830207391465,18.238325787402868,0.6725323523293205,0.7481813430786133,16.699404 +341,Regression,Adaptive Random Forest,ChickWeights,11.700671911575691,18.698639858183288,0.6917798823884449,0.750828742980957,17.953966 +352,Regression,Adaptive Random Forest,ChickWeights,12.012928806619971,19.028065448277466,0.7098550919958697,0.779423713684082,19.24314 +363,Regression,Adaptive Random Forest,ChickWeights,12.590729727774809,20.061815233276363,0.6868102538385266,0.8219194412231445,20.584825000000002 +374,Regression,Adaptive Random Forest,ChickWeights,13.29572445199132,21.688967498502105,0.6634948622954009,0.8368387222290039,21.949532 +385,Regression,Adaptive Random Forest,ChickWeights,13.850252347511734,22.377982941031114,0.6830616430184708,0.8398981094360352,23.337733 +396,Regression,Adaptive Random Forest,ChickWeights,13.995508749414423,22.434927630401365,0.7029833246789492,0.8451242446899414,24.808931 +407,Regression,Adaptive Random Forest,ChickWeights,14.855647843034443,23.972462409994428,0.6847772413527866,0.8440675735473633,26.305221 +418,Regression,Adaptive Random Forest,ChickWeights,15.648428200057216,25.832735423225586,0.6563908585574095,0.8621377944946289,27.821711999999998 +429,Regression,Adaptive Random Forest,ChickWeights,16.477960681723363,27.016517310630082,0.6660339910533338,0.8826723098754883,29.421276999999996 +440,Regression,Adaptive Random Forest,ChickWeights,16.794784005292485,27.277386650758192,0.6836500576952018,0.8854074478149414,31.044845999999996 +451,Regression,Adaptive Random Forest,ChickWeights,17.2443539228967,27.815314781786782,0.6850336806962379,0.915654182434082,32.692344999999996 +462,Regression,Adaptive Random Forest,ChickWeights,18.21783864235053,29.965283642676138,0.6566601868655235,0.9476785659790039,34.453267999999994 +473,Regression,Adaptive Random Forest,ChickWeights,19.154558799374207,31.279498058996012,0.6696542166515442,0.9631280899047852,36.23863899999999 +484,Regression,Adaptive Random Forest,ChickWeights,19.653022199172934,31.710924929172922,0.6790841892096586,0.979741096496582,38.04845199999999 +495,Regression,Adaptive Random Forest,ChickWeights,20.17748759588543,32.35841629000369,0.6856630158751376,1.0035409927368164,39.89288999999999 +506,Regression,Adaptive Random Forest,ChickWeights,20.994447812000203,33.88452895368057,0.6653322556738073,1.0402307510375977,41.77183299999999 +517,Regression,Adaptive Random Forest,ChickWeights,21.74940325928189,34.92971521251369,0.6643962418834424,1.0591440200805664,43.682228999999985 +528,Regression,Adaptive Random Forest,ChickWeights,22.718068194641532,36.272080231437364,0.6746268651566016,1.0802621841430664,45.622796999999984 +539,Regression,Adaptive Random Forest,ChickWeights,22.976084812890594,36.32299861842887,0.6871862958215178,1.1105661392211914,47.62064799999998 +550,Regression,Adaptive Random Forest,ChickWeights,23.812560792713985,37.68037385984369,0.6737446986071818,1.1564149856567383,49.63871399999998 +561,Regression,Adaptive Random Forest,ChickWeights,24.744158926088524,38.956389615090316,0.6665241448790927,1.171940803527832,51.68634399999998 +572,Regression,Adaptive Random Forest,ChickWeights,25.965548256363952,40.779089345824126,0.6619939776632806,1.1861085891723633,53.83172099999997 +578,Regression,Adaptive Random Forest,ChickWeights,26.10164191353107,40.80941552099692,0.669724624616493,1.1904268264770508,56.00600499999997 +20,Regression,Adaptive Random Forest,TrumpApproval,4.656196028844478,13.301506400077992,-414.0076115498352,0.20158100128173828,0.057323 +40,Regression,Adaptive Random Forest,TrumpApproval,3.307191630717303,9.514843640593101,-35.39725790498291,0.28950977325439453,0.159522 +60,Regression,Adaptive Random Forest,TrumpApproval,2.3916587233350866,7.783560456255013,-31.83725667748105,0.32280826568603516,0.43784 +80,Regression,Adaptive Random Forest,TrumpApproval,2.0172424359013847,6.770328731809264,-23.921456088954436,0.36927127838134766,0.749169 +100,Regression,Adaptive Random Forest,TrumpApproval,2.069330341220504,6.141775226189047,-11.868015650386665,0.4076700210571289,1.082433 +120,Regression,Adaptive Random Forest,TrumpApproval,2.013474643057227,5.653544639730099,-8.249866206703038,0.4241609573364258,1.485703 +140,Regression,Adaptive Random Forest,TrumpApproval,1.8943659201342373,5.255534318342925,-7.260127227254786,0.44395923614501953,2.052392 +160,Regression,Adaptive Random Forest,TrumpApproval,1.9423634360618716,4.987168106592344,-5.559462264629689,0.45576953887939453,2.6505280000000004 +180,Regression,Adaptive Random Forest,TrumpApproval,1.9639788846395134,4.758402061618727,-4.244508869717663,0.47258472442626953,3.3297560000000006 +200,Regression,Adaptive Random Forest,TrumpApproval,1.9045329443413326,4.539431452034987,-3.787127034775958,0.5018167495727539,4.037089000000001 +220,Regression,Adaptive Random Forest,TrumpApproval,1.7801675790175082,4.332908187325825,-3.7047348470369075,0.539036750793457,4.871381000000001 +240,Regression,Adaptive Random Forest,TrumpApproval,1.7262455165213564,4.162317120423255,-3.3742337174670682,0.5583086013793945,5.726343000000002 +260,Regression,Adaptive Random Forest,TrumpApproval,1.6726006855046047,4.010034080286883,-3.1147254000977194,0.586766242980957,6.608691000000002 +280,Regression,Adaptive Random Forest,TrumpApproval,1.6001254820213158,3.8693841240389197,-3.01116046378164,0.6034517288208008,7.537396000000002 +300,Regression,Adaptive Random Forest,TrumpApproval,1.5903246290151523,3.758572865099384,-2.72204992570884,0.6344270706176758,8.563678000000001 +320,Regression,Adaptive Random Forest,TrumpApproval,1.5306703522535514,3.644833568467773,-2.673500446315358,0.6524057388305664,9.653495000000001 +340,Regression,Adaptive Random Forest,TrumpApproval,1.462120415173825,3.538151879462345,-2.658070572544154,0.6771516799926758,10.778313 +360,Regression,Adaptive Random Forest,TrumpApproval,1.4104873891633294,3.442715407420023,-2.491830651593505,0.712040901184082,12.006044000000001 +380,Regression,Adaptive Random Forest,TrumpApproval,1.3577274631021345,3.3535534396577877,-2.4279222429470106,0.7612085342407227,13.264975000000002 +400,Regression,Adaptive Random Forest,TrumpApproval,1.328889471148693,3.2750276755937473,-2.3616646758926834,0.7830896377563477,14.608014 +420,Regression,Adaptive Random Forest,TrumpApproval,1.2856838141339133,3.198005596242657,-2.3114858734875385,0.8153314590454102,15.987435000000001 +440,Regression,Adaptive Random Forest,TrumpApproval,1.2502461578606217,3.1277634460074983,-2.1102975482726696,0.8549776077270508,17.476653000000002 +460,Regression,Adaptive Random Forest,TrumpApproval,1.2118787702501406,3.0607885313580625,-1.8245276191275441,0.8641138076782227,19.005275 +480,Regression,Adaptive Random Forest,TrumpApproval,1.1755519926992437,2.997482691409013,-1.6465763687209671,0.904881477355957,20.582478000000002 +500,Regression,Adaptive Random Forest,TrumpApproval,1.1542746800420942,2.9412002898427465,-1.494663742459657,0.9429025650024414,22.213321 +520,Regression,Adaptive Random Forest,TrumpApproval,1.1232655769227813,2.8856256311301474,-1.4054721071787495,0.9187402725219727,23.93785 +540,Regression,Adaptive Random Forest,TrumpApproval,1.0927628011224122,2.8324064719208977,-1.3090698562992058,0.9784936904907227,25.72016 +560,Regression,Adaptive Random Forest,TrumpApproval,1.0798076211233283,2.7860066009246953,-1.2872677886963872,0.8415918350219727,27.559893 +580,Regression,Adaptive Random Forest,TrumpApproval,1.0533259806656756,2.7386650773118006,-1.2648586320750757,0.926945686340332,29.430927 +600,Regression,Adaptive Random Forest,TrumpApproval,1.0370277841126194,2.695306817676886,-1.1694452334238137,1.0152063369750977,31.394822 +620,Regression,Adaptive Random Forest,TrumpApproval,1.0220360797787769,2.6548714349996483,-1.0727572654712625,0.9687509536743164,33.420245 +640,Regression,Adaptive Random Forest,TrumpApproval,1.006223169156282,2.6150891537993277,-0.9735122270872276,0.8030519485473633,35.538976 +660,Regression,Adaptive Random Forest,TrumpApproval,0.9862189251721106,2.576116595691222,-0.901357605879683,0.7759256362915039,37.763356 +680,Regression,Adaptive Random Forest,TrumpApproval,0.9658028732124053,2.5385905860617046,-0.8755448750460146,0.8428354263305664,40.028227 +700,Regression,Adaptive Random Forest,TrumpApproval,0.9580702867531661,2.506070409170758,-0.8758066430098239,0.9465646743774414,42.353807 +720,Regression,Adaptive Random Forest,TrumpApproval,0.9436099236768006,2.472715624364642,-0.8663281360281503,1.0379304885864258,44.723793 +740,Regression,Adaptive Random Forest,TrumpApproval,0.9279645732871133,2.4402852992549255,-0.8166009677654511,1.1119890213012695,47.149898 +760,Regression,Adaptive Random Forest,TrumpApproval,0.9159099470417099,2.410261116608071,-0.7913739428902633,1.1737489700317383,49.62861 +780,Regression,Adaptive Random Forest,TrumpApproval,0.8968362370347621,2.379411736746614,-0.7536320120768303,1.261582374572754,52.179919 +800,Regression,Adaptive Random Forest,TrumpApproval,0.8878342141912964,2.3513921402434717,-0.7280625702741705,1.3552255630493164,54.784302 +820,Regression,Adaptive Random Forest,TrumpApproval,0.8775558321142263,2.3237456817971016,-0.7062000372474271,1.4321069717407227,57.452517 +840,Regression,Adaptive Random Forest,TrumpApproval,0.8672496542857573,2.297532908897418,-0.6834092983276716,1.4874773025512695,60.173386 +860,Regression,Adaptive Random Forest,TrumpApproval,0.8593706057522699,2.272389423762812,-0.6439286158863597,1.5595178604125977,62.976048 +880,Regression,Adaptive Random Forest,TrumpApproval,0.8551106332542915,2.2487703297155224,-0.6019328469057044,1.619084358215332,65.856537 +900,Regression,Adaptive Random Forest,TrumpApproval,0.8437512715146732,2.224375873084905,-0.5739713521606553,1.1261072158813477,68.798174 +920,Regression,Adaptive Random Forest,TrumpApproval,0.8344220404851989,2.2010168562801016,-0.5664083310843888,1.167832374572754,71.779904 +940,Regression,Adaptive Random Forest,TrumpApproval,0.825939320609599,2.179009982884269,-0.5482654902802699,1.1199464797973633,74.829165 +960,Regression,Adaptive Random Forest,TrumpApproval,0.8156984309758435,2.1571048007400404,-0.5331573747015668,1.1733713150024414,77.929016 +980,Regression,Adaptive Random Forest,TrumpApproval,0.806477335746804,2.1360065895495888,-0.5325097322303367,1.2283296585083008,81.05698100000001 +1000,Regression,Adaptive Random Forest,TrumpApproval,0.8008625237630099,2.1159877488140326,-0.5292346593649373,1.2836008071899414,84.241983 +1001,Regression,Adaptive Random Forest,TrumpApproval,0.800378499538596,2.1149541843634605,-0.5287610996295022,1.2846193313598633,87.445729 +11,Regression,Aggregated Mondrian Forest,ChickWeights,1.0878895070954884,1.3778002085324723,-1.2599207317049026,0.17917156219482422,0.003809 +22,Regression,Aggregated Mondrian Forest,ChickWeights,1.15171477394762,1.5218208011368886,-1.3974856828423898,0.33136653900146484,0.017797 +33,Regression,Aggregated Mondrian Forest,ChickWeights,1.2596040860169628,1.630698561429495,-0.8214033882315572,0.48356151580810547,0.056943 +44,Regression,Aggregated Mondrian Forest,ChickWeights,1.1470025325021567,1.5136945038262,-0.7860708992998826,0.6357030868530273,0.120895 +55,Regression,Aggregated Mondrian Forest,ChickWeights,1.7448650745312246,2.8901942810902064,-0.6023944619968462,0.795161247253418,0.34128800000000004 +66,Regression,Aggregated Mondrian Forest,ChickWeights,1.9741736434582027,3.1122799656868354,0.1967701507194095,0.949946403503418,0.602886 +77,Regression,Aggregated Mondrian Forest,ChickWeights,2.3465039451978784,3.868783481489585,0.16539043694478717,1.1044378280639648,0.885897 +88,Regression,Aggregated Mondrian Forest,ChickWeights,2.3152944739841907,3.751470845606434,0.286453015670338,1.2595434188842773,1.213122 +99,Regression,Aggregated Mondrian Forest,ChickWeights,2.4851266884813286,3.8753788781661265,0.3615628965518305,1.4176397323608398,1.708637 +110,Regression,Aggregated Mondrian Forest,ChickWeights,2.679180085056696,4.098463178184459,0.5005082908479199,1.580409049987793,2.233256 +121,Regression,Aggregated Mondrian Forest,ChickWeights,2.993112128155013,4.501608187312601,0.5353065115430311,1.7385053634643555,2.789253 +132,Regression,Aggregated Mondrian Forest,ChickWeights,3.049130101089184,4.474860576824222,0.624267329970883,1.8972959518432617,3.530617 +143,Regression,Aggregated Mondrian Forest,ChickWeights,3.129389359320645,4.535626207267123,0.6870855629914132,2.0540571212768555,4.307795 +154,Regression,Aggregated Mondrian Forest,ChickWeights,3.2350921629171503,4.614317779917637,0.7245583098520811,2.21335506439209,5.238077 +165,Regression,Aggregated Mondrian Forest,ChickWeights,3.615407192454655,5.434402308521257,0.6928112980835472,2.370730400085449,6.2153469999999995 +176,Regression,Aggregated Mondrian Forest,ChickWeights,3.842899644735678,5.8926781106586255,0.7087106044563829,2.5251951217651367,7.308114 +187,Regression,Aggregated Mondrian Forest,ChickWeights,3.939333513046091,5.936527515565436,0.7578865873655871,2.680434226989746,8.444739 +198,Regression,Aggregated Mondrian Forest,ChickWeights,4.1526220464224926,6.160116941975886,0.7926170753106898,2.8339643478393555,9.747256 +209,Regression,Aggregated Mondrian Forest,ChickWeights,4.486090256229248,6.857164593682279,0.7881489392686998,2.9901113510131836,11.107311 +220,Regression,Aggregated Mondrian Forest,ChickWeights,5.095083445923365,8.268326900050806,0.7303274183124314,3.147219657897949,12.601650999999999 +231,Regression,Aggregated Mondrian Forest,ChickWeights,5.345901760482457,8.651953805757511,0.7474084291359289,3.301150321960449,14.172197999999998 +242,Regression,Aggregated Mondrian Forest,ChickWeights,5.775936882313693,9.234098241635358,0.7685060952534608,3.4575910568237305,15.876551999999998 +253,Regression,Aggregated Mondrian Forest,ChickWeights,6.050411841877211,9.480574702158652,0.7880472652798773,3.6158742904663086,17.675130999999997 +264,Regression,Aggregated Mondrian Forest,ChickWeights,6.7396819662512994,10.861908099063555,0.7457953067175733,3.77274227142334,19.599012999999996 +275,Regression,Aggregated Mondrian Forest,ChickWeights,7.418933110619537,12.596893007879746,0.699156722346497,3.926619529724121,21.625010999999997 +286,Regression,Aggregated Mondrian Forest,ChickWeights,7.830180870941,13.02165358749325,0.7215679622698357,4.082179069519043,23.771257999999996 +297,Regression,Aggregated Mondrian Forest,ChickWeights,8.059624975297776,13.201631143135529,0.7517949935656911,4.237311363220215,26.095146999999997 +308,Regression,Aggregated Mondrian Forest,ChickWeights,8.517266870602596,14.029786197157003,0.750336177123377,4.390841484069824,28.501656999999998 +319,Regression,Aggregated Mondrian Forest,ChickWeights,9.872910663629112,17.67011178426297,0.6406578335650022,4.544772148132324,31.026265 +330,Regression,Aggregated Mondrian Forest,ChickWeights,10.355957081475973,18.251720539867826,0.671924837978655,4.698409080505371,33.677707999999996 +341,Regression,Aggregated Mondrian Forest,ChickWeights,10.779061369126929,18.644503325392748,0.6934349036095702,4.852313041687012,36.483968999999995 +352,Regression,Aggregated Mondrian Forest,ChickWeights,10.97013178962945,18.690294927177735,0.7199342471488321,5.009421348571777,39.412921 +363,Regression,Aggregated Mondrian Forest,ChickWeights,11.836385670325258,20.411474322578705,0.6756306209292051,5.165541648864746,42.477176 +374,Regression,Aggregated Mondrian Forest,ChickWeights,12.650208752532226,22.152599191433616,0.6487731216482631,5.323611259460449,45.685202 +385,Regression,Aggregated Mondrian Forest,ChickWeights,13.26433375884341,22.74111870549559,0.672536034672935,5.478930473327637,49.011466999999996 +396,Regression,Aggregated Mondrian Forest,ChickWeights,13.285084454056173,22.62858691877232,0.6976709876564118,5.63400936126709,52.447627 +407,Regression,Aggregated Mondrian Forest,ChickWeights,14.297859574888522,24.603705237609702,0.6677818184200794,5.789168357849121,55.986709 +418,Regression,Aggregated Mondrian Forest,ChickWeights,15.277775247208368,26.91758918665374,0.6267299649165277,5.945448875427246,59.629264 +429,Regression,Aggregated Mondrian Forest,ChickWeights,16.148002577856595,27.91235298687263,0.643353503146777,6.099112510681152,63.379205999999996 +440,Regression,Aggregated Mondrian Forest,ChickWeights,16.450833155107055,28.053185003016473,0.6652347923635655,6.252856254577637,67.23493599999999 +451,Regression,Aggregated Mondrian Forest,ChickWeights,16.938736394119786,28.680885446607185,0.6649461832952053,6.4079084396362305,71.205463 +462,Regression,Aggregated Mondrian Forest,ChickWeights,18.465286457846624,32.222162406640614,0.6027938253530374,6.562827110290527,75.286683 +473,Regression,Aggregated Mondrian Forest,ChickWeights,19.368786292726078,33.403615991184594,0.6231055060350865,6.717398643493652,79.474485 +484,Regression,Aggregated Mondrian Forest,ChickWeights,19.88015130963188,33.764210229402664,0.6360141173956066,6.872824668884277,83.769797 +495,Regression,Aggregated Mondrian Forest,ChickWeights,20.57744796303998,34.830627929035586,0.6356250399764956,7.029131889343262,88.176271 +506,Regression,Aggregated Mondrian Forest,ChickWeights,21.43571571603741,36.40788480688662,0.6134430891768862,7.184878349304199,92.694929 +517,Regression,Aggregated Mondrian Forest,ChickWeights,22.34914238062968,37.807266067412606,0.6066317565796657,7.340197563171387,97.332285 +528,Regression,Aggregated Mondrian Forest,ChickWeights,23.191315994328228,38.81894260965106,0.6271697641288401,7.495863914489746,102.073572 +539,Regression,Aggregated Mondrian Forest,ChickWeights,23.34075784343543,38.827434948624926,0.6423969913213963,7.652411460876465,106.92036399999999 +550,Regression,Aggregated Mondrian Forest,ChickWeights,24.125457325549842,39.99965605849559,0.6321733437483394,7.811335563659668,111.876093 +561,Regression,Aggregated Mondrian Forest,ChickWeights,24.859407485948264,40.9180834433101,0.6319179521646502,7.9698591232299805,116.942977 +572,Regression,Aggregated Mondrian Forest,ChickWeights,25.582967433016186,41.65667948828452,0.6471310448579161,8.12806224822998,122.12293 +578,Regression,Aggregated Mondrian Forest,ChickWeights,25.674172622955844,41.71227980537356,0.65479005999511,8.21412181854248,127.41509599999999 +20,Regression,Aggregated Mondrian Forest,TrumpApproval,0.38127899900663437,0.4856864156914124,0.4734504440676397,0.3661947250366211,0.012803 +40,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3903932807396207,0.4802129236445582,0.908098150441852,0.7077703475952148,0.066318 +60,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3553562094560649,0.44754485397583466,0.8908737885344612,1.0465993881225586,0.161186 +80,Regression,Aggregated Mondrian Forest,TrumpApproval,0.37850782280668976,0.48181042919820394,0.8725877843301283,1.386988639831543,0.310355 +100,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3456451525369771,0.45047631187257403,0.9301027529077078,1.7262754440307617,0.520575 +120,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3369671927041528,0.4421642502671299,0.9427860880043346,2.0684385299682617,0.828496 +140,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3170957614007029,0.42172730009164255,0.9461011323760549,2.404311180114746,1.349971 +160,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3307037984070857,0.4315519243898653,0.9502341257934384,2.7412595748901367,1.938101 +180,Regression,Aggregated Mondrian Forest,TrumpApproval,0.32391175568062514,0.4198186275021798,0.9586532343987925,3.0777502059936523,2.6707590000000003 +200,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3216990324082377,0.4152317221827294,0.959406710806771,3.4146718978881836,3.5252660000000002 +220,Regression,Aggregated Mondrian Forest,TrumpApproval,0.31954266191014236,0.40981538467826617,0.9573113010689299,3.7566747665405273,4.592894 +240,Regression,Aggregated Mondrian Forest,TrumpApproval,0.32058338930030683,0.40973424053171104,0.9569909127297425,4.096526145935059,5.7511 +260,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30963502752669864,0.3977121378847216,0.958918388812615,4.436213493347168,7.098098 +280,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3021514917324106,0.38814569807877647,0.9590118642619369,4.772730827331543,8.655558000000001 +300,Regression,Aggregated Mondrian Forest,TrumpApproval,0.302378432925199,0.38793022611752936,0.9597410141114792,5.1073408126831055,10.324296 +320,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30426355112773323,0.38808206022274244,0.9577019017103428,5.440821647644043,12.208908000000001 +340,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30611168054734034,0.391104192749731,0.9546026616524738,5.773791313171387,14.314788 +360,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3092553456162419,0.39535170942611675,0.9532980843409116,6.1096906661987305,16.588556 +380,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3076082401740039,0.39602056260851237,0.9515479192781826,6.445483207702637,19.05639 +400,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3014609513967445,0.3890658925747077,0.951945723428325,6.780200004577637,21.697867000000002 +420,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29711382936926317,0.3834166944817816,0.9518088379060109,7.1165571212768555,24.511965000000004 +440,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29823263394549426,0.3845174635941775,0.952475485177767,7.451247215270996,27.506667000000004 +460,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2946274143925286,0.38012238021570716,0.9560358463571957,7.7864484786987305,30.697063000000004 +480,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29339680191338824,0.3767220961039539,0.9578595845961764,8.120524406433105,34.045785 +500,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2879691623817681,0.37098658104945786,0.9600287871527097,8.45413875579834,37.543006000000005 +520,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2858512762877924,0.36803361403114826,0.9606162857565064,8.791060447692871,41.194255000000005 +540,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28337361966731006,0.3643610952909248,0.9615619008486129,9.128493309020996,45.008334000000005 +560,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28829960115327347,0.3724151060029248,0.9588932716362207,9.463343620300293,48.989560000000004 +580,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28931551719826604,0.37295833877986023,0.957758997678865,9.801314353942871,53.13082800000001 +600,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28804453093670845,0.3714720231862385,0.9585805866298193,10.13992977142334,57.44083900000001 +620,Regression,Aggregated Mondrian Forest,TrumpApproval,0.285913070387579,0.3694806072400069,0.9596689430521099,10.477011680603027,61.917696000000014 +640,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2840899599351511,0.36696425721615267,0.9609793991220156,10.80936336517334,66.56801000000002 +660,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2804371592513609,0.3629082548929102,0.9621252180674722,11.1486234664917,71.37699000000002 +680,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2832721366095781,0.36464822523085283,0.9611623224331389,11.485033988952637,76.34970200000002 +700,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28537572628090535,0.3664704272728199,0.959741096022156,11.82703685760498,81.48889300000002 +720,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2846995678315971,0.3663721647933076,0.9588788359612517,12.1649808883667,86.79821800000002 +740,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28438969208914106,0.36562346066591106,0.9590812376103318,12.5026273727417,92.27027700000002 +760,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2812524955317089,0.36227858565351356,0.9593969665626949,12.84118938446045,97.89633500000002 +780,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2784736419799919,0.35904953945649953,0.9599459478528628,13.181014060974121,103.67983800000002 +800,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28122680710979,0.3614117991183927,0.9590552347518728,13.522452354431152,109.62063100000002 +820,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2798414038154103,0.35991057058708614,0.9589530251045719,13.858756065368652,115.71824100000002 +840,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2792299366421054,0.358810295818463,0.9588293518961978,14.199065208435059,121.97670500000002 +860,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2757234931419036,0.35572944295397174,0.9596100235239657,14.53821849822998,128.39866500000002 +880,Regression,Aggregated Mondrian Forest,TrumpApproval,0.27250879183678145,0.3526976163964828,0.9604999212537026,14.877989768981934,134.987331 +900,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2707423985398595,0.35053221584455113,0.9608236269181287,15.214400291442871,141.746125 +920,Regression,Aggregated Mondrian Forest,TrumpApproval,0.27149682778681117,0.35071953709177356,0.960138783514385,15.551268577575684,148.677278 +940,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2704179121014184,0.350599255928843,0.9598313134304426,15.892088890075684,155.784091 +960,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2707082259086565,0.3516525290937312,0.9591696518431827,16.229090690612793,163.071856 +980,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2709225398475326,0.3517696828828596,0.9583487676398438,16.574454307556152,170.541967 +1000,Regression,Aggregated Mondrian Forest,TrumpApproval,0.26869541998932756,0.349579763566054,0.9581740736545136,16.91306972503662,178.198313 +1001,Regression,Aggregated Mondrian Forest,TrumpApproval,0.268533139095725,0.34942113435685235,0.9581842100200093,16.932257652282715,186.03359 +11,Regression,Adaptive Model Rules,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.019614219665527344,0.000715 +22,Regression,Adaptive Model Rules,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.021178245544433594,0.002248 +33,Regression,Adaptive Model Rules,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.02634716033935547,0.0045000000000000005 +44,Regression,Adaptive Model Rules,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.027434349060058594,0.007522000000000001 +55,Regression,Adaptive Model Rules,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.034033775329589844,0.011341 +66,Regression,Adaptive Model Rules,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.043257713317871094,0.016081 +77,Regression,Adaptive Model Rules,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.04948711395263672,0.021988 +88,Regression,Adaptive Model Rules,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.05513286590576172,0.050945000000000004 +99,Regression,Adaptive Model Rules,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.05624675750732422,0.083616 +110,Regression,Adaptive Model Rules,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.057623863220214844,0.119642 +121,Regression,Adaptive Model Rules,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.05775737762451172,0.157993 +132,Regression,Adaptive Model Rules,ChickWeights,5.216037157212015,7.551012267266295,-0.0719037453282565,0.05781078338623047,0.197604 +143,Regression,Adaptive Model Rules,ChickWeights,5.030848211447775,7.321940337412501,0.18367095381254994,0.058394432067871094,0.238591 +154,Regression,Adaptive Model Rules,ChickWeights,4.907406922429448,7.137924382310331,0.3406662269828342,0.058447837829589844,0.28091 +165,Regression,Adaptive Model Rules,ChickWeights,5.132506734403487,7.341156657504303,0.439370571581684,0.058447837829589844,0.32452 +176,Regression,Adaptive Model Rules,ChickWeights,5.292049153445915,7.468652514259996,0.5321251372519638,0.059058189392089844,0.369448 +187,Regression,Adaptive Model Rules,ChickWeights,5.31698748044205,7.461166418014795,0.6176873420824156,0.059111595153808594,0.415771 +198,Regression,Adaptive Model Rules,ChickWeights,5.300228902480157,7.425148329077998,0.6988181014109027,0.059058189392089844,0.474994 +209,Regression,Adaptive Model Rules,ChickWeights,5.830499581707958,9.648698249017793,0.5807452540036802,0.02527141571044922,0.54427 +220,Regression,Adaptive Model Rules,ChickWeights,6.400718854692065,10.45569246424029,0.5689754490886993,0.03142070770263672,0.614353 +231,Regression,Adaptive Model Rules,ChickWeights,6.611665150046439,10.617456980307361,0.6198209084753062,0.036536216735839844,0.6853290000000001 +242,Regression,Adaptive Model Rules,ChickWeights,7.029624246247838,11.197269958950692,0.6597654020329642,0.041068077087402344,0.7572760000000001 +253,Regression,Adaptive Model Rules,ChickWeights,7.254490759785878,11.350610231674398,0.6963529412438163,0.044592857360839844,0.830219 +264,Regression,Adaptive Model Rules,ChickWeights,7.784750145903498,12.258358647532567,0.6764200982742594,0.044699668884277344,0.9041760000000001 +275,Regression,Adaptive Model Rules,ChickWeights,8.342804112650073,13.247943494163705,0.6674407623884211,0.044699668884277344,0.986871 +286,Regression,Adaptive Model Rules,ChickWeights,8.88061114203256,14.075280539927816,0.6748649197186086,0.045203208923339844,1.0705630000000002 +297,Regression,Adaptive Model Rules,ChickWeights,9.500784996803779,14.855892526018593,0.6858683144490312,0.04522991180419922,1.1552540000000002 +308,Regression,Adaptive Model Rules,ChickWeights,10.070788242104461,15.77018489177999,0.6847321098344714,0.045256614685058594,1.2409280000000003 +319,Regression,Adaptive Model Rules,ChickWeights,10.988840488902909,17.80174938329892,0.6354464447499208,0.045256614685058594,1.3276340000000002 +330,Regression,Adaptive Model Rules,ChickWeights,11.635092222175304,18.61329763011445,0.6589287557789436,0.04528331756591797,1.4153970000000002 +341,Regression,Adaptive Model Rules,ChickWeights,11.7817306308102,18.651657721342477,0.6933268021215234,0.04528331756591797,1.5042310000000003 +352,Regression,Adaptive Model Rules,ChickWeights,11.878812775671825,18.699040402285984,0.7198024587207095,0.04528331756591797,1.5941090000000002 +363,Regression,Adaptive Model Rules,ChickWeights,12.712200605470676,19.934033697107445,0.690787203614232,0.045310020446777344,1.6850070000000001 +374,Regression,Adaptive Model Rules,ChickWeights,13.202927457133043,20.9603625224819,0.6857237785454591,0.04533672332763672,1.7769460000000001 +385,Regression,Adaptive Model Rules,ChickWeights,13.5542070698499,21.51079994203591,0.707149447507495,0.04533672332763672,1.8699420000000002 +396,Regression,Adaptive Model Rules,ChickWeights,13.642433072457155,21.454130101613703,0.7283852775805406,0.04533672332763672,1.9639870000000001 +407,Regression,Adaptive Model Rules,ChickWeights,14.50232093628697,22.86556238504221,0.7132152539462153,0.045676231384277344,2.060696 +418,Regression,Adaptive Model Rules,ChickWeights,15.245933470432924,24.220098655355127,0.6979521608965717,0.045729637145996094,2.158386 +429,Regression,Adaptive Model Rules,ChickWeights,15.766409258920858,25.08619072251902,0.7120527209837302,0.045729637145996094,2.2570650000000003 +440,Regression,Adaptive Model Rules,ChickWeights,15.931210335947624,25.166941851240068,0.7307081986676882,0.04564952850341797,2.4439120000000005 +451,Regression,Adaptive Model Rules,ChickWeights,16.418312975299003,25.736739737917958,0.7303482652633313,0.046179771423339844,2.6336030000000004 +462,Regression,Adaptive Model Rules,ChickWeights,17.4982370763817,27.78944281741256,0.7047111429028308,0.04692363739013672,2.8262370000000003 +473,Regression,Adaptive Model Rules,ChickWeights,18.254684132762545,29.056725346353634,0.7149358826261665,0.04697704315185547,3.0200670000000005 +484,Regression,Adaptive Model Rules,ChickWeights,18.58513038702809,29.354635254956722,0.7250038129485413,0.046950340270996094,3.2149970000000003 +495,Regression,Adaptive Model Rules,ChickWeights,19.01404260598322,29.86038018890717,0.7323226450377984,0.046896934509277344,3.430483 +506,Regression,Adaptive Model Rules,ChickWeights,19.88342353555136,31.26600741511644,0.7150584356224581,0.04697704315185547,3.648791 +517,Regression,Adaptive Model Rules,ChickWeights,20.595063111922975,32.24616798680886,0.713982273554131,0.047003746032714844,3.869932 +528,Regression,Adaptive Model Rules,ChickWeights,21.380474467010046,33.43504054753495,0.7235347994633756,0.047003746032714844,4.098743 +539,Regression,Adaptive Model Rules,ChickWeights,21.53249764026729,33.42135235584957,0.735168024057878,0.047003746032714844,4.328606 +550,Regression,Adaptive Model Rules,ChickWeights,22.499187843294454,35.002118414433774,0.7184757368310433,0.04703044891357422,4.559521999999999 +561,Regression,Adaptive Model Rules,ChickWeights,23.191634121895575,35.912468657285935,0.7166015220750928,0.047003746032714844,4.791517 +572,Regression,Adaptive Model Rules,ChickWeights,24.040656821383894,37.100860859043735,0.7202195492645626,0.046950340270996094,5.02459 +578,Regression,Adaptive Model Rules,ChickWeights,24.194319129377014,37.21658551958108,0.7253190778127725,0.04697704315185547,5.258551 +20,Regression,Adaptive Model Rules,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.053809165954589844,0.004347 +40,Regression,Adaptive Model Rules,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.07615184783935547,0.011776 +60,Regression,Adaptive Model Rules,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.08839702606201172,0.021496 +80,Regression,Adaptive Model Rules,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.09804439544677734,0.033628000000000005 +100,Regression,Adaptive Model Rules,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.10713481903076172,0.04833900000000001 +120,Regression,Adaptive Model Rules,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.11132335662841797,0.06604700000000001 +140,Regression,Adaptive Model Rules,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.11333751678466797,0.086317 +160,Regression,Adaptive Model Rules,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.1174459457397461,0.109348 +180,Regression,Adaptive Model Rules,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.1194601058959961,0.13519 +200,Regression,Adaptive Model Rules,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.017618179321289062,0.169486 +220,Regression,Adaptive Model Rules,TrumpApproval,2.0738811328897206,4.417664564856108,-3.890594467356201,0.03579998016357422,0.205189 +240,Regression,Adaptive Model Rules,TrumpApproval,1.9726100065438286,4.2375242409752385,-3.5337340888030546,0.04149913787841797,0.24247600000000002 +260,Regression,Adaptive Model Rules,TrumpApproval,1.8594315384151245,4.074751007989252,-3.248610147038553,0.048842430114746094,0.28140600000000004 +280,Regression,Adaptive Model Rules,TrumpApproval,1.7773205119132678,3.936654153117972,-3.1518424972300867,0.06378841400146484,0.322149 +300,Regression,Adaptive Model Rules,TrumpApproval,1.8265705896173514,3.8591002097544127,-2.923813511442849,0.07349681854248047,0.364943 +320,Regression,Adaptive Model Rules,TrumpApproval,1.7442837931334845,3.739506488697679,-2.866813933026025,0.08107662200927734,0.40978200000000004 +340,Regression,Adaptive Model Rules,TrumpApproval,1.6994316865849048,3.6380049904847285,-2.8674589929341425,0.08619213104248047,0.45684600000000003 +360,Regression,Adaptive Model Rules,TrumpApproval,1.6868885299887,3.5545855692388106,-2.7224500036418355,0.0937795639038086,0.506202 +380,Regression,Adaptive Model Rules,TrumpApproval,1.637461983479605,3.464628975063406,-2.658760364179245,0.09889507293701172,0.560423 +400,Regression,Adaptive Model Rules,TrumpApproval,1.622197889515682,3.392154183911459,-2.6064142473473755,0.10611248016357422,0.624493 +420,Regression,Adaptive Model Rules,TrumpApproval,1.6252883623828789,3.3313119696358306,-2.5933132470830746,0.11011409759521484,0.691125 +440,Regression,Adaptive Model Rules,TrumpApproval,1.663593439145693,3.2993129689970107,-2.4608371725208844,0.11575984954833984,0.760369 +460,Regression,Adaptive Model Rules,TrumpApproval,1.6928806013876205,3.26900202016339,-2.221881423949668,0.12384319305419922,0.832438 +480,Regression,Adaptive Model Rules,TrumpApproval,1.6463369530072471,3.2036213976345094,-2.0231064080329655,0.13150310516357422,0.907505 +500,Regression,Adaptive Model Rules,TrumpApproval,1.6312675040418116,3.1569789450171624,-1.8741285299844175,0.07843685150146484,0.99002 +520,Regression,Adaptive Model Rules,TrumpApproval,1.6486177246548734,3.1232792518100463,-1.81800645719813,0.08357906341552734,1.082168 +540,Regression,Adaptive Model Rules,TrumpApproval,1.664948820150162,3.091452157271598,-1.7507490735781142,0.0873861312866211,1.179534 +560,Regression,Adaptive Model Rules,TrumpApproval,1.6361907885919602,3.043459997537018,-1.7295303491345493,0.08855342864990234,1.466782 +580,Regression,Adaptive Model Rules,TrumpApproval,1.6082012495575047,2.9965453347231947,-1.7114709556760634,0.08905696868896484,1.757406 +600,Regression,Adaptive Model Rules,TrumpApproval,1.622569336171024,2.97009213510141,-1.6343417506962359,0.09091472625732422,2.050417 +620,Regression,Adaptive Model Rules,TrumpApproval,1.6368903964872519,2.946158197159977,-1.5525460315178896,0.0923452377319336,2.345765 +640,Regression,Adaptive Model Rules,TrumpApproval,1.652159107256621,2.9245287804119107,-1.4681901897894076,0.09443950653076172,2.643466 +660,Regression,Adaptive Model Rules,TrumpApproval,1.6570267761004454,2.8972896524900835,-1.4050084478390592,0.09603023529052734,2.943569 +680,Regression,Adaptive Model Rules,TrumpApproval,1.6362052297782712,2.859601997032609,-1.379870428705038,0.09812450408935547,3.2460560000000003 +700,Regression,Adaptive Model Rules,TrumpApproval,1.608205636538717,2.8213269237454877,-1.377433396876134,0.10156917572021484,3.5543920000000004 +720,Regression,Adaptive Model Rules,TrumpApproval,1.5855230254631891,2.785659545407005,-1.3686218528413674,0.1027364730834961,3.8755710000000003 +740,Regression,Adaptive Model Rules,TrumpApproval,1.583695771004626,2.7597111871599203,-1.3233016566851918,0.1038503646850586,4.202987 +760,Regression,Adaptive Model Rules,TrumpApproval,1.5704020318609786,2.7290361106702816,-1.2965538228485634,0.1038503646850586,4.532954 +780,Regression,Adaptive Model Rules,TrumpApproval,1.5638796853366008,2.702190403614744,-1.2616800152467116,0.10642147064208984,4.876142 +800,Regression,Adaptive Model Rules,TrumpApproval,1.5494799828615766,2.674411214594314,-1.2354538504080876,0.10703182220458984,5.2219169999999995 +820,Regression,Adaptive Model Rules,TrumpApproval,1.533437809889996,2.6465115200139584,-1.2130964074464639,0.1085958480834961,5.570086999999999 +840,Regression,Adaptive Model Rules,TrumpApproval,1.5202839319169328,2.6201051582792827,-1.1892919715417847,0.11015987396240234,5.920738999999999 +860,Regression,Adaptive Model Rules,TrumpApproval,1.5178574341866524,2.5988091386120904,-1.1501373691585313,0.11077022552490234,6.280333 +880,Regression,Adaptive Model Rules,TrumpApproval,1.4962844530295305,2.571223801389781,-1.0942757338776041,0.11124706268310547,6.652339 +900,Regression,Adaptive Model Rules,TrumpApproval,1.4724252749133646,2.5436398469986066,-1.0582196084183888,0.11164379119873047,7.031402 +920,Regression,Adaptive Model Rules,TrumpApproval,1.4596881679466962,2.5220256913044325,-1.056635177134157,0.11167049407958984,7.413564 +940,Regression,Adaptive Model Rules,TrumpApproval,1.452139596196528,2.5028075284250018,-1.0425932823285438,0.11270427703857422,7.802334 +960,Regression,Adaptive Model Rules,TrumpApproval,1.4364147887122178,2.481230554777158,-1.0285162299402342,0.11320781707763672,8.193766 +980,Regression,Adaptive Model Rules,TrumpApproval,1.4186260884044517,2.45780687839372,-1.0290538610685447,0.11381816864013672,8.587828 +1000,Regression,Adaptive Model Rules,TrumpApproval,1.3997779646996387,2.434572696055838,-1.024386017127401,0.11442852020263672,8.984547 +1001,Regression,Adaptive Model Rules,TrumpApproval,1.3984653255896196,2.433357833975862,-1.0237164038272608,0.11442852020263672,9.38293 +11,Regression,Streaming Random Patches,ChickWeights,4.674710287324511,12.709622005759085,-206.93269654300337,0.14386653900146484,0.053261 +22,Regression,Streaming Random Patches,ChickWeights,2.741934273684416,9.017856101646904,-85.12629469646626,0.16807842254638672,0.14276 +33,Regression,Streaming Random Patches,ChickWeights,2.3543476002974106,7.430504888974863,-37.34585890537725,0.20960521697998047,0.266148 +44,Regression,Streaming Random Patches,ChickWeights,1.9327820011330463,6.452362261246447,-31.77814024428305,0.24174785614013672,0.646479 +55,Regression,Streaming Random Patches,ChickWeights,2.2606373648191784,6.1461368420669364,-6.374120366305681,0.30608272552490234,1.057843 +66,Regression,Streaming Random Patches,ChickWeights,2.3521495161457713,5.750947689984691,-1.7831107407377038,0.35672664642333984,1.521792 +77,Regression,Streaming Random Patches,ChickWeights,2.707478618787897,5.832856917221716,-0.9188552689556648,0.3732900619506836,2.259814 +88,Regression,Streaming Random Patches,ChickWeights,2.60389034076398,5.525482549715508,-0.5612341217350767,0.41286373138427734,3.03269 +99,Regression,Streaming Random Patches,ChickWeights,2.7646559934763433,5.466320467144536,-0.27797322073999386,0.4623746871948242,3.8541800000000004 +110,Regression,Streaming Random Patches,ChickWeights,2.880719733615897,5.407041915578862,0.12681954319141486,0.5318593978881836,4.717509000000001 +121,Regression,Streaming Random Patches,ChickWeights,3.0896780011355176,5.466874267225462,0.31254594053869156,0.5604543685913086,5.631386000000001 +132,Regression,Streaming Random Patches,ChickWeights,3.270943191870578,5.7618521847151385,0.37587775495273845,0.1946859359741211,6.649151000000001 +143,Regression,Streaming Random Patches,ChickWeights,3.2470159770350198,5.633009027852055,0.5168368848346436,0.22883892059326172,7.703019000000001 +154,Regression,Streaming Random Patches,ChickWeights,3.2192007860807728,5.520141144427338,0.6056681999848637,0.2577199935913086,8.869892000000002 +165,Regression,Streaming Random Patches,ChickWeights,3.5165819956804767,5.874797514643079,0.6409683688760216,0.26273250579833984,10.065309000000003 +176,Regression,Streaming Random Patches,ChickWeights,3.700430602978386,6.068185859760413,0.6911390854727877,0.29411983489990234,11.324773000000002 +187,Regression,Streaming Random Patches,ChickWeights,3.8037309027428843,6.1218084380222,0.7426259865968339,0.3320951461791992,12.629055000000003 +198,Regression,Streaming Random Patches,ChickWeights,4.006649900662983,6.578339511639692,0.7635979888713487,0.25274181365966797,13.981910000000003 +209,Regression,Streaming Random Patches,ChickWeights,4.229383118423565,6.982583803939909,0.780430075854037,0.32021617889404297,15.391493000000002 +220,Regression,Streaming Random Patches,ChickWeights,4.825249759558611,8.423350501384354,0.720252540483964,0.35108089447021484,16.880127 +231,Regression,Streaming Random Patches,ChickWeights,5.088028806665401,8.669832171958772,0.7465054715218886,0.32001399993896484,18.399834000000002 +242,Regression,Streaming Random Patches,ChickWeights,5.462442991686406,9.175585237136575,0.7715339230013022,0.3710927963256836,20.020863000000002 +253,Regression,Streaming Random Patches,ChickWeights,5.5636194675564115,9.260101660572655,0.797901929856006,0.41926097869873047,21.677784000000003 +264,Regression,Streaming Random Patches,ChickWeights,6.261116867150435,10.599777327157994,0.7580584961853923,0.3416013717651367,23.387217000000003 +275,Regression,Streaming Random Patches,ChickWeights,6.742618468073929,11.802240597789721,0.7360625543191792,0.3569021224975586,25.127202000000004 +286,Regression,Streaming Random Patches,ChickWeights,7.039594962415952,12.249488193444416,0.7537446936710837,0.39655208587646484,26.911059000000005 +297,Regression,Streaming Random Patches,ChickWeights,7.148800229885712,12.311677740983953,0.7842510327710687,0.4258260726928711,28.739538000000007 +308,Regression,Streaming Random Patches,ChickWeights,7.753683144422786,13.244190950829555,0.7776398094561477,0.45011425018310547,30.593856000000006 +319,Regression,Streaming Random Patches,ChickWeights,8.773143666519827,15.939753659782179,0.7077199430319765,0.4718656539916992,32.508295000000004 +330,Regression,Streaming Random Patches,ChickWeights,9.312574124937234,16.796832021919023,0.7222505421616592,0.3236379623413086,34.514388000000004 +341,Regression,Streaming Random Patches,ChickWeights,9.544591695703465,16.94083213248977,0.7470058810264122,0.3676939010620117,36.550995 +352,Regression,Streaming Random Patches,ChickWeights,9.680039805071173,17.006622056031052,0.7682275557667291,0.36295223236083984,38.648436000000004 +363,Regression,Streaming Random Patches,ChickWeights,10.417098847501563,18.381838377902795,0.7370670774420947,0.3571195602416992,40.771216 +374,Regression,Streaming Random Patches,ChickWeights,11.080869197293334,19.965812195666537,0.7148404591067161,0.39987850189208984,42.937715000000004 +385,Regression,Streaming Random Patches,ChickWeights,11.60940210623338,20.687378926969966,0.7291406299077132,0.3288450241088867,45.213373000000004 +396,Regression,Streaming Random Patches,ChickWeights,11.737814918904208,20.678726756260627,0.7476640805491588,0.4024953842163086,47.506899000000004 +407,Regression,Streaming Random Patches,ChickWeights,12.682108791666492,22.361726245252385,0.7257144517605874,0.4475545883178711,49.84079500000001 +418,Regression,Streaming Random Patches,ChickWeights,13.622708961705229,24.146094170569185,0.6997951546356598,0.4968290328979492,52.212878 +429,Regression,Streaming Random Patches,ChickWeights,14.165217959113354,24.88032134675199,0.7167593971826183,0.5376424789428711,54.630802 +440,Regression,Streaming Random Patches,ChickWeights,14.411006646174876,24.97835315387625,0.7347289581181171,0.5657072067260742,57.077112 +451,Regression,Streaming Random Patches,ChickWeights,14.766578325445964,25.376772271610328,0.7378384948653763,0.25832653045654297,59.570857 +462,Regression,Streaming Random Patches,ChickWeights,16.09445226127713,28.12961105819035,0.6974376845026461,0.3047628402709961,62.101037999999996 +473,Regression,Streaming Random Patches,ChickWeights,16.916275460891086,29.341089843915018,0.7093290035354769,0.3544912338256836,64.67061 +484,Regression,Streaming Random Patches,ChickWeights,17.222566694739786,29.549549967606488,0.7213397403469026,0.39832592010498047,67.26974899999999 +495,Regression,Streaming Random Patches,ChickWeights,17.854950072386483,30.34354672604944,0.7235900637963901,0.43242549896240234,69.89740799999998 +506,Regression,Streaming Random Patches,ChickWeights,18.84874733203415,31.79966974813451,0.7052484004379906,0.46784114837646484,72.66954099999998 +517,Regression,Streaming Random Patches,ChickWeights,19.785853660032195,33.20181112471305,0.6967783021275082,0.49755001068115234,75.49558399999998 +528,Regression,Streaming Random Patches,ChickWeights,20.52664258005787,34.100310164439925,0.7124234805234935,0.5351285934448242,78.36005299999998 +539,Regression,Streaming Random Patches,ChickWeights,20.766026265849113,34.21097619783695,0.7225061795517687,0.576685905456543,81.26779099999997 +550,Regression,Streaming Random Patches,ChickWeights,21.840503815170695,36.158966072689324,0.6995590139862528,0.4753904342651367,84.25568299999998 +561,Regression,Streaming Random Patches,ChickWeights,22.596906243133255,37.108967777985264,0.6974029137241997,0.5189352035522461,87.27382099999997 +572,Regression,Streaming Random Patches,ChickWeights,23.534320250737128,38.28067851879141,0.7021424273708647,0.5585355758666992,90.32555299999997 +578,Regression,Streaming Random Patches,ChickWeights,23.709683411591413,38.441629018276466,0.7069383356385298,0.3551816940307617,93.40141099999997 +20,Regression,Streaming Random Patches,TrumpApproval,2.677140920600926,9.804891856735377,-224.4966127051096,0.2373647689819336,0.078317 +40,Regression,Streaming Random Patches,TrumpApproval,2.42676306487335,7.1506632844470275,-19.556918338481843,0.3270711898803711,0.26006399999999996 +60,Regression,Streaming Random Patches,TrumpApproval,1.8116457742622056,5.852230884873156,-17.563213740222555,0.34931087493896484,0.628767 +80,Regression,Streaming Random Patches,TrumpApproval,1.5261658032230543,5.084894428469453,-13.057813649460385,0.3968191146850586,1.163603 +100,Regression,Streaming Random Patches,TrumpApproval,1.4048851039170587,4.580518627305071,-6.157363117938322,0.41078853607177734,1.7740749999999998 +120,Regression,Streaming Random Patches,TrumpApproval,1.2872329076385731,4.198963935277897,-4.1024421406573515,0.4562673568725586,2.4559889999999998 +140,Regression,Streaming Random Patches,TrumpApproval,1.4191481295394186,4.9019146331166,-6.185954638838571,0.20268917083740234,3.2282499999999996 +160,Regression,Streaming Random Patches,TrumpApproval,1.3292908695512107,4.594852312450113,-4.568052693162012,0.32157421112060547,4.121231 +180,Regression,Streaming Random Patches,TrumpApproval,1.2559271503392595,4.341680890984575,-3.3661470093978725,0.4017667770385742,5.147797 +200,Regression,Streaming Random Patches,TrumpApproval,1.169313410896163,4.12134361195162,-2.94593273053925,0.47729015350341797,6.3380719999999995 +220,Regression,Streaming Random Patches,TrumpApproval,1.1066399346497042,3.9344660517514094,-2.8792501333638807,0.5200605392456055,7.615136 +240,Regression,Streaming Random Patches,TrumpApproval,1.0535228972416337,3.7704097053754206,-2.5892915995637975,0.590418815612793,8.98983 +260,Regression,Streaming Random Patches,TrumpApproval,1.0002808672832586,3.6243313765079748,-2.3612477765650133,0.677699089050293,10.49221 +280,Regression,Streaming Random Patches,TrumpApproval,0.9484528900796008,3.4935839886686573,-2.269856713922535,0.7459287643432617,12.09809 +300,Regression,Streaming Random Patches,TrumpApproval,0.9319658343242508,3.3810597344709987,-2.011909605217061,0.8300580978393555,13.828035999999999 +320,Regression,Streaming Random Patches,TrumpApproval,0.9015525068993323,3.2761126415748776,-1.9678527090537403,0.8134641647338867,15.697273999999998 +340,Regression,Streaming Random Patches,TrumpApproval,0.9086073156973856,3.2065165500712434,-2.0044577988421626,0.7938528060913086,17.678026 +360,Regression,Streaming Random Patches,TrumpApproval,0.9209686764414103,3.130698586248577,-1.8875761151685357,0.8660383224487305,19.789752 +380,Regression,Streaming Random Patches,TrumpApproval,0.9054594388814018,3.0518145886207013,-1.8388130234405762,0.9304952621459961,22.025823 +400,Regression,Streaming Random Patches,TrumpApproval,0.9021459083449618,2.9892737243691805,-1.8006304820861794,0.9046812057495117,24.377516 +420,Regression,Streaming Random Patches,TrumpApproval,0.9000900027115483,2.937103674242639,-1.7932063526136273,0.2984609603881836,26.835964 +440,Regression,Streaming Random Patches,TrumpApproval,0.884833385913356,2.873027664171451,-1.6243016536608597,0.3453207015991211,29.357596 +460,Regression,Streaming Random Patches,TrumpApproval,0.8690754265879537,2.8131168800056585,-1.3859136859847618,0.3807516098022461,31.984181 +480,Regression,Streaming Random Patches,TrumpApproval,0.8473225380080763,2.7551095271995596,-1.235881587238783,0.44824886322021484,34.68911 +500,Regression,Streaming Random Patches,TrumpApproval,0.8286186581223807,2.701061117260836,-1.1039316995995572,0.49751949310302734,37.472375 +520,Regression,Streaming Random Patches,TrumpApproval,0.8308331247066605,2.6760879938297655,-1.0688124961584973,0.37538814544677734,40.349897 +540,Regression,Streaming Random Patches,TrumpApproval,0.8063786739892863,2.6263308571208617,-0.9852938090834253,0.40636730194091797,43.316687 +560,Regression,Streaming Random Patches,TrumpApproval,0.7997929461413226,2.582727501713279,-0.9656668153374994,0.43744945526123047,46.3679 +580,Regression,Streaming Random Patches,TrumpApproval,0.7908850979728871,2.5414571653673215,-0.950423138286411,0.49677181243896484,49.479346 +600,Regression,Streaming Random Patches,TrumpApproval,0.7789627943481009,2.500361162030882,-0.8669718089652279,0.569575309753418,52.654549 +620,Regression,Streaming Random Patches,TrumpApproval,0.7682254429218135,2.4616773328611172,-0.7820656947310429,0.6584272384643555,55.885269 +640,Regression,Streaming Random Patches,TrumpApproval,0.756836908225871,2.4246570785119217,-0.6965531574296129,0.7120962142944336,59.181412 +660,Regression,Streaming Random Patches,TrumpApproval,0.7406340846119412,2.388088765643962,-0.6339309775627988,0.8089780807495117,62.54643 +680,Regression,Streaming Random Patches,TrumpApproval,0.7257657440750075,2.3532857176647086,-0.6117268738432657,0.8398160934448242,65.979515 +700,Regression,Streaming Random Patches,TrumpApproval,0.7284794894639326,2.3250297797266612,-0.6145762958857721,0.9275884628295898,69.48963400000001 +720,Regression,Streaming Random Patches,TrumpApproval,0.7231955460113827,2.297270435922827,-0.6108826519065647,0.9087285995483398,73.071921 +740,Regression,Streaming Random Patches,TrumpApproval,0.7217944839950929,2.2699024953355416,-0.5717835473178023,0.8719320297241211,76.723943 +760,Regression,Streaming Random Patches,TrumpApproval,0.7121024512438853,2.241058668519108,-0.5486900768629306,0.9295186996459961,80.452493 +780,Regression,Streaming Random Patches,TrumpApproval,0.7019422114012909,2.213016497735788,-0.5169405784918113,1.0446271896362305,84.252351 +800,Regression,Streaming Random Patches,TrumpApproval,0.7005931807120314,2.1884283322336215,-0.49683523063919743,1.1031560897827148,88.13395700000001 +820,Regression,Streaming Random Patches,TrumpApproval,0.6997484436891046,2.169363820936814,-0.48702250637871436,1.0328702926635742,92.08914500000002 +840,Regression,Streaming Random Patches,TrumpApproval,0.6949195885567419,2.14957176369946,-0.4735678496288336,0.7475957870483398,96.10836100000002 +860,Regression,Streaming Random Patches,TrumpApproval,0.6920590805093112,2.1264870362465933,-0.439603572136809,0.8119535446166992,100.18224400000001 +880,Regression,Streaming Random Patches,TrumpApproval,0.6882027439938415,2.1038322601427426,-0.40209138862407245,0.8655576705932617,104.313167 +900,Regression,Streaming Random Patches,TrumpApproval,0.6818594219129391,2.085410616994055,-0.38345052454273,0.8467855453491211,108.50343900000001 +920,Regression,Streaming Random Patches,TrumpApproval,0.6756248333192205,2.0637081851469703,-0.37706620583821104,0.9400205612182617,112.75318600000001 +940,Regression,Streaming Random Patches,TrumpApproval,0.6689624136970388,2.0428592411141833,-0.3608300191067024,0.8168668746948242,117.06153900000001 +960,Regression,Streaming Random Patches,TrumpApproval,0.6627773066160889,2.022520414368002,-0.3478143420231987,0.9120321273803711,121.43268700000002 +980,Regression,Streaming Random Patches,TrumpApproval,0.6600305544016135,2.003593726688123,-0.34839580077130505,0.9782476425170898,125.86239700000002 +1000,Regression,Streaming Random Patches,TrumpApproval,0.657029407021691,1.9853014454830338,-0.3461726175729922,1.0593442916870117,130.37428000000003 +1001,Regression,Streaming Random Patches,TrumpApproval,0.6566965628025029,1.9843359394661053,-0.34576147632314735,1.0646085739135742,134.90266400000002 +11,Regression,Bagging,ChickWeights,10.955590565999659,17.7409835250609,-404.147256051216,0.1553668975830078,0.005094 +22,Regression,Bagging,ChickWeights,5.88626580700965,12.566688603347808,-166.25182631838038,0.1681652069091797,0.018278 +33,Regression,Bagging,ChickWeights,4.383857039198176,10.299865918219764,-72.67921052893462,0.20520591735839844,0.039075 +44,Regression,Bagging,ChickWeights,3.446496162870555,8.931116231999566,-61.79980671874969,0.2209186553955078,0.065167 +55,Regression,Bagging,ChickWeights,3.3513349782155033,8.247717183177938,-12.279242202465667,0.2687969207763672,0.09656600000000001 +66,Regression,Bagging,ChickWeights,3.889627188952696,8.0458642201752,-4.4474976461238604,0.3383464813232422,0.144605 +77,Regression,Bagging,ChickWeights,4.337751636727128,7.9681159743419645,-2.5808890563388096,0.3940753936767578,0.201306 +88,Regression,Bagging,ChickWeights,4.489908334389532,7.740787033322287,-2.0640641214272355,0.4405231475830078,0.274421 +99,Regression,Bagging,ChickWeights,4.7831270806190425,7.705843596650206,-1.5396388125269618,0.4615802764892578,0.37284300000000004 +110,Regression,Bagging,ChickWeights,4.73395080514245,7.47334250555501,-0.6680701376440403,0.4910602569580078,0.5845670000000001 +121,Regression,Bagging,ChickWeights,4.733710015085173,7.331306378435282,-0.23631246502535208,0.5042209625244141,0.806575 +132,Regression,Bagging,ChickWeights,4.565752852065114,7.0976416640915465,0.05294854472396571,0.5126438140869141,1.039517 +143,Regression,Bagging,ChickWeights,4.439022558662509,6.895745596080793,0.2759386934515202,0.5216274261474609,1.284562 +154,Regression,Bagging,ChickWeights,4.362170284876481,6.736533340066285,0.4127346685162743,0.5262050628662109,1.543379 +165,Regression,Bagging,ChickWeights,4.647894929983432,7.008861526290804,0.48897532974091285,0.5290126800537109,1.82575 +176,Regression,Bagging,ChickWeights,4.817744211127824,7.136288548419971,0.5728405597677722,0.5066156387329102,2.145522 +187,Regression,Bagging,ChickWeights,4.867036081233358,7.152118590173611,0.6487028411390494,0.4790925979614258,2.491984 +198,Regression,Bagging,ChickWeights,4.887061675652274,7.15502256260352,0.720333392468735,0.3514680862426758,2.944156 +209,Regression,Bagging,ChickWeights,5.070260383978678,7.484020932149266,0.7477619935177958,0.3277406692504883,3.412471 +220,Regression,Bagging,ChickWeights,5.671227628293087,8.602358503958763,0.7082361492582977,0.3353548049926758,3.897275 +231,Regression,Bagging,ChickWeights,5.872335352103937,8.83175934179542,0.7369479677711775,0.37228870391845703,4.389179 +242,Regression,Bagging,ChickWeights,6.107145463120707,9.222510821361377,0.769191114739663,0.3991250991821289,4.889463 +253,Regression,Bagging,ChickWeights,6.19844823305091,9.33633324769997,0.7945607849348244,0.42070865631103516,5.506109 +264,Regression,Bagging,ChickWeights,6.823605404288741,10.586090935492885,0.758682880699561,0.43335819244384766,6.131382 +275,Regression,Bagging,ChickWeights,7.289576170484155,11.670233638164651,0.7419337665758028,0.4418039321899414,6.777342 +286,Regression,Bagging,ChickWeights,7.579012857443305,12.145524073459754,0.75790700185782,0.45081424713134766,7.439278 +297,Regression,Bagging,ChickWeights,7.564986803201262,12.135208564512551,0.7903915740986345,0.45409488677978516,8.115354 +308,Regression,Bagging,ChickWeights,8.103353916061925,13.02855032884451,0.7848217554522041,0.4577798843383789,8.913327 +319,Regression,Bagging,ChickWeights,9.2182891996096,15.75502466975724,0.7144552709875674,0.4609994888305664,9.721339 +330,Regression,Bagging,ChickWeights,9.685083231372472,16.400765556025647,0.7351946818510116,0.4719209671020508,10.546217 +341,Regression,Bagging,ChickWeights,9.903299441393282,16.527032528363478,0.759214288686034,0.47708606719970703,11.387382 +352,Regression,Bagging,ChickWeights,10.047801743751695,16.62843798311862,0.778421004008311,0.48488330841064453,12.279877 +363,Regression,Bagging,ChickWeights,10.963674059851892,18.110346084278056,0.7447765461541069,0.48735523223876953,13.191450000000001 +374,Regression,Bagging,ChickWeights,11.492835005144466,19.28081121430548,0.7340717056357994,0.4739046096801758,14.120497000000002 +385,Regression,Bagging,ChickWeights,11.898657720927194,19.943213385356135,0.7482768269892894,0.4488801956176758,15.069160000000002 +396,Regression,Bagging,ChickWeights,12.0617729851989,19.965773188137263,0.7647640178574115,0.4161386489868164,16.114715 +407,Regression,Bagging,ChickWeights,12.97304553348899,21.57136186484404,0.7447607843499935,0.40607547760009766,17.182106 +418,Regression,Bagging,ChickWeights,13.747411939847145,23.06575414024212,0.7260576137332548,0.42958927154541016,18.259955 +429,Regression,Bagging,ChickWeights,14.305030376306712,23.986335540211613,0.7367482003695625,0.4628152847290039,19.347052 +440,Regression,Bagging,ChickWeights,14.526268288674354,24.074522605416067,0.7535790611891788,0.4899911880493164,20.478651000000003 +451,Regression,Bagging,ChickWeights,15.027594657922048,24.671918598232004,0.7521995995009789,0.5175485610961914,21.623166 +462,Regression,Bagging,ChickWeights,16.208274238827823,27.03842360672758,0.7204560380476568,0.5713090896606445,22.784126 +473,Regression,Bagging,ChickWeights,16.99589357541462,28.364120175265192,0.7283636722963454,0.5903940200805664,23.973479 +484,Regression,Bagging,ChickWeights,17.304815327407063,28.547476213614512,0.739918935022724,0.6084756851196289,25.225522 +495,Regression,Bagging,ChickWeights,17.747173803776352,29.064129392830434,0.7464079684248853,0.6325922012329102,26.517497000000002 +506,Regression,Bagging,ChickWeights,18.655435380807354,30.57773482627066,0.7274654471662664,0.6401453018188477,27.826703000000002 +517,Regression,Bagging,ChickWeights,19.38212758204995,31.566942752755278,0.7259045847606268,0.5900964736938477,29.161388000000002 +528,Regression,Bagging,ChickWeights,20.162555380752366,32.73116726334994,0.7350525453098611,0.6013956069946289,30.577640000000002 +539,Regression,Bagging,ChickWeights,20.34377517847412,32.75647044736101,0.7456003073902285,0.6148271560668945,32.006392000000005 +550,Regression,Bagging,ChickWeights,21.397093652240404,34.43808497807088,0.7274757471078548,0.6217546463012695,33.45442800000001 +561,Regression,Bagging,ChickWeights,22.130535392790673,35.39551310036421,0.7247017706467436,0.6181917190551758,34.916661000000005 +572,Regression,Bagging,ChickWeights,22.976096797270415,36.539264510866154,0.7286255260499503,0.6243486404418945,36.463466000000004 +578,Regression,Bagging,ChickWeights,23.114298050830314,36.631109645590286,0.7338934315030725,0.6280336380004883,38.020312000000004 +20,Regression,Bagging,TrumpApproval,6.57361785669815,13.877675781396096,-450.73930630825186,0.3875865936279297,0.030628 +40,Regression,Bagging,TrumpApproval,4.357601810962072,9.93598927447802,-38.690592530050864,0.5688495635986328,0.09352200000000001 +60,Regression,Bagging,TrumpApproval,3.120546196671925,8.124382016407804,-34.775930157070896,0.6946392059326172,0.180013 +80,Regression,Bagging,TrumpApproval,2.5823668216656817,7.068571931029129,-26.165472568815836,0.7988948822021484,0.365225 +100,Regression,Bagging,TrumpApproval,2.6103510398716643,6.439797187103485,-13.147122820254193,0.9021625518798828,0.589181 +120,Regression,Bagging,TrumpApproval,2.5653436103516496,5.96335184363353,-9.29140495411716,0.9421710968017578,0.842127 +140,Regression,Bagging,TrumpApproval,2.4314692166818666,5.556159680491977,-8.232140838080387,0.9627094268798828,1.43468 +160,Regression,Bagging,TrumpApproval,2.270493582871441,5.217534738727647,-6.179445803611509,1.0016803741455078,2.055929 +180,Regression,Bagging,TrumpApproval,2.1841879014169865,4.9594120506005,-4.6969569828406526,0.9622507095336914,2.757735 +200,Regression,Bagging,TrumpApproval,2.030794616399332,4.7110231793054895,-4.155876544063708,0.5397500991821289,3.551711 +220,Regression,Bagging,TrumpApproval,1.922882727301643,4.50300441964265,-4.081371242371108,0.37749576568603516,4.407055 +240,Regression,Bagging,TrumpApproval,1.8390508968191754,4.321014818317665,-3.7141474735663893,0.43126392364501953,5.281937999999999 +260,Regression,Bagging,TrumpApproval,1.7379678526387645,4.155226166492631,-3.4180849750109363,0.4941263198852539,6.197258999999999 +280,Regression,Bagging,TrumpApproval,1.7042826877160742,4.0269186303191065,-3.3444224917120184,0.5997896194458008,7.197467999999999 +300,Regression,Bagging,TrumpApproval,1.6796571065333832,3.9174008876388,-3.043265693703045,0.6906900405883789,8.219126999999999 +320,Regression,Bagging,TrumpApproval,1.5891460162001483,3.7936804881645676,-2.979662055693274,0.757817268371582,9.288978999999998 +340,Regression,Bagging,TrumpApproval,1.5335884019062007,3.685003453386448,-2.968029890458662,0.7969903945922852,10.383406999999998 +360,Regression,Bagging,TrumpApproval,1.54418408246079,3.606838798974545,-2.832696164635261,0.8751077651977539,11.515104999999998 +380,Regression,Bagging,TrumpApproval,1.5054402320411853,3.5177984273762375,-2.771919367898169,0.9264421463012695,12.700466999999998 +400,Regression,Bagging,TrumpApproval,1.4723491084260332,3.435525460733128,-2.699225318590951,0.9911317825317383,13.924277999999997 +420,Regression,Bagging,TrumpApproval,1.429579158986208,3.3562143901072865,-2.6472359354971333,0.9703760147094727,15.190464999999998 +440,Regression,Bagging,TrumpApproval,1.3992424504019558,3.2853835752695946,-2.4316761923151153,1.0316247940063477,16.527614999999997 +460,Regression,Bagging,TrumpApproval,1.365864594828797,3.217129802398014,-2.120443637805541,1.1097803115844727,17.906522 +480,Regression,Bagging,TrumpApproval,1.3301487592579586,3.151695763852486,-1.9259010739386908,1.1814966201782227,19.341224 +500,Regression,Bagging,TrumpApproval,1.2968821746115176,3.0904885141585767,-1.7543370405557224,1.2276010513305664,20.84359 +520,Regression,Bagging,TrumpApproval,1.2678501702074907,3.0331483120333496,-1.6577103317872361,1.2783823013305664,22.421222999999998 +540,Regression,Bagging,TrumpApproval,1.2343399552126226,2.9775564396478966,-1.551795811786203,1.2796869277954102,24.086657999999996 +560,Regression,Bagging,TrumpApproval,1.220715255826944,2.9300367331798807,-1.5298738258017646,1.2170305252075195,25.790580999999996 +580,Regression,Bagging,TrumpApproval,1.1924146311245054,2.8809984439523286,-1.5063937515255619,1.0756006240844727,27.649708999999994 +600,Regression,Bagging,TrumpApproval,1.1793821598427467,2.837096711415006,-1.4037015974174163,0.9519128799438477,29.574726999999996 +620,Regression,Bagging,TrumpApproval,1.1645342598465298,2.7961228756221597,-1.2991852345603885,0.9679117202758789,31.579397999999998 +640,Regression,Bagging,TrumpApproval,1.1398425529628198,2.753175690936408,-1.1874325743915652,0.936314582824707,33.628169 +660,Regression,Bagging,TrumpApproval,1.1198821044801988,2.7133180185933967,-1.1092797015680484,0.9601030349731445,35.724068 +680,Regression,Bagging,TrumpApproval,1.103543375947734,2.675681585437618,-1.0835838801311364,1.0132951736450195,37.850435000000004 +700,Regression,Bagging,TrumpApproval,1.0951788603095205,2.6436744635360436,-1.0874567422761272,1.0955934524536133,40.041881000000004 +720,Regression,Bagging,TrumpApproval,1.073603439060177,2.607328103868497,-1.0750617689055861,1.1506471633911133,42.267731000000005 +740,Regression,Bagging,TrumpApproval,1.052216298294681,2.5725220480150233,-1.0188151031858665,1.1900300979614258,44.534549000000005 +760,Regression,Bagging,TrumpApproval,1.0329729065757678,2.539265324444459,-0.9882648176410449,1.2228517532348633,46.85075200000001 +780,Regression,Bagging,TrumpApproval,1.0152010354431573,2.5074934380267417,-0.9475063222432678,1.2825212478637695,49.22632000000001 +800,Regression,Bagging,TrumpApproval,1.007374170791078,2.4793027589713232,-0.9211818120686948,1.3126497268676758,51.66223500000001 +820,Regression,Bagging,TrumpApproval,1.002386830132377,2.4547936379778226,-0.9040692252875042,1.3594255447387695,54.13772100000001 +840,Regression,Bagging,TrumpApproval,0.9929186057762197,2.428247138481691,-0.8804076589484491,1.397130012512207,56.66204300000001 +860,Regression,Bagging,TrumpApproval,0.9756374748391698,2.400488599154532,-0.8344958433267429,1.4288606643676758,59.23948900000001 +880,Regression,Bagging,TrumpApproval,0.964181960731443,2.37450147105602,-0.7860721035239808,1.4496355056762695,61.87688900000001 +900,Regression,Bagging,TrumpApproval,0.9549728240782616,2.3497625798451978,-0.7564202624419067,1.3498811721801758,64.59788000000002 +920,Regression,Bagging,TrumpApproval,0.9412862327577896,2.3247867434211136,-0.747529388757789,1.1945161819458008,67.39612200000002 +940,Regression,Bagging,TrumpApproval,0.934469347520636,2.302477656767798,-0.7286928787152502,1.2202577590942383,70.24630900000002 +960,Regression,Bagging,TrumpApproval,0.9259837543593707,2.280476047701142,-0.7135440601163805,1.2635469436645508,73.13750900000002 +980,Regression,Bagging,TrumpApproval,0.9196316545824527,2.2597103614150886,-0.715155968132227,1.2794008255004883,76.07904100000002 +1000,Regression,Bagging,TrumpApproval,0.9087747756519627,2.2382775608394114,-0.7111012811273396,1.3157854080200195,79.06112300000002 +1001,Regression,Bagging,TrumpApproval,0.9082029688272106,2.2371845268363217,-0.710571805505718,1.3157854080200195,82.06893700000002 +11,Regression,Exponentially Weighted Average,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.06525707244873047,0.004749 +22,Regression,Exponentially Weighted Average,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.07764339447021484,0.02229 +33,Regression,Exponentially Weighted Average,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.09733104705810547,0.042479 +44,Regression,Exponentially Weighted Average,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.10875988006591797,0.065964 +55,Regression,Exponentially Weighted Average,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.13167858123779297,0.11433299999999999 +66,Regression,Exponentially Weighted Average,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.1604146957397461,0.17057299999999997 +77,Regression,Exponentially Weighted Average,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.18252849578857422,0.23188899999999998 +88,Regression,Exponentially Weighted Average,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.2035512924194336,0.308411 +99,Regression,Exponentially Weighted Average,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.21530437469482422,0.393152 +110,Regression,Exponentially Weighted Average,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.2280874252319336,0.484559 +121,Regression,Exponentially Weighted Average,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.23778057098388672,0.583103 +132,Regression,Exponentially Weighted Average,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.24797725677490234,0.792791 +143,Regression,Exponentially Weighted Average,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.22072410583496094,1.018133 +154,Regression,Exponentially Weighted Average,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.2406787872314453,1.2542229999999999 +165,Regression,Exponentially Weighted Average,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.25681114196777344,1.5160939999999998 +176,Regression,Exponentially Weighted Average,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.2727985382080078,1.7876089999999998 +187,Regression,Exponentially Weighted Average,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.2873516082763672,2.0795459999999997 +198,Regression,Exponentially Weighted Average,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.30018043518066406,2.3857599999999994 +209,Regression,Exponentially Weighted Average,ChickWeights,57.5645933014354,59.46206220864915,-14.922837840066967,0.27625083923339844,2.7086299999999994 +220,Regression,Exponentially Weighted Average,ChickWeights,58.69090909090908,60.81327606250582,-13.581197962556498,0.2926349639892578,3.0480419999999993 +231,Regression,Exponentially Weighted Average,ChickWeights,60.25541125541125,62.66764529032318,-12.244451024360147,0.30736351013183594,3.437715999999999 +242,Regression,Exponentially Weighted Average,ChickWeights,62.17355371900826,65.06963847478845,-10.489760184397113,0.32158851623535156,3.8493299999999993 +253,Regression,Exponentially Weighted Average,ChickWeights,63.936758893280626,67.17295239601157,-9.634560128382748,0.3348064422607422,4.393024 +264,Regression,Exponentially Weighted Average,ChickWeights,65.10606060606062,68.57980310513724,-9.127665748505592,0.3451099395751953,4.977281 +275,Regression,Exponentially Weighted Average,ChickWeights,66.61454545454548,70.46451073219248,-8.408339126213217,0.35480308532714844,5.586244 +286,Regression,Exponentially Weighted Average,ChickWeights,68.48951048951052,72.8020594498525,-7.6983532427125105,0.3655261993408203,6.228505 +297,Regression,Exponentially Weighted Average,ChickWeights,70.55218855218858,75.3669362796119,-7.08492451355157,0.3711223602294922,6.899176000000001 +308,Regression,Exponentially Weighted Average,ChickWeights,72.39285714285718,77.65033596401675,-6.643510181414674,0.3809375762939453,7.597348 +319,Regression,Exponentially Weighted Average,ChickWeights,73.45454545454551,79.15086186624424,-6.206879640065647,0.39273643493652344,8.345726 +330,Regression,Exponentially Weighted Average,ChickWeights,75.77878787878792,82.20832738177494,-5.653192449779911,0.40398597717285156,9.225203 +341,Regression,Exponentially Weighted Average,ChickWeights,77.92375366568919,84.89106353805269,-5.352795814687307,0.4136791229248047,10.124705 +352,Regression,Exponentially Weighted Average,ChickWeights,80.04545454545458,87.49376601169416,-5.134510311668016,0.4233722686767578,11.048248000000001 +363,Regression,Exponentially Weighted Average,ChickWeights,80.99724517906337,88.57562798692558,-5.105139086016474,0.43306541442871094,11.989622 +374,Regression,Exponentially Weighted Average,ChickWeights,82.77807486631018,90.83029071422122,-4.901675845817959,0.45247840881347656,12.967698 +385,Regression,Exponentially Weighted Average,ChickWeights,85.1766233766234,93.99517810235533,-4.591702735915359,0.4681224822998047,14.032748 +396,Regression,Exponentially Weighted Average,ChickWeights,87.26767676767678,96.48964983485283,-4.494054297851511,0.48465919494628906,15.12168 +407,Regression,Exponentially Weighted Average,ChickWeights,89.00737100737103,98.71879502607636,-4.345544683073043,0.4991130828857422,16.236767999999998 +418,Regression,Exponentially Weighted Average,ChickWeights,90.57416267942587,100.72635724110245,-4.224084264201084,0.5109386444091797,17.416859999999996 +429,Regression,Exponentially Weighted Average,ChickWeights,93.12121212121215,104.19735398794236,-3.9677178403495805,0.5206584930419922,18.643557999999995 +440,Regression,Exponentially Weighted Average,ChickWeights,95.41818181818185,107.03565676064125,-3.8710119659250104,0.5303516387939453,19.910713999999995 +451,Regression,Exponentially Weighted Average,ChickWeights,97.16629711751665,109.07665280092142,-3.843505105397095,0.5280742645263672,21.225085999999994 +462,Regression,Exponentially Weighted Average,ChickWeights,98.71645021645023,111.17636431671961,-3.72620239405422,0.5443019866943359,22.607732999999993 +473,Regression,Exponentially Weighted Average,ChickWeights,101.54122621564484,115.20584573786859,-3.4812404756668593,0.5577869415283203,24.015635999999994 +484,Regression,Exponentially Weighted Average,ChickWeights,103.77066115702482,117.90601559037043,-3.4365483842712585,0.5712184906005859,25.474227999999993 +495,Regression,Exponentially Weighted Average,ChickWeights,106.02424242424244,120.71525892518193,-3.37467008920777,0.5825443267822266,26.96779499999999 +506,Regression,Exponentially Weighted Average,ChickWeights,107.31620553359684,122.26004165941237,-3.356924458603192,2.7805843353271484,30.43480299999999 +517,Regression,Exponentially Weighted Average,ChickWeights,109.39651837524178,124.91233289427785,-3.291877964737682,2.825040817260742,33.93932499999999 +528,Regression,Exponentially Weighted Average,ChickWeights,112.36553030303028,129.1106745698386,-3.1225038051323804,2.876035690307617,37.48648499999999 +539,Regression,Exponentially Weighted Average,ChickWeights,114.52504638218922,131.65752925403248,-3.109734667916423,2.927671432495117,41.07985699999999 +550,Regression,Exponentially Weighted Average,ChickWeights,115.89999999999996,133.35909826820617,-3.0866973064470367,2.9773387908935547,44.71417799999999 +561,Regression,Exponentially Weighted Average,ChickWeights,117.86452762923346,135.8046463151548,-3.0526234314410727,3.0208263397216797,48.39640499999999 +572,Regression,Exponentially Weighted Average,ChickWeights,120.54020979020974,139.4624607986965,-2.953338846956928,3.065652847290039,52.120071999999986 +578,Regression,Exponentially Weighted Average,ChickWeights,121.81833910034597,141.00422703423635,-2.942935834251463,3.092409133911133,55.885136999999986 +20,Regression,Exponentially Weighted Average,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.1445150375366211,0.017697 +40,Regression,Exponentially Weighted Average,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.21173763275146484,0.059669 +60,Regression,Exponentially Weighted Average,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.25832653045654297,0.116093 +80,Regression,Exponentially Weighted Average,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.3003568649291992,0.19454100000000002 +100,Regression,Exponentially Weighted Average,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.3407926559448242,0.29552300000000004 +120,Regression,Exponentially Weighted Average,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.3709287643432617,0.510712 +140,Regression,Exponentially Weighted Average,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.3974485397338867,0.760252 +160,Regression,Exponentially Weighted Average,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.3402233123779297,1.044132 +180,Regression,Exponentially Weighted Average,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.3811016082763672,1.367502 +200,Regression,Exponentially Weighted Average,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.3127880096435547,1.794637 +220,Regression,Exponentially Weighted Average,TrumpApproval,39.727043136363626,39.77723500009918,-395.50198072931875,0.3578624725341797,2.266575 +240,Regression,Exponentially Weighted Average,TrumpApproval,39.56323079166665,39.61325406766278,-395.19837684116754,0.3875255584716797,2.775551 +260,Regression,Exponentially Weighted Average,TrumpApproval,39.42014538461535,39.46968290441584,-397.63185900832246,0.42084693908691406,3.358614 +280,Regression,Exponentially Weighted Average,TrumpApproval,39.33200189285712,39.37942345737111,-414.45601593500356,0.47014808654785156,4.097303 +300,Regression,Exponentially Weighted Average,TrumpApproval,39.18435719999999,39.23275803924839,-404.5402138221895,0.5117359161376953,4.882924 +320,Regression,Exponentially Weighted Average,TrumpApproval,39.13568690624999,39.1818628962716,-423.5167725219512,0.5480670928955078,5.740529 +340,Regression,Exponentially Weighted Average,TrumpApproval,39.14620944117645,39.18989510023786,-447.7943063391533,0.5615406036376953,6.72918 +360,Regression,Exponentially Weighted Average,TrumpApproval,39.24072974999997,39.28395553300239,-453.6543473793619,0.5990085601806641,7.76074 +380,Regression,Exponentially Weighted Average,TrumpApproval,39.29597665789471,39.337699215460226,-470.6701690846498,0.6312580108642578,8.924786000000001 +400,Regression,Exponentially Weighted Average,TrumpApproval,39.35730624999997,39.39781946688104,-485.4842825426507,0.6682605743408203,10.154856 +420,Regression,Exponentially Weighted Average,TrumpApproval,39.40549083333331,39.44465897881697,-502.77995042269276,0.6983966827392578,11.469727 +440,Regression,Exponentially Weighted Average,TrumpApproval,39.49730674999998,39.53710368662846,-495.9856416828035,0.7316226959228516,12.901862000000001 +460,Regression,Exponentially Weighted Average,TrumpApproval,39.61474728260867,39.65658853240579,-473.14358309219216,0.7705020904541016,14.448849000000001 +480,Regression,Exponentially Weighted Average,TrumpApproval,39.71032456249997,39.753047582709755,-464.4916761787406,0.8079357147216797,16.094285 +500,Regression,Exponentially Weighted Average,TrumpApproval,39.80313951999997,39.84667590965187,-456.87508245086684,2.9298267364501953,19.825027 +520,Regression,Exponentially Weighted Average,TrumpApproval,39.873547134615364,39.916931033645376,-459.2932847271911,3.0076160430908203,23.629967 +540,Regression,Exponentially Weighted Average,TrumpApproval,39.94649651851849,39.98996046818772,-459.28610565666287,3.1059207916259766,27.509295 +560,Regression,Exponentially Weighted Average,TrumpApproval,39.976066142857114,40.018487723609816,-470.92618770667195,3.203706741333008,31.453258 +580,Regression,Exponentially Weighted Average,TrumpApproval,40.00338510344825,40.044755101652726,-483.2331705341176,3.296670913696289,35.460995000000004 +600,Regression,Exponentially Weighted Average,TrumpApproval,40.07393431666663,40.11569326301364,-479.5746686678817,3.391347885131836,39.544454 +620,Regression,Exponentially Weighted Average,TrumpApproval,40.1459417741935,40.18827077358568,-473.96334667177865,3.4906063079833984,43.697410000000005 +640,Regression,Exponentially Weighted Average,TrumpApproval,40.219438156249964,40.26249426545423,-466.8085709746123,3.5870800018310547,47.924640000000004 +660,Regression,Exponentially Weighted Average,TrumpApproval,40.28296777272724,40.32626722721455,-464.9172853497744,3.6864986419677734,52.218165000000006 +680,Regression,Exponentially Weighted Average,TrumpApproval,40.31998279411761,40.36256991107017,-473.1325264408024,3.782560348510742,56.58359200000001 +700,Regression,Exponentially Weighted Average,TrumpApproval,40.31359012857138,40.35509446667054,-485.40526703956544,3.816682815551758,61.02307200000001 +720,Regression,Exponentially Weighted Average,TrumpApproval,40.31730695833329,40.357915759594896,-496.1610725544049,3.917215347290039,65.52718500000002 +740,Regression,Exponentially Weighted Average,TrumpApproval,40.36653568918915,40.407119416424955,-497.07428037101636,4.021100997924805,70.10250300000001 +760,Regression,Exponentially Weighted Average,TrumpApproval,40.403143671052604,40.443256311482514,-503.3712175162706,4.113973617553711,74.743136 +780,Regression,Exponentially Weighted Average,TrumpApproval,40.44545064102563,40.48534274444009,-506.6856716110208,4.221334457397461,79.45160200000001 +800,Regression,Exponentially Weighted Average,TrumpApproval,40.478548249999996,40.518050685964006,-512.1052117095793,4.33137321472168,84.22367000000001 +820,Regression,Exponentially Weighted Average,TrumpApproval,40.50894034146341,40.5479845946661,-518.5068774177179,4.393171310424805,89.06994900000001 +840,Regression,Exponentially Weighted Average,TrumpApproval,40.5406558690476,40.579310897365986,-524.140575335229,4.501546859741211,93.98653900000001 +860,Regression,Exponentially Weighted Average,TrumpApproval,40.58371181395347,40.62239247493601,-524.3496319016275,4.600507736206055,98.97766000000001 +880,Regression,Exponentially Weighted Average,TrumpApproval,40.62855514772725,40.66738601007716,-522.897851512946,4.697576522827148,104.03937600000002 +900,Regression,Exponentially Weighted Average,TrumpApproval,40.664104233333326,40.702738445808535,-526.020768835918,4.774164199829102,109.17863200000002 +920,Regression,Exponentially Weighted Average,TrumpApproval,40.68274704347825,40.72073961991632,-535.1540147256861,4.872934341430664,114.38942200000002 +940,Regression,Exponentially Weighted Average,TrumpApproval,40.70972619148935,40.74737437775791,-540.4099749760601,4.975519180297852,119.67171900000002 +960,Regression,Exponentially Weighted Average,TrumpApproval,40.734006364583315,40.771242977826994,-546.7118652484228,5.07771110534668,125.02115000000002 +980,Regression,Exponentially Weighted Average,TrumpApproval,40.74031829795916,40.776840159239676,-557.5026042066913,5.174932479858398,130.44017000000002 +1000,Regression,Exponentially Weighted Average,TrumpApproval,40.75359492299998,40.78950075300399,-567.2567645513548,5.274145126342773,135.92720000000003 +1001,Regression,Exponentially Weighted Average,TrumpApproval,40.75458054545452,40.7904615623717,-567.6629514867817,5.276128768920898,141.45243100000002 +11,Regression,River MLP,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.012152671813964844,0.004659 +22,Regression,River MLP,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012152671813964844,0.035685 +33,Regression,River MLP,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.012152671813964844,0.08499899999999999 +44,Regression,River MLP,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.012152671813964844,0.154054 +55,Regression,River MLP,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.012152671813964844,0.236038 +66,Regression,River MLP,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.012152671813964844,0.334153 +77,Regression,River MLP,ChickWeights,43.46421300395698,43.662828265715675,-106.52347713504811,0.012152671813964844,0.44537099999999996 +88,Regression,River MLP,ChickWeights,43.359772412267546,43.583709810639945,-96.13505707304522,0.012152671813964844,0.810611 +99,Regression,River MLP,ChickWeights,39.34760833403674,41.28110871337288,-71.88434940071843,0.012152671813964844,1.1795 +110,Regression,River MLP,ChickWeights,36.27694842893514,39.43109665219568,-45.43679588251114,0.012152671813964844,1.5519370000000001 +121,Regression,River MLP,ChickWeights,33.384495302116036,37.629851771248454,-31.570957412576163,0.012152671813964844,1.9276810000000002 +132,Regression,River MLP,ChickWeights,30.760956861305427,36.03410144813446,-23.410283906032372,0.012152671813964844,2.306782 +143,Regression,River MLP,ChickWeights,28.636527077105512,34.63559483719005,-17.266619380249022,0.012152671813964844,2.6893580000000004 +154,Regression,River MLP,ChickWeights,26.848366395937333,33.39569685051843,-13.432529594168455,0.012152671813964844,3.0876050000000004 +165,Regression,River MLP,ChickWeights,25.68994399106157,32.40192925941153,-9.921659843170621,0.012152671813964844,3.5625080000000002 +176,Regression,River MLP,ChickWeights,24.410830110997512,31.436328147747602,-7.289127728255313,0.012152671813964844,4.041374 +187,Regression,River MLP,ChickWeights,23.27797268834062,30.533192270092133,-5.402500905628177,0.012152671813964844,4.523515000000001 +198,Regression,River MLP,ChickWeights,22.21718064008457,29.702466677215767,-3.8195183008370286,0.012152671813964844,5.01074 +209,Regression,River MLP,ChickWeights,21.553196218218126,29.080353233595055,-2.8083766468986493,0.012152671813964844,5.506907 +220,Regression,River MLP,ChickWeights,21.477175147376318,28.875533005215566,-2.287429329651187,0.012152671813964844,6.006767 +231,Regression,River MLP,ChickWeights,20.952511346795177,28.337840419816686,-1.7081998467380504,0.012152671813964844,6.546588 +242,Regression,River MLP,ChickWeights,20.676711476832995,27.952207916118613,-1.120246781438643,0.012152671813964844,7.091828 +253,Regression,River MLP,ChickWeights,20.0995501155626,27.40917062551413,-0.77060809797316,0.012152671813964844,7.64039 +264,Regression,River MLP,ChickWeights,20.480542447806847,27.75611161124588,-0.6589532289622051,0.012312889099121094,8.194426 +275,Regression,River MLP,ChickWeights,20.614496901205563,28.041519628611827,-0.48996193124700227,0.012312889099121094,8.751968 +286,Regression,River MLP,ChickWeights,20.565278129073285,27.847512109279503,-0.27268965368266684,0.012312889099121094,9.312993 +297,Regression,River MLP,ChickWeights,20.274958424672484,27.625687239183637,-0.08627660996898667,0.012312889099121094,9.883614000000001 +308,Regression,River MLP,ChickWeights,20.45327995927562,27.912230969749725,0.012367732509979579,0.012312889099121094,10.459328000000001 +319,Regression,River MLP,ChickWeights,21.510251079645446,30.009813508826145,-0.03600670421086383,0.012312889099121094,11.038549000000001 +330,Regression,River MLP,ChickWeights,21.665590739767385,30.044848619277115,0.11133412773326878,0.012312889099121094,11.621241000000001 +341,Regression,River MLP,ChickWeights,21.89048226594194,30.334415208142868,0.18882940014056426,0.012312889099121094,12.207835000000001 +352,Regression,River MLP,ChickWeights,21.893952955210157,30.339570241246864,0.2623598804373043,0.012312889099121094,12.797939000000001 +363,Regression,River MLP,ChickWeights,22.90533819708786,32.054250800509514,0.20046341020600145,0.012312889099121094,13.393067000000002 +374,Regression,River MLP,ChickWeights,23.763742364378043,33.85198840163,0.1802483296948254,0.012312889099121094,13.997035000000002 +385,Regression,River MLP,ChickWeights,24.571542788077334,34.76894700117602,0.23490387687324887,0.012312889099121094,14.642537000000003 +396,Regression,River MLP,ChickWeights,24.36284577450351,34.48838301267373,0.2980969129506975,0.012312889099121094,15.292230000000002 +407,Regression,River MLP,ChickWeights,25.734101326034633,37.062399123466015,0.24654151138402014,0.012312889099121094,15.945417000000003 +418,Regression,River MLP,ChickWeights,27.11119186006235,39.928390080533305,0.17910517682254135,0.012312889099121094,16.606412000000002 +429,Regression,River MLP,ChickWeights,28.3590406143643,42.09339312084405,0.18927902325132573,0.012312889099121094,17.270793 +440,Regression,River MLP,ChickWeights,29.357663164641018,43.68705243766196,0.18853885804982917,0.012312889099121094,17.938627 +451,Regression,River MLP,ChickWeights,30.691095011752385,45.674890437608916,0.15071943546696187,0.012312889099121094,18.61722 +462,Regression,River MLP,ChickWeights,32.58469239048663,49.95891321104316,0.04563754616882043,0.012312889099121094,19.304175 +473,Regression,River MLP,ChickWeights,34.457204466549335,53.836031572602984,0.02142234380208996,0.012312889099121094,19.9947 +484,Regression,River MLP,ChickWeights,37.61656772325176,59.47905507802133,-0.1290194303344141,0.012312889099121094,20.688692000000003 +495,Regression,River MLP,ChickWeights,39.835053793820734,63.30264151550531,-0.20299724250356888,0.012312889099121094,21.389042000000003 +506,Regression,River MLP,ChickWeights,40.84375246428709,64.75828138125749,-0.2223668969879733,0.012312889099121094,22.096762000000002 +517,Regression,River MLP,ChickWeights,42.3259867290128,66.7403629812066,-0.22521937114525392,0.012312889099121094,22.959028000000004 +528,Regression,River MLP,ChickWeights,43.903763311459905,69.27444073484561,-0.18681443911135398,0.012312889099121094,23.825387000000003 +539,Regression,River MLP,ChickWeights,45.11725117254523,70.97054614693485,-0.19420442814828975,0.012312889099121094,24.695185000000002 +550,Regression,River MLP,ChickWeights,46.19146939493793,72.84904016514736,-0.2194804408254527,0.012312889099121094,25.570022 +561,Regression,River MLP,ChickWeights,48.0255382358928,75.55118081102547,-0.25426558573377567,0.012312889099121094,26.453531 +572,Regression,River MLP,ChickWeights,49.60861685612801,77.95895414232838,-0.23532548305844236,0.012312889099121094,27.340524000000002 +578,Regression,River MLP,ChickWeights,51.40782550111089,80.92025038917566,-0.298583502299673,0.012312889099121094,28.229463000000003 +20,Regression,River MLP,TrumpApproval,28.203089584036217,31.678254793976468,-2352.839799462937,0.013110160827636719,0.018592 +40,Regression,River MLP,TrumpApproval,17.631407237579232,23.536801219235826,-221.7205207554288,0.013110160827636719,0.053933999999999996 +60,Regression,River MLP,TrumpApproval,13.441671937224772,19.739075566761823,-210.18539534147195,0.013110160827636719,0.098078 +80,Regression,River MLP,TrumpApproval,11.196749290061339,17.292913087737123,-161.5886474703317,0.013110160827636719,0.159515 +100,Regression,River MLP,TrumpApproval,9.529407951935296,15.54264880746251,-81.40884208187767,0.013110160827636719,0.228076 +120,Regression,River MLP,TrumpApproval,8.478754286735066,14.272499783288554,-57.95136830581733,0.013110160827636719,0.332328 +140,Regression,River MLP,TrumpApproval,7.525552058981039,13.242333407520347,-51.44233495767236,0.013110160827636719,0.527456 +160,Regression,River MLP,TrumpApproval,6.729532853932534,12.401843141618142,-39.56324503441056,0.013110160827636719,0.7296090000000001 +180,Regression,River MLP,TrumpApproval,6.20494414148211,11.727398222866162,-30.855608065765253,0.013110160827636719,0.938725 +200,Regression,River MLP,TrumpApproval,5.707613016041334,11.135875265707485,-27.808506433676282,0.013110160827636719,1.173453 +220,Regression,River MLP,TrumpApproval,5.35235544657082,10.636236352263047,-27.34993958822678,0.013110160827636719,1.423387 +240,Regression,River MLP,TrumpApproval,4.997211310189409,10.191758203807838,-25.225868327846438,0.013110160827636719,1.696442 +260,Regression,River MLP,TrumpApproval,4.698339965696975,9.799142308635478,-23.570888426665658,0.013350486755371094,2.044166 +280,Regression,River MLP,TrumpApproval,4.429952698677103,9.448184269747657,-22.91569767610472,0.013350486755371094,2.398587 +300,Regression,River MLP,TrumpApproval,4.185436867704573,9.131292683908228,-20.968518634865898,0.013350486755371094,2.759636 +320,Regression,River MLP,TrumpApproval,3.989857840855361,8.848493522992882,-20.65027251080777,0.013350486755371094,3.127609 +340,Regression,River MLP,TrumpApproval,3.793510888989401,8.58729864958044,-20.548263158423392,0.013350486755371094,3.5072970000000003 +360,Regression,River MLP,TrumpApproval,3.624008920532304,8.350732680595982,-19.544713512132038,0.013350486755371094,3.994133 +380,Regression,River MLP,TrumpApproval,3.4723941591948395,8.130732570333006,-19.15022282865096,0.013350486755371094,4.488831 +400,Regression,River MLP,TrumpApproval,3.327129028584169,7.926491124989248,-18.691844486765735,0.013350486755371094,4.996594 +420,Regression,River MLP,TrumpApproval,3.1988914312464622,7.737168953530663,-18.383340677896445,0.013350486755371094,5.512642 +440,Regression,River MLP,TrumpApproval,3.0905418652204037,7.562941397323993,-17.185096454486196,0.013350486755371094,6.041213 +460,Regression,River MLP,TrumpApproval,2.9841087219087,7.398658950448711,-15.50384711789857,0.013350486755371094,6.671723 +480,Regression,River MLP,TrumpApproval,2.878027938067315,7.243616567021465,-14.455461195017083,0.013350486755371094,7.311147 +500,Regression,River MLP,TrumpApproval,2.790174040420949,7.099353406661882,-13.534510391092628,0.013350486755371094,7.962598 +520,Regression,River MLP,TrumpApproval,2.702735665583147,6.962851553400364,-13.005371203657658,0.013350486755371094,8.621042 +540,Regression,River MLP,TrumpApproval,2.619923274637493,6.8334980874356335,-12.440396167539378,0.013350486755371094,9.286045999999999 +560,Regression,River MLP,TrumpApproval,2.556848015725479,6.714676061099242,-12.286263553450382,0.013350486755371094,9.968405999999998 +580,Regression,River MLP,TrumpApproval,2.4920466556079988,6.599727625727584,-12.1527109643015,0.013350486755371094,10.662936999999998 +600,Regression,River MLP,TrumpApproval,2.4260558633236777,6.490118235625791,-11.578750092830703,0.013350486755371094,11.381531999999998 +620,Regression,River MLP,TrumpApproval,2.3708694907142864,6.387338726311831,-10.997792654674662,0.013350486755371094,12.131809999999998 +640,Regression,River MLP,TrumpApproval,2.309077397643504,6.287531812954472,-10.40846497658843,0.013350486755371094,12.902857999999998 +660,Regression,River MLP,TrumpApproval,2.253417256192923,6.192441467899733,-9.986430076809746,0.013350486755371094,13.765244 +680,Regression,River MLP,TrumpApproval,2.1933714736526424,6.100884478116631,-9.832475924452435,0.013350486755371094,14.638727 +700,Regression,River MLP,TrumpApproval,2.1444840100167193,6.014053532220149,-9.80279442231031,0.013350486755371094,15.530555 +720,Regression,River MLP,TrumpApproval,2.0889149793500317,5.930058413028094,-9.733901359629304,0.013350486755371094,16.434104 +740,Regression,River MLP,TrumpApproval,2.038014375475162,5.849577408393744,-9.43824097776182,0.013350486755371094,17.347853 +760,Regression,River MLP,TrumpApproval,1.990139846596363,5.772270602035659,-9.274280741061778,0.013350486755371094,18.296023 +780,Regression,River MLP,TrumpApproval,1.946515411702069,5.69815370877383,-9.05697999731458,0.013350486755371094,19.263123 +800,Regression,River MLP,TrumpApproval,1.9088971171085878,5.627293726045093,-8.897112526821198,0.013350486755371094,20.241821 +820,Regression,River MLP,TrumpApproval,1.8732261968689674,5.559226632327132,-8.765208356441784,0.013350486755371094,21.227083 +840,Regression,River MLP,TrumpApproval,1.8347271749400444,5.492864392938861,-8.621969919695442,0.013350486755371094,22.223141000000002 +860,Regression,River MLP,TrumpApproval,1.8001515928803729,5.4291765082898245,-8.383942958793503,0.013350486755371094,23.230427000000002 +880,Regression,River MLP,TrumpApproval,1.762610565031098,5.367211780988228,-8.125392758933815,0.013350486755371094,24.244373000000003 +900,Regression,River MLP,TrumpApproval,1.7278213800286457,5.307357454879676,-7.9606012045493255,0.013350486755371094,25.346781000000004 +920,Regression,River MLP,TrumpApproval,1.6959197142820022,5.249600105314111,-7.910676780558388,0.013350486755371094,26.455778000000002 +940,Regression,River MLP,TrumpApproval,1.6672680101890094,5.193857129216991,-7.796440957593809,0.013350486755371094,27.578615000000003 +960,Regression,River MLP,TrumpApproval,1.6372087427382507,5.139738169534271,-7.704147396017831,0.013350486755371094,28.708325000000002 +980,Regression,River MLP,TrumpApproval,1.6082702309133736,5.087224346398139,-7.692803163516414,0.013350486755371094,29.852185000000002 +1000,Regression,River MLP,TrumpApproval,1.582128560540168,5.036439630005545,-7.663534315499042,0.013350486755371094,31.047385000000002 +1001,Regression,River MLP,TrumpApproval,1.5805783932319006,5.033923389051291,-7.660658200179739,0.013350486755371094,32.243154000000004 +11,Regression,[baseline] Mean predictor,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.0004901885986328125,0.000258 +22,Regression,[baseline] Mean predictor,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.0004901885986328125,0.000737 +33,Regression,[baseline] Mean predictor,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.0004901885986328125,0.00134 +44,Regression,[baseline] Mean predictor,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.0004901885986328125,0.002066 +55,Regression,[baseline] Mean predictor,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.0004901885986328125,0.0029100000000000003 +66,Regression,[baseline] Mean predictor,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.0004901885986328125,0.0038720000000000004 +77,Regression,[baseline] Mean predictor,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.0004901885986328125,0.004952000000000001 +88,Regression,[baseline] Mean predictor,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.0004901885986328125,0.006149000000000001 +99,Regression,[baseline] Mean predictor,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.0004901885986328125,0.007464000000000001 +110,Regression,[baseline] Mean predictor,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.0004901885986328125,0.008896000000000001 +121,Regression,[baseline] Mean predictor,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.0004901885986328125,0.010446 +132,Regression,[baseline] Mean predictor,ChickWeights,5.875758254207378,8.194624755054596,-0.2624191661321591,0.0004901885986328125,0.012113 +143,Regression,[baseline] Mean predictor,ChickWeights,6.530760796045927,8.870097879563003,-0.19803554240449484,0.0004901885986328125,0.013898 +154,Regression,[baseline] Mean predictor,ChickWeights,7.121466111912466,9.458403141043558,-0.15770278521517955,0.0004901885986328125,0.015801 +165,Regression,[baseline] Mean predictor,ChickWeights,7.772438504082036,10.375670403553157,-0.11989999304508925,0.0004901885986328125,0.017821999999999998 +176,Regression,[baseline] Mean predictor,ChickWeights,8.565827130563894,11.410434180005831,-0.09206765686265328,0.0004901885986328125,0.019960999999999996 +187,Regression,[baseline] Mean predictor,ChickWeights,9.429958588641576,12.495061319237752,-0.07221531716282037,0.0004901885986328125,0.022216999999999997 +198,Regression,[baseline] Mean predictor,ChickWeights,10.47731537859646,13.900491647656429,-0.05555027037575888,0.0004901885986328125,0.024589999999999997 +209,Regression,[baseline] Mean predictor,ChickWeights,11.43172675762076,15.229123619635446,-0.04445651287163721,0.0004901885986328125,0.027079 +220,Regression,[baseline] Mean predictor,ChickWeights,11.974320980081139,16.22368260926648,-0.03775608698471111,0.0004901885986328125,0.029685 +231,Regression,[baseline] Mean predictor,ChickWeights,12.9382196746461,17.489503190785292,-0.03157819728271183,0.0004901885986328125,0.032406 +242,Regression,[baseline] Mean predictor,ChickWeights,14.229204186206863,19.43725798629848,-0.02523677186741935,0.0004901885986328125,0.035243 +253,Regression,[baseline] Mean predictor,ChickWeights,15.339413196393396,20.820238312545918,-0.021649789303838762,0.0004901885986328125,0.041904 +264,Regression,[baseline] Mean predictor,ChickWeights,15.948617107030818,21.75817315507082,-0.019440185124094622,0.0004901885986328125,0.048726 +275,Regression,[baseline] Mean predictor,ChickWeights,16.794155127707494,23.16724301729152,-0.016999619323781356,0.0004901885986328125,0.055688 +286,Regression,[baseline] Mean predictor,ChickWeights,17.990009992534457,24.865985915258104,-0.014754713395529917,0.0004901885986328125,0.062787 +297,Regression,[baseline] Mean predictor,ChickWeights,19.34919450213405,26.676209297603677,-0.012890456560007202,0.0004901885986328125,0.070018 +308,Regression,[baseline] Mean predictor,ChickWeights,20.46881241431745,28.248013022827834,-0.011537481517321035,0.0004901885986328125,0.077383 +319,Regression,[baseline] Mean predictor,ChickWeights,20.993702124162965,29.638141143499492,-0.010503673119392376,0.0004901885986328125,0.08488399999999999 +330,Regression,[baseline] Mean predictor,ChickWeights,22.586872779548433,32.01796640002603,-0.009220237952050514,0.0004901885986328125,0.09251699999999999 +341,Regression,[baseline] Mean predictor,ChickWeights,23.973458872107372,33.821533603903084,-0.008387701903732392,0.0004901885986328125,0.10028199999999998 +352,Regression,[baseline] Mean predictor,ChickWeights,25.315991788770976,35.461698606860665,-0.007731302158646702,0.0004901885986328125,0.10817799999999998 +363,Regression,[baseline] Mean predictor,ChickWeights,25.615062978866305,35.981300981590465,-0.007443749031205149,0.0004901885986328125,0.11620599999999998 +374,Regression,[baseline] Mean predictor,ChickWeights,26.673321526932543,37.51836715700961,-0.006935846124255907,0.0004901885986328125,0.12436199999999997 +385,Regression,[baseline] Mean predictor,ChickWeights,28.27694482780972,39.8753298933956,-0.006332510983879436,0.0004901885986328125,0.13263999999999998 +396,Regression,[baseline] Mean predictor,ChickWeights,29.55612496209691,41.288487059450155,-0.005980181891907188,0.0004901885986328125,0.14104099999999997 +407,Regression,[baseline] Mean predictor,ChickWeights,30.561677112682855,42.81802042618151,-0.005646723150046551,0.0004901885986328125,0.14956599999999998 +418,Regression,[baseline] Mean predictor,ChickWeights,31.39346669137945,44.18765357092498,-0.0053697143301307815,0.0004901885986328125,0.15821399999999997 +429,Regression,[baseline] Mean predictor,ChickWeights,33.10612890637694,46.865579751152914,-0.004966366070605188,0.0004901885986328125,0.16698499999999997 +440,Regression,[baseline] Mean predictor,ChickWeights,34.54914638861108,48.61167278858254,-0.004716123854972665,0.0004901885986328125,0.17588299999999996 +451,Regression,[baseline] Mean predictor,ChickWeights,35.43263419295921,49.67507127970072,-0.004553693807187953,0.0004901885986328125,0.18490599999999996 +462,Regression,[baseline] Mean predictor,ChickWeights,36.308550382896186,51.2507761435036,-0.004357377489546899,0.0004901885986328125,0.19405499999999995 +473,Regression,[baseline] Mean predictor,ChickWeights,38.26330298063241,54.532250497281034,-0.004051661204895529,0.0004901885986328125,0.20332799999999995 +484,Regression,[baseline] Mean predictor,ChickWeights,39.598662348008276,56.08659790201894,-0.0039023944795495424,0.0004901885986328125,0.21272499999999994 +495,Regression,[baseline] Mean predictor,ChickWeights,40.94697327298068,57.823326559810994,-0.0037535911132069444,0.0004901885986328125,0.22224499999999994 +506,Regression,[baseline] Mean predictor,ChickWeights,41.42384714758024,58.679845942015916,-0.003665234721119459,0.0004901885986328125,0.23188899999999996 +517,Regression,[baseline] Mean predictor,ChickWeights,42.72663002099646,60.40151056768402,-0.0035345422299792872,0.0004901885986328125,0.24165999999999996 +528,Regression,[baseline] Mean predictor,ChickWeights,44.77321528369677,63.69509749878913,-0.0033415055563215112,0.0004901885986328125,0.25155399999999994 +539,Regression,[baseline] Mean predictor,ChickWeights,45.99579764939489,65.0494992510053,-0.0032526095626370655,0.0004901885986328125,0.26157099999999994 +550,Regression,[baseline] Mean predictor,ChickWeights,46.57020777663759,66.07332710234044,-0.0031815200825582313,0.0004901885986328125,0.2717109999999999 +561,Regression,[baseline] Mean predictor,ChickWeights,47.758257606406204,67.5643396193493,-0.0030950009187136196,0.0004901885986328125,0.28197199999999994 +572,Regression,[baseline] Mean predictor,ChickWeights,49.49138874897682,70.24569214117749,-0.002971942406188699,0.0004901885986328125,0.29235599999999995 +578,Regression,[baseline] Mean predictor,ChickWeights,50.250899455914585,71.11438743304103,-0.002929468639104371,0.0004901885986328125,0.30283499999999997 +20,Regression,[baseline] Mean predictor,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.0004901885986328125,0.001338 +40,Regression,[baseline] Mean predictor,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.0004901885986328125,0.0038250000000000003 +60,Regression,[baseline] Mean predictor,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.0004901885986328125,0.00717 +80,Regression,[baseline] Mean predictor,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.0004901885986328125,0.011356999999999999 +100,Regression,[baseline] Mean predictor,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.0004901885986328125,0.020929 +120,Regression,[baseline] Mean predictor,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.0004901885986328125,0.030834 +140,Regression,[baseline] Mean predictor,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.0004901885986328125,0.041039 +160,Regression,[baseline] Mean predictor,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.0004901885986328125,0.051538 +180,Regression,[baseline] Mean predictor,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.0004901885986328125,0.062312 +200,Regression,[baseline] Mean predictor,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.0004901885986328125,0.073408 +220,Regression,[baseline] Mean predictor,TrumpApproval,1.9409519346841397,3.56902990398404,-2.19210047340805,0.0004901885986328125,0.084777 +240,Regression,[baseline] Mean predictor,TrumpApproval,1.9366756524315063,3.4612902974772624,-2.024876884626847,0.0004901885986328125,0.096419 +260,Regression,[baseline] Mean predictor,TrumpApproval,1.9250039777458068,3.363327951159923,-1.8945640461454523,0.0004901885986328125,0.108333 +280,Regression,[baseline] Mean predictor,TrumpApproval,1.8726934920539138,3.257010428159885,-1.8420037280027222,0.0004901885986328125,0.120517 +300,Regression,[baseline] Mean predictor,TrumpApproval,1.8907476896224937,3.1958821895815714,-1.6910252267675165,0.0004901885986328125,0.133002 +320,Regression,[baseline] Mean predictor,TrumpApproval,1.819623890420079,3.103812605138666,-1.663886258690169,0.0004901885986328125,0.145758 +340,Regression,[baseline] Mean predictor,TrumpApproval,1.7396293145937214,3.014220627768389,-1.654906383755708,0.0004901885986328125,0.158784 +360,Regression,[baseline] Mean predictor,TrumpApproval,1.7350691203787965,2.9569384317632506,-1.5759385016835008,0.0004901885986328125,0.172076 +380,Regression,[baseline] Mean predictor,TrumpApproval,1.6987131960417108,2.8893997308323693,-1.5446951110541192,0.0004901885986328125,0.185636 +400,Regression,[baseline] Mean predictor,TrumpApproval,1.673610627740774,2.82935583501861,-1.5089937655143242,0.0004901885986328125,0.199488 +420,Regression,[baseline] Mean predictor,TrumpApproval,1.6410137122925974,2.7701802079251965,-1.484737486096575,0.0004901885986328125,0.213608 +440,Regression,[baseline] Mean predictor,TrumpApproval,1.6565972573555454,2.7427790467379385,-1.391750010744973,0.0004901885986328125,0.227993 +460,Regression,[baseline] Mean predictor,TrumpApproval,1.699464840115161,2.7394674040138396,-1.2626191030939884,0.0004901885986328125,0.242643 +480,Regression,[baseline] Mean predictor,TrumpApproval,1.7224824441896143,2.7219018737730583,-1.182307732575659,0.0004901885986328125,0.25756 +500,Regression,[baseline] Mean predictor,TrumpApproval,1.7446092142173422,2.7058035442295596,-1.1113262021905803,0.0004901885986328125,0.272747 +520,Regression,[baseline] Mean predictor,TrumpApproval,1.7464998751860934,2.677192702589883,-1.0705208906620065,0.0004901885986328125,0.288233 +540,Regression,[baseline] Mean predictor,TrumpApproval,1.7535492786865425,2.653885630983747,-1.0271707062792519,0.0004901885986328125,0.303987 +560,Regression,[baseline] Mean predictor,TrumpApproval,1.7201019899937544,2.614359234374483,-1.0141103337708768,0.0004901885986328125,0.320009 +580,Regression,[baseline] Mean predictor,TrumpApproval,1.6887559504032665,2.5757257291728384,-1.0033760803823184,0.0004901885986328125,0.336298 +600,Regression,[baseline] Mean predictor,TrumpApproval,1.701917368353294,2.5614247637328695,-0.9592753712060649,0.0004901885986328125,0.35287999999999997 +620,Regression,[baseline] Mean predictor,TrumpApproval,1.7178157166185173,2.5513468959681562,-0.9142580419512063,0.0004901885986328125,0.369731 +640,Regression,[baseline] Mean predictor,TrumpApproval,1.7365901196485038,2.545046385321895,-0.8692105635365064,0.0004901885986328125,0.386852 +660,Regression,[baseline] Mean predictor,TrumpApproval,1.7465677425181807,2.532051562790666,-0.8368676529707118,0.0004901885986328125,0.40424 +680,Regression,[baseline] Mean predictor,TrumpApproval,1.731617734826669,2.5042261861708606,-0.8251107974736909,0.0004901885986328125,0.42189499999999996 +700,Regression,[baseline] Mean predictor,TrumpApproval,1.6973720107412233,2.4702678919797196,-0.8225927549994396,0.0004901885986328125,0.439849 +720,Regression,[baseline] Mean predictor,TrumpApproval,1.6698372433333928,2.4400355004771073,-0.81732226470892,0.0004901885986328125,0.458072 +740,Regression,[baseline] Mean predictor,TrumpApproval,1.6732482399922957,2.425592833263792,-0.7947920429290933,0.0004901885986328125,0.47656299999999996 +760,Regression,[baseline] Mean predictor,TrumpApproval,1.6653913599894004,2.404136439714782,-0.7822814452716051,0.0004901885986328125,0.49532099999999996 +780,Regression,[baseline] Mean predictor,TrumpApproval,1.6644612180457288,2.387561393188575,-0.7656652158374817,0.0004901885986328125,0.514347 +800,Regression,[baseline] Mean predictor,TrumpApproval,1.6556359332933146,2.368497267913513,-0.7532954885990883,0.0004901885986328125,0.533661 +820,Regression,[baseline] Mean predictor,TrumpApproval,1.6452077788467467,2.348678653798561,-0.7430103139622937,0.0004901885986328125,0.5532450000000001 +840,Regression,[baseline] Mean predictor,TrumpApproval,1.6374623223784903,2.3305035344735936,-0.7320713255917544,0.0004901885986328125,0.5730930000000001 +860,Regression,[baseline] Mean predictor,TrumpApproval,1.6419505315856449,2.3202080137162757,-0.7138439732116804,0.0004901885986328125,0.6284980000000001 +880,Regression,[baseline] Mean predictor,TrumpApproval,1.6490002164922652,2.3126155324510744,-0.6941855677649247,0.0004901885986328125,0.6842080000000001 +900,Regression,[baseline] Mean predictor,TrumpApproval,1.6474991175923384,2.299197536504521,-0.6816400531907807,0.0004901885986328125,0.7401880000000002 +920,Regression,[baseline] Mean predictor,TrumpApproval,1.6301006788336792,2.2779225390149764,-0.6777843948800273,0.0004901885986328125,0.7964830000000002 +940,Regression,[baseline] Mean predictor,TrumpApproval,1.6221876471839873,2.2623787372500574,-0.6690049120995847,0.0004901885986328125,0.8530460000000002 +960,Regression,[baseline] Mean predictor,TrumpApproval,1.6124120493571745,2.245866476718547,-0.6619276404267609,0.0004901885986328125,0.9098760000000002 +980,Regression,[baseline] Mean predictor,TrumpApproval,1.5867001120604314,2.223758235975506,-0.661013659831075,0.0004901885986328125,0.9669740000000002 +1000,Regression,[baseline] Mean predictor,TrumpApproval,1.5681359363812417,2.2037391763141216,-0.6587014308970958,0.0004901885986328125,1.0243380000000002 +1001,Regression,[baseline] Mean predictor,TrumpApproval,1.567554989468773,2.202858861923226,-0.6584830635688459,0.0004901885986328125,1.081765 From f27e7cbd7f290662fdaab37f968372999e0beccb Mon Sep 17 00:00:00 2001 From: Hoang Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 9 Oct 2023 21:13:50 +0700 Subject: [PATCH 140/347] DBSTREAM fix (related to issue #1324) (#1336) * Adding negative signs before fading_factor for steps within Algoritjm 2 of the paper by Hashler and Bolanos to allow clusters with low weight removed. * Change clustering_is_up_to_date to True after every time the function recluster is called. * initiate new micro cluster based on the maximum key of the existing micro clusters, or indexed as 0 if the list of micro clusters is still empty. * Add description to the UNRELEASED.md file --- docs/releases/unreleased.md | 8 ++++++++ river/cluster/dbstream.py | 19 ++++++++++++++----- 2 files changed, 22 insertions(+), 5 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 502f934d12..d7f30c07ac 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -6,6 +6,14 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Added `anomaly.LocalOutlierFactor`, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation. +## clustering + +- Add fixes to `cluster.DBSTREAM` algorithm, including: + - Addition of the `-` sign before the `fading_factor` in accordance with the algorithm 2 proposed by Hashler and Bolanos (2016) to allow clusters with low weights to be removed. + - The new `micro_cluster` is added with the key derived from the maximum key of the existing micro clusters. If the set of micro clusters is still empty (`len = 0`), a new micro cluster is added with key 0. + - `cluster_is_up_to_date` is set to `True` at the end of the `self._recluster()` function. + + ## datasets - Added `datasets.WebTraffic`, which is a dataset that counts the occurrences of events on a website. It is a multi-output regression dataset with two outputs. diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 693f30b80f..d474ed6b7e 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -182,9 +182,14 @@ def _update(self, x): if len(neighbor_clusters) < 1: # create new micro cluster - self._micro_clusters[len(self._micro_clusters)] = DBSTREAMMicroCluster( - x=x, last_update=self._time_stamp, weight=1 - ) + if len(self._micro_clusters) > 0: + self._micro_clusters[max(self._micro_clusters.keys()) + 1] = DBSTREAMMicroCluster( + x=x, last_update=self._time_stamp, weight=1 + ) + else: + self._micro_clusters[0] = DBSTREAMMicroCluster( + x=x, last_update=self._time_stamp, weight=1 + ) else: # update existing micro clusters current_centers = {} @@ -253,7 +258,9 @@ def _cleanup(self): micro_clusters = copy.deepcopy(self._micro_clusters) for i, micro_cluster_i in self._micro_clusters.items(): try: - value = 2 ** (self.fading_factor * (self._time_stamp - micro_cluster_i.last_update)) + value = 2 ** ( + -self.fading_factor * (self._time_stamp - micro_cluster_i.last_update) + ) except OverflowError: continue @@ -266,7 +273,7 @@ def _cleanup(self): for i in self.s.keys(): for j in self.s[i].keys(): try: - value = 2 ** (self.fading_factor * (self._time_stamp - self.s_t[i][j])) + value = 2 ** (-self.fading_factor * (self._time_stamp - self.s_t[i][j])) except OverflowError: continue @@ -375,6 +382,8 @@ def _recluster(self): self._n_clusters, self._clusters = self._generate_clusters_from_labels(labels) self._centers = {i: self._clusters[i].center for i in self._clusters.keys()} + self.clustering_is_up_to_date = True + def learn_one(self, x, sample_weight=None): self._update(x) From 3a78523df2bb2f8f24a9d5a92752a7c714973e0f Mon Sep 17 00:00:00 2001 From: Marek Wadinger <50716630+MarekWadinger@users.noreply.github.com> Date: Tue, 10 Oct 2023 15:38:48 +0200 Subject: [PATCH 141/347] FIX: LOF with QuantileFilter raises IndexError (#1330) * FIX: LOF with QuantileFilter raises IndexError * FIX: score_one changes internal state of LOF * FIX: black refactor * Update initial score in LOF Co-authored-by: Max Halford * Add tests for stateless score_one and fix doctest * Minor line length fix * Add LOF fixtures to release notes --------- Co-authored-by: Max Halford --- docs/releases/unreleased.md | 2 ++ river/anomaly/lof.py | 41 ++++++++++++++++++++++++++++--------- river/anomaly/test_lof.py | 30 +++++++++++++++++++++++++++ 3 files changed, 63 insertions(+), 10 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index d7f30c07ac..c6edd98865 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -5,6 +5,8 @@ River's mini-batch methods now support pandas v2. In particular, River conforms ## anomaly - Added `anomaly.LocalOutlierFactor`, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation. + - Made `score_one` method of `anomaly.LocalOutlierFactor` stateless + - Defined default score for uninitialized detector ## clustering diff --git a/river/anomaly/lof.py b/river/anomaly/lof.py index 631593394c..ea6b3624fb 100644 --- a/river/anomaly/lof.py +++ b/river/anomaly/lof.py @@ -1,5 +1,6 @@ from __future__ import annotations +import copy import functools import pandas as pd @@ -220,7 +221,26 @@ class LocalOutlierFactor(anomaly.base.AnomalyDetector): ... scores.append(lof.score_one(x)) >>> [round(score, 3) for score in scores] - [1.802, 1.937, 1.567, 1.181, 1.28] + [1.802, 1.936, 1.566, 1.181, 1.272] + + >>> X = [0.5, 0.45, 0.43, 0.44, 0.445, 0.45, 0.0] + >>> lof = anomaly.LocalOutlierFactor() + + >>> for x in X[:3]: + ... lof.learn_one({'x': x}) # Warming up + + >>> for x in X: + ... features = {'x': x} + ... print( + ... f'Anomaly score for x={x:.3f}: {lof.score_one(features):.3f}') + ... lof.learn_one(features) + Anomaly score for x=0.500: 0.000 + Anomaly score for x=0.450: 0.000 + Anomaly score for x=0.430: 0.000 + Anomaly score for x=0.440: 1.020 + Anomaly score for x=0.445: 1.032 + Anomaly score for x=0.450: 0.000 + Anomaly score for x=0.000: 0.980 References ---------- @@ -342,10 +362,11 @@ def score_one(self, x: dict): self.x_scores.append(x) self.x_scores, equal = check_equal(self.x_scores, self.x_list) - if len(self.x_scores) == 0: - return None + if len(self.x_scores) == 0 or len(self.x_list) == 0: + return 0.0 x_list_copy = self.x_list.copy() + ( nm, x_list_copy, @@ -359,13 +380,13 @@ def score_one(self, x: dict): ) = expand_objects( self.x_scores, x_list_copy, - self.neighborhoods, - self.rev_neighborhoods, - self.k_dist, - self.reach_dist, - self.dist_dict, - self.local_reach, - self.lof, + self.neighborhoods.copy(), + self.rev_neighborhoods.copy(), + self.k_dist.copy(), + copy.deepcopy(self.reach_dist), + copy.deepcopy(self.dist_dict), + self.local_reach.copy(), + self.lof.copy(), ) neighborhoods, rev_neighborhoods, k_dist, dist_dict = self._initial_calculations( diff --git a/river/anomaly/test_lof.py b/river/anomaly/test_lof.py index 9703b8cdc9..c4933b0091 100644 --- a/river/anomaly/test_lof.py +++ b/river/anomaly/test_lof.py @@ -80,3 +80,33 @@ def test_issue_1328(): X = [{"a": 1, "b": 1}, {"a": 1, "b": 1}] for x in X: lof.learn_one(x) + + +def test_issue_1331(): + import copy + + from river import anomaly + + lof = anomaly.LocalOutlierFactor() + + X = [{"a": 1, "b": 1}, {"a": 1, "b": 1}] + for x in X: + lof.learn_one(x) + + neighborhoods_ = lof.neighborhoods.copy() + rev_neighborhoods = lof.rev_neighborhoods.copy() + k_dist_ = lof.k_dist.copy() + reach_dist_ = copy.deepcopy(lof.reach_dist) + dist_dict_ = copy.deepcopy(lof.dist_dict) + local_reach_ = lof.local_reach.copy() + lof_ = lof.lof.copy() + + lof.score_one({"a": 0.5, "b": 1}) + + assert neighborhoods_ == lof.neighborhoods + assert rev_neighborhoods == lof.rev_neighborhoods + assert k_dist_ == lof.k_dist + assert reach_dist_ == lof.reach_dist + assert dist_dict_ == lof.dist_dict + assert local_reach_ == lof.local_reach + assert lof_ == lof.lof From 23143c6bb019a3f50773c350539378a9aaf0f20c Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Wed, 11 Oct 2023 17:21:45 -0300 Subject: [PATCH 142/347] Fixes #1248 (#1337) * bug fix * update affected test --- docs/releases/unreleased.md | 9 ++++++--- river/ensemble/streaming_random_patches.py | 2 +- river/tree/extremely_fast_decision_tree.py | 2 +- river/tree/splitter/nominal_splitter_classif.py | 15 ++++++++++----- 4 files changed, 18 insertions(+), 10 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index c6edd98865..bf39d3e7f0 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -11,11 +11,10 @@ River's mini-batch methods now support pandas v2. In particular, River conforms ## clustering - Add fixes to `cluster.DBSTREAM` algorithm, including: - - Addition of the `-` sign before the `fading_factor` in accordance with the algorithm 2 proposed by Hashler and Bolanos (2016) to allow clusters with low weights to be removed. - - The new `micro_cluster` is added with the key derived from the maximum key of the existing micro clusters. If the set of micro clusters is still empty (`len = 0`), a new micro cluster is added with key 0. + - Addition of the `-` sign before the `fading_factor` in accordance with the algorithm 2 proposed by Hashler and Bolanos (2016) to allow clusters with low weights to be removed. + - The new `micro_cluster` is added with the key derived from the maximum key of the existing micro clusters. If the set of micro clusters is still empty (`len = 0`), a new micro cluster is added with key 0. - `cluster_is_up_to_date` is set to `True` at the end of the `self._recluster()` function. - ## datasets - Added `datasets.WebTraffic`, which is a dataset that counts the occurrences of events on a website. It is a multi-output regression dataset with two outputs. @@ -31,3 +30,7 @@ River's mini-batch methods now support pandas v2. In particular, River conforms ## proba - Added `_from_state` method to `proba.MultivariateGaussian` to warm start from previous knowledge. + +## tree + +- Fix a bug in `tree.splitter.NominalSplitterClassif` that generated a mismatch between the number of existing tree branches and the number of tracked branches. diff --git a/river/ensemble/streaming_random_patches.py b/river/ensemble/streaming_random_patches.py index 6c2d023df0..07ee620c1a 100644 --- a/river/ensemble/streaming_random_patches.py +++ b/river/ensemble/streaming_random_patches.py @@ -409,7 +409,7 @@ class SRPClassifier(BaseSRPEnsemble, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 72.77% + Accuracy: 71.97% Notes ----- diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index b158f14145..944505a65b 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -432,7 +432,7 @@ def _reevaluate_best_split(self, node, parent, branch_index, **kwargs): # update EFDT if parent is None: - # Root case : replace the root node by a new split node + # Root case : replace the root node by the new node self._root = best_split else: parent.children[branch_index] = best_split diff --git a/river/tree/splitter/nominal_splitter_classif.py b/river/tree/splitter/nominal_splitter_classif.py index 988078d64d..f1fcf09c77 100644 --- a/river/tree/splitter/nominal_splitter_classif.py +++ b/river/tree/splitter/nominal_splitter_classif.py @@ -1,7 +1,6 @@ from __future__ import annotations import collections -import functools from ..utils import BranchFactory from .base import Splitter @@ -18,9 +17,7 @@ def __init__(self): super().__init__() self._total_weight_observed = 0.0 self._missing_weight_observed = 0.0 - self._att_dist_per_class = collections.defaultdict( - functools.partial(collections.defaultdict, float) - ) + self._att_dist_per_class = collections.defaultdict(dict) self._att_values = set() @property @@ -32,12 +29,20 @@ def update(self, att_val, target_val, sample_weight): self._missing_weight_observed += sample_weight else: self._att_values.add(att_val) - self._att_dist_per_class[target_val][att_val] += sample_weight + + try: + self._att_dist_per_class[target_val][att_val] += sample_weight + except KeyError: + self._att_dist_per_class[target_val][att_val] = sample_weight self._total_weight_observed += sample_weight def cond_proba(self, att_val, target_val): class_dist = self._att_dist_per_class[target_val] + + if att_val not in class_dist: + return 0.0 + value = class_dist[att_val] try: return value / sum(class_dist.values()) From 0a0d9ed4f00e9c96cd1ca1e98547368e5b31ecb1 Mon Sep 17 00:00:00 2001 From: Donatas Povilaitis Date: Sat, 14 Oct 2023 13:03:37 +0300 Subject: [PATCH 143/347] DBSTREAM: fix density graph timestamps (#1338) --- docs/releases/unreleased.md | 1 + river/cluster/dbstream.py | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index bf39d3e7f0..51712519dc 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -14,6 +14,7 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Addition of the `-` sign before the `fading_factor` in accordance with the algorithm 2 proposed by Hashler and Bolanos (2016) to allow clusters with low weights to be removed. - The new `micro_cluster` is added with the key derived from the maximum key of the existing micro clusters. If the set of micro clusters is still empty (`len = 0`), a new micro cluster is added with key 0. - `cluster_is_up_to_date` is set to `True` at the end of the `self._recluster()` function. + - shared density graph update timestamps are initialized with the current timestamp value ## datasets diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index d474ed6b7e..dbef212096 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -228,10 +228,10 @@ def _update(self, x): except KeyError: try: self.s[i][j] = 0 - self.s_t[i][j] = 0 + self.s_t[i][j] = self._time_stamp except KeyError: self.s[i] = {j: 0} - self.s_t[i] = {j: 0} + self.s_t[i] = {j: self._time_stamp} # prevent collapsing clusters for i in neighbor_clusters.keys(): From bfb4ea61c84de3269caa433d8ee2879464184c0f Mon Sep 17 00:00:00 2001 From: Donatas Povilaitis Date: Sat, 14 Oct 2023 17:53:07 +0300 Subject: [PATCH 144/347] DBSTREAM: fix microcluster labeling (#1339) * Fix microcluster labeling * Add tests * Update unreleased.md * Fix linter errors * Fix imports order --- docs/releases/unreleased.md | 3 +- river/cluster/dbstream.py | 2 +- river/cluster/test_dbstream.py | 120 +++++++++++++++++++++++++++++++++ 3 files changed, 123 insertions(+), 2 deletions(-) create mode 100644 river/cluster/test_dbstream.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 51712519dc..bee7de13e4 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -14,7 +14,8 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Addition of the `-` sign before the `fading_factor` in accordance with the algorithm 2 proposed by Hashler and Bolanos (2016) to allow clusters with low weights to be removed. - The new `micro_cluster` is added with the key derived from the maximum key of the existing micro clusters. If the set of micro clusters is still empty (`len = 0`), a new micro cluster is added with key 0. - `cluster_is_up_to_date` is set to `True` at the end of the `self._recluster()` function. - - shared density graph update timestamps are initialized with the current timestamp value + - Shared density graph update timestamps are initialized with the current timestamp value + - `neighbour_neighbours` are appended correctly to the `seed_set` when generating cluster labels ## datasets diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index dbef212096..d7b942e486 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -337,7 +337,7 @@ def _generate_labels(self, weighted_adjacency_list): ) # add new neighbors to seed set for neighbor_neighbor in neighbor_neighbors: - if labels[neighbor_neighbor] is not None: + if labels[neighbor_neighbor] is None: seed_set.append(neighbor_neighbor) return labels diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py new file mode 100644 index 0000000000..c49089d415 --- /dev/null +++ b/river/cluster/test_dbstream.py @@ -0,0 +1,120 @@ +from __future__ import annotations + +import pytest + +from river.cluster import DBSTREAM + + +@pytest.fixture +def dbstream(): + return DBSTREAM( + fading_factor=0.001, clustering_threshold=1, cleanup_interval=1, intersection_factor=0.05 + ) + + +def assert_micro_cluster_properties(cluster, center, last_update=None): + assert cluster.center == pytest.approx(center) + if last_update is not None: + assert cluster.last_update == last_update + + +def test_cluster_formation_and_cleanup(dbstream: DBSTREAM): + X = [ + {1: 1}, + {1: 3}, + {1: 3}, + {1: 3}, + {1: 5}, + {1: 7}, + {1: 9}, + {1: 11}, + {1: 11}, + {1: 13}, + {1: 11}, + {1: 15}, + {1: 17}, + ] + + for x in X: + dbstream.learn_one(x) + + assert len(dbstream._micro_clusters) == 3 + assert_micro_cluster_properties(dbstream.micro_clusters[1], center={1: 3}, last_update=3) + assert_micro_cluster_properties(dbstream.micro_clusters[5], center={1: 11}, last_update=10) + assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 17}, last_update=12) + + +def test_with_two_micro_clusters(dbstream: DBSTREAM): + # First micro-cluster + dbstream.learn_one({1: 1, 2: 1}) + for _ in range(25): + dbstream.learn_one({1: 1.7, 2: 1.7}) + + # Second micro-cluster + dbstream.learn_one({1: 3, 2: 3}) + for _ in range(25): + dbstream.learn_one({1: 2.3, 2: 2.3}) + + # Points in the middle of two micro-clusters + for _ in range(5): + dbstream.learn_one({1: 2, 2: 2}) + + assert len(dbstream._micro_clusters) == 2 + assert_micro_cluster_properties( + dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56 + ) + assert_micro_cluster_properties( + dbstream.micro_clusters[1], center={1: 2.402677, 2: 2.402677}, last_update=56 + ) + + assert dbstream.s == {0: {1: 3.995844478090532}} + assert dbstream.s_t == {0: {1: 56}} + + dbstream._recluster() + assert len(dbstream.clusters) == 1 + assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.003033, 2: 2.003033}) + + +def test_density_graph_with_three_micro_clusters(dbstream: DBSTREAM): + # First micro-cluster + dbstream.learn_one({1: 1, 2: 1}) + for _ in range(25): + dbstream.learn_one({1: 1.7, 2: 1.7}) + + # Second micro-cluster + dbstream.learn_one({1: 3, 2: 3}) + for _ in range(25): + dbstream.learn_one({1: 2.3, 2: 2.3}) + + # Points in the middle of first and second micro-clusters + for _ in range(5): + dbstream.learn_one({1: 2, 2: 2}) + + # Third micro-cluster + dbstream.learn_one({1: 4, 2: 4}) + for _ in range(25): + dbstream.learn_one({1: 3.3, 2: 3.3}) + + # Points in the middle of second and third micro-clusters + for _ in range(4): + dbstream.learn_one({1: 3, 2: 3}) + + assert len(dbstream._micro_clusters) == 3 + + assert_micro_cluster_properties( + dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56 + ) + assert_micro_cluster_properties( + dbstream.micro_clusters[1], center={1: 2.461654, 2: 2.461654}, last_update=86 + ) + assert_micro_cluster_properties( + dbstream.micro_clusters[2], center={1: 3.430485, 2: 3.430485}, last_update=86 + ) + + assert dbstream.s[0] == pytest.approx({1: 3.995844}) + assert dbstream.s[1] == pytest.approx({2: 2.997921}) + assert dbstream.s_t == {0: {1: 56}, 1: {2: 86}} + + dbstream._recluster() + assert len(dbstream.clusters) == 1 + assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.489894, 2: 2.489894}) From 424cc384fe0c626e9adfb082d44ae298af248303 Mon Sep 17 00:00:00 2001 From: Donatas Povilaitis Date: Sun, 15 Oct 2023 20:13:22 +0300 Subject: [PATCH 145/347] dbstream: fix adjacency matrix building (#1340) * dbstream: fix adj matrix building * Update unreleased.md * Update docs/releases/unreleased.md Co-authored-by: Max Halford --------- Co-authored-by: Max Halford --- docs/releases/unreleased.md | 1 + river/cluster/dbstream.py | 29 +++++++----- river/cluster/test_dbstream.py | 85 ++++++++++++++++++++++------------ 3 files changed, 73 insertions(+), 42 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index bee7de13e4..79f2c12327 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -16,6 +16,7 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - `cluster_is_up_to_date` is set to `True` at the end of the `self._recluster()` function. - Shared density graph update timestamps are initialized with the current timestamp value - `neighbour_neighbours` are appended correctly to the `seed_set` when generating cluster labels + - When building weighted adjacency matrix the algorithm accounts for possibly orphaned entries in shared density graph ## datasets diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index d7b942e486..10966aa284 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -287,18 +287,23 @@ def _generate_weighted_adjacency_matrix(self): weighted_adjacency_matrix = {} for i in list(self.s.keys()): for j in list(self.s[i].keys()): - if ( - self._micro_clusters[i].weight >= self.minimum_weight - and self._micro_clusters[j].weight >= self.minimum_weight - ): - value = self.s[i][j] / ( - (self._micro_clusters[i].weight + self._micro_clusters[j].weight) / 2 - ) - if value > self.intersection_factor: - try: - weighted_adjacency_matrix[i][j] = value - except KeyError: - weighted_adjacency_matrix[i] = {j: value} + try: + if ( + self._micro_clusters[i].weight <= self.minimum_weight + or self._micro_clusters[j].weight <= self.minimum_weight + ): + continue + except KeyError: + continue + + value = self.s[i][j] / ( + (self._micro_clusters[i].weight + self._micro_clusters[j].weight) / 2 + ) + if value > self.intersection_factor: + try: + weighted_adjacency_matrix[i][j] = value + except KeyError: + weighted_adjacency_matrix[i] = {j: value} return weighted_adjacency_matrix diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py index c49089d415..255c71e7a3 100644 --- a/river/cluster/test_dbstream.py +++ b/river/cluster/test_dbstream.py @@ -5,20 +5,30 @@ from river.cluster import DBSTREAM -@pytest.fixture -def dbstream(): +def build_dbstream(fading_factor=0.001, intersection_factor=0.05): return DBSTREAM( - fading_factor=0.001, clustering_threshold=1, cleanup_interval=1, intersection_factor=0.05 + fading_factor=fading_factor, + clustering_threshold=1, + cleanup_interval=1, + intersection_factor=intersection_factor, ) +def add_cluster(dbstream, initial_point, move_towards, times=1): + dbstream.learn_one(initial_point) + for _ in range(times): + dbstream.learn_one(move_towards) + + def assert_micro_cluster_properties(cluster, center, last_update=None): assert cluster.center == pytest.approx(center) if last_update is not None: assert cluster.last_update == last_update -def test_cluster_formation_and_cleanup(dbstream: DBSTREAM): +def test_cluster_formation_and_cleanup(): + dbstream = build_dbstream() + X = [ {1: 1}, {1: 3}, @@ -44,18 +54,12 @@ def test_cluster_formation_and_cleanup(dbstream: DBSTREAM): assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 17}, last_update=12) -def test_with_two_micro_clusters(dbstream: DBSTREAM): - # First micro-cluster - dbstream.learn_one({1: 1, 2: 1}) - for _ in range(25): - dbstream.learn_one({1: 1.7, 2: 1.7}) - - # Second micro-cluster - dbstream.learn_one({1: 3, 2: 3}) - for _ in range(25): - dbstream.learn_one({1: 2.3, 2: 2.3}) +def test_with_two_micro_clusters(): + dbstream = build_dbstream() - # Points in the middle of two micro-clusters + add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25) + add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25) + # Points in the middle of first and second micro-clusters for _ in range(5): dbstream.learn_one({1: 2, 2: 2}) @@ -75,26 +79,16 @@ def test_with_two_micro_clusters(dbstream: DBSTREAM): assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.003033, 2: 2.003033}) -def test_density_graph_with_three_micro_clusters(dbstream: DBSTREAM): - # First micro-cluster - dbstream.learn_one({1: 1, 2: 1}) - for _ in range(25): - dbstream.learn_one({1: 1.7, 2: 1.7}) - - # Second micro-cluster - dbstream.learn_one({1: 3, 2: 3}) - for _ in range(25): - dbstream.learn_one({1: 2.3, 2: 2.3}) +def test_density_graph_with_three_micro_clusters(): + dbstream = build_dbstream() + add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25) + add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25) # Points in the middle of first and second micro-clusters for _ in range(5): dbstream.learn_one({1: 2, 2: 2}) - # Third micro-cluster - dbstream.learn_one({1: 4, 2: 4}) - for _ in range(25): - dbstream.learn_one({1: 3.3, 2: 3.3}) - + add_cluster(dbstream, initial_point={1: 4, 2: 4}, move_towards={1: 3.3, 2: 3.3}, times=25) # Points in the middle of second and third micro-clusters for _ in range(4): dbstream.learn_one({1: 3, 2: 3}) @@ -118,3 +112,34 @@ def test_density_graph_with_three_micro_clusters(dbstream: DBSTREAM): dbstream._recluster() assert len(dbstream.clusters) == 1 assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.489894, 2: 2.489894}) + + +def test_density_graph_with_removed_microcluster(): + dbstream = build_dbstream(fading_factor=0.1, intersection_factor=0.3) + + add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25) + add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25) + # Points in the middle of first and second micro-clusters + for _ in range(5): + dbstream.learn_one({1: 2, 2: 2}) + + add_cluster(dbstream, initial_point={1: 4, 2: 4}, move_towards={1: 3.3, 2: 3.3}, times=25) + # Points in the middle of second and third micro-clusters + for _ in range(4): + dbstream.learn_one({1: 3, 2: 3}) + + assert len(dbstream._micro_clusters) == 2 + assert_micro_cluster_properties( + dbstream.micro_clusters[1], center={1: 2.461654, 2: 2.461654}, last_update=86 + ) + assert_micro_cluster_properties( + dbstream.micro_clusters[2], center={1: 3.430485, 2: 3.430485}, last_update=86 + ) + + assert dbstream.s[0] == pytest.approx({1: 3.615835}) + assert dbstream.s[1] == pytest.approx({2: 2.803583}) + assert dbstream.s_t == {0: {1: 56}, 1: {2: 86}} + + dbstream._recluster() + assert len(dbstream.clusters) == 1 + assert_micro_cluster_properties(dbstream.clusters[0], center={1: 3.152231, 2: 3.152231}) From 4fc1575a991b4413f6af0c7fbb44d54000baed55 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 21 Oct 2023 12:12:08 +0200 Subject: [PATCH 146/347] Split events into timestamps and datum (#1345) --- docs/releases/unreleased.md | 4 ++++ river/utils/rolling.py | 12 ++++++++---- river/utils/test_rolling.py | 14 +++++++++++++- 3 files changed, 25 insertions(+), 5 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 79f2c12327..ed1d466c6d 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -37,3 +37,7 @@ River's mini-batch methods now support pandas v2. In particular, River conforms ## tree - Fix a bug in `tree.splitter.NominalSplitterClassif` that generated a mismatch between the number of existing tree branches and the number of tracked branches. + +## utils + +- `utils.TimeRolling` now works correctly if two samples with the same timestamp are added in a row. diff --git a/river/utils/rolling.py b/river/utils/rolling.py index 849f88ba0b..6e01eb232b 100644 --- a/river/utils/rolling.py +++ b/river/utils/rolling.py @@ -125,12 +125,15 @@ class TimeRolling(BaseRolling): def __init__(self, obj: Rollable, period: dt.timedelta): super().__init__(obj) self.period = period - self._events: list[tuple[dt.datetime, typing.Any]] = [] + self._timestamps: list[dt.datetime] = [] + self._datum: list[typing.Any] = [] self._latest = dt.datetime(1, 1, 1) def update(self, *args, t: dt.datetime, **kwargs): self.obj.update(*args, **kwargs) - bisect.insort_left(self._events, (t, (args, kwargs))) + i = bisect.bisect_left(self._timestamps, t) + self._timestamps.insert(i, t) + self._datum.insert(i, (args, kwargs)) # There will only be events to revert if the new event if younger than the previously seen # youngest event @@ -138,7 +141,7 @@ def update(self, *args, t: dt.datetime, **kwargs): self._latest = t i = 0 - for ti, (argsi, kwargsi) in self._events: + for ti, (argsi, kwargsi) in zip(self._timestamps, self._datum): if ti > t - self.period: break self.obj.revert(*argsi, **kwargsi) @@ -146,6 +149,7 @@ def update(self, *args, t: dt.datetime, **kwargs): # Remove expired events if i > 0: - self._events = self._events[i:] + self._timestamps = self._timestamps[i:] + self._datum = self._datum[i:] return self diff --git a/river/utils/test_rolling.py b/river/utils/test_rolling.py index 6d1cedb4de..fef2b764ba 100644 --- a/river/utils/test_rolling.py +++ b/river/utils/test_rolling.py @@ -4,7 +4,7 @@ import pytest -from river import stats, utils +from river import proba, stats, utils def test_with_counter(): @@ -38,3 +38,15 @@ def test_rolling_with_not_rollable(): def test_time_rolling_with_not_rollable(): with pytest.raises(ValueError): utils.TimeRolling(stats.Quantile(), period=dt.timedelta(seconds=10)) + + +def test_issue_1343(): + """ + + https://github.com/online-ml/river/issues/1343 + + """ + rmean = utils.TimeRolling(proba.MultivariateGaussian(), period=dt.timedelta(microseconds=1)) + t = dt.datetime.utcnow() + rmean.update({"a": 0}, t=t) + rmean.update({"a": 1}, t=t) From 1b07a7c7a69ffc0a6defa383afb94936fcbf7926 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 21 Oct 2023 12:13:09 +0200 Subject: [PATCH 147/347] Remove dict2numpy and numpy2dict (#1344) * remove dict2numpy and numpy2dict * Update unreleased.md --- docs/releases/unreleased.md | 1 + river/anomaly/test_lof.py | 3 +- river/utils/__init__.py | 3 -- river/utils/data_conversion.py | 56 ---------------------------------- 4 files changed, 2 insertions(+), 61 deletions(-) delete mode 100644 river/utils/data_conversion.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index ed1d466c6d..31c69c7ce2 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -40,4 +40,5 @@ River's mini-batch methods now support pandas v2. In particular, River conforms ## utils +- Removed `utils.dict2numpy` and `utils.numpy2dict` functions. They were not used anywhere in the library. - `utils.TimeRolling` now works correctly if two samples with the same timestamp are added in a row. diff --git a/river/anomaly/test_lof.py b/river/anomaly/test_lof.py index c4933b0091..f34c4a64a4 100644 --- a/river/anomaly/test_lof.py +++ b/river/anomaly/test_lof.py @@ -5,7 +5,6 @@ from sklearn import neighbors from river import anomaly, datasets -from river.utils import dict2numpy np.random.seed(42) @@ -57,7 +56,7 @@ def test_batch_lof_scores(): under different batch sizes. """ cc_df = pd.DataFrame(datasets.CreditCard()) - cc_df_np = [dict2numpy(i) for i in cc_df[0].to_dict().values()] + cc_df_np = [np.array(x.values()) for x in cc_df[0].to_dict().values()] batch_sizes = [20, 50, 100] diff --git a/river/utils/__init__.py b/river/utils/__init__.py index 6474b84c5e..3ac6e56a3b 100644 --- a/river/utils/__init__.py +++ b/river/utils/__init__.py @@ -3,20 +3,17 @@ from . import inspect, math, norm, pretty, random from .context_managers import log_method_calls -from .data_conversion import dict2numpy, numpy2dict from .param_grid import expand_param_grid from .rolling import Rolling, TimeRolling from .sorted_window import SortedWindow from .vectordict import VectorDict __all__ = [ - "dict2numpy", "expand_param_grid", "inspect", "log_method_calls", "math", "pretty", - "numpy2dict", "random", "norm", "Rolling", diff --git a/river/utils/data_conversion.py b/river/utils/data_conversion.py deleted file mode 100644 index 660da2ed03..0000000000 --- a/river/utils/data_conversion.py +++ /dev/null @@ -1,56 +0,0 @@ -from __future__ import annotations - -import numpy as np - - -def dict2numpy(data) -> np.ndarray: - """Convert a dictionary containing data to a numpy array. - - There is not restriction to the type of keys in `data`, but values must - be strictly numeric. To make sure random permutations of the features - do not impact on the learning algorithms, keys are first converted to - strings and then sorted prior to the conversion. - - Parameters - ---------- - data - A dictionary whose keys represent input attributes and the values - represent their observed contents. - - Returns - ------- - An array representation of the values in `data`. - - Examples - -------- - >>> from river.utils import dict2numpy - >>> dict2numpy({'a': 1, 'b': 2, 3: 3}) - array([3, 1, 2]) - - """ - data_ = {str(k): v for k, v in data.items()} - return np.asarray(list(x for _, x in sorted(data_.items()))) - - -def numpy2dict(data: np.ndarray) -> dict: - """Convert a numpy array to a dictionary. - - Parameters - ---------- - data - An one-dimensional numpy.array. - - Returns - ------- - A dictionary where keys are integers $k \\in \\left{0, 1, ..., |\\text{data}| - 1\\right}$, - and the values are each one of the $k$ entries in `data`. - - Examples - -------- - >>> import numpy as np - >>> from river.utils import numpy2dict - >>> numpy2dict(np.array([1.0, 2.0, 3.0])) - {0: 1.0, 1: 2.0, 2: 3.0} - - """ - return {k: v for k, v in enumerate(data)} From c735492ad040f7d10398b0f7b3a3f428a2a24f5c Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 21 Oct 2023 12:15:45 +0200 Subject: [PATCH 148/347] fix test --- river/anomaly/test_lof.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/anomaly/test_lof.py b/river/anomaly/test_lof.py index f34c4a64a4..0b8767324f 100644 --- a/river/anomaly/test_lof.py +++ b/river/anomaly/test_lof.py @@ -56,7 +56,7 @@ def test_batch_lof_scores(): under different batch sizes. """ cc_df = pd.DataFrame(datasets.CreditCard()) - cc_df_np = [np.array(x.values()) for x in cc_df[0].to_dict().values()] + cc_df_np = [np.array(list(x.values())) for x in cc_df[0].to_dict().values()] batch_sizes = [20, 50, 100] From ba5b19d238246d8e7ef96cafdb75f25a7e250df1 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Tue, 24 Oct 2023 20:22:11 -0300 Subject: [PATCH 149/347] Fix bug in EFDT (#1347) --- docs/releases/unreleased.md | 1 + river/tree/extremely_fast_decision_tree.py | 119 ++++++++++++--------- 2 files changed, 67 insertions(+), 53 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 31c69c7ce2..bb58355b93 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -37,6 +37,7 @@ River's mini-batch methods now support pandas v2. In particular, River conforms ## tree - Fix a bug in `tree.splitter.NominalSplitterClassif` that generated a mismatch between the number of existing tree branches and the number of tracked branches. +- Fix a bug in `tree.ExtremelyFastDecisionTreeClassifier` where the split re-evaluation failed when the current branch's feature was not available as a split option. The fix also enables the tree to pre-prune a leaf via the tie-breaking mechanism. ## utils diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index 944505a65b..9b948e356b 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -451,59 +451,67 @@ def _reevaluate_best_split(self, node, parent, branch_index, **kwargs): # Manage memory self._enforce_size_limit() - - elif ( - x_best.merit - x_current.merit > hoeffding_bound or hoeffding_bound < self.tau - ) and (id_current != id_best): - # Create a new branch - branch = self._branch_selector(x_best.numerical_feature, x_best.multiway_split) - leaves = tuple( - self._new_leaf(initial_stats, parent=node) - for initial_stats in x_best.children_stats - ) - - new_split = x_best.assemble(branch, node.stats, node.depth, *leaves, **kwargs) - # Update weights in new_split - new_split.last_split_reevaluation_at = node.total_weight - - n_active = n_inactive = 0 - for leaf in node.iter_leaves(): - if leaf.is_active(): - n_active += 1 + elif x_current is not None: + if ( + x_best.merit - x_current.merit > hoeffding_bound + or hoeffding_bound < self.tau + ) and (id_current != id_best): + # Create a new branch + branch = self._branch_selector( + x_best.numerical_feature, x_best.multiway_split + ) + leaves = tuple( + self._new_leaf(initial_stats, parent=node) + for initial_stats in x_best.children_stats + ) + + new_split = x_best.assemble( + branch, node.stats, node.depth, *leaves, **kwargs + ) + # Update weights in new_split + new_split.last_split_reevaluation_at = node.total_weight + + n_active = n_inactive = 0 + for leaf in node.iter_leaves(): + if leaf.is_active(): + n_active += 1 + else: + n_inactive += 1 + + self._n_active_leaves -= n_active + self._n_inactive_leaves -= n_inactive + self._n_active_leaves += len(leaves) + + if parent is None: + # Root case : replace the root node by a new split node + self._root = new_split else: - n_inactive += 1 - - self._n_active_leaves -= n_active - self._n_inactive_leaves -= n_inactive - self._n_active_leaves += len(leaves) - - if parent is None: - # Root case : replace the root node by a new split node - self._root = new_split - else: - parent.children[branch_index] = new_split - - stop_flag = True - - # Manage memory - self._enforce_size_limit() - - elif ( - x_best.merit - x_current.merit > hoeffding_bound or hoeffding_bound < self.tau - ) and (id_current == id_best): - branch = self._branch_selector(x_best.numerical_feature, x_best.multiway_split) - # Change the branch but keep the existing children nodes - new_split = x_best.assemble( - branch, node.stats, node.depth, *tuple(node.children), **kwargs - ) - # Update weights in new_split - new_split.last_split_reevaluation_at = node.total_weight - - if parent is None: - # Root case : replace the root node by a new split node - self._root = new_split - else: - parent.children[branch_index] = new_split + parent.children[branch_index] = new_split + + stop_flag = True + + # Manage memory + self._enforce_size_limit() + + elif ( + x_best.merit - x_current.merit > hoeffding_bound + or hoeffding_bound < self.tau + ) and (id_current == id_best): + branch = self._branch_selector( + x_best.numerical_feature, x_best.multiway_split + ) + # Change the branch but keep the existing children nodes + new_split = x_best.assemble( + branch, node.stats, node.depth, *tuple(node.children), **kwargs + ) + # Update weights in new_split + new_split.last_split_reevaluation_at = node.total_weight + + if parent is None: + # Root case : replace the root node by a new split node + self._root = new_split + else: + parent.children[branch_index] = new_split return stop_flag @@ -551,7 +559,12 @@ def _attempt_to_split(self, node, parent, branch_index, **kwargs): node.total_weight, ) - if x_best.merit - x_null.merit > hoeffding_bound or hoeffding_bound < self.tau: + if x_best.feature is None: + # Pre-pruning - null wins + node.deactivate() + self._n_inactive_leaves += 1 + self._n_active_leaves -= 1 + elif x_best.merit - x_null.merit > hoeffding_bound or hoeffding_bound < self.tau: # Create a new branch branch = self._branch_selector(x_best.numerical_feature, x_best.multiway_split) leaves = tuple( From 8a7cae7483b71eb2a4617fd3a2d5557f1b5c65a6 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Thu, 26 Oct 2023 22:23:22 -0300 Subject: [PATCH 150/347] Add the NoDrift dummy drift detector (#1350) * add NoDrift detector * lint * update test * Update river/drift/no_drift.py Co-authored-by: Max Halford * Update river/drift/no_drift.py Co-authored-by: Max Halford * Update river/drift/no_drift.py Co-authored-by: Max Halford * streamline test --------- Co-authored-by: Max Halford --- docs/releases/unreleased.md | 12 ++-- river/drift/__init__.py | 2 + river/drift/no_drift.py | 76 ++++++++++++++++++++++++++ river/forest/adaptive_random_forest.py | 57 +++++++++---------- 4 files changed, 113 insertions(+), 34 deletions(-) create mode 100644 river/drift/no_drift.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index bb58355b93..9a6f0ba2d3 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -8,6 +8,10 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Made `score_one` method of `anomaly.LocalOutlierFactor` stateless - Defined default score for uninitialized detector +## covariance + +- Added `_from_state` method to `covariance.EmpiricalCovariance` to warm start from previous knowledge. + ## clustering - Add fixes to `cluster.DBSTREAM` algorithm, including: @@ -22,13 +26,13 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Added `datasets.WebTraffic`, which is a dataset that counts the occurrences of events on a website. It is a multi-output regression dataset with two outputs. -## forest +## drift -- Simplify inner the structures of `forest.ARFClassifier` and `forest.ARFRegressor` by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole. +- Add `drift.NoDrift` to allow disabling the drift detection capabilities of models. This detector does nothing and always returns `False` when queried whether or not a concept drift was detected. -## covariance +## forest -- Added `_from_state` method to `covariance.EmpiricalCovariance` to warm start from previous knowledge. +- Simplify inner the structures of `forest.ARFClassifier` and `forest.ARFRegressor` by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole. ## proba diff --git a/river/drift/__init__.py b/river/drift/__init__.py index 70ba3746c1..b434cd7a5b 100644 --- a/river/drift/__init__.py +++ b/river/drift/__init__.py @@ -12,6 +12,7 @@ from .adwin import ADWIN from .dummy import DummyDriftDetector from .kswin import KSWIN +from .no_drift import NoDrift from .page_hinkley import PageHinkley from .retrain import DriftRetrainingClassifier @@ -22,6 +23,7 @@ "DriftRetrainingClassifier", "DummyDriftDetector", "KSWIN", + "NoDrift", "PageHinkley", "PeriodicTrigger", ] diff --git a/river/drift/no_drift.py b/river/drift/no_drift.py new file mode 100644 index 0000000000..c5173aca04 --- /dev/null +++ b/river/drift/no_drift.py @@ -0,0 +1,76 @@ +from __future__ import annotations + +from river import base +from river.base.drift_detector import DriftDetector + + +class NoDrift(base.DriftDetector): + """Dummy class used to turn off concept drift detection capabilities of adaptive models. + It always signals that no concept drift was detected. + Examples + -------- + + >>> from river import drift + >>> from river import evaluate + >>> from river import forest + >>> from river import metrics + >>> from river.datasets import synth + + >>> dataset = synth.ConceptDriftStream( + ... seed=8, + ... position=500, + ... width=40, + ... ).take(700) + + We can turn off the warning detection capabilities of Adaptive Random Forest (ARF) or + other similar models. Thus, the base models will reset immediately after identifying a drift, + bypassing the background model building phase: + + >>> adaptive_model = forest.ARFClassifier( + ... leaf_prediction="mc", + ... warning_detector=drift.NoDrift(), + ... seed=8 + ... ) + + We can also turn off the concept drift handling capabilities completely: + + >>> stationary_model = forest.ARFClassifier( + ... leaf_prediction="mc", + ... warning_detector=drift.NoDrift(), + ... drift_detector=drift.NoDrift(), + ... seed=8 + ... ) + + Let's put that to test: + + >>> for x, y in dataset: + ... adaptive_model = adaptive_model.learn_one(x, y) + ... stationary_model = stationary_model.learn_one(x, y) + + The adaptive model: + + >>> adaptive_model.n_drifts_detected() + 2 + + >>> adaptive_model.n_warnings_detected() + 0 + + The stationary one: + + >>> stationary_model.n_drifts_detected() + 0 + + >>> stationary_model.n_warnings_detected() + 0 + + """ + + def __init__(self): + super().__init__() + + def update(self, x: int | float) -> DriftDetector: + return self + + @property + def drift_detected(self): + return False diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index 8e7a9c6645..c96d83e231 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -10,7 +10,7 @@ import numpy as np from river import base, metrics, stats -from river.drift import ADWIN +from river.drift import ADWIN, NoDrift from river.tree.hoeffding_tree_classifier import HoeffdingTreeClassifier from river.tree.hoeffding_tree_regressor import HoeffdingTreeRegressor from river.tree.nodes.arf_htc_nodes import ( @@ -32,8 +32,8 @@ def __init__( n_models: int, max_features: bool | str | int, lambda_value: int, - drift_detector: base.DriftDetector | None, - warning_detector: base.DriftDetector | None, + drift_detector: base.DriftDetector, + warning_detector: base.DriftDetector, metric: metrics.base.MultiClassMetric | metrics.base.RegressionMetric, disable_weighted_vote, seed, @@ -50,20 +50,21 @@ def __init__( self._rng = random.Random(self.seed) - self._warning_detectors: list[base.DriftDetector] = ( - None # type: ignore - if self.warning_detector is None - else [self.warning_detector.clone() for _ in range(self.n_models)] - ) - self._drift_detectors: list[base.DriftDetector] = ( - None # type: ignore - if self.drift_detector is None - else [self.drift_detector.clone() for _ in range(self.n_models)] - ) + self._warning_detectors: list[base.DriftDetector] + self._warning_detection_disabled = True + if not isinstance(self.warning_detector, NoDrift): + self._warning_detectors = [self.warning_detector.clone() for _ in range(self.n_models)] + self._warning_detection_disabled = False + + self._drift_detectors: list[base.DriftDetector] + self._drift_detection_disabled = True + if not isinstance(self.drift_detector, NoDrift): + self._drift_detectors = [self.drift_detector.clone() for _ in range(self.n_models)] + self._drift_detection_disabled = False # The background models self._background: list[BaseTreeClassifier | BaseTreeRegressor | None] = ( - None if self.warning_detector is None else [None] * self.n_models # type: ignore + None if self._warning_detection_disabled else [None] * self.n_models # type: ignore ) # Performance metrics used for weighted voting/aggregation @@ -71,10 +72,10 @@ def __init__( # Drift and warning logging self._warning_tracker: dict = ( - collections.defaultdict(int) if self.warning_detector is not None else None # type: ignore + collections.defaultdict(int) if not self._warning_detection_disabled else None # type: ignore ) self._drift_tracker: dict = ( - collections.defaultdict(int) if self.drift_detector is not None else None # type: ignore + collections.defaultdict(int) if not self._drift_detection_disabled else None # type: ignore ) @property @@ -101,12 +102,10 @@ def _drift_detector_input( def _new_base_model(self) -> BaseTreeClassifier | BaseTreeRegressor: raise NotImplementedError - def n_warnings_detected(self, tree_id: int | None = None) -> int | None: + def n_warnings_detected(self, tree_id: int | None = None) -> int: """Get the total number of concept drift warnings detected, or the number on an individual tree basis (optionally). - If warning detection is disabled, will return `None`. - Parameters ---------- tree_id @@ -119,20 +118,18 @@ def n_warnings_detected(self, tree_id: int | None = None) -> int | None: """ - if self.warning_detector is None: - return None + if self._warning_detection_disabled: + return 0 if tree_id is None: return sum(self._warning_tracker.values()) return self._warning_tracker[tree_id] - def n_drifts_detected(self, tree_id: int | None = None) -> int | None: + def n_drifts_detected(self, tree_id: int | None = None) -> int: """Get the total number of concept drifts detected, or such number on an individual tree basis (optionally). - If drift detection is disabled, will return `None`. - Parameters ---------- tree_id @@ -145,8 +142,8 @@ def n_drifts_detected(self, tree_id: int | None = None) -> int | None: """ - if self.drift_detector is None: - return None + if self._drift_detection_disabled: + return 0 if tree_id is None: return sum(self._drift_tracker.values()) @@ -171,13 +168,13 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs): k = poisson(rate=self.lambda_value, rng=self._rng) if k > 0: - if self.warning_detector is not None and self._background[i] is not None: + if not self._warning_detection_disabled and self._background[i] is not None: self._background[i].learn_one(x=x, y=y, sample_weight=k) # type: ignore model.learn_one(x=x, y=y, sample_weight=k) drift_input = None - if self.drift_detector is not None and self.warning_detector is not None: + if not self._warning_detection_disabled: drift_input = self._drift_detector_input(i, y, y_pred) self._warning_detectors[i].update(drift_input) @@ -189,7 +186,7 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs): # Update warning tracker self._warning_tracker[i] += 1 - if self.drift_detector is not None: + if not self._drift_detection_disabled: drift_input = ( drift_input if drift_input is not None @@ -198,7 +195,7 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs): self._drift_detectors[i].update(drift_input) if self._drift_detectors[i].drift_detected: - if self.warning_detector is not None and self._background[i] is not None: + if not self._warning_detection_disabled and self._background[i] is not None: self.data[i] = self._background[i] self._background[i] = None self._warning_detectors[i] = self.warning_detector.clone() From b72077d052deb3b1d941997ff550a6e659bd00b7 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 30 Oct 2023 11:02:58 +0100 Subject: [PATCH 151/347] add yield_predictions to iter_progressive_val_score --- docs/releases/unreleased.md | 4 +++ river/evaluate/progressive_validation.py | 33 +++++++++++++++++++++--- river/utils/pretty.py | 2 +- 3 files changed, 35 insertions(+), 4 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 31c69c7ce2..378f4f626c 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -22,6 +22,10 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Added `datasets.WebTraffic`, which is a dataset that counts the occurrences of events on a website. It is a multi-output regression dataset with two outputs. +## evaluate + +- Added a `yield_predictions` parameter to `evaluate.iter_progressive_val_score`, which allows including predictions in the output. + ## forest - Simplify inner the structures of `forest.ARFClassifier` and `forest.ARFRegressor` by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole. diff --git a/river/evaluate/progressive_validation.py b/river/evaluate/progressive_validation.py index 9aa1714276..45e9e0d4f8 100644 --- a/river/evaluate/progressive_validation.py +++ b/river/evaluate/progressive_validation.py @@ -19,6 +19,7 @@ def _progressive_validation( delay: str | int | dt.timedelta | typing.Callable | None = None, measure_time=False, measure_memory=False, + yield_predictions=False, ): # Check that the model and the metric are in accordance if not metric.works_with(model): @@ -45,7 +46,7 @@ def _progressive_validation( if measure_time: start = time.perf_counter() - def report(): + def report(y_pred): if isinstance(metric, metrics.base.Metrics): state = {m.__class__.__name__: m for m in metric} else: @@ -58,6 +59,9 @@ def report(): state["Time"] = dt.timedelta(seconds=now - start) if measure_memory: state["Memory"] = model._raw_memory_usage + if yield_predictions: + state["Prediction"] = y_pred + return state for i, x, y, *kwargs in stream.simulate_qa(dataset, moment, delay, copy=True): @@ -90,13 +94,13 @@ def report(): # Yield current results n_total_answers += 1 if n_total_answers == next_checkpoint: - yield report() + yield report(y_pred=y_pred) prev_checkpoint = next_checkpoint next_checkpoint = next(checkpoints, None) else: # If the dataset was exhausted, we need to make sure that we yield the final results if prev_checkpoint and n_total_answers != prev_checkpoint: - yield report() + yield report(y_pred=None) def iter_progressive_val_score( @@ -108,6 +112,7 @@ def iter_progressive_val_score( step=1, measure_time=False, measure_memory=False, + yield_predictions=False, ) -> typing.Generator: """Evaluates the performance of a model on a streaming dataset and yields results. @@ -143,6 +148,9 @@ def iter_progressive_val_score( Whether or not to measure the elapsed time. measure_memory Whether or not to measure the memory usage of the model. + yield_predictions + Whether or not to include predictions. If step is 1, then this is equivalent to yielding + the predictions at every iterations. Otherwise, not all predictions will be yielded. Examples -------- @@ -180,6 +188,24 @@ def iter_progressive_val_score( {'ROCAUC': ROCAUC: 95.07%, 'Step': 1200} {'ROCAUC': ROCAUC: 95.07%, 'Step': 1250} + The `yield_predictions` parameter can be used to include the predictions in the results: + + >>> steps = evaluate.iter_progressive_val_score( + ... model=model, + ... dataset=datasets.Phishing(), + ... metric=metrics.ROCAUC(), + ... step=1, + ... yield_predictions=True + ... ) + + >>> for step in itertools.islice(steps, 100, 105): + ... print(step) + {'ROCAUC': ROCAUC: 94.68%, 'Step': 101, 'Prediction': {False: 0.966..., True: 0.033...}} + {'ROCAUC': ROCAUC: 94.75%, 'Step': 102, 'Prediction': {False: 0.035..., True: 0.964...}} + {'ROCAUC': ROCAUC: 94.82%, 'Step': 103, 'Prediction': {False: 0.043..., True: 0.956...}} + {'ROCAUC': ROCAUC: 94.89%, 'Step': 104, 'Prediction': {False: 0.816..., True: 0.183...}} + {'ROCAUC': ROCAUC: 94.96%, 'Step': 105, 'Prediction': {False: 0.041..., True: 0.958...}} + References ---------- [^1]: [Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation](http://hunch.net/~jl/projects/prediction_bounds/progressive_validation/coltfinal.pdf) @@ -196,6 +222,7 @@ def iter_progressive_val_score( delay=delay, measure_time=measure_time, measure_memory=measure_memory, + yield_predictions=yield_predictions, ) diff --git a/river/utils/pretty.py b/river/utils/pretty.py index acc0125100..3ddc4469f4 100644 --- a/river/utils/pretty.py +++ b/river/utils/pretty.py @@ -34,7 +34,7 @@ def print_table( raise ValueError("all the columns must be of the same length") # Determine the width of each column based on the maximum length of it's elements - col_widths = [max(len(col) if col else 0, len(header)) for header, col in zip(headers, columns)] + col_widths = [max(max(map(len, col)), len(header)) for header, col in zip(headers, columns)] # Make a template to print out rows one by one row_format = " ".join(["{:" + str(width + 2) + "s}" for width in col_widths]) From b9f73666adcdfb00c8016e9d2acc359c8594e26c Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 30 Oct 2023 11:07:51 +0100 Subject: [PATCH 152/347] Update progressive_validation.py --- river/evaluate/progressive_validation.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/river/evaluate/progressive_validation.py b/river/evaluate/progressive_validation.py index 45e9e0d4f8..0fbb63884a 100644 --- a/river/evaluate/progressive_validation.py +++ b/river/evaluate/progressive_validation.py @@ -190,6 +190,8 @@ def iter_progressive_val_score( The `yield_predictions` parameter can be used to include the predictions in the results: + >>> import itertools + >>> steps = evaluate.iter_progressive_val_score( ... model=model, ... dataset=datasets.Phishing(), From 0e87b804efc7f55d75402bf5cfdea87e66cd9232 Mon Sep 17 00:00:00 2001 From: Hoang Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 31 Oct 2023 00:05:57 +0700 Subject: [PATCH 153/347] Implementation of the incremental Kolmogorov-Smirnov Statistics (#1354) * Initial implementation of incremental KS statistics * Update docstring * Refactor incremental KS from metrics to stats. * Modify import in test_ks.py file * Refactor import in test_ks.py file * Refactor import for test_ks.py * Add test to assert whether the two sliding windows follow the same distribution with the result from the KS test. * Run black code formatter on kolmogorov_smirnov.py file * Fix test_ks.py with ruff v0.1.2 * Commit from suggestion of code reviewer. Co-authored-by: Max Halford * Update code from reviewer's suggestion. Co-authored-by: Max Halford * Update code from reviewer's suggestion. Co-authored-by: Max Halford * Trim trailing whitespace of kolmogorov_smirnov.py file * Add description to UNRELEASED.md --------- Co-authored-by: Max Halford --- docs/releases/unreleased.md | 4 + river/stats/__init__.py | 2 + river/stats/kolmogorov_smirnov.py | 291 ++++++++++++++++++++++++++++++ river/stats/test_ks.py | 45 +++++ 4 files changed, 342 insertions(+) create mode 100644 river/stats/kolmogorov_smirnov.py create mode 100644 river/stats/test_ks.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 1dd67a69b6..4973eb8e35 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -47,6 +47,10 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Fix a bug in `tree.splitter.NominalSplitterClassif` that generated a mismatch between the number of existing tree branches and the number of tracked branches. - Fix a bug in `tree.ExtremelyFastDecisionTreeClassifier` where the split re-evaluation failed when the current branch's feature was not available as a split option. The fix also enables the tree to pre-prune a leaf via the tie-breaking mechanism. +## stats + +- Implementation of the incremental Kolmogorov-Smirnov statistics (at `stats.KolmogorovSmirnov`), with the option to calculate either the original KS or Kuiper's test. + ## utils - Removed `utils.dict2numpy` and `utils.numpy2dict` functions. They were not used anywhere in the library. diff --git a/river/stats/__init__.py b/river/stats/__init__.py index 50c0625ef5..0b88c19d4b 100644 --- a/river/stats/__init__.py +++ b/river/stats/__init__.py @@ -9,6 +9,7 @@ from .ewmean import EWMean from .ewvar import EWVar from .iqr import IQR, RollingIQR +from .kolmogorov_smirnov import KolmogorovSmirnov from .kurtosis import Kurtosis from .link import Link from .mad import MAD @@ -37,6 +38,7 @@ "EWMean", "EWVar", "IQR", + "KolmogorovSmirnov", "Kurtosis", "Link", "MAD", diff --git a/river/stats/kolmogorov_smirnov.py b/river/stats/kolmogorov_smirnov.py new file mode 100644 index 0000000000..873e3dd731 --- /dev/null +++ b/river/stats/kolmogorov_smirnov.py @@ -0,0 +1,291 @@ +from __future__ import annotations + +import math +import random + +from river import base, stats + +__all__ = ["KolmogorovSmirnov"] + + +class Treap(base.Base): + """Class representing Treap (Cartesian Tree) used to calculate the Incremental KS statistics.""" + + def __init__(self, key, value=0): + self.key = key + self.value = value + self.priority = random.random() + self.size = 1 + self.height = 1 + self.lazy = 0 + self.max_value = value + self.min_value = value + self.left = None + self.right = None + + @staticmethod + def sum_all(node, value): + if node is None: + return + node.value += value + node.max_value += value + node.min_value += value + node.lazy += value + + @classmethod + def unlazy(cls, node): + cls.sum_all(node.left, node.lazy) + cls.sum_all(node.right, node.lazy) + node.lazy = 0 + + @classmethod + def update(cls, node): + if node is None: + return + cls.unlazy(node) + node.size = 1 + node.height = 0 + node.max_value = node.value + node.min_value = node.value + + if node.left is not None: + node.size += node.left.size + node.height += node.left.height + node.max_value = max(node.max_value, node.left.max_value) + node.min_value = min(node.min_value, node.left.min_value) + + if node.right is not None: + node.size += node.right.size + node.height = max(node.height, node.right.height) + node.max_value = max(node.max_value, node.right.max_value) + node.min_value = min(node.min_value, node.right.min_value) + + node.height += 1 + + @classmethod + def split_keep_right(cls, node, key): + if node is None: + return None, None + + left, right = None, None + + cls.unlazy(node) + + if key <= node.key: + left, node.left = cls.split_keep_right(node.left, key) + right = node + else: + node.right, right = cls.split_keep_right(node.right, key) + left = node + + cls.update(left) + cls.update(right) + + return left, right + + @classmethod + def merge(cls, left, right): + if left is None: + return right + if right is None: + return left + + node = None + + if left.priority > right.priority: + cls.unlazy(left) + left.right = cls.merge(left.right, right) + node = left + else: + cls.unlazy(right) + right.left = cls.merge(left, right.left) + node = right + + cls.update(node) + + return node + + @classmethod + def split_smallest(cls, node): + if node is None: + return None, None + + left, right = None, None + + cls.unlazy(node) + + if node.left is not None: + left, node.left = cls.split_smallest(node.left) + right = node + else: + right = node.right + node.right = None + left = node + + cls.update(left) + cls.update(right) + + return left, right + + @classmethod + def split_greatest(cls, node): + if node is None: + return None, None + + cls.unlazy(node) + + if node.right is not None: + node.right, right = cls.split_greatest(node.right) + left = node + else: + left = node.left + node.left = None + right = node + + cls.update(left) + cls.update(right) + + return left, right + + @staticmethod + def get_size(node): + return 0 if node is None else node.size + + @staticmethod + def get_height(node): + return 0 if node is None else node.height + + +class KolmogorovSmirnov(stats.base.Bivariate): + r"""Incremental Kolmogorov-Smirnov statistics. + + The two-sample Kolmogorov-Smirnov test quantifies the distance between the empirical functions of two samples, + with the null distribution of this statistic is calculated under the null hypothesis that the samples are drawn from + the same distribution. The formula can be described as + + $$ + D_{n, m} = \sup_x \| F_{1, n}(x) - F_{2, m}(x) \|. + $$ + + This implementation is the incremental version of the previously mentioned statistics, with the change being in + the ability to insert and remove an observation thorugh time. This can be done using a randomized tree called + Treap (or Cartesian Tree) [^2] with bulk operation and lazy propagation. + + The implemented algorithm is able to perform the insertion and removal operations + in O(logN) with high probability and calculate the Kolmogorov-Smirnov test in O(1), + where N is the number of sample observations. This is a significant improvement compared + to the O(N logN) cost of non-incremental implementation. + + This implementation also supports the calculation of the Kuiper statistics. Different from the orginial + Kolmogorov-Smirnov statistics, Kuiper's test [^3] calculates the sum of the absolute sizes of the most positive and + most negative differences between the two cumulative distribution functions taken into account. As such, + Kuiper's test is very sensitive in the tails as at the median. + + Last but not least, this implementation is also based on the original implementation within the supplementary + material of the authors of paper [^1], at + [the following Github repository](https://github.com/denismr/incremental-ks/tree/master). + + Parameters + ---------- + statistic + The method used to calculate the statistic, can be either "ks" or "kuiper". + The default value is set as "ks". + + Examples + -------- + + >>> import numpy as np + >>> from river import stats + + >>> stream_a = [1, 1, 2, 2, 3, 3, 4, 4] + >>> stream_b = [1, 1, 1, 1, 2, 2, 2, 2] + + >>> incremental_ks = stats.KolmogorovSmirnov(statistic="ks") + >>> for a, b in zip(stream_a, stream_b): + ... incremental_ks.update(a, b) + + >>> incremental_ks + KolmogorovSmirnov: 0.5 + + >>> incremental_ks.n_samples + 8 + + References + ---------- + [^1]: dos Reis, D.M. et al. (2016) ‘Fast unsupervised online drift detection using incremental Kolmogorov-Smirnov + test’, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. + doi:10.1145/2939672.2939836. + [^2]: C. R. Aragon and R. G. Seidel. Randomized search trees. In FOCS, pages 540–545. IEEE, 1989. + [^3]: Kuiper, N. H. (1960). "Tests concerning random points on a circle". + Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, Series A. 63: 38–47. + + """ + + def __init__(self, statistic="ks"): + self.treap = None + self.n_samples = 0 + self.statistic = statistic + + @staticmethod + def ca(p_value): + return (-0.5 * math.log(p_value)) ** 0.5 + + @classmethod + def ks_threshold(cls, p_value, n_samples): + return cls.ca(p_value) * (2.0 * n_samples / n_samples**2) + + def update(self, x, y): + keys = ((x, 0), (y, 1)) + + self.n_samples += 1 + + for key in keys: + left, left_g, right, val = None, None, None, None + + left, right = Treap.split_keep_right(self.treap, key) + left, left_g = Treap.split_greatest(left) + val = 0 if left_g is None else left_g.value + + left = Treap.merge(left, left_g) + right = Treap.merge(Treap(key, val), right) + Treap.sum_all(right, 1 if key[1] == 0 else -1) + + self.treap = Treap.merge(left, right) + + def revert(self, x, y): + keys = ((x, 0), (y, 1)) + + self.n_samples -= 1 + + for key in keys: + left, right, right_l = None, None, None + + left, right = Treap.split_keep_right(self.treap, key) + right_l, right = Treap.split_smallest(right) + + if right_l is not None and right_l.key == key: + Treap.sum_all(right, -1 if key[1] == 0 else 1) + else: + right = Treap.merge(right_l, right) + + self.treap = Treap.merge(left, right) + + def get(self): + assert self.statistic in ["ks", "kuiper"] + if self.n_samples == 0: + return 0 + + if self.statistic == "ks": + return max(self.treap.max_value, -self.treap.min_value) / self.n_samples + elif self.statistic == "kuiper": + return max(self.treap.max_value - self.treap.min_value) / self.n_samples + else: + raise ValueError(f"Unknown statistic {self.statistic}, expected one of: ks, kuiper") + + def test_ks_threshold(self, ca): + """ + Test whether the reference and sliding window follows the same or different probability distribution. + This test will return `True` if we **reject** the null hypothesis that + the two windows follow the same distribution. + """ + return self.get() > ca * (2 * self.n_samples / self.n_samples**2) ** 0.5 diff --git a/river/stats/test_ks.py b/river/stats/test_ks.py new file mode 100644 index 0000000000..baa979189e --- /dev/null +++ b/river/stats/test_ks.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +from collections import deque + +import numpy as np +from scipy.stats import ks_2samp + +from river import stats + + +def test_incremental_ks_statistics(): + initial_a = np.random.normal(loc=0, scale=1, size=500) + initial_b = np.random.normal(loc=1, scale=1, size=500) + + stream_a = np.random.normal(loc=0, scale=1, size=5000) + stream_b = np.random.normal(loc=1, scale=1, size=5000) + + incremental_ks_statistics = [] + incremental_ks = stats.KolmogorovSmirnov(statistic="ks") + sliding_a = deque(initial_a) + sliding_b = deque(initial_b) + + for a, b in zip(initial_a, initial_b): + incremental_ks.update(a, b) + for a, b in zip(stream_a, stream_b): + incremental_ks.revert(sliding_a.popleft(), sliding_b.popleft()) + sliding_a.append(a) + sliding_b.append(b) + incremental_ks.update(a, b) + incremental_ks_statistics.append(incremental_ks.get()) + + ks_2samp_statistics = [] + sliding_a = deque(initial_a) + sliding_b = deque(initial_b) + + for a, b in zip(stream_a, stream_b): + sliding_a.popleft() + sliding_b.popleft() + sliding_a.append(a) + sliding_b.append(b) + ks_2samp_statistics.append(ks_2samp(sliding_a, sliding_b).statistic) + + assert np.allclose(np.array(incremental_ks_statistics), np.array(ks_2samp_statistics)) + + assert incremental_ks.test_ks_threshold(ca=incremental_ks.ca(p_value=0.05)) is True From 3d0d6b46bf8ee75843798432cd4667dd93c801a9 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Wed, 1 Nov 2023 18:14:31 +0100 Subject: [PATCH 154/347] tidy ks --- river/stats/kolmogorov_smirnov.py | 14 +++++--------- .../{test_ks.py => test_kolmogorov_smirnov.py} | 2 +- 2 files changed, 6 insertions(+), 10 deletions(-) rename river/stats/{test_ks.py => test_kolmogorov_smirnov.py} (93%) diff --git a/river/stats/kolmogorov_smirnov.py b/river/stats/kolmogorov_smirnov.py index 873e3dd731..e3689a9613 100644 --- a/river/stats/kolmogorov_smirnov.py +++ b/river/stats/kolmogorov_smirnov.py @@ -226,14 +226,6 @@ def __init__(self, statistic="ks"): self.n_samples = 0 self.statistic = statistic - @staticmethod - def ca(p_value): - return (-0.5 * math.log(p_value)) ** 0.5 - - @classmethod - def ks_threshold(cls, p_value, n_samples): - return cls.ca(p_value) * (2.0 * n_samples / n_samples**2) - def update(self, x, y): keys = ((x, 0), (y, 1)) @@ -282,7 +274,11 @@ def get(self): else: raise ValueError(f"Unknown statistic {self.statistic}, expected one of: ks, kuiper") - def test_ks_threshold(self, ca): + @staticmethod + def _ca(p_value): + return (-0.5 * math.log(p_value)) ** 0.5 + + def _test_ks_threshold(self, ca): """ Test whether the reference and sliding window follows the same or different probability distribution. This test will return `True` if we **reject** the null hypothesis that diff --git a/river/stats/test_ks.py b/river/stats/test_kolmogorov_smirnov.py similarity index 93% rename from river/stats/test_ks.py rename to river/stats/test_kolmogorov_smirnov.py index baa979189e..24b2fd4f97 100644 --- a/river/stats/test_ks.py +++ b/river/stats/test_kolmogorov_smirnov.py @@ -42,4 +42,4 @@ def test_incremental_ks_statistics(): assert np.allclose(np.array(incremental_ks_statistics), np.array(ks_2samp_statistics)) - assert incremental_ks.test_ks_threshold(ca=incremental_ks.ca(p_value=0.05)) is True + assert incremental_ks._test_ks_threshold(ca=incremental_ks._ca(p_value=0.05)) is True From bc1b7cb005f90e822c75acfd78e7f0b6584888ec Mon Sep 17 00:00:00 2001 From: Hoang Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Thu, 2 Nov 2023 15:46:50 +0700 Subject: [PATCH 155/347] Implementation of the StandardAbsoluteDeviation (SAD) anomaly detection algorithm (#1357) Implementation of the SAD algorithm, adapted from the version implemented in PySAD (Python Streaming Anomaly Detection). --- docs/releases/unreleased.md | 1 + river/anomaly/__init__.py | 2 + river/anomaly/sad.py | 86 +++++++++++++++++++++++++++++++++++++ 3 files changed, 89 insertions(+) create mode 100644 river/anomaly/sad.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 4973eb8e35..f2a7b85220 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -7,6 +7,7 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Added `anomaly.LocalOutlierFactor`, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation. - Made `score_one` method of `anomaly.LocalOutlierFactor` stateless - Defined default score for uninitialized detector +- Implementation of the `anomaly.StandardAbsoluteDeviation` algorithm, which is a uni-variate anomaly detection algorithm, based on the implementation in [PySAD](https://github.com/selimfirat/pysad/blob/master/pysad/models/knn_cad.py) (Python Streaming Anomaly Detection) ## covariance diff --git a/river/anomaly/__init__.py b/river/anomaly/__init__.py index ac0cd08269..ca5ffd0888 100644 --- a/river/anomaly/__init__.py +++ b/river/anomaly/__init__.py @@ -18,6 +18,7 @@ from .gaussian import GaussianScorer from .hst import HalfSpaceTrees from .lof import LocalOutlierFactor +from .sad import StandardAbsoluteDeviation from .svm import OneClassSVM __all__ = [ @@ -27,6 +28,7 @@ "HalfSpaceTrees", "OneClassSVM", "QuantileFilter", + "StandardAbsoluteDeviation", "ThresholdFilter", "LocalOutlierFactor", ] diff --git a/river/anomaly/sad.py b/river/anomaly/sad.py new file mode 100644 index 0000000000..80c272c5a2 --- /dev/null +++ b/river/anomaly/sad.py @@ -0,0 +1,86 @@ +from __future__ import annotations + +from river import anomaly, stats + +__all__ = ["StandardAbsoluteDeviation"] + + +class StandardAbsoluteDeviation(anomaly.base.SupervisedAnomalyDetector): + r"""Standard Absolute Deviation (SAD). + + SAD is the model that calculates the anomaly score by using the deviation from the mean/median, divided by the + standard deviation of all the points seen within the data stream. The idea of this model is based on + the $3 \times \sigma$ rule described in [^1]. + + This implementation is adapted from the [implementation](https://github.com/selimfirat/pysad/blob/master/pysad/models/standard_absolute_deviation.py) + within PySAD (Python Streaming Anomaly Detection) [^2]. + + As a univariate anomaly detection algorithm, this implementation is adapted to `River` in a similar way as that of + the `GaussianScorer` algorithm, with the variable taken into the account at the learning phase and scoring phase + under variable `y`, ignoring `x`. + + Parameters + ---------- + sub_stat + The statistic to be subtracted, then divided by the standard deviation for scoring. + This parameter must be either "mean" or "median". + + References + ---------- + [^1]: Hochenbaum, J., Vallis, O.S., Kejariwal, A., 2017. Automatic Anomaly Detection in the Cloud Via + Statistical Learning. https://doi.org/10.48550/ARXIV.1704.07706. + [^2]: Yilmaz, S.F., Kozat, S.S., 2020. PySAD: A Streaming Anomaly Detection Framework in Python. + https://doi.org/10.48550/ARXIV.2009.02572. + + Examples + -------- + + >>> import numpy as np + >>> from river import anomaly + >>> from river import stream + + >>> np.random.seed(42) + + >>> X = np.random.randn(150) + + >>> model = anomaly.StandardAbsoluteDeviation(sub_stat="mean") + + >>> for x in X: + ... model = model.learn_one(None, x) + + >>> model.score_one(None, 2) + 2.209735291993561 + + >>> model.score_one(None, 0) + 0.08736408615569183 + + >>> model.score_one(None, 1) + 1.1485496890746263 + + """ + + def __init__(self, sub_stat=None): + self.variance = stats.Var() + self.sub_stat = sub_stat + + if self.sub_stat == "mean": + self.subtracted_statistic_estimator = stats.Mean() + elif self.sub_stat == "median": + self.subtracted_statistic_estimator = stats.Quantile(q=0.5) + else: + raise ValueError( + f"Unknown subtracted statistic {self.sub_stat}, expected one of median, mean." + ) + + def learn_one(self, x, y): + self.variance.update(y) + self.subtracted_statistic_estimator.update(y) + + return self + + def score_one(self, x, y): + score = (y - self.subtracted_statistic_estimator.get()) / ( + self.variance.get() ** 0.5 + 1e-10 + ) + + return abs(score) From f514ad0cbca6e2cf565f7c2f72cca1ead57d2103 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 2 Nov 2023 18:38:45 +0100 Subject: [PATCH 156/347] Use Poetry (#1346) * start using poetry * Update pyproject.toml * update poetry * Update __version__.py * Update unit-tests.yml * make groups optional * no need to source activate * Update __version__.py * Update __version__.py * bumps * wip * wip * add pytest-xdist[psutil] * Update pyproject.toml * Update build.py * wip * Update action.yml * Update unit-tests.yml * Update unit-tests.yml * Update unit-tests.yml * Update unit-tests.yml * Update unit-tests.yml * Update build.py * try this * now try this * Update unit-tests.yml * Update sad.py * Update sad.py * fix tests * stuff * Update action.yml * Update CONTRIBUTING.md * Update action.yml * try adding a build-root param * wip * Update unit-tests.yml * wip * Update action.yml * wip * Update unit-tests.yml --- .github/actions/install-env/action.yml | 53 + .github/workflows/build-river.yml | 71 - .github/workflows/ci.yml | 47 - .github/workflows/code-quality.yml | 28 +- .github/workflows/delete-caches.yml | 2 +- .github/workflows/{docs.yml => dev-docs.yml} | 18 +- .github/workflows/pypi.yml | 2 +- .github/workflows/release-docs.yml | 20 +- .github/workflows/unit-tests.yml | 46 +- CONTRIBUTING.md | 24 +- build.py | 72 + poetry.lock | 5667 ++++++++++++++++++ pyproject.toml | 72 +- river/__version__.py | 4 +- river/anomaly/sad.py | 41 +- setup.py | 212 +- 16 files changed, 6013 insertions(+), 366 deletions(-) create mode 100644 .github/actions/install-env/action.yml delete mode 100644 .github/workflows/build-river.yml delete mode 100644 .github/workflows/ci.yml rename .github/workflows/{docs.yml => dev-docs.yml} (72%) create mode 100644 build.py create mode 100644 poetry.lock diff --git a/.github/actions/install-env/action.yml b/.github/actions/install-env/action.yml new file mode 100644 index 0000000000..719b9793db --- /dev/null +++ b/.github/actions/install-env/action.yml @@ -0,0 +1,53 @@ +name: Install env + +inputs: + python-version: + description: "Python version to use" + required: true + build-root: + default: "true" + options: + - true + - false + +runs: + using: "composite" + steps: + - name: Check out repository + uses: actions/checkout@v3 + + - name: Set up Python + id: set-up-python + uses: actions/setup-python@v4 + with: + python-version: ${{ inputs.python-version }} + + - name: Load cached Poetry installation + uses: actions/cache@v3 + with: + path: ~/.local # the path depends on the OS + key: poetry-1 # increment to reset cache + + - name: Install poetry + uses: snok/install-poetry@v1 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + + - name: Load cached virtual env + id: cached-poetry-dependencies + uses: actions/cache@v3 + with: + path: .venv + key: venv-${{ runner.os }}-${{ steps.set-up-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} + + - name: Install dependencies + shell: bash + if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' + run: poetry install --no-interaction --no-ansi --no-root + + - name: Build + shell: bash + if: ${{ inputs.build-root == 'true' }} + run: poetry install --no-interaction --no-ansi diff --git a/.github/workflows/build-river.yml b/.github/workflows/build-river.yml deleted file mode 100644 index e89f435527..0000000000 --- a/.github/workflows/build-river.yml +++ /dev/null @@ -1,71 +0,0 @@ -name: build-river - -on: - workflow_call: - inputs: - python: - type: string - os: - type: string - -jobs: - build-river: - runs-on: ${{ inputs.os }} - - # Instead of using two matrices in the calling Workflow, we can use conditionals here - if: (inputs.python == '3.11') || github.event_name == 'push' - - steps: - - uses: actions/checkout@v3 - - - name: set up rust - if: runner.os != 'Linux' - uses: actions-rs/toolchain@v1 - with: - profile: minimal - toolchain: nightly - override: true - - - run: curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show - if: runner.os == 'Linux' - - - name: Set up Python ${{ inputs.python }} - uses: actions/setup-python@v4 - with: - python-version: ${{ inputs.python }} - - - name: Cache the Python environment - uses: actions/cache@v3 - id: cache-venv - with: - path: ~/.venv - key: ${{ runner.os }}-${{ inputs.python }}-venv-${{ hashFiles('**/setup.py') }} - restore-keys: | - venv-${{ runner.os }}-${{ inputs.python }} - ${{ runner.os }}-${{ inputs.python }}-venv- - - - name: Install Python dependencies - if: ${{ steps.cache-venv.outputs.cache-hit != 'true' }} - run: | - python -m pip install --upgrade pip - python -m venv ~/.venv - source ~/.venv/bin/activate - pip install wheel - pip install scikit-learn sqlalchemy - pip install pytest-xdist[psutil] - pip install numpydoc jupyter - - - name: Build River - run: | - source ~/.venv/bin/activate - pip install -e ".[dev,docs]" - - # We should delete the git project from the build cache to avoid conflicts - - name: Delete the Git project - run: rm -r .git - - - uses: actions/cache/save@v3 - id: cache-river - with: - path: ${{ github.workspace }} - key: river-build-${{ runner.os }}-${{ inputs.python }} diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml deleted file mode 100644 index 1d273ba88e..0000000000 --- a/.github/workflows/ci.yml +++ /dev/null @@ -1,47 +0,0 @@ -name: ci - -on: - push: - branches: - - main - - pull_request: - branches: - - "*" - -jobs: - build-river: - strategy: - fail-fast: false - matrix: - python: ["3.10", "3.11"] - os: [ubuntu-latest, macos-latest] - - uses: ./.github/workflows/build-river.yml - with: - python: ${{ matrix.python }} - os: ${{ matrix.os }} - - unit-tests: - needs: build-river - strategy: - fail-fast: false - matrix: - python: ["3.10", "3.11"] - os: [ubuntu-latest, macos-latest] - - uses: ./.github/workflows/unit-tests.yml - with: - python: ${{ matrix.python }} - os: ${{ matrix.os }} - - dev-docs: - if: github.event_name == 'push' - needs: unit-tests # The workflow will actually update docs, so it's best to wait for unit tests too. - uses: ./.github/workflows/docs.yml - secrets: inherit - - branch-docs: - if: github.event_name == 'pull_request' - needs: build-river # The workflow will only build docs, so no need to wait for unit tests. - uses: ./.github/workflows/docs.yml diff --git a/.github/workflows/code-quality.yml b/.github/workflows/code-quality.yml index c349ae1e99..39b544cebd 100644 --- a/.github/workflows/code-quality.yml +++ b/.github/workflows/code-quality.yml @@ -12,29 +12,13 @@ jobs: ubuntu: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - - name: Set up Python - uses: actions/setup-python@v4 + - name: Build River + uses: ./.github/actions/install-env with: - python-version: 3.11 - - - run: curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show - if: matrix.os == 'ubuntu-latest' - - - name: Cache Python dependencies - uses: actions/cache@v2 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ hashFiles('**/setup.py') }} - restore-keys: | - ${{ runner.os }}-pip- - - - name: Install Python dependencies - run: | - python -m pip install --upgrade pip - pip install wheel - pip install -e ".[dev]" + python-version: "3.12" + build-root: false - name: Run pre-commit on all files - run: pre-commit run --all-files + run: poetry run pre-commit run --all-files diff --git a/.github/workflows/delete-caches.yml b/.github/workflows/delete-caches.yml index 96fc9f892e..e4bedb060b 100644 --- a/.github/workflows/delete-caches.yml +++ b/.github/workflows/delete-caches.yml @@ -7,7 +7,7 @@ on: jobs: my-job: name: Delete all caches - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - name: Clear caches diff --git a/.github/workflows/docs.yml b/.github/workflows/dev-docs.yml similarity index 72% rename from .github/workflows/docs.yml rename to .github/workflows/dev-docs.yml index 376c0d2429..b86a03d2e0 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/dev-docs.yml @@ -1,7 +1,9 @@ name: docs on: - workflow_call: + push: + branches: + - main jobs: docs: @@ -10,21 +12,25 @@ jobs: steps: - uses: actions/checkout@v3 - - name: Retrieve the environment and the River build - uses: ./.github/actions/retrieve-env + - name: Build River + uses: ./.github/actions/install-env with: - python: "3.11" + python-version: "3.12" + build-root: false - - name: Install Ubuntu dependencies + - name: Install extra Ubuntu dependencies run: sudo apt-get install graphviz pandoc + - name: Install extra Python dependencies + run: | + poetry install --with docs + - name: Build docs run: | source ~/.venv/bin/activate make doc - name: Deploy docs - if: github.event_name == 'push' env: GH_TOKEN: ${{ secrets.GitHubToken }} run: | diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 43d85a99d0..7557de4dab 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -34,7 +34,7 @@ jobs: arch: alt steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: set up rust if: matrix.os != 'ubuntu-latest' diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index fdb4a1430d..0572eb1ea1 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -10,24 +10,20 @@ jobs: ubuntu: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - - name: Install Ubuntu dependencies - run: sudo apt-get install graphviz pandoc - - - name: Set up Python - uses: actions/setup-python@v4 + - name: Build River + uses: ./.github/actions/install-env with: - python-version: "3.9" + python-version: "3.12" - - run: curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show - if: matrix.os == 'ubuntu-latest' + - name: Install extra Ubuntu dependencies + run: sudo apt-get install graphviz pandoc - - name: Install Python dependencies + - name: Install extra Python dependencies run: | python -m pip install --upgrade pip - pip install wheel - pip install -e ".[compat,dev,docs]" + poetry install --with compat --with docs pip install rich python -m spacy download en_core_web_sm diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 7f75012a5a..1ee099a9d4 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -1,28 +1,29 @@ name: unit-tests on: - workflow_call: - inputs: - python: - type: string - os: - type: string + pull_request: + branches: + - "*" + push: + branches: + - main jobs: - test: - runs-on: ${{ inputs.os }} + run: + strategy: + matrix: + os: [ubuntu-latest] + python-version: [3.12, 3.11, 3.10] - # Instead of using two matrices in the calling Workflow, we can use conditionals here - # if: (inputs.os == 'ubuntu-latest' && inputs.python == '3.11') || github.event_name == 'push' - if: (inputs.python == '3.11') || github.event_name == 'push' + runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v3 - - name: Retrieve the environment and the River build - uses: ./.github/actions/retrieve-env + - name: Build River + uses: ./.github/actions/install-env with: - python: ${{ inputs.python }} + python-version: "3.12" - name: Cache River datasets uses: actions/cache@v3 @@ -38,18 +39,9 @@ jobs: - name: Download datasets run: | - source ~/.venv/bin/activate - python -c "from river import datasets; datasets.CreditCard().download(); datasets.Elec2().download(); datasets.SMSSpam().download()" - python -c "from river import bandit; bandit.datasets.NewsArticles().download()" + poetry run python -c "from river import datasets; datasets.CreditCard().download(); datasets.Elec2().download(); datasets.SMSSpam().download()" + poetry run python -c "from river import bandit; bandit.datasets.NewsArticles().download()" - - name: pytest [Branch] - if: github.event_name == 'pull_request' + - name: pytest run: | - source ~/.venv/bin/activate - pytest --durations=10 -n logical # Run pytest on all logical CPU cores - - - name: pytest [Main] - if: github.event_name == 'push' - run: | - source ~/.venv/bin/activate - pytest -m "not datasets" --durations=10 -n logical # Run pytest on all logical CPU cores + poetry run pytest -m "not datasets" --durations=10 -n logical # Run pytest on all logical CPU cores diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 25e7662a7d..6a7f992e30 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -18,33 +18,29 @@ The typical workflow for contributing to River is: ## Local setup -We encourage you to use a virtual environment. You'll want to activate it every time you want to work on River. +Start by cloning the repository: ```sh -python -m venv .venv -source .venv/bin/activate +git clone https://github.com/online-ml/river ``` -You can also create a virtual environment via `conda`: +Next, you'll need a Python environment. A nice way to manage your Python versions is to use pyenv, which can installed [here](https://github.com/pyenv/pyenv-installer). Once you have pyenv, you can install the latest Python version River supports: ```sh -conda create -n river -y python=3.9 -conda activate river +pyenv install -v $(cat .python-version) ``` -Yet another option is to use `pyenv`: +You need a `Rust` compiler you can install it by following this [link](https://www.rust-lang.org/fr/tools/install). You'll also need [Poetry](https://python-poetry.org/): ```sh -pyenv virtualenv 3.10 river310 -pyenv activate river310 +curl -sSL https://install.python-poetry.org | python3 - ``` -You need a `Rust` compiler you can install it by following this [link](https://www.rust-lang.org/fr/tools/install) - -Then, navigate to your cloned fork and install River and the required dependencies in [development mode](https://stackoverflow.com/questions/19048732/python-setup-py-develop-vs-install): +Now you're set to install River and activate the virtual environment: ```sh -pip install -e ".[dev]" +poetry install +poetry shell ``` Finally, install the [pre-commit](https://pre-commit.com/) push hooks. This will run some code quality checks every time you push to GitHub. @@ -100,7 +96,7 @@ If you're adding a class or a function, then you'll need to add a docstring. We To build the documentation, you need to install some extra dependencies: ```sh -pip install -e ".[docs]" +poetry install --with docs pip install git+https://github.com/MaxHalford/yamp ``` diff --git a/build.py b/build.py new file mode 100644 index 0000000000..6511455d42 --- /dev/null +++ b/build.py @@ -0,0 +1,72 @@ +import platform +from distutils.command.build_ext import build_ext +from distutils.errors import CCompilerError, DistutilsExecError, DistutilsPlatformError +import setuptools +from setuptools_rust import Binding, RustExtension + +try: + from numpy import __version__ as numpy_version + from numpy import get_include +except ImportError: + subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"]) + from numpy import __version__ as numpy_version + from numpy import get_include + +try: + from Cython.Build import cythonize +except ImportError: + subprocess.check_call([sys.executable, "-m", "pip", "install", "Cython"]) + from Cython.Build import cythonize # type: ignore + + +ext_modules = cythonize( + module_list=[ + setuptools.Extension( + "*", + sources=["**/*.pyx"], + include_dirs=[get_include()], + libraries=[] if platform.system() == "Windows" else ["m"], + define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")], + ) + ], + compiler_directives={ + "language_level": 3, + "binding": True, + "embedsignature": True, + }, +) + +rust_extensions = [RustExtension("river.stats._rust_stats", binding=Binding.PyO3)] + + +class BuildFailed(Exception): + pass + + +class ExtBuilder(build_ext): + def run(self): + try: + build_ext.run(self) + except (DistutilsPlatformError, FileNotFoundError): + raise BuildFailed("File not found. Could not compile C extension.") + + def build_extension(self, ext): + try: + build_ext.build_extension(self, ext) + except (CCompilerError, DistutilsExecError, DistutilsPlatformError, ValueError): + raise BuildFailed("Could not compile C extension.") + + +def build(setup_kwargs): + """ + This function is mandatory in order to build the extensions. + """ + setup_kwargs.update( + { + "ext_modules": ext_modules, + "cmdclass": {"build_ext": ExtBuilder}, + "rust_extensions": rust_extensions, + "zip_safe": False, + "include_package_data": True, + } + ) diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000000..eae3a46351 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,5667 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "alabaster" +version = "0.7.13" +description = "A configurable sidebar-enabled Sphinx theme" +optional = false +python-versions = ">=3.6" +files = [ + {file = 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+mypy = "^1.6.1" +pre-commit = "^3.5.0" +pytest = "^7.4.2" +ruff = "^0.1.1" +scikit-learn = "^1.3.1" +sqlalchemy = "^2.0.22" +sympy = "^1.10.1" +pytest-xdist = {extras = ["psutil"], version = "^3.3.1"} + +[tool.poetry.group.compat] +optional = true + +[tool.poetry.group.compat.dependencies] +scikit-learn = "^1.0.1" +sqlalchemy = "^2.0.0" +vaex = "^4.0.0" + +[tool.poetry.group.docs] +optional = true + +[tool.poetry.group.docs.dependencies] +"dominate" = "*" +"flask" = "*" +"ipykernel" = "*" +"jupyter-client" = "*" +"mike" = "*" +"mkdocs" = "*" +"mkdocs-awesome-pages-plugin" = "*" +"mkdocs-charts-plugin" = "*" +"mkdocs-material" = "*" +"nbconvert" = "*" +"numpydoc" = "*" +"python-slugify" = "*" +"spacy" = "*" +"tabulate" = "*" +"watermark" = "*" + +[tool.poetry.group.benchmark] +optional = true + +[tool.poetry.group.benchmark.dependencies] +"dominate" = "2.8.0" +"scikit-learn" = "1.3.1" +"scipy" = "1.11.3" +"tabulate" = "0.9.0" +"vowpalwabbit" = "9.9.0" +"watermark" = "2.4.3" [tool.pytest.ini_options] addopts = [ "--doctest-modules", "--doctest-glob=README.md", + "--ignore=build.py", "--ignore=benchmarks", "--ignore=docs/scripts", "--verbose", diff --git a/river/__version__.py b/river/__version__.py index dc93f05d71..148d5ca59a 100644 --- a/river/__version__.py +++ b/river/__version__.py @@ -1,5 +1,3 @@ from __future__ import annotations -VERSION = (0, 19, 0) - -__version__ = ".".join(map(str, VERSION)) # noqa: F401 +__version__ = "0.19.0" diff --git a/river/anomaly/sad.py b/river/anomaly/sad.py index 80c272c5a2..6b5b52d429 100644 --- a/river/anomaly/sad.py +++ b/river/anomaly/sad.py @@ -23,7 +23,7 @@ class StandardAbsoluteDeviation(anomaly.base.SupervisedAnomalyDetector): ---------- sub_stat The statistic to be subtracted, then divided by the standard deviation for scoring. - This parameter must be either "mean" or "median". + Defaults to `stats.Mean()`. References ---------- @@ -35,52 +35,41 @@ class StandardAbsoluteDeviation(anomaly.base.SupervisedAnomalyDetector): Examples -------- - >>> import numpy as np + >>> import random >>> from river import anomaly + >>> from river import stats >>> from river import stream - >>> np.random.seed(42) + >>> rng = random.Random(42) - >>> X = np.random.randn(150) + >>> model = anomaly.StandardAbsoluteDeviation(sub_stat=stats.Mean()) - >>> model = anomaly.StandardAbsoluteDeviation(sub_stat="mean") - - >>> for x in X: - ... model = model.learn_one(None, x) + >>> for _ in range(150): + ... y = rng.gauss(0, 1) + ... model = model.learn_one(None, y) >>> model.score_one(None, 2) - 2.209735291993561 + 2.057... >>> model.score_one(None, 0) - 0.08736408615569183 + 0.084... >>> model.score_one(None, 1) - 1.1485496890746263 + 0.986... """ - def __init__(self, sub_stat=None): + def __init__(self, sub_stat: stats.base.Univariate = None): self.variance = stats.Var() - self.sub_stat = sub_stat - - if self.sub_stat == "mean": - self.subtracted_statistic_estimator = stats.Mean() - elif self.sub_stat == "median": - self.subtracted_statistic_estimator = stats.Quantile(q=0.5) - else: - raise ValueError( - f"Unknown subtracted statistic {self.sub_stat}, expected one of median, mean." - ) + self.sub_stat = sub_stat or stats.Mean() def learn_one(self, x, y): self.variance.update(y) - self.subtracted_statistic_estimator.update(y) + self.sub_stat.update(y) return self def score_one(self, x, y): - score = (y - self.subtracted_statistic_estimator.get()) / ( - self.variance.get() ** 0.5 + 1e-10 - ) + score = (y - self.sub_stat.get()) / (self.variance.get() ** 0.5 + 1e-10) return abs(score) diff --git a/setup.py b/setup.py index 18b302366a..049fb45f07 100644 --- a/setup.py +++ b/setup.py @@ -1,140 +1,82 @@ -from __future__ import annotations +# -*- coding: utf-8 -*- +from setuptools import setup -import io -import os -import platform -import subprocess -import sys +packages = \ +['river', + 'river.active', + 'river.anomaly', + 'river.bandit', + 'river.bandit.datasets', + 'river.bandit.envs', + 'river.base', + 'river.checks', + 'river.cluster', + 'river.compat', + 'river.compose', + 'river.conf', + 'river.covariance', + 'river.datasets', + 'river.datasets.synth', + 'river.drift', + 'river.drift.binary', + 'river.drift.datasets', + 'river.ensemble', + 'river.evaluate', + 'river.facto', + 'river.feature_extraction', + 'river.feature_selection', + 'river.forest', + 'river.imblearn', + 'river.linear_model', + 'river.metrics', + 'river.metrics.efficient_rollingrocauc', + 'river.metrics.multioutput', + 'river.misc', + 'river.model_selection', + 'river.multiclass', + 'river.multioutput', + 'river.naive_bayes', + 'river.neighbors', + 'river.neighbors.ann', + 'river.neural_net', + 'river.optim', + 'river.preprocessing', + 'river.proba', + 'river.reco', + 'river.rules', + 'river.sketch', + 'river.stats', + 'river.stream', + 'river.time_series', + 'river.tree', + 'river.tree.mondrian', + 'river.tree.nodes', + 'river.tree.split_criterion', + 'river.tree.splitter', + 'river.utils'] -import setuptools # type: ignore -from setuptools_rust import Binding, RustExtension # type: ignore +package_data = \ +{'': ['*'], 'river.metrics.efficient_rollingrocauc': ['cpp/*']} -try: - from numpy import __version__ as numpy_version - from numpy import get_include -except ImportError: - subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"]) - from numpy import __version__ as numpy_version - from numpy import get_include +install_requires = \ +['pandas>=2.1,<3.0', 'scipy>=1.8.1,<2.0.0'] -try: - from Cython.Build import cythonize -except ImportError: - subprocess.check_call([sys.executable, "-m", "pip", "install", "Cython"]) - from Cython.Build import cythonize # type: ignore +setup_kwargs = { + 'name': 'river', + 'version': '0.19.0', + 'description': 'Online machine learning in Python', + 'long_description': 'None', + 'author': 'Max Halford', + 'author_email': 'maxhalford25@gmail.com', + 'maintainer': 'None', + 'maintainer_email': 'None', + 'url': 'None', + 'packages': packages, + 'package_data': package_data, + 'install_requires': install_requires, + 'python_requires': '>=3.9,<3.13', +} +from build import * +build(setup_kwargs) -NAME = "river" -DESCRIPTION = "Online machine learning in Python" -LONG_DESCRIPTION_CONTENT_TYPE = "text/markdown" -URL = "https://github.com/online-ml/river" -EMAIL = "maxhalford25@gmail.com" -AUTHOR = "Max Halford" -REQUIRES_PYTHON = ">=3.8.0" - -here = os.path.abspath(os.path.dirname(__file__)) -with open(os.path.join(here, "README.md"), encoding="utf-8") as f: - long_description = "\n" + f.read() - -about: dict = {} -with open(os.path.join(here, NAME, "__version__.py")) as f: - exec(f.read(), about) - -setuptools.setup( - name=NAME, - version=about["__version__"], - description=DESCRIPTION, - long_description=long_description, - long_description_content_type=LONG_DESCRIPTION_CONTENT_TYPE, - author=AUTHOR, - author_email=EMAIL, - python_requires=REQUIRES_PYTHON, - url=URL, - packages=setuptools.find_packages(exclude=("tests",)), - install_requires=(base_packages := [f"numpy>={numpy_version}", "scipy>=1.5", "pandas>=1.3"]), - extras_require={ - "dev": base_packages - + [ - "black>=22.1.0", - "graphviz>=0.10.1", - "gym>=0.26.1", - "matplotlib>=3.0.2", - "mypy>=0.980", - "pre-commit>=2.9.2", - "pytest>=4.5.0", - "ruff>=0.0.213", - "scikit-learn>=1.0.1", - "sqlalchemy>=2.0.0", - "sympy>=1.10.1", - ], - "compat": base_packages - + [ - "scikit-learn", - "sqlalchemy>=2.0.0", - "vaex", - ], - "docs": base_packages - + [ - "dominate", - "flask", - "ipykernel", - "jupyter-client", - "mike", - "mkdocs", - "mkdocs-awesome-pages-plugin", - "mkdocs-charts-plugin", - "mkdocs-material", - "nbconvert", - "numpydoc", - "python-slugify", - "spacy", - "tabulate", - "watermark", - ], - "extra": ["river_extra"], - "deep": ["deep-river"], - "torch": ["river_torch"], - "benchmark": base_packages - + [ - "dominate==2.8.0", - "scikit-learn==1.3.1", - "scipy==1.11.3", - "tabulate==0.9.0", - "vowpalwabbit==9.9.0", - "watermark==2.4.3", - ], - ":python_version == '3.6'": ["dataclasses"], - }, - include_package_data=True, - license="BSD-3", - classifiers=[ - # Trove classifiers - # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers - "License :: OSI Approved :: BSD License", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: Implementation :: CPython", - "Programming Language :: Python :: Implementation :: PyPy", - ], - ext_modules=cythonize( - module_list=[ - setuptools.Extension( - "*", - sources=["**/*.pyx"], - include_dirs=[get_include()], - libraries=[] if platform.system() == "Windows" else ["m"], - define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")], - ) - ], - compiler_directives={ - "language_level": 3, - "binding": True, - "embedsignature": True, - }, - ), - rust_extensions=[RustExtension("river.stats._rust_stats", binding=Binding.PyO3)], - # rust extensions are not zip safe, just like C-extensions. - zip_safe=False, -) +setup(**setup_kwargs) From e6bf46aeb0877e69ec2b8daa89ef6b9d70da3a84 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 2 Nov 2023 18:39:55 +0100 Subject: [PATCH 157/347] merge --- .github/workflows/unit-tests.yml | 46 +++++----- setup.py | 140 ------------------------------- 2 files changed, 19 insertions(+), 167 deletions(-) delete mode 100644 setup.py diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 7f75012a5a..0d7c00e016 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -1,28 +1,29 @@ name: unit-tests on: - workflow_call: - inputs: - python: - type: string - os: - type: string + pull_request: + branches: + - "*" + push: + branches: + - main jobs: - test: - runs-on: ${{ inputs.os }} + run: + strategy: + matrix: + os: [ubuntu-latest] + python-version: ["3.12", "3.11", "3.10"] - # Instead of using two matrices in the calling Workflow, we can use conditionals here - # if: (inputs.os == 'ubuntu-latest' && inputs.python == '3.11') || github.event_name == 'push' - if: (inputs.python == '3.11') || github.event_name == 'push' + runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v3 - - name: Retrieve the environment and the River build - uses: ./.github/actions/retrieve-env + - name: Build River + uses: ./.github/actions/install-env with: - python: ${{ inputs.python }} + python-version: "3.12" - name: Cache River datasets uses: actions/cache@v3 @@ -38,18 +39,9 @@ jobs: - name: Download datasets run: | - source ~/.venv/bin/activate - python -c "from river import datasets; datasets.CreditCard().download(); datasets.Elec2().download(); datasets.SMSSpam().download()" - python -c "from river import bandit; bandit.datasets.NewsArticles().download()" + poetry run python -c "from river import datasets; datasets.CreditCard().download(); datasets.Elec2().download(); datasets.SMSSpam().download()" + poetry run python -c "from river import bandit; bandit.datasets.NewsArticles().download()" - - name: pytest [Branch] - if: github.event_name == 'pull_request' + - name: pytest run: | - source ~/.venv/bin/activate - pytest --durations=10 -n logical # Run pytest on all logical CPU cores - - - name: pytest [Main] - if: github.event_name == 'push' - run: | - source ~/.venv/bin/activate - pytest -m "not datasets" --durations=10 -n logical # Run pytest on all logical CPU cores + poetry run pytest -m "not datasets" --durations=10 -n logical # Run pytest on all logical CPU cores diff --git a/setup.py b/setup.py deleted file mode 100644 index 18b302366a..0000000000 --- a/setup.py +++ /dev/null @@ -1,140 +0,0 @@ -from __future__ import annotations - -import io -import os -import platform -import subprocess -import sys - -import setuptools # type: ignore -from setuptools_rust import Binding, RustExtension # type: ignore - -try: - from numpy import __version__ as numpy_version - from numpy import get_include -except ImportError: - subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"]) - from numpy import __version__ as numpy_version - from numpy import get_include - -try: - from Cython.Build import cythonize -except ImportError: - subprocess.check_call([sys.executable, "-m", "pip", "install", "Cython"]) - from Cython.Build import cythonize # type: ignore - -NAME = "river" -DESCRIPTION = "Online machine learning in Python" -LONG_DESCRIPTION_CONTENT_TYPE = "text/markdown" -URL = "https://github.com/online-ml/river" -EMAIL = "maxhalford25@gmail.com" -AUTHOR = "Max Halford" -REQUIRES_PYTHON = ">=3.8.0" - -here = os.path.abspath(os.path.dirname(__file__)) -with open(os.path.join(here, "README.md"), encoding="utf-8") as f: - long_description = "\n" + f.read() - -about: dict = {} -with open(os.path.join(here, NAME, "__version__.py")) as f: - exec(f.read(), about) - -setuptools.setup( - name=NAME, - version=about["__version__"], - description=DESCRIPTION, - long_description=long_description, - long_description_content_type=LONG_DESCRIPTION_CONTENT_TYPE, - author=AUTHOR, - author_email=EMAIL, - python_requires=REQUIRES_PYTHON, - url=URL, - packages=setuptools.find_packages(exclude=("tests",)), - install_requires=(base_packages := [f"numpy>={numpy_version}", "scipy>=1.5", "pandas>=1.3"]), - extras_require={ - "dev": base_packages - + [ - "black>=22.1.0", - "graphviz>=0.10.1", - "gym>=0.26.1", - "matplotlib>=3.0.2", - "mypy>=0.980", - "pre-commit>=2.9.2", - "pytest>=4.5.0", - "ruff>=0.0.213", - "scikit-learn>=1.0.1", - "sqlalchemy>=2.0.0", - "sympy>=1.10.1", - ], - "compat": base_packages - + [ - "scikit-learn", - "sqlalchemy>=2.0.0", - "vaex", - ], - "docs": base_packages - + [ - "dominate", - "flask", - "ipykernel", - "jupyter-client", - "mike", - "mkdocs", - "mkdocs-awesome-pages-plugin", - "mkdocs-charts-plugin", - "mkdocs-material", - "nbconvert", - "numpydoc", - "python-slugify", - "spacy", - "tabulate", - "watermark", - ], - "extra": ["river_extra"], - "deep": ["deep-river"], - "torch": ["river_torch"], - "benchmark": base_packages - + [ - "dominate==2.8.0", - "scikit-learn==1.3.1", - "scipy==1.11.3", - "tabulate==0.9.0", - "vowpalwabbit==9.9.0", - "watermark==2.4.3", - ], - ":python_version == '3.6'": ["dataclasses"], - }, - include_package_data=True, - license="BSD-3", - classifiers=[ - # Trove classifiers - # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers - "License :: OSI Approved :: BSD License", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: Implementation :: CPython", - "Programming Language :: Python :: Implementation :: PyPy", - ], - ext_modules=cythonize( - module_list=[ - setuptools.Extension( - "*", - sources=["**/*.pyx"], - include_dirs=[get_include()], - libraries=[] if platform.system() == "Windows" else ["m"], - define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")], - ) - ], - compiler_directives={ - "language_level": 3, - "binding": True, - "embedsignature": True, - }, - ), - rust_extensions=[RustExtension("river.stats._rust_stats", binding=Binding.PyO3)], - # rust extensions are not zip safe, just like C-extensions. - zip_safe=False, -) From 49daad328f39cd0245bd3455adb67d97a9721b3f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 2 Nov 2023 18:55:47 +0100 Subject: [PATCH 158/347] Bump werkzeug from 3.0.0 to 3.0.1 (#1438) Bumps [werkzeug](https://github.com/pallets/werkzeug) from 3.0.0 to 3.0.1. - [Release notes](https://github.com/pallets/werkzeug/releases) - [Changelog](https://github.com/pallets/werkzeug/blob/main/CHANGES.rst) - [Commits](https://github.com/pallets/werkzeug/compare/3.0.0...3.0.1) --- updated-dependencies: - dependency-name: werkzeug dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index eae3a46351..f1b7730e91 100644 --- a/poetry.lock +++ b/poetry.lock @@ -5586,13 +5586,13 @@ files = [ [[package]] name = "werkzeug" -version = "3.0.0" +version = "3.0.1" description = "The comprehensive WSGI web application library." optional = false python-versions = ">=3.8" files = [ - {file = "werkzeug-3.0.0-py3-none-any.whl", hash = "sha256:cbb2600f7eabe51dbc0502f58be0b3e1b96b893b05695ea2b35b43d4de2d9962"}, - {file = "werkzeug-3.0.0.tar.gz", hash = "sha256:3ffff4dcc32db52ef3cc94dff3000a3c2846890f3a5a51800a27b909c5e770f0"}, + {file = "werkzeug-3.0.1-py3-none-any.whl", hash = "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"}, + {file = "werkzeug-3.0.1.tar.gz", hash = "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc"}, ] [package.dependencies] From 869c33e108f21411493a83c65943477c9a32f46a Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 2 Nov 2023 18:57:28 +0100 Subject: [PATCH 159/347] Update README.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 21a9c678aa..0f26363f84 100644 --- a/README.md +++ b/README.md @@ -4,8 +4,12 @@

- - CI Pipeline + + unit-tests + + + + code-quality From cc8ee1bd1be93459e70bd14ddbf8a93c059825f4 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 2 Nov 2023 19:06:46 +0100 Subject: [PATCH 160/347] ellipsis --- river/naive_bayes/bernoulli.py | 2 +- river/preprocessing/random_projection.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/river/naive_bayes/bernoulli.py b/river/naive_bayes/bernoulli.py index 7ddba2acb0..d5b2efc92d 100644 --- a/river/naive_bayes/bernoulli.py +++ b/river/naive_bayes/bernoulli.py @@ -62,7 +62,7 @@ class BernoulliNB(base.BaseNB): 0.25 >>> model.predict_proba_one("test") - {'yes': 0.8831539823829913, 'no': 0.11684601761700895} + {'yes': 0.883..., 'no': 0.116...} >>> model.predict_one("test") 'yes' diff --git a/river/preprocessing/random_projection.py b/river/preprocessing/random_projection.py index 13e2152f04..979c249595 100644 --- a/river/preprocessing/random_projection.py +++ b/river/preprocessing/random_projection.py @@ -39,7 +39,7 @@ class GaussianRandomProjector(base.Transformer): ... x = model.transform_one(x) ... print(x) ... break - {0: -61289.37139206629, 1: 141312.51039283074, 2: 279165.99370457436} + {0: -61289.371..., 1: 141312.510..., 2: 279165.993...} >>> model = ( ... preprocessing.GaussianRandomProjector( @@ -50,7 +50,7 @@ class GaussianRandomProjector(base.Transformer): ... linear_model.LinearRegression() ... ) >>> evaluate.progressive_val_score(dataset, model, metrics.MAE()) - MAE: 0.933502 + MAE: 0.933... References ---------- From 7a61af96018f9682f4bc9a45b84e20e08185fe7c Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 2 Nov 2023 19:08:11 +0100 Subject: [PATCH 161/347] mmm --- .github/actions/retrieve-env/action.yml | 26 ------------------------- .github/workflows/dev-docs.yml | 6 +++--- 2 files changed, 3 insertions(+), 29 deletions(-) delete mode 100644 .github/actions/retrieve-env/action.yml diff --git a/.github/actions/retrieve-env/action.yml b/.github/actions/retrieve-env/action.yml deleted file mode 100644 index d9ea3a7845..0000000000 --- a/.github/actions/retrieve-env/action.yml +++ /dev/null @@ -1,26 +0,0 @@ -name: Create Environment -description: Retrieves Python - -inputs: - python: - description: "Python version" - -runs: - using: "composite" - - steps: - - name: Retrieve the Python environment - uses: actions/cache/restore@v3 - id: retrieve-venv - with: - path: ~/.venv - key: venv-${{ runner.os }}-${{ inputs.python }} - restore-keys: | - ${{ runner.os }}-${{ inputs.python }}-venv - - - name: Retrieve River - uses: actions/cache/restore@v3 - id: retrieve-river - with: - path: ${{ github.workspace }} - key: river-build-${{ runner.os }}-${{ inputs.python }} diff --git a/.github/workflows/dev-docs.yml b/.github/workflows/dev-docs.yml index b86a03d2e0..84194ffe32 100644 --- a/.github/workflows/dev-docs.yml +++ b/.github/workflows/dev-docs.yml @@ -1,4 +1,4 @@ -name: docs +name: dev-docs on: push: @@ -27,14 +27,14 @@ jobs: - name: Build docs run: | - source ~/.venv/bin/activate + source $VENV make doc - name: Deploy docs env: GH_TOKEN: ${{ secrets.GitHubToken }} run: | - source ~/.venv/bin/activate + source $VENV git config user.name github-actions git config user.email github-actions@github.com git config pull.rebase false From 4256435da1323d85bc39e1c99abdce6b5c6feb40 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 2 Nov 2023 19:33:51 +0100 Subject: [PATCH 162/347] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 0f26363f84..cfeda16c48 100644 --- a/README.md +++ b/README.md @@ -5,11 +5,11 @@

- unit-tests + unit-tests - code-quality + code-quality From 738687c7e11e57c786d828623f7dec2bc7b7c1c8 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Fri, 3 Nov 2023 10:12:08 +0100 Subject: [PATCH 163/347] Update README.md --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index cfeda16c48..35132f2237 100644 --- a/README.md +++ b/README.md @@ -19,10 +19,6 @@ discord - - - roadmap - pypi From c154716be424ee592a439d7f34e865c7f5d4a4aa Mon Sep 17 00:00:00 2001 From: Max Halford Date: Wed, 8 Nov 2023 17:22:00 +0100 Subject: [PATCH 164/347] add ipykernel --- poetry.lock | 8 ++++---- pyproject.toml | 1 + 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/poetry.lock b/poetry.lock index f1b7730e91..60bf91aaa5 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1546,13 +1546,13 @@ test = ["nbval (>=0.9.2)", "pytest (>=4)", "pytest-cov"] [[package]] name = "ipykernel" -version = "6.25.2" +version = "6.26.0" description = "IPython Kernel for Jupyter" optional = false python-versions = ">=3.8" files = [ - {file = "ipykernel-6.25.2-py3-none-any.whl", hash = "sha256:2e2ee359baba19f10251b99415bb39de1e97d04e1fab385646f24f0596510b77"}, - {file = "ipykernel-6.25.2.tar.gz", hash = "sha256:f468ddd1f17acb48c8ce67fcfa49ba6d46d4f9ac0438c1f441be7c3d1372230b"}, + {file = "ipykernel-6.26.0-py3-none-any.whl", hash = "sha256:3ba3dc97424b87b31bb46586b5167b3161b32d7820b9201a9e698c71e271602c"}, + {file = "ipykernel-6.26.0.tar.gz", hash = "sha256:553856658eb8430bbe9653ea041a41bff63e9606fc4628873fc92a6cf3abd404"}, ] [package.dependencies] @@ -5664,4 +5664,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "84d9fd905dde15672f5aaa38974c8e13e519a1aee5e2f513d8f75cde1314f61f" +content-hash = "0013d71e92c2e5fa1b6edece64bb6629c29dcd69f9db3918aa6b1e5ee8681693" diff --git a/pyproject.toml b/pyproject.toml index d6e023a1b6..2fa6af1128 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,7 @@ scikit-learn = "^1.3.1" sqlalchemy = "^2.0.22" sympy = "^1.10.1" pytest-xdist = {extras = ["psutil"], version = "^3.3.1"} +ipykernel = "^6.26.0" [tool.poetry.group.compat] optional = true From 6e296116d1cb2e4879951915fa51e269d2a25e32 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Wed, 8 Nov 2023 17:32:47 +0100 Subject: [PATCH 165/347] Make confusion matrix work with evaluate module (#1444) * make cm work with evaluate * Update test_confusion.py --- docs/releases/unreleased.md | 4 ++++ river/metrics/confusion.py | 7 +++++++ river/metrics/test_confusion.py | 16 ++++++++++++++++ 3 files changed, 27 insertions(+) create mode 100644 river/metrics/test_confusion.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index f2a7b85220..29275fe295 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -39,6 +39,10 @@ River's mini-batch methods now support pandas v2. In particular, River conforms - Simplify inner the structures of `forest.ARFClassifier` and `forest.ARFRegressor` by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole. +## metrics + +- `metrics.ConfusionMatrix` may now be used with `evaluate.progressive_val_score` and `evaluate.iter_progressive_val_score`. + ## proba - Added `_from_state` method to `proba.MultivariateGaussian` to warm start from previous knowledge. diff --git a/river/metrics/confusion.py b/river/metrics/confusion.py index 8146aadefb..7c16809fa4 100644 --- a/river/metrics/confusion.py +++ b/river/metrics/confusion.py @@ -127,3 +127,10 @@ def total_false_positives(self): @property def total_false_negatives(self): return sum(self.false_negatives(label) for label in self.classes) + + def works_with(self, model) -> bool: + return utils.inspect.isclassifier(model) + + @property + def requires_labels(self): + return True diff --git a/river/metrics/test_confusion.py b/river/metrics/test_confusion.py new file mode 100644 index 0000000000..55d3e7f793 --- /dev/null +++ b/river/metrics/test_confusion.py @@ -0,0 +1,16 @@ +from __future__ import annotations + +from river import datasets, evaluate, linear_model, metrics, optim, preprocessing + + +def test_issue_1443(): + dataset = datasets.Phishing() + + model = preprocessing.StandardScaler() | linear_model.LogisticRegression( + optimizer=optim.SGD(0.1) + ) + + metric = metrics.ConfusionMatrix() + + for _ in evaluate.iter_progressive_val_score(dataset, model, metric): + pass From c51e79b5de26b78b6428e7f64c55ffa2954342c2 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 08:24:56 +0100 Subject: [PATCH 166/347] bump to 0.20 --- Makefile | 1 + docs/examples/batch-to-online.ipynb | 82 +- docs/examples/bike-sharing-forecasting.ipynb | 66 +- .../building-a-simple-nowcasting-model.ipynb | 102 +- docs/examples/content-personalization.ipynb | 218 +- docs/examples/debugging-a-pipeline.ipynb | 44 +- docs/examples/imbalanced-learning.ipynb | 74 +- .../part-1.ipynb | 84 +- .../part-2.ipynb | 114 +- .../part-3.ipynb | 2 +- .../quantile-regression-uncertainty.ipynb | 20 +- docs/examples/sentence-classification.ipynb | 138 +- .../examples/the-art-of-using-pipelines.ipynb | 92 +- .../binary-classification.ipynb | 98 +- .../concept-drift-detection.ipynb | 22 +- .../multiclass-classification.ipynb | 130 +- .../getting-started/regression.ipynb | 66 +- docs/recipes/active-learning.ipynb | 26 +- docs/recipes/bandits-101.ipynb | 14146 +++++++++++++++- docs/recipes/cloning-and-mutating.ipynb | 58 +- docs/recipes/feature-extraction.ipynb | 2 +- docs/recipes/hyperparameter-tuning.ipynb | 2 +- docs/recipes/mini-batching.ipynb | 35 +- docs/recipes/model-evaluation.ipynb | 2 +- docs/recipes/on-hoeffding-trees.ipynb | 130 +- docs/recipes/pipelines.ipynb | 82 +- docs/recipes/reading-data.ipynb | 86 +- docs/recipes/rolling-computations.ipynb | 11 +- docs/releases/{unreleased.md => 0.20.0.md} | 2 +- pyproject.toml | 2 +- river/__version__.py | 2 +- 31 files changed, 14942 insertions(+), 997 deletions(-) rename docs/releases/{unreleased.md => 0.20.0.md} (99%) diff --git a/Makefile b/Makefile index 748db226f2..007d750115 100644 --- a/Makefile +++ b/Makefile @@ -4,6 +4,7 @@ format: pre-commit run --all-files execute-notebooks: + export PYDEVD_DISABLE_FILE_VALIDATION = 1 jupyter nbconvert --execute --to notebook --inplace docs/introduction/*/*.ipynb --ExecutePreprocessor.timeout=-1 jupyter nbconvert --execute --to notebook --inplace docs/recipes/*.ipynb --ExecutePreprocessor.timeout=-1 jupyter nbconvert --execute --to notebook --inplace docs/examples/*.ipynb --ExecutePreprocessor.timeout=-1 diff --git a/docs/examples/batch-to-online.ipynb b/docs/examples/batch-to-online.ipynb index e4f53a3191..99c9cdb717 100644 --- a/docs/examples/batch-to-online.ipynb +++ b/docs/examples/batch-to-online.ipynb @@ -25,10 +25,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:49.758896Z", - "iopub.status.busy": "2023-09-02T00:48:49.758379Z", - "iopub.status.idle": "2023-09-02T00:48:50.245713Z", - "shell.execute_reply": "2023-09-02T00:48:50.238220Z" + "iopub.execute_input": "2023-11-09T07:13:26.584039Z", + "iopub.status.busy": "2023-11-09T07:13:26.583739Z", + "iopub.status.idle": "2023-11-09T07:13:27.166882Z", + "shell.execute_reply": "2023-11-09T07:13:27.156212Z" }, "tags": [] }, @@ -102,10 +102,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:50.266250Z", - "iopub.status.busy": "2023-09-02T00:48:50.265746Z", - "iopub.status.idle": "2023-09-02T00:48:50.312281Z", - "shell.execute_reply": "2023-09-02T00:48:50.311460Z" + "iopub.execute_input": "2023-11-09T07:13:27.189863Z", + "iopub.status.busy": "2023-11-09T07:13:27.189347Z", + "iopub.status.idle": "2023-11-09T07:13:27.208208Z", + "shell.execute_reply": "2023-11-09T07:13:27.206462Z" }, "tags": [] }, @@ -128,10 +128,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:50.319696Z", - "iopub.status.busy": "2023-09-02T00:48:50.319239Z", - "iopub.status.idle": "2023-09-02T00:48:50.358037Z", - "shell.execute_reply": "2023-09-02T00:48:50.356950Z" + "iopub.execute_input": "2023-11-09T07:13:27.213628Z", + "iopub.status.busy": "2023-11-09T07:13:27.212467Z", + "iopub.status.idle": "2023-11-09T07:13:27.233529Z", + "shell.execute_reply": "2023-11-09T07:13:27.232804Z" }, "tags": [] }, @@ -167,10 +167,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:50.363772Z", - "iopub.status.busy": "2023-09-02T00:48:50.361677Z", - "iopub.status.idle": "2023-09-02T00:48:50.407335Z", - "shell.execute_reply": "2023-09-02T00:48:50.406622Z" + "iopub.execute_input": "2023-11-09T07:13:27.239697Z", + "iopub.status.busy": "2023-11-09T07:13:27.239252Z", + "iopub.status.idle": "2023-11-09T07:13:27.277655Z", + "shell.execute_reply": "2023-11-09T07:13:27.276221Z" }, "tags": [] }, @@ -235,10 +235,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:50.411311Z", - "iopub.status.busy": "2023-09-02T00:48:50.410781Z", - "iopub.status.idle": "2023-09-02T00:48:50.500815Z", - "shell.execute_reply": "2023-09-02T00:48:50.500338Z" + "iopub.execute_input": "2023-11-09T07:13:27.282609Z", + "iopub.status.busy": "2023-11-09T07:13:27.281556Z", + "iopub.status.idle": "2023-11-09T07:13:27.363041Z", + "shell.execute_reply": "2023-11-09T07:13:27.361561Z" }, "tags": [] }, @@ -284,10 +284,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:50.502765Z", - "iopub.status.busy": "2023-09-02T00:48:50.502650Z", - "iopub.status.idle": "2023-09-02T00:48:50.525202Z", - "shell.execute_reply": "2023-09-02T00:48:50.524912Z" + "iopub.execute_input": "2023-11-09T07:13:27.368371Z", + "iopub.status.busy": "2023-11-09T07:13:27.367591Z", + "iopub.status.idle": "2023-11-09T07:13:27.391267Z", + "shell.execute_reply": "2023-11-09T07:13:27.390641Z" }, "tags": [] }, @@ -327,10 +327,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:50.526794Z", - "iopub.status.busy": "2023-09-02T00:48:50.526708Z", - "iopub.status.idle": "2023-09-02T00:48:50.540858Z", - "shell.execute_reply": "2023-09-02T00:48:50.540594Z" + "iopub.execute_input": "2023-11-09T07:13:27.395942Z", + "iopub.status.busy": "2023-11-09T07:13:27.395217Z", + "iopub.status.idle": "2023-11-09T07:13:27.410988Z", + "shell.execute_reply": "2023-11-09T07:13:27.410729Z" }, "tags": [] }, @@ -366,10 +366,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:50.542496Z", - "iopub.status.busy": "2023-09-02T00:48:50.542388Z", - "iopub.status.idle": "2023-09-02T00:48:50.584646Z", - "shell.execute_reply": "2023-09-02T00:48:50.584295Z" + "iopub.execute_input": "2023-11-09T07:13:27.412488Z", + "iopub.status.busy": "2023-11-09T07:13:27.412405Z", + "iopub.status.idle": "2023-11-09T07:13:27.446197Z", + "shell.execute_reply": "2023-11-09T07:13:27.445827Z" }, "tags": [] }, @@ -395,10 +395,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:50.586405Z", - "iopub.status.busy": "2023-09-02T00:48:50.586308Z", - "iopub.status.idle": "2023-09-02T00:48:50.679307Z", - "shell.execute_reply": "2023-09-02T00:48:50.679021Z" + "iopub.execute_input": "2023-11-09T07:13:27.447825Z", + "iopub.status.busy": "2023-11-09T07:13:27.447750Z", + "iopub.status.idle": "2023-11-09T07:13:27.495443Z", + "shell.execute_reply": "2023-11-09T07:13:27.495189Z" }, "tags": [] }, @@ -451,10 +451,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:50.680871Z", - "iopub.status.busy": "2023-09-02T00:48:50.680771Z", - "iopub.status.idle": "2023-09-02T00:48:50.809990Z", - "shell.execute_reply": "2023-09-02T00:48:50.809706Z" + "iopub.execute_input": "2023-11-09T07:13:27.496931Z", + "iopub.status.busy": "2023-11-09T07:13:27.496855Z", + "iopub.status.idle": "2023-11-09T07:13:27.609803Z", + "shell.execute_reply": "2023-11-09T07:13:27.609550Z" }, "tags": [] }, @@ -538,7 +538,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/bike-sharing-forecasting.ipynb b/docs/examples/bike-sharing-forecasting.ipynb index 1903bf8fbc..470363bdfc 100644 --- a/docs/examples/bike-sharing-forecasting.ipynb +++ b/docs/examples/bike-sharing-forecasting.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:52.388443Z", - "iopub.status.busy": "2023-09-02T00:48:52.387754Z", - "iopub.status.idle": "2023-09-02T00:48:52.818022Z", - "shell.execute_reply": "2023-09-02T00:48:52.817578Z" + "iopub.execute_input": "2023-11-09T07:13:28.595456Z", + "iopub.status.busy": "2023-11-09T07:13:28.595182Z", + "iopub.status.idle": "2023-11-09T07:13:29.004623Z", + "shell.execute_reply": "2023-11-09T07:13:29.004315Z" }, "tags": [] }, @@ -67,10 +67,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:52.819985Z", - "iopub.status.busy": "2023-09-02T00:48:52.819815Z", - "iopub.status.idle": "2023-09-02T00:48:59.871195Z", - "shell.execute_reply": "2023-09-02T00:48:59.870876Z" + "iopub.execute_input": "2023-11-09T07:13:29.006292Z", + "iopub.status.busy": "2023-11-09T07:13:29.006158Z", + "iopub.status.idle": "2023-11-09T07:13:34.564864Z", + "shell.execute_reply": "2023-11-09T07:13:34.564546Z" }, "tags": [] }, @@ -131,10 +131,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:59.872853Z", - "iopub.status.busy": "2023-09-02T00:48:59.872756Z", - "iopub.status.idle": "2023-09-02T00:49:11.119053Z", - "shell.execute_reply": "2023-09-02T00:49:11.118705Z" + "iopub.execute_input": "2023-11-09T07:13:34.566502Z", + "iopub.status.busy": "2023-11-09T07:13:34.566385Z", + "iopub.status.idle": "2023-11-09T07:13:43.686695Z", + "shell.execute_reply": "2023-11-09T07:13:43.686371Z" }, "tags": [] }, @@ -201,10 +201,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:11.121222Z", - "iopub.status.busy": "2023-09-02T00:49:11.121092Z", - "iopub.status.idle": "2023-09-02T00:49:11.141944Z", - "shell.execute_reply": "2023-09-02T00:49:11.141668Z" + "iopub.execute_input": "2023-11-09T07:13:43.688298Z", + "iopub.status.busy": "2023-11-09T07:13:43.688204Z", + "iopub.status.idle": "2023-11-09T07:13:43.702197Z", + "shell.execute_reply": "2023-11-09T07:13:43.701978Z" }, "tags": [] }, @@ -412,10 +412,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:11.143621Z", - "iopub.status.busy": "2023-09-02T00:49:11.143509Z", - "iopub.status.idle": "2023-09-02T00:49:11.635268Z", - "shell.execute_reply": "2023-09-02T00:49:11.634940Z" + "iopub.execute_input": "2023-11-09T07:13:43.703680Z", + "iopub.status.busy": "2023-11-09T07:13:43.703602Z", + "iopub.status.idle": "2023-11-09T07:13:44.089579Z", + "shell.execute_reply": "2023-11-09T07:13:44.089307Z" }, "tags": [] }, @@ -467,14 +467,14 @@ "\n", "3. LinearRegression\n", "-------------------\n", - "Name Value Weight Contribution \n", - "Intercept 1.00000 6.58252 6.58252 \n", - "pressure 1.05314 3.78529 3.98646 \n", - "humidity 0.42247 1.44921 0.61225 \n", - "y_mean_by_station_and_hour -0.59098 0.54167 -0.32011 \n", - " clouds 0.47566 -1.92255 -0.91448 \n", - " wind 2.21104 -0.77720 -1.71843 \n", - "temperature -1.22098 2.47030 -3.01619 \n", + "Name Value Weight Contribution \n", + " Intercept 1.00000 6.58252 6.58252 \n", + " pressure 1.05314 3.78529 3.98646 \n", + " humidity 0.42247 1.44921 0.61225 \n", + "y_mean_by_station_and_hour -0.59098 0.54167 -0.32011 \n", + " clouds 0.47566 -1.92255 -0.91448 \n", + " wind 2.21104 -0.77720 -1.71843 \n", + " temperature -1.22098 2.47030 -3.01619 \n", "\n", "Prediction: 5.21201\n" ] @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:11.637463Z", - "iopub.status.busy": "2023-09-02T00:49:11.637338Z", - "iopub.status.idle": "2023-09-02T00:49:22.900309Z", - "shell.execute_reply": "2023-09-02T00:49:22.900026Z" + "iopub.execute_input": "2023-11-09T07:13:44.091270Z", + "iopub.status.busy": "2023-11-09T07:13:44.091166Z", + "iopub.status.idle": "2023-11-09T07:13:53.376511Z", + "shell.execute_reply": "2023-11-09T07:13:53.376206Z" }, "tags": [] }, @@ -594,7 +594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/building-a-simple-nowcasting-model.ipynb b/docs/examples/building-a-simple-nowcasting-model.ipynb index 1ee82ca211..500ffaa590 100644 --- a/docs/examples/building-a-simple-nowcasting-model.ipynb +++ b/docs/examples/building-a-simple-nowcasting-model.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:24.420171Z", - "iopub.status.busy": "2023-09-02T00:49:24.419780Z", - "iopub.status.idle": "2023-09-02T00:49:24.797328Z", - "shell.execute_reply": "2023-09-02T00:49:24.796994Z" + "iopub.execute_input": "2023-11-09T07:13:54.533808Z", + "iopub.status.busy": "2023-11-09T07:13:54.533370Z", + "iopub.status.idle": "2023-11-09T07:13:54.963329Z", + "shell.execute_reply": "2023-11-09T07:13:54.963037Z" }, "tags": [] }, @@ -59,10 +59,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:24.799326Z", - "iopub.status.busy": "2023-09-02T00:49:24.799159Z", - "iopub.status.idle": "2023-09-02T00:49:24.859695Z", - "shell.execute_reply": "2023-09-02T00:49:24.859293Z" + "iopub.execute_input": "2023-11-09T07:13:54.965002Z", + "iopub.status.busy": "2023-11-09T07:13:54.964864Z", + "iopub.status.idle": "2023-11-09T07:13:54.985089Z", + "shell.execute_reply": "2023-11-09T07:13:54.984832Z" }, "tags": [] }, @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:24.861616Z", - "iopub.status.busy": "2023-09-02T00:49:24.861498Z", - "iopub.status.idle": "2023-09-02T00:49:25.082191Z", - "shell.execute_reply": "2023-09-02T00:49:25.081885Z" + "iopub.execute_input": "2023-11-09T07:13:54.986720Z", + "iopub.status.busy": "2023-11-09T07:13:54.986638Z", + "iopub.status.idle": "2023-11-09T07:13:55.216775Z", + "shell.execute_reply": "2023-11-09T07:13:55.216512Z" }, "tags": [] }, @@ -153,17 +153,17 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:25.084045Z", - "iopub.status.busy": "2023-09-02T00:49:25.083910Z", - "iopub.status.idle": "2023-09-02T00:49:25.274882Z", - "shell.execute_reply": "2023-09-02T00:49:25.274032Z" + "iopub.execute_input": "2023-11-09T07:13:55.218516Z", + "iopub.status.busy": "2023-11-09T07:13:55.218372Z", + "iopub.status.idle": "2023-11-09T07:13:55.341470Z", + "shell.execute_reply": "2023-11-09T07:13:55.341081Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -188,17 +188,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:25.280931Z", - "iopub.status.busy": "2023-09-02T00:49:25.279720Z", - "iopub.status.idle": "2023-09-02T00:49:25.472417Z", - "shell.execute_reply": "2023-09-02T00:49:25.472038Z" + "iopub.execute_input": "2023-11-09T07:13:55.343297Z", + "iopub.status.busy": "2023-11-09T07:13:55.343193Z", + "iopub.status.idle": "2023-11-09T07:13:55.453441Z", + "shell.execute_reply": "2023-11-09T07:13:55.453155Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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ajQkf/ACAyWQCANTX1yM3N5dK4AghhBBCklzSNTzoWONjNpsHeCYkEXT8nNDaMEIIIYSQ5Jd0wU+HZMhMkL5HPyeEEEIIIYNH0gY/hBBCCCGEENIVBT+k1x544AFMmjRpoKeBM844A3feeedAT4MQQgghhMQpCn7iSG1tLe644w6MGDECRqMReXl5mDFjBp577jm4XK6Bnl6PrVy5EhzHxaz1eKxfjxBCCCGEDA5J1+0tUR06dAgzZsxAeno6Hn30UUyYMAEGgwHbt2/Hiy++iKKiIlx00UVBn+v3+6HT6fp5xrHn8/mg1+sHehqEEEIIISRJJX3mR2ISbIJrwP6RmBTRPG+99VZotVps3LgRl19+OcaOHYthw4Zh3rx5+Oyzz3DhhRcGzuU4Ds899xwuuugiWCwWPPLIIwCA5557DsOHD4der8fo0aPx2muvBZ5z5MgRcByHLVu2BMZaW1vBcRxWrlwJoDOjsnz5cpSXl8NsNmP69OnYu3evbK6PP/448vLykJqaiptuugkejyfk5zpy5AjOPPNMAEBGRgY4jsP1118PoL1M7fbbb8edd96J7OxszJkzp9t5hns9AJAkCb/5zW+QmZmJ/Px8PPDAAxF9/4QQQgghJPklfebHIXrw8/3PD9j7vzDyF7Bqw7fdbmpqwtKlS/Hoo4/CYrEEPUfZleyBBx7A448/jieffBJarRYffvgh7rjjDjz55JOYPXs2Fi9ejBtuuAHFxcWBYCFSf/jDH/CPf/wDOTk5+MUvfoEbb7wRq1atAgB88MEHePDBB/HMM8/g1FNPxWuvvYann34aw4YNC/paJSUleP/99zF//nzs3bsXVqs1sL8OALz66qu45ZZbAq/fnUhe7+6778a6deuwZs0aXH/99ZgxYwbOPvvsqL4DQgghhBCSfJI++EkEBw4cAGMMo0ePlo1nZ2cHsiq33XYb/vrXvwYeu/rqq3HDDTcEjq+66ipcf/31uPXWWwEAd999N9auXYu///3vUQc/jzzyCGbOnAkA+N3vfoe5c+fC4/HAYDDgmWeewY033oibbroJAPDwww9j2bJlIbM/Go0GmZmZAIDc3Fykp6fLHh85ciT+9re/BY6PHDkSdm7dvd7EiRPx5z//OfDa//rXv7B8+XIKfgghhBBCSPKXvSWy9evXY8uWLRg/fjy8Xq/ssfLyctnx7t27MWPGDNnYjBkzsHv37qjfd+LEiYE/FxQUAADq6+sBAPv27cPUqVNl51dUVET9Hh0mT57c4+cG03XuQPv8O+ZOCCGEEJLsmvx2PF+9BE8dX4wjHroHUqLMTxwYMWIEOI5Tra3pKCXrWtbVIVR5XCg83x7nMsYCY36/P+i5XZsndJTbSVJka5eipfwc0cwzGGXjB47j+mzuhBBCCCHx5t81X2Gr8wgAYIezCv8Ydj2sWvW95GCV9MFPisaIF0b+YkDfvztZWVk4++yz8a9//Qu//OUvow5sAGDs2LFYtWoVFixYEBhbtWoVxo0bBwDIyckBANTU1OCkk04CAFlTgUiNGjUK69atk73P2rVrwz6no4ObKIrdvn4k84zm9QghhBBCBguRSdjmPBo4dohuLG3Zgstyel6lk2ySPvjhOb7bhgPx4Nlnn8WMGTNQXl6OBx54ABMnTgTP89iwYQP27NnTbXnYvffei8svvxwnnXQSZs+ejU8//RQffPABli1bBqA9ezRt2jQ8/vjjGDp0KOrr63H//fdHPc9bb70Vv/jFLzBlyhTMmDEDixYtws6dO0M2PACAsrIycByHxYsX4/zzz4fJZEJKSkrQcyOZZzSvRwghhBAyWNhFNxiYbOzLls24IGsyjDxtJwLQmp+4MXz4cGzevBmzZ8/GfffdhxNPPBHl5eX45z//iXvuuQd/+ctfwj7/4osvxlNPPYW///3vGD9+PF544QUsXLgQZ5xxRuCc//znPxAEAZMnT8add96Jhx9+OOp5zp8/H/fffz9+85vfYPLkyTh69ChuueWWsM8pKirCgw8+iN/97nfIy8vD7bffHvb87uYZ7esRQgghhAwGbYJLNeYUPVjRunMAZhOfONZ1cUWCsNlsSEtLQ1tbG6xWq+wxj8eDw4cPY+jQoTAauy85I5FjjMHpdMJisahabycq+nlpJwgC1q1bh6lTp0KrTfqE8KBB1zU50XVNTnRdk1d/XtvtzqN4tPJ91XiWzoonh98ALafp0/cfKOFiAyXK/BBCCCGEEJIE7GLwrUea/Dasse0N+thgQ8EPIYQQQgghScAWpOytw6dNG5GABV8xR8EPIYQQQgghScAmukM+VuVtxJYfW2APZhT8EEIIIYQQkgTsYTI/APBp04Z+mkn8ouCHEEIIIYSQJKDM/OTo0mTHu13HsN9d059TijsU/BBCCCGEEJIEbKI883N2xonI0Mr3Qhzs2R8KfgghhBBCCEkCNkGe+cnSpeK8zJNlYxvtB9EmOPtzWnEl6uDn+PHjuPbaa5GVlQWTyYQJEyZg48aNgccZY/jTn/6EgoICmEwmzJ49G/v375e9RnNzM6655hpYrVakp6fjpptugsPh6P2nIYQQQgghZJBSlr2laow4K30CdFzn/kIMDNW+lv6eWtyIKvhpaWnBjBkzoNPp8MUXX2DXrl34xz/+gYyMjMA5f/vb3/D000/j+eefx7p162CxWDBnzhx4PJ19x6+55hrs3LkTX331FRYvXoxvv/0WP/vZz2L3qQghhBBCCBlEJCbBqdjnx6oxw6wxIF1rkY07QuwHNBhEtc3sX//6V5SUlGDhwoWBsaFDhwb+zBjDk08+ifvvvx/z5s0DAPz3v/9FXl4ePvroI1x55ZXYvXs3vvzyS2zYsAHl5eUAgH/+8584//zz8fe//x2FhYWx+FwkjOuvvx6tra346KOPAABnnHEGJk2ahCeffLLHrxmL1yCEEEIIIT1jF91gkO/jY9WaAACpGhMa/G2ycwerqIKfTz75BHPmzMFPfvITfPPNNygqKsKtt96Km2++GQBw+PBh1NbWYvbs2YHnpKWlYerUqVizZg2uvPJKrFmzBunp6YHABwBmz54Nnuexbt06XHLJJar39Xq98Hq9gWObzQYAEAQBgiDIzhUEAYyxwD+J5IYbbsCrr74KANDpdCgtLcV1112H3//+99Bqo7pUEen4ft5//33odLpuvy/GGL799lvMnTsXzc3NSE9PDzwW6WvEm46fk2A/S4OJKIpgjEEUxYGeCokhuq7Jia5rcqLrmrz669q2eB2q+zAT00EQBFh4g+yxNp8zqe57ovksUd1RHzp0CM899xzuvvtu/P73v8eGDRvwq1/9Cnq9HgsWLEBtbS0AIC8vT/a8vLy8wGO1tbXIzc2VT0KrRWZmZuAcpcceewwPPviganzjxo2wWORpPI7jYDab4XK5Eu4vEL/fj7PPPhvPPfccvF4vli5dirvvvhuMMdxzzz2yc30+H/R6fY/fRxAEOJ3ti90MBgMABI5DYYxBkqTAuTqdLvBYpK8Rb7xeL3w+H7Zt25ZwgVssSZIEu92O9evXg+epD0qyoOuanOi6Jie6rsmrv67tUa4FTl3nfZiR6bBpffu6fJu2CU6+87Edtj3I3Z9Y98nhRHP/GVXwI0kSysvL8eijjwIATjrpJOzYsQPPP/88FixYEN0so3Dffffh7rvvDhzbbDaUlJSgvLwcVqtVdq7H40FlZSXMZjOMRiOYJIG5Bu6GnDNbwEX4g67T6WA2mzFs2DAAwNixY/H5559jyZIlOHLkCFpbW1FeXo5nn30WBoMBhw4dQlVVFe655x4sXboUPM/jtNNOw5NPPokhQ4YAaP9tw7333ouFCxdCo9HgxhtvhFarhVarDQSOZ555Jk488cRAyZrX68Wf/vQnvPnmm6ivr0dJSQl+97vfYdasWbjwwgsBACUlJQCABQsWYOHCharXaGlpwZ133olPP/0UXq8XM2fOxFNPPYWRI0cCAF555RXcddddeOutt3DXXXehqqoKp556Kv7zn/+goKAgFl99RDQaDfR6PUaMGAGj0dhv7xtvRFHEhg0bMGXKFGg0moGeDokRuq7Jia5rcqLrmrz669oy+z5YavYGjvP1GZg6ZCoAYF+9B0da7YHHsqx5mJo/tc/m0t86qsIiEVXwU1BQgHHjxsnGxo4di/fffx8AkJ+fDwCoq6uT3cDW1dVh0qRJgXPq6+tlryEIApqbmwPPVzIYDIHMgmzyP97EK8c4jgv8w9wutPzpbtVz+0vmX54Al5Ia1XM4jgv82WQyoampCQCwfPlyWK1WfPXVVwDav7dzzz0XFRUV+O6776DVavHwww/jvPPOw7Zt26DX6/F///d/ePXVV/Gf//wHY8eOxT/+8Q98+OGHmDVrlux9Or4voD2gWbNmDZ5++mmceOKJOHz4MBobG1FaWorXX38d1157Lfbu3Qur1QqTyRR4XtfXuOGGG7B//3588sknsFqt+O1vf4u5c+di165d0Ol04DgOLpcL//jHP/Daa6+B53lce+21uPfee7Fo0aKef+FR6phzsJ+lwYbjOGg0mkH/PSQbuq7Jia5rcqLrmrz649o6mU92b5emNQfeL01vkT3mZL6k+jmL5rNE9alnzJiBvXv3ysb27duHsrIyAO3ND/Lz87F8+fJAsGOz2bBu3TrccsstAICKigq0trZi06ZNmDx5MgDg66+/hiRJmDo1eSLQ3mKMYfny5ViyZAl++ctfoqGhARaLBS+99FKg3O3111+HJEl46aWXAj/QCxcuRHp6OlauXIlzzjkHTz75JO677z5ceumlAIDnn38eS5YsCfm++/btwzvvvIOvvvoqsHarIxPFGENmZiYAIDc3V7bmp6uOoGfVqlWYPn06AGDRokUoKSnBRx99hJ/85CcA2svvnn/+eQwfPhwAcPvtt+Ohhx7qzddGCCGEEDIoKZsYWLXmwJ9TNaaw5w4mUQU/d911F6ZPn45HH30Ul19+OdavX48XX3wRL774IoD2qPbOO+/Eww8/jJEjR2Lo0KH44x//iMLCQlx88cUA2jNF5557Lm6++WY8//zz8Pv9uP3223HllVdSpzcAixcvRkpKCvx+PyRJwtVXX40HHngAt912GyZMmCBb57N161YcOHAAqanyzJLH48HBgwfR1taGmpoaWVCp1WpRXl4ecn3Lli1boNFoMHPmzB5/ht27d0Or1creNysrC6NHj8bu3bsDY2azORD4AO2ZRWVWkBBCCCGEdM8mumTH1i4BT4pGXtrvoOAnMlOmTMGHH36I++67Dw899BCGDh2KJ598Etdcc03gnN/85jdwOp342c9+htbWVpx66qn48ssvZespFi1ahNtvvx1nnXUWeJ7H/Pnz8fTTT8fuUyWwM888E8899xz0ej0KCwtlaTxlcweHw4HJkycHLRPLycnp0fubTKbuT4qRrg0TgPbgeTA3HSCEEEII6SmboMj8aMJlfmifn4hdcMEFuOCCC0I+znEcHnroobDlS5mZmXjjjTeifese4cwWZP7liX55r1DvHw2LxYIRI0ZEdO7JJ5+Mt99+G7m5uarGDx0KCgqwbt06nH766QDa1wlt2rQJJ598ctDzJ0yYAEmS8M0338halnfoCFjCddIbO3YsBEHAunXrAmVvTU1N2Lt3r2rNGCGEEEII6T1V5kcbOvPjFD2QmASeG3ydBZP+E3M8Dz4ldcD+ibTTW09cc801yM7Oxrx58/Ddd9/h8OHDWLlyJX71q1/h2LFjAIA77rgDjz/+OD766CPs2bMHt956K1pbW0O+5pAhQ7BgwQLceOON+OijjwKv+c477wAASktLwXEcFi9ejIaGBjgcDtVrjBw5EvPmzcPNN9+M77//Hlu3bsW1116LoqKiwOa3hBBCCCEkdlRrfrpke5SZHwYGp+TFYJT0wU8yM5vN+Pbbb1FaWopLL70UY8eOxU033QSPxxPIBP3617/GddddhwULFqCiogKpqalBN5Lt6rnnnsNll12GW2+9FWPGjMHNN98c6J9eWFiIBx54AL/73e+Ql5eH22+/PehrLFy4EJMnT8YFF1yAiooKMMbw+eefq0rdCCGEEEJI7ynL3lK7NDywatTLGhyDtPSNYwm4yMJmsyEtLQ1tbW1B9/k5fPgwhg4dOqj3bekLjDE4nU5YLPJ2iYmMfl7adZQpTp06NalaXw52dF2TE13X5ETXNXn1x7WVmIRr9zwFhs7b+seGXoshxtzA8YI9/4SP+QPHD5ZdiVHm5Gg2Fi42UKLMDyGEEEIIIQnMIXplgQ+gLnVL1VK7a4CCH0IIIYQQQhKastkBoC51S1U0PRisHd8o+CGEEEIIISSBKbM4Zt4AHS8vsaONTttR8EMIIYQQQkgCswnyzI+yxA2gjU47UPBDCCGEEEJIArOp2lybVefQRqftkjb4kSRpoKdAEgD9nBBCCCEk0akyP0FaWyvHBmvmJ+l6Ker1evA8j+rqauTk5ECv1ydNW+aBxhiD1+uFRqNJ+O+UMQafz4eGhgbwPA+9Xj/QUyKEEEII6RFV5ieisrfBmflJuuCH53kMHToUNTU1qK6uHujpJJWOgCGZAkqz2YzS0lLwfNImQQkhhBCS5JTNC4JtakoND9olXfADtGd/SktLIQgCRFEc6OkkDUEQsG3bNowYMSIpNmDTaDTQarVJE8gRQgghZHCyCfJAJk0bbM2PPPOjfM5gkfh3sCFwHAedTgedTjfQU0kagiCAMQaj0ZgUwQ8hhBBCSDJoE7tf85OiWvPjAWNs0P0SmGp9CCGEEEIISWDKhgdBu70p1gFJkOCWfH06r3hEwQ8hhBBCCCEJSmKSes1PBA0PgMG57oeCH0IIIYQQQhKUS/KBgcnGgmV+jJwOOk6+bGEw7vVDwQ8hhBBCCCEJSlnyBgTv9sZxXJB215T5IYQQQgghhCQI5R4/Rl4PHR+8MRW1u6bghxBCCCGEDEKMMdT4WtDktw/0VHrFJiqbHaizPh2UmZ/BGPxQv2JCCCGEEDKoSEzC8zVL8V3bLmg5DW4uOBunp40b6Gn1iHK/HmuQPX46qIIfgdb8EEIIIYQQktS+bNmM79p2AQAEJuL9hjUDPKOeiybzo3yM1vwQQgghhBCSxKq9zXirfpVsrN7fBpFJAzSj3rErMj/BNjjtoNzoVLleaDCg4IcQQgghhAwKEpPwXM0S+JmgeixRsyDKAEa5man8MWW3Nyp7I4QQQgghJCktbt6EA+6aoI8l6p436rK30Gt+lFkhCn4IIYQQQghJQlXeRrzbsDrk44na+UxZ9pYWtuEBtbqm4IcQQgghhCQ1gYl4tvpLCEwMeY6ya1qiaIui4UGwzA9jrE/mFa8o+CGEEEIIIUnt48b1OOKpD3tOIq75YYypsjfhGh6kKlpd+5kAD/P3ydziFQU/hBBCCCEkaTlFDz5u2iAbK9JnYby5VDaWiGt+XJJX1aUu3D4/wQKjwbbuh4IfQgghhBCStA566mTd3ThwuKVwDjJ1KbLzEnH9S7BW1eHK3ky8Hrzi9l+5ZijZUfBDCCGEEEKSVqvglB0XG7Iw3JSPFEUJWCIGP8rARc/pYOB1Ic/nOE71uROx3K83KPghhBBCCCFJq00R/KRrLQDUJWCJGPwo21yH6/TWwapVfm4qeyOEEEIIISQptEQa/CRg+Zdqg9MwJW8dkiHj1RsU/BBCCCGEkKTVJgTPjiTDhp/Kz6bM6gSTDJ+7Nyj4IYQQQgghSatFcMiOM37M/KjLvxIvA6Kcc7hmBx0G+0anFPwQQgghhJCkpcr8aIKXvbkkb9hNUOORcmNWq6b7NT/KvX4o+CGEEEIIISRJKLu9da75MarOTbQSMGXDg0jK3ijzQwghhBBCSBLySwJcklc21hH8WIIEP4kWCCjnm9qDzE+iBXy9RcEPIYQQQghJSspObwCQ/mPDAy2ngZk3yB5LtECg2S9fz5QeQatrdYvvxPrMvUXBDyGEEEIISUptojz40XFaWcCTqigTU66hiWc+SVCVvWXrrN0+LxlafPcGBT+EEEIIISQptQZpc81xXOA4kTc6bRLsqrEsXWq3z1MGfD7mh08SYjaveEfBDyGEEEIISUptIZoddEjkzmeNfpvs2KIxwsTru32ecpNTILE+d29R8EMIIYQQQpKSMvOjDn4SOPPjl2d+srXdZ30AwMIbwIGTjSXS5+4tCn4IIYQQQkhSUrW5VnRDS+Tgp1ER/GRFsN4HAHiOV3W6S6TP3VsU/BBCCCGEkKSk7PaW1l3mJ4EW/yvL3rIjWO/TQVn6lmhd7nqDgh9CCCGEEJKUul3zo03cts/KzE80wY8y6KPghxBCCCGEkATXqmgFnZFEa34aBXnmJ9KyNyCxP3dvUfBDCCGEEEKSDmNMlflRlr0py78SJQiQmKTa4DTShgdAsC53lPkhhBBCCCEkYTklLwQmysbSteEbHngkH/wJsOeNXXTDz+TzjGSD0w6JvNaptyj4IYQQQgghSUfZ6Q0A0hTd3qyKIABIjCyIcr2PhuNVgV046oYHFPwQQgghhBCSsJTBj0VjhI7XqsaUe94kQiCg7PSWqU0Fz0V+W5/IjR56i4IfQgghhBCSdJTrfZTNDoD2jIlZY5CNJUIg0JtOb0DirnWKBQp+CCGEEEJI0lHt8aNRBz9AYnY+axSUG5xGF/yoW13H/2eOFQp+CCGEEEJI0mkT5G2uQ62JScTgp0lR9pYTRbMDQP2Z3ZIPfkVziGRFwQ8hhBBCCEk6rWL4NtcdEjH4UZa9ZUXR5hpQt7oGAGcClPvFAgU/hBBCCCEk6SgbHqSHDH4Sb88bZcODaNpcA0BK0C538R/0xQIFP4QQQgghBACww1mJ/zv2CRbWfg1HAgQB4aiDnxBlb8rOZ3G+541X8qsClWjX/Gg4HhZF0NekyCYlq6iCnwceeAAcx8n+GTNmTOBxj8eD2267DVlZWUhJScH8+fNRV1cne43KykrMnTsXZrMZubm5uPfeeyEI8b+ZFCGEEEJIMqv0NOCvVR9ig/0AlrZswVv13w/0lHqlVbXmJ3jmR7nXT7xnQIIFKdF2ewOAAn2G7Lja19zjOSUSbfenyI0fPx7Lli3rfAFt50vcdddd+Oyzz/Duu+8iLS0Nt99+Oy699FKsWrUKACCKIubOnYv8/HysXr0aNTU1+OlPfwqdTodHH300Bh+HEEIIIYT0xNsNqyB0WfS+xXF4AGfTO34mqjqYhQp+lCVg8R78KDu9pWhMMPL6qF+nQJ+BA+6awHGNt6XXc0sEUQc/Wq0W+fn5qvG2tja8/PLLeOONNzBr1iwAwMKFCzF27FisXbsW06ZNw9KlS7Fr1y4sW7YMeXl5mDRpEv7yl7/gt7/9LR544AHo9dFfOEIIIYQQ0jt7XMfxg+OQbCwR1r6EYlNkfYBwra4Ta88b9Xqf6LM+QLDMTwuYIMC/bxfE+jpAEsEkCZAkQBTBW9NgmDwNnFHdLCGRRB387N+/H4WFhTAajaioqMBjjz2G0tJSbNq0CX6/H7Nnzw6cO2bMGJSWlmLNmjWYNm0a1qxZgwkTJiAvLy9wzpw5c3DLLbdg586dOOmkk2LzqQghhBBCSEQYY3iz/jvVuI/54ZcE6PiobxcHnHK9j4bjkaLYzLRDonV7622ntw6F+kzZMTt0AK1vPACxvjbkc9zfLIP1f34JTW5eyHPiXVQ/zVOnTsUrr7yC0aNHo6amBg8++CBOO+007NixA7W1tdDr9UhPT5c9Jy8vD7W17V9ibW2tLPDpeLzjsVC8Xi+8Xm/g2GZrj3gFQaD1Qv1IFEUwxiCKg6MP/GBC1zY50XVNTnRdk9NAXtfNjsPY6zoe9LFWnwMZ2pR+nlHvNXltYIwFjq0aEyRRggRJda4Zetm5HtEHt88Ts6Av1te2wdsqm2+mxtKj++FcjRWMMRg8fsz8/iBO2FULvyEHPMeFfI5YX4OW//sLLNf+D3RjTujR/PtCNJ8/qqt63nnnBf48ceJETJ06FWVlZXjnnXdgMqlb5sXKY489hgcffFA1vnHjRlgswVOYJPYkSYLdbsf69evB89QoMJnQtU1OdF2TE13X5DRQ11UCw0Ldejg5Z9DHv/9hHXJZ4gU/W/jjcGo7P1Mq02DdunVBz3XDD6de/vm/2bAKqYhNeVesr+0u7UE4+c75trbVY93h4J8tHIGJGFpVidlrj8Di9kMC0OayQwdN+Cc6nXA89Ve0Ta6A44STgTDBUn9xOoP//AbTq5A2PT0do0aNwoEDB3D22WfD5/OhtbVVlv2pq6sLrBHKz8/H+vXrZa/R0Q0u2DqiDvfddx/uvvvuwLHNZkNJSQnKy8thtUbX15z0nCiK2LBhA6ZMmQKNppt/MUhCoWubnOi6Jie6rslpoK7rKtseuGoZLAj+y+QRxaMx1lzcb/OJleqmdbA0VQWOR1hKMbVoatBzJSbh5f0/gKEzmzJy3DiUGXJiMpdYX9u3D++Bxe8LHE8tOAlTU0dG/TqeFUtg/fYo/EwC+PZ56XR6pGiM4HMLwGdmArwGHM9DrK+FpCiHS9mzFTqTAebLrwOnG9h1+x1VYZHoVfDjcDhw8OBBXHfddZg8eTJ0Oh2WL1+O+fPnAwD27t2LyspKVFRUAAAqKirwyCOPoL6+Hrm5uQCAr776ClarFePGjQv5PgaDAQaDuk5Tq9XKus2RvsdxHDQaDX3vSYiubXKi65qc6Lomp/6+rn4m4v3mdeDC/ObeBX9C/pzZmUf2uTL0qWE/R4rWJOsO54Ivpp87VtdWYhJaRIfss+Ua06N+XbGlGd4ln0DHayB0Kcfz6jXIm3cVjKfOAtclS8V8XjjeegXezRtkr+PfsgHOxjqk3ng7NBnyNUT9KZrPH1Xu7Z577sE333yDI0eOYPXq1bjkkkug0Whw1VVXIS0tDTfddBPuvvturFixAps2bcINN9yAiooKTJs2DQBwzjnnYNy4cbjuuuuwdetWLFmyBPfffz9uu+22oMENIYQQQgjpG1+3bEeDv002puPkN5Hxvvg/FPUGp+GXSag7vsVnp7s20SVrRw70rNub++svwERRdr0PDMvGiv+ZC9Pps2WBDwBwegNSrvsZzHMvASAPloVjlfCsWhH1HAZKVGHisWPHcNVVV6GpqQk5OTk49dRTsXbtWuTktKcFn3jiCfA8j/nz58Pr9WLOnDl49tlnA8/XaDRYvHgxbrnlFlRUVMBisWDBggV46KGHYvupCCGEEEJISF7Jjw8a18rGRpoKYeb12Oo8EhhzxGkQ0B31BqfmsOenakyoQec+N3YhPoM+Zac3DccjTRP+sylJbS3wrm3fwFbPtZe7bZtQiGWzRqPM4Av5PI7jYJ49F9qCYthf+zeYt/1nQzd8NMznzYtqDgMpquDnrbfeCvu40WjEM888g2eeeSbkOWVlZfj888+jeVtCCCGEEBJDO11VsInyAOGq3FPxdet22ZgzYYMfeeYn1B4/HZTtrpUbpMaLpiBtrnkuuiYK7q+XgAl+AICO10LUcFg7pQwAUONrgcSksK+pH38i0u78Pez/eQYQBKRe/wtwmsQpjUycmRJCCCGEkJio8bbIjocZ8zHWXIz19gOy8UQse2OMoU0R/GR0V/amTYy9ftQbnEbX+EuytcGz+pvAsY7TYse4AjhS28v+fExAk+BATjevq80vRNqdv4dkt4FP6dk+QwOFemQSQgghhAwy9Yq1PoWGDACJs/YlHLfkg4/J933pbs1PSoJ8bnXwE13g4V7RmfUBAI1Gi22njJCdU+Ntjui1eLMF2ryCqN4/HlDwQwghhBAyyCgbHeTq0gCog4B4Lf8KR1nyBgBp3az5sWoSJfOjKHuLIvMj2W2qxgTGU2YgNbtINlbtk2cFkw0FP4QQQgghg0yDIoOQEwh+lGtf4jMDEk6bYi2TidfDwOvCPke55ideg58mQR78RJP5ca9YCubvzPpwHA/T7Lko0GfIzquh4IcQQgghhCQLxhjqffLgJ08fKvOTeMFPi7LZQTclb0Cw4Cc+P7cy85OtjSz4kRx2VdbHMKUCmqzsQMljh2pfZGVviYqCH0IIIYSQQcQmuuBjftlYxwJ39ZofNxhj/Ta3WIi22QEQJPiJw1bXHsmnKkOMtOGB+5uvwHzewHF71ud8AEChXr45aXWEa34SFQU/hBBCCCGDSL2i5I0Hj0xtCgB12RsDg1sKvfdLPFJmfrprdgCou735mB9eyR/i7IGhzPoAQFYEZW/M64Xne3nWRz95KjQ5eQCgKntrFhzwJNg1jwYFP4QQQgghg4hyvU+2rnOvGGXZGxC/619CUWZ+elL2BsRfyZ9yj59UjanbtUwA4P1hHZin6zXkYD57buCoQJ8BDpzsObW+1t5MNa5R8EMIIYQQMojU+xSd3n5c7wMARk4HLaeRPR5vQUB3WgV5w4N0TfhObwBg5vWqACDegj5lm+uIsj6MqbM+4yZAk5vfecxrVa+VzB3fKPghhBBCCBlElG2uOzq9AQDHcUH2vImvIKA7Pcn88Byvyv7Y4uxzqzu9db/eRzhyEEJ1lWzMeOqZqvOUpW/JvO6Hgh9CCCGEkEFEucFpjuImOtE7vqnX/HSf+QHUpW/x9rlVnd4iCH6UWR9NVg50o8erzitSND1I5nbXFPwQQgghhAwiyjU/uV0yP0D8BwHhiExSZaoiaXgAAKlaRcYrzjq+KTN23bW5luw2+LZulI0Zp88Ex6tv/wsGUbtrCn4IIYQQQgYJiUmqtSO5+uTJ/LQKTjDIW3NH0uoaUHe6i6dyvz2u49jjOi4b6y7z41n7HZgoBo45rQ6GqacGPTfYRqeJ1uI8UhT8EEIIIYSEwRiDU/RAYGL3J8e5FsEJkUmyMWXmJ56DgO7UKbqU6TktrBE0PACCbXQaH5/bJwl4oWapbEzPaTHeUhLyOUwU4V29UjZmOPkU8JaUoOcr9/rxSn40C46eTTjOaQd6AoQQQggh8aZFcGCHsxLbnEexw1mJVsGJbJ0V9xTPQ5kxZ6Cn12PK9T7BgoNEzvwo16rk6dPBcVyIs+WCbfAaD95rXINaxef6Sc70oG3JO/h3bYPYKn+OccYZIc/P1KbAwOtkextV+5oj6iiXaCj4IYQQQgj50RrbXnzYuA5V3kbVY41+Gz5qWoc7ii4YgJnFhrLNdY4uTRUcxGsQEIlaf6vsWFnOFU48Zn4OumuxuEm+bme4MR/nZ54c9nnKRgfakiHQlg4NeT7HcSjQZ+CIpz4wVuNrwQRLWQ9mHd+o7I0QQgghBMBRTwOePv5Z0MCnwxFPQz/OKPaUzQ5y9Op1I8qyt0TK/CjL3rruYdQdVfAzwA0P/EzECzVLZWuYNByPXxTOCWxKG4xYXwffvl2ysWDtrZWUpW/J2u6agh9CCCGEEACbHAe7PafJb0/oheDKsjfleh8gscveahXBT1SZH218ZX4+blyvCsQvzZ6GYkNW2Od5VsmzPrzZAsNJU7p9P9VeP0na7pqCH0IIIYQQAMcVv+k28npMtAyRjfmZAJvo6sdZxZZ6g9NgmR9l8DPw5V+RkJikyvz0puzNJrohKZpD9JcqbyM+alonGysz5OCirPBBjHC8Ep5VK2VjhqmngdPpu33PQsPg2OuHgh9CCCGEEADHvE2y46tzT8NvSy4GB/maGOVmk4mkwacoewuS+VEGAW7JlxCd7loEJ3xMkI0Fy2yFkqmVd0ITmIi2AQp0P2vaJOvKx4HDzwvPgZbThHwO83ph/++LYGLnd8BxPIzTT4/oPQsVgWKT3w6fJIQ4O3FR8EMIIYSQQU9kkmpjx2JDFniOV3W8StTgx89EVfvivCBrYpQNDwDAmQClb8HaXCsDmnAytBZVcKEMFvvLfneN7Hhu1mQMNeaFfY7zo7cg1tfKxoxnnA1Ndm5E75mnT5cdMzA0CYn5sx4OBT+EEEIIGfTqfK2q7EaRvn1thbI0rEkYmBvi3mry21QbgAYre7MECX7sCRD89KbNNQDwHK/aOFS5Rqo/+CRB9VlOSR0R9jneLRvhWfudbExbXAbz+ZdE/L4mXg8jLy+PaxWcET8/UVDwQwghhJBBr0pR8mbVmGH9cQF8smR+6hWd3sy8IWigo+U0MCluggd68X8ketPmuoMyGByI4Oe4r0kVpBYbskOeLzY3wvH2q7IxTm9A6nU/A6eNblcbZaasOUF/1sOh4IcQQgghg95xnzz46dpRK1uryPwk6A2heo8fddanQyK2u1aWvSnLuCKhXCOk/M76Q6VH3uEtV5emCkY7MFGE47V/g3nkwanlsmugyQ1fJhdMpk4R/CjKJJMBbXJKCCGEkEFP2eyga/CTLJkfZae3cHvgpGiMsvMTYc2Pss11fk+CH8V30ui3w39gL3zbN4N53OCt6eCsaeDT0sFb06AtLAanN/Ri1mrK9tYlIbI+Yn0dHO+9Dv8ReYt2w+RpMJRX9Oi9M5SZnyQse6PghxBCCCGDnjL4Kema+VEFP4m55ke1wWmYTmjKdtfxXvbW2zbXHbqu+TG7fBi7dAXajnwZ8nzOYIT5nAtgnDkbnCY2t9WViuCn1CgPfpjfD/fXX8K97HMwwS97TJOVA8tl10S11qmrwVD2RsEPIYQQQgY1kUmqBeZFsuBHXh5mE13wSQL0fGLdRilLuMK1gVa2u473hge9bXPd9TmcxHDCrhqc9v1BGL0CYApdPsa8Hjg/fQ/eTeuQcsVPoS0dGvV7KqmCH0NO4M/+A3vheOc1iA21yqeB43mkXHczeKNJ9ViklJmfFsr8EEIIIYQkl9ognd7Clb0BQJNg71FmYSCpMj/6cGt+lBudxnfwo25zrYuqzXWHbLsfl7+/GUXVnYGiwMSw++sAgFBdhbYnH4PxtFkwzbkIvNkc9XsDgE1woU0RcJQYsiHU1cD12Qfwbd8c9Hl8WgZSrroeurJhPXrfDhk6i+y4hdb8EEIIIYQkF2XJm1VjlmU+TLweFo1Rtu6l0W9LqODHI/lgU2zYGU3mxxHnZW/KzF1+lG2uAYB5PJBe+BeKa2yyXmsCE6E3p0I/qRzM6YBka4Nka4PY3AR0OZMxCe5vl8H97TJwBiN4axp4axpgTkFm9XE4tq8DJwhgPh8gitDk5EF/wonQjZ0A3tIeqCmzPulOAakffoK29avBumx62oHjeBhPnwXzuReDM6o790UrUysP9FsEByQmgeeSp0caBT+EEEIIGdSUnd6CLTDP1qbKgp9E6/gWrElDuG5vyhbY8Z75Uba57kmzA8+qFZCam6DlNPB3KaFrPWE0Cq/8JfhU+fflP3oIzrdfhVBzXPVazOuB2OCB2FAHMAaT0wmhsRboEpAJ1VXwbt0IjuOhHTocutHj4XRV4vSGA9B7RZg8fow5aoNPG/yzaEuGIOXyn0JbXBr1Zw0lQyvP/IhMgl10I00xnsgo+CGEEELIoKbu9JapOidLZ8VRb0PgWFlCFu+U632sGjMMvC7k+amqhgfxHfz0ts0183rhXrEEAKDjNPBDQJvViGWzRuHkyWfghFR1oKgrG4a0X/8R7hVL4V7yqar5QMTvzST4D+2H/9B+pPltKBc6s2ymIPsw8WYLTOfOg3HGGeD42GZk0rUWcOBk+wy1CE4KfgghhBBCkoVyg9NgG0oqO74lWuZHuVlnuDbXQLA1P/Fe9tYqO4428+NZtQKSs319S8f6nk/PH4/6PCuKfaEDXU6jhXn2+TBMKofzw7fg370jaHlapHySvGlD16YanE4P0xlnw3jmHPCmnq0p6o6G45GmNaO1y7qjZsGBIcjtk/cbCBT8EEIIIWTQEpiIGl+zbKwoSOZH3e46sYOfcCVvQLA1Px4wxnrcQrkvSUxCfS/aXDOfF+4VSwPHWk6DQ0OzUJ/X/h0p90cKRpOdC+vNv2pfN2RrDawLkuw2CLZWVFdWInvUaGiMJnB6PSD44duzE/69O8H8nRkjZcc6PacDx/EwVJwG8zkXgk9Lj/hz9VSmNlUe/PiTq+kBBT+EEEIIGbTqfG0QFb+pD7bmJ0sRLDQKiVX2pizT664NtDLzIzARHuaHidPHfG69FazNdZ4uPeLne1Z/C8nR+f3oOA3WnjIkcBxNiSNnNEJjzIcmNz8wJggC7OvWwTh1KrTazltv44wzwfw++Pfthm/nVjjqqnDQJcBj0MJn0MJj0OLqYecjfcIUaLJygr1dn1Cu+0m2jm8U/BBCCCFk0FKu90nTWlQ3/kB7w4Oumv2J1QWrwde7zA/Qnv0x8fEX/ARrc628gQ+F+X1wf/2FbIwfMw61+YbAcYvggF8SoOuDfZ04nR768SdCP/5E7LLvx0fHPg08lqoxIXPkef2ebcvUKTY6TbLgJzH+jSWEEEII6QPHFK2FS7rs79OVcq8fPxNgj/N1MF2pMj/drPkx8XpwkN90x+u6n960ufas+RaSXf7dpM25WHVefzS4qPQofxazB6TMULXRaZKVvVHwQwghhJBB65iizXWxPnjwk6G1gFfcNjUkyLofh+iBS/LKxrore+M4Lui6n3jU0zbXzO+He/mXsjH9qHFIHT5W1epbuWaqL1QpAvFSY/+VunWlCn4Um64mOip7I4QQQsigdcyrbHYQPPjhOR5ZulTZ4vcmvw0jTPlBz48njYqsBQcOmYpMVjApGqNsY9R4bXfd0zbXnrXfQbLJn2s69yIA7cHh4S6ft18yP8rgJ8jas/4QSdnb/1Z9jDStGSWGLBQbsjDSVABjHJZEBkPBDyGEEEIGpWCd3opDBD9Ae8e3rsFPonR8U968pmnN0P3YzjmcRGl3rWxzHUmnt/asj3ytj27kWOiGjgDQvibqsKcu8FhfZ368kl8VxAVrvNEfMhWZH4folq15coge/OA4KDvnf4ctCPvvTjyhsjdCCCGEDEq1vlZVp7dwN3DKdT+NQoIEP4o1G8qyplBSEqDsLVib60jK3rzrv4fUJl8rZD7ngsCflWuilJvExtoxb5NsY1EOXNDNdvuDMvgB5AG0skmIhuOj3ldpIFHwQwghhJBB6bjiJi49RKe3DtlaeYe0pn4ohYqFVsWajUiDn1TFdxGPZW89aXPNBAHuZZ/LxnTDR0M3YnTgWLkmqq+zfMr1Prn6tAErIzPxeug5nWys67ofZZOQQn1mYGPYREDBDyGEEEIGpSpF8NNd2Y4y89Mf60BiQblPS2aEbaDVDQ/ir+ytJ22uvetXQWxVZH3mXCg7VrYC7+uyt3hZ7wO0N7sIt+4n2n9v4g0FP4QQQggZlI4rO711c8OZrQh+mhJ0zU+GLrLMj7LjWTyWvUXb5pqJQbI+w0ZC2yXrAwA5isyPQ3TDLfl6OdvQlJmfgVrv00G10am/a/ATX3ONFgU/hBBCCBmUlGsXivXh11hkK7IBdtENr+SP+bxiTblPS7A1HcGoGx7EX/ATbZtr74Y1EFvk190050JVwJQbZBPYvlz3o9zjp9QwMG2uO2QqN/X9MYBmjKkyP6H2xopXFPwQQgghZNBp7/Qmzxp09xtsZdkbkBjZH+U+LZGv+ZGXvcXjpq7RtLlmogD3V5/JxnRDhkM3cqzqXB2vRboi+9HQR6VvbYJT1lIcAEqNA5z50SkyPz8GPzbRpSp/pMwPIYQQQkicC9bpraib7lomXq8qBYv3jm9+JqpurLtbE9NB2fAgHjM/0bS59m5aB7FZnmExzbkoZJmcsvStr9b9KNf76Dkt8rrZhLavKbODHcFPsLkq10fFOwp+CCGEEDLoKDMG6VqLKrAJJltRDqTcQDTeKDu9AZGv+VGWvblELyRFwDiQomlzzUQR7qWLZWO6smHQjR4X8vWVHd8afH1zrZVraIoN2eC5gb1FVwY/He3SVaWicTDXaNEmp4QQQggZdJoUGRvlep5QsnRWHPU2BI7jfaNT5XofHadFCt99kAeo9/lhYHCIXqTyBgiVhyHWVoO3pEA7YjR4kzlmc45Uja814jbXvs3rITY1yMaCrfXpKlev7PgWffDjED3Y7ahCG0JnzZTrfeJhDY2yNLJFcIIxFiT4GZi9iHqDgh9CCCGEDDrKtTpZWvV6nmByVB3f4jvzo1zvk661hL3h76pr2ZvB48eQymY4178MYf8BSM7OoIrjeGiHtGdRdKPHQ1s6FBzf99mAHxyHZMfpWkvQkj7m8cClyPpoS4ZAN+aEsK+vyvxEWfbW5LfjwaPvoN7XCpfehRLXcEywDlGdd8BdKzse6PU+AFStrv1MgEPyBGlzPfBzjRYFP4QQQggZdJTtn5U3e6FkKTJE8Z75UX3OCJsdAO2L/vXQYsrqfZiyqRK8xCAZaiHx8g0wGZPgP3wA/sMHgC8/gSYrBynX3ATd0BEx+QyhbFYEPyenDFMFdkwUYH/1OYgNdbJxczdZH0CdDWzw28AYizh4/E/t14GAiYHhg6Z1quCnTXCqWq6PMhVF9Pp9KS1IENnktwdpcz3wWapoJVaRHiGEEEL63WbHYbxY8xVWtu6IqzUfvaHM/Cj38Akl0fb6UW5wquzi1Z1xlTZM3XAUvMQAQNUkIhixqQG2Z/4O78Y1Ub1XNByiB3tcx2VjJ6cMkx0zSYLjzVfg27NTNq4tGQLduIndvocy8+ORfLBH2PRho/0AfnAclI3t9VTDJXplY7sVn8HI6zHUmBvRe/QlHaeBVSMvZTzgroVHsddRonV6Ayj4IYQQQkgY+901+FvVh1jRuh0v1CzFK3UrwBgb6Gn1WrNq75seBj+CPa4DQlXwE0Xmh4kiKr7fJxuT0PlZNZnZ4DTBi4iYKMC+6GU4P/sATIr997PVcRgMnT+HOk6LEyylsnNci9+Hd9Na2RhvtiD12v+JKHuTpUsFB/l5kZS+uSUfXqldoRqXmIQdrkrZ2G7XMdnxaFMhNHHSQECZDd3uPCo7NvOGqDKJ8YLK3gghhBAS0hrbXtnxVy1bkaIx4vKcGQM0o95jjPW4HCxLKy+FEpiINtEVVVDRn1r8PdvjBwC8a79DRrNTtlTfNrQEeeXnQD9uIjRZ2WBeL/yH9sG/dyd8u7arysvcyz6HVF+LlKtvAmcw9OajyPzgOCw7PsFSAkOXcjz311/CvWKJ7BxOp0fqz+6AJjc/ovfQcDyydVZZwFPvb8NwU/jnv9ewRtVQo8MWxxGckjoycLzLVSV7fJylJKK59YdMbQqOoD5wvMMpD9yKDVkRlwDGEwp+CCGEEBLSEU+9auzDxnWw8EbMzZo8ADPqPbvohl/RJSzYBqbBpGvN0HC8rPyr0W+P2+CnWXETnhnhHj+Sxw3Xl59A06VIqC43Fc6r5mJi/mmBMc5ggH7sBOjHToD5wp/A9fE7cH+3XPZa3m0/QKyvg+nci6CfcFKvmyEITMQWRfBzcsrw9nm7XPB88xVcSz+VPc7xPFJvuAW6MnlpXHdyFMFPQzcNLo546vFF8w8hH9/qOBxYN2QTXKruaWPNA7/ep4PyZ9olyUv2ErHkDaDghxBCCCEhMMZUmxp2eL3+G1g0BpyRHr5jVjxSZn04cEiPMCjgOR5Z2lTZhpdNfjtGmgpiOsdYUXZ7y4wwyPMs/xKSwwa+y2/2vzltOMYwb8jncBoNLJdeBU1eAZzvvwHWJUAUao/D/spz0OTkwXTmHBjKK8DpdCFfK5x9rmrVjfgkZMP12Ydwf7cczKtel2O58nrox06I+r2UG3jW+0KXvUlMwsu1y2XleBw42XGz4ECVtxGlxhzVeh8Dr8NQY17Uc+wr3W2GW5yAzQ4ACn4IIYQQEkKz4IAzzALvF2u+glljkJXxJAJlk4IMrSWqdRZZOnnwE68bnboln2qBeiRBntjSDPfKpQAQyPwcHJaFY8UZKIpgwb9xxhngc3LheOV5SG6X/LUb6uB4579wffERDCefAm1xGTSFJdDk5cvWDx321OE/tV/DLfpwRe4MTEnt7By36ccubyaXD/l1Npx83Adu/1/g8vuDzsdy4WUwTpne7byDydUr212HvtZft27HAXeNbOzS7GlY2boDTnQGoVudR1BqzFGVvI0yFULLaXo0z77QXaBMmR9CCCGEJJWjHvmmkMrfYjMw/PP453hkyNUoNeb09/R6TLkeI9JsSAdlC+R47fim3OAU6P63+QDg+uwDMKE9kOA5HhLP4dsZ7WVljgi7nelHjUPanb+H/T/PQqirVj0u2W1wf7MscMxptNAUFEI/ZgKMp56Bfzcvw2FP+/qhJ459ij+W/QSjRSt8WzchZdN7uOl4PdJs7XNJ11rAQrQqN59zIUyzzo1ozsEoO77V+lqCnmcTXHiz/nvZWL4+A/OypqDZZ8fR1s6gaLPjMC7MmqJqdjDOHD/rfYDuf1YSsc010Mtub48//jg4jsOdd94ZGPN4PLjtttuQlZWFlJQUzJ8/H3V18sVvlZWVmDt3LsxmM3Jzc3HvvfdCEAQQQgghJH4c9cqDn9HmQlyYNUU2JjARn4dZ4xCP1J3eoluvo+z41t06kIGiLHkz8wYYeX3Y5whVR2Qd0jQch20TCtGS2X4j7BDdEb+/Jjcfaff+GanX3QxtYfgbeyYKEI5VwrXsMzQ99FuM+nQlspqc0AgSRu6vw95nHkTjn+9Gy3v/RfHuykDgAwBmjbKRAgfDieVIv+dPMJ83L+L5BlOoz5QdN/ht8ErqDNNq215VKd6N+bOg47WYZBkiG9/rqkadr1W1Z844c3Gv5hpr4f69sGrMsGrNIR+PZz3O/GzYsAEvvPACJk6U90m/66678Nlnn+Hdd99FWloabr/9dlx66aVYtWoVAEAURcydOxf5+flYvXo1ampq8NOf/hQ6nQ6PPvpo7z4NIYQQQmJG2eygzJCDq3JORZvgxLdtuwLjyiAp3ikzP5E2Owicr2iLHa9lb6pmB91s5MoYg/Pjd2VjnMGENacMCRwrA6rucBoNDCdPhf6kU+DfuxPu5V/Cf2BP2OcIoh/jdzVi/K5a+PQa6H0iAKBeo4dJEbxpOD7Q5Y3jeOjLp8F01nnQ5sVmDVahIUN2zMBQ42vBEMVePMp/V8pTR2CCpQwAMNZcLGscIUHCe43yPZD0nA7DTPGz3gcInxFN1JI3oIfBj8PhwDXXXIN///vfePjhhwPjbW1tePnll/HGG29g1qxZAICFCxdi7NixWLt2LaZNm4alS5di165dWLZsGfLy8jBp0iT85S9/wW9/+1s88MAD0OvD/0aCEEIIIf1D2eygzJgLjuNwWto4WfBT42sJdLBKBMoyNWUw052cKNaBDKRWRaDS3Xof3/bN8B+UtzbXzT4XbnNni2Ob6IJD9CBFY4xqLhzHQT/mBOjHnACh8jB8O7dBOF4JsfoYxBZ5xzORiYE/dwQ+AOAWffAosi4GgwW6YSOhHTYSxmmnQ5MV25tyI69Hji5N1vHtmLdJFfwc9zXLjseYOru2mXg9iqV0NKIzM7SqTR4AjjbH13ofALDwBug4raozIgAUGzKDPCMx9Cj4ue222zB37lzMnj1bFvxs2rQJfr8fs2fPDoyNGTMGpaWlWLNmDaZNm4Y1a9ZgwoQJyMvrjG7nzJmDW265BTt37sRJJ52kej+v1wuvt/MHxmZr/0tGEAQql+tHoiiCMQZRFLs/mSQUurbJia5rcuqv6+qR/Kj1tsjW+BRpMyAIAnJ5q2yjU4/oQ6O3LW7bPSs1+uyy+afz5qjuJzI4s+z5TtGDNq8DligDgq764ro2eG2yeaaF+ZzM74Pzo7eBLudz6ZnIPO088Ef/LQtIqlwNvetuV1gCfWEJOn7dLbmcEKuPwb91I3wb1kAQ3Ai1ja5fw+HgsBxUFaejJs+K6yZcAYu1vRkCA/rkvrBQl4F6X2vguNLdAMHS2eSDMYYqT6Psu87XpgXmIooihkmZaGA1P84Ssn+vAGC0oTAu72nTNWZZc48OhT/+XRAvoplL1MHPW2+9hR9++AEbNmxQPVZbWwu9Xo/09HTZeF5eHmprawPndA18Oh7veCyYxx57DA8++KBqfOPGjbBYImtNSXpPkiTY7XasX78efC979JP4Qtc2OdF1TU79dV2Pc21w6DrXxnDgUL31IBpwBBIYvHoPBHTeEC/74XuUsoxgLxVXGBgq9TUQ0dmG+djuwwBrDvMsOQESnHoX0OUGdvmm75HHossgddUX13WXdj+cfGf2x2ZrxLqj64Kem7p1A6xVR2VjzeWnwr15C/Q6EY1c5+us2LEWzVJhTOYoUzwCfFYhGvauhHXPdhi9P97QchyOFqRi24gc7BmaCZ++PUOiAQ/X7nqsQ1OYF+09QWOHU9P5+Tfad6LsYGeWpg0eNOnljRDqdxzFOrSveZckCblOA5waJ0IlR4XWZqw7EPzaDCRB65L9DHVo2VeDdcwV5BkDw+mMvBwzquCnqqoKd9xxB7766isYjT3/7Ua07rvvPtx9992BY5vNhpKSEpSXl8NqtYZ5JoklURSxYcMGTJkyBRpNfKVmSe/QtU1OdF2TU39d1+Wt22Gp7/wFY6E+E6cO6WwXPPLoQVlZXFZuEaamR7+PSn+zi24YD8pvMs8YWhH1up+3D+2S7RdUWDgU5T9utNkTfXFdv6w8Coun86ZwUu54TE0/UXWe1NoC20evA11+oawZNhJFV1wLjuOwtroRGxwHAo+lZGRjas7UmMwxmNfG8Xi3IQ0jDjZA5xdxfGg+mlLavxPdj/8AwImWITi1qGctrKPhbUvFjrrOn3VJb8DUIZ2ff6vzCCzHtwSOTbwes4efHigDFUUR2LAeZelNqvVmAKDntLhoxGzo4qzsDQA21DSj2b5PNX7e8JlBGk0MnI6qsEhEFfxs2rQJ9fX1OPnkkwNjoiji22+/xb/+9S8sWbIEPp8Pra2tsuxPXV0d8vPzAQD5+flYv3697HU7usF1nKNkMBhgMKi/YK1WC62WunX3J47joNFo6HtPQnRtkxNd1+TUH9f1mL9JtoZnqClP9n4FhkxU+Tp/494g2hPi56xNcMs+FwcO2ca0qPb5AdrX/bSInYFFs+To9eeP9XVtlVyyz5ptsAZ9bfvnHwKCHx1pCY7jkTr/amh/3IS02JiNjc6DgfNrhNY+vdatkguCXos9Y9tL6y7IKsdBd62qNfQU64h++ZkrM+fKvsd6fxsYD+j49veuFlpljxcbs6FTbODKczxOtAzBCtsO1euPthTBpIufQKKrLL1VtZYvS5sKqyG+Kq+i+TmI6t/0s846C9u3b8eWLVsC/5SXl+Oaa64J/Fmn02H58uWB5+zduxeVlZWoqKgAAFRUVGD79u2or+/sivHVV1/BarVi3Lhx0UyHEEIIIX3kqKrZgXwfH2UL4OoQ+5/Em95ucNpBvfllfO31wxhDiyBv6R1sTZb/4D54N8t/KW2oOA3aotLAcZFicftxb+Qlgj2h7CiXrbXitsLzkKoxBcZ0nBYn9yLTFo1CxednYLKf9+NeedldcYj9b5QtrzuMjbMW110Fa3cd6vMliqjC5dTUVJxwwgmyMYvFgqysrMD4TTfdhLvvvhuZmZmwWq345S9/iYqKCkybNg0AcM4552DcuHG47rrr8Le//Q21tbW4//77cdtttwXN7hBCCCGkf0lMQqWifXWporVtvj5ddhxq88d409sNTjvkKDY6bXQ1w/XZh/Dt2gZOrweXYgVvtYJPTQNvTYNu1FhosnNDvFrs2UU3RCbJxpQ3skyS4PzgTdkYbzLDfN7FsjFl8NPot8EnCdDzfZN1aVYGbToLsnSpuK/0UrxUsxxOyYPLc2Z027o7Vky8Hlk6K5q6dPU75m0K/ELgmCL4CbX551hzMbScBgITVePxKiPId5zIba6BXuzzE8oTTzwBnucxf/58eL1ezJkzB88++2zgcY1Gg8WLF+OWW25BRUUFLBYLFixYgIceeijWUyGEEEJID9T721QbOSozPwV6eXODOn8rRCb1KIvSn3q7wWmHHF1n5iejxYXJSz6Byxb6torjeBhPPROm8y8GbzSFPC9WlAEEBw5pik0pvWu/g1BdJRsznTcPfIo8IFRe6/bMR7Oq3XMsSExCq6C8Ru3zGWrMwyNDr475e0aiWJ8pC36O/1jyyRhTtbku0gcPfky8HmPMRdjh7GwdruO0GGEMvuwjHgz6zE8wK1eulB0bjUY888wzeOaZZ0I+p6ysDJ9//nlv35oQQgghfeCoR571sWrMSNfIa/zzFTfEIpPQ6LchT5ERije93eC0Q47OCjCGcbtrcdbK/dALEhAmGGBMgvu75fBu+wGWS6+CfsJJfbovkrJ0LFVjku0jIzbWw/XZB7JztHmFME4/Q/VaRl6PbJ1VtpnrcW/fBD/BMlYZ3exP1B+KDVnY6jwSOO4o/WsU7PBIPtW5oZyUMlQW/Iw2FwbWDsWjYMFPomd+4vvXM4QQQgjpd0cVJW9lxhzVjXqqxqja16YmAUrfervBaYdsSY/zlu7GuV/tgc7fvkePpLhpD0Zqa4F94bOwv/wvCFVHwcS+2StFud6na4mY2NIE27P/gOSSB0jmS68EF6LTXJFijZcy2xEryqCNA9ft5qz9QRnQVP24Jk5Z8mbi9WGzibPSJwQyaRqOx+U5M2I809jK0qXKvv9UjSlkWV+iiN9QkxBCCCED4qhH0ezAkKM6h+M4FOgzcMBdExir8bViUl9PrpeU5WA9WTfCPB4Ynn8eYw/WycYFJsKcXwzDjDPAnA5ItlZIdhv8e3aCCfIyQt/OrfDt3ApOq4OmsBh8USnMLg+Eglzw+UXgzfIStWi1hCjvk9paYHv2HxBb5DfthhMnQz8qdOOpQkOmIvPRN3vrKMsS03vYkCLWlOuean2t8EsCjikagxQbssNm9Iy8Hn8d9lMccNeg2JAla+IQjzQcj58VnIP/1C4HA3B93hlxnamKRGLPnhBCCCExp8z8lBqDl7nk69NlwU+8Nz1gjKlurnuS+XF//QXE6mPQcBqIXRavt5VPQsEVt4LTyxs4iY31cL63CL69O9VzEvwQKg8DRw8hw+mEY8tagOPAp6RCk5MHTW4+dOMmQn/CJHBRbH6qzKBkaFMgOexoe+7/IDbWyx7TFpbAcvlPw76eMvNT3UeZH1Wzgx6uyYo15ToeBoZaf6uq810k62F0nCaumxwonZQyFP8c8T8DPY2YoeCHEEIIIQEO0SNb2A0Ez/wA6oXw8V72Zhc98DN5mVm0a37E1ha4Vy4F0H4TKzIRXr0GX80egykzTsNYvbpzrSY7F6k/vxO+zevh/PBtSI7uN2SUHHZIDjv8hw/As+57aPIKYJ59PvQnnaIqTWN+PyCKgMEQyDp0lL1xEoPF5UOxsxW2Ff8Hsa5G9lxtXiGst9wN3hy+tKxIcVNf42vpkwYX6uBn4EveAMCsMSBTmyKb3zFvE6pUba4zlU8lcYaCH0IIIYQEVCqaHWg5jWqfkw7KpgfxHvw0K5od9GQ9ifuLj9qDDQBajofEc3j7spPRmJOCIf7QQQ3HcTCcPBW6MSfAvXQxfNs3Q2xuDHm+klhXA/uil6H58hPoT5oCZmuD2NQAsbEBUlvLj+/BgzOZwJnMmCLWYpLbiVS7BxqRIVu/A4KixEqTkwfrrb9WdXcLRln2JTIJ9f42VQDcW81+ZSvy+Mj8AO0BYNfgp8rbFOj61qE4RKc3Ej8o+CGEEEJIgLLkrdiQJesS1lWh4sa3yW+HXxLidk1Abzc4FY5XwrN+deBYy2mwfVwBGnPab9AbwwQ/HXizBZaLr4Dl4isgOR0Qjh2FUHUE/qOHIezcDjAJAAv5fLGpAe5lwTvmMiaBuZyAy4lUT7OsAYNW0eNKk5kN6y2/Bm9NU75MUKkaE1I1JthFd2Cs2tsc8+CnNUi5XrwoNmRhu/No4Hir44iqJbwyQ0biT3z+7UQIIYSQAaFscx2q5A2Aqq01A0Odvy1u9wFRNzuIvOSNMQbnx++ia2CiMZqwZtrQwHG9vy2q+fCWFOhHj4d+9HgIgoAd69ahrLwcfFsLxPpaiPW18G5aC6H6WFSvC6g7z2m6BLB8Wgast94DTUZ0JVpFhkzscR0PHB/3NWMyhkc5t/BU1yjOgp+uDnlqZcdm3hBX8yXBUfBDCCGEkIBImx0A7W19M7QpsrbK1b7muA1+lJmfaG5U/Xt2wL9/t2xMOmMWXObO76vBbwNjrFf793AaDTS5+dDktm98aTxzDvw7t8K1dDGEqiMRvYZynxwA0OoM0GTmQFtcAvPc+dBkRb9XS5E+Sx789EHHt1h04+sr3ZW0FRmy+nTvJhIbFPwQQgghBEB7q2blviVlhvAbWebr02XBT62vtS+mFhM93eCUiSJcH78rG+PTMmA98zyg8r+BMa/kh130wKqNXftijuOgP2ESdONPhH/fLng3rQOz28BnZkGTlQM+OxearGxwRjOY2wXmdqGqtQpLji6FoOPRZjXBmWbB85N+DT6KbnHBFBrkJW7KTme95ZMEOEWPbCyeyt6U656UEn3/m8GCgh9CCCGEAGhvWCB0ad0MtG9wGk6BPgO7XZ1lWdUxviGOpZ5mfrzrV0Goq5aNmc+/GDpzJjhwYF1K4Rr8bTENfjpwHBcoketOg02DnSkFgeMcXVqvAx9A3e75uK+515murpQbswLxVfZm0RhVmc6uuguOSHwY+F2jCCGEEBIXKhWbm2ZpU5GiMYZ9jrLjWzxnfpQlVZFkfpjHA9fnH8nGtIUlMJRXQMPxyNZZZY81RND0oK8pmwZE29EuFOXNvUfyqb7T3lC+lpHXw6xRtw4fSOECnGJD9KWEpP9R8EMIIYQQAECdv1V2HKrFteycBGl33dMNTt3fLlPty2Oe95PAhqM5quAnuqYHfUHZ0jtW2ZMsbSoMvE42FsvNTpXXJ1ZBWyyFW/dDba4TAwU/hBBCCAEA1PvkN+55uu7bICszPzbRBZfojem8YqEnG5xKLifcX38pG9OPOQH6UeMCx8rgpz4OMj8tynbRMWoawHEcCvXygDiW636U5WTxVPLWIVQrazNviJsNWUl4FPwQQgghBIC6VXOuvvvgJ1efBg7yNR/xmP0JtsFpmtYc9jnur78E88oX4JsvuFR2rMr8+AY+86MOImJ3U64s+zoey8xPHHd66xCqk2ExdXpLGBT8EEIIIQSAOmuRq0vv9jk6TqMKAGrjMPhRNjtI11pCbt4KAJKtDZ5vl8nGDCdNgbaoVDamDBAbFe8zEFr8fbdRqDrzE7t218qgLZ46vXUIFfyU0HqfhEHBDyGEEBIlxhg22g/g/YY1qFRsCpqo/ExEs+LGPS+CzA/Q3vGtq5o4bHoQbbMD97LPwPz+wDHH8TCfe7HqPHXDgzYwxlTn9ae+DCKUwU91DANd5ZqfeCx7S9EYkRYkk0ad3hIHBT+EEEJIlFa27cQ/jn2C9xrX4MGj78RlpiNajX6brGUzAORGsOYHUAc/8fh9RNPmWmxuhGf1N7IxwynTocnNU52r/I58TIBNdPVipuE1+e2qbm5deSQfXJJ8zVUsy8eUN/ltglO1N09PqdYqxWHwAwRvbBCvG/sSNQp+CCGEkCh93bo98GeX5MUnTRsHcDaxoWx2YNEYI24zrGx6EI+Zn2g2OHUv+RRM7NzviNNoYZpzYdBzM7QWaDj57VRfNT34tGkjfnngJdyy/wV81Lg+6Dl1QdYcxbJrWr4+Hbzi9jEWTQ8YY+q1SnG45gcInuWh4CdxUPBDCCGERIExptrI8/u23bAJfffb/v6gbHaQF8F6nw4FBmXw0zzgpV9KkZZUifW18G5YIxszTp8JTUbwm1ue45Gt7fu9ftySD+81rAlk595u+B5HPPWq85a1bJUdWzVmmHh9zOah5TTI16fLxqJpd80Yw7sNq/HbQ6/h1bqVEJkEALCLbtUGu/FY9gYAJYpAx6IxIl1Dnd4SBQU/hBBCSBTsokdVVuRnApZ3yQYlImXmJ1dvDXGmmrLszS35+rT0qyeUmZ/MEJkf15cfg/14Qw4AnE4P09lzw752jr7vO75Ve5vhY37Z2Fv138uOm/0OrGzbKRubkTYm5nNRdXyLIvPzvW03Pmhci0pvA75s/gEfN7VnsJRrsiLpxjdQxltKZR0OJ1hKqdNbAqHghxBCCIlCqPUsS1u2wK/4zXUiUbW5jnC9DwBkaVOg47SysVguhO+OTXDBEWbdSbANTrODbHDqP3wA3s0bZGPGmbPBp4YPBNVND2Kf+QnWPnyr8wh2OasCx581b5JlT7ScBhdklsd8LkWKNS/HfZF3fFvZKg/OljS3/3ujXO9j1ZrDduMbSAX6DPxPwWwUG7JwUspQXJN7+kBPiURB2/0phBBCCOkQag+bVsGJ9bZ9mJE2tp9nFBt1inU60QQ/PMcjT5+GY13aHtf5WjHWXByr6YX0XsMavN+4BjpOi6tzT8O5mSepzgm2walyPYnkccPx+kuyMc5ogunMOd3OQfld9UXwEyqYfLPhezxkvhIOyYNlLdtkj81MG98n62YKFWWOkQa6NsGF3a5j8jHRhQ32A3ArNsaN15K3DrPSJ2BW+oSBngbpAcr8EEIIIVGoDbOY//PmzXG31iUSjLEebXDalbL0rT8yP22CEx80rgXQXnr4at0KVQAAAEe98rUxHDhVEwDne4sgNjfKxsxnzwVv7n4th3KfI+V3GQs1IdbVHHDXYJPjIL5o3iwri+PA4cKs2Gd9AKBA0e663tcWUdZzo+OgqqMg0L5OKVE6vZHER5kfQgghJAqhMj8AcMhTi/3uGowyF/bjjHrPIXnglnyysWgaHgDqjm/90e66ytukupn+T+1ymDUGTLeOBgAcdNfiqeOfyc5RbnDq3bQO3k1rZefoho6AcebZEc1DGfw0+e2QmASei93vmGu8ob/PN+u/V7W/npE2BnmKxgSxUqi41gwMdb7WbjuerbftDzq+23UMQpd1VkD8dnojiY8yP4QQQkgUurup/7z5h36aSewomx1w4LrdBFRJeUMcLkiMlcYg5WUMDM9Wf4EtjsPY767GI5XvqfahOSllaODPYlMjHO++JnucMxiRcs3/gNNEtuYkR1H25mcC2mLY8EFiUtjvs9rXrGrCMS/rlJi9v5JZY4BVI29G0N2/F07Rgx2uypCP73dXy47jveyNJC4KfgghhJAIMcZUe9h0vZEGgPX2/X2y5qMv1SnKtHJ0VtXeNd1RZhnqfG2QFL/Nj7VGxcalHUQm4Yljn+LRyg9UGa2x5mJcmzcTAMBEEY7X/w3mlQdHKZdfB01WdsTzSA+yOL/BF7ufgWbBAZ9izZIy4OrqlNSRfb7vjLLjW3fB7g+OQ4G21pHIiOHeRIR0RcEPIYQQEqH2m1B5u+Grc0+Hscs+KgwMS1u29PPMekfd5jq69T6AOvPjZwKaFO2LYy1Y5qeDjwnwKAKfCZYy/LbkEph4PZjHA+dHb8F/5KDsHEN5BQwnT41qHjzHB+n4Frt1P8r1U0ZejxvyZ4U8vy+zPh2UZY7dtbtebz8gOx5qzJO1i1aizA/pKxT8EEIIIRFSdkTTczoU6jNweto42fjXLdtVGYd41psNTjsE20yzr0vflPNWlmJ1NSllKO4tngedywPXFx+j5S+/hef7FbJzNFk5SJl/TY/mom56ELvMj3JT3UJ9JiZZhgTtpjfRMgTDTHkxe+9QlA0uwl1rt+TDVscR2dg5GZNwUsqwkM8JtQ8TIb1FwQ8hhBASIeVv4PP16eA5XtVe2SV5sVHxm+54FovMD8dxqi5g4Rbpx4Ky7O2neTNxcpAb6skpw3GHcRp8H72Llod+A9fSTyG55A0COI5HynU3gzMaezQXZbvrWHZ8UwYWBfoMcByHK3NOVZ17cXbfZ32A6NZ4bXUckbUa58GjPHU4zsk4MeRzqOyN9BXq9kYIIYRESJn5yf9xnUuBPgMnWoZgq/NI4LF97hqcpsgIxavebHDaVYE+HYc8tYHjWl8L/EcPQzi4F+A4cJYU8JZUcCkp4FOs4DOzwHGhS5/CkZiEJkXwk6fPwB1FF+Dvxz7GdudRcBLDhQ0pOO/bQ3DsWQwEabPcjoP5kiuhKwudiehOniJg7K4MLBrVijbXHfvsjDIX4pyMSYEyy1PTxvbL3krtc5AHunbRDYfoQYpGHTyut8u7vI23lCBFY8QESynydOmo87fKHtdzOph5Q8znTAhAwQ8hhBASMWXmp2vpz3hLiSz4OeiuRSIQmKhaO9PT4KfrOhCTy4f85YvRdjB0BkSTkw/zuRdBP6kcHB9dMUqL4IQE+QL6HF0q9LwWv8meg8PbP4Rx/UaYbE4IIV4DAPTjT4TprPOgGzoiqvdXKjHIGyRUeRtj1u5amUHr+nO3IO8MzLCOgQgJo0z912I9R2cFD152DWp8zRipmINfErDZcVg2dkrqSADta6XOypiIN+q/lT2eqUvpcVBMSHco+CGEEEIipM78dN6EDjfmyx476mmATxKg5+P7P7VNfrtqr5yelL0B7ZkfMIbR++sxa+V+pHokwBi6a5rYUAv7ay9Cu+JLmOfOh270uIhvepUd9XScFqkwwLNqBVxffIwMZ+hmCxzPQ3/yVJhmnQttQVFE79cdZfDjkXxo9Nsj/i4b/G3YxzdgnOhGhrZzvYtH8qFJkGe4ugY/PMcPyL5SWk6DPH2arNyt2tuiCn62OY/KGk9w4FCeOjxwfEb6eLzbsFpWFkclb6QvxfffyIQQQkickJikKs8p6NLeeaipvXtVRyAhQcIRT33cb3iqbHNt5g1BS5cike/hMW/xdgw/1AQAENDe/S5cVy8AEI5VwvbCE9CNGAPdqLHgjCbwJhM4ownQ6wGfD8znBfO2/wNJhMPsgZXzwJZqADgOE4+5YfvoIQh11SHfhzOaYJw6A8bTZ0OTGXkr60hkalNg0RhlewpVeRu7DX4a/Ta817AG37TuhEPrwHeHj+Gvw38aaKBQqwi4AXWzgYFSoM+UBT/B1v0oS95GmwuR3iW4SdWYUGEdhW/bdgXG+mpzVkIACn4IIYSQiDT67RCYKBvrehNq4vUoMmThmLcxMHbAUxP3wU8smh0AgNjcCOszz2N4fZNsXJBEGCxWaIrLwJwOSE4HmNMBJvhVr+E/sAf+A3sier90wYn/8TvgMWjhSDGgpNUPIURQoC0qhfHUM2E4+RRw+r5ZS8JxHEoN2djtOhYYq/Q2YnKXLEdXdtGNjxvXY2nLVviZEAiaXZIXnzSux00FswGoSy2zdFYYeF2ffIZoFeoz0HVLX2XwIzARmxyHZGMdJW9dzc+uwBbHEdhEF7ScBmenh26EQEhvUfBDCCGEREB5Y2fmDUjVmGRjw415suDnoLuuX+bWG8psVk/W+zDG4Hz7v+DsdvAcL9vc1DF2JPKuug18WrrsfN+2H+D67EOIDT1bG9URiBq9AoxeAVqtSXWO/oRJMM0+H9rSof2yhqREEfxUdflZ6MAYwxctP+D9hrVwSd6gr7PefgDX58+ChuNRo2pzHR9ZHwAoMMjnomzMsMtZJcuEAcGDn1x9Gh4deg32u2sw0lSALGpzTfoQBT+EEEJIBJTBT/6P7Ya7GmEqwDdtOwPHidD0IBaZH+/6VfDtay9b0nMaeJgEl1mPr88YifLp52Bkl8AHaM+SGE6cDP0Jk+DduAauLz6G1BZdW2xlFk7LaTr/XFgCy8VXQDdyTNSfpTeU634qPergZ2XbTrxW903Y17GJLuxyVWGCpSxom+t4oZxLna9N1uRBmfUZZswPGdhk6VIp6CH9goIfQgghJAK1EdyEjjDJmx7U+VthE9ywBslKxAtl44BoNziV2lrh+vidwLGW08KewuP1q8rhNutRrMgsdcVpNDBOPRWGk6fCu2E1/If2g3ncYG7Xj//vBvP7wOkN7f8YDIDeAEginIc2gnehy/tqwKdaYZ57KQxTpkfdPS4WShXBT7WvGX5JgK5L04s1tr2q5/HgYdTo4ETn3kNrbfswwVKmKnsrVOylNJCUWSg/E2RNHnY4K2WPl4coASSkP1HwQwghhESgJsQeP12VGLKh57TwdelcdchTi0kpQ/t4dj2n2uMniswPYwyO9xZBcndGIXpeg2WzRsBt1gMIv/llB06ng3H6TBinz4z4fZ/d8zQMdidyGhzIbnLgoqKZyJgxt8eblMZCiaKzHQPDcV8zhhhzAQAik7DfXSM7Z3LKcFyXNxNrWvfiZduSwPh6+wHckD9LnfkxxE/mx6oxw8wbZOV71b5m5OrT0OS3q8rgJlrK+nuKhKj0/69FCCGEkAQUSeZHw/EYasyTjR2I49I3h+hRrcnIi2LNj2/rJvh2bJaNCZMm4fDQziAgWLey3rKJLvggwp5qxKFh2Vg/ZQjSTz9nQAMfoL3pRY7i++u67qfK2yhr+wwANxfMRp4+HVNT5fsMOUQ3vm/bozo/ntb8cByn2uy0I1hTZn3MvAFDfwwCCRlIFPwQQggh3fAzUVUeFizzAwDDFaVvBxS/6Y8nDYqsDwcu4nUXktMB5/tvyMb4lFRYLrlSNtYiOOBW3MD3VoNfvu8NDz5u9oYJt+5nr0vehjtPn460H+edo0tDAbPKHv+oaZ3sWM9pkalNieV0e03570FHmd4Olzz4OcFSGpMNXwnpLfopJIQQQrpR72tTbQQaauG5Mvg56KkDYyzouQNN2ewgW2eVNQ4Ix/nhW5Ac8oDQcslVyEsvUu3ro9wctrcaFYFoli41bm6slet+Krtkfva6j8seG63YEHSsKM8aKr+3An1G3HzODso1SDXeFjDGsN2pDn4IiQfx9W8QIYQQEoeUJW9WjRlmTfD9YkYY5cGPQ3Sr1tXEC9V6nwhL3nz7dsG7aa1sTD/+ROhPmgIdr1Vlj5SL9ntLmYXLjqMuYaWKdT8dZW+MMex1KYIfc5HseIwUviwsntb7dFCW4dX4mnHM14Q2wSkbp+CHxAsKfgghhJBuRNNuOEdnhVVjlo3F67qfuh60uWaSJOvuBgCc0YSUn1wbaP2t/H6UwWNvKTM/OTpriDP7n7LsrUVwwC660SjY0Sw4ZI+NUmR+rDBipLEg5GsXxFGntw7Ka90sOLDRflA2lq2zIj/KLoKE9BUKfgghhJBuKBfth1rvA7QvAh9ukpcvHfTEZ/DTk8yPd+MaCNXHZGOWCy8Dn9Z5E6y8IY6k41s0lMFPdhwFPwX6DOg4eTPdKk8j9imyPikaE4qCBDNTg2wC2qEojpoddMjXZ6jKHJe3bJMdn2Ap7ZdNZgmJBAU/hBBCSDeULXu722hyhEn+2/t4zfxEu8Ep83nh+uxD2Zg2vwiGaafJxvJVmZ/Wnk8yCGXZWzxlfjQcjyJFB7RKbyP2uOXNDkaZCoMGBKekjFQFEx3iMfOj57Wq4LNJkDekmEAtrkkcoeCHEEII6YZy4bny5l5puGLdzxFPPQQmxnpavSIyKcgGp+GDH/c3yyDZWmVj5osuU20oWqDsAOZtjmnTh3jO/ADq0rcqbyP2KTq9jTbLS946ZOpSQj4Wj2t+gO5/GTDeXNxPMyGkexT8EEIIIWF4JJ9qrYby5l5J2fHNzwTZfi/xoMlvhwRJNhYu8yPZbXAv/0I2phs5FroxJ6jOVd4MuyQv7Ir9hHrKKXpUrbPjKfMDqIOfva5q1fVXdnrralrqaNVYhjYFJl4fmwnGWLh/H8oMOYF23oTEAwp+CCGEkDCClWx1l/lJ0RhV5/Rn6dsWx2E8dPQdPHlsMXa7jqkel5iElW07ZGNGXo8UPvQmoa4vPwHzdg1gOFjm/SRo6Va2zgqNoiVzrJoeKLNVHDhkxlG3N0Dd7vq4r0nWKl3LaTBMsRluV1Ot6tK37rIrAylcOR6VvJF4Q8EPIYQQEoYy+MnSpkLPa4Of3IWy9O1gPwU/NsGNp45/ht2uY1hn34eHjr6Dv1V9iEpPA4D2dT4PV76HDxvlG2jm6dJCLkoX6mrgXfOtbMwwpQLaouDtizUcjzxFd6+eND3Y767B8pZtsrJDZclbhtYCXYR7E/UXZbtrpWHGPOjC/Aylay0Yo2iDrWwpHU8Kw5TjUYtrEm+6/9ubEEIIGcRUba4jXHcx3JSPVbbdgeP+6vi201UJj6IsbLPjMLY4jqA8dTi2O9WPA0B56oiQr+n69D0w1lkix2l1MJ9/Sdh55OszZI0iom168EXzZrxWtxIMDFaNGQ8MuQIF+gw0+OWL6bPirOQNANI1FqRoTHCI7qCPjwqxpqeri7KmyLJ2Z6SrywvjhXKj0w5aThNy/RIhA4UyP4QQQkgYyuAn0v1KRijW/Rz3NsMlemM1rZCqvE1BxxkYNtgPqAIfDhwuyCrHpdlTgz7Pt3s7fDu3ysZMZ5wDTXr4IFCZqVB2zAtni+NwIPABAJvowhv13wGI7z1+OnAchxJDVsjHx5iKQj7WYVLKUPym5BLMzZyMP5VdrlpHFk8ytBboOZ1qfKSpAMY4XadEBi/K/BBCCCFhHPfKb9oLw9zUdjXEkAMNx0P8MWPCwHDIU9fnZUDRNFbI0aXhlsI5GBuiG5fY0gzH6y/JxviUVBjPOrfb11buhRRp5ueYtwlPH/9MtkYGADbaD2C/uzohgh+gfaF/sPVWQGSZHwA4KWUoTkoZGstp9Qme41GgT8dRb4NsnNb7kHhEmR9CCCEkBIlJOKbIpBQbIttrRcdrUWbIlY0d8tTFbG6hKIOfCZayoL99n5k2Ho8PvTZk4MNEAY7/vgDJ5ZSNm8+7GLzR1O08lAv0a32tkJgU4ux2DtGDv1d9rOrm1uHN+u9VDQ/irc11h5IQ634K9ZlI1XT//SWaYOWgFPyQeESZH0IIISSERr8dPuaXjRXpI8v8AMAwYy4OdVnrc8RTH7O5BeORfKqNS6/MORVZuhR81Lgeq217kaY147KcCpySOjLsa7k+fR/+IwdlY/oTToKh4vSI5qLsdudjfrQITmSF6MwmMBFPHV+MOn9ryNfc7Tqm6oKWHWed3joo2113GG3uvuQtESk7vpl5A4YZc0OcTcjAoeCHEEIICeG4Yp2KmTcgI4o9S8oUN399Hfwc9zbLysU4cCgyZMLA67Ag/0z8NO+MkB3duvJu+wHub76SjWmycpBy1Q0RPR9oXwdi4HXwSp3BY42vJWTw81rdN9jhrJSNDTXmwSa40CR0NjlQlsPFa+an2JAFDpxqvqPC7O+TyMYoPtdJKcPAc1RgROIP/VQSQgghIShL3ooMWRHf/APAEEXwU+trDdppLVaUJW95+nQY+M6F6JHMXWxqgOPNhbIxTqNF6oJfgDebI54Lx3FBSt+Ct7teZ9uPpS1bZGPpWgt+XXwRLsupCPs+8brmx8TrkatTbxo7Jkm7n02wlOGcjEkw8DqMMBXgytwZAz0lQoKKKvh57rnnMHHiRFitVlitVlRUVOCLLzp3e/Z4PLjtttuQlZWFlJQUzJ8/H3V18vrmyspKzJ07F2azGbm5ubj33nshCEJsPg0hhBASQ8dV630iL3kDgJIff/vfgYGh0hN5Q4JoKTu9hes4psQkCd6tm2B7/gkwj7xFs+WSK6EtiX79hjL4CdWJThn46Dgtfl08D1m6VJyWNi5kK2WrxiwL7uKNct2PVWNW7X+ULDiOww35s/DK6F/iL0OuituMHCFRBT/FxcV4/PHHsWnTJmzcuBGzZs3CvHnzsHPnTgDAXXfdhU8//RTvvvsuvvnmG1RXV+PSSy8NPF8URcydOxc+nw+rV6/Gq6++ildeeQV/+tOfYvupCCGE9Du35MM7DavwTPUX2OeqHujpxERPmx10MPA6FCmeo+yIFUvKzE+odSdddQQ9bX9/EPZXnoPYKC/NM5w8FYbpM3s0H+X7B2v4IDFJNX5d3sxAq3ANx+PynOBZhHi/wR5iyJEdjzEXRZU5JITEXlRrfi688ELZ8SOPPILnnnsOa9euRXFxMV5++WW88cYbmDVrFgBg4cKFGDt2LNauXYtp06Zh6dKl2LVrF5YtW4a8vDxMmjQJf/nLX/Db3/4WDzzwAPR66gVPCCGJ6q367wO/wV/Vtgd/KL0M4y0lAzupXmCMqdb8FEfR7KBDmSFHFkQd9cRH8MMkCb7tP8C95FMINceDnqPJzUfKT67r8Q27cq+jI556+JkIHacJjFX7WlSlgOWpw2XHp6SOwDBjvqx5BBC/zQ46nJF+Ar5o2Qyn6IGG4zE3c/JAT4mQQa/Ha35EUcRbb70Fp9OJiooKbNq0CX6/H7Nnzw6cM2bMGJSWlmLNmjUAgDVr1mDChAnIy8sLnDNnzhzYbLZA9ogQQkjiYYxhrW1f5zEYnjj+Keoi3NslHjUJdtVNeVGUZW+AuunB4T5qemAX3WgV5G2pgwU/7ZmejWj73wdhf+X5kIGPtrAY1p/dAc5o7PGchhnzZMcCE1GpCP6UWZ9MbQoytCmyMY7jcFXuqarXj9f1Ph2ydKn4+7AFuKPoAvx92IKI9/chhPSdqLu9bd++HRUVFfB4PEhJScGHH36IcePGYcuWLdDr9UhPT5edn5eXh9ra9t/U1NbWygKfjsc7HgvF6/XC6+3cFdtma+/xLwgCrRfqR6IogjEGURQHeiokxujaJqf+vK4tggNtihtvh+DGXys/xIMlV8CkSbzM/lFXPRjr7NRl4vWwwhj1f3dKdJmy16n0NMDr90HTw05Yoa7rEVed7H10nBbZfEpgvkyS4N++GZ6vFkOqDV2WyOcVwHj2BdBNPBmM53v131kDtMjXpaOmS6ODvc7jKNN1BmUHnNWyeQ8x5AZ9zzGGQow3lWCHq7Mj3BB9TtzfB6TAgHLzMAAIO1f6ezh50bXte9H8PRB18DN69Ghs2bIFbW1teO+997BgwQJ888030b5MVB577DE8+OCDqvGNGzfCYom85SjpHUmSYLfbsX79evA8NQpMJnRtk1N/XteDXCOcOqdqfL/TiT82vYL5wkTwSKy1Duv5Sji1nZ8pTdJi/fr1Ub+OCz449fLv5ssNK5DNUkI8I7xQ13UTf0w231yWio3rNwAA9PU1SF/3LXRNobNOQnombCeeAveQ4YBXBDZs6NH8lIxaEU6+c17fHfwBaYIncLxOu1P2OGxOrKtaF/S1ypGJw7pjqOfsGC3lgrU0Yx2Cn5to6O/h5EXXtu85ner//oQSdfCj1+sxYsQIAMDkyZOxYcMGPPXUU7jiiivg8/nQ2toqy/7U1dUhP7+95jc/P1/1H46ObnAd5wRz33334e677w4c22w2lJSUoLy8HFZrfKe8k4koitiwYQOmTJkCjUbT/RNIwqBrm5z687rWNm2ApSn4L6Nq4caRDAFX5ajLluLZtjo7LG2dn+lE6yhMzZ/ao9f68NA+NAuOwHF6fhGmWsf06LVCXdeddU7ZfCdZR2GKeQzci9+H/4cfA4QgvzDk8wpgPOdC6CacBK4PbszaWo04XL8ycOzVazF1SPv3KDARzx/YCAvrnNfsommYYAndWW42ZsIvCdDxybVVIf09nLzo2va9jqqwSPT6bw5JkuD1ejF58mTodDosX74c8+fPBwDs3bsXlZWVqKho79FfUVGBRx55BPX19cjNba+B/uqrr2C1WjFu3LiQ72EwGGAwGNST12qh1SbXX37xjuM4aDQa+t6TEF3b5NRf17XS3xh2UfznrT+gzJyL09NC/10fb6r9LbLPVGrK6fH3ONSUjxbHwfYDxtBycAf89mNgHjeYxw3J4wFzu8HpdNCPmwD9ieXgdKFbOCuvKxMENDUfQ6bNBb1fhN4notx1BPa1X4B5PUCQa6PNL4JpzoXQTzy5T4KeDqMt8g5nNf4WeDkBFo0RxzzNECDKHh+ZUgStJvz3rE3SPdrp7+HkRde2b0XzvUZ1Be677z6cd955KC0thd1uxxtvvIGVK1diyZIlSEtLw0033YS7774bmZmZsFqt+OUvf4mKigpMmzYNAHDOOedg3LhxuO666/C3v/0NtbW1uP/++3HbbbcFDW4IIYQkBmUHs5lp4/Fd225IkAJjL9Usw0RLGdK18V+uzBjr9R4/XZUZc/CD4yBS7R7M/novhlethlOxB04H7+b14D9+F8YZZ8A4fSZ4a+dGmczvg9jYAGPlIXjsTWD1NRCrj0NoqMXZrlrZ2pk8fTqYRv3fVk1OPsxzL4Z+Qt8GPR1KjTnQchoIrHO9wyFPHSZYynDQLV/vm6tLQ4qm5w0WCCGkO1EFP/X19fjpT3+KmpoapKWlYeLEiViyZAnOPvtsAMATTzwBnucxf/58eL1ezJkzB88++2zg+RqNBosXL8Ytt9yCiooKWCwWLFiwAA899FBsPxUhhJB+4xK9qPO3ysbOzTwJI00FeKl2WWDMzwRstB/E7IyJ/TzD6DULDrgVnd56FfzosnDituM4bdVB6H0ifN00O5AcNriWfAL3ss+gHTYKzOWE1NoMyekAGEOW0wmPxRLI6AhMlAU+AGBQlIVxBiPM586D8bQzwXWTWYklHafBEGMuDrhrAmMH3LWYYClTdXobbgpdAk8IIbEQ1d9+L7/8ctjHjUYjnnnmGTzzzDMhzykrK8Pnn38ezdsSQgiJY8pNO3nwKNJnYogxF5sdh7Gpo9wLwAF3TUIEP8qsj5HXI0vbsz1lxPo6lL35Ls7a3dkKXGISRCZCw4Wv/2eiCP/+3d2+h0+SdzriOU722sZTZsB8wXzwqQOzTna4MU8V/ADqNtfK1tiEEBJrVHhICCGkV5QlbyWGrMBi9BMspbLgZ3+XG+B4dswnD36K9Jk92ujTf2AvbC8+Ba3fB57jIHXJzrjNRmSNOhGcyQTOYARnNMF/YC/8B/dG/z6sM/gRtDx0BjM06dnQFJXAdNb50JUNjfo1Y2mEqQBLftwAF2gPgn2SgEqPfFNWyvwQQvoaBT+EEEJ65Yhi084yY07gzyNNBbLHqn3NcIieuF/XcdzbLDvuScmb2NoC+6vPg/nby+f0nA4e1v7n7eMLkDnvclxceqbqeUJ1FTzffQ3vxrVggj/oazO9AZqhI6ErKoG2oAhfao/ga20tvAYtJA2Ps9InorxgdtDnDgRlUGMTXfjBcUi2JowDhyGKDWEJISTWKPghhBDSK8qyt643sKXGHOg4rSwzcdBdixNThvTX9HrkmKLsrciQGdXzmSDA8erzkBz2wJie16IuhcdXZ41GZWkmpvD2oM/VFpYg5YoFMF8wH75tP0BqawWflg4+PQNCmhXfsuPYXnUYN066ECnG9jK2HYdeg9vbuZFsiSE76GsPlHxdOiwaI5xi5/4+S7tkggCgUJ8JE594m+ESQhILBT+EEEJ6zM9EVHnlpUtdg5+Oxe773dWBsf3umrgOfhhjQYKf6DI/rk/ehf/IQdmYNGo0/jszBX59+396jyjKBZV4SwqMFacHjut8rfjHsU9Q6WmAU+PE/qOL8Ieyy1BsyMJxnzxTVWqMr+CH4zgMN+Zjm/NIYGy365jsnGEmWu9DCOl7tM0sIYSQHjvubYLIJNlYqSLroCx9OxDn635aRSdcklc2VqyPPPjx/rAe7u+Wy8Y0mdkwXXtjIPABgAZ/mywTEs5251H84cgbskDTJrrw0NF38F3bblkbaaB3nen6SnfreYYbab0PIaTvUfBDCCGkx5TZixxdGiyK9Tyq4MdTC0kRMMUTZdZHz+mQrYus05tQWw3H26/KxjitDqk33IqijBJoFd3dlM0ilBhj+KxpEx6r/CBooOSSvHihZolsLF1rQarGFNF8+9OIboKfYSZa70MI6XsU/BBCCOkx9XqfHNU5yuDHKXpQ62vty2n1irLZQZEhE3w3+/IAAPN4YH/lOTCfPGtkmX81tMWl0HEaVUbmiFfeLKIrnyTg2Zov8Xr9N2BgIc9Tirf1Ph2Gh2ljzYPHEAMFP4SQvkfBDyGEkB47quj0FqxbV6Y2BRnaFNlYPLe8VmZ+Ii0hc376HsQ6+ecynjIDxmmnBY7LDPLgMNy6n3/XfoXv29R7/ExOGY5hUug5xWvwk6a1IEeXFvSxUmN2oD06IYT0JQp+CCGE9AhjTN3m2qDO/HAcl1Drfo77og9+hOOV8K75VjamLSyB5bJrZGPK4DBU2ZtfErC6Tb3fz2XZFbij4HzMFybilJSRQZ8br8EPELr0jTY3JYT0Fwp+CCGE9Ei9vw1uyScbC7VPi/Km94Cnts/m1RuMMVQpO73pw7e5ZozB+eFbYF3WMXE6HVJvuAWcTt66Wfn9HPM2wS8JUDrua5btgQMAvy6+CPNzKsBzPDTgcXvBuZiZNl713KFxvFdOqKYGFPwQQvoLBT+EENLPGGPY4jiMla07VcFDIlGWbKVoTMhUlLd1GKHI/Bz1NMATh5/dJrpUjQW6y/z4tmyE/+A+2ZjprPOhyVYHIcpOeBIkVbAFQNU+PFtnRXnqCNkYz/H4WcHZuCCzHBw4AMApqSNlm8zGm1Ad37rrBEcIIbFCBbaEENKPJCbh+Zql+K5tFwDg8+ZNeHzotREtqI83wZodcBwX9Nxhxjxw4AIL9xkYDnvqMdZc3OfzjMYxRbMDPadFjs4a8nzm88L1yTuyMU1mNkyz5gQ936wxIE+fjrouDR8Oe+pUe9xUKoIfZdDUged4XJN3Os5MPwFOyYsRcd4ueqgxV/ZzAAA6ThuXrbkJIcmJgh9CCOknjDG8WrcyEPgA7b/h3+mqwgRL2QDOrGdUzQ7CdOsy8DqUGXNka4QOuGsGNPgRmIiD7lrsc9egzteKOn+rKgtT2E2nN/fyLyG2tsjGzBf9RFXu1tVQY64i+FF3fKvyyIOf7tbxFBrCl+bFCwOvQ6khWxY4lxlzVC3ACSGkr1DwQwgh/eS9xjVY2rJFNb7fXZOQwY+q2UE35VYjTQWy5/R3xzfGGGp8LdjurMQ251HsclV1W3pXFGZzU7G5Ee6vv5SN6UaMgX7iyWFfc5gxD2ttnWVyQYMfb3TBTyIZaS6UBT/KkkhCCOlLFPwQQkg/+KL5B3zQuDboYwfddf08m96zCW40Cw7ZWKhmBx1GmgrwVcvWwPE+dw0YYyFL5WLJLwl44vin2Ow4HNXzxltKQj7m+uRdMMEfOOY4HpZLruz28ww1KkvcGuBnInQ/Zj8cokf13ZYakyf4OS/jJKxq2w235IOZN+CcjBMHekqEkEGEgh9CCOlj37btwn/rVoZ8/EA/BgGxclSxOaeO06JAnxH2Ocrf8LcJTjQJdmSHWVMTK9+07Yoq8OHAYbp1NE5LGxv0cd+enfBu3SQbM8yYCW1h92V8ym5sAhNxzNsYCIqUWR8Nx3f73SaSQkMm/m/4DTjkqcNwYx7StJaBnhIhZBCh4IcQQvrQFsdhPF+9JOw5NtGFRsEedmF9vFGWvJUasqHppmlDvi4dFo1R1k1tv7umX4Kf3a5jIR8rMWRjmDEPefp05OvTkadr/3+zxhD0fP/hA7C/8pxsjDdbYD7v4ojmYtEYVU0PDrrrQgY/hfrMpFsTk6614OSUYQM9DULIIETBDyGE9BHGGBbWfi3rbAUAV+ScisXNG2VBwEF3bUIFP8p1Kt2VvAGdm51u6ZKB2e+uQYV1dMznp3TIIy8tnGApw2lpYzHBUob0KDIP/qOHYXvhSTCvvB22+fyLwZsjf51hxjxZ8NM1mFQ2OwjV6Y0QQkj0Eq+3KiGEJIj97hrU+9tkYxdklWNe1hRVS+KD7vjc9DMYxhj2uo7LxiIJfoD2dT9dHeiHz+0Svaj1yTuyXZEzA6eljYsq8BGqjsD2/P+pAh/9CZNgqJgZ1ZyU634Odtn0VdnmOpmaHRBCyECj4IcQQvrIGtte2XGBPgNX55wGjuNUmzoe8PRv57PeqPe3qRbkjzEXRfRc5bqfI556+CUhZnMLRrkfEQ8+6myKcLwStuefAPO4ZeP6sROQ+tOfg+Oj+8/pcEXwU+VthF8SwBjDMUW77ZIkanZACCEDjcreCCGkD0hMwhr7PtnYdOuYQFMDZfBzyF0PkUndrpuJB7sU62esGjOK9JHtMzPCmC/b5NLPBBzxNqgyQrF0WFHyVmLIgo7v/j9/jDEIRw7Cu34VvD+sB/N5ZY/rx4xH6g23gtPpop6TMlMmMgmV3kakac1wSfL3obI3QgiJHQp+CCGkD+xxHUeb4JSNTbOOCvxZWfbmY35UeRsjLh8bSLtcVbLjsebiiDvVmTUGFBkyZdmN/e7qPg5+olufJLW1wLNhNbzrV0NsCN6GXDdyLFJvvK1HgQ/Q/j3k6zNk5XiHPfXI1KXIzjPxemRpU3v0HoQQQtQo+CGEkD6wWlHyVmbIQbGhc8NMq9aMHF0aGrqsCTroro374Icxht2K9T7jLN23d+5qlKlQFvzscR3H+ZmT219fkqIuIeuOsjPdMFOe6hwmSfDv3QnP6m/g37kNjEkhX083fDSs/3M7OJ2+V/MaZsxTBD91siYYAFBsyE6oFuiEEBLvKPghhJAYE5iIdfb9srFpQTqajTDly4MfTx3O6vPZ9U6D34Ymv002Ns4ceiPQYMaYi/B163YAACcx2PZug2NVK3w7tkBy2MBbUsCnpoFLtYK3pkGTlQ39ieUR7aGj5JF8OO5tlo113WdHsrXBs+57eNd+B7G5Ufl0BQ6GyVOR8pPrwOmDt8GOxlBjLlbb9gSOD3nq4JX8snOo5I0QQmKLgh9CCImxnc4qOET5wviKLiVvHUaY8mVNEQ6447/pgXK9T6rGFPF6nw6jTUXIabDjhJ01GHWgARanD3bDgcA6HMnpgOR0ALWdGSbX0sXQDR8F46mzoJ8wCZwmsv98HfU0yFqNc+BQqsuGb98ueFd/A9/2zWBS6CwPAGhy8mA4ZToM5dOhSY/dZqPKdV+VnkZ4FMEPdXojhJDYouCHEEJiTNnlbbgxH3n6dNV5wxXrfo55m+CWfDDxvSun6ku7e7HeB2gvm0tZ+R2u/2gzREkMjHskf7dNCPwH98F/cB94azoMk6eBz8gEb7GAM6eAN5vBGMDsbZDsNkh2G5jDBqfjGGb4K+E1aOHVa1AomuD64AGIjfVh34vTG2A4+RQYTpkB7ZDhfVJ6VmbIkR1LkFQtuUup0xshhMQUBT+EEBJDfknABvsB2dj0tOCbeA415sk6nzEwHPHUY6w5+vKu/qJa7xNFyRtjDK5P34d7xZcwcjo40Rn8uCUfUmGK6HUkWyvcK76M6FyTrw1Tu6yjSdEYIerTQp6vLSyBcfpM6CdPBW+MbD49ZdYYUKDPQI0i4OmKMj+EEBJbFPwQQkgMbXMeVbUqnpqqLnkDAD2vRZkxR7Yg/4C7Jm6Dnwa/TbZGCQDGRri/D5MkON97HZ413wJo72LWdXF/dZYRQ2fOh27kGEhuF5jdBsnWBrGlCb4tmyA5bKFeOiwvk+8hZODV3dk4nQ6Gk06BYfoZ0JYO6dcGA8OMeSGDn0xtClI0xn6bCyGEDAYU/BBCSAwpu7yNMRchSxe6VfFwY74i+Knts7n11m7Feh+LxijrYBcKEwU43lgI7w/rAmPGH4OQDZNLsWNcPloyLThxxAzk6Kzq58+7Ar5tm+D57mv4jxyMeL6MMdUGqvouwY82vwiG6TPbS+jM5ohfN5aGmfKwqkvTg64o60MIIbFHwQ8hhMSIV/Jjk0N+c14RpMtbVyNM+Vjeui1wfDCOg59dTvl6n3HmYvDdbMrK/H7YX30evp1bZeM6jQ4rzz0RP4zubJawx3UMOWnjVK/BabUwnDwVhpOnQjhWCe/G1RCbGsGcDjCXC5LLAeZyAuDAp6aCS00Dn2pFixHYZtsJvVeAwSfA6BUwzDwEhuIhMEw7rc/W8kRjqFHddrtDCa33IYSQmKPghxASN5yiB15JUG30mCg2Ow7LWhVz4DA1dWTY54xQbO7ZJNjRIjiQoY2/70CZ+emuPI9JEhyLXlIFPpxGg5TrfgY+6zjQZX3UHtdxnBYk+OlKW1wKbXGpatwuuCFBQprWEhhb27IFy2o7Mz9F+ixcNHxB2Nfvb0OMubJ1X11Rm2tCCIk9Cn4IIXFhResO/LvmKwDAxdmn4PKcGQM8o+itbN0hOx5vKZHdjAdTqM+AkdfDI/kCYwfdtShPHdEnc+ypJr8d9ar1PuGDH/cXH8O7dZNsjNPpkHrjbdCPOQFjmoCNiuCnJ5a2bMErtSsAAJdkT8Vl2RXgOA6H3HWy8+JxA1kTr0eBPgPVvmbVY1T2RgghsRfbbbQJIaQHqryNeKlmGdiP//uocT0a/T1b4D5Q9riOY6vziGysu5I3AOA5HsMUpU8HFTft8SDYep9wmQnvxjVwLftMNsYZjLD+4m7ox5wAoH09VFfVvma0Cc6o5nXU04BXalcEfnY+aFyLpS3tmaaua6kAYJgp/oIfIHjpGwcu6v2TCCGEdI+CH0LIgJKYhJdqlkFC50aTDKzHWYCBwBjDm/XfycasGnNEwQ+g3uzygCf+NjtVbm46xlQUcr2P//ABON56VTbGcTxSb7wVumGdZYBDjLmq7mt7XNURz4kxhtfrv1GVjL1atwI/OA6hytskGw+3vmYgBQvKCvWZ3e57RAghJHoU/BBCBtQ3bbuwz62+4d3vHrgAQGQSmvx22fqdcDY7D6s+wyXZUyPerHSEIvg56K6FxKQQZw8MZeZnnCV4yZvY1Aj7f54BE+Vd1izzr4J+lHw9j4bjMcpUKBvb64486N3kOIgdzkrVOAPD/x37RBZQA+pNReNFsKAski56hBBCoke/ViKEDBib4MKi+m+DPhYsIOoPtb4WPFr5QWA/mzStBXm6NOTq01Cgz8Cp1rHI7bJJpsQkvFX/vew1cnRpOCt9QsTvOdwoD37ckg/V3mbkN7rg270dzO0Cn5IK3poGLtUKPtUKTWYOOGPv94Cp8jZir+s4xppLUGQIXmbV7HegVrEXTbDNTSWXC/aX/gnJYZeNm06bBeOMM4O+9hhzEbY7jwaOlUFWKH5JwOt1wX92gPYAtqt8fQbMGkNEr93fgjU9KKVOb4QQ0ico+CGEDJjX67+VbXTZ1VFPA9ySL+LsSax82rRRtpFnm+BEm+AMBGMfNK7F/+TPxhnp7etWVtv2osrbKHuNn+RURFWylKVLRaY2BS0+Owpq2zDyQAOcb/wRrY7QmSeO56GfNAWms+dCm18Y8rxwDrhr8cDRtyAyCRqOxy8L52KqVd2dThmQmHmDar2Pb8cWON5bBKlNHiTpx4yHed4VIeegbJpw1NMAl+jtNlD5smUL6vytsrFCfWbQxgEAMDQOmx10MPF6lBiyUeltCIzFa4keIYQkOip7I4QMiJ3OKnzXtivk4wxM1a2rP3S3yajIJLxQsxSv130DnyTgnYbVsseLDdmYYR0T9ftWNOtxw3/X4sp3N2Py5mMQmhvCns8kCd4f1qH1r3+G/b8vQKiJfo3UN607AhkSkUn4V/Xn2NYlCwO0tx//vPkH2dgYc+d6H8lhh/2/L8L28r9UgY82rxApP/05OI0m5ByGG/Oh6bJ2iIF1m/VrE5z4sHGtbGyEqQAPD706ZGlbvAcTl2ZPBf/jf5JHmApwoqVsgGdE/r+9O4+Purr3x//6fGZfMpnsC0lI2Pd9hwICshQRq611a92utlbvraW3t1++9Wr1tvW2tz+31tb7ba1LLW211g0VZVNQIgQEZIewBchGlslMJrN/zu+PmEk+M5OQkEkyJK/n48FDP+dzPp85nzkZmHfOOe9DRP0TR36IqNcFlCCer9ykKjPJeti0ZlT5HeGy455yjLVET6/qKSGhtDtyEOnduj0ocZWqRokA4KaMuZfc+DNSoPQY5r76EWrcraNgHsUPAQEJl9qEU8C3twS+vSXQj5kA3dAR0OQVQJs3GNB3PHpyIeJZgyKEJ86/jZ8U3IDhplw0hrz4RdnrOO1VB6FjTHkIVVfCf+wwPBvegtIUnaFNttqQdM+/QjaZO2yDXtZiqDFbFfAcayrHJGtRu9e8drEYnjapwQHg9qyrYJL1+GH+ajx0eh2coSbV+UQe+QGAmbYReMKYibpgI0aYcrv8M0RERJ3D4IeIet07dbtREbGG5JsZ81Dhr8MH/n3hst5e91PldyAoQqqyB3K/ioaQG6WeShQ7j6nORe57M9yUiynWIV16zcDJ43D+4RmYQ+ovu0II+JQALBmDoC0ohNLYCOFyQml0Rq2paeE//AX8h78IH0vJKUiTtfBUn4MuKweajCzIGVmQk+2QJEkVaLbwKQH88tybWDNoFV6u+ghlnmrYnR7YHR5kXmxEYVUTZtSfQb2nKeraFvoRY2D55rehSe3cupVR5kGqvj7qaX/dz1nvRWxxHFCVzUseHU4akaGzYU3eKvxX2WvhUS2TrI9KJ56IsvR2ZOntfd0MIqJ+jcEPEfWqan8D3qzZpSobYszG1SkT8JnrBD6o3xcuP+GpgCKUXvsteGRqZJvGjLnJrVPYJlgG44+Vm6IW07e4OXMeJOlSIzWtAqdL4fzDMxB+HzSSDL2shV8JojbVjOPDMzF42mJcO2511D2VJje8H2+E5+NNEL7Ya6YAQDjqYHS74aurhq/NPWSLFZrJ0yBnVAIZ1nC5pAik1zSi6OwZHKjYhUWOJiQ3eCErzQvxNZKMHH0KJFlEvRYAyGYLzKtvhGH6nC69D6PMeXi7tiR8XOqpREAJxlw39ZfqbarEAHpJh5sy5qnqjDQPwpq8a/Gnyi0IihC+nbUwYZMdEBFR72LwQ0S96qWqjxAQrWmQJUj4l5zFkCUZI0w5qrrukBcVfke7Wcji7YJfHfzkRyzqX2gfhyy9HU+cfweNIY/q3CRrUdTi/Y4EzpyE83+fUgUvJtmAQ0V2vLtiLBSNjApjI1bHCCJkswXmFdfBuGApvNs3wfvxJigdjMREUtyN8G7bhG/7alGZlYTjwzORVutG0dk6mJv8Ma/RSDJyDKnQSbHX7xgmToXl+lsg25Jjnu/ISFOuKttZUIRwzFOOcZYCVb2agFOVGQ4AVqdPR5ouKeqeU6xDMGVY10bhiIio/2PwQ0S95vPGU/i88aSqbGnKxPBi9DRtElK0VtQHG8PnT3jKey34OR8x8hPrdUeb8/Czwpvx6/NvhevrJC2+mTG3068TOHsKzueejBq10Y2biHfn6aBomke6znir0RB0I1lriXkf2WyGedm1MM5fAv/+PQiWnUbwfBlCFRcggh3vUdQSgGZXuZBd5YIsydBJGvhi1NVKGuQYUqCNCHxkmx26omEwzJgD/ZgJnXz6aGaNAUXGLJzytiab2N94Jir42d94RnVs0RixMnXqZb8uERENPAx+iKhXBJQgXqrcqiqzacz4Rsac8LEkSRhuysEu14lw2XFPRTitdE+LnPaW385Gk1l6Ox4dfBM+qN+Hcn8drkoeh8JOLqgPlB6D84+/iQp89GMmYNAd34H+1B/gbbOY/4C7DPOSR3d4T9lkhnHWV4BZXwEAiFAQoapK+MtOo6JkJ1IsJojaGoQuVkIEmoOiyLVNekmLLH0yKvz18CutI3MtgY/eYoOcngltQSF0RcOhLRoK2Z7apeltHZloLVQFP/vcp3Er5qvqRGaim2AZDIOsi8vrExHRwMDgh4h6xTt1u6MSBNyS+RVYNOqNOkeac1XBz4leSnoQFCFURGQ/G9RO8AM0j1Z8LX1ml17Df/QQXH/6bTgAaaEfPR5Jd9wHSafDGHO+anTsC/fZSwY/kSSNFtrcPCAzG86QDMvMmdBqtRCKAqWmGr6SYlzY/hbgaB1h08kayJKMbH0KTlsUfJFnhG7wEKwasRypOUMgW6wdvGL3TbIUqtJXn/fVoibgRLrOBqA5E99Bd5nqmglMB01ERF3E4IeIelysJAfDTbn4Sowv9cMj1v2c99WiMeSFNSJIirdKvyMqkUHkmp/u8B/cB9eLz0GEgqpy/ahxSLrze5B0zSMYEyyDo4IfIURcRlgkWYYmMxvmlV/D22N88B75AmMPVyK5wQN9eiEsU5ZAP2ocsjKyMEMokCDFbWTnUoaZsmHRGFWb3u5rPIMlKc3T6U56KtGkqCfljWfwQ0REXcTgh4h63Msxkhzclb0oZha3QkMmdJJWVb/UU9Hhvi/xcMGnHvVJ1lriFnD5Pt+Fxlf+CBERXOnHTUbSt+8NBz4AMME6GGizrU5D0I2zvoudnlbXWZVBJ+qK0nGqqDnAe3DQKphsw8Pne3ufGVmSMdFSiB3Oo+GyfY2nw8FP5JS3PEN6zEQHREREHeEuakTUo/Y2nsaeiCQHV6dMbPfLvE7WRm1IedxT0WPta3HeV6M6bm+9T1eIUAhNG9+NGfgYJs9A0h3fUQU+AJCtsyNDp86YFpnhrLt8SgB1bZJKAEBOAuwvM8laqDo+1HQOgS/XJu13n1Gdm8hRHyIiugwMfoioR71Rs1N1bNOYcWObJAexjDDlqo57Y93P+Yg013n67gU/wfLzaHjqF2h6742owMc4cx6st/0LJE304LskSVFf7CNHPbor1uamibC5ZuQaHq/ix7GmC2gMeXHSU9lhXSIios7gtDci6jE+JYDSiFGbmzPnRSU5iDTcnAO0mYVW6qns8c1Oo9NcX17wI0JBeDa9D8/G9RChUNR501cWwXzdTZDk9p9lgrUQmxxfhI+PNl2AV/HDKOsvq02RKiKCn1StNSGypiVrLRhizI5Ked0Y8qo2NtVJWozqwp5KRERELRj8EFGPOe2tVn1plSBhlm3EJa+LHPnxKn6U+Wrivu6lRXOmt3pVWVenvQlFgf/gPng+eAfB8nNR5yVJhmnpNTAtW3XJJAJjzflRm34eabqAyXFa91QdcKiOE2HUp0WslNeNijot+GhzHvQy//kiIqKu478eRNRjIqcq5RvSOzV6YddakKFLxsU2qbFPeCp6LPiJlektr5PBjwgE4NtdDM/WDxC6WBWzjiYrB9ab74Ru8JBO3dOsMWCEKRfHPBfCZV80nolb8BM58pOtT4nLfeMhVsrr+qBbVWeilVPeiIjo8jD4IaIeU+pVT3kbZsru9LUjTLmq4Oe4pxxXp0yMW9vaipzylqK1XnJqnggE4N2+GZ6PPoTicsasI0kyjIuXw7x0VVRig0uZYB2sCn4iF/x3R2XEKFciJDtoESvlddv/B4AJlsJebhUREfUXTHhARD3mpEc9EjK0K8GPWb3fz4mmnsv4Fhn8XGrUJ1B6DI7/+Snc7/yj3cBHm5WL5AfXwrLy+i4HPgAwMeILfoW/HrUBV5fvE0tkwoNMnT0u942HlpTX7UnVWjFIn9p7DSIion6FIz9E1CMagm7VyA0ADDV2beSnraqAA7UBV4/s7XIuIs11e8GP0uRG01uvwrvr03bvpc3Nh2nRcugnTYOk0Vx2m4qMmVEjIEebzmNujI1huyJR01y3Ncmq3u+nrQnWwl7beJWIiPofBj9E1CMiR30Msq5LSQRa1gd5FX+4bFvDYXwtfWbc2tgicoPTyOBHCAH/3l1wv/F3KI2xR3p0w0fBtGgFdCPHxOXLuSzJGGnKxeeNp8JlR5sudDv4SdQ01211lMaaKa6JiKg7OO2NiHpEqVed7GCIMatLqao1koyZScNVZVsdB6FEJCborkCMTG9t9/hR3I1ofOk5uP78h5iBj7agCMkP/gTJ3/t36EeNjeuoxCjzINXx0TZrgC6lwl+P9+o/x+fyeXiVQJtyh6peoqS5bqsl5XUkCRLGM/ghIqJu6FLw8/jjj2P69OlISkpCZmYmrrvuOhw7dkxVx+v14v7770daWhqsVituuOEGVFWpfwNcVlaGlStXwmw2IzMzEz/60Y8QDAa7/zRElDAiM711Zb1Pi0X28arji4EGHGqKTiPdHZX+eiiInenNf/QQHL96BL79e6KukwxGWK6/BcnfXwvd4PhkYYsUGfyc99XCFfK0Wz8gQih2HsPPzv4Da06+gHUXt+ND7TE8V/lBuE4ip7lua6K1MKpsqCkb1kskoiAiIupIl4Kfjz/+GPfffz8+++wzbNy4EYFAAEuXLoXb3ZqG9Ac/+AHeeecdvPbaa/j4449RXl6O66+/Pnw+FAph5cqV8Pv92LFjB1566SW8+OKLePjhh+P3VETUp4QQOBkx8jOsC+t9Wgw35URNQdviONCttkU6F5HsIFVrhUmR4P7nOjj/90kozoaoa/RjJ8L+48dg+sqiDjcr7a5CYxb0knpU5lhT9OiPO+TFX6u344ETf8AzF97FoaYy1fndjSdxwlMOILHTXLc1OUbwwylvRETUXV1a87NhwwbV8YsvvojMzEzs2bMH8+fPR0NDA55//nmsW7cOixYtAgC88MILGD16ND777DPMmjULH374IQ4fPoxNmzYhKysLkyZNwn/913/hxz/+MX76059Cr4/PDuZEV6KGoBuOYJOqTAKQo0+B7gra1LEy4IhKT3w5Iz+SJGGRfQJertoKAJBDCmr2f4bajyugczVC+HwQfj+Ev/m/stEEbdFQaIuGQTd0BOTU9PA0NBEKQTS5obgaoDS6IJxOKI0uKJX7sbT6OMweP8xNfmT7tagL7oUIBqLaI5stsHzjW9BPnNori+51kgbDTTmqYOZI0wVMSxoWPg4JBf919jWc9V3s8F5v1+7GD/OuTeg0120NNWbDpjHDGWr9PEyKERARERF1Rbe+TTU0NP9GNDW1Oe3onj17EAgEsGTJknCdUaNGoaCgAMXFxZg1axaKi4sxfvx4ZGVlhessW7YM9913Hw4dOoTJkydHvY7P54PP5wsfO53N8+6DwSCny/WiUCgEIQRCoVBfN6VferF6KzY7DkBARJ3TSzo8mLuyx37zHe++PdZ4HkK0PkeyxoJkmC7r8zrLPAybK9/BiCPlGHmiGiZPAA7tSSRrzVF1FQDBygtA8TYAgGSzQzKZIBpdEE1uQES/tymBBowNtf79YtOaIbTRa2C0I8fCfOO3ISfbe/UzMMKYjYPus+HjI+5zqvfxgLsMZ7zVMa9teVwhgN3OUpQ1VaPSV6/qmzQ5KWH/Hr01/St4rvJDKFAw1zYKRbrMhG1rb+Lfxf0T+7X/Yt/2vK7823DZwY+iKHjwwQcxd+5cjBs3DgBQWVkJvV4Pu92uqpuVlYXKyspwnbaBT8v5lnOxPP7443j00Uejynfv3g2LxXK5j0BdpCgKXC4Xdu3aBbkHp/oMRGVSPd7Sfd7ueTeAXx57DfcF5kCHy0+f3J549+1HmmNwa1qnw+YoJuzatavL99HVVCH1k834VsN5eNA8EqMAqPe7oPFFBzJR2kzJbaFAqAJMj+RVrflRfEE0+tpcp9HAMW0e3KPGA0fVaxx7Q0Cqh1vX2p6D7tPYXvEp9F/+9f2e5ojqvQaAfMWO8UoONmlOIKSE4Ha7IUnAbw+8gXOyeg1m1ZGz2CnUU/8ShQbAHZgEj+RHer0VO8/u7OsmJQT+Xdw/sV/7L/Ztz3PH+Pe+PZcd/Nx///04ePAgPvnkk8u9RaetXbsWa9asCR87nU7k5+dj2rRpsNlsPf761CwUCqGkpATTp0+Hphv7l1C0g1WbYWm4dCDvzDDiqylT4v768e7b98rOwOJtfZ55aZMxM216l+4RPHcWjW+vAwJeGIzJqoxsCgCNXg9TJ7KUhYSAR/HDq/jhUfwIiMjfvEmQ2wSUSXoLjF/eV1M0DOav34a8rBz0lUlKAO+eLFVlubOPyMd4SwECIoTnT+6FRWl9r29Mn4trU6cBAJKqP8HfL2yD2WyBJAFlaIQF6p+zpcPmJ1y2N+oY/y7un9iv/Rf7tue1zArrjMsKfh544AGsX78e27ZtQ15eXrg8Ozsbfr8fDodDNfpTVVWF7OzscJ3I3wC3ZINrqRPJYDDAYDBEN16rhVZ75ayD6A8kSYJGo+H7HkdBEUKJ+6RqDYmEL9epREyBe7f+cyxLm9wjX1Yv1beKUOBVAjBroj+LbQWUIMr8NarnGWHN7dLPTLCqAk3P/wbw+wBJgkmjh07WIKg0By7lOTZUjxmDJTkzAb0eksEASatH6GIVgqdPIHDqBNzOWtQHGuFTItbuSIDXqEOTWY8mkw5us775/806eEx6/Ouom2BMSoFsT4XG3vfJALTQYqgpG6WeinDZCX8lJicPwReuMniEX/VeL0gZG36vV6ROwT8ufAJJQsw1SqlaKyx6U88/BMUd/y7un9iv/Rf7tmd15X3tUg8IIfCv//qveOONN/DRRx+hqEid3nXq1KnQ6XTYvHkzbrjhBgDAsWPHUFZWhtmzZwMAZs+ejZ///Oeorq5GZmYmAGDjxo2w2WwYM2ZMV5pD1C8cdJdFJQd4etjdyNDZcMJTjofP/C1c7gw1YYvjAFakxn/0pyPnfbX477J/ojbowmzbSNyXuxw6KfZvr8p8NQhGjK4MMWbFrBtLqK4Gzt8/AcXdqCo3pmdjY5EOR0ZlwWE3QydpsXD47OjUxwuvRl3AhV/veRaplfWAEF8GOq0BjpBjJysYbMhA0pBpnW5rbxllHqQKfo42nQcA7HCqp+GNMOUiTZcUPk7WmjE+lIMTcMS8b6KmuSYiIuopXZp4eP/99+OVV17BunXrkJSUhMrKSlRWVsLjad53Ijk5GXfffTfWrFmDrVu3Ys+ePbjzzjsxe/ZszJo1CwCwdOlSjBkzBt/61rewf/9+fPDBB3jooYdw//33xxzdIervIr/ADjflIkNnC/9/5KaOb9eWIKD03qJvRSj47YX3UBt0AQCKncfwbm30vjctSiP298nVp8LSyb1ZFJcTzueehNKgzkimGz4aOWv/G7tmD4XD3pzoICCC+KThSMz7fOY6jupkPY6OzMLRUdkoK0hFTYYVTRZDu4FPtj4Fd+cs7lQ7e9sok3q/n1JPJZpCPuxpPKkqn2MbFXXtjFAB5Hb+qk/UNNdEREQ9pUsjP7///e8BAAsXLlSVv/DCC7jjjjsAAE8++SRkWcYNN9wAn8+HZcuW4Xe/+124rkajwfr163Hfffdh9uzZsFgsuP322/HYY49170mIrkABJYgSV6mqbLZthOr46+mzcaBNti9H0I3NjgNYnhqdGbEnFDuPRaVR/mfNZ5htGxFz5KDUW6E67myKa6WpCc7nnkToonpBvragCLa77odkNGKadRh2uo6Hz21xHMCylElRU7p2Ok+ojvWSFqPMeRhvKcA4SwHyDGnhqYUtNFLiLkIdaR4ECVJ4GmRABPF6zWfwKv5wHQkSZtqGR12bCjOmWYeixF0adS5R01wTERH1lC5Pe7sUo9GIZ599Fs8++2y7dQYPHoz33nuvKy9N1C/tdZ+O/gKbpA5+RphzMc5SgIPu1r1e3q4twWL7+B7f+ycgQnj14o4Y5UG8ULkFP87/WlTgcTJi5GdoJzY3VRpdcP6/pxEsP6cq12YPgu07D0IyNo8cLUoZrwp+zvlqUOqtxHBTa0KCukAjjn+5oWeL7+ddgynWIZdsR6KyaozIM6ThnK8mXLahbq+qzhhzPuza2EkzVqVOjRn8ZOrscW0nERFRokvcX3USDQCfOY+rjkeZByFVZ42qd336LNVxfbARWxwHe7RtALCl/gCqAw0xz+13n8FOl3qEpTHkVWVlA4Bhlxj5CTnq0fDbXyF47oyqXJOWAdt3fwDZ3PqFfpw5Hxm6ZFW9zfVfqI4jR9JMsh7jzQUdtuFKMNqcpzpum54biB4xbKvImIVxluj3gCM/REQ00DD4IeojXsWPPa5TqrI5tpEx644252GMOV9V9nbtrh5d++NR/PhnzWcd1nmpaiua2mwQesqrnrKmk7QoMGa0e32ougrOpx9HqEo9VU62JcN23xrIyXZ1uSTjKvs4VVmx87iqDW1HhgBgatLQHh8h6w0jzYPaPSdDxowYU97aWhWRalwraZjwgIiIBhwGP0R95PPG0/CL1jTMEiTMSGr/C+zXM2arjuuCjfio4VCPte/9us/hDDWpypZHZJlzBN149eKn4ePIKW+DjRntZoULnjuLht/8N0KOOlW5xp6K5Pt/BE1a7KBpQfJY1XodvwiEk0Y0BN042nRBVb+j9/RKEpn0oK3xlgIkaTpOWT3eXIDZbYLrpSmTuL8PERENOFf+r0OJrlA7Go6qjsdZCmDTmtutP9qch9HmPBz5Ms0xALxbtwdL7BNi7uHSHc5gE96p3a0qm2QtwrczF6DCV4f97jPh8g/r98OiMeKkpxKH27QNAIa1s94ncPwInH96FsKnTvGtycyG7btroElJbbdtqTorpliHqDKdbXEcwJKUCShxnVTtjWSQdZhoKbzU414RUnVWZOnsqAo4os7NSY7O8hZJkiR8L3c5FtnHQ4YUNY2OiIhoIODID1EfaAr5VAEE0P6Ut7Yi1/5U+R1Rmdji4c3aXVGJGG7KmAdJknBX9mLopNbfmwgI/LPmM+x3n0FAqKfhRWZ6E0LA89GHcD73ZFTgo80vRPK//rjDwKfFopTxquPT3iqc8VZjV8QapCnWIdD3gylvLUbFmPqmlTSYZh3aqeu1kgbjLAUYY8mPe8BMRER0JWDwQ9QHSlylqo1ANZKM6UnDLnnd2BgL/iO/8HdXhb8eG+v3q8rmJo/C4C/X7mTqk6OCsFiMsh4T2uxRJHw+NP75/8H91qsQQr1YXzd8FJK/9++QrUmRt4lpkqUQqVp1Yoi3anepMuIB/WfKW4tYwc8kaxHMGu6RRkRE1BkMfogu02H3Ofz7yZfw4Mk/YbcrOo1we4QQ+NSpnvI20VLUqY1AJUnCzIgv9J85j3cqDX1nuENe/M+5N6MCs2+kz1HVW5k2FXmGtNhthIThplz8KG91eBpfqKYaDU//Ar69JVH19eMnw3bP98PprDtDlmQsjEh88JnzuGrKm17SYqK1sNP3vBLECn5md2LEkIiIiJr1n/kgRL1IEQp+X/EBagJOAMDTF97Fk0PvRLrO1uF1ASWI5ys3qzYtBYA5HaQpjjTDNgzr61rX41T463HBX9duMNJZCgSerfwgKlX11SmTkKlXjzbpJA2+P+ga/Orcm7gYaECK1ooJlsGYaC3EOFM+zPUuBI+dgftcMYLnziBYdhoiEEAk87JrYVp6DSS567+HWWgfhzdqdqoCnrYmWYtgkvVdvm8iy9LZMcyUg1JPc3a8VK0VU6xFfdwqIiKiKweDH6LLUOarCQc+ABAUIbxVW4K7sxe3e01twIUnz7+Dk151RjSDrMOUpM6t2QCaNw1N1VpRF2wMl+10HkdeRDa4rtqqKcVhd61qLchgQwZuzJgTs36eIQ1PDrkD3tpKaM5dQOhIGYLn34H/3Fn4vJ4OX0sympB0279AP3biZbc3Q2fDBMvgqLVTLfrblDegNWnBP2s+Q0AJYXX6dBj7WYBHRETUkxj8EF2GIxHplAHgI8dBXJc2A2m66HUrx5vK8cSFd9AQdEeduz3rqi6NUMiSjBm2EdhQ93m4bKfrBG7oRvDzccMhlGjKYEHrhqI2jRk/zF8ds21Kkxu+z7bD++lHCNXVdOm1tFm5SLrrfmgysy67vS0WpYyPGfxoJQ0m99MRkRx9Cu7PXdHXzSAiIroiMfghugxHI1I6A82jP2/XluDO7EWq8u0Nh/G/FR8iFLHI3yDrcF/Ocsy8xOaUscxIGqYKfs75alDhr0eOPqXL9zrWdAF/qt6qKtNIMtbkrUJGxDS+YPk5eLdvgW/PZzGnsXVEkmTop82C9fpburS+pyNTrENg05ij9iOaYClkEgAiIiKKwuCHEtp5Xy0+bTgKV0g9jUpCcxrl+cljIEu9m7dDCBG1kWaLrY6DWJ02A6m65kxkh93n8PvyD6LWpWTqkvHv+auRb0i/rDaMNOUiWWtRjSTtdJ7AdekzunSfz12n8PuKDxBqk+AAAP4lewlGmgdBeL0InDmJwMljCBw/gmDZ6U7fW7baoM0fDG1+YfN/C4og25IvfWEXaCUNFtrH4u1adSKFywkoiYiIqP9j8EMJqz7YiEfPvorGUOz1I5scX+CCrw63Zs3v1XZVBhxRIw0tAiKId2pLcHv2VagJOPH0hXejAp/xlsH4t0ErYe1Edrf2yJKMGUnDsLFuH/IuODD+UAVyzpegVpMKtKzZkSRIsgw5JQ3aoqHQFQ2DtmgYNClpcIe8+HPVx9hWfxC6QAg2rx9p1U5kBVyYF8rCtH0lcFS+hdD5sxCK0nFjAMgWa3OQkzcYmoJCaPMLISfbe2UvmUX28Xindnf4fdZJWkyxDunx1yUiIqIrD4MfSlhb6g+0G/i0eLduD2bZRkRtptmT2hv1abHZcQArUqfgqQvro4KkpSmT8O2shdB0c7RKaajHV/aUI3/7Ttgdre9RwGiEVtKEjwWa1+cEL5TB+0nz1Da3xYCzgVqM8/owxR+EJL6sp4Rg1ZqQbaiHF5cmyTL0E6fB+JVF0BYO7bNNM7P0dtyYMRevXvwUEiTcmjm/W4ElERER9V8MfighCSFQ7Dx+6XoQ+GPlJvys8JZuBxSdFbneZ4QpFye9leE1PQERxENn1kVN1ZtiHYLbsxZ2a5qeCATQtP51eLdvQYpQ4PT60HZcxh3yIllraff6uoALDTVVMMU4Z4AOmbpLT0uTrTYY5y6EcfZ8yMn2Lj9DT7gufQYW2ccDAGzaWE9HRERExOCHEtQ5Xw0u+GtVZV9JHgOzrMfFgBOfN54Kl5/xVuPD+n1YkTqlV9oWmeltjm0U8g3p2Oz4IlwWGfhk61Pwvdzl3Qp8QtWVcL30vwiWnwuXWTQGuIKtr+UO+doNftwhLxqC0dP1JElCutYKyatAjjF6I8kytAVF0A0bBe3QEdANGwlJm3h/dTDoISIioktJvG8wREDUqE+azobv5iyFLMkIiBD+7+lXcN7XGhy9enEHZiQNj5lmOp5qAy5cDDSoykaZB2GytQhbHQehIHp9jFHW44d518LSjalY3pIdcP/jLxB+n6rcIhvhgge1aRYcGJuDimwb/m/+DUjWmgEAwu9r3mT09EmcPVoM+NX3NWn0SNfZoIUMl9YPOTsX2rQMyKlp0KSkQZObD13RMEgGZk4jIiKiKx+DH0o4QgjscB5Vlc1OGhEeNdFJGtyVvRiPnX01fN6r+PFy1Uf4Qd6qHm1b5Hofs2xAviENsiRjgX0stjoORF1zX+4y5BnSLuv1hNeLxtf/At/u4qhzklaH5Blz8VxOOU5lGsOJDkrSgliROixcTz9yLC4GnPj/jgeQ4mhCal0TFI2E+VnTsCh7OmSzGYpOj2P79iN/1ixoE3BUh4iIiCgeejdHMFEnnPJWoTpidGW2baTqeLQ5DwuSx6rKdrlOqKbD9YSjHnXwM8o8KByUrU6bDgnqaWNfS5+JGUmXl3Y5dLEKDU/9Imbgo83KRfIP/xO2m+9E/ohprRneAHzkOAQh1BnmPm04CiFLqEu1oHRYBiqHDsKiyddCNygfmpQ0SEaT6h5ERERE/RGDH0o4xc5jquMsvR1FxsyoerdmzYdVo17n8WLlVviUrm2+2RWRIz+jzIPC/5+lt+PbWQvDxwuSx+Lr6bMv63X8xw+j4alfIFhVHnXOOGs+ktf8BNrsXADALNsI1fky30Xsd58JHwsh8EnDEVWd2baR0Mkc4SEiIqKBhd9+KKEoQokKfmYnjYyZRjlJY8KtmfPxvxUfhMsuBhrwVm0JbsyYE/e2uUIenPfVqMraBj8AsDx1MqYmDYVPCVzWVDchBLzbN6PpzVchhHr9kGQwwnrjt2GYot7IdIJlMHL0Kajw14fL3q4twSRrEQDgrO9iVPKIucmjutw2IiIioisdR34ooRz3VKAu2Kgqm5M8sp3awILkMRhtzlOVfeQ4GDXtKy5ta1KPwuglLQqNWVH1MnS2ywt8AgG4//Yi3G/8LSrw0eYMgv3fH44KfIDmDU9XpU1XlR1pOo9STyUAYHvEqE+6zoaRptwut4+IiIjoSsfghxJK5KhPniEN+Yb0dutLkqSaagYA9cFGlEWM0MRD5JS34aZc6NpsKHq5RCgI765P4fifR+Hd9WnUef34yUj+t7XQpEdP/WsxzzYKKVqrquyd2hIoQsGnEckj5tlGdyvlNhEREdGVitPeKGGEhILPIlJcRyY6iGWwIQMZumRVCup9jacx2JgR1/YdidjcNHLKW1eJQAC+XZ/Cs2UDQnWxgzXzsmthWnoNJLnjYEUna7EidTLWVW8Pl5W4SrGx/gs0BN2qupzyRkRERAMVf/1LCeNw0zk4Q+pNODsT/EiShMlfrm9psc99Oq5t8yh+nPZWq8pGmi9v6pjS4EDTh+tR/7O1aPzHKzEDH0mnR9Id98G8/NpLBj4tFtsnwCy37scjIPBy1UeqOkXGrMtOu01ERER0pePIDyWMyFGfQmMmcvQpnbp2krUIH9bvCx8fayqHO+Tt1saibR1wn1VtYCpDxnBTTqevF4qCQOlR+HZ8DP+BvRBK9GaoLbR5BbDedAe0gwq61EazxoAlKRPwdm1JuCxy09V5HPUhIiKiAYzBzxVop/MEtjUchlfxq8o1kozR5jxcmzYdmitsTUdAhLDTdUJVNqcToz4txpjzoJO0CIgggOZRjwPusqg00F0VFCG8XVuC1y9+piofYsqCUdZ3eK0IhRA8cxL+w1/Af2AvQherOqyvGzwEpqXXQDd6fMzsdp2xPHUy3qv7HEERijonQcIcG4MfIiIiGrgY/Fxh9jeewdMX1kMgdjazA+6zcIY8uD0iCUCiO+I+B3fIqyqbmdT5wMUg6zDGnKfa32Zf4+luBT/nfDX4XfkGnImY7gYA48yxR2VEKAT/F5/Df3AfAkcPQmlyx6zXlm7YKJivXgnt8FGXHfS0SNFaMT95DLY4DkSdG28ZDLvW0q37ExEREV3JGPxcQUJCwSvVH7cb+LT4oG4v5tlGYagpu5da1n27G0+qjocas5GpT+7SPSZZi9TBj/sMFKF0ObNZSCh4p3Y3Xq8pjjmCMtiQgRWpU6LKA6XH4H59HYKVF6LORZJ0OhimzIRxzgJoC4ouWb8rrkmbhq2Og1E/J5zyRkRERAMdg58ryFbHQZz31V6ynoDAHys34WeFt1wR09+EENjjUgc/05KGdfk+k6yFeKnNzDJPUwPKjpUgz5IFOTkFkjXpkskDzvtq8Vz5BzjprYw6J0HC6rQZuD59JnRy60cnVF+HprdfhW/f7ku2UZuVC8PcBTBMnQ3ZbO78w3VBjj4F05OGYVebaYR6SXdZ7ykRERFRf8Lg5wrhUfx47eIOVVmeIQ2L7RMAAGe81fi44VD43BlvNT6s348VqZN7tZ0thBCdnsJ1ylsVtbHptKShXX7NbH0K8oUV1qOlGFFajcKzdQjKe+H4cqqXJMuQbXbI9hRoC4qgnzQN2sFDIMkyFKHg3brP8drFHeF1Q20N0qfhvtxl4dE0xd2IUGU5AieOwrNlA0TAH3VNC21uHnRjJkA/ZgK0hUO7PbWtM76WPhN7Gk8i9OVmqQvtY2G6xBolIiIiov6Owc8VYn3t7qg00LdnXYVxlua1JwERwklvpWpk6NWLn2JG0jCk6ZJ6rZ27XaV4uepjaCQZN6TPwrzk0Ze8Zk/ElLdsfQoG6VO79Lr+44fh/XgT7jpQDJe/NZBqEr7wOhehKAg56hBy1CFw5iQ82zZBY0+Bd/w4/C2nEUflBlhCCrRBBRpFgS4QgqUpgHnSIMyACdLO99BQX4dQZTmURmeH7dHm5sM4dyF0o8dDk9K1Z4mHQmMmfpi3GlscB5CjT8HX02f3ehuIiIiIEg2DnytAXaAR62v3qMomWYvCgQ8A6CQN7spejMfOvhou8yp+vFz1EX6Qt6pX2ukIuvHMhffCIyfPlr+Pk94q3JY5v8Ppd7sjp7xZOz86ojTUw/3m38NTzizQwdXmvE8JdLjux1N3EeUbX8c8ITAv4pxW1iBTlwyDXIP2x3XUZLMF5q9eB8PsBZ3en6enTLYWRe1/RERERDSQMfi5ArxWswN+EQgfS5Bwa+b8qHqjzXlYkDxWNf1tl+sE9jae7pUvwR/U7YuaMrah7nOc89bg3wathE1rirqmyu/AOZ96k8+pnZjyJkIheLdvQdOGtyB8rVnijLIOkiRBiObF/oosoUkEYJUMMe/jDDWF67aVrDUjRWvtwhQ1CcY582H+6tcgW6ydvIaIiIiIehODnwR31nsRHzsOqcquso9DniEtZv1bMudjT+MpNIY84bIXKrdgzJBvwyDrmr/oCxH3UQmfEsBGx/6Y5w41leGhM+uwJm8VCo2ZqnORU95sGjNGXGLz0MCZk3C/9gqC5eeizkmSBIPOiEN5ZhwflolTRWmYnjEe9yXNhtLgQMhRj1D5efj27kKouhJNIZ/qeq2kQYbedsk9fABA0mihycyCNr8QxnmLoM0ffMlriIiIiKjvMPhJcOuqt6tSFhtkHb6e0f76DZvWhFsz5+OP597H2CMVGHqqBuamAEpRghzFCOH1QIQUaLNyoBs9DvrR46EdMhyStns/CtsaDkft09PWxUADHjnzN/zboJWqkZ3IKW+TrUXtTlETAT+a3nsTno82AjHSfctmC8wrrkPlMDPeqv80XL6/6SwwaDm0yc2JDjBhCkzLVuHC2YPYsfl/MeJENVIczcFitt4OnayDpNNC0ukhWayQbcmtf5KSIadnQpszCHJaBiSNpitvExERERH1IQY/CeyA+yy+aLNvDQCsSp2GFG3706pEIICZByphXr8PUkNDuNwruREypIencQWryhGsKofnow8hGYzQDR8Fw+QZ0E+c2uUv9IpQ8F7d56qyImMWHEE36ttkcfOLIJ658B4eL7oVuYZUuEIeHG1S74kzvZ10zIHTpWj86wsIXayKed44Yy7Mq74O2ZqEif4GoE3w4wp5cMpbjWFt9j2SJAlf2Pz4dM4QfDpnCPT+IOxaK54ceQ8kjbZXMrIRERERUe9i8NMLip3H8I+LxTDKesy2jcAC+1gkaaLXv7QlhMDfqj9Vldm1FqxMmxq7fsAPb/E2eDZvgOJ0IE/Rom1YoQgFbsULa4zXFT4v/Af3wX9wHzQbsmBa8lUYps7qdBC0t/E0Kv31qrKbM+chz5CGJ8+vxwlPebjcLwL4Tfl7eGzwTfjcdUo1qqWXtKokDgAg/L7m0Z6PNyHWaI82ZxAs3/gWdEWtQVOmPhm5+lSU++vCZfsaT6uCHwDY33i2tV16LcbYh0HW6jr1zERERER05WHw08Mq/fV4tvz98H4rp7yVePXiDsxIGo7FKeMxyjQo5ijDLlcpTkVstHlD+uyYa1ECpceaR0XqWhMH6GUtjBo9vKHWPGUNQU/M4Ket0MUqNP71BXg+XA/T1SthmDIDkq7j9S/v1qkz0RUYMjDOXABJkvCfBV/H7ys+QLHzWPj8GW81Xr24A1UBh+q6CdbC5nVJgQACxw7Bt283/Af3qRIatJA0WpiWXwvTVUshaaJ/jCdZi1Be1xr8fOI8ghvSZ4Xfa78SxJGm86prJloKO3xOIiIiIrqyMfjpYW/VloQDnxYBEcSnziP41HkEBYYM3Je7TJUIICQU/P3iJ6prsvUpuMo+TlUmfD641/8D3k+2xnztZI0ZJ3LMODIqC16DDn69BvcOWY0CjR2BY4fhP3IAwXNnYl4bqr2Ixr+9CPffX4YmJxfavMGQcvOgr2uACASAL9cInfJURQURX02dEg4ydLIW96YthPfEEQSrK2Bz+ZDk8sLs+hwTG/2YIAT8ei38eg1GpDjgNJchUHoMwuuJalMLbUERrDffCW12brt1ZiYNx3ttgrIqvwNHms5jjCUfAHC06bwqM50EKWrUiYiIiIj6FwY/Pag24ML2hsMd1inzXcR/nX0NjxbeFM7gtq3hMCoippF9M2Ouaq+cQOkxNP7tRYRqL8a8r274aGQtXYmt+AQ1gdYNOd831+CBQdOhGzIc5hWrobic8B85AO/2LQiePxt1HyEUBMvPI1h+HhACGW43Gj75ENpBBdAVDsEeax3sSU3w65t/lGxaM2bJufAfPYTgyeMInDyGYNlp3Bbw4YK/DoiRVrpFet15+KXyds9LWh3MK1bDuODqS07JG27KQZ4hTbXp6xbHwXDws9+tftahpmxYNcYO70lEREREVzYGPz1ofe1u1aiPRpIhQUJQhFT1mhQfHi97HY8V3gybxoR/XNyhOl9kzMKMpGEQQiB45iS8Oz6Gb3dxzNfUFQ2DedXXw2tglta6sK56e/j8Z65j+FZwPpK1FgCAnGSDccZcGKbPQeDwATR98Ha7o0FhoRCC587AV3YShd4a3NXmVIrOCpf2i6hL9LIWaVoragOuqHNA8/487WV5AyToR42BefU3OxztUV0hSbjKPg5/rvo4XLbLdQKNoatg1RjxRUTwM8HCNNVERERE/R2Dnx7SEHRji+OAqmyxfQJuSJ+NbQ2HsMnxBar8jvC5er8Lf9j1PGbValHoOolMix5NJh3cFj1uyV8A7+YN8O36tN1sZ5JOB/M1N8A4b5FqD5+F9nF47WJxeIpXSCjY7DiA69Nnqa+XJOjHToBuzHgEjh6EZ/P7CJw8gVhJBlrUBdxR97B1sKbIpjWjSfHB02YdUgtzjFEXXdEw6CfPgGHCFMjJ9nbv2555tjFYV709HIAGRBCfNhzFtKShOB+xsSrX+xARERH1fwx+esh7dZ/D32ZNiQwZ16RNg01rwjVp03B1ykT84vTf4TtxFMNOXcTQU7WwNjZvuLmwzX2Msh7ZhjI0dfBauqJhsN58JzQZWVHnkjQmzLGNxMcNrRulbqr/AtemTYdWip46JkkS9KPHQz96PITXi2D5OQTPnUXw/FkEyk4Dp5v35WkK+VQbqba8VnujN5IkQ5OTi2z7WLyrnEa1VQOnzQhFlmDwBXFv2gJYgjKE1ws52Q7duEnQ2FM6eOpLs2lNmJ40DJ85j4fLtjgOwCCrf+zNsgFDIzLBEREREVH/w+CnB7hDXnxYv19V9pXk0cjQ2QA0p7EWxZ/g/nc/Q7njPAJKKNZtAACpuvb39JG0OphXXg/j/MWq0Z5Iy1InqYKf+mAjdrtOYpZtRIfPIRmN0A0ZDt2Q4QCAYDCIo59sx6gMG97f+zfYL2iQU+mEyROAJElI1ppbr5VlaAuKoB06ArqhI6ArGg7J2Dy6M7HxNH557o1w3aHGbGQXreywLZfrKvt4VfBT5rsYlZ1unKVAtZ6KiIiIiPonBj894IP6ffAqrVO7JEi4Nm06AEAoCpre/Bs827dARnMWt3JfXVRGOAAwawwwyNH7zmgysmCcOQ+G6XMg25Iv2Z4iYxaGm3JV++18WL/vksFPLEKnxyu2ShRPzQGm5gBCwOAL4o7MhchMHhNOaCAZTZB0sffMmWQtwn25y/FGzU4kaUy4N+fqLrejs8aZ85Gus6mSPrRNggBwvQ8RERHRQMHgJ848ih/v1+1Vlc20DUeuIRUi4Ifrz3+A/0Drea2kaQ6A/HUQQqA+xYRGiwFmTwCDlFTA2xxESQYjDJOmwTBzHrSFQ2PuDdSRZSmTVMHPkabzOOu9iMHGjJj1T3gqcMpTheGmHAwxtU6nOyZXY4frVOvrSxJGpQ3HgtwZXWrT/OQxmJ88pkvPcDlkScZV9nF4LSKJRFsTrYU93g4iIiIi6nsMfuIoIEJ4qXJr1FqY1WkzoDS64PrjbxA4eyrqOnPhcNhHDMYzKWW4mNI8NWx5ymTkZl8FEQxCeJogWawdTm27lBm24UiutqAh2JqkYH3dbtyfuyKq7i7XCTx5/p3wcaExE4vtEzDWOAgfaI5BQuuIjlk24J6cq7scjPWmBclj8Y+LxRAxkjfk6lOR/uV0RCIiIiLq3xj8xIkj6MaT59/BcY96n5rJ1iLkOYJoeP5xhGqqVeckjQbWm++CYepM2AH82FeHzY4vkKWz4+qUCc11tFpISd3/cq6TNFhin4DXa1pTZH/acBTXp89Cjr41sUBAhFTpoQHgjLcaz1dughACTZIfljbBz+3ZVyFNl9Tt9vWkNF0SJloLsa/xdNQ5TnkjIiIiGji4yjsOTnoq8ZPTf4kKfHKqGnHThlI4fvlIdOBjNMH23TUwTJ0ZLhtkSMW3sxZiWeqkDva8uXxLUyap1hAJCLxZs1NV5yPHQdX6mI5MsQ7FV2yj49rGnnKVfVzM8gmc8kZEREQ0YHDkp5u2NRzGHys2hffRAYDccgfm7SzDlMpgzP1rNPYUJH3nB53esDNebFoTlqZMwju1JeGy7Q1HcH36LGTp7fArQbwREQy1x6Ix4p6cJQk93a2tKdYhsGnMcIZak4ZrJQ1Gmwf1YauIiIiIqDdx5OcyhYSCl6s+wu/LN6gCn6Ena3DLP7/AzKrmACGSNjcfyQ/+314PfFpckzoVekk9+tMS8Gxy7Ed9sFFV/5bMr2CKdSgkqIOcu7MXw6619HyD40QrabDAPlZVNso8CEZZ30ctIiIiIqLexpGfy7TDeRTv130eVb7guAv5utSoaWuS0QTTvEUwLfkqJIOht5oZxaY1Y2nKRKyv2x0u29ZwGF9NnYK3akpUdcdZCrAqbTpWpQG1ARe21H2BEtdBXJczH7NtI3u76d12TepUFDuPoSbghAQJ30if09dNIiIiIqJexODnMs2zjcZu10nscp0Ily1LmYRpigshqTXbm2y1wbTwahjmLoRsNPVFU6NckzYVH9bvg//LESsBgZ+Xva6aEgYAN2a0BgdpuiR8LW0mckuBGUnDe7W98WLTmvHfRbfhWFM5BhszEj5RAxERERHFF6e9XSZJkvDd3GXIN6RDK2nwnZyluCN7EeBRp7m23vgtmBavSJjABwCStRYsSZmoKosMfCZbizDc1DdT83qSRWPElKQhDHyIiIiIBiCO/HSDSdbjh3nXwhlqCgcKSpNbVUcymfuiaZe0Km0aNtbvV61XausbGZwSRkRERET9S5dHfrZt24ZVq1YhNzcXkiThzTffVJ0XQuDhhx9GTk4OTCYTlixZghMnTqjq1NXV4dZbb4XNZoPdbsfdd9+Nxkb1QvsrRZbeHg58hKJAeNUjP5I5MZMC2LUWLPlyL6FIM5KGo8iY1cstIiIiIiLqWV0OftxuNyZOnIhnn3025vlf/epXeOaZZ/Dcc89h586dsFgsWLZsGbxeb7jOrbfeikOHDmHjxo1Yv349tm3bhnvvvffynyJBRAY+QOIGP0Dz6I9OUg/+SZDw9YzZfdQiIiIiIqKe0+VpbytWrMCKFStinhNC4KmnnsJDDz2E1atXAwBefvllZGVl4c0338RNN92EI0eOYMOGDSgpKcG0adMAAL/5zW/w1a9+Fb/+9a+Rm3vlrjMREVPeAEBO0GlvAJCitWKxfTw21O8Nl822jUS+Ib0PW0VERERE1DPimvDg9OnTqKysxJIlS8JlycnJmDlzJoqLiwEAxcXFsNvt4cAHAJYsWQJZlrFzZ+c22ExUkcGPpNEA+sTeR+YbGXMw4stpe4MNGfh21oI+bhERERERUc+Ia8KDyspKAEBWlnq9SFZWVvhcZWUlMjMz1Y3QapGamhquE8nn88Hn84WPnU4nACAYDCIYjL1gvy8EGl2AEK0FRjNCoVDfNagT9NDgP/O+jsaQFxbZAAlSu+9pKBSCECLhn4m6jn3bP7Ff+yf2a//Efu2/2Lc9ryvxwBWR7e3xxx/Ho48+GlW+e/duWCyJs6bGdPoEUt2toz9BjQ5Hr/DRrLYURYHL5cKuXbsgy8yS3p+wb/sn9mv/xH7tn9iv/Rf7tue53dFLT9oT1+AnOzsbAFBVVYWcnJxweVVVFSZNmhSuU11drbouGAyirq4ufH2ktWvXYs2aNeFjp9OJ/Px8TJs2DTabLZ6P0C0+xQdPm2BMk5ePwpkz+7BF8RUKhVBSUoLp06dDo9H0dXMojti3/RP7tX9iv/ZP7Nf+i33b81pmhXVGXIOfoqIiZGdnY/PmzeFgx+l0YufOnbjvvvsAALNnz4bD4cCePXswdepUAMCWLVugKApmthMoGAwGGAyG6MZrtdBqE2fwyu/zApIUPtaYLQnVvniQJAkajabfPRexb/sr9mv/xH7tn9iv/Rf7tmd15X3tcg80NjaitLQ0fHz69Gns27cPqampKCgowIMPPoif/exnGD58OIqKivCf//mfyM3NxXXXXQcAGD16NJYvX4577rkHzz33HAKBAB544AHcdNNNV3SmNwAQTU2q40ROc01ERERENNB0OfjZvXs3rrrqqvBxy3S022+/HS+++CL+4z/+A263G/feey8cDgfmzZuHDRs2wGg0hq/5y1/+ggceeACLFy+GLMu44YYb8Mwzz8ThcfqW8EQGP4mb5pqIiIiIaKDpcvCzcOFCiLYZzSJIkoTHHnsMjz32WLt1UlNTsW7duq6+dMITHvViq0Te44eIiIiIaKBhyok4UjjtjYiIiIgoYTH4iaPIkR+JIz9ERERERAmDwU8cMeEBEREREVHiYvATR5EJD7jmh4iIiIgocTD4iROhKFA8HlUZs70RERERESUOBj9xIrweAOoseJKJ096IiIiIiBIFg584iZzyBnDaGxERERFRImHwEyeiKSLTmywDBkMftYaIiIiIiCIx+IkTxROd6U2SpD5qDRERERERRWLwEyeR0964xw8RERERUWJh8BMnUXv8MPghIiIiIkooDH7iJGqPH25wSkRERESUUBj8xIkSmfCAIz9ERERERAmFwU+cRGV748gPEREREVFCYfATJ1HT3jjyQ0RERESUUBj8xElUwgMzgx8iIiIiokTC4CdOYu3zQ0REREREiYPBT5xErfnhtDciIiIiooTC4CdOmOqaiIiIiCixMfiJA6EoEB6PqkwymfqoNUREREREFAuDnzgQPi+EUFRlXPNDRERERJRYGPzEQWSmNwCQTQx+iIiIiIgSCYOfOIhc7yNJMmAw9FFriIiIiIgoFgY/cRCV6c1shiRJfdQaIiIiIiKKhcFPHHCPHyIiIiKixMfgJw64xw8RERERUeJj8BMH0Xv8MPghIiIiIko0DH7iIGraG0d+iIiIiIgSDoOfOIie9sY1P0REREREiYbBTxxET3tj8ENERERElGgY/MRB5CanksnURy0hIiIiIqL2MPiJAyVqnx+O/BARERERJRoGP3EQOe2NCQ+IiIiIiBIPg584iEx4wDU/RERERESJh8FPNwkhIDweVZnEfX6IiIiIiBIOg59uEj4vhFBUZZz2RkRERESUeBj8dFPklDeACQ+IiIiIiBIRg59uikp2IMmQ9IY+ag0REREREbWHwU83xdrjR5L5thIRERERJRp+S++mqD1+uN6HiIiIiCghMfjppqhpb1zvQ0RERESUkBj8dFPUHj8c+SEiIiIiSkgMfrqJe/wQEREREV0ZGPx0E9f8EBERERFdGRj8dJPwRAQ/XPNDRERERJSQGPx0U2TCA5nBDxERERFRQmLw001K1D4/nPZGRERERJSIGPx0U3SqawY/RERERESJiMFPN0WmuubIDxERERFRYmLw0w1CCIiIaW+yiWt+iIiIiIgSEYOf7vD5IISiKmK2NyIiIiKixMTgpxuUiDTXAKe9ERERERElKgY/3RC53geQIBmNfdIWIiIiIiLqGIOfbhAej+pYNpshyXxLiYiIiIgSEb+pd4PCTG9ERERERFcMBj/dELXHD4MfIiIiIqKExeCnG7jHDxERERHRlaNPg59nn30WhYWFMBqNmDlzJnbt2tWXzemyqD1+mOaaiIiIiChh9Vnw8/e//x1r1qzBI488gs8//xwTJ07EsmXLUF1d3VdN6jIlctqbmSM/RERERESJqs+CnyeeeAL33HMP7rzzTowZMwbPPfcczGYz/vSnP/VVk7qM096IiIiIiK4c2r54Ub/fjz179mDt2rXhMlmWsWTJEhQXF0fV9/l88Pl84WOn0wkACAaDCAaDPd/gdoSaGgEhwsfCYOzT9vS0UCgEIQRCoVBfN4XijH3bP7Ff+yf2a//Efu2/2Lc9ryvfv/sk+KmpqUEoFEJWVpaqPCsrC0ePHo2q//jjj+PRRx+NKt+9ezcslr5bZ5Nx9iz07tbRn/PnL8C9c2eftaenKYoCl8uFXbt2QeZ+Rv0K+7Z/Yr/2T+zX/on92n+xb3ue2+2+dKUv9Unw01Vr167FmjVrwsdOpxP5+fmYNm0abDZbn7XLufVdKJ7W4Ctz4iToJ07ts/b0tFAohJKSEkyfPh0ajaavm0NxxL7tn9iv/RP7tX9iv/Zf7Nue1zIrrDP6JPhJT0+HRqNBVVWVqryqqgrZ2dlR9Q0GAwwGQ1S5VquFVtt38VvSrXdBaXRBNLkhPE3QFxRC04ft6Q2SJEGj0fTp+049g33bP7Ff+yf2a//Efu2/2Lc9qyvva5/0gF6vx9SpU7F582Zcd911AJqHBDdv3owHHnigL5p0WXRFw/q6CURERERE1El9Fn6uWbMGt99+O6ZNm4YZM2bgqaeegtvtxp133tlXTSIiIiIion6sz4Kfb37zm7h48SIefvhhVFZWYtKkSdiwYUNUEgQiIiIiIqJ46NOJhw888MAVNc2NiIiIiIiuXMy3R0REREREAwKDHyIiIiIiGhAY/BARERER0YDA4IeIiIiIiAYEBj9ERERERDQgMPghIiIiIqIBgcEPERERERENCAx+iIiIiIhoQGDwQ0REREREAwKDHyIiIiIiGhAY/BARERER0YDA4IeIiIiIiAYEBj9ERERERDQgaPu6AZdDCAEAcDqdfdySgSUYDMLtdsPpdEKrvSJ/dKgd7Nv+if3aP7Ff+yf2a//Fvu15LTFBS4zQkSuyB1wuFwAgPz+/j1tCRERERESJwOVyITk5ucM6kuhMiJRgFEVBeXk5kpKSIElSXzdnwHA6ncjPz8e5c+dgs9n6ujkUR+zb/on92j+xX/sn9mv/xb7teUIIuFwu5ObmQpY7XtVzRY78yLKMvLy8vm7GgGWz2fjh7afYt/0T+7V/Yr/2T+zX/ot927MuNeLTggkPiIiIiIhoQGDwQ0REREREAwKDH+o0g8GARx55BAaDoa+bQnHGvu2f2K/9E/u1f2K/9l/s28RyRSY8ICIiIiIi6iqO/BARERER0YDA4IeIiIiIiAYEBj9ERERERDQgMPghIiIiIqIBgcHPALNt2zasWrUKubm5kCQJb775pup8VVUV7rjjDuTm5sJsNmP58uU4ceKEqs7ChQshSZLqz3e/+11VnbKyMqxcuRJmsxmZmZn40Y9+hGAw2NOPN6D1Rt/u378fN998M/Lz82EymTB69Gg8/fTTvfF4A1ZvfWZb1NbWIi8vD5IkweFw9NBTUW/264svvogJEybAaDQiMzMT999/f08+2oDWW/1aUlKCxYsXw263IyUlBcuWLcP+/ft7+vEGtHj0LQAUFxdj0aJFsFgssNlsmD9/PjweT/h8XV0dbr31VthsNtjtdtx9991obGzs6ccbUBj8DDButxsTJ07Es88+G3VOCIHrrrsOp06dwltvvYW9e/di8ODBWLJkCdxut6ruPffcg4qKivCfX/3qV+FzoVAIK1euhN/vx44dO/DSSy/hxRdfxMMPP9zjzzeQ9Ubf7tmzB5mZmXjllVdw6NAh/OQnP8HatWvx29/+tsefb6DqjX5t6+6778aECRN65FmoVW/16xNPPIGf/OQn+D//5//g0KFD2LRpE5YtW9ajzzaQ9Ua/NjY2Yvny5SgoKMDOnTvxySefICkpCcuWLUMgEOjxZxyo4tG3xcXFWL58OZYuXYpdu3ahpKQEDzzwAGS59ev4rbfeikOHDmHjxo1Yv349tm3bhnvvvbdXnnHAEDRgARBvvPFG+PjYsWMCgDh48GC4LBQKiYyMDPGHP/whXLZgwQLx/e9/v937vvfee0KWZVFZWRku+/3vfy9sNpvw+XxxfQaKraf6Npbvfe974qqrrupuk6kTerpff/e734kFCxaIzZs3CwCivr4+jq2n9vRUv9bV1QmTySQ2bdrUE82mS+ipfi0pKREARFlZWbjsiy++EADEiRMn4voMFNvl9u3MmTPFQw891O59Dx8+LACIkpKScNn7778vJEkSFy5ciO9DDGAc+aEwn88HADAajeEyWZZhMBjwySefqOr+5S9/QXp6OsaNG4e1a9eiqakpfK64uBjjx49HVlZWuGzZsmVwOp04dOhQDz8FxRKvvo2loaEBqamp8W80XVI8+/Xw4cN47LHH8PLLL6t+C0m9L179unHjRiiKggsXLmD06NHIy8vDjTfeiHPnzvXOg5BKvPp15MiRSEtLw/PPPw+/3w+Px4Pnn38eo0ePRmFhYa88C6l1pm+rq6uxc+dOZGZmYs6cOcjKysKCBQtUfV9cXAy73Y5p06aFy5YsWQJZlrFz585eepr+j//CUdioUaNQUFCAtWvXor6+Hn6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\n", 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" ] @@ -234,17 +234,17 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:25.474454Z", - "iopub.status.busy": "2023-09-02T00:49:25.474315Z", - "iopub.status.idle": "2023-09-02T00:49:25.632682Z", - "shell.execute_reply": "2023-09-02T00:49:25.632380Z" + "iopub.execute_input": "2023-11-09T07:13:55.455166Z", + "iopub.status.busy": "2023-11-09T07:13:55.455031Z", + "iopub.status.idle": "2023-11-09T07:13:55.570302Z", + "shell.execute_reply": "2023-11-09T07:13:55.569976Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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\n", 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" ] @@ -290,10 +290,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:25.634449Z", - "iopub.status.busy": "2023-09-02T00:49:25.634329Z", - "iopub.status.idle": "2023-09-02T00:49:25.652868Z", - "shell.execute_reply": "2023-09-02T00:49:25.652527Z" + "iopub.execute_input": "2023-11-09T07:13:55.571899Z", + "iopub.status.busy": "2023-11-09T07:13:55.571799Z", + "iopub.status.idle": "2023-11-09T07:13:55.583309Z", + "shell.execute_reply": "2023-11-09T07:13:55.582997Z" }, "tags": [] }, @@ -339,17 +339,17 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:25.654618Z", - "iopub.status.busy": "2023-09-02T00:49:25.654506Z", - "iopub.status.idle": "2023-09-02T00:49:25.808203Z", - "shell.execute_reply": "2023-09-02T00:49:25.807926Z" + "iopub.execute_input": "2023-11-09T07:13:55.585204Z", + "iopub.status.busy": "2023-11-09T07:13:55.585085Z", + "iopub.status.idle": "2023-11-09T07:13:55.695779Z", + "shell.execute_reply": "2023-11-09T07:13:55.695478Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -396,17 +396,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:25.809909Z", - "iopub.status.busy": "2023-09-02T00:49:25.809792Z", - "iopub.status.idle": "2023-09-02T00:49:25.966550Z", - "shell.execute_reply": "2023-09-02T00:49:25.966146Z" + "iopub.execute_input": "2023-11-09T07:13:55.697342Z", + "iopub.status.busy": "2023-11-09T07:13:55.697247Z", + "iopub.status.idle": "2023-11-09T07:13:55.808959Z", + "shell.execute_reply": "2023-11-09T07:13:55.808709Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -456,17 +456,17 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:25.968406Z", - "iopub.status.busy": "2023-09-02T00:49:25.968270Z", - "iopub.status.idle": "2023-09-02T00:49:26.122429Z", - "shell.execute_reply": "2023-09-02T00:49:26.122044Z" + "iopub.execute_input": "2023-11-09T07:13:55.810582Z", + "iopub.status.busy": "2023-11-09T07:13:55.810475Z", + "iopub.status.idle": "2023-11-09T07:13:55.920717Z", + "shell.execute_reply": "2023-11-09T07:13:55.920475Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -496,10 +496,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:26.124297Z", - "iopub.status.busy": "2023-09-02T00:49:26.124157Z", - "iopub.status.idle": "2023-09-02T00:49:26.145411Z", - "shell.execute_reply": "2023-09-02T00:49:26.145136Z" + "iopub.execute_input": "2023-11-09T07:13:55.922261Z", + "iopub.status.busy": "2023-11-09T07:13:55.922164Z", + "iopub.status.idle": "2023-11-09T07:13:55.935603Z", + "shell.execute_reply": "2023-11-09T07:13:55.935386Z" } }, "outputs": [ @@ -723,7 +723,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" }, "vscode": { "interpreter": { diff --git a/docs/examples/content-personalization.ipynb b/docs/examples/content-personalization.ipynb index a3d8ced170..b246c6cdae 100644 --- a/docs/examples/content-personalization.ipynb +++ b/docs/examples/content-personalization.ipynb @@ -31,10 +31,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:27.695411Z", - "iopub.status.busy": "2023-09-02T00:49:27.695233Z", - "iopub.status.idle": "2023-09-02T00:49:27.716059Z", - "shell.execute_reply": "2023-09-02T00:49:27.715602Z" + "iopub.execute_input": "2023-11-09T07:13:57.050580Z", + "iopub.status.busy": "2023-11-09T07:13:57.050354Z", + "iopub.status.idle": "2023-11-09T07:13:57.074268Z", + "shell.execute_reply": "2023-11-09T07:13:57.073904Z" } }, "outputs": [ @@ -88,10 +88,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:27.718298Z", - "iopub.status.busy": "2023-09-02T00:49:27.718142Z", - "iopub.status.idle": "2023-09-02T00:49:27.897952Z", - "shell.execute_reply": "2023-09-02T00:49:27.897309Z" + "iopub.execute_input": "2023-11-09T07:13:57.076466Z", + "iopub.status.busy": "2023-11-09T07:13:57.076302Z", + "iopub.status.idle": "2023-11-09T07:13:57.256665Z", + "shell.execute_reply": "2023-11-09T07:13:57.256383Z" } }, "outputs": [], @@ -156,16 +156,16 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:27.899930Z", - "iopub.status.busy": "2023-09-02T00:49:27.899768Z", - "iopub.status.idle": "2023-09-02T00:49:28.220808Z", - "shell.execute_reply": "2023-09-02T00:49:28.220472Z" + "iopub.execute_input": "2023-11-09T07:13:57.258402Z", + "iopub.status.busy": "2023-11-09T07:13:57.258280Z", + "iopub.status.idle": "2023-11-09T07:13:57.506299Z", + "shell.execute_reply": "2023-11-09T07:13:57.500295Z" } }, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -206,16 +206,16 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.222721Z", - "iopub.status.busy": "2023-09-02T00:49:28.222477Z", - "iopub.status.idle": "2023-09-02T00:49:28.340049Z", - "shell.execute_reply": "2023-09-02T00:49:28.339765Z" + "iopub.execute_input": "2023-11-09T07:13:57.512046Z", + "iopub.status.busy": "2023-11-09T07:13:57.510704Z", + "iopub.status.idle": "2023-11-09T07:13:57.614851Z", + "shell.execute_reply": "2023-11-09T07:13:57.614595Z" } }, "outputs": [ { "data": { - "image/png": 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", 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\n", "text/plain": [ "
" ] @@ -242,10 +242,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.341934Z", - "iopub.status.busy": "2023-09-02T00:49:28.341784Z", - "iopub.status.idle": "2023-09-02T00:49:28.354013Z", - "shell.execute_reply": "2023-09-02T00:49:28.353613Z" + "iopub.execute_input": "2023-11-09T07:13:57.616481Z", + "iopub.status.busy": "2023-11-09T07:13:57.616370Z", + "iopub.status.idle": "2023-11-09T07:13:57.625528Z", + "shell.execute_reply": "2023-11-09T07:13:57.625279Z" } }, "outputs": [ @@ -253,13 +253,13 @@ "data": { "text/plain": [ "defaultdict(Zeros (),\n", - " {'politics': 0.06389451550325113,\n", - " 'music': -0.04041254194187752,\n", - " 'finance': -0.040319730234734,\n", - " 'camping': -0.03581829597317823,\n", - " 'food': -0.037778771188204816,\n", - " 'health': -0.04029646665611086,\n", - " 'sports': -0.03661678982763635})" + " {'finance': 0.0,\n", + " 'politics': 0.0,\n", + " 'music': 0.0,\n", + " 'health': 0.0,\n", + " 'sports': 0.0,\n", + " 'camping': 0.0,\n", + " 'food': 0.0})" ] }, "execution_count": 5, @@ -284,16 +284,16 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.355858Z", - "iopub.status.busy": "2023-09-02T00:49:28.355742Z", - "iopub.status.idle": "2023-09-02T00:49:28.470356Z", - "shell.execute_reply": "2023-09-02T00:49:28.469937Z" + "iopub.execute_input": "2023-11-09T07:13:57.627378Z", + "iopub.status.busy": "2023-11-09T07:13:57.627242Z", + "iopub.status.idle": "2023-11-09T07:13:57.706788Z", + "shell.execute_reply": "2023-11-09T07:13:57.706478Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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\n", 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" ] @@ -327,16 +327,16 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.472267Z", - "iopub.status.busy": "2023-09-02T00:49:28.472146Z", - "iopub.status.idle": "2023-09-02T00:49:28.623349Z", - "shell.execute_reply": "2023-09-02T00:49:28.623062Z" + "iopub.execute_input": "2023-11-09T07:13:57.708558Z", + "iopub.status.busy": "2023-11-09T07:13:57.708446Z", + "iopub.status.idle": "2023-11-09T07:13:57.831339Z", + "shell.execute_reply": "2023-11-09T07:13:57.831092Z" } }, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -383,10 +383,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.625183Z", - "iopub.status.busy": "2023-09-02T00:49:28.625070Z", - "iopub.status.idle": "2023-09-02T00:49:28.638642Z", - "shell.execute_reply": "2023-09-02T00:49:28.638322Z" + "iopub.execute_input": "2023-11-09T07:13:57.833117Z", + "iopub.status.busy": "2023-11-09T07:13:57.833018Z", + "iopub.status.idle": "2023-11-09T07:13:57.841532Z", + "shell.execute_reply": "2023-11-09T07:13:57.841238Z" } }, "outputs": [], @@ -419,10 +419,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.640459Z", - "iopub.status.busy": "2023-09-02T00:49:28.640372Z", - "iopub.status.idle": "2023-09-02T00:49:28.653081Z", - "shell.execute_reply": "2023-09-02T00:49:28.652733Z" + "iopub.execute_input": "2023-11-09T07:13:57.843341Z", + "iopub.status.busy": "2023-11-09T07:13:57.843256Z", + "iopub.status.idle": "2023-11-09T07:13:57.851215Z", + "shell.execute_reply": "2023-11-09T07:13:57.850947Z" } }, "outputs": [], @@ -464,10 +464,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.655075Z", - "iopub.status.busy": "2023-09-02T00:49:28.654960Z", - "iopub.status.idle": "2023-09-02T00:49:28.667223Z", - "shell.execute_reply": "2023-09-02T00:49:28.666879Z" + "iopub.execute_input": "2023-11-09T07:13:57.853951Z", + "iopub.status.busy": "2023-11-09T07:13:57.853844Z", + "iopub.status.idle": "2023-11-09T07:13:57.862054Z", + "shell.execute_reply": "2023-11-09T07:13:57.861773Z" } }, "outputs": [ @@ -500,16 +500,16 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.668994Z", - "iopub.status.busy": "2023-09-02T00:49:28.668865Z", - "iopub.status.idle": "2023-09-02T00:49:28.813901Z", - "shell.execute_reply": "2023-09-02T00:49:28.813617Z" + "iopub.execute_input": "2023-11-09T07:13:57.863720Z", + "iopub.status.busy": "2023-11-09T07:13:57.863619Z", + "iopub.status.idle": "2023-11-09T07:13:57.985531Z", + "shell.execute_reply": "2023-11-09T07:13:57.985269Z" } }, "outputs": [ { "data": { - "image/png": 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", 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Pz89izM/PD8XFxaioqDB/4N21YmNjsWTJEnuVSERERDZ04cKFGz4Ms9UHnsZYsGCB+XNtgOrPmmnfvj3S0tLg5ubWrPsyGAzYuXMnRo0a1agPDaT6YZ/tg322D/bZfthr+7BVn0tKShAWFlav9+5WH3j8/f2RnZ1tMZadnQ13d3erR3cAQKPRQKPR1Br38vKCu7t7s9ZnMBjg7OwMb29v/s9kQ+yzfbDP9sE+2w97bR+26nPNtupzOYrD3aXVUOHh4YiPj7cYi4uLQ3h4eAtVRERERI7G4QJPaWkpkpOTkZycDKD6tvPk5GRkZmYCqD4dNW3aNPP8mTNn4vz58/j3v/+N06dPY/Xq1di4cWOtTyQmIiKim5fDBZ5Dhw6hX79+6NevHwAgOjoa/fr1w6JFiwAAV65cMYcfAAgLC8PmzZsRFxeHPn36YPny5fjoo48QGRnZIvUTERGR43G4a3hGjhyJ6z0ayNpTlEeOHIkjR47YsCoiIiJqzRzuCA8RERFRc2PgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJc8jAs2rVKoSGhkKr1WLw4ME4ePDgdeevXLkSXbt2hZOTE4KDg/H000+jsrLSTtUSERGRo3O4wLNhwwZER0cjJiYGhw8fRp8+fRAZGYmcnByr87/44gs8//zziImJwalTp/Dxxx9jw4YNWLhwoZ0rJyIiIkflcIFnxYoVeOqppzBjxgz06NED77//PpydnbFmzRqr8/ft24ehQ4fi0UcfRWhoKMaOHYvJkyff8KgQERER3TyULV3AtfR6PZKSkrBgwQLzmFwuR0REBBITE62uc9ttt+Gzzz7DwYMHMWjQIJw/fx5btmzB1KlT69yPTqeDTqczf19cXAwAMBgMMBgMzfRqYN7mtf8l22Cf7YN9tg/22X7Ya/uwVZ8bsj2HCjy5ubkwGo3w8/OzGPfz88Pp06etrvPoo48iNzcXw4YNgxACVVVVmDlz5nVPacXGxmLJkiW1xrdv3w5nZ+emvYg6xMXF2WS7ZIl9tg/22T7YZ/thr+2juftcXl5e77kOFXgaIyEhAUuXLsXq1asxePBgnD17FnPnzsXLL7+Ml156yeo6CxYsQHR0tPn74uJiBAcHY+zYsXB3d2/W+gwGA+Li4jBmzBioVKpm3Tb9iX22D/bZPthn+2Gv7cNWfa45Q1MfDhV4fHx8oFAokJ2dbTGenZ0Nf39/q+u89NJLmDp1Kp588kkAQK9evVBWVoa///3veOGFFyCX175MSaPRQKPR1BpXqVQ2+4G35bbpT+yzfbDP9sE+2w97bR/N3eeGbMuhLlpWq9UYMGAA4uPjzWMmkwnx8fEIDw+3uk55eXmtUKNQKAAAQgjbFUtERESthkMd4QGA6OhoTJ8+HQMHDsSgQYOwcuVKlJWVYcaMGQCAadOmISgoCLGxsQCA8ePHY8WKFejXr5/5lNZLL72E8ePHm4MPERER3dwcLvBMmjQJV69exaJFi5CVlYW+ffti27Zt5guZMzMzLY7ovPjii5DJZHjxxRdx6dIltG3bFuPHj8err77aUi+BiIiIHIzDBR4AiIqKQlRUlNVlCQkJFt8rlUrExMQgJibGDpURERFRa+RQ1/AQERER2QIDDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJnkMGnlWrViE0NBRarRaDBw/GwYMHrzu/sLAQc+bMQUBAADQaDbp06YItW7bYqVoiIiJydMqWLuCvNmzYgOjoaLz//vsYPHgwVq5cicjISKSmpsLX17fWfL1ejzFjxsDX1xdff/01goKCkJGRAU9PT/sXT0RERA7J4QLPihUr8NRTT2HGjBkAgPfffx+bN2/GmjVr8Pzzz9eav2bNGuTn52Pfvn1QqVQAgNDQUHuWTERERA7OoQKPXq9HUlISFixYYB6Ty+WIiIhAYmKi1XV++OEHhIeHY86cOfj+++/Rtm1bPProo5g/fz4UCoXVdXQ6HXQ6nfn74uJiAIDBYIDBYGjGVwTz9pp7u2SJfbYP9tk+2Gf7Ya/tw1Z9bsj2HCrw5Obmwmg0ws/Pz2Lcz88Pp0+ftrrO+fPnsWPHDkyZMgVbtmzB2bNnMXv2bBgMBsTExFhdJzY2FkuWLKk1vn37djg7Ozf9hVgRFxdnk+2SJfbZPthn+2Cf7Ye9to/m7nN5eXm95zpU4GkMk8kEX19ffPDBB1AoFBgwYAAuXbqEN954o87As2DBAkRHR5u/Ly4uRnBwMMaOHQt3d/dmrc9gMCAuLg5jxowxn3Kj5sc+2wf7bB/ss/2w1/Zhqz7XnKGpD4cKPD4+PlAoFMjOzrYYz87Ohr+/v9V1AgICoFKpLE5fde/eHVlZWdDr9VCr1bXW0Wg00Gg0tcZVKpXNfuBtuW36E/tsH+yzfbDP9sNe20dz97kh23Ko29LVajUGDBiA+Ph485jJZEJ8fDzCw8OtrjN06FCcPXsWJpPJPPb7778jICDAatghIiKim49DBR4AiI6OxocffohPPvkEp06dwqxZs1BWVma+a2vatGkWFzXPmjUL+fn5mDt3Ln7//Xds3rwZS5cuxZw5c1rqJRAREZGDcahTWgAwadIkXL16FYsWLUJWVhb69u2Lbdu2mS9kzszMhFz+Z04LDg7Gzz//jKeffhq9e/dGUFAQ5s6di/nz57fUSyAiIiIH43CBBwCioqIQFRVldVlCQkKtsfDwcOzfv9/GVREREVFr5XCntIiIiIiaGwMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSZ7NA09CQoKtd0FERER0XTYLPHv37sXo0aMxevRoW+2CiIiIqF6UDV3BYDDgiy++QFJSEpRKJYYNG4b777/fvDw5ORnPP/884uLiIITAwIEDm7VgIiIiooZqUOApKSnB8OHDcezYMQghAABvvfUW7r//fnz11VdYtGgRli5dCpPJhP79+2Px4sW45557bFI4ERERUX01KPC89tprOHr0KPr06YMpU6YAAD777DN8++23eOSRR7Bx40Z06tQJb775Ju69916bFExERETUUA0KPN9//z1CQkJw4MABqNVqAEBUVBS6deuGr776CnfddRe+/fZbaDQamxRLRERE1BgNumj5/PnzGDdunDnsAIBWq8Xdd98NAHjzzTcZdoiIiMjhNCjwVFRUwM/Pr9a4r68vAKBr167NUxURERFRM2rW29Llcj7HkIiIiBxPg29LT0lJwcaNG2uNAcBXX31lvnvrWg8//HAjyyMiIiJqugYHnm+++QbffPONxVhNyHnkkUdqjctkMgYeIiIialENCjyLFi2CTCazVS1ERERENtGgwLN48WIblUFERERkOw26yvjxxx/HDz/8YKtaiIiIiGyiQYFn3bp1SE5OtlEpRERERLbB+8iJiIhI8hh4iIiISPIYeIiIiEjyGvwcnk2bNiE9Pb3e82UyGT7++OOG7oaIiIio2TQ48CQnJzfowmUGHiIiImppDQ48jz32GKZPn26LWoiIiIhsosGBJzQ0FCNGjLBFLUREREQ2wYuWiYiISPIYeIiIiEjyGHiIiIhI8hoUeNauXQuFQoGFCxfCYDDUOU+v12PhwoVYtmxZkwskIiIiaqoGBZ6goCAsWrQI3t7eUKlUdc5Tq9Xw8fHBCy+8gJ07dza5SCIiIqKmaFDg+fTTT9GmTRtERUXdcO6cOXPg5eWFtWvXNro4IiIioubQoMCzb98+REREQKPR3HCuRqNBREQE9u7d2+jiiIiIiJpDgwLP5cuX0aFDh3rPDwsLw5UrVxpcFBEREVFzalDgkcvl171Y+a8MBgPkct4IRkRERC2rQWkkMDAQKSkp9Z6fkpKCoKCgBhdFRERE1JwaFHhuv/127Nixo16flp6eno4dO3Zg+PDhja2NiIiIqFk0KPDMmTMHBoMBDz74IHJzc+ucl5eXh4ceeghVVVWYNWtWk4skIiIiaooGfXho//79MW/ePKxcuRI9evTAzJkzMWrUKLRr1w4AcOnSJcTHx+ODDz7A1atXER0djf79+9ukcCIiIqL6avCnpS9fvhxarRZvvPEGXn31Vbz66qsWy4UQUCgUWLBgAV555ZVmK5SIiIiosRoceGQyGZYuXYonnngCa9euxb59+5CVlQUA8Pf3x9ChQ/HYY4+hY8eOzV4sERERUWM0OPDU6NixI4/gEBERUavAh+QQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQ5bOBZtWoVQkNDodVqMXjwYBw8eLBe661fvx4ymQwTJkywbYFERETUajhk4NmwYQOio6MRExODw4cPo0+fPoiMjEROTs5110tPT8ezzz6L22+/3U6VEhERUWvgkIFnxYoVeOqppzBjxgz06NED77//PpydnbFmzZo61zEajZgyZQqWLFmCDh062LFaIiIicnTKli7gr/R6PZKSkrBgwQLzmFwuR0REBBITE+tc7z//+Q98fX3xxBNP4Ndff73uPnQ6HXQ6nfn74uJiAIDBYIDBYGjiK7BUs73m3i5ZYp/tg322D/bZfthr+7BVnxuyPYcLPLm5uTAajfDz87MY9/Pzw+nTp62us2fPHnz88cdITk6u1z5iY2OxZMmSWuPbt2+Hs7Nzg2uuj7i4OJtslyyxz/bBPtsH+2w/7LV9NHefy8vL6z3X4QJPQ5WUlGDq1Kn48MMP4ePjU691FixYgOjoaPP3xcXFCA4OxtixY+Hu7t6s9RkMBsTFxWHMmDFQqVTNum36E/tsH+yzfbDP9sNe24et+lxzhqY+HC7w+Pj4QKFQIDs722I8Ozsb/v7+teafO3cO6enpGD9+vHnMZDIBAJRKJVJTU9GxY0eLdTQaDTQaTa1tqVQqm/3A23Lb9Cf22T7YZ/tgn+2HvbaP5u5zQ7blcBctq9VqDBgwAPHx8eYxk8mE+Ph4hIeH15rfrVs3HD9+HMnJyeY/9957L0aNGoXk5GQEBwfbs3wiIiJyQA53hAcAoqOjMX36dAwcOBCDBg3CypUrUVZWhhkzZgAApk2bhqCgIMTGxkKr1aJnz54W63t6egJArXEiIiK6OTlk4Jk0aRKuXr2KRYsWISsrC3379sW2bdvMFzJnZmZCLne4g1NERETkoBwy8ABAVFQUoqKirC5LSEi47rrr1q1r/oKIiIio1eJhEiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiImo2QghU6I0oqjDAZBItXY6ZsqULICIiIsBkEjidVYKrpToEeGjhrlXBTatEZn45KgxGCAFcLqyAwWhCmd4Id60SAR5OKNNVATKgs68rhABcNEoUlOvhpFJArZTjYkEFTl0pxpnsUuiNRvi7a9GujTPK9FUoqjAgu6gSMpkMRpNAlcmEogoDXDVKuGtVUCvlqDAYcSG/AhqlHB7OKrioFTAYBdy1SlSZBFKzSuCqVcLfQ4vMvHIkZRQgp0QHANAo5fB0VqHKKOCnkmPcuJbrLwMPEcFgNOFqiQ6p2SXYfz4P3i5q5JXq0c7LGeN6+kOrUkAmA1Sylq5UWioNRmiUcshkMphMAnK59QYLISAEUFCuR36ZHkkZBdCqFPBx1UAhl8FJrcCVwgqcySmFWilHUYUBZ7JL0dZNg65+rnDWKJFTXIkrRZWoNJjQ1k2DvsGe8PfQQgYgp0SHiwXlyCqqRKXBCKVCDh9XDUK8ndHZ1xVhPi5QKuQQQkBvNKFMZ0SV0QQPZxU0SgUu5Jcjp0QHN60SKoUcBeV6ZOaV4/zVUhy5UAhntQK3hnqZ96mUy+HnroFM1vAfKCEEsot1yC3VQauqfq1ymQy6KhM0Sjk0SgWyiitwKL0AGfnl0FeZoFbKEeTpBA8nFboHuCHQ0wleLmq4qJVQ1/M8h8lU/dqrTAJluiq4a1XIK9PhUkF1368UVeDE5WJkFVXiYkEF2rio0NZVg5wSHeQyGXoGuaO7vzuUCjnK9VVw1SiRkV+OhNM50BtNaOOsRmGFAVf/CApSoasyIbu4+jW5ubbsPyAMPEQ3me0nsvDernO4raM37uoZgNd/TsXu36/WOf+lTSnmr100CpTplJibuB2Dw7wwNTwEOcU6KP54o+4e4I6s4koMDGmDAA8tdFUmnMkuRfcANygVf76zCCEgk8lQVGGAu1YJo0mgwmCEq0ZZrzfB7OJK6AwmuDsp4aRWQF9lgpNKYbGP+jCZBLKKK+GsViAttwwXCyrg7arGLQEeMJhM8HZRA4DVmkwmgSvFlcgv1cNVq0SAhxYH0vKRXVyJgjI9XDRK3BrqBbkMOJ9bBoPRBH2VCWdySnE2pxQH0/L/eLMGrj3qr1XJ0dnXFfIKOb7JTcKVIh3O5JQ26HXZglZV3dtKg8k8JpcBbd005je06/n5RHatMTetEgq5DCHeLvB2UcPTSQWjEKgyCrTzckJbVw1UCjlkMiC3VI8D5/NwMD0fohnPkrTz1EJZpcC3uYdRpjcit1SHKpOATAaUVFbBaBRw0ShRWKG3eO03UqqrwoX8CvP3mfnl2HI8q875uaV6ANVHRHzdNcgv1aNMbwQAqJVy89GU9l7O0KoUcFYrUFpZhaziSijkMijlMqTnlcNo5RSSh5MKYT4u6ObvBi8XdXUoK6yAVqWAp5MKvm4aAIBCIYPRKOCmVUFXZUS53ogKvRFuWiW8XTVQKWQoLDeguNIArUqBksoqVBlNCPVxgUwGZBVVQqOUY2RXX3T1d4OrRomsokrkl+shjEYcPbi33v2zBQYeoiZKzSpBWzcNvFzUFm/cN5JfpkdpZRXK9FV4+aeTyCnR4baO3njq9g4I9nKuc72MvDIkpF7FnT394eeuRZXRhPxyPdy1KshkwMs/ncRn+zMBAE8MC8PskR3h7arBlaIK3PP2HuSVVf/DeiSzEKt2nmvQay3TGc1fH0jLx4G0/Hqv2yvIA5cKK5D/x/7r4qZVwkmlwLheAegR6I68Uj3OXS3Fb+n5yMwvv+6bXXsvZ9zW0Rsluio4qxQIa+sCIYBdqVdxoaAclQYjuvm7w02rxNVSHY5kFtardqVchgBPLbxcNCiprD4FUPNm1FR/fX+qNJhw/FIxADmQn2d1HX93LVRKGUoqq1BpMKLSYIKzWoFQbxfI5YCnk/qPnyGBqyV6XC2phJeLGp18XQEABeUGnLxcjCtFFSjVVSHYyxk+rhq0ddPA7Y/QWVShR1puOdJzy1Dxxz6s1V4Tdlw1SvMpEa1KgeA2zujq74YADy2cVAocvViEI5kF5p8/oDpQAEBheWGD++amUaJMXwVPZzWqjNVHcfRVJuiNJjirlRgY0gbdA9zRxlmFCoMJmfllKCw34ExOKbKKKlGqq973xcJKADKkn8mtc18lf8z9K7kM8HRWo3uAG7xcNOjs64oObV3Q2dcNBeV6ZBVVwkWjhFYlx6krxThxuRhGk4CrRomiCgMUchlu7+yDTr5uKKk0QKNUoF97T2hVCgghYDQJlOqq4OGkqtcvAvoqExRyGapMJihkMijk1Ue+tCpFg/vbXEJ9XBAKFxgMBlw81mJlAGDgoVZKCIHM/HIEt3Gu8zQAABRVGOCsVkBVx2/+VUZTnUcFqowmHEzPxy0BHvBwVgEAynRVuFhQgX3nctG7nSde23oaB9Nv/Kbfv70nrpbqLH7js+ZsTik+Tcwwfz9rZEeM6eEHk0ngo1/TsO3En78hxvxw4ob7/XhPGj7ek3bDeQEeWrz7aH90D3CDs1ppPgIjhEBSRgGKKw1QKeSIP5mF3SkZ6B4SAHdnFbamZKGw3HDD7QPA8UtF9ZpXUlmFksoqrNuXXq/518rML0dmfvl15ySetx4iajipFKgwWIaZKpPAhfyKG/79AUCQpxN0VUZUmYS5Ny5qhflNyyQEOrR1QTd/dwz440iYq0YJkwBKdQaU641Iv1qKXYdS0LFjR/h7OqFHgDsEqsNgjwD3Rp0Kqsv1TqUB1f8f5JfpUVRhgADg6aSCu5MKCrkMeaV6pOeVIdTbBf4e2nrtr8pogkD10YArRZUQovooW1GFATnFOigVMmhVCmTml+PCHwG30mCEi6Y6xNzZ0x+ezmq0ddPAaBLmo4sNcW2YSLlYgM27DqBbj55QKBTwc9eajzpqVApolHLoqkxw1SjR1k0DIQQ8nFQo0xnhpK6+RqY+bu/ctkE1ymQyKBUyeDqr671OTS0K+Z8BpyXDjqNh4CGHlluqg0Ypx+msEjz5ySEUVdR+c106sRdW7zyDi4XVp1r6t/eERqmo9ca2ekp/jOsVgIsF5TibU4q1e9Ox649TOV88NRi3dfSBEAJv/JyK1QkNO/JxI4freSThr95LOIf3GlnL6G6+yCnR1Qoa9/YJxFuP9IVMJsOF/HIEeGhrhb6aN1SZTIaBoV7m8SGhnuiH8xg3rjdUKhVixt+Ccr0RXi5//qMshMC5q6U4klmIMl0V7h/QDum5ZfjlZDaOXSpCcBtndAtww+9ZJegT7AkPJxWc1dWnplQKGU5dKUHKpSJcLChH/OkcqORyCFT/Vjyqmy96B3ngtk4+8HPTokRngFohR5VJILu4EmdzSrHl+BX8nl2Krv5uyC/T42qJDgajCX2DPdHxjyMcJy8Xw89dgzYuajw+NAwVeiMCPZ2gUlRfuHk6qwS+bhoUVRigqzLhaqkOOcWVyMwvh6tGhUBPLcJ8XNAryAMymQxVRhPOXS1DUBunWkf3GvOmPCTUE+5Xj2Hc2M5QqVQNWrehrhd2AECpkMPXXQtf99qBxt9DW++gc+32ACDYy/m6RzLrozFhB7AME4PDvJB3SmDcoOAG9drDmTc5tzYMPNRi9FUmPPT+Phy9WP2G/K/RnXF/vyCE+rgAAPaezcWUjw7ccDsLvztu8X1d4WL254fr3MajH954P3XxdFbh0UHt8X/7M+DtosaDA9ph5S9nUFXH7Zi923ngSlElrpbo8PH0gfjq0EW89kBveDirIITAL6dysOG3TPxyKsfq+l393PDDP4fi2MUifHv4IioNJsyL6Iz8Mj0uFFRgSJiX+c1JCIGfT2Rja8oVDOngjQl9g+Ck/vM3vqa+4WhVilq/QcpkMnTydUMnX7drXrMnerfzrNc2bwn0wIMD2tVrbs2RNwAI9HRCv/Zt8NDA4HqtWxelQoaeQR4AYPVN3vo6cnT1d7O6rLFvykTUvBh4yGaKyg3Yey4XPQLcEeLtbD5qIIRAQbkB63/LNIcdAHg7/gzejj/ToH24apTmc/HWDO3kjdNXSiyuG6ivQWFemDmiA1IuFWNF3O9w1yoR5uOCAA8nvHB3d6iVcrholObf6P99ZzfzulF3dK61vUqD0erh5dHd/cxfy2QyjOnhhzE9/hwrLNdDIZfBRa20+G381lAv3HrN0ZcQbxf0a9/GYtsymQx39vTHnT39G/z6iYikhIGHbOLt+DNYEfd7rfFvZ9+G+1fva9Q2X77vFvxtSIjF9QtCCJy4XAwFTPj90G6MGzcOBiFDTrHOfKSoxtq9aVjy40nc0zsA08JDkVVciYjuvnBSKfB10kU89/WfV9SdefUu83U/d3Tzw79G1w4wDdXYc+kNOYdPRETWMfBQs4vemIxvD1+yusxa2PklegQCPbXYdzYPi75PweWiSgDVt8H+PG842v9x2sXahZoyWfXpB4PBgJp45axWItSn9o/2jKFhmDE0zGpdDw0MbvKpECIiclwMPNQkQgiMXr4L3QPcMbFfEE5eKa4z7FjTN9jTfKtsRA8/RFxzKoeIiKi5OGzgWbVqFd544w1kZWWhT58+eOeddzBo0CCrcz/88EN8+umnSEmpfkDagAEDsHTp0jrnU/NZ+F0KzueW4XxuGTYfv2Kx7NvZt6H/NdeUXCmqQHjsDnTzd8PWubcDsH7UhoiIqLk55H11GzZsQHR0NGJiYnD48GH06dMHkZGRyMmxftdKQkICJk+ejJ07dyIxMRHBwcEYO3YsLl2q/5EGapjCcj2eWPcbvjyYWeec/n+5gDbAwwnpy+7GtnnDIZPJGHaIiMhuHDLwrFixAk899RRmzJiBHj164P3334ezszPWrFljdf7nn3+O2bNno2/fvujWrRs++ugjmEwmxMfH27ly6SqqMGDt3jRk5pVjdcJZ9P1PHOJP/xlA+7Srvo33sdtCMaJLW6TFtuAnxBEREf2Fw53S0uv1SEpKwoIFC8xjcrkcERERSExMrNc2ysvLYTAY4OXlZXW5TqeDTvfnZ78UFxcDAAwGAwyG+j01tr5qttfc27W3Pku2AwCW/HjS6vKv/zHY4vuqqrpvFbcFqfTZ0bHP9sE+2w97bR+26nNDtudwgSc3NxdGoxF+fpYXr/r5+eH06dP12sb8+fMRGBiIiIgIq8tjY2OxZMmSWuPbt2+Hs3PTHsRWl7i4OJts1x4qjUBdPyr9vE0YE2TCli1b7FpTXVpzn1sT9tk+2Gf7Ya/to7n7XF5+/Y+SuZbDBZ6mWrZsGdavX4+EhARotdafkrpgwQJER0ebvy8uLjZf9+Pu7t6s9RgMBsTFxWHMmDE2f0S8rfx07Apw0PJpxgEeWkwa2A5zRnZooaosSaHPrQH7bB/ss/2w1/Zhqz7XnKGpD4cLPD4+PlAoFMjOzrYYz87Ohr//9Z8W++abb2LZsmX45Zdf0Lt37zrnaTQaaDSaWuMqlcpmP/C23LYtVeiNePqr6rDjolbgtxcj4Kx2uB8bs9ba59aGfbYP9tl+2Gv7aO4+N2RbDnfRslqtxoABAywuOK65ADk8PLzO9V5//XW8/PLL2LZtGwYOHGiPUiUt7mQ2Oi7cgu6LtpnHnh7TxaHDDhERUV0c8t0rOjoa06dPx8CBAzFo0CCsXLkSZWVlmDFjBgBg2rRpCAoKQmxsLADgtddew6JFi/DFF18gNDQUWVlZAABXV1e4urq22OtobYQQMAlALgOe+vRQreWP1/GUYiIiIkfnkIFn0qRJuHr1KhYtWoSsrCz07dsX27ZtM1/InJmZCbn8z4NT7733HvR6PR588EGL7cTExGDx4sX2LL3VqP4U7Sx4u2qQmVeOD3afR2p2CVQKGQzG2p/y/eu/R1l8cCUREVFr4pCBBwCioqIQFRVldVlCQoLF9+np6bYvqJXTVRmx92wubuvog9e2ncbavelW5/017Hw1Mxzd/N3gpuW5bSIiar0cNvBQ05Xrq/DLqRz868sjjVo/ZUkkXDX8ESEiotaP72YScig9H+29nOGqVSLlUjH2nM3F2/Fn6rVuJ19XRI3qhLTcMsSdzMa6Gbcy7BARkWTwHU0iTl4uxoPvJ8JVo0RXfzckZRRcd36Hti7Y8cxIq8ueHtPFBhUSERG1HAYeiZi25iAAoFRXZTXsrHi4D7xdNfjPjycwKMwbsff3sneJRERELYaBRwLejj+D3FJdncuPLx5rvug4vo6jOkRERFLGwCMBK+J+tzr+4bSB6OrHO6yIiIgYeFqpy4UVOHG5GKU6y0+K/SV6BArK9ejTzhNqpcM9SJuIiKhFMPC0UvPWJ+Nger7F2LNju6CTL58sTURE9Fc8BNBK/TXsAEDUHZ1boBIiIiLHx8DTChWU6WuNJS8a0wKVEBERtQ4MPK3Q5aIK89dt3TR4/28D4OmsbsGKiIiIHBuv4WmFDpz/83TWby9EtGAlRERErQOP8LRCmfnlLV0CERFRq8LA0wqpFDIAwPTwkBauhIiIqHVg4GllhBD48Nc0AICvu7aFqyEiImodeA1PK1FpMGLpllPofM1zdty0/OsjIiKqD75jthLdXtpWa+zhgcEtUAkREVHrw1NarUBOcaXVca1KYedKiIiIWicGnlbgqpVPQp8zqmMLVEJERNQ68ZRWK1BQ9ucHhL71SF8YjAL39wtqwYqIiIhaFwaeViDlchEAoE87D9zXl0GHiIiooXhKqxVYtvU0AODoxaIWroSIiKh1YuBpRdp7Obd0CURERK0SA4+DE0KYv37pnh4tWAkREVHrxcDj4H48dsX89e2dfVqwEiIiotaLgcfBLd18yvw1n7tDRETUOAw8Dq5vsCcAQK3gXxUREVFj8V3UwVWZqq/hWXzvLS1cCRERUevFwOPgSiqrHzrIDwolIiJqPAYeB1dcWQUAcHdStXAlRERErRcDj4OrOcLjziM8REREjcbA4+CKK2pOafEIDxERUWMx8Dgwk0lcc0qLR3iIiIgai4HHge1PyzN/7c4jPERERI3GwOPAak5nAXzoIBERUVMw8DiwMp0RADC0k3cLV0JERNS6MfA4sBc2HQcAHDif38KVEBERtW4MPA4qt1SHSoMJAODlom7haoiIiFo3Bh4Hdexiofnr5yK7tlwhREREEsDA46D0VSbz13f3DmjBSoiIiFo/Bh4HlF+mx8zPDgMABoV6wVnNZ/AQERE1BQOPA5r0v0Tz14cyeMEyERFRUzHwOBghBM7klJq/nzOqUwtWQ0REJA0MPA5GbzRZfD93dOcWqoSIiEg6GHgczFeHLpq/jnt6OJQK/hURERE1Fd9NHcyLm1LMX3f2c2vBSoiIiKSDgceBCCFaugQiIiJJYuBxICmXis1fr5zUt+UKISIikhgGHgdSdM2no9/bJ7AFKyEiIpIWBh4H8lt69TN3VAoZ5HJZC1dDREQkHQw8DkSrUgAADEZey0NERNScGHgciOmPi5YfHtiuhSshIiKSFgYeB1KmqwIAfnYWERFRM2PgcSDleiMAwFmtaOFKiIiIpIWBx4FUMPAQERHZBAOPAyk31AQentIiIiJqTgw8DuT81epPSecRHiIioubFwOMgSnVVOHG5+knLTgw8REREzYqBx0HkFFeav+4Z5NGClRAREUkPA4+D0FWZAADuWiU6tnVt4WqIiIikhYHHQdQEHjetqoUrISIikh4GHgeh++MOLY2KfyVERETNzWHfXVetWoXQ0FBotVoMHjwYBw8evO78r776Ct26dYNWq0WvXr2wZcsWO1XaPLafzAYAqBUO+1dCRETUajnku+uGDRsQHR2NmJgYHD58GH369EFkZCRycnKszt+3bx8mT56MJ554AkeOHMGECRMwYcIEpKSk2LnyxtFXmfDxnjQAgEbpkH8lRERErZpDvruuWLECTz31FGbMmIEePXrg/fffh7OzM9asWWN1/ltvvYU777wTzz33HLp3746XX34Z/fv3x7vvvmvnyhunsspo/jrqjs4tWAkREZE0OdwjffV6PZKSkrBgwQLzmFwuR0REBBITE62uk5iYiOjoaIuxyMhIbNq0yZal3lBhuR57fs/B0TwZFCeyoVRaf75OSWWV+evR3XztVR4REdFNw+ECT25uLoxGI/z8/CzG/fz8cPr0aavrZGVlWZ2flZVldb5Op4NOpzN/X1RUBADIz8+HwWBoSvkWjl4swuxPkqq/OW49rF1LrZSjoCC/2fZ/MzEYDCgvL0deXh5UKt7pZivss32wz/bDXtuHrfpcUlICABBC3HCuwwUee4iNjcWSJUtqjYeFhbVANZZ83mzpCoiIiFqXkpISeHhc/6G9Dhd4fHx8oFAokJ2dbTGenZ0Nf39/q+v4+/s3aP6CBQssToGZTCbk5+fD29sbMpmsia/AUnFxMYKDg3HhwgW4u7s367bpT+yzfbDP9sE+2w97bR+26rMQAiUlJQgMDLzhXIcLPGq1GgMGDEB8fDwmTJgAoDqQxMfHIyoqyuo64eHhiI+Px7x588xjcXFxCA8Ptzpfo9FAo9FYjHl6ejZH+XVyd3fn/0x2wD7bB/tsH+yz/bDX9mGLPt/oyE4Nhws8ABAdHY3p06dj4MCBGDRoEFauXImysjLMmDEDADBt2jQEBQUhNjYWADB37lyMGDECy5cvx913343169fj0KFD+OCDD1ryZRAREZGDcMjAM2nSJFy9ehWLFi1CVlYW+vbti23btpkvTM7MzIRc/ucd9bfddhu++OILvPjii1i4cCE6d+6MTZs2oWfPni31EoiIiMiBOGTgAYCoqKg6T2ElJCTUGnvooYfw0EMP2biqhtNoNIiJial1Co2aF/tsH+yzfbDP9sNe24cj9Fkm6nMvFxEREVEr5pBPWiYiIiJqTgw8REREJHkMPERERCR5DDxEREQkeQw8NrRq1SqEhoZCq9Vi8ODBOHjwYEuX5NB2796N8ePHIzAwEDKZrNaHvwohsGjRIgQEBMDJyQkRERE4c+aMxZz8/HxMmTIF7u7u8PT0xBNPPIHS0lKLOceOHcPtt98OrVaL4OBgvP7667Z+aQ4lNjYWt956K9zc3ODr64sJEyYgNTXVYk5lZSXmzJkDb29vuLq64oEHHqj1NPPMzEzcfffdcHZ2hq+vL5577jlUVVVZzElISED//v2h0WjQqVMnrFu3ztYvz2G899576N27t/lBa+Hh4di6dat5OXtsG8uWLYNMJrN4EC173XSLFy+GTCaz+NOtWzfz8lbRY0E2sX79eqFWq8WaNWvEiRMnxFNPPSU8PT1FdnZ2S5fmsLZs2SJeeOEF8e233woA4rvvvrNYvmzZMuHh4SE2bdokjh49Ku69914RFhYmKioqzHPuvPNO0adPH7F//37x66+/ik6dOonJkyeblxcVFQk/Pz8xZcoUkZKSIr788kvh5OQk/ve//9nrZba4yMhIsXbtWpGSkiKSk5PFuHHjRPv27UVpaal5zsyZM0VwcLCIj48Xhw4dEkOGDBG33XabeXlVVZXo2bOniIiIEEeOHBFbtmwRPj4+YsGCBeY558+fF87OziI6OlqcPHlSvPPOO0KhUIht27bZ9fW2lB9++EFs3rxZ/P777yI1NVUsXLhQqFQqkZKSIoRgj23h4MGDIjQ0VPTu3VvMnTvXPM5eN11MTIy45ZZbxJUrV8x/rl69al7eGnrMwGMjgwYNEnPmzDF/bzQaRWBgoIiNjW3BqlqPvwYek8kk/P39xRtvvGEeKywsFBqNRnz55ZdCCCFOnjwpAIjffvvNPGfr1q1CJpOJS5cuCSGEWL16tWjTpo3Q6XTmOfPnzxddu3a18StyXDk5OQKA2LVrlxCiuq8qlUp89dVX5jmnTp0SAERiYqIQojqcyuVykZWVZZ7z3nvvCXd3d3Nv//3vf4tbbrnFYl+TJk0SkZGRtn5JDqtNmzbio48+Yo9toKSkRHTu3FnExcWJESNGmAMPe908YmJiRJ8+fawuay095iktG9Dr9UhKSkJERIR5TC6XIyIiAomJiS1YWeuVlpaGrKwsi556eHhg8ODB5p4mJibC09MTAwcONM+JiIiAXC7HgQMHzHOGDx8OtVptnhMZGYnU1FQUFBTY6dU4lqKiIgCAl5cXACApKQkGg8Gi1926dUP79u0tet2rVy/z08+B6j4WFxfjxIkT5jnXbqNmzs34/4DRaMT69etRVlaG8PBw9tgG5syZg7vvvrtWP9jr5nPmzBkEBgaiQ4cOmDJlCjIzMwG0nh4z8NhAbm4ujEajxV8sAPj5+SErK6uFqmrdavp2vZ5mZWXB19fXYrlSqYSXl5fFHGvbuHYfNxOTyYR58+Zh6NCh5o9iycrKglqtrvWBun/t9Y36WNec4uJiVFRU2OLlOJzjx4/D1dUVGo0GM2fOxHfffYcePXqwx81s/fr1OHz4sPnzFa/FXjePwYMHY926ddi2bRvee+89pKWl4fbbb0dJSUmr6bHDfrQEEdnenDlzkJKSgj179rR0KZLUtWtXJCcno6ioCF9//TWmT5+OXbt2tXRZknLhwgXMnTsXcXFx0Gq1LV2OZN11113mr3v37o3BgwcjJCQEGzduhJOTUwtWVn88wmMDPj4+UCgUta5Qz87Ohr+/fwtV1brV9O16PfX390dOTo7F8qqqKuTn51vMsbaNa/dxs4iKisJPP/2EnTt3ol27duZxf39/6PV6FBYWWsz/a69v1Me65ri7u7eafyCbSq1Wo1OnThgwYABiY2PRp08fvPXWW+xxM0pKSkJOTg769+8PpVIJpVKJXbt24e2334ZSqYSfnx97bQOenp7o0qULzp4922p+nhl4bECtVmPAgAGIj483j5lMJsTHxyM8PLwFK2u9wsLC4O/vb9HT4uJiHDhwwNzT8PBwFBYWIikpyTxnx44dMJlMGDx4sHnO7t27YTAYzHPi4uLQtWtXtGnTxk6vpmUJIRAVFYXvvvsOO3bsQFhYmMXyAQMGQKVSWfQ6NTUVmZmZFr0+fvy4RcCMi4uDu7s7evToYZ5z7TZq5tzM/w+YTCbodDr2uBmNHj0ax48fR3JysvnPwIEDMWXKFPPX7HXzKy0txblz5xAQENB6fp6b5dJnqmX9+vVCo9GIdevWiZMnT4q///3vwtPT0+IKdbJUUlIijhw5Io4cOSIAiBUrVogjR46IjIwMIUT1bemenp7i+++/F8eOHRP33Xef1dvS+/XrJw4cOCD27NkjOnfubHFbemFhofDz8xNTp04VKSkpYv369cLZ2fmmui191qxZwsPDQyQkJFjcYlpeXm6eM3PmTNG+fXuxY8cOcejQIREeHi7Cw8PNy2tuMR07dqxITk4W27ZtE23btrV6i+lzzz0nTp06JVatWnVT3cb7/PPPi127dom0tDRx7Ngx8fzzzwuZTCa2b98uhGCPbenau7SEYK+bwzPPPCMSEhJEWlqa2Lt3r4iIiBA+Pj4iJydHCNE6eszAY0PvvPOOaN++vVCr1WLQoEFi//79LV2SQ9u5c6cAUOvP9OnThRDVt6a/9NJLws/PT2g0GjF69GiRmppqsY28vDwxefJk4erqKtzd3cWMGTNESUmJxZyjR4+KYcOGCY1GI4KCgsSyZcvs9RIdgrUeAxBr1641z6moqBCzZ88Wbdq0Ec7OzmLixIniypUrFttJT08Xd911l3BychI+Pj7imWeeEQaDwWLOzp07Rd++fYVarRYdOnSw2IfUPf744yIkJESo1WrRtm1bMXr0aHPYEYI9tqW/Bh72uukmTZokAgIChFqtFkFBQWLSpEni7Nmz5uWtoccyIYRonmNFRERERI6J1/AQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEJFNyWQyjBw5sqXLaDYJCQmQyWRYvHhxS5dCRA3AwENEdvfYY49BJpMhPT29pUuxSmohjYgAZUsXQETSdurUKTg7O7d0Gc1m0KBBOHXqFHx8fFq6FCJqAAYeIrKpbt26tXQJzcrZ2Vlyr4noZsBTWkQEwPLalEOHDmHMmDFwc3ODh4cHJk6c2OjTT389PRQaGopPPvkEABAWFgaZTGb1FFJaWhqefPJJtG/fHhqNBgEBAXjssceQkZFR5z4uXbqEadOmwd/fH3K5HAkJCQCAnTt34vHHH0fXrl3h6uoKV1dXDBw4EB988IHVHgDArl27zLXJZDKsW7euVp/+KiUlBQ8//DB8fX2h0WgQFhaGefPmIS8vr9bc0NBQhIaGorS0FHPnzkVgYCA0Gg169+6Nr7/+utb8oqIiLFq0CD169ICrqyvc3d3RqVMnTJ8+3WpPiMgSj/AQkYXffvsNr7/+OkaNGoV//OMfOHLkCDZt2oTjx48jJSUFWq22SdufN28e1q1bh6NHj2Lu3Lnw9PQEUB0Aahw4cACRkZEoKyvDPffcg86dOyM9PR2ff/45tm7disTERHTo0MFiu3l5eQgPD4eXlxceeeQRVFZWwt3dHQDw2muv4ezZsxgyZAgmTpyIwsJCbNu2Df/4xz+QmpqK5cuXm2uIiYnBkiVLEBISgscee8y8/b59+173de3ZsweRkZHQ6/V48MEHERoaisTERLz11lv46aefsH///lqnwQwGA8aOHYuCggI88MADKC8vx/r16/Hwww9j27ZtGDt2LABACIHIyEgcOHAAQ4cOxZ133gm5XI6MjAz88MMPmDp1KkJCQhrxt0F0E2m2z10nolZt586dAoAAINavX2+xbOrUqQKA+PLLLxu8XQBixIgRFmPTp08XAERaWlqt+Xq9XoSGhgo3Nzdx+PBhi2W//vqrUCgU4p577qm1DwBixowZoqqqqtY2z58/X2vMYDCIMWPGCIVCITIyMm5Yc42aPsXExJjHjEaj6NixowAgtm3bZjH/ueeeEwDE448/bjEeEhIiAIj77rtP6HQ68/gvv/wiAIjIyEjz2LFjxwQAMWHChFr1VFZWipKSEqu1EtGfeEqLiCwMHz4ckyZNshh7/PHHAVQf/bG1n376Cenp6XjuuefQr18/i2XDhg3Dfffdhy1btqC4uNhimVqtxuuvvw6FQlFrm2FhYbXGlEolZs6cCaPRiJ07dzap5r179+LcuXO46667EBkZabFs0aJF8PLywhdffAG9Xl9r3f/+979Qq9Xm70ePHo2QkBCrvXZycqo1ptFo4Orq2qT6iW4GPKVFRBYGDBhQa6xdu3YAgMLCQpvvf//+/QCA1NRUq9fJZGVlwWQy4ffff8fAgQPN42FhYXXeOVVSUoI333wTmzZtwrlz51BWVmax/PLly02q+ciRIwBg9Vb2muuFtm/fjtTUVPTq1cu8zNPT02oYa9euHRITE83fd+/eHb1798aXX36JixcvYsKECRg5ciT69u0LuZy/txLVBwMPEVmoue7lWkpl9T8VRqPR5vvPz88HAHz++efXnffX0OLn52d1nl6vx8iRI3H48GH069cPU6dOhbe3N5RKJdLT0/HJJ59Ap9M1qeaao0111RAQEGAxr4aHh4fV+UqlEiaTyeL7HTt2YPHixfjmm2/wzDPPAADatm2LqKgovPDCC1aPbBHRnxh4iMih1ASuH3/8Effcc0+916u5u+qvvv/+exw+fBhPPPEEPvroI4tl69evN98x1hQ1NWdnZ1tdnpWVZTGvMby9vfHOO+/g7bffxunTp7Fjxw688847iImJgUqlwoIFCxq9baKbAY+FEpHd1RyNsHbEaPDgwQBgcUqnKc6dOwcAuO+++2ot+/XXX62uI5fLG3Q0q+Zao5rb4K9VVlaGQ4cOwcnJCV27dq33Nusik8nQvXt3zJkzB3FxcQCAH374ocnbJZI6Bh4isjsvLy8AwIULF2otu++++9C+fXusWLECu3fvrrXcYDBgz5499d5Xze3af11n165d+PDDD+us7+LFi/Xex9ChQ9GxY0ds3boVv/zyi8WyV155BXl5eZg8ebLFxckNkZ6ebvU5SDVHlJr6qACimwFPaRGR3d1xxx1488038fe//x0PPPAAXFxcEBISgqlTp0Kj0eDrr7/GXXfdhREjRuCOO+5Ar169IJPJkJGRgV9//RXe3t44ffp0vfY1fvx4hIaG4vXXX0dKSgp69uyJ1NRU/PTTT5g4caLVh/zdcccd2LhxIyZMmIB+/fpBoVDg3nvvRe/eva3uQy6XY926dYiMjMS4cePw0EMPISQkBImJiUhISEDHjh2xbNmyRvcrOTkZ999/PwYNGoQePXrA398fly5dwqZNmyCXy/H00083ettENwsGHiKyu7vuuguvv/46PvzwQyxfvhwGgwEjRozA1KlTAQC33norjh49ijfeeANbtmzB3r17odFoEBQUhAkTJmDy5Mn13perqyt27NiB5557Drt370ZCQgJuueUWfP755/Dz87MaeN566y0AwI4dO/Djjz/CZDKhXbt2dQYeoPqW+f379+M///kPtm/fjqKiIgQGBmLu3Ll48cUXm/TZWwMHDsT8+fORkJCAzZs3o7CwEP7+/oiIiMBzzz2HIUOGNHrbRDcLmRBCtHQRRERERLbEa3iIiIhI8hh4iIiISPJ4DQ8RNcjKlSvr9cTlxx57zOIDQYmIWhKv4SGiBgkNDUVGRsYN5+3cudPqRy0QEbUEBh4iIiKSPF7DQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREkvf/E9tKnYfRI7AAAAAASUVORK5CYII=\n", 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" ] @@ -537,16 +537,16 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.815797Z", - "iopub.status.busy": "2023-09-02T00:49:28.815645Z", - "iopub.status.idle": "2023-09-02T00:49:28.968182Z", - "shell.execute_reply": "2023-09-02T00:49:28.967884Z" + "iopub.execute_input": "2023-11-09T07:13:57.987101Z", + "iopub.status.busy": "2023-11-09T07:13:57.987012Z", + "iopub.status.idle": "2023-11-09T07:13:58.115909Z", + "shell.execute_reply": "2023-11-09T07:13:58.115635Z" } }, "outputs": [ { "data": { - "image/png": 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", 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bG3vVm2JVVVVZ3CrdZDKhoKAAPj4+vF06ERFREyGEQElJCYKCgq757L4mH3iys7Ph7+9vsczf3x/FxcWoqKiAi4tLnW0SExOtcjMyIiIikt7JkyctHnBbnyYfeK7HzJkzER8fb35fVFSE1q1bIzMzE25ublY9lsFgwMaNGzFw4ECr3cWW6mI/2wf72T7Yz/bDvrYPW/VzSUkJwsPDG/Td3eQDT0BAAHJyciyW5eTkwN3dvd7RHaDmqcb1PUDP29sb7u7uVq3PYDBAp9PBx8eHf5lsiP1sH+xn+2A/2w/72j5s1c+1+2rIdBSHu0qrsaKjo5GcnGyxLCkpCdHR0RJVRERERI7G4QJPaWkp0tLSzA8WzMzMRFpaGk6cOAGg5nTUuHHjzO2feuopHDt2DP/+979x8OBBfPDBB/jmm2/w7LPPSlE+EREROSCHCzy7du1Cjx490KNHDwBAfHw8evTogVmzZgEAzp49aw4/ABAeHo5ff/0VSUlJ6N69OxYsWIBPPvkEsbGxktRPREREjsfh5vAMGDAAV7s1UH13UR4wYAD27t1rw6qIiIioKXO4ER4iIiIia2PgISIiIqfHwENEREROj4GHiIiInB4DDxERETk9Bh4iIiJyegw8RERE5PQYeIiIiMjpMfAQERGR02PgISIiIqfHwENEREROj4GHiIiInB4DDxERETk9Bh4iIiJyegw8RERE5PQYeIiIiMjpMfAQERGR02PgISIiIqfHwENEREROj4GHiIiInB4DDxERETk9Bh4iIiJyegw8RERE5PQYeIiIiMjpMfAQERGR02PgISIiIqfHwENEREROj4GHiIiInB4DDxERETk9Bh4iIiJyegw8RERE5PQYeIiIiMjpMfAQERGR02PgISIiIqfHwENEREROj4GHiIiInB4DDxERETk9Bh4iIiJyegw8RERE5PQYeIiIiMjpMfAQERGR02PgISIiIqfHwENEREROj4GHiIiInB4DDxERETk9Bh4iIiJyegw8RERE5PQYeIiIiMjpMfAQERGR02PgISIiIqfHwENEREROj4GHiIiInB4DDxERETk9Bh4iIiJyegw8RERE5PQYeIiIiMjpMfAQERGR02PgISIiIqfHwENEREROj4GHiIiInB4DDxERETk9Bh4iIiJyegw8RERE5PQcMvAsWbIEYWFh0Gq1iIqKws6dO6/aftGiRejYsSNcXFwQEhKCZ599FpWVlXaqloiIiBydwwWelStXIj4+HgkJCdizZw+6d++O2NhY5Obm1tv+q6++wowZM5CQkIADBw7g008/xcqVK/Hiiy/auXIiIiJyVA4XeBYuXIjJkydj4sSJ6NKlC5YuXQqdTodly5bV237btm3o06cPHnnkEYSFhWHo0KEYPXr0NUeFiIiIqPlQSl3ApfR6PXbv3o2ZM2eal8nlcsTExCA1NbXebW677TZ88cUX2LlzJ3r37o1jx45hzZo1GDt27BWPU1VVhaqqKvP74uJiAIDBYIDBYLDSp4F5n5f+TrbBfrYP9rN9sJ/th31tH7bq58bsz6ECT15eHoxGI/z9/S2W+/v74+DBg/Vu88gjjyAvLw99+/aFEALV1dV46qmnrnpKKzExEXPmzKmzfN26ddDpdDf2Ia4gKSnJJvslS+xn+2A/2wf72X7Y1/Zh7X4uLy9vcFuHCjzXIyUlBW+88QY++OADREVF4ciRI5g6dSpee+01vPLKK/VuM3PmTMTHx5vfFxcXIyQkBEOHDoW7u7tV6zMYDEhKSsKQIUOgUqmsum+6iP1sH+xn+2A/2w/72j5s1c+1Z2gawqECj6+vLxQKBXJyciyW5+TkICAgoN5tXnnlFYwdOxaPP/44AODmm29GWVkZnnjiCbz00kuQy+tOU9JoNNBoNHWWq1Qqm/2Bt+W+6SL2s32wn+2D/Ww/7Gv7sHY/N2ZfDjVpWa1Wo1evXkhOTjYvM5lMSE5ORnR0dL3blJeX1wk1CoUCACCEsF2xRERE1GQ41AgPAMTHx2P8+PGIjIxE7969sWjRIpSVlWHixIkAgHHjxiE4OBiJiYkAgBEjRmDhwoXo0aOH+ZTWK6+8ghEjRpiDDxERETVvDhd4Ro0ahXPnzmHWrFnIzs5GREQE1q5da57IfOLECYsRnZdffhkymQwvv/wyTp8+jZYtW2LEiBGYO3euVB+BiIiIHIzDBR4AiIuLQ1xcXL3rUlJSLN4rlUokJCQgISHBDpURERFRU+RQc3iIiIiIbIGBh4iIiJweAw8RERE5PQYeIiIicnoMPEREROT0GHiIiIjI6THwEBERkdNj4CEiIiKnx8BDRERETs8h77RMRETNT7m+Ggq5DAqZDIdzS3HsXBkMRhNKq6qhUcrRo7Un2vm53fBxhBCoMBiRX6rHXycLkHxahqMbjsIEGXxd1dCplaisNkKlkEMuA8r1RrioFPDUqVBtEmihUaKwXI8WaiW8WqjRKcANrholZDLZVY956nwF9EYTXFQK5JVWQa2Uo6O/21W3I+th4CEioisSQqC0qhrnSqrMX9Y6tRICAicLKpCVV4YQbx06+LuioEyPCkNNUAj00MJNqzLvp7jSgD3Hz+NsUSVKK6uRXVyJkwXlqKw24UxhBfJKq1BYbrhmPaE+OujUSoR4uaCw3AB3FxWqqo0AgEqDEaVVRmiUckSEeKKDvxtctUqUVBpwNLcMeaVVOJhdjFPnK1CuN16yVwVw4ugN91W4bwuE+ugQ5tMC50qrcKawAi4qBRRyGf4+XVTv53PTKhHqo0NxRTWUChl6h3kj0MMFOSWVOFdShbKqavi4auCqUaCq2oRgTxeoFHKolXIUVRhw+nwFZDJACOBQTgn01SbojSbIL4Socn01fF01aO2tQ3t/V3i6qHHyfDlOFpRDpZDDTauCr5sa1UYBuQzQV5ug0yhRXlWNkspqlOmroVMr4aVTQyYDCsr0OF+uh1ohR2GFAfpqE0J9dDCaBE4XVkAll6NfB1+083OFRqXA2cJK5JdWwWQyIT9PhjtvuJevHwMPETkVIQSEAORyGcr11Ug/XQw/t5p/8OVyGUoqDfj7VBFaaJToGOAGrUpRZx+5JZXQqhQ4kV+OvNIqFFUYcDy/HIdySqBWyuGtU0OnViDEWweVQo680iqcOl8BjVKO7iGeKKowoEJvRFZ+Gc6XG+DTQg2NSg59tQkVeiNaebmga7AHurXyhJtWCaVchvPlBpRV1QSB/acLsSFTju0/7YePqxYBHlqYhEClwYiIEC90D/FAaWU1svLLUVxpQEGpHjq1Au393QAIuKiVCPLQokxvRPrpIhRXGKBRKRDsqUWlwYTckkqknSjE0XNlqDAYEeChRQu1Ap46NTRKOQxGgZziShzJLcXB7GLkleqv62fh66qGr6sGcpkMB7OLYRIN39ZVo4S/uwY6tRIqhcz8MzieXw4AOHC2+Krbp50svOYx5DKgg78b5JVF6BTeCgYTcL6s5rOqFDJzKPLUqVBUYUBZlRFGk4DBaIKrVokKvRE5xZU4fyHIZOaVITOvDMC5eo+nlMugVMigrzbBTVuzz5LKmj+jtY6dK7tm3Y2VV6rHwewSrNufY/V9A3X7emdWQb3twlylnUXDwENEDmnPifP4Ke0MjCaBogoD3F2UuL9nK/i00MDPXQO1Qo5SfTVKK6uRX6rH/rNFOHauDN/tOYW8Uj0UchmMDfiGbaFWQKtSoExfjRCvmv+pHsuz/pdO48mB7FNSF2GmUsigUsgtRka8W6gR7tsC/5wpQqXBBJVCBheVAqVV1TCJmi/aS8NSKy8XBHu6oIVGiWBPF/i4qqFRKtDezxX+7loEetYEOyEAX1cNFHLLUz15pVX461QhThdWovzCaS690QSNUgGjSaCFpmb0qaBMj30nC3HqfAWMQkCnVqC1tw4+rhrcHOyBUB8d/N200GkUgMmINWvW4M47u0KlUuF6FJUbkFtSiQPZJThXUjOy09JNg0APLfJL9VAp5ejeygOdAtyhlMtgEgJKhRylVdXYfOgcqk0Cvq4alFQasO1oPvLL9Aj2dIGnTgUvnQpnCiuhN5qgkMmQU1wJoxCoMpjgoVMhxEuHSoMRSrkM7f1doZDL4aZVorSyGi00SujUCpwtqsChnFIcPVeKapNAK08XtPbRocpgQkllNc6VVkIprwkjQgiUVFXDXauCTws1tCoF9EYTTp2vgE6tgHcLNdy1SlSbBLx0agDA4dwSuGtVCPJ0wZnCCuw9UYgTBeUwCQF/dy18XNUwGU2ozJf2zzMDDxHZjMkkIJfLUFxpwNq/s5F6LB8t3TTwaaFG2slCKBVytPauOTVRrq85FbH/bDH+OlVU7/6+2H6iwcduSNgBgDK9EWUXvsQP55bW28ZTp0K4bwu08tJBX21EUYUB58sMOFdahWqjCcFeOujUCpwprDlVUqE3wlOnQkSIJ9RKOSoNJijkgAwynCosx4n8chRXVtd7LJkM6BLoBq2+CAGBQSgoM+BYXilUCjlyiithMF78XCqFDFqVAu5aFQrL9ebPcakAdy28WqhxvkyPgjI9TEIg0FOLnq290N7PFSqFHAVlepwrqcK50ioIAWgvjAa1aemKLkHuaO/nCs8LX25Gk4BJ1PzSKGtGx/TVJhiMJujUCshkMgghcK60CicLyvH3qSK4aVWIbuuDIE+XBv1MrsTXVYNBnfxvaB+XM5jq9lljeehU8NCpLoywXZscNUHOVaPEsJsDLdYNvSnghuu5XPcQT9zR1eq7bRSDwYA1a05KWgMDDxFdt4zsEhzMLsbNwR7YcDAX5XojjuSWIruo0jys3dCRlsupFDK08tJBBuBEQTmqG7CPvu18cW+PYFRWG+GmVeHWcG+0dNPgcG4p/jpVc2onqo03ugS6w2AUWLc/G2cv/O+52iigNxoR7KlDZJgXFHIZgj1doFHKbTKptNJgxOGcUvh7aFBlMMFdq4KbVgm5XHbhy2EN7ryzW51RB5NJIKek8sIkWrXFOiEEDEZxYX5NOdy0Kvi7ay3W3+hnUchlUMByH2plzZySWjKZDH5uWvi5adEr1PuGjkdkLQw8RA6uXF+NkwUVMAmBDv5uMBhN5nknRpNAQZkeLd00yC2uRPLBXPRo7Wm+8uPPrAJMX7UPWRfmPchkwK//uh1dgtwhhMDB7BIEebrAw+XqQ/lCCBSWG/D5tkz88o8C357bDROArUfyr1l/bdhx1ShRWlUzqlEbgmQyoKWrxvx52rZsgQ4BbmjpqsFd3YIQ4GH5ZX2+3AAhBLYcyUOghwuCPLXwddVApZDXOf1xqQ7+buhw2f++1UoZ7uoWdM36bUWrUuDmVh6N3k4ulyHQo/6REplMBrWyph/qu5qJVwNRc8bAQ+RghBDYkVmAHccK8P3eU+ZJmpeKCPFs0KTMuvsG7nx3MwBAo5SjqtpkXteztSfctDWnbsr11TiSW4qyKiMyckou24sMR4rrDzreLdRQymWICPHE4M5+MAng9PkK9O/YEpGhXuYv3OsZaZDJZPBuUTOicU9EcKO2JSJi4CGyg1Pny/FnVgHuuCkQaqUci9YfglwmQ1Qbb8z47m98PC4SSoUMe08U4vlV+665v8aEnTtuCoBKKccfGbkW80YuDTsAsOdEzT7/OFT/FSaXUspluKNrAJ4e0Bbhvi0AADp1w/854UgDEdkbAw+RlZ0v0+P+D7fVe6XPs7gszCTX/Ba7aFO9++od5o1xt4Xi1jY+2HGsABk5JXg3+TAAQKuSY3psJ4R4uSD9dBHu6BqIzoFu2JiRi5nf/w21Uo7FD/dAz9ZeFvuc8/M/+GHvadzdPQiT+oYj9Wg+Znz/d73H7xzojoEdWyLQQ4uOAe7oHuSKtWt/w5133nndV7QQEUmBgYcINRNI300+jA9Sam4+lpl4p3kUIml/Dib/d9dVt4/p7I/1B6xzj4tgTxcsfbQXuga7W4yEDO8WiOEIRPyQDnVOCV16ZcegTv7Y8eKVr2RJGHETEkbcZH4f6tMCD/du3aDaDIZr3xiOiMgRMfBQs1V7yXR+aRXu/3CbeWIvAITPXNOofV0r7CyfeAtaeemwfFsmdGolHusTjvyyKhiMAuG+LbBq10ncHOyB1j66K05IvRRPCRERNQ4DDzUbH6Qcwfy1GTY/TqCHFmun9oOHru4pn9dH3mx+fekVSI/f3sbmdRERNWcMPOT0iisNmPLlHmw+nHfFNt4t1Ph4XCQiQjzx+q/78dnWLPO6CbeFYdZdXSCT1T+ycuBssfnmbkRE5JgYeMipFJTpETVvXaO3+/lffRF84S6wl89xuZbOge6NPh4REdkXAw81acfzy9D/rZQL75RAasoV2z7YqxXmP9DNYpSmQm+Ei7ruwyOJiMi5MPCQw6s0GGESwuI+L1l5ZTiSW4rHr3H1VK1tMwbV+xwfhh0iouaBgYccSrm+Gp9szsTCpEPXvY8tLwxEKy+dFasiIqKmjoGHJGM0CYvnH+WWVKL33ORG72fttNvR1sflwsMWeUM8IiKqi4GH7MZgNGHS57tw7Fwp+rT1xcpdJwHU3OTvof+k4s+s8w3e19Au/gjzbYF/DWoHN62KN8QjIqKrYuAhu3lldTo2XXhOU23YAere5O/l4Z0xJioULmoFkvbnwE2rxK1tfOxaKxERORcGHrKqU+fL8depItx5c6B5WVGFAc98uRtbj9T/hO1L/W9Sb9zevqX5/ZAuV35EAhERUUMx8NB12ZlZgCBPLXYcK8CgTn5ooVHi821ZmLvmgLnNqqeisebvsxY38Qv3bYH7ewbj650n8c6oCDz0n1QAwH09g7Hgwe58ZAIREdkEAw9dkdEkEPl6Es6XG/DKXV0wqW84AGDN32fxzJd7rrn9g0tT6yz7evKtCPDQIm5QewBA1rzh1i2aiIioHgw8ZGH7sXzM+jEdh3JKLZa/9st+/Pb3Wew63vCJxZeK6eyHDx/tBZVCbo0yiYiIGoWBh8xMJoGHP9p+xfUNCTs/xfWBi0qBIe9sAgB0CXTHoocj0MHfzWp1EhERNRYDD5kt3XS03uWRoV51ws49EUEo1xvxQK9WePJ/uwEAh14fBrWyZgSHp6qIiMiRMPAQAOCjTUcxf22G+f0tYV74M+s80mYNgatGiXYv/WZed/C1O6BVXXwkA8MNERE5OgYewrvJhy0e5fDd09HoFept0WbRqAhMW5mGzf8eaBF2iIiImgIGnmZu48Fci7DTwd8VPVt71Wk3skcwRvYItmdpREREVsNLZpqxgjI9Ji7/0/w+uo0P1k7tx3vhEBGR0+EITzPWZ94G8+veYd74+olbJayGiIjIdjjC00w98vF2VBiMAIBADy2+eSpa4oqIiIhshyM8zUx2USVuTUy2WLY+vr9E1RAREdkHR3iakRP55XXCzvwHuqGFhrmXiIicGwNPM/LRZssbCz7Zrw0eigyRqBoiIiL74X/tm5GsvHLz61VPReOWMO+rtCYiInIeDDzNxObD57DlSB4AYH18P7Tz47OtiIio+eAprWagqNyAsZ/uNL9v29JVwmqIiIjsj4GnGVi3P9viPW8sSEREzQ0Dj5M7nl+G6d/+ZX5/4NU7JKyGiIhIGgw8TkxfbUL/t1LM7+fcfRNc1HzwJxERNT8MPE7KYDShw8u/WSwbe2uoRNUQERFJi4HHSS1af8ji/YFX74Bczrk7RETUPDHwOKnPtx03v3ZRKXgqi4iImjUGHidUXGlAaVW1+f3OlwZLWA0REZH0GHicjL7ahG6z15nf73llCNy0KgkrIiIikh4Dj5NZnXba4r13C7VElRARETkOBh4n8+9L7rnzRL82ElZCRETkOBh4nMip8xcfDuqmUWLmsE4SVkNEROQ4GHicyIGzJebXW2YM4iMkiIiILmDgcSL/nCkCAPTr0BIeLpyoTEREVIuBx4mkny4GAAzo0FLiSoiIiByLUuoC6Mbpq00Y+HYKThdWAABuCnKXuCIiIiLH4pAjPEuWLEFYWBi0Wi2ioqKwc+fOq7YvLCzElClTEBgYCI1Ggw4dOmDNmjV2qlZ6KRm55rADAF0YeIiIiCw43AjPypUrER8fj6VLlyIqKgqLFi1CbGwsMjIy4OfnV6e9Xq/HkCFD4Ofnh2+//RbBwcE4fvw4PD097V+8BIwmgSf+t9tiGW80SEREZMnhAs/ChQsxefJkTJw4EQCwdOlS/Prrr1i2bBlmzJhRp/2yZctQUFCAbdu2QaWq+aIPCwuzZ8mSWvrHUYv3K564VaJKiIiIHJdDBR69Xo/du3dj5syZ5mVyuRwxMTFITU2td5uffvoJ0dHRmDJlCn788Ue0bNkSjzzyCF544QUoFPU/MLOqqgpVVVXm98XFNZN9DQYDDAaDFT8RzPuz9n5rvfV7hvn1tMHt0CvE3WbHcmS27meqwX62D/az/bCv7cNW/dyY/TlU4MnLy4PRaIS/v7/Fcn9/fxw8eLDebY4dO4YNGzZgzJgxWLNmDY4cOYJnnnkGBoMBCQkJ9W6TmJiIOXPm1Fm+bt066HS6G/8g9UhKSrL6Pg8VyQBcDHUt8g9izZr6+6m5sEU/U13sZ/tgP9sP+9o+rN3P5eXl1250gUMFnuthMpng5+eHjz76CAqFAr169cLp06fx1ltvXTHwzJw5E/Hx8eb3xcXFCAkJwdChQ+Hubt0JvwaDAUlJSRgyZIj5lJu1vP/eVgBlAIAlo7tjaBf/q2/gxGzZz3QR+9k+2M/2w762D1v1c+0ZmoZwqMDj6+sLhUKBnJwci+U5OTkICAiod5vAwECoVCqL01edO3dGdnY29Ho91Oq6D8/UaDTQaDR1lqtUKpv9gbfFvnu09sLh3JrAc2e3YN5ZGbb9GdJF7Gf7YD/bD/vaPqzdz43Zl0Ndlq5Wq9GrVy8kJyebl5lMJiQnJyM6Orrebfr06YMjR47AZDKZlx06dAiBgYH1hh1nYTQJfLPrFABg4UPdGXaIiIiuwqECDwDEx8fj448/xueff44DBw7g6aefRllZmfmqrXHjxllMan766adRUFCAqVOn4tChQ/j111/xxhtvYMqUKVJ9BJv7Me00hrzzh/l9S7e6o1VERER0kUOd0gKAUaNG4dy5c5g1axays7MRERGBtWvXmicynzhxAnL5xZwWEhKC33//Hc8++yy6deuG4OBgTJ06FS+88IJUH8GmTp0vx9QVaRbLbmvrK00xRERETYTDBR4AiIuLQ1xcXL3rUlJS6iyLjo7G9u3bbVyVY+j75kaL94M7+UEh5+ksIiKiq3G4U1rUOPlleqlLICIicngMPE0cHxRKRER0bQw8TUhZVbX5dZ92PnDTKhE/pIOEFRERETUNDjmHh+r35tqLd1H+72NRnLtDRETUQBzhaULkl9xrh2GHiIio4Rh4mqDRvUOkLoGIiKhJYeBpQk4XVgAAOgdyojIREVFjMPA0IafO1wSeYE8XiSshIiJqWhh4moiSSgMOnK15KmywFwMPERFRYzDwNBGr9542v+YIDxERUeMw8DQRGqXC/NpNq5KwEiIioqaHgaeJePnHdABARIintIUQERE1QbzxoAM7lFOCoe9sQpdAd+irTQCAtJOF0hZFRETUBHGEx4GNX7YTALD/wmRlAHhtZFepyiEiImqybB54UlJSbH0Ip3W2qLLOsmFdAySohIiIqGmzWeDZunUrBg8ejMGDB9vqEM2St04tdQlERERNTqPn8BgMBnz11VfYvXs3lEol+vbti/vuu8+8Pi0tDTNmzEBSUhKEEIiMjLRqwc1F7ZydS22aPhByPkOLiIio0RoVeEpKStCvXz/89ddfEEIAABYvXoz77rsPq1atwqxZs/DGG2/AZDKhZ8+emD17Nu666y6bFO7sFiRl1FnW2kcnQSVERERNX6MCz5tvvol9+/ahe/fuGDNmDADgiy++wPfff4+HH34Y33zzDdq1a4e3334bd999t00Kbi4+2nTM4v3LwztLVAkREVHT16jA8+OPPyI0NBQ7duyAWl0zlyQuLg6dOnXCqlWrMGzYMHz//ffQaDQ2KbY5uTCABgA49sadPJVFRER0Axo1afnYsWO48847zWEHALRaLYYPHw4AePvttxl2rOTWNt4AAI1SzrBDRER0gxoVeCoqKuDv719nuZ+fHwCgY8eO1qmKUGGombS84KHuEldCRETU9Fn1snS5nPcxtIZqown7LtxR2YuXoRMREd2wRl+Wnp6ejm+++abOMgBYtWqV+eqtSz300EPXWV7zdOkNBxU8nUVERHTDGh14vvvuO3z33XcWy2pDzsMPP1xnuUwmY+BppJLKavPrbq08JKyEiIjIOTQq8MyaNQsyGUccbK2wXA8ACPZ0gU7N57sSERHdqEZ9m86ePdtGZdClzpcbANQEHiIiIrpxjZpl/Nhjj+Gnn36yVS10QcGFER5PnUriSoiIiJxDowLP8uXLkZaWZqNSqFZhWU3g4RVaRERE1sHryB1Q7SktzxYc4SEiIrIGBh4HVDtp2ZsjPERERFbBwOOAvt97GgBPaREREVlLo695Xr16NbKyshrcXiaT4dNPP23sYZqtg9nF5tectExERGQdjQ48aWlpjZq4zMDTONmX3GXZVcN78BAREVlDo79RJ0yYgPHjx9uiFgJw6ZM5VEqecSQiIrKGRgeesLAw9O/f3xa1EIDiSoP5dY8QT+kKISIiciIcQnAwxRU1geeOmwKgVPDHQ0REZA38RnUwRRcCj4cLJywTERFZCwOPgzEHHl6hRUREZDWNCjyfffYZFAoFXnzxRRgMhiu20+v1ePHFFzFv3rwbLrC54QgPERGR9TUq8AQHB2PWrFnw8fGBSnXlL2S1Wg1fX1+89NJL2Lhx4w0X2ZzUBh53Bh4iIiKraVTg+e9//wsvLy/ExcVds+2UKVPg7e2Nzz777LqLa45+/ycHANBCrZC4EiIiIufRqMCzbds2xMTEQKPRXLOtRqNBTEwMtm7det3FNTfikpvwHMoplbASIiIi59KowHPmzBm0adOmwe3Dw8Nx9uzZRhfVXL3+6wHz68Gd/SSshIiIyLk0KvDI5fKrTla+nMFggFzOC8Ea6tMtmebXkaFeElZCRETkXBqVRoKCgpCent7g9unp6QgODm50UVTzDDIiIiKyjkYFnttvvx0bNmxo0NPSs7KysGHDBvTr1+96ayMiIiKyikYFnilTpsBgMOCBBx5AXl7eFdvl5+fjwQcfRHV1NZ5++ukbLrI5OFdSZX796K2tJayEiIjI+TTq4aE9e/bEtGnTsGjRInTp0gVPPfUUBg4ciFatWgEATp8+jeTkZHz00Uc4d+4c4uPj0bNnT5sU7myWbDxifj11cAcJKyEiInI+jX5a+oIFC6DVavHWW29h7ty5mDt3rsV6IQQUCgVmzpyJ119/3WqFOruCMr35tZu20T8WIiIiuopGf7PKZDK88cYbmDRpEj777DNs27YN2dnZAICAgAD06dMHEyZMQNu2ba1ebHOhUfLKNiIiImu67qGEtm3bcgTHitr5uZpf8wotIiIi6+JQgoMo1xsBAI/1CZe4EiIiIufDwOMgyqqqAQCuGj5Di4iIyNoYeBxEbeBpoeGEZSIiImtj4HEQpQw8RERENsPA4yDK9LWntBh4iIiIrI2Bx0GUVtVMWuYIDxERkfUx8DiIi3N4OGmZiIjI2hh4HERpJU9pERER2QoDjwOoqjYiu7gSAE9pERER2QIDjwN449cD5tc6NU9pERERWRsDjwP450yx+bWHi0rCSoiIiJyTwwaeJUuWICwsDFqtFlFRUdi5c2eDtluxYgVkMhlGjhxp2wKtaHi3QPNrnZqntIiIiKzNIQPPypUrER8fj4SEBOzZswfdu3dHbGwscnNzr7pdVlYWnn/+edx+++12qtQ6hKj5/a5Lgg8RERFZj0MGnoULF2Ly5MmYOHEiunTpgqVLl0Kn02HZsmVX3MZoNGLMmDGYM2cO2rRpY8dqb1yF4cI9eDi6Q0REZBMO9w2r1+uxe/duzJw507xMLpcjJiYGqampV9zu1VdfhZ+fHyZNmoTNmzdf9RhVVVWoqqoyvy8urplDYzAYYDAYbvATWKrd39X2W1KhBwBolDKrH7+5aEg/041jP9sH+9l+2Nf2Yat+bsz+HC7w5OXlwWg0wt/f32K5v78/Dh48WO82W7Zswaeffoq0tLQGHSMxMRFz5syps3zdunXQ6XSNrrkhkpKSrrju2z0KADJkZmZhzZpjNjl+c3G1fibrYT/bB/vZftjX9mHtfi4vL29wW4cLPI1VUlKCsWPH4uOPP4avr2+Dtpk5cybi4+PN74uLixESEoKhQ4fC3d3dqvUZDAYkJSVhyJAhUKnqvwJrauo6AMDmHDmWxd1h1eM3Fw3pZ7px7Gf7YD/bD/vaPmzVz7VnaBrC4QKPr68vFAoFcnJyLJbn5OQgICCgTvujR48iKysLI0aMMC8zmUwAAKVSiYyMDLRt29ZiG41GA41GU2dfKpXKZn/gG7Jvd62Sf+FukC1/hnQR+9k+2M/2w762D2v3c2P25XCTltVqNXr16oXk5GTzMpPJhOTkZERHR9dp36lTJ/z9999IS0sz/7r77rsxcOBApKWlISQkxJ7l35B593eTugQiIiKn5HAjPAAQHx+P8ePHIzIyEr1798aiRYtQVlaGiRMnAgDGjRuH4OBgJCYmQqvVomvXrhbbe3p6AkCd5Y4qyEOLM0WVCPZ0kboUIiIip+SQgWfUqFE4d+4cZs2ahezsbERERGDt2rXmicwnTpyAXO5wg1PXrfaydD5WgoiIyDYcMvAAQFxcHOLi4updl5KSctVtly9fbv2CbKg28GhVDDxERES24DzDJE2UySRQaaiZZO3CER4iIiKbYOCRWGW10fyap7SIiIhsg4FHYhX6i4FHq2TgISIisgUGHomV62vn78ghl8skroaIiMg5MfBIrPLChGUXTlgmIiKyGQYeiZ0pqgQAnC/ng+uIiIhshYFHYq/9sl/qEoiIiJweA4/EjuSWSl0CERGR02PgISIiIqfHwENEREROj4GHiIiInB4Dj4MY1jVA6hKIiIicFgOPxAI9tACApwe0lbgSIiIi58XAI7Gq6poHh/JJ6URERLbDwCOx2jst8zlaREREtsPAIyEhxMXAo+KPgoiIyFb4LSshg1HAJGpea3hKi4iIyGYYeCSUeizf/Fqj5I+CiIjIVvgtK6Fj5y4+VoKBh4iIyHb4LSuh9n5u5tcymUzCSoiIiJwbA4+EDMaaS9JvDvaQuBIiIiLnxsAjoarqmiu0eDqLiIjItvhNKyHedJCIiMg+GHgkVFxZDQDg9B0iIiLbYuCR0Cur0wEAmw/nSVwJERGRc2PgISIiIqfHwCOhMB8dAKC1t07iSoiIiJwbA4+EerT2AgCMiw6VuBIiIiLnxsAjoQ0HcwEACjlnLRMREdkSA4+EiioMAICf9p2RuBIiIiLnxsDjAEy1j0wnIiIim2DgkUj1hcdKAMD9vVpJWAkREZHzY+CRiP6SwNPOz1XCSoiIiJwfA49E9NUXA88tYd4SVkJEROT8GHgkUjvCI5MBSl6lRUREZFMMPBKpHeFRKeSQ8WFaRERENsXAI5HawKNR8EdARERka/y2lUjtKS21kj8CIiIiW+O3rURqR3gYeIiIiGyP37YSYeAhIiKyH37bSsR8SotzeIiIiGyO37YS4QgPERGR/fDbViJ5pXoAQE5xpcSVEBEROT8GHom8sjodwMXgQ0RERLbDwCORCoNR6hKIiIiaDQYeiYyPDgUAhPu2kLgSIiIi58fAIxGdRgkAGNTJT+JKiIiInB8Dj0QufZYWERER2Ra/bSViMN+Hhw8OJSIisjUGHokY+CwtIiIiu+G3rUT01QIAT2kRERHZA79tJVI7wsPAQ0REZHv8tpXIzswCAICSc3iIiIhsjoFHItkXHinxU9oZiSshIiJyfgw8Eqs2CalLICIicnoMPBJ7dkgHqUsgIiJyegw8EjBdMqqTV1IlYSVERETNAwOPBAwmk/l1Ky8XCSshIiJqHhh4JFBtvDjC062Vp3SFEBERNRMMPBI4eq7U/JqXpRMREdkeA48E7n5/q/m1Us7AQ0REZGsMPBKTyRh4iIiIbI2Bx86KKw1Sl0BERNTsOGzgWbJkCcLCwqDVahEVFYWdO3dese3HH3+M22+/HV5eXvDy8kJMTMxV20sp9Wi+1CUQERE1Ow4ZeFauXIn4+HgkJCRgz5496N69O2JjY5Gbm1tv+5SUFIwePRobN25EamoqQkJCMHToUJw+fdrOlV+bq0YpdQlERETNjkMGnoULF2Ly5MmYOHEiunTpgqVLl0Kn02HZsmX1tv/yyy/xzDPPICIiAp06dcInn3wCk8mE5ORkO1d+bVqVQuoSiIiImh2HG27Q6/XYvXs3Zs6caV4ml8sRExOD1NTUBu2jvLwcBoMB3t7e9a6vqqpCVdXFOxwXFxcDAAwGAwwG686xqd3f5b9fvp5uzJX6l6yL/Wwf7Gf7YV/bh636uTH7c7jAk5eXB6PRCH9/f4vl/v7+OHjwYIP28cILLyAoKAgxMTH1rk9MTMScOXPqLF+3bh10Ol3ji26ApKQkAMDhIhmAi6M8a9asscnxmqvafibbYj/bB/vZftjX9mHtfi4vL29wW4cLPDdq3rx5WLFiBVJSUqDVauttM3PmTMTHx5vfFxcXm+f9uLu7W7Ueg8GApKQkDBkyBCqVCluP5gP7dwMAZo/ojDt7h1j1eM3V5f1MtsF+tg/2s/2wr+3DVv1ce4amIRwu8Pj6+kKhUCAnJ8dieU5ODgICAq667dtvv4158+Zh/fr16Nat2xXbaTQaaDSaOstVKpXN/sCb9y2rmTbVNdgdE/q0scmxmjNb/gzpIvazfbCf7Yd9bR/W7ufG7MvhJi2r1Wr06tXLYsJx7QTk6OjoK243f/58vPbaa1i7di0iIyPtUep1+f2fmiCXfrrhqZSIiIhujMON8ABAfHw8xo8fj8jISPTu3RuLFi1CWVkZJk6cCAAYN24cgoODkZiYCAB48803MWvWLHz11VcICwtDdnY2AMDV1RWurq6SfY76fL3zhNQlEBERNTsOGXhGjRqFc+fOYdasWcjOzkZERATWrl1rnsh84sQJyOUXB6c+/PBD6PV6PPDAAxb7SUhIwOzZs+1ZOhERETkghww8ABAXF4e4uLh616WkpFi8z8rKsn1BRERE1GQ53BweIiIiImtj4CEiIiKnx8BDRERETo+BRyJvP9hd6hKIiIiaDQYeO2vtXfPoinDfFhJXQkRE1Hww8NiZ0SQAAEq5TOJKiIiImg8GHjurDTwKBh4iIiK7YeCxs2oGHiIiIrtj4LEzo8kEgKe0iIiI7ImBx87OlxsAAHIGHiIiIrth4LGj/WcuPiGdIzxERET2w8BjRwVleqlLICIiapYYeOzITXvxWa0Go0nCSoiIiJoXBh47kl1yFqttS1fpCiEiImpmGHjsaPZP/5hfy2Scw0NERGQvDDx2UlhuwJ4ThVKXQURE1Cwx8NiJUQipSyAiImq2GHjshYGHiIhIMgw8dmJk3iEiIpIMA4+dmC4Z4fFuoZawEiIiouaHgcdOTKaLgaeowiBhJURERM0PA4+dXJJ3YDTx/BYREZE9MfDYSYXBaH7t56aRsBIiIqLmh4HHTlannTG//vyx3hJWQkRE1Pww8NiJl+7iROXOge4SVkJERNT8MPDYSUd/PjuLiIhIKgw8dlJ7p+VurTwkroSIiKj5YeCxE+OFOw/K+dBQIiIiu2PgsZPaER6lnIGHiIjI3hh47KT23jtyBh4iIiK7Y+Cxk9rAwxEeIiIi+2PgsZPawKNg4CEiIrI7Bh47qZ3Dw8BDRERkfww8dmIe4eFVWkRERHbHwGMnRlPN7xzhISIisj8GHjsxmmoSDwMPERGR/THw2MmF+w4y8BAREUmAgcdOeJUWERGRdBh47ISBh4iISDpKqQtoDpJPy/BT6iEAQHXtuS0iIiKyG47w2FhJZTV+OqEwv/9p3xkJqyEiImqeGHiIiIjI6THw2BxPYREREUmNgcfGxGV558l+baQphIiIqBlj4LGxy8d3+rb3laQOIiKi5oyBx87kfJYWERGR3THw2Njlp7SYd4iIiOyPgcfGxGUntWRg4iEiIrI3Bh4bu3yEhzdaJiIisj8GHhu7fNKynImHiIjI7hh4bO2yIR7mHSIiIvtj4LGxy0d4TLwPIRERkd0x8NjY5XN48kqqpCmEiIioGWPgsSNfVzVua8sbDxIREdmbUuoCnF3tAI9MBmyfORhKBTMmERGRvfHb18bEhXNaMoBhh4iISCL8BraxiyM8vDyLiIhIKgw8NlY7aZlxh4iISDoMPHbCAR4iIiLpMPDYmLj8unQiIiKyOwYeG+McHiIiIukx8NgJ4w4REZF0HDbwLFmyBGFhYdBqtYiKisLOnTuv2n7VqlXo1KkTtFotbr75ZqxZs8ZOlV4dz2gRERFJzyEDz8qVKxEfH4+EhATs2bMH3bt3R2xsLHJzc+ttv23bNowePRqTJk3C3r17MXLkSIwcORLp6el2rrwuceGkFs9oERERScchA8/ChQsxefJkTJw4EV26dMHSpUuh0+mwbNmyetsvXrwYd9xxB6ZPn47OnTvjtddeQ8+ePfH+++/bufK6eFk6ERGR9Bwu8Oj1euzevRsxMTHmZXK5HDExMUhNTa13m9TUVIv2ABAbG3vF9vZSWK7H7/tzAHDSMhERkZQc7llaeXl5MBqN8Pf3t1ju7++PgwcP1rtNdnZ2ve2zs7PrbV9VVYWqqotPLS8qKgIAFBQUwGAw3Ej5FvadKsK8n/YBAGQyBfLz8622b7JkMBhQXl6O/Px8qFQqqctxWuxn+2A/2w/72j5s1c8lJSUAGnYLGIcLPPaQmJiIOXPm1FkeHh5u0+P6zrPp7omIiJqlkpISeHh4XLWNwwUeX19fKBQK5OTkWCzPyclBQEBAvdsEBAQ0qv3MmTMRHx9vfm8ymVBQUAAfHx+rn3oqLi5GSEgITp48CXd3d6vumy5iP9sH+9k+2M/2w762D1v1sxACJSUlCAoKumZbhws8arUavXr1QnJyMkaOHAmgJpAkJycjLi6u3m2io6ORnJyMadOmmZclJSUhOjq63vYajQYajcZimaenpzXKvyJ3d3f+ZbID9rN9sJ/tg/1sP+xr+7BFP19rZKeWwwUeAIiPj8f48eMRGRmJ3r17Y9GiRSgrK8PEiRMBAOPGjUNwcDASExMBAFOnTkX//v2xYMECDB8+HCtWrMCuXbvw0UcfSfkxiIiIyEE4ZOAZNWoUzp07h1mzZiE7OxsRERFYu3ateWLyiRMnIJdfvMDstttuw1dffYWXX34ZL774Itq3b4/Vq1eja9euUn0EIiIiciAOGXgAIC4u7oqnsFJSUuose/DBB/Hggw/auKrG02g0SEhIqHMKjayL/Wwf7Gf7YD/bD/vaPhyhn2WCj/MmIiIiJ+dwNx4kIiIisjYGHiIiInJ6DDxERETk9Bh4iIiIyOkx8NjQkiVLEBYWBq1Wi6ioKOzcuVPqkhzapk2bMGLECAQFBUEmk2H16tUW64UQmDVrFgIDA+Hi4oKYmBgcPnzYok1BQQHGjBkDd3d3eHp6YtKkSSgtLbVo89dff+H222+HVqtFSEgI5s+fb+uP5lASExNxyy23wM3NDX5+fhg5ciQyMjIs2lRWVmLKlCnw8fGBq6sr7r///jp3Mz9x4gSGDx8OnU4HPz8/TJ8+HdXV1RZtUlJS0LNnT2g0GrRr1w7Lly+39cdzGB9++CG6detmvtFadHQ0fvvtN/N69rFtzJs3DzKZzOJGtOzrGzd79mzIZDKLX506dTKvbxJ9LMgmVqxYIdRqtVi2bJn4559/xOTJk4Wnp6fIycmRujSHtWbNGvHSSy+J77//XgAQP/zwg8X6efPmCQ8PD7F69Wqxb98+cffdd4vw8HBRUVFhbnPHHXeI7t27i+3bt4vNmzeLdu3aidGjR5vXFxUVCX9/fzFmzBiRnp4uvv76a+Hi4iL+85//2OtjSi42NlZ89tlnIj09XaSlpYk777xTtG7dWpSWlprbPPXUUyIkJEQkJyeLXbt2iVtvvVXcdttt5vXV1dWia9euIiYmRuzdu1esWbNG+Pr6ipkzZ5rbHDt2TOh0OhEfHy/2798v3nvvPaFQKMTatWvt+nml8tNPP4lff/1VHDp0SGRkZIgXX3xRqFQqkZ6eLoRgH9vCzp07RVhYmOjWrZuYOnWqeTn7+sYlJCSIm266SZw9e9b869y5c+b1TaGPGXhspHfv3mLKlCnm90ajUQQFBYnExEQJq2o6Lg88JpNJBAQEiLfeesu8rLCwUGg0GvH1118LIYTYv3+/ACD+/PNPc5vffvtNyGQycfr0aSGEEB988IHw8vISVVVV5jYvvPCC6Nixo40/kePKzc0VAMQff/whhKjpV5VKJVatWmVuc+DAAQFApKamCiFqwqlcLhfZ2dnmNh9++KFwd3c39+2///1vcdNNN1kca9SoUSI2NtbWH8lheXl5iU8++YR9bAMlJSWiffv2IikpSfTv398ceNjX1pGQkCC6d+9e77qm0sc8pWUDer0eu3fvRkxMjHmZXC5HTEwMUlNTJays6crMzER2drZFn3p4eCAqKsrcp6mpqfD09ERkZKS5TUxMDORyOXbs2GFu069fP6jVanOb2NhYZGRk4Pz583b6NI6lqKgIAODt7Q0A2L17NwwGg0Vfd+rUCa1bt7bo65tvvtl893Ogph+Li4vxzz//mNtcuo/aNs3x74DRaMSKFStQVlaG6Oho9rENTJkyBcOHD6/TH+xr6zl8+DCCgoLQpk0bjBkzBidOnADQdPqYgccG8vLyYDQaLX6wAODv74/s7GyJqmraavvtan2anZ0NPz8/i/VKpRLe3t4Wberbx6XHaE5MJhOmTZuGPn36mB/Fkp2dDbVaXeeBupf39bX68UptiouLUVFRYYuP43D+/vtvuLq6QqPR4KmnnsIPP/yALl26sI+tbMWKFdizZ4/5+YqXYl9bR1RUFJYvX461a9fiww8/RGZmJm6//XaUlJQ0mT522EdLEJHtTZkyBenp6diyZYvUpTiljh07Ii0tDUVFRfj2228xfvx4/PHHH1KX5VROnjyJqVOnIikpCVqtVupynNawYcPMr7t164aoqCiEhobim2++gYuLi4SVNRxHeGzA19cXCoWizgz1nJwcBAQESFRV01bbb1fr04CAAOTm5lqsr66uRkFBgUWb+vZx6TGai7i4OPzyyy/YuHEjWrVqZV4eEBAAvV6PwsJCi/aX9/W1+vFKbdzd3ZvMP5A3Sq1Wo127dujVqxcSExPRvXt3LF68mH1sRbt370Zubi569uwJpVIJpVKJP/74A++++y6USiX8/f3Z1zbg6emJDh064MiRI03mzzMDjw2o1Wr06tULycnJ5mUmkwnJycmIjo6WsLKmKzw8HAEBARZ9WlxcjB07dpj7NDo6GoWFhdi9e7e5zYYNG2AymRAVFWVus2nTJhgMBnObpKQkdOzYEV5eXnb6NNISQiAuLg4//PADNmzYgPDwcIv1vXr1gkqlsujrjIwMnDhxwqKv//77b4uAmZSUBHd3d3Tp0sXc5tJ91LZpzn8HTCYTqqqq2MdWNHjwYPz9999IS0sz/4qMjMSYMWPMr9nX1ldaWoqjR48iMDCw6fx5tsrUZ6pjxYoVQqPRiOXLl4v9+/eLJ554Qnh6elrMUCdLJSUlYu/evWLv3r0CgFi4cKHYu3evOH78uBCi5rJ0T09P8eOPP4q//vpL3HPPPfVelt6jRw+xY8cOsWXLFtG+fXuLy9ILCwuFv7+/GDt2rEhPTxcrVqwQOp2uWV2W/vTTTwsPDw+RkpJicYlpeXm5uc1TTz0lWrduLTZs2CB27doloqOjRXR0tHl97SWmQ4cOFWlpaWLt2rWiZcuW9V5iOn36dHHgwAGxZMmSZnUZ74wZM8Qff/whMjMzxV9//SVmzJghZDKZWLdunRCCfWxLl16lJQT72hqee+45kZKSIjIzM8XWrVtFTEyM8PX1Fbm5uUKIptHHDDw29N5774nWrVsLtVotevfuLbZv3y51SQ5t48aNAkCdX+PHjxdC1Fya/sorrwh/f3+h0WjE4MGDRUZGhsU+8vPzxejRo4Wrq6twd3cXEydOFCUlJRZt9u3bJ/r27Ss0Go0IDg4W8+bNs9dHdAj19TEA8dlnn5nbVFRUiGeeeUZ4eXkJnU4n7r33XnH27FmL/WRlZYlhw4YJFxcX4evrK5577jlhMBgs2mzcuFFEREQItVot2rRpY3EMZ/fYY4+J0NBQoVarRcuWLcXgwYPNYUcI9rEtXR542Nc3btSoUSIwMFCo1WoRHBwsRo0aJY4cOWJe3xT6WCaEENYZKyIiIiJyTJzDQ0RERE6PgYeIiIicHgMPEREROT0GHiIiInJ6DDxERETk9Bh4iIiIyOkx8BAREZHTY+AhIpuSyWQYMGCA1GVYTUpKCmQyGWbPni11KUTUCAw8RGR3EyZMgEwmQ1ZWltSl1MvZQhoRAUqpCyAi53bgwAHodDqpy7Ca3r1748CBA/D19ZW6FCJqBAYeIrKpTp06SV2CVel0Oqf7TETNAU9pEREAy7kpu3btwpAhQ+Dm5gYPDw/ce++913366fLTQ2FhYfj8888BAOHh4ZDJZPWeQsrMzMTjjz+O1q1bQ6PRIDAwEBMmTMDx48eveIzTp09j3LhxCAgIgFwuR0pKCgBg48aNeOyxx9CxY0e4urrC1dUVkZGR+Oijj+rtAwD4448/zLXJZDIsX768Tj9dLj09HQ899BD8/Pyg0WgQHh6OadOmIT8/v07bsLAwhIWFobS0FFOnTkVQUBA0Gg26deuGb7/9tk77oqIizJo1C126dIGrqyvc3d3Rrl07jB8/vt4+ISJLHOEhIgt//vkn5s+fj4EDB+LJJ5/E3r17sXr1avz9999IT0+HVqu9of1PmzYNy5cvx759+zB16lR4enoCqAkAtXbs2IHY2FiUlZXhrrvuQvv27ZGVlYUvv/wSv/32G1JTU9GmTRuL/ebn5yM6Ohre3t54+OGHUVlZCXd3dwDAm2++iSNHjuDWW2/Fvffei8LCQqxduxZPPvkkMjIysGDBAnMNCQkJmDNnDkJDQzFhwgTz/iMiIq76ubZs2YLY2Fjo9Xo88MADCAsLQ2pqKhYvXoxffvkF27dvr3MazGAwYOjQoTh//jzuv/9+lJeXY8WKFXjooYewdu1aDB06FAAghEBsbCx27NiBPn364I477oBcLsfx48fx008/YezYsQgNDb2OnwZRM2K1564TUZO2ceNGAUAAECtWrLBYN3bsWAFAfP31143eLwDRv39/i2Xjx48XAERmZmad9nq9XoSFhQk3NzexZ88ei3WbN28WCoVC3HXXXXWOAUBMnDhRVFdX19nnsWPH6iwzGAxiyJAhQqFQiOPHj1+z5lq1/ZSQkGBeZjQaRdu2bQUAsXbtWov206dPFwDEY489ZrE8NDRUABD33HOPqKqqMi9fv369ACBiY2PNy/766y8BQIwcObJOPZWVlaKkpKTeWonoIp7SIiIL/fr1w6hRoyyWPfbYYwBqRn9s7ZdffkFWVhamT5+OHj16WKzr27cv7rnnHqxZswbFxcUW69RqNebPnw+FQlFnn+Hh4XWWKZVKPPXUUzAajdi4ceMN1bx161YcPXoUw4YNQ2xsrMW6WbNmwdvbG1999RX0en2dbd955x2o1Wrz+8GDByM0NLTevnZxcamzTKPRwNXV9YbqJ2oOeEqLiCz06tWrzrJWrVoBAAoLC21+/O3btwMAMjIy6p0nk52dDZPJhEOHDiEyMtK8PDw8/IpXTpWUlODtt9/G6tWrcfToUZSVlVmsP3PmzA3VvHfvXgCo91L22vlC69atQ0ZGBm6++WbzOk9Pz3rDWKtWrZCammp+37lzZ3Tr1g1ff/01Tp06hZEjR2LAgAGIiIiAXM7/txI1BAMPEVmonfdyKaWy5p8Ko9Fo8+MXFBQAAL788surtrs8tPj7+9fbTq/XY8CAAdizZw969OiBsWPHwsfHB0qlEllZWfj8889RVVV1QzXXjjZdqYbAwECLdrU8PDzqba9UKmEymSzeb9iwAbNnz8Z3332H5557DgDQsmVLxMXF4aWXXqp3ZIuILmLgISKHUhu4fv75Z9x1110N3q726qrL/fjjj9izZw8mTZqETz75xGLdihUrzFeM3YjamnNycupdn52dbdHuevj4+OC9997Du+++i4MHD2LDhg147733kJCQAJVKhZkzZ173vomaA46FEpHd1Y5G1DdiFBUVBQAWp3RuxNGjRwEA99xzT511mzdvrncbuVzeqNGs2rlGtZfBX6qsrAy7du2Ci4sLOnbs2OB9XolMJkPnzp0xZcoUJCUlAQB++umnG94vkbNj4CEiu/P29gYAnDx5ss66e+65B61bt8bChQuxadOmOusNBgO2bNnS4GPVXq59+TZ//PEHPv744yvWd+rUqQYfo0+fPmjbti1+++03rF+/3mLd66+/jvz8fIwePdpicnJjZGVl1XsfpNoRpRu9VQBRc8BTWkRkd4MGDcLbb7+NJ554Avfffz9atGiB0NBQjB07FhqNBt9++y2GDRuG/v37Y9CgQbj55pshk8lw/PhxbN68GT4+Pjh48GCDjjVixAiEhYVh/vz5SE9PR9euXZGRkYFffvkF9957b703+Rs0aBC++eYbjBw5Ej169IBCocDdd9+Nbt261XsMuVyO5cuXIzY2FnfeeScefPBBhIaGIjU1FSkpKWjbti3mzZt33f2VlpaG++67D71790aXLl0QEBCA06dPY/Xq1ZDL5Xj22Weve99EzQUDDxHZ3bBhwzB//nx8/PHHWLBgAQwGA/r374+xY8cCAG655Rbs27cPb731FtasWYOtW7dCo9EgODgYI0eOxOjRoxt8LFdXV2zYsAHTp0/Hpk2bkJKSgptuuglffvkl/P396w08ixcvBgBs2LABP//8M0wmE1q1anXFwAPUXDK/fft2vPrqq1i3bh2KiooQFBSEqVOn4uWXX76hZ29FRkbihRdeQEpKCn799VcUFhYiICAAMTExmD59Om699dbr3jdRcyETQgipiyAiIiKyJc7hISIiIqfHwENEREROj3N4iKhRFi1a1KA7Lk+YMMHigaBERFLiHB4iapSwsDAcP378mu02btxY76MWiIikwMBDRERETo9zeIiIiMjpMfAQERGR02PgISIiIqfHwENEREROj4GHiIiInB4DDxERETk9Bh4iIiJyegw8RERE5PQYeIiIiMjp/T9YMpQSAgqfIwAAAABJRU5ErkJggg==\n", 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" ] @@ -583,10 +583,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:28.970266Z", - "iopub.status.busy": "2023-09-02T00:49:28.970152Z", - "iopub.status.idle": "2023-09-02T00:49:29.025413Z", - "shell.execute_reply": "2023-09-02T00:49:29.025061Z" + "iopub.execute_input": "2023-11-09T07:13:58.117688Z", + "iopub.status.busy": "2023-11-09T07:13:58.117560Z", + "iopub.status.idle": "2023-11-09T07:13:58.162543Z", + "shell.execute_reply": "2023-11-09T07:13:58.162305Z" } }, "outputs": [ @@ -594,25 +594,25 @@ "data": { "text/html": [ "\n", - "\n", + "
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 preferencepreference
itemcampingfinancefoodhealthmusicpoliticssportscampingfinancefoodhealthmusicpoliticssports
user
Anna@afternoon-0.059041-0.0181050.0692220.0328650.1683531.0000000.195960Anna@afternoon0.0097270.0428770.005421-0.089900-0.1434531.000000-0.113309
Anna@morning-0.136399-0.1175770.0763000.0811310.1544830.2218901.000000Anna@morning0.0000000.0000000.000000-0.044113-0.1641950.000000-0.013165
Tom@afternoon-0.2330710.057220-0.074671-0.0271151.0000000.1636070.141781Tom@afternoon-0.0442990.0992390.037974-0.0972141.000000-0.1724450.012231
Tom@morning-0.050107-0.0285620.061163-0.0054280.0634831.0000000.125515Tom@morning-0.0236930.020042-0.002125-0.114500-0.1658901.000000-0.096596
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -716,7 +716,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/debugging-a-pipeline.ipynb b/docs/examples/debugging-a-pipeline.ipynb index ac14e935af..dedc043a77 100644 --- a/docs/examples/debugging-a-pipeline.ipynb +++ b/docs/examples/debugging-a-pipeline.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:30.394238Z", - "iopub.status.busy": "2023-09-02T00:49:30.393986Z", - "iopub.status.idle": "2023-09-02T00:49:41.362597Z", - "shell.execute_reply": "2023-09-02T00:49:41.362266Z" + "iopub.execute_input": "2023-11-09T07:13:59.191790Z", + "iopub.status.busy": "2023-11-09T07:13:59.191005Z", + "iopub.status.idle": "2023-11-09T07:14:08.483952Z", + "shell.execute_reply": "2023-11-09T07:14:08.483636Z" }, "tags": [] }, @@ -105,10 +105,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:41.364443Z", - "iopub.status.busy": "2023-09-02T00:49:41.364253Z", - "iopub.status.idle": "2023-09-02T00:49:41.386868Z", - "shell.execute_reply": "2023-09-02T00:49:41.386606Z" + "iopub.execute_input": "2023-11-09T07:14:08.485645Z", + "iopub.status.busy": "2023-11-09T07:14:08.485481Z", + "iopub.status.idle": "2023-11-09T07:14:08.500960Z", + "shell.execute_reply": "2023-11-09T07:14:08.500710Z" }, "tags": [] }, @@ -331,10 +331,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:41.388613Z", - "iopub.status.busy": "2023-09-02T00:49:41.388503Z", - "iopub.status.idle": "2023-09-02T00:49:41.403489Z", - "shell.execute_reply": "2023-09-02T00:49:41.403226Z" + "iopub.execute_input": "2023-11-09T07:14:08.502443Z", + "iopub.status.busy": "2023-11-09T07:14:08.502348Z", + "iopub.status.idle": "2023-11-09T07:14:08.511537Z", + "shell.execute_reply": "2023-11-09T07:14:08.511292Z" }, "tags": [] }, @@ -405,15 +405,15 @@ "\n", "4. LinearRegression\n", "-------------------\n", - "Name Value Weight Contribution \n", - "Intercept 1.00000 9.19960 9.19960 \n", - "y_ewm_0.5_by_station 0.19640 9.19349 1.80562 \n", - "humidity 1.16366 1.01680 1.18320 \n", - "temperature -0.51938 -0.41575 0.21593 \n", - " wind -0.69426 -0.03810 0.02645 \n", - "pressure 0.04916 0.18321 0.00901 \n", - "y_mean_by_station_and_hour -0.27110 0.19553 -0.05301 \n", - " clouds 1.54778 -0.32838 -0.50827 \n", + "Name Value Weight Contribution \n", + " Intercept 1.00000 9.19960 9.19960 \n", + " y_ewm_0.5_by_station 0.19640 9.19349 1.80562 \n", + " humidity 1.16366 1.01680 1.18320 \n", + " temperature -0.51938 -0.41575 0.21593 \n", + " wind -0.69426 -0.03810 0.02645 \n", + " pressure 0.04916 0.18321 0.00901 \n", + "y_mean_by_station_and_hour -0.27110 0.19553 -0.05301 \n", + " clouds 1.54778 -0.32838 -0.50827 \n", "\n", "Prediction: 11.87854\n" ] @@ -447,7 +447,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/imbalanced-learning.ipynb b/docs/examples/imbalanced-learning.ipynb index 815b19b31c..bfa927a870 100644 --- a/docs/examples/imbalanced-learning.ipynb +++ b/docs/examples/imbalanced-learning.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:42.902654Z", - "iopub.status.busy": "2023-09-02T00:49:42.902258Z", - "iopub.status.idle": "2023-09-02T00:49:45.426420Z", - "shell.execute_reply": "2023-09-02T00:49:45.425895Z" + "iopub.execute_input": "2023-11-09T07:14:09.563203Z", + "iopub.status.busy": "2023-11-09T07:14:09.562696Z", + "iopub.status.idle": "2023-11-09T07:14:12.104372Z", + "shell.execute_reply": "2023-11-09T07:14:12.103931Z" }, "tags": [] }, @@ -69,10 +69,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:49:45.428695Z", - "iopub.status.busy": "2023-09-02T00:49:45.428513Z", - "iopub.status.idle": "2023-09-02T00:50:06.626723Z", - "shell.execute_reply": "2023-09-02T00:50:06.626362Z" + "iopub.execute_input": "2023-11-09T07:14:12.106717Z", + "iopub.status.busy": "2023-11-09T07:14:12.106483Z", + "iopub.status.idle": "2023-11-09T07:14:29.763539Z", + "shell.execute_reply": "2023-11-09T07:14:29.763248Z" }, "tags": [] }, @@ -126,10 +126,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:50:06.628489Z", - "iopub.status.busy": "2023-09-02T00:50:06.628395Z", - "iopub.status.idle": "2023-09-02T00:50:27.706492Z", - "shell.execute_reply": "2023-09-02T00:50:27.706173Z" + "iopub.execute_input": "2023-11-09T07:14:29.765284Z", + "iopub.status.busy": "2023-11-09T07:14:29.765167Z", + "iopub.status.idle": "2023-11-09T07:14:47.409515Z", + "shell.execute_reply": "2023-11-09T07:14:47.409255Z" }, "tags": [] }, @@ -174,10 +174,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:50:27.708255Z", - "iopub.status.busy": "2023-09-02T00:50:27.708123Z", - "iopub.status.idle": "2023-09-02T00:50:48.906869Z", - "shell.execute_reply": "2023-09-02T00:50:48.906567Z" + "iopub.execute_input": "2023-11-09T07:14:47.411111Z", + "iopub.status.busy": "2023-11-09T07:14:47.410990Z", + "iopub.status.idle": "2023-11-09T07:15:04.959874Z", + "shell.execute_reply": "2023-11-09T07:15:04.959580Z" }, "tags": [] }, @@ -223,10 +223,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:50:48.908750Z", - "iopub.status.busy": "2023-09-02T00:50:48.908631Z", - "iopub.status.idle": "2023-09-02T00:51:07.329925Z", - "shell.execute_reply": "2023-09-02T00:51:07.329621Z" + "iopub.execute_input": "2023-11-09T07:15:04.961552Z", + "iopub.status.busy": "2023-11-09T07:15:04.961454Z", + "iopub.status.idle": "2023-11-09T07:15:21.026123Z", + "shell.execute_reply": "2023-11-09T07:15:21.025870Z" }, "tags": [] }, @@ -271,10 +271,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:51:07.331679Z", - "iopub.status.busy": "2023-09-02T00:51:07.331568Z", - "iopub.status.idle": "2023-09-02T00:51:07.352762Z", - "shell.execute_reply": "2023-09-02T00:51:07.352509Z" + "iopub.execute_input": "2023-11-09T07:15:21.027820Z", + "iopub.status.busy": "2023-11-09T07:15:21.027720Z", + "iopub.status.idle": "2023-11-09T07:15:21.042562Z", + "shell.execute_reply": "2023-11-09T07:15:21.042314Z" }, "tags": [] }, @@ -486,10 +486,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:51:07.354551Z", - "iopub.status.busy": "2023-09-02T00:51:07.354436Z", - "iopub.status.idle": "2023-09-02T00:51:29.235692Z", - "shell.execute_reply": "2023-09-02T00:51:29.235410Z" + "iopub.execute_input": "2023-11-09T07:15:21.044039Z", + "iopub.status.busy": "2023-11-09T07:15:21.043955Z", + "iopub.status.idle": "2023-11-09T07:15:39.917985Z", + "shell.execute_reply": "2023-11-09T07:15:39.917704Z" }, "tags": [] }, @@ -534,10 +534,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:51:29.237458Z", - "iopub.status.busy": "2023-09-02T00:51:29.237345Z", - "iopub.status.idle": "2023-09-02T00:51:47.662009Z", - "shell.execute_reply": "2023-09-02T00:51:47.661713Z" + "iopub.execute_input": "2023-11-09T07:15:39.919655Z", + "iopub.status.busy": "2023-11-09T07:15:39.919554Z", + "iopub.status.idle": "2023-11-09T07:15:55.141745Z", + "shell.execute_reply": "2023-11-09T07:15:55.141444Z" }, "tags": [] }, @@ -583,10 +583,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:51:47.663651Z", - "iopub.status.busy": "2023-09-02T00:51:47.663535Z", - "iopub.status.idle": "2023-09-02T00:52:06.237014Z", - "shell.execute_reply": "2023-09-02T00:52:06.236703Z" + "iopub.execute_input": "2023-11-09T07:15:55.143849Z", + "iopub.status.busy": "2023-11-09T07:15:55.143695Z", + "iopub.status.idle": "2023-11-09T07:16:10.513167Z", + "shell.execute_reply": "2023-11-09T07:16:10.512873Z" }, "tags": [] }, @@ -636,7 +636,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb index a127737eb7..5df71eab68 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb @@ -92,10 +92,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:23:57.769553Z", - "iopub.status.busy": "2023-09-02T09:23:57.768668Z", - "iopub.status.idle": "2023-09-02T09:23:59.110942Z", - "shell.execute_reply": "2023-09-02T09:23:59.110580Z" + "iopub.execute_input": "2023-11-09T07:19:30.152437Z", + "iopub.status.busy": "2023-11-09T07:19:30.152168Z", + "iopub.status.idle": "2023-11-09T07:19:30.653284Z", + "shell.execute_reply": "2023-11-09T07:19:30.653022Z" } }, "outputs": [ @@ -103,8 +103,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading https://maxhalford.github.io/files/datasets/ml_100k.zip (1.83 MB)\n", - "Uncompressing into /Users/max/river_data/MovieLens100K\n", "x = {\n", " \"user\": \"259\",\n", " \"item\": \"255\",\n", @@ -144,10 +142,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:23:59.113085Z", - "iopub.status.busy": "2023-09-02T09:23:59.112881Z", - "iopub.status.idle": "2023-09-02T09:23:59.144637Z", - "shell.execute_reply": "2023-09-02T09:23:59.144346Z" + "iopub.execute_input": "2023-11-09T07:19:30.673956Z", + "iopub.status.busy": "2023-11-09T07:19:30.673778Z", + "iopub.status.idle": "2023-11-09T07:19:30.702127Z", + "shell.execute_reply": "2023-11-09T07:19:30.701862Z" } }, "outputs": [], @@ -180,10 +178,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:23:59.146456Z", - "iopub.status.busy": "2023-09-02T09:23:59.146348Z", - "iopub.status.idle": "2023-09-02T09:24:00.520498Z", - "shell.execute_reply": "2023-09-02T09:24:00.520052Z" + "iopub.execute_input": "2023-11-09T07:19:30.703858Z", + "iopub.status.busy": "2023-11-09T07:19:30.703762Z", + "iopub.status.idle": "2023-11-09T07:19:31.804702Z", + "shell.execute_reply": "2023-11-09T07:19:31.804425Z" } }, "outputs": [ @@ -191,10 +189,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.934259, RMSE: 1.124469 – 00:00:00 – 514 B\n", - "[50,000] MAE: 0.923893, RMSE: 1.105 – 00:00:00 – 514 B\n", - "[75,000] MAE: 0.937359, RMSE: 1.123696 – 00:00:01 – 514 B\n", - "[100,000] MAE: 0.942162, RMSE: 1.125783 – 00:00:01 – 514 B\n" + "[25,000] MAE: 0.934259, RMSE: 1.124469 – 00:00:00 – 898 B\n", + "[50,000] MAE: 0.923893, RMSE: 1.105 – 00:00:00 – 898 B\n", + "[75,000] MAE: 0.937359, RMSE: 1.123696 – 00:00:00 – 898 B\n", + "[100,000] MAE: 0.942162, RMSE: 1.125783 – 00:00:01 – 898 B\n" ] } ], @@ -243,10 +241,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:00.522525Z", - "iopub.status.busy": "2023-09-02T09:24:00.522377Z", - "iopub.status.idle": "2023-09-02T09:24:02.249242Z", - "shell.execute_reply": "2023-09-02T09:24:02.248988Z" + "iopub.execute_input": "2023-11-09T07:19:31.806401Z", + "iopub.status.busy": "2023-11-09T07:19:31.806321Z", + "iopub.status.idle": "2023-11-09T07:19:33.203936Z", + "shell.execute_reply": "2023-11-09T07:19:33.203678Z" } }, "outputs": [ @@ -254,10 +252,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761844, RMSE: 0.960972 – 00:00:00 – 173.6 KB\n", - "[50,000] MAE: 0.753292, RMSE: 0.951223 – 00:00:00 – 242.23 KB\n", - "[75,000] MAE: 0.754177, RMSE: 0.953376 – 00:00:01 – 286.04 KB\n", - "[100,000] MAE: 0.754651, RMSE: 0.954148 – 00:00:01 – 309.64 KB\n" + "[25,000] MAE: 0.761844, RMSE: 0.960972 – 00:00:00 – 161.03 KB\n", + "[50,000] MAE: 0.753292, RMSE: 0.951223 – 00:00:00 – 216.34 KB\n", + "[75,000] MAE: 0.754177, RMSE: 0.953376 – 00:00:01 – 254.81 KB\n", + "[100,000] MAE: 0.754651, RMSE: 0.954148 – 00:00:01 – 278.41 KB\n" ] } ], @@ -314,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:02.250998Z", - "iopub.status.busy": "2023-09-02T09:24:02.250911Z", - "iopub.status.idle": "2023-09-02T09:24:04.707646Z", - "shell.execute_reply": "2023-09-02T09:24:04.707360Z" + "iopub.execute_input": "2023-11-09T07:19:33.205610Z", + "iopub.status.busy": "2023-11-09T07:19:33.205516Z", + "iopub.status.idle": "2023-11-09T07:19:35.421840Z", + "shell.execute_reply": "2023-11-09T07:19:35.421552Z" } }, "outputs": [ @@ -325,10 +323,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 1.070136, RMSE: 1.397014 – 00:00:00 – 570.35 KB\n", - "[50,000] MAE: 0.99174, RMSE: 1.290666 – 00:00:01 – 716 KB\n", - "[75,000] MAE: 0.961072, RMSE: 1.250842 – 00:00:01 – 844.09 KB\n", - "[100,000] MAE: 0.944883, RMSE: 1.227688 – 00:00:02 – 945.19 KB\n" + "[25,000] MAE: 1.070136, RMSE: 1.397014 – 00:00:00 – 557.99 KB\n", + "[50,000] MAE: 0.99174, RMSE: 1.290666 – 00:00:01 – 690.31 KB\n", + "[75,000] MAE: 0.961072, RMSE: 1.250842 – 00:00:01 – 813.07 KB\n", + "[100,000] MAE: 0.944883, RMSE: 1.227688 – 00:00:02 – 914.17 KB\n" ] } ], @@ -382,10 +380,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:04.709466Z", - "iopub.status.busy": "2023-09-02T09:24:04.709346Z", - "iopub.status.idle": "2023-09-02T09:24:07.491578Z", - "shell.execute_reply": "2023-09-02T09:24:07.491259Z" + "iopub.execute_input": "2023-11-09T07:19:35.423652Z", + "iopub.status.busy": "2023-11-09T07:19:35.423543Z", + "iopub.status.idle": "2023-11-09T07:19:37.907491Z", + "shell.execute_reply": "2023-11-09T07:19:37.907194Z" } }, "outputs": [ @@ -393,10 +391,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761818, RMSE: 0.961057 – 00:00:00 – 669.27 KB\n", - "[50,000] MAE: 0.751667, RMSE: 0.949443 – 00:00:01 – 869.85 KB\n", - "[75,000] MAE: 0.749653, RMSE: 0.948723 – 00:00:02 – 1 MB\n", - "[100,000] MAE: 0.748559, RMSE: 0.947854 – 00:00:02 – 1.11 MB\n" + "[25,000] MAE: 0.761818, RMSE: 0.961057 – 00:00:00 – 643.81 KB\n", + "[50,000] MAE: 0.751667, RMSE: 0.949443 – 00:00:01 – 817.72 KB\n", + "[75,000] MAE: 0.749653, RMSE: 0.948723 – 00:00:01 – 964.02 KB\n", + "[100,000] MAE: 0.748559, RMSE: 0.947854 – 00:00:02 – 1.05 MB\n" ] } ], @@ -455,7 +453,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb index 9eb28d3aa6..88ce4f1c7d 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb @@ -81,10 +81,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:08.860210Z", - "iopub.status.busy": "2023-09-02T09:24:08.859842Z", - "iopub.status.idle": "2023-09-02T09:24:09.275194Z", - "shell.execute_reply": "2023-09-02T09:24:09.274737Z" + "iopub.execute_input": "2023-11-09T07:19:38.980694Z", + "iopub.status.busy": "2023-11-09T07:19:38.980368Z", + "iopub.status.idle": "2023-11-09T07:19:39.414414Z", + "shell.execute_reply": "2023-11-09T07:19:39.414064Z" } }, "outputs": [], @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:09.277314Z", - "iopub.status.busy": "2023-09-02T09:24:09.277147Z", - "iopub.status.idle": "2023-09-02T09:24:15.610729Z", - "shell.execute_reply": "2023-09-02T09:24:15.610317Z" + "iopub.execute_input": "2023-11-09T07:19:39.416570Z", + "iopub.status.busy": "2023-11-09T07:19:39.416390Z", + "iopub.status.idle": "2023-11-09T07:19:44.873993Z", + "shell.execute_reply": "2023-11-09T07:19:44.873596Z" } }, "outputs": [ @@ -122,10 +122,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761761, RMSE: 0.960662 – 00:00:01 – 818.86 KB\n", - "[50,000] MAE: 0.751922, RMSE: 0.949783 – 00:00:03 – 948.77 KB\n", - "[75,000] MAE: 0.749822, RMSE: 0.948634 – 00:00:04 – 1.07 MB\n", - "[100,000] MAE: 0.748393, RMSE: 0.94776 – 00:00:06 – 1.19 MB\n" + "[25,000] MAE: 0.761761, RMSE: 0.960662 – 00:00:01 – 778.29 KB\n", + "[50,000] MAE: 0.751922, RMSE: 0.949783 – 00:00:02 – 908.2 KB\n", + "[75,000] MAE: 0.749822, RMSE: 0.948634 – 00:00:03 – 1.03 MB\n", + "[100,000] MAE: 0.748393, RMSE: 0.94776 – 00:00:05 – 1.15 MB\n" ] } ], @@ -189,10 +189,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:15.612862Z", - "iopub.status.busy": "2023-09-02T09:24:15.612710Z", - "iopub.status.idle": "2023-09-02T09:24:15.630272Z", - "shell.execute_reply": "2023-09-02T09:24:15.629805Z" + "iopub.execute_input": "2023-11-09T07:19:44.876288Z", + "iopub.status.busy": "2023-11-09T07:19:44.876139Z", + "iopub.status.idle": "2023-11-09T07:19:44.889117Z", + "shell.execute_reply": "2023-11-09T07:19:44.888503Z" } }, "outputs": [ @@ -245,10 +245,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:15.632338Z", - "iopub.status.busy": "2023-09-02T09:24:15.632212Z", - "iopub.status.idle": "2023-09-02T09:24:15.648735Z", - "shell.execute_reply": "2023-09-02T09:24:15.648479Z" + "iopub.execute_input": "2023-11-09T07:19:44.890935Z", + "iopub.status.busy": "2023-11-09T07:19:44.890832Z", + "iopub.status.idle": "2023-11-09T07:19:44.904119Z", + "shell.execute_reply": "2023-11-09T07:19:44.903789Z" } }, "outputs": [], @@ -272,10 +272,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:15.650350Z", - "iopub.status.busy": "2023-09-02T09:24:15.650257Z", - "iopub.status.idle": "2023-09-02T09:24:15.664513Z", - "shell.execute_reply": "2023-09-02T09:24:15.664250Z" + "iopub.execute_input": "2023-11-09T07:19:44.905952Z", + "iopub.status.busy": "2023-11-09T07:19:44.905856Z", + "iopub.status.idle": "2023-11-09T07:19:44.916868Z", + "shell.execute_reply": "2023-11-09T07:19:44.916467Z" } }, "outputs": [], @@ -303,10 +303,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:15.666206Z", - "iopub.status.busy": "2023-09-02T09:24:15.666117Z", - "iopub.status.idle": "2023-09-02T09:24:33.502271Z", - "shell.execute_reply": "2023-09-02T09:24:33.501964Z" + "iopub.execute_input": "2023-11-09T07:19:44.918905Z", + "iopub.status.busy": "2023-11-09T07:19:44.918752Z", + "iopub.status.idle": "2023-11-09T07:20:00.959021Z", + "shell.execute_reply": "2023-11-09T07:20:00.958727Z" } }, "outputs": [ @@ -314,10 +314,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.760059, RMSE: 0.961415 – 00:00:04 – 935.54 KB\n", - "[50,000] MAE: 0.751429, RMSE: 0.951504 – 00:00:08 – 1.06 MB\n", - "[75,000] MAE: 0.750568, RMSE: 0.951592 – 00:00:13 – 1.22 MB\n", - "[100,000] MAE: 0.75018, RMSE: 0.951622 – 00:00:17 – 1.37 MB\n" + "[25,000] MAE: 0.760059, RMSE: 0.961415 – 00:00:03 – 895.78 KB\n", + "[50,000] MAE: 0.751429, RMSE: 0.951504 – 00:00:07 – 1.02 MB\n", + "[75,000] MAE: 0.750568, RMSE: 0.951592 – 00:00:11 – 1.18 MB\n", + "[100,000] MAE: 0.75018, RMSE: 0.951622 – 00:00:16 – 1.33 MB\n" ] } ], @@ -386,10 +386,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:24:33.504144Z", - "iopub.status.busy": "2023-09-02T09:24:33.504021Z", - "iopub.status.idle": "2023-09-02T09:25:38.175942Z", - "shell.execute_reply": "2023-09-02T09:25:38.175657Z" + "iopub.execute_input": "2023-11-09T07:20:00.960832Z", + "iopub.status.busy": "2023-11-09T07:20:00.960735Z", + "iopub.status.idle": "2023-11-09T07:21:03.334574Z", + "shell.execute_reply": "2023-11-09T07:21:03.334266Z" } }, "outputs": [ @@ -397,10 +397,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761379, RMSE: 0.96214 – 00:00:16 – 1.73 MB\n", - "[50,000] MAE: 0.751998, RMSE: 0.951589 – 00:00:32 – 2.03 MB\n", - "[75,000] MAE: 0.750994, RMSE: 0.951616 – 00:00:48 – 2.36 MB\n", - "[100,000] MAE: 0.750849, RMSE: 0.952142 – 00:01:04 – 2.66 MB\n" + "[25,000] MAE: 0.761379, RMSE: 0.96214 – 00:00:15 – 1.67 MB\n", + "[50,000] MAE: 0.751998, RMSE: 0.951589 – 00:00:31 – 1.97 MB\n", + "[75,000] MAE: 0.750994, RMSE: 0.951616 – 00:00:46 – 2.3 MB\n", + "[100,000] MAE: 0.750849, RMSE: 0.952142 – 00:01:02 – 2.6 MB\n" ] } ], @@ -471,10 +471,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:25:38.177709Z", - "iopub.status.busy": "2023-09-02T09:25:38.177604Z", - "iopub.status.idle": "2023-09-02T09:26:04.802711Z", - "shell.execute_reply": "2023-09-02T09:26:04.802410Z" + "iopub.execute_input": "2023-11-09T07:21:03.336221Z", + "iopub.status.busy": "2023-11-09T07:21:03.336122Z", + "iopub.status.idle": "2023-11-09T07:21:28.075174Z", + "shell.execute_reply": "2023-11-09T07:21:28.074930Z" } }, "outputs": [ @@ -482,10 +482,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.758339, RMSE: 0.959047 – 00:00:06 – 2.16 MB\n", - "[50,000] MAE: 0.749833, RMSE: 0.948531 – 00:00:13 – 2.54 MB\n", - "[75,000] MAE: 0.749631, RMSE: 0.949418 – 00:00:19 – 2.96 MB\n", - "[100,000] MAE: 0.749776, RMSE: 0.950131 – 00:00:26 – 3.35 MB\n" + "[25,000] MAE: 0.758339, RMSE: 0.959047 – 00:00:05 – 2.04 MB\n", + "[50,000] MAE: 0.749833, RMSE: 0.948531 – 00:00:12 – 2.41 MB\n", + "[75,000] MAE: 0.749631, RMSE: 0.949418 – 00:00:18 – 2.82 MB\n", + "[100,000] MAE: 0.749776, RMSE: 0.950131 – 00:00:24 – 3.19 MB\n" ] } ], @@ -551,10 +551,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:26:04.804485Z", - "iopub.status.busy": "2023-09-02T09:26:04.804363Z", - "iopub.status.idle": "2023-09-02T09:26:39.079247Z", - "shell.execute_reply": "2023-09-02T09:26:39.078802Z" + "iopub.execute_input": "2023-11-09T07:21:28.076978Z", + "iopub.status.busy": "2023-11-09T07:21:28.076874Z", + "iopub.status.idle": "2023-11-09T07:21:58.474830Z", + "shell.execute_reply": "2023-11-09T07:21:58.474562Z" } }, "outputs": [ @@ -562,10 +562,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761435, RMSE: 0.962211 – 00:00:08 – 834.1 KB\n", - "[50,000] MAE: 0.754063, RMSE: 0.953248 – 00:00:17 – 964.01 KB\n", - "[75,000] MAE: 0.754729, RMSE: 0.95507 – 00:00:25 – 1.08 MB\n", - "[100,000] MAE: 0.755697, RMSE: 0.956542 – 00:00:34 – 1.21 MB\n" + "[25,000] MAE: 0.761435, RMSE: 0.962211 – 00:00:07 – 792.94 KB\n", + "[50,000] MAE: 0.754063, RMSE: 0.953248 – 00:00:15 – 922.85 KB\n", + "[75,000] MAE: 0.754729, RMSE: 0.95507 – 00:00:22 – 1.04 MB\n", + "[100,000] MAE: 0.755697, RMSE: 0.956542 – 00:00:30 – 1.17 MB\n" ] } ], @@ -615,7 +615,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb index d2be37bc56..037a839cea 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb @@ -31,7 +31,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/quantile-regression-uncertainty.ipynb b/docs/examples/quantile-regression-uncertainty.ipynb index 1bfa244ca9..2865544aad 100644 --- a/docs/examples/quantile-regression-uncertainty.ipynb +++ b/docs/examples/quantile-regression-uncertainty.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:52:07.634046Z", - "iopub.status.busy": "2023-09-02T00:52:07.633637Z", - "iopub.status.idle": "2023-09-02T00:52:07.828667Z", - "shell.execute_reply": "2023-09-02T00:52:07.828369Z" + "iopub.execute_input": "2023-11-09T07:16:11.977143Z", + "iopub.status.busy": "2023-11-09T07:16:11.976368Z", + "iopub.status.idle": "2023-11-09T07:16:12.234088Z", + "shell.execute_reply": "2023-11-09T07:16:12.233810Z" } }, "outputs": [], @@ -39,17 +39,17 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:52:07.830510Z", - "iopub.status.busy": "2023-09-02T00:52:07.830367Z", - "iopub.status.idle": "2023-09-02T00:52:08.513721Z", - "shell.execute_reply": "2023-09-02T00:52:08.512874Z" + "iopub.execute_input": "2023-11-09T07:16:12.236861Z", + "iopub.status.busy": "2023-11-09T07:16:12.236152Z", + "iopub.status.idle": "2023-11-09T07:16:12.989619Z", + "shell.execute_reply": "2023-11-09T07:16:12.989322Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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LowYWqa54e9KkSdq+fbs6dOggqa4WYfr06VHPv/zyywM7qvuXO05UkP/QQw9p2bJlGjJkiLp166bdu3fr5Zdf1sqVK3XLLbeoZ8+eEa+ZMWOGcnJy9P3vfz/qe0arNVy1apUkaejQoSGLBixevFgDBw7Ufffdp/vvv1+S1LVrV91+++36/e9/r9raWvXr109z5szRkiVLNGPGjEANyemnnx4Rqvz3e9JJJ4W0oz73+cc//lETJ07UokWLdN5550mqW2Xr1Vdf1aWXXqrdu3dH9EFwsJwwYYJmz56t73//+7rjjjtUUlKiKVOmqLa2NvD/ovz8/Kif05w5c/Tuu++GPFdRUaHBgwdrz549uuuuu/SPf/wj5DXHHntsSIjyT3vu1q2b1q9fr8mTJ6tNmzYh08lOOOEEnXDCCRHXl+rqcepTLwoAzVWzDC2rV6+WbduB3yL5VVdXq23bthHnv/LKK6qoqEj4gwUAuOEf//iH9u7dG3ck5tJLL9Xjjz+uF154Qbfeequkuv+mXXvttVHPX79+fSC0JGvYsGFat26dnn32We3YsUO5ubnq06ePpk6dGvW/j+Xl5frHP/4RKERvLH/NTvCogiQ98sgjat26tf7yl79o2rRpOu644zR9+vS4m3TGU9/7DOcPXf/7v/8bdfW24NDSsWNHLV26VHfeeaeefPJJ1dbWasCAAZo+fbpOPfXUerd9165d2rRpkyRF/UXbmDFjQkLLqaeeqqlTpwb2kBkxYoQmTpwYCL4A0FJYxoRVRzZFIywrZPWwWbNmadSoUfr0008j9gQoLCxUp06dQo5dcMEFKi4ujjo8DwBIj5///Od6/vnn9dVXX0Vd5hgAgIZqliMtp512mmzb1vbt2xPWqKxfv16LFi3Sq6++mqbWAQCiWbRokX71q18RWAAAKddkoWX//v2BfQykuvCxatUqtWnTRscff7xGjRql0aNH6/HHH9dpp52mHTt2aOHCherTp0/IXgfPPvusOnfuHLKLMAAg/VauXNnUTQAAZKgmmx7mL9gMN2bMGE2bNk21tbV68MEH9de//lVff/212rVrp29/+9uaOHGiTjnlFEl1uzh3795do0eP1kMPPZTuWwAAAACQBs2ipgUAAAAAYvE0dQMAAAAAIB5CCwAAAIBmLe2F+I7j6JtvvlFRUZEsy0r35QEAAAA0E8YYVVRUqEuXLvJ4Yo+npD20fPPNNyorK0v3ZQEAAAA0U5s2bVLXrl1jPp/20FJUVCSprmHFxcXpvnyL5fP59N5776lv377KymqW2/OgAejXzES/Zi76NjPRr5mJfk2P8vJylZWVBTJCLGnvAf+UsOLiYkJLGvl8PhUUFKi4uJhvvAxCv2Ym+jVz0beZiX7NTPRreiUqG6EQHwAAAECzRmgBAAAA0KwRWgAAAAA0a0zQAwAAOELYtq3a2tqmbkaL4PP5ZFmWqqqqqGlphFatWsnr9Tb6fegBAACAZs4Yo61bt2rv3r1N3ZQWwxij/Px8bdy4kb0FG6m0tFSdOnVq1OdIaAEAAGjm/IGlQ4cOys/P54foNDDG6MCBA3zejeD/DLdv3y5J6ty5c4Pfi9ACAADQjNm2HQgsbdu2bermtBjGGNm2rdzcXEJLI+Tl5UmStm/frg4dOjR4qhiF+AAAAM2Yv4YlPz+/iVsCNIz/725j6rEILQAAAEcAftuPI1Uq/u4SWgAAAAA0a4QWAAAAHLEWL14sy7JYWS3DEVoAAACQUpZlxf26//77G/S+5513nm6//faUthVHBlYPAwAAQEpt2bIl8OdZs2bp17/+tdasWRM4VlhYGPizf5UuNnBEPPUaabn//vsjkvIJJ5zgVtsAAAAQxjGOyn0HmuzLMU7CNnbq1CnwVVJSIsuyAo+/+OILFRUV6fXXX9cZZ5yhnJwcLV26VGPHjtVll10W8j633367zjvvPEnS2LFj9eabb2rSpEmBn0M3bNgQOPf9999X3759lZ+fr7POOiskJOHIV+9Ie9JJJ+mf//zn4TcgFQMAAKTNfrtKP147pcmu/5fjfqLirMYvv3zPPffoscce0zHHHKPWrVsnPH/SpEn68ssvdfLJJ+uBBx6QJLVv3z4QXH75y1/q8ccfV/v27fWTn/xE119/vZYtW9bodqJ5qHfiyMrKUqdOndxoCwAAAFqIBx54QBdeeGHS55eUlCg7O1v5+flRfxZ96KGHdO6550qqC0TDhg1TVVWVcnNzU9ZmNJ16F+KvXbtWXbp00THHHKNRo0Zp48aNbrQLAAAAGaxv374pfb8+ffoE/ty5c2dJdbuwIzPUa6Slf//+mjZtmnr16qUtW7Zo4sSJOvvss/XJJ5+oqKgo6muqq6tVXV0deFxeXi5J8vl88vl8jWg66sO27UChGzIH/ZqZ6NfMRd9mJrf71efzyRgT8tWU6tsG/7nh/87Pzw95H8uyIt67pqYm5DXRru//c1ZWVkS7/H3TEM3l884E/s8x2s//yeaBeoWWoUOHBv7cp08f9e/fX927d9fs2bN1ww03RH3Nww8/rIkTJ0Ycf++991RQUFCfyzcZ5+BBWV6vrOzspm5KgzmOo4qKCr377rvyeFjpOlPQr5mJfs1c9G1mcrtfLctSfn6+Dhw4INu2ZRlHj3e5NuXXSbo9VbYqrcqkz/f/8rqysu41VVVVgcetWrUKnFdaWqrVq1cHzpOkDz74QK1atQoc83q9qq6uDjkn2vsdPHhQknTgwIGQc+vDGCPHcVRZWZmSHd1bsurqatXU1Ojjjz+OCIHJ9k+jquhLS0t1/PHH66uvvop5zoQJE3THHXcEHpeXl6usrEx9+/ZVcXFxYy6fNjUffyBPUbGyju7Z1E1pMNu2tXLlSvXr109er7epm4MUoV8zE/2auejbzOR2v1ZVVWnjxo3Kz88P1GcUKfoMl+YoJydHkgK/rPbfQ0FBQcgvsAcPHqxJkybp5Zdf1oABAzR9+nR9/vnnOu200wLnHXPMMfrggw+0Y8cOFRYWqk2bNlHfLy8vT1LdaE5Df0lujFFlZaUKCgoILY3k9XqVnZ2tnj17RtQY+WdhJdKo0LJ//36tW7dO114bO+3n5OQE/rKGXDgr64hZeczn2PI4R/764ZZlyev1HvH3gVD0a2aiXzMXfZuZ3OzXrKyskO0mjjT+Nkf7d/D9DBkyRL/61a909913q6qqStdff71Gjx6t1atXB8676667NGbMGJ100kk6ePCg1q9fH/X9Yl2jIW0/Uj/35sT/GUb7+T/Z75l6fWfdeeeduvTSS9W9e3d98803uu++++T1ejVy5Mj6vM2RxxiZoLocAAAAJGfs2LEaO3Zs4PF5550Xs05k4sSJUcsK/I4//ngtX7485FiPHj0i3u9b3/oWtSgZpl6hZfPmzRo5cqR27dql9u3b67vf/a7eeecdtW/f3q32NQ+OkamuaupWAAAAAC1SvULLCy+84FY7mjfjyFSz0hkAAADQFFi6JEmmppphRgAAAKAJEFqSYYxk2xL7ygAAAABpR2hJgjFGxrZlfLVN3RQAAACgxSG0JIORFgAAAKDJEFqSYRzJtmUILQAAAEDaEVqS4RjJsSWmhwEAAABpR2hJhjEyto+RFgAAAKAJEFqSQk0LAABAczV27FhddtllgcfnnXeebr/99ka958CBA3X33Xc3rmFIGUJLMoyRsR1WDwMAAKiHsWPHyrIsWZal7Oxs9ezZUw888IB8Lv8i+O9//7t+85vfJHXu4sWLZVmW9u7dG3L85Zdf1r333utC69AQWU3dgCOBMZJsHyMtAAAA9TRkyBBNnTpV1dXVeu2113TzzTerVatWmjBhQsh5NTU1ys7OTsk127Rpk5L3qKysTEFrkAqMtCTDOJKMnJqqpm4JAABo4YzjyNlf0WRfxnHq1d6cnBx16tRJ3bt314033qhBgwbp1VdfDUzpeuihh9SlSxf16tVLkrRp0yaNGDFCpaWlatOmjYYPH64NGzYE3s+2bd1xxx0qLS1V27Zt9fOf/1zGmJBrhk8Pq66u1t13362ysjLl5OSoZ8+eeuaZZ7RhwwYNHDhQktS6dWtZlqWxY8dKipwetmfPHo0ePVqtW7dWfn6+hg4dqrVr1waenzZtmkpLSzV//nz17t1bhYWFGjJkiLZs2VKvzwvRMdKSQN03gZEsj1RV3dTNAQAALZw5UKndv/ppk12/zW+elFVY1ODX5+XladeuXZKkhQsXqri4WAsWLJAk1dbWavDgwRowYICWLFmirKwsPfjggxoyZIg+/vhjZWdn6/HHH9e0adP07LPPqnfv3nr88cf1yiuv6Pzzz495zdGjR2v58uX6wx/+oFNPPVXr16/Xzp07VVZWppdfflnf//73tWbNGhUXFysvLy/qe4wdO1Zr167Vq6++quLiYt199926+OKL9dlnn6lVq1aSpAMHDuixxx7T3/72N3k8Hl1zzTW68847NWPGjAZ/XqhDaEmGkeT1ytQQWgAAABrCGKOFCxdq/vz5uuWWW7Rjxw4VFBTo6aefDkwLmz59uhzH0dNPPy3LsiRJU6dOVWlpqRYvXqyLLrpITz31lCZMmKArrrhCkjRlyhTNnz8/5nW//PJLzZ49WwsWLNCgQYMkScccc0zgef9Usg4dOqi0tDTqe/jDyrJly3TWWWdJkmbMmKGysjLNmTNHP/jBDyTVha4pU6bo2GOPlSSNHz9eDzzwQEM/MgQhtCRyaLjRysqSqWZ6GAAAQH3MmzdPhYWFqq2tleM4uvrqq3X//ffr5ptv1imnnBJSx/LRRx/pq6++UlFR6EhOVVWV1q1bp3379mnLli3q379/4LmsrCz17ds3YoqY36pVq+T1enXuuec2+B4+//xzZWVlhVy3bdu26tWrlz7//PPAsfz8/EBgkaTOnTtr+/btDb4uDiO0JGLMoZGWLBmfT8b2yfLysQEAACRj4MCBmjx5srKzs9WlSxdlZR3+OaqgoCDk3P379+uMM86IOp2qffv2Dbp+rOlebvBPE/OzLCtmmEL98NN3QodqWrxeGduWfLZEaAEAAE3Eyi9Qm9882aTXr4+CggL17NkzqXNPP/10zZo1Sx06dFBxcXHUczp37qwVK1bonHPOkST5fD69//77Ov3006Oef8opp8hxHL355puB6WHB/CM9tm3HbFfv3r3l8/m0YsWKwPSwXbt2ac2aNTrxxBOTujc0DquHJWLq/mF5syTHZq8WAADQpCyPR57Coib7sjzu/fg4atQotWvXTsOHD9eSJUu0fv16LV68WLfeeqs2b94sSbrtttv0yCOPaM6cOfriiy900003ReyxEqxHjx4aM2aMrr/+es2ZMyfwnrNnz5Ykde/eXZZlad68edqxY4f2798f8R7HHXechg8frnHjxmnp0qX66KOPdM011+ioo47S8OHDXfksEIrQkgwjKcsr2bZEaAEAAHBFfn6+3nrrLXXr1k1XXHGFevfurRtuuEFVVVWBkZef/exnuvbaazVmzBgNGDBARUVFuvzyy+O+7+TJk3XllVfqpptu0gknnKBx48YF9mA56qijNHHiRN1zzz3q2LGjxo8fH/U9pk6dqjPOOEOXXHKJBgwYIGOMXnvttYgpYXCHZdI80a68vFwlJSXat29fzGG/5sTU1urgov+TPJbM/grlnn2+vG0bNqeyKfmHNPv37x8ylxRHNvo1M9GvmYu+zUxu92tVVZXWr1+vo48+Wrm5uSl/f0RnjFFlZaUKCgoCK5mhYeL9HU42GzDSktChTOfxyBgjU8tICwAAAJBOhJZEAptLWrIkyfY1cYMAAACAloXQkohRYKk6I8n4CC0AAABAOhFaEgkq+bEkielhAAAAQFoRWhIw/poWy5KxJKe2pmkbBAAAALQwhJZkHBptsTxeqaa6iRsDAAAAtCyElkSMqfuyLMmbJVNFaAEAAADSidCSiAn8Q/J6ZaqrmrI1AAAAQItDaEkoqBDf65Xx1cg4ThO2BwAAAGhZCC2JmMOF+PJ6JZ/DXi0AAKBZMNXVcir3p+XLVDffKfJjx47VZZddFnh83nnn6fbbb2/Uew4cOFB333134xqWwOLFi2VZlvbu3evqddxmWZbmzJnj6jWyXH33TGBM3d6SsmS8Xpnqaplan6xW2U3dMgAA0IKZ6mpVrVgiZ//+tFzPU1io3P5ny8rJSer8sWPH6rnnnpMktWrVSt26ddPo0aP1i1/8QllZ7v4I+ve//12tWrVK6tzFixdr4MCB2rNnj0pLSwPHX375ZdXUuLtq7FlnnaUtW7aopKQk6deMHTtWe/fudT0kNDeElmQYI1l1q4cZx5Z87NUCAACalvHVytm/X1Z2tqzs5IJEg69VUy1n/34ZX23SoUWShgwZoqlTp6q6ulqvvfaabr75ZrVq1UoTJkyIOLempkbZ2an5pXCbNm1S8h6VlZUpaE1s2dnZ6tSpk6vXiCWVn3c6MD0sKUbSoelhtiPD9DAAANBMWNk5snJz3f1qYCjKyclRp06d1L17d914440aNGiQXn31VUmHp3Q99NBD6tKli3r16iVJ2rRpk0aMGKHS0lK1adNGw4cP14YNGwLvadu27rjjDpWWlqpt27b6+c9/LhO0GbgUOT2surpad999t8rKypSTk6OePXvqmWee0YYNGzRw4EBJUuvWrWVZlsaOHSspcnrYnj17NHr0aLVu3Vr5+fkaOnSo1q5dG3h+2rRpKi0t1fz589W7d28VFhZqyJAh2rJlS8zPJ3x6WKL3uP/++/Xcc89p7ty5sixLlmVp8eLFSX1u0T7vX/ziF+rfv39Eu0499VQ98MADkqSVK1fqwgsvVLt27VRSUqJzzz1XH3zwQcx7cguhJZFATYskj7eunqWW0AIAAFBfeXl5IVOuFi5cqDVr1mjBggWaN2+eamtrNXjwYBUVFWnJkiVatmxZ4Ad3/+sef/xxTZs2Tc8++6yWLl2q3bt365VXXol73dGjR+v555/XH/7wB33++ef6y1/+osLCQpWVlenll1+WJK1Zs0ZbtmzRpEmTor7H2LFj9d577+nVV1/V8uXLZYzRxRdfrNrawzNwDhw4oMcee0x/+9vf9NZbb2njxo2688476/UZxXuPO++8UyNGjAgEmS1btuiss85K6nOL9nmPGjVK7777rtatWxc459NPP9XHH3+sq6++WpJUUVGhMWPGaOnSpXrnnXd03HHH6eKLL1ZFRUW97quxmB6WSCC516VZIyPD9DAAAICkGWO0cOFCzZ8/X7fcckvgeEFBgZ5++unANKXp06fLcRw9/fTTsixLkjR16lSVlpZq8eLFuuiii/TUU09pwoQJuuKKKyRJU6ZM0fz582Ne+8svv9Ts2bO1YMECDRo0SJJ0zDHHBJ73TyXr0KFDSE1LsLVr1+rVV1/VsmXLdNZZZ0mSZsyYobKyMs2ZM0c/+MEPJEm1tbWaMmWKjj32WEnS+PHjAyMWyYr3HoWFhcrLy1N1dXXItLJkPjcp8vOW6kZVZs6cqV/96leB++rfv7969uwpSTr//PND2vff//3fKi0t1ZtvvqlLLrmkXvfWGIy0JHKoEP8wi5oWAACAJMybN0+FhYXKzc3V0KFDddVVV+n+++8PPH/KKaeE/AD90Ucf6auvvlJRUZEKCwtVWFioNm3aqKqqSuvWrdO+ffu0ZcuWkClNWVlZ6tu3b8w2rFq1Sl6vV+eee26D7+Pzzz9XVlZWyHXbtm2rXr166fPPPw8cy8/PD4QNSercubO2b99er2s15D0SfW5+4Z+3JI0aNUozZ86UVBcun3/+eY0aNSrw/LZt2zRu3Dgdd9xxKikpUXFxsfbv36+NGzfW674ai5GWhIwOLR8mqe5fxrabskEAAABHhIEDB2ry5MnKzs5Wly5dIlYNKygoCHm8f/9+nXHGGZoxY0bEe7Vv375BbcjLy2vQ6xoifMUyy7Ii6m3ceI9kP7fwz1uSRo4cqbvvvlsffPCBDh48qE2bNumqq64KPD9mzBjt2rVLkyZNUvfu3ZWTk6MBAwa4vrJaOEJLMsyhQnxJRkaqZaQFAAAgkYKCgsA0o2ScfvrpmjVrljp06KDi4uKo53Tu3FkrVqzQOeecI0ny+Xx6//33dfrpp0c9/5RTTpHjOHrzzTcD08OC+Uce7Di/lO7du7d8Pp9WrFgRmB62a9curVmzRieeeGLS95cK2dnZEW1N5nOLpWvXrjr33HM1Y8YMHTx4UBdeeKE6dOgQeH7ZsmX685//rIsvvlhSXcH/zp07G38j9cT0sATMoYEW/0iLPF451VVN2CIAAIDMNGrUKLVr107Dhw/XkiVLtH79ei1evFi33nqrNm/eLEm67bbb9Mgjj2jOnDn64osvdNNNN8XdnLFHjx4aM2aMrr/+es2ZMyfwnrNnz5Ykde/eXZZlad68edqxY4f2R9n35rjjjtPw4cM1btw4LV26VB999JGuueYaHXXUURo+fLgrn0W8+/n444+1Zs0a7dy5U7W1tUl9bvGMGjVKL7zwgl588cWQqWFS3b3/7W9/0+eff64VK1Zo1KhRaR298iO0JBJUiC9JljdLhtACAACaCVNTLVNV5e5XTXVa7iU/P19vvfWWunXrpiuuuEK9e/fWDTfcoKqqqsAIws9+9jNde+21GjNmjAYMGKCioiJdfvnlcd938uTJuvLKK3XTTTfphBNO0Lhx4wJ7sBx11FGaOHGi7rnnHnXs2FHjx4+P+h5Tp07VGWecoUsuuUQDBgyQMUavvfZa0ptYpsq4cePUq1cv9e3bV+3bt9eyZcuS+tziufLKK7Vr1y4dOHBAl112WchzzzzzjPbs2aPTTz9d1157rW699daQkZh0sUx9J9o1Unl5uUpKSrRv3756D181BXvPLlUtWSRP+/ayvFmyd+2Up7hEed85r6mbVi/+Ic3+/fu7vgst0od+zUz0a+aibzOT2/1aVVWl9evX6+ijj1Zubm7guKmuVtWKJXKijAy4wVNYqNz+Z9drc8kjmTFGlZWVKigoCKzIhYaJ9XdYSj4b8F/MRALzww7xemTSXHgEAAAQzsrJUW7/s9O2FYOV1arFBBY0P4SWRIxCCvEtb5Zk18r4fLL4LRkAAGhCVk4OQQItAjUtCZm60OIfFfR6ZWyHDSYBAACANCG0JM0/0uKVfL66LwAAAACuI7Qk4l+nIGikRY4tQ2gBAABplOa1k4CUScXfXUJLIsYELXssyeOVbFtiehgAAEgD/5K6Bw4caOKWAA3j/7vbmOWhqSRPJJBXDk0P83hkjGGkBQAApIXX61Vpaam2b98uqW4vE5bgdZ8xRtXV1fJ6vXzeDWSM0YEDB7R9+3aVlpbK6/U2+L0ILQkZGZmQv6yWMVItIy0AACA9OnXqJEmB4AL3GWNUU1Oj7OxsQksjlZaWBv4ONxShJYFoc/CMZcnYjLQAAID0sCxLnTt3VocOHVTLL07Twufz6eOPP1bPnj3ZDLYRWrVq1agRFj96IJGgPVr8LInVwwAAQNp5vd6U/ACIxHw+n4wxys3NJbQ0AxTiJyV0tMV4LDnV1U3UFgAAAKBlIbQkJWykxeOVaqqaqC0AAABAy0JoSSTautJerwwjLQAAAEBaEFoSMUbh08MILQAAAED6EFoSilKI782Sqa2Vse2maRIAAADQghBakhCxMrfXKzm2xLLHAAAAgOsILYlEq2nxeGRsnwzLHgMAAACuI7QkYhQRXCxvlmQ7Eps7AQAAAK4jtCRijGSFTRDzeiVGWgAAAIC0ILQkZMLXDqubHkZNCwAAAJAWhJZEotS0WJYlyZLxMT0MAAAAcBuhJYFodfiB52oZaQEAAADcRmhpKGPqlj0GAAAA4CpCS0LRh1qs8OJ8AAAAAK4gtCRiTOw5Yo6T3rYAAAAALRChJZFoSx4HPwcAAADAVYSWBoqyEDIAAAAAFxBaEjFGMatXGGkBAAAAXEdoSSRmMLEILQAAAEAaEFqSES2cWJKhEB8AAABwHaElkZiF+JaMIbQAAAAAbiO0NJRlSQ7TwwAAAAC3NSq0PPLII7IsS7fffnuKmtMMGRN7pTDHTm9bAAAAgBaowaFl5cqV+stf/qI+ffqksj3NT6xie4tCfAAAACAdGhRa9u/fr1GjRul//ud/1Lp161S3qRmKrGmxJImaFgAAAMB1DQotN998s4YNG6ZBgwaluj3NjpGJllkky0NNCwAAAJAGWfV9wQsvvKAPPvhAK1euTOr86upqVVdXBx6Xl5dLknw+n3w+X30vn3a2z5YtyQqbCmZLsnz2EXEPkmTbtowxsm3qcDIJ/ZqZ6NfMRd9mJvo1M9Gv6ZHsz9L1Ci2bNm3SbbfdpgULFig3Nzep1zz88MOaOHFixPH33ntPBQUF9bl8k3Aq98tRtqzd5WFPeKWdu+VdsaJpGlZPjuOooqJC7777rjweFo3LFPRrZqJfMxd9m5no18xEv6ZHZWVlUudZxiRfTT5nzhxdfvnl8nq9gWO2bcuyLHk8HlVXV4c8J0UfaSkrK9OuXbtUXFyc7KWbTM3nn6j232uU1emokOP2ju3ytu+onDP6N1HL6se2ba1cuVL9+vWL6CMcuejXzES/Zi76NjPRr5mJfk2P8vJytW3bVvv27YubDeo10nLBBRdo9erVIceuu+46nXDCCbr77rujdmhOTo5ycnIiL5yVpayses9OSzvbIzmy5A3fYNJjySsdEffgZ1mWvF7vEdVmJEa/Zib6NXPRt5mJfs1M9Kv7kv1s69UDRUVFOvnkk0OOFRQUqG3bthHHM0bMYnuL1cMAAACANGCCXjLCR1mkQ5mF0AIAAAC4rdFjXYsXL05BM5oxY6KueFyXWljyGAAAAHAbIy2JxJoCZjE9DAAAAEgHQksijok5PUxMDwMAAABcR2hJxBgp6gQxi9ACAAAApAGhJRFjYmYWSloAAAAA9xFaEjDGUbTUYlkealoAAACANCC0NIqRYbgFAAAAcBWhJZGY08MsyRFzxAAAAACXEVoScWIV4kuSYYoYAAAA4DJCSyLGiT3SIiMx0AIAAAC4itCSSKwlj5keBgAAAKQFoSWRWDUtdU+KoRYAAADAXYSWBOoiSfSRFmMMIy0AAACAywgtiSQKJQ6hBQAAAHAToSWRuIX4kmF6GAAAAOAqQksisZY8tizJcZgeBgAAALiM0JJIokJ8MgsAAADgKkJLIsZRzJGWuhPS2RoAAACgxSG0JCNqZrHq8orjpL05AAAAQEtCaEmgbnZY1Ep86lkAAACANCC0JGJijKRYUl1NC8EFAAAAcBOhJQ5jDu14b8UaaRHTwwAAAACXEVriMXFWB7Ms1T3JSAsAAADgJkJLIkbRR1osSY5hdhgAAADgMkJLPCbeSIoVdA4AAAAAtxBa4ooTSPyF+EwPAwAAAFxFaInHHPpHzEJ8IzmEFgAAAMBNhJZ4EhXix50+BgAAACAVCC1xmfiF+BI1LQAAAIDLCC3xJCrEZ3oYAAAA4DpCSwLGRK9psSxLhkJ8AAAAwHWElngS5hGrLtQAAAAAcA2hJZ5kAgmhBQAAAHAVoSUO4x9qibrkMQAAAIB0ILTEY0yCkRQjOU7amgMAAAC0RISWRGIU4texRCE+AAAA4C5CSzwm8I+oLImaFgAAAMBlhJa4kqhpIbMAAAAAriK0xHNoFMVS9NBSNxBDTQsAAADgJkJLPMbUJZN4i4cxPQwAAABwFaElkQShhMwCAAAAuIvQEo8xSmKoJU2NAQAAAFomQksy4mUW9mkBAAAAXEVoicdf0xI3tQAAAABwE6ElHn/BSozMwj4tAAAAgPsILXH5a1rinUJoAQAAANxEaImjrg4/QSE+oQUAAABwFaElngT7tBgZQgsAAADgMkJLXP5AwkgLAAAA0FQILQmZ2JnFsupGWwAAAAC4htASjzEJalosGdtOZ4sAAACAFofQEk+iQRRLksNICwAAAOAmQktch0ZaYpa0WJKcNLYHAAAAaHkILfGYBIX4lsVICwAAAOAyQksyYmUWSTKMtAAAAABuIrTEk6gQ37Ikm9ACAAAAuInQEs+hmV+WFXv1MPZpAQAAANxFaInLxN+HxSK0AAAAAG4jtMRhEgUSSzLUtAAAAACuIrTEE3djSdU9R00LAAAA4CpCS2NYYvUwAAAAwGWElsagpgUAAABwHaElHmOkeIX4shLXvQAAAABoFEJLPEkU4sux09IUAAAAoKUitMSVRCG+w0gLAAAA4CZCSzzGxI0sdYX4hBYAAADATYSWeBLlEcti9TAAAADAZYSWeBKMoliyZJgeBgAAALiK0JJA3EhiWZIcVhADAAAAXERoiSvRksf+UwgtAAAAgFsILfGYBKuHWRahBQAAAHAZoSWOhFnEspTUaAwAAACABiO0xJVEGDGGzAIAAAC4iNDSGIHpYSx7DAAAALilXqFl8uTJ6tOnj4qLi1VcXKwBAwbo9ddfd6ttTc+Y+HPE/NPDqGkBAAAAXFOv0NK1a1c98sgjev/99/Xee+/p/PPP1/Dhw/Xpp5+61b6mZcyhYBLvFKaHAQAAAG7Kqs/Jl156acjjhx56SJMnT9Y777yjk046KaUNOyL4Aw0jLQAAAIBr6hVagtm2rRdffFGVlZUaMGBAzPOqq6tVXV0deFxeXi5J8vl88vl8Db18Wti2LUeSHSOUOMbIOI5qbZ88R8C9GGNk23ZTNwUpRL9mJvo1c9G3mYl+zUz0a3okmwcsU8/t3FevXq0BAwaoqqpKhYWFmjlzpi6++OKY599///2aOHFixPH58+eroKCgPpdOO+fgAZn9FVJObowTbMl25CltLcvrTW/j6slxHFVUVKioqEgeD+svZAr6NTPRr5mLvs1M9Gtmol/To7KyUoMHD9a+fftUXFwc87x6h5aamhpt3LhR+/bt00svvaSnn35ab775pk488cSo50cbaSkrK9OuXbviNqw58P17rWo+/UjeLl2jPu9UHZSprFTu2efLk5ef5tbVj23bWrlypfr16ydvMw9YSB79mpno18xF32Ym+jUz0a/pUV5errZt2yYMLfWeHpadna2ePXtKks444wytXLlSkyZN0l/+8peo5+fk5CgnJyfywllZyspq8Oy0tDAej7ySvDGK8T2WR46kLI9HnmZ+L5JkWZa8Xm+z/9xRP/RrZqJfMxd9m5no18xEv7ov2c+20WNdjuOEjKRkGhNvaTBLiZdFBgAAANAo9YqNEyZM0NChQ9WtWzdVVFRo5syZWrx4sebPn+9W+5pWojDCPi0AAACA6+oVWrZv367Ro0dry5YtKikpUZ8+fTR//nxdeOGFbrWvaSUMI1bdHi2EFgAAAMA19QotzzzzjFvtaJbqpobF2VzSqjuL0AIAAAC4h/Xb4nFM3MxSN9JipHh1LwAAAAAahdAShzGOEo60GMk4hBYAAADALYSWROKNtASWQia0AAAAAG4htMSTcATFkoxDTQsAAADgIkJLXMlMDzMMtAAAAAAuIrTEk2ikxT89zDjutwUAAABooQgt8RgTVLcSjXX4PAAAAACuILTEY5wEdfhW3QpjZBYAAADANYSWeJIOI6QWAAAAwC2ElngSTg+TJEvGoaYFAAAAcAuhJR5jFH+jFgAAAABuI7TEY0wSmcVQiA8AAAC4iNAShzEJ9mmR6p4ntAAAAACuIbTEk8RIS93ThBYAAADALYSWhJKoaUm0CSUAAACABiO0xOMkU9MipocBAAAALiK0xGMSL2Vsgv4JAAAAIPUILfEkueSxYXoYAAAA4BpCSxwmqSWPJUZaAAAAAPcQWuJJZqTFsE8LAAAA4CZCSzzGSWKbFvZpAQAAANxEaEkofmpJavYYAAAAgAbLauoGNGfm4EFVL39Lzv5yterZS9mn95dlRYkpTuJVxgAAAAA0DCMtcdR89rF8mzbIVJSr5sOVsr/eFP1EpocBAAAAriG0xOHs2hHy2N6yOeIcw8phAAAAgKsILXEYO7QQ39m3N/qJTA8DAAAAXENoicfxKTi1OOX7opxkMdoCAAAAuIjQEk/YCIpTvq9uw8lgliSH0AIAAAC4hdASgzEmctqXr1bm4IHQY5YlY5geBgAAALiF0BKLMXU1LWEi61osaloAAAAAFxFaYok20iLJRKtrIbQAAAAAriG0xGSkKNO+IorxLYt9WgAAAAAXEVriiTKC4pTvDXlsSVHDDQAAAIDUILTEYiTj2BGHI0daPKweBgAAALiI0BJLjJoWp3xv6LLHlqhpAQAAAFxEaIkpemhRba1M1cGgAyx5DAAAALiJ0BJLjJEWSTLByx5TiA8AAAC4itASh3GculASJqQY3xI1LQAAAICLCC0xGNuJOYISWoxvSVEK9gEAAACkBqElFp8v5lMhocVSaGE+AAAAgJQitMRgnDihJbimRdS0AAAAAG4itMRgEoy0BEZXLIvNJQEAAAAXEVpi8cWpU6mtObzsMfu0AAAAAK4itMRixx5pkSQTqGuxCC0AAACAiwgtMZgEK4L5i/Ety6KkBQAAAHARoSWWQzUtkbu01Ans1UJNCwAAAOAqQksM8QrxpfAVxAzLHgMAAAAuIbTEYic3PUyWJTli2WMAAADAJYSWWGyfJKNYE8Sc8r1BoyuGKWIAAACASwgtMQSmh8UqaqmpkamuqhtpkanLNwAAAABSjtASi88XEkSsnBzJCv24zL59TA8DAAAAXEZoiSV875WsVvIUFYee4l9BTEYMtQAAAADuILTEYOza0AOWR1ZJScghp7xupMUYw0gLAAAA4BJCSwzGF7p6mOXxyFNcGnLs8EiLJIfQAgAAALiB0BJL+JLHUUPLoZoWSYbpYQAAAIArCC0xGCdsc0mPR57i0OlhpnzvoUJ8h+lhAAAAgEsILbHYYYX4Ho88JaUhh0x1tUx1teqWPCa0AAAAAG4gtMRih460WF6vrMKiyGWP91cc+gOhBQAAAHADoSUGYzsKWcbY46krxi8qCjnPqdh3aMVjQgsAAADgBkJLLD7/ksd1hfby1H1UVnhdS2WlZMKmkgEAAABIGUJLLFFWD5Mkq1VOyOHAfi6MtAAAAACuILTEYPyh5dBAi+Xx1v0hyxt6os+umx7mMNoCAAAAuIHQEovtCylpCYy0eLNCz3NsHSpqSVfLAAAAgBaF0BJLlCWPJUlZoaHF2LbkGGaHAQAAAC4htMRgnNCaFv/0MCsstMh3aGlkUgsAAADgCkJLDMYXuk9LYKTFG1rTYhz/0siEFgAAAMANhJZY7OihJaKmxfbVjbI4hBYAAADADYSWWMJXA/OPsISFFuOzD00NI7QAAAAAbiC0xGDs8JqWQyMt4TUtNjUtAAAAgJsILbHEqmmJFlqYHgYAAAC4htASS9jqYbEK8WXbMhTiAwAAAK4htMQQPj0sViF+3XmWDNPDAAAAAFfUK7Q8/PDD6tevn4qKitShQwdddtllWrNmjVtta1rh+7RYMaaH+Wrr/k1oAQAAAFxRr9Dy5ptv6uabb9Y777yjBQsWqLa2VhdddJEqKyvdal/TscNWD/NvLhlt9TAAAAAArslKfMphb7zxRsjjadOmqUOHDnr//fd1zjnnpLRhTS1iepjXP9ISXtPikzFO5BLJAAAAAFKiXqEl3L59+yRJbdq0iXlOdXW1qqurA4/Ly8slST6fT77wFbqaEePzhZTWO5ZHtjFyPN6IknvHkWzbjlxxrBmxbVvGmLp2ImPQr5mJfs1c9G1mol8zE/2aHsnmAcs0sILccRx973vf0969e7V06dKY591///2aOHFixPH58+eroKCgIZdOi/ZzZip751bpUC3Lrn7fVWWP4+Q9UKmj/jE75NxNQ74vtWsvT05uUzQ1KY7jqKKiQkVFRfJ4WH8hU9CvmYl+zVz0bWaiXzMT/ZoelZWVGjx4sPbt26fi4uKY5zV4pOXmm2/WJ598EjewSNKECRN0xx13BB6Xl5errKxMffv2jduwprbv/16RzxhZreqmg7UuKVRWm2KZ/GwdCJsi1sc+qNxjj5H3qLKmaGpSbNvWypUr1a9fP3nDl23GEYt+zUz0a+aibzMT/ZqZ6Nf08M/CSqRBoWX8+PGaN2+e3nrrLXXt2jXuuTk5OcrJyYm8cFaWssJX4mpGLMeRJck69Njj8cprWTKtsgLHAufatrweq1nfjyRZliWv19vs24n6oV8zE/2auejbzES/Zib61X3Jfrb16gFjjG655Ra98sorWrx4sY4++ugGNe5IYJLdXFKSbB9LHgMAAAAuqVdoufnmmzVz5kzNnTtXRUVF2rp1qySppKREeXl5rjSwyYQVXVmHwopleeqWPw4ONbZDZgEAAABcUq+qosmTJ2vfvn0677zz1Llz58DXrFmz3Gpf0wlfKSK4ACuspqVuVIbUAgAAALih3tPDWoqY08NUt8GkUc3h52ybfVoAAAAAl7B+Wyx2aAixrKCPyhuW9QgsAAAAgGsILbGEj7QEFeBb4ascUIgPAAAAuIbQEoOJW9MSHlpsQgsAAADgEkJLFMaYyClfITUtYYX4hBYAAADANYSWaMJHWSRZnqCgElbTYhxCCwAAAOAWQks0Pl/kseCRFqaHAQAAAGlDaIkiYrljKbSmJcrqYcawghgAAADgBkJLNFGmh8XdXNK2ZVj2GAAAAHAFoSWKupXDQqd7Bde0WBEjLbbkMD0MAAAAcAOhJRr/SIsVdCzukseOJEZaAAAAADcQWqKx7fCBlrAlj6OsHsZICwAAAOAKQksUCQvxw2pa6kIOIy0AAACAGwgt0QSWPD40P8zyyLIOzxWLrGlxDk0RAwAAAJBqhJYoIgrxPWEfU3hoYZ8WAAAAwDWElmjCp4d5Qz8mK8qSx4QWAAAAwB2Elmh8vpCBluDljiVF2VzSjl4HAwAAAKDRCC1RJJweFrHkMauHAQAAAG4htERjh42ahIWWqEses3oYAAAA4ApCSxTG9oU8tpIZaaGmBQAAAHAFoSWaiJGW0JoWyxtZiG8ILQAAAIArCC3R2HZISUvCJY8dO2J0BgAAAEBqEFqiqAsgsQvxrfDpYUZBG1ICAAAASCVCSzT+6WHWoX8lqmmRCC0AAACASwgtUZhATcuh1BJR0xIWWizJ1Na63zAAAACgBSK0RJOwpiVss0lZMr4at1sFAAAAtEiElmjCd7ePmB4WHlok+XysIAYAAAC4gNASRd30sMMBJLymxbI8EVPGDHu1AAAAAK4gtESTYJ8WSZGjLT4foQUAAABwAaEliog9V8KnhymyGD9imWQAAAAAKUFoiSZipCXKxxS+gpjPR2YBAAAAXEBoiSZs9TDLG2WkJXyvFp8tGcflhgEAAAAtD6ElCuMLm+oVraYleNljy6qbHkZNCwAAAJByhJZoEi15rMiRFsP0MAAAAMAVhJYoTFhNS/iSx5Iia1pY8hgAAABwBaElGp+vbtDEOvQ4yvSwyJoWnww1LQAAAEDKEVqicfyjJodSS9SRlvDNJZkeBgAAALiB0BJF+PSwpJY8Zp8WAAAAwBWElijCN5eMVtMSPD3M0qGgQ00LAAAAkHKElmh8oaEl6pLHUWpaCC0AAABA6hFaovElseRx8PQwy2L1MAAAAMAlhJYojBM+0pJMIT6hBQAAAHADoSWaJPZpscJCi2ymhwEAAABuILREk8zqYVmtoryG0AIAAACkGqElioglj8NHVSRZWZHTw4xDaAEAAABSjdASTcTqYcns08JICwAAAOAGQksU4SMt0WpaIkZfqGkBAAAAXEFoica/Ephl1T2OurlkaE0Lq4cBAAAA7iC0RGMns7lk0DHLYqQFAAAAcAmhJYqIQvxEm0tKku0QWgAAAAAXEFqiSaqmJTS0GMfH6mEAAACACwgtURgnfKQl2pLHYSMtRpJd616jAAAAgBaK0BLGGJPc5pJR9m5xampcahUAAADQchFawoWPsijG9LDwkRZLUi0jLQAAAECqEVrChY+ySFFHVSIK8Y1kfIQWAAAAINUILWGM73Bosfx/iDrSEmUZZKaHAQAAAClHaAnnRNkkMtqSx5YnokDf1BJaAAAAgFQjtIQL2Viybqwlak2LFDltjJoWAAAAIOUILWEOTw8zh+eHxQgt4cseG0ILAAAAkHKElnDRCvFjjrSEFeNTiA8AAACkHKElTGBjyeCyliRHWpwaQgsAAACQaoSWcCE1LZIsq67oPprgmhZLjLQAAAAALiC0hAufHhZrapgkKzi0GMn4WD0MAAAASDVCSxhTj9CirFahjynEBwAAAFKO0BLODt2nxfJE2UTS/1xW+D4thBYAAAAg1QgtYUx4TUu8kZaI1cN80c8DAAAA0GCElnD1mR7mZZ8WAAAAwG2ElnARoSXe9DD2aQEAAADcRmgJc7gQv66uxYo70kJNCwAAAOA2Qku4kJEWK/6Sx2EjLYaRFgAAACDlCC1hQgrxLSVf02KJJY8BAAAAFxBawoUveeyNU9MSPj0sfOUxAAAAAI1GaAnnnx7mzy1Jby5pydTWuNUqAAAAoMWqd2h56623dOmll6pLly6yLEtz5sxxoVlNx9RryeOwUZhaRloAAACAVKt3aKmsrNSpp56qP/3pT260p+lFbC6Z/JLHFOIDAAAAqZeV+JRQQ4cO1dChQ91oS7MQPtJSnyWP5WOkBQAAAEg1alrC1WN6mBVS08JICwAAAOCGeo+01Fd1dbWqq6sDj8vLyyVJPp9PvmY4MmHX1sgYE6jDN5ZHdtBqYsEcr1fBz5ja5nlPkmTbtowxssNDGY5o9Gtmol8zF32bmejXzES/pkeyPzu7HloefvhhTZw4MeL4e++9p4KCArcvX28l//63CquqJcuSfLYqa2q1a3d51HOz9x9UJ5//L7IlU1WlL1esSF9j68FxHFVUVOjdd9+VJ96UNxxR6NfMRL9mLvo2M9GvmYl+TY/KysqkznM9tEyYMEF33HFH4HF5ebnKysrUt29fFRcXu335eju49T+q+vITOZW1slp5VZyfpx5torfTMdU6mHWorqXWJ1nSmWeeKcuy0tji5Ni2rZUrV6pfv37yxtl7BkcW+jUz0a+Zi77NTPRrZqJf08M/CysR10NLTk6OcnJyIi+claWsLNcvX2+WjCwp8OXxeuWNEUKsVtnyP2OsutdmKXJVsebCsix5vd5m+bmj4ejXzES/Zi76NjPRr5mJfnVfsp9tvXtg//79+uqrrwKP169fr1WrVqlNmzbq1q1bfd+u+fE1Yp8WY2Rqa2W1ahX9fAAAAAD1Vu/Q8t5772ngwIGBx/6pX2PGjNG0adNS1rAm49QjtERLhqwgBgAAAKRUvUPLeeedJxNjNa1MUJ99Wqwo8xtNLaEFAAAASCWWQggXvuxavMKrKCMtprYmxQ0CAAAAWjZCS5jwkZa4m0tantDnjZEILQAAAEBKEVrCObYUvGVkonW5vaGjLUwPAwAAAFKL0BLOthVcsmN54q/LHVje2LJkJBkK8QEAAICUIrSEMeE1LfUZaTFGqmF6GAAAAJBKhJZw9VnyWJEriDmEFgAAACClCC1hIpc8jj89LGIFMQrxAQAAgJQitISr5/Sw8JEWU1Od6hYBAAAALRqhJVw9ljyWJGW1CnnI6mEAAABAahFawpj61rRkhU0fo6YFAAAASClCSzg7dJ8WK9FIiydsnxaWPAYAAABSitASJrwQXwkL8Q89b1kseQwAAAC4gNASzraDB1qSmB4WXtNCaAEAAABSidASzq7v5pLhq4cRWgAAAIBUIrSEqe8+LVYWNS0AAACAmwgt4eq75HHYSAubSwIAAACpRWgJYoyJLMT3Jtpcsm6kxfK/B/u0AAAAAClFaAnmOKqrwjeHU0jCzSXDpodR0wIAAACkFKElWPjGkkpc0yKvP7RYkozESAsAAACQUoSWIIGpYfVa8vhQaDk0MsP0MAAAACC1CC3BfL7IY/UpxDeSWD0MAAAASClCS7BAEb6Rf+ik3kses3oYAAAAkFKEliAmSk1L4pGW8NDCSAsAAACQSoSWYNGmhyW55LEfoQUAAABILUJLkIYU4isrbPqYr1bGmOjnAgAAAKg3QkswJ7imRZIsWVai1cNahR4wJqg2BgAAAEBjEVqChU8PSzA1rO4c/0jLoX1aRDE+AAAAkEqEliAmfIQk0dQw6XBosYKOUdcCAAAApAyhJVhYTYuVRGgJWfLYSDKGYnwAAAAghQgtQSKWPE6wR4skKTi0HBptYXoYAAAAkDqElmB2WCF+MiMtlufwef76fUILAAAAkDKElmDBNS2WpAQrhwV4Q0dkTLT9XgAAAAA0CKElSGghviUrmdXDFLbssTFMDwMAAABSiNASzD40QuKf5pVMTYsUMtJijJFqCC0AAABAqhBagjRoyWMFjbRYkhxHpro6tQ0DAAAAWjBCS7AGFOJLkpVf4P+T5Niy9+xMedMAAACAlorQEswOLaBPZp8WSfIUFR16gSU5jpxdu1LdMgAAAKDFIrQEiZwellxNi1VUfOgPlowxsnduS3HLAAAAgJaL0BKsgTUtnsK60GJZHsk4cnbtSHXLAAAAgBaL0BLEBFYPq6tpSXbJY09gpKXutfbuXTKO40ILAQAAgJaH0BIsBdPDZCTV1sop35fatgEAAAAtFKElWHhosZJcPSwvL2KvFmc3K4gBAAAAqUBoCRJSiG9JSnJ6mGV55Cn0ryCmuhXECC0AAABAShBagh2qaTlU0ZL0ksdS8BQxT91eLbtZ9hgAAABIBUJLkMBIi/FvLplcTYt0eAUxWZaM48hmBTEAAAAgJQgtwRq45LEUvILYoQ0md25PYcMAAACAlovQEsy2/XPD6jRgephlWZKMbEILAAAAkBKEliAmbKSlPjUtwdPDZIycPbsj3g8AAABA/RFagjm2QoZa6jXSUnT4gZFk23L27UlZ0wAAAICWitASxPh8oQfqUYhv5eZJWVmH38s4clhBDAAAAGg0QkuwRhTiW5YVOkXMcWSzVwsAAADQaISWYE7Da1qk4L1aLMlmpAUAAABIBUJLEOMLH2lJfnqYFLrssXFsVhADAAAAUoDQEswOr2mp38cTvoIYoQUAAABoPEJLkJAlii0rEFqMbcsYJ+Hr/SuIWf5lj3fucKWdAAAAQEuSlfiUFsQ5tLmkMZJlyfLWTQ+zt2yWjORp116evPyYLw+eHiZj5JTvlfH5ZGXxMQMAAAANxUhLMF/kPi3GsWV5vMrq1kNm7x7ZO3fIGBP15YHQ4mfbcvbudq+9AAAAQAtAaAkSsYO9xyNTVSXl5in7pFOV0/fbsnJy5Hy9WaamJuL1Vk6ulJ19+P1Y9hgAAABoNOYtBYuy5LGpOihPfoGsvHxl5RfIU9JatV98qtqN6+Vp3UaegsKQ13gKi+Xs3nloipgjZxehBQAAAGgMRlqCGF/46mFemaoqedq2qyuul+QpLFL2aX3VqteJcvZETv0KqWuxbfZqAQAAABqJ0BIsyvQwOY68RSUhhy1vlrI6HSUrq5VMbeg0MStkrxZH9i5WEAMAAAAag9ASLGx6mGRkeb2yCooiTvWUtpanpFROeXnocfZqAQAAAFKK0BIkuBDfkmRqa6XcvIi6FUmyvF55j+oqc/BAyHFPxF4thBYAAACgMQgtwWy7bo+WQ0xtrTyFRbJyc6Oe7m3bXlZ2tkx1deBY8PQwycgp31cXfgAAAAA0CKElWHhNi69WnjZtY57uKWktT2lrORWHp4iF7NVyaKNKZw/F+AAAAEBDEVoOMY4jYxz/I0mWLFnyFhbHfI3l8SirS5lM1cHDx1pl1+3XEvS+7NUCAAAANByhxS9slMXISK2yZRVG1rME87ZtJysnp24TykNCpog57NUCAAAANAahxS84tBhJjiMrLz9qEX4wq7hUntZt5VTsCxwLWUHMsRlpAQAAABqB0HKICV/u2DjyFBfLys6J+zrLspTVpatMVZXMoSJ+/wpi7NUCAAAANB6hxc/nC31sjDzt2if1Um+bdrLy8gO1LSHTw4yRs4NljwEAAICGIrQcYsJXDpMlb1FJUq+1iorlbdtW5tAqYv4VxPx7tTDSAgAAADQcocUvfHqYZYUuXxyHZVnyduoq1VTLGCOrMGykpXxfyF4uAAAAAJLXoNDypz/9ST169FBubq769++vd999N9XtSjvjnx7m31zS45FVXJr0671t20l5+TIHKg/XtBx6H1N1QJVvzJVTW5O6BgMAAAAtRL1Dy6xZs3THHXfovvvu0wcffKBTTz1VgwcP1vbtR3jdRtj0MMvjkZWdnfTLPYVFyurURWbPbllZrWTl5de9T1aWZHl04JUXtPvnN2v/nFmyd2wLFO0DAAAAiC+rvi944oknNG7cOF133XWSpClTpugf//iHnn32Wd1zzz0pb6AbKiv3aus3a0OOebbvULZTKxlbjuXIauXVuqpt9Xpfc1SJqnYYWfu/VmHbYmVtrqh7wqu6EZe9O1Q170Xt++erUm6eTOvWclq3lmndRqa4WMrNlcnJkXKyZbJzpFatJCv4ClboBa2wxzFPtWQ7tvbu/Err13rk9da729FM2bZPe3euo18zDP2auejbzES/ZqaW0K+l7bqoTZujmroZSbFMPX7lX1NTo/z8fL300ku67LLLAsfHjBmjvXv3au7cuQnfo7y8XCUlJdq3b5+Ki5OrGUm1Lz5epIo/PxH1OSMjOY5qs7P0x5vPq/d7m6oqOQcPqMvual2+4EvlHawNO8FIsuoChyXJsmSFhxEX1G09Y8vj8abhakgX+jUz0a+Zi77NTPRrZmoJ/Vp78cU663s3Nmkbks0G9YqNO3fulG3b6tixY8jxjh076osvvoj6murqalUHFaGXl9etsOXz+eQLX2Y4TWzbUSCpGSf0yUMZzvF4GjaFKztbqqnRN+08evqqU9X7q106ec0Oddqxv+55/+iIceq+G2TJWHX/VtC/XGOC7h2Zg37NTPRr5qJvMxP9mpkytV8tj4xjmuzncb9kr+/6WNfDDz+siRMnRhx/7733VFBQ4Pblo9q1ZZ06+FcLi/G3cEtpriorKxv0/kaWZKQDHktvH99Obx/fTh13HdC3vtyhk9ftUl61rZB0YoIa4vJ3hUMtTUaiXzMT/Zq56NvMRL9mpoztV8vWtu3btGLFiiZtRrI/b9crtLRr105er1fbtoXWemzbtk2dOnWK+poJEybojjvuCDwuLy9XWVmZ+vbt22TTw9Z+WqO9b8Yust/VtlDLLjxJrQsbGqqMzP4KmdraQDF/TcdsrWhXpHf7lamwVio+aKtkf42KK6pVvL9aeQdrlF1rq1WtrVY1trJrbXntw6NAVsT3i4nzXLQmGdmOI6/Hk7FDnC2SkWzHltfjdX+UDulDv2Yu+jYz0a+ZqQX0a8fOndW/f/8mbYN/FlYi9Qot2dnZOuOMM7Rw4cJATYvjOFq4cKHGjx8f9TU5OTnKycmJvHBWlrKymqaoqfepA6X/Ghj3nEsaeQ1753ZVvbNUVmGhPPkFsvfskqqqlHXscco+/kRZ2ZGfiZt8Pp9WrFihM/v3b7LPHann79d+9GtGoV8zF32bmejXzES/pkeyn229e+COO+7QmDFj1LdvX5155pl66qmnVFlZGVhNDHU8bdsrq9vRqv3qCzn79srKzVPOaf3k7dpdloc9PQEAAIBk1Tu0XHXVVdqxY4d+/etfa+vWrfrWt76lN954I6I4v6WzLEutjukpe/tWWTk5yj6pj7xt2jV1swAAAIAjToPGusaPHx9zOhgO8xQWKeeM/rLy8uTJzWvq5gAAAABHJCbouczbuk1TNwEAAAA4olFcAQAAAKBZI7QAAAAAaNYILQAAAACaNUILAAAAgGaN0AIAAACgWSO0AAAAAGjWCC0AAAAAmjVCCwAAAIBmjdACAAAAoFkjtAAAAABo1ggtAAAAAJo1QgsAAACAZo3QAgAAAKBZI7QAAAAAaNYILQAAAACaNUILAAAAgGaN0AIAAACgWctK9wWNMZKk8vLydF+6RfP5fKqsrFR5ebmystLe7XAJ/ZqZ6NfMRd9mJvo1M9Gv6eHPBP6MEEvae6CiokKSVFZWlu5LAwAAAGiGKioqVFJSEvN5yySKNSnmOI6++eYbFRUVybKsdF66RSsvL1dZWZk2bdqk4uLipm4OUoR+zUz0a+aibzMT/ZqZ6Nf0MMaooqJCXbp0kccTu3Il7SMtHo9HXbt2TfdlcUhxcTHfeBmIfs1M9Gvmom8zE/2amehX98UbYfGjEB8AAABAs0ZoAQAAANCsEVpaiJycHN13333Kyclp6qYghejXzES/Zi76NjPRr5mJfm1e0l6IDwAAAAD1wUgLAAAAgGaN0AIAAACgWSO0AAAAAGjWCC0AAAAAmjVCyxHkrbfe0qWXXqouXbrIsizNmTMn5Plt27Zp7Nix6tKli/Lz8zVkyBCtXbs25JzzzjtPlmWFfP3kJz8JOWfjxo0aNmyY8vPz1aFDB911113y+Xxu316LlY5+/eijjzRy5EiVlZUpLy9PvXv31qRJk9Jxey1Wur5f/Xbt2qWuXbvKsizt3bvXpbtCOvt12rRp6tOnj3Jzc9WhQwfdfPPNbt5ai5euvl25cqUuuOAClZaWqnXr1ho8eLA++ugjt2+vxUpFv0rS8uXLdf7556ugoEDFxcU655xzdPDgwcDzu3fv1qhRo1RcXKzS0lLdcMMN2r9/v9u316IQWo4glZWVOvXUU/WnP/0p4jljjC677DL9+9//1ty5c/Xhhx+qe/fuGjRokCorK0POHTdunLZs2RL4evTRRwPP2batYcOGqaamRm+//baee+45TZs2Tb/+9a9dv7+WKh39+v7776tDhw6aPn26Pv30U/3yl7/UhAkT9Mc//tH1+2up0tGvwW644Qb16dPHlXvBYenq1yeeeEK//OUvdc899+jTTz/VP//5Tw0ePNjVe2vp0tG3+/fv15AhQ9StWzetWLFCS5cuVVFRkQYPHqza2lrX77ElSkW/Ll++XEOGDNFFF12kd999VytXrtT48ePl8Rz+MXrUqFH69NNPtWDBAs2bN09vvfWWfvSjH6XlHlsMgyOSJPPKK68EHq9Zs8ZIMp988kngmG3bpn379uZ//ud/AsfOPfdcc9ttt8V839dee814PB6zdevWwLHJkyeb4uJiU11dndJ7QCS3+jWam266yQwcOLCxTUYS3O7XP//5z+bcc881CxcuNJLMnj17Uth6xOJWv+7evdvk5eWZf/7zn240G0lwq29XrlxpJJmNGzcGjn388cdGklm7dm1K7wGRGtqv/fv3N/fee2/M9/3ss8+MJLNy5crAsddff91YlmW+/vrr1N5EC8ZIS4aorq6WJOXm5gaOeTwe5eTkaOnSpSHnzpgxQ+3atdPJJ5+sCRMm6MCBA4Hnli9frlNOOUUdO3YMHBs8eLDKy8v16aefunwXCJeqfo1m3759atOmTeobjYRS2a+fffaZHnjgAf31r38N+a0f0i9V/bpgwQI5jqOvv/5avXv3VteuXTVixAht2rQpPTeCCKnq2169eqlt27Z65plnVFNTo4MHD+qZZ55R79691aNHj7TcCw5Lpl+3b9+uFStWqEOHDjrrrLPUsWNHnXvuuSH9vnz5cpWWlqpv376BY4MGDZLH49GKFSvSdDeZj//DZYgTTjhB3bp104QJE7Rnzx7V1NTod7/7nTZv3qwtW7YEzrv66qs1ffp0LVq0SBMmTNDf/vY3XXPNNYHnt27dGhJYJAUeb926NT03g4BU9Wu4t99+W7NmzWLouomkql+rq6s1cuRI/f73v1e3bt2a4lYQJFX9+u9//1uO4+i3v/2tnnrqKb300kvavXu3LrzwQtXU1DTFrbV4qerboqIiLV68WNOnT1deXp4KCwv1xhtv6PXXX1dWVlZT3FqLlky//vvf/5Yk3X///Ro3bpzeeOMNnX766brgggsCtS9bt25Vhw4dQt47KytLbdq04WenFOI7JEO0atVKf//733XDDTeoTZs28nq9GjRokIYOHSpjTOC84B9STznlFHXu3FkXXHCB1q1bp2OPPbYpmo443OjXTz75RMOHD9d9992niy66KG33gsNS1a8TJkxQ79694wZUpE+q+tVxHNXW1uoPf/hD4Hv0+eefV6dOnbRo0SJqW5pAqvr24MGDuuGGG/Sd73xHzz//vGzb1mOPPaZhw4Zp5cqVysvLa4rba7GS6VfHcSRJP/7xj3XddddJkk477TQtXLhQzz77rB5++OEma39Lw0hLBjnjjDO0atUq7d27V1u2bNEbb7yhXbt26Zhjjon5mv79+0uSvvrqK0lSp06dtG3btpBz/I87derkUssRTyr61e+zzz7TBRdcoB/96Ee69957XW034ktFv/7rX//Siy++qKysLGVlZemCCy6QJLVr10733Xef+zeBCKno186dO0uSTjzxxMA57du3V7t27bRx40YXW494UtG3M2fO1IYNGzR16lT169dP3/72tzVz5kytX79ec+fOTct9IFSifo32/ShJvXv3Dnw/durUSdu3bw953ufzaffu3fzslEKElgxUUlKi9u3ba+3atXrvvfc0fPjwmOeuWrVK0uFvygEDBmj16tUh33wLFixQcXFxxDcs0qsx/SpJn376qQYOHKgxY8booYcecru5SFJj+vXll1/WRx99pFWrVmnVqlV6+umnJUlLlixhedwm1ph+/c53viNJWrNmTeCc3bt3a+fOnerevbt7jUZSGtO3Bw4ckMfjkWVZgXP8j/2/0UfTiNWvPXr0UJcuXUK+HyXpyy+/DHw/DhgwQHv37tX7778feP5f//qXHMcJBFekQFOuAoD6qaioMB9++KH58MMPjSTzxBNPmA8//ND85z//McYYM3v2bLNo0SKzbt06M2fOHNO9e3dzxRVXBF7/1VdfmQceeMC89957Zv369Wbu3LnmmGOOMeecc07gHJ/PZ04++WRz0UUXmVWrVpk33njDtG/f3kyYMCHt99tSpKNfV69ebdq3b2+uueYas2XLlsDX9u3b036/LUU6+jXcokWLWD3MZenq1+HDh5uTTjrJLFu2zKxevdpccskl5sQTTzQ1NTVpvd+WJB19+/nnn5ucnBxz4403ms8++8x88skn5pprrjElJSXmm2++Sfs9twSN7VdjjHnyySdNcXGxefHFF83atWvNvffea3Jzc81XX30VOGfIkCHmtNNOMytWrDBLly41xx13nBk5cmRa7zXTEVqOIP4fSMK/xowZY4wxZtKkSaZr166mVatWplu3bubee+8NWaZ448aN5pxzzjFt2rQxOTk5pmfPnuauu+4y+/btC7nOhg0bzNChQ01eXp5p166d+dnPfmZqa2vTeastSjr69b777ot6je7du6f5bluOdH2/RrsmocU96erXffv2meuvv96UlpaaNm3amMsvvzxkmVykXrr69v/+7//Md77zHVNSUmJat25tzj//fLN8+fJ03mqL0th+9Xv44YdN165dTX5+vhkwYIBZsmRJyPO7du0yI0eONIWFhaa4uNhcd911pqKiIh232GJYxgRVkAEAAABAM0NNCwAAAIBmjdACAAAAoFkjtAAAAABo1ggtAAAAAJo1QgsAAACAZo3QAgAAAKBZI7QAAAAAaNYILQAAAACaNUILAAAAgGaN0AIAAACgWSO0AAAAAGjWCC0AAAAAmrX/D/REKaJdW53NAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -160,7 +160,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/sentence-classification.ipynb b/docs/examples/sentence-classification.ipynb index 0c7af11fe4..f95c117985 100644 --- a/docs/examples/sentence-classification.ipynb +++ b/docs/examples/sentence-classification.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:52:10.006055Z", - "iopub.status.busy": "2023-09-02T00:52:10.005732Z", - "iopub.status.idle": "2023-09-02T00:52:10.388324Z", - "shell.execute_reply": "2023-09-02T00:52:10.388002Z" + "iopub.execute_input": "2023-11-09T07:16:14.097055Z", + "iopub.status.busy": "2023-11-09T07:16:14.096808Z", + "iopub.status.idle": "2023-11-09T07:16:14.492429Z", + "shell.execute_reply": "2023-11-09T07:16:14.492035Z" }, "tags": [] }, @@ -62,10 +62,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:52:10.390078Z", - "iopub.status.busy": "2023-09-02T00:52:10.389940Z", - "iopub.status.idle": "2023-09-02T00:52:10.405704Z", - "shell.execute_reply": "2023-09-02T00:52:10.405433Z" + "iopub.execute_input": "2023-11-09T07:16:14.494071Z", + "iopub.status.busy": "2023-11-09T07:16:14.493926Z", + "iopub.status.idle": "2023-11-09T07:16:14.506089Z", + "shell.execute_reply": "2023-11-09T07:16:14.505825Z" }, "tags": [] }, @@ -103,10 +103,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:52:10.407372Z", - "iopub.status.busy": "2023-09-02T00:52:10.407258Z", - "iopub.status.idle": "2023-09-02T01:59:03.297216Z", - "shell.execute_reply": "2023-09-02T01:59:03.296855Z" + "iopub.execute_input": "2023-11-09T07:16:14.507584Z", + "iopub.status.busy": "2023-11-09T07:16:14.507506Z", + "iopub.status.idle": "2023-11-09T07:16:31.431294Z", + "shell.execute_reply": "2023-11-09T07:16:31.430945Z" }, "tags": [] }, @@ -162,10 +162,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T01:59:03.299143Z", - "iopub.status.busy": "2023-09-02T01:59:03.299031Z", - "iopub.status.idle": "2023-09-02T01:59:03.319929Z", - "shell.execute_reply": "2023-09-02T01:59:03.319675Z" + "iopub.execute_input": "2023-11-09T07:16:31.433018Z", + "iopub.status.busy": "2023-11-09T07:16:31.432918Z", + "iopub.status.idle": "2023-11-09T07:16:31.443389Z", + "shell.execute_reply": "2023-11-09T07:16:31.443153Z" }, "tags": [] }, @@ -173,9 +173,9 @@ { "data": { "text/plain": [ - " False True \n", - "False 4,809 17 \n", - "True 102 645 " + " False True \n", + "False 4,809 17 \n", + " True 102 645 " ] }, "execution_count": 4, @@ -206,10 +206,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T01:59:03.321642Z", - "iopub.status.busy": "2023-09-02T01:59:03.321540Z", - "iopub.status.idle": "2023-09-02T02:34:10.621635Z", - "shell.execute_reply": "2023-09-02T02:34:10.621358Z" + "iopub.execute_input": "2023-11-09T07:16:31.445057Z", + "iopub.status.busy": "2023-11-09T07:16:31.444961Z", + "iopub.status.idle": "2023-11-09T07:16:40.630373Z", + "shell.execute_reply": "2023-11-09T07:16:40.630094Z" }, "tags": [] }, @@ -274,10 +274,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T02:34:10.623520Z", - "iopub.status.busy": "2023-09-02T02:34:10.623413Z", - "iopub.status.idle": "2023-09-02T02:34:10.644643Z", - "shell.execute_reply": "2023-09-02T02:34:10.644254Z" + "iopub.execute_input": "2023-11-09T07:16:40.632089Z", + "iopub.status.busy": "2023-11-09T07:16:40.631980Z", + "iopub.status.idle": "2023-11-09T07:16:40.642424Z", + "shell.execute_reply": "2023-11-09T07:16:40.642131Z" }, "tags": [] }, @@ -285,9 +285,9 @@ { "data": { "text/plain": [ - " False True \n", - "False 4,570 255 \n", - "True 41 706 " + " False True \n", + "False 4,570 255 \n", + " True 41 706 " ] }, "execution_count": 6, @@ -304,10 +304,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T02:34:10.646211Z", - "iopub.status.busy": "2023-09-02T02:34:10.646098Z", - "iopub.status.idle": "2023-09-02T02:34:10.667391Z", - "shell.execute_reply": "2023-09-02T02:34:10.667130Z" + "iopub.execute_input": "2023-11-09T07:16:40.644084Z", + "iopub.status.busy": "2023-11-09T07:16:40.643976Z", + "iopub.status.idle": "2023-11-09T07:16:40.658816Z", + "shell.execute_reply": "2023-11-09T07:16:40.658527Z" }, "tags": [] }, @@ -479,10 +479,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T02:34:10.668961Z", - "iopub.status.busy": "2023-09-02T02:34:10.668858Z", - "iopub.status.idle": "2023-09-02T02:34:11.240468Z", - "shell.execute_reply": "2023-09-02T02:34:11.240041Z" + "iopub.execute_input": "2023-11-09T07:16:40.660408Z", + "iopub.status.busy": "2023-11-09T07:16:40.660299Z", + "iopub.status.idle": "2023-11-09T07:16:41.186575Z", + "shell.execute_reply": "2023-11-09T07:16:41.186314Z" }, "tags": [] }, @@ -545,10 +545,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T02:34:11.242530Z", - "iopub.status.busy": "2023-09-02T02:34:11.242375Z", - "iopub.status.idle": "2023-09-02T02:34:11.265325Z", - "shell.execute_reply": "2023-09-02T02:34:11.265063Z" + "iopub.execute_input": "2023-11-09T07:16:41.188113Z", + "iopub.status.busy": "2023-11-09T07:16:41.188014Z", + "iopub.status.idle": "2023-11-09T07:16:41.198225Z", + "shell.execute_reply": "2023-11-09T07:16:41.197984Z" }, "tags": [] }, @@ -556,9 +556,9 @@ { "data": { "text/plain": [ - " False True \n", - "False 4,584 243 \n", - "True 55 692 " + " False True \n", + "False 4,584 243 \n", + " True 55 692 " ] }, "execution_count": 9, @@ -575,10 +575,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T02:34:11.267012Z", - "iopub.status.busy": "2023-09-02T02:34:11.266890Z", - "iopub.status.idle": "2023-09-02T02:34:11.285944Z", - "shell.execute_reply": "2023-09-02T02:34:11.285665Z" + "iopub.execute_input": "2023-11-09T07:16:41.199736Z", + "iopub.status.busy": "2023-11-09T07:16:41.199651Z", + "iopub.status.idle": "2023-11-09T07:16:41.209129Z", + "shell.execute_reply": "2023-11-09T07:16:41.208914Z" }, "tags": [] }, @@ -838,10 +838,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T02:34:11.287769Z", - "iopub.status.busy": "2023-09-02T02:34:11.287663Z", - "iopub.status.idle": "2023-09-02T02:34:12.381997Z", - "shell.execute_reply": "2023-09-02T02:34:12.381591Z" + "iopub.execute_input": "2023-11-09T07:16:41.210822Z", + "iopub.status.busy": "2023-11-09T07:16:41.210727Z", + "iopub.status.idle": "2023-11-09T07:16:43.345840Z", + "shell.execute_reply": "2023-11-09T07:16:43.345508Z" }, "tags": [] }, @@ -874,10 +874,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T02:34:12.384046Z", - "iopub.status.busy": "2023-09-02T02:34:12.383784Z", - "iopub.status.idle": "2023-09-02T05:14:59.324748Z", - "shell.execute_reply": "2023-09-02T05:14:59.324481Z" + "iopub.execute_input": "2023-11-09T07:16:43.347911Z", + "iopub.status.busy": "2023-11-09T07:16:43.347693Z", + "iopub.status.idle": "2023-11-09T07:17:42.083336Z", + "shell.execute_reply": "2023-11-09T07:17:42.082851Z" }, "tags": [] }, @@ -936,10 +936,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T05:14:59.326421Z", - "iopub.status.busy": "2023-09-02T05:14:59.326305Z", - "iopub.status.idle": "2023-09-02T05:14:59.357330Z", - "shell.execute_reply": "2023-09-02T05:14:59.357071Z" + "iopub.execute_input": "2023-11-09T07:17:42.086641Z", + "iopub.status.busy": "2023-11-09T07:17:42.086502Z", + "iopub.status.idle": "2023-11-09T07:17:42.105326Z", + "shell.execute_reply": "2023-11-09T07:17:42.105096Z" }, "tags": [] }, @@ -947,9 +947,9 @@ { "data": { "text/plain": [ - " False True \n", - "False 4,537 290 \n", - "True 85 662 " + " False True \n", + "False 4,537 290 \n", + " True 85 662 " ] }, "execution_count": 13, @@ -966,10 +966,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T05:14:59.358867Z", - "iopub.status.busy": "2023-09-02T05:14:59.358782Z", - "iopub.status.idle": "2023-09-02T05:14:59.388148Z", - "shell.execute_reply": "2023-09-02T05:14:59.387899Z" + "iopub.execute_input": "2023-11-09T07:17:42.106909Z", + "iopub.status.busy": "2023-11-09T07:17:42.106826Z", + "iopub.status.idle": "2023-11-09T07:17:42.119123Z", + "shell.execute_reply": "2023-11-09T07:17:42.118725Z" }, "tags": [] }, @@ -1192,7 +1192,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" }, "vscode": { "interpreter": { diff --git a/docs/examples/the-art-of-using-pipelines.ipynb b/docs/examples/the-art-of-using-pipelines.ipynb index 4ffb09e24d..7f872fcd9a 100644 --- a/docs/examples/the-art-of-using-pipelines.ipynb +++ b/docs/examples/the-art-of-using-pipelines.ipynb @@ -25,10 +25,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T05:15:01.267796Z", - "iopub.status.busy": "2023-09-02T05:15:01.267477Z", - "iopub.status.idle": "2023-09-02T05:15:02.822438Z", - "shell.execute_reply": "2023-09-02T05:15:02.822133Z" + "iopub.execute_input": "2023-11-09T07:17:43.488129Z", + "iopub.status.busy": "2023-11-09T07:17:43.487739Z", + "iopub.status.idle": "2023-11-09T07:17:44.001211Z", + "shell.execute_reply": "2023-11-09T07:17:44.000818Z" }, "tags": [] }, @@ -37,8 +37,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading https://maxhalford.github.io/files/datasets/kaggle_recruit_restaurants.zip (4.28 MB)\n", - "Uncompressing into /Users/max/river_data/Restaurants\n", "{'area_name': 'Tōkyō-to Nerima-ku Toyotamakita',\n", " 'date': datetime.datetime(2016, 1, 1, 0, 0),\n", " 'genre_name': 'Izakaya',\n", @@ -72,10 +70,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T05:15:02.824128Z", - "iopub.status.busy": "2023-09-02T05:15:02.823996Z", - "iopub.status.idle": "2023-09-02T05:47:05.985901Z", - "shell.execute_reply": "2023-09-02T05:47:05.985566Z" + "iopub.execute_input": "2023-11-09T07:17:44.003374Z", + "iopub.status.busy": "2023-11-09T07:17:44.003166Z", + "iopub.status.idle": "2023-11-09T07:17:52.097932Z", + "shell.execute_reply": "2023-11-09T07:17:52.097668Z" }, "tags": [] }, @@ -146,10 +144,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T05:47:05.987651Z", - "iopub.status.busy": "2023-09-02T05:47:05.987558Z", - "iopub.status.idle": "2023-09-02T05:47:06.008112Z", - "shell.execute_reply": "2023-09-02T05:47:06.007846Z" + "iopub.execute_input": "2023-11-09T07:17:52.099582Z", + "iopub.status.busy": "2023-11-09T07:17:52.099477Z", + "iopub.status.idle": "2023-11-09T07:17:52.109947Z", + "shell.execute_reply": "2023-11-09T07:17:52.109685Z" }, "tags": [] }, @@ -181,10 +179,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T05:47:06.009719Z", - "iopub.status.busy": "2023-09-02T05:47:06.009604Z", - "iopub.status.idle": "2023-09-02T06:36:12.768771Z", - "shell.execute_reply": "2023-09-02T06:36:12.768397Z" + "iopub.execute_input": "2023-11-09T07:17:52.111601Z", + "iopub.status.busy": "2023-11-09T07:17:52.111480Z", + "iopub.status.idle": "2023-11-09T07:18:05.021317Z", + "shell.execute_reply": "2023-11-09T07:18:05.021035Z" }, "tags": [] }, @@ -246,10 +244,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T06:36:12.770564Z", - "iopub.status.busy": "2023-09-02T06:36:12.770453Z", - "iopub.status.idle": "2023-09-02T07:26:26.731827Z", - "shell.execute_reply": "2023-09-02T07:26:26.731553Z" + "iopub.execute_input": "2023-11-09T07:18:05.023001Z", + "iopub.status.busy": "2023-11-09T07:18:05.022896Z", + "iopub.status.idle": "2023-11-09T07:18:21.447044Z", + "shell.execute_reply": "2023-11-09T07:18:21.446714Z" }, "tags": [] }, @@ -299,10 +297,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T07:26:26.733434Z", - "iopub.status.busy": "2023-09-02T07:26:26.733325Z", - "iopub.status.idle": "2023-09-02T08:05:06.669495Z", - "shell.execute_reply": "2023-09-02T08:05:06.669211Z" + "iopub.execute_input": "2023-11-09T07:18:21.448692Z", + "iopub.status.busy": "2023-11-09T07:18:21.448585Z", + "iopub.status.idle": "2023-11-09T07:18:38.125086Z", + "shell.execute_reply": "2023-11-09T07:18:38.124793Z" }, "tags": [] }, @@ -346,10 +344,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T08:05:06.671159Z", - "iopub.status.busy": "2023-09-02T08:05:06.671044Z", - "iopub.status.idle": "2023-09-02T08:05:06.691706Z", - "shell.execute_reply": "2023-09-02T08:05:06.691444Z" + "iopub.execute_input": "2023-11-09T07:18:38.126825Z", + "iopub.status.busy": "2023-11-09T07:18:38.126718Z", + "iopub.status.idle": "2023-11-09T07:18:38.137524Z", + "shell.execute_reply": "2023-11-09T07:18:38.137270Z" }, "tags": [] }, @@ -384,10 +382,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T08:05:06.693283Z", - "iopub.status.busy": "2023-09-02T08:05:06.693194Z", - "iopub.status.idle": "2023-09-02T08:54:09.757923Z", - "shell.execute_reply": "2023-09-02T08:54:09.757617Z" + "iopub.execute_input": "2023-11-09T07:18:38.139108Z", + "iopub.status.busy": "2023-11-09T07:18:38.139011Z", + "iopub.status.idle": "2023-11-09T07:18:55.081379Z", + "shell.execute_reply": "2023-11-09T07:18:55.081112Z" }, "tags": [] }, @@ -430,10 +428,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T08:54:09.759593Z", - "iopub.status.busy": "2023-09-02T08:54:09.759478Z", - "iopub.status.idle": "2023-09-02T09:23:35.110833Z", - "shell.execute_reply": "2023-09-02T09:23:35.110177Z" + "iopub.execute_input": "2023-11-09T07:18:55.084448Z", + "iopub.status.busy": "2023-11-09T07:18:55.084338Z", + "iopub.status.idle": "2023-11-09T07:19:11.593184Z", + "shell.execute_reply": "2023-11-09T07:19:11.592935Z" }, "tags": [] }, @@ -478,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:23:35.114559Z", - "iopub.status.busy": "2023-09-02T09:23:35.114415Z", - "iopub.status.idle": "2023-09-02T09:23:55.183639Z", - "shell.execute_reply": "2023-09-02T09:23:55.183338Z" + "iopub.execute_input": "2023-11-09T07:19:11.594836Z", + "iopub.status.busy": "2023-11-09T07:19:11.594736Z", + "iopub.status.idle": "2023-11-09T07:19:27.786139Z", + "shell.execute_reply": "2023-11-09T07:19:27.785845Z" }, "tags": [] }, @@ -524,10 +522,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T09:23:55.185691Z", - "iopub.status.busy": "2023-09-02T09:23:55.185441Z", - "iopub.status.idle": "2023-09-02T09:23:55.213909Z", - "shell.execute_reply": "2023-09-02T09:23:55.213609Z" + "iopub.execute_input": "2023-11-09T07:19:27.787941Z", + "iopub.status.busy": "2023-11-09T07:19:27.787835Z", + "iopub.status.idle": "2023-11-09T07:19:27.805692Z", + "shell.execute_reply": "2023-11-09T07:19:27.805442Z" }, "tags": [] }, @@ -778,7 +776,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/introduction/getting-started/binary-classification.ipynb b/docs/introduction/getting-started/binary-classification.ipynb index 30df2f0f20..d9e8c990c5 100644 --- a/docs/introduction/getting-started/binary-classification.ipynb +++ b/docs/introduction/getting-started/binary-classification.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:17.434889Z", - "iopub.status.busy": "2023-09-02T00:45:17.434128Z", - "iopub.status.idle": "2023-09-02T00:45:18.027890Z", - "shell.execute_reply": "2023-09-02T00:45:18.027583Z" + "iopub.execute_input": "2023-11-09T07:10:38.073183Z", + "iopub.status.busy": "2023-11-09T07:10:38.072499Z", + "iopub.status.idle": "2023-11-09T07:10:38.719433Z", + "shell.execute_reply": "2023-11-09T07:10:38.719120Z" } }, "outputs": [ @@ -70,10 +70,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.041516Z", - "iopub.status.busy": "2023-09-02T00:45:18.041318Z", - "iopub.status.idle": "2023-09-02T00:45:18.059626Z", - "shell.execute_reply": "2023-09-02T00:45:18.059374Z" + "iopub.execute_input": "2023-11-09T07:10:38.735763Z", + "iopub.status.busy": "2023-11-09T07:10:38.735574Z", + "iopub.status.idle": "2023-11-09T07:10:38.749221Z", + "shell.execute_reply": "2023-11-09T07:10:38.748919Z" } }, "outputs": [], @@ -95,10 +95,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.061201Z", - "iopub.status.busy": "2023-09-02T00:45:18.061119Z", - "iopub.status.idle": "2023-09-02T00:45:18.075142Z", - "shell.execute_reply": "2023-09-02T00:45:18.074885Z" + "iopub.execute_input": "2023-11-09T07:10:38.750971Z", + "iopub.status.busy": "2023-11-09T07:10:38.750874Z", + "iopub.status.idle": "2023-11-09T07:10:38.760618Z", + "shell.execute_reply": "2023-11-09T07:10:38.760327Z" }, "tags": [] }, @@ -132,10 +132,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.076772Z", - "iopub.status.busy": "2023-09-02T00:45:18.076664Z", - "iopub.status.idle": "2023-09-02T00:45:18.089842Z", - "shell.execute_reply": "2023-09-02T00:45:18.089591Z" + "iopub.execute_input": "2023-11-09T07:10:38.762298Z", + "iopub.status.busy": "2023-11-09T07:10:38.762158Z", + "iopub.status.idle": "2023-11-09T07:10:38.771704Z", + "shell.execute_reply": "2023-11-09T07:10:38.771450Z" } }, "outputs": [ @@ -167,10 +167,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.091471Z", - "iopub.status.busy": "2023-09-02T00:45:18.091386Z", - "iopub.status.idle": "2023-09-02T00:45:18.160160Z", - "shell.execute_reply": "2023-09-02T00:45:18.159890Z" + "iopub.execute_input": "2023-11-09T07:10:38.773320Z", + "iopub.status.busy": "2023-11-09T07:10:38.773228Z", + "iopub.status.idle": "2023-11-09T07:10:38.793532Z", + "shell.execute_reply": "2023-11-09T07:10:38.793286Z" } }, "outputs": [ @@ -207,10 +207,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.161731Z", - "iopub.status.busy": "2023-09-02T00:45:18.161621Z", - "iopub.status.idle": "2023-09-02T00:45:18.177328Z", - "shell.execute_reply": "2023-09-02T00:45:18.177058Z" + "iopub.execute_input": "2023-11-09T07:10:38.795051Z", + "iopub.status.busy": "2023-11-09T07:10:38.794957Z", + "iopub.status.idle": "2023-11-09T07:10:38.804706Z", + "shell.execute_reply": "2023-11-09T07:10:38.804479Z" } }, "outputs": [], @@ -231,10 +231,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.179063Z", - "iopub.status.busy": "2023-09-02T00:45:18.178977Z", - "iopub.status.idle": "2023-09-02T00:45:18.193209Z", - "shell.execute_reply": "2023-09-02T00:45:18.192956Z" + "iopub.execute_input": "2023-11-09T07:10:38.806177Z", + "iopub.status.busy": "2023-11-09T07:10:38.806098Z", + "iopub.status.idle": "2023-11-09T07:10:38.815839Z", + "shell.execute_reply": "2023-11-09T07:10:38.815589Z" } }, "outputs": [ @@ -266,10 +266,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.194798Z", - "iopub.status.busy": "2023-09-02T00:45:18.194697Z", - "iopub.status.idle": "2023-09-02T00:45:18.208875Z", - "shell.execute_reply": "2023-09-02T00:45:18.208617Z" + "iopub.execute_input": "2023-11-09T07:10:38.817413Z", + "iopub.status.busy": "2023-11-09T07:10:38.817309Z", + "iopub.status.idle": "2023-11-09T07:10:38.826675Z", + "shell.execute_reply": "2023-11-09T07:10:38.826435Z" } }, "outputs": [ @@ -301,10 +301,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.210425Z", - "iopub.status.busy": "2023-09-02T00:45:18.210338Z", - "iopub.status.idle": "2023-09-02T00:45:18.254722Z", - "shell.execute_reply": "2023-09-02T00:45:18.254440Z" + "iopub.execute_input": "2023-11-09T07:10:38.828197Z", + "iopub.status.busy": "2023-11-09T07:10:38.828118Z", + "iopub.status.idle": "2023-11-09T07:10:38.864356Z", + "shell.execute_reply": "2023-11-09T07:10:38.864086Z" }, "tags": [] }, @@ -348,10 +348,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.256289Z", - "iopub.status.busy": "2023-09-02T00:45:18.256179Z", - "iopub.status.idle": "2023-09-02T00:45:18.302491Z", - "shell.execute_reply": "2023-09-02T00:45:18.302238Z" + "iopub.execute_input": "2023-11-09T07:10:38.866017Z", + "iopub.status.busy": "2023-11-09T07:10:38.865919Z", + "iopub.status.idle": "2023-11-09T07:10:38.900686Z", + "shell.execute_reply": "2023-11-09T07:10:38.900450Z" } }, "outputs": [ @@ -388,10 +388,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.304245Z", - "iopub.status.busy": "2023-09-02T00:45:18.304129Z", - "iopub.status.idle": "2023-09-02T00:45:18.326675Z", - "shell.execute_reply": "2023-09-02T00:45:18.326410Z" + "iopub.execute_input": "2023-11-09T07:10:38.902171Z", + "iopub.status.busy": "2023-11-09T07:10:38.902084Z", + "iopub.status.idle": "2023-11-09T07:10:38.918876Z", + "shell.execute_reply": "2023-11-09T07:10:38.918645Z" }, "tags": [] }, @@ -570,10 +570,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:18.328153Z", - "iopub.status.busy": "2023-09-02T00:45:18.328049Z", - "iopub.status.idle": "2023-09-02T00:45:18.391428Z", - "shell.execute_reply": "2023-09-02T00:45:18.391165Z" + "iopub.execute_input": "2023-11-09T07:10:38.920413Z", + "iopub.status.busy": "2023-11-09T07:10:38.920323Z", + "iopub.status.idle": "2023-11-09T07:10:38.967256Z", + "shell.execute_reply": "2023-11-09T07:10:38.966975Z" }, "tags": [] }, @@ -611,7 +611,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" }, "vscode": { "interpreter": { diff --git a/docs/introduction/getting-started/concept-drift-detection.ipynb b/docs/introduction/getting-started/concept-drift-detection.ipynb index 6cdab5c912..ae3398bac9 100644 --- a/docs/introduction/getting-started/concept-drift-detection.ipynb +++ b/docs/introduction/getting-started/concept-drift-detection.ipynb @@ -45,10 +45,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2023-09-02T00:45:19.877606Z", - "iopub.status.busy": "2023-09-02T00:45:19.877215Z", - "iopub.status.idle": "2023-09-02T00:45:20.252739Z", - "shell.execute_reply": "2023-09-02T00:45:20.252378Z" + "iopub.execute_input": "2023-11-09T07:10:40.159076Z", + "iopub.status.busy": "2023-11-09T07:10:40.158722Z", + "iopub.status.idle": "2023-11-09T07:10:40.508285Z", + "shell.execute_reply": "2023-11-09T07:10:40.508002Z" }, "jupyter": { "outputs_hidden": false @@ -61,7 +61,7 @@ "outputs": [ { "data": { - "image/png": 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z5k2rD8qpuAMs/zYfWy4o7rOPE1oMHOHIFPV26OAh3D5jenMsV+8HLlYoItyI3BBp7yIQDlh9doKPP/4Y8+bNw4ABA8Dj8dC7d2/MmTNHZ/cDwLorg0ilUuTl5WHChAkQCoW43diMrwsrMCUmBD39dAfIX/xRDuCcxraUlBQIBXx89FsZPt2jOdAiLi4OA4M64vXD+keJpqam4q+Lt/Gfk3+pXis9X5gLABg1chSGdO+k2n4q9xx+u1au+HcNHzf9IjBrRA/wSsT4puy4znzUZd8OwL6L1Sht9sZPC7RXIVEew+cLALmildW/SxecE3UGoLjWZjkPvB5DMXFwEJYd/x2AVOvc+tTdlWLhX7+rXk+cOFFrxodbByqA8jOqfJXlioqKwjdlJRpp1fcHBgZiX7M7sk+I8XJyP/QN6IC4MD+tMrR9P7gqqgcFR68H5fu7q39XpKbGWu08xZeq8fz/DmtsM/a+dkTKeouPj8eIXtqfA7pw/X7gYoUiQkgrk4JYf39/CAQC1fJ0SpWVlTqXquvatSu2bduGpqYm3Lp1CyEhIVi4cCF69eql8zy2WBlEmddr24rx2+kqbDx0GcWLk3Smfz/3nNY2oVAIoYCvFcACgJubAG5GlFUoFEIgEGi81s7LTWM7j6/ZlXnFzjPo1dUbIvfWNIbqad95xcCQkmv1etOqt1nweHz8t82o6H9tOY6LMaGobmh9TMaWH9uUZG5tGnZWZJ/FiimDNbbx1a5VPV9l+dUdv9Y6UwOfz8cPRYrZFJb+choAcPGdSXhr5yn4erkj7f4+aG6RY/mvp9ChjodUa6864ySsvvqOk3D0euDxeFYtX9EV7WDLkevDWAI3gVnXwdX7oT3UISGOxKSBXe7u7oiNjUV+fr5qm1wuR35+PhIS9K8p7OHhgW7duqGlpQX/93//hwcffNC8EnPs4AVFMFTTaHpfJX3jmhjGesvOsg2o+qHoMtwFrX9Omb6pE8xk7AID4tomjH53Nz4tUMwQcP7GHUQty8WaPec1E7Yp4jcHtAeR6bqKbcXXtLaVtxkY19bFmw348o9yvL+rFADw7cFL2PzXFawtpfk2CXEFPJqegJB2xeTZCTIyMvDll19iw4YNOH36NBYsWICGhgbMmTMHADBr1iyNgV8HDx7E1q1bceHCBfzxxx9ISUmBXC7HK6+8wt1VWMKCzzQGjM4R+gy0FztgY06wyXaErM3CBy1y4+acNUR9BgBdQezGNsHnqt/O4srtu3gvRxEsLvqpBHVNLXjn1zOqsn5WcB5HLt/WyosrbEVtamnth62YQsz0dbAJcQTWXrGuvYZ6xnwmE0Kch8l9YqdNm4YbN25g8eLFEIvFiI6ORk5OjmqwV0VFhcYj4KamJixatAgXLlxAx44dkZqaim+++Qa+vr6cXYSp5Axwo16CED+hxR/Wlra2DlycgynRIRaWQnFN6tfy6JpCBPp44MtZwyzO25BF2zT7pra0CcyViyUo/Vh0Ge/msA8EbMvc+mX7caDeCmOtVnJCCCGE2IZZA7vS09ORnp7Ouq+goEDj9X333YdTp6wzIb+5VhwVoPrAHvz4TILOpWP7vf4rmmVynHtrIoQC9gbrV348jjfb9OHUYESg1Nwix/eHr2gfqiPKOnGlFvvO3dTaLpcz+LOsdfvxK7UAag0XwATGPoprO+uY+kuGYXCu0rxVxiKX7jI6bf4Z9sU3VOWA9VuzSPtw5XYjArw94O5m1gKHxIFQdwJC2heX+1S+XtuEaonig+zdnDOoZVnW9MCFW2i+twTszLUHdU6qv734ms6VoSxdCKAtuZzBtwcvYfJ/9+EUy0T+cobB53u1B5hxXQ5jtA3K1X8oyBnTgkf1bhH1Tfqn+DHlWvNOifFZwXnDCYlLO1RejdHv/o5H1uy3d1EIIYS0YfUpthyNooVS4a+L7H0y1QcIHbhQjS//0L1O+ZWauzr3WRI+to3Hth69itd/KmFPDECm42SmxrD/+u4oruq4JnNbLtVbZuUmFGjXSTHezjau2wFguL6TV7Uu6/vMxiNG50tc1/eHLwPQ/Nwg9tfcIqeWcUKI6wWxxgRR2Seua7w+oe8LTFfwCMv6XbY99MSVGr3p/yrnZi31n49pj/q3FM/MIPaf3xSZdJ5XfjxuUnpCiPMpFdcjedVePDUqHIsnR9i7OITYTNjCnfYugsNxuSDWGH+06XOqrwVyZ5uAV8WCAPbY5Rp8mHfWqPMr3ZWyr4DGZWeCW3eazTqOr96dgJtJEwghVqRrrIAj+OjeZ+O6P8spiHUlS+8t+LOUnoqYQl/ge/GdSTYsiXW43PMYc6ZPNffz3JzpXEqu1uLB1X9i79kbrfkw5n+pcNkn9o7E9GUnW2RyzSDWyPLYoy8vIcR4khYZMr4vxi9WeHpDCCHGcLkg1lBwxLbf3BGt5sRhBy7c0trWpKOVlWuG6sacOPr/jlxBndrgOTnLyl1sbtTTHK6EOLJvD1Rg65GreO67o/YuCiHERbleEGtoP1sCM1tiG5tNDz7Zzr/k55PmFQCmdScwFHSbUw2v/t8J1Ku14J4R1+tJDTQ2t6BJKqMpyQlxcDfvON8PTQfuJUEIMYPL9YnVF6g9veEwunf21NpuzuceAwaPf3nAjCO1nRHXY0SvLmYda0prsKFH/RdvNZpVBnWPrinEP4Z117k/YrFiLthx/btafC5CiPXQD01CiL25YBCr+6P3t9OVrNv3n9d+xG/4PECVGY/EdfWjNac/KqB7wBebX47bpm8b2+IObRWU3jCYhhBCDKH+9YS0Xy7XncCcgV3VDeaNyjfH+aoG1u0/FhkO/NhELcs1Ou2LW46ZdQ5CCLEV6hJACFFyuSDWnBkDzPG/P8rNOm7LvcnVCSHORy5n2nXL3/biqyi5qpjiqB1fJgBqwSXEGbhgdwLbnKeQZZYBQkj71SKTI3nVXoT4euKbufH2Lg7n9pfdxPObiwEo5pe0VYOApcz5zM87VYlXfjyGDx+J5L5AxKVEbjD9PXRi9gkrlKR9csGWWNJW2MKdSNtEy7ASYomT1+pw/kaD1mIpzuBcZT1+PnZNb+ujoZlFnIGxPRHmfX0YtxuleOpr+lwkxJG5YEsshbFsdh7XsfIYIcThcL2i1oSP9gIAvEVuuH9AAKd5E0KItbheSyzFsIQQK7DlgCNr/Rg/ca+/q1HX4oSfpU5YZEKIHi4XxJozOwFp/xqbzZvCjBBX5Swfpc5STkKI6VwuiP36wCV7F4E4oJ+O0vrvxDLmLk9t1rnsMM+Uo0xtZUk5HOQSCCEccbkg9vLtu/YuAnFA1FpDiPMGeZduNeCp9X/hr4vV9i4KIcSGXC6IFThKcwJxKPZo2SLtS3t6CxnVJdaBBhg8++0R7D5ThUfXFGrtc6RyEkK45XJBLAUrhA29KwiXKHCyrcvVjUalo89/QtoXFwxi7V0C4oj49MYgLMx9VzhrDGvKbeBI12hsUejHBSHti1lB7OrVqxEWFgYPDw/Ex8fj0KFDetOvWrUK/fv3h6enJ0JDQ/Hiiy+iqanJrAJbikIVwoZiWMLG3JCH7bi9Z2/g49/OQa42RUqTVIa/ffIHlv9yyswz2Y76LVJV1+RY/cj1FMahysmBrKwsDB8+HN7e3ggICMCUKVNQWlqqkaapqQlpaWno0qULOnbsiKlTp6KyslIjTUVFBSZNmgQvLy8EBATg5ZdfRksLzdJCnIvJQeyWLVuQkZGBJUuW4MiRI4iKikJycjKqqqpY02/atAkLFy7EkiVLcPr0aaxduxZbtmzBa6+9ZnHhzUEtboQNn94WhENsLX6z1h3CR7+dRXZJ68IiOSVilFytw7o/y21ZPIPYHruful6n+ve8b4psWRyD5Ea2sLaH7gR79uxBWloaDhw4gLy8PEilUiQlJaGhoUGV5sUXX8Qvv/yCH374AXv27MG1a9fw8MMPq/bLZDJMmjQJzc3N2L9/PzZs2ID169dj8eLF9rgkQsxmchC7cuVKzJs3D3PmzEFERATWrFkDLy8vrFu3jjX9/v37MWrUKDz++OMICwtDUlISHnvsMYOtt9bCd7kOFIQQW1CPj/SFVL+WiLF2XznqmqRocaKJq78/fEX172OXazjLt75JatJjfrapzJynFi2Xk5ODJ598EoMGDUJUVBTWr1+PiooKFBUpfljU1tZi7dq1WLlyJR544AHExsbiq6++wv79+3HgwAEAQG5uLk6dOoWNGzciOjoaEydOxIoVK7B69Wo0Nzfb8/IIMYlJy842NzejqKgImZmZqm18Ph+JiYkoLNQeFQoAI0eOxMaNG3Ho0CHExcXhwoULyM7OxsyZM3WeRyKRQCKRqF7X1SlaAKRSKaRSqSlF1mLLuRyJ85DJZBa/t5yZ8tpduQ4A7XqQy+Va+3RRfxTb3CwF3Nh/Me88fh07j1/H0UvVGNO3i9H5a2DkVvlbbTpYgUmDAyGTyQymVU9jblkKL9zCrK+KMCMuFEsnDwQAlIrr0bmDOwK8RazHsP1N1IPgtmVpbmlN39LSoresjc0tOHmtXms7V3Vtjb9Zba1ilTU/Pz8AQFFREaRSKRITE1VpBgwYgB49eqCwsBAjRoxAYWEhIiMjERgYqEqTnJyMBQsW4OTJk4iJieG8nIRYg0lB7M2bNyGTyTTe+AAQGBiIM2fOsB7z+OOP4+bNmxg9ejQYhkFLSwueeeYZvd0JsrKysGzZMq3tubm58PLyMqXIWqTNAlDPWNLWyZOnkH3zpL2LYXd5eXn2LoJDUNbDlSt8KB9YZWdn6z3magOg/EjNyclhiWE1P27zT11Hp8arAAQAgJ07s/HFGT6kciAtQq6jn7Yij6qqGwbLYxpFvtdqm5C0ai/+3kOuKpcu5eUXYWzd6PLRCcXn8beHLqNnczl2VPBxvFqR58cJ7P0zr4u1/yYtLa2f623LoohhFdd34EAhbujpfryqRIDyeu2K5+q+aGw0bhYFY8nlcrzwwgsYNWoUBg8eDAAQi8Vwd3eHr6+vRtrAwECIxWJVGrbvceU+NtZsXNKL76H5mqNzicD+I8kR6KpPkYDbZw72brTg4vwmBbHmKCgowNtvv41PP/0U8fHxKCsrw/PPP48VK1bgjTfeYD0mMzMTGRkZqtd1dXUIDQ1FUlISfHx8LCrPWyUFqJPS4xKiadCgCKTG9bR3MexGKpUiLy8PEyZMgFAotHdx7KZtPezZWoJDNxSruaWmpuo99vT1erx3XPFEKjklBaI2Uezzhbkar4VCIYZE9ce35xU/nu5LTMILB3YDAKJH3Yduvp5a51Dm0bVrV6SmxppxhezUyyaV89B/wADg4jm9x4SFh2GPuAKA4brR5asrB4E7ipbEt4s1v4505ZlTdwzFtyo10rz812/KaFXruOYWOf598DcAwIgRCRge1llnedr+jZS4ui+UgR9X0tLSUFJSgn379nGaLxtrNi7pFfWF5muOfry94csefzgCXT8K34uzzXlshYsfdSYFsf7+/hAIBFqjHCsrKxEUFMR6zBtvvIGZM2fi6aefBgBERkaioaEB8+fPx+uvvw4+SydVkUgEkUj7V5JQKLT4g2RU7y74qfi64YTEpQgEApcO3pS4uMfaA2U9qH8+GaoXNzc3jX8LhfpbMnk8HgQCzWM0j9d9Pj6fb9W/k0Cgv+zKMiiZWxZ9A6105cl2XvUuA22PY3it+wzVq76ycFHfXP7N0tPTsWPHDuzduxfdu3dXbQ8KCkJzczNqamo0WmPVv6eDgoK0xqUov9d1fZdbs3FJr6zumq8zr7CnM1HCpgRO8rGGwsfZu2cOXrqL0/OULE3mND9TcfGjzqQg1t3dHbGxscjPz8eUKVMAKB5n5OfnIz09nfWYxsZGrUBV+QFpjzn7Xk3pT0Es0ULTRxI2pnQ8snTgu/pb0N6j6I1bscvy85iVh4lVw7SzYV8Mw+C5557DTz/9hIKCAoSHh2vsj42NhVAoRH5+PqZOnQoAKC0tRUVFBRISFIFbQkIC3nrrLVRVVSEgIACAosuEj48PIiIiWM9rzcYlveRtpuPk6FwSSAwnshNd9SmRcfu5YO8GCy7Ob3J3goyMDMyePRvDhg1DXFwcVq1ahYaGBsyZMwcAMGvWLHTr1g1ZWVkAgMmTJ2PlypWIiYlRdSd44403MHnyZKN+7XPNz4tamQghxjF7ntj2FTc5rP3nbyIuzM/o9LV3pfjH54V4ZGh3/GN4qBVLZj1paWnYtGkTtm/fDm9vb1Uf1k6dOsHT0xOdOnXC3LlzkZGRAT8/P/j4+OC5555DQkICRowYAQBISkpCREQEZs6ciffeew9isRiLFi1CWloaa6BKiKMyOYidNm0abty4gcWLF0MsFiM6Oho5OTmqTuEVFRUaLa+LFi0Cj8fDokWLcPXqVXTt2hWTJ0/GW2+9xd1VmMDeLRyEEOcgkzM4WnHb6PSaU2wZjmJr70rRIDFvcvm7zYZnD3AE12ruYvNfl/FEfA8E+Hho7bf04/jxLw/iXw/0MTr9f/LP4cTVWhwqr3baIPazzz4DAIwbN05j+1dffYUnn3wSAPDRRx+Bz+dj6tSpkEgkSE5OxqeffqpKKxAIsGPHDixYsAAJCQno0KEDZs+ejeXLl9vqMgjhhFkDu9LT03V2HygoKNA8gZsblixZgiVLlphzKkIIsYv3d5Xi/I0GwwlZGNsSu+Tn1hkx1I8xFNsduliN2kYpOjn4k6WZaw/i/I0G7D17A9vSRmnt56LF+psDl/TuVz9HXZPzTyFnTDc8Dw8PrF69GqtXr9aZpmfPnnYf2EOIpWjqf0IIYbFmz3mT0nM5B3XbFsoGSQuKLlVrbDtYfsvs/H8+dg0f5Z3VGRAZ00KqfuwRHS3Wyh8BxWYsjvDKj8fwxrYSw+UwOWdt245exU9HuRkwRAixHatPseWI/jWoBf856ZKXTggxEp8HmLugFtddYqd/cQAnrtZylt+/vjsKABjVxx9x4cb3KVWnfo1PrjuE4xyPdFauELZw4gB0EOn+vK5pNL519XK19pQ+jc0teGFLscnlI4TYn0u2xPa24mwgxD5WTBls0fGdPB37sSyxPUv6z5s184pGdwLNc7MFsOU3GzSmltJFLmdw5Tb7fIw373AzQlvCUo4zYm7mRJXKDF+jsdh+lEik3OVPCLEtlwxiSfviLXJDckSg4YR6dOnozlFpiCuqbmjGHUlri6AyVnpzxym89tMJo/IwdSqorF/PYPoX7PNJqnvx+2KMfvd3bC++qrVPZm5TsxHe/ZV9FUdTNbcJYrkemktjfQlxXhTEOqglk9nn6rOVR2K7G07kKHjcP74lxNjYprG5BUNX5GHqZ60BJcMAkhYZ/revHJsOVuBazV2Tzm1sQHukosZgmu3FihXH/ru7TGufXFefWCPObaixmauZYFpkdHcTQthREOug3LUXXrcpvhO1TvB5PJqXk3DO2Bjsym2WAJXRDPIqWPpi6rPt6DWT0huDLWDVFcQaw1aLCFgaxOq6xNq7ipZzLgfkEUJsi4JYB9U/0Nuu5+c70TM2Hg/o6GHZQD0KgklblgQ3EpkMN+pb+5tO/+KAwWPU34N7zlaZfW5d2HoOcNjdVItRrblGpJHKrVPIqGW52HrkCvf9EwghNkNBrINJGRSE11IHYJieVWj8OrhjVkJPq5bD2RaF6Chyw/yxvexdDEIAAOM/3IMx7/1u8/PmnarEaz+dgKRFezGE8psNuNVmIJdFLbFqh7Ll0vYjpNaEWQTUya3Yb/fV/ztutbwJIdZHQayDmZnQE/PH9tabZmxff/S1ckutrbsT3N+/q9a2PgEdjTpW+WU6uo8/l0Uirs6Ce6C+yfSVuNRDtQMXqlWPu00x7+vD2HSwAhsPVLDufy+nVON1g6RFK7AFwNFoJ808opbnGkhhHfq6PTAMsO/cTRuUghBiDRTEOhi2D/WpQzUHWcWFd7H6hz9bd4LkQZbNAKAPW7e3fwwzbnCZWdMZEWKAMfeYpEVmta4o3xReNPtYcS37QLK2gfGyX04h9s3fzD6PLpV1TRa18lrClBkXWuQMNlhQz4QQ+6IglkMPDAgwuvVQJ5ZvTvVHg4O7+WCaDdb8ZmuJXfNErNUGnLEFoqY+RbSk8chWg1SI89D1fpLK5LjbLENNYzMGL9mFWesOcnK+tveAJU/Rv/yjHLknxVrbjb1Hlv5y2mAa9eKpz1d78MItxL+dj91n9PfrZRjGrJW8DNlXZlrL6qHyasOJCCEOiYJYDjEMg41z4y3Kg20wSZPaZNxTh3aHwAbP+tn6xPJ4PIgE1nnLsLXaCHWca2gPX43XFH4Sa9A1sOuBDwswcHEOth65CqmMQWUdNwsG/PObIk7yUZrPcX5t6Wpo/frAJaOO//mYeTMwGOqvL22z8AI9qCGk/aIglkPDwvwQ1MnDojzYPp9FwtY/k61mDdB5Hiudnm0AsodQ++352YyhyHp4CGseNFUO4VKLjlHxl6sVj+q5XAYWAA5fus1pfo7uTyNbTC2NQWetO2RhDoQQR0VBLIuMCf1MPmbFg4Mwb4zlo+PVw7BV06IRFeqL11MHqraZ0whrzvXoOo+1wkS2llhPoUBr28TIYHi5a28nhEtyOQOpgflJrd0X2xr3Gre/gS27flv8IGcYBkUu9uOAEFdCQSyLZ8fpnx2AzcyEME76i/LVoscpMd2wPW0UQnw9WxOY8cE/b0wvhPp5Gk6oRleXBVO6MkR174TCzAeMSssWD2hct96DFf8zVDVJepampUeORN1dqfYUVW25+lvG0nvG2Gn8LDkP3deEtG8UxLIwdY7UmDZ9NC06t4X72Xi6C0x+1K6rDkwJYt0EfAR3MhyI9vDzYm2JVd80KMRH51K4Y/sppueKCPbRe574Xl0MloUQwLgA1RkDJC673LBdf32TFH+cvWHU8c60KiAhxDFREMvC1M/Wcf0CdO5bOjnCtHMbOHm4fweT8tOVb08/LwDA+1MHm1QOU4LYN6ew593W9rRRkLEFsWqhxM5/jcEHj0ZppXkhsS/efjgSANC5gzsOvT4eJ5YmsZ6HpuIiXLL2u6mJZcECR9L2h2dzixyz1x1CnZ45ctXvQWO7E1gycwjd8YS0by4fxA4I0l40oO1n68Mx3TTSdfUWGZX3mL7+eHJUuMF0j2q0MLJ/sH80LQovJ/fHyN7ctCZumBOLjxNaMCkyiHW/rq8XgQmt1MZMN7Y9bRQ6d3BnnU5oQBB7y6qHWl/ZZ+7rjU6eQtXrAG8PeHsI2Q6Duxsfu14Ya7BMhBjze+cXM0fXG2v17+fxdrbhqa6UjlbYt+/nuPd/x5GKGr1p1OvVWi2xX+0vx96zN/Dgf/dhe/FV65yEEOIQXD6IHdK9k9a2to/SV06Lxtonh6tezxqhf8nXbWmj8FBMN7z/iHbLodKkIcGqf88wkB8APBTTHWn39zHY1eHx+B6s29t+KRv6km57mpRBimCXb8I3jzEplS27g0K0A1a/Du7Yv/ABFC+eoLG9q7cI/3qgD15O7q8R0Krb8FScxuthPTvj0dhQBPlYNnsEIbb0xd4LyD9daTDdwQu38NCn+w1nyGHg2PYj5Fptk0nHmNsn1tBRf5bdwqx1h3DsSi0yvj9m1DkIIc7J5YNYY/uIqaealRCGbWmjEOijaJFNGazZmhkd6ouPpkWrptsSsQz4Wjp5EGvelrROHF6UiBUPsj/Cb/vor2tHd715ta0X5feNmylB7L2D5o7W3RqtfKS4cOIALGAZUBfi6wlfL+2yZiT1R9r9fXTme18/zWVsf1wwEp40qwFxQnM3HDaYZu854/qh7jx+HVuPXLG0SADM6xNsTncCQgjRxeWDWHPw+IpAdc/L96Mw8wH0Z+mSoK4byyh79c9vzX+b/8Hu31F3Nwf1L5xji5MguteCqSsobVsM5Wv1lti4MD+95VEmDdDT/ULZEuvjIcSrKQPw4b1+rx9Pj9abt7loZS5iLkeeqsmUgJCr1klz7iX1I4z9PUxd2QkhupgVxK5evRphYWHw8PBAfHw8Dh3SPZn0uHHjwOPxtP6bNGmS2YXmkpvA/KDRQygwavQ9G57Gv3ms2401IMgb79wb3KTrePUWkE5erX1GeTweRvfxN/pcHm6trZmrZwzFv8b31eiTqo4tII/q3knVNQHQ/iKbGtsdZ9+ciAejuxldJkK4px05Tf1sP27d4WZ1Li7duiPBJ7vL7F0Mo2j0iaXpCQghFjI5iN2yZQsyMjKwZMkSHDlyBFFRUUhOTkZVFfs62Vu3bsX169dV/5WUlEAgEODRRx+1uPBcaDtx/ht/M202AS7oapU1Vs4LYzE9TtEXVtcXg7512P92r3+ur3pw27aM97asnBaF4E4eeHdqJLp6i5AxoZ/x87kC2J4+Gv99PEb1mm22Ay7m29VFV/cRau0hxhDXGe73aWsLt56wz4nN6U4A285OQAhp30yOFlauXIl58+Zhzpw5iIiIwJo1a+Dl5YV169axpvfz80NQUJDqv7y8PHh5eTlMEKu+KlT/QG+9/TfN9Te1QVxKuroNWGvpVLZ5WJUeHRaK/80ahtwX1Ubua/UnUPxvQJAPCjPHY9pw9gFkxnAT8JE4MABx4X4I62LelGHmEljQ8k5chzP9qDl+pUbnvjd3nLLaefV9pujiTPVKCHF8JgWxzc3NKCoqQmJiYmsGfD4SExNRWFhoVB5r167F9OnT0aGDbYOXthJ6KfpzTlWb3krXY3EA6KI2EMpLx4h4XZ4b3xefz4zVaGHU6E6g9sLT3bg/iakttvq+OwR8HhIjAhHgrXvkvr7TGSoK27n/N3s4vv9ngs0fKXYUudn0fKR9ccQgTN8P3//tK7faec2pit/PtD6xM7aF1RHrnFjZ0k6t/xGih0nf6Ddv3oRMJkNgoObynYGBgThz5ozB4w8dOoSSkhKsXbtWbzqJRAKJpLXvWV1dHQBAKpVCKpWaUmQtyuO/eHwIpAwP3h5C/GfaEKzZW463p0Sw5i+VSsEHcGDhOPB5ACOXQSo3bSLyB/p1QZCPCBXVdxV5trSep6WlBRmJfXCroRk9O3sYdY0tahOhG0ovlUohV+tPoF6Puo5l2lwfI2d0ptXVIqNML5MZX1ZrMVhHLS12K5sjMPR+cBXK62/WUQ8tLbon8rcf+0R5LTK5yceUXK1B4gBFH3y5kce3tLk35XLTz2spru4LV7+/COGaTZul1q5di8jISMTFxelNl5WVhWXLlmltz83NhZeXFydl2Z3/m8brf4YBJw8W4KRqi6JqBneWIzs7m5NzTusOrGsQYHIPOX7L+011jj/37UPPDkBPANnZF4zKq0TMA6BoEW5bvsGd+Si53dqim52djSaJAMo2U/X0eXl5bXJWlOncuXPwEfJRJ1Ucc/36NWRns0/NU1/Xmrc65XnK9ZTVulrf3prn1X7bFxUVob6Mmny03w+u52YTkLVlD5TvWXX79u2DjT82DWpqagKnE8Aa6dq1azC1R1rZuTJkS84CAM5f4ht1/L59+3BJbd2Ua9eMO45LXN0XjY2NnORDCFEw6dPY398fAoEAlZWak29XVlYiKIh95SelhoYGbN68GcuXLzd4nszMTGRkZKhe19XVITQ0FElJSfDxYV/FyVhSqRR5eXmYMGEChELd3QeeL8wFAMT0D0Nq6gCLzqlu/r3/196V4rXDvwMAxowZw7pymD6dL9zCD+VFAIDU1FSNfSkpDCrrJdh06DLG9O2CuDA/LD32Oxrutf6mpqbqrAfldffr1w8fzg3HoGWKYD8kJASpqUNYy/JZeSHQWK+1XVmuB6QyXNt4FGP6+iN1dJhJ12kJ5bWol0V9+/gBXVF0qQY1d6WIjY3FGD3LB7d3xt4X7Z1UKkXE8t917h89ejQ+OHHAhiUyzMvTEzXNth9wdvSW6YFkn759kDpeMb/ziV1nsfvaRYPHRA8fgfjw1un8fms4jiO3xCaf2xJc3RfKp4qEEG6YFMS6u7sjNjYW+fn5mDJlCgDFo538/Hykp6frPfaHH36ARCLBE088YfA8IpEIIpH23KJCoZCzL1hj8xLw+Vb5UheqPVVyc3Mz+Rxj+gXi7Yci0S+wI+uxPUTuWJjaOtOC+uwE6ul11YObQIAOnq1/A5FQdxl1DVJTphcKhdg0P0H/BVmBu4CPZpkc3Tt7spa9o4cQXb3dUXNXCjc3gUsHb0pc3mPt0c/HDa+eZWvGrJTlKNb9eQkvpwwEoBhPYYwn1h3GS0n9ENndF94ebkYfxyWu7gu6twjhlsnPxTIyMjB79mwMGzYMcXFxWLVqFRoaGjBnzhwAwKxZs9CtWzdkZWVpHLd27VpMmTIFXbp04abkzs7CabV4PJ7OJWbZmDqSWFmkxX+LwMaDl/Bycn+DaR3NtrRRWP17Gf6d1I91v/oUPzR4hBhj3Z/WGyjlCu5KZcgpEWutcmjIB7lnVf9+MDqE62IRQpyUyUHstGnTcOPGDSxevBhisRjR0dHIyclRDfaqqKjQ+qVcWlqKffv2ITc3ly1Ll6QxN6wNwsCpQ7tj/f6LiO3Z2aj0yvI9NTocT1lh2jFbiAjxweoZQ3Xu58E2dU8IafXMxiJcfGeSxgIshBBiDrNGKKSnp+vsPlBQUKC1rX///vSBZWcLJw5AfLgfRhq5Opcpy98661/WkiV+CSGWoa8EQoilbN+5iADQPU+stXgIBZgYGax3LlxX0eHeKm33D+hq55IQ4ppkcsaqc9i2d3v37sXkyZMREhICHo+Hbdu2aex/8skntZZ6T0lJ0UhTXV2NGTNmwMfHB76+vpg7dy7u3Lljw6sgxHIUxDoAR2oPHHavu8Hfo9pvv7OCl+/HN3PjMCmydSU1ahQixHYm/ecPexfBqTU0NCAqKgqrV6/WmSYlJUVjyffvvvtOY/+MGTNw8uRJ5OXlYceOHdi7dy/mz5+vIzdCHJNjTXjoQhz1UfaWfyagobkFPh7Gt9g65pXo1tVbhK7eilZYB/0zENKunRFrT8lnLLplgYkTJ2LixIl604hEIp1TX54+fRo5OTn466+/MGzYMADAJ598gtTUVHzwwQcICWm/jRikfaGWWAMiQiybl1YXW3cnMJaAzzMpgCWEEOJ4CgoKEBAQgP79+2PBggW4deuWal9hYSF8fX1VASwAJCYmgs/n4+DBg/YoLiFmoZZYHXb+azQOX7yNR4Z2t8HZHCiKNUOIrwdOXVdM4r3hqTg8800R3pkaaedSEUKIa0pJScHDDz+M8PBwnD9/Hq+99homTpyIwsJCCAQCiMViBARoLu7i5uYGPz8/iMXsC0lYczl4LXwP3fs4OpcI2nPROwpd9SkScNvxzd7LIHNxfgpidRgU0gmDQjpZLX+ehfPEOpK3HooEn1eC2SPDMKqPP04uSwaf71wXRSOlCXEOPx+7Zu8iOLzp06er/h0ZGYkhQ4agd+/eKCgowPjx483K0xbLwatEfaF7H0dLl7/h+wYn+ViDruXZ34uzzXlshYtlmCmItZP2ND9poI8HvpjV+ljKmQJY5ykpIQTQXH2QGKdXr17w9/dHWVkZxo8fj6CgIFRVVWmkaWlpQXV1tc5+tNZcDl5Llp4noJlXODlFwibbryJprMLHC1m3D166i9PzlCxN5jQ/U3GxDDMFsQ6AAilCCCHWcuXKFdy6dQvBwYoZWRISElBTU4OioiLExsYCAHbv3g25XI74+HjWPGyxHLyKXM9SyhydSwKJ4UR2oqs+JTJuowV7L4PMxfkpiLUTze4EFMYSQggxzp07d1BWVqZ6XV5ejuLiYvj5+cHPzw/Lli3D1KlTERQUhPPnz+OVV15Bnz59kJysaHkbOHAgUlJSMG/ePKxZswZSqRTp6emYPn26489MsFStm9/SWvuVgzgEmp3AAVAIa38MzRRLCHEShw8fRkxMDGJiYgAAGRkZiImJweLFiyEQCHD8+HH8/e9/R79+/TB37lzExsbijz/+0GhJ/fbbbzFgwACMHz8eqampGD16NL74Qk9fVEIcELXEOgBqiLUjqnxCiJMZN26c3qXcd+0y3HfSz88PmzZt4rJYhNgctcQSQgghhBCnQ0Gsnaj/iG5PMxU4LepNQAghhDgVCmLtRKA2DZW3B/XqsBf6+UAIIYQ4J4qe7MTdjY8vZsaiWSZH5w7u9i4OIYQQFrlXeEi1dyEIIawoiLWjpEHsk0oTQghxDDsvC7DK3oUghLCi7gSEgLrEEkIIIc6Gglji0miGLUIIIcQ5URBLCCH3XLzVYO8iEEIIMRIFsYQQcs/WI9fsXQRCCCFGoiCWEEDv6jfEdbgJqH8JIYQ4C7OC2NWrVyMsLAweHh6Ij4/HoUOH9KavqalBWloagoODIRKJ0K9fP2RnZ5tVYEK4RH1iiToBn37XE0KIszB5iq0tW7YgIyMDa9asQXx8PFatWoXk5GSUlpYiICBAK31zczMmTJiAgIAA/Pjjj+jWrRsuXboEX19fLspPCCGccePTrxpCCHEWJgexK1euxLx58zBnzhwAwJo1a7Bz506sW7cOCxcu1Eq/bt06VFdXY//+/RAKhQCAsLAwy0pNCCFWQN0JCCHEeZj07Ky5uRlFRUVITExszYDPR2JiIgoLC1mP+fnnn5GQkIC0tDQEBgZi8ODBePvttyGTySwrOSEcoh6xBKCWWEIIcSYmtcTevHkTMpkMgYGBGtsDAwNx5swZ1mMuXLiA3bt3Y8aMGcjOzkZZWRmeffZZSKVSLFmyhPUYiUQCiUSiel1XVwcAkEqlkEqlphRZi/J4S/NxdlQP99yLXltaZC5dF/R+UODRAD/Cgqv7wtXvL0K4ZvVlZ+VyOQICAvDFF19AIBAgNjYWV69exfvvv68ziM3KysKyZcu0tufm5sLLy4uTcuXl5XGSj7Nz9XqoqxMA4OHo0aNovkgBjKu/H86IeQAE9i4GcTBc3ReNjY2c5EMIUTApiPX394dAIEBlZaXG9srKSgQFBbEeExwcDKFQCIGg9Yth4MCBEIvFaG5uhru7u9YxmZmZyMjIUL2uq6tDaGgokpKS4OPjY0qRtUilUuTl5WHChAmqPrquiOpB4YuLhUBDPWJiYpAYwf4edgX0flC4VXgRKD9r72IQB8PVfaF8qkgI4YZJQay7uztiY2ORn5+PKVOmAFC0tObn5yM9PZ31mFGjRmHTpk2Qy+Xg35u+5uzZswgODmYNYAFAJBJBJBJpbRcKhZx9wXKZlzNz9Xrg3ZtjSyAQuHQ9KLn6+8HNjVphiTau7gtXvresJXJDpL2LQOzI5EkRMzIy8OWXX2LDhg04ffo0FixYgIaGBtVsBbNmzUJmZqYq/YIFC1BdXY3nn38eZ8+exc6dO/H2228jLS2Nu6sgxEw0TyxRd/Ryrb2LQAghxEgm94mdNm0abty4gcWLF0MsFiM6Oho5OTmqwV4VFRWqFlcACA0Nxa5du/Diiy9iyJAh6NatG55//nm8+uqr3F0FIYRw4KejtOwsIYQ4C7MGdqWnp+vsPlBQUKC1LSEhAQcOHDDnVITYBA3pIoQQQpwLrbFIXBr1JiCEEEKcEwWxhBBCCCHE6VAQSwghhBBCnA4FsYQAYGilJkIIIcSpUBBLXBt1iiWEEEKcEgWxhBBCCCHE6VAQSwghhBBCnA4FsYQANFEsIYQQ4mQoiCUujUedYgkhhBCnREEsIYQQQghxOhTEEkIIIYQQp0NBLCGgLrGEEEKIs6Eglrg0HnWJJYQ4mb1792Ly5MkICQkBj8fDtm3bNPYzDIPFixcjODgYnp6eSExMxLlz5zTSVFdXY8aMGfDx8YGvry/mzp2LO3fu2PAqCLGcm70LQAghhBDjNTQ0ICoqCk899RQefvhhrf3vvfce/vOf/2DDhg0IDw/HG2+8geTkZJw6dQoeHh4AgBkzZuD69evIy8uDVCrFnDlzMH/+fGzatMnWl0PsJGzhTp37Lr4zyYYlMR8FsYQQQogTmThxIiZOnMi6j2EYrFq1CosWLcKDDz4IAPj6668RGBiIbdu2Yfr06Th9+jRycnLw119/YdiwYQCATz75BKmpqfjggw8QEhJis2shxBIUxBICgKFOsYSQdqC8vBxisRiJiYmqbZ06dUJ8fDwKCwsxffp0FBYWwtfXVxXAAkBiYiL4fD4OHjyIhx56SCtfiUQCiUSiel1XVwcAkEqlkEql3F4E38O4dFIpRBBxe24HoKs+RQLbfVFx/je10jkoiCUujbrEEkLaE7FYDAAIDAzU2B4YGKjaJxaLERAQoLHfzc0Nfn5+qjRtZWVlYdmyZVrbc3Nz4eXlxUXRW0V9YVy67Gy84fsGt+d2ANnZ2azb34uzfxm41NjYaHEeFMQSQgghRK/MzExkZGSoXtfV1SE0NBRJSUnw8fHh9mRZ3Y0s1BUkbErg9twOoPDxQtbtg5fuslkZSpYmW/0cytZ8S1AQSwgAhibZIsRl5L44Fkkf7bV3MawiKCgIAFBZWYng4GDV9srKSkRHR6vSVFVVaRzX0tKC6upq1fFtiUQiiETaj+6FQiGEQiFHpb9H3mRcOqEQEkgMp3MyuupTIrPds0PO/6ZWOgcFscSl8WiOLUJcDr8d3/bh4eEICgpCfn6+Kmitq6vDwYMHsWDBAgBAQkICampqUFRUhNjYWADA7t27IZfLER8fb6+ik3v0zRpANFEQSwghxMU4dxR7584dlJWVqV6Xl5ejuLgYfn5+6NGjB1544QW8+eab6Nu3r2qKrZCQEEyZMgUAMHDgQKSkpGDevHlYs2YNpFIp0tPTMX36dJqZgDgVsxY7WL16NcLCwuDh4YH4+HgcOnRIZ9r169eDx+Np/Kecp44QQgixNWdviT18+DBiYmIQExMDAMjIyEBMTAwWL14MAHjllVfw3HPPYf78+Rg+fDju3LmDnJwcje/eb7/9FgMGDMD48eORmpqK0aNH44svjBxQRYiDMLkldsuWLcjIyMCaNWsQHx+PVatWITk5GaWlpVqjHZV8fHxQWlqqek2PcImjoSm2COHW66kD8Vb2aXsXg1WXDs49LdO4cePA6PnQ4vF4WL58OZYvX64zjZ+fHy1sQJyeyS2xK1euxLx58zBnzhxERERgzZo18PLywrp163Qew+PxEBQUpPqv7dQfhNgL/ZwixDrmje1l1nFj+3XluCTaOnkJMbgbxyPqCSE2Z1IQ29zcjKKiIo1JlPl8PhITE1FYyD4lBKDov9OzZ0+EhobiwQcfxMmTJ80vMSGEkHbLW2SboRrx4V1sch5CiPWY9Glx8+ZNyGQy1kmUz5w5w3pM//79sW7dOgwZMgS1tbX44IMPMHLkSJw8eRLdu7PPBWfNlUGUx9tiNQpHRvWgoHwkJ5PJXLou6P3gmrp2dMeNO81Wydvc95JcLue4JNqkUqlJ5+HqvqD7ixjDe+BCk4+pP/2OFUri+Kz+kzchIQEJCa2TEY8cORIDBw7E559/jhUrVrAeY4uVQfLy8jjJx9m5ej3crhEA4KH42DHgSrG9i2N3rv5+cLUJWxSNBdbpVKNY8cf0+hSLr8PMMcdGy87ORvlFvtHn4eq+4GKFIkJIK5M+Yfz9/SEQCFBZWamxvbKyUucEyW0JhULExMRoTA/SljVXBpFKpcjLy8OECRNsMpmvo6J6UNhw5SDK62sRFTUEqUO62bs4dkPvB4XnC3PtXQSb8vDwQJ3UOpPFp6amIru2GLtOVRlOrCa8R3ccvXXNKmVSSk1NRfGvpSi4fsmo9FzdF1ysUEQIaWVSEOvu7o7Y2Fjk5+er5puTy+XIz89Henq6UXnIZDKcOHECqampOtPYYmUQq6wy4oRcvR749+baEQjcXLoelFz9/UC4IxQK8f4/orFrqWk/DF6dOBBbj1o3iBUKheDxjG/t5eq+oHuLEG6Z/KwnIyMDs2fPxrBhwxAXF4dVq1ahoaEBc+bMAQDMmjUL3bp1Q1ZWFgBg+fLlGDFiBPr06YOamhq8//77uHTpEp5++mlur4QQQlyEr5cQNY2W96+09myHPh6mB22BPjSPOCHEOCYHsdOmTcONGzewePFiiMViREdHIycnRzXYq6KiAnx+6y/c27dvY968eRCLxejcuTNiY2Oxf/9+REREcHcVhFhI35yLhDiaP199AFv+uozlO07ZuygOp6u3CB9Pj8bjXx7Um46mKyfE+Zk1iiE9PV1n94GCggKN1x999BE++ugjc05DCCGERQeRG0J8PW12vvhwPxwsrzY6/Yopgzk5b0gnD3w7bwTu/6DA6GMWTRqI2J6dde7/exQtq0pIe2HdIaCEEEIcFs/ImQmCO5n2iD/Qm5sVsSZHh8Cvg7tJxyQP0j/IWLnkLD18IcT5URBLCCEcMDXYslz7j8KMDbJNOa791xohroOCWEII4YC5XSxTI42bntAaeDzg0xlDDaZTzuJhD/bsuzqmTxe8OLjFfgVwNUs7Kf4jxEgUxBKXxqPRHcQG/lz4gM59qx83HERaS8rgIKRGBhuV9vTyFCuXhp05d6gxt7UxadbNjkWYtxkFIITYBAWxhBCL1DQ2I/90JVpk1l8ulEtyObcPlvXl1k3HIKwdz402+4cUF306X0jsZ3RaT3eB0WlNKVq4fwed+3g8zR+a/xzby4Sc9aM+sYQ4PwpiCQF9oVniH58XYu6Gw1iz57y9i2K07cVXEbUsF3+cu2HXcgzuZptHpy8n98e2tFFa20Vuln8FBFk4r+tvGffp3Nc2vH9iRE+j8qTnK4S4BgpiiUujLzvLna28AwD4+Zh1V1ni0vObi1EvacHsdYeMPmbSkGCk3d+bszI8FtfDouNNacDl83gI6+KltV354+3R2O5a+5YMNa4vqIeQj06ehhc16OQpREwPX63tAgP9bekeJYToQkEsIYQTztiabUqPAgGPB19P3TMQmLpgRkSw7TpbGgp43344EtvSRqGjqHXqcD+WWbJG9u7Cenzba1d/+VrqAADAJ4/F4Nun4/WWo3+gdp2ol526sBNC1FEQS4idHKm4jb8uGj+BPBd2HL+GbUevamy7VnMXl283Wpy3ehizvfgqjl2usThPLu07dxOPfLYfZVX1Zh3vaAGUKTFz/yD2gJm591cTCviIDvWFoUkI2LofPDAgUG8f2Plje6P0zRSM7dfV4JRZbWdB4PHM+3FEAzYJcQ0UxBIC8+aOlMsZFF+ugaRFprVvz9kb2HFc9+P15hY5Hv50Px5dU4j6JqnW/qs1dzFl9Z/4hcNH9E1SGdI3HcULW4pR09gMAJDJGYx8ZzceWLkPEu3LMImyNa7oUjWe31yMB1f/aWmROfXE2oM4fOk2Fmw8YtbxpoRFKx4cZESGtgu0xhkRQOqj69j3HhmCV1L6Gzxe5KYYFGbOJcvVolg+BaeEEDUUxBKXZsl34pd/XMCU1X8i7VvNoKjoUjVmrzuE9E1Hcb32LuuxzWoj+eubWvse1jQ2I23TEdz33u8ovlyD5747an4B22hRe3be2KyIWKVq5bijHUubRBlrnLvXR9YRMAyDyromjW23GprNysuU1r2ZCWFmnUOfR+71W31qVDgAwNfL+MUVuGqZVM/Hv6M7/jEsFB5CAcuvQPafhYaK0bZbQtvg2dips/Ql6+Gn3TeYEOKcKIglBEDNXSmOVNzGezln0CBhH9DSIpOjql4REJVV1eO/v5cBAH47XaVK0ySVYepnharXt+5oB0y/n6lC8kd7Wc/xbs4Z7Dx+XSPgNMbthma8/MMxHLq3vn2DpAXPfXcUv564zjr1FRfdV2VyBjK1cir/1dBsWZPujuPX8PSGv1B718KoGsBrP5Ug/u18/Fh0RbXN3HDO0HECvnU/TtPu74PfXxqHN/42EAAwopefSVNOdfRw0+jzCpj+qN7Ufr9ttQ1Ko0J9DR7jplavyhZdXd6cMthgmgXjuBucR+yMFkZweW6GkxDS/i395bTq3w2SFix7cLBWmmlfHEDRpdt4428RWLHjFGs+d9oEwGzB6Jz1f2m8Vm9dulbTBHOs2HkKW49cxQ9FV/DcA33A4/Hwy7Fr+OXYNfB4wJczh2EEy6Ac9Zhk+yU+Zhp5PrmcQcqqvRplZxgG/919Dh/kntV53Pkbd9BR5IYuHdzhJmAP+tI3KVqfP8k/h0V/iwCg+AGhK70+3x2qAAC89MMx1TazGyV5rX1I1cWF+aFbZ09cutWAm3ckZmZu1OkRpjanKo/HQ2bqQHy+94JRxwv4PBS9kYhrNU24/4MCANwtwWpsPupdXkf27oJ1Tw7X2M/WYuzpLsCiSQMhkzMGl/Y1NAXX/LG94OVOX3uEtBfUEktIG0cqali3F126DQA6A1hAu7VO1iaIbW6xzoIAO45dV/37k91luFzdOlCLYYCnvz6sVRZAMyg7Vs1HfZMUKav2ImzhTnx/+DLKbzZodDlQutXQjHNVd1TTawHAxVuNegPYG/USjP9wD+LfzkfEkl3IKbmOM+I6LP35JG6xBH/V9x77X625i8iluVi8vUQrjVzO4FxlvYkthOZFsbr6hS6eHIGPpkWb/Mh+RLgf6/bDixJNLpuxRG4C+Kl1Q3AzcTlZQ9NhKen6c6jXUcaEfoquCHookz89phf+eZ/xLajUdZYQ10BBLHFpbN91l241YMn2EpytNH4Ue1lVPVpkctyVaj5KVx+UUtsoxZBlu1jO14hfT1zH94cvm/Xl+03hRY0+tgBYA8/ban1BP99zHocvVuNvn+zTSPPZnnKcESuu+5Ufj+P+DwqQ8f0xWOJuswxVdU04p1afzS1yPLPxCFJW/YH1+y8i9s3fsL34qkYXgq33ZlH4cu8F3JXK8HXhJYhrNVuqV+w8hQkf7cVHeYrgWS5nUHGrUXVeLin6Whr/B/rnfZqP+nf+a7RGXn1ZppMCAP+OLHNbcaiTlxCfPBaDz2YMNRhEtqVrYJWxPyIcKbYM96e+sYQ4O3quQkgbdU0t2FB4CRsKL6FkWTI6itzQ2Kx/4vfElXsxIMhbFQAqqbd+7jolRpNUO7ic/sUB1b/Z5slUz+vZb4vg31GEtx6KVG1/Y/tJrbS/na7U2jbu3iNkAPi68BK+LryklebLfRe1tv1y7BqmDu2Gbr6eOgMvfQYuzgEAPPdAH73pnt9cbDCvEVn5WDt7GMYPDAQAfPWnorz/2V2GjKT+WPxzCTYeqMDwsM746+Jt1jzMbaUzdFjb/S8m9sONOgmSBinKOiiktf9e34CO5hWCI5OjQsw6TldLrLHt4BrdT9jy4WiyYV2t4upbH4vrgT1nb6rulT4BHVFW1fpkIU5HSzkhxHFQEEuIHrPWHsTWZ0fhrZ2nDaZtG8ACwDeFl7Dlr8t4NWUAPswtNZjH7UbtgWBHK25jzZ7ziOzWCbtOKr5wUyODMaqPv8582IJlSzz5laIf78V3JgFg7xvKRn1O2k92l5l83ms1d7Hj+HWNbXM3HMagEB98OWuYxvYjFbex8YCiD6yuABawYGCXiQd6CAVYOS2adZ+1F4bwcheoZqDgUtt5XHXRdXnqwaUxdWBKlft4mPZ15ibg499J/VRBrPq5Di9KhJ+XO2Qy41YtI4TYBwWxxKUZ+lI+UlGD3JNifHuwwqz8d55QBGA/tVlgQJeqeu2+oQ99uh8AVAEsAPz7+2PIeWGMVQcSsWlsbsGfZbfQq2sHw4kBvLCl2KLzjXu/QKurBACcvFaHV//vuMa2h+/VkyFt67hJKoPIjY8jFboDX8C0rgTW0NnAoCZ13/8zQaurCBd09aE1JyjnqtUVAHp28cLa2cMMpjP2jJ5CAfh8HmTc/w4ghHCIglji0thWIGpr/jdFNiiJacR1TViw8QgKL9yy6XlTP/4DF29ZvrqXsdgCWKU/zt3k5BwD3shBdKgvig2sMGZKEMmlB6NDMHtkGDp5Co1KP35AAAZ3s87UQwIOR0wZFVAaeb53pw5BnwDLlvGlwWCEOB8a2EVcmrsZ0zY5ClsHsABsGsDakr4Als8DEgcGIO1++8wv+lrqQAzt0dmotD4ebshMHWi1shjdncCICJXLLhXGxp9t06mXQb2lnQJaQpwDtcQSl2ZoYnRC/h4VglXTYwyms0bgs2paNAJ9PIxKy+MBxYuTjA40zaGzO4EZM86acwwAHHptPK7W3EVUd1/0ei3brDzYJPTuglITZiQhhNif8zZDEcKBy7fbZ8siaR88hKZ9RKsHsMopvqYPD+WsPJoBsmmDtLSYObArwMcDMT06cxKsq//wmB7HXT0RQmzDrCB29erVCAsLg4eHB+Lj43Ho0CGjjtu8eTN4PB6mTJlizmkJ4dzRy7X2LgIhnGgbSL6SPAC/pI/Gm1O0V58z1/39A9TPqLssRkSoVp6ggV2buFe9zrjs70sIsQ2Tg9gtW7YgIyMDS5YswZEjRxAVFYXk5GRUVVXpPe7ixYt46aWXMGbMGLMLSwjX/DoYN1iGEEB/cGbJ7AVJEYFmH6uLgM9DZPdOJi3XO6S7r+pYNokDA1i3m9UQy2EU6yYwsu71nLOTV+tngdCJ+8oDwNKlS8Hj8TT+GzBggGp/U1MT0tLS0KVLF3Ts2BFTp05FZaX23NKEODqT79SVK1di3rx5mDNnDiIiIrBmzRp4eXlh3bp1Oo+RyWSYMWMGli1bhl69eulMR4it9fSjVXsIUVo5LQpPjgzDL2kJrPs1FxEwsHKBAca01hpqHH1iRA+M6euPmFDjBr7py99bJMTWZ0fi5/RRTh/EAsCgQYNw/fp11X/79rVOufbiiy/il19+wQ8//IA9e/bg2rVrePjhh+1YWkLMY9LArubmZhQVFSEzM1O1jc/nIzExEYWFhTqPW758OQICAjB37lz88ccfBs8jkUggkbTO5VhXVwcAkEqlkEqlug4zivJ4S/NxdlQPClYcA0PaCbmcUd0ncrn2lF8tLS2QSqWQM637DN1XDMNopHkwKgi5pyrRP7CjxnZpi8yke9TS+7mzhwCvT+wHqVSKc2rb5YxcK2/1a2gbkLboKff9/f1RVtWAmO4+2mna1Itcrn1edUsmKVoXZbIWo+Z0lbXJTyptUfu3FJHBHVX/Zvu/pWz5eevm5oagoCCt7bW1tVi7di02bdqEBx54AADw1VdfYeDAgThw4ABGjBhhszISYimTgtibN29CJpMhMFDz0VdgYCDOnDnDesy+ffuwdu1aFBcXG32erKwsLFu2TGt7bm4uvLy4aTnLy8vjJB9n5+r1UFfDB41vJG35iRhUSxS/cK5eu4rs7MsAgNPXeAA0Z7TYt28fLnUEblcLoGydzM7WNWpe8ZF7584djTQMA7wyBOjqUXNvuyLdkSNHIL9kqMWy9WNc93ktc/XKFWRnKxf8UJxP0ixRnU8ub712ADh69Ch4l9nL/WBngOkM5OfmqG1V5FlXX69x/efOnkX2XcMr3bHT/norv3AB2dmtK8ddbWhNt2vXLrjrmKyEq8/JxkbbDSQ9d+4cQkJC4OHhgYSEBGRlZaFHjx4oKiqCVCpFYmKiKu2AAQPQo0cPFBYW6gxirdm4pMI3biYOdSKIuDm3k2sWcNvL3BY/uLg4h1Wn2Kqvr8fMmTPx5Zdfwt9f9xKZbWVmZiIjI0P1uq6uDqGhoUhKSoKPj49FZZJKpcjLy8OECRMgFLpuf0iqB4XN4r9wrk7/Sk3E9fy4YCQeWKV4utQtpBtSUyMBANf/vIjtl85qpB09ejQGhfhg4/W/cL5e8V5KTU1lzff5wlwAQMeOHZGaOkrn+ZXphg4dipRB+vvLKtPqO6+plJ8PSt26d0dq6mCN83mIREhNHQcAeOlQHmSy1i/RmJgYpEZqtwLqoszTx9sbqakjVa/79uuHVDPn51WvF6VevXohNbmf6vUZcT3eO674O6ekJMNDqBnFcv05qQz8rC0+Ph7r169H//79cf36dSxbtgxjxoxBSUkJxGIx3N3d4evrq3FMYGAgxGKxzjxt0biEqC9MPuQNbs7s/OK4XV7OWj+I1XHxo86kINbf3x8CgUCrA3hlZSXrY4vz58/j4sWLmDx5smqb8nGcm5sbSktL0bu39geUSCSCSKT960ooFHIWcHGZlzNz9XoQGrFiF3E9Xbw9Vf/m83mqe0TA126qEwjcIBQKNfqLGrqneDyeUfedm0Bg0v1prXuZz+Oz5K37Gvgmlrs1S808Tb1+Q9yFmvkJBK1fgYrPQvamWK4+J231WTtx4kTVv4cMGYL4+Hj07NkT33//PTw9PfUcqZs1G5dUsrqbfEhCT5oaDQDqzy7lNL+Spcmc5seGix91JgWx7u7uiI2NRX5+vmqaLLlcjvz8fKSnp2ulHzBgAE6cOKGxbdGiRaivr8fHH3+M0FB68xH74tO0OoSFJTMNuCJLZxrw7+iOm3eaMa4/++wH5ogP98PB8mqNbfPGuObAYl9fX/Tr1w9lZWWYMGECmpubUVNTo9Eaq6sxSskWjUuQN5l8iAQSw4lcgETG7WeWLX5wcXEOk5uhMjIy8OWXX2LDhg04ffo0FixYgIaGBsyZMwcAMGvWLNXALw8PDwwePFjjP19fX3h7e2Pw4MFwd7fPWuSEKI0fYNqXZny4n5VKQrg2JTrE7GNlJkRlyt9B1gh77TKXKgtDv/UsLeeO58bgvUeG4IXEvprntSDPzfM1+3YOCvGBr5drfufcuXMH58+fR3BwMGJjYyEUCpGfn6/aX1paioqKCiQksM9KQYijMrlP7LRp03Djxg0sXrwYYrEY0dHRyMnJUQ32qqioAJ9Pj2iJc/hHbDeUnzmBeu8eOHG1DmfE+ped1DV/JnE8PSyYPq2DrhE+LLic79Qcaff3xurfz+ONv0XYtyBqGBMrJaiTB/4xjNsnc7w2kfc3c+M5zd+RvfTSS5g8eTJ69uyJa9euYcmSJRAIBHjsscfQqVMnzJ07FxkZGfDz84OPjw+ee+45JCQk0MwExOmYNbArPT2dtfsAABQUFOg9dv369eackhCr4PN5iOrCIDV1EIRCIcIW7tSb3pF7H3T1FuFGPT1aU+puRhDbp2sHRHao0wqAHNlLSf0xc0QYgjqZPrLbWkQO2Nfcr4N2K6z6n9mJ/uQGXblyBY899hhu3bqFrl27YvTo0Thw4AC6du0KAPjoo4/A5/MxdepUSCQSJCcn49NPP7VzqQkxneN90hDiwGzdh9avgzvef2SITc9pCf+Opk138+w480aeG6Os6o7Jx/z6r1EYF2y9ptWXk/sDAKdLwfJ4PKsFsIHeir/nxMH6ZxpQb3m9r19XJA7kZgUya99u9m5Ft5bNmzfj2rVrkEgkuHLlCjZv3qwxiNrDwwOrV69GdXU1GhoasHXrVr39YQlxVBTEEmKCti10fy5UTBYuNHbZSyME+rQGgjwA3h7Gd343pbvDqD5dNF4/0Yd9ihZdS42yyZjQDyeWJuHMihR4qT2S/+EZ9r52fQI6Gp23uq+eHK53f1y4HyRSbqec4ULa/X1w7q2JiO/VxXBiB/Drv0Zie9ooPGCg7/iiSYquDHNHh2PDU3EmLXVLCCHmok8aQkzQtq9fN19PXHxnEs6+ORFPjgwzKg9fLyH+eOV+uPF5eCyuh9b+tiOoJ0QEauR9X7+urPk2NcuwUUe/v/v7d8W/xrcOmkkZFIRvn9bs/9ZJx5gXuQmtVeH+HeDtIYSHUKDRyjU8zA8FL41Dv8COWPmPKLPyVjeuf2sdLJms3Rd02d8HoVmmvbqWPv+8j33kuqEiioSmfYyasqSpvVsKvT2EiAr1Ndi94qnR4dj36v1YNGmgjUpmnKjunfTu1+hOQDNSEOJ0KIglxAS6Bp/weDxkpg4weHz2v8bgz1cfQKifF04tT0HWw5Ea+zfPH4GkCM3HegI+D0v/Pkj1Wlerb0SIDxJ6d8HFdyZh/ZzhKHhpnFoefGRMaJ3k3ZNl4JKur3C5kZHU+48MQUJv3S2MYf4dkPvifXh4aOtckPoGAC2cqLs+1YMqttZnAZ+HsX3Zg302I3t3QeZE0wKwjAn98MSIHuh7rzW5PfWpNEf3zl6c9yW2OD8Dx9v7RwIhxDJWXbGLEGfz3tQheDfnDFbPGIrbDc1Y8O0R1b7sf43BwGBvPPfdUdZjRW7sI9rH9PXH36NC0KtrB0SEtE4K7s4y+GVEry6orGudK7GFpamybdC2dHIELt5qxPyxrS2JbefbbJFrtkr2C/QGAMT08MXRihpEdvMBD5pzaiq1LcL8sb3wxd4Lqtc7nhuNQB8PdPU2fflHBsD/Zg3DX5eq8fme1jy/fioOY/t1xTu/si9nra5toOPf0R1hXTqgb0BHjO7jj31lN1X7+gd6g8eD1iwUPnq6bOgKg9RbtomDMmOqNEKI86AglhA1/xgeikeHddcKjHp28dIIQI3RJ6Ajvv9nAuuoaH3UB0d1FGnfou5tguWe/h3w5KhwvXkqR4tvTxuFgtIbeGp0GADg0xlD8e2BCkwbFoK9v+9Wpf+/BQmY+pliOU65WhTbL7AjXksdqBHEDu6m/5GtPgzDIDEiEIkRgaogNjrUF2PvdZmYEBGIvFOVqsnw2cSE+uLbp+NRe1eKpIhAyJnWHwifz4zFoCW7VGl5POB/s4dh9Lu/m11mQghp73TN1HPxnUk2Lol+FMQS0gbbI0xTR90DiuDPmAB21bRovLClGG8/pOhaoGtw1hMjeuBQeTUmRQbjl2PXWsurJ+/3pg7B2n3lqoE3UaG+iAr1Ve0P7uSJl5L7QyqVoqMQ2J0xGj5eHhrXq96dwJQ+rB093HDXwOAqtvymDu2m+vcnj8XgaEUNhod1xo7j1/HClmLVvj9euR/Xau7qDaI7iNzw4zMJeGRNoWpb984crfPehjX6VDIOs9yBNmq5JFyLDNceI0CIPtQnlhATdfPVvfb4WLVBV8b2JZ0S0w1nVqTg8fjWD/B/PdAHgGKAktKbUyKR++J9rP1ZdfnH8FDsenEsQo2cMzW0s5dWwK5+HaZMYr929jD07toBa2cP09qnnLlgpJ4+tADgIRQgoXcXuAn4qsFc0feC8FA/L6NG+Q8L015l7fXUgehiYgs5AAzt2dnkY9orW/QntWWgTDE5Ic6HWmIJMZGbnum01j85HL1eywZgWqulh1AzMM1I6o/59/Vm7U5g60XD1LvT+ngaP93XkO6+yP/3ONZ9h15PxO2GZqODawDw9XLH6eUpFk2kr2xlnze2F54eE47wzGyTjo/t2Rmbno43qdyEEEKsg1piCeEQn8/D0B6+AIBHYrvrT2wAWwALAJ3aBJLWXgpXzjD4ctYwRIf64sNHozT2zU7oaVaeHUVuZgWCnu4C8M24XmUdxYW1tqQaO/K97W+RkX38KYiFbVpJadorQog+1BJLCMe+nhuPoxW3kWClCe0ju3XCs+N649OC8xjaw9dq51GSMwwmRARiQoT2Kkzm9BU2pKs396tP/ZZxH3JKxJhlZtBtDFfrI2qL7gRtF+QwlSlFdKalhgkhChTEEmKEwWozE3Rw13/bdBS5YYwJc5Saisfj4ZWUAXglxfC8tFwI1tMH2NzFCtiseWIojl6uQRJLsGypcP8OWGDFJW4BYGJkMPafv4XunXXXl6lcdR7TI29MMDhojxBCKIglRI9fnx+D7BPX8c/7WgOgj6dHI23TETw/vp+eI53ft0/H49uDl1hXxPp7VAhySsSYHse++IM5UgYHI2VwMGf5GWNMX3/8ce4mJy20M+J6oKefF4YYWCXKFG27jjgSazZc+nVwN3lqOktROywhzoeCWEL0GBjsg4HBmvPD9g30Ru6L99mpRLYzqo8/RvXxZ9338fRoSGUM64INzmT9nDjcqJcgqJPlXRj4fJ7G7BSWeO+RIThzvR5j+rLXvyMI79LB3kUghLg4CmIJISbj8Xhwd3P+tisBn8dJAMs1XcsbO4JtaaOwbl+53mWBCSHEFiiIJYQQYrToUF/857EYexeDczSuixDnQ0EsIYQQQuxn6b1+5LRiFzGRc3doI4QQQnR4OEaxhPGgEB8DKQkhzohaYgkhhLRLsxLCMCDYx6ipumieWOLMvAcuNPmY+tPvWKEktkVBLCGEkHaJz+dhhJUXAyGE2A91JyCEEEIIIU7HrCB29erVCAsLg4eHB+Lj43Ho0CGdabdu3Yphw4bB19cXHTp0QHR0NL755huzC0wIIYQQQojJQeyWLVuQkZGBJUuW4MiRI4iKikJycjKqqqpY0/v5+eH1119HYWEhjh8/jjlz5mDOnDnYtWuXxYUnhBBCCCGuyeQgduXKlZg3bx7mzJmDiIgIrFmzBl5eXli3bh1r+nHjxuGhhx7CwIED0bt3bzz//PMYMmQI9u3bZ3HhCSGEEEKIazJpYFdzczOKioqQmZmp2sbn85GYmIjCwkKDxzMMg927d6O0tBTvvvuuznQSiQQSiUT1uq6uDgAglUohlUpNKbIW5fGW5uPsqB4UqB4UqB4U2OpBLpe7XL24yvuhpaVF9W+2a+W6Htp7fRJiayYFsTdv3oRMJkNgYKDG9sDAQJw5c0bncbW1tejWrRskEgkEAgE+/fRTTJgwQWf6rKwsLFu2TGt7bm4uvLy8TCmyTnl5eZzk4+yoHhSoHhSoHhQU9aD4eLx27Rqys6/Yt0B20t7fDxIZoPw7Z2dn60zHVT00NjZykg8hRMEmU2x5e3ujuLgYd+7cQX5+PjIyMtCrVy+MGzeONX1mZiYyMjJUr+vq6hAaGoqkpCT4+Fg2abVUKkVeXh4mTJgAoVBoUV7OjOpBgepBgepBQb0epkpK8X9HrmHJP0ZhYLC3vYtmU670fnggUQqhgAcvd+2vQ67rQflUkRDCDZOCWH9/fwgEAlRWVmpsr6ysRFBQkM7j+Hw++vTpAwCIjo7G6dOnkZWVpTOIFYlEEIlEWtuFQiFnH6hc5uXMqB4UqB4UqB4UhEIhPng0Gm9OGQJPd4G9i2M3rvB+8Dfi+riqh/ZelyZZangBCkIMMWlgl7u7O2JjY5Gfn6/aJpfLkZ+fj4SEBKPzkcvlGn1eCSHE0fB4PJcOYAkhxNGZ3J0gIyMDs2fPxrBhwxAXF4dVq1ahoaEBc+bMAQDMmjUL3bp1Q1ZWFgBF/9Zhw4ahd+/ekEgkyM7OxjfffIPPPvuM2yshhBBCCCEuw+Qgdtq0abhx4wYWL14MsViM6Oho5OTkqAZ7VVRUgM9vbeBtaGjAs88+iytXrsDT0xMDBgzAxo0bMW3aNO6ughBCCCEOI3JDpP4E4T1sUxDCqbCFO3Xuu/jOJBuWRMGsgV3p6elIT09n3VdQUKDx+s0338Sbb75pzmkIIYQQQghhZZPZCQghhBBCiOPwHrjQrOPqT7/DcUnMZ/KKXYQQQghpH1avXo2wsDB4eHggPj4ehw4dsneRCDEaBbGEEEKIC9qyZQsyMjKwZMkSHDlyBFFRUUhOTkZVVZW9i0aIUSiIJYQQQlzQypUrMW/ePMyZMwcRERFYs2YNvLy8sG7dOnsXjRCjOEWfWIZhAHCz2olUKkVjYyPq6upceuJpqgcFqgcFqgcFqgcFqgcFrutB+R2m/E6zp+bmZhQVFSEzM1O1jc/nIzExEYWFhVrpJRKJxvzutbW1AIDq6mpIpVKt9G53nSK8IGboHLaIdXvMGvbtAPDbo79pbauvrwdg2f3gFO8y5YWGhobauSSEEEKIZerr69Gpk31XrLp58yZkMplqekylwMBAnDlzRit9VlYWli1bprU9PDzcamUk7Yf/An+d+yy5H5wiiA0JCcHly5fh7e0NHo9nUV51dXUIDQ3F5cuX4ePjw1EJnQ/VgwLVgwLVgwLVgwLVgwLX9cAwDOrr6xESEsJB6WwrMzMTGRkZqtdyuRzV1dXo0qWLxd/LtkLva/thq3su7genCGL5fD66d+/OaZ4+Pj70JgbVgxLVgwLVgwLVgwLVgwKX9WDvFlglf39/CAQCVFZWamyvrKxEUFCQVnqRSASRSKSxzdfX15pFtBp6X9tP27q39H6ggV2EEEKIi3F3d0dsbCzy8/NV2+RyOfLz85GQkGDHkhFiPKdoiSWEEEIItzIyMjB79mwMGzYMcXFxWLVqFRoaGjBnzhx7F40Qo7hcECsSibBkyRKtxyKuhupBgepBgepBgepBgepBob3Xw7Rp03Djxg0sXrwYYrEY0dHRyMnJ0Rrs1V6097+nI7NW3fMYR5jrgxBCCCGEEBNQn1hCCCGEEOJ0KIglhBBCCCFOh4JYQgghhBDidCiIJYQQQgghTsflgtjVq1cjLCwMHh4eiI+Px6FDh+xdJM4sXboUPB5P478BAwao9jc1NSEtLQ1dunRBx44dMXXqVK2JrisqKjBp0iR4eXkhICAAL7/8MlpaWmx9KSbZu3cvJk+ejJCQEPB4PGzbtk1jP8MwWLx4MYKDg+Hp6YnExEScO3dOI011dTVmzJgBHx8f+Pr6Yu7cubhz545GmuPHj2PMmDHw8PBAaGgo3nvvPWtfmkkM1cOTTz6p9f5ISUnRSNMe6iErKwvDhw+Ht7c3AgICMGXKFJSWlmqk4epeKCgowNChQyESidCnTx+sX7/e2pdnNGPqYdy4cVrviWeeeUYjjTPXw2effYYhQ4aoJlhPSEjAr7/+qtrvCu8DV2PKd/z69eu13v8eHh42LG37Yej7hw0n9w3jQjZv3sy4u7sz69atY06ePMnMmzeP8fX1ZSorK+1dNE4sWbKEGTRoEHP9+nXVfzdu3FDtf+aZZ5jQ0FAmPz+fOXz4MDNixAhm5MiRqv0tLS3M4MGDmcTERObo0aNMdnY24+/vz2RmZtrjcoyWnZ3NvP7668zWrVsZAMxPP/2ksf+dd95hOnXqxGzbto05duwY8/e//50JDw9n7t69q0qTkpLCREVFMQcOHGD++OMPpk+fPsxjjz2m2l9bW8sEBgYyM2bMYEpKSpjvvvuO8fT0ZD7//HNbXaZBhuph9uzZTEpKisb7o7q6WiNNe6iH5ORk5quvvmJKSkqY4uJiJjU1lenRowdz584dVRou7oULFy4wXl5eTEZGBnPq1Cnmk08+YQQCAZOTk2PT69XFmHq47777mHnz5mm8J2pra1X7nb0efv75Z2bnzp3M2bNnmdLSUua1115jhEIhU1JSwjCMa7wPXImp3/FfffUV4+Pjo/H+F4vFNi51+2Do+6ctru4blwpi4+LimLS0NNVrmUzGhISEMFlZWXYsFXeWLFnCREVFse6rqalhhEIh88MPP6i2nT59mgHAFBYWMgyjeBPy+XyNm/izzz5jfHx8GIlEYtWyc6XtzSOXy5mgoCDm/fffV22rqalhRCIR89133zEMwzCnTp1iADB//fWXKs2vv/7K8Hg85urVqwzDMMynn37KdO7cWaMeXn31VaZ///5WviLz6ApiH3zwQZ3HtMd6YBiGqaqqYgAwe/bsYRiGu3vhlVdeYQYNGqRxrmnTpjHJycnWviSztK0HhlEEsc8//7zOY9pjPXTu3Jn53//+57Lvg/bM1O/4r776iunUqZONSuc6jAliubpvXKY7QXNzM4qKipCYmKjaxufzkZiYiMLCQjuWjFvnzp1DSEgIevXqhRkzZqCiogIAUFRUBKlUqnH9AwYMQI8ePVTXX1hYiMjISI2JrpOTk1FXV4eTJ0/a9kI4Ul5eDrFYrHHdnTp1Qnx8vMZ1+/r6YtiwYao0iYmJ4PP5OHjwoCrN2LFj4e7urkqTnJyM0tJS3L5920ZXY7mCggIEBASgf//+WLBgAW7duqXa117roba2FgDg5+cHgLt7obCwUCMPZRpH/TxpWw9K3377Lfz9/TF48GBkZmaisbFRta891YNMJsPmzZvR0NCAhIQEl30ftFfmfsffuXMHPXv2RGhoKB588EGn/a5zNlzdNy4TxN68eRMymUxrJZLAwECIxWI7lYpb8fHxWL9+PXJycvDZZ5+hvLwcY8aMQX19PcRiMdzd3eHr66txjPr1i8Vi1vpR7nNGynLr+7uLxWIEBARo7Hdzc4Ofn1+7qpuUlBR8/fXXyM/Px7vvvos9e/Zg4sSJkMlkANpnPcjlcrzwwgsYNWoUBg8eDACc3Qu60tTV1eHu3bvWuByzsdUDADz++OPYuHEjfv/9d2RmZuKbb77BE088odrfHurhxIkT6NixI0QiEZ555hn89NNPiIiIcMn3QXtmznd8//79sW7dOmzfvh0bN26EXC7HyJEjceXKFVsU2aVxdd+43LKz7dnEiRNV/x4yZAji4+PRs2dPfP/99/D09LRjyYgjmD59uurfkZGRGDJkCHr37o2CggKMHz/ejiWznrS0NJSUlGDfvn32Lopd6aqH+fPnq/4dGRmJ4OBgjB8/HufPn0fv3r1tXUyr6N+/P4qLi1FbW4sff/wRs2fPxp49e+xdLOIAEhISkJCQoHo9cuRIDBw4EJ9//jlWrFhhx5IRY7lMS6y/vz8EAoHWyNPKykoEBQXZqVTW5evri379+qGsrAxBQUFobm5GTU2NRhr16w8KCmKtH+U+Z6Qst76/e1BQEKqqqjT2t7S0oLq6ul3XTa9eveDv74+ysjIA7a8e0tPTsWPHDvz+++/o3r27ajtX94KuND4+Pg71o1FXPbCJj48HAI33hLPXg7u7O/r06YPY2FhkZWUhKioKH3/8scu9D9o7Lr7jhUIhYmJiVO9/Yj1c3TcuE8S6u7sjNjYW+fn5qm1yuRz5+fkav8Takzt37uD8+fMIDg5GbGwshEKhxvWXlpaioqJCdf0JCQk4ceKERiCTl5cHHx8fRERE2Lz8XAgPD0dQUJDGddfV1eHgwYMa111TU4OioiJVmt27d0Mul6u+1BMSErB3715IpVJVmry8PPTv3x+dO3e20dVw68qVK7h16xaCg4MBtJ96YBgG6enp+Omnn7B7926Eh4dr7OfqXkhISNDIQ5nGUT5PDNUDm+LiYgDQeE84ez20JZfLIZFIXOZ94Cq4+I6XyWQ4ceKE6v1PrIez+8a0MWfObfPmzYxIJGLWr1/PnDp1ipk/fz7j6+vbbqbU+Pe//80UFBQw5eXlzJ9//skkJiYy/v7+TFVVFcMwiulkevTowezevZs5fPgwk5CQwCQkJKiOV04nk5SUxBQXFzM5OTlM165dHX6Krfr6eubo0aPM0aNHGQDMypUrmaNHjzKXLl1iGEYxxZavry+zfft25vjx48yDDz7IOsVWTEwMc/DgQWbfvn1M3759NaaWqqmpYQIDA5mZM2cyJSUlzObNmxkvLy+HmlpKXz3U19czL730ElNYWMiUl5czv/32GzN06FCmb9++TFNTkyqP9lAPCxYsYDp16sQUFBRoTJ3T2NioSsPFvaCcIubll19mTp8+zaxevdqhplYyVA9lZWXM8uXLmcOHDzPl5eXM9u3bmV69ejFjx45V5eHs9bBw4UJmz549THl5OXP8+HFm4cKFDI/HY3JzcxmGcY33gSsx9B0/c+ZMZuHChar0y5YtY3bt2sWcP3+eKSoqYqZPn854eHgwJ0+etNclOC1D38MLFy5kZs6cqUrP1X3jUkEswzDMJ598wvTo0YNxd3dn4uLimAMHDti7SJyZNm0aExwczLi7uzPdunVjpk2bxpSVlan23717l3n22WeZzp07M15eXsxDDz3EXL9+XSOPixcvMhMnTmQ8PT0Zf39/5t///jcjlUptfSkm+f333xkAWv/Nnj2bYRjFNFtvvPEGExgYyIhEImb8+PFMaWmpRh63bt1iHnvsMaZjx46Mj48PM2fOHKa+vl4jzbFjx5jRo0czIpGI6datG/POO+/Y6hKNoq8eGhsbmaSkJKZr166MUChkevbsycybN0/rB1x7qAe2OgDAfPXVV6o0XN0Lv//+OxMdHc24u7szvXr10jiHvRmqh4qKCmbs2LGMn58fIxKJmD59+jAvv/yyxjyxDOPc9fDUU08xPXv2ZNzd3ZmuXbsy48ePVwWwDOMa7wNXo+87/r777lN9LzAMw7zwwguqtIGBgUxqaipz5MgRO5Ta+Rn6Hp49ezZz3333aR1j6X3DYxiGMa3tlhBCCCGEEPtymT6xhBBCCCGk/aAglhBCCCGEOB0KYgkhhBBCiNOhIJYQQgghhDgdCmIJIYQQQojToSCWEEIIIYQ4HQpiCSGEEEKI06EglhBCCCGEOB0KYgkhhBBCiNOhIJYQQgghhDgdCmIJIYQQQojToSCWEEIIIYQ4nf8HHlgaWc7FUJgAAAAASUVORK5CYII=", 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\n", 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" ] @@ -122,10 +122,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2023-09-02T00:45:20.254722Z", - "iopub.status.busy": "2023-09-02T00:45:20.254540Z", - "iopub.status.idle": "2023-09-02T00:45:20.885226Z", - "shell.execute_reply": "2023-09-02T00:45:20.882399Z" + "iopub.execute_input": "2023-11-09T07:10:40.510069Z", + "iopub.status.busy": "2023-11-09T07:10:40.509945Z", + "iopub.status.idle": "2023-11-09T07:10:41.029675Z", + "shell.execute_reply": "2023-11-09T07:10:41.029399Z" }, "jupyter": { "outputs_hidden": false @@ -146,7 +146,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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\n", "text/plain": [ "
" ] @@ -207,7 +207,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" }, "vscode": { "interpreter": { diff --git a/docs/introduction/getting-started/multiclass-classification.ipynb b/docs/introduction/getting-started/multiclass-classification.ipynb index 54910c5956..60617cd4eb 100644 --- a/docs/introduction/getting-started/multiclass-classification.ipynb +++ b/docs/introduction/getting-started/multiclass-classification.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:22.384019Z", - "iopub.status.busy": "2023-09-02T00:45:22.383719Z", - "iopub.status.idle": "2023-09-02T00:45:22.764865Z", - "shell.execute_reply": "2023-09-02T00:45:22.764514Z" + "iopub.execute_input": "2023-11-09T07:10:42.224562Z", + "iopub.status.busy": "2023-11-09T07:10:42.224156Z", + "iopub.status.idle": "2023-11-09T07:10:42.628788Z", + "shell.execute_reply": "2023-11-09T07:10:42.628519Z" } }, "outputs": [ @@ -72,10 +72,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:22.766683Z", - "iopub.status.busy": "2023-09-02T00:45:22.766546Z", - "iopub.status.idle": "2023-09-02T00:45:22.795125Z", - "shell.execute_reply": "2023-09-02T00:45:22.794865Z" + "iopub.execute_input": "2023-11-09T07:10:42.630519Z", + "iopub.status.busy": "2023-11-09T07:10:42.630390Z", + "iopub.status.idle": "2023-11-09T07:10:42.653795Z", + "shell.execute_reply": "2023-11-09T07:10:42.653510Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:22.796768Z", - "iopub.status.busy": "2023-09-02T00:45:22.796675Z", - "iopub.status.idle": "2023-09-02T00:45:22.810815Z", - "shell.execute_reply": "2023-09-02T00:45:22.810551Z" + "iopub.execute_input": "2023-11-09T07:10:42.655574Z", + "iopub.status.busy": "2023-11-09T07:10:42.655470Z", + "iopub.status.idle": "2023-11-09T07:10:42.666775Z", + "shell.execute_reply": "2023-11-09T07:10:42.666521Z" } }, "outputs": [ @@ -142,10 +142,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:22.812271Z", - "iopub.status.busy": "2023-09-02T00:45:22.812168Z", - "iopub.status.idle": "2023-09-02T00:45:22.825425Z", - "shell.execute_reply": "2023-09-02T00:45:22.825199Z" + "iopub.execute_input": "2023-11-09T07:10:42.668327Z", + "iopub.status.busy": "2023-11-09T07:10:42.668223Z", + "iopub.status.idle": "2023-11-09T07:10:42.676692Z", + "shell.execute_reply": "2023-11-09T07:10:42.676449Z" } }, "outputs": [ @@ -177,10 +177,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:22.826975Z", - "iopub.status.busy": "2023-09-02T00:45:22.826886Z", - "iopub.status.idle": "2023-09-02T00:45:22.899444Z", - "shell.execute_reply": "2023-09-02T00:45:22.899168Z" + "iopub.execute_input": "2023-11-09T07:10:42.678226Z", + "iopub.status.busy": "2023-11-09T07:10:42.678137Z", + "iopub.status.idle": "2023-11-09T07:10:42.709366Z", + "shell.execute_reply": "2023-11-09T07:10:42.709107Z" } }, "outputs": [ @@ -217,10 +217,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:22.901156Z", - "iopub.status.busy": "2023-09-02T00:45:22.900959Z", - "iopub.status.idle": "2023-09-02T00:45:22.917152Z", - "shell.execute_reply": "2023-09-02T00:45:22.916895Z" + "iopub.execute_input": "2023-11-09T07:10:42.710857Z", + "iopub.status.busy": "2023-11-09T07:10:42.710764Z", + "iopub.status.idle": "2023-11-09T07:10:42.720273Z", + "shell.execute_reply": "2023-11-09T07:10:42.720037Z" } }, "outputs": [ @@ -249,10 +249,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:22.918722Z", - "iopub.status.busy": "2023-09-02T00:45:22.918636Z", - "iopub.status.idle": "2023-09-02T00:45:22.935428Z", - "shell.execute_reply": "2023-09-02T00:45:22.935168Z" + "iopub.execute_input": "2023-11-09T07:10:42.721662Z", + "iopub.status.busy": "2023-11-09T07:10:42.721588Z", + "iopub.status.idle": "2023-11-09T07:10:42.732033Z", + "shell.execute_reply": "2023-11-09T07:10:42.731797Z" } }, "outputs": [ @@ -287,31 +287,31 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:22.937156Z", - "iopub.status.busy": "2023-09-02T00:45:22.937040Z", - "iopub.status.idle": "2023-09-02T00:45:23.575411Z", - "shell.execute_reply": "2023-09-02T00:45:23.575063Z" + "iopub.execute_input": "2023-11-09T07:10:42.733503Z", + "iopub.status.busy": "2023-11-09T07:10:42.733423Z", + "iopub.status.idle": "2023-11-09T07:10:43.165894Z", + "shell.execute_reply": "2023-11-09T07:10:43.165636Z" } }, "outputs": [ { "data": { "text/plain": [ - " Precision Recall F1 Support \n", - " \n", - " brickface 77.13% 84.85% 80.81% 330 \n", - " cement 78.92% 83.94% 81.35% 330 \n", - " foliage 65.69% 20.30% 31.02% 330 \n", - " grass 100.00% 96.97% 98.46% 330 \n", - " path 90.63% 91.19% 90.91% 329 \n", - " sky 99.08% 98.18% 98.63% 330 \n", - " window 43.50% 67.88% 53.02% 330 \n", - " \n", - " Macro 79.28% 77.62% 76.31% \n", - " Micro 77.61% 77.61% 77.61% \n", - " Weighted 79.27% 77.61% 76.31% \n", + " Precision Recall F1 Support \n", + " \n", + "brickface 77.13% 84.85% 80.81% 330 \n", + " cement 78.92% 83.94% 81.35% 330 \n", + " foliage 65.69% 20.30% 31.02% 330 \n", + " grass 100.00% 96.97% 98.46% 330 \n", + " path 90.63% 91.19% 90.91% 329 \n", + " sky 99.08% 98.18% 98.63% 330 \n", + " window 43.50% 67.88% 53.02% 330 \n", + " \n", + " Macro 79.28% 77.62% 76.31% \n", + " Micro 77.61% 77.61% 77.61% \n", + " Weighted 79.27% 77.61% 76.31% \n", "\n", - " 77.61% accuracy " + " 77.61% accuracy " ] }, "execution_count": 8, @@ -348,31 +348,31 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:23.577203Z", - "iopub.status.busy": "2023-09-02T00:45:23.577088Z", - "iopub.status.idle": "2023-09-02T00:45:24.242152Z", - "shell.execute_reply": "2023-09-02T00:45:24.241854Z" + "iopub.execute_input": "2023-11-09T07:10:43.167450Z", + "iopub.status.busy": "2023-11-09T07:10:43.167352Z", + "iopub.status.idle": "2023-11-09T07:10:43.621666Z", + "shell.execute_reply": "2023-11-09T07:10:43.621416Z" } }, "outputs": [ { "data": { "text/plain": [ - " Precision Recall F1 Support \n", - " \n", - " brickface 77.13% 84.85% 80.81% 330 \n", - " cement 78.92% 83.94% 81.35% 330 \n", - " foliage 65.69% 20.30% 31.02% 330 \n", - " grass 100.00% 96.97% 98.46% 330 \n", - " path 90.63% 91.19% 90.91% 329 \n", - " sky 99.08% 98.18% 98.63% 330 \n", - " window 43.50% 67.88% 53.02% 330 \n", - " \n", - " Macro 79.28% 77.62% 76.31% \n", - " Micro 77.61% 77.61% 77.61% \n", - " Weighted 79.27% 77.61% 76.31% \n", + " Precision Recall F1 Support \n", + " \n", + "brickface 77.13% 84.85% 80.81% 330 \n", + " cement 78.92% 83.94% 81.35% 330 \n", + " foliage 65.69% 20.30% 31.02% 330 \n", + " grass 100.00% 96.97% 98.46% 330 \n", + " path 90.63% 91.19% 90.91% 329 \n", + " sky 99.08% 98.18% 98.63% 330 \n", + " window 43.50% 67.88% 53.02% 330 \n", + " \n", + " Macro 79.28% 77.62% 76.31% \n", + " Micro 77.61% 77.61% 77.61% \n", + " Weighted 79.27% 77.61% 76.31% \n", "\n", - " 77.61% accuracy " + " 77.61% accuracy " ] }, "execution_count": 9, @@ -409,7 +409,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/introduction/getting-started/regression.ipynb b/docs/introduction/getting-started/regression.ipynb index 4c32dd4c2e..25d7a91ccc 100644 --- a/docs/introduction/getting-started/regression.ipynb +++ b/docs/introduction/getting-started/regression.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:25.707518Z", - "iopub.status.busy": "2023-09-02T00:45:25.707204Z", - "iopub.status.idle": "2023-09-02T00:45:26.094554Z", - "shell.execute_reply": "2023-09-02T00:45:26.094173Z" + "iopub.execute_input": "2023-11-09T07:10:44.634128Z", + "iopub.status.busy": "2023-11-09T07:10:44.633948Z", + "iopub.status.idle": "2023-11-09T07:10:45.050152Z", + "shell.execute_reply": "2023-11-09T07:10:45.049874Z" } }, "outputs": [ @@ -71,10 +71,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:26.096271Z", - "iopub.status.busy": "2023-09-02T00:45:26.096140Z", - "iopub.status.idle": "2023-09-02T00:45:26.113530Z", - "shell.execute_reply": "2023-09-02T00:45:26.113248Z" + "iopub.execute_input": "2023-11-09T07:10:45.051791Z", + "iopub.status.busy": "2023-11-09T07:10:45.051670Z", + "iopub.status.idle": "2023-11-09T07:10:45.063670Z", + "shell.execute_reply": "2023-11-09T07:10:45.063443Z" } }, "outputs": [], @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:26.115160Z", - "iopub.status.busy": "2023-09-02T00:45:26.115076Z", - "iopub.status.idle": "2023-09-02T00:45:26.128786Z", - "shell.execute_reply": "2023-09-02T00:45:26.128528Z" + "iopub.execute_input": "2023-11-09T07:10:45.065255Z", + "iopub.status.busy": "2023-11-09T07:10:45.065173Z", + "iopub.status.idle": "2023-11-09T07:10:45.074734Z", + "shell.execute_reply": "2023-11-09T07:10:45.074417Z" } }, "outputs": [ @@ -137,10 +137,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:26.130304Z", - "iopub.status.busy": "2023-09-02T00:45:26.130222Z", - "iopub.status.idle": "2023-09-02T00:45:26.148212Z", - "shell.execute_reply": "2023-09-02T00:45:26.147967Z" + "iopub.execute_input": "2023-11-09T07:10:45.076494Z", + "iopub.status.busy": "2023-11-09T07:10:45.076376Z", + "iopub.status.idle": "2023-11-09T07:10:45.090050Z", + "shell.execute_reply": "2023-11-09T07:10:45.089788Z" } }, "outputs": [ @@ -177,10 +177,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:26.149704Z", - "iopub.status.busy": "2023-09-02T00:45:26.149608Z", - "iopub.status.idle": "2023-09-02T00:45:26.162699Z", - "shell.execute_reply": "2023-09-02T00:45:26.162432Z" + "iopub.execute_input": "2023-11-09T07:10:45.091538Z", + "iopub.status.busy": "2023-11-09T07:10:45.091446Z", + "iopub.status.idle": "2023-11-09T07:10:45.101556Z", + "shell.execute_reply": "2023-11-09T07:10:45.101323Z" } }, "outputs": [], @@ -201,10 +201,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:26.164248Z", - "iopub.status.busy": "2023-09-02T00:45:26.164161Z", - "iopub.status.idle": "2023-09-02T00:45:26.177605Z", - "shell.execute_reply": "2023-09-02T00:45:26.177371Z" + "iopub.execute_input": "2023-11-09T07:10:45.103238Z", + "iopub.status.busy": "2023-11-09T07:10:45.103165Z", + "iopub.status.idle": "2023-11-09T07:10:45.112098Z", + "shell.execute_reply": "2023-11-09T07:10:45.111849Z" } }, "outputs": [ @@ -236,10 +236,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:26.179257Z", - "iopub.status.busy": "2023-09-02T00:45:26.179157Z", - "iopub.status.idle": "2023-09-02T00:45:28.368646Z", - "shell.execute_reply": "2023-09-02T00:45:28.368368Z" + "iopub.execute_input": "2023-11-09T07:10:45.113659Z", + "iopub.status.busy": "2023-11-09T07:10:45.113555Z", + "iopub.status.idle": "2023-11-09T07:10:46.876088Z", + "shell.execute_reply": "2023-11-09T07:10:46.875793Z" } }, "outputs": [ @@ -282,10 +282,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:28.370301Z", - "iopub.status.busy": "2023-09-02T00:45:28.370188Z", - "iopub.status.idle": "2023-09-02T00:45:30.543139Z", - "shell.execute_reply": "2023-09-02T00:45:30.542840Z" + "iopub.execute_input": "2023-11-09T07:10:46.877715Z", + "iopub.status.busy": "2023-11-09T07:10:46.877619Z", + "iopub.status.idle": "2023-11-09T07:10:48.610244Z", + "shell.execute_reply": "2023-11-09T07:10:48.609993Z" } }, "outputs": [ @@ -329,7 +329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/recipes/active-learning.ipynb b/docs/recipes/active-learning.ipynb index d79aff4ece..79eba9268c 100644 --- a/docs/recipes/active-learning.ipynb +++ b/docs/recipes/active-learning.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:32.751403Z", - "iopub.status.busy": "2023-09-02T00:45:32.751058Z", - "iopub.status.idle": "2023-09-02T00:45:33.602726Z", - "shell.execute_reply": "2023-09-02T00:45:33.602405Z" + "iopub.execute_input": "2023-11-09T07:10:50.584308Z", + "iopub.status.busy": "2023-11-09T07:10:50.583447Z", + "iopub.status.idle": "2023-11-09T07:10:51.424051Z", + "shell.execute_reply": "2023-11-09T07:10:51.423788Z" } }, "outputs": [ @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:33.614078Z", - "iopub.status.busy": "2023-09-02T00:45:33.613917Z", - "iopub.status.idle": "2023-09-02T00:45:33.630138Z", - "shell.execute_reply": "2023-09-02T00:45:33.629896Z" + "iopub.execute_input": "2023-11-09T07:10:51.438209Z", + "iopub.status.busy": "2023-11-09T07:10:51.438034Z", + "iopub.status.idle": "2023-11-09T07:10:51.447763Z", + "shell.execute_reply": "2023-11-09T07:10:51.447500Z" } }, "outputs": [ @@ -142,10 +142,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:33.631781Z", - "iopub.status.busy": "2023-09-02T00:45:33.631672Z", - "iopub.status.idle": "2023-09-02T00:45:34.092919Z", - "shell.execute_reply": "2023-09-02T00:45:34.092647Z" + "iopub.execute_input": "2023-11-09T07:10:51.449385Z", + "iopub.status.busy": "2023-11-09T07:10:51.449300Z", + "iopub.status.idle": "2023-11-09T07:10:51.890865Z", + "shell.execute_reply": "2023-11-09T07:10:51.890624Z" } }, "outputs": [ @@ -242,7 +242,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" }, "vscode": { "interpreter": { diff --git a/docs/recipes/bandits-101.ipynb b/docs/recipes/bandits-101.ipynb index 0ba4c6debd..ddd0d93e63 100644 --- a/docs/recipes/bandits-101.ipynb +++ b/docs/recipes/bandits-101.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:35.575425Z", - "iopub.status.busy": "2023-09-02T00:45:35.575172Z", - "iopub.status.idle": "2023-09-02T00:45:35.634396Z", - "shell.execute_reply": "2023-09-02T00:45:35.634109Z" + "iopub.execute_input": "2023-11-09T07:10:52.894617Z", + "iopub.status.busy": "2023-11-09T07:10:52.894487Z", + "iopub.status.idle": "2023-11-09T07:10:52.934553Z", + "shell.execute_reply": "2023-11-09T07:10:52.934260Z" } }, "outputs": [], @@ -35,7 +35,7 @@ "\n", "for k in gym.envs.registry:\n", " if k.startswith('river_bandits'):\n", - " print(k)" + " print(k)\n" ] }, { @@ -51,10 +51,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:35.636137Z", - "iopub.status.busy": "2023-09-02T00:45:35.636026Z", - "iopub.status.idle": "2023-09-02T00:45:36.107306Z", - "shell.execute_reply": "2023-09-02T00:45:36.106935Z" + "iopub.execute_input": "2023-11-09T07:10:52.936515Z", + "iopub.status.busy": "2023-11-09T07:10:52.936399Z", + "iopub.status.idle": "2023-11-09T07:10:53.338264Z", + "shell.execute_reply": "2023-11-09T07:10:53.337989Z" } }, "outputs": [], @@ -81,7 +81,7 @@ " env=env,\n", " reward_stat=stats.Mean(),\n", " n_episodes=(n_episodes := 2000),\n", - ")" + ")\n" ] }, { @@ -97,10 +97,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:45:36.109239Z", - "iopub.status.busy": "2023-09-02T00:45:36.109080Z", - "iopub.status.idle": "2023-09-02T00:46:16.897601Z", - "shell.execute_reply": "2023-09-02T00:46:16.897128Z" + "iopub.execute_input": "2023-11-09T07:10:53.340072Z", + "iopub.status.busy": "2023-11-09T07:10:53.339932Z", + "iopub.status.idle": "2023-11-09T07:11:26.506543Z", + "shell.execute_reply": "2023-11-09T07:11:26.506114Z" } }, "outputs": [ @@ -108,9 +108,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/6000000 [00:00441\n", " 632\n", " 0\n", - " 4\n", - " -0.036801\n", - " 0.457068\n", + " 3\n", + " -2.082302\n", + " 0.768603\n", " \n", " \n", " 3566176\n", " 1188\n", " 725\n", " 1\n", - " 5\n", - " 1.837321\n", - " 2.220956\n", + " 8\n", + " 1.021648\n", + " 0.589201\n", " \n", " \n", " 1109043\n", " 369\n", " 681\n", " 0\n", - " 6\n", - " 0.616991\n", - " 1.324610\n", + " 9\n", + " 2.678262\n", + " 1.831294\n", " \n", " \n", " 4286042\n", " 1428\n", " 680\n", " 2\n", - " 3\n", - " 0.236458\n", - " 0.570999\n", + " 7\n", + " 0.358914\n", + " 0.006951\n", " \n", " \n", " 5395174\n", @@ -185,8 +185,8 @@ " 391\n", " 1\n", " 1\n", - " -0.851223\n", - " 0.446835\n", + " 0.212899\n", + " 0.571724\n", " \n", " \n", "\n", @@ -194,11 +194,11 @@ ], "text/plain": [ " episode step policy_idx arm reward reward_stat\n", - "1324896 441 632 0 4 -0.036801 0.457068\n", - "3566176 1188 725 1 5 1.837321 2.220956\n", - "1109043 369 681 0 6 0.616991 1.324610\n", - "4286042 1428 680 2 3 0.236458 0.570999\n", - "5395174 1798 391 1 1 -0.851223 0.446835" + "1324896 441 632 0 3 -2.082302 0.768603\n", + "3566176 1188 725 1 8 1.021648 0.589201\n", + "1109043 369 681 0 9 2.678262 1.831294\n", + "4286042 1428 680 2 7 0.358914 0.006951\n", + "5395174 1798 391 1 1 0.212899 0.571724" ] }, "execution_count": 3, @@ -214,7 +214,7 @@ " env._max_episode_steps\n", " )\n", "))\n", - "trace_df.sample(5, random_state=42)" + "trace_df.sample(5, random_state=42)\n" ] }, { @@ -230,28 +230,7044 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:46:16.899601Z", - "iopub.status.busy": "2023-09-02T00:46:16.899491Z", - "iopub.status.idle": "2023-09-02T00:46:17.665862Z", - "shell.execute_reply": "2023-09-02T00:46:17.665055Z" + "iopub.execute_input": "2023-11-09T07:11:26.509068Z", + "iopub.status.busy": "2023-11-09T07:11:26.508969Z", + "iopub.status.idle": "2023-11-09T07:11:27.335099Z", + "shell.execute_reply": "2023-11-09T07:11:27.334817Z" } }, "outputs": [ { "data": { - "text/plain": [ - "" + "text/html": [ + " \n", + " " ] }, - "execution_count": 4, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" }, { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -265,20 +7281,14 @@ " 2: 'ε = 0 (greedy)'\n", "}\n", "\n", - "colors = {\n", - " 'ε = 0.1': 'tab:blue',\n", - " 'ε = 0.01': 'tab:red',\n", - " 'ε = 0 (greedy)': 'tab:green'\n", - "}\n", - "\n", "(\n", " trace_df\n", " .assign(policy=trace_df.policy_idx.map(policy_names))\n", " .groupby(['step', 'policy'])\n", " ['reward'].mean()\n", " .unstack()\n", - " .plot(color=colors)\n", - ")" + " .plot()\n", + ")\n" ] }, { @@ -306,10 +7316,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:46:17.670738Z", - "iopub.status.busy": "2023-09-02T00:46:17.670193Z", - "iopub.status.idle": "2023-09-02T00:46:55.332617Z", - "shell.execute_reply": "2023-09-02T00:46:55.332268Z" + "iopub.execute_input": "2023-11-09T07:11:27.353460Z", + "iopub.status.busy": "2023-11-09T07:11:27.353266Z", + "iopub.status.idle": "2023-11-09T07:11:58.339423Z", + "shell.execute_reply": "2023-11-09T07:11:58.338741Z" } }, "outputs": [ @@ -317,9 +7327,11 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/6000000 [00:00" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" ] }, "metadata": {}, @@ -413,8 +14355,8 @@ " .groupby(['step', 'policy'])\n", " ['is_action_optimal'].mean()\n", " .unstack()\n", - " .plot(color=colors)\n", - ")" + " .plot()\n", + ")\n" ] }, { @@ -442,10 +14384,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:46:55.803532Z", - "iopub.status.busy": "2023-09-02T00:46:55.803411Z", - "iopub.status.idle": "2023-09-02T00:47:10.886177Z", - "shell.execute_reply": "2023-09-02T00:47:10.885753Z" + "iopub.execute_input": "2023-11-09T07:11:58.758656Z", + "iopub.status.busy": "2023-11-09T07:11:58.758571Z", + "iopub.status.idle": "2023-11-09T07:12:10.603815Z", + "shell.execute_reply": "2023-11-09T07:12:10.603493Z" } }, "outputs": [ @@ -453,7 +14395,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|████████████████████████████████| 180000/180000 [00:14<00:00, 12305.42it/s]\n" + " 0%| | 0/180000 [00:00\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -638,10 +638,10 @@ 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" ] @@ -851,17 +851,17 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:47:39.548276Z", - "iopub.status.busy": "2023-09-02T00:47:39.548181Z", - "iopub.status.idle": "2023-09-02T00:47:50.829755Z", - "shell.execute_reply": "2023-09-02T00:47:50.829454Z" + "iopub.execute_input": "2023-11-09T07:12:35.872146Z", + "iopub.status.busy": "2023-11-09T07:12:35.872058Z", + "iopub.status.idle": "2023-11-09T07:12:44.214838Z", + "shell.execute_reply": "2023-11-09T07:12:44.214473Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -906,17 +906,17 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:47:50.832249Z", - "iopub.status.busy": "2023-09-02T00:47:50.831992Z", - "iopub.status.idle": "2023-09-02T00:47:58.646407Z", - "shell.execute_reply": "2023-09-02T00:47:58.646096Z" + "iopub.execute_input": "2023-11-09T07:12:44.217237Z", + "iopub.status.busy": "2023-11-09T07:12:44.217130Z", + "iopub.status.idle": "2023-11-09T07:12:49.774043Z", + "shell.execute_reply": "2023-11-09T07:12:49.773780Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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Zo+3bt2vjxo1auXKlMjIyCs9pMpl07NgxPf/883r//ff19NNPa8KECfLx8XFb3wEAAAAAAPB/dh5O14QpmxWbXPKkAA2Daumzcd3VI9Lxs6GK9tRlbbUh/qS2HkqzKzMM6dHpWzT/4YEKC/ZzSzwou7TTeXp85jYt2H3MaV1fb7MeGdpKdw9sXmEvss/qERmq4Z0aaf72o3ZlR9Jz9M3KOD0wpGWFtlmV5ORb9P6/+zR5eawczBXi0Lq4VL3zzz49fXnbyg0OAAAAAFBu+RarZm1K1EeLYpR40n71quLU8jbrmu7NNLpnM3UND5HJZKq0GK/vGa5Ji2J04pT97OU/rDqoey9srhB/30prHwAAoLrxSKL53Llz9dhjjykmJqYw+Vs6kwjevn17jRo1SiNHjlT37t1lNrv+MqdevXqqV6+eOnXqpOuvv16SZLVatWnTJs2ZM0ezZ8/Wrl27CusnJyfriSee0Oeff673339fI0aMqNiOAgAAAAAAoFiGYeinNfF6Zd5u5RWUPGNx76hQfXxTNzUMcl9St6+3WR+P6aYRk5YrI6fArjwlK08PTd2sn+/qI+8KTkhGxVl/MFUPT93s0uxpPSLr6q1rO6tlw8BKi+fJS9vq313HlG+xz7T+bMkB3dArXPUDa1Va+56yLTFNj83Yqv3HM0t97OdLD6hXVF1d3C6sEiIDAAAAAJRXgcWq2ZuT9NGiGCWknnb5OF9vs8b1idT4wc3d9szHz8dL9wxsrtfm77Yry8qz6LtVB/XI0NZuiQUAAKA68MgbsKuuuqowyVySgoKCNH78eG3YsEE7duzQK6+8op49e5Yqybw4ZrNZPXv21CuvvKIdO3Zo48aNGj9+vIKDgwvrxMTEaOTIkeVuCwAAAAAAAK5Jz87XfT9t0n/n7HSaZH57/yj9fHcftyaZnxUe6q93ru9SbPnauFR9uHC/GyOCq6xWQ58sjtGNk9c4TTKv7eOlF69srxn39q3UJHNJiqofoHEXRDosy8wt0IcLatbvU16BVe/9s1ejPl1VpiTzs/4zY6sOlSJZAQAAAABQ+QosVv26MVFD31uqJ37Z5nKSua+3Wbf1i9LyJ4fohSvbu/2Zz9g+Earr7+Ow7NuVB3UqJ9+t8QAAAFRlHptqyTAMNW3aVBMnTtShQ4f06aefqnv37pXebrdu3fTpp5/q0KFDmjhxopo0aVIYDwAAAAAAACrfpoSTGv7hcv2182iJ9fx8zPrghq568coO8vHgjOGXdGikOwdEF1v+8eIYLdt3wo0RwZnjp3J0yzfr9Pbfe2Wxlvzcr3/Levrn0Qt1e/9oeZkrb2nucz10USsF+TlebHLKugTFlCMhuyrZfSRDV3+yUpMWxTj9c2hcx0/vXt9FIcW86E/PztcDUzYpt8BSGaECAAAAAErBYjX02+YkXfL+Mj02c6sOpriYYO5l1i19I7XsiSF66aoOCgt2/6QCkhRQy7vYZz3p2fn6aU2CmyMCAACoujzyhq5+/fp6//33deDAAT3++OMKCgpyewyBgYF6/PHHdeDAAb333nuqX7++22MAAAAAAAA4n1ithj5fekCjP1+tpLTsEutGhPpr1n39dXW3pm6KrmRPXdZWXcJDHJYZhvTo9C06llHyrNlwj+X7T2j4h8u1Iia5xHp+Pma9PqqTfrqzj8JD/d0U3Rl1A3z14JCWDsssVkNv/rnHrfFUtAKLVZ8sjtFVH6/QriMZTutf272Z/nrkQl3bo5neH9212HrbEtP12jz7pc0BAAAAAO5hsRqasyVJl7y/VI9M36LY5CyXjvPxMmncBRFa8sRg/W9kRzWq45kE83Pd0i+q2I/Av1oeq+w8PnQGAACQPJRoHhsbq4cffli+vr6eaN6Gr6+vHnnkER04cMDToQAAAAAAANRYyZm5uv279Xrzzz0qcDKz8YhOjTX3oQFq3yTYTdE55+tt1idjuym4mBeQKVl5emjqZhVYrG6ODGcVWKya+Nce3fLNOiVn5pVYt3VYoH5/cIDG9omQyeSeWcyLurVflJqG1HZYtmD3Ma2JTXFzRBUj5nimrv18td7+e6/yLSX/Xa8fWEtf3tJT747uojq1z8xkPqRtQz0wpEWxx/ywOl6/bz1coTEDAAAAAEpmtRr6Y+thXfbBMj08bYsOnHAtwdzbbNKY3hFa/PhgvXp1JzUp5j7YE4L9fHRbvyiHZSlZeZq6jlnNAQAAJA8lmgcGBnqi2RJVxZgAAAAAAABqglUHkjX8w+Vauu9EifVqeZv12qiO+nhsNwX7+bgpOtc1q+uvd0uYbXltXKo+XLjffQGhUFJatm6YvEafLjkgo+TcZo3pHa45DwxQ6zD3r7J4Lj8fLz15WZtiy1+fv1tWJx9lVCUWq6GvlsdqxKTl2noozWn9Kzo31r+PXqhh7cPsyh4d2loXNA8t9tinf92mmOOZ5QkXAAAAAOACq9XQ/O1HdPmHyzVh6mbtd/FezMts0o29wrX48cF645pOalbXvSuJuer2/tHy9/VyWPbFsgPKLWBWcwAAAI8kmgMAAAAAAKDmK7BY9d6/+3TTV2t1/FRuiXVbNAjQnAf766Y+kR6bYdoVw9qH6a4B0cWWf7w4RsucJNSjYv2765iGf7hcG+NPllgvsJa3Jo3ppjeu6azaxbxEdrcrOzdR52Z1HJZtS0zXH9uqx8zd8SlZunHyar06b7dyC0qe1b+uv48+HttNH4/trroBjle89PYya9KYbmoQVMth+ek8i+7/eaNO5xWUO3YAAAAAgD2r1dBfO45o+KTluv/nTdp77JRLx3mZTbq+RzMtfmyw3ry2s8JDq2aC+VmhAb4ad0Gkw7JjGbn6ZWOimyMCAACoekg0BwAAAAAAQIU7mp6jsV+t1aSF+53OMH1dj2b6Y8IAtW0U7J7gyunJy9qqa3iIwzLDkB6dvkXHMnLcG9R5KLfAopd+36m7f9ig9Oz8Eut2alpHcycM0FVdmrgpOteYzSY9O7xdseUT/9qrnPyqO3ua1Wrox9UHddkHy7X+YMmJ/tKZDzX+eXSQrujs/M+hYZCfJt3YTeZivjvZdyxTz/+2Q4azf2AAAAAAAC7LK7Bq5oZDuvzD5Rr/0ybtOepagrnZJF3bvZkW/meQ3r6+iyLqVe0E83PdNTBavt6O06c+W3JA+ZaSP6gGAACo6Ug0BwAAAAAAQIVatOeYLv9wmdbFpZZYz9/XS+/f0EXvXN9F/r7eboqu/Hy9zfp4bDcF+zmOOSUrTw9N3awCXkRWmoPJWbr2s1X6btVBp3Xv6B+tX+7rq6j6AZUfWBlc0LyehrUPc1iWlJat713ooyckpWXrlm/W6b9zdirbSTJ8kJ+33hvdRZNv7lHsLOWO9G1RT49d0qbY8lmbkjR9/SGXzwcAAAAAcCw9O1+fLz2ggRMX6Ylftrk8g7nZJI3q1lQL/jNI747uUmXvvUvSMMhPY3qFOyxLPJmtOVuqx2pjAAAAlYVEcwAAAAAAAFSIvAKrXp27S3d8t0EnT5c8w3T7xsGaO2GARnVr5qboKlazuv56d3TXYsvXxqXqw4X73RfQeWTOliRd8dEK7UjKKLFeiL+Pvrqlp164sr1qeXu5KbqyefrytvIqZurujxfHKDUrz80RFc8wDM1Yf0iXvb9MK2KSnda/sHUD/fPohbqmezOZTMVMT16C+wa10JA2DYotf+H3ndp5OL3U5wUAAAAASIfTsvXq3F3q/+YivfnnHh3LyHXpOJNJGtm1if55dJDev6GrmjcIrORIK9c9g1rIx8vxPeuni2NksbKaFgAAOH9Vn6minLBYLNqyZYsOHTqk3NxcNWzYUK1bt1bTpk09HRoAAAAAAECNl5ByWhOmbtLWROcJn7f2jdQzw9vJz6dqJ/86M6x9mO4aEK2vVsQ5LP94cYx6RYXqwtbFJ8nCddl5Fr30+05N3+B8ButeUXX14Y3d1CSkthsiK78WDQI1tneEflwTb1d2KqdAkxbu10tXdfBAZLaOZ+To6VnbtWjPcad1A3y99PwV7XVjr/AyJZifZTab9N7orrrioxVKSsu2K88rsOr+nzfpjwkDFOznU+Z2AAAAAOB8svNwur5cFqu5246ooBRJ1CaTNKJTYz18cSu1CguqxAjdq2lIbV3bvZmmOVg1KzY5S3/uOKIrOjfxQGQAAACeVyUTzQsKCpScnKzatWurTp06JdY9cuSI/ve//+nnn39WVlaWXXmnTp30+OOPa9y4cZUVLgAAAAAAwHlt7rbDeubX7TqVW1BivWA/b028rosu69jITZFVvicva6sN8Se15VCaXZlhSI9O36L5Dw9UWLCf+4OrQfYePaUHp2zS/uOZJdYzmaQHh7TUwxe3krdX9VrM8eGhrTR7c5IyHfw9+mlNvG7tF6VoDy1BbhiGft96WC/M2an07JJXK5CkC5qH6u3ruig81L9C2q8b4KtPbuqu6z9fpXyLfQJEfMppPTlzmz4b171cSe0AAAAAUJMZhqHl+5M1eVmsSytUFTWiU2M9PLSVWtegBPNz3Te4hWZsOCRHefcfL4rR8I6NZS5mNTIAAICarMq8bcnKytLLL7+sDh06yM/PT02bNlVoaKgaNmyom266SZs2bbI75p9//lGHDh00efJkZWZmyjAMu59t27bp1ltv1WWXXabsbPsZbwAAAAAAAFA2OfkWPTt7ux6cstlpknm3iBDNf3hgjUoylyRfb7M+HttNwX6O53NIycrTQ1M3q8BidXNkNYNhGJq6LkFXfbzCaZJ5/cBa+unOPnrskjbVLslcOhP/fYNbOCwrsBqa+NceN0d0xvGMHN3/8yY9PG2L0yRzPx+zXryyvabcdUGFJZmf1TU8RM8Nb1ds+V87j+qblQcrtE0AAAAAqAnyLVbN2pSoyz9crlu+WVeqJHOTSRreqZH+fHigPrmpe41NMpekyHoBGtm1qcOyPUdPaaELq3sBAADURFXijcuOHTvUtm1b/e9//9Pu3btltVoLE8WTk5M1bdo09enTRx9//HHhMWvXrtVVV12ltLQ0GYYhk8lU7I9hGPr33391xRVXeLCXAAAAAAAANUfM8VO6+pOVmrI2wWnd8YNaaMa9fdWsbsUmnlYVzer6693RXYstXxuXqkmLYtwXUA1xKidfD03bomdmbVduQcmJ+gNb1defDw9U/5b13RRd5bhzQLQa13E8+/2fO45qw8FUt8WSV2DVl8tiddG7S/XnjqNO63ePCNH8hwbq9v7RlTbD2639ojSic+Niy9+Yv1sb409WStsAAAAAUN2cysnXl8tideHExfrPjK3ac/SUy8fW8jZr3AURWvzYYH16Uw+1axxciZFWHfcPbqHiFsr6eNF+GYaD6c4BAABqOI8nmiclJemiiy5SUlJSsQnjkmSxWPTII49o+fLlslqtuvnmm5WXl1dY7mg287MDvLPJ5kuWLNGHH37osb4CAAAAAABUd4ZhaMaGQ7ryo5VOX1DWC/DV93f01tOXt5VPNZxhujSGtQ/TXQOiiy3/dHGM9h1z/YXu+W5bYpqu+GiF/th6uMR6XmaTnrysjb6/vbcaBNVyU3SVx8/HS49f0qbY8tfm73bLS+3l+0/o8g+X6bX5u5XpZLUCXy+znr68rWaO76fmDQIrNS6TyaS3ru2s5vUDHJYXWA09OGWTUrPyKjUOVxxKPa3fNidpXVyqrI7WXQcAAACASnI0PUdvzN+tfm8s0mvzd+tIeo7Lx4YG+OqRoa206umL9OrVnRRVzP1XTdUqLEiXF7Ma39bEdC3f7/ps8AAAADWFx9/wPfzww0pOTraZfdzRj8lkktVq1UsvvaRffvlFMTExhfX79eunadOmKSEhQTk5OTp16pR27typd999V02bNi083jAMvfbaayooKPnlCAAAAAAAAOxl5hbo0elb9OQv25Sdbymxbr8W9fTnwwM1qHUDN0XneU9e1lZdw0MclhVYDT3/2w5mvnLCMAx9vSJO1362SvEpp0us2zSktmbce4HuH9yy0mbQ9oRR3ZqqfTEzxW1OSNP87c5nFy+rQ6mnNf7Hjbr563U6cCLLaf1OTeto7kMDNH5QC3m56c8gsJa3Ph3XXX4+jh9tH0nP0SPTt3gsuTvmeKbu/XGDBk5crEemb9HoL1brms9WKcfJv5kAAAAAUF57jmboPzO2aMBbi/TFslidcvLh8Lmi6vnr1as7auVTF+mRoa1VL7D6f8xdVvcPblls2cesWAcAAM5DHk00j42N1axZs2wSzO+44w6tW7dOmZmZysrK0oYNG3TPPfdIOjNjzbJly/Tll18WnuPhhx/WihUrNHr0aDVr1ky+vr4KCAhQu3bt9Oijj2rr1q3q3bt3Yf2UlBTNmTPH7X0FAAAAAACoznYeTteVH63Qb1tKnmHabJIeG9ZaP97ZRw2D/dwUXdXg623Wx2O7KdjP22H5urhU/bopyc1RVR8ns/J09w8b9MrcXcq3lJwkfEn7MM17aIB6RIa6KTr3MZtNem5Eu2LL3/prj3ILKjZpOSffog8W7NPQ95bqr53OE9m9zSY9OrS1Zt3fT63Dgio0Fle0bRSsV0Z2LLZ82b4T+nixe1/+H8/I0TOztuvSD5bp753HbMq2HErT6/N3uzUeAAAAAOcHw5DWxJ3Urd+s02UfLNesTUkqKMWHt90jQvT5uB5a+NhgjbsgUrV9vSox2uqhY9M6uqhtQ4dl6w6mam1sipsjAgAA8CyPJppPmzZNkgpnHH/99df11VdfqWfPnvL391ft2rXVvXt3ff7555o4caIMw5DVatWiRYskSZ07d9Z7771XYht169bVtGnT5O3tLZPpzKw6Z48HAAAAAABAyQzD0PerDmrUJ6sUl1zyDMeNgv009e4LNOHiVm6b3biqaVbXX69cXXwC7OvzdyvtdJ4bI6oe1sSmaPik5Vqw+3iJ9Xy9zHr5qg764uYeCvH3dVN07te/ZX0NaeN4NYCE1NP6aU1ChbRjGIb+3nlUQ99bqg8W7FdugdXpMW0bBem3B/rr4aGt5OPlucfL1/cM1+iezYotf3/BPq2MqfwlzU/l5Ovdf/Zq0NtLNHVdgizFJHRMX39IJ7P4uw8AAACgYlis0oYTJr2z3Uv3TN2hpftOuHysyXTmA+5fxvfVrPv767KOjc7b5zjFeWBICbOau/nDZgAAAE/zaKL5unXrCv+7RYsWeuqpp4qt+5///Edt2rSR9H+J6ePHjy9MHi9JVFSUrrjiisKliTdu3FjOyAEAAAAAAGq+jJx83f/zJr34+07lWUpOQL24bUPNf3ig+jSv56boqq6rujRR/5aO/z+kZuXprb/2ujmiqivfYtXEv/ZozJdrdCQ9p8S6UfX8Nev+frq1X5RLzwSru2eGt1Nx7/knLdyv9NP55Tr/gROZuuWbdbr3x41KPJnttH5gLW89P6Kd/pgwQB2b1ilX2xXlfyM7qm0jxzOqG4b08LTNOpZR8u9VWeUVWPXtyjgNenuJPloUo+z8kmeZzy2wasaGQ5USCwAAAIDzR1xylr5bk6hXNnvpxxgvJWa5fn/s623W2D4RWvCfQZp8S0/1jKp5q4RVlB6RdYt9trN8f7I2J5x0c0QAAACe49FE8x07dkiSTCaTrrnmmhJfEJlMJo0aNaowWVySBgwY4HJbAwcOlHQmST0xMbGMEQMAAAAAAJwftiem64pJK/TnjqMl1vPxMun5Ee301a09FRpQc2eYLg2TyaT/jewo32Jme566LkGbeCGpuOQsXffZKn265IAMJ6t6X921ieY+NLDKJDi7Q+uwIN3QK8JhWXp2vj5evL9M583MLdAb83frsg+Wafl+12b8vqZ7Uy16fJDuGtjco7OYF+Xn46XPxvVQYC1vh+XJmXmaMGWzCpx8KFMaVquh37ce1tD3lurlP3YptRSzlP+4Jr7YGc8BAAAAwJHTeQVauPuYXpizQ4PeXqwh7yzRe4vidDLP9QTzEH8fPXRRS616+iK9PqqTWjQIrMSIa44Hh7QqtuwTZjUHAADnEcdP4N0kNTW18L87d+7stH7HjrbLDkdGRrrcVlRUVOF/p6enu3wcAAAAAADA+cQwDP2wOl6vzdvtdBbziFB/fTSmm7qEh7gnuGqkRYNA3TuouT5a5PjF43Ozd+iPB/vLuwol7bqLYRiauSFRL/2xU6fzSp4FuraPl14e2UHX92h2XsxiXtSjw1ppzpYkh/+fvl8Vr1v6Rik81N+lcxmGoTlbDuv1+bt1/FSuS8d0bBqsl6/qoB6RVXeWu+j6AZp4XWfd//Mmh+XrDqbq7X/26pnL25W7rZUxyXrzzz3anlS258uJJ7O1eM9xDW0fVu5YAAAAANRMhmHowIlMLdl7Qkv3ndDa2FSnz2eKExHqr7sGRuu6Hs3k7+vR9KBq6YLmoeoZWVcb4u0nC1iw+7h2Hk5XhybnzwfxAADg/OXRkeSpU6cKXxCFhjp/WVG0TmCg619Znlv39OnTLh8HAAAAAABwvsjIydfTv27T/O0lz2IuSSM6N9Yb13RSsJ+PGyKrnh4Y0lJzthxWQqr9s6jdRzL0/ep43Tkg2gOReU766Xw9O3u75m0/4rRum7AgfXJTN7VsGOSGyKqmhkF+uvfCFnp/wT67sjyLVRP/3quPxnRzep6dh9P10u87tf6gazPph/j76IlL2+jGXhHyMlf9BP/hnRrrtn5R+m7VQYflXyyNVc/IUA0rY4L3rsMZevOvPVq270Q5ojzj+9UHSTQHAAAAYCMzt0ArY5K1dN8JLd17Qklp2eU6X5dmdXTPhS10WcdG1eKerqoymUx68KKWuu3b9Q7LP118QJ/c1N3NUQEAALifRxPNLRZLYaK5r6/zpZV9fMr+4vJ8nPEIAAAAAADAVTuS0nX/z5scJkWfq5a3WS9e2UFjeofzvMUJPx8v/W9kh2JfSL73z14N79RIjevUdnNknrEmNkWPTt+iI+k5TuuOuyBCz49oLz8fLzdEVrXdfWG0fl4b73AW8j+2HtYd/aPULaKuw2PTTufpnX/2asraBFkN522ZTdJNfSL12CWtFeLv/HltVfLs8HbacihNWw6lOSx/bMYWzXtooMszwEtS4snTevefffptS5IMF/7/SVJdfx9NuKiVNiac1Lxt9h9ULN+frNgTmWrOUvUAAADAecswDO05ekpL953Qkr3HteHgSRW4ctPmxNB2DXX3wObqHR3KM5sKMqh1A3VqWsfhylbzdxxRzPFT5/UH8gAA4PzA2jgAAAAAAADnMcMw9NOaeL0yd7fTpZib1w/QJzd1V7vGwW6Krvob3Kahhndq5HCW+Kw8i16Zu0uf3tTDA5G5T77Fqvf/3afPlh5wmqwbGuCrt67tXOaZp2sif19vPXZJaz3163aH5a/P360Z9/a1SSKwWA1NW5+gd/7eq5On811qp2dkXb08skO1Xfbb19usT27qrhGTlivNQZ8zcgr0wJRNmjm+r2p5l/wBw8msPH2yOEY/rI53eYl6Px+z7hrQXPcMaq5gPx91TQhxmGguST+uideLV3Zw6bwAAAAAaob07HytjEnWkr3HtXTfCR3LsP+YuCy8TIau6txID1zchoTnSnB2VvN7f9xoV2YYZ2Y1f++Gru4PDAAAwI1INAcAAAAAADhPZeTk65lft2vedsfJkOca2bWJXhvVSYG1eJxUWi9c0UFL955QVp7Frmz+9qNasve4Brdp6IHIKl9ccpYenrZZ2xLtZ/4qamCr+nr3+i5qGOznhsiql+t6hOubFQe199gpu7L1B0/qn13HdGmHRpKkjfGpevH3ndqRlOHSuRsG1dKzw9tpZNcm1X7Gu6YhtfX+DV11ezGrCGxLTNerc3frlas7OizPybfom5Vx+mzJAZ3KKXCpTS+zSaN7huuRoa0Uds7vbrfwkGJnvftlQ6Iev6SNAvj3FAAAoEbJyMnXPzuPaf+xU+rQtI4uaR/GKk3nMavV0K4jGYWJ5ZsS0mSpgFnLJckkKSLQUIe6Vl3Q0NDVl7ZWcDBJ5pVlWLswtQkLcnhPPmfrYT08tJUi6wV4IDIAAAD34Ek2AAAAAADAeWhHUroemLJJ8SmnS6zn623Wy1d10I29wqt9EqqnNKrjp0eHtdar83Y7LH9hzk7982i9GpWAYBiGZm5I1Et/7NRpBwn25/L1MuvJy9rojv7RMpv5HXPEy2zSsyPa6dZv1jksf/PPPerUtI7e+WevZm1Kcumc3maT7hwQrQkXt6pRH5AMadNQDw5pqY8Xxzgs/3FNvHpG1dXIrk0L91mshn7dmKj3/t2noxk5Lrd1SfswPXlZW7VsGGhXZjKZdHPfSD35yza7slO5BZq9OUnjLoh0uS0AAABUXcdP5ejblQf10+p4ncr9vw8WB7aqry9v6Vmj7vVQsoycfC3ecyaxfNm+ZCVnVsys5ZJUL8BXF7ZuoMFtGqhLWC1tXbeyws6NkpnNJj1wUUs9NHWzXZnFaujzpQf0xjWdPRAZAACAe9ScNwgAAAAAAABwyjAM/bQmXq/M3a08i7XEus3rB+jjsd3Vvkmwm6KruW7rF6VfNyVp9xH7WaYTUk/r08Ux+s8lbTwQWcVLP52vZ2Zv0/ztR53WbdkwUB/e2FUdmtRxQ2TV26DWDTSwVX0t359sVxaXnKULJy5WgYuz4w1sVV8vXtnBYYJ0TfDosNbaGH9Sq2NTHJY/M2u7OjQJVosGgVq4+7gm/r1H+45lunz+HpF19czlbdUzKrTEeld1aaLX5+9W2ul8u7IfVh/UTX0i+IAHAACgGotPydLkZbGauTFReQX299fL9yfr/X/36Znh7TwQHdxp1+EM/bjmoH7bfFjZ+SV/bO0qs0nqFlFXg/5/cnnHJnUKP87OyHBtBStUnBGdGuv9f/cpLjnLruyXjYmacFErNQmp7YHIAAAAKl+VSTTngToAAAAAAEDlOpWTr6dnbde8bUec1r2qSxO9fk2nGjXTsSd5e5n16tUdde1nqxyWf740Vld3a6rmDap34u/qAyn6z4wtOpLufFbocRdE6Lnh7VXbl9n9XPXM5e20Ima5DAf55K4kmTerW1v/vaK9LmkfVqOfx3qZTfpwTFeNmLRCJ07ZzyB4Os+i8T9tUqi/r9YdTHX5vC0aBOipy9pqmIv///x8vHRDr3B9sTTWrmzfsUytjUvVBc3rudw+AAAAqoYdSen6fOkBzd9+RM6G4V+tiNNVXZvwcW0NlFdg1Z87jujH1fHaEH+yQs7ZIKhWYWL5gJb1FeLvWyHnRfl5mU26f3ALPeFg1ap8i6HJy2L10lUdPBAZAABA5asSbwoNw9DQoUNLfYyXV+leQplMJhmO3sIAAAAAAADUcDuS0vXglE06mHK6xHq+3ma9dGUHjekdXqMTUT2hR2RdjekdrqnrDtmV5Vms+u+cHfrpzj7V8v97vsWq9/7dp8+XHnCYBH2u0ABfvXVtZw1rH+ae4GqQ9k2CdV33Zpq5MbFUx9XyNuv+wS1176Dm8vM5PxL7Gwb56aMx3TT2yzUOk39ijrs+g3nDoFp6dFhrXd+jmby9zKWKY1yfSE1eFuvw78UPqw+SaA4AAFBNGIah1bEp+nxprJbtO+HycRaroWdnbdes+/vLy1z97vVg72h6jqasjdeUdYeUnGn/YWtpeJtN6h5ZV4PbNNCg1g3UvnFwtXwmcL64ultTfbBgv5LSsu3Kpq5L0P1DWqhhkJ8HIgMAAKhcVSLRXFKZEsBLe0x1G5CPGTNG06ZNs9kXGRmpgwcPeiYgAAAAAABQ7RiGoZ/XJuh/c3c5XMr7XNH1A/TJ2O5q3yTYTdGdf566rK3+3nlMqVl5dmUrY1L0+9bDGtm1qQciK7u45Cw9PG2ztiWmO607sFV9vXt9FzUM5sVrWT12SRv9se2wcvJL/vt81mUdGum5Ee0UHupfyZFVPRc0r6fHL22jiX/tLdPxQbW8NX5wC93eP0r+vmV7lB4e6q+L2zbUgt3H7cr+3nlMR9Kz1bgOy6sDAABUVVaroX92HdNnSw9o66G0Mp1ja2K6flx9ULf1j67Y4OA2Zz80+HF1vP7ZdUwWF1aUKk7jOn6FieX9WtZXsJ9PBUaKyuTjZdZ9g1vo+d922JXlFlj19fI4PTO8nQciAwAAqFxVItG8uiWAu8Mff/xhl2QOAAAAAABQGqdy8vXMrO2au+2I07pXdmmiN67ppMBaVeJxUY0V4u+rZy5v63CpZUl6dd5uDWnbsFq8aDYMQzM3JOqlP3bqdJ6lxLq+XmY9eVkb3dE/WmZm8SuXRnX8dPfA5vpoUUyJ9Vo0CNBLV3XQwFYN3BRZ1TT+whbaePCkFu6xT/Qujo+XSeMuiNSEi1opNKD8S9Xf0jfKYaK5xWpoytoEPXZJm3K3AQAAgIqVV2DVb1uS9PnSA4o9kVXu8739915d0qGRmoTwkWF1kplboNmbEvXD6njtL8WqSOfy8TKpd3SoBrVuoMFtGqpVw0ByZKqx63o006SF+3X8lP1s9j+uidf4QS1UtwLuIwEAAKoSj785LMtM5jVdenq67rvvPk+HAQAAAAAAqrGdh9P1wM+bdDDldIn1fL3NevHK9hrbO4IXnW5yXY9mmrkhUesOptqVnTiVq3f/3quXR3b0QGSuSzudp2dnb9f87Ued1m3ZMFAf3thVHZrUcUNk54d7B7XQ1GKWaQ+s5a2HL26lW/tFydfb7IHoqhaz2aR3R3fRiEkrHC5vXtTIrk30+CVtKnQG+AEt66t5/QDFJtsnKE1dl6AHL2qpWt5eFdYeAAAAyi4zt0DT1iXoq+VxOpqRU6pjG9fxU2iAr3YezrAry8qz6MXfd+rLW3pWVKioRPuPndKPa+I1a1OSMnMLSn18g6BaurRDmAa3bqi+LeopgI/6aww/Hy/dc2FzvTpvt13Z6TyLvl0Zp//wMTEAAKhhPDqajYuL82TzVdYTTzyhpKQkSVJAQICyssr/hTQAAAAAADg/GIahKesS9PIfu5RXYC2xblQ9f31yU3cSgN3MZDLp1VEdNfzD5SpwsNz2j2vidV2PcHVqVjX/XFYfSNF/ZmzRkXTnSRfjLojQc8Pbq7YvSbQVKbCWtz4f1123fLPOZjb5a7o11dOXt1XDYD8PRlf1hPj76tObuuu6z1cp3+J44pMBLevr6cvbqmPTiv97ZzabdHPfSL38xy67suTMPP2146hGdm1a4e0CAADAdSmZufp+1UF9vzpe6dn5pTq2ZcNAjR/UQld1aaLjp3I07L1lys63X/Xp313H9NeOo7qsY6OKChsVqMBi1b+7jumH1fFaHZtSpnP0jgrVzX0jdWmHRnz4W4ON7ROhT5ccUGpWnl3Zt6sO6q4Lm1eLleoAAABc5dFE88jISE82XyUtWbJEX331lSTJbDbrxRdf1JNPPunhqAAAAAAAQHWQmVugZ2Zt1x9bDzute0Xnxnrjmk4K4sWXR7QOC9KdA6P1xdJYuzKrIT3323bNvr+/vMxVZ5b5vAKr3l+wT58vPSBnixSGBvjqrWs7a1j7MPcEdx7qGRWqvx6+UAt2H1O+xaohbRuqdViQp8OqsrqEh+iNazrrqV+3yXLOBx4dmgTr6cvbamCrBpXa/rU9muntv/fafBhw1verDpJoDgAA4CGHUk/rq+Wxmr7hkHLyS/5Yu6huESG6b1ALDW0XJvP/v3drVtdfj13S2uFsx5L00u871b9lPe7Fq5ATp3I1bV2CpqxLcOmD6qJq+3hpVPemuqVvpNo2Cq6ECFHV+Pt6684B0Xr77712ZadyCvTj6ng9MKSlByIDAACoHKzPU4VkZ2frrrvukvH/39RNmDBBvXr18nBUAAAAAACgOth1OEMPTNmkuOSSV0bz9TbrhSva66Y+ETKZqk4S8/no4Ytbae7WI0pKy7Yr25aYrilr43Vz3yj3B+ZAXHKWHp62WdsS053WHdiqvt69vguzartBRD1/3TEg2tNhVBvX9WimqHr+mrvtiHLyLRrcpqEuaf9/SUGVKdjPR6O6NdXPaxPsyjYlpGlHUnqlzKYOAAAAx/YczdAXS2P1+9bDNh8iumJwmwa6b1AL9Y4OdXhffVu/KP22JUk7kjLsyo5m5Oidv/fq5ZEdyxw7ys8wDG2MP6kfVsfrzx1Hil35qCTN6wfo5r6RurZHM2avPg/d0jdSXyw9oIycAruyr5bH6vb+UfL3JSULAADUDIxqqpD//ve/OnDggCQpIiJCr776qjZs2ODhqAAAAAAAQFVmGIamrjukl/7YqbyCkmdfi6znr0/GdieZsYrw9/XWS1d10N0/OH7+M/Hvvbq0YyM1DPJcwrZhGJq5MVEv/b7T4UzM5/L1MuvJy9rojv7RbkncBcqiZ1SoekaFeqTtW/pGOUw0l6QfVh/UxOu6uDkiAACA88/6g6n6fMkBLdxzvFTHmU3SFZ2baPygFmrfpORZq729zHpjVGeN/GSFHOWw/7AmXld3a6puEXVLFQPK73RegeZsOawfV8dr1xH7DwGcMZuki9uF6Za+kerfoj73vuexID8f3dY/WpMW7rcrO3k6X1PWJuiugc09EBkAAEDFI9G8ili/fr0++OCDwu1PPvlEgYGBngsIAAAAAABUeZm5BXp21nb9vvWw07ojOjXWm9d2YnnuKmZY+zANbRemBbuP2ZWdyinQ6/N264Mbu3kgsjO/X8/P3q7ftjj//WrZMFAf3thVHZrwEQNQnDaNgnRB81CtiU21K5uz5bCeubyd6gb4eiAyAACAms0wDC3Ze0KfLI7RhviTpTq2lrdZo3uG6+6BzRVRz9/l4zo1q6Pb+0fr6xVxDuKRnpm1XX9MGCAfL3Op4kHZ7Dt2StPXH9LMDYcczkDtTGiAr27sFa6xfSLUrK7rvweo2W7vF6Wvl8cqy8GH+V8si9W4CyLl5+PlgcgAAAAqFonmVUB+fr7uvPNOWSxnBp/XX3+9rrjiCg9HBQAAAAAAqrI9RzN0/0+bFJucVWI9Xy+z/ntle43rE+FwSW943ktXtdfKmGRl59u/mPxty2Fd3zNc/VvWd2tMOw+na8KUzU5/vyRp3AURem54e9X25eUp4MytfaMcJprnFlg1Y8Mh3TuohQeiAgAAqLk2xp/Um3/u1vqDpUswD/Lz1i19I3Vbv2g1CKpVprb/M6y1/tpxVElp2XZle46e0tcr4jSe8V+lOZqeo9+3Jmn25sPaXYbZyyWpa3iIbu0XqeGdGquWN/e8sFU3wFfj+kbqi6WxdmUnTuVq5oZDurlvlPsDAwAAqGAkmlcBb7zxhrZv3y5JCgkJ0aRJkzwcEQAAAAAAqMp+2Zio53/brpx8a4n1Iuv565Ox3dWxKbNMV2XN6vrroYtb6a2/9jgs/+9vO/TnIwPd8lLbMAz9tDZBr8zdpbyCkn+/QgN89da1nTWsfVilxwXUFMPah6lRsJ+OZuTYlf24Jl53DWwuLzMfBQEAAJTXgROZmvjXHv290371qJI0DKqluwZGa0zviHKvCBZQy1v/G9lBd36/wWH5Bwv2aUSnxgoPZYbsipKRk6+/th/V7M1JWhOXIsMo/TlqeZt1VZcmuqVvlDo143kKSnbXgOb6buVB5Tp4hvL50liN6R0hb1YuAAAA1RyJ5h62a9cuvfbaa4Xbb731lho1auTBiAAAAAAAQFWVk2/RS7/v1LT1h5zWHdGpsd64tpOCy/liHO5x54BozdqUqP3HM+3KYpOzNHlprCZc3KpSY8jIydczv27XvO1HnNYd2Kq+3r2+ixoG+1VqTEBN4+1l1k19IvTuv/vsyhJPZmvxnuMayscbAAAAZXY8I0cfLNyv6esPyWJ1Pcs4un6A7r2wuUZ1b1qhH/le3C5Mwzs10vztR+3KcvKteu63Hfr+9l6sQFYOuQUWLdl7QnO2JGnB7uNOP5ouTnhobY3rE6nRPcNVN8C3gqNETdUgqJbG9I7Qd6sO2pUlpWVr/cGT6tuinvsDAwAAqEAeTTS/4447PNKuyWTS119/7ZG2z2W1WnXnnXcqLy9PkjRw4EDdfffdHo4KAAAAAABURQeTs3T/z5u0y8lyz75eZj1/RTvdfEEkL6qrEV9vs169uqNumLzGYfnHi2M0smtTRdSrnJnuth5K04NTN+lQqv2S7ufy8TLpqcva6o7+0TIz6zJQJjf2jtCkRfuVb7FPfPphTTyJ5gAAAGVwKidfk5fF6qvlccrOt7h8XKemdXT/4Ba6pEOjSltZ5sUrO2j5vmSdyi2wK1u274R+33pYI7s2rZS2ayqr1dCG+JOavTlJ87cfUXp2fpnPNbhNA93SN1KDWjdkdSGUyb2DmuvntfEO7/G2J6WRaA4AAKo9jyaaf/fdd25/4WkYRpVJNJ80aZLWrDnz8tDX11eTJ0/2yAvg48eP68SJE6U6JiYmxmY7MzNTGRklv+hG5cvKyipxGwAA1EyMAQCg5luwJ1kvzNunzNySX5Y3C/HTO6Paqn3jIJ06dcpN0aGitKvvo6s6NdTv24/bleUWWPXsrC36ZHQHm+dH5R0HGIahn9Yf1vuL4lTgZLa/ZiF+entUW3VoHKTMTH6/gLKqJemStvU1b6f9M9ll+05oW9xRRVXSRyUAag6eBQDAGfkWq2ZsOqLJKxJ0Mts+kbs4F0SF6I6+zdQnKkQmk0lZlXiPU1vSQ4Mj9drfBxyWv/z7TnVv7Kc6tVmRzJmYE1mat+O45u88oSMZuWU+T5Cft0Z1DtPo7o0VEVpbkir1d6CiMQ6oWgJMUq+IOloVl2ZXtiU+RRkZDdwfFACgRmIMcP7KzLRfDdedTIZhuL5eVAUzm80ymUw6N4TKTrQ+m2husbj+FXNliIuLU6dOnQr/sr/44ot66aWX7OotWbJEQ4YMKdyOjIzUwYMHKzSWl156SS+//HK5zjFp0iRFRERUUEQAAAAAAECSLFbp9wSzlhwxO63bOdSqMS2s8vfotAIor8x86bXNXjptcfyM7PbWFnWtVzGP87LypSkHzNpx0vnvV9dQq25sYVVtfr+AChF3Svpgh+O/UIMaWXVNtNXNEQEAAFQvVkPakmLS3ASzUnJdzzFoW8eqERFWRQRWYnAOWA3pwx1eOpjpONa+Dc/cc8FeWq60KcWkDSfMSjpdvnySFkGGejWwqkd9Q75eFRQgIOn3eLMWHrZ/vtKotqFnuno2PwkAAFR/CQkJeuihhwq3d+zYoQ4dOrit/SrxauhscrlhGPJg3rtb3XPPPYVJ5m3bttWzzz7r4YgAAAAAAEBVkpYrfbffS3GnSn6JajYZuirCqsGNDXlgoTRUsEAf6cpIq6bHOn7jPeugWW1DLPIr5wvxuFPSd/u8lJZX8i+Nt8nQqCir+ofx+wVUpKhAqVmAocQs+79Ya0+YNCJCqkXiCwAAgEN70036I96sQw7GUsVpFmDoqkir2tTxTD6C2STd0MKit7d5yWrYx736uFk9G1jVMtgDwVVB2QXS1lSTNpwwKSbDJENlvyFtVNtQz/+fXB5aqwKDBM7RLMDxvy3HsqU8i/iwAQAAVGtVItHcMAx5eXnpoosu0o033qjQ0FBPh1Spvv76ay1YsEDSmST7yZMny9fX18NRAQAAAACAqmJPmkk/7Dcrq6DkF6l1fA3d3tqi6CA3BQa3uKChobXHDYcz3aXnmfTnIbNGRZVtpjurIS06bNK8BLOsTl7UN/QzdFtri5oGlKkpACUwmaSBjayaesA+2yDHYtKGZJP6h50fk7IAAAC4KinrzKzBe9Kdr8p0Vr1ahkZEWNWtniGzhz+ebeIvXdTE0IIkx4FMP+Clp7pY5O1692qUAqu0K82kjSdM2nHSpAIHCfmuquNrqEd9Qz3rW9XEX3w4jUrXtJhEc0MmHTktRfLsDgAAVGNVItHcZDLJarVqwYIFWrZsmUaMGKFbb71Vw4cPl5dXzfqs78iRI3r88ccLt++66y4NHDjQgxFJ999/v66//vpSHRMTE6Orr766cLt3795q165dBUeG0srKytK6desKt3v37q2AAN4GAwBQ0zEGAICaw2I19MWKBH2xO0HO0gv7RofojavaKDSAj9droqYdMnXjN5tldfCLsPyoWQ+O6KnWDQNKNQ5IycrTc3/s06qEk07bH9GhgZ6/rKUCalWJx4dAjdQ336L5H69TenaBXdmmU0F67obuhauBAkBRPAsAcD45nJ6jj5fGa96O407vlc+qW9tb9wyI0OjujeXjVXUyt/vmW7T3q006dDLHrux4jkkxvs1138BID0TmGYZhaNOhDM3bcVz/7ElWRo792NhVgbW8NKxtfY3o0FA9IurIy9NfFlQixgFVj8Vq6L2dq5STbz8xQGB4Ww3p1tgDUQEAahrGAOev3bt3e7R9j74puuWWWzRr1ixlZmZKOpNwnpubq9mzZ2v27NmqX7++xo4dq5tvvlndu3f3ZKgV5oEHHlBaWpokqVGjRpo4caJnA5LUsGFDNWzYsFznCAwMVHAw63hVNQEBAfy5AABwHmIMAFQNxzNy9PPaBKVm5alzszq6rkczksVQopTMXD0yc4uW708usZ7JJD18cStNuKhVjX5per7rHRys2/ql6ZuVcXZlFkN6/Z9Y/TK+n11ZceOANbEpemjqFh0/lVtiu34+Zv1vZEddz79ZQKULlnRj7wh9sTTWrizmxGntTinQBc3ruT8wDzAMQ5sS0rQuLlVNQvw0pG1DBfv5eDosoFrhWQCAmuhkVp4+WRyjH1bHK8/i2qpOtX28dNfAaN1zYXMFVcHxRLCkN67ponFfr3VY/vWqRF3bK1otGwa6NzA3S87M1a8bEzV9/SHFJmeV+Tw+XiYNadNQo7o11ZC2DeXnU7MmMnQV44CqoV3jYG1OSLPbfyA1jz8fAEClYAxw/ggM9Oz9gUcTzb/77jt9+umn+uWXX/TDDz9o8eLFks48VJakEydOaNKkSZo0aZLat2+v2267TTfddJMaNWrkybDLbObMmZo9e3bh9ocffqiQkBDPBQQAAAAAqBRztx3Wk79s0+k8S+G+RXuO66Mx3eRdhWbRQtWxMT5VD/y8WUcz7Gc0O1dogK8+vLGrBrZq4KbI4En/uaS15m8/4vD3YlNCmmZsOKThbUNKPIfFaujjRTH6cOE+h7Ojn6t1WKA+HttdrcNYzxlwl3F9IjV5WawMB38/f1h98LxINN+ccFKvzdutDfH/t9pC8/oB+uW+fqzaAQDAeSon36JvVsbpsyUHdMrFGa69zCaN7hmuR4e2UsNgv0qOsHwGtKqvUd2aavbmJLuyPItVz83ermn3XFDjPv61Wg2tiEnWtPUJ+nfXMeVbXJ2f3l7v6FBd3bWphndqpBB/xoyoGjo0cZxovutIhvuDAQAAqEAef7vt7++vW265RQsWLNDBgwf1yiuvqHXr1jZ1DMPQrl279OSTTyo8PFyXX365pk+frtzckmdgqmqeeOKJwv8eMWKERo8e7cFoAAAAAAAVrcBi1WvzdunBKZttkswl6c8dR/Xuv/s8FBmqKsMw9NXyWN3wxRqnSeY9Iutq3kMDSDI/jwTW8tYLV7YvtvzNv/bo5On8YsuPZ+To5q/X6v0FzpPMb+gZrjkPDCDJHHCz8FB/XdzW8WqTf+88piPp2W6OyH0OpZ7Wg1M2adSnq2ySzCUpNjlLT/26zUORAQAAT7FYDc1Yf0iD316iiX/tdTnJ/JL2Yfr7kQv1xjWdqnyS+VnPj2inEH/HM66vjUvVzA2Jbo6o8hxNz9Gkhfs1cOJi3fLNOs3ffrRMSeatwwL15GVttOKpIZpxb1+N7RNBkjmqlPaN6zjcv+fIKVmcPZgBAACowjw6o3lR4eHheu655/Tcc89pzZo1+u677zRjxgylpaXJMAyZTCZZLBb9888/+ueffxQUFKTRo0fr5ptv1sCBAz0dvlNpaWmF/z1v3rwyfYEcHx9vd9zmzZvVtWvXckYHAAAAACiP5MxcTZiyWatjU4qt89mSA+oZWVcXtwtzY2SoqjJy8vXEzK36e+cxp3XvGhCtpy5vKx9mxD/vXN6xkQa1bqCl+07YlaWdztf7i+I0JMD+uOX7T+jR6VuUnJlX4vkDfL302qhOurpb04oKGUAp3dI3Sgt2H7fbb7Eamro2Qf+5pI0Hoqo86afz9fHi/fp+VbzyLNZi6/2765h2JKWrY1PHyRoAAKDmMAxDC3cf11t/7dH+45kuH9czsq6eGd5WPSJDKzG6ylEvsJaeHd5OT/7i+OO61+bv1kXtGqp+YC03R1YxCixWLd57QtPWJWjx3uNOP34uTqNgP43s2kQjuzZVu8ZBNW6Wd9Qs7ZsEO9yfnW9RXHKWWjYMdHNEAAAAFaPKvp284IIL9Pnnn+vo0aOaNm2ahg8fLrP5TLjG/19HNCMjQ19//bUGDx6sFi1a6H//+5/i4uI8GTYAAAAA4Dy05VCarvxoRYlJ5mc9On2LDqWedkNUqMp2Hk7XlR+tcJpkHlTLW5+P667nr2hPkvl5ymQy6X8jO6iWt+M//9+2HdOBc1ZgLrAaevvvPbrlm3VOk8zbNQ7WHxMGkGQOeNiAlvXVvL6DL0YkTVmXoNwCi8Oy6ia3wKKvlsfqwrcX68vlcSUmmZ/1wQJWgwEAoCazWA0t23dCN3yxRnf9sMHlJPMWDQI0+eYemjm+b7VMMj/r+h7NdEFzx/GnZ+fr1bm73BxR+R1KPa13/t6r/m8t0t0/bNDCPaVPMg+q5a3RPZtpyt19tPLpi/TM8HZq3ySYJHNUeW0bBclczK/priMZjgsAAACqgSr/htLX11ejR4/W3LlzlZiYqLfffludOnUqTDaXziSex8XF6eWXX1bLli114YUX6uuvv1ZGBgM1AAAAAEDlmrouQaM/X60j6Tku1c/IKdD9P2+qMUljKB3DMDR9fYJGfbpK8Sklf3DQ/v8nAV/WsbGbokNVFVkvQA8MaVls+YxYL1msUlqudOdP2/TJ4gMynLzIv/mCSM2+v5+aN2A2LcDTzGaTxl0Q6bAsOTNPf+046uaIKpZhGJq37YiGvbdMr87brfTsfJePXbD7uLYlplVecAAAwCMSUk7rvX/2auBbi3TLN+u07mCqS8eFBdfSm9d00t+PXKhLOjSq9onHJpNJr43qJN9iPiz/bcthLd9vv7pVVZNXYNXcbYd189drNXDiYn28OEbHMnJLdQ4vs0lD24Xps5u6a/3zQzXxui7q16K+vIrL2gWqID8fL7Uo5jnLzsPpbo4GAACg4nh7OoDSCAsL02OPPabHHntMW7Zs0XfffaepU6fqxIkzN1dnk89XrlyplStX6uGHH1ZycrL8/Pw8GXahOXPmKD/f9YfokrR161Y9/vjjhdthYWH66aefbOq0bFn8i0YAAAAAQOXILbDoxTk7NW39oVIfuz0pXa/M3aVXr+5UCZGhqsrOs+j533bo102JTuve2CtcL13VQX4+Xm6IDNXBvYOa67fNSYpNzrIrO5pt0s8HzNqTZlJWQckTLwTV8tZb13XW8E58wABUJdf2aKZ3/tmr03n2H6J9v+qgRnatnisPbIxP1WvzdmtTQlqZz/Hhgv36+rZeFRcUAADwiOw8i/7ccUQzNyS6tCLcuYJqeWv84Ba6o3+0avvWrPvkFg0C9cCQlnq/mJVcnpu9Q38/cmGV7HfM8UxNX5+gXzclKTWr5BW1ihMeWls39orQdT2aKSy4auR1AOXRoUmww9UZdh1mokwAAFB9VatE83N17dpVH3zwgd555x3Nnz9f33//vebNm1eYyG0YhrKzs2W1Ol9+010GDRpU6mO8vW3/iPz8/DR06NCKCgkAAAAAUAaH07J1308btTWx5JlovM0mFRSzPvBPaxLUKyq02iaOoXQOnMjU/T9t0t5jp0qs5+dj1mtXd9K1PZq5KTJUF7W8vfTK1R1101drHZZvTHa+cGHnZnX08ZjuiqjnX9HhASinOrV9NKpbU/28NsGubFNCmnYkpatj0zoeiKxsDiZnaeLfezR/u+uzsXuZTbI4GDct3HNcWw+lqUt4SAVGCAAA3MEwDG05lKYZGxI1d+thncotKNXxPl4m3XxBlB68qKVCA3wrKUrPGz+4uX7fmqQDJ+w/LE5IPa1Ji/brqcvaeiAyezn5Fs3ffkTT1h1yeSb6ony8TLq0QyON6R2hvs3rycys5ahB2jcJ1m9bDtvt33U4Q4ZhVPuVGAAAwPmp2iaan+Xt7a2rrrpK7du3V6NGjfTZZ58xMAMAAAAAVJpVB5I1YcpmpTiZqalDk2B9elN3Pf3r9mJn6npm1na1bxysVmFBlREqqoi52w7rqV+2KcvBLLXnal4/QJ+O6662jYLdFBmqm/4t6+uqLk30+1b7F5bO3DkgWk9d1la+3s4T0gF4xi19oxwmmkvSD6sPauJ1XdwcUemdzMrTpEX79dOaeOVbHH9sV1RYcC09dkkbhfr76q4fNjis8+HC/fqGWc0BAKg2TpzK1ezNiZqxIVExDmb2dcXVXZvosUvaKDy05n8oW8vbS29c01mjv1jtsPzLZbEa2bWJR58X7DqcoenrEzR7c5Iyckr3wcBZLRoEaEzvCF3TvVmN/nAA57f2jR1/IJySlafjp3KZuR8AAFRL1TrRPD09XdOmTdMPP/ygNWvWSBJJ5gAAAACASmEYhr5cHqs3/9yjYiYpL3RN96Z6fVQn+fl46cMxXXXFpBU6firXrt7pPIvu+3mT5jzQXwG1qvUtOhzIK7Dq9fm79d2qg07rXtG5sd68trMC+T2AE89f0U6L9xx3eSbAOrV99M71XTSsfVglRwagvNo0ClKf6FCtjbOfGXLOlsN65vJ2qltFE3Jy8i36YfVBfbQoRqdcTDzy9/XS+EEtdNfAaPn7esswDHUJD9HWQ2l2dRftOa4th9LUlVnNAQCosvItVi3ec1wzNiRq8d7jDlcqccXAVvX11GVtq9VqLhWhd3SoxvQO19R1h+zKCqyGnpm1Xb+O7+fW2b8zcwv0x9bDmrYuwemqfsXx8zFreKfGGtM7Qj0j65LPgRqvfZPiPwjZdTiDRHMAAFAtVbu3lxaLRX/++ad++OEHzZ07V7m5Z17Un11ixjDO3LA2bdpU48aNk58fgzQAAAAAQPlk5RboyV+2ad72IyXW8zab9OKV7TXugsjCF2cNg/z00ZhuGvvVWocvWWOOZ+qZWdv14Y1dedlWgxxKPa0JUzdri4NkuXP5eJn03yva6+ZzfmeAkjQM8tPjl7bRi7/vdFq3R2RdTRrTTU1DarshMgAV4dZ+UQ4TzXMLrJqx4ZDuHdTCA1EVzzAM/bHtiCb+tUeJJ7NdOsZskm7oFaFHh7VSw6D/e35vMpn0yNBWuv3b9Q6P+3DBPn17e+8KiRsAAFSc/cdOaebGRM3alKjkzJJXfyuOr5dZl3QI0019ItW3Rb0KjrD6ePqydvp313ElZ9pPVrA5IU0/r43XzX2jKjUGi9XQ6gMp+nVTov7acVTZ+SWvzlac9o2DNaZ3uK7q2lR1avtUcJRA1RUa4KvGdfx0JD3Hrmzn4XQNadvQA1EBAACUT7VJNN+8ebN++OEHTZ06VSdOnJBkn1xeu3ZtjRo1SrfeeqsuvvhiXtACAAAAAMot9kSm7v1xo/Y7Weq5YVAtfTauu3pEhtqV9WleT09c2kZv/rnH4bG/bz2sXtGhuvmCyAqJGZ5jtRr6eW283vhzj07nlfwytmlIbX1yU3dmZ0WpjbsgUr9sTNT2pOJnlLtvcAv9Z1hr+XiZ3RgZgPIa1j5MjYL9dDTDPinhp7Xxumtgc3m5cRbLkqyLS9Vr83c7nIG8OBe1bainL2+r1mFBDssHt26gruEhDj/UWrz3hDYnnFS3iLpljBgAAFSUjJx8zd16RDM2HHL6gXVJOjQJ1uie4RrZtYlC/Kvmyi3uVMffRy9c2V4PTd3ssPytv/ZqWPtGalSn4ifbizmeqV83Jeq3zUkOE2RdEeDrpau6NtWY3uHq1LQO+Ro4b3VoEuzw79GuIxkeiAYAAKD8qnSi+ZEjR/TTTz/pxx9/1M6dZ2ZpOptUfq5Bgwbp1ltv1XXXXafAwEB3hwkAAAAAqKH+3XVM/5m+RadyC0qs1yuqrj65qbvNrJxF3TOwuTYcPKkFu485LH/lj13q0qyOOjcLKU/I8KBDqaf15C/btDo2xWndIW0a6L3RXVU3gBfpKD0vs0mvjer4/9i76/CozrQN4PfMxN3dXSBAEiS4t0UKFGuxOlKlW9l2691ufT+q0BbaUrQ4RVpa3DWBKIG4e4j7zPn+YKHQORPPzCS5f9e115Z5jzwhZHLmnPt9XsxYeUZppQRLI12smNsfo/3ZIYuoO9KVSTF/sBv+e/C60lhWaS2OXSvEuEB7DVT2l9SiKnz0eyL+TBC/phET5GiG1ycHYpiPTbPb3epq/oiqruaHk7CWXc2JiIg0QqEQcC6tBNsuZeP3uDzUNSradRwLI11M7++M2eEuCHYy7+Qqu7+pIY7YEZmN49eLlMaq6pvwzp54fLswrFPOdaO6AXtjcrEjMhvR2aonMrekv6sFHhrkiikhTjDW1+oICpFaBDma4dDVQqXXE3IZNCciIqLuSeuu8uvq6rBz506sW7cOhw8fhkJx8wPqre7lt/j6+mLhwoVYuHAh3N3Z8Y2IiIiIiIg6j1wh4PND1/HVkeQWt31kqAdenxzYYtdgqVSC/87uhylfn0RWaa3SeINcgWUborD/ueHs4tXNKBQCNpzPwEet6GIulQAvTvTHslHekGpJR1rqnkJcLPDtgjA8vTESDfKbYfMgCwW+XDgAPs62Gq6OiDriwUFu+PJIEhrlyk1Xfj6bobGgeUlVPb48nISN5zPRpFCuTYyDmQFevscfMwY4t/r33qhmupofu1aEqMwbCGVXcyIiIrXJKavFjshsbIvMEr2f0RpSCTDSzxZzwl0xLtAO+jqyTq6y55BIJHh/eh9MWHFcNMx/ID4fBxMKMCGofdeEDU0KHLtWiB1R2TiSWCh6zdkaZgY6eCDUBQ8OckWAg1m7jkHUUwU5if9MpJfUoLKuEaYGumquiIiIiKhjtCZofvz4caxbtw7bt29HVdXN5chvhctvdTE3NzfHnDlz8PDDDyMiIkKT5arN6NGjRbu4ExERERERUdcoq2nA879cEe0cdScDXSk+eiAE0wc4t/rY5ka6WDU/DA+sOoOGJuWHhTlltXhxazRWLwpnCLmbyCypwcvbo3E+rbTFbW1M9PHlQ/0x1Lv5bq5ErTUhyB7Hlg/B5gMnYakHWBsAdqb6mi6LiDrI1lQfk/o64tcruUpjJ64XIbWoCl626lvZs65Rjp9Op2Pl0eQWV3m5xVhPhqfG+OCxYZ4w1GtbkEwikeCFCX54+McLouNfHErCz4+xqzkREVFXEgQBx64V4cfTaTiVXIz2Pq72sDbC7HBXzAx1gYO56lXg6G6uVkZ4YbwfPvw9UXT8rV/jEOFtDZNWdg8XBAGxOeXYGZWDPdG5KK1uaHdtgz2t8NAgN9zbxwEGupwwQCSmudUaEvMrMdDDSo3VEBEREXWcRoPmSUlJWLduHTZs2IDMzEwAyuFymUyGiRMn4uGHH8b9998PfX0+LCMiIiIiIqKukZBbgSUbLrXYocvVyhDfLQhX2Z2mOX2czfH21CC8vitOdPxwYiG+PZGCp0b7tPnYpD4KhYD15252Ma9tbL6LOXDzQexXDw2AnRkfrFPnMtHXgQ+bxxH1OIsiPESD5gCw/lwG3p4a3OU1CIKA32Lz8cFvV5FT1rrupTKpBA8NcsXz4/xg24GJLyN9bTDAzQKXM8uUxo5fL0Jkxg2EubOrORERUWerb5Jjz5VcrD6ZiusFVe06hpGeDJP6OmJOuCsGeljetWo5td5jwz2x+0ouruZVKI3lldfhv39ea/GaML+8Drsu52BnVDaSCtv3/QQAGxM9zAx1wdyBrmqd8EjUXblYGsLUQAeVdcoTdeNzyhk0JyIiom5Ho0Fzf3//u0LltwiCgH79+mHRokWYP38+7OzsNFQhERERERER9Ra7LmfjtZ2xossS32mUny2+eLA/LIz02n2ueYPccCn9BnZdzhEd/+yPaxjgaokIb+t2n6MzFVXWY/25DJxLLYGLhSGeGevTqx8sZpRU4+XtMbjQii7mBrpSvHJPAB4Z6sEu9URE1Gqhbhbo42yGuBzlYNH2yGy8NNEfxq3sYNke8bnleHdvQqt+190yPtAer94XAB+7jl8jSCQSvDDeD4tUdDX//NB1rH98cIfPQ0RERDeV1zZi0/lM/HQ6DYWV9e06Rri7JeaEu2JSiGOrO22TaroyKT58oC9mrDwt2lH+5zPpmDHAGSEuFne9Xtsgxx/x+dgRlY3TycVQtLMbvZ6OFBOC7DEr1AUjfG2gI5O270BEvZBEIkGQo5noCogJIpNHiIiIiLSdVnzCuzWL2d7eHvPmzcPDDz+Mvn37argqIiIiIiIi6g0a5Qr8Z/9VrD2T3uK2z431wfPj/SDrYGBYIpHgPzP6ID63XLRDmEIAnt18Gb89N1zjHbAPxOXhtZ2xuFHTCAC4AOD3uHz89OhADPHSjiC8uigUAn4+m45PDlxrVRfzQR5W+GRWCDxsjNVQHRER9SQSiQSLIjzwyvYYpbHKuibsvpKD+YPdO/28JVX1+OzP69hyMbPVoaS+zub416TATp8gN8LXBmHulojMuKE0djKpGJEZpQhzZydAIiKijsgpq8WPp9Lwy4VMVDe0/Dn37+xM9TEzzAWzwlzg3YsnpHeV/q4WeDjCQ/SelUIAXt0Riz3PDINUIsGF9FLsiMzGb7F57fpe3hLmbomZoS6Y3NcR5ka6HaieqHcLcmLQnIiIiHoOrQiaC4IAmUwGGxsbHDlyBEeOHOnS80kkEkRGRnbpOYiIiIiIiEj7FVbU4elNUbiYrhxgupOpvg5WzO2P8UH2nXZuIz0drJwfivu/Po0akQeAxVX1eHbzZWx8YrBGukaV1zbi3T3x2CnSdb22UY4l6yOxY9nQTula2h2kF1fjle0xuJDecmdXQ10Z/nmvPxZFsIs5ERG13/39nPDBb1dR9r/JXndadyYD8wa53W7i0lGNcgXWnc3A54euiy7vLsbZwhAv3+OP+/s5dcnvO4lEguXjfbHwB1VdzZPY1ZyIiKid4nLKsfpkKvbF5EHexpbXujIJxgfaY3a4C0b62rLTdRd7caIfDsTlI7+iTmksIa8CSzdEIjG/Etk3att9DmcLQ8wMdcYDoS6cLE/USYIczURfv55fhUa5Arp87yQiIqJuRCuC5gCgUCgQHx8P4GbwvCtIJBIIgtBpN9+JiIiIiIio+4rMKMWyDVEtLgntZ2+C7xaGw7MLHrT52Jnio5kheG7zZdHx82ml+O/B6/jnvQGdfu7mnE4uxsvbopFbrvwQ85by2kY8uvYCdj01DDYm+mqsTr0UCgFrz6Tjkz8SUdeoaHH7QZ5W+HRWCNyt+WCWiIg6xkBXhrnhrvjuRKrS2LWCSpxPK+2U1UWOXSvEv/clIKWoulXbm+rr4KkxPnh0mAcMdGUdPn9zhvvYINzdEpdUdDW/lF6KcA92NVcHhULgBDoiom5OEAScSCrG9ydScDq5pM37BziYYna4K6b3d4J1D74PoG1MDXTx7rRgLFkv3kjv0NXCdh3XWE+GSX0dMTPMBYM8rPh7nqiTBTuZi77eIFcgubAKgSqC6ERERETaSCuC5n8PfjMITkRERERERF1FEARsOJeB9/YloFHe/ETnKSGO+HhmCIz1u+7j8/39nHApvRTrzmaIjq86loJwd0uMC+y8buqq1DXK8fGBRPx0Or1V22eV1uKJny/hl8VDujxopgnpxdV4eXt0ix3vgZtdzF+9LwALh7jz4SwREXWaBUPc8f3JVIj1Zll/NqNDQfPUoiq8v/8qjiS2Lpwkk0owf7Abnh/nq7Zw2c2u5n5Y8MN50fHPDyVhwxPsat5V8svrsOl8BvbF5iGrtAb9XS3w5pQghLhYaLo0IiJqg4YmBfZG52L1yVQk5le2aV+JBLg32AFPjPBCqJsFn+NryD3BDpgYZI8/Ewo6dByJ5OZEvpmhLrgn2AGGej3vXg6RtvCxM4GuTCJ6/zkht4JBcyIiIupWNB4076ru5URERERERER/V9coxxu747A9MrvZ7WRSCV67LwCPD/dUy0PU1ycHIjqrDNHZ5aLjL2y5gv3PjYCrlVGX1RCTXYYXtlxpdTfTW65klWH5L1ewcn5ojwlYKxQCfjqTjk9b2cV8sKcVPmEXcyIi6gKuVkYYF2An2qnyQHw+8svr4GBu0KZjVtQ14usjyfjpdFqLk+5uGeZjjbemBMPfwbRN5+oMw3ysMdDDUnTi16nkYlxML8VAdjXvNIIg4FxqKdafS8cf8QWQK/76N3Ix/QYWrDmPPc8Mh0cXrPZDRESdq7KuEZsvZOLHU+nIr1C9YpkYA10pZoe54okRnvysqyXenRaMMyklqKpvavO+PnYmmBnqghkDnNt87UhE7aOnI4WfvSnicyuUxuJzKzAzTANFEREREbWTRoPmaWlpmjw9ERERERER9SK5ZbVYuiESMSrC3LdYG+vhq3kDMNTbRk2VAfo6MnwzPxSTvzyF8tpGpfGKuiY8tTEK25dFQF+nc7tNNcoVWHk0BV8dSUKTon2TwQ/E5+PD36/i9clBnVqbJqQVV+OVNnQxf21SABYMZhdzIiLqOgsjPESD5nKFgE3nM/CPif6tOo5cIWB7ZBY+/eMaiqsaWrWPm5URXp8ciIlB9hrrYHqrq/n8Naq6ml/HxieGqLmqnqeqvgm7orKx/lwGrhdUqdyuoq4Jb+2Jx8+PDmRXWyIiLZVXXoufTqdj8/lMVLYxlGxtrIdFER5YGOEOK2O9LqqQ2sPR3BAvTfTDO3sTWrW9pZEupvV3xgOhzujrbM7f20QaEORoJho0T8hr/v40ERERkbbRaNDc3d1dk6cnIiIiIiKiXuJcagme3hiFkurmQ1X9XC2wan4onCwM1VTZX1wsjbBibj88tvaS6HhsTjn+vS8B70/v22nnTCmqwj+2XFHZSf1OHtZGMNCVqVxme/XJNLhZGWFhhEen1adOcoWAn06n4dM/rqG+qeUu5kO8rPDJzH5ws+66LvNEREQAMMLHBp42xkgrVl51ZNOFLDwz1hd6OtJmj3ExvRTv7o1HXI5yyEGMkZ4Mz4z1wWPDPGGg27mT3NpjqLc1BnlY4UJ6qdLY6eQSXEgrxSBPdjVvj+TCSqw/m4EdUTmt7pB64noR/ogvwL19HLq4OiIiaoureRVYfSIVe6Jz2zyR3NPGGE+M8MTMUBet+N1P4hZGeGDXlVxEZ5WJjuvKJBgbYIcHQl0wxt+uxWtEIupaQU5mQKTy6wm5FRAEgRNAiIiIqNvQaNCciIiIiIiIqCsJgoCfz6Tj/f1XW3zI+tAgN7xzf1Cndwxvi7EB9nh6jDe+OZoiOr7hXCYGelhhWn/nDp1HoRCw7mw6Pvw9sVWh6gVD3PCvSYGoqG3CjJWnkVcuvuT223vi4WxpiLEB9h2qT91Si6rw8vYYRGa03MXcSE+G1+4LwHx2MSciIjWRSiVYOMQd7+1T7l5ZXFWP3+PyVF4b5JbV4qPfE7EnOrfV55sZ6oJX7vWHvZlBu2vubDe7mvtiXjNdzTc9ya7mrdUkV+DQ1QKsO5uBMykl7TrGv/clYJSfLQz1GEYkItIkQRBwOrkE359MxYnrRW3eP8zdEotHemF8oD1k/Iyr9WRSCVbND8VDq88ho6Tm9uv9XMwxM8wFU0Kc2ImeSIsEO5mLvl5R14TsG7VwtWIDCyIiIuoeGDQnIiIiIiKiHqmuUY7Xd8VhR1R2s9vpyaR4b1owHhzkpqbKmvfCeD9EZtzAuVTljp0A8NrOWAQ5msHX3rRdx88tq8Ur22NwKrm4xW3tTPXx6ex+GOVnCwAw0tPBj48MxOxvz4p2vFQIwDObLmPrkgj0cRZ/kKJN2trFPMLLGp/MCuFDICIiUruZYS747M9rqGmQK42tO5uhFDSva5Tju+OpWHU8GXWNLf+OA4D+rhZ45/5g9He16IySO12EtzUGeVrhQpryNdKZlBKcTy3BYC9rDVTWfRRV1mPLxUxsPJ+pcuJga+WU1eKbo8l46R7/TqqOiIjaolGuwP6YPHx/IhUJea1bseQWiQSYGGSPxSO9EObOFUG6GycLQ/yxfCSOXy9CXaMcwU7m8LEz0XRZRCQi0FH1/duEvAreYyQiIqJug0FzIiIiIiIi6nFyy2qxdEMkYrLLm93OwcwA3y4M06pAlY5Mii8fGoDJX55CUWW90nhNgxzLNkbh16eHwVi/9R/rBUHAr1dy8eavcaisUw6J/92UEEe8P70PLIzu7oQV6GiGb+aH4rG1FyEX6RJf0yDHY2svYvfTw+BkYdjq+tQtpagKL2+LRlRmWYvbGunJ8NqkQMwf5MYu5kREpBHmhrqYPsAZm85nKo1FZtxAXE45+jibQxAE/Babjw9+u4qcstpWHdvOVB+v3heA6f2dtfr33O2u5qtVdTVPwubFDJr/nSAIiMosw7qz6fgtNg+N8uZX+WmL70+k4oFQZ3jZMtxGRKQu+eV12HIxC79cbPukIX0dKWaFueDx4Z587+7mDHRluCfYQdNlEFELTA104W5tdNcKBLfE51bw55iIiIi6DQbNiYiIiKjVYrLLEJNdDi9bYwzxtNbqEAIR9V7nU0vw1MYolFQ3NLvdIA8rfDM/FLam+mqqrPXsTA3w9UMDMG/NedEwd3JhFf61Kxafz+0PiaTl9+LS6ga8sTsWv8Xmt7ituaEu/j29D+7v56Rym1F+tnh/eh+8tjNWdLywsh6Prb2IbUsjYGqg2+I51UmhEPDDqTR89mfrupgP9bbGxzPZxZyIiDRvUYS7aNAcANadTcfDQz3w7t4E0Y7fYvR0pHhyhCeeGu3TpslrmjTU2waDPa1wXuRrPJtagnOpJRjCruYAgNoGOfZE52Dd2QzE57at0y1ws9vtuAA7LIzwwKGEAqw/l6G0TYNcgbf3xGPdY4NadU1KnU8hAE0KQE+m6UqIqCspFAJOJRdj4/kMHLpaKHqfoDmWRrpYGOGBRRHusDHRvnsgREQ9WZCjmWjQPKEd1+hEREREmtI97h4TERERkUYpFALe25eAtWfSb782NsAOX88bACM9XlISkXYQBAHrzmbg3/sS0NTCQ9dFEe54Y3IQ9HSkaqqu7QZ7WePle/zx0e+JouO/XsnFQA8rLBji3uxxjiQW4J87YkW7o//dSD9bfDIzBA7mBi1u+9AgN2SW1mDVsRTR8cT8Sjy1MQo/PjIQujLt+HsuqKjDP7Zewenkkha3NdaT4V+TAzFvkBuDU0REpBUCHMxUhqx3RuVgW2Q2hFbmzu4NdsC/JgXCzbr7TaRaPt4PD60+Jzr2+aHr+GVxhJor0i4ZJdXYcC4DWy9lo7y2sc37WxrpYu5AN8wf7HZ7ol1/Vwv8FpsnOpHzZFIx/ojPx719HDtcO7VebHY5vjh4FaeSZWhUAIEWAkKHNMLMTNOVEVFnKqmqx7bIbGw6n4nMUuWQYkvcrY3wxHBPzApzhSFnpBARaUSQoxl+j1Nu/pGQ2/xKnERERETaRCOpoPj4eAQHB2vi1CppY01ERERE2uKrI8l3hcwB4EhiIV7ZHoOvHhrAAB4RaVxdoxxv7I7D9sjsZrfTk0nx/ow+mBPuqqbKOmbxCC9cSi/FoauFouPv7U1AiIs5QlwslMaq65vw/v6r2HxBvPPpnQx1b4aqFwxuW6j65Yn+yCqtwb6YPNHxk0nFeHN3HD58oK/Gf1ccvlqAl7fHoLSFTvcAMMzHGh89wC7mRESkfR4e6iEaNG9pkt0t/vameGtqEIb52HR2aWoT4W2NIV5WOJeq/PdwLrUUZ1NKEOHdu7qayxUCjl8vxLqzGTh+vajVEw7u1M/VAouGuGNyiCMMdO8OI5ob6uLV+wLw8vYY0X3f25uAkX62nIiuBudTS/DNsRScuF70v1duXmPH3ZDguW0J2P7UcMi4+hxRtyYIAi6klWLj+Uz8HpeHRnnb39T7u1pgyUgvTAx24HsCEZGGBTuLzwTMLa/DjeoGWBrrqbkiIiIiorbTyF2//v37Y8GCBXjnnXfg7t5857WulpGRgbfeegubNm1CY2Pbu3sQERER9XR/xOdjxaHromP7YvLQ19kcS0Z5q7kqIqK/5JbVYumGSMRkN98FxsHMAN8uDEN/Vwv1FNYJpFIJ/ju7PyZ/dRLZN2qVxhvkCizbEIX9zw2HhdFfDyUupZfiH1ujW9XxrL+rBVbM7Q9PG+N21ffZ7H7IL6/DpYwbotv8cjELrlZGeHqMT5uP3xnqm+T48LdEpQlTYtjFnIiItN2EIHs4mBkgv6KuTftZGOnixQl+eGiQG3S0ZKWRjlg+3g8Pfq+6q3mEd+/oal5W04Ctl7Kw/lwGskqVrxVboqcjxdQQJyyKcEe/Fq6RZ4a64JeLWYgUuebLLa/D10eS8cq9AW2ugVomCAKOXy/CN0eTcTFd/JobAC5nV2BPdA5mDHBRY3VE1FnKaxuxMyobG89nIrmwql3HGB9ojyWjvBDubsnPtEREWiLI0Vzl2NW8CgztxpOAiYiIqPfQSNBcLpdj3bp12LhxI+bOnYuXX34ZISEhaq0hOjoan376KbZt24ampia1npuIiIiou7iWX4l/bLnS7DYfH0hEkJMZRvjaqqcoIqI7nE8twdObolBc1XyX6oEelvhmfijsTA3UVFnnMTfSxar5YZi56gwa5Aql8ZyyWry4NRqrF4WjUaHA54eS8N3xFLTU2FRHKsHy8b5YOsq7Q4EzA10Zvl8UjgdWnkZ6iXiw/dM/rsHVygj393Nq93naI7mwCs9uvoyreRUtbjvcxwYfzewLF0t2MSciIu2lK5Ni3mA3/N9B8cnAfyeTSrBwiDuWj/e9a1JadzfEyxoRXtY4m1qiNHY+rRRnUoox1LvnBjYS8yuw9nQ6dl3OQX2T8vVhS1wsDbFgiDvmhLvCqpUdFKVSCd6bFoypX50Svc5cfTIVs8Jc4GVr0uZ6SJxCIeDPhHx8czQFsTnNT6q95f8OXsfkvk7Q0+n+E0qIegNBEBCdXY6N5zKwNyYXdY1tf0/X15FixgBnPDHCCz52fA8mItI29mb6sDbWQ4nIKovxuQyaExERUfegkaC5np4eGhoa0NTUhE2bNmHTpk0YMmQIFi9ejFmzZsHYuO1d1FqjuroaW7duxZo1a3Du3M1uJ8L/1pA0MOh+YQMiIiKirlRW04An111CdYO82e0UAvDs5svY+8xwuFoxnEdE6iEIAtadzcC/9yWgqYVE9cIh7nhzSlC3Dlv0dTHH2/cH4fVdcaLjhxML8d6+BJxLLUFifmWLx/O1M8GKuf3Rx1l1R522sDLWw0+PDsIDK0/jRo34amEvbY2Go7kBBnpYdco5myMIArZeysI7exJQ29j87zFjPRlenxyEhwa5suMbERF1Cw8OcsVXR5LQKG/+Gmi4jw3emhoEP3tTNVWmXsvH++Ls98pBcwD4/FASIryse9TvdrlCwKGrBVh7Ol00YN8ao/xssSjCHaP97SCTtv3vJtjJHIsiPERXimmUC3h7TzzWPTaoR/29a0KTXIG9MblYeTQFSW3sapxVWostl7KwcIhmVxOmrlXT0IRtl7Kx83IOUgurEOJqjn9NCkSwU+d8vqOuV13fhF+v5GLj+QzE57Y8MVqMl60x5g92x8xQ5x41mYyIqKeRSCQIcjLDyaRipbGEVjTHICIiItIGGgmax8fH44UXXsC+fftuB73PnTuHc+fOYdmyZZgwYQKmTZuGMWPGwNPTs0PnSktLw9GjR7F7924cOnQI9fX1AP4KmAPA1KlTsWLFig6dh4iIiKgnaZIr8Ozmy8gsFe9M+3dlNY1YvD4SO5cNhaGerIurI6Lerq5Rjjd2x2F7ZHaz2+nJpHh/eh/MGeiqpsq61rxBbriYVordV3JFx8UCP38nkQCPD/PES/f4w0C3c9+vPW2MsXpROOatOY8Gkc6aDXIFnlx3CbueGgZPm66ZYA7cXG789V2x2BeT1+K2/Vwt8OWD/eFu3XX1EBERdTY7UwNM6uuIX1VcE7hZGeGNyYGYEGTfowO/g72sMdTbGmdSlEPXF9JKcTalpEd0ByyvacTWS1n4+Ww6sm/Utnl/MwMdzA53xYIh7p1yDfbCBD/si8kVXVHoZFIxDsTl476+jh0+T29U3yTHjsgcfHs8pdX3Y8R8dTgJs0JdeH+mByqqrMe6s+lYfy4DZXdM8D2dXIKHvj+HXU8PgzdXFdBqV/MqsPF8BnZfzkVVfdtX3NaVSXBPsAPmD3bHEC+rHv17noioJwlyFA+ax+e2btUaIiIiIk3TSNDc29sbe/bswcGDB/HGG2/g4sWLEAQBEokEdXV12LdvH/bt2wcAcHZ2Rnh4OPr06QN/f3+4uLjA0dERJiYmMDQ0hCAIqKurQ2VlJfLy8pCdnY1r164hNjYWUVFRyMnJuX3eW+e4FTIfOHAg/vOf/2D8+PGa+GsgIiIi0lof/Z4oetOrOVfzKvDPHTH44sH+fMhBRF0mt6wWSzdEIia7+ZvwDmYG+HZhGPq7WqinMDWQSCT4z4y+iM+taHNnQwBwtjDEZ7P7IcLbuguquyncwwr/nd0Pz26+LDpeVtOIR366gJ3LhsLaRL/Tzx+ZUYrnNl9BTlnLIaylo7zx4kQ/6Mq6b6d7IiLqvf55bwCOJhaiou6vkJqxngzPjPXFY8M9oK/TOwKmy8f74UzKWdGxzw8lIcK7+3Y1Ty6sxNoz6dgRmdPiCi1ighzNsCjCHdP6O3dq4NjcUBev3ReIF7dFi46/ty8Bo/xtYaSnkcdP3VJNQxM2X8jC6hOpyK+o6/DxCivrsfZMOpaN9u6E6kgbpBRVYc3JNOyIyhad1AsAFXVNeG1HLH5ZPATSdqxYQF2nrlGO/TF52Hg+A1GZZe06houlIeYNdsPsMFfYmnb+Z2kiIupaQU5moq+nFFWjrlHe6Q1BiIiIiDqbRu/0TZgwARMmTMDevXvx8ccf48yZM7fHboXBs7OzkZOTg19//bXNx7+za/mtm8mCIGDYsGF49dVXMXny5A5+BUREREQ9z47IbKw5laZy3NZUH0WV9aJje6JzEeJijidGeHVVeUTUi51PLcHTm6JEuyfeaaCHJb6ZHwo7UwM1VaY+xvo6WLUgFPd/fRo1Da0PHM0Oc8FbU4NgaqDbhdXdNLWfE7Ju1OCTA9dExzNKarB4fSQ2PjG40x6iyBUCVh1LxopDSZArhGa3tTXVx//N6YcRvradcm4iIiJNcLIwxP7nRtzufDzA1QILhrjDzqznXf80Z5CnFYb5WON0skhX8/RSnEkpwbBu1NVcoRBw7Hohfjqd3ubJ38DNTrf39XHEogh3hLlbdlnI/oFQZ/xyMRMX028ojeWV1+GrI8n4570BXXLunqS8thHrz6bjx9PpKK1u/jPO3w3zscb0PrZ47deraBKUv8/fHk/BvMFuMDfs+ut/6hqCIOBSxg18fyIVh64WQGj+Yw6Am+97v1zMwrzBbl1fILUopagKm85nYntkNsprG1ve4W+kEmBcoD3mD3bDSF9bTiAgIurGglUEzeUKAdcLKhHiYqHegoiIiIjaSCtaSkydOhVTp05FVFQUvvnmG+zYsQMVFRUAcNeNUKE1d1Hu2OfO7uWmpqaYNWsWli1bhrCwsE7+CoiIiIh6hitZZXhtV6zKcT97E2xbMhTLNkaKLk8OAB/8dhWBjmbd6mE+EWk3QRCw/lwG3tubgKYWQsQLh7jjzSlB0NPpuV2qfexM8eEDffH8L1da3NbaWA8fPtAXE4Mdur6wOywb5Y2s0hpsvpAlOh6ZcQMvbovGVw8O6PDD8vzyOizfchnnUktb3HaMvy0+nd0PNl3QTZ2IiEjdXK2M8J8ZfTVdhsa9MN4Pp5NVdTW/jqHdoKt5ZV0jtl3Kxrqz6UgvqWnz/vZm+lgw2B0PDnJTS6dbiUSCd+/vgylfnYTY5fmak6mYGeoCHzuTLq+lOyqpqsePp9Ow7kwGKuubWt7hDuMD7fD0GB8McLNERUUF9p9PwLE85X/f5bWNWH0iFS/d499ZZZOayBUCDibk47sTqbjcju7XH/5+FeMD7XrdxCNt0ShX4GBCATacy1B577Qldqb6eHCQGx4c6AonC8NOrpCIiDTB08YEBrpS1DUqr0wSn1vBoDkRERFpPa0Imt8SGhqKH374AStXrsT+/fuxb98+/Pnnn8jNzVXa9u83hu8Mod/6bycnJ0ycOBGTJ0/GlClToK/PB8lEREREqhRW1GHJ+ksql+A1N9TF6kXhMDfSxdfzQjH1q1PIKatV2k4hAM9sisKeZ4bD1cqoq8smoh6urlGON3fHYVtkdrPb6cmk+Pf0YMwd2Ds6t03r74xL6Tew/lyGym0mBNnjwwf6aiRULZFI8N60Psi+UauyG+f+mDy4Whrh1fva3+3yYEIBXt4ejbKa5rvD6cokePW+QDw2zEPrg2ZERETUNuEeVhjhayN6zXEx/QZOJ5dguK92ToROLarCurMZ2HYpC9VtWK3mljB3Szwy1AP39nGArky9Ey2DnMywKMIDa8+kK401ygW8syce6x8fxGuvO+SX1+H7E6nYfCETtY2t/35LJMDkvo54eowPAh3v7oY5wVmBs4US1MuV/55/PJ2Gh4d6qGXyAXVcbYMc26Oy8cPJ1HZNOLmlsq4Jb++Jx6oFbLqlToUVddh8IQubLmSgoEJ8JciWjPC1wfzB7hgXaKf293QiIupaMqkEAQ5muJJVpjSWkFuh/oKIiIiI2kirgua36Ovr44EHHsADDzwAAEhNTUVkZCRiYmKQlpaGrKwslJeXo6bm5o0WIyMjWFhYwNXVFR4eHggJCUFYWBi8vLw0+WUQERERdRv1TXIs2RCp8kGIVAJ8PW8A3K2NAQBWxnr4bmEYZq46g3qRYPqNmkYsWR+JHcuGwlBP1qW1E1HPlVdei6XrIxGdXd7sdvZm+vh2QRgGuFmqqTLt8MaUQMRklyn9/Zjo6+DtqUGYFeai0WCPrkyKlfNDMfvbs0jMrxTd5tvjKXCzMmrz0u51jXJ8+NtV/HxWddD+Fi8bY3z50AD0cTZv0zmIiIio+1g+3lfl5LYVh65jmI/2dDVXKAScTC7G2tNpOHqtqM3768okmBrihEeGeWi88+ELE/ywLyYPxVXK9xJOJRfjt9h8TA5x1EBl2iWzpAarjqdgR2Q2GuTik/vF6EglmDHAGctGe8PLVrw7vIkuMMZRgQPZyvdeahrk+OZoMt65P7jdtVPXK6mqx7qzGVh/LgOl1Q2dcszf4/LxR3w+7lHzyla9jSAIuJRxAz+fSceBuPwWV2ATY2Wsh9nhLpg3yO32fVciIuqZgpzEg+bxuc3f+yYiIiLSBloZNP87Ly8veHl5Yfbs2ZouhYiIiKjHEQQBb+yKa3Y53n9NCsQIX9u7XuvjbI6PZvbFC1uiRfdJyKvAaztjsGJuf615oH+nzJIa/BGfD5lUgin9HGFnyiWFibTJhbRSPLUxEsVVzT9oD3e3xMoFob3yZ1hfR4b1TwzGP7fH4GBCAaQSCSYE2ePV+wK0ZkUJUwNd/PjIQMxYeVrlZKY3f42Dk4UBRvvbteqYSQWVeHbzZZXh9TvNDnPBO/cHw1i/W9z+ICIionYKc1fd1Twy4wZOJRcrfaZVt+r6JuyMysbaM+lIKapu8/62pvpYMNgdDw121ZprX3NDXfxrUgD+sVX8vsD7+xMw2t+2116LJRVUYuWxFOyJzoW8DQFUfR0pHhzoisWjvOFsYdji9mMcBZzMF1DdpHzvZeP5DDwxwhMultrx+YD+klZcjTUnU7E9Mlu0iUNznMwN8NhwT1TUNuLLI8mi27z1axwivK1hZqDbGeXSHWoamrD7ci7WnU1v1edSMYM8rTB/sBvu7eMAfR026SAi6g2CncxEX0/Mr4RcIUAm1b7naERERES39M67e0RERER0289n0rEtMlvl+AOhznh8uKfo2IwBLojNrsCPp9NEx3dfyUVfFwuV+2uCQiFg1fEU/N/B67cf9P73z2vY+OQQ9He10GxxRARBELD+XAbe25vQYjewBUPc8NaUYOjp9N4lpc0MdLFqQRga5Qo0NCm0MsTjZGGIHx4eiDnfnUVNg1xpXK4Q8PTGKGxbOhRBKh64ADf/bfxyMQvv7o1HXWPzQQxTfR28P6MPpvV37nD9RERE1D0sH++nuqv5wesY7mOjkUnQmSU1+PlsOrZeykJlXVOb9+/nYo5Hh3liUl9HrbzunTHAGZsvZOJi+g2lsbzyOnx1JBmv3heggco0JzqrDKuOpeBAfH6b9jPWk2FBhDseH+7ZpskEBjrABGcFdmcoh1Ub5QI+P5SEz2b3a1Mt1HUiM25g9YlU/JGQD6GNDbADHc2weKQnpoQ4QVcmRaNcgYNXC3E1r0Jp24KKenxyIBHvT+/bSZVTalEV1p/LwPbI7Ha9n5sa6GBmqAvmD3aDr71pF1RIRETaLMhR/L5nTYMcGSXVKlewISIiItIG2vcEmoiIiIjU5kxKMf69/6rK8X4u5vhgRt9mH8a/NikACXnlOJdaKjr+wW9XEehoiqHeNh2ut6Mq6xrx4tZo/JlQcNfr1Q1yPL0xCof+MQqGeuwiRKQpdY1yvLk7rtnJLwCgJ5Pi39ODMXegm5oq0366Mil0ZdoXPLqlj7M5vpkXisd/vgix+QPVDXI8tvYidj89DA7myqGa8ppGvLYrBr/FthzWGeBmgS8fHKA1Xd2JiIhIPcLcLTHSzxYnrhcpjUVlluFkUjFG+qmnq7kgCDiTUoKfTqfjcGJBm8OkOlIJJvV1xCPDPBDqZtk1RXYSiUSC96b1wZSvTol27V5zMhWzwpzhY9ezQ5WCIOB0cglWHU/G6eSSNu1rbqiLR4d54JGhHrAw0mvX+Yc7CDhbqoeCSuUVoXZGZWPJSC8GWzVIoRBw6GoBvj+RiksZypMyWjLC1waLR3opTZjRlUnx0QN9MWPladHPWRvOZWJ6f2eEe1h1pPxeTa4QcCSxEOvOpquczNSSfq4WmD/YDVNDnHjfkYioFwtwMINUAtHf2fG5FQyaExERkVZj0JyIiIiol8oqrcHTG6NULt9sa6qP7xaGw0C3+QcgujIpvp4Xivu/OoXc8jqlcblCwDObLmPPM8M0ulRzUkEllqyPRGqx+BLlOWW1WHUsGf+Y6K/myogIAK7lV+K5zZdxraD5ZaftzfTx7YIwDNDywA0pGxNgh3en9cGbu+NEx/Mr6vDo2ovYtjQCJnd0Zr+UXornf7mCnLLaZo8vkQBPjfbG8vF+Wh26JyIioq6zfLyvaNAcAFYcuo4Rvl3b1bygog47orKx/VK2ys+ezbE21sO8wW5YMMQd9mat72itaYGOZng4wkN0tbMmhYC398Rjw+ODNdJRvqvJFQIOxOVj1fFkxOUod5Vujo2JPp4c4Yn5Q9zvuv5tD10psGS4G977PVlpTCEA/3fwOlYtCOvQOajt6hrl2BmVgzUnU9v8nqAjlWBqPyc8OcKr2ZWf+rla4NFhnvjhlPhqg6/ujMX+54ZDX4cB57YoqarHlktZ2Hgus8XPomL0daSY1t8JC4d4oK+LeRdUSERE3Y2hngxetiZILqxSGkvIq8DUfk4aqIqIiIiodRg0JyIiIuqFquub8OS6S7hR0yg6rieT4tsFYaJdZcXYmNwMpc/89gwamhRK46XVDVi6IRLblw5tMbjeFX6LzcNL26JR0yBvdrtvT6RiVpgr3KzZBZdIXQRBwMbzmfj3vgTUi7x/3Cnc3RIrF4S2aRl50i4Lh7gjq7QG359IFR2/mleBpzdG4YeHwyGRSPDN0WR8fui6aKefO9mZ6mPF3P4Y5qP51TOIiIhIc0LdLDHKzxbHRcLmlzPLcCKpGKM6uat5Q5MCh68WYFtkNo5dK2zxukVMkKMZHh3mgan9nDTymbkzLJ/gi70xuSiqrEwyfvsAAO25SURBVFcaO51cgv2xeZgS0nPCM/VNNwPE3x1PQXpJTZv2dbYwxJJRXpgT7tqp3+/p/Ryw/mIe0kQCzb/H5SMmuwwhLhaddj5S7UZ1A9afy8C6s+korlLuMt8cE30dPDTIFY8O84SThWGr9vnHBD8ciMsXDUQnF1Zh1bEULB/v16Y6eqsrWWVYdyYd+2LzRO9xtsTNyggLh7hjdrhLu1coICKinivI0Uw8aJ7btgmLREREROrGoDkRERFRLyMIAl7aFo3EfNVdg9+f0Qdh7m3rFtzXxRwfzuiLF7dFi47H5VTgXztj8d85/dTWxaxJrsCnf17Dd8fFA41/19CkwHv7ErDm4fAuroyIAKCspgGv7ojFgfj8FrddMMQNb00Jhp4OO1V3d6/eG4Cs0hr8Hif+fT9+vQiv7IhB9o1aXEgrbfF4YwPs8OmsEFib6Hd2qURERNQNLR/vKxo0B4AVB69jZCd1NU/Mr8DWi9nYfSUHpdVtC5ICgEwqwT3B9nh0mCfC3S27fbdvMwNd/GtSAF7YIn5P4N/7EjDa367Dnbs1rbKuERvPZ+LHU2koFAnVN8fTxhjLRntjen/nLvlcoyOV4B8T/PDs5sui45/+cQ3rHx/c6eelv1zLr8TaM2nYdTkHdY1tCynbm+nj0WGeeGiQG8wNddu0r7G+Dv4zow8e+emi6PjKoymYEuIIHzvTNh23t6hrlGNvdC7Wn8tATHZ5m/eXSIAx/nZYGOGOUb62kEq79/s5ERF1nWAnM+yJzlV6PZ5BcyIiItJy3fuOHhERERG12ddHklWG+wDgkaEemBPu2q5jzwxzQWxOOdaeSRcd33k5B31dzPHoMM92Hb8tSqrq8dwvl3E6uaRN+x26WoCjiYUYE2DXRZUREQCcTy3B8i1XkFde1+x2ejIp/j09GHMHuqmpMupqUqkEK+b2R37FOVzOLBPdZmdUTovH0ZNJ8a9JAXh4qEe3D2YRERFR5xngZonR/rY4dk05bH4lqwzHrxdhtH/7Pu+V1zRiT3QOtl7KRmxO28OIAGBhpIuHBrlhwRB3OLeyW3F3Mb2/Mzafz8KFdOXJggUV9fjqcBJemxSogco6rqiyHj+dTsP6cxmorGtq074BDqZ4eowPJvV1hKyLA6iT+zpi1bEUJOQph5VOJhXjTEoxhnpzFaDOJFcIOHy1AGvPpONMStvuQQGAv70pnhzphfv7OXVoAsJofztM6++EX68oh9ca5Aq8uiMWW5dEMAR9h6zSGmw4l4Etl7JQpmLVx+ZYGOliTrgrFgx25+qIRETUKkFOZqKvF1fVo7Cyjit5EhERkdZi0JyIiIioF/kzPh//PXhd5fhQb2u8PrljD31fnxyIhLwKlV1o399/FYGOZhjiZd2h8zQnJrsMS9dHIreFAKsq7+6Nx1Afa+jrdM8ly4m0WZNcga+OJOOrI0lQCM1v62BmgJULQhHq1rYVFkj7GejKsHpROGasPI2sUuXl3VvibWuMLx8agGAn8y6ojoiIiLq75eP9RIPmALDiUBJG+dm2eqKaQiHgdEoxtl7Kxh/x+WhoaluX4lv87U3x6DAPTOvvDEO9nvlZUyKR4L3pwZj85SnIRS72fziVhtnhLt2qq3JmSQ2+P5mCrZey2/y9H+hhiaWjvDE2wE5tEyOlUglevscfj64V72z96R/XsHOZNSdqdoLy2kZsu5SFn8+mt+szzVBvaywe6dWm96OWvDklCMevF4mGpi9l3MCmC5lYMMS9U87VXSkUAk4kFWH92QwcuVYIoYX7EmL6OJthUYQH7u/nBAPdnvl+TkREXSPIUTxoDtzsam7nz6A5ERERaScGzYmIiIh6iesFlXhhyxWV465WhvhmXih0ZR1bvllXJsXK+aGY+tUp0U7FcoWApzdGYc+zw7uke9vWi1l449e4Fh8Ajw+0R2FlneiSuOklNVhzMg1Pj/Hp9PqIerOcslq88MsV0Q6HfzchyB6fzAyBpbGeGiojTbAx0cdPjwzCzFVnUF7b+u5xDw50xVtTg2Ckx1saREREJK6/qwXG+NviqEjYPDqrDMeuF2FMC13Ns0prsC0yGzsis5FT1vYQKQDIpBKMDbDDo8M8EOHVO8K9AQ5meGSoB344laY01qQQ8Nav8dj4xGCt/7uIzy3Ht8dTsT8mt8UJsn83LsAOS0d7Y6CHVdcU14LR/rYY6GGJi+k3lMYuZ5bh0NVCTAiy10BlPUNyYRV+PpOOHVHZqGmQt2lfmVSCSX0dsXiEF/q6dP6kWRsTfbwxOQgvbYsWHf/490SMD7SHg3nvC7E1yhXYGZWNVcdSkF5S0+b99WRSTA5xxMIIdwxwtdD69zAiItJO1ib6cDAzQH6F8rOzhNyKFj+jEBEREWkKn8oSERER9QJlNQ14ct0lVKt4AGakd7OzbGcFOm1M9PHtgjDM/u6saOC7pLoBS9dHYtvSiE7r/FPfJMe7exOw6Xxms9tJJMCLE/zw1GgfxOSUY/o3p0W3+/pIMh4IdYajec9aypxIU36PzcM/d8SgooVl5vV0pHhzciAWDHHng9tewMfOBN8vDMPCHy6gQd78BCFTAx18+EBfTAlxUlN1RERE1J0tH+8nGjQHgM8PXsdokS7CtQ1yHIjPw9aL2TibWtLuc3vZGmNOuCseGOAMO7PeF+hcPt4Xe6JzUVRZrzR2JqUE+2LyMLWf9l3TCYKA82mlWHUsBcevi//bUUUmlWBaPycsGeUNfwfNdmyXSCR4+Z4AzPnurOj4Z39cw7gAO0il/LzVWgqFgOPXi/Dj6TScTCpu8/5GejLMHeiKx4Z5wtXKqAsq/MvMUGfsvpyDU8nKdVbWN+GtX+Pw/aLwLq1BmzTJFdh5OQdfH0lGZmnbA+bOFoaYN9gNcwe6wsZEvwsqJCKi3ibIyUw8aJ5XoYFqiIiIiFqHQXMiIiKiHq5JrsCzmy8jo5luPf+d3Q8BDqqX7GuPfq4W+M/0Pnh5e4zoeGxOOV7fFYfPZod0OEyaV16LZRuicCWrrNntzA118cWD/TH6f10h+rtaYG64K7ZcylLatrZRjv/sv4qv54V2qDai3q62QY5/7295EggA+NqZ4Kt5Azr9/Yi022Ava3wyKwTLm1l1I9TNAl88OKDLQxlERETUc/RztcDYADscSSxUGovOLsfRa4UYG2APQRBwJasMWy9lY190Lirrm58YqYqxngxT+zlhdrgrQt16d7dbUwNdvDE5EM//ckV0/P39CRgTYAcTfe14RKVQCDh4tQDfHk/B5cyyNu1roCvFgwPd8Pjwrg8Qt8UgTyuM9rfFMZHJFtcKKrEnOhfTBzhroLLupbKuEdsjs/HzmfR2dcG2M9XHw0M9sGCwO8yNdLugQmUSiQT/mdEH93x+AnWNypN5/0wowIG4PNzbx1Et9WhKk1yBX6/k4ssjSc3eE1VluI8NFkW4Y2yAHXQ6uPojERHRnYKdzEQ/oyTkMmhORERE2ks77uIRERERUZf55I9rzXZbem6sD+7r2zUPl2aHuyI2pxzrzmaIju+IykaIizkeHurR7nOcTSnBs5ujUFzV0Ox2gY5m+G5BGNys737w+8q9/vg9Lk+0y/K+mDzMG1yMod427a6PqDe7ll+JZzdH4XpBVYvbzhvshjcnB8FQr3NWOaDuZfoAZ2SV1uC/B6/f9bpEAjwzxgfPj/Plw30iIiJqs+XjfUVDHADwfwevI6WwGlsvZSGpsOXrVVUGe1phTrgr7uvrACM9PnK55f5+Tth0PhPn00qVxgoq6vHl4ST8a1KgBir7S0OTAr9eycG3x1OQUlTdpn3NDXXxcIQ7Hh7qAWst7XL80kR/0aA5cPPf/6S+jtDT4TW2mLTiavx8Jh3bI7NR1Y7JJwPcLPDoME/cG+ygkb9jd2tjLB/vh49+TxQdf+vXeER428DcUD3hd3WSKwTsic7Bl4eTkVbctp9rU30dzAxzwcIId3jbmnRRhURE1NsFOYo3WUkvqUZVfZPWTMYkIiIiuhOvUIiIiIh6sF2Xs/H9iVSV4+MD7bF8vF+X1vDmlCAk5lXiQrryw2UA+Pe+BAQ4mGKwl3WbjisIAn44lYYPf0+EXCE0u+2MAc74YEZf0QCrtYk+Xpzoj7f3xIvu+86eeOx/bgR0GXAkajVBELDhXAbe338V9U3KHdTuZGagg49nhnTZhBfqPp4Z6wN7MwN8+uc1FFXWY7CnFZaP90OEd9t+PxARERHdEuJigXEBdjgsEjaPy6lAXE77ugY6mhtgVpgLZoW5wN3auKNl9kgSiQT/nt4H931xUvQz+4+n0jArzAV+9qZqr626vgm/XMzCmpOpyCuva9O+DmYGeGKEJx4a5AZjLQ8B9XE2x5QQR+yLyVMayyytwdZLWVgwxF0DlWknQRBwMqkYa8+k4+i1QgjN32pSoiuTYEqIEx4e6oH+rhZdUmNbPDHcE3ujcxEv0h21sLIeHx9IxAcz+mqgsq4hVwjYF5OLLw8ntXniiL+9KRZGuGPGAGet/7kmIqLuL8hJPGguCEBiXgXCPazUXBERERFRy/hpmYiIiKiHiskuwz93xKoc97UzwYq5/SCVdu1y3royKb6ZH4qpX51CfoXyA9wmhYCnN0VhzzPD4WRh2Kpj1jQ04ZXtMaIPS++kI5XgzSlBWBTh3uyy5fMHu2HzhUwk5lcqjV0vqML6sxl4bLhnq2oj6u1uVDfglR0xOJhQ0OK2Az0s8fmDA+Dcyp996tkkEgnmDHTFnIGuaJQrOMGHiIiIOsXy8X6iQfO20pNJMTHYHrPDXTHcxwayLv4s3RP42Zvi0aEeWHMqTWmsSSHgrV/jsPnJIc1+Xu9MuWW12HwhE+vOZqC8trFN+3rbGmPpKG9M6+/crbqA/2OCH36PyxcN+395OAkzQ116/apS1fVN2Hk5B2tPp7U5oAwANiZ6mD/YHfMHu8HOzKALKmwfHZkUHz0QgmnfnIJYf4ZN5zMxvb8zBnl27zCbQiFgf2wevjichOQ2rE4hk0pwbx8HLBrijkGeVmp7HyIiInK1NIKpvg4qRVZNSWDQnIiIiLQUg+ZEREREPVBhZR0Wr4tEg4pOwmYGOli9KBymBupZItfWVB+rFoRi7nfn0CBXrqm4qgHLNkRiy5IIGOg2/4AzrbgaS9dH4lqBcij87+dcOT8UA1txU05HJsW79wdj7vfnRMdXHLyOqf2cYGuqncthE2mLc6kleGHLlRa7AkolwLNjffHsWB/oMExMIhgyJyIios7S18Uc4wPtcOhq+8LmfZzNMDvMFdP6O8HCSK+Tq+v5nh/viz3RuSisrFcaO5daij3RuZjW37nLzi9XCDiRVISN5zJxJLFANHDbnH6uFnhqtDcmBNp3+UT9ruBla4LZYS745WKW0lhhZT1+PpuOpaO8NVCZ5mWV1uDnM+nYcikLlXXKQa+W9HU2x6PDPDA5xBH6OtoZ1u/rYo7Hh3ti9UnlyR4A8NrOGPz2/Aitrb85CoWAA/H5+OJQUov3CO8kk0owK9QFz4z1gauVURdWSEREJE4qlSDQ0Ux0FeAEkZVIiIiIiLQBg+ZEREREPUx9kxzLNkSJdg8HbgY8v54XCg8b9S7vPcDNEu9P74NXdsSIjkdnl+PN3XH4ZFaIyi5Ch68WYPmWKy0+AAxzt8TK+aGwb0MnqcFe1pjW3wm/XslVGqusb8LHBxLx2ex+rT4eUW/SJFfgyyPJ+PpIUovBDUdzA3w+tz8Ge1mrpzgiIiIi6vWWj/drU9DcwkgX0/s7Y3a4C4KdzLuwsp7P1EAXr08OxPO/XBEd/8/+qxgXaA8T/c59XFVUWY+tl7Kw+UImsm/Utnn/kX62WDbKG0O8un+n4+fH+2Ln5RzRZgSrjqVg3mA3mKmpEYGmCYKAsykl+OlMOg5dLYDQxokHMqkE9/VxwKPDPBDqZtkt/m288L+u9mI/BylF1fjmaAr+McFPA5W1j0Ih4M+EfHx+KEl0ZUJVZFIJHhjgjGfH+sLNmgFzIiLSrCAn8aB5PIPmREREpKUYNCciIiLqQQRBwFu74xGZcUPlNv+aFIiRfrZqrOovcwa6IianDBvOZYqOb4vMRoiLORZGeNz1ukIh4IvDSfjicFKL51gU4Y43Jge1aynrf00KxKGEAlQ3yJXGtkdm46FBbghzt2zzcYl6suwbNVj+yxVcauZ955Z7gu3x8cwQdoIkIiIiIrXq42yO8YH2OHS1QOU2UsnNcPGccFeMC7Trlh1+tdX9/Zyw+UImzqUqh2kKK+vxxaHreH1yUIfPIwgCzqaWYOP5TPwZn49GedtSxFIJMDnECUtGeqGPc8+ZYOBobohFQ9yx5pRyV+vy2kasPpGKFyf6a6Ay9WmSK/BbXD6+PZaChLy2B7gsjXQxb7AbFgxxh6O5YRdU2HWM9HTwwYy+WPTjBdHxVceSMSXEEX72pmqurG0EQcCfCQX4/FASrrbheyiVADMGuODZsT5qb7pBRESkSpCTmejr1woq0ShXcLVHIiIi0joMmhMRERH1IOvPZWDLJeXlkG95YIAzHh/uqcaKlL01JRiJeZUqQ6nv7k1AgKMZBnpYAQDKaxqxfMtlHL1W1Oxx9XWk+GBGX8wMc2l3bfZmBnhunC8+/D1RdPydPfHY/fQwyLrhctlEXeG32Dy8uiMGFS2sMqCvI8WbU4Iwf7Bbt+j4RkREREQ9z9tTg3A58wZKqhvuet3D2gizw10xM9QFDuatXxWLWk8ikeC9aX0w6YuTaBJZAunH0+mYFeYKf4f2BV3LahqwPTIbmy5kIrWous376+lIMTvMBYtHesHdumcGUZeN9sbmC5miE+t/OJWGRREesDXV10BlXauuUY7tkdn4/kQqMktr2rx/oKMZHh3mgfv7OcFAt/tOPhnpZ4sZA5yx63KO0lijXMCrO2KwfelQSLXwfpcgCDh8tRCfH76OuJy2Bcyn9XfGs2N94GVr0oUVEhERtV2Qo3jQvKFJgZSiKgQ4iI8TERERaYpWB80jIiKwZMkSzJ07F4aG3atDABEREZG6nU0pwbt7E1SO93MxxwcP9NV4yFNPR4qVC0Ix9atTKKioVxpvUghYtiEK+54djhs1DViyPrLFh4Eulob4dkFYp3Qce3SYJ7ZcyhJ9OB2bU44tF7Mwb7Bbh89D1J3VNsjx3r4EbL4gvjrBnfzsTfDVQ6HtDo0QEREREXUGVysjbFkSgR9PpyG9uBpetsa4v58zBnpYavxzcm/gZ2+Kx4Z74vsTqUpjcoWAt36Nwy+Lh7T6eyEIAqIyy7DxfAb2x+ShvknR5ppM9XWwMMIdjwzzgJ1pz55kYG2ijydGeImuFFfTIMfKY8l4e2qwBirrGuW1jdhwLgM/nU5DcVVDyzvcQSoB7gl2wCNDPTDI06rHvD+8MTkQx64V4kZNo9JYVGYZNpzPwKK/rTCoSYIg4Oi1Qnx+KAkx2eWt3k8iubmKwrNjfeFjx4A5ERFpJz97U+jKJKIr8CTkVjBoTkRERFpHq4Pm58+fx4ULF7B8+XLMmzcPTzzxBEJDQzVdFhEREZHWySqtwVMbIyEX6QwGALam+vhuYbjWdF+yMzXAqgVhmPvdWdEbacVV9Zi/5hxyympR19j8w+KRfrb4Ym5/WBrrdUptejpSvDM1WOWSwp/8kYj7+jh02vmIupureRV4dvNlJBdWtbjtgiFueGNykNa89xARERFR7+ZjZ4IPZvTVdBm91nPjfPHrlRzRSefn00qxJzoX0/o7N3uMyrpG7L6Si43nMpCYX9muOnzsTDB/sBtmhrnAzEC3Xcfojp4Y4Yl1Z9NFg8Ybz2Xi8eGecLE00kBlnaewog4/nE7DxnOZqKpvfuWtvzM31MWDg1yxcIh7t/97EGNtoo83pwThH1ujRcc/OXANE4Ls4Wiu2cZfgiDg+PUirDiUhOisslbvJ5EAk/s64vlxvvC150R3IiLSbno6UvjYmeJqnvJqHQm5FXiAsSgiIiLSMlodNL+loqIC3333Hb777jv0798fixcvxrx582BqyhsFRERERDUNTVi8PlL0QSEA6Mmk+HZBmNYtAR7qZon3pvXBaztjRcdTWrHc9TNjfPDCBD/IOnlp35F+trgn2B5/xBcojZXVNOK/B6/h/ekMJ1DvIggC1p/LwPv7r6KhhW6B5oa6+HhmCO7t46Cm6oiIiIiISNuZ6Ovg9clBeG7zZdHx9/dfxdgAO5iKhL/jcsqx6UImfr2cg+oGeZvPrSuT4L4+jpg/2K1HdaluC1MDXTw12gf/+e2q0liDXIEvDiXh09n9NFBZx6UVV+P7EynYEZmDBnnbutv72ZvgkaGemDHAGYZ6PXuS9IwBzth1OQcnk4qVxqrqm/Dm7nisXhSmkZ8PQRBwMqkYKw5dx+XMsjbtO7mvI54f7ws/BsyJiKgbCXYyEw2ax+cqv0ZERESkaVofNBcE4fYNDUEQcPnyZTz11FN46aWXMGfOHDzxxBOIiIjQcJVEREREmiEIAl7eFiN6M+qW96f3QZi7pRqrar2HBrkhNqccm85ntmk/E30d/HdOP9wT3HUh1jcmB+HYtSLR5bc3ns/EgwPd0MfZvMvOT6RNiirr8drOGBy6WtjitoM8rPD5g/3hZKHZLmhERERERKR9poY4YvP5TJxNLVEaK6qsx+eHkvDmlCAAQG2DHPticrHxfCautKGz8Z3crIwwb7AbZoW5wMZEvyOl9wgLI9zxw6k05FfUKY3tiMrGklFe8LHrPmHd2OxyfHs8Bb/F5UEQX+RPpfGBdnh0mCeGelv3mokHEokEH8zoi4krTqC2UXnCxqGrBfg9Lh+T+jqqrSZBEHDsehG+OZKMSxk32rTvvcEOeH68LwIdzbqoOiIioq4TpOL3V0JexV05KSIiIiJtoNVB8+joaHz//ffYuHEjysrKANy8CSIIAqqrq7F27VqsXbsWgYGBWLx4MRYuXAhLS+0MURERERF1hZXHUrA/Nk/l+MMR7pgz0FWNFbXd21ODkJhXgahWdivysTPBdwvD4G1r0qV1uVoZYdlob3x+KElpTBCAt/fEY/vSCN7sox7vQFw+Xt8Vi5Lqhma3k0qA58f54ZmxPp2+ygAREREREfUMEokE700Lxn1fnESTQjkZvPZMOgZ5WuFcagl2RGajoq6pzeeQSSUYH2iH+YPdMdzHBlJ+PrnNQFeG58f7iq4upxCA/zt4HSvnh2mgstYTBAFnUkqw6lgKTiUrd+Zujo5UgukDnLFkpBd8e2n3a1crI/xjgp9oZ3vg5v2uYd42MDdSXlmgMykUAv6Iz8c3x5IRl9O2zq0Tg+zx/HhfBDuxAQQREXVfQU7iQfPy2kbkltfBmY1ciIiISItINV1Ac/r27YuvvvoKubm5+PnnnzFy5EgI/2tJcGeX84SEBLzwwgtwdnbGggULcPz4cU2WTURERKQWhxIK8Nmf11SOD/Gywhv/6wKmzfR1ZPh2QRjsTFvuLDaprwN2Pz2sy0Pmtywd5Q0XS/GbeZEZN7Drco5a6mjJ9cJqbE6R4odrUhzNlaCxjctEE4mpqGvEi1ujsXRDZIshcydzA2xZEoHnx/syZE5ERERERM3ytTfF48M9RcfkCgFL1kfip9PpbQ6ZO5gZ4IXxfjj9z7H4bmE4RvrZMmQuYlaYCzysjUTHfovNR2x2uZorah25QsDvsXmY9s1pzF9zvk0hc0NdGR4b5okTr4zBZ7P79dqQ+S2PDvNAXxWr9BVV1uOjA+Ih9M7QJFdgZ1Q2Jn5+Ass2RrUpZD4+0A77nh2O7xeFM2RORETdnqqgOQDE52jn9RgRERH1XlodNL/FwMAACxcuxLFjx5CYmIgXX3wRtra2d4XOBUFAXV0dNm/ejLFjx8Lf3x+fffYZioqKNFw9ERERUedLLqzE8i1XVC4L7GxhiJXzw6Ar6xaXe7AzM8CqBaHQlYk/AJZKgNfuC8A380Jhoq++RXkMdGV4q5mw/ge/JaKyrlFt9fydIAj44VQa5v4QhXOFUsSUSrE7Q4aXdiVqrCbqGc6kFOO+z09iR1R2i9ve18cBvz8/EgM9rNRQGRERERER9QTPjvOFg5lBh48jkQCj/W2xelE4Tv1zDJ4f7wsH844ftyfTlUnxj4n+Ksc/+UO77inUN8nxy4VMjP+/41i2MQoxbQjCWxrp4oXxfjjz6li8NTUITuwMCgDQkUnx4QN9VU4U33whC+dSSzr1nPVNcmw8n4Ex/z2Gf2yNRnJhVav3HRtghz3PDMOahweij4qAPBERUXdjZqALVyvxa5OEvLat9kFERETU1bpH8ugOfn5++PTTT5GdnY2tW7di4sSJt7ub39nlPCkpCf/85z/h6uqKOXPm4M8//9Rk2URERESdprymEU+ui0RVvXhnL0NdGVYvCoeVsZ6aK+uYMHcrvDetj9Lrlka6WP/4YCwZ5X37ek+dJgTZY5SfrehYcVU9vjiUpOaKbqprlOMfW6Px730JkP9twsHR6yW4kFaqkbqoe6trlOPf+xIwb/V55JTVNrutvo4UH8zoi5XzQ7t8SW0iIiIiIupZTPR18MaUwHbvb2Oih6dGe+PEy2Ow9tFBmBBkD51uMtleG0zp64ggR/EumieTinE2pXNDxu1RWdeI70+kYMTHR/HqzlikFVe3el8ncwO8PTUIp18di+fH+8Kym90jU4c+zuZ4QsXKAgDwr52xqGuUd/g8NQ1NWHMyFSM/OYrXd8Uhq7T5ew13Gu1vi91PD8OPjwxEiItFh2shIiLSNqqux+JzGTQnIiIi7aK+dpCdTEdHB7NmzcKsWbOQmZmJNWvWYO3atcjOvtlx71aX84aGBuzYsQM7duyAm5sbnnjiCTz66KNwcnLS8FdARERE1HZyhYBnf7nc7MO1z2b3a3bJPW320CA3OJgb4KvDSSiqqsdYfzssGeWt0Y5TEokEb08Nwj2fn0Dj3xPdANaeScfcga5qXXY5+0YNlqyPbPZm45/x+RjkyQ7T1HpxOeV4YcsVJLWiq1iQoxk+f7A//Hr5cuNERERERNR+k/s6YrNPJk4ntz7UHOFljflD3DAxyAF6OgyWt5dUKsHL9/jj0bUXRcc//SMRO5YN1ciE/+Kqevx0Og3rz2agok68yYIqfvYmWDrKG1P7OXWbVf40afl4P/wel4/M0hqlsdTianxzNBkvNtP9vjnltY1YfzYdP55OR2l1Q5v2HeFrgxcm+CHUzbJd5yYiIuougp3M8Ud8gdLrCQyaExERkZbptkHzO7m5ueG9997DO++8g99//x2rV6/Gb7/9hqammzegBOFmICgjIwNvvfUW3n33Xdx333148sknMWnSJEilvNlERERE3cMnBxJx4nqRyvFnx/pgcoijGivqfGP87TDG307TZdzFy9YEjw/3wrfHU5TGmhQC3tkbjw2PD1bLA9gzKcV4ZtPlFh/SnUou7vJaqGdokiuw6lgKvjichCaF8mSKO0klwLLR3nh+nB9DHURERERE1CESiQTv3t8H930hPrH7FjMDHcwKc8W8wW7wsTNRY4U922h/W4S7W+JSxg2lsajMMhy+WojxQfZqqyertAbfn0jF1ktZqG9StGnfMHdLLBvljbEBdpBK1R+O764M9WT4z4w+WPjDBdHxVcdSMDnEEQEOrW9oUVJVjx9O3ZwoUKliNUZVxgfa45mxPujvatGm/YiIiLorVR3Nc8pqUV7TyJVEiYiISGv0iKD5LVKpFJMnT8bkyZORn5+PtWvXYs2aNUhNTb0d+hEEAU1NTdi3bx/27dsHZ2dnLF26FIsXL4aNjY2GvwIiIiIi1XZfzsF3J1JVjo8PtMML4/3UWFHv8uxYH+y6nI2CinqlsdPJJfg9Lh+T+nZdyF8QBPx4Oh0f/HYV8hbCwACQmF+Jwso62JkadFlN1P2lFlXhha3RiM4qa3Fbd2sj/N+cfghzZ6d8IiIiIiLqHD52JvjHBH98fCBRaWyAmwXmD3bHlBBHGOjKNFBdzyaRSPDKvQGY891Z0fHP/rzW5cHthiYFjiQWYOulbBy7VohW3O64y9gAOywb7Y2BHvyc2l4jfG3xQKgzdkblKI01KQS8uiMWO5YNhayFfwd55bX4/kQqNl/IRF1j6ycKSCXA5BAnPD3Gu02BdiIiop4g2Fn17774vHIM9WaGiYiIiLRDjwqa38nBwQGhoaHo168fUlP/CmTd2WVSEARkZ2fjzTffxIcffohnn30Wb775JgwNDTVRMhEREZFKsdnl+OeOGJXjPnYmWDG3P7s2dSFjfR28PjkIz22+LDr+/r4EjPa3hZFe519i1zXK8a+dsdh5WfmhX3NOJxdjxgCXTq+Huj9BELD+XAY++O1qqx4Azx/shn9NCoSxfo/9CElERERERBqydJQXzAx1sOViFuQKAQPcLPDQIDcEO5lrurQeb5CnFUb72+LYNeXV8xLzK7E3JhfT+jt3+nmTCyux5WIWdkbloKSFFdv+TiaVYGqII5aM8kagii6g1DZvTA7CsWtFoqvnXckqw/qz6XhkmKfovhkl1fj2eAq2R2Y3uzLB3+lIJXgg1BnLRvvA08a43bUTERF1Zw5mBrA00sWNmkalsYTcCgbNiYiISGv0uJRAbm4ufvzxR/z444/IyMgAoBwuv5NEIoEgCKiursbHH3+MrVu3Ys+ePQgKClJr3URERESqFFXWY/H6SyqXDTYz0MHqReEwNeASel1taogjNp7LwPm0UqWx3PI6rDqWghcn+nfqOXPKarFk/SXE5VS0ed+TSQyak7L88jq8vD0aJ5OKW9zWzlQfH88KwRh/OzVURkREREREvZFEIsH8we6YP9hd06X0Si9N9BcNmgPA/x28jkl9HaErk3b4PFX1Tdgfk4stF7MQlVnW5v31daR4cKArnhjhBVcrow7XQ3+xMtbDW1OCsHzLFdHxT/+4honBDnCy+KtR1/WCSqw8mow90blt6kSvryPFQ4Pc8ORILzhbsPEXERH1bhKJBEFOZjidXKI0lpDb9mdCRERERF2lRwTNFQoF9u/fj9WrV+PAgQOQy+W3A+W3guQA4OHhgSeffBKPPvoorl69itWrV2PXrl2or6+/vV1qairGjx+P2NhYWFtba/LLIiIiIkJDkwLLNkQir7xOdFwqAb6aF8rOP2oikUjwzv3BmPLVKchFnqJ9dzwVM0Nd4NFJ34+zKSV4elOUaEepOxnoSFEnMhHhVFIxBEG4a+Il9V6CIGBPdC7e3B2HirqmFrefHOKI96f1gaWxnhqqIyIiIiIiIk3o42yOySGO2B+TpzSWUVKDrZey2j0JQBAERGbcwJaLWdgfm4eaBnmbj2FmoIOHh3rg4aEesDHRb1cd1LJp/Z2w63IOjl9XnnRQ3SDHm7vjsObhcMTlVODro0n4I76gTcc31pNhYYQHHh/uCVtTfh+JiIhuCXYyFw+a5zFoTkRERNqjWwfN09PTsWbNGqxduxZ5eTdvgN0K0twKjkulUkyZMgVLlizBPffccztk4+DggDFjxqCkpAQrVqzAihUrUFdXB0EQUFBQgBUrVuD999/X5JdHRESkdWKzy7HqeDJKqhowPtAeC4a4w1BPpumyeixBEPD2njhcyrihcptX7wvAKD9bNVZFgY5mWDjEHWvPpCuNNcgV+Pe+BPzwyMAOnUMQBPx0Oh3/+e2qaKD9To8M9UAfewO8tCtRaaywsh5JhVXwszftUD3U/d2obsAbv8aJBgf+zsxAB/+e3qdLlkcnIiIiIiIi7fPiBD8ciMsXvQfx5eEkzAx1gYFu6+8BFlXWY2dUNrZeykJKUXW7arI308eTI7zw4CA3mOh368eZ3YJEIsH70/tg4ooTqG1UnhBwOLEQ0745jZjs8jYd18JIF48O9cQjQz1gbsTVGImIiP4uyNFM9PXkwirUNcrbdA1GRERE1FW63Z2ZpqYm7Nq1C6tXr8aRI0cgCMJd3cuBm8EcZ2dnPPHEE3jyySfh5OSk8njW1tZ4//33MX/+fAwdOhQVFRUQBAF79+5l0JyIiOgO+2JysfyXK2j63wOn82mluJJVhm/mh2q4sp5rw/lMbL6QpXJ8en8nPDnCS40V0S0vTPDD3uhclIh0Gj+cWIgjiQUYG2DfrmPXNcrxr52x2Hk5p9nt9HSk+HBGX8wMc0FOYSmkEKCAcufyk0nFDJr3ckevFeKf22NQWFnf4rYjfG3w6ax+cDA3UENlREREREREpA28bE0wK9QFWy4p34cqqKjHz2fSsWSUd7PHaJIrcOxaEbZeysKRxMLb9xDbytfOBE+O8MK0AU7Q12GwSp1crYzw4kQ/vL//quh4W0Lmtqb6eHKEJ+YNdudEASIiomYEO4kHzZsUApIKqtDXxVzNFREREREp6zaf7K9fv47Vq1dj3bp1KC4uBqDcvVwikeDee+/FkiVLMGXKFEil0lYfPzAwEM888wz+85//AABSU1O75OsgIiLqjuJyyvHi1milB0T7Y/OwKLUEg72sNVRZz3UutQTv7olXOd7X2RwfzQy5PdGO1MvcUBf/vC8Ar2yPER1/d28ChnrbtLnTRE5ZLZauj0RsTvMP7hzNDfDdwjCEuFgAAEwNdOBuCqRVKm97KqkIjw/3bFMd1DNU1zfhg9+uYuP5zBa3NdCV4l+TArFwiDvfV4iIiIiIiHqh58f7YteVHDQ0KZTGVh1PwUOD3WBmoNyROq24GlsvZWFHZHarJjiLMdKTYWqIE+YMdEWomwU/l2rQo8M8sSc6t82dy29xtjDE0lFemB3uyg6sREREreBpYwx9HSnqRa7BEvLKGTQnIiIiraDVQfP6+nps27YNq1evxqlTpwBAtHu5g4MDHnvsMTz55JNwd3dv9/lCQ//qyFpTU9OByomIiHqO4qp6LF53SfQGBwD8HpfPoHkny75Rg6c2Rqns/GRjoo/vF4XxYY2GzQp1wabzmbiSVaY0llFSgx9OpeHpMT6tPt7ZlBI8sylKtEv6nQZ5WmHl/FDYmOjf9bq/uYC0SuUHsefTStHQpICeTusnYVL3F5lRin9sjUZGScufa/q7WuD/5vSDl62JGiojIiIiIiIibeRkYYiFQ9zxw6k0pbGymkasOZGKf0z0BwDUNsjxW2wetlzKwoW00nafM8zdEnPDXTE5xBHG7HqtFWRSCT56IARTvz4FeRu60nvZGGPZaG9MH+AMXRnvQREREbWWjkyKAAdTRItM8orPrdBARURERETKtPqujYODAyoqbl44/b17OQCMGzcOS5cuxbRp06Cj0/Evxczs5pI07JRARER0U0OTAk9tiEJueZ3KbQ4mFODtqUH8/dlJahqasHhdJEpVhI11ZRJ8uyAUjuaGaq6M/k4qleDd+4MxfeVpCCLP3b46koTpA5zhbNH890oQBKw9k473919t8QHeI0M98PrkQNEHdv7mChzIVn69pkGOqMwbGMIJIb1CQ5MCnx+6jm+Pp6Cl58E6UgmeH+eLZaO9ocOHwERERERERL3eU6O98cuFTFQ3yJXG1pxKQ7iHFQ7E52PvlVxU1je16xw2Jnp4INQFc8Jd4GNn2tGSqQsEOZnhyRFe+PZ4SovbBjqa4ekx3rivjyNkUt4fJiIiao8gJ3PRoHkCg+ZERESkJbQ6aF5eXn5XaE0QBNjY2OCRRx7B4sWL4ePT+g6RbXEr1E5ERNTbvbs3HhfSm+9KlFNWi/jcCvRx5tJtHSUIAl7ZHoOEPNU3jt6b1gfhHlZqrIqa08/VAg8OdMXmC1lKY3WNCnyw/yq+mR8qsuetbeT4165Y7IzKafY8ejpSfDCjL2aFuajcxt0E0JcJqJcrX8eeSipm0LwXuJZfieVbruBqM+8ht/jYmWDFnP5cdpOIiIiIiIhuszbRx+MjvPDl4SSlsZoGORb9eKFdx5VKgDH+dpgz0BVjA+zY8bobWD7eF7/H5alcKW2AmwWeGeODsQF2fKZKRETUQUFOZqKvX82rgEIhQMrJXERERKRhWh00B3C7e/mIESOwdOlSzJw5E3p6el1yruHDhyMtTXlJQCIiot5ow7kMbDyf2aptDyYUMGjeCVYdT8G+mDyV44si3PHQIDc1VkSt8fI9AfgtNh/ltY1KY/tj8zA/uRhDfWyUxnLKarF0fSRic5S7VNzJ0dwA3y0MQ4iLRbPbyaSAr5mAuBvKNxxPJhfjpXv8m/9CqNuSKwT8cCoVn/1xHQ1yRYvbPz7cEy/f4w8DXZkaqiMiIiIiIqLu5MkRnlh/Nh03apTvc7SVh7URZoe7YlaYC+zNDDqhOlIXA10ZvlsYhgVrzqO46q+VF4d6W+OZMT6I8LZmwJyIiKiTBKsImlc3yJFRWgNPG2M1V0RERER0N60Ompubm+Phhx/GkiVLEBgY2OXn09fXh7u7e5efh4iISNtdSCvFO3viW739wYQCvDDBrwsr6vmOJBbg0z+uqRwf7GmFN6cEqbEiai0rYz28ONEPb/0q/jPz9p54/Pb8iLu6dZ1LLcHTG6NQUt0gus8tgzytsHJ+KGxM9FtVi7+5gLgbyq/HZpehvKYR5ka6rToOdR/55XV4YcsVnE0taXFbZwtDfDo7BEO9lSc+EBEREREREQGAqYEunhrtg//8drVd+xvoSjGpryPmhrtikKcVw8jdWICDGX5/fiQOXy1ATYMcgzyt2GyEiIioCwQ4mEIiAf7Xh/MuCbkVDJoTERGRxml10DwvLw8GBuxwQEREpE45ZbVYtiESTQqRuxkqJORVIKu0Bq5WRl1YWc+VXFiF5zdfEb2BBNwMh66cH8plhbXYvEFu2HwhC1fzKpTGkgqr8POZdDwxwguCIGDtmXS8v/8q5C38jD0y1AOvTw5s0/fd30L8mAoBOJNSjPv6Orb6WKT9DiUU4OXt0a3qMjcrzAVvTQ2CmQEnGxAREREREVHzFka444dTacivqGv1Pv1czDFnoCum9nPiZ88exNZUHw9yhUUiIqIuZaSnA08bY6QWVSuNxeeWY3IIn+0QERGRZmlt0Pzy5ctYv3797T/PmzcP4eHhGqyIiIio56ttkGPxuksquyzrSCUqA+iHrhbg0WGeXVlej1Re24jF6y6hsr5JdNxAV4rvF4XBupUdrUkzdGRSvDctGLO/PSs6/vmhJNwT7IAVh65jZ1ROs8fS05HiP9P7YHa4a5vrsDMALPQElDUodws7mcygeU9R1yjHR78nYu2Z9Ba3tTLWwwcz+uLePg5dXxgRERERERH1CAa6Mjw3zhf/2hXb7HYWRrqYMcAZcwe6IsDBTE3VEREREfU8wU7mokHzBJEGR0RERETqprVB8+PHj+Pzzz+HRCKBTCbDa6+9pumSiIiIejRBEPDPHTGIz1V9w+Kd+4Ox+UKm6DZ/xjNo3lZyhYDnf7mM1GLlG0e3fDa7H4KduCRtdzDQwwozBjhj12XlIHlVfRMmrDiOukZFs8dwNDfAtwvC0M/Vol01SCSAv7mA80XKQfNTScXtOiZpl+TCSjyz6TIS8ytb3HZ8oB0+fCAEtqacqEJERERERERtMzvcBbsv5+BCeuldr0skwAhfW8wJd8GEIHvo68g0VCERERFRzxHkaIa90blKryc089yWiIiISF20NmheV3dzOT5BEODi4gJbW1sNV0RERNSzfXciFXtEbmDcMm+wGxYMcUdJVYNo0PxCeinKahpgYaTXlWX2KJ/+cQ3HrhWpHH96jDemhDipsSLqqNfuC8Cf8fmobpArjbUUMh/kYYVv5od2OBQcYCHgvMg/q8zSGmSW1MDN2qhDxyfNEAQBv1zMwrt741v8t2Sir4O3pgZhdpgLJBLlSQdERERERERELdGVSbF6UTj+7+A1HLteBCtjPYz2s8OscBc4WxhqujwiIiKiHiXYSXx1mMLKehRV1rOhDBEREWmU1gbNHRxuLu0ukUhgZ2en4WqIiIh6tqOJhfj4QKLK8YEelnhnajAAYEKQPVYcuq60jVwh4Oi1QswY4NJldfYkv17JwbfHU1SOjwuww4sT/NVYEXUGOzMDPD/eFx/8pvrnSczDEe54Y0oQdGXSDtfgZy6oHDuZXIT51u4dPgepV3lNI17bFYPfYvNb3HaAmwW+fHAAXK04oYCIiIiIiIg6xtxIF+9O66PpMoiIiIh6vEBH8aA5ACTkVWCUKZtzEhERkeZ0PMnSRZyc/ureWVpa2syWRERE1BEpRVV47pfLEFRkU53MDbByfhj0dG5eNgQ6mqrsWvRnfEFXldmjxGaX45XtMSrHvW2NseLB/pBK2Ym4O3pkqCe8bY1bta2ejhSfzgrBu9P6dErIHABMdIEAe/Hzn0oq7pRzkPpEZpRi0pcnWwyZSyQ3V0HYuiSCIXMiIiIiIiIiIiIiom7E1lQfdiq6lieIrDRNRNRTVNc3oby2UdNlEFELtLaj+bBhw2BkZISamhqkpaWhtLQUVlZWmi6LiIioR6moa8ST6y6hsq5JdFxfR4rvF4XftRybRCLBxGB7/HQ6XWn749eLUNcoh4GurKtK7vaKq+qxZP0l1DcpRMdNDXSwelE4zAx01VwZdRY9HSneuT8YC3+40Ox2DmYG+G5hGPq5WnR6DRGelkgsqFZ6/UxKCeQKATJOYtB6coWAb44m44vDSZArVHepBwA7U318Prc/hvrYqKk6IiIiIiIiIiIiIiLqTMFOZii8VqT0enxuuQaqISLqOkkFldgbnYu9MXlIK66GjlSCySGO+HhmCLMmRFpKazuaGxsb4/777wcAKBQKrF+/XsMVERER9SxyhYDlv1xBapFyGPWWT2aFoI+zudLrE4LsRbevaZDjbEpJp9XY0zQ0KfDUhijklteJjkslwFcPDYCXrYmaK6PONsLXFvcGO6gcH+Rhhb3PDu+SkDkARHiKH7e8thGxObwhqe3yymsxb/U5/N/B6y2GzMcF2OHA8pEMmRMRERERERERERERdWNBTmairyfksaM5EXV/mSU1+OZoMu79/AQmrDiBL48kI634ZlalSSHg1yu5eOvXOA1XSUSqaG3QHADefPNN6OnpAQDeffddJCUlabgiIiKinuO/f17DkcRCleNLR3ljWn9n0bFBHlYwNxTvuP1nQn6n1NcTvbs3HhfSS1WO//PeAIz2t1NjRdSV3pgSCEsj5Z+TRRHu2PDE4LtWCuhsA1zNoa8jfql/Kkm5GwZpjz/i83HfFydxPk31ewUA6MmkeGdqENY8HA4rYz01VUdERERERERERERERF0h2Em5+RcApBVXo7pefHVqIiJtll9ehx9OpWHaN6cx8tOj+PSPa0jMr1S5/dZL2UgqUD1ORJqj1UHzwMBA/PDDD5BIJCgrK8Po0aNx6NAhTZdFRETU7e2NzsXKYykqx0f72+Lle/xVjuvIpBgXIB6IPnS1EIoWOvD2RhvOZWDj+UyV49P6O2HxSC81VkRdzcXSCOsfH4wIL2sY6srgZ2+Cr+cNwHvT+kBPRQi8s+jrSDHI00p07GRScZeem9qnrlGON3fHYcn6SJTVNDa7rbetMXY9PRSPDPOERCJRU4VERERERERERERERNRVghzFO5oLApoNZhIRaZPS6gZsOJeBud+dRcRHh/HvfQmIzipr9f7N5ViISHN0NF1AS+bPnw8rKys89thjyMvLwz333IPhw4fjoYcewqBBg+Dp6QkzMzPIZDJNl0pERNQtxOeW4+Xt0SrHvWyM8cWDAyCTNh9enBBkj52Xc5ReL6qsx5XsMoS6WXa41p7iQlop3tkTr3K8j7MZPp4ZwsBoD9TH2RybFw/RyLmH+9iIhsqjMm+gur4Jxvpa/1Gg17heUIlnN13GtVbM0H9woCvemhoEIz1+/4iIiIiIiIiIiIiIego3KyOY6OugSqR7eUJeBcLc+eyViLRTRV0j/owvwN7oXJxKLoa8A40J90Tn4oXxfnCzNurEComoo7Q6nfD38LhEIoEgCDh16hROnTrV7uNKJBI0NXFZGSIi6n1KquqxeF0k6hoVouOm+jr4flE4zA11WzzWSD9b6OlI0dCkfKw/4wsYNP+fnLJaLNsQiSYVH6ZsTPTw3cJwGOhy0hx1ruG+NsDvyq83ygVcSCvFGBWrEpD6CIKATRcy8d7eBNSLvJfeydRABx8+0BdTQpzUVB0REREREREREREREamLVCpBoKMpLqbfUBpLyC3XQEVERKrVNshxOPFmuPzotSLR3Eh7yBUCVh1PwYcP9O2U4xFR55BquoDmCIJw+3+33Or0eedYe/5HRETU2zTKFVi2MQo5ZbWi4xIJ8PmD/eFjZ9Kq4xnr62C4j43o2MGE/HbX2ZPUNsixeN0llFQ3iI7ryiRYtSAMzhaGaq6MeoNABzNYG+uJjp1IKlJzNfR3ZTUNWLYhCq/vimsxZB7qZoHfnhvBkDkRERERERERERERUQ8W5Ggm+npCboWaKyEiUtbQpMChhAI8/8tlhL1/EM9suow/4gvaHTI3VNGMb0dkNvLL6zpSKhF1Mq3uaA781cW8M8Lht45FRETUG723NwEX0kpVjr800R/jAu3bdMwJQfY4klio9HpKUTVSiqrgbdu60HpPJAgCXtkRg/hmbvy8e38fDPSwUmNV1JtIpRIM87HBnuhcpbFTScUaqIhuuZBWiuW/XEZuCzdIJBLgmTE+eH6cL3RkWj1HmIiIiIiIiIiIiIiIOijISTxonphfiSa5gs8KiEjtmuQKnEstxd7oXPwel4eKuqYOHS/ExRxTQ5wwOcQRp5OL8fL2GKVtGuQKrD6ZijenBHXoXETUebQ6aD5y5MjbHcyJiIio/Tadz8T6cxkqx6eEOOKp0d5tPu64QDtIJIDYPK6DCQXwHtV7g+bfHk/FXpGA7y0Lhrhh3mA3NVZEvdFwX/GgeVJhFfLL6+BgbqCBqnqvJrkCXx9NxpeHk6BoYf6rvZk+Vsztj6He4itHEBERERERERERERFRzxLsZC76en2TAqnF1fCzN1VzRUTUGwmCgMiMG9gbnYv9sXkorhJfwb21fO1McH8/J0zt5wQPG+Pbr08f4IzPDyUhp6xWaZ9N5zPx9BgfWKlYwZuI1Eurg+bHjh3TdAlERETd3qX0Ury9J07leJCjGT6ZFdKuyV12pgYY4GqBqMwypbGDCQVYOqrt4fWe4GhiIT75I1Hl+CBPK7w1JViNFVFvNcJXdUj5VHIxZoW5qLGa3i23rBbLf7mCC+mqV5a4ZXygHT6Z1Y83ToiIiIiIiIiIiIiIehFfexPoSCVoEulWk5BbwaA5EXWpvPJa7IjMxvbIbKSX1HToWG5WRrfD5f4O4u9dujIplozywlu/xiuN1TbK8eOpNLx0j3+H6iCizqHVQXMiIiLqmNyyWizdEIVGuXjrXCtjPXy/KAxGeu2/JJgQ5CAaNI/KvIGiynrYmuq3+9jdUUpRFZ775bJol3cAcDI3wMr5odDT4dJ21PUczQ3hbWuMlKJqpbFTSUUMmqvJgbg8/HNHLMprG5vdTk9HitcnBWJRhDtXdiIiIiIiIiIiIiIi6mX0dWTwsTNBYn6l0lh8bjmmD3DWQFVE1JPVN8lxMKEA2y5l42RSUYurMjfHwcwAU0IcMbWfE0JczFv1vHNOuCu+PJyM4qp6pbGfz6Zj8SgvmBnotr8oIuoUDJoTERH1UHWNcixZHyl6QQ4AOlIJVs4PhYulUYfOMyHIHh8fUO7eLQjA4asFeHCQW4eO351U1DXiyXWXUFnXJDpuoCvF94vCYWPSu8L3pFkjfG3Fg+bJJRAEgYHmLlTXKMe/9yVg4/nMFrf1sTPBVw8NQKCjmRoqIyIiIiIiIiIiIiIibRTkZCYaNE/Iq9BANUTUU8XllGN7ZDZ2X8lBWU3zzbKaY2Wsh0l9HTA1xAkDPawglbbt2bOBrgxPjvDEh78rZ04q65qw/mwGnh7j0+76iKhzMGhORETUAwmCgFd3xCA2p1zlNm9PDcIQL+sOn8vHzgRetsZIFQmyHkzoPUFzuULA8l+uiP493PLprH7o42yuxqqIgOE+Nlh7Jl3p9eKqeiTmVzLY3EWu5Vfi2c1RuF5Q1eK2Dw1yxZtTgjq0ugQREREREREREREREXV/QY5m2IkcpdfjcyvYQIiIOuRGdQN+vZKDrZeyOzR5xVRfB/f0ccDUfk4Y6m0NXVnHVnOfP8QdK4+liK4O/eOpNDw2zBOGerIOnYOIOoZJBiIioh5o9clU7L6Sq3L8oUGuWDDEvdPONyHIHt8dT1V6/WRyMarrm2Cs3/MvOf775zUcSSxUOb5stDem9nNSY0VENw3xtoaOVIImkXXOTiUVM2jeyQRBwIbzmXh/XwLqmxTNbmtmoIOPZoZgUl9HNVVHRERERERERERERETaLNhJvGlVWU0j8srr4GRhqOaKiKg7kysEnEwqwrZL2TiYUIAGefPPL1Ux0JVifKA9pvZzwig/Wxjodl7w20RfB48M9cAXh5OUxkqqG7D5QiYeG+7Zaecjorbr1qmvpqYmFBQU4MaNG6isrISpqSksLS1hb28PHZ1u/aURERG127FrhfhIZFmhW8LdLfHu/X06dbb7RBVB84YmBU4mFeHePj07RLk3Ohcrj6WoHB/jb4uXJvqrsSKiv5jo62CAmwUupt9QGjuZXIwnR3ppoKqeqaymAa9sj8GfCQUtbhvubonPH+wPF0sjNVRGRERERERERERERETdQVAzDYIScisYNCeiVkkvrsa2yCzsiMxBfkVdu46hI5VgtL8tpvZzwvhA+y5tMPjoMA+sOZmK6ga50tj3J1KxYIg79HQ61jmdiNqv26WxU1JSsGbNGpw4cQKXL19GfX290jb6+voIDQ3FqFGj8MQTT8DTU3tntAiCgPT0dMTGxiI7OxtlZWXQ19eHpaUlfH19MXDgQBgYGGi6TCIi6iZSi6rw7ObLEGlcDABwNDfAqgVhnX4B3t/VEjYm+iiuUv69/GdCQY8OmsfllOPl7dEqx71sjfHFQwMgk3IZO9Kc4T62okHzC2klqGuUd+qM897qXGoJXthyBXnlzd+okUiAZ8f44LlxvtDp4DJyRERERERERERERETUs5gb6cLF0hDZN2qVxuJzKzA+yF4DVRFRd1Bd34TfYvOwLTIbF9JK230cXzsTzAl3xfQBzrA11e/EClWzMNLDgiHu+O6EcoPD/Io67IzKxoOD3NRSCxEp6zZB8/z8fDz11FPYs2cPBOFmeu7W//9dXV0dzp49i7Nnz+Ljjz/G9OnT8fXXX8PBwUGdJat048YN7N69GwcOHMCRI0dQXFyscltdXV1MnjwZy5cvx6hRo9RYJRERdTeVdY1YvD4SlXVNouP6OlJ8tzCsSz4IyKQSjA+0wy8Xs5TGjiQWokmu6JGByuKqeixZH4m6RvHlpUz1dbB6UTjMDHTVXBnR3Yb72mDFoetKr9c1KhCVcQNDfWw0UFXP0CRX4Msjyfj6SJLKST63OJgZYMXc/ojwtlZPcURERERERERERERE1O0EOZqJBs0T8so1UA0RaTNBEBCZcQPbLmVjX0yuaEfw1jDV18HU/k6YE+6Kfi7mkEjU30jv8eGe+OlMOhqalPMXq46nYFaYS4/MnRB1B90iaH7w4EHMmzcPpaWlt8PlEonk9hvanYHzO9/kBEGAIAjYtWsXjh8/js2bN2P8+PHqLf5vnn76aaxZswYNDQ2t2r6xsRG7d+/G7t27sWjRInz11VcwM1O9TA4REfVOCoWAF7ZcQXJhlcptPp4ZghAXiy6rYUKQvWjQvKymERfTb/S4YGWjXIGnNkYhp0z5Jg9ws2vxlw8NgLetiZorI1LWz8UcpgY6ohNRTiYXM2jeTtk3arD8lyu4lKHcLf7vJgTZ45OZIbA01lNDZURERERERERERERE1F0FOZnhz4QCpdcT8io0UA0RaaOCijrsjMrBtktZSC2ubvdxhnpbY064K+4JdoChnmZXwbYzM8DccFesP5ehNJZRUoP9sXmY1t9ZA5URkdYHzU+fPo3p06ejtvZmiEsikdwOkOvo6CAgIAA2NjYwNjZGdXU1iouLce3aNTQ2Nt61fUlJCaZPn46DBw8iIiJCY1/P+fPnRUPmMpkMjo6OsLe3R2NjIzIyMlBefvdMxHXr1iExMRGHDx+GiQlDa0RE9JcVh67j0NVCleNLRnph+oCuveAe5mMDIz0ZakRmyB5MKOhxQfP39iY0u9zUK/cEYEyAnRorIlJNRybFUG9r/BGvfFPyVFIx/nmvBorq5n6LzcOrO2JQoWIViVv0dKR4c3IgFgxx18jMfyIiIiIiIiIiIiIi6l6CncxFX88qrUV5bSPMDbmaMlFvVN8kx9HEQmy9lI1j1wpbXG1ZFWcLQ8wKc8GsMBe4Whl1bpEdtGSUFzZfyESTyBe38mgKpoY4QSrlM1ciddPqoHl1dTVmz56N2tra24FxAHjwwQfx2GOPYeTIkdDTU+4I2NDQgJMnT+KHH37Ali1bbgc6ampqMHv2bFy/fh1GRpp/k7SwsMC8efMwefJkjBgxAqamprfH5HI5Tp48ibfeegsnT568/fqFCxfwyCOPYPv27ZoomYiItND+mDx8dSRZ5fgoP1u8cm9Al9dhoCvDSF9bHIjPVxr7MyEfb04J7DEhy03nM0Vn0d4ytZ8Tlo7yUmNFRC0b7msrGjSPyy3HjeoGdtpupdoGOd7bl4DNFzJb3NbXzgRfzRuAAAeuSERERERERERERERERK0T5KT6ucLVvAoM8eoZDb4q6xpxKeMGCsrrMCbADvZmBpouiUjrCIKA6Oxy7IjMxt6YXJTVNLbrOHo6UtzXxwGzw1wx1Ntaa8PaLpZGmD7AGdsjs5XGrhVU4tDVAkwMdtBAZUS9m1YHzT/55BPk5+ffDpk7Oztj27ZtGDJkSLP76enpYdy4cRg3bhyWL1+O2bNnIzv75ptPXl4ePv30U7z99tvq+BJEeXh44I033sC8efNgaGgouo1MJsPo0aNx9OhRPPXUU/j+++9vj+3YsQNHjx7FmDFj1FUyERFpqYTcCry0LVrluKeNMb58cABkavqQMDHYXjRonn2jFon5lQh07P5hy4vppXh7T5zK8WAnM3wyM6THhOqp5xjhYyP6uiAAp1OKMSXESc0VdT9X8yrw7ObLSC6sanHbeYPd8ObkII0vMUdERERERERERERERN2Lk7kBLIx0RQOl8bndP2heWt2An06nYe2ZdFT+b+VYiQT44sEBuL8fn1cRAUBuWS12Xc7BzqhspBRVt/s4IS7mmB3uivtDnGBu1D1WQ1g22hs7orIhiHRs/+ZoMiYE2TOPQaRmWh00//HHH2+HzK2trXH69Gm4ubm16RiDBg3CyZMnER4ejpKSEgiCgDVr1mgsaP7uu+9iwoQJop3YxchkMqxcuRJRUVG4dOnS7dfXrFnDoDkRUS9XWt2AJ9ddQm2jXHTcRF8HqxeFqfXDwtgAO8ikEshFljE6mFDQ7YPmuWW1WLYhEo1y8TWorI318P2icAZLSSu5WxvBxdIQ2TdqlcZOJTFo3hxBELD+XAbe338VDU2KZrc1M9DBJ7NCcG8fRzVVR0REREREREREREREPYlEIkGQoxnOpJQojSXkVmigos5RUFGH1SdSsfF8ptIzbkEAXtoWjX4u5nC3NtZQhUSaVdPQhANx+dgRlY0zKSWiQevWsDLWw4wBzpgd7tItV172tjXBpD6O2B+bpzQWnV2OU8nFGOFrq4HKiHovqaYLUCUmJgY5OTkAbl5Affzxx20Omd/i5uaGDz/8EML/3n1zc3MRExPTabW2xeTJk1sdMr9FJpPhlVdeueu1P/74ozPLIiKibqZRrsBTGyORU6YcGAVuzvj+fG5/+NiZqrUuCyM9DPSwFB37M0G503l3Utsgx+L1l1Bc1SA6riOVYOX8UDhbiK9WQqRpEokEI3zFu5qfTCq+fa1Md7tR3YAn10XirV/jWwyZD/SwxO/LRzJkTkREREREREREREREHRKkooFXQl73C5pnldbg9V2xGPHxUaw5laaykVpDkwLfnUhVc3VEmqVQCDiTUowXt0Yj/P1D+MfWaJxObnvIXCoBxgXY4dsFYTj32ji8OSWoW4bMb3lqjLfKsa+PJKuxEiICtLijeXx8PICb3QMNDQ3x4IMPduh4Dz30EJ577jnU1tbePn5ISEiH61SXESNG3PXnkpIS1NTUwMjISEMVERGRJr2/LwHnUktVjr84wQ/jg+zVWNFfJgY5iNYWl1OB3LJaOHXDILYgCHh1ZwziclTfuHnn/mAM7ubL1FHPN9zHFpsvZCm9nlNWi/SSGnjasEPEnc6mlOCFLVeQX1HX7HZSCfDcOF88M8YHOjKtnctLRERERERERERERETdRLCzeEA0qaAS9U1y6Oto/wrLyYWVWHksBb9eyRVdEVvM9kvZWD7OF3ZmBl1cHZFmpRZVYWdUDnZdzlHZYLA1vGyNMSfcFQ8McO5RPzfBTuYYG2CHI4mFSmPn00pxKb0U4R5WGqiMqHfS2qB5YeHNNwmJRAJPT88OB6qNjIzg6emJhISEu47fXVhaKneHLS8vZ9CciKgX2nIxEz+fzVA5PrmvI54e46PGiu42Icge7+1LEB07mFCAh4d6qLegTvD9iVT8eiVX5fi8wW5YMMRdjRURtc9Qb2tIJBCdAX8qqYhB8/9pkivwxeEkfH00ucVuAU7mBvj8wQEY5MkbGURERERERERERERE1DmCHM1FX29SCEgqqEIfZ/FxbRCXU45vjibjQHx+m7syN8gV+OFUGl6bFNg1xRFpUHlNI/bG5GJHVDYuZ5a1+zim+jqY1NcRcwa6INTNEhKJpPOK1CJPj/EWDZoDwDdHk/HTo4PUXBFR76W1QfO6ur+6Bhoadk7nUwODv2bt1NfXd8ox1SUnJ0fpNWtrdk0lIuptIjNK8cbuOJXjAQ6m+HR2iEY/SLhaGSHAwRSJ+ZVKY90xaH7sWiE+OpCocnyghyXemRqsxoqI2s/SWA99nc0Rk12uNHYyqRgLIzzUX5SWySqtwfO/XEZUK27u3BNsj49nhsDCSK/rCyMiIiIiIiIiIiIiol7D29YYejpSNDQplMYSciu0Mmh+Kb0UXx9NxrFrRR06zoZzGXhqtA/MjXQ7qTIizWmUK3D8WhF2Xs7GoYRCNMiVf6ZbQyoBRvjaYmaYCyYG2cNAV/tXNeioMHcrDPGywrnUUqWxo9eKEJdTrpXvhUQ9kdYGzW1tbQEAgiAgMzOzU46ZlZV1+79tbGw65ZjqcvLkybv+7O7uDj09BlqIiHqTvPJaLFkfhUa5+LRvSyNdrF4UDiM9zf96nxjsIBo0P5dagvLaRpgbdo+bAqlFVXh282WVM+0dzQ2wcn4Y9HSk6i2MqAOG+9iIBs3PppSgSa6Ajqz3/nveH5OHV3fGoLKuqdnt9HWkeGtqEOYNcuuxHQKIiIiIiIiIiIiIiEhzdGRSBDiYij7TScir0EBF4gRBwKnkYnx9JBnn05TDoM3R15GiXiRIX90gx7qz6Xh2nG9nlUmkVoIgID63AjujcrAnOgfFVQ3tPpa/vSlmhjljWn9n2JsZtLxDD/PMGF+cSz0vOrbyWDJWzg9Tc0VEvZPmk2gquLq63v7v4uJinD9/HoMHD2738c6fP4+ior9mzLm5uXWoPnX78ccf7/rzpEmTNFQJERFpQl2jHEvWR6K4SnxFDplUgpXzw+BqZaTmysRNDLLHl4eTlF5vUgg4dq0Q0/o7a6Cqtqmsa8ST6y6pDJzq60jx/cJw2Jrqq7kyoo4Z7muDlcdSlF6vrG9CdHY5wtwtNVCVZtU0NOG9vQn45WJWi9v625viq3kD4GdvqobKiIiIiIiIiIiIiIiotwpyNBMPmudqPmiuUAg4dLUA3xxNRrRIjc1xMjfAklHeuL+fE8b933GUViuHcH86k44nRnjBUK/nd22mnqOwog67r+RgZ1SOaGO+1rI21sP9/Z0wM9QFwU5mvbrx1TAfa/RztUB0VpnS2O9x+UgurIKPnYn6CyPqZbQ2aD58+HAYGBigvv5moO7VV1/F0aNH232811577fZ/6+vrY/jw4R2uUV1+++03nDhx4q7XHnnkEc0UQ0REaicIAl7bGSt6E+GWt6cGIcLbWo1VNS/YyQxO5gbILa9TGvszoUDrg+YKhYDlv1xBSlG1ym0+mRWCvi5chom6nzB3SxjqylDbKFcaO5VU3OuC5gm5FXh2c1SzP++3LBzijtcnB/aKpeiIiIiIiIiIiIiIiEizgp3MRF9PyKuAQiFAKlV/+LRJrsD+2DysPJqCawVtC9J62hhj2ShvTB/gfHvF6EeHeuC/B68rbVta3YBfLmbi0WGenVI3UWeqaWhCSmE1kosqkVxYdft/acXVUKhYLb0lejIpxgXaYWaoC0b520K3F69CfSeJRIKnR3tj8fpIpTFBAFYdS8F/5/TTQGVEvYvWBs0NDQ0xceJE7NmzBwBw4sQJPP7441i9ejWk0ta/kQqCgKVLl+LYsWO3Z/fcc889MDDoHktJlJaWYsmSJXe9Nn36dAwaNKjTzlFYWHhXt/fWSE5OvuvPVVVVqKjQ/IzJ3q66urrZPxNR97TufDZ2Xc5ROf5AfwdMC7LUuvfhUT6W2ByZp/T6scRCFJeW3b55oI2+OpaOw4mFKscfHeKCMV6mWvd3Tr1XW68BQl3NcDr1htLrxxLz8egg+06tTVsJgoDNl3Lx3yNpaJQ3f8fHzEAH7032xVh/GzTUVqOhVk1FEhERtQLvBRAREfVOvAYgIiLqvXgd0Hu4m4vHuqrqm3A1qxCuloZqq6VRrsCe2EL8eDYLWTeUm401x9fWCE8MdcXEQFvIpBLU1VTh1hFm9LHCquMy1DQoN0j67ngK7g+yZOCWNOZGTSNSi2uQVlKD1OIapJbUIq24BnkV4ivRt0dfJ1Pc39cO9wbZwtxQFwBQW10FPo78yyAXQ/jYGiG5qEZpbPflbDwxxBHOFt0jC9pRvAbovaqqqjR6fokgCO2cR9P1oqOjERYWBkEQIAgCJBIJ+vfvj88++wxjxoxpcf9jx47h5ZdfRlRUFICbYRKZTIaoqCj07du3q8vvMIVCgSlTpuD333+//Zq5uTliY2Ph6uraaed555138O6773boGF9++SXc3Nw6qSIiIrolsUyCb69KIUB8NrqnqYBnguTQxsz2tTIJVl4V7/q7NECOQEvtvAS5XCzB2iTV3YoDLRRYHKCABhoEEHWao7kS7M5Q/nculQj4cKAcBj28YXdNE7AxWYq4Gy2/eXqbCljoK4elvhoKIyIiIiIiIiIiIiIi+p96OfDPCzLRZ8WP+snR37rrn7c2yIGzhRIcyZWirKFtD0jdTQRMcFYg2FJo9tnqrxlSHMkVf2Yzz1uOwXba+VyZegaFAJQ1AAU1EuTXAgW1kv/9D6hu6ppQgIWegIG2AgbaKmCvvvki3VpksQTrVOQ4htkrMMdLoeaKiNQrMzMTzz333O0/x8XFITg4WG3n19qO5gDQr18/vPjii/j0008hkUggCAIuX76M8ePHw83NDWPGjEFISAhsbGxgbGyM6upqlJSUIDo6GseOHUNGRgYA3A6pSyQSvPjii90iZA4AL7/88l0hcwD47rvvOjVkTkRE2quoFlh7XXXI3FxPwGN+2hkyBwAfMwGGMgG1cuX6Y29ItDJonl0NbEpR/RdqayBgkS9D5tT9+ZuL//wpBAmSyyXoY6V9P5+dJb8GWHNNhqK65n+QJRBwn6sCE5ybv/lJRERERERERERERETUFfRlgK0BUCjSQDynWtKlQfO6JuBkgQTHcqWoamPY1sdMgYnOAvzMBUhasetoRwVO5EnQJChvfChHioG2cj6roQ6TK4CiOtwOkef/L1BeWAs0KLr+H5ieVEB/65sBcx8zPn9sqwHWAn7LElAs8oz3fKEE97gA5noaKIyol9DqoDkAfPzxx8jPz8f69esh+d/VhyAIyMjIwM8//6xyv1uN2m8FzAVBwKJFi/DRRx+ppe6O+vLLL/F///d/d732yiuvYO7cuRqqiIiI1KmuCVh9TSYa0gYAHYmAJ/zlMNPiC2WZFAiyFBBZrPw1xJVKMMsTWvXhqaoR+OGaTOWHSAOZgCcD5DDS+qsnopY5GgFmugIqGpX/vV/rwUHzmFI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" ] @@ -987,17 +987,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:47:58.648463Z", - "iopub.status.busy": "2023-09-02T00:47:58.648373Z", - "iopub.status.idle": "2023-09-02T00:48:21.211763Z", - "shell.execute_reply": "2023-09-02T00:48:21.211450Z" + "iopub.execute_input": "2023-11-09T07:12:49.776942Z", + "iopub.status.busy": "2023-11-09T07:12:49.776838Z", + "iopub.status.idle": "2023-11-09T07:13:05.965442Z", + "shell.execute_reply": "2023-11-09T07:13:05.965103Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -1051,17 +1051,17 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:21.213893Z", - "iopub.status.busy": "2023-09-02T00:48:21.213730Z", - "iopub.status.idle": "2023-09-02T00:48:40.890890Z", - "shell.execute_reply": "2023-09-02T00:48:40.890553Z" + "iopub.execute_input": "2023-11-09T07:13:05.968078Z", + "iopub.status.busy": "2023-11-09T07:13:05.967962Z", + "iopub.status.idle": "2023-11-09T07:13:19.811749Z", + "shell.execute_reply": "2023-11-09T07:13:19.811473Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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X+gcPHjT++9//GldeeaXRpEkTt9dn3WfdBwD4z4wZM1zWofbt2xvz5883srOzTXWPHTtmTJo0yeXTTevUqWN6v7wg2dnZRqdOnUztatasaXz11VdGVlaWo15SUpLxzDPPuOyufv/993s8p+nTpxf494ddu3aZ6i1cuNDo3LmzqV5kZKSxb98+j/rx5ZwAAMjzzjvvFLojdkRExHm/J++M1+rlY04oHQTNARQoMTHRiIyMNP2jPXny5ELrJyQkGM2aNTPVf+6553w4YgAAypYePXoYzZo1Mz777DMjLS3Nbf2cnBzjzjvvdHlx5RyqLsw//vEPU7tevXoZp06dKrCu3W536atFixYub5IXJCYmxggICDC1/fbbbwutv3XrVqN69eqm+lOnTi1TcwIAoLQsXrzYsFgshpR7Y9brr79+3m9qP/XUU6a20dHRpjdgnb388sum+tWqVXP7R27D8O37Ab6aEwAA3rR7924jNDTUsR4FBQUV+brYmSdrF+s+6z4AwH86duxoWoN69uxppKSkFNlm0aJFRmBgoKndK6+8UmSbjz/+2FS/Ro0axrZt2wqt/80335jqBwYGugTFC5KZmemyft99992m8Fh+ycnJRs+ePU31b7rpJrf9+HJOAADklxc0j4yMNIYMGWJMnDjRmDFjhhEXF2csXrzYa0FzXquXjzmhdBA0B1Cgxx9/3PSP9aBBgwp9sZln4cKFpjaRkZFGYmKij0YMAEDZMm/ePCMzM7NYbXJyclzewB0/frzbdlu3bjXt/BEcHGxs3769yDbp6elGq1atTH198sknbvu65pprTG1uvPFGt22cd3qLiooy7V7i7zkBAFAa0tLSjBYtWjjWpAcffPC839Q+duyYy84rCxcuLLKN3W43Bg0aZGrz9NNPu+3LV+8H+HJOAAB404UXXmhai77//nuvXp91v2RzAgCgJPbs2WNaeyQZa9eu9ait8y6fffv2LbRuZmam0aRJE1P9zz//3G0fN9xwQ7H/fvDhhx+a2rRq1cpIT08vss22bdtMu7QHBAQYMTExRbbx5ZwAAMgvNjbW2LZtm2Gz2VzOeStozmv18jEnlB6rAMCJ3W7XF198YTr2wgsvyGKxFNlu2LBhGjhwoKN85swZff/996UyRgAAyrqRI0cqODi4WG0CAgL0+OOPm4799ttvbttNnTpVdrvdUb7uuuvUrl27ItuEhobqySefNB377LPPimxz8uRJzZo1y1G2WCx64YUX3I7vlltuUVRUlKO8f/9+LVy4sMg2vpoTAACl5Z///Kf27NkjSWratKleeuml877WtGnTlJKS4igPGjRIw4YNK7KNxWLR888/bzo2depUGYZRaBtfvh/gqzkBAOBNc+fO1eLFix3lq6++WldffbVX+2Ddz8W6DwDwh507d5rKjRs3Vq9evTxqe+WVV5rKsbGxhdb97bffdODAAUe5WbNmuuWWW9z24bxWz5gxQ6dOnSqyjfN75E899ZRCQ0OLbNO+fXtde+21jrLNZnN53uDMl3MCACC/Fi1aqH379rJaSy8Ky2v1XGV9Tig9BM0BuFi1apWOHz/uKDdv3lxDhgzxqO1tt91mKs+ZM8eLIwMAoOLL/2JJkpKSkpSWllZkmx9//NFUdl6PC3PttdcqPDzcUV63bp0OHTpUaP358+crJyfHUR4yZIiaN2/uth+r1eryhrK75wi+mhMAAKVh3bp1evfddx3lf//734qIiDjv682dO9dU9nRdvPDCCxUdHe0oHzlyRH/++Weh9X35foCv5gQAgDd98sknprLzH1e9gXX/HNZ9AICvnThxwlRu0qSJx22bNm1qKicnJxda13ltvOWWW9yGraTcIN3gwYMd5ezsbP3888+F1k9ISNBff/3lKEdEROiaa65x24/kul47j9mZr+YEAIA/8Fr9nLI8J5QeguYAXMyfP99UHjFihEcvAvPq5rdkyRKlpqZ6bWwAAFR0NWrUcDlW1O4dO3fuNO2MEh4ern79+nnUl3NdwzBcngfk53zuoosu8qgfyfU5wrx58wqt68s5AQDgbdnZ2brttttks9kk5e50evnll5/39VJSUrRs2TLTMU/XYIvFouHDh5uOFbUG++r9AF/OCQAAbzl48KDpU8e6du2qDh06eLUP1n0z1n0AgK9Vq1bNVE5PT/e4rXPd2rVrF1rXV++1O/fTv39/00YtRenfv7+qVKniKO/cuVO7d+/2uK/SmhMAAL7Ga3WzsjonlC6C5gBcbNq0yVT2NNglSQ0bNlSzZs0c5aysLG3fvt1LIwMAoOI7ePCgy7FatWoVWt953e7du7cCAwM97q9///5FXq+oc8V5jtCjRw+FhIQ4yocOHTLduVxUP6U5JwAAvG3y5MnasmWLJKl69eqaMmVKia63bds2ZWdnO8rR0dGqX7++x+19tdYX5/0AX84JAABv+fXXXx03kkm5O3h5G+u+K9Z9AIAvde3a1VSOiYnxOMy0du1aU7l3794F1jt69KiOHDniKIeEhKh79+4ej9FX631gYKDLHArry5dzAgDA13it7qoszgmli6A5ABcxMTGmcvv27YvV3rm+8/UAAEDhli9fbipHRUUpODi40Pq+Wrezs7NNu4wXt6+QkBC1aNHCo754LgIAKK+2b9+ul19+2VF+7bXXivXmbEF8uS76qi/WegBAebRu3TpTuUuXLo7HGzdu1AMPPKAuXbqoRo0aqlKlipo1a6YRI0bozTffLPCm8oKw7p9/PwAAeEPjxo1N4afMzEyPbiDPzMzUu+++azp22223FVjXeS1r2bJlkX8DcOa8NsbGxionJ8ejvny13pfmnAAA8DVeq59/P77uC6WHoDkAk/T0dMXHx5uONWnSpFjXcK6/c+fOEo8LAIDKYurUqabyZZddVmR953W2tNbtvXv3mt7YDQsLK/KjP0vSl6/mBACAN9ntdt12223KysqSJA0cOFB33HFHia/r7XVx//79ysjIcKnny/cDfDUnAAC8yTlo3rx5c6WkpOi2225T9+7d9f7772vz5s1KTk5Wenq69u/fr4ULF2rixIlq1aqVnn76adNuYQVh3XffD+s+AKC0vfbaa7Jaz0VpnnvuOX311VeF1k9OTtZVV11lCj2NGjVKo0aNKrB+SdfGOnXqKDQ01FHOysrSvn37SqUvX633xZkTAAC+xmt19/2UhTmhdBE0B2CSmJgowzAc5aCgINWtW7dY12jUqJGpfOzYMa+MDQCAiu7nn3/WsmXLTMduvvnmIts4r7ONGzcuVp/O6/bx48c96se53fn0VdhzBF/NCQAAb5oyZYr+/PNPSVJwcLA++eQTWSyWEl+3pOtivXr1FBgY6Cjb7XYlJSW51PPl+wG+mhMAAN7k/ClfVqtVgwYNcrlhvCDp6emaPHmyLrvsMp05c6bQeqz7rlj3AQC+NmDAAH3wwQeO1/Q5OTm6+eab1bt3b7366quaPXu2fv31V/3vf//T/fffrxYtWmjevHmO9iNGjNB3331X6PVLujZKUsOGDYu8Zh7n98ZL+l57aa33kudzAgDA13it7qoszgmlK9B9FQCVSUpKiqlcpUqVYv9hPDw8vMhrAgAAVydOnNBdd91lOjZmzBj17t27yHbO66zzOuyOc/3s7GxlZmYqJCTEq/0U1Kaw5wi+mhMAAN6yb98+Pfvss47yU089pbZt23rl2iVdFy0Wi8LCwkyhtoLWYF++H+CrOQEA4C12u90lIP7AAw9o48aNknLXpssvv1yXXXaZGjdurNTUVG3cuFH//e9/dejQIUebhQsX6uabb9YPP/xQYD+s+65Y9wEA/nDPPfeoTZs2euCBB7Rt2zZJuZ9u4vwJJ/k1b95cjz/+uO644w7TjujOfPVee3p6umw2W4n68tV6X5y+AADwNV6ruyqLc0LpYkdzACbO/xjn/4gqT4WFhRV5TQAAYGa323XDDTcoISHBcaxatWqaMmWK27YlXbud1+2CrumNfgrqy9MXtqU1JwAAvOXOO+9UamqqJKlt27Z6+umnvXZtX63B5WmtL05fAAB4w6lTp0w7cEnSX3/9JUmqVauWli5dqh9//FF33323Lr/8cl177bV69dVXtXPnTo0fP97UbtasWfr6668L7Id1v2R9AQDgTUOHDtW6dev02GOPKSAgoMi6TZs21WOPPabx48cXGTKX/Lfen09frPcAAPBavSR98Ryh4iBoDsAkIyPDVA4ODi72NZx3C01PTy/RmAAAqOgmTpyoX375xXTs448/VpMmTdy2LenaXdAu3wWt3b58juCrOQEA4A2ff/65Fi5cKCl3F49PPvnkvNbJwvhqDS5Pa31x+gIAwBsK+yNmQECA5s+fr4EDBxZ4PiIiQv/973910UUXmY6/8sorLsF1iXW/pH0BAOBNH330kVq0aKE333zTZWdwZ/Hx8br33nvVrFkzTZ06tci6/lrvz6cv1nsAAHitXpK+eI5QcRA0B2DifOdQVlZWsa+RmZlZ5DUBAMA5U6ZM0dtvv2069vjjj+vaa6/1qH1J127ndbuga3qjn4L6Kuw5gq/mBABASR0+fFiPPfaYo3z77bcXGjQ7X75ag8vTWl+cvgAA8IbC1pnbb79dffr0KbKt1WrVf/7zH9Pupjt37tTSpUvd9sO6X7y+AADwhuzsbF111VW65557dPjwYUlSzZo19dxzz2nt2rU6efKksrKydOjQIf34448aO3asLBaLJOnEiRO67bbbNHHixEKv76/1/nz6Yr0HAIDX6iXpi+cIFQdBcwAmERERpnJBdzq743znkPM1AQBArm+//VYPPfSQ6djNN9+sV1991eNrlHTtLuiO34LWbl8+R/DVnAAAKKn77rtPycnJkqT69evr9ddf93ofvlqDy9NaX5y+AADwhsLWmTvuuMOj9s2bN9fw4cNNxwoKmrPul6wvAAC84Z577tEPP/zgKPfu3Vvbtm3TpEmT1KtXL1WvXl1BQUFq0KCBRo0apVmzZmnOnDmm0NObb76pL774osDr+2u9P5++WO8BAOC1ekn64jlCxUHQHICJ8z/GaWlpBX6EZ1FSU1OLvCYAAJDmzZunCRMmmNbZcePG6bPPPnPsfuIJ53XWeR12x7l+YGBggXcBl7Sfgtp4+sK2tOYEAEBJzJgxQ7Nnz3aU33vvPVWvXt3r/ZR0XTQM47ze8C3N9wN8NScAALwlLCxMAQEBpmORkZHq1q2bx9cYPHiwqbx+/XqXOqz7rlj3AQC+tGTJEn3++eeOct26dTVv3jzVr1+/yHZXXHGF/v3vf5uOTZw40aNNUUrrvfaCnr+U9L320lrvi9MXAAC+xmt1V2VxTihdBM0BmNSuXdsUbsvOztaxY8eKdY2DBw+aynXr1vXK2AAAqCgWL16sq6++Wjk5OY5jI0aM0Hfffefyxq87zutsQkJCsdo7r9t16tTxqB/ndufTV2HPEXw1JwAASiL/x2CPHDlS11xzTan0U9J18ejRo6bnHFarVbVr13ap58v3A3w1JwAAvMl5/WrZsqWsVs//zNamTRtTuaB1lnXfFes+AMCXpkyZYio/9NBDHr+/fPPNN6t169aOclJSkmbNmuVSr6RroyQdOnSoyGvmcR57Sd9rL631XvJ8TgAA+Bqv1V2VxTmhdBE0B2ASFhampk2bmo7Fx8cX6xrO9du2bVvicQEAUFGsWbNGV1xxheljofr166fZs2crODi42Ndz/kN1aa3bzZs3V2BgoKOcnp6u48ePl0pfvpoTAAAlkZyc7Hg8f/58WSwWt18XXnih6Rr79+93qbNp0yZTHW+vi1FRUQV+0ocv3w/w1ZwAAPCmdu3amcpVq1YtVnvn+idPnnSpw7rvvh/WfQBAaTEMQ3/88Yfp2KhRozxub7VaNXLkSNOxZcuWudQr6dp47Ngx098XgoOD1bx58wLr+uq9dl/OCQAAX+O1uvt+ysKcULoImgNw4fwP8vbt24vVPiYmpsjrAQBQWW3evFmXXnqpUlJSHMe6deumn3/+WeHh4ed1TV+t20FBQWrRosV595WZmam9e/d61BfPRQAAOMeX66Kv+mKtBwCUR+3btzeVMzMzi9U+f3hKkqpUqeJSh3X//PsBAKCkTp48qVOnTpmORUdHF+sazvUL+mRQ57Vsz549ysrK8rgP57WxRYsWpk1iiurLV+t9ac4JAABf47X6+ffj675QegiaA3DRtWtXU3nVqlUetz18+LDi4uIc5aCgIJc34AEAqIx27typESNGmHYsa9eunX777TdVq1btvK/rvG6vW7fO9DFV7qxcubLI6xV1rjjPETZs2GD6I3yDBg0K/VgrX84JAICyrkOHDgoKCnKU4+LidPjwYY/b+2qtL877Ab6cEwAA3tK9e3dT+ejRo8Vq7/zR0LVq1XKpw7rvinUfAOArBd1EVtywc/41T5JsNptLnfr166t+/fqmfjds2OBxH75a73NycrR27VqP+vLlnAAA8DVeq7sqi3NC6SJoDsDF5ZdfbiovXLhQhmF41Pb33383lS+88EJFRER4bWwAAJRH+/fv1/Dhw01/VI6OjtaCBQtUp06dEl27bdu2pp3GU1NTPX5xlpqaqtWrVzvKFovF5XlAfs7nFixY4PE4nesW9ZGjvpwTAADna+7cuVqwYEGxvt58803TNerVq+dSp2XLlqY6kZGRGjRokOmYp2uwYRhauHCh6VhRa7Cv3g/w5ZwAAPCWkSNHymo992e1ffv26cSJEx63dw5bOX9MtcS674x1HwDgSwXdBHbo0KFiXcN5B/PC3v8fOXKkqVxa77U797Nq1SqlpqZ61M/KlSuVlpbmKLdu3VqtW7f2uK/SmhMAAL7Ga3WzsjonlC6C5gBc9OvXT7Vr13aU9+7dqyVLlnjU9vPPPzeVR48e7c2hAQBQ7hw+fFjDhg1TQkKC41ijRo20aNEiNWrUyCt9XHHFFaay83pcmOnTpyslJcVR7tmzpxo2bFho/csuu8y0g8uSJUu0d+9et/0YhqEvv/zSdMzdcwRfzQkAgPM1ePBgDR8+vFhfPXr0MF0jNDTUpU5Bb5Ke77q4ePFi7du3z1GuV6+e+vTpU2h9X74f4Ks5AQDgLXXr1lX//v1Nx2bNmuVR25ycHM2ePdt0bMiQIQXWZd0/h3UfAOBLwcHBatCggenYH3/8UaxrLFq0yFTOv6FKfs5r4xdffOFR4GrPnj1aunSpoxwUFKTLLrus0PpNmjRRt27dHOWUlBR9//33bvuRSr7el9acAADwB16rn1OW54TSQ9AcgAur1aqbb77ZdGzSpEluXwguWrRIy5cvd5QjIyN1zTXXlMYQAQAoF06cOKERI0Zoz549jmN16tTRggULFB0d7bV+br31VlksFkd52rRpiomJKbJNRkaGXn31VdOx2267rcg2NWvW1JgxYxxlwzD0wgsvuB3f1KlTTR9pFRUVpeHDhxfZxldzAgCgPLjuuusUHh7uKC9btsztH7sNw9CkSZNMx2655RbTTqzOfPl+gK/mBACAN911112m8htvvKHMzEy37T799FMdOXLEUa5ataouvvjiAuuy7udi3QcA+MOwYcNM5XfffVc5OTketV26dKnp0zYLul6eiy++WI0bN3aU4+Li9MUXX7jt44UXXjCt1VdeeaWqVatWZBvn98hfffVVZWRkFNkmJiZG06dPd5QLet7gzJdzAgDA13itnquszwmlyACAAhw/ftyIiIgwJDm+Jk+eXGj9hIQEo1mzZqb6zz77rA9HDABA2XL69GmjV69eprWxevXqxsaNG0ulv2uvvdbUV69evYxTp04VWNdutxt33XWXqX7z5s2NrKwst/1s27bNsFqtprbffvttkfWrV69uqv/ZZ5+VqTkBAOArixcvNq1VUVFRHrd94oknTG2jo6ONgwcPFlr/5ZdfNtWvVq2akZSU5LYfX74f4Ks5AQDgLTabzejUqZNpPZowYYJhs9kKbfPnn3+6rK1PPvlkkf2w7rPuAwD849dffzWtP5KMO+64o8i13jAMY/fu3UbDhg1N7Vq1amXk5OQU2uY///mPqX6NGjWMbdu2FVr/m2++MdUPCAgwdu7c6XZOmZmZRtOmTU1t7777bsNutxdY/9SpU0bPnj1N9W+44Qa3/fhyTgAAeKok78k747V6+ZgTSgdBcwCFeuWVV1xeSN9zzz2mBcVmsxmzZ892eXHasGFD4+TJk/4bPAAAfjZkyBCXdfTFF180FixYUOyvEydOuO1v9+7dRpUqVUz9denSxVi8eLGp3s6dO41x48a5jO3777/3eG533nmnqa3VajX++c9/msaZlZVlfPHFF0aNGjVMdTt37mxkZ2d71I8v5wQAgC+U5E3tpKQko379+i7t586da/rj8IEDB1xuvpJkvP766x735av3A3w5JwAAvGXhwoWGxWIxrUnDhw831q9fb6qXnJxsvPXWWy5/SG3durVx+vTpIvtg3WfdBwD4z4UXXuiyDg0YMMBYuHChy3vbiYmJxptvvmlUq1bNpc2MGTOK7CcrK8vo0KGDqU3NmjWNr776ytRPUlKS8eyzz7psAHPvvfd6PKdvv/3WZXxXXXWVsWvXLlO9RYsWGZ07dzbVi4iIMPbu3etRP76cEwAA+a1YsaLAv7O/+eabprWmXr16hf5NvqibowyD1+rlZU4oHRbDcLMHPYBKy263a/To0Zo3b57peEBAgKKiolStWjXt27dPycnJpvNhYWFasGCB+vfv78PRAgBQtlgsFq9da/HixRoyZIjbetOmTdP48eNdPmaqTp06atq0qY4dO6aEhASX8/fff7+mTJni8XjS0tI0ePBgrV+/3nQ8ODhY0dHRCgkJ0d69e5WSkmI6X7t2ba1cuVKtW7f2uC9fzQkAAF9YsmSJLrzwQkc5KipKcXFxHrdftmyZLr74YpePuK5evbqio6OVnJys+Ph42Ww20/nRo0dr9uzZHj8/8eX7Ab6aEwAA3vTaa6/pySefdDlev359NW7cWKmpqdqzZ4+ysrJM52vVqqXFixerU6dObvtg3T//OQEAUBJHjhxRv379tG/fPpdzERERio6OVlhYmJKSkrR3716X96Yl6dFHH9Wbb77ptq+YmBgNGDBAJ06ccOmnRYsWSk9P1759+5SdnW0637t3by1ZskRhYWEez+vee+/Vf/7zH9Mxi8WiJk2aqE6dOtq/f78SExNN561Wq6ZPn66rrrrK4358OScAAPI0a9ZM+/fvL9E1JkyYoC+//LLIOrxWLx9zQinwY8gdQDmQnp5uXHfddS53FBX2VatWLZddRgEAqIw8XTs9+SrO2vrtt98aYWFhHl/7scceK/QjMouSlJRkDB061ON+mjVrZmzevLnY/fhyTgAAlDZvfEznokWLjJo1a3q8Lo4fP97IyMgodj++fD/AV3MCAMCbpkyZYgQFBXm8frVp08Zl11B3WPdZ9wEA/hEfH1/gp5a6+woKCjJeffXVYr0/vWnTJiMqKsrjPoYPH35eO3rabDbj4Ycf9rifKlWqGNOnTy92P76cEwAAeYqz7hT2NWHCBI/64rV6+ZgTvMsqAChCaGiovvvuO82cOVNdu3YttF54eLjuvfdebd++3aMdVwEAQOn4xz/+oa1bt2r8+PEKCgoqtN6gQYO0ZMkSvfHGG+e1I1jNmjW1YMECffLJJ2rZsmWR9Z5++mlt2bLFox3bCuKrOQEAUB4MHTpU27dv1z333KMqVaoUWq9bt2764Ycf9M033ygkJKTY/fjy/QBfzQkAAG+6//77tXnzZl177bVFvlaNjo7We++9p82bN6tVq1bF6oN1n3UfAOAfTZo00aJFi/T9999ryJAhslqLjtZUq1ZN99xzj7Zs2aInnniiWO9Pd+nSRVu2bNFTTz2lGjVqFFqvVatW+vTTT/X777+revXqHl8/j9Vq1dtvv60//vhDAwcOLLRecHCwrr/+em3dulXXXHNNsfuRfDcnAAD8gdfq5WNO8C6LYRTwOT4AUIjY2FitWbNGBw8eVFZ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"text/plain": [ "
" ] @@ -1125,7 +1125,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/recipes/pipelines.ipynb b/docs/recipes/pipelines.ipynb index 9a7a431866..e5b9533737 100644 --- a/docs/recipes/pipelines.ipynb +++ b/docs/recipes/pipelines.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:42.421626Z", - "iopub.status.busy": "2023-09-02T00:48:42.420909Z", - "iopub.status.idle": "2023-09-02T00:48:42.759593Z", - "shell.execute_reply": "2023-09-02T00:48:42.759147Z" + "iopub.execute_input": "2023-11-09T07:13:21.052106Z", + "iopub.status.busy": "2023-11-09T07:13:21.051775Z", + "iopub.status.idle": "2023-11-09T07:13:21.381473Z", + "shell.execute_reply": "2023-11-09T07:13:21.381222Z" }, "tags": [] }, @@ -54,10 +54,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:42.761738Z", - "iopub.status.busy": "2023-09-02T00:48:42.761619Z", - "iopub.status.idle": "2023-09-02T00:48:42.775627Z", - "shell.execute_reply": "2023-09-02T00:48:42.775366Z" + "iopub.execute_input": "2023-11-09T07:13:21.383218Z", + "iopub.status.busy": "2023-11-09T07:13:21.383116Z", + "iopub.status.idle": "2023-11-09T07:13:21.391122Z", + "shell.execute_reply": "2023-11-09T07:13:21.390898Z" }, "tags": [] }, @@ -82,10 +82,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:42.777613Z", - "iopub.status.busy": "2023-09-02T00:48:42.777493Z", - "iopub.status.idle": "2023-09-02T00:48:42.790303Z", - "shell.execute_reply": "2023-09-02T00:48:42.790024Z" + "iopub.execute_input": "2023-11-09T07:13:21.392637Z", + "iopub.status.busy": "2023-11-09T07:13:21.392566Z", + "iopub.status.idle": "2023-11-09T07:13:21.399859Z", + "shell.execute_reply": "2023-11-09T07:13:21.399636Z" }, "tags": [] }, @@ -108,10 +108,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:42.792053Z", - "iopub.status.busy": "2023-09-02T00:48:42.791972Z", - "iopub.status.idle": "2023-09-02T00:48:42.809051Z", - "shell.execute_reply": "2023-09-02T00:48:42.808598Z" + "iopub.execute_input": "2023-11-09T07:13:21.401312Z", + "iopub.status.busy": "2023-11-09T07:13:21.401230Z", + "iopub.status.idle": "2023-11-09T07:13:21.414775Z", + "shell.execute_reply": "2023-11-09T07:13:21.414515Z" }, "tags": [] }, @@ -297,10 +297,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:42.810930Z", - "iopub.status.busy": "2023-09-02T00:48:42.810799Z", - "iopub.status.idle": "2023-09-02T00:48:42.967393Z", - "shell.execute_reply": "2023-09-02T00:48:42.967087Z" + "iopub.execute_input": "2023-11-09T07:13:21.416182Z", + "iopub.status.busy": "2023-11-09T07:13:21.416092Z", + "iopub.status.idle": "2023-11-09T07:13:21.548154Z", + "shell.execute_reply": "2023-11-09T07:13:21.547921Z" }, "tags": [] }, @@ -342,10 +342,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:42.969138Z", - "iopub.status.busy": "2023-09-02T00:48:42.968997Z", - "iopub.status.idle": "2023-09-02T00:48:42.986924Z", - "shell.execute_reply": "2023-09-02T00:48:42.986634Z" + "iopub.execute_input": "2023-11-09T07:13:21.549730Z", + "iopub.status.busy": "2023-11-09T07:13:21.549592Z", + "iopub.status.idle": "2023-11-09T07:13:21.559482Z", + "shell.execute_reply": "2023-11-09T07:13:21.559259Z" }, "tags": [] }, @@ -377,10 +377,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:42.988739Z", - "iopub.status.busy": "2023-09-02T00:48:42.988622Z", - "iopub.status.idle": "2023-09-02T00:48:43.006141Z", - "shell.execute_reply": "2023-09-02T00:48:43.005784Z" + "iopub.execute_input": "2023-11-09T07:13:21.560881Z", + "iopub.status.busy": "2023-11-09T07:13:21.560805Z", + "iopub.status.idle": "2023-11-09T07:13:21.569542Z", + "shell.execute_reply": "2023-11-09T07:13:21.569327Z" }, "tags": [] }, @@ -420,10 +420,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:43.007822Z", - "iopub.status.busy": "2023-09-02T00:48:43.007708Z", - "iopub.status.idle": "2023-09-02T00:48:43.025846Z", - "shell.execute_reply": "2023-09-02T00:48:43.025585Z" + "iopub.execute_input": "2023-11-09T07:13:21.570977Z", + "iopub.status.busy": "2023-11-09T07:13:21.570908Z", + "iopub.status.idle": "2023-11-09T07:13:21.579655Z", + "shell.execute_reply": "2023-11-09T07:13:21.579439Z" }, "tags": [] }, @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:43.027465Z", - "iopub.status.busy": "2023-09-02T00:48:43.027354Z", - "iopub.status.idle": "2023-09-02T00:48:43.044487Z", - "shell.execute_reply": "2023-09-02T00:48:43.044207Z" + "iopub.execute_input": "2023-11-09T07:13:21.581059Z", + "iopub.status.busy": "2023-11-09T07:13:21.580992Z", + "iopub.status.idle": "2023-11-09T07:13:21.590688Z", + "shell.execute_reply": "2023-11-09T07:13:21.590482Z" }, "tags": [] }, @@ -686,10 +686,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:43.046037Z", - "iopub.status.busy": "2023-09-02T00:48:43.045956Z", - "iopub.status.idle": "2023-09-02T00:48:43.062148Z", - "shell.execute_reply": "2023-09-02T00:48:43.061800Z" + "iopub.execute_input": "2023-11-09T07:13:21.592485Z", + "iopub.status.busy": "2023-11-09T07:13:21.592401Z", + "iopub.status.idle": "2023-11-09T07:13:21.601115Z", + "shell.execute_reply": "2023-11-09T07:13:21.600876Z" }, "tags": [] }, @@ -729,7 +729,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/recipes/reading-data.ipynb b/docs/recipes/reading-data.ipynb index 9b49a16165..ffe9fabd12 100644 --- a/docs/recipes/reading-data.ipynb +++ b/docs/recipes/reading-data.ipynb @@ -25,10 +25,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:44.738314Z", - "iopub.status.busy": "2023-09-02T00:48:44.737893Z", - "iopub.status.idle": "2023-09-02T00:48:45.178807Z", - "shell.execute_reply": "2023-09-02T00:48:45.178378Z" + "iopub.execute_input": "2023-11-09T07:13:22.614405Z", + "iopub.status.busy": "2023-11-09T07:13:22.614052Z", + "iopub.status.idle": "2023-11-09T07:13:23.018338Z", + "shell.execute_reply": "2023-11-09T07:13:23.018013Z" }, "tags": [] }, @@ -83,10 +83,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:45.181005Z", - "iopub.status.busy": "2023-09-02T00:48:45.180761Z", - "iopub.status.idle": "2023-09-02T00:48:45.199685Z", - "shell.execute_reply": "2023-09-02T00:48:45.199400Z" + "iopub.execute_input": "2023-11-09T07:13:23.020193Z", + "iopub.status.busy": "2023-11-09T07:13:23.020070Z", + "iopub.status.idle": "2023-11-09T07:13:23.029968Z", + "shell.execute_reply": "2023-11-09T07:13:23.029739Z" }, "tags": [] }, @@ -126,10 +126,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:45.201882Z", - "iopub.status.busy": "2023-09-02T00:48:45.201730Z", - "iopub.status.idle": "2023-09-02T00:48:45.221025Z", - "shell.execute_reply": "2023-09-02T00:48:45.220688Z" + "iopub.execute_input": "2023-11-09T07:13:23.031463Z", + "iopub.status.busy": "2023-11-09T07:13:23.031373Z", + "iopub.status.idle": "2023-11-09T07:13:23.039934Z", + "shell.execute_reply": "2023-11-09T07:13:23.039687Z" }, "tags": [] }, @@ -172,10 +172,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:45.223054Z", - "iopub.status.busy": "2023-09-02T00:48:45.222932Z", - "iopub.status.idle": "2023-09-02T00:48:45.240375Z", - "shell.execute_reply": "2023-09-02T00:48:45.240063Z" + "iopub.execute_input": "2023-11-09T07:13:23.041472Z", + "iopub.status.busy": "2023-11-09T07:13:23.041400Z", + "iopub.status.idle": "2023-11-09T07:13:23.050253Z", + "shell.execute_reply": "2023-11-09T07:13:23.050041Z" }, "tags": [] }, @@ -220,10 +220,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:45.242310Z", - "iopub.status.busy": "2023-09-02T00:48:45.242169Z", - "iopub.status.idle": "2023-09-02T00:48:45.260871Z", - "shell.execute_reply": "2023-09-02T00:48:45.260499Z" + "iopub.execute_input": "2023-11-09T07:13:23.051634Z", + "iopub.status.busy": "2023-11-09T07:13:23.051564Z", + "iopub.status.idle": "2023-11-09T07:13:23.060533Z", + "shell.execute_reply": "2023-11-09T07:13:23.060305Z" }, "tags": [] }, @@ -279,10 +279,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:45.262878Z", - "iopub.status.busy": "2023-09-02T00:48:45.262737Z", - "iopub.status.idle": "2023-09-02T00:48:45.302619Z", - "shell.execute_reply": "2023-09-02T00:48:45.302347Z" + "iopub.execute_input": "2023-11-09T07:13:23.062000Z", + "iopub.status.busy": "2023-11-09T07:13:23.061924Z", + "iopub.status.idle": "2023-11-09T07:13:23.150089Z", + "shell.execute_reply": "2023-11-09T07:13:23.149850Z" }, "tags": [] }, @@ -290,16 +290,16 @@ { "data": { "text/plain": [ - "({'age': 0.0380759064334241,\n", - " 'sex': 0.0506801187398187,\n", - " 'bmi': 0.0616962065186885,\n", - " 'bp': 0.0218723549949558,\n", - " 's1': -0.0442234984244464,\n", - " 's2': -0.0348207628376986,\n", - " 's3': -0.0434008456520269,\n", - " 's4': -0.00259226199818282,\n", - " 's5': 0.0199084208763183,\n", - " 's6': -0.0176461251598052},\n", + "({'age': 0.038075906433423026,\n", + " 'sex': 0.05068011873981862,\n", + " 'bmi': 0.061696206518683294,\n", + " 'bp': 0.0218723855140367,\n", + " 's1': -0.04422349842444599,\n", + " 's2': -0.03482076283769895,\n", + " 's3': -0.04340084565202491,\n", + " 's4': -0.002592261998183278,\n", + " 's5': 0.019907486170462722,\n", + " 's6': -0.01764612515980379},\n", " 151.0)" ] }, @@ -333,10 +333,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:45.304284Z", - "iopub.status.busy": "2023-09-02T00:48:45.304171Z", - "iopub.status.idle": "2023-09-02T00:48:45.367887Z", - "shell.execute_reply": "2023-09-02T00:48:45.367585Z" + "iopub.execute_input": "2023-11-09T07:13:23.151543Z", + "iopub.status.busy": "2023-11-09T07:13:23.151448Z", + "iopub.status.idle": "2023-11-09T07:13:23.208472Z", + "shell.execute_reply": "2023-11-09T07:13:23.208213Z" }, "tags": [] }, @@ -365,10 +365,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:45.369776Z", - "iopub.status.busy": "2023-09-02T00:48:45.369621Z", - "iopub.status.idle": "2023-09-02T00:48:45.387961Z", - "shell.execute_reply": "2023-09-02T00:48:45.387690Z" + "iopub.execute_input": "2023-11-09T07:13:23.210222Z", + "iopub.status.busy": "2023-11-09T07:13:23.210088Z", + "iopub.status.idle": "2023-11-09T07:13:23.220180Z", + "shell.execute_reply": "2023-11-09T07:13:23.219956Z" }, "tags": [] }, @@ -409,7 +409,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/recipes/rolling-computations.ipynb b/docs/recipes/rolling-computations.ipynb index 2f8f9c6f81..a0c30b926a 100644 --- a/docs/recipes/rolling-computations.ipynb +++ b/docs/recipes/rolling-computations.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-09-02T00:48:46.868460Z", - "iopub.status.busy": "2023-09-02T00:48:46.867919Z", - "iopub.status.idle": "2023-09-02T00:48:47.400738Z", - "shell.execute_reply": "2023-09-02T00:48:47.400269Z" + "iopub.execute_input": "2023-11-09T07:13:24.199721Z", + "iopub.status.busy": "2023-11-09T07:13:24.199487Z", + "iopub.status.idle": "2023-11-09T07:13:24.675669Z", + "shell.execute_reply": "2023-11-09T07:13:24.675399Z" } }, "outputs": [ @@ -86,6 +86,7 @@ "proba.MultivariateGaussian\n", "stats.BayesianMean\n", "stats.Cov\n", + "stats.KolmogorovSmirnov\n", "stats.Mean\n", "stats.PearsonCorr\n", "stats.SEM\n", @@ -126,7 +127,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/releases/unreleased.md b/docs/releases/0.20.0.md similarity index 99% rename from docs/releases/unreleased.md rename to docs/releases/0.20.0.md index 29275fe295..48406246ea 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/0.20.0.md @@ -1,4 +1,4 @@ -# Unreleased +# 0.20.0 - 2023-11-09 River's mini-batch methods now support pandas v2. In particular, River conforms to pandas' new sparse API. diff --git a/pyproject.toml b/pyproject.toml index 2fa6af1128..4c4f669477 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "river" -version = "0.19.0" +version = "0.20.0" description = "Online machine learning in Python" authors = ["Max Halford "] diff --git a/river/__version__.py b/river/__version__.py index 148d5ca59a..353196af4f 100644 --- a/river/__version__.py +++ b/river/__version__.py @@ -1,3 +1,3 @@ from __future__ import annotations -__version__ = "0.19.0" +__version__ = "0.20.0" From 69b6085adc587443934ff54bfcee2ed62851d58f Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 09:29:20 +0100 Subject: [PATCH 167/347] fix --- .github/workflows/pypi.yml | 8 ++++---- CONTRIBUTING.md | 3 +++ docs/releases/0.20.0.md | 4 +++- 3 files changed, 10 insertions(+), 5 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 7557de4dab..0497cb707a 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -62,7 +62,7 @@ jobs: - name: Build wheels uses: pypa/cibuildwheel@v2.12.3 env: - CIBW_BUILD: "cp38-* cp39-* cp310-* cp311-* cp312-*" + CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_BEFORE_BUILD: > pip install setuptools-rust cython && rustup default nightly && @@ -96,10 +96,10 @@ jobs: name: Build source distribution runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - - name: Build sdist - run: pipx run build --sdist + - name: Build + run: poetry build - uses: actions/upload-artifact@v2 with: diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6a7f992e30..9058023527 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -190,6 +190,9 @@ make execute-notebooks ```sh RIVER_VERSION=$(python -c "import river; print(river.__version__)") echo $RIVER_VERSION +``` + +```sh git tag $RIVER_VERSION git push origin $RIVER_VERSION ``` diff --git a/docs/releases/0.20.0.md b/docs/releases/0.20.0.md index 48406246ea..53bfa71c5e 100644 --- a/docs/releases/0.20.0.md +++ b/docs/releases/0.20.0.md @@ -1,6 +1,8 @@ # 0.20.0 - 2023-11-09 -River's mini-batch methods now support pandas v2. In particular, River conforms to pandas' new sparse API. +- River's mini-batch methods now support pandas v2. In particular, River conforms to pandas' new sparse API. +- Dropped support for Python 3.8. +- Added support for Python 3.12. ## anomaly From 8286e70f106af32e1cc359df3af19e20e95433c1 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 09:33:53 +0100 Subject: [PATCH 168/347] Update release-docs.yml --- .github/workflows/release-docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 0572eb1ea1..7262d80c55 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -15,7 +15,7 @@ jobs: - name: Build River uses: ./.github/actions/install-env with: - python-version: "3.12" + python-version: "3.11" - name: Install extra Ubuntu dependencies run: sudo apt-get install graphviz pandoc From 79427a2b9d5a0b7064cd5a98808eb89053605134 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 09:35:32 +0100 Subject: [PATCH 169/347] Update pypi.yml --- .github/workflows/pypi.yml | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 0497cb707a..752a9b7d97 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -98,7 +98,12 @@ jobs: steps: - uses: actions/checkout@v3 - - name: Build + - name: Build River + uses: ./.github/actions/install-env + with: + python-version: "3.12" + + - name: Build dist run: poetry build - uses: actions/upload-artifact@v2 From 17c0762fc7d9b02d81bb6a0a716f556774280eab Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 09:55:10 +0100 Subject: [PATCH 170/347] Update release-docs.yml --- .github/workflows/release-docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 7262d80c55..ac57033626 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -15,7 +15,7 @@ jobs: - name: Build River uses: ./.github/actions/install-env with: - python-version: "3.11" + python-version: "3.10" - name: Install extra Ubuntu dependencies run: sudo apt-get install graphviz pandoc From 9cadfb6d1882a56f3057412ca35b5c10478ee065 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 10:07:43 +0100 Subject: [PATCH 171/347] wip --- .github/workflows/release-docs.yml | 9 ++++----- pyproject.toml | 1 - 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index ac57033626..1334dd9ec0 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -15,17 +15,16 @@ jobs: - name: Build River uses: ./.github/actions/install-env with: - python-version: "3.10" + python-version: "3.12" - name: Install extra Ubuntu dependencies run: sudo apt-get install graphviz pandoc - name: Install extra Python dependencies run: | - python -m pip install --upgrade pip - poetry install --with compat --with docs - pip install rich - python -m spacy download en_core_web_sm + poetry install --with docs --with compat + poetry install rich + poetry run python -m spacy download en_core_web_sm - name: Use Rich in notebooks run: | diff --git a/pyproject.toml b/pyproject.toml index 4c4f669477..0db8a679d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,7 +38,6 @@ optional = true [tool.poetry.group.compat.dependencies] scikit-learn = "^1.0.1" sqlalchemy = "^2.0.0" -vaex = "^4.0.0" [tool.poetry.group.docs] optional = true From 9f65d1c71841b822cb992d95eea24b7f93430c5e Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 10:17:23 +0100 Subject: [PATCH 172/347] Update release-docs.yml --- .github/workflows/release-docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 1334dd9ec0..b999c93fff 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -23,7 +23,7 @@ jobs: - name: Install extra Python dependencies run: | poetry install --with docs --with compat - poetry install rich + poetry run pip install rich poetry run python -m spacy download en_core_web_sm - name: Use Rich in notebooks From 958fdad4a5467b68bc73b21a2894c13a19f971e0 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 10:38:09 +0100 Subject: [PATCH 173/347] update poetry --- .github/workflows/release-docs.yml | 4 +- poetry.lock | 1369 +--------------------------- pyproject.toml | 2 + 3 files changed, 11 insertions(+), 1364 deletions(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index b999c93fff..6e4782cbbe 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -23,17 +23,15 @@ jobs: - name: Install extra Python dependencies run: | poetry install --with docs --with compat - poetry run pip install rich poetry run python -m spacy 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{extras = ["psutil"], version = "^3.3.1"} ipykernel = "^6.26.0" +ipython = "^8.17.2" +rich = "^13.6.0" [tool.poetry.group.compat] optional = true From 1d98c0756896d9316aa8319ba32668f088263e7e Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 10:49:56 +0100 Subject: [PATCH 174/347] Update pypi.yml --- .github/workflows/pypi.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 752a9b7d97..66911c6360 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -68,7 +68,7 @@ jobs: rustup default nightly && rustup show # rust doesn't seem to be available for musl linux on i686 - CIBW_SKIP: "*-musllinux_i686" + CIBW_SKIP: "*-musllinux_{i686,aarch64}" # we build for "alt_arch_name" if it exists, else 'auto CIBW_ARCHS: ${{ matrix.alt_arch_name || 'auto' }} From ebc20a98e5646626caef5cea6fb6eb2530b784cd Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 10:50:30 +0100 Subject: [PATCH 175/347] Update Makefile --- Makefile | 1 - 1 file changed, 1 deletion(-) diff --git a/Makefile b/Makefile index 007d750115..748db226f2 100644 --- a/Makefile +++ b/Makefile @@ -4,7 +4,6 @@ format: pre-commit run --all-files execute-notebooks: - export PYDEVD_DISABLE_FILE_VALIDATION = 1 jupyter nbconvert --execute --to notebook --inplace docs/introduction/*/*.ipynb --ExecutePreprocessor.timeout=-1 jupyter nbconvert --execute --to notebook --inplace docs/recipes/*.ipynb --ExecutePreprocessor.timeout=-1 jupyter nbconvert --execute --to notebook --inplace docs/examples/*.ipynb --ExecutePreprocessor.timeout=-1 From 5eafef33b68dc6ba4d7b892fb2e906450169ccdc Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 10:57:37 +0100 Subject: [PATCH 176/347] add jupyter --- poetry.lock | 672 ++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 1 + 2 files changed, 672 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 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= "Send2Trash-1.8.2-py3-none-any.whl", hash = "sha256:a384719d99c07ce1eefd6905d2decb6f8b7ed054025bb0e618919f945de4f679"}, + {file = "Send2Trash-1.8.2.tar.gz", hash = "sha256:c132d59fa44b9ca2b1699af5c86f57ce9f4c5eb56629d5d55fbb7a35f84e2312"}, +] + +[package.extras] +nativelib = ["pyobjc-framework-Cocoa", "pywin32"] +objc = ["pyobjc-framework-Cocoa"] +win32 = ["pywin32"] + [[package]] name = "setuptools" version = "68.2.2" @@ -3386,6 +3958,17 @@ ssh = ["paramiko"] test = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "moto[server]", "paramiko", "pytest", "pytest-rerunfailures", "requests", "responses"] webhdfs = ["requests"] +[[package]] +name = "sniffio" +version = "1.3.0" +description = "Sniff out which async library your code is running under" +optional = false +python-versions = ">=3.7" +files = [ + {file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"}, + {file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"}, +] + [[package]] name = "snowballstemmer" version = "2.2.0" @@ -3836,6 +4419,26 @@ files = [ [package.extras] widechars = ["wcwidth"] +[[package]] +name = "terminado" +version = "0.17.1" +description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library." +optional = false +python-versions = ">=3.7" +files = [ + {file = "terminado-0.17.1-py3-none-any.whl", hash = "sha256:8650d44334eba354dd591129ca3124a6ba42c3d5b70df5051b6921d506fdaeae"}, + {file = "terminado-0.17.1.tar.gz", hash = "sha256:6ccbbcd3a4f8a25a5ec04991f39a0b8db52dfcd487ea0e578d977e6752380333"}, +] + +[package.dependencies] +ptyprocess = {version = "*", markers = "os_name != \"nt\""} +pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""} +tornado = ">=6.1.0" + +[package.extras] +docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] +test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"] + [[package]] name = "text-unidecode" version = "1.3" @@ -4044,6 +4647,17 @@ dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2 doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"] test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"] +[[package]] +name = "types-python-dateutil" +version = "2.8.19.14" +description = "Typing stubs for python-dateutil" +optional = false +python-versions = "*" +files = [ + {file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"}, + {file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"}, +] + [[package]] name = "typing-extensions" version = "4.8.0" @@ -4066,6 +4680,20 @@ files = [ {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"}, ] +[[package]] +name = "uri-template" +version = "1.3.0" +description = "RFC 6570 URI Template Processor" +optional = false +python-versions = ">=3.7" +files = [ + {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"}, + {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"}, +] + +[package.extras] +dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"] + [[package]] name = "urllib3" version = "2.0.7" @@ -4265,6 +4893,21 @@ srsly = ">=2.4.3,<3.0.0" typer = ">=0.3.0,<0.10.0" wasabi = ">=0.9.1,<1.2.0" +[[package]] +name = "webcolors" +version = "1.13" +description = "A library for working with the color formats defined by HTML and CSS." +optional = false +python-versions = ">=3.7" +files = [ + {file = "webcolors-1.13-py3-none-any.whl", hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf"}, + {file = "webcolors-1.13.tar.gz", hash = "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"}, +] + +[package.extras] +docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"] +tests = ["pytest", "pytest-cov"] + [[package]] name = "webencodings" version = "0.5.1" @@ -4276,6 +4919,22 @@ files = [ {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, ] +[[package]] +name = "websocket-client" +version = "1.6.4" +description = "WebSocket client for Python with low level API options" +optional = false +python-versions = ">=3.8" +files = [ + {file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"}, + {file = "websocket_client-1.6.4-py3-none-any.whl", hash = "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24"}, +] + +[package.extras] +docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"] +optional = ["python-socks", "wsaccel"] +test = ["websockets"] + [[package]] name = "werkzeug" version = "3.0.1" @@ -4293,6 +4952,17 @@ MarkupSafe = ">=2.1.1" [package.extras] watchdog = ["watchdog (>=2.3)"] +[[package]] +name = "widgetsnbextension" +version = "4.0.9" +description = "Jupyter interactive widgets for Jupyter Notebook" +optional = false +python-versions = ">=3.7" +files = [ + {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"}, + {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"}, +] + [[package]] name = "zipp" version = "3.17.0" @@ -4311,4 +4981,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "76bee1bf0656ed0c5a2fe7c83186ebda3ee54df96bcac50e438fce4a823d3c18" +content-hash = "b658dd32bf42004643f9b39e736e9257ef70b62bade016804ac0b0bb793cb09a" diff --git a/pyproject.toml b/pyproject.toml index c89b85e66e..441af5d7f7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,6 +33,7 @@ pytest-xdist = {extras = ["psutil"], version = "^3.3.1"} ipykernel = "^6.26.0" ipython = "^8.17.2" rich = "^13.6.0" +jupyter = "^1.0.0" [tool.poetry.group.compat] optional = true From fc4bafbb8ecc486f164629fa8f2cfe0209f6d966 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 11:12:31 +0100 Subject: [PATCH 177/347] Update release-docs.yml --- .github/workflows/release-docs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 6e4782cbbe..878631b9ea 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -32,6 +32,7 @@ jobs: - name: Execute notebooks run: | + poetry shell make execute-notebooks - name: Build docs From 4c72b02391c8fed8b6bf7270d905c612019b7f8f Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 11:21:54 +0100 Subject: [PATCH 178/347] upgrade cibuildwheel --- .github/workflows/pypi.yml | 2 +- pyproject.toml | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 66911c6360..d12b29e5c3 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -60,7 +60,7 @@ jobs: platforms: all - name: Build wheels - uses: pypa/cibuildwheel@v2.12.3 + uses: pypa/cibuildwheel@v2 env: CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_BEFORE_BUILD: > diff --git a/pyproject.toml b/pyproject.toml index 441af5d7f7..3493e69c7b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,6 +6,9 @@ build-backend = "poetry.core.masonry.api" name = "river" version = "0.20.0" description = "Online machine learning in Python" +readme = "README.md" +homepage = "https://riverml.xyz/" +repository = "https://github.com/online-ml/river/" authors = ["Max Halford "] [tool.poetry.build] From 338ba01173a13db1b74e1ef113d960ded9bfc503 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 11:23:42 +0100 Subject: [PATCH 179/347] Update release-docs.yml --- .github/workflows/release-docs.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 878631b9ea..07a28981b7 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -32,11 +32,13 @@ jobs: - name: Execute notebooks run: | - poetry shell + source $VENV make execute-notebooks - name: Build docs - run: make doc + run: | + source $VENV + make doc - name: Deploy docs env: From 168617fc4dc736719cbb5a805930028d30d30426 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 11:56:54 +0100 Subject: [PATCH 180/347] wip --- .github/workflows/pypi.yml | 2 +- .github/workflows/release-docs.yml | 4 +--- poetry.lock | 17 ++++++++++------- pyproject.toml | 1 + 4 files changed, 13 insertions(+), 11 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index d12b29e5c3..6444956204 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -60,7 +60,7 @@ jobs: platforms: all - name: Build wheels - uses: pypa/cibuildwheel@v2 + uses: pypa/cibuildwheel@v2.16.2 env: CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_BEFORE_BUILD: > diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 07a28981b7..5fdfc5324e 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -44,17 +44,15 @@ jobs: env: GH_TOKEN: ${{ secrets.GitHubToken }} run: | + source $VENV git config user.name github-actions git config user.email github-actions@github.com git config pull.rebase false - git add --all git commit -m "Execute notebooks" - git fetch git checkout gh-pages git pull - git checkout main RIVER_VERSION=$(python -c "import river; print(river.__version__)") mike deploy ${RIVER_VERSION} latest --update-aliases --push --force --remote https://${GH_TOKEN}@github.com/online-ml/river.git diff --git a/poetry.lock b/poetry.lock index 807c007d6f..78c08a6119 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2060,24 +2060,27 @@ files = [ [[package]] name = "mike" -version = "1.1.2" +version = "2.0.0" description = "Manage multiple versions of your MkDocs-powered documentation" optional = false python-versions = "*" files = [ - {file = "mike-1.1.2-py3-none-any.whl", hash = "sha256:4c307c28769834d78df10f834f57f810f04ca27d248f80a75f49c6fa2d1527ca"}, - {file = "mike-1.1.2.tar.gz", hash = "sha256:56c3f1794c2d0b5fdccfa9b9487beb013ca813de2e3ad0744724e9d34d40b77b"}, + {file = "mike-2.0.0-py3-none-any.whl", hash = "sha256:87f496a65900f93ba92d72940242b65c86f3f2f82871bc60ebdcffc91fad1d9e"}, + {file = "mike-2.0.0.tar.gz", hash = "sha256:566f1cab1a58cc50b106fb79ea2f1f56e7bfc8b25a051e95e6eaee9fba0922de"}, ] [package.dependencies] -jinja2 = "*" +importlib-metadata = "*" +importlib-resources = "*" +jinja2 = ">=2.7" mkdocs = ">=1.0" +pyparsing = ">=3.0" pyyaml = ">=5.1" verspec = "*" [package.extras] -dev = ["coverage", "flake8 (>=3.0)", "shtab"] -test = ["coverage", "flake8 (>=3.0)", "shtab"] +dev = ["coverage", "flake8 (>=3.0)", "flake8-quotes", "shtab"] +test = ["coverage", "flake8 (>=3.0)", "flake8-quotes", "shtab"] [[package]] name = "mistune" @@ -4981,4 +4984,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "b658dd32bf42004643f9b39e736e9257ef70b62bade016804ac0b0bb793cb09a" +content-hash = "87961d28c7c601a8799ac7c4294ef9d40565d0e954a0a6f2444da971767a4065" diff --git a/pyproject.toml b/pyproject.toml index 3493e69c7b..8d612c6e27 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,7 @@ ipykernel = "^6.26.0" ipython = "^8.17.2" rich = "^13.6.0" jupyter = "^1.0.0" +mike = "^2.0.0" [tool.poetry.group.compat] optional = true From 266d0cd6da8807aeb124bb9a01e83807bde69236 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 13:09:28 +0100 Subject: [PATCH 181/347] Update pypi.yml --- .github/workflows/pypi.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 6444956204..4436e9d9e6 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -119,7 +119,7 @@ jobs: name: artifact path: dist - - uses: pypa/gh-action-pypi-publish@v1.4.2 + - uses: pypa/gh-action-pypi-publish@v1.8.10 with: - user: ${{ secrets.pypi_user }} - password: ${{ secrets.pypi_password }} + user: __token__ + password: ${{ secrets.PYPI_API_TOKEN }} From 358b959ccff27d743489303ed8081465da9720dd Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 13:10:45 +0100 Subject: [PATCH 182/347] Update release-docs.yml --- .github/workflows/release-docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 5fdfc5324e..e152015000 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -55,4 +55,4 @@ jobs: git pull git checkout main RIVER_VERSION=$(python -c "import river; print(river.__version__)") - mike deploy ${RIVER_VERSION} latest --update-aliases --push --force --remote https://${GH_TOKEN}@github.com/online-ml/river.git + mike deploy ${RIVER_VERSION} latest --update-aliases --push --remote https://${GH_TOKEN}@github.com/online-ml/river.git From ab7c53d2176a92ba1854eb6125cbc04860ad1373 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 14:32:44 +0100 Subject: [PATCH 183/347] 0.20.1 --- .github/workflows/pypi.yml | 4 ++-- docs/releases/0.20.1.md | 3 +++ pyproject.toml | 2 +- river/__version__.py | 2 +- 4 files changed, 7 insertions(+), 4 deletions(-) create mode 100644 docs/releases/0.20.1.md diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 4436e9d9e6..42f8d226e5 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -106,7 +106,7 @@ jobs: - name: Build dist run: poetry build - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: path: dist/*.tar.gz @@ -114,7 +114,7 @@ jobs: needs: [build_wheels, build_sdist] runs-on: ubuntu-latest steps: - - uses: actions/download-artifact@v2 + - uses: actions/download-artifact@v3 with: name: artifact path: dist diff --git a/docs/releases/0.20.1.md b/docs/releases/0.20.1.md new file mode 100644 index 0000000000..d16524c7d4 --- /dev/null +++ b/docs/releases/0.20.1.md @@ -0,0 +1,3 @@ +# 0.20.1 - 2023-11-09 + +Dummy release to make wheels available. No actual difference with v0.20.0. diff --git a/pyproject.toml b/pyproject.toml index 8d612c6e27..41d9b3e64e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "river" -version = "0.20.0" +version = "0.20.1" description = "Online machine learning in Python" readme = "README.md" homepage = "https://riverml.xyz/" diff --git a/river/__version__.py b/river/__version__.py index 353196af4f..6008fcce9d 100644 --- a/river/__version__.py +++ b/river/__version__.py @@ -1,3 +1,3 @@ from __future__ import annotations -__version__ = "0.20.0" +__version__ = "0.20.1" From 3758eb1e0ae7042cd648f352ed1c16fac51b55db Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 9 Nov 2023 15:51:14 +0100 Subject: [PATCH 184/347] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 35132f2237..b7e8fb7c74 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@

- river_logo + river_logo

From 48ca13ee2612c75957550f1de5053bebfe84a227 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 18 Nov 2023 10:46:59 +0100 Subject: [PATCH 185/347] make cm inherit from multi-class metric --- river/linear_model/pa.py | 6 ++++-- river/metrics/base.py | 15 ++++++++++----- river/metrics/confusion.py | 13 +++++++------ river/metrics/test_confusion.py | 21 +++++++++++++++++++++ 4 files changed, 42 insertions(+), 13 deletions(-) diff --git a/river/linear_model/pa.py b/river/linear_model/pa.py index bf65f72163..7a751fe1c5 100644 --- a/river/linear_model/pa.py +++ b/river/linear_model/pa.py @@ -73,7 +73,8 @@ class PARegressor(BasePA, base.Regressor): ... metric = metric.update(yi, y_pred) >>> print(metric) - MAE: 9.809402, MSE: 472.393532 + MAE: 9.809402 + MSE: 472.393532 References ---------- @@ -156,7 +157,8 @@ class PAClassifier(BasePA, base.Classifier): ... metric = metric.update(yi, model.predict_proba_one(xi)) >>> print(metric) - Accuracy: 88.46%, LogLoss: 0.325727 + Accuracy: 88.46% + LogLoss: 0.325727... References ---------- diff --git a/river/metrics/base.py b/river/metrics/base.py index b34b571cac..9af8f6190d 100644 --- a/river/metrics/base.py +++ b/river/metrics/base.py @@ -7,8 +7,6 @@ from river import base, stats, utils -from . import confusion - __all__ = [ "BinaryMetric", "ClassificationMetric", @@ -79,9 +77,16 @@ class ClassificationMetric(Metric): _fmt = ".2%" # output a percentage, e.g. 0.427 becomes "42,7%" - def __init__(self, cm: confusion.ConfusionMatrix | None = None): + def __init__(self, cm: "confusion.ConfusionMatrix" | None = None): + # HACK: there is a circular dependency between ConfusionMatrix and ClassificationMetric. We + # use ConfusionMatrix here so as to express metrics in terms of false/true + # positives/negatives. But for UX reasons, we also want ConfusionMatrix to be a + # ClassificationMetric, so that it can used in places a ClassificationMetric can be used, + # such as evaluate.progressive_val_score. + from river import metrics + if cm is None: - cm = confusion.ConfusionMatrix() + cm = metrics.ConfusionMatrix() self.cm = cm def update(self, y_true, y_pred, sample_weight=1.0): @@ -220,7 +225,7 @@ class Metrics(Metric, collections.UserList): """ - def __init__(self, metrics, str_sep=", "): + def __init__(self, metrics, str_sep="\n"): super().__init__(metrics) self.str_sep = str_sep diff --git a/river/metrics/confusion.py b/river/metrics/confusion.py index 7c16809fa4..a2be281857 100644 --- a/river/metrics/confusion.py +++ b/river/metrics/confusion.py @@ -3,10 +3,11 @@ import functools from collections import defaultdict +from river import metrics from river import utils -class ConfusionMatrix: +class ConfusionMatrix(metrics.base.MultiClassMetric): """Confusion Matrix for binary and multi-class classification. Parameters @@ -128,9 +129,9 @@ def total_false_positives(self): def total_false_negatives(self): return sum(self.false_negatives(label) for label in self.classes) - def works_with(self, model) -> bool: - return utils.inspect.isclassifier(model) - @property - def requires_labels(self): - return True + def bigger_is_better(self): + raise NotImplementedError + + def get(self): + raise NotImplementedError diff --git a/river/metrics/test_confusion.py b/river/metrics/test_confusion.py index 55d3e7f793..a30f4f2102 100644 --- a/river/metrics/test_confusion.py +++ b/river/metrics/test_confusion.py @@ -14,3 +14,24 @@ def test_issue_1443(): for _ in evaluate.iter_progressive_val_score(dataset, model, metric): pass + + +def test_confusion_and_other_metrics(): + """ + + >>> dataset = datasets.Phishing() + + >>> model = preprocessing.StandardScaler() | linear_model.LogisticRegression( + ... optimizer=optim.SGD(0.1) + ... ) + + >>> metric = metrics.ConfusionMatrix() + metrics.F1() + metrics.Accuracy() + + >>> evaluate.progressive_val_score(dataset, model, metric) + False True + False 613 89 + True 49 499 + F1: 87.85% + Accuracy: 88.96% + + """ From 7b2f1b867db7b107f9f5498ec550bfe9cb228220 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADlia=20Takaguti=20Dicezare?= <85309092+mariliatd@users.noreply.github.com> Date: Thu, 23 Nov 2023 07:25:26 -0300 Subject: [PATCH 186/347] Rename sample_weight to w (code quality) (#1457) * rename sample_weight to w in cluster module files Signed-off-by: mariliatd * rename sample_weight to w in ensemble module files Signed-off-by: mariliatd * rename sample_weight to w in facto module files Signed-off-by: mariliatd * rename sample_weight to w in forest and tree modules files Signed-off-by: mariliatd * rename sample_weight to w in metrics module files Signed-off-by: mariliatd * rename sample_weight to w in tree splitter module files Signed-off-by: mariliatd * add note to unreleased.md file Signed-off-by: mariliatd --------- Signed-off-by: mariliatd --- docs/releases/unreleased.md | 27 +++++++++++++ river/cluster/dbstream.py | 4 +- river/cluster/denstream.py | 4 +- river/cluster/streamkmeans.py | 4 +- river/cluster/textclust.py | 4 +- river/ensemble/streaming_random_patches.py | 18 ++++----- river/facto/base.py | 8 ++-- river/forest/adaptive_random_forest.py | 4 +- river/forest/online_extra_trees.py | 4 +- river/metrics/base.py | 38 +++++++++---------- river/metrics/confusion.py | 20 +++++----- river/metrics/mse.py | 4 +- river/metrics/multioutput/base.py | 8 ++-- river/metrics/multioutput/confusion.py | 8 ++-- river/metrics/multioutput/macro.py | 8 ++-- river/metrics/multioutput/micro.py | 8 ++-- river/metrics/multioutput/per_output.py | 8 ++-- river/metrics/multioutput/sample_average.py | 8 ++-- river/metrics/r2.py | 12 +++--- river/metrics/roc_auc.py | 8 ++-- river/metrics/silhouette.py | 4 +- river/metrics/test_metrics.py | 2 +- river/tree/extremely_fast_decision_tree.py | 24 ++++++------ .../hoeffding_adaptive_tree_classifier.py | 6 +-- .../tree/hoeffding_adaptive_tree_regressor.py | 6 +-- river/tree/hoeffding_tree_classifier.py | 10 ++--- river/tree/hoeffding_tree_regressor.py | 10 ++--- river/tree/isoup_tree_regressor.py | 6 +-- river/tree/nodes/efdtc_nodes.py | 18 ++++----- river/tree/nodes/hatc_nodes.py | 20 +++++----- river/tree/nodes/hatr_nodes.py | 18 ++++----- river/tree/nodes/htc_nodes.py | 16 ++++---- river/tree/nodes/htr_nodes.py | 16 ++++---- river/tree/nodes/isouptr_nodes.py | 16 ++++---- river/tree/nodes/leaf.py | 14 +++---- river/tree/splitter/base.py | 4 +- river/tree/splitter/ebst_splitter.py | 28 +++++++------- river/tree/splitter/exhaustive_splitter.py | 26 ++++++------- river/tree/splitter/gaussian_splitter.py | 4 +- river/tree/splitter/histogram_splitter.py | 4 +- .../tree/splitter/nominal_splitter_classif.py | 10 ++--- river/tree/splitter/nominal_splitter_reg.py | 14 +++---- river/tree/splitter/qo_splitter.py | 18 ++++----- river/tree/splitter/random_splitter.py | 12 +++--- river/tree/splitter/tebst_splitter.py | 4 +- 45 files changed, 272 insertions(+), 245 deletions(-) create mode 100644 docs/releases/unreleased.md diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md new file mode 100644 index 0000000000..efdbb9dd40 --- /dev/null +++ b/docs/releases/unreleased.md @@ -0,0 +1,27 @@ +# Unreleased + +## cluster + +- Renamed `sample_weight` to `w`. + +## ensemble + +- Renamed `sample_weight` to `w`. + +## facto + +- Renamed `sample_weight` to `w`. + +## forest + +- Renamed `sample_weight` to `w`. + +## tree + +- Renamed `sample_weight` to `w`. + +## metrics + +- Renamed `sample_weight` to `w`. + + diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 10966aa284..88bdc0149f 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -389,7 +389,7 @@ def _recluster(self): self.clustering_is_up_to_date = True - def learn_one(self, x, sample_weight=None): + def learn_one(self, x, w=None): self._update(x) if self._time_stamp % self.cleanup_interval == 0: @@ -399,7 +399,7 @@ def learn_one(self, x, sample_weight=None): return self - def predict_one(self, x, sample_weight=None): + def predict_one(self, x, w=None): self._recluster() min_distance = math.inf diff --git a/river/cluster/denstream.py b/river/cluster/denstream.py index c6e16bbff2..e663cc1506 100644 --- a/river/cluster/denstream.py +++ b/river/cluster/denstream.py @@ -313,7 +313,7 @@ def _initial_dbscan(self): else: item.covered = False - def learn_one(self, x, sample_weight=None): + def learn_one(self, x, w=None): self._n_samples_seen += 1 # control the stream speed if self._n_samples_seen % self.stream_speed == 0: @@ -352,7 +352,7 @@ def learn_one(self, x, sample_weight=None): self.o_micro_clusters.pop(j) return self - def predict_one(self, x, sample_weight=None): + def predict_one(self, x, w=None): # This function handles the case when a clustering request arrives. # implementation of the DBSCAN algorithm proposed by Ester et al. if not self.initialized: diff --git a/river/cluster/streamkmeans.py b/river/cluster/streamkmeans.py index 083d24ae3e..76a02d9d45 100644 --- a/river/cluster/streamkmeans.py +++ b/river/cluster/streamkmeans.py @@ -84,7 +84,7 @@ def __init__(self, chunk_size=10, n_clusters=2, **kwargs): self._temp_chunk = {} self.centers = {} - def learn_one(self, x, sample_weight=None): + def learn_one(self, x, w=None): self.time_stamp += 1 index = self.time_stamp % self.chunk_size @@ -107,7 +107,7 @@ def learn_one(self, x, sample_weight=None): return self - def predict_one(self, x, sample_weight=None): + def predict_one(self, x, w=None): def get_distance(c): return utils.math.minkowski_distance(self.centers[c], x, 2) diff --git a/river/cluster/textclust.py b/river/cluster/textclust.py index aa42d81be1..0407991043 100644 --- a/river/cluster/textclust.py +++ b/river/cluster/textclust.py @@ -153,7 +153,7 @@ def __init__( self.micro_distance = self.distances(self.micro_distance) self.macro_distance = self.distances(self.macro_distance) - def learn_one(self, x, t=None, sample_weight=None): + def learn_one(self, x, t=None, w=None): localdict = {} for key in x.keys(): new_key = key @@ -213,7 +213,7 @@ def learn_one(self, x, t=None, sample_weight=None): ## predicts the cluster number. The type specifies whether this should happen on micro-cluster ## or macro-cluster level - def predict_one(self, x, sample_weight=None, type="micro"): + def predict_one(self, x, w=None, type="micro"): localdict = {} for key in x.keys(): new_key = key diff --git a/river/ensemble/streaming_random_patches.py b/river/ensemble/streaming_random_patches.py index 07ee620c1a..7cd9f87992 100644 --- a/river/ensemble/streaming_random_patches.py +++ b/river/ensemble/streaming_random_patches.py @@ -109,7 +109,7 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs): k = poisson(rate=self.lam, rng=self._rng) if k == 0: continue - model.learn_one(x=x, y=y, sample_weight=k, n_samples_seen=self._n_samples_seen) + model.learn_one(x=x, y=y, w=k, n_samples_seen=self._n_samples_seen) return self @@ -532,7 +532,7 @@ def learn_one( x: dict, y: base.typing.ClfTarget, *, - sample_weight: int, + w: int, n_samples_seen: int, **kwargs, ): @@ -543,8 +543,8 @@ def learn_one( # Use all features x_subset = x - # TODO Find a way to verify if the model natively supports sample_weight - for _ in range(int(sample_weight)): + # TODO Find a way to verify if the model natively supports sample_weight (w) + for _ in range(int(w)): self.model.learn_one(x=x_subset, y=y, **kwargs) if self._background_learner: @@ -552,7 +552,7 @@ def learn_one( # Note: Pass the original instance x so features are correctly # selected based on the corresponding subspace self._background_learner.learn_one( - x=x, y=y, sample_weight=sample_weight, n_samples_seen=n_samples_seen # type: ignore + x=x, y=y, w=w, n_samples_seen=n_samples_seen # type: ignore ) if not self.disable_drift_detector and not self.is_background_learner: @@ -830,7 +830,7 @@ def learn_one( x: dict, y: base.typing.RegTarget, *, - sample_weight: int, + w: int, n_samples_seen: int, **kwargs, ): @@ -842,8 +842,8 @@ def learn_one( # Use all features x_subset = x - # TODO Find a way to verify if the model natively supports sample_weight - for _ in range(int(sample_weight)): + # TODO Find a way to verify if the model natively supports sample_weight (w) + for _ in range(int(w)): self.model.learn_one(x=x_subset, y=y, **kwargs) # Drift detection input @@ -860,7 +860,7 @@ def learn_one( # Note: Pass the original instance x so features are correctly # selected based on the corresponding subspace self._background_learner.learn_one( - x=x, y=y, sample_weight=sample_weight, n_samples_seen=n_samples_seen # type: ignore + x=x, y=y, w=w, n_samples_seen=n_samples_seen # type: ignore ) if not self.disable_drift_detector and not self.is_background_learner: diff --git a/river/facto/base.py b/river/facto/base.py index 802e161d6f..46957e8933 100644 --- a/river/facto/base.py +++ b/river/facto/base.py @@ -65,20 +65,20 @@ def __init__( def _init_latents(self) -> collections.defaultdict: """Initializes latent weights dict.""" - def learn_one(self, x, y, sample_weight=1.0): + def learn_one(self, x, y, w=1.0): x = self._ohe_cat_features(x) if self.sample_normalization: x_l2_norm = sum(xj**2 for xj in x.values()) ** 0.5 x = {j: xj / x_l2_norm for j, xj in x.items()} - return self._learn_one(x, y, sample_weight=sample_weight) + return self._learn_one(x, y, w=w) def _ohe_cat_features(self, x): """One hot encodes string features considering them as categorical.""" return dict((f"{j}_{xj}", 1) if isinstance(xj, str) else (j, xj) for j, xj in x.items()) - def _learn_one(self, x, y, sample_weight=1.0): + def _learn_one(self, x, y, w=1.0): # Calculate the gradient of the loss with respect to the raw output g_loss = self.loss.gradient(y_true=y, y_pred=self._raw_dot(x)) @@ -86,7 +86,7 @@ def _learn_one(self, x, y, sample_weight=1.0): g_loss = utils.math.clamp(g_loss, minimum=-self.clip_gradient, maximum=self.clip_gradient) # Apply the sample weight - g_loss *= sample_weight + g_loss *= w # Update the intercept intercept_lr = self.intercept_lr.get(self.weight_optimizer.n_iterations) diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index c96d83e231..d432ac997a 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -169,9 +169,9 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs): k = poisson(rate=self.lambda_value, rng=self._rng) if k > 0: if not self._warning_detection_disabled and self._background[i] is not None: - self._background[i].learn_one(x=x, y=y, sample_weight=k) # type: ignore + self._background[i].learn_one(x=x, y=y, w=k) # type: ignore - model.learn_one(x=x, y=y, sample_weight=k) + model.learn_one(x=x, y=y, w=k) drift_input = None if not self._warning_detection_disabled: diff --git a/river/forest/online_extra_trees.py b/river/forest/online_extra_trees.py index ce904c9b83..1e43b9e661 100644 --- a/river/forest/online_extra_trees.py +++ b/river/forest/online_extra_trees.py @@ -314,10 +314,10 @@ def learn_one(self, x, y): if w == 0: # Skip model update if w is zero continue - model.learn_one(x, y, sample_weight=w) + model.learn_one(x, y, w=w) if i in self._background_trees: - self._background_trees[i].learn_one(x, y, sample_weight=w) + self._background_trees[i].learn_one(x, y, w=w) trained.append(i) diff --git a/river/metrics/base.py b/river/metrics/base.py index b34b571cac..af9997a8e6 100644 --- a/river/metrics/base.py +++ b/river/metrics/base.py @@ -84,19 +84,19 @@ def __init__(self, cm: confusion.ConfusionMatrix | None = None): cm = confusion.ConfusionMatrix() self.cm = cm - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): self.cm.update( y_true, y_pred, - sample_weight=sample_weight, + w=w, ) return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): self.cm.revert( y_true, y_pred, - sample_weight=sample_weight, + w=w, ) return self @@ -148,21 +148,21 @@ def update( self, y_true: bool, y_pred: bool | float | dict[bool, float], - sample_weight=1.0, + w=1.0, ) -> BinaryMetric: if self.requires_labels: y_pred = y_pred == self.pos_val - return super().update(y_true == self.pos_val, y_pred, sample_weight) + return super().update(y_true == self.pos_val, y_pred, w) def revert( self, y_true: bool, y_pred: bool | float | dict[bool, float], - sample_weight=1.0, + w=1.0, ) -> BinaryMetric: if self.requires_labels: y_pred = y_pred == self.pos_val - return super().revert(y_true == self.pos_val, y_pred, sample_weight) + return super().revert(y_true == self.pos_val, y_pred, w) class MultiClassMetric(ClassificationMetric): @@ -224,7 +224,7 @@ def __init__(self, metrics, str_sep=", "): super().__init__(metrics) self.str_sep = str_sep - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): # If the metrics are classification metrics, then we have to handle the case where some # of the metrics require labels, whilst others need to be fed probabilities if hasattr(self, "requires_labels") and not self.requires_labels: @@ -239,19 +239,19 @@ def update(self, y_true, y_pred, sample_weight=1.0): m.update(y_true, y_pred) return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): # If the metrics are classification metrics, then we have to handle the case where some # of the metrics require labels, whilst others need to be fed probabilities if hasattr(self, "requires_labels") and not self.requires_labels: for m in self: if m.requires_labels: - m.revert(y_true, max(y_pred, key=y_pred.get), sample_weight) + m.revert(y_true, max(y_pred, key=y_pred.get), w) else: - m.revert(y_true, y_pred, sample_weight) + m.revert(y_true, y_pred, w) return self for m in self: - m.revert(y_true, y_pred, sample_weight) + m.revert(y_true, y_pred, w) return self def get(self): @@ -333,12 +333,12 @@ def __init__(self): def _eval(self, y_true, y_pred): pass - def update(self, y_true, y_pred, sample_weight=1.0): - self._mean.update(x=self._eval(y_true, y_pred), w=sample_weight) + def update(self, y_true, y_pred, w=1.0): + self._mean.update(x=self._eval(y_true, y_pred), w=w) return self - def revert(self, y_true, y_pred, sample_weight=1.0): - self._mean.revert(x=self._eval(y_true, y_pred), w=sample_weight) + def revert(self, y_true, y_pred, w=1.0): + self._mean.revert(x=self._eval(y_true, y_pred), w=w) return self def get(self): @@ -354,11 +354,11 @@ class ClusteringMetric(base.Base, abc.ABC): _fmt = ",.6f" # Use commas to separate big numbers and show 6 decimals @abc.abstractmethod - def update(self, x, y_pred, centers, sample_weight=1.0) -> ClusteringMetric: + def update(self, x, y_pred, centers, w=1.0) -> ClusteringMetric: """Update the metric.""" @abc.abstractmethod - def revert(self, x, y_pred, centers, sample_weight=1.0) -> ClusteringMetric: + def revert(self, x, y_pred, centers, w=1.0) -> ClusteringMetric: """Revert the metric.""" @abc.abstractmethod diff --git a/river/metrics/confusion.py b/river/metrics/confusion.py index 7c16809fa4..ccf321b592 100644 --- a/river/metrics/confusion.py +++ b/river/metrics/confusion.py @@ -62,22 +62,22 @@ def __getitem__(self, key): """Syntactic sugar for accessing the counts directly.""" return self.data[key] - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): self.n_samples += 1 - self._update(y_true, y_pred, sample_weight) + self._update(y_true, y_pred, w) return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): self.n_samples -= 1 - # Revert is equal to subtracting so we pass the negative sample_weight - self._update(y_true, y_pred, -sample_weight) + # Revert is equal to subtracting so we pass the negative sample_weight (w) + self._update(y_true, y_pred, -w) return self - def _update(self, y_true, y_pred, sample_weight): - self.data[y_true][y_pred] += sample_weight - self.total_weight += sample_weight - self.sum_row[y_true] += sample_weight - self.sum_col[y_pred] += sample_weight + def _update(self, y_true, y_pred, w): + self.data[y_true][y_pred] += w + self.total_weight += w + self.sum_row[y_true] += w + self.sum_col[y_pred] += w @property def classes(self): diff --git a/river/metrics/mse.py b/river/metrics/mse.py index 6fcd750e8a..526d2af933 100644 --- a/river/metrics/mse.py +++ b/river/metrics/mse.py @@ -81,5 +81,5 @@ class RMSLE(RMSE): """ - def update(self, y_true, y_pred, sample_weight=1.0): - return super().update(math.log(y_true + 1), math.log(y_pred + 1), sample_weight) + def update(self, y_true, y_pred, w=1.0): + return super().update(math.log(y_true + 1), math.log(y_pred + 1), w) diff --git a/river/metrics/multioutput/base.py b/river/metrics/multioutput/base.py index 9eee7b21b9..e37431e31e 100644 --- a/river/metrics/multioutput/base.py +++ b/river/metrics/multioutput/base.py @@ -37,10 +37,10 @@ def update( y_true: dict[str | int, base.typing.ClfTarget], y_pred: dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]], - sample_weight=1.0, + w=1.0, ) -> MultiOutputClassificationMetric: """Update the metric.""" - self.cm.update(y_true, y_pred, sample_weight) + self.cm.update(y_true, y_pred, w) return self def revert( @@ -48,10 +48,10 @@ def revert( y_true: dict[str | int, base.typing.ClfTarget], y_pred: dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]], - sample_weight=1.0, + w=1.0, ) -> MultiOutputClassificationMetric: """Revert the metric.""" - self.cm.revert(y_true, y_pred, sample_weight) + self.cm.revert(y_true, y_pred, w) return self def works_with(self, model) -> bool: diff --git a/river/metrics/multioutput/confusion.py b/river/metrics/multioutput/confusion.py index 7d20b613ac..f5c6b5b4c9 100644 --- a/river/metrics/multioutput/confusion.py +++ b/river/metrics/multioutput/confusion.py @@ -51,24 +51,24 @@ class MultiLabelConfusionMatrix: def __init__(self): self.data = dict() - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): for label, yt in y_true.items(): try: cm = self.data[label] except KeyError: cm = metrics.ConfusionMatrix() self.data[label] = cm - cm.update(yt, y_pred[label], sample_weight) + cm.update(yt, y_pred[label], w) return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): for label, yt in y_true.items(): try: cm = self.data[label] except KeyError: cm = metrics.ConfusionMatrix() self.data[label] = cm - cm.update(yt, y_pred[label], sample_weight) + cm.update(yt, y_pred[label], w) return self def __repr__(self): diff --git a/river/metrics/multioutput/macro.py b/river/metrics/multioutput/macro.py index 45fbf0b1d7..cbd5699ea1 100644 --- a/river/metrics/multioutput/macro.py +++ b/river/metrics/multioutput/macro.py @@ -37,14 +37,14 @@ def works_with(self, model) -> bool: return utils.inspect.ismoclassifier(model) return utils.inspect.ismoregressor(model) - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): for i in y_true: - self.metrics[i].update(y_true[i], y_pred[i], sample_weight) + self.metrics[i].update(y_true[i], y_pred[i], w) return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): for i in y_true: - self.metrics[i].revert(y_true[i], y_pred[i], sample_weight) + self.metrics[i].revert(y_true[i], y_pred[i], w) return self def get(self): diff --git a/river/metrics/multioutput/micro.py b/river/metrics/multioutput/micro.py index 3449563929..079a613753 100644 --- a/river/metrics/multioutput/micro.py +++ b/river/metrics/multioutput/micro.py @@ -30,14 +30,14 @@ def works_with(self, model) -> bool: return utils.inspect.ismoclassifier(model) return utils.inspect.ismoregressor(model) - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): for i in y_true: - self.metric.update(y_true[i], y_pred[i], sample_weight) + self.metric.update(y_true[i], y_pred[i], w) return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): for i in y_true: - self.metric.revert(y_true[i], y_pred[i], sample_weight) + self.metric.revert(y_true[i], y_pred[i], w) return self def get(self): diff --git a/river/metrics/multioutput/per_output.py b/river/metrics/multioutput/per_output.py index db75519add..0e810e8858 100644 --- a/river/metrics/multioutput/per_output.py +++ b/river/metrics/multioutput/per_output.py @@ -35,14 +35,14 @@ def works_with(self, model) -> bool: return utils.inspect.ismoclassifier(model) return utils.inspect.ismoregressor(model) - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): for i in y_true: - self.metrics[i].update(y_true[i], y_pred[i], sample_weight) + self.metrics[i].update(y_true[i], y_pred[i], w) return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): for i in y_true: - self.metrics[i].revert(y_true[i], y_pred[i], sample_weight) + self.metrics[i].revert(y_true[i], y_pred[i], w) return self def get(self): diff --git a/river/metrics/multioutput/sample_average.py b/river/metrics/multioutput/sample_average.py index 17278f0d12..66c522c62b 100644 --- a/river/metrics/multioutput/sample_average.py +++ b/river/metrics/multioutput/sample_average.py @@ -53,18 +53,18 @@ def works_with(self, model) -> bool: return utils.inspect.ismoclassifier(model) return utils.inspect.ismoregressor(model) - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): metric = self.metric.clone() for i in y_true: metric.update(y_true[i], y_pred[i]) - self._avg.update(metric.get(), sample_weight) + self._avg.update(metric.get(), w) return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): metric = self.metric.clone() for i in y_true: metric.update(y_true[i], y_pred[i]) - self._avg.revert(metric.get(), sample_weight) + self._avg.revert(metric.get(), w) return self def get(self): diff --git a/river/metrics/r2.py b/river/metrics/r2.py index 88685e4b28..479591fc2b 100644 --- a/river/metrics/r2.py +++ b/river/metrics/r2.py @@ -51,15 +51,15 @@ def __init__(self): def bigger_is_better(self): return True - def update(self, y_true, y_pred, sample_weight=1.0): - self._y_var.update(y_true, w=sample_weight) - squared_error = (y_true - y_pred) * (y_true - y_pred) * sample_weight + def update(self, y_true, y_pred, w=1.0): + self._y_var.update(y_true, w=w) + squared_error = (y_true - y_pred) * (y_true - y_pred) * w self._residual_sum_of_squares += squared_error return self - def revert(self, y_true, y_pred, sample_weight=1.0): - self._y_var.update(y_true, w=-sample_weight) - self._residual_sum_of_squares -= (y_true - y_pred) * (y_true - y_pred) * sample_weight + def revert(self, y_true, y_pred, w=1.0): + self._y_var.update(y_true, w=-w) + self._residual_sum_of_squares -= (y_true - y_pred) * (y_true - y_pred) * w return self def get(self): diff --git a/river/metrics/roc_auc.py b/river/metrics/roc_auc.py index 7f46432300..2e4a2d3c9f 100644 --- a/river/metrics/roc_auc.py +++ b/river/metrics/roc_auc.py @@ -67,16 +67,16 @@ def works_with(self, model) -> bool: or utils.inspect.isanomalyfilter(model) ) - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred for t, cm in zip(self.thresholds, self.cms): - cm.update(y_true == self.pos_val, p_true > t, sample_weight) + cm.update(y_true == self.pos_val, p_true > t, w) return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred for t, cm in zip(self.thresholds, self.cms): - cm.revert(y_true == self.pos_val, p_true > t, sample_weight) + cm.revert(y_true == self.pos_val, p_true > t, w) return self @property diff --git a/river/metrics/silhouette.py b/river/metrics/silhouette.py index 9d5e44e9ae..3866eb5f1f 100644 --- a/river/metrics/silhouette.py +++ b/river/metrics/silhouette.py @@ -68,7 +68,7 @@ def _find_distance_second_closest_center(centers, x): distances = {i: math.sqrt(utils.math.minkowski_distance(centers[i], x, 2)) for i in centers} return sorted(distances.values())[-2] - def update(self, x, y_pred, centers, sample_weight=1.0): + def update(self, x, y_pred, centers, w=1.0): distance_closest_centroid = math.sqrt(utils.math.minkowski_distance(centers[y_pred], x, 2)) self._sum_distance_closest_centroid += distance_closest_centroid @@ -77,7 +77,7 @@ def update(self, x, y_pred, centers, sample_weight=1.0): return self - def revert(self, x, y_pred, centers, sample_weight=1.0): + def revert(self, x, y_pred, centers, w=1.0): distance_closest_centroid = math.sqrt(utils.math.minkowski_distance(centers[y_pred], x, 2)) self._sum_distance_closest_centroid -= distance_closest_centroid diff --git a/river/metrics/test_metrics.py b/river/metrics/test_metrics.py index df8c07e942..fa66a9fc6b 100644 --- a/river/metrics/test_metrics.py +++ b/river/metrics/test_metrics.py @@ -236,7 +236,7 @@ def test_metric(metric, sk_metric): m = copy.deepcopy(metric) for i, (yt, yp, w) in enumerate(zip(y_true, y_pred, sample_weights)): if metric.works_with_weights: - m.update(y_true=yt, y_pred=yp, sample_weight=w) + m.update(y_true=yt, y_pred=yp, w=w) else: m.update(y_true=yt, y_pred=yp) diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index 9b948e356b..935f42b74b 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -204,7 +204,7 @@ def _branch_selector(self, numerical_feature=True, multiway_split=False) -> type else: return EFDTNominalMultiwayBranch - def learn_one(self, x, y, *, sample_weight=1.0): + def learn_one(self, x, y, *, w=1.0): """Incrementally train the model Parameters @@ -213,7 +213,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): Instance attributes. y The label of the instance. - sample_weight + w The weight of the sample. Notes @@ -234,20 +234,20 @@ def learn_one(self, x, y, *, sample_weight=1.0): # Updates the set of observed classes self.classes.add(y) - self._train_weight_seen_by_model += sample_weight + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() self._n_active_leaves = 1 # Sort instance X into a leaf - self._sort_to_leaf(x, y, sample_weight) + self._sort_to_leaf(x, y, w) # Process all nodes, starting from root to the leaf where the instance x belongs. - self._process_nodes(x, y, sample_weight, self._root, None, None) + self._process_nodes(x, y, w, self._root, None, None) return self - def _sort_to_leaf(self, x, y, sample_weight): + def _sort_to_leaf(self, x, y, w): """For a given instance, find the corresponding leaf and update it. Private function where leaf learn from instance. @@ -262,7 +262,7 @@ def _sort_to_leaf(self, x, y, sample_weight): Instance attributes. y The instance label. - sample_weight + w The weight of the sample. """ @@ -287,12 +287,12 @@ def _sort_to_leaf(self, x, y, sample_weight): node = node.traverse(x, until_leaf=False) if isinstance(node, HTLeaf): break - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - def _process_nodes(self, x, y, sample_weight, node, parent, branch_index): + def _process_nodes(self, x, y, w, node, parent, branch_index): """Process nodes from the root to the leaf where the instance belongs. 1. If the node is internal: @@ -306,7 +306,7 @@ def _process_nodes(self, x, y, sample_weight, node, parent, branch_index): Instance attributes. y The label of the instance. - sample_weight + w The weight of the sample. node The node to process. @@ -317,7 +317,7 @@ def _process_nodes(self, x, y, sample_weight, node, parent, branch_index): """ if isinstance(node, BaseEFDTBranch): # Update split nodes as the tree is traversed - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) old_weight = node.last_split_reevaluation_at new_weight = node.total_weight @@ -342,7 +342,7 @@ def _process_nodes(self, x, y, sample_weight, node, parent, branch_index): child = node.children[child_index] except KeyError: child_index, child = node.most_common_path() - self._process_nodes(x, y, sample_weight, child, node, child_index) + self._process_nodes(x, y, w, child, node, child_index) elif self._growth_allowed and node.is_active(): if node.depth >= self.max_depth: # Max depth reached node.deactivate() diff --git a/river/tree/hoeffding_adaptive_tree_classifier.py b/river/tree/hoeffding_adaptive_tree_classifier.py index 98c3b06df2..05807fe031 100644 --- a/river/tree/hoeffding_adaptive_tree_classifier.py +++ b/river/tree/hoeffding_adaptive_tree_classifier.py @@ -217,17 +217,17 @@ def summary(self): ) return summ - def learn_one(self, x, y, *, sample_weight=1.0): + def learn_one(self, x, y, *, w=1.0): # Updates the set of observed classes self.classes.add(y) - self._train_weight_seen_by_model += sample_weight + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() self._n_active_leaves = 1 - self._root.learn_one(x, y, sample_weight=sample_weight, tree=self) + self._root.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() diff --git a/river/tree/hoeffding_adaptive_tree_regressor.py b/river/tree/hoeffding_adaptive_tree_regressor.py index eeab272d0b..8b1193f274 100644 --- a/river/tree/hoeffding_adaptive_tree_regressor.py +++ b/river/tree/hoeffding_adaptive_tree_regressor.py @@ -227,13 +227,13 @@ def summary(self): ) return summ - def learn_one(self, x, y, *, sample_weight=1.0): - self._train_weight_seen_by_model += sample_weight + def learn_one(self, x, y, *, w=1.0): + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() self._n_active_leaves = 1 - self._root.learn_one(x, y, sample_weight=sample_weight, tree=self) + self._root.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() diff --git a/river/tree/hoeffding_tree_classifier.py b/river/tree/hoeffding_tree_classifier.py index 91d09f79c2..7d7feb19bb 100755 --- a/river/tree/hoeffding_tree_classifier.py +++ b/river/tree/hoeffding_tree_classifier.py @@ -318,7 +318,7 @@ def _attempt_to_split(self, leaf: HTLeaf, parent: DTBranch, parent_branch: int, # Manage memory self._enforce_size_limit() - def learn_one(self, x, y, *, sample_weight=1.0): + def learn_one(self, x, y, *, w=1.0): """Train the model on instance x and corresponding target y. Parameters @@ -327,7 +327,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): Instance attributes. y Class label for sample x. - sample_weight + w Sample weight. Returns @@ -349,7 +349,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): # Updates the set of observed classes self.classes.add(y) - self._train_weight_seen_by_model += sample_weight + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() @@ -369,7 +369,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): node = self._root if isinstance(node, HTLeaf): - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._growth_allowed and node.is_active(): if node.depth >= self.max_depth: # Max depth reached node.deactivate() @@ -403,7 +403,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): if isinstance(node, HTLeaf): break # Learn from the sample - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() diff --git a/river/tree/hoeffding_tree_regressor.py b/river/tree/hoeffding_tree_regressor.py index f5a873f7a0..b7afa030d3 100644 --- a/river/tree/hoeffding_tree_regressor.py +++ b/river/tree/hoeffding_tree_regressor.py @@ -217,7 +217,7 @@ def _new_leaf(self, initial_stats=None, parent=None): return new_adaptive - def learn_one(self, x, y, *, sample_weight=1.0): + def learn_one(self, x, y, *, w=1.0): """Train the tree model on sample x and corresponding target y. Parameters @@ -226,7 +226,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): Instance attributes. y Target value for sample x. - sample_weight + w The weight of the sample. Returns @@ -234,7 +234,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): self """ - self._train_weight_seen_by_model += sample_weight + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() @@ -254,7 +254,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): node = self._root if isinstance(node, HTLeaf): - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._growth_allowed and node.is_active(): if node.depth >= self.max_depth: # Max depth reached node.deactivate() @@ -288,7 +288,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): if isinstance(node, HTLeaf): break # Learn from the sample - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() diff --git a/river/tree/isoup_tree_regressor.py b/river/tree/isoup_tree_regressor.py index c61a9c9f3a..d4413674e1 100644 --- a/river/tree/isoup_tree_regressor.py +++ b/river/tree/isoup_tree_regressor.py @@ -207,7 +207,7 @@ def _new_leaf(self, initial_stats=None, parent=None): return new_adaptive - def learn_one(self, x, y, *, sample_weight: float = 1.0) -> iSOUPTreeRegressor: # type: ignore + def learn_one(self, x, y, *, w: float = 1.0) -> iSOUPTreeRegressor: # type: ignore """Incrementally train the model with one sample. Training tasks: @@ -225,13 +225,13 @@ def learn_one(self, x, y, *, sample_weight: float = 1.0) -> iSOUPTreeRegressor: Instance attributes. y Target values. - sample_weight + w The weight of the passed sample. """ # Update target set self.targets.update(y.keys()) - super().learn_one(x, y, sample_weight=sample_weight) # type: ignore + super().learn_one(x, y, w=w) # type: ignore return self diff --git a/river/tree/nodes/efdtc_nodes.py b/river/tree/nodes/efdtc_nodes.py index 34c870da13..5fb611f034 100644 --- a/river/tree/nodes/efdtc_nodes.py +++ b/river/tree/nodes/efdtc_nodes.py @@ -111,13 +111,13 @@ def total_weight(self) -> float: def new_nominal_splitter(): return NominalSplitterClassif() - def update_stats(self, y, sample_weight): + def update_stats(self, y, w): try: - self.stats[y] += sample_weight + self.stats[y] += w except KeyError: - self.stats[y] = sample_weight + self.stats[y] = w - def update_splitters(self, x, y, sample_weight, nominal_attributes): + def update_splitters(self, x, y, w, nominal_attributes): for att_id, att_val in x.items(): if att_id in self._disabled_attrs: continue @@ -133,9 +133,9 @@ def update_splitters(self, x, y, sample_weight, nominal_attributes): splitter = copy.deepcopy(self.splitter) self.splitters[att_id] = splitter - splitter.update(att_val, y, sample_weight) + splitter.update(att_val, y, w) - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): """Update branch with the provided sample. Parameters @@ -144,13 +144,13 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): Sample attributes for updating the node. y Target value. - sample_weight + w Sample weight. tree Tree to update. """ - self.update_stats(y, sample_weight) - self.update_splitters(x, y, sample_weight, tree.nominal_attributes) + self.update_stats(y, w) + self.update_splitters(x, y, w, tree.nominal_attributes) def prediction(self, x, *, tree=None): return normalize_values_in_dict(self.stats, inplace=False) diff --git a/river/tree/nodes/hatc_nodes.py b/river/tree/nodes/hatc_nodes.py index 0f9fa241e5..bab558192d 100644 --- a/river/tree/nodes/hatc_nodes.py +++ b/river/tree/nodes/hatc_nodes.py @@ -47,12 +47,12 @@ def __init__(self, stats, depth, splitter, drift_detector, rng, **kwargs): def kill_tree_children(self, hat): pass - def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_branch=None): + def learn_one(self, x, y, *, w=1.0, tree=None, parent=None, parent_branch=None): if tree.bootstrap_sampling: # Perform bootstrap-sampling k = poisson(rate=1, rng=self.rng) if k > 0: - sample_weight *= k + w *= k aux = self.prediction(x, tree=tree) y_pred = max(aux, key=aux.get) if aux else None @@ -71,7 +71,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b self._mean_error = self._mean_error.clone() # Update statistics - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) weight_seen = self.total_weight @@ -176,7 +176,7 @@ def iter_leaves(self): if isinstance(child, AdaBranchClassifier) and child._alternate_tree: yield from child._alternate_tree.iter_leaves() - def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_branch=None): + def learn_one(self, x, y, *, w=1.0, tree=None, parent=None, parent_branch=None): leaf = super().traverse(x, until_leaf=True) aux = leaf.prediction(x, tree=tree) y_pred = max(aux, key=aux.get) if aux else None @@ -185,9 +185,9 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b # Update stats as traverse the tree to improve predictions (in case split nodes are used # to provide responses) try: - self.stats[y] += sample_weight + self.stats[y] += w except KeyError: - self.stats[y] = sample_weight + self.stats[y] = w old_error = self._mean_error.get() @@ -250,7 +250,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b self._alternate_tree.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=parent, parent_branch=parent_branch, @@ -265,7 +265,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b child.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=self.branch_no(x), @@ -280,7 +280,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b leaf.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=self.branch_no(x), @@ -292,7 +292,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b child.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=child_id, diff --git a/river/tree/nodes/hatr_nodes.py b/river/tree/nodes/hatr_nodes.py index a8e134ddd4..aa496c4f4d 100644 --- a/river/tree/nodes/hatr_nodes.py +++ b/river/tree/nodes/hatr_nodes.py @@ -47,14 +47,14 @@ def __init__(self, stats, depth, splitter, drift_detector, rng, **kwargs): def kill_tree_children(self, hatr): pass - def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_branch=None): + def learn_one(self, x, y, *, w=1.0, tree=None, parent=None, parent_branch=None): y_pred = self.prediction(x, tree=tree) if tree.bootstrap_sampling: # Perform bootstrap-sampling k = poisson(rate=1, rng=self.rng) if k > 0: - sample_weight *= k + w *= k drift_input = abs(y - y_pred) old_error = self._error_tracker.mean.get() @@ -69,7 +69,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b self._error_tracker = self._error_tracker.clone() # Update learning model - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) weight_seen = self.total_weight @@ -149,13 +149,13 @@ def iter_leaves(self): if isinstance(child, AdaBranchRegressor) and child._alternate_tree: yield from child._alternate_tree.iter_leaves() - def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_branch=None): + def learn_one(self, x, y, *, w=1.0, tree=None, parent=None, parent_branch=None): leaf = super().traverse(x, until_leaf=True) y_pred = leaf.prediction(x, tree=tree) # Update stats as traverse the tree to improve predictions (in case split nodes are used # to provide responses) - self.stats.update(y, sample_weight) + self.stats.update(y, w) drift_input = abs(y - y_pred) old_error = self._error_tracker.mean.get() @@ -224,7 +224,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b self._alternate_tree.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=parent, parent_branch=parent_branch, @@ -238,7 +238,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b child.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=self.branch_no(x), @@ -253,7 +253,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b leaf.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=self.branch_no(x), @@ -265,7 +265,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b child.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=child_id, diff --git a/river/tree/nodes/htc_nodes.py b/river/tree/nodes/htc_nodes.py index 6063af90e3..d35549ee79 100644 --- a/river/tree/nodes/htc_nodes.py +++ b/river/tree/nodes/htc_nodes.py @@ -31,11 +31,11 @@ def __init__(self, stats, depth, splitter, **kwargs): def new_nominal_splitter(): return NominalSplitterClassif() - def update_stats(self, y, sample_weight): + def update_stats(self, y, w): try: - self.stats[y] += sample_weight + self.stats[y] += w except KeyError: - self.stats[y] = sample_weight + self.stats[y] = w def prediction(self, x, *, tree=None): return normalize_values_in_dict(self.stats, inplace=False) @@ -164,7 +164,7 @@ def __init__(self, stats, depth, splitter, **kwargs): self._mc_correct_weight = 0.0 self._nb_correct_weight = 0.0 - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): """Update the node with the provided instance. Parameters @@ -173,7 +173,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): Instance attributes for updating the node. y Instance class. - sample_weight + w The instance's weight. tree The Hoeffding Tree to update. @@ -184,13 +184,13 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): # Empty node (assume the majority class will be the best option) or majority # class prediction is correct if len(self.stats) == 0 or max(mc_pred, key=mc_pred.get) == y: - self._mc_correct_weight += sample_weight + self._mc_correct_weight += w nb_pred = do_naive_bayes_prediction(x, self.stats, self.splitters) if len(nb_pred) > 0 and max(nb_pred, key=nb_pred.get) == y: - self._nb_correct_weight += sample_weight + self._nb_correct_weight += w - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) def prediction(self, x, *, tree=None): """Get the probabilities per class for a given instance. diff --git a/river/tree/nodes/htr_nodes.py b/river/tree/nodes/htr_nodes.py index 13a04d484a..248164399a 100644 --- a/river/tree/nodes/htr_nodes.py +++ b/river/tree/nodes/htr_nodes.py @@ -64,8 +64,8 @@ def manage_memory(self, criterion, last_check_ratio, last_check_vr, last_check_e pre_split_dist=self.stats, ) - def update_stats(self, y, sample_weight): - self.stats.update(y, sample_weight) + def update_stats(self, y, w): + self.stats.update(y, w) def prediction(self, x, *, tree=None): return self.stats.mean.get() @@ -133,13 +133,13 @@ def __init__(self, stats, depth, splitter, leaf_model, **kwargs): sign = inspect.signature(leaf_model.learn_one).parameters self._model_supports_weights = "sample_weight" in sign or "w" in sign - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + def learn_one(self, x, y, *, w=1.0, tree=None): + super().learn_one(x, y, w=w, tree=tree) if self._model_supports_weights: - self._leaf_model.learn_one(x, y, sample_weight) + self._leaf_model.learn_one(x, y, w) else: - for _ in range(int(sample_weight)): + for _ in range(int(w)): self._leaf_model.learn_one(x, y) def prediction(self, x, *, tree=None): @@ -173,14 +173,14 @@ def __init__(self, stats, depth, splitter, leaf_model, **kwargs): self._fmse_mean = 0.0 self._fmse_model = 0.0 - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): pred_mean = self.stats.mean.get() pred_model = self._leaf_model.predict_one(x) self._fmse_mean = tree.model_selector_decay * self._fmse_mean + (y - pred_mean) ** 2 self._fmse_model = tree.model_selector_decay * self._fmse_model + (y - pred_model) ** 2 - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) def prediction(self, x, *, tree=None): if self._fmse_mean < self._fmse_model: # Act as a regression tree diff --git a/river/tree/nodes/isouptr_nodes.py b/river/tree/nodes/isouptr_nodes.py index bfdb632094..4a20f6328f 100644 --- a/river/tree/nodes/isouptr_nodes.py +++ b/river/tree/nodes/isouptr_nodes.py @@ -33,9 +33,9 @@ def __init__(self, stats, depth, splitter, **kwargs): stats = stats if stats else VectorDict(default_factory=functools.partial(Var)) super().__init__(stats, depth, splitter, **kwargs) - def update_stats(self, y, sample_weight): + def update_stats(self, y, w): for t in y: - self.stats[t].update(y[t], sample_weight) + self.stats[t].update(y[t], w) def prediction(self, x, *, tree=None): return {t: self.stats[t].mean.get() if t in self.stats else 0.0 for t in tree.targets} @@ -82,8 +82,8 @@ def __init__(self, stats, depth, splitter, leaf_models, **kwargs): sign = inspect.signature(self._leaf_models[t].learn_one).parameters self._model_supports_weights[t] = "sample_weight" in sign or "w" in sign - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + def learn_one(self, x, y, *, w=1.0, tree=None): + super().learn_one(x, y, w=w, tree=tree) for target_id, y_ in y.items(): try: @@ -107,9 +107,9 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): # Now the proper training if self._model_supports_weights[target_id]: - model.learn_one(x, y_, sample_weight) + model.learn_one(x, y_, w) else: - for _ in range(int(sample_weight)): + for _ in range(int(w)): model.learn_one(x, y_) def prediction(self, x, *, tree=None): @@ -145,7 +145,7 @@ def __init__(self, stats, depth, splitter, leaf_models, **kwargs): self._fmse_mean = collections.defaultdict(float) self._fmse_model = collections.defaultdict(float) - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): pred_mean = {t: self.stats[t].mean.get() if t in self.stats else 0.0 for t in tree.targets} pred_model = super().prediction(x, tree=tree) @@ -157,7 +157,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): tree.model_selector_decay * self._fmse_model[t] + (y[t] - pred_model[t]) ** 2 ) - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) def prediction(self, x, *, tree=None): pred = {} diff --git a/river/tree/nodes/leaf.py b/river/tree/nodes/leaf.py index 0333815970..dfab307504 100644 --- a/river/tree/nodes/leaf.py +++ b/river/tree/nodes/leaf.py @@ -77,7 +77,7 @@ def new_nominal_splitter(): pass @abc.abstractmethod - def update_stats(self, y, sample_weight): + def update_stats(self, y, w): pass def _iter_features(self, x) -> typing.Iterable: @@ -90,7 +90,7 @@ def _iter_features(self, x) -> typing.Iterable: """ yield from x.items() - def update_splitters(self, x, y, sample_weight, nominal_attributes): + def update_splitters(self, x, y, w, nominal_attributes): for att_id, att_val in self._iter_features(x): if att_id in self._disabled_attrs: continue @@ -106,7 +106,7 @@ def update_splitters(self, x, y, sample_weight, nominal_attributes): splitter = self.splitter.clone() self.splitters[att_id] = splitter - splitter.update(att_val, y, sample_weight) + splitter.update(att_val, y, w) def best_split_suggestions(self, criterion, tree) -> list[BranchFactory]: """Find possible split candidates. @@ -149,7 +149,7 @@ def disable_attribute(self, att_id): del self.splitters[att_id] self._disabled_attrs.add(att_id) - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): """Update the node with the provided sample. Parameters @@ -158,7 +158,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): Sample attributes for updating the node. y Target value. - sample_weight + w Sample weight. tree Tree to update. @@ -169,9 +169,9 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): All classes overriding this method should include a call to `super().learn_one` to guarantee the learning process happens consistently. """ - self.update_stats(y, sample_weight) + self.update_stats(y, w) if self.is_active(): - self.update_splitters(x, y, sample_weight, tree.nominal_attributes) + self.update_splitters(x, y, w, tree.nominal_attributes) @abc.abstractmethod def prediction(self, x, *, tree=None) -> dict: diff --git a/river/tree/splitter/base.py b/river/tree/splitter/base.py index 98056dbffb..8524e14b8b 100644 --- a/river/tree/splitter/base.py +++ b/river/tree/splitter/base.py @@ -21,7 +21,7 @@ class Splitter(base.Estimator, abc.ABC): """ @abc.abstractmethod - def update(self, att_val, target_val: base.typing.Target, sample_weight: float): + def update(self, att_val, target_val: base.typing.Target, w: float): """Update statistics of this observer given an attribute value, its target value and the weight of the instance observed. @@ -31,7 +31,7 @@ def update(self, att_val, target_val: base.typing.Target, sample_weight: float): The value of the monitored attribute. target_val The target value. - sample_weight + w The weight of the instance. """ diff --git a/river/tree/splitter/ebst_splitter.py b/river/tree/splitter/ebst_splitter.py index 17bffc6269..f11bce48e4 100644 --- a/river/tree/splitter/ebst_splitter.py +++ b/river/tree/splitter/ebst_splitter.py @@ -43,14 +43,14 @@ def __init__(self): def is_target_class(self) -> bool: return False - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: return else: if self._root is None: - self._root = EBSTNode(att_val, target_val, sample_weight) + self._root = EBSTNode(att_val, target_val, w) else: - self._root.insert_value(att_val, target_val, sample_weight) + self._root.insert_value(att_val, target_val, w) def cond_proba(self, att_val, target_val): """Not implemented in regression splitters.""" @@ -237,7 +237,7 @@ def _remove_bad_split_nodes(self, current_node, parent=None, is_left_child=True) class EBSTNode: - def __init__(self, att_val, target_val, sample_weight): + def __init__(self, att_val, target_val, w): self.att_val = att_val if isinstance(target_val, dict): @@ -250,23 +250,23 @@ def __init__(self, att_val, target_val, sample_weight): self.estimator = Var() self._update_estimator = self._update_estimator_univariate - self._update_estimator(self, target_val, sample_weight) + self._update_estimator(self, target_val, w) self._left = None self._right = None @staticmethod - def _update_estimator_univariate(node, target, sample_weight): - node.estimator.update(target, sample_weight) + def _update_estimator_univariate(node, target, w): + node.estimator.update(target, w) @staticmethod - def _update_estimator_multivariate(node, target, sample_weight): + def _update_estimator_multivariate(node, target, w): for t in target: - node.estimator[t].update(target[t], sample_weight) + node.estimator[t].update(target[t], w) # Incremental implementation of the insert method. Avoiding unnecessary # stack tracing must decrease memory costs - def insert_value(self, att_val, target_val, sample_weight): + def insert_value(self, att_val, target_val, w): current = self antecedent = None is_right = False @@ -274,10 +274,10 @@ def insert_value(self, att_val, target_val, sample_weight): while current is not None: antecedent = current if att_val == current.att_val: - self._update_estimator(current, target_val, sample_weight) + self._update_estimator(current, target_val, w) return elif att_val < current.att_val: - self._update_estimator(current, target_val, sample_weight) + self._update_estimator(current, target_val, w) current = current._left is_right = False @@ -287,6 +287,6 @@ def insert_value(self, att_val, target_val, sample_weight): # Value was not yet added to the tree if is_right: - antecedent._right = EBSTNode(att_val, target_val, sample_weight) + antecedent._right = EBSTNode(att_val, target_val, w) else: - antecedent._left = EBSTNode(att_val, target_val, sample_weight) + antecedent._left = EBSTNode(att_val, target_val, w) diff --git a/river/tree/splitter/exhaustive_splitter.py b/river/tree/splitter/exhaustive_splitter.py index 7ba3b8ad83..945f00daea 100644 --- a/river/tree/splitter/exhaustive_splitter.py +++ b/river/tree/splitter/exhaustive_splitter.py @@ -30,14 +30,14 @@ def __init__(self): super().__init__() self._root = None - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: return else: if self._root is None: - self._root = ExhaustiveNode(att_val, target_val, sample_weight) + self._root = ExhaustiveNode(att_val, target_val, w) else: - self._root.insert_value(att_val, target_val, sample_weight) + self._root.insert_value(att_val, target_val, w) def cond_proba(self, att_val, target_val): """The underlying data structure used to monitor the input does not allow probability @@ -148,27 +148,27 @@ def _search_for_best_split_option( class ExhaustiveNode: - def __init__(self, att_val, target_val, sample_weight): + def __init__(self, att_val, target_val, w): self.class_count_left = defaultdict(float) self.class_count_right = defaultdict(float) self._left = None self._right = None self.cut_point = att_val - self.class_count_left[target_val] += sample_weight + self.class_count_left[target_val] += w - def insert_value(self, val, label, sample_weight): + def insert_value(self, val, label, w): if val == self.cut_point: - self.class_count_left[label] += sample_weight + self.class_count_left[label] += w elif val < self.cut_point: - self.class_count_left[label] += sample_weight + self.class_count_left[label] += w if self._left is None: - self._left = ExhaustiveNode(val, label, sample_weight) + self._left = ExhaustiveNode(val, label, w) else: - self._left.insert_value(val, label, sample_weight) + self._left.insert_value(val, label, w) else: - self.class_count_right[label] += sample_weight + self.class_count_right[label] += w if self._right is None: - self._right = ExhaustiveNode(val, label, sample_weight) + self._right = ExhaustiveNode(val, label, w) else: - self._right.insert_value(val, label, sample_weight) + self._right.insert_value(val, label, w) diff --git a/river/tree/splitter/gaussian_splitter.py b/river/tree/splitter/gaussian_splitter.py index b7edb9d4ea..cdf50f5cc5 100644 --- a/river/tree/splitter/gaussian_splitter.py +++ b/river/tree/splitter/gaussian_splitter.py @@ -30,7 +30,7 @@ def __init__(self, n_splits: int = 10): self._att_dist_per_class: dict[ClfTarget, Gaussian] = {} self.n_splits = n_splits - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: return else: @@ -46,7 +46,7 @@ def update(self, att_val, target_val, sample_weight): self._min_per_class[target_val] = att_val self._max_per_class[target_val] = att_val - val_dist.update(att_val, sample_weight) + val_dist.update(att_val, w) def cond_proba(self, att_val, target_val): if target_val in self._att_dist_per_class: diff --git a/river/tree/splitter/histogram_splitter.py b/river/tree/splitter/histogram_splitter.py index 55a5f6bdf7..cde9d490c3 100644 --- a/river/tree/splitter/histogram_splitter.py +++ b/river/tree/splitter/histogram_splitter.py @@ -33,8 +33,8 @@ def __init__(self, n_bins: int = 256, n_splits: int = 32): functools.partial(sketch.Histogram, max_bins=self.n_bins) ) - def update(self, att_val, target_val, sample_weight): - for _ in range(int(sample_weight)): + def update(self, att_val, target_val, w): + for _ in range(int(w)): self.hists[target_val].update(att_val) def cond_proba(self, att_val, target_val): diff --git a/river/tree/splitter/nominal_splitter_classif.py b/river/tree/splitter/nominal_splitter_classif.py index f1fcf09c77..189d04b3d5 100644 --- a/river/tree/splitter/nominal_splitter_classif.py +++ b/river/tree/splitter/nominal_splitter_classif.py @@ -24,18 +24,18 @@ def __init__(self): def is_numeric(self): return False - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: - self._missing_weight_observed += sample_weight + self._missing_weight_observed += w else: self._att_values.add(att_val) try: - self._att_dist_per_class[target_val][att_val] += sample_weight + self._att_dist_per_class[target_val][att_val] += w except KeyError: - self._att_dist_per_class[target_val][att_val] = sample_weight + self._att_dist_per_class[target_val][att_val] = w - self._total_weight_observed += sample_weight + self._total_weight_observed += w def cond_proba(self, att_val, target_val): class_dist = self._att_dist_per_class[target_val] diff --git a/river/tree/splitter/nominal_splitter_reg.py b/river/tree/splitter/nominal_splitter_reg.py index ebe7bf7b5e..a2ebff001c 100644 --- a/river/tree/splitter/nominal_splitter_reg.py +++ b/river/tree/splitter/nominal_splitter_reg.py @@ -28,16 +28,16 @@ def is_numeric(self): return False @staticmethod - def _update_estimator_univariate(estimator, target, sample_weight): - estimator.update(target, sample_weight) + def _update_estimator_univariate(estimator, target, w): + estimator.update(target, w) @staticmethod - def _update_estimator_multivariate(estimator, target, sample_weight): + def _update_estimator_multivariate(estimator, target, w): for t in target: - estimator[t].update(target[t], sample_weight) + estimator[t].update(target[t], w) - def update(self, att_val, target_val, sample_weight): - if att_val is None or sample_weight is None: + def update(self, att_val, target_val, w): + if att_val is None or w is None: return else: try: @@ -49,7 +49,7 @@ def update(self, att_val, target_val, sample_weight): else: self._statistics[att_val] = Var() estimator = self._statistics[att_val] - self._update_estimator(estimator, target_val, sample_weight) + self._update_estimator(estimator, target_val, w) def cond_proba(self, att_val, target_val): """Not implemented in regression splitters.""" diff --git a/river/tree/splitter/qo_splitter.py b/river/tree/splitter/qo_splitter.py index 8e246e17a4..269249a19b 100644 --- a/river/tree/splitter/qo_splitter.py +++ b/river/tree/splitter/qo_splitter.py @@ -66,11 +66,11 @@ def __init__(self, radius: float = 0.25, allow_multiway_splits=False): self.allow_multiway_splits = allow_multiway_splits - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: return else: - self._quantizer.update(att_val, target_val, sample_weight) + self._quantizer.update(att_val, target_val, w) def cond_proba(self, att_val, target_val): raise NotImplementedError @@ -162,12 +162,12 @@ def _init_estimator(self, y): self.y_stats = stats.Var() self._update_estimator = self._update_estimator_univariate - def _update_estimator_univariate(self, target, sample_weight): - self.y_stats.update(target, sample_weight) + def _update_estimator_univariate(self, target, w): + self.y_stats.update(target, w) - def _update_estimator_multivariate(self, target, sample_weight): + def _update_estimator_multivariate(self, target, w): for t in target: - self.y_stats[t].update(target[t], sample_weight) + self.y_stats[t].update(target[t], w) def __iadd__(self, o): self.x_stats += o.x_stats @@ -175,9 +175,9 @@ def __iadd__(self, o): return self - def update(self, x, y, sample_weight): - self.x_stats.update(x, sample_weight) - self._update_estimator(y, sample_weight) + def update(self, x, y, w): + self.x_stats.update(x, w) + self._update_estimator(y, w) class FeatureQuantizer: diff --git a/river/tree/splitter/random_splitter.py b/river/tree/splitter/random_splitter.py index f198083414..4fd099f379 100644 --- a/river/tree/splitter/random_splitter.py +++ b/river/tree/splitter/random_splitter.py @@ -29,17 +29,17 @@ def clone(self, new_params: dict | None = None, include_attributes=False): return super().clone(new_params, include_attributes) @abc.abstractmethod - def _update_stats(self, branch, target_val, sample_weight): + def _update_stats(self, branch, target_val, w): pass def cond_proba(self, att_val, class_val) -> float: """This attribute observer does not support probability density estimation.""" raise NotImplementedError - def update(self, att_val, target_val, sample_weight) -> Splitter: + def update(self, att_val, target_val, w) -> Splitter: if self.threshold is None: if len(self._buffer) < self.buffer_size: - self._buffer.append((att_val, target_val, sample_weight)) + self._buffer.append((att_val, target_val, w)) return self mn = min(self._buffer, key=lambda t: t[0])[0] @@ -53,7 +53,7 @@ def update(self, att_val, target_val, sample_weight) -> Splitter: return self - self._update_stats(0 if att_val <= self.threshold else 1, target_val, sample_weight) + self._update_stats(0 if att_val <= self.threshold else 1, target_val, w) return self @@ -76,8 +76,8 @@ def __init__(self, seed, buffer_size): super().__init__(seed, buffer_size) self.stats = {0: stats.Var(), 1: stats.Var()} - def _update_stats(self, branch, target_val, sample_weight): - self.stats[branch].update(target_val, sample_weight) + def _update_stats(self, branch, target_val, w): + self.stats[branch].update(target_val, w) @property def is_target_class(self) -> bool: diff --git a/river/tree/splitter/tebst_splitter.py b/river/tree/splitter/tebst_splitter.py index f5b623060e..68e2212b82 100644 --- a/river/tree/splitter/tebst_splitter.py +++ b/river/tree/splitter/tebst_splitter.py @@ -22,10 +22,10 @@ def __init__(self, digits: int = 1): super().__init__() self.digits = digits - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): try: att_val = round(att_val, self.digits) - super().update(att_val, target_val, sample_weight) + super().update(att_val, target_val, w) except TypeError: # feature value is None pass From 704ca0d7d5c170900be345abc1dd96370bcd15de Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 14:55:31 +0100 Subject: [PATCH 187/347] wip --- .github/workflows/pypi.yml | 11 +- poetry.lock | 1758 ++++++++++++++++++------------------ pyproject.toml | 1 + 3 files changed, 874 insertions(+), 896 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 42f8d226e5..a8f5a749b5 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -6,6 +6,15 @@ on: tags: - "*" + # Cython-3.0.6-cp310-cp310-macosx_10_9_x86_64.whl + # Cython-3.0.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl + # Cython-3.0.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl + # Cython-3.0.6-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl + # Cython-3.0.6-cp310-cp310-musllinux_1_1_aarch64.whl + # Cython-3.0.6-cp310-cp310-musllinux_1_1_x86_64.whl + # Cython-3.0.6-cp310-cp310-win32.whl + # Cython-3.0.6-cp310-cp310-win_amd64.whl + jobs: build_wheels: name: Build wheels on ${{ matrix.os }} @@ -68,7 +77,7 @@ jobs: rustup default nightly && rustup show # rust doesn't seem to be available for musl linux on i686 - CIBW_SKIP: "*-musllinux_{i686,aarch64}" + # CIBW_SKIP: "*-musllinux_{i686,aarch64}" # we build for "alt_arch_name" if it exists, else 'auto CIBW_ARCHS: ${{ matrix.alt_arch_name || 'auto' }} diff --git a/poetry.lock b/poetry.lock index 78c08a6119..f80b6514cb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -24,13 +24,13 @@ files = [ [[package]] name = "anyio" -version = "4.0.0" +version = "4.1.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.0.0-py3-none-any.whl", hash = "sha256:cfdb2b588b9fc25ede96d8db56ed50848b0b649dca3dd1df0b11f683bb9e0b5f"}, - {file = "anyio-4.0.0.tar.gz", hash = "sha256:f7ed51751b2c2add651e5747c891b47e26d2a21be5d32d9311dfe9692f3e5d7a"}, + {file = "anyio-4.1.0-py3-none-any.whl", hash = "sha256:56a415fbc462291813a94528a779597226619c8e78af7de0507333f700011e5f"}, + {file = "anyio-4.1.0.tar.gz", hash = "sha256:5a0bec7085176715be77df87fc66d6c9d70626bd752fcc85f57cdbee5b3760da"}, ] [package.dependencies] @@ -39,9 +39,9 @@ idna = ">=2.8" sniffio = ">=1.1" [package.extras] -doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"] -test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] -trio = ["trio (>=0.22)"] +doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (>=0.23)"] [[package]] name = "appnope" @@ -132,20 +132,21 @@ test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock [[package]] name = "asttokens" -version = "2.4.0" +version = "2.4.1" description = "Annotate AST trees with source code positions" optional = false python-versions = "*" files = [ - 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+python-versions = ">=3.8" files = [ - {file = "urllib3-2.0.7-py3-none-any.whl", hash = "sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"}, - {file = "urllib3-2.0.7.tar.gz", hash = "sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84"}, + {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"}, + {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"}, ] [package.extras] brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] -secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] @@ -4730,19 +4698,19 @@ test = ["coverage", "flake8 (>=3.7)", "mypy", "pretend", "pytest"] [[package]] name = "virtualenv" -version = "20.24.5" +version = "20.25.0" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.7" files = [ - {file = "virtualenv-20.24.5-py3-none-any.whl", hash = "sha256:b80039f280f4919c77b30f1c23294ae357c4c8701042086e3fc005963e4e537b"}, - {file = "virtualenv-20.24.5.tar.gz", hash = "sha256:e8361967f6da6fbdf1426483bfe9fca8287c242ac0bc30429905721cefbff752"}, + {file = "virtualenv-20.25.0-py3-none-any.whl", hash = "sha256:4238949c5ffe6876362d9c0180fc6c3a824a7b12b80604eeb8085f2ed7460de3"}, + {file = "virtualenv-20.25.0.tar.gz", hash = "sha256:bf51c0d9c7dd63ea8e44086fa1e4fb1093a31e963b86959257378aef020e1f1b"}, ] [package.dependencies] distlib = ">=0.3.7,<1" filelock = ">=3.12.2,<4" -platformdirs = ">=3.9.1,<4" +platformdirs = ">=3.9.1,<5" [package.extras] docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] @@ -4865,24 +4833,24 @@ bracex = ">=2.1.1" [[package]] name = "wcwidth" -version = "0.2.8" +version = "0.2.12" description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = "*" files = [ - {file = "wcwidth-0.2.8-py2.py3-none-any.whl", hash = "sha256:77f719e01648ed600dfa5402c347481c0992263b81a027344f3e1ba25493a704"}, - {file = "wcwidth-0.2.8.tar.gz", hash = "sha256:8705c569999ffbb4f6a87c6d1b80f324bd6db952f5eb0b95bc07517f4c1813d4"}, + {file = "wcwidth-0.2.12-py2.py3-none-any.whl", hash = "sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c"}, + {file = "wcwidth-0.2.12.tar.gz", hash = "sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02"}, ] [[package]] name = "weasel" -version = "0.3.3" +version = "0.3.4" description = "Weasel: A small and easy workflow system" optional = false python-versions = ">=3.6" files = [ - {file = "weasel-0.3.3-py3-none-any.whl", hash = "sha256:141b12fd0d38599ff8c567208d1db0f5af1b532363fadeba27d7bc87d751d88a"}, - {file = "weasel-0.3.3.tar.gz", hash = "sha256:924962dfc9d89602552e7332846e95d264eca18aebe2b96c2527d46b7bb7cf9c"}, + {file = "weasel-0.3.4-py3-none-any.whl", hash = "sha256:ee48a944f051d007201c2ea1661d0c41035028c5d5a8bcb29a0b10f1100206ae"}, + {file = "weasel-0.3.4.tar.gz", hash = "sha256:eb16f92dc9f1a3ffa89c165e3a9acd28018ebb656e0da4da02c0d7d8ae3f6178"}, ] [package.dependencies] @@ -4984,4 +4952,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "87961d28c7c601a8799ac7c4294ef9d40565d0e954a0a6f2444da971767a4065" +content-hash = "bb6e52f5b8fa93ff13f392560473b7ed6778f3a523e96b3b12d9b255d56b3a41" diff --git a/pyproject.toml b/pyproject.toml index 41d9b3e64e..2e53888fbd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,7 @@ script = "build.py" [tool.poetry.dependencies] python = ">=3.9,<3.13" +numpy = "^1.23.0" scipy = "^1.8.1" pandas = "^2.1" From 715942d245ca8dc6cda1426890aec7381b2791e1 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 14:57:01 +0100 Subject: [PATCH 188/347] ruff --- river/metrics/base.py | 2 +- river/metrics/confusion.py | 3 +-- river/stats/_rust_stats.pyi | 2 ++ 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/river/metrics/base.py b/river/metrics/base.py index a018d5cb8a..5a0a28cf8d 100644 --- a/river/metrics/base.py +++ b/river/metrics/base.py @@ -77,7 +77,7 @@ class ClassificationMetric(Metric): _fmt = ".2%" # output a percentage, e.g. 0.427 becomes "42,7%" - def __init__(self, cm: "confusion.ConfusionMatrix" | None = None): + def __init__(self, cm=None): # HACK: there is a circular dependency between ConfusionMatrix and ClassificationMetric. We # use ConfusionMatrix here so as to express metrics in terms of false/true # positives/negatives. But for UX reasons, we also want ConfusionMatrix to be a diff --git a/river/metrics/confusion.py b/river/metrics/confusion.py index be2628e36c..ece59e61e9 100644 --- a/river/metrics/confusion.py +++ b/river/metrics/confusion.py @@ -3,8 +3,7 @@ import functools from collections import defaultdict -from river import metrics -from river import utils +from river import metrics, utils class ConfusionMatrix(metrics.base.MultiClassMetric): diff --git a/river/stats/_rust_stats.pyi b/river/stats/_rust_stats.pyi index b342a08715..c163809bd0 100644 --- a/river/stats/_rust_stats.pyi +++ b/river/stats/_rust_stats.pyi @@ -1,3 +1,5 @@ +from __future__ import annotations + class RsQuantile: def __init__(self, q: float): ... def update(self, x: float): ... From 5615dd6a9970b5dd6f7e9b5b58f90a72fecbb182 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 16:58:09 +0100 Subject: [PATCH 189/347] move stuff around --- .github/workflows/pypi.yml | 42 +++++++++++++++----------------------- 1 file changed, 17 insertions(+), 25 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index a8f5a749b5..eb48ab57eb 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -21,26 +21,8 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest, windows-latest, macos-latest] + os: [ubuntu-latest, windows-latest, macos-11, macos-latest] arch: [main, alt] - include: - - os: ubuntu-latest - platform: linux - - os: windows-latest - ls: dir - - os: macos-latest - arch: alt - alt_arch_name: arm64 - - os: ubuntu-latest - platform: linux - alt_arch_name: aarch64 - exclude: - - os: windows-latest - arch: alt - - os: macos-latest - arch: alt - - os: ubuntu-latest - arch: alt steps: - uses: actions/checkout@v3 @@ -72,28 +54,38 @@ jobs: uses: pypa/cibuildwheel@v2.16.2 env: CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" - CIBW_BEFORE_BUILD: > - pip install setuptools-rust cython && + CIBW_BEFORE_ALL: > rustup default nightly && rustup show # rust doesn't seem to be available for musl linux on i686 # CIBW_SKIP: "*-musllinux_{i686,aarch64}" # we build for "alt_arch_name" if it exists, else 'auto - CIBW_ARCHS: ${{ matrix.alt_arch_name || 'auto' }} + CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" + CIBW_ARCHS_MACOS: "x86_64 arm64 universal2" + CIBW_ARCHS_WINDOWS: "AMD64 x86 ARM64" CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" - CIBW_MUSLLINUX_X86_64_IMAGE: "musllinux_1_1" + CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" CIBW_MANYLINUX_AARCH64_IMAGE: "manylinux2014" + CIBW_MANYLINUX_PPC64LE_IMAGE: "manylinux2014" + CIBW_MANYLINUX_S390X_IMAGE: "manylinux2014" + CIBW_MANYLINUX_PYPY_X86_64_IMAGE: "manylinux2014" + CIBW_MANYLINUX_PYPY_I686_IMAGE: "manylinux2014" + CIBW_MANYLINUX_PYPY_AARCH64_IMAGE: "manylinux2014" + + CIBW_MUSLLINUX_X86_64_IMAGE: "musllinux_1_1" + CIBW_MUSLLINUX_I686_IMAGE: "musllinux_1_1" CIBW_MUSLLINUX_AARCH64_IMAGE: "musllinux_1_1" + CIBW_MUSLLINUX_PPC64LE_IMAGE: "musllinux_1_1" + CIBW_MUSLLINUX_S390X_IMAGE: "musllinux_1_1" + CIBW_ENVIRONMENT: 'PATH="$HOME/.cargo/bin:$PATH"' # Fix the following error: error: cargo rustc --lib --message-format=json-render-diagnostics --manifest-path Cargo.toml --release -v --features pyo3/extension-module -- --crate-type cdylibfailed with code -9 # You need to set a second environment variable CARGO_NET_GIT_FETCH_WITH_CLI="true" for linux environments # Solutio found here: https://github.com/rust-lang/cargo/issues/10583 CIBW_ENVIRONMENT_LINUX: 'PATH="$HOME/.cargo/bin:$PATH" CARGO_NET_GIT_FETCH_WITH_CLI="true"' - CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" CIBW_ENVIRONMENT_WINDOWS: 'PATH="$UserProfile\.cargo\bin;$PATH"' CIBW_BEFORE_BUILD_LINUX: > - pip install cython numpy setuptools wheel setuptools-rust && curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show From 740ce0da2acefc8bd175da356beacff00aa48c51 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 17:04:08 +0100 Subject: [PATCH 190/347] Update pypi.yml --- .github/workflows/pypi.yml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index eb48ab57eb..fa1a5105e2 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -54,9 +54,6 @@ jobs: uses: pypa/cibuildwheel@v2.16.2 env: CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" - CIBW_BEFORE_ALL: > - rustup default nightly && - rustup show # rust doesn't seem to be available for musl linux on i686 # CIBW_SKIP: "*-musllinux_{i686,aarch64}" # we build for "alt_arch_name" if it exists, else 'auto @@ -85,6 +82,10 @@ jobs: # Solutio found here: https://github.com/rust-lang/cargo/issues/10583 CIBW_ENVIRONMENT_LINUX: 'PATH="$HOME/.cargo/bin:$PATH" CARGO_NET_GIT_FETCH_WITH_CLI="true"' CIBW_ENVIRONMENT_WINDOWS: 'PATH="$UserProfile\.cargo\bin;$PATH"' + + CIBW_BEFORE_BUILD: > + rustup default nightly && + rustup show CIBW_BEFORE_BUILD_LINUX: > curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show From 0207424e4139f3ead6a36216f10b763d0d4e742b Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 17:18:50 +0100 Subject: [PATCH 191/347] Update pypi.yml --- .github/workflows/pypi.yml | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index fa1a5105e2..43497788cf 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -6,15 +6,6 @@ on: tags: - "*" - # Cython-3.0.6-cp310-cp310-macosx_10_9_x86_64.whl - # Cython-3.0.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl - # Cython-3.0.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl - # Cython-3.0.6-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl - # Cython-3.0.6-cp310-cp310-musllinux_1_1_aarch64.whl - # Cython-3.0.6-cp310-cp310-musllinux_1_1_x86_64.whl - # Cython-3.0.6-cp310-cp310-win32.whl - # Cython-3.0.6-cp310-cp310-win_amd64.whl - jobs: build_wheels: name: Build wheels on ${{ matrix.os }} @@ -22,7 +13,6 @@ jobs: strategy: matrix: os: [ubuntu-latest, windows-latest, macos-11, macos-latest] - arch: [main, alt] steps: - uses: actions/checkout@v3 @@ -90,6 +80,17 @@ jobs: curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show + # See Poetry note here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile + # It explains why wheels have be re-tagged for macos + - run: pip install wheel==0.40 + if: matrix.os == 'macos-latest' || matrix.os == 'macos-11' + # create pendulum-2.1.2-cp311-cp311-macosx_12_0_arm64.whl + - run: wheel tags --platform-tag macosx_12_0_arm64 ./wheelhouse/pendulum-2.1.2-cp311-cp311-macosx_12_0_x86_64.whl + if: matrix.os == 'macos-latest' || matrix.os == 'macos-11' + # remove the incorrectly tagged arm64 wheel + - run: rm ./wheelhouse/pendulum-2.1.2-cp311-cp311-macosx_12_0_x86_64.whl + if: matrix.os == 'macos-latest' || matrix.os == 'macos-11' + - uses: actions/upload-artifact@v2 with: path: ./wheelhouse/*.whl From 8f7ed9f130c0485826f41029fc6e0e0b4e5a57eb Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 17:26:28 +0100 Subject: [PATCH 192/347] Update pypi.yml --- .github/workflows/pypi.yml | 15 +++------------ 1 file changed, 3 insertions(+), 12 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 43497788cf..a36de37efe 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -48,7 +48,9 @@ jobs: # CIBW_SKIP: "*-musllinux_{i686,aarch64}" # we build for "alt_arch_name" if it exists, else 'auto CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" - CIBW_ARCHS_MACOS: "x86_64 arm64 universal2" + # We don't build arm64 wheels yet because there's an issue with Poetry. + # More information here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile (CTRL + F "poetry") + CIBW_ARCHS_MACOS: "x86_64 universal2" CIBW_ARCHS_WINDOWS: "AMD64 x86 ARM64" CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" @@ -80,17 +82,6 @@ jobs: curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show - # See Poetry note here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile - # It explains why wheels have be re-tagged for macos - - run: pip install wheel==0.40 - if: matrix.os == 'macos-latest' || matrix.os == 'macos-11' - # create pendulum-2.1.2-cp311-cp311-macosx_12_0_arm64.whl - - run: wheel tags --platform-tag macosx_12_0_arm64 ./wheelhouse/pendulum-2.1.2-cp311-cp311-macosx_12_0_x86_64.whl - if: matrix.os == 'macos-latest' || matrix.os == 'macos-11' - # remove the incorrectly tagged arm64 wheel - - run: rm ./wheelhouse/pendulum-2.1.2-cp311-cp311-macosx_12_0_x86_64.whl - if: matrix.os == 'macos-latest' || matrix.os == 'macos-11' - - uses: actions/upload-artifact@v2 with: path: ./wheelhouse/*.whl From 1bed0ad5a97666bd00a76ff95dbacb0c26cef96a Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 17:32:42 +0100 Subject: [PATCH 193/347] Update pypi.yml --- .github/workflows/pypi.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index a36de37efe..650ad76a75 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -48,9 +48,9 @@ jobs: # CIBW_SKIP: "*-musllinux_{i686,aarch64}" # we build for "alt_arch_name" if it exists, else 'auto CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" - # We don't build arm64 wheels yet because there's an issue with Poetry. + # We don't build arm64 and universal2 wheels yet because there's an issue with Poetry. # More information here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile (CTRL + F "poetry") - CIBW_ARCHS_MACOS: "x86_64 universal2" + CIBW_ARCHS_MACOS: "x86_64" CIBW_ARCHS_WINDOWS: "AMD64 x86 ARM64" CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" From c3825e5133cfd812fd1803f217cedea1359af6c3 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 17:33:18 +0100 Subject: [PATCH 194/347] Update pypi.yml --- .github/workflows/pypi.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 650ad76a75..4c5e15a31c 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -43,7 +43,9 @@ jobs: - name: Build wheels uses: pypa/cibuildwheel@v2.16.2 env: - CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" + # TODO + # CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" + CIBW_BUILD: "cp311-*" # rust doesn't seem to be available for musl linux on i686 # CIBW_SKIP: "*-musllinux_{i686,aarch64}" # we build for "alt_arch_name" if it exists, else 'auto From 48c9710bcd3072cf8fb4729fac759dbae43f29ae Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 18:11:53 +0100 Subject: [PATCH 195/347] Update pypi.yml --- .github/workflows/pypi.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 4c5e15a31c..ea1f858ae6 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -53,7 +53,8 @@ jobs: # We don't build arm64 and universal2 wheels yet because there's an issue with Poetry. # More information here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile (CTRL + F "poetry") CIBW_ARCHS_MACOS: "x86_64" - CIBW_ARCHS_WINDOWS: "AMD64 x86 ARM64" + # We don't build ARM64 wheels yet because there's a Rust issue + CIBW_ARCHS_WINDOWS: "AMD64 x86" CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" From 977a5b4067194e17e5feffe56364d14bac010def Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 2 Dec 2023 20:13:09 +0100 Subject: [PATCH 196/347] Update pypi.yml --- .github/workflows/pypi.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index ea1f858ae6..d7b820262d 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -47,7 +47,7 @@ jobs: # CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_BUILD: "cp311-*" # rust doesn't seem to be available for musl linux on i686 - # CIBW_SKIP: "*-musllinux_{i686,aarch64}" + CIBW_SKIP: "*-musllinux_i686" # we build for "alt_arch_name" if it exists, else 'auto CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" # We don't build arm64 and universal2 wheels yet because there's an issue with Poetry. From b4f0b39607a0e733025a5d771b26287456fe3821 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sun, 3 Dec 2023 14:00:51 +0100 Subject: [PATCH 197/347] Update pypi.yml --- .github/workflows/pypi.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index d7b820262d..f1b30046d3 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -46,15 +46,14 @@ jobs: # TODO # CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_BUILD: "cp311-*" - # rust doesn't seem to be available for musl linux on i686 - CIBW_SKIP: "*-musllinux_i686" - # we build for "alt_arch_name" if it exists, else 'auto CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" # We don't build arm64 and universal2 wheels yet because there's an issue with Poetry. # More information here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile (CTRL + F "poetry") CIBW_ARCHS_MACOS: "x86_64" # We don't build ARM64 wheels yet because there's a Rust issue CIBW_ARCHS_WINDOWS: "AMD64 x86" + # Rust doesn't seem to be available for musl linux on i686 and ppc64le + CIBW_SKIP: "*-musllinux_i686 *-musllinux_ppc64le" CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" From f8b5cf9e5575041ed078fd750db39a21bfda0cc6 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sun, 3 Dec 2023 15:49:39 +0100 Subject: [PATCH 198/347] Update pypi.yml --- .github/workflows/pypi.yml | 33 ++++++++++++++++++--------------- 1 file changed, 18 insertions(+), 15 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index f1b30046d3..ea0b7d9c33 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -12,12 +12,19 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest, windows-latest, macos-11, macos-latest] + os: + [ + ubuntu-latest, + windows-latest, + macos-11, + macos-latest, + macos-latest-xlarge, + ] steps: - uses: actions/checkout@v3 - - name: set up rust + - name: Set up rust if: matrix.os != 'ubuntu-latest' uses: actions-rs/toolchain@v1 with: @@ -25,15 +32,6 @@ jobs: toolchain: nightly override: true - - run: rustup target add aarch64-apple-darwin - if: matrix.os == 'macos-latest' - - - run: rustup toolchain install stable-i686-pc-windows-msvc - if: matrix.os == 'windows-latest' - - - run: rustup target add i686-pc-windows-msvc - if: matrix.os == 'windows-latest' - - name: Set up QEMU if: matrix.os == 'ubuntu-latest' uses: docker/setup-qemu-action@v3 @@ -47,13 +45,18 @@ jobs: # CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_BUILD: "cp311-*" CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" - # We don't build arm64 and universal2 wheels yet because there's an issue with Poetry. - # More information here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile (CTRL + F "poetry") - CIBW_ARCHS_MACOS: "x86_64" + CIBW_ARCHS_MACOS: "x86_64 arm64" # We don't build ARM64 wheels yet because there's a Rust issue CIBW_ARCHS_WINDOWS: "AMD64 x86" # Rust doesn't seem to be available for musl linux on i686 and ppc64le - CIBW_SKIP: "*-musllinux_i686 *-musllinux_ppc64le" + CIBW_SKIP: "*-musllinux_i686 *-musllinux_ppc64le *-musllinux_s390x" + + # arm64 and universal2 wheels are tagged with x86_64 because there's an issue with Poetry + # More information here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile (CTRL + F "poetry") + # https://github.com/pypa/cibuildwheel/issues/1415 + CIBW_REPAIR_WHEEL_COMMAND_MACOS: > + delocate-wheel --require-archs {delocate_archs} -w {dest_dir} -v {wheel} && + for file in {dest_dir}/*.whl ; do mv $file ${file//x86_64/arm64} ; done CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" From 9bed64dca01d8bbc473a10b6823319fc8fd0c837 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sun, 3 Dec 2023 15:50:33 +0100 Subject: [PATCH 199/347] Update pypi.yml --- .github/workflows/pypi.yml | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index ea0b7d9c33..18ad69f49c 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -12,14 +12,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: - [ - ubuntu-latest, - windows-latest, - macos-11, - macos-latest, - macos-latest-xlarge, - ] + os: [ubuntu-latest, windows-latest, macos-11, macos-latest] steps: - uses: actions/checkout@v3 @@ -48,7 +41,7 @@ jobs: CIBW_ARCHS_MACOS: "x86_64 arm64" # We don't build ARM64 wheels yet because there's a Rust issue CIBW_ARCHS_WINDOWS: "AMD64 x86" - # Rust doesn't seem to be available for musl linux on i686 and ppc64le + # Rust nighlty doesn't seem to be available for musl linux on i686 and ppc64le and s390x (yet) CIBW_SKIP: "*-musllinux_i686 *-musllinux_ppc64le *-musllinux_s390x" # arm64 and universal2 wheels are tagged with x86_64 because there's an issue with Poetry From 25a89733a71258d6fc35159dde05762ed434ef2e Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sun, 3 Dec 2023 15:58:14 +0100 Subject: [PATCH 200/347] Update pypi.yml --- .github/workflows/pypi.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 18ad69f49c..7ec9993c97 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -49,7 +49,7 @@ jobs: # https://github.com/pypa/cibuildwheel/issues/1415 CIBW_REPAIR_WHEEL_COMMAND_MACOS: > delocate-wheel --require-archs {delocate_archs} -w {dest_dir} -v {wheel} && - for file in {dest_dir}/*.whl ; do mv $file ${file//x86_64/arm64} ; done + for file in {dest_dir}/*.whl ; do mv $file ${file//x86_64/arm64} 2>/dev/null ; done CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" From f2225747c28e43bf53293d96c2030a949b73d340 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sun, 3 Dec 2023 16:07:28 +0100 Subject: [PATCH 201/347] Update pypi.yml --- .github/workflows/pypi.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 7ec9993c97..285eb862ac 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -38,7 +38,8 @@ jobs: # CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_BUILD: "cp311-*" CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" - CIBW_ARCHS_MACOS: "x86_64 arm64" + # CIBW_ARCHS_MACOS: "x86_64 arm64" + CIBW_ARCHS_MACOS: "universal2" # We don't build ARM64 wheels yet because there's a Rust issue CIBW_ARCHS_WINDOWS: "AMD64 x86" # Rust nighlty doesn't seem to be available for musl linux on i686 and ppc64le and s390x (yet) @@ -48,8 +49,9 @@ jobs: # More information here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile (CTRL + F "poetry") # https://github.com/pypa/cibuildwheel/issues/1415 CIBW_REPAIR_WHEEL_COMMAND_MACOS: > + ls {dest_dir} && delocate-wheel --require-archs {delocate_archs} -w {dest_dir} -v {wheel} && - for file in {dest_dir}/*.whl ; do mv $file ${file//x86_64/arm64} 2>/dev/null ; done + for file in {dest_dir}/*.whl ; do mv $file ${file//x86_64/universal2} ; done CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" From 673a3b730694927ca08c00280f513201848ce919 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sun, 3 Dec 2023 16:59:39 +0100 Subject: [PATCH 202/347] wip --- .github/workflows/pypi.yml | 9 +++++++++ MANIFEST.in | 1 + 2 files changed, 10 insertions(+) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 285eb862ac..464ced3d9a 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -25,6 +25,15 @@ jobs: toolchain: nightly override: true + - run: rustup target add aarch64-apple-darwin + if: matrix.os == 'macos-latest' + + - run: rustup toolchain install stable-i686-pc-windows-msvc + if: matrix.os == 'windows-latest' + + - run: rustup target add i686-pc-windows-msvc + if: matrix.os == 'windows-latest' + - name: Set up QEMU if: matrix.os == 'ubuntu-latest' uses: docker/setup-qemu-action@v3 diff --git a/MANIFEST.in b/MANIFEST.in index ac3faa3ca1..0bc743b1e0 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,4 @@ +global-include *.cpp global-include *.pyx global-include *.pxd include river/datasets/*.csv From 60c2b29fb7d6ca4a07f91a25eccd1e189790be40 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sun, 3 Dec 2023 17:04:43 +0100 Subject: [PATCH 203/347] Update pypi.yml --- .github/workflows/pypi.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 464ced3d9a..2fcc324b53 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -26,7 +26,7 @@ jobs: override: true - run: rustup target add aarch64-apple-darwin - if: matrix.os == 'macos-latest' + if: matrix.os == 'macos-latest' || matrix.os == 'macos-11' - run: rustup toolchain install stable-i686-pc-windows-msvc if: matrix.os == 'windows-latest' From d6c5c41cc16369a3becdf60fb9d3b9d618288e34 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADlia=20Takaguti=20Dicezare?= <85309092+mariliatd@users.noreply.github.com> Date: Sun, 3 Dec 2023 15:02:38 -0300 Subject: [PATCH 204/347] No more return self (#1461) * remove return self from learn_one of active learning Signed-off-by: mariliatd * remove return self from learn_one of cluster module Signed-off-by: mariliatd * remove return self from learn_one of factorization machines classes Signed-off-by: mariliatd * remove return self from learn_one of forest classes Signed-off-by: mariliatd * remove return self from learn_one of ranking models classes Signed-off-by: mariliatd * remove return self from learn_one of tree models classes Signed-off-by: mariliatd * remove return self from learn_one of mondrian tree models classes Signed-off-by: mariliatd * remove return self from learn_one of compose module Signed-off-by: mariliatd * remove return self from learn_one of preprocessing module Signed-off-by: mariliatd * remove return self from learn_one of anomaly detector classes Signed-off-by: mariliatd * remove return self from learn_one of base module Signed-off-by: mariliatd * remove return self from learn_one of feature extraction module Signed-off-by: mariliatd * remove return self from learn_one of linear models classes Signed-off-by: mariliatd * remove return self from learn_one of naive bayes models classes Signed-off-by: mariliatd * remove return self from learn_one of remaining files Signed-off-by: mariliatd * remove return self from learn_one of remaining files Signed-off-by: mariliatd * remove return self from learn_many of all classes Signed-off-by: mariliatd * remove unnecessary learn_one assignment to variable in jupyter notebooks Signed-off-by: mariliatd --------- Signed-off-by: mariliatd --- README.md | 2 +- benchmarks/model_adapters/vw.py | 1 - docs/examples/batch-to-online.ipynb | 7 ++-- docs/examples/debugging-a-pipeline.ipynb | 4 +-- .../examples/the-art-of-using-pipelines.ipynb | 3 +- .../binary-classification.ipynb | 2 +- .../getting-started/regression.ipynb | 2 +- river/active/base.py | 1 - river/active/entropy.py | 2 +- river/anomaly/base.py | 17 ++-------- river/anomaly/filter.py | 3 +- river/anomaly/gaussian.py | 3 +- river/anomaly/hst.py | 8 ++--- river/anomaly/sad.py | 4 +-- river/anomaly/svm.py | 6 ++-- river/anomaly/test_hst.py | 2 +- river/base/base.py | 2 +- river/base/classifier.py | 10 +----- river/base/clusterer.py | 4 --- river/base/multi_output.py | 12 ++----- river/base/regressor.py | 12 ++----- river/base/test_base.py | 2 +- river/base/transformer.py | 32 +++++------------- river/checks/clf.py | 4 +-- river/cluster/clustream.py | 10 +++--- river/cluster/dbstream.py | 4 +-- river/cluster/denstream.py | 5 ++- river/cluster/k_means.py | 3 +- river/cluster/streamkmeans.py | 8 ++--- river/cluster/textclust.py | 2 +- river/compat/river_to_sklearn.py | 3 +- river/compat/sklearn_to_river.py | 4 --- river/compose/grouper.py | 1 - river/compose/pipeline.py | 6 +--- river/compose/target_transform.py | 1 - river/compose/test_product.py | 2 +- river/compose/union.py | 6 ++-- river/conf/jackknife.py | 4 +-- river/drift/no_drift.py | 4 +-- river/drift/retrain.py | 1 - river/dummy.py | 9 ++--- river/ensemble/bagging.py | 6 ---- river/ensemble/boosting.py | 3 -- river/ensemble/stacking.py | 2 -- river/ensemble/streaming_random_patches.py | 2 -- river/ensemble/voting.py | 1 - river/evaluate/progressive_validation.py | 2 +- river/facto/base.py | 4 +-- river/facto/ffm.py | 4 +-- river/facto/fm.py | 4 +-- river/facto/fwfm.py | 4 +-- river/facto/hofm.py | 4 +-- river/feature_extraction/agg.py | 16 ++++----- river/feature_extraction/kernel_approx.py | 4 +-- river/feature_extraction/test_agg.py | 13 +++++--- river/feature_extraction/vectorize.py | 8 ++--- river/feature_selection/k_best.py | 4 +-- river/feature_selection/variance.py | 5 ++- river/forest/adaptive_random_forest.py | 2 -- river/forest/aggregated_mondrian_forest.py | 4 --- river/forest/online_extra_trees.py | 2 -- river/forest/test_amf.py | 4 +-- river/imblearn/chebyshev.py | 4 --- river/imblearn/hard_sampling.py | 2 -- river/imblearn/random.py | 10 ++---- river/linear_model/alma.py | 2 -- river/linear_model/base.py | 4 +-- river/linear_model/bayesian_lin_reg.py | 2 -- river/linear_model/pa.py | 8 ++--- river/linear_model/softmax.py | 2 -- river/linear_model/test_glm.py | 21 ++++++++---- river/metrics/silhouette.py | 2 +- river/model_selection/bandit.py | 4 --- river/model_selection/greedy.py | 2 -- river/model_selection/sh.py | 2 -- river/multiclass/occ.py | 2 -- river/multiclass/ovo.py | 2 -- river/multiclass/ovr.py | 6 +--- river/multioutput/chain.py | 10 ++---- river/multioutput/encoder.py | 4 +-- river/naive_bayes/bernoulli.py | 16 ++------- river/naive_bayes/complement.py | 16 ++------- river/naive_bayes/gaussian.py | 4 +-- river/naive_bayes/multinomial.py | 16 ++------- river/naive_bayes/test_naive_bayes.py | 8 ++--- river/neighbors/knn_classifier.py | 1 - river/neighbors/knn_regressor.py | 2 -- river/neural_net/mlp.py | 9 +++-- river/preprocessing/impute.py | 16 ++++----- river/preprocessing/lda.py | 3 +- river/preprocessing/one_hot.py | 24 ++++++-------- river/preprocessing/ordinal.py | 6 ++-- river/preprocessing/pred_clipper.py | 3 +- river/preprocessing/scale.py | 33 ++++++++----------- river/preprocessing/test_lda.py | 2 +- river/preprocessing/test_scale.py | 2 +- river/reco/baseline.py | 4 +-- river/reco/biased_mf.py | 4 +-- river/reco/funk_mf.py | 4 +-- river/reco/normal.py | 3 +- river/rules/amrules.py | 13 ++------ river/stats/shift.py | 6 ++-- river/time_series/holt_winters.py | 6 ++-- river/time_series/snarimax.py | 6 ++-- river/time_series/test_evaluate.py | 1 - river/tree/extremely_fast_decision_tree.py | 6 ---- .../hoeffding_adaptive_tree_classifier.py | 2 -- .../tree/hoeffding_adaptive_tree_regressor.py | 2 -- river/tree/hoeffding_tree.py | 2 +- river/tree/hoeffding_tree_classifier.py | 6 ---- river/tree/hoeffding_tree_regressor.py | 5 --- river/tree/isoup_tree_regressor.py | 2 -- .../tree/mondrian/mondrian_tree_classifier.py | 1 - .../tree/mondrian/mondrian_tree_regressor.py | 1 - river/tree/stochastic_gradient_tree.py | 4 +-- 115 files changed, 193 insertions(+), 451 deletions(-) diff --git a/README.md b/README.md index b7e8fb7c74..0edef73742 100644 --- a/README.md +++ b/README.md @@ -92,7 +92,7 @@ Now let's run the model on the dataset in a streaming fashion. We sequentially i >>> for x, y in dataset: ... y_pred = model.predict_one(x) # make a prediction ... metric = metric.update(y, y_pred) # update the metric -... model = model.learn_one(x, y) # make the model learn +... model.learn_one(x, y) # make the model learn >>> metric Accuracy: 89.28% diff --git a/benchmarks/model_adapters/vw.py b/benchmarks/model_adapters/vw.py index 03807ba2a5..381b9ea4a8 100644 --- a/benchmarks/model_adapters/vw.py +++ b/benchmarks/model_adapters/vw.py @@ -22,7 +22,6 @@ def learn_one(self, x, y): ex = self._format_x(x) ex = f"{y_vw} | {ex}" self.vw.learn(ex) - return self def predict_proba_one(self, x): ex = "| " + self._format_x(x) diff --git a/docs/examples/batch-to-online.ipynb b/docs/examples/batch-to-online.ipynb index 99c9cdb717..fc5f8255db 100644 --- a/docs/examples/batch-to-online.ipynb +++ b/docs/examples/batch-to-online.ipynb @@ -380,7 +380,7 @@ "scaler = preprocessing.StandardScaler()\n", "\n", "for xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer()):\n", - " scaler = scaler.learn_one(xi)" + " scaler.learn_one(xi)" ] }, { @@ -425,7 +425,8 @@ "for xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer(), shuffle=True, seed=42):\n", " \n", " # Scale the features\n", - " xi_scaled = scaler.learn_one(xi).transform_one(xi)\n", + " scaler.learn_one(xi)\n", + " xi_scaled = scaler.transform_one(xi)\n", " \n", " # Test the current model on the new \"unobserved\" sample\n", " yi_pred = log_reg.predict_proba_one(xi_scaled)\n", @@ -538,7 +539,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/examples/debugging-a-pipeline.ipynb b/docs/examples/debugging-a-pipeline.ipynb index dedc043a77..2f20450584 100644 --- a/docs/examples/debugging-a-pipeline.ipynb +++ b/docs/examples/debugging-a-pipeline.ipynb @@ -87,7 +87,7 @@ " # Answer\n", " else:\n", " metric.update(y, questions[i])\n", - " model = model.learn_one(x, y)\n", + " model.learn_one(x, y)\n", " \n", " if i >= 30000 and i % 30000 == 0:\n", " print(i, metric)" @@ -447,7 +447,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/examples/the-art-of-using-pipelines.ipynb b/docs/examples/the-art-of-using-pipelines.ipynb index 7f872fcd9a..ae03f17dc0 100644 --- a/docs/examples/the-art-of-using-pipelines.ipynb +++ b/docs/examples/the-art-of-using-pipelines.ipynb @@ -120,7 +120,8 @@ " x.pop(key)\n", " \n", " # Rescale the data\n", - " x = scaler.learn_one(x).transform_one(x)\n", + " scaler.learn_one(x)\n", + " x = scaler.transform_one(x)\n", " \n", " # Fit the linear regression\n", " y_pred = lin_reg.predict_one(x)\n", diff --git a/docs/introduction/getting-started/binary-classification.ipynb b/docs/introduction/getting-started/binary-classification.ipynb index d9e8c990c5..17a9725c61 100644 --- a/docs/introduction/getting-started/binary-classification.ipynb +++ b/docs/introduction/getting-started/binary-classification.ipynb @@ -215,7 +215,7 @@ }, "outputs": [], "source": [ - "model = model.learn_one(x, y)" + "model.learn_one(x, y)" ] }, { diff --git a/docs/introduction/getting-started/regression.ipynb b/docs/introduction/getting-started/regression.ipynb index 25d7a91ccc..eea7e154c5 100644 --- a/docs/introduction/getting-started/regression.ipynb +++ b/docs/introduction/getting-started/regression.ipynb @@ -185,7 +185,7 @@ }, "outputs": [], "source": [ - "model = model.learn_one(x, y)" + "model.learn_one(x, y)" ] }, { diff --git a/river/active/base.py b/river/active/base.py index aa0acc398f..e4b8f7c057 100644 --- a/river/active/base.py +++ b/river/active/base.py @@ -71,4 +71,3 @@ def predict_one(self, x, **kwargs): def learn_one(self, x, y, **kwargs): self.classifier.learn_one(x, y) - return self diff --git a/river/active/entropy.py b/river/active/entropy.py index e40663472a..09858ae1f4 100644 --- a/river/active/entropy.py +++ b/river/active/entropy.py @@ -50,7 +50,7 @@ class EntropySampler(ActiveLearningClassifier): ... metric = metric.update(y, y_pred) ... if ask: ... n_samples_used += 1 - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> metric Accuracy: 86.60% diff --git a/river/anomaly/base.py b/river/anomaly/base.py index b265269c3e..9c8fc42c50 100644 --- a/river/anomaly/base.py +++ b/river/anomaly/base.py @@ -15,7 +15,7 @@ def _supervised(self): return False @abc.abstractmethod - def learn_one(self, x: dict) -> AnomalyDetector: + def learn_one(self, x: dict): """Update the model. Parameters @@ -23,10 +23,6 @@ def learn_one(self, x: dict) -> AnomalyDetector: x A dictionary of features. - Returns - ------- - self - """ @abc.abstractmethod @@ -52,7 +48,7 @@ class SupervisedAnomalyDetector(base.Estimator): """A supervised anomaly detector.""" @abc.abstractmethod - def learn_one(self, x: dict, y: base.typing.Target) -> SupervisedAnomalyDetector: + def learn_one(self, x: dict, y: base.typing.Target): """Update the model. Parameters @@ -60,10 +56,6 @@ def learn_one(self, x: dict, y: base.typing.Target) -> SupervisedAnomalyDetector x A dictionary of features. - Returns - ------- - self - """ @abc.abstractmethod @@ -153,11 +145,6 @@ def learn_one(self, *args, **learn_kwargs): args Depends on whether the underlying anomaly detector is supervised or not. - Returns - ------- - self - """ if self.protect_anomaly_detector and not self.classify(self.score_one(*args)): self.anomaly_detector.learn_one(*args, **learn_kwargs) - return self diff --git a/river/anomaly/filter.py b/river/anomaly/filter.py index 968168b18b..a3a685fe26 100644 --- a/river/anomaly/filter.py +++ b/river/anomaly/filter.py @@ -144,7 +144,7 @@ class QuantileFilter(anomaly.base.AnomalyFilter): >>> for x, y in datasets.CreditCard().take(2000): ... score = model.score_one(x) ... is_anomaly = model['QuantileFilter'].classify(score) - ... model = model.learn_one(x) + ... model.learn_one(x) ... report = report.update(y, is_anomaly) >>> report @@ -180,7 +180,6 @@ def learn_one(self, *args, **learn_kwargs): if not self.protect_anomaly_detector or not self.classify(score): self.anomaly_detector.learn_one(*args, **learn_kwargs) self.quantile.update(score) - return self @classmethod def _unit_test_params(cls): diff --git a/river/anomaly/gaussian.py b/river/anomaly/gaussian.py index 1044fe7bc1..352d10138b 100644 --- a/river/anomaly/gaussian.py +++ b/river/anomaly/gaussian.py @@ -32,7 +32,7 @@ class GaussianScorer(anomaly.base.SupervisedAnomalyDetector): >>> detector = anomaly.GaussianScorer() >>> for y in (rng.gauss(0, 1) for _ in range(100)): - ... detector = detector.learn_one(None, y) + ... detector.learn_one(None, y) >>> detector.score_one(None, -3) 0.999477... @@ -59,7 +59,6 @@ def __init__(self, window_size=None, grace_period=100): def learn_one(self, x, y): self.gaussian.update(y) - return self def score_one(self, x, y): if self.gaussian.n_samples < self.grace_period: diff --git a/river/anomaly/hst.py b/river/anomaly/hst.py index d795348250..6f47f18f7b 100644 --- a/river/anomaly/hst.py +++ b/river/anomaly/hst.py @@ -138,11 +138,11 @@ class HalfSpaceTrees(anomaly.base.AnomalyDetector): ... ) >>> for x in X[:3]: - ... hst = hst.learn_one({'x': x}) # Warming up + ... hst.learn_one({'x': x}) # Warming up >>> for x in X: ... features = {'x': x} - ... hst = hst.learn_one(features) + ... hst.learn_one(features) ... print(f'Anomaly score for x={x:.3f}: {hst.score_one(features):.3f}') Anomaly score for x=0.500: 0.107 Anomaly score for x=0.450: 0.071 @@ -170,7 +170,7 @@ class HalfSpaceTrees(anomaly.base.AnomalyDetector): >>> for x, y in datasets.CreditCard().take(2500): ... score = model.score_one(x) - ... model = model.learn_one(x) + ... model.learn_one(x) ... auc = auc.update(y, score) >>> auc @@ -268,8 +268,6 @@ def learn_one(self, x): self._first_window = False self.counter = 0 - return self - def score_one(self, x): if self._first_window: return 0 diff --git a/river/anomaly/sad.py b/river/anomaly/sad.py index 6b5b52d429..68cc1835ee 100644 --- a/river/anomaly/sad.py +++ b/river/anomaly/sad.py @@ -46,7 +46,7 @@ class StandardAbsoluteDeviation(anomaly.base.SupervisedAnomalyDetector): >>> for _ in range(150): ... y = rng.gauss(0, 1) - ... model = model.learn_one(None, y) + ... model.learn_one(None, y) >>> model.score_one(None, 2) 2.057... @@ -67,8 +67,6 @@ def learn_one(self, x, y): self.variance.update(y) self.sub_stat.update(y) - return self - def score_one(self, x, y): score = (y - self.sub_stat.get()) / (self.variance.get() ** 0.5 + 1e-10) diff --git a/river/anomaly/svm.py b/river/anomaly/svm.py index 297ad3ed22..6611591350 100644 --- a/river/anomaly/svm.py +++ b/river/anomaly/svm.py @@ -48,7 +48,7 @@ class OneClassSVM(linear_model.base.GLM, anomaly.base.AnomalyDetector): >>> for x, y in datasets.CreditCard().take(2500): ... score = model.score_one(x) ... is_anomaly = model.classify(score) - ... model = model.learn_one(x) + ... model.learn_one(x) ... auc = auc.update(y, is_anomaly) >>> auc @@ -102,10 +102,10 @@ def _get_intercept_update(self, loss_gradient): ) def learn_one(self, x): - return super().learn_one(x, y=1) + super().learn_one(x, y=1) def learn_many(self, X): - return super().learn_many(X, y=pd.Series(True, index=X.index)) + super().learn_many(X, y=pd.Series(True, index=X.index)) def score_one(self, x): return self._raw_dot_one(x) - self.intercept diff --git a/river/anomaly/test_hst.py b/river/anomaly/test_hst.py index ffcafcccc0..ac5f7a8767 100644 --- a/river/anomaly/test_hst.py +++ b/river/anomaly/test_hst.py @@ -24,7 +24,7 @@ def test_missing_features(): >>> for x, y in datasets.CreditCard().take(8000): ... del x[random.choice(features)] ... score = model.score_one(x) - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) ... auc = auc.update(y, score) >>> auc diff --git a/river/base/base.py b/river/base/base.py index af760f58c2..16e4f829b3 100644 --- a/river/base/base.py +++ b/river/base/base.py @@ -453,7 +453,7 @@ def log_method_calls( >>> with utils.log_method_calls(class_condition): ... for x, y in datasets.CreditCard().take(1): ... score = model.score_one(x) - ... model = model.learn_one(x) + ... model.learn_one(x) >>> print(logs.getvalue()) MinMaxScaler.transform_one diff --git a/river/base/classifier.py b/river/base/classifier.py index 95dd25dc70..19f01be18f 100644 --- a/river/base/classifier.py +++ b/river/base/classifier.py @@ -25,10 +25,6 @@ def learn_one(self, x: dict, y: base.typing.ClfTarget) -> Classifier: y A label. - Returns - ------- - self - """ def predict_proba_one(self, x: dict) -> dict[base.typing.ClfTarget, float]: @@ -85,7 +81,7 @@ class MiniBatchClassifier(Classifier): """A classifier that can operate on mini-batches.""" @abc.abstractmethod - def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchClassifier: + def learn_many(self, X: pd.DataFrame, y: pd.Series): """Update the model with a mini-batch of features `X` and boolean targets `y`. Parameters @@ -95,10 +91,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchClassifier: y A series of boolean target values. - Returns - ------- - self - """ def predict_proba_many(self, X: pd.DataFrame) -> pd.DataFrame: diff --git a/river/base/clusterer.py b/river/base/clusterer.py index fd036920fa..07c6ac7f3d 100644 --- a/river/base/clusterer.py +++ b/river/base/clusterer.py @@ -21,10 +21,6 @@ def learn_one(self, x: dict) -> Clusterer: x A dictionary of features. - Returns - ------- - self - """ @abc.abstractmethod diff --git a/river/base/multi_output.py b/river/base/multi_output.py index d862d68ad9..a05904621a 100644 --- a/river/base/multi_output.py +++ b/river/base/multi_output.py @@ -10,7 +10,7 @@ class MultiLabelClassifier(Estimator, abc.ABC): """Multi-label classifier.""" @abc.abstractmethod - def learn_one(self, x: dict, y: dict[FeatureName, bool]) -> MultiLabelClassifier: + def learn_one(self, x: dict, y: dict[FeatureName, bool]): """Update the model with a set of features `x` and the labels `y`. Parameters @@ -20,10 +20,6 @@ def learn_one(self, x: dict, y: dict[FeatureName, bool]) -> MultiLabelClassifier y A dictionary of labels. - Returns - ------- - self - """ def predict_proba_one(self, x: dict, **kwargs) -> dict[FeatureName, dict[bool, float]]: @@ -72,7 +68,7 @@ class MultiTargetRegressor(Estimator, abc.ABC): """Multi-target regressor.""" @abc.abstractmethod - def learn_one(self, x: dict, y: dict[FeatureName, RegTarget], **kwargs) -> MultiTargetRegressor: + def learn_one(self, x: dict, y: dict[FeatureName, RegTarget], **kwargs): """Fits to a set of features `x` and a real-valued target `y`. Parameters @@ -82,10 +78,6 @@ def learn_one(self, x: dict, y: dict[FeatureName, RegTarget], **kwargs) -> Multi y A dictionary of numeric targets. - Returns - ------- - self - """ @abc.abstractmethod diff --git a/river/base/regressor.py b/river/base/regressor.py index 48f353ea0f..775a23a33e 100644 --- a/river/base/regressor.py +++ b/river/base/regressor.py @@ -15,7 +15,7 @@ class Regressor(estimator.Estimator): """A regressor.""" @abc.abstractmethod - def learn_one(self, x: dict, y: base.typing.RegTarget) -> Regressor: + def learn_one(self, x: dict, y: base.typing.RegTarget): """Fits to a set of features `x` and a real-valued target `y`. Parameters @@ -25,10 +25,6 @@ def learn_one(self, x: dict, y: base.typing.RegTarget) -> Regressor: y A numeric target. - Returns - ------- - self - """ @abc.abstractmethod @@ -51,7 +47,7 @@ class MiniBatchRegressor(Regressor): """A regressor that can operate on mini-batches.""" @abc.abstractmethod - def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchRegressor: + def learn_many(self, X: pd.DataFrame, y: pd.Series): """Update the model with a mini-batch of features `X` and real-valued targets `y`. Parameters @@ -61,10 +57,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchRegressor: y A series of numbers. - Returns - ------- - self - """ @abc.abstractmethod diff --git a/river/base/test_base.py b/river/base/test_base.py index 2154f58a53..412303b93b 100644 --- a/river/base/test_base.py +++ b/river/base/test_base.py @@ -75,7 +75,7 @@ def test_mutate(): >>> for x, y in datasets.TrumpApproval(): ... _ = model.predict_one(x) - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> len(model[-1].weights) 6 diff --git a/river/base/transformer.py b/river/base/transformer.py index defc0c80f8..5313ba618d 100644 --- a/river/base/transformer.py +++ b/river/base/transformer.py @@ -57,7 +57,7 @@ class Transformer(base.Estimator, BaseTransformer): def _supervised(self): return False - def learn_one(self, x: dict) -> Transformer: + def learn_one(self, x: dict): """Update with a set of features `x`. A lot of transformers don't actually have to do anything during the `learn_one` step @@ -70,12 +70,8 @@ def learn_one(self, x: dict) -> Transformer: x A dictionary of features. - Returns - ------- - self - """ - return self + return class SupervisedTransformer(base.Estimator, BaseTransformer): @@ -85,7 +81,7 @@ class SupervisedTransformer(base.Estimator, BaseTransformer): def _supervised(self): return True - def learn_one(self, x: dict, y: base.typing.Target) -> SupervisedTransformer: + def learn_one(self, x: dict, y: base.typing.Target): """Update with a set of features `x` and a target `y`. Parameters @@ -95,12 +91,8 @@ def learn_one(self, x: dict, y: base.typing.Target) -> SupervisedTransformer: y A target. - Returns - ------- - self - """ - return self + return class MiniBatchTransformer(Transformer): @@ -121,7 +113,7 @@ def transform_many(self, X: pd.DataFrame) -> pd.DataFrame: """ - def learn_many(self, X: pd.DataFrame) -> Transformer: + def learn_many(self, X: pd.DataFrame): """Update with a mini-batch of features. A lot of transformers don't actually have to do anything during the `learn_many` step @@ -134,12 +126,8 @@ def learn_many(self, X: pd.DataFrame) -> Transformer: X A DataFrame of features. - Returns - ------- - self - """ - return self + return class MiniBatchSupervisedTransformer(Transformer): @@ -150,7 +138,7 @@ def _supervised(self): return True @abc.abstractmethod - def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchSupervisedTransformer: + def learn_many(self, X: pd.DataFrame, y: pd.Series): """Update the model with a mini-batch of features `X` and targets `y`. Parameters @@ -160,12 +148,8 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchSupervisedTransf y A series of boolean target values. - Returns - ------- - self - """ - return self + return @abc.abstractmethod def transform_many(self, X: pd.DataFrame) -> pd.DataFrame: diff --git a/river/checks/clf.py b/river/checks/clf.py index 3c22797bb2..ce9d754214 100644 --- a/river/checks/clf.py +++ b/river/checks/clf.py @@ -15,7 +15,7 @@ def check_predict_proba_one(classifier, dataset): for x, y in dataset: xx, yy = copy.deepcopy(x), copy.deepcopy(y) - classifier = classifier.learn_one(x, y) + classifier.learn_one(x, y) y_pred = classifier.predict_proba_one(x) if utils.inspect.isactivelearner(classifier): @@ -37,7 +37,7 @@ def check_predict_proba_one_binary(classifier, dataset): for x, y in dataset: y_pred = classifier.predict_proba_one(x) - classifier = classifier.learn_one(x, y) + classifier.learn_one(x, y) assert set(y_pred.keys()) == {False, True} diff --git a/river/cluster/clustream.py b/river/cluster/clustream.py index 1e6b9445ca..74a73e9329 100644 --- a/river/cluster/clustream.py +++ b/river/cluster/clustream.py @@ -103,7 +103,7 @@ class CluStream(base.Clusterer): ... ) >>> for x, _ in stream.iter_array(X): - ... clustream = clustream.learn_one(x) + ... clustream.learn_one(x) >>> clustream.predict_one({0: 1, 1: 1}) 1 @@ -216,7 +216,7 @@ def learn_one(self, x, w=1.0): if len(self.micro_clusters) == self.max_micro_clusters: self._initialized = True - return self + return # Determine the closest micro-cluster with respect to the new point instance closest_id, closest_dist = self._get_closest_mc(x) @@ -236,7 +236,7 @@ def learn_one(self, x, w=1.0): if closest_dist < radius: closest_mc.insert(x, w, self._timestamp) - return self + return # If the new point does not fit in the micro-cluster, micro-clusters # whose relevance stamps are less than the threshold are deleted. @@ -253,12 +253,10 @@ def learn_one(self, x, w=1.0): n_clusters=self.n_macro_clusters, seed=self.seed, **self.kwargs ) for center in self._mc_centers.values(): - self._kmeans_mc = self._kmeans_mc.learn_one(center) + self._kmeans_mc.learn_one(center) self.centers = self._kmeans_mc.centers - return self - def predict_one(self, x): index, _ = self._get_closest_mc(x) try: diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 88bdc0149f..93a1cea682 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -119,7 +119,7 @@ class DBSTREAM(base.Clusterer): ... ) >>> for x, _ in stream.iter_array(X): - ... dbstream = dbstream.learn_one(x) + ... dbstream.learn_one(x) >>> dbstream.predict_one({0: 1, 1: 2}) 0 @@ -397,8 +397,6 @@ def learn_one(self, x, w=None): self.clustering_is_up_to_date = False - return self - def predict_one(self, x, w=None): self._recluster() diff --git a/river/cluster/denstream.py b/river/cluster/denstream.py index e663cc1506..4d060795de 100644 --- a/river/cluster/denstream.py +++ b/river/cluster/denstream.py @@ -117,7 +117,7 @@ class DenStream(base.Clusterer): ... n_samples_init=10) >>> for x, _ in stream.iter_array(X): - ... denstream = denstream.learn_one(x) + ... denstream.learn_one(x) >>> denstream.predict_one({0: -1, 1: -2}) 0 @@ -326,7 +326,7 @@ def learn_one(self, x, w=None): self._initial_dbscan() self.initialized = True del self._init_buffer - return self + return # Merge self._merge(x) @@ -350,7 +350,6 @@ def learn_one(self, x, w=None): if o_micro_cluster_j.calc_weight(self.timestamp) < xi: # c_o might not grow into a p-micro-cluster, we can safely delete it self.o_micro_clusters.pop(j) - return self def predict_one(self, x, w=None): # This function handles the case when a clustering request arrives. diff --git a/river/cluster/k_means.py b/river/cluster/k_means.py index 9df9386ed3..149c791123 100644 --- a/river/cluster/k_means.py +++ b/river/cluster/k_means.py @@ -65,7 +65,7 @@ class KMeans(base.Clusterer): >>> k_means = cluster.KMeans(n_clusters=2, halflife=0.1, sigma=3, seed=42) >>> for i, (x, _) in enumerate(stream.iter_array(X)): - ... k_means = k_means.learn_one(x) + ... k_means.learn_one(x) ... print(f'{X[i]} is assigned to cluster {k_means.predict_one(x)}') [1, 2] is assigned to cluster 1 [1, 4] is assigned to cluster 1 @@ -118,7 +118,6 @@ def learn_predict_one(self, x): def learn_one(self, x): self.learn_predict_one(x) - return self def predict_one(self, x): def get_distance(c): diff --git a/river/cluster/streamkmeans.py b/river/cluster/streamkmeans.py index 76a02d9d45..df6faba1f7 100644 --- a/river/cluster/streamkmeans.py +++ b/river/cluster/streamkmeans.py @@ -63,7 +63,7 @@ class STREAMKMeans(base.Clusterer): >>> streamkmeans = cluster.STREAMKMeans(chunk_size=3, n_clusters=2, halflife=0.5, sigma=1.5, seed=0) >>> for x, _ in stream.iter_array(X): - ... streamkmeans = streamkmeans.learn_one(x) + ... streamkmeans.learn_one(x) >>> streamkmeans.predict_one({0: 1, 1: 0}) 0 @@ -99,14 +99,12 @@ def learn_one(self, x, w=None): if index == 0: kmeans_i = cluster.KMeans(n_clusters=self.n_clusters, **self.kwargs) for point_j in self._temp_chunk.values(): - kmeans_i = kmeans_i.learn_one(point_j) + kmeans_i.learn_one(point_j) for center_j in kmeans_i.centers.values(): - self._kmeans = self._kmeans.learn_one(center_j) + self._kmeans.learn_one(center_j) self.centers = self._kmeans.centers - return self - def predict_one(self, x, w=None): def get_distance(c): return utils.math.minkowski_distance(self.centers[c], x, 2) diff --git a/river/cluster/textclust.py b/river/cluster/textclust.py index 0407991043..bf0ffce194 100644 --- a/river/cluster/textclust.py +++ b/river/cluster/textclust.py @@ -97,7 +97,7 @@ class TextClust(base.Clusterer): ... y_pred = model.predict_one(x["text"]) ... y = x["cluster"] ... metric = metric.update(y,y_pred) - ... model = model.learn_one(x["text"]) + ... model.learn_one(x["text"]) >>> print(metric) AdjustedRand: -0.17647058823529413 diff --git a/river/compat/river_to_sklearn.py b/river/compat/river_to_sklearn.py index 7329c207d1..b80781f5ed 100644 --- a/river/compat/river_to_sklearn.py +++ b/river/compat/river_to_sklearn.py @@ -530,7 +530,8 @@ def _partial_fit(self, X, y): # Call learn_one for each observation self.labels_ = np.empty(len(X), dtype=np.int32) for i, (x, _) in enumerate(STREAM_METHODS[type(X)](X)): - label = self.instance_.learn_one(x).predict_one(x) + self.instance_.learn_one(x) + label = self.instance_.predict_one(x) self.labels_[i] = label return self diff --git a/river/compat/sklearn_to_river.py b/river/compat/sklearn_to_river.py index 61506012b2..bb16ad76c7 100644 --- a/river/compat/sklearn_to_river.py +++ b/river/compat/sklearn_to_river.py @@ -104,11 +104,9 @@ class SKL2RiverRegressor(SKL2RiverBase, base.Regressor): def learn_one(self, x, y): self.estimator.partial_fit(X=[self._align_dict(x)], y=[y]) - return self def learn_many(self, X, y): self.estimator.partial_fit(X=self._align_df(X), y=y) - return self def predict_one(self, x): try: @@ -176,11 +174,9 @@ def _multiclass(self): def learn_one(self, x, y): self.estimator.partial_fit(X=[self._align_dict(x)], y=[y], classes=self.classes) - return self def learn_many(self, X, y): self.estimator.partial_fit(X=self._align_df(X), y=y, classes=self.classes) - return self def predict_proba_one(self, x): try: diff --git a/river/compose/grouper.py b/river/compose/grouper.py index 9e6bb44074..7d19f50ebf 100644 --- a/river/compose/grouper.py +++ b/river/compose/grouper.py @@ -43,7 +43,6 @@ def _get_key(self, x): def learn_one(self, x): key = self._get_key(x) self.transformers[key].learn_one(x) - return self def transform_one(self, x): key = self._get_key(x) diff --git a/river/compose/pipeline.py b/river/compose/pipeline.py index 5ff21d9cae..66b93e9f1b 100644 --- a/river/compose/pipeline.py +++ b/river/compose/pipeline.py @@ -243,7 +243,7 @@ class Pipeline(base.Estimator): >>> model = (tfidf + counts) | mnb >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> x = dataset[0][0] >>> report = model.debug_one(dataset[0][0]) @@ -477,8 +477,6 @@ def learn_one(self, x: dict, y=None, **params): else: step.learn_one(x=x) - return self - def _transform_one(self, x: dict): """This methods takes care of applying the first n - 1 steps of the pipeline, which are supposedly transformers. It also returns the final step so that other functions can do @@ -705,8 +703,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series | None = None, **params): else: last_step.learn_many(X=X, **params) - return self - def _transform_many(self, X: pd.DataFrame): """This methods takes care of applying the first n - 1 steps of the pipeline, which are supposedly transformers. It also returns the final step so that other functions can do diff --git a/river/compose/target_transform.py b/river/compose/target_transform.py index 4d1804ac25..ad6193a5d1 100644 --- a/river/compose/target_transform.py +++ b/river/compose/target_transform.py @@ -66,7 +66,6 @@ def _update(self, y): def learn_one(self, x, y): self._update(y) self.regressor.learn_one(x, self.func(y)) - return self def predict_one(self, x): y_pred = self.regressor.predict_one(x) diff --git a/river/compose/test_product.py b/river/compose/test_product.py index 72bcda639a..9f557af6d1 100644 --- a/river/compose/test_product.py +++ b/river/compose/test_product.py @@ -49,7 +49,7 @@ def test_issue_1243(): >>> group1 = compose.Select('z') >>> group2 = compose.Select('x') | preprocessing.StandardScaler() >>> model = group1 + group2 + group1 * group2 - >>> model = model.learn_many(X) + >>> model.learn_many(X) >>> for x in X.to_dict('records'): ... print(model.transform_one(x)) {'z*x': 0.785..., 'x': 0.785..., 'z': 1} diff --git a/river/compose/union.py b/river/compose/union.py index 6198a988aa..0210745a1e 100644 --- a/river/compose/union.py +++ b/river/compose/union.py @@ -66,7 +66,7 @@ class TransformerUnion(base.MiniBatchTransformer): >>> from pprint import pprint >>> for x in X: - ... agg = agg.learn_one(x) + ... agg.learn_one(x) ... pprint(agg.transform_one(x)) {'revenue_count_by_place': 1, 'revenue_mean_by_place': 42.0} {'revenue_count_by_place': 1, 'revenue_mean_by_place': 16.0} @@ -144,7 +144,7 @@ class TransformerUnion(base.MiniBatchTransformer): ... (compose.Select("revenue") | preprocessing.StandardScaler()) ... ) - >>> _ = agg.learn_many(X) + >>> agg.learn_many(X) >>> agg.transform_many(X) place revenue 0 2 0.441250 @@ -269,7 +269,6 @@ def learn_one(self, x, y=None): t.learn_one(x, y) else: t.learn_one(x) - return self def transform_one(self, x): """Passes the data through each transformer and packs the results together.""" @@ -294,7 +293,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series | None = None): t.learn_many(X, y) else: t.learn_many(X) - return self def transform_many(self, X): """Passes the data through each transformer and packs the results together.""" diff --git a/river/conf/jackknife.py b/river/conf/jackknife.py index 7f64b5e2eb..1d0726142c 100644 --- a/river/conf/jackknife.py +++ b/river/conf/jackknife.py @@ -58,7 +58,7 @@ class RegressionJackknife(base.Wrapper, base.Regressor): ... interval = model.predict_one(x, with_interval=True) ... validity = validity.update(y in interval) ... efficiency = efficiency.update(interval.width) - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) The interval's validity is the proportion of times the true value is within the interval. We specified a confidence level of 90%, so we expect the validity to be around 90%. @@ -117,8 +117,6 @@ def learn_one(self, x, y, **kwargs): self.regressor.learn_one(x, y, **kwargs) - return self - def predict_one(self, x, with_interval=False, **kwargs): """Predict the output of features `x`. diff --git a/river/drift/no_drift.py b/river/drift/no_drift.py index c5173aca04..d0b8273992 100644 --- a/river/drift/no_drift.py +++ b/river/drift/no_drift.py @@ -44,8 +44,8 @@ class NoDrift(base.DriftDetector): Let's put that to test: >>> for x, y in dataset: - ... adaptive_model = adaptive_model.learn_one(x, y) - ... stationary_model = stationary_model.learn_one(x, y) + ... adaptive_model.learn_one(x, y) + ... stationary_model.learn_one(x, y) The adaptive model: diff --git a/river/drift/retrain.py b/river/drift/retrain.py index a73bbb95c1..3602203e37 100644 --- a/river/drift/retrain.py +++ b/river/drift/retrain.py @@ -67,7 +67,6 @@ def predict_proba_one(self, x, **kwargs): def learn_one(self, x, y, **kwargs): self._update_detector(x, y) self.model.learn_one(x, y, **kwargs) - return self def _update_detector(self, x, y): y_pred = self.model.predict_one(x) diff --git a/river/dummy.py b/river/dummy.py index 967bcf8643..1980c9bae8 100644 --- a/river/dummy.py +++ b/river/dummy.py @@ -44,7 +44,7 @@ class NoChangeClassifier(base.Classifier): >>> model = dummy.NoChangeClassifier() >>> for sentence, label in sentences: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> new_sentence = 'glad sad miserable pleasant glad' >>> model.predict_one(new_sentence) @@ -66,7 +66,6 @@ def _multiclass(self): def learn_one(self, x, y): self.last_class = y self.classes.add(y) - return self def predict_one(self, x): return self.last_class @@ -108,7 +107,7 @@ class PriorClassifier(base.Classifier): >>> model = dummy.PriorClassifier() >>> for sentence, label in sentences: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> new_sentence = 'glad sad miserable pleasant glad' >>> model.predict_one(new_sentence) @@ -133,7 +132,6 @@ def _multiclass(self): def learn_one(self, x, y): self.counts.update([y]) self.n += 1 - return self def predict_proba_one(self, x): return {label: count / self.n for label, count in self.counts.items()} @@ -163,7 +161,7 @@ class StatisticRegressor(base.Regressor): >>> model = dummy.StatisticRegressor(stats.Mean()) >>> for sentence, score in sentences: - ... model = model.learn_one(sentence, score) + ... model.learn_one(sentence, score) >>> new_sentence = 'glad sad miserable pleasant glad' >>> model.predict_one(new_sentence) @@ -180,7 +178,6 @@ def _unit_test_params(cls): def learn_one(self, x, y): self.statistic.update(y) - return self def predict_one(self, x): return self.statistic.get() diff --git a/river/ensemble/bagging.py b/river/ensemble/bagging.py index 9b11e0b43e..19d35d4d82 100644 --- a/river/ensemble/bagging.py +++ b/river/ensemble/bagging.py @@ -19,8 +19,6 @@ def learn_one(self, x, y, **kwargs): for _ in range(utils.random.poisson(1, self._rng)): model.learn_one(x, y, **kwargs) - return self - class BaggingClassifier(BaseBagging, base.Classifier): """Online bootstrap aggregation for classification. @@ -244,8 +242,6 @@ def learn_one(self, x, y, **kwargs): self.models[max_error_idx] = self.model.clone() self._drift_detectors[max_error_idx] = drift.ADWIN() - return self - class LeveragingBaggingClassifier(BaggingClassifier): """Leveraging Bagging ensemble classifier. @@ -407,8 +403,6 @@ def learn_one(self, x, y, **kwargs): self[max_error_idx] = self.model.clone() self._drift_detectors[max_error_idx] = drift.ADWIN(delta=self.adwin_delta) - return self - @property def bagging_methods(self): """Valid bagging_method options.""" diff --git a/river/ensemble/boosting.py b/river/ensemble/boosting.py index 40a13f8153..8c780100f3 100644 --- a/river/ensemble/boosting.py +++ b/river/ensemble/boosting.py @@ -91,7 +91,6 @@ def learn_one(self, x, y, **kwargs): lambda_poisson *= (self.correct_weight[i] + self.wrong_weight[i]) / ( 2 * self.wrong_weight[i] ) - return self def predict_proba_one(self, x, **kwargs): y_proba = collections.Counter() @@ -204,7 +203,6 @@ def learn_one(self, x, y, **kwargs): self._drift_detectors[max_error_idx] = drift.ADWIN() self.correct_weight[max_error_idx] = 0 self.wrong_weight[max_error_idx] = 0 - return self class BOLEClassifier(AdaBoostClassifier): @@ -326,7 +324,6 @@ def learn_one(self, x, y, **kwargs): self.wrong_weight[pos] += lambda_poisson lambda_poisson *= (self.instances_seen) / (2 * self.wrong_weight[pos]) correct = False - return self def predict_proba_one(self, x, **kwargs): y_proba = collections.Counter() diff --git a/river/ensemble/stacking.py b/river/ensemble/stacking.py index ff9dab74dd..d9edb0b388 100644 --- a/river/ensemble/stacking.py +++ b/river/ensemble/stacking.py @@ -81,8 +81,6 @@ def learn_one(self, x, y): # Update the meta-classifier using the predictions from the base classifiers self.meta_classifier.learn_one(oof, y) - return self - def predict_proba_one(self, x): oof = { f"oof_{i}_{k}": p diff --git a/river/ensemble/streaming_random_patches.py b/river/ensemble/streaming_random_patches.py index 7cd9f87992..5c739baf00 100644 --- a/river/ensemble/streaming_random_patches.py +++ b/river/ensemble/streaming_random_patches.py @@ -111,8 +111,6 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs): continue model.learn_one(x=x, y=y, w=k, n_samples_seen=self._n_samples_seen) - return self - def _generate_subspaces(self, features: list): n_features = len(features) diff --git a/river/ensemble/voting.py b/river/ensemble/voting.py index 1ee81730ec..332ec4a591 100644 --- a/river/ensemble/voting.py +++ b/river/ensemble/voting.py @@ -62,7 +62,6 @@ def _multiclass(self): def learn_one(self, x, y): for model in self: model.learn_one(x, y) - return self def predict_one(self, x): if self.use_probabilities: diff --git a/river/evaluate/progressive_validation.py b/river/evaluate/progressive_validation.py index 0fbb63884a..1a2f8fde28 100644 --- a/river/evaluate/progressive_validation.py +++ b/river/evaluate/progressive_validation.py @@ -338,7 +338,7 @@ def progressive_val_score( >>> for x, y in datasets.Phishing(): ... y_pred = model.predict_proba_one(x) ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> metric ROCAUC: 95.07% diff --git a/river/facto/base.py b/river/facto/base.py index 46957e8933..2574c7d6ce 100644 --- a/river/facto/base.py +++ b/river/facto/base.py @@ -72,7 +72,7 @@ def learn_one(self, x, y, w=1.0): x_l2_norm = sum(xj**2 for xj in x.values()) ** 0.5 x = {j: xj / x_l2_norm for j, xj in x.items()} - return self._learn_one(x, y, w=w) + self._learn_one(x, y, w=w) def _ohe_cat_features(self, x): """One hot encodes string features considering them as categorical.""" @@ -99,8 +99,6 @@ def _learn_one(self, x, y, w=1.0): # Update the latent weights self._update_latents(x, g_loss) - return self - def _raw_dot(self, x): # Start with the intercept y_pred = self.intercept diff --git a/river/facto/ffm.py b/river/facto/ffm.py index f7e9bde9df..01991887c2 100644 --- a/river/facto/ffm.py +++ b/river/facto/ffm.py @@ -194,7 +194,7 @@ class FFMRegressor(FFM, base.Regressor): ... ) >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14}) 5.319945 @@ -341,7 +341,7 @@ class FFMClassifier(FFM, base.Classifier): ... ) >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14}) True diff --git a/river/facto/fm.py b/river/facto/fm.py index 08a7499094..efe0c47ee6 100644 --- a/river/facto/fm.py +++ b/river/facto/fm.py @@ -179,7 +179,7 @@ class FMRegressor(FM, base.Regressor): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'Bob': 1, 'Harry Potter': 1}) 5.236504 @@ -320,7 +320,7 @@ class FMClassifier(FM, base.Classifier): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'Bob': 1, 'Harry Potter': 1}) True diff --git a/river/facto/fwfm.py b/river/facto/fwfm.py index 980fffd1fb..963522fc4d 100644 --- a/river/facto/fwfm.py +++ b/river/facto/fwfm.py @@ -215,7 +215,7 @@ class FwFMRegressor(FwFM, base.Regressor): ... ) >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'Bob': 1, 'Harry Potter': 1}) 5.236501 @@ -363,7 +363,7 @@ class FwFMClassifier(FwFM, base.Classifier): ... ) >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'Bob': 1, 'Harry Potter': 1}) True diff --git a/river/facto/hofm.py b/river/facto/hofm.py index db184f2ee8..330b5a55af 100644 --- a/river/facto/hofm.py +++ b/river/facto/hofm.py @@ -203,7 +203,7 @@ class HOFMRegressor(HOFM, base.Regressor): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14}) 5.311745 @@ -353,7 +353,7 @@ class HOFMClassifier(HOFM, base.Classifier): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14}) True diff --git a/river/feature_extraction/agg.py b/river/feature_extraction/agg.py index a059ba0afe..80f7426231 100644 --- a/river/feature_extraction/agg.py +++ b/river/feature_extraction/agg.py @@ -62,7 +62,7 @@ class Agg(base.Transformer): ... ) >>> for x in X: - ... agg = agg.learn_one(x) + ... agg.learn_one(x) ... print(agg.transform_one(x)) {'revenue_mean_by_place': 42.0} {'revenue_mean_by_place': 16.0} @@ -84,7 +84,7 @@ class Agg(base.Transformer): ... ) >>> for x in X: - ... agg = agg.learn_one(x) + ... agg.learn_one(x) ... print(agg.transform_one(x)) {'revenue_max_by_place_and_country': 42} {'revenue_max_by_place_and_country': 16} @@ -105,7 +105,7 @@ class Agg(base.Transformer): >>> import pprint >>> for x in X: - ... agg = agg.learn_one(x) + ... agg.learn_one(x) ... pprint.pprint(agg.transform_one(x)) {'revenue_max_by_place_and_country': 42, 'revenue_mean_by_place': 42.0} {'revenue_max_by_place_and_country': 16, 'revenue_mean_by_place': 16.0} @@ -155,7 +155,7 @@ class Agg(base.Transformer): ... "value": string.ascii_lowercase.index(g) + random.random(), ... } ... t = dt.datetime(2023, 1, 1) + dt.timedelta(days=day) - ... agg = agg.learn_one(x, t=t) + ... agg.learn_one(x, t=t) >>> len(agg.state) 26 @@ -193,7 +193,6 @@ def learn_one(self, x, t=None): self._groups[key].update(x[self.on], t=t) else: self._groups[key].update(x[self.on]) - return self def transform_one(self, x): return {self._feature_name: self._groups[self._make_key(x)].get()} @@ -267,7 +266,7 @@ class TargetAgg(base.SupervisedTransformer, Agg): >>> for x, y in dataset: ... print(agg.transform_one(x)) - ... agg = agg.learn_one(x, y) + ... agg.learn_one(x, y) {'y_bayes_mean_by_place': 3.0} {'y_bayes_mean_by_place': 3.0} {'y_bayes_mean_by_place': 9.5} @@ -290,7 +289,7 @@ class TargetAgg(base.SupervisedTransformer, Agg): >>> for x, y in dataset: ... print(agg.transform_one(x)) - ... agg = agg.learn_one(x, y) + ... agg.learn_one(x, y) {'y_bayes_mean_by_place_and_country': 3.0} {'y_bayes_mean_by_place_and_country': 3.0} {'y_bayes_mean_by_place_and_country': 3.0} @@ -328,7 +327,7 @@ class TargetAgg(base.SupervisedTransformer, Agg): ... x = {"group": g} ... y = string.ascii_lowercase.index(g) + random.random() ... t = dt.datetime(2023, 1, 1) + dt.timedelta(days=day) - ... agg = agg.learn_one(x, y, t=t) + ... agg.learn_one(x, y, t=t) References ---------- @@ -354,4 +353,3 @@ def learn_one(self, x, y, t=None): self._groups[key].update(y, t=t) else: self._groups[key].update(y) - return self diff --git a/river/feature_extraction/kernel_approx.py b/river/feature_extraction/kernel_approx.py index 697da3f4e0..8282829132 100644 --- a/river/feature_extraction/kernel_approx.py +++ b/river/feature_extraction/kernel_approx.py @@ -39,7 +39,7 @@ class RBFSampler(base.Transformer): >>> model = lm.LogisticRegression(optimizer=optim.SGD(.1)) >>> for x, y in stream.iter_array(X, Y): - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) ... y_pred = model.predict_one(x) ... print(y, int(y_pred)) 0 0 @@ -53,7 +53,7 @@ class RBFSampler(base.Transformer): ... ) >>> for x, y in stream.iter_array(X, Y): - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) ... y_pred = model.predict_one(x) ... print(y, int(y_pred)) 0 0 diff --git a/river/feature_extraction/test_agg.py b/river/feature_extraction/test_agg.py index d19e9e4d87..78b9fad198 100644 --- a/river/feature_extraction/test_agg.py +++ b/river/feature_extraction/test_agg.py @@ -29,7 +29,8 @@ def test_agg_lag(): ... Agg("revenue", None, Shift(2)) ... ) >>> for x in X: - ... print(agg.learn_one(x).transform_one(x)) + ... agg.learn_one(x) + ... print(agg.transform_one(x)) {'revenue_shift_2': None, 'customers_shift_2': None} {'revenue_shift_2': None, 'customers_shift_2': None} {'revenue_shift_2': 420, 'customers_shift_2': 10} @@ -50,7 +51,8 @@ def test_agg_lag(): ... for d in [1, 2, 3] ... ]) >>> for x in X: - ... print(agg.learn_one(x).transform_one(x)) + ... agg.learn_one(x) + ... print(agg.transform_one(x)) {'customers_shift_3': None, 'customers_shift_2': None, 'customers_shift_1': None} {'customers_shift_3': None, 'customers_shift_2': None, 'customers_shift_1': 10} {'customers_shift_3': None, 'customers_shift_2': 10, 'customers_shift_1': 10} @@ -67,7 +69,8 @@ def test_agg_lag(): >>> agg = Agg("customers", "shop", Shift(1)) >>> for x in X: - ... print(agg.learn_one(x).transform_one(x)) + ... agg.learn_one(x) + ... print(agg.transform_one(x)) {'customers_shift_1_by_shop': None} {'customers_shift_1_by_shop': None} {'customers_shift_1_by_shop': 10} @@ -104,7 +107,7 @@ def test_target_agg_lag(): >>> agg = TargetAgg(None, Shift(1)) + TargetAgg(None, Shift(2)) >>> for x, y in dataset: ... print(agg.transform_one(x)) - ... agg = agg.learn_one(x, y) + ... agg.learn_one(x, y) {'y_shift_2': None, 'y_shift_1': None} {'y_shift_2': None, 'y_shift_1': None} {'y_shift_2': None, 'y_shift_1': 42} @@ -119,7 +122,7 @@ def test_target_agg_lag(): >>> agg = TargetAgg("country", Shift(1)) + TargetAgg("country", Shift(2)) >>> for x, y in dataset: ... print(agg.transform_one(x)) - ... agg = agg.learn_one(x, y) + ... agg.learn_one(x, y) {'y_shift_2_by_country': None, 'y_shift_1_by_country': None} {'y_shift_2_by_country': None, 'y_shift_1_by_country': None} {'y_shift_2_by_country': None, 'y_shift_1_by_country': None} diff --git a/river/feature_extraction/vectorize.py b/river/feature_extraction/vectorize.py index a4970a3887..f4d6aff840 100644 --- a/river/feature_extraction/vectorize.py +++ b/river/feature_extraction/vectorize.py @@ -359,7 +359,7 @@ def transform_many(self, X: pd.Series) -> pd.DataFrame: ) def learn_many(self, X): - return self + return class TFIDF(BagOfWords): @@ -421,7 +421,7 @@ class TFIDF(BagOfWords): ... ] >>> for sentence in corpus: - ... tfidf = tfidf.learn_one(sentence) + ... tfidf.learn_one(sentence) ... print(tfidf.transform_one(sentence)) {'this': 0.447, 'is': 0.447, 'the': 0.447, 'first': 0.447, 'document': 0.447} {'this': 0.333, 'document': 0.667, 'is': 0.333, 'the': 0.333, 'second': 0.469} @@ -435,7 +435,7 @@ class TFIDF(BagOfWords): >>> for sentence in corpus: ... x = {'sentence': sentence} - ... tfidf = tfidf.learn_one(x) + ... tfidf.learn_one(x) ... print(tfidf.transform_one(x)) {'this': 0.447, 'is': 0.447, 'the': 0.447, 'first': 0.447, 'document': 0.447} {'this': 0.333, 'document': 0.667, 'is': 0.333, 'the': 0.333, 'second': 0.469} @@ -474,8 +474,6 @@ def learn_one(self, x): # Increment the global document counter self.n += 1 - return self - def transform_one(self, x): term_counts = super().transform_one(x) n_terms = sum(term_counts.values()) diff --git a/river/feature_selection/k_best.py b/river/feature_selection/k_best.py index bf029a8a11..f798b4ae98 100644 --- a/river/feature_selection/k_best.py +++ b/river/feature_selection/k_best.py @@ -46,7 +46,7 @@ class SelectKBest(base.SupervisedTransformer): ... ) >>> for xi, yi, in stream.iter_array(X, y): - ... selector = selector.learn_one(xi, yi) + ... selector.learn_one(xi, yi) >>> pprint(selector.leaderboard) Counter({9: 0.7898, @@ -81,8 +81,6 @@ def learn_one(self, x, y): for i, xi in x.items(): self.leaderboard[i] = self.similarities[i].update(xi, y).get() - return self - def transform_one(self, x): best_features = {pair[0] for pair in self.leaderboard.most_common(self.k)} diff --git a/river/feature_selection/variance.py b/river/feature_selection/variance.py index c2f20d2bd5..8d80e0fdad 100644 --- a/river/feature_selection/variance.py +++ b/river/feature_selection/variance.py @@ -35,7 +35,8 @@ class VarianceThreshold(base.Transformer): >>> selector = feature_selection.VarianceThreshold() >>> for x, _ in stream.iter_array(X): - ... print(selector.learn_one(x).transform_one(x)) + ... selector.learn_one(x) + ... print(selector.transform_one(x)) {0: 0, 1: 2, 2: 0, 3: 3} {1: 1, 2: 4} {1: 1, 2: 1} @@ -51,8 +52,6 @@ def learn_one(self, x): for i, xi in x.items(): self.variances[i].update(xi) - return self - def check_feature(self, feature): if feature not in self.variances: return True diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index d432ac997a..a3553c1d9d 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -209,8 +209,6 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs): # Update warning tracker self._drift_tracker[i] += 1 - return self - def _init_ensemble(self, features: list): self._set_max_features(len(features)) self.data = [self._new_base_model() for _ in range(self.n_models)] diff --git a/river/forest/aggregated_mondrian_forest.py b/river/forest/aggregated_mondrian_forest.py index c2a392eacb..e062e15bb0 100644 --- a/river/forest/aggregated_mondrian_forest.py +++ b/river/forest/aggregated_mondrian_forest.py @@ -196,8 +196,6 @@ def learn_one(self, x, y): for tree in self: tree.learn_one(x, y) - return self - def predict_proba_one(self, x): # Checking that the model has been trained once at least # Otherwise return the default empty dict @@ -317,8 +315,6 @@ def learn_one(self, x, y): self.iteration += 1 - return self - def predict_one(self, x): # Checking that the model has been trained once at least if not self._is_initialized: diff --git a/river/forest/online_extra_trees.py b/river/forest/online_extra_trees.py index 1e43b9e661..aa02cb9306 100644 --- a/river/forest/online_extra_trees.py +++ b/river/forest/online_extra_trees.py @@ -324,8 +324,6 @@ def learn_one(self, x, y): # Increase by one the count of instances observed by each trained model self._sample_counter.update(trained) - return self - # Properties @property def n_warnings(self) -> collections.Counter: diff --git a/river/forest/test_amf.py b/river/forest/test_amf.py index 177009228c..f424bbf896 100644 --- a/river/forest/test_amf.py +++ b/river/forest/test_amf.py @@ -10,9 +10,9 @@ def test_issue_1272(): >>> from river import forest, metrics >>> model = forest.ARFClassifier(metric=metrics.CrossEntropy()) - >>> model = model.learn_one({"x": 1}, True) + >>> model.learn_one({"x": 1}, True) >>> model = forest.ARFClassifier() - >>> model = model.learn_one({"x": 1}, True) + >>> model.learn_one({"x": 1}, True) """ diff --git a/river/imblearn/chebyshev.py b/river/imblearn/chebyshev.py index 4db150341d..5bc1a769bc 100644 --- a/river/imblearn/chebyshev.py +++ b/river/imblearn/chebyshev.py @@ -110,8 +110,6 @@ def learn_one(self, x, y, **kwargs): else: self.regressor.learn_one(x, y, **kwargs) - return self - @classmethod def _unit_test_params(cls): yield {"regressor": linear_model.LinearRegression(), "seed": 42} @@ -205,8 +203,6 @@ def learn_one(self, x, y, **kwargs): else: self.regressor.learn_one(x, y, **kwargs) - return self - @classmethod def _unit_test_params(cls): yield {"regressor": linear_model.LinearRegression()} diff --git a/river/imblearn/hard_sampling.py b/river/imblearn/hard_sampling.py index ed1036cde6..a7eb07e745 100644 --- a/river/imblearn/hard_sampling.py +++ b/river/imblearn/hard_sampling.py @@ -66,8 +66,6 @@ def learn_one(self, x, y, **kwargs): else: self.model.learn_one(x, y) - return self - class HardSamplingRegressor(HardSampling, base.Regressor): """Hard sampling regressor. diff --git a/river/imblearn/random.py b/river/imblearn/random.py index dd4ddec930..67d7e623c9 100644 --- a/river/imblearn/random.py +++ b/river/imblearn/random.py @@ -91,7 +91,7 @@ def learn_one(self, x, y, **kwargs): self._pivot = max(g.keys(), key=lambda y: f[y] / g[y]) else: self.classifier.learn_one(x, y, **kwargs) - return self + return # Determine the sampling ratio if the class is not the pivot M = f[self._pivot] / g[self._pivot] # Likelihood ratio @@ -100,8 +100,6 @@ def learn_one(self, x, y, **kwargs): if ratio < 1 and self._rng.random() < ratio: self.classifier.learn_one(x, y, **kwargs) - return self - class RandomOverSampler(ClassificationSampler): """Random over-sampling. @@ -166,7 +164,7 @@ def learn_one(self, x, y, **kwargs): self._pivot = max(g.keys(), key=lambda y: g[y] / f[y]) else: self.classifier.learn_one(x, y, **kwargs) - return self + return M = g[self._pivot] / f[self._pivot] rate = M * f[y] / g[y] @@ -174,8 +172,6 @@ def learn_one(self, x, y, **kwargs): for _ in range(utils.random.poisson(rate, rng=self._rng)): self.classifier.learn_one(x, y, **kwargs) - return self - class RandomSampler(ClassificationSampler): """Random sampling by mixing under-sampling and over-sampling. @@ -253,5 +249,3 @@ def learn_one(self, x, y, **kwargs): for _ in range(utils.random.poisson(rate, rng=self._rng)): self.classifier.learn_one(x, y, **kwargs) - - return self diff --git a/river/linear_model/alma.py b/river/linear_model/alma.py index c46e5caf73..45a300a6c5 100644 --- a/river/linear_model/alma.py +++ b/river/linear_model/alma.py @@ -85,5 +85,3 @@ def learn_one(self, x, y): self.w[i] /= max(1, norm) self.k += 1 - - return self diff --git a/river/linear_model/base.py b/river/linear_model/base.py index b2e2bb6904..02846efb18 100644 --- a/river/linear_model/base.py +++ b/river/linear_model/base.py @@ -163,7 +163,7 @@ def _eval_gradient_one(self, x: dict, y: float, w: float) -> tuple[dict, float]: def learn_one(self, x, y, w=1.0): with self._learn_mode(x): - return self._fit(x, y, w, get_grad=self._eval_gradient_one) + self._fit(x, y, w, get_grad=self._eval_gradient_one) # Mini-batch methods @@ -191,4 +191,4 @@ def _eval_gradient_many( def learn_many(self, X: pd.DataFrame, y: pd.Series, w: float | pd.Series = 1): self._y_name = y.name with self._learn_mode(set(X)): - return self._fit(X, y, w, get_grad=self._eval_gradient_many) + self._fit(X, y, w, get_grad=self._eval_gradient_many) diff --git a/river/linear_model/bayesian_lin_reg.py b/river/linear_model/bayesian_lin_reg.py index 55148e59eb..5370d64436 100644 --- a/river/linear_model/bayesian_lin_reg.py +++ b/river/linear_model/bayesian_lin_reg.py @@ -194,8 +194,6 @@ def learn_one(self, x, y): self._set_arrays(x.keys(), m_arr, ss_arr, ss_inv_arr) - return self - def predict_one(self, x, with_dist=False): """Predict the output of features `x`. diff --git a/river/linear_model/pa.py b/river/linear_model/pa.py index 7a751fe1c5..b3f23ee579 100644 --- a/river/linear_model/pa.py +++ b/river/linear_model/pa.py @@ -69,7 +69,7 @@ class PARegressor(BasePA, base.Regressor): >>> for xi, yi in stream.iter_array(X, y): ... y_pred = model.predict_one(xi) - ... model = model.learn_one(xi, yi) + ... model.learn_one(xi, yi) ... metric = metric.update(yi, y_pred) >>> print(metric) @@ -97,8 +97,6 @@ def learn_one(self, x, y): if self.learn_intercept: self.intercept += step - return self - def predict_one(self, x): return utils.math.dot(x, self.weights) + self.intercept @@ -149,7 +147,7 @@ class PAClassifier(BasePA, base.Classifier): ... ) >>> for xi, yi in stream.iter_array(X_train, y_train): - ... y_pred = model.learn_one(xi, yi) + ... model.learn_one(xi, yi) >>> metric = metrics.Accuracy() + metrics.LogLoss() @@ -180,8 +178,6 @@ def learn_one(self, x, y): if self.learn_intercept: self.intercept += step - return self - def predict_proba_one(self, x): y_pred = utils.math.sigmoid(utils.math.dot(x, self.weights) + self.intercept) return {False: 1.0 - y_pred, True: y_pred} diff --git a/river/linear_model/softmax.py b/river/linear_model/softmax.py index fbacd49a62..019bf09a5a 100644 --- a/river/linear_model/softmax.py +++ b/river/linear_model/softmax.py @@ -95,8 +95,6 @@ def learn_one(self, x, y): gradient = {i: xi * loss + self.l2 * weights.get(i, 0) for i, xi in x.items()} self.weights[label] = self.optimizers[label].step(w=weights, g=gradient) - return self - def predict_proba_one(self, x): return utils.math.softmax( {label: utils.math.dot(weights, x) for label, weights in self.weights.items()} diff --git a/river/linear_model/test_glm.py b/river/linear_model/test_glm.py index 2d95f2ce73..f274b39873 100644 --- a/river/linear_model/test_glm.py +++ b/river/linear_model/test_glm.py @@ -79,7 +79,8 @@ def test_finite_differences(lm, dataset): eps = 1e-6 for x, y in dataset: - x = scaler.learn_one(x).transform_one(x) + scaler.learn_one(x) + x = scaler.transform_one(x) # Store the current gradient and weights gradient, _ = lm._eval_gradient_one(x, y, 1) @@ -214,7 +215,8 @@ def test_lin_reg_sklearn_coherence(river_params, sklearn_params): sk = sklm.SGDRegressor(**sklearn_params) for x, y in datasets.TrumpApproval().take(100): - x = ss.learn_one(x).transform_one(x) + ss.learn_one(x) + x = ss.transform_one(x) rv.learn_one(x, y) sk.partial_fit([list(x.values())], [y]) @@ -238,7 +240,8 @@ def test_lin_reg_sklearn_learn_many_coherence(river_params, sklearn_params): sk = sklm.SGDRegressor(**sklearn_params) for x, y in datasets.TrumpApproval().take(100): - x = ss.learn_one(x).transform_one(x) + ss.learn_one(x) + x = ss.transform_one(x) rv.learn_many(pd.DataFrame([x]), pd.Series([y])) sk.partial_fit([list(x.values())], [y]) @@ -320,7 +323,8 @@ def test_log_reg_sklearn_coherence(river_params, sklearn_params): sk = sklm.SGDClassifier(**sklearn_params) for x, y in datasets.Bananas().take(100): - x = ss.learn_one(x).transform_one(x) + ss.learn_one(x) + x = ss.transform_one(x) rv.learn_one(x, y) sk.partial_fit([list(x.values())], [y], classes=[False, True]) @@ -360,7 +364,8 @@ def test_perceptron_sklearn_coherence(river_params, sklearn_params): sk = sklm.Perceptron(**sklearn_params) for x, y in datasets.Bananas().take(100): - x = ss.learn_one(x).transform_one(x) + ss.learn_one(x) + x = ss.transform_one(x) rv.learn_one(x, y) sk.partial_fit([list(x.values())], [y], classes=[False, True]) @@ -403,7 +408,8 @@ def test_lin_reg_sklearn_l1_non_regression(): ) for xi, yi in stream.iter_pandas(X, y): - xi_tr = ss.learn_one(xi).transform_one(xi) + ss.learn_one(xi) + xi_tr = ss.transform_one(xi) rv.learn_one(xi_tr, yi) sk.partial_fit([list(xi_tr.values())], [yi]) @@ -455,7 +461,8 @@ def test_log_reg_sklearn_l1_non_regression(): rv_pred = list() sk_pred = list() for xi, yi in stream.iter_pandas(X, y): - xi_tr = ss.learn_one(xi).transform_one(xi) + ss.learn_one(xi) + xi_tr = ss.transform_one(xi) rv.learn_one(xi_tr, yi) sk.partial_fit([list(xi_tr.values())], [yi], classes=[False, True]) diff --git a/river/metrics/silhouette.py b/river/metrics/silhouette.py index 3866eb5f1f..cbc57bfa1a 100644 --- a/river/metrics/silhouette.py +++ b/river/metrics/silhouette.py @@ -42,7 +42,7 @@ class Silhouette(metrics.base.ClusteringMetric): >>> metric = metrics.Silhouette() >>> for x, _ in stream.iter_array(X): - ... k_means = k_means.learn_one(x) + ... k_means.learn_one(x) ... y_pred = k_means.predict_one(x) ... metric = metric.update(x, y_pred, k_means.centers) diff --git a/river/model_selection/bandit.py b/river/model_selection/bandit.py index 89ccaed63d..1db7b7054d 100644 --- a/river/model_selection/bandit.py +++ b/river/model_selection/bandit.py @@ -134,8 +134,6 @@ def learn_one(self, x, y): self.policy.update(arm_id, y, y_pred) model.learn_one(x, y) - return self - class BanditClassifier(BanditModelSection, model_selection.base.ModelSelectionClassifier): """Bandit-based model selection for classification. @@ -208,5 +206,3 @@ def learn_one(self, x, y): ) self.policy.update(arm_id, y, y_pred) model.learn_one(x, y) - - return self diff --git a/river/model_selection/greedy.py b/river/model_selection/greedy.py index ae006b0859..fce0b87e70 100644 --- a/river/model_selection/greedy.py +++ b/river/model_selection/greedy.py @@ -69,8 +69,6 @@ def learn_one(self, x, y): self._best_model = model self._best_metric = metric - return self - @property def best_model(self): return self._best_model diff --git a/river/model_selection/sh.py b/river/model_selection/sh.py index 5ef28d99c2..534e306238 100644 --- a/river/model_selection/sh.py +++ b/river/model_selection/sh.py @@ -92,8 +92,6 @@ def learn_one(self, x, y): self._s = cutoff self._r = math.floor(self.budget / (self._s * math.ceil(math.log(self._n, self.eta)))) - return self - class SuccessiveHalvingRegressor(SuccessiveHalving, ModelSelectionRegressor): r"""Successive halving algorithm for regression. diff --git a/river/multiclass/occ.py b/river/multiclass/occ.py index e3ada07d56..1a2b3af60c 100644 --- a/river/multiclass/occ.py +++ b/river/multiclass/occ.py @@ -145,8 +145,6 @@ def learn_one(self, x, y, **kwargs): for i, c in enumerate(code): self.classifiers[i].learn_one(x, c, **kwargs) - return self - def predict_one(self, x, **kwargs): if not self.code_book: # it's empty return None diff --git a/river/multiclass/ovo.py b/river/multiclass/ovo.py index b8a0ee0632..59c0a46dc0 100644 --- a/river/multiclass/ovo.py +++ b/river/multiclass/ovo.py @@ -80,8 +80,6 @@ def learn_one(self, x, y, **kwargs): pair = (c, y) if c < y else (y, c) self.classifiers[pair].learn_one(x, y=c < y, **kwargs) - return self - def predict_one(self, x, **kwargs): if not self.classifiers: # is empty return None diff --git a/river/multiclass/ovr.py b/river/multiclass/ovr.py index 9597e61014..b4506fac07 100644 --- a/river/multiclass/ovr.py +++ b/river/multiclass/ovr.py @@ -57,7 +57,7 @@ class OneVsRestClassifier(base.Wrapper, base.Classifier): >>> for X in pd.read_csv(dataset.path, chunksize=64): ... y = X.pop('category') ... y_pred = model.predict_many(X) - ... model = model.learn_many(X, y) + ... model.learn_many(X, y) """ @@ -87,8 +87,6 @@ def learn_one(self, x, y, **kwargs): for label, model in self.classifiers.items(): model.learn_one(x, y == label, **kwargs) - return self - def predict_proba_one(self, x, **kwargs): y_pred = {} total = 0.0 @@ -114,8 +112,6 @@ def learn_many(self, X, y, **kwargs): for label, model in self.classifiers.items(): model.learn_many(X, y == label, **kwargs) - return self - def predict_proba_many(self, X, **kwargs): y_pred = pd.DataFrame(columns=self.classifiers.keys(), index=X.index) diff --git a/river/multioutput/chain.py b/river/multioutput/chain.py index 13b381818f..9ea8d4ca0b 100644 --- a/river/multioutput/chain.py +++ b/river/multioutput/chain.py @@ -81,7 +81,7 @@ class ClassifierChain(BaseChain, base.MultiLabelClassifier): ... y = {i: yi == 'TRUE' for i, yi in y.items()} ... y_pred = model.predict_one(x) ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> metric MicroAverage(Jaccard): 41.81% @@ -146,8 +146,6 @@ def learn_one(self, x, y, **kwargs): if o not in self.order: self.order.append(o) - return self - def predict_proba_one(self, x, **kwargs): x = copy.copy(x) y_pred = {} @@ -251,8 +249,6 @@ def learn_one(self, x, y, **kwargs): if o not in self.order: self.order.append(o) - return self - def predict_one(self, x, **kwargs): x = copy.copy(x) y_pred = {} @@ -305,7 +301,7 @@ class ProbabilisticClassifierChain(ClassifierChain): ... y_pred = model.predict_one(x) ... y_pred = {k: y_pred.get(k, 0) for k in y} ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> metric MicroAverage(Jaccard): 51.84% @@ -400,7 +396,7 @@ class MonteCarloClassifierChain(ProbabilisticClassifierChain): ... y_pred = model.predict_one(x) ... y_pred = {k: y_pred.get(k, 0) for k in y} ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> metric MicroAverage(Jaccard): 51.79% diff --git a/river/multioutput/encoder.py b/river/multioutput/encoder.py index 86a49748eb..6d7b0c488d 100644 --- a/river/multioutput/encoder.py +++ b/river/multioutput/encoder.py @@ -41,7 +41,7 @@ class MultiClassEncoder(base.MultiLabelClassifier): ... y_pred = model.predict_one(x) ... y_pred = {k: y_pred.get(k, 0) for k in y} ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> metric MicroAverage(Jaccard): 95.10% @@ -71,8 +71,6 @@ def learn_one(self, x, y): # Update the classifier self.model.learn_one(x, y_encoded) - return self - def predict_proba_one(self, x, **kwargs): enc_probas = self.model.predict_proba_one(x, **kwargs) diff --git a/river/naive_bayes/bernoulli.py b/river/naive_bayes/bernoulli.py index d5b2efc92d..111dbabf89 100644 --- a/river/naive_bayes/bernoulli.py +++ b/river/naive_bayes/bernoulli.py @@ -54,7 +54,7 @@ class BernoulliNB(base.BaseNB): ... ) >>> for sentence, label in docs: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> model["nb"].p_class("yes") 0.75 @@ -86,7 +86,7 @@ class BernoulliNB(base.BaseNB): ... ("nb", naive_bayes.BernoulliNB(alpha=1)) ... ) - >>> model = model.learn_many(X, y) + >>> model.learn_many(X, y) >>> unseen = pd.Series(["Taiwanese Taipei", "Chinese Shanghai"]) @@ -122,18 +122,12 @@ def learn_one(self, x, y): y Target class. - Returns - ------- - self - """ self.class_counts.update((y,)) for i, xi in x.items(): self.feature_counts[i].update({y: xi > self.true_threshold}) - return self - def p_feature_given_class(self, f: str, c: str) -> float: num = self.feature_counts.get(f, {}).get(c, 0.0) + self.alpha den = self.class_counts[c] + self.alpha * 2 @@ -186,10 +180,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): y Target classes. - Returns - ------- - self - """ # One hot encode y and convert it into sparse matrix y = base.one_hot_encode(y) @@ -224,8 +214,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): for f, count in dict_count.items(): self.feature_counts[f].update(count) - return self - def _feature_log_prob(self, columns: list) -> pd.DataFrame: """Compute log probabilities of input features. diff --git a/river/naive_bayes/complement.py b/river/naive_bayes/complement.py index 71160339c8..80598b25eb 100644 --- a/river/naive_bayes/complement.py +++ b/river/naive_bayes/complement.py @@ -56,7 +56,7 @@ class ComplementNB(base.BaseNB): ... ) >>> for sentence, label in docs: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> model["nb"].p_class("yes") 0.5 @@ -92,7 +92,7 @@ class ComplementNB(base.BaseNB): ... ("nb", naive_bayes.ComplementNB(alpha=1)) ... ) - >>> model = model.learn_many(X, y) + >>> model.learn_many(X, y) >>> unseen = pd.Series(["Taiwanese Taipei", "Chinese Shanghai"]) @@ -133,10 +133,6 @@ def learn_one(self, x, y): y Target class. - Returns - ------- - self - """ self.class_counts.update((y,)) @@ -145,8 +141,6 @@ def learn_one(self, x, y): self.feature_totals.update({f: frequency}) self.class_totals.update({y: frequency}) - return self - def p_class(self, c): return self.class_counts[c] / sum(self.class_counts.values()) @@ -196,10 +190,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): y Target classes. - Returns - ------- - self - """ y = base.one_hot_encode(y) columns, classes = X.columns, y.columns @@ -232,8 +222,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): for f, count in dict_count.items(): self.feature_counts[f].update(count) - return self - def _feature_log_prob(self, unknown: list, columns: list) -> pd.DataFrame: """Compute log probabilities of input features. diff --git a/river/naive_bayes/gaussian.py b/river/naive_bayes/gaussian.py index 3f9c991fa8..b4ca430b52 100644 --- a/river/naive_bayes/gaussian.py +++ b/river/naive_bayes/gaussian.py @@ -33,7 +33,7 @@ class GaussianNB(base.Classifier): >>> model = naive_bayes.GaussianNB() >>> for x, y in stream.iter_array(X, Y): - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({0: -0.8, 1: -1}) 1 @@ -52,8 +52,6 @@ def learn_one(self, x, y): for i, xi in x.items(): self.gaussians[y][i].update(xi) - return self - def predict_proba_one(self, x): """Return probabilities using the log-likelihoods.""" jll = self.joint_log_likelihood(x) diff --git a/river/naive_bayes/multinomial.py b/river/naive_bayes/multinomial.py index b30f631342..97ce314d50 100644 --- a/river/naive_bayes/multinomial.py +++ b/river/naive_bayes/multinomial.py @@ -57,7 +57,7 @@ class MultinomialNB(base.BaseNB): ... ) >>> for sentence, label in docs: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> model["nb"].p_class("yes") 0.5 @@ -93,7 +93,7 @@ class MultinomialNB(base.BaseNB): ... ("nb", naive_bayes.MultinomialNB(alpha=1)) ... ) - >>> model = model.learn_many(X, y) + >>> model.learn_many(X, y) >>> unseen = pd.Series(["Taiwanese Taipei", "Chinese Shanghai"]) @@ -132,10 +132,6 @@ def learn_one(self, x, y): y Target class. - Returns - ------- - self - """ self.class_counts.update((y,)) @@ -143,8 +139,6 @@ def learn_one(self, x, y): self.feature_counts[f].update({y: frequency}) self.class_totals.update({y: frequency}) - return self - @property def classes_(self): return list(self.class_counts.keys()) @@ -197,10 +191,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): y Target classes. - Returns - ------- - self - """ y = base.one_hot_encode(y) columns, classes = X.columns, y.columns @@ -229,8 +219,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): for f, count in dict_count.items(): self.feature_counts[f].update(count) - return self - def _feature_log_prob(self, columns: list, known: list, unknown: list) -> pd.DataFrame: """Compute log probabilities of input features. diff --git a/river/naive_bayes/test_naive_bayes.py b/river/naive_bayes/test_naive_bayes.py index 384e0e0bc4..2dd15bb8a3 100644 --- a/river/naive_bayes/test_naive_bayes.py +++ b/river/naive_bayes/test_naive_bayes.py @@ -82,7 +82,7 @@ def test_learn_one_methods(model): assert model.predict_one("not fitted yet") is None for x, y in yield_dataset(): - model = model.learn_one(x, y) + model.learn_one(x, y) # Check class methods. if isinstance(model["model"], naive_bayes.ComplementNB) or isinstance( @@ -116,10 +116,10 @@ def test_learn_many_vs_learn_one(model, batch_model): ComplementNB with differents alpha parameters.. """ for x, y in yield_dataset(): - model = model.learn_one(x, y) + model.learn_one(x, y) for x, y in yield_batch_dataset(): - batch_model = batch_model.learn_many(x, y) + batch_model.learn_many(x, y) assert model["model"].p_class("yes") == batch_model["model"].p_class("yes") assert model["model"].p_class("no") == batch_model["model"].p_class("no") @@ -216,7 +216,7 @@ def test_river_vs_sklearn(model, sk_model, bag): ComplementNB with differents alpha parameters. """ for x, y in yield_batch_dataset(): - model = model.learn_many(x, y) + model.learn_many(x, y) X = pd.concat([x for x, _ in yield_batch_dataset()]) y = pd.concat([y for _, y in yield_batch_dataset()]) diff --git a/river/neighbors/knn_classifier.py b/river/neighbors/knn_classifier.py index eb3f9931a2..67264abf3d 100644 --- a/river/neighbors/knn_classifier.py +++ b/river/neighbors/knn_classifier.py @@ -133,7 +133,6 @@ def learn_one(self, x, y): # Ensure classes known to instance reflect window self._run_class_cleanup() - return self def _run_class_cleanup(self): """Helper function to run class cleanup, accounting for _cleanup_counter.""" diff --git a/river/neighbors/knn_regressor.py b/river/neighbors/knn_regressor.py index 0cc2bbf162..9fdefd3b5e 100644 --- a/river/neighbors/knn_regressor.py +++ b/river/neighbors/knn_regressor.py @@ -103,8 +103,6 @@ def _check_aggregation_method(self, method): def learn_one(self, x, y): self._nn.append((x, y)) - return self - def predict_one(self, x, **kwargs): # Find the nearest neighbors! nearest = self._nn.search((x, None), n_neighbors=self.n_neighbors, **kwargs) diff --git a/river/neural_net/mlp.py b/river/neural_net/mlp.py index 9bdb6d7d6a..ecd9b849bd 100644 --- a/river/neural_net/mlp.py +++ b/river/neural_net/mlp.py @@ -162,8 +162,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.DataFrame): z, a = self._forward(X) self._backward(z, a, y) - return self - def __call__(self, X: pd.DataFrame): """Make predictions. @@ -261,7 +259,7 @@ class MLPRegressor(base.Regressor, MLP): ... for xb in pd.read_csv(dataset.path, chunksize=batch_size): ... yb = xb.pop('five_thirty_eight') ... y_pred = model.predict_many(xb) - ... model = model.learn_many(xb, yb) + ... model.learn_many(xb, yb) >>> model.predict_many(xb) five_thirty_eight @@ -310,10 +308,11 @@ def predict_many(self, X): def learn_one(self, x, y): # Multi-output if isinstance(y, dict): - return self.learn_many(X=pd.DataFrame([x]), y=pd.DataFrame([y])) + self.learn_many(X=pd.DataFrame([x]), y=pd.DataFrame([y])) + return # Single output - return self.learn_many(X=pd.DataFrame([x]), y=pd.Series([y])) + self.learn_many(X=pd.DataFrame([x]), y=pd.Series([y])) def predict_one(self, x): y_pred = self.predict_many(X=pd.DataFrame([x])) diff --git a/river/preprocessing/impute.py b/river/preprocessing/impute.py index baf14fdb41..a6cc70b734 100644 --- a/river/preprocessing/impute.py +++ b/river/preprocessing/impute.py @@ -17,7 +17,7 @@ class PreviousImputer(base.Transformer): >>> imputer = preprocessing.PreviousImputer() - >>> imputer = imputer.learn_one({'x': 1, 'y': 2}) + >>> imputer.learn_one({'x': 1, 'y': 2}) >>> imputer.transform_one({'y': None}) {'y': 2} @@ -34,8 +34,6 @@ def learn_one(self, x): if v is not None: self._latest[i] = v - return self - def transform_one(self, x): for i, v in x.items(): if v is None: @@ -80,7 +78,7 @@ class StatImputer(base.Transformer): >>> imp = preprocessing.StatImputer(('temperature', stats.Mean())) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'temperature': 1} {'temperature': 8} @@ -104,7 +102,7 @@ class StatImputer(base.Transformer): >>> imp = preprocessing.StatImputer(('weather', stats.Mode())) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'weather': 'sunny'} {'weather': 'rainy'} @@ -119,7 +117,7 @@ class StatImputer(base.Transformer): >>> imp = preprocessing.StatImputer(('weather', 'missing')) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'weather': 'sunny'} {'weather': 'rainy'} @@ -149,7 +147,7 @@ class StatImputer(base.Transformer): ... ) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'weather': 'sunny', 'temperature': 8} {'weather': 'rainy', 'temperature': 3} @@ -187,7 +185,7 @@ class StatImputer(base.Transformer): ... ) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'weather': 'sunny', 'temperature': 8} {'weather': 'rainy', 'temperature': 3} @@ -216,8 +214,6 @@ def learn_one(self, x): if x[i] is not None: self.stats[i].update(x[i]) - return self - def transform_one(self, x): # Transformers are supposed to be pure, therefore we make a copy of the features x = x.copy() diff --git a/river/preprocessing/lda.py b/river/preprocessing/lda.py index d3f6908868..4830e8bb73 100644 --- a/river/preprocessing/lda.py +++ b/river/preprocessing/lda.py @@ -95,7 +95,7 @@ class LDA(base.Transformer): ... ) >>> for x in X: - ... lda = lda.learn_one(x) + ... lda.learn_one(x) ... topics = lda.transform_one(x) ... print(topics) {0: 0.5, 1: 2.5} @@ -195,7 +195,6 @@ def learn_transform_one(self, x: dict) -> dict: def learn_one(self, x): self.learn_transform_one(x) - return self def transform_one(self, x): # Extracts words of the document as a list of words: diff --git a/river/preprocessing/one_hot.py b/river/preprocessing/one_hot.py index dbe2309f57..59d3337fae 100644 --- a/river/preprocessing/one_hot.py +++ b/river/preprocessing/one_hot.py @@ -57,7 +57,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> oh = preprocessing.OneHotEncoder() >>> for x in X[:2]: - ... oh = oh.learn_one(x) + ... oh.learn_one(x) ... pprint(oh.transform_one(x)) {'c1_u': 1, 'c2_d': 1} {'c1_a': 1, 'c1_u': 0, 'c2_d': 0, 'c2_x': 1} @@ -67,7 +67,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> oh = preprocessing.OneHotEncoder(drop_zeros=True) >>> for x in X: - ... oh = oh.learn_one(x) + ... oh.learn_one(x) ... pprint(oh.transform_one(x)) {'c1_u': 1, 'c2_d': 1} {'c1_a': 1, 'c2_x': 1} @@ -78,7 +78,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> oh = preprocessing.OneHotEncoder(drop_first=True, drop_zeros=True) >>> for x in X: - ... oh = oh.learn_one(x) + ... oh.learn_one(x) ... pprint(oh.transform_one(x)) {'c2_d': 1} {'c2_x': 1} @@ -93,7 +93,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> pp = compose.Select('c1') | preprocessing.OneHotEncoder() >>> for x in X: - ... pp = pp.learn_one(x) + ... pp.learn_one(x) ... pprint(pp.transform_one(x)) {'c1_u': 1} {'c1_a': 1, 'c1_u': 0} @@ -106,7 +106,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> pp += compose.Select('c2') >>> for x in X: - ... pp = pp.learn_one(x) + ... pp.learn_one(x) ... pprint(pp.transform_one(x)) {'c1_u': 1, 'c2': 'd'} {'c1_a': 1, 'c1_u': 0, 'c2': 'x'} @@ -123,7 +123,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> oh = preprocessing.OneHotEncoder(drop_zeros=True) >>> for x in X: - ... oh = oh.learn_one(x) + ... oh.learn_one(x) ... pprint(oh.transform_one(x)) {'c1_a': 1, 'c1_u': 1, 'c2_d': 1} {'c1_a': 1, 'c1_b': 1, 'c2_x': 1} @@ -171,8 +171,8 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> oh = preprocessing.OneHotEncoder(drop_zeros=False) >>> X_init = pd.DataFrame([{"c1": "Oranges", "c2": "Apples"}]) - >>> oh = oh.learn_many(X_init) - >>> oh = oh.learn_many(X) + >>> oh.learn_many(X_init) + >>> oh.learn_many(X) >>> df = oh.transform_many(X) >>> df.sort_index(axis="columns") @@ -201,7 +201,7 @@ def __init__(self, drop_zeros=False, drop_first=False): def learn_one(self, x): if self.drop_zeros: - return self + return for i, xi in x.items(): if isinstance(xi, list) or isinstance(xi, set): @@ -210,8 +210,6 @@ def learn_one(self, x): else: self.values[i].add(xi) - return self - def transform_one(self, x, y=None): oh = {} @@ -234,13 +232,11 @@ def transform_one(self, x, y=None): def learn_many(self, X): if self.drop_zeros: - return self + return for col in X.columns: self.values[col].update(X[col].unique()) - return self - def transform_many(self, X): oh = pd.get_dummies(X, columns=X.columns, sparse=True, dtype="uint8") diff --git a/river/preprocessing/ordinal.py b/river/preprocessing/ordinal.py index 199e6522ba..01b981ac86 100644 --- a/river/preprocessing/ordinal.py +++ b/river/preprocessing/ordinal.py @@ -54,7 +54,7 @@ class OrdinalEncoder(base.MiniBatchTransformer): >>> encoder = preprocessing.OrdinalEncoder() >>> for x in X: ... print(encoder.transform_one(x)) - ... encoder = encoder.learn_one(x) + ... encoder.learn_one(x) {'country': 0, 'place': 0} {'country': -1, 'place': -1} {'country': 0, 'place': 0} @@ -75,7 +75,7 @@ class OrdinalEncoder(base.MiniBatchTransformer): 2 0 0 3 0 0 - >>> encoder = encoder.learn_many(xb1) + >>> encoder.learn_many(xb1) >>> encoder.transform_many(xb2) country place 4 0 0 @@ -113,7 +113,6 @@ def learn_one(self, x): for i, xi in x.items(): if xi is not None and xi not in self.categories[i]: self.categories[i][xi] = next(self._counters[i]) - return self def transform_many(self, X): return pd.DataFrame( @@ -133,4 +132,3 @@ def learn_many(self, X, y=None): for xi in X[i].dropna().unique(): if xi not in self.categories[i]: self.categories[i][xi] = next(self._counters[i]) - return self diff --git a/river/preprocessing/pred_clipper.py b/river/preprocessing/pred_clipper.py index 6fa53ccfd0..24dfec8c65 100644 --- a/river/preprocessing/pred_clipper.py +++ b/river/preprocessing/pred_clipper.py @@ -35,7 +35,7 @@ class PredClipper(base.Wrapper, base.Regressor): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'a': -100, 'b': -200}) 0 @@ -56,7 +56,6 @@ def _wrapped_model(self): def learn_one(self, x, y, **kwargs): self.regressor.learn_one(x=x, y=y, **kwargs) - return self def predict_one(self, x, **kwargs): y_pred = self.regressor.predict_one(x=x, **kwargs) diff --git a/river/preprocessing/scale.py b/river/preprocessing/scale.py index 8283c2d190..25ab2328dd 100644 --- a/river/preprocessing/scale.py +++ b/river/preprocessing/scale.py @@ -52,7 +52,8 @@ class Binarizer(base.Transformer): >>> binarizer = river.preprocessing.Binarizer() >>> for x in X: - ... print(binarizer.learn_one(x).transform_one(x)) + ... binarizer.learn_one(x) + ... print(binarizer.transform_one(x)) {'x1': False, 'x2': False} {'x1': True, 'x2': True} {'x1': True, 'x2': True} @@ -114,7 +115,8 @@ class StandardScaler(base.MiniBatchTransformer): >>> scaler = preprocessing.StandardScaler() >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 0.0, 'y': 0.0} {'x': -0.999, 'y': 0.999} {'x': 0.937, 'y': 1.350} @@ -129,8 +131,8 @@ class StandardScaler(base.MiniBatchTransformer): >>> X = pd.DataFrame.from_dict(X) >>> scaler = preprocessing.StandardScaler() - >>> scaler = scaler.learn_many(X[:3]) - >>> scaler = scaler.learn_many(X[3:]) + >>> scaler.learn_many(X[:3]) + >>> scaler.learn_many(X[3:]) You can then call `transform_many` to scale a mini-batch of features: @@ -166,8 +168,6 @@ def learn_one(self, x): (xi - old_mean) * (xi - self.means[i]) - self.vars[i] ) / self.counts[i] - return self - def transform_one(self, x): if self.with_std: return {i: safe_div(xi - self.means[i], self.vars[i] ** 0.5) for i, xi in x.items()} @@ -217,8 +217,6 @@ def learn_many(self, X: pd.DataFrame): self.vars[col] = a * old_var + b * new_var + a * b * (old_mean - new_mean) ** 2 self.counts[col] += new_count - return self - def transform_many(self, X: pd.DataFrame): """Scale a mini-batch of features. @@ -280,7 +278,8 @@ class MinMaxScaler(base.Transformer): >>> scaler = preprocessing.MinMaxScaler() >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 0.0} {'x': 0.0} {'x': 0.406920} @@ -298,8 +297,6 @@ def learn_one(self, x): self.min[i].update(xi) self.max[i].update(xi) - return self - def transform_one(self, x): return { i: safe_div(xi - self.min[i].get(), self.max[i].get() - self.min[i].get()) @@ -338,7 +335,8 @@ class MaxAbsScaler(base.Transformer): >>> scaler = preprocessing.MaxAbsScaler() >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 1.0} {'x': 0.767216} {'x': 0.861940} @@ -354,8 +352,6 @@ def learn_one(self, x): for i, xi in x.items(): self.abs_max[i].update(xi) - return self - def transform_one(self, x): return {i: safe_div(xi, self.abs_max[i].get()) for i, xi in x.items()} @@ -403,7 +399,8 @@ class RobustScaler(base.Transformer): >>> scaler = preprocessing.RobustScaler() >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 0.0} {'x': -1.0} {'x': 0.0} @@ -427,8 +424,6 @@ def learn_one(self, x): if self.with_scaling: self.iqr[i].update(xi) - return self - def transform_one(self, x): x_tf = {} @@ -525,7 +520,8 @@ class AdaptiveStandardScaler(base.Transformer): >>> scaler = preprocessing.AdaptiveStandardScaler(fading_factor=.6) >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 0.0} {'x': -0.816} {'x': 0.812} @@ -546,7 +542,6 @@ def learn_one(self, x): for i, xi in x.items(): self.vars[i].update(xi) self.means[i].update(xi) - return self def transform_one(self, x): return { diff --git a/river/preprocessing/test_lda.py b/river/preprocessing/test_lda.py index 8858fe6878..8f229bbf38 100644 --- a/river/preprocessing/test_lda.py +++ b/river/preprocessing/test_lda.py @@ -264,7 +264,7 @@ def test_learn_transform(): for document in DOC_SET: tokens = {token: 1 for token in document.split(" ")} - lda = lda.learn_one(x=tokens) + lda.learn_one(x=tokens) components_list.append(lda.transform_one(x=tokens)) diff --git a/river/preprocessing/test_scale.py b/river/preprocessing/test_scale.py index 36e4e1b7c1..2d4fa2df02 100644 --- a/river/preprocessing/test_scale.py +++ b/river/preprocessing/test_scale.py @@ -80,7 +80,7 @@ def test_issue_1313(): feat_2 float32 dtype: object - >>> model = model.learn_many(X) + >>> model.learn_many(X) >>> X1 = model.transform_many(X) >>> X1.dtypes feat_1 float32 diff --git a/river/reco/baseline.py b/river/reco/baseline.py index fb8213fa59..7d62458e0d 100644 --- a/river/reco/baseline.py +++ b/river/reco/baseline.py @@ -69,7 +69,7 @@ class Baseline(reco.base.Ranker): >>> model = reco.Baseline(optimizer=optim.SGD(0.005)) >>> for x, y in dataset: - ... _ = model.learn_one(**x, y=y) + ... model.learn_one(**x, y=y) >>> model.predict_one(user='Bob', item='Harry Potter') 6.538120 @@ -130,5 +130,3 @@ def learn_one(self, user, item, y, x=None): # Update biases self.u_biases = self.u_optimizer.step(self.u_biases, u_grad_bias) self.i_biases = self.i_optimizer.step(self.i_biases, i_grad_bias) - - return self diff --git a/river/reco/biased_mf.py b/river/reco/biased_mf.py index 4032b31daa..2b4515e4b0 100644 --- a/river/reco/biased_mf.py +++ b/river/reco/biased_mf.py @@ -100,7 +100,7 @@ class BiasedMF(Ranker): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(**x, y=y) + ... model.learn_one(**x, y=y) >>> model.predict_one(user='Bob', item='Harry Potter') 6.489025 @@ -226,5 +226,3 @@ def learn_one(self, user, item, y, x=None): self.i_biases = self.i_bias_optimizer.step(self.i_biases, i_grad_bias) self.u_latents = self.u_latent_optimizer.step(self.u_latents, u_latent_grad) self.i_latents = self.i_latent_optimizer.step(self.i_latents, i_latent_grad) - - return self diff --git a/river/reco/funk_mf.py b/river/reco/funk_mf.py index 163f7b42be..a7edf2ade7 100644 --- a/river/reco/funk_mf.py +++ b/river/reco/funk_mf.py @@ -76,7 +76,7 @@ class FunkMF(reco.base.Ranker): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(**x, y=y) + ... model.learn_one(**x, y=y) >>> model.predict_one(user='Bob', item='Harry Potter') 1.866272 @@ -143,5 +143,3 @@ def learn_one(self, user, item, y, x=None): # Update latent weights self.u_latents = self.u_optimizer.step(self.u_latents, u_latent_grad) self.i_latents = self.i_optimizer.step(self.i_latents, i_latent_grad) - - return self diff --git a/river/reco/normal.py b/river/reco/normal.py index b9acadc3ec..8bab210a16 100644 --- a/river/reco/normal.py +++ b/river/reco/normal.py @@ -44,7 +44,7 @@ class RandomNormal(reco.base.Ranker): >>> model = reco.RandomNormal(seed=42) >>> for x, y in dataset: - ... _ = model.learn_one(**x, y=y) + ... model.learn_one(**x, y=y) >>> model.predict_one(user='Bob', item='Harry Potter') 6.147299621751425 @@ -60,7 +60,6 @@ def __init__(self, seed=None): def learn_one(self, user, item, y, x=None): self.mean.update(y) self.variance.update(y) - return self def predict_one(self, user, item, x=None): μ = self.mean.get() or 0 diff --git a/river/rules/amrules.py b/river/rules/amrules.py index 3ec132f3e5..245f71c1f4 100644 --- a/river/rules/amrules.py +++ b/river/rules/amrules.py @@ -19,7 +19,6 @@ def __init__(self): def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1): self.mean.update(y, w) - return self def predict_one(self, x: dict): return self.mean.get() @@ -51,8 +50,6 @@ def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1): for _ in range(int(w)): self.model_predictor.learn_one(x, y) - return self - def predict_one(self, x: dict): if self._mae_mean <= self._mae_model: return self.mean_predictor.predict_one(x) @@ -147,8 +144,6 @@ def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1): # type: ign self.update(x, y, w) self.pred_model.learn_one(x, y, w) - return self - def predict_one(self, x: dict): return self.pred_model.predict_one(x) @@ -356,7 +351,7 @@ def _new_rule(self) -> RegRule: drift_detector=self.drift_detector.clone(), ) - def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1) -> AMRules: + def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1): any_covered = False to_del = set() @@ -411,8 +406,6 @@ def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1) -> AMRules: for rule_id in to_del: del self._rules[rule_id] - return self - def predict_one(self, x: dict) -> base.typing.RegTarget: y_pred = 0 hits = 0 @@ -466,7 +459,7 @@ def anomaly_score(self, x) -> tuple[float, float, float]: ... if i == 1000: ... # Skip the last example ... break - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.anomaly_score(x) (1.0168907243483933, 0.13045786430817402, 1.0) @@ -517,7 +510,7 @@ def debug_one(self, x) -> str: ... if i == 1000: ... # Skip the last example ... break - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> print(model.debug_one(x)) Rule 0: 3 > 0.5060 and 0 > 0.2538 diff --git a/river/stats/shift.py b/river/stats/shift.py index b4b6d09737..4acbadd655 100644 --- a/river/stats/shift.py +++ b/river/stats/shift.py @@ -70,7 +70,7 @@ class Shift(stats.base.Univariate): Now let's call the `learn_one` method to update our feature extractor. >>> x = next(X) - >>> agg = agg.learn_one(x) + >>> agg.learn_one(x) At this point, the average defaults to the initial value of `stats.Mean`, which is 0. @@ -79,11 +79,11 @@ class Shift(stats.base.Univariate): We can now update our feature extractor with the next data point and check the output. - >>> agg = agg.learn_one(next(X)) + >>> agg.learn_one(next(X)) >>> agg.transform_one(x) {'sales_mean_of_shift_1_by_shop': 10.0} - >>> agg = agg.learn_one(next(X)) + >>> agg.learn_one(next(X)) >>> agg.transform_one(x) {'sales_mean_of_shift_1_by_shop': 12.5} diff --git a/river/time_series/holt_winters.py b/river/time_series/holt_winters.py index 52241be66a..0daed4c096 100644 --- a/river/time_series/holt_winters.py +++ b/river/time_series/holt_winters.py @@ -192,11 +192,11 @@ def learn_one(self, y, x=None): self.trend.update(y, self.level) if self.season is not None: self.season.update(y, self.level, self.trend) - return self + return self._first_values.append(y) if len(self._first_values) < max(2, self.seasonality): - return self + return # The components can be initialized now that enough values have been observed self.level.append(statistics.mean(self._first_values)) @@ -208,8 +208,6 @@ def learn_one(self, y, x=None): self._initialized = True - return self - def forecast(self, horizon, xs=None): op = operator.mul if self.multiplicative else operator.add return [ diff --git a/river/time_series/snarimax.py b/river/time_series/snarimax.py index e543737a11..150f15c2bb 100644 --- a/river/time_series/snarimax.py +++ b/river/time_series/snarimax.py @@ -176,7 +176,7 @@ class SNARIMAX(time_series.base.Forecaster): ... ) >>> for t, (x, y) in enumerate(datasets.AirlinePassengers()): - ... model = model.learn_one(y) + ... model.learn_one(y) >>> horizon = 12 >>> future = [ @@ -244,7 +244,7 @@ class SNARIMAX(time_series.base.Forecaster): ... ) >>> for x, y in datasets.AirlinePassengers(): - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> forecast = model.forecast(horizon=horizon) >>> for x, y_pred in zip(future, forecast): @@ -349,8 +349,6 @@ def learn_one(self, y, x=None): self.y_hist.appendleft(y) - return self - def forecast(self, horizon, xs=None): if xs is None: xs = [{}] * horizon diff --git a/river/time_series/test_evaluate.py b/river/time_series/test_evaluate.py index 369b8ae95c..7644598b92 100644 --- a/river/time_series/test_evaluate.py +++ b/river/time_series/test_evaluate.py @@ -9,7 +9,6 @@ def __init__(self): def learn_one(self, y, x=None): self.mean.update(y) - return self def forecast(self, horizon, xs=None): return [self.mean.get()] * horizon diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index 935f42b74b..bac7ed8be6 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -226,10 +226,6 @@ def learn_one(self, x, y, *, w=1.0): * Reevaluate the best split for each internal node. * Attempt to split the leaf. - Returns - ------- - self - """ # Updates the set of observed classes self.classes.add(y) @@ -245,8 +241,6 @@ def learn_one(self, x, y, *, w=1.0): # Process all nodes, starting from root to the leaf where the instance x belongs. self._process_nodes(x, y, w, self._root, None, None) - return self - def _sort_to_leaf(self, x, y, w): """For a given instance, find the corresponding leaf and update it. diff --git a/river/tree/hoeffding_adaptive_tree_classifier.py b/river/tree/hoeffding_adaptive_tree_classifier.py index 05807fe031..290d34dad1 100644 --- a/river/tree/hoeffding_adaptive_tree_classifier.py +++ b/river/tree/hoeffding_adaptive_tree_classifier.py @@ -232,8 +232,6 @@ def learn_one(self, x, y, *, w=1.0): if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - return self - # Override HoeffdingTreeClassifier def predict_proba_one(self, x): proba = {c: 0.0 for c in self.classes} diff --git a/river/tree/hoeffding_adaptive_tree_regressor.py b/river/tree/hoeffding_adaptive_tree_regressor.py index 8b1193f274..a2066a9e91 100644 --- a/river/tree/hoeffding_adaptive_tree_regressor.py +++ b/river/tree/hoeffding_adaptive_tree_regressor.py @@ -238,8 +238,6 @@ def learn_one(self, x, y, *, w=1.0): if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - return self - def predict_one(self, x): pred = 0.0 if self._root is not None: diff --git a/river/tree/hoeffding_tree.py b/river/tree/hoeffding_tree.py index f1e1a0e168..9a7addeeef 100644 --- a/river/tree/hoeffding_tree.py +++ b/river/tree/hoeffding_tree.py @@ -394,7 +394,7 @@ def draw(self, max_depth: int | None = None): ... tau=0.05, ... ) >>> for x, y in datasets.Phishing(): - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> dot = model.draw() .. image:: ../../docs/img/dtree_draw.svg diff --git a/river/tree/hoeffding_tree_classifier.py b/river/tree/hoeffding_tree_classifier.py index 7d7feb19bb..6ce2c09f57 100755 --- a/river/tree/hoeffding_tree_classifier.py +++ b/river/tree/hoeffding_tree_classifier.py @@ -330,10 +330,6 @@ def learn_one(self, x, y, *, w=1.0): w Sample weight. - Returns - ------- - self - Notes ----- Training tasks: @@ -408,8 +404,6 @@ def learn_one(self, x, y, *, w=1.0): if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - return self - def predict_proba_one(self, x): proba = {c: 0.0 for c in sorted(self.classes)} if self._root is not None: diff --git a/river/tree/hoeffding_tree_regressor.py b/river/tree/hoeffding_tree_regressor.py index b7afa030d3..1bcc1c96b3 100644 --- a/river/tree/hoeffding_tree_regressor.py +++ b/river/tree/hoeffding_tree_regressor.py @@ -229,9 +229,6 @@ def learn_one(self, x, y, *, w=1.0): w The weight of the sample. - Returns - ------- - self """ self._train_weight_seen_by_model += w @@ -293,8 +290,6 @@ def learn_one(self, x, y, *, w=1.0): if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - return self - def predict_one(self, x): """Predict the target value using one of the leaf prediction strategies. diff --git a/river/tree/isoup_tree_regressor.py b/river/tree/isoup_tree_regressor.py index d4413674e1..2f92a34756 100644 --- a/river/tree/isoup_tree_regressor.py +++ b/river/tree/isoup_tree_regressor.py @@ -233,8 +233,6 @@ def learn_one(self, x, y, *, w: float = 1.0) -> iSOUPTreeRegressor: # type: ign super().learn_one(x, y, w=w) # type: ignore - return self - def predict_one(self, x): pred = {} if self._root is not None: diff --git a/river/tree/mondrian/mondrian_tree_classifier.py b/river/tree/mondrian/mondrian_tree_classifier.py index 51e96a7dd2..c2c786ee65 100644 --- a/river/tree/mondrian/mondrian_tree_classifier.py +++ b/river/tree/mondrian/mondrian_tree_classifier.py @@ -456,7 +456,6 @@ def learn_one(self, x, y): # Incrementing iteration self.iteration += 1 - return self def predict_proba_one(self, x): """Predict the probability of the samples. diff --git a/river/tree/mondrian/mondrian_tree_regressor.py b/river/tree/mondrian/mondrian_tree_regressor.py index c83d30cbbd..54258346bd 100644 --- a/river/tree/mondrian/mondrian_tree_regressor.py +++ b/river/tree/mondrian/mondrian_tree_regressor.py @@ -377,7 +377,6 @@ def learn_one(self, x, y): # Incrementing iteration self.iteration += 1 - return self def predict_one(self, x): """Predict the label of the samples. diff --git a/river/tree/stochastic_gradient_tree.py b/river/tree/stochastic_gradient_tree.py index 0312e67868..4a3821d6ca 100644 --- a/river/tree/stochastic_gradient_tree.py +++ b/river/tree/stochastic_gradient_tree.py @@ -124,7 +124,7 @@ def learn_one(self, x, y, *, w=1.0): node.update(x, grad_hess, self, w) if node.total_weight - node.last_split_attempt_at < self.grace_period: - return self + return # Update split attempt data node.last_split_attempt_at = node.total_weight @@ -150,8 +150,6 @@ def learn_one(self, x, y, *, w=1.0): p_branch = p_node.branch_no(x) if isinstance(p_node, DTBranch) else None node.apply_split(best_split, p_node, p_branch, self) - return self - @staticmethod def _compute_p_value(merit, n_observations): # Null hypothesis: expected loss is zero From 936a5d75495e5e48ca21bd5717b27a3298659c2c Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sun, 3 Dec 2023 19:03:16 +0100 Subject: [PATCH 205/347] Update pypi.yml --- .github/workflows/pypi.yml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 2fcc324b53..9d0a294add 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -43,9 +43,7 @@ jobs: - name: Build wheels uses: pypa/cibuildwheel@v2.16.2 env: - # TODO - # CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" - CIBW_BUILD: "cp311-*" + CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" # CIBW_ARCHS_MACOS: "x86_64 arm64" CIBW_ARCHS_MACOS: "universal2" From 4e561f8660a7c0da9abbc9ca3b88d7924e280275 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 4 Dec 2023 18:16:50 +0100 Subject: [PATCH 206/347] remove return self from update and revert --- .pre-commit-config.yaml | 6 --- README.md | 2 +- docs/releases/0.21.0.md | 5 ++ docs/releases/unreleased.md | 27 ---------- poetry.lock | 56 +++----------------- pyproject.toml | 5 -- river/active/entropy.py | 6 ++- river/anomaly/filter.py | 9 ++-- river/anomaly/hst.py | 2 +- river/anomaly/svm.py | 2 +- river/anomaly/test_hst.py | 2 +- river/bandit/base.py | 2 - river/bandit/bayes_ucb.py | 5 +- river/bandit/envs/candy_cane.py | 10 ++-- river/bandit/epsilon_greedy.py | 11 ++-- river/bandit/exp3.py | 19 ++++--- river/bandit/lin_ucb.py | 1 - river/bandit/random.py | 4 +- river/bandit/thompson.py | 9 ++-- river/bandit/ucb.py | 15 ++++-- river/base/drift_detector.py | 8 --- river/cluster/clustream.py | 13 +++-- river/cluster/textclust.py | 57 ++++++++++++++++----- river/conf/jackknife.py | 17 ++++-- river/drift/adwin.py | 4 +- river/drift/adwin_c.pyx | 2 +- river/drift/binary/ddm.py | 4 +- river/drift/binary/eddm.py | 4 +- river/drift/binary/hddm_a.py | 4 +- river/drift/binary/hddm_w.py | 4 +- river/drift/dummy.py | 8 ++- river/drift/kswin.py | 4 +- river/drift/no_drift.py | 2 +- river/drift/page_hinkley.py | 4 +- river/evaluate/progressive_validation.py | 6 ++- river/feature_selection/k_best.py | 3 +- river/forest/online_extra_trees.py | 10 ++-- river/linear_model/pa.py | 8 +-- river/metrics/accuracy.py | 2 +- river/metrics/balanced_accuracy.py | 4 +- river/metrics/base.py | 24 +++++---- river/metrics/confusion.py | 4 +- river/metrics/cross_entropy.py | 6 ++- river/metrics/fbeta.py | 27 ++++++---- river/metrics/fowlkes_mallows.py | 3 +- river/metrics/geometric_mean.py | 2 +- river/metrics/jaccard.py | 12 +++-- river/metrics/kappa.py | 2 +- river/metrics/log_loss.py | 2 +- river/metrics/mae.py | 3 +- river/metrics/mape.py | 2 +- river/metrics/mcc.py | 2 +- river/metrics/mse.py | 10 ++-- river/metrics/multioutput/base.py | 2 - river/metrics/multioutput/confusion.py | 8 +-- river/metrics/multioutput/exact_match.py | 2 +- river/metrics/multioutput/macro.py | 2 - river/metrics/multioutput/micro.py | 2 - river/metrics/multioutput/per_output.py | 2 - river/metrics/multioutput/sample_average.py | 5 +- river/metrics/mutual_info.py | 13 +++-- river/metrics/precision.py | 12 +++-- river/metrics/r2.py | 5 +- river/metrics/rand.py | 15 ++++-- river/metrics/recall.py | 12 +++-- river/metrics/report.py | 2 +- river/metrics/roc_auc.py | 6 +-- river/metrics/rolling_roc_auc.py | 12 ++--- river/metrics/silhouette.py | 6 +-- river/metrics/smape.py | 2 +- river/metrics/vbeta.py | 9 ++-- river/misc/sdft.py | 7 +-- river/misc/skyline.py | 10 ++-- river/multioutput/chain.py | 6 +-- river/multioutput/encoder.py | 2 +- river/preprocessing/impute.py | 2 +- river/proba/beta.py | 10 ++-- river/proba/gaussian.py | 31 ++++++----- river/proba/multinomial.py | 23 +++++---- river/proba/test_gaussian.py | 2 +- river/sketch/counter.py | 8 ++- river/sketch/heavy_hitters.py | 4 +- river/sketch/histogram.py | 10 ++-- river/sketch/set.py | 2 - river/stats/auto_corr.py | 11 ++-- river/stats/count.py | 1 - river/stats/cov.py | 9 ++-- river/stats/entropy.py | 14 ++--- river/stats/ewmean.py | 4 +- river/stats/ewvar.py | 4 +- river/stats/iqr.py | 6 +-- river/stats/kurtosis.py | 7 +-- river/stats/link.py | 7 ++- river/stats/mad.py | 4 +- river/stats/maximum.py | 18 +++---- river/stats/mean.py | 11 ++-- river/stats/minimum.py | 5 +- river/stats/mode.py | 14 ++--- river/stats/n_unique.py | 11 ++-- river/stats/pearson.py | 8 +-- river/stats/ptp.py | 8 +-- river/stats/quantile.py | 4 +- river/stats/sem.py | 6 ++- river/stats/shift.py | 3 +- river/stats/skew.py | 7 +-- river/stats/summing.py | 8 +-- river/stats/test_parallel.py | 9 ++-- river/stats/test_quantile.py | 4 +- river/stats/var.py | 9 ++-- river/time_series/metrics.py | 4 -- river/tree/splitter/base.py | 2 + river/tree/splitter/ebst_splitter.py | 1 + river/utils/pretty.py | 8 ++- river/utils/rolling.py | 9 ++-- river/utils/test_rolling.py | 2 +- 115 files changed, 456 insertions(+), 461 deletions(-) create mode 100644 docs/releases/0.21.0.md delete mode 100644 docs/releases/unreleased.md diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index eed258f2bd..8bdd4f2a2a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,12 +11,6 @@ repos: - repo: local hooks: - - id: black - name: black - language: python - types: [python] - entry: black - - id: ruff name: ruff language: python diff --git a/README.md b/README.md index 0edef73742..c1ffd72905 100644 --- a/README.md +++ b/README.md @@ -91,7 +91,7 @@ Now let's run the model on the dataset in a streaming fashion. We sequentially i >>> for x, y in dataset: ... y_pred = model.predict_one(x) # make a prediction -... metric = metric.update(y, y_pred) # update the metric +... metric.update(y, y_pred) # update the metric ... model.learn_one(x, y) # make the model learn >>> metric diff --git a/docs/releases/0.21.0.md b/docs/releases/0.21.0.md new file mode 100644 index 0000000000..fb2a8e4123 --- /dev/null +++ b/docs/releases/0.21.0.md @@ -0,0 +1,5 @@ +# 0.21.0 - 2023-12-04 + +- The `learn_one` and `learn_many` methods of each estimator don't not return anything anymore. This is to emphasize that the estimators are stateful. +- The `update` and `revert` method of classes that have also cease to return anything. +- `sample_weight` has been renamed to `w`. diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md deleted file mode 100644 index efdbb9dd40..0000000000 --- a/docs/releases/unreleased.md +++ /dev/null @@ -1,27 +0,0 @@ -# Unreleased - -## cluster - -- Renamed `sample_weight` to `w`. - -## ensemble - -- Renamed `sample_weight` to `w`. - -## facto - -- Renamed `sample_weight` to `w`. - -## forest - -- Renamed `sample_weight` to `w`. - -## tree - -- Renamed `sample_weight` to `w`. - -## metrics - -- Renamed `sample_weight` to `w`. - - diff --git a/poetry.lock b/poetry.lock index f80b6514cb..20c4bb5322 100644 --- a/poetry.lock +++ b/poetry.lock @@ -215,48 +215,6 @@ soupsieve = ">1.2" html5lib = ["html5lib"] lxml = ["lxml"] -[[package]] -name = "black" -version = "23.11.0" -description = "The uncompromising code formatter." -optional = false -python-versions = ">=3.8" -files = [ - 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[[package]] name = "bleach" version = "6.1.0" @@ -1180,13 +1138,13 @@ files = [ [[package]] name = "importlib-metadata" -version = "6.9.0" +version = "7.0.0" description = "Read metadata from Python packages" optional = false python-versions = ">=3.8" files = [ - {file = "importlib_metadata-6.9.0-py3-none-any.whl", hash = "sha256:1c8dc6839ddc9771412596926f24cb5a553bbd40624ee2c7e55e531542bed3b8"}, - {file = "importlib_metadata-6.9.0.tar.gz", hash = "sha256:e8acb523c335a91822674e149b46c0399ec4d328c4d1f6e49c273da5ff0201b9"}, + {file = "importlib_metadata-7.0.0-py3-none-any.whl", hash = "sha256:d97503976bb81f40a193d41ee6570868479c69d5068651eb039c40d850c59d67"}, + {file = "importlib_metadata-7.0.0.tar.gz", hash = "sha256:7fc841f8b8332803464e5dc1c63a2e59121f46ca186c0e2e182e80bf8c1319f7"}, ] [package.dependencies] @@ -4892,13 +4850,13 @@ files = [ [[package]] name = "websocket-client" -version = "1.6.4" +version = "1.7.0" description = "WebSocket client for Python with low level API options" optional = false python-versions = ">=3.8" files = [ - 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[tool.ruff] select = ["E", "F", "I", "UP"] # https://beta.ruff.rs/docs/rules/ line-length = 100 diff --git a/river/active/entropy.py b/river/active/entropy.py index 09858ae1f4..f980b9fc7a 100644 --- a/river/active/entropy.py +++ b/river/active/entropy.py @@ -47,7 +47,7 @@ class EntropySampler(ActiveLearningClassifier): >>> n_samples_used = 0 >>> for x, y in dataset: ... y_pred, ask = model.predict_one(x) - ... metric = metric.update(y, y_pred) + ... metric.update(y, y_pred) ... if ask: ... n_samples_used += 1 ... model.learn_one(x, y) @@ -63,7 +63,9 @@ class EntropySampler(ActiveLearningClassifier): """ - def __init__(self, classifier: base.Classifier, discount_factor: float = 3, seed=None): + def __init__( + self, classifier: base.Classifier, discount_factor: float = 3, seed=None + ): super().__init__(classifier, seed=seed) self.discount_factor = discount_factor diff --git a/river/anomaly/filter.py b/river/anomaly/filter.py index a3a685fe26..856f96ca13 100644 --- a/river/anomaly/filter.py +++ b/river/anomaly/filter.py @@ -86,7 +86,9 @@ class ThresholdFilter(anomaly.base.AnomalyFilter): """ - def __init__(self, anomaly_detector, threshold: float, protect_anomaly_detector=True): + def __init__( + self, anomaly_detector, threshold: float, protect_anomaly_detector=True + ): super().__init__( anomaly_detector=anomaly_detector, protect_anomaly_detector=protect_anomaly_detector, @@ -145,7 +147,7 @@ class QuantileFilter(anomaly.base.AnomalyFilter): ... score = model.score_one(x) ... is_anomaly = model['QuantileFilter'].classify(score) ... model.learn_one(x) - ... report = report.update(y, is_anomaly) + ... report.update(y, is_anomaly) >>> report Precision Recall F1 Support @@ -186,6 +188,7 @@ def _unit_test_params(cls): from river import preprocessing yield { - "anomaly_detector": preprocessing.StandardScaler() | anomaly.OneClassSVM(nu=0.2), + "anomaly_detector": preprocessing.StandardScaler() + | anomaly.OneClassSVM(nu=0.2), "q": 0.995, } diff --git a/river/anomaly/hst.py b/river/anomaly/hst.py index 6f47f18f7b..8fca772e10 100644 --- a/river/anomaly/hst.py +++ b/river/anomaly/hst.py @@ -171,7 +171,7 @@ class HalfSpaceTrees(anomaly.base.AnomalyDetector): >>> for x, y in datasets.CreditCard().take(2500): ... score = model.score_one(x) ... model.learn_one(x) - ... auc = auc.update(y, score) + ... auc.update(y, score) >>> auc ROCAUC: 91.15% diff --git a/river/anomaly/svm.py b/river/anomaly/svm.py index 6611591350..2593211d8e 100644 --- a/river/anomaly/svm.py +++ b/river/anomaly/svm.py @@ -49,7 +49,7 @@ class OneClassSVM(linear_model.base.GLM, anomaly.base.AnomalyDetector): ... score = model.score_one(x) ... is_anomaly = model.classify(score) ... model.learn_one(x) - ... auc = auc.update(y, is_anomaly) + ... auc.update(y, is_anomaly) >>> auc ROCAUC: 74.68% diff --git a/river/anomaly/test_hst.py b/river/anomaly/test_hst.py index ac5f7a8767..341f6697db 100644 --- a/river/anomaly/test_hst.py +++ b/river/anomaly/test_hst.py @@ -25,7 +25,7 @@ def test_missing_features(): ... del x[random.choice(features)] ... score = model.score_one(x) ... model.learn_one(x, y) - ... auc = auc.update(y, score) + ... auc.update(y, score) >>> auc ROCAUC: 88.68% diff --git a/river/bandit/base.py b/river/bandit/base.py index 564499d9f4..0359686cae 100644 --- a/river/bandit/base.py +++ b/river/bandit/base.py @@ -113,7 +113,6 @@ def update(self, arm_id, *reward_args, **reward_kwargs): self._rewards[arm_id].update(*reward_args, **reward_kwargs) self._counts[arm_id] += 1 self._n += 1 - return self @property def ranking(self) -> list[ArmID]: @@ -214,4 +213,3 @@ def update(self, arm_id, context, *reward_args, **reward_kwargs): self._rewards[arm_id].update(*reward_args, **reward_kwargs) self._counts[arm_id] += 1 self._n += 1 - return self diff --git a/river/bandit/bayes_ucb.py b/river/bandit/bayes_ucb.py index 7eec7f1de9..4c62b59d8e 100644 --- a/river/bandit/bayes_ucb.py +++ b/river/bandit/bayes_ucb.py @@ -45,8 +45,8 @@ class BayesUCB(bandit.base.Policy): >>> while True: ... action = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(action) - ... policy = policy.update(action, reward) - ... metric = metric.update(reward) + ... policy.update(action, reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -86,4 +86,3 @@ def update(self, arm_id, *reward_args, **reward_kwargs): super().update(arm_id, *reward_args, **reward_kwargs) reward = reward_args[0] self._posteriors[arm_id].update(reward) - return self diff --git a/river/bandit/envs/candy_cane.py b/river/bandit/envs/candy_cane.py index 5bda95f241..421af13c62 100644 --- a/river/bandit/envs/candy_cane.py +++ b/river/bandit/envs/candy_cane.py @@ -36,7 +36,7 @@ class CandyCaneContest(gym.Env): >>> while True: ... arm = env.action_space.sample() ... observation, reward, terminated, truncated, info = env.step(arm) - ... metric = metric.update(reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -58,8 +58,12 @@ def __init__(self, n_machines=100, reward_decay=0.03): self.action_space = gym.spaces.Discrete(n_machines) self.observation_space = gym.spaces.Dict( { - "attempts": gym.spaces.Tuple([gym.spaces.Discrete(self.n_steps)] * n_machines), - "successes": gym.spaces.Tuple([gym.spaces.Discrete(self.n_steps)] * n_machines), + "attempts": gym.spaces.Tuple( + [gym.spaces.Discrete(self.n_steps)] * n_machines + ), + "successes": gym.spaces.Tuple( + [gym.spaces.Discrete(self.n_steps)] * n_machines + ), } ) self.reward_range = (0.0, 1.0) diff --git a/river/bandit/epsilon_greedy.py b/river/bandit/epsilon_greedy.py index 15568043b0..e8f52559bd 100644 --- a/river/bandit/epsilon_greedy.py +++ b/river/bandit/epsilon_greedy.py @@ -49,8 +49,8 @@ class EpsilonGreedy(bandit.base.Policy): >>> while True: ... arm = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(arm) - ... policy = policy.update(arm, reward) - ... metric = metric.update(reward) + ... policy.update(arm, reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -64,7 +64,12 @@ class EpsilonGreedy(bandit.base.Policy): """ def __init__( - self, epsilon: float, decay=0.0, reward_obj=None, burn_in=0, seed: int | None = None + self, + epsilon: float, + decay=0.0, + reward_obj=None, + burn_in=0, + seed: int | None = None, ): super().__init__(reward_obj=reward_obj, burn_in=burn_in) self.epsilon = epsilon diff --git a/river/bandit/exp3.py b/river/bandit/exp3.py index 7f077674cf..00e8fb62ca 100644 --- a/river/bandit/exp3.py +++ b/river/bandit/exp3.py @@ -52,8 +52,8 @@ class Exp3(bandit.base.Policy): >>> while True: ... action = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(action) - ... policy = policy.update(action, reward) - ... metric = metric.update(reward) + ... policy.update(action, reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -70,9 +70,16 @@ class Exp3(bandit.base.Policy): _REQUIRES_UNIVARIATE_REWARD = True def __init__( - self, gamma: float, reward_obj=None, reward_scaler=None, burn_in=0, seed: int | None = None + self, + gamma: float, + reward_obj=None, + reward_scaler=None, + burn_in=0, + seed: int | None = None, ): - super().__init__(reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in) + super().__init__( + reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in + ) self.seed = seed self.gamma = gamma self._rng = random.Random(seed) @@ -84,7 +91,8 @@ def __init__( def _pull(self, arm_ids): total = sum(self._weights[arm_id] for arm_id in arm_ids) self._probabilities = { - arm_id: (1 - self.gamma) * (self._weights[arm_id] / total) + self.gamma / len(arm_ids) + arm_id: (1 - self.gamma) * (self._weights[arm_id] / total) + + self.gamma / len(arm_ids) for arm_id in arm_ids } return self._rng.choices(arm_ids, weights=self._probabilities.values())[0] @@ -94,7 +102,6 @@ def update(self, arm_id, *reward_args, **reward_kwargs): reward = reward_args[0] reward /= self._probabilities[arm_id] self._weights[arm_id] *= math.exp(self.gamma * reward / len(self._weights)) - return self @classmethod def _unit_test_params(cls): diff --git a/river/bandit/lin_ucb.py b/river/bandit/lin_ucb.py index 5ba387c8e3..32c4bbde86 100644 --- a/river/bandit/lin_ucb.py +++ b/river/bandit/lin_ucb.py @@ -91,4 +91,3 @@ def update(self, arm_id, context, *reward_args, **reward_kwargs): super().update(arm_id, None, *reward_args, **reward_kwargs) reward = reward_args[0] self._bayes_lin_regs[arm_id].learn_one(x=context, y=reward) - return self diff --git a/river/bandit/random.py b/river/bandit/random.py index f1455b22a6..4e7f1c4de1 100644 --- a/river/bandit/random.py +++ b/river/bandit/random.py @@ -40,8 +40,8 @@ class RandomPolicy(bandit.base.Policy): >>> while True: ... action = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(action) - ... policy = policy.update(action, reward) - ... metric = metric.update(reward) + ... policy.update(action, reward) + ... metric.update(reward) ... if terminated or truncated: ... break diff --git a/river/bandit/thompson.py b/river/bandit/thompson.py index e600bb4504..88df098b1c 100644 --- a/river/bandit/thompson.py +++ b/river/bandit/thompson.py @@ -57,8 +57,8 @@ class ThompsonSampling(bandit.base.Policy): >>> while True: ... arm = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(arm) - ... policy = policy.update(arm, reward) - ... metric = metric.update(reward) + ... policy.update(arm, reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -72,7 +72,10 @@ class ThompsonSampling(bandit.base.Policy): """ def __init__( - self, reward_obj: proba.base.Distribution = None, burn_in=0, seed: int | None = None + self, + reward_obj: proba.base.Distribution = None, + burn_in=0, + seed: int | None = None, ): super().__init__(reward_obj=reward_obj, burn_in=burn_in) self.seed = seed diff --git a/river/bandit/ucb.py b/river/bandit/ucb.py index 06c4fe585c..a31ae03a72 100644 --- a/river/bandit/ucb.py +++ b/river/bandit/ucb.py @@ -53,8 +53,8 @@ class UCB(bandit.base.Policy): >>> while True: ... arm = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(arm) - ... policy = policy.update(arm, reward) - ... metric = metric.update(reward) + ... policy.update(arm, reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -70,9 +70,16 @@ class UCB(bandit.base.Policy): """ def __init__( - self, delta: float, reward_obj=None, reward_scaler=None, burn_in=0, seed: int = None + self, + delta: float, + reward_obj=None, + reward_scaler=None, + burn_in=0, + seed: int = None, ): - super().__init__(reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in) + super().__init__( + reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in + ) self.delta = delta self.seed = seed self._rng = random.Random(seed) diff --git a/river/base/drift_detector.py b/river/base/drift_detector.py index 25f0719f2a..c5972728f6 100644 --- a/river/base/drift_detector.py +++ b/river/base/drift_detector.py @@ -65,10 +65,6 @@ def update(self, x: int | float) -> DriftDetector: x Input value. - Returns - ------- - self - """ @@ -88,10 +84,6 @@ def update(self, x: bool) -> BinaryDriftDetector: x Input boolean. - Returns - ------- - self - """ diff --git a/river/cluster/clustream.py b/river/cluster/clustream.py index 74a73e9329..b7adecb0f4 100644 --- a/river/cluster/clustream.py +++ b/river/cluster/clustream.py @@ -278,8 +278,13 @@ def __init__( self.x = x self.w = w self.timestamp = timestamp - self.var_x = {k: stats.Var().update(x[k], w) for k in x} - self.var_time = stats.Var().update(timestamp, w) + self.var_x = {} + for k in x: + v = stats.Var() + v.update(x[k], w) + self.var_x[k] = v + self.var_time = stats.Var() + self.var_time.update(timestamp, w) @property def center(self): @@ -344,5 +349,7 @@ def inverse_error(x): def __iadd__(self, other: CluStreamMicroCluster): self.var_time += other.var_time - self.var_x = {k: self.var_x[k] + other.var_x.get(k, stats.Var()) for k in self.var_x} + self.var_x = { + k: self.var_x[k] + other.var_x.get(k, stats.Var()) for k in self.var_x + } return self diff --git a/river/cluster/textclust.py b/river/cluster/textclust.py index bf0ffce194..0a848d0f3f 100644 --- a/river/cluster/textclust.py +++ b/river/cluster/textclust.py @@ -96,13 +96,12 @@ class TextClust(base.Clusterer): >>> for x in corpus: ... y_pred = model.predict_one(x["text"]) ... y = x["cluster"] - ... metric = metric.update(y,y_pred) + ... metric.update(y,y_pred) ... model.learn_one(x["text"]) >>> print(metric) AdjustedRand: -0.17647058823529413 - """ # constructor with default specification @@ -192,7 +191,12 @@ def learn_one(self, x, t=None, w=None): self.micro_clusters[clusterId].n += 1 self.micro_clusters[clusterId].merge( - mc, self.t, self.omega, self.fading_factor, self.term_fading, self.realtime + mc, + self.t, + self.omega, + self.fading_factor, + self.term_fading, + self.realtime, ) self._dist_mean += min_dist @@ -256,7 +260,9 @@ def _get_closest_mc(self, mc, idf, distance): if counter > 1: ## our threshold mu = (sumdist - min_dist) / (counter - 1) - threshold = mu - self.sigma * math.sqrt(squaresum / (counter - 1) - mu**2) + threshold = mu - self.sigma * math.sqrt( + squaresum / (counter - 1) - mu**2 + ) if min_dist < threshold: clusterId = smallest_key @@ -282,7 +288,9 @@ def _calculateIDF(self, micro_clusters): # update weights according to the fading factor def _updateweights(self): for micro in self.micro_clusters.values(): - micro.fade(self.t, self.omega, self.fading_factor, self.term_fading, self.realtime) + micro.fade( + self.t, self.omega, self.fading_factor, self.term_fading, self.realtime + ) # delete micro clusters with a weight smaller omega for key in list(self.micro_clusters.keys()): @@ -329,7 +337,9 @@ def _mergemicroclusters(self): j = i + 1 while j < len(self.micro_clusters): m_dist = self.micro_distance.dist( - self.micro_clusters[micro_keys[i]], self.micro_clusters[micro_keys[j]], idf + self.micro_clusters[micro_keys[i]], + self.micro_clusters[micro_keys[j]], + idf, ) ## lets merge them @@ -363,7 +373,9 @@ def _get_distance_matrix(self, clusters): ids = list(clusters.keys()) # initialize all distances to 0 - distances = pd.DataFrame(np.zeros((numClusters, numClusters)), columns=ids, index=ids) + distances = pd.DataFrame( + np.zeros((numClusters, numClusters)), columns=ids, index=ids + ) for idx, row in enumerate(ids): for col in ids[idx + 1 :]: @@ -443,7 +455,10 @@ def get_macroclusters(self): numClusters = min([self.num_macro, len(self.micro_clusters)]) # create empty clusters - macros = {x: self.microcluster({}, self.t, 0, self.realtime, x) for x in range(numClusters)} + macros = { + x: self.microcluster({}, self.t, 0, self.realtime, x) + for x in range(numClusters) + } # merge micro clusters to macro clusters for key, value in self.microToMacro.items(): @@ -463,7 +478,9 @@ def get_macroclusters(self): def showclusters(self, topn, num, type="micro"): # first clusters are sorted according to their respective weights if type == "micro": - sortedmicro = sorted(self.micro_clusters.values(), key=lambda x: x.weight, reverse=True) + sortedmicro = sorted( + self.micro_clusters.values(), key=lambda x: x.weight, reverse=True + ) else: sortedmicro = sorted( self.get_macroclusters().values(), key=lambda x: x.weight, reverse=True @@ -493,7 +510,10 @@ def showclusters(self, topn, num, type="micro"): ] for rep in representatives: print( - "weight: " + str(round(rep[1], 2)) + "\t token: " + str(rep[0]).expandtabs(10) + "weight: " + + str(round(rep[1], 2)) + + "\t token: " + + str(rep[0]).expandtabs(10) ) print("-------------------------------------------") @@ -519,7 +539,9 @@ def get_assignment(self, x, type): # identify the closest micro cluster using the predefined distance measure for key in self.micro_clusters.keys(): if self.micro_clusters[key].weight > self.min_weight: - cur_dist = self.micro_distance.dist(mc, self.micro_clusters[key], idf) + cur_dist = self.micro_distance.dist( + mc, self.micro_clusters[key], idf + ) if cur_dist < dist: dist = cur_dist closest = key @@ -594,7 +616,9 @@ def __init__(self, type): ## generic method that is called for each distance def dist(self, m1, m2, idf): - return getattr(self, self.type, lambda: "Invalid distance measure")(m1, m2, idf) + return getattr(self, self.type, lambda: "Invalid distance measure")( + m1, m2, idf + ) ##calculate cosine similarity directly and fast def tfidf_cosine_distance(self, mc, microcluster, idf): @@ -604,12 +628,17 @@ def tfidf_cosine_distance(self, mc, microcluster, idf): for k in list(mc.tf.keys()): if k in idf: if k in microcluster.tf: - sum += (mc.tf[k]["tf"] * idf[k]) * (microcluster.tf[k]["tf"] * idf[k]) + sum += (mc.tf[k]["tf"] * idf[k]) * ( + microcluster.tf[k]["tf"] * idf[k] + ) tfidflen += mc.tf[k]["tf"] * idf[k] * mc.tf[k]["tf"] * idf[k] tfidflen = math.sqrt(tfidflen) for k in list(microcluster.tf.keys()): microtfidflen += ( - microcluster.tf[k]["tf"] * idf[k] * microcluster.tf[k]["tf"] * idf[k] + microcluster.tf[k]["tf"] + * idf[k] + * microcluster.tf[k]["tf"] + * idf[k] ) microtfidflen = math.sqrt(microtfidflen) if tfidflen == 0 or microtfidflen == 0: diff --git a/river/conf/jackknife.py b/river/conf/jackknife.py index 1d0726142c..57582a1209 100644 --- a/river/conf/jackknife.py +++ b/river/conf/jackknife.py @@ -56,8 +56,8 @@ class RegressionJackknife(base.Wrapper, base.Regressor): >>> for x, y in dataset: ... interval = model.predict_one(x, with_interval=True) - ... validity = validity.update(y in interval) - ... efficiency = efficiency.update(interval.width) + ... validity.update(y in interval) + ... efficiency.update(interval.width) ... model.learn_one(x, y) The interval's validity is the proportion of times the true value is within the interval. We @@ -91,7 +91,9 @@ def __init__( alpha = (1 - confidence_level) / 2 self._lower = ( - stats.RollingQuantile(alpha, window_size) if window_size else stats.Quantile(alpha) + stats.RollingQuantile(alpha, window_size) + if window_size + else stats.Quantile(alpha) ) self._upper = ( stats.RollingQuantile(1 - alpha, window_size) @@ -107,7 +109,11 @@ def _wrapped_model(self): def _unit_test_params(cls): from river import linear_model, preprocessing - yield {"regressor": (preprocessing.StandardScaler() | linear_model.LinearRegression())} + yield { + "regressor": ( + preprocessing.StandardScaler() | linear_model.LinearRegression() + ) + } def learn_one(self, x, y, **kwargs): # Update the quantiles @@ -138,5 +144,6 @@ def predict_one(self, x, with_interval=False, **kwargs): return y_pred return interval.Interval( - lower=y_pred + (self._lower.get() or 0), upper=y_pred + (self._upper.get() or 0) + lower=y_pred + (self._lower.get() or 0), + upper=y_pred + (self._upper.get() or 0), ) diff --git a/river/drift/adwin.py b/river/drift/adwin.py index 6899043ddd..7ca6720e3b 100644 --- a/river/drift/adwin.py +++ b/river/drift/adwin.py @@ -51,7 +51,7 @@ class ADWIN(DriftDetector): >>> # Update drift detector and verify if change is detected >>> for i, val in enumerate(data_stream): - ... _ = adwin.update(val) + ... adwin.update(val) ... if adwin.drift_detected: ... print(f"Change detected at index {i}, input value: {val}") Change detected at index 1023, input value: 4 @@ -133,5 +133,3 @@ def update(self, x): self._reset() self._drift_detected = self._helper.update(x) - - return self diff --git a/river/drift/adwin_c.pyx b/river/drift/adwin_c.pyx index 322859c780..bddebd63a1 100644 --- a/river/drift/adwin_c.pyx +++ b/river/drift/adwin_c.pyx @@ -88,7 +88,7 @@ cdef class AdaptiveWindowing: If True then a change is detected. """ - return self._update(value) + self._update(value) cdef bint _update(self, double value): # Increment window with one element diff --git a/river/drift/binary/ddm.py b/river/drift/binary/ddm.py index 24706d6137..1ef45f5643 100644 --- a/river/drift/binary/ddm.py +++ b/river/drift/binary/ddm.py @@ -79,7 +79,7 @@ class DDM(base.BinaryDriftAndWarningDetector): >>> print_warning = True >>> # Update drift detector and verify if change is detected >>> for i, x in enumerate(data_stream): - ... _ = ddm.update(x) + ... ddm.update(x) ... if ddm.warning_detected and print_warning: ... print(f"Warning detected at index {i}") ... print_warning = False @@ -145,5 +145,3 @@ def update(self, x): if p_i + s_i > self._p_min + self.drift_threshold * self._s_min: self._drift_detected = True self._warning_detected = False - - return self diff --git a/river/drift/binary/eddm.py b/river/drift/binary/eddm.py index ccb960b63b..4e65a0f0d3 100644 --- a/river/drift/binary/eddm.py +++ b/river/drift/binary/eddm.py @@ -69,7 +69,7 @@ class EDDM(base.BinaryDriftAndWarningDetector): >>> print_warning = True >>> # Update drift detector and verify if change is detected >>> for i, x in enumerate(data_stream): - ... _ = eddm.update(x) + ... eddm.update(x) ... if eddm.warning_detected and print_warning: ... print(f"Warning detected at index {i}") ... print_warning = False @@ -162,5 +162,3 @@ def update(self, x): # Update the index of the last error/failure detected self._last_error = self._n - - return self diff --git a/river/drift/binary/hddm_a.py b/river/drift/binary/hddm_a.py index 74eb85c57a..7b8239970e 100644 --- a/river/drift/binary/hddm_a.py +++ b/river/drift/binary/hddm_a.py @@ -50,7 +50,7 @@ class HDDM_A(base.BinaryDriftAndWarningDetector): >>> print_warning = True >>> # Update drift detector and verify if change is detected >>> for i, x in enumerate(data_stream): - ... _ = hddm_a.update(x) + ... hddm_a.update(x) ... if hddm_a.warning_detected and print_warning: ... print(f"Warning detected at index {i}") ... print_warning = False @@ -141,8 +141,6 @@ def update(self, x): elif self._mean_decr(self.warning_confidence): self._warning_detected = True - return self - # Check if the global mean increased def _mean_incr(self, confidence: float): if self._x_min.n == self._z.n: diff --git a/river/drift/binary/hddm_w.py b/river/drift/binary/hddm_w.py index 48303bc231..3d29742046 100644 --- a/river/drift/binary/hddm_w.py +++ b/river/drift/binary/hddm_w.py @@ -53,7 +53,7 @@ class HDDM_W(base.BinaryDriftAndWarningDetector): >>> print_warning = True >>> # Update drift detector and verify if change is detected >>> for i, x in enumerate(data_stream): - ... _ = hddm_w.update(x) + ... hddm_w.update(x) ... if hddm_w.warning_detected and print_warning: ... print(f"Warning detected at index {i}") ... print_warning = False @@ -139,8 +139,6 @@ def update(self, x): elif self._detect_mean_decr(self.warning_confidence): self._warning_detected = True - return self - def _has_mean_changed( self, sample1: SampleInfo, sample2: SampleInfo, confidence: float ) -> bool: diff --git a/river/drift/dummy.py b/river/drift/dummy.py index 9919421b7d..a9cb428852 100644 --- a/river/drift/dummy.py +++ b/river/drift/dummy.py @@ -51,7 +51,7 @@ class DummyDriftDetector(base.DriftDetector): >>> ptrigger = DummyDriftDetector(t_0=500, seed=42) >>> for i, v in enumerate(data): - ... _ = ptrigger.update(v) + ... ptrigger.update(v) ... if ptrigger.drift_detected: ... print(f"Drift detected at instance {i}.") Drift detected at instance 499. @@ -67,7 +67,7 @@ class DummyDriftDetector(base.DriftDetector): ... seed=42 ... ) >>> for i, v in enumerate(data): - ... _ = rtrigger.update(v) + ... rtrigger.update(v) ... if rtrigger.drift_detected: ... print(f"Drift detected at instance {i}.") Drift detected at instance 368. @@ -86,7 +86,7 @@ class DummyDriftDetector(base.DriftDetector): >>> rtrigger = rtrigger.clone() >>> for i, v in enumerate(data): - ... _ = rtrigger.update(v) + ... rtrigger.update(v) ... if rtrigger.drift_detected: ... print(f"Drift detected at instance {i}.") Drift detected at instance 429. @@ -175,8 +175,6 @@ def update(self, x): self._drift_detected = False self._trigger() - return self - def clone(self): new = ( super().clone( diff --git a/river/drift/kswin.py b/river/drift/kswin.py index 6a959f9f4d..82547faa52 100644 --- a/river/drift/kswin.py +++ b/river/drift/kswin.py @@ -65,7 +65,7 @@ class KSWIN(DriftDetector): >>> # Update drift detector and verify if change is detected >>> for i, val in enumerate(data_stream): - ... _ = kswin.update(val) + ... kswin.update(val) ... if kswin.drift_detected: ... print(f"Change detected at index {i}, input value: {val}") Change detected at index 1016, input value: 6 @@ -157,8 +157,6 @@ def update(self, x): else: # Not enough samples in the sliding window for a valid test self._drift_detected = False - return self - @classmethod def _unit_test_params(cls): yield {"seed": 1} diff --git a/river/drift/no_drift.py b/river/drift/no_drift.py index d0b8273992..98c2a010a3 100644 --- a/river/drift/no_drift.py +++ b/river/drift/no_drift.py @@ -69,7 +69,7 @@ def __init__(self): super().__init__() def update(self, x: int | float) -> DriftDetector: - return self + ... @property def drift_detected(self): diff --git a/river/drift/page_hinkley.py b/river/drift/page_hinkley.py index d332822eda..7202710020 100644 --- a/river/drift/page_hinkley.py +++ b/river/drift/page_hinkley.py @@ -40,7 +40,7 @@ class PageHinkley(DriftDetector): >>> # Update drift detector and verify if change is detected >>> for i, val in enumerate(data_stream): - ... _ = ph.update(val) + ... ph.update(val) ... if ph.drift_detected: ... print(f"Change detected at index {i}, input value: {val}") Change detected at index 1006, input value: 5 @@ -125,5 +125,3 @@ def update(self, x): test_increase = self._sum_increase - self._min_increase test_decrease = self._max_decrease - self._sum_decrease self._drift_detected = self._test_drift(test_increase, test_decrease) - - return self diff --git a/river/evaluate/progressive_validation.py b/river/evaluate/progressive_validation.py index 1a2f8fde28..9e2373fcf0 100644 --- a/river/evaluate/progressive_validation.py +++ b/river/evaluate/progressive_validation.py @@ -23,7 +23,9 @@ def _progressive_validation( ): # Check that the model and the metric are in accordance if not metric.works_with(model): - raise ValueError(f"{metric.__class__.__name__} metric is not compatible with {model}") + raise ValueError( + f"{metric.__class__.__name__} metric is not compatible with {model}" + ) # Determine if predict_one or predict_proba_one should be used in case of a classifier if utils.inspect.isanomalydetector(model) or utils.inspect.isanomalyfilter(model): @@ -337,7 +339,7 @@ def progressive_val_score( >>> for x, y in datasets.Phishing(): ... y_pred = model.predict_proba_one(x) - ... metric = metric.update(y, y_pred) + ... metric.update(y, y_pred) ... model.learn_one(x, y) >>> metric diff --git a/river/feature_selection/k_best.py b/river/feature_selection/k_best.py index f798b4ae98..887772541f 100644 --- a/river/feature_selection/k_best.py +++ b/river/feature_selection/k_best.py @@ -79,7 +79,8 @@ def _unit_test_params(cls): def learn_one(self, x, y): for i, xi in x.items(): - self.leaderboard[i] = self.similarities[i].update(xi, y).get() + self.similarities[i].update(xi, y) + self.leaderboard[i] = self.similarities[i].get() def transform_one(self, x): best_features = {pair[0] for pair in self.leaderboard.most_common(self.k)} diff --git a/river/forest/online_extra_trees.py b/river/forest/online_extra_trees.py index aa02cb9306..6ed958df46 100644 --- a/river/forest/online_extra_trees.py +++ b/river/forest/online_extra_trees.py @@ -222,10 +222,10 @@ def _detection_mode_all( warning_detector: base.DriftDetector, detector_input: int | float, ) -> tuple[bool, bool]: - in_warning = warning_detector.update(detector_input).drift_detected - in_drift = drift_detector.update(detector_input).drift_detected + warning_detector.update(detector_input) + drift_detector.update(detector_input) - return in_drift, in_warning + return drift_detector.drift_detected, warning_detector.drift_detected @staticmethod def _detection_mode_drop( @@ -233,9 +233,9 @@ def _detection_mode_drop( warning_detector: base.DriftDetector, detector_input: int | float, ) -> tuple[bool, bool]: - in_drift = drift_detector.update(detector_input).drift_detected + drift_detector.update(detector_input) - return in_drift, False + return drift_detector.drift_detected, False @staticmethod def _detection_mode_off( diff --git a/river/linear_model/pa.py b/river/linear_model/pa.py index b3f23ee579..5ae9f286c1 100644 --- a/river/linear_model/pa.py +++ b/river/linear_model/pa.py @@ -13,7 +13,9 @@ class BasePA: def __init__(self, C, mode, learn_intercept): self.C = C self.mode = mode - self.calc_tau = {0: self._calc_tau_0, 1: self._calc_tau_1, 2: self._calc_tau_2}[mode] + self.calc_tau = {0: self._calc_tau_0, 1: self._calc_tau_1, 2: self._calc_tau_2}[ + mode + ] self.learn_intercept = learn_intercept self.weights = collections.defaultdict(float) self.intercept = 0.0 @@ -70,7 +72,7 @@ class PARegressor(BasePA, base.Regressor): >>> for xi, yi in stream.iter_array(X, y): ... y_pred = model.predict_one(xi) ... model.learn_one(xi, yi) - ... metric = metric.update(yi, y_pred) + ... metric.update(yi, y_pred) >>> print(metric) MAE: 9.809402 @@ -152,7 +154,7 @@ class PAClassifier(BasePA, base.Classifier): >>> metric = metrics.Accuracy() + metrics.LogLoss() >>> for xi, yi in stream.iter_array(X_test, y_test): - ... metric = metric.update(yi, model.predict_proba_one(xi)) + ... metric.update(yi, model.predict_proba_one(xi)) >>> print(metric) Accuracy: 88.46% diff --git a/river/metrics/accuracy.py b/river/metrics/accuracy.py index 8e3cbd8bae..c7b6018823 100644 --- a/river/metrics/accuracy.py +++ b/river/metrics/accuracy.py @@ -25,7 +25,7 @@ class Accuracy(metrics.base.MultiClassMetric): >>> metric = metrics.Accuracy() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric Accuracy: 60.00% diff --git a/river/metrics/balanced_accuracy.py b/river/metrics/balanced_accuracy.py index dcfec44680..5d581ea2d0 100644 --- a/river/metrics/balanced_accuracy.py +++ b/river/metrics/balanced_accuracy.py @@ -27,7 +27,7 @@ class BalancedAccuracy(metrics.base.MultiClassMetric): >>> metric = metrics.BalancedAccuracy() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric BalancedAccuracy: 62.50% @@ -36,7 +36,7 @@ class BalancedAccuracy(metrics.base.MultiClassMetric): >>> y_pred = [0, 1, 0, 0, 0, 1] >>> metric = metrics.BalancedAccuracy() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric BalancedAccuracy: 62.50% diff --git a/river/metrics/base.py b/river/metrics/base.py index 5a0a28cf8d..ec34ceb726 100644 --- a/river/metrics/base.py +++ b/river/metrics/base.py @@ -95,7 +95,6 @@ def update(self, y_true, y_pred, w=1.0): y_pred, w=w, ) - return self def revert(self, y_true, y_pred, w=1.0): self.cm.revert( @@ -103,7 +102,6 @@ def revert(self, y_true, y_pred, w=1.0): y_pred, w=w, ) - return self @property def bigger_is_better(self): @@ -192,11 +190,15 @@ class RegressionMetric(Metric): _fmt = ",.6f" # use commas to separate big numbers and show 6 decimals @abc.abstractmethod - def update(self, y_true: numbers.Number, y_pred: numbers.Number) -> RegressionMetric: + def update( + self, y_true: numbers.Number, y_pred: numbers.Number + ) -> RegressionMetric: """Update the metric.""" @abc.abstractmethod - def revert(self, y_true: numbers.Number, y_pred: numbers.Number) -> RegressionMetric: + def revert( + self, y_true: numbers.Number, y_pred: numbers.Number + ) -> RegressionMetric: """Revert the metric.""" @property @@ -238,11 +240,10 @@ def update(self, y_true, y_pred, w=1.0): m.update(y_true, max(y_pred, key=y_pred.get)) else: m.update(y_true, y_pred) - return self + return for m in self: m.update(y_true, y_pred) - return self def revert(self, y_true, y_pred, w=1.0): # If the metrics are classification metrics, then we have to handle the case where some @@ -253,11 +254,10 @@ def revert(self, y_true, y_pred, w=1.0): m.revert(y_true, max(y_pred, key=y_pred.get), w) else: m.revert(y_true, y_pred, w) - return self + return for m in self: m.revert(y_true, y_pred, w) - return self def get(self): return [m.get() for m in self] @@ -322,7 +322,11 @@ def __metaclass__(self): def __repr__(self): name = self.__class__.__name__ - return super().__repr__().replace(name, f"{name}({self.metric.__class__.__name__})") + return ( + super() + .__repr__() + .replace(name, f"{name}({self.metric.__class__.__name__})") + ) class MeanMetric(abc.ABC): @@ -340,11 +344,9 @@ def _eval(self, y_true, y_pred): def update(self, y_true, y_pred, w=1.0): self._mean.update(x=self._eval(y_true, y_pred), w=w) - return self def revert(self, y_true, y_pred, w=1.0): self._mean.revert(x=self._eval(y_true, y_pred), w=w) - return self def get(self): return self._mean.get() diff --git a/river/metrics/confusion.py b/river/metrics/confusion.py index ece59e61e9..95737c6c84 100644 --- a/river/metrics/confusion.py +++ b/river/metrics/confusion.py @@ -25,7 +25,7 @@ class ConfusionMatrix(metrics.base.MultiClassMetric): >>> cm = metrics.ConfusionMatrix() >>> for yt, yp in zip(y_true, y_pred): - ... cm = cm.update(yt, yp) + ... cm.update(yt, yp) >>> cm ant bird cat @@ -65,13 +65,11 @@ def __getitem__(self, key): def update(self, y_true, y_pred, w=1.0): self.n_samples += 1 self._update(y_true, y_pred, w) - return self def revert(self, y_true, y_pred, w=1.0): self.n_samples -= 1 # Revert is equal to subtracting so we pass the negative sample_weight (w) self._update(y_true, y_pred, -w) - return self def _update(self, y_true, y_pred, w): self.data[y_true][y_pred] += w diff --git a/river/metrics/cross_entropy.py b/river/metrics/cross_entropy.py index fe1d66568a..5b22d1741a 100644 --- a/river/metrics/cross_entropy.py +++ b/river/metrics/cross_entropy.py @@ -26,7 +26,7 @@ class CrossEntropy(metrics.base.MeanMetric, metrics.base.MultiClassMetric): >>> metric = metrics.CrossEntropy() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) ... print(metric.get()) 1.222454 1.169691 @@ -53,6 +53,8 @@ def _eval(self, y_true, y_pred): for label, proba in y_pred.items(): if y_true == label: - total += math.log(utils.math.clamp(x=proba, minimum=1e-15, maximum=1 - 1e-15)) + total += math.log( + utils.math.clamp(x=proba, minimum=1e-15, maximum=1 - 1e-15) + ) return -total diff --git a/river/metrics/fbeta.py b/river/metrics/fbeta.py index cd399215a5..f3a8df0e77 100644 --- a/river/metrics/fbeta.py +++ b/river/metrics/fbeta.py @@ -52,7 +52,7 @@ class FBeta(metrics.base.BinaryMetric): >>> metric = metrics.FBeta(beta=2) >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric FBeta: 35.71% @@ -100,7 +100,8 @@ class MacroFBeta(metrics.base.MultiClassMetric): >>> metric = metrics.MacroFBeta(beta=.8) >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroFBeta: 100.00% MacroFBeta: 31.06% MacroFBeta: 54.04% @@ -164,7 +165,7 @@ class MicroFBeta(metrics.base.MultiClassMetric): >>> metric = metrics.MicroFBeta(beta=2) >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric MicroFBeta: 60.00% @@ -217,7 +218,8 @@ class WeightedFBeta(metrics.base.MultiClassMetric): >>> metric = metrics.WeightedFBeta(beta=0.8) >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedFBeta: 100.00% WeightedFBeta: 31.06% WeightedFBeta: 54.04% @@ -287,7 +289,8 @@ class MultiFBeta(metrics.base.MultiClassMetric): ... ) >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MultiFBeta: 100.00% MultiFBeta: 25.76% MultiFBeta: 62.88% @@ -300,7 +303,9 @@ def __init__(self, betas, weights, cm=None): super().__init__(cm) self.betas = betas self.weights = ( - collections.defaultdict(functools.partial(int, 1)) if weights is None else weights + collections.defaultdict(functools.partial(int, 1)) + if weights is None + else weights ) def get(self): @@ -353,7 +358,7 @@ class F1(FBeta): >>> metric = metrics.F1() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric F1: 40.00% @@ -387,7 +392,8 @@ class MacroF1(MacroFBeta): >>> metric = metrics.MacroF1() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroF1: 100.00% MacroF1: 33.33% MacroF1: 55.56% @@ -423,7 +429,7 @@ class MicroF1(MicroFBeta): >>> metric = metrics.MicroF1() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric MicroF1: 60.00% @@ -464,7 +470,8 @@ class WeightedF1(WeightedFBeta): >>> metric = metrics.WeightedF1() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedF1: 100.00% WeightedF1: 33.33% WeightedF1: 55.56% diff --git a/river/metrics/fowlkes_mallows.py b/river/metrics/fowlkes_mallows.py index 2b9b9b2d6d..b15fcf4c8e 100644 --- a/river/metrics/fowlkes_mallows.py +++ b/river/metrics/fowlkes_mallows.py @@ -48,7 +48,8 @@ class FowlkesMallows(metrics.base.MultiClassMetric): >>> metric = metrics.FowlkesMallows() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) FowlkesMallows: 0.00% FowlkesMallows: 100.00% FowlkesMallows: 57.74% diff --git a/river/metrics/geometric_mean.py b/river/metrics/geometric_mean.py index 3fbeb528b4..ce0bd87c66 100644 --- a/river/metrics/geometric_mean.py +++ b/river/metrics/geometric_mean.py @@ -39,7 +39,7 @@ class GeometricMean(metrics.base.MultiClassMetric): >>> metric = metrics.GeometricMean() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric GeometricMean: 69.34% diff --git a/river/metrics/jaccard.py b/river/metrics/jaccard.py index 87738acd11..04b2ef533e 100644 --- a/river/metrics/jaccard.py +++ b/river/metrics/jaccard.py @@ -27,7 +27,8 @@ class Jaccard(metrics.base.BinaryMetric): >>> metric = metrics.Jaccard() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) Jaccard: 0.00% Jaccard: 50.00% Jaccard: 66.67% @@ -69,7 +70,8 @@ class MacroJaccard(metrics.base.MultiClassMetric): >>> metric = metrics.MacroJaccard() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroJaccard: 100.00% MacroJaccard: 25.00% MacroJaccard: 50.00% @@ -115,7 +117,8 @@ class MicroJaccard(metrics.base.MultiClassMetric): >>> metric = metrics.MicroJaccard() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MicroJaccard: 100.00% MicroJaccard: 33.33% MicroJaccard: 50.00% @@ -156,7 +159,8 @@ class WeightedJaccard(metrics.base.MultiClassMetric): >>> metric = metrics.WeightedJaccard() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedJaccard: 100.00% WeightedJaccard: 25.00% WeightedJaccard: 50.00% diff --git a/river/metrics/kappa.py b/river/metrics/kappa.py index 9218b1de68..303f78f9c5 100644 --- a/river/metrics/kappa.py +++ b/river/metrics/kappa.py @@ -37,7 +37,7 @@ class CohenKappa(metrics.base.MultiClassMetric): >>> metric = metrics.CohenKappa() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric CohenKappa: 42.86% diff --git a/river/metrics/log_loss.py b/river/metrics/log_loss.py index 858c8f83fb..e998d1a372 100644 --- a/river/metrics/log_loss.py +++ b/river/metrics/log_loss.py @@ -20,7 +20,7 @@ class LogLoss(metrics.base.MeanMetric, metrics.base.BinaryMetric): >>> metric = metrics.LogLoss() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) ... print(metric.get()) 0.105360 0.105360 diff --git a/river/metrics/mae.py b/river/metrics/mae.py index fb4fb44d4c..78bd4ed629 100644 --- a/river/metrics/mae.py +++ b/river/metrics/mae.py @@ -19,7 +19,8 @@ class MAE(metrics.base.MeanMetric, metrics.base.RegressionMetric): >>> metric = metrics.MAE() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.5 0.5 0.333 diff --git a/river/metrics/mape.py b/river/metrics/mape.py index 3fdedf7bee..46df50084e 100644 --- a/river/metrics/mape.py +++ b/river/metrics/mape.py @@ -18,7 +18,7 @@ class MAPE(metrics.base.MeanMetric, metrics.base.RegressionMetric): >>> metric = metrics.MAPE() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric MAPE: 32.738095 diff --git a/river/metrics/mcc.py b/river/metrics/mcc.py index 9f5eb70a81..e4d5d1c4c8 100644 --- a/river/metrics/mcc.py +++ b/river/metrics/mcc.py @@ -29,7 +29,7 @@ class MCC(metrics.base.BinaryMetric): >>> mcc = metrics.MCC() >>> for yt, yp in zip(y_true, y_pred): - ... mcc = mcc.update(yt, yp) + ... mcc.update(yt, yp) >>> mcc MCC: -0.333333 diff --git a/river/metrics/mse.py b/river/metrics/mse.py index 526d2af933..bda70174d8 100644 --- a/river/metrics/mse.py +++ b/river/metrics/mse.py @@ -21,7 +21,8 @@ class MSE(metrics.base.MeanMetric, metrics.base.RegressionMetric): >>> metric = metrics.MSE() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.25 0.25 0.1666 @@ -46,7 +47,8 @@ class RMSE(MSE): >>> metric = metrics.RMSE() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.5 0.5 0.408248 @@ -74,7 +76,7 @@ class RMSLE(RMSE): >>> metric = metrics.RMSLE() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric RMSLE: 0.357826 @@ -82,4 +84,4 @@ class RMSLE(RMSE): """ def update(self, y_true, y_pred, w=1.0): - return super().update(math.log(y_true + 1), math.log(y_pred + 1), w) + super().update(math.log(y_true + 1), math.log(y_pred + 1), w) diff --git a/river/metrics/multioutput/base.py b/river/metrics/multioutput/base.py index e37431e31e..f2935391a8 100644 --- a/river/metrics/multioutput/base.py +++ b/river/metrics/multioutput/base.py @@ -41,7 +41,6 @@ def update( ) -> MultiOutputClassificationMetric: """Update the metric.""" self.cm.update(y_true, y_pred, w) - return self def revert( self, @@ -52,7 +51,6 @@ def revert( ) -> MultiOutputClassificationMetric: """Revert the metric.""" self.cm.revert(y_true, y_pred, w) - return self def works_with(self, model) -> bool: return utils.inspect.ismoclassifier(model) diff --git a/river/metrics/multioutput/confusion.py b/river/metrics/multioutput/confusion.py index f5c6b5b4c9..b669cf7b1e 100644 --- a/river/metrics/multioutput/confusion.py +++ b/river/metrics/multioutput/confusion.py @@ -28,7 +28,7 @@ class MultiLabelConfusionMatrix: ... ] >>> for yt, yp in zip(y_true, y_pred): - ... cm = cm.update(yt, yp) + ... cm.update(yt, yp) >>> cm 0 @@ -59,7 +59,6 @@ def update(self, y_true, y_pred, w=1.0): cm = metrics.ConfusionMatrix() self.data[label] = cm cm.update(yt, y_pred[label], w) - return self def revert(self, y_true, y_pred, w=1.0): for label, yt in y_true.items(): @@ -69,10 +68,11 @@ def revert(self, y_true, y_pred, w=1.0): cm = metrics.ConfusionMatrix() self.data[label] = cm cm.update(yt, y_pred[label], w) - return self def __repr__(self): return "\n\n".join( - "\n".join([str(label)] + textwrap.indent(repr(cm), prefix=" ").splitlines()) + "\n".join( + [str(label)] + textwrap.indent(repr(cm), prefix=" ").splitlines() + ) for label, cm in self.data.items() ) diff --git a/river/metrics/multioutput/exact_match.py b/river/metrics/multioutput/exact_match.py index 288cbea168..3bfb14b91c 100644 --- a/river/metrics/multioutput/exact_match.py +++ b/river/metrics/multioutput/exact_match.py @@ -38,7 +38,7 @@ class ExactMatch(metrics.base.MeanMetric, MultiOutputClassificationMetric): >>> metric = metrics.multioutput.ExactMatch() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric ExactMatch: 33.33% diff --git a/river/metrics/multioutput/macro.py b/river/metrics/multioutput/macro.py index cbd5699ea1..873de0c0e5 100644 --- a/river/metrics/multioutput/macro.py +++ b/river/metrics/multioutput/macro.py @@ -40,12 +40,10 @@ def works_with(self, model) -> bool: def update(self, y_true, y_pred, w=1.0): for i in y_true: self.metrics[i].update(y_true[i], y_pred[i], w) - return self def revert(self, y_true, y_pred, w=1.0): for i in y_true: self.metrics[i].revert(y_true[i], y_pred[i], w) - return self def get(self): return statistics.mean(metric.get() for metric in self.metrics.values()) diff --git a/river/metrics/multioutput/micro.py b/river/metrics/multioutput/micro.py index 079a613753..9f5b9479ef 100644 --- a/river/metrics/multioutput/micro.py +++ b/river/metrics/multioutput/micro.py @@ -33,12 +33,10 @@ def works_with(self, model) -> bool: def update(self, y_true, y_pred, w=1.0): for i in y_true: self.metric.update(y_true[i], y_pred[i], w) - return self def revert(self, y_true, y_pred, w=1.0): for i in y_true: self.metric.revert(y_true[i], y_pred[i], w) - return self def get(self): return self.metric.get() diff --git a/river/metrics/multioutput/per_output.py b/river/metrics/multioutput/per_output.py index 0e810e8858..9762b16618 100644 --- a/river/metrics/multioutput/per_output.py +++ b/river/metrics/multioutput/per_output.py @@ -38,12 +38,10 @@ def works_with(self, model) -> bool: def update(self, y_true, y_pred, w=1.0): for i in y_true: self.metrics[i].update(y_true[i], y_pred[i], w) - return self def revert(self, y_true, y_pred, w=1.0): for i in y_true: self.metrics[i].revert(y_true[i], y_pred[i], w) - return self def get(self): return dict(self.metrics) diff --git a/river/metrics/multioutput/sample_average.py b/river/metrics/multioutput/sample_average.py index 66c522c62b..246dd1553e 100644 --- a/river/metrics/multioutput/sample_average.py +++ b/river/metrics/multioutput/sample_average.py @@ -34,7 +34,8 @@ class SampleAverage(MultiOutputMetric, metrics.base.WrapperMetric): >>> sample_jaccard = metrics.multioutput.SampleAverage(metrics.Jaccard()) >>> for yt, yp in zip(y_true, y_pred): - ... sample_jaccard = sample_jaccard.update(yt, yp) + ... sample_jaccard.update(yt, yp) + >>> sample_jaccard SampleAverage(Jaccard): 58.33% @@ -58,14 +59,12 @@ def update(self, y_true, y_pred, w=1.0): for i in y_true: metric.update(y_true[i], y_pred[i]) self._avg.update(metric.get(), w) - return self def revert(self, y_true, y_pred, w=1.0): metric = self.metric.clone() for i in y_true: metric.update(y_true[i], y_pred[i]) self._avg.revert(metric.get(), w) - return self def get(self): return self._avg.get() diff --git a/river/metrics/mutual_info.py b/river/metrics/mutual_info.py index 7b09f13b54..de07a33a53 100644 --- a/river/metrics/mutual_info.py +++ b/river/metrics/mutual_info.py @@ -60,7 +60,8 @@ class MutualInfo(metrics.base.MultiClassMetric): >>> metric = metrics.MutualInfo() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.0 0.0 0.0 @@ -144,7 +145,8 @@ class NormalizedMutualInfo(metrics.base.MultiClassMetric): >>> metric = metrics.NormalizedMutualInfo() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 @@ -257,7 +259,8 @@ class AdjustedMutualInfo(metrics.base.MultiClassMetric): >>> metric = metrics.AdjustedMutualInfo() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 @@ -332,7 +335,9 @@ def get(self): else: denominator = max(denominator, np.finfo("float64").eps) - adjusted_mutual_info_score = (mutual_info_score - expected_mutual_info_score) / denominator + adjusted_mutual_info_score = ( + mutual_info_score - expected_mutual_info_score + ) / denominator return adjusted_mutual_info_score diff --git a/river/metrics/precision.py b/river/metrics/precision.py index 676d03b657..7ae807f670 100644 --- a/river/metrics/precision.py +++ b/river/metrics/precision.py @@ -27,7 +27,8 @@ class Precision(metrics.base.BinaryMetric): >>> metric = metrics.Precision() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) Precision: 100.00% Precision: 50.00% Precision: 50.00% @@ -66,7 +67,8 @@ class MacroPrecision(metrics.base.MultiClassMetric): >>> metric = metrics.MacroPrecision() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroPrecision: 100.00% MacroPrecision: 25.00% MacroPrecision: 50.00% @@ -114,7 +116,8 @@ class MicroPrecision(metrics.base.MultiClassMetric): >>> metric = metrics.MicroPrecision() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MicroPrecision: 100.00% MicroPrecision: 50.00% MicroPrecision: 66.67% @@ -160,7 +163,8 @@ class WeightedPrecision(metrics.base.MultiClassMetric): >>> metric = metrics.WeightedPrecision() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedPrecision: 100.00% WeightedPrecision: 25.00% WeightedPrecision: 50.00% diff --git a/river/metrics/r2.py b/river/metrics/r2.py index 479591fc2b..e2785ead3c 100644 --- a/river/metrics/r2.py +++ b/river/metrics/r2.py @@ -30,7 +30,8 @@ class R2(metrics.base.RegressionMetric): >>> metric = metrics.R2() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.0 0.9183 0.9230 @@ -55,12 +56,10 @@ def update(self, y_true, y_pred, w=1.0): self._y_var.update(y_true, w=w) squared_error = (y_true - y_pred) * (y_true - y_pred) * w self._residual_sum_of_squares += squared_error - return self def revert(self, y_true, y_pred, w=1.0): self._y_var.update(y_true, w=-w) self._residual_sum_of_squares -= (y_true - y_pred) * (y_true - y_pred) * w - return self def get(self): if self._y_var.mean.n > 1: diff --git a/river/metrics/rand.py b/river/metrics/rand.py index c3a037c72e..0992a865d4 100644 --- a/river/metrics/rand.py +++ b/river/metrics/rand.py @@ -26,7 +26,9 @@ def _pair_confusion(cm): false_negatives -= sum_squares - true_negatives = cm.n_samples * cm.n_samples - (false_positives + false_negatives) - sum_squares + true_negatives = ( + cm.n_samples * cm.n_samples - (false_positives + false_negatives) - sum_squares + ) pair_confusion_matrix[0][0] = true_negatives pair_confusion_matrix[0][1] = false_positives @@ -78,7 +80,7 @@ class Rand(metrics.base.MultiClassMetric): >>> metric = metrics.Rand() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric Rand: 0.666667 @@ -147,7 +149,8 @@ class AdjustedRand(metrics.base.MultiClassMetric): >>> metric = metrics.AdjustedRand() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 @@ -186,8 +189,10 @@ def get(self): 2.0 * (true_positives * true_negatives - false_negatives * false_positives) / ( - (true_positives + false_negatives) * (false_negatives + true_negatives) - + (true_positives + false_positives) * (false_positives + true_negatives) + (true_positives + false_negatives) + * (false_negatives + true_negatives) + + (true_positives + false_positives) + * (false_positives + true_negatives) ) ) except ZeroDivisionError: diff --git a/river/metrics/recall.py b/river/metrics/recall.py index ada235e906..fe5eab9ec6 100644 --- a/river/metrics/recall.py +++ b/river/metrics/recall.py @@ -27,7 +27,8 @@ class Recall(metrics.base.BinaryMetric): >>> metric = metrics.Recall() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) Recall: 100.00% Recall: 100.00% Recall: 50.00% @@ -65,7 +66,8 @@ class MacroRecall(metrics.base.MultiClassMetric): >>> metric = metrics.MacroRecall() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroRecall: 100.00% MacroRecall: 50.00% MacroRecall: 66.67% @@ -110,7 +112,8 @@ class MicroRecall(metrics.MicroPrecision): >>> metric = metrics.MicroRecall() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MicroRecall: 100.00% MicroRecall: 50.00% MicroRecall: 66.67% @@ -147,7 +150,8 @@ class WeightedRecall(metrics.base.MultiClassMetric): >>> metric = metrics.WeightedRecall() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedRecall: 100.00% WeightedRecall: 50.00% WeightedRecall: 66.67% diff --git a/river/metrics/report.py b/river/metrics/report.py index 1711323c57..0b47846885 100644 --- a/river/metrics/report.py +++ b/river/metrics/report.py @@ -34,7 +34,7 @@ class ClassificationReport(metrics.base.MultiClassMetric): >>> report = metrics.ClassificationReport() >>> for yt, yp in zip(y_true, y_pred): - ... report = report.update(yt, yp) + ... report.update(yt, yp) >>> print(report) Precision Recall F1 Support diff --git a/river/metrics/roc_auc.py b/river/metrics/roc_auc.py index 2e4a2d3c9f..3e7f780564 100644 --- a/river/metrics/roc_auc.py +++ b/river/metrics/roc_auc.py @@ -34,7 +34,7 @@ class ROCAUC(metrics.base.BinaryMetric): >>> metric = metrics.ROCAUC() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric ROCAUC: 87.50% @@ -45,7 +45,7 @@ class ROCAUC(metrics.base.BinaryMetric): >>> metric = metrics.ROCAUC(n_thresholds=20) >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric ROCAUC: 75.00% @@ -71,13 +71,11 @@ def update(self, y_true, y_pred, w=1.0): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred for t, cm in zip(self.thresholds, self.cms): cm.update(y_true == self.pos_val, p_true > t, w) - return self def revert(self, y_true, y_pred, w=1.0): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred for t, cm in zip(self.thresholds, self.cms): cm.revert(y_true == self.pos_val, p_true > t, w) - return self @property def requires_labels(self): diff --git a/river/metrics/rolling_roc_auc.py b/river/metrics/rolling_roc_auc.py index 68421af9fe..7ac8aae4a9 100644 --- a/river/metrics/rolling_roc_auc.py +++ b/river/metrics/rolling_roc_auc.py @@ -43,7 +43,7 @@ class RollingROCAUC(metrics.base.BinaryMetric): >>> metric = metrics.RollingROCAUC(window_size=4) >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric RollingROCAUC: 75.00% @@ -53,7 +53,7 @@ class RollingROCAUC(metrics.base.BinaryMetric): def __init__(self, window_size=1000, pos_val=True): self.window_size = window_size self.pos_val = pos_val - self.__metric = EfficientRollingROCAUC(pos_val, window_size) + self._metric = EfficientRollingROCAUC(pos_val, window_size) def works_with(self, model) -> bool: return ( @@ -64,13 +64,11 @@ def works_with(self, model) -> bool: def update(self, y_true, y_pred): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred - self.__metric.update(y_true, p_true) - return self + self._metric.update(y_true, p_true) def revert(self, y_true, y_pred): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred - self.__metric.revert(y_true, p_true) - return self + self._metric.revert(y_true, p_true) @property def requires_labels(self) -> bool: @@ -81,4 +79,4 @@ def works_with_weights(self) -> bool: return False def get(self): - return self.__metric.get() + return self._metric.get() diff --git a/river/metrics/silhouette.py b/river/metrics/silhouette.py index cbc57bfa1a..d3c6d0a27d 100644 --- a/river/metrics/silhouette.py +++ b/river/metrics/silhouette.py @@ -44,7 +44,7 @@ class Silhouette(metrics.base.ClusteringMetric): >>> for x, _ in stream.iter_array(X): ... k_means.learn_one(x) ... y_pred = k_means.predict_one(x) - ... metric = metric.update(x, y_pred, k_means.centers) + ... metric.update(x, y_pred, k_means.centers) >>> metric Silhouette: 0.568058 @@ -75,8 +75,6 @@ def update(self, x, y_pred, centers, w=1.0): distance_second_closest_centroid = self._find_distance_second_closest_center(centers, x) self._sum_distance_second_closest_centroid += distance_second_closest_centroid - return self - def revert(self, x, y_pred, centers, w=1.0): distance_closest_centroid = math.sqrt(utils.math.minkowski_distance(centers[y_pred], x, 2)) self._sum_distance_closest_centroid -= distance_closest_centroid @@ -84,8 +82,6 @@ def revert(self, x, y_pred, centers, w=1.0): distance_second_closest_centroid = self._find_distance_second_closest_center(centers, x) self._sum_distance_second_closest_centroid -= distance_second_closest_centroid - return self - def get(self): try: return self._sum_distance_closest_centroid / self._sum_distance_second_closest_centroid diff --git a/river/metrics/smape.py b/river/metrics/smape.py index 802eba61d4..813bf3b1c0 100644 --- a/river/metrics/smape.py +++ b/river/metrics/smape.py @@ -18,7 +18,7 @@ class SMAPE(metrics.base.MeanMetric, metrics.base.RegressionMetric): >>> metric = metrics.SMAPE() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric SMAPE: 37.869392 diff --git a/river/metrics/vbeta.py b/river/metrics/vbeta.py index 9e20caf1f1..78ee42bcc5 100644 --- a/river/metrics/vbeta.py +++ b/river/metrics/vbeta.py @@ -50,7 +50,8 @@ class Homogeneity(metrics.base.MultiClassMetric): >>> metric = metrics.Homogeneity() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 @@ -145,7 +146,8 @@ class Completeness(metrics.base.MultiClassMetric): >>> metric = metrics.Completeness() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 1.0 @@ -242,7 +244,8 @@ class VBeta(metrics.base.MultiClassMetric): >>> metric = metrics.VBeta(beta=1.0) >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 diff --git a/river/misc/sdft.py b/river/misc/sdft.py index 8c21b82eab..0f715f22e3 100644 --- a/river/misc/sdft.py +++ b/river/misc/sdft.py @@ -32,14 +32,13 @@ class SDFT(base.Base): >>> sdft = misc.SDFT(window_size) >>> for i, x in enumerate(X): - ... sdft = sdft.update(x) - ... + ... sdft.update(x) ... if i + 1 >= window_size: ... assert np.allclose(sdft.coefficients, np.fft.fft(X[i+1 - window_size:i+1])) References ---------- - [^1]: [Jacobsen, E. and Lyons, R., 2003. The sliding DFT. IEEE Signal Processing Magazine, 20(2), pp.74-80.](https://www.comm.utoronto.ca/~dimitris/ece431/slidingdft.pdf) + [^1]: [Jacobsen, E. asample_average.pynd Lyons, R., 2003. The sliding DFT. IEEE Signal Processing Magazine, 20(2), pp.74-80.](https://www.comm.utoronto.ca/~dimitris/ece431/slidingdft.pdf) [^2]: [Understanding and Implementing the Sliding DFT](https://www.dsprelated.com/showarticle/776.php) """ @@ -68,5 +67,3 @@ def update(self, x): for i, c in enumerate(self.coefficients): self.coefficients[i] = (c + diff) * np.exp(2j * np.pi * i / self.window_size) self.window.append(x) - - return self diff --git a/river/misc/skyline.py b/river/misc/skyline.py index 193762c301..8c0c543150 100644 --- a/river/misc/skyline.py +++ b/river/misc/skyline.py @@ -46,7 +46,7 @@ class Skyline(collections.UserList, base.Base): >>> for _ in range(100): ... house = random_house() - ... skyline = skyline.update(house) + ... skyline.update(house) >>> print(len(skyline)) 13 @@ -94,7 +94,7 @@ class Skyline(collections.UserList, base.Base): ... ) >>> for kart in karts: - ... skyline = skyline.update(kart._asdict()) + ... skyline.update(kart._asdict()) >>> best_cart_names = [kart['name'] for kart in skyline] >>> for name in best_cart_names: @@ -159,7 +159,7 @@ def update(self, x): # If the skyline is empty then the first element is part of the skyline if not self: self.append(x) - return self + return to_drop = [] is_dominated = False @@ -177,7 +177,7 @@ def update(self, x): to_drop.append(i) if is_dominated: - return self + return # Remove dominated elements if to_drop: @@ -187,4 +187,4 @@ def update(self, x): # Add x to the skyline self.append(x) - return self + return diff --git a/river/multioutput/chain.py b/river/multioutput/chain.py index 9ea8d4ca0b..e4516fd708 100644 --- a/river/multioutput/chain.py +++ b/river/multioutput/chain.py @@ -80,7 +80,7 @@ class ClassifierChain(BaseChain, base.MultiLabelClassifier): ... # Convert y values to booleans ... y = {i: yi == 'TRUE' for i, yi in y.items()} ... y_pred = model.predict_one(x) - ... metric = metric.update(y, y_pred) + ... metric.update(y, y_pred) ... model.learn_one(x, y) >>> metric @@ -300,7 +300,7 @@ class ProbabilisticClassifierChain(ClassifierChain): >>> for x, y in dataset: ... y_pred = model.predict_one(x) ... y_pred = {k: y_pred.get(k, 0) for k in y} - ... metric = metric.update(y, y_pred) + ... metric.update(y, y_pred) ... model.learn_one(x, y) >>> metric @@ -395,7 +395,7 @@ class MonteCarloClassifierChain(ProbabilisticClassifierChain): >>> for x, y in dataset: ... y_pred = model.predict_one(x) ... y_pred = {k: y_pred.get(k, 0) for k in y} - ... metric = metric.update(y, y_pred) + ... metric.update(y, y_pred) ... model.learn_one(x, y) >>> metric diff --git a/river/multioutput/encoder.py b/river/multioutput/encoder.py index 6d7b0c488d..e7d6e94202 100644 --- a/river/multioutput/encoder.py +++ b/river/multioutput/encoder.py @@ -40,7 +40,7 @@ class MultiClassEncoder(base.MultiLabelClassifier): >>> for x, y in dataset: ... y_pred = model.predict_one(x) ... y_pred = {k: y_pred.get(k, 0) for k in y} - ... metric = metric.update(y, y_pred) + ... metric.update(y, y_pred) ... model.learn_one(x, y) >>> metric diff --git a/river/preprocessing/impute.py b/river/preprocessing/impute.py index a6cc70b734..de15e9fdc6 100644 --- a/river/preprocessing/impute.py +++ b/river/preprocessing/impute.py @@ -238,7 +238,7 @@ def __init__(self, value: typing.Any): self.value = value def update(self, x): - return self + ... def get(self): return self.value diff --git a/river/proba/beta.py b/river/proba/beta.py index 0dbd02f927..d23718c350 100644 --- a/river/proba/beta.py +++ b/river/proba/beta.py @@ -53,9 +53,9 @@ class Beta(base.ContinuousDistribution): (0.867..., 0.165...) >>> for success in range(100): - ... beta = beta.update(True) + ... beta.update(True) >>> for failure in range(200): - ... beta = beta.update(False) + ... beta.update(False) >>> beta(.21), beta(.35) (2.525...e-05, 0.841...) @@ -85,18 +85,18 @@ def update(self, x): self.alpha += 1 else: self.beta += 1 - return self def revert(self, x): if x: self.alpha -= 1 else: self.beta -= 1 - return self def __call__(self, p: float): return ( - p ** (self.alpha - 1) * (1 - p) ** (self.beta - 1) / _beta_func(self.alpha, self.beta) + p ** (self.alpha - 1) + * (1 - p) ** (self.beta - 1) + / _beta_func(self.alpha, self.beta) ) def sample(self): diff --git a/river/proba/gaussian.py b/river/proba/gaussian.py index 5d922e9e54..ded37ca6e5 100644 --- a/river/proba/gaussian.py +++ b/river/proba/gaussian.py @@ -25,7 +25,9 @@ class Gaussian(base.ContinuousDistribution): >>> from river import proba - >>> p = proba.Gaussian().update(6).update(7) + >>> p = proba.Gaussian() + >>> p.update(6) + >>> p.update(7) >>> p 𝒩(μ=6.500, σ=0.707) @@ -34,6 +36,7 @@ class Gaussian(base.ContinuousDistribution): 0.564189 >>> p.revert(7) + >>> p 𝒩(μ=6.000, σ=0.000) """ @@ -65,17 +68,17 @@ def __repr__(self): def update(self, x, w=1.0): self._var.update(x, w) - return self def revert(self, x, w=1.0): self._var.revert(x, w) - return self def __call__(self, x): var = self._var.get() if var: try: - return math.exp((x - self.mu) ** 2 / (-2 * var)) / math.sqrt(math.tau * var) + return math.exp((x - self.mu) ** 2 / (-2 * var)) / math.sqrt( + math.tau * var + ) except ValueError: return 0.0 except OverflowError: @@ -133,7 +136,7 @@ class MultivariateGaussian(base.MultivariateContinuousDistribution): 0.0 >>> for x in X.to_dict(orient="records"): - ... p = p.update(x) + ... p.update(x) >>> p.var blue green red blue 0.076119 0.020292 -0.010128 @@ -176,7 +179,7 @@ class MultivariateGaussian(base.MultivariateContinuousDistribution): >>> p = utils.Rolling(MultivariateGaussian(), window_size=5) >>> for x in X.to_dict(orient="records"): - ... p = p.update(x) + ... p.update(x) >>> p.var blue green red blue 0.087062 -0.022873 0.007765 @@ -189,7 +192,7 @@ class MultivariateGaussian(base.MultivariateContinuousDistribution): >>> X.index = [dt(2023, 3, 28, 0, 0, 0) + td(seconds=x) for x in range(8)] >>> p = utils.TimeRolling(MultivariateGaussian(), period=td(seconds=5)) >>> for t, x in X.iterrows(): - ... p = p.update(x.to_dict(), t=t) + ... p.update(x.to_dict(), t=t) >>> p.var blue green red blue 0.087062 -0.022873 0.007765 @@ -201,8 +204,8 @@ class MultivariateGaussian(base.MultivariateContinuousDistribution): >>> multi = proba.MultivariateGaussian() >>> single = proba.Gaussian() >>> for x in X.to_dict(orient='records'): - ... multi = multi.update(x) - ... single = single.update(x['blue']) + ... multi.update(x) + ... single.update(x['blue']) >>> multi.mu['blue'] == single.mu True >>> multi.sigma['blue']['blue'] == single.sigma @@ -239,7 +242,9 @@ def mu(self) -> dict: @property def var(self) -> pd.DataFrame: """The variance of the distribution.""" - variables = sorted(list({var for cov in self._var.matrix.keys() for var in cov})) + variables = sorted( + list({var for cov in self._var.matrix.keys() for var in cov}) + ) # Initialize the covariance matrix array cov_array = np.zeros((len(variables), len(variables))) @@ -264,19 +269,19 @@ def sigma(self) -> pd.DataFrame: def __repr__(self): mu_str = ", ".join(f"{m:.3f}" for m in self.mu.values()) - var_str = self.var.to_string(float_format="{:0.3f}".format, header=False, index=False) + var_str = self.var.to_string( + float_format="{:0.3f}".format, header=False, index=False + ) var_str = " [" + var_str.replace("\n", "]\n [") + "]" return f"𝒩(\n μ=({mu_str}),\n σ^2=(\n{var_str}\n )\n)" def update(self, x): # TODO: add support for weigthed samples self._var.update(x) - return self def revert(self, x): # TODO: add support for weigthed samples self._var.revert(x) - return self def __call__(self, x: dict[str, float]): """PDF(x) method.""" diff --git a/river/proba/multinomial.py b/river/proba/multinomial.py index 9418e60cbc..19ff5be4b8 100644 --- a/river/proba/multinomial.py +++ b/river/proba/multinomial.py @@ -24,16 +24,17 @@ class Multinomial(base.DiscreteDistribution): >>> from river import proba >>> p = proba.Multinomial(['green'] * 3) - >>> p = p.update('red') - + >>> p.update('red') >>> p('red') 0.25 - >>> p = p.update('red').update('red') + >>> p.update('red') + >>> p.update('red') >>> p('green') 0.5 - >>> p = p.revert('red').revert('red') + >>> p.revert('red') + >>> p.revert('red') >>> p('red') 0.25 @@ -49,7 +50,7 @@ class Multinomial(base.DiscreteDistribution): ... ) >>> for x in X: - ... dist = dist.update(x) + ... dist.update(x) ... print(dist) ... print() P(red) = 1.000 @@ -82,7 +83,7 @@ class Multinomial(base.DiscreteDistribution): ... ) >>> for x, day in zip(X, days): - ... dist = dist.update(x, t=dt.datetime(2019, 1, day)) + ... dist.update(x, t=dt.datetime(2019, 1, day)) ... print(dist) ... print() P(red) = 1.000 @@ -123,15 +124,15 @@ def mode(self): def update(self, x): self.counts.update([x]) self._n += 1 - return self def revert(self, x): self.counts.subtract([x]) self._n -= 1 - return self def sample(self): - return self._rng.choices(list(self.counts.keys()), weights=list(self.counts.values()))[0] + return self._rng.choices( + list(self.counts.keys()), weights=list(self.counts.values()) + )[0] def __call__(self, x): try: @@ -140,4 +141,6 @@ def __call__(self, x): return 0.0 def __repr__(self): - return "\n".join(f"P({c}) = {self(c):.3f}" for c, _ in self.counts.most_common()) + return "\n".join( + f"P({c}) = {self(c):.3f}" for c, _ in self.counts.most_common() + ) diff --git a/river/proba/test_gaussian.py b/river/proba/test_gaussian.py index bb445cea16..92a1639fe7 100644 --- a/river/proba/test_gaussian.py +++ b/river/proba/test_gaussian.py @@ -26,7 +26,7 @@ def test_univariate_multivariate_consistency(p): single = {c: proba.Gaussian() for c in X.columns} for x in X.to_dict(orient="records"): - multi = multi.update(x) + multi.update(x) for c, s in single.items(): s.update(x[c]) diff --git a/river/sketch/counter.py b/river/sketch/counter.py index 3c82744b88..1e54b7b8e5 100644 --- a/river/sketch/counter.py +++ b/river/sketch/counter.py @@ -72,7 +72,7 @@ class Counter(base.Base): >>> vals = [] >>> for _ in range(10000): ... v = rng.randint(-1000, 1000) - ... cms = cms.update(v) + ... cms.update(v) ... counter[v] += 1 ... vals.append(v) @@ -107,7 +107,7 @@ class Counter(base.Base): >>> cms_a = sketch.Counter(epsilon=0.001, delta=0.01, seed=0) >>> for v in vals: - ... cms_a = cms_a.update(v) + ... cms_a.update(v) >>> cms_a[7] 5 @@ -123,7 +123,7 @@ class Counter(base.Base): >>> for _ in range(10000): ... v = rng.randint(-1000, 1000) - ... cms_b = cms_b.update(v) + ... cms_b.update(v) Now, we can define a cosine distance function: @@ -181,8 +181,6 @@ def __len__(self): def update(self, x: typing.Hashable, w: int = 1): self._cms[self._hash(x)] += w - return self - def total(self) -> int: """Return the total count.""" return sum(self._cms[0, :]) diff --git a/river/sketch/heavy_hitters.py b/river/sketch/heavy_hitters.py index 75e29d32d7..0d1b124a95 100644 --- a/river/sketch/heavy_hitters.py +++ b/river/sketch/heavy_hitters.py @@ -54,7 +54,7 @@ class HeavyHitters(base.Base): We will feed the counter with printable ASCII characters: >>> for _ in range(10_000): - ... hh = hh.update(rng.choice(string.printable)) + ... hh.update(rng.choice(string.printable)) We can retrieve estimates of the `n` top elements and their frequencies. Let's try `n=3` >>> hh.most_common(3) @@ -119,8 +119,6 @@ def update(self, x: typing.Hashable): self._delta = self._bucket_width + self._delta * self.fading_factor - return self - def most_common(self, n: int | None = None) -> list[tuple[typing.Hashable, float]]: res = [] for key in self._entries: diff --git a/river/sketch/histogram.py b/river/sketch/histogram.py index 6a3428172f..1f1b497827 100644 --- a/river/sketch/histogram.py +++ b/river/sketch/histogram.py @@ -101,7 +101,7 @@ class Histogram(collections.UserList, base.Base): >>> hist = sketch.Histogram(max_bins=15) >>> for x in values: - ... hist = hist.update(x) + ... hist.update(x) >>> for bin in hist: ... print(bin) @@ -140,7 +140,7 @@ def update(self, x): # Insert the bin if the histogram is empty if not self: self.append(b) - return self + return # Use bisection to find where to insert # We don't use the bisect module in order to save some CPU cycles @@ -169,8 +169,6 @@ def update(self, x): if len(self) > self.max_bins: self._shrink(1) - return self - def _shrink(self, k): """Shrinks the histogram by merging the two closest bins.""" @@ -208,7 +206,7 @@ def iter_cdf(self, X, verbose=False): >>> hist = sketch.Histogram() >>> for x in range(4): - ... hist = hist.update(x) + ... hist.update(x) >>> print(hist) [0.00000, 0.00000]: 1 @@ -255,7 +253,7 @@ def cdf(self, x): >>> hist = sketch.Histogram() >>> for x in range(4): - ... hist = hist.update(x) + ... hist.update(x) >>> print(hist) [0.00000, 0.00000]: 1 diff --git a/river/sketch/set.py b/river/sketch/set.py index 48e614de36..344b1f6548 100644 --- a/river/sketch/set.py +++ b/river/sketch/set.py @@ -172,8 +172,6 @@ def update(self, values: typing.Iterable): for x in values: self.add(x) - return self - def __contains__(self, x: typing.Hashable): proj = [] pos = self._hash(x) diff --git a/river/stats/auto_corr.py b/river/stats/auto_corr.py index dd65882473..9764d823ef 100644 --- a/river/stats/auto_corr.py +++ b/river/stats/auto_corr.py @@ -25,7 +25,8 @@ class AutoCorr(stats.base.Univariate): >>> auto_corr = stats.AutoCorr(lag=1) >>> for x in [0.25, 0.5, 0.2, -0.05]: - ... print(auto_corr.update(x).get()) + ... auto_corr.update(x) + ... print(auto_corr.get()) 0 0 -1.0 @@ -33,7 +34,8 @@ class AutoCorr(stats.base.Univariate): >>> auto_corr = stats.AutoCorr(lag=2) >>> for x in [0.25, 0.5, 0.2, -0.05]: - ... print(auto_corr.update(x).get()) + ... auto_corr.update(x) + ... print(auto_corr.get()) 0 0 0 @@ -41,7 +43,8 @@ class AutoCorr(stats.base.Univariate): >>> auto_corr = stats.AutoCorr(lag=1) >>> for x in [1, 0, 0, 0]: - ... print(auto_corr.update(x).get()) + ... auto_corr.update(x) + ... print(auto_corr.get()) 0 0 0 @@ -66,7 +69,5 @@ def update(self, x): # Add x to the window self.window.append(x) - return self - def get(self): return self.pearson.get() diff --git a/river/stats/count.py b/river/stats/count.py index 0b89b920b4..94abbab9ec 100644 --- a/river/stats/count.py +++ b/river/stats/count.py @@ -18,7 +18,6 @@ def __init__(self): def update(self, x=None): self.n += 1 - return self def get(self): return self.n diff --git a/river/stats/cov.py b/river/stats/cov.py index 0310a45573..9d8d114f44 100644 --- a/river/stats/cov.py +++ b/river/stats/cov.py @@ -26,7 +26,8 @@ class Cov(stats.base.Bivariate): >>> cov = stats.Cov() >>> for xi, yi in zip(x, y): - ... print(cov.update(xi, yi).get()) + ... cov.update(xi, yi) + ... print(cov.get()) 0.0 -1.044999 -4.286 @@ -41,7 +42,8 @@ class Cov(stats.base.Bivariate): >>> rcov = utils.Rolling(stats.Cov(), window_size=3) >>> for xi, yi in zip(x, y): - ... print(rcov.update(xi, yi).get()) + ... rcov.update(xi, yi) + ... print(rcov.get()) 0.0 -1.045 -4.286 @@ -79,21 +81,18 @@ def update(self, x, y, w=1.0): self.mean_x.update(x, w) self.mean_y.update(y, w) self.cov += w * (dx * (y - self.mean_y.get()) - self.cov) / max(self.n - self.ddof, 1) - return self def revert(self, x, y, w=1.0): dx = x - self.mean_x.get() self.mean_x.revert(x, w) self.mean_y.revert(y, w) self.cov -= w * (dx * (y - self.mean_y.get()) - self.cov) / max(self.n - self.ddof, 1) - return self def update_many(self, X: np.ndarray, Y: np.ndarray): dx = X - self.mean_x.get() self.mean_x.update_many(X) self.mean_y.update_many(Y) self.cov += (dx * (Y - self.mean_y.get()) - self.cov).sum() / max(self.n - self.ddof, 1) - return self def get(self): return self.cov diff --git a/river/stats/entropy.py b/river/stats/entropy.py index 1eef016dc9..c7e4e1dbb7 100644 --- a/river/stats/entropy.py +++ b/river/stats/entropy.py @@ -35,8 +35,8 @@ class Entropy(stats.base.Univariate): >>> from river import stats >>> def entropy_list(labels, base=None): - ... value,counts = np.unique(labels, return_counts=True) - ... return entropy(counts, base=base) + ... value,counts = np.unique(labels, return_counts=True) + ... return entropy(counts, base=base) >>> SEED = 42 * 1337 >>> random.seed(SEED) @@ -49,7 +49,7 @@ class Entropy(stats.base.Univariate): >>> random.shuffle(list_animal) >>> for animal in list_animal: - ... _ = entro.update(animal) + ... entro.update(animal) >>> print(f'{entro.get():.6f}') 1.058093 @@ -83,7 +83,11 @@ def update(self, x): fading_factor = self.fading_factor entropy = self.entropy - entropy = (n + eps) / (n + 1) * (fading_factor * entropy - math.log((n + eps) / (n + 1))) + entropy = ( + (n + eps) + / (n + 1) + * (fading_factor * entropy - math.log((n + eps) / (n + 1))) + ) entropy -= (cx + 1) / (n + 1) * math.log((cx + 1) / (n + 1)) entropy += (cx + eps) / (n + 1) * math.log((cx + eps) / (n + 1)) self.entropy = entropy @@ -91,7 +95,5 @@ def update(self, x): self.n += 1 self.counter.update([x]) - return self - def get(self): return self.entropy diff --git a/river/stats/ewmean.py b/river/stats/ewmean.py index a788d69be4..df3a7db14a 100644 --- a/river/stats/ewmean.py +++ b/river/stats/ewmean.py @@ -25,7 +25,8 @@ class EWMean(stats.base.Univariate): >>> X = [1, 3, 5, 4, 6, 8, 7, 9, 11] >>> ewm = stats.EWMean(fading_factor=0.5) >>> for x in X: - ... print(ewm.update(x).get()) + ... ewm.update(x) + ... print(ewm.get()) 1.0 2.0 3.5 @@ -58,7 +59,6 @@ def name(self): def update(self, x): self._ewmean.update(x) - return self def get(self): return self._ewmean.get() diff --git a/river/stats/ewvar.py b/river/stats/ewvar.py index 8b4c48e974..6bd06c7b29 100644 --- a/river/stats/ewvar.py +++ b/river/stats/ewvar.py @@ -28,7 +28,8 @@ class EWVar(stats.base.Univariate): >>> X = [1, 3, 5, 4, 6, 8, 7, 9, 11] >>> ewv = stats.EWVar(fading_factor=0.5) >>> for x in X: - ... print(ewv.update(x).get()) + ... ewv.update(x) + ... print(ewv.get()) 0.0 1.0 2.75 @@ -61,7 +62,6 @@ def name(self): def update(self, x): self._ewvar.update(x) - return self def get(self): return self._ewvar.get() diff --git a/river/stats/iqr.py b/river/stats/iqr.py index 34e566819d..c5d56c38de 100644 --- a/river/stats/iqr.py +++ b/river/stats/iqr.py @@ -22,7 +22,7 @@ class IQR(stats.base.Univariate): >>> iqr = stats.IQR(q_inf=0.25, q_sup=0.75) >>> for i in range(0, 1001): - ... iqr = iqr.update(i) + ... iqr.update(i) ... if i % 100 == 0: ... print(iqr.get()) 0.0 @@ -59,7 +59,6 @@ def update(self, x): self._iqr.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): return self._iqr.get() @@ -98,7 +97,7 @@ class RollingIQR(stats.base.RollingUnivariate): ... ) >>> for i in range(0, 1001): - ... rolling_iqr = rolling_iqr.update(i) + ... rolling_iqr.update(i) ... if i % 100 == 0: ... print(rolling_iqr.get()) 0.0 @@ -130,7 +129,6 @@ def update(self, x): self._rolling_iqr.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): # HACK: Avoid crash if get is called before update diff --git a/river/stats/kurtosis.py b/river/stats/kurtosis.py index 63524ae353..2813fa5ec2 100644 --- a/river/stats/kurtosis.py +++ b/river/stats/kurtosis.py @@ -24,7 +24,8 @@ class Kurtosis(stats.base.Univariate): >>> kurtosis = stats.Kurtosis(bias=False) >>> for x in X: - ... print(kurtosis.update(x).get()) + ... kurtosis.update(x) + ... print(kurtosis.get()) -3.0 -2.0 -1.5 @@ -50,7 +51,8 @@ class Kurtosis(stats.base.Univariate): >>> kurtosis = stats.Kurtosis(bias=True) >>> for x in X: - ... print(kurtosis.update(x).get()) + ... kurtosis.update(x) + ... print(kurtosis.get()) -3.0 -2.0 -1.5 @@ -87,7 +89,6 @@ def __init__(self, bias=False): def update(self, x): self._kurtosis.update(x) - return self def get(self): return self._kurtosis.get() diff --git a/river/stats/link.py b/river/stats/link.py index cb0c43e8bf..9f27efae11 100644 --- a/river/stats/link.py +++ b/river/stats/link.py @@ -34,7 +34,7 @@ class Link(stats.base.Univariate): Let us now call `update`. - >>> stat = stat.update(1) + >>> stat.update(1) The output from `get` will still be 0. The reason is that `stats.Shift` has not enough values, and therefore outputs it's default value, which is `None`. The `stats.Mean` @@ -47,13 +47,13 @@ class Link(stats.base.Univariate): therefore the mean can be updated. The mean is therefore equal to 1, because that's the only value from the past. - >>> stat = stat.update(3) + >>> stat.update(3) >>> stat.get() 1.0 On the subsequent call to update, the mean will be updated with the value 3. - >>> stat = stat.update(4) + >>> stat.update(4) >>> stat.get() 2.0 @@ -73,7 +73,6 @@ def update(self, x): y = self.left.get() if y is not None: self.right.update(y) - return self def get(self): return self.right.get() diff --git a/river/stats/mad.py b/river/stats/mad.py index 7e34efa0b0..0f1bdd9cb0 100644 --- a/river/stats/mad.py +++ b/river/stats/mad.py @@ -22,7 +22,8 @@ class MAD(quantile.Quantile): >>> mad = stats.MAD() >>> for x in X: - ... print(mad.update(x).get()) + ... mad.update(x) + ... print(mad.get()) 0.0 2.0 1.0 @@ -49,4 +50,3 @@ def __init__(self): def update(self, x): self.median.update(x) super().update(abs(x - self.median.get())) - return self diff --git a/river/stats/maximum.py b/river/stats/maximum.py index 692f2312c7..6581b7a970 100644 --- a/river/stats/maximum.py +++ b/river/stats/maximum.py @@ -19,9 +19,10 @@ class Max(stats.base.Univariate): >>> from river import stats >>> X = [1, -4, 3, -2, 5, -6] - >>> _max = stats.Max() + >>> maximum = stats.Max() >>> for x in X: - ... print(_max.update(x).get()) + ... maximum.update(x) + ... print(maximum.get()) 1 1 3 @@ -37,7 +38,6 @@ def __init__(self): def update(self, x): if x > self.max: self.max = x - return self def get(self): return self.max @@ -59,7 +59,8 @@ class RollingMax(stats.base.RollingUnivariate): >>> X = [1, -4, 3, -2, 2, 1] >>> rolling_max = stats.RollingMax(window_size=2) >>> for x in X: - ... print(rolling_max.update(x).get()) + ... rolling_max.update(x) + ... print(rolling_max.get()) 1 1 3 @@ -78,7 +79,6 @@ def window_size(self): def update(self, x): self.window.append(x) - return self def get(self): try: @@ -103,7 +103,8 @@ class AbsMax(stats.base.Univariate): >>> X = [1, -4, 3, -2, 5, -6] >>> abs_max = stats.AbsMax() >>> for x in X: - ... print(abs_max.update(x).get()) + ... abs_max.update(x) + ... print(abs_max.get()) 1 4 4 @@ -119,7 +120,6 @@ def __init__(self): def update(self, x): if abs(x) > self.abs_max: self.abs_max = abs(x) - return self def get(self): return self.abs_max @@ -141,7 +141,8 @@ class RollingAbsMax(stats.base.RollingUnivariate): >>> X = [1, -4, 3, -2, 2, 1] >>> rolling_absmax = stats.RollingAbsMax(window_size=2) >>> for x in X: - ... print(rolling_absmax.update(x).get()) + ... rolling_absmax.update(x) + ... print(rolling_absmax.get()) 1 4 4 @@ -160,7 +161,6 @@ def window_size(self): def update(self, x): self.window.append(abs(x)) - return self def get(self): try: diff --git a/river/stats/mean.py b/river/stats/mean.py index 81f6ae02a9..7fe2e7e140 100644 --- a/river/stats/mean.py +++ b/river/stats/mean.py @@ -24,7 +24,8 @@ class Mean(stats.base.Univariate): >>> X = [-5, -3, -1, 1, 3, 5] >>> mean = stats.Mean() >>> for x in X: - ... print(mean.update(x).get()) + ... mean.update(x) + ... print(mean.get()) -5.0 -4.0 -3.0 @@ -40,7 +41,8 @@ class Mean(stats.base.Univariate): >>> rmean = utils.Rolling(stats.Mean(), window_size=2) >>> for x in X: - ... print(rmean.update(x).get()) + ... rmean.update(x) + ... print(rmean.get()) 1.0 1.5 2.5 @@ -63,14 +65,12 @@ def __init__(self): def update(self, x, w=1.0): self.n += w self._mean += (w / self.n) * (x - self._mean) - return self def update_many(self, X: np.ndarray): a = self.n / (self.n + len(X)) b = len(X) / (self.n + len(X)) self._mean = a * self._mean + b * np.mean(X) self.n += len(X) - return self def revert(self, x, w=1.0): self.n -= w @@ -80,7 +80,6 @@ def revert(self, x, w=1.0): self._mean = 0.0 else: self._mean -= (w / self.n) * (x - self._mean) - return self def get(self): return self._mean @@ -148,11 +147,9 @@ def name(self): def update(self, x): self._mean.update(x) - return self def revert(self, x): self._mean.revert(x) - return self def get(self): # Uses the notation from https://www.wikiwand.com/en/Bayes_estimator#/Practical_example_of_Bayes_estimators diff --git a/river/stats/minimum.py b/river/stats/minimum.py index 58728058fa..bf07d1374b 100644 --- a/river/stats/minimum.py +++ b/river/stats/minimum.py @@ -21,7 +21,6 @@ def __init__(self): def update(self, x): if x < self.min: self.min = x - return self def get(self): return self.min @@ -43,7 +42,8 @@ class RollingMin(stats.base.RollingUnivariate): >>> X = [1, -4, 3, -2, 2, 1] >>> rolling_min = stats.RollingMin(2) >>> for x in X: - ... print(rolling_min.update(x).get()) + ... rolling_min.update(x) + ... print(rolling_min.get()) 1 -4 -4 @@ -62,7 +62,6 @@ def window_size(self): def update(self, x): self.window.append(x) - return self def get(self): try: diff --git a/river/stats/mode.py b/river/stats/mode.py index b35afa44ac..dc1553b10c 100644 --- a/river/stats/mode.py +++ b/river/stats/mode.py @@ -29,7 +29,8 @@ class Mode(stats.base.Univariate): >>> X = ['sunny', 'cloudy', 'cloudy', 'rainy', 'rainy', 'rainy'] >>> mode = stats.Mode(k=2) >>> for x in X: - ... print(mode.update(x).get()) + ... mode.update(x) + ... print(mode.get()) sunny sunny cloudy @@ -39,7 +40,8 @@ class Mode(stats.base.Univariate): >>> mode = stats.Mode(k=-1) >>> for x in X: - ... print(mode.update(x).get()) + ... mode.update(x) + ... print(mode.get()) sunny sunny cloudy @@ -60,7 +62,6 @@ def name(self): def update(self, x): if self.k == -1 or x in self.counts or len(self.counts) < self.k: self.counts[x] += 1 - return self def get(self): return max(self.counts, key=self.counts.get, default=None) @@ -89,7 +90,8 @@ class RollingMode(stats.base.RollingUnivariate): >>> X = ['sunny', 'sunny', 'sunny', 'rainy', 'rainy', 'rainy', 'rainy'] >>> rolling_mode = stats.RollingMode(window_size=2) >>> for x in X: - ... print(rolling_mode.update(x).get()) + ... rolling_mode.update(x) + ... print(rolling_mode.get()) sunny sunny sunny @@ -100,7 +102,8 @@ class RollingMode(stats.base.RollingUnivariate): >>> rolling_mode = stats.RollingMode(window_size=5) >>> for x in X: - ... print(rolling_mode.update(x).get()) + ... rolling_mode.update(x) + ... print(rolling_mode.get()) sunny sunny sunny @@ -131,7 +134,6 @@ def update(self, x): self.counts[x] += 1 self.window.append(x) - return self def get(self): return max(self.counts, key=self.counts.get, default=None) diff --git a/river/stats/n_unique.py b/river/stats/n_unique.py index 608433fcf0..527398be06 100644 --- a/river/stats/n_unique.py +++ b/river/stats/n_unique.py @@ -37,14 +37,16 @@ class NUnique(stats.base.Univariate): >>> alphabet = string.ascii_lowercase >>> n_unique = stats.NUnique(error_rate=0.2, seed=42) - >>> n_unique.update('a').get() + >>> n_unique.update('a') + >>> n_unique.get() 1 - >>> n_unique.update('b').get() + >>> n_unique.update('b') + >>> n_unique.get() 2 >>> for letter in alphabet: - ... n_unique = n_unique.update(letter) + ... n_unique.update(letter) >>> n_unique.get() 31 @@ -52,7 +54,7 @@ class NUnique(stats.base.Univariate): >>> n_unique = stats.NUnique(error_rate=0.01, seed=42) >>> for letter in alphabet: - ... n_unique = n_unique.update(letter) + ... n_unique.update(letter) >>> n_unique.get() 26 @@ -87,7 +89,6 @@ def update(self, x): i = x & NUnique.P32 - 1 >> 32 - self.n_bits z = 35 - len(bin(NUnique.P32 - 1 & x << self.n_bits | 1 << self.n_bits - 1)) self.buckets[i] = max(self.buckets[i], z) - return self def get(self): a = ( diff --git a/river/stats/pearson.py b/river/stats/pearson.py index 2679553d9c..1080293bc2 100644 --- a/river/stats/pearson.py +++ b/river/stats/pearson.py @@ -31,7 +31,8 @@ class PearsonCorr(stats.base.Bivariate): >>> pearson = stats.PearsonCorr() >>> for xi, yi in zip(x, y): - ... print(pearson.update(xi, yi).get()) + ... pearson.update(xi, yi) + ... print(pearson.get()) 0 0 0 @@ -50,7 +51,8 @@ class PearsonCorr(stats.base.Bivariate): >>> pearson = utils.Rolling(stats.PearsonCorr(), window_size=4) >>> for xi, yi in zip(x, y): - ... print(pearson.update(xi, yi).get()) + ... pearson.update(xi, yi) + ... print(pearson.get()) 0 0 0 @@ -74,13 +76,11 @@ def update(self, x, y): self.var_x.update(x) self.var_y.update(y) self.cov_xy.update(x, y) - return self def revert(self, x, y): self.var_x.revert(x) self.var_y.revert(y) self.cov_xy.revert(x, y) - return self def get(self): var_x = self.var_x.get() diff --git a/river/stats/ptp.py b/river/stats/ptp.py index 30aaecfaae..802812b2ca 100644 --- a/river/stats/ptp.py +++ b/river/stats/ptp.py @@ -15,7 +15,8 @@ class PeakToPeak(stats.base.Univariate): >>> X = [1, -4, 3, -2, 2, 4] >>> ptp = stats.PeakToPeak() >>> for x in X: - ... print(ptp.update(x).get()) + ... ptp.update(x) + ... print(ptp.get()) 0. 5. 7. @@ -37,7 +38,6 @@ def update(self, x): self._ptp.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): if not self._is_updated: @@ -68,7 +68,8 @@ class RollingPeakToPeak(stats.base.RollingUnivariate): >>> X = [1, -4, 3, -2, 2, 1] >>> ptp = stats.RollingPeakToPeak(window_size=2) >>> for x in X: - ... print(ptp.update(x).get()) + ... ptp.update(x) + ... print(ptp.get()) 0 5 7 @@ -89,7 +90,6 @@ def window_size(self): def update(self, x): self.max.update(x) self.min.update(x) - return self def get(self): maximum = self.max.get() diff --git a/river/stats/quantile.py b/river/stats/quantile.py index d4ab469e77..5292c3331b 100644 --- a/river/stats/quantile.py +++ b/river/stats/quantile.py @@ -65,7 +65,6 @@ def update(self, x): self._quantile.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): # HACK: Avoid this following error in `QuantileFilter` @@ -106,7 +105,7 @@ class RollingQuantile(stats.base.RollingUnivariate): ... ) >>> for i in range(1001): - ... rolling_quantile = rolling_quantile.update(i) + ... rolling_quantile.update(i) ... if i % 100 == 0: ... print(rolling_quantile.get()) 0.0 @@ -141,7 +140,6 @@ def update(self, x): self._rolling_quantile.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): if not self._is_updated: diff --git a/river/stats/sem.py b/river/stats/sem.py index 6459c04a1e..38ebac7ac1 100644 --- a/river/stats/sem.py +++ b/river/stats/sem.py @@ -26,7 +26,8 @@ class SEM(var.Var): >>> sem = stats.SEM() >>> for x in X: - ... print(sem.update(x).get()) + ... sem.update(x) + ... print(sem.get()) 0.0 1.0 0.577350 @@ -40,7 +41,8 @@ class SEM(var.Var): >>> rolling_sem = utils.Rolling(stats.SEM(ddof=1), window_size=3) >>> for x in X: - ... print(rolling_sem.update(x).get()) + ... rolling_sem.update(x) + ... print(rolling_sem.get()) 0.0 1.5 0.881917 diff --git a/river/stats/shift.py b/river/stats/shift.py index 4acbadd655..a84940356a 100644 --- a/river/stats/shift.py +++ b/river/stats/shift.py @@ -35,7 +35,7 @@ class Shift(stats.base.Univariate): >>> stat = stats.Shift(1) | stats.Mean() >>> for i in range(5): - ... stat = stat.update(i) + ... stat.update(i) ... print(stat.get()) 0.0 0.0 @@ -96,7 +96,6 @@ def __init__(self, amount=1, fill_value=None): def update(self, x): self.buffer.append(x) - return self def get(self): try: diff --git a/river/stats/skew.py b/river/stats/skew.py index 66ff7daf9b..0ed1196af5 100644 --- a/river/stats/skew.py +++ b/river/stats/skew.py @@ -23,7 +23,8 @@ class Skew(stats.base.Univariate): >>> skew = stats.Skew(bias=False) >>> for x in X: - ... print(skew.update(x).get()) + ... skew.update(x) + ... print(skew.get()) 0.0 0.0 -1.4802398132849872 @@ -37,7 +38,8 @@ class Skew(stats.base.Univariate): >>> skew = stats.Skew(bias=True) >>> for x in X: - ... print(skew.update(x).get()) + ... skew.update(x) + ... print(skew.get()) 0.0 0.0 -0.6043053732501439 @@ -66,7 +68,6 @@ def name(self): def update(self, x): self._skew.update(x) - return self def get(self): return self._skew.get() diff --git a/river/stats/summing.py b/river/stats/summing.py index 93f1a2de27..eb8c99b0ee 100644 --- a/river/stats/summing.py +++ b/river/stats/summing.py @@ -19,7 +19,8 @@ class Sum(stats.base.Univariate): >>> X = [-5, -3, -1, 1, 3, 5] >>> mean = stats.Sum() >>> for x in X: - ... print(mean.update(x).get()) + ... mean.update(x) + ... print(mean.get()) -5.0 -8.0 -9.0 @@ -32,7 +33,8 @@ class Sum(stats.base.Univariate): >>> X = [1, -4, 3, -2, 2, 1] >>> rolling_sum = utils.Rolling(stats.Sum(), window_size=2) >>> for x in X: - ... print(rolling_sum.update(x).get()) + ... rolling_sum.update(x) + ... print(rolling_sum.get()) 1.0 -3.0 -1.0 @@ -47,11 +49,9 @@ def __init__(self): def update(self, x): self.sum += x - return self def revert(self, x): self.sum -= x - return self def get(self): return self.sum diff --git a/river/stats/test_parallel.py b/river/stats/test_parallel.py index a29017729c..4032094c23 100644 --- a/river/stats/test_parallel.py +++ b/river/stats/test_parallel.py @@ -22,7 +22,8 @@ def test_add_mean(): for i, (x, y, w) in enumerate(zip(X, Y, W)): A.update(x, w) B.update(y, w) - C.update(x, w).update(y, w) + C.update(x, w) + C.update(y, w) D = A + B assert math.isclose(C.get(), D.get()) @@ -55,7 +56,8 @@ def test_add_var(ddof): for i, (x, y, w) in enumerate(zip(X, Y, W)): A.update(x, w) B.update(y, w) - C.update(x, w).update(y, w) + C.update(x, w) + C.update(y, w) D = A + B assert math.isclose(C.get(), D.get()) @@ -89,7 +91,8 @@ def test_sub(stat): for x, y, w in zip(X, Y, W): A.update(x, w) B.update(y, w) - C.update(x, w).update(y, w) + C.update(x, w) + C.update(y, w) D = C - B assert math.isclose(D.get(), A.get()) diff --git a/river/stats/test_quantile.py b/river/stats/test_quantile.py index 6e245f8f17..51a32f9482 100644 --- a/river/stats/test_quantile.py +++ b/river/stats/test_quantile.py @@ -14,7 +14,7 @@ def test_issue_1178(): >>> q = stats.Quantile(0.01) >>> for x in [5, 0, 0, 0, 0, 0, 0, 0]: - ... q = q.update(x) + ... q.update(x) ... print(q) Quantile: 5. Quantile: 0. @@ -27,7 +27,7 @@ def test_issue_1178(): >>> q = stats.Quantile(0.99) >>> for x in [5, 0, 0, 0, 0, 0, 0, 0]: - ... q = q.update(x) + ... q.update(x) ... print(q) Quantile: 5. Quantile: 5. diff --git a/river/stats/var.py b/river/stats/var.py index 67a06910b5..6532e22bec 100644 --- a/river/stats/var.py +++ b/river/stats/var.py @@ -35,7 +35,8 @@ class Var(stats.base.Univariate): >>> var = stats.Var() >>> for x in X: - ... print(var.update(x).get()) + ... var.update(x) + ... print(var.get()) 0.0 2.0 1.0 @@ -50,7 +51,8 @@ class Var(stats.base.Univariate): >>> X = [1, 4, 2, -4, -8, 0] >>> rvar = utils.Rolling(stats.Var(ddof=1), window_size=3) >>> for x in X: - ... print(rvar.update(x).get()) + ... rvar.update(x) + ... print(rvar.get()) 0.0 4.5 2.333333 @@ -82,21 +84,18 @@ def update(self, x, w=1.0): self.mean.update(x, w) mean_new = self.mean.get() self._S += w * (x - mean_old) * (x - mean_new) - return self def revert(self, x, w=1.0): mean_old = self.mean.get() self.mean.revert(x, w) mean_new = self.mean.get() self._S -= w * (x - mean_old) * (x - mean_new) - return self def update_many(self, X: np.ndarray): mean_old = self.mean.get() self.mean.update_many(X) mean_new = self.mean.get() self._S += np.sum(np.multiply(np.subtract(X, mean_old), np.subtract(X, mean_new))) - return self def get(self): if self.n > self.ddof: diff --git a/river/time_series/metrics.py b/river/time_series/metrics.py index 8dfa50ca4f..587ce2e645 100644 --- a/river/time_series/metrics.py +++ b/river/time_series/metrics.py @@ -19,10 +19,6 @@ def update(self, y_true: list[Number], y_pred: list[Number]) -> ForecastingMetri y_pred Predicted values at each time step of the horizon. - Returns - ------- - self - """ @abc.abstractmethod diff --git a/river/tree/splitter/base.py b/river/tree/splitter/base.py index 8524e14b8b..70e4216835 100644 --- a/river/tree/splitter/base.py +++ b/river/tree/splitter/base.py @@ -33,6 +33,7 @@ def update(self, att_val, target_val: base.typing.Target, w: float): The target value. w The weight of the instance. + """ @abc.abstractmethod @@ -49,6 +50,7 @@ def cond_proba(self, att_val, target_val: base.typing.ClfTarget) -> float: Returns ------- Probability for an attribute value given a class. + """ @abc.abstractmethod diff --git a/river/tree/splitter/ebst_splitter.py b/river/tree/splitter/ebst_splitter.py index f11bce48e4..ae5ac39aa8 100644 --- a/river/tree/splitter/ebst_splitter.py +++ b/river/tree/splitter/ebst_splitter.py @@ -33,6 +33,7 @@ class EBSTSplitter(Splitter): data streams. Data mining and knowledge discovery, 23(1), 128-168. [^2]: [Osojnik, Aljaž. 2017. Structured output prediction on Data Streams (Doctoral Dissertation)](http://kt.ijs.si/theses/phd_aljaz_osojnik.pdf) + """ def __init__(self): diff --git a/river/utils/pretty.py b/river/utils/pretty.py index 3ddc4469f4..6a3678128e 100644 --- a/river/utils/pretty.py +++ b/river/utils/pretty.py @@ -34,7 +34,9 @@ def print_table( raise ValueError("all the columns must be of the same length") # Determine the width of each column based on the maximum length of it's elements - col_widths = [max(max(map(len, col)), len(header)) for header, col in zip(headers, columns)] + col_widths = [ + max(max(map(len, col)), len(header)) for header, col in zip(headers, columns) + ] # Make a template to print out rows one by one row_format = " ".join(["{:" + str(width + 2) + "s}" for width in col_widths]) @@ -48,7 +50,9 @@ def print_table( row_format.format(*headers) + "\n" + "\n".join( - row_format.format(*[col[i].rjust(width) for col, width in zip(columns, col_widths)]) + row_format.format( + *[col[i].rjust(width) for col, width in zip(columns, col_widths)] + ) for i in order ) ) diff --git a/river/utils/rolling.py b/river/utils/rolling.py index 6e01eb232b..c5abc9806e 100644 --- a/river/utils/rolling.py +++ b/river/utils/rolling.py @@ -60,7 +60,8 @@ class Rolling(BaseRolling): >>> rmean = utils.Rolling(stats.Mean(), window_size=3) >>> for x in X: - ... print(rmean.update(x).get()) + ... rmean.update(x) + ... print(rmean.get()) 1.0 2.0 3.0 @@ -81,7 +82,6 @@ def update(self, *args, **kwargs): self.obj.revert(*self.window[0][0], **self.window[0][1]) self.obj.update(*args, **kwargs) self.window.append((args, kwargs)) - return self class TimeRolling(BaseRolling): @@ -114,7 +114,8 @@ class TimeRolling(BaseRolling): >>> rmean = utils.TimeRolling(stats.Mean(), period=dt.timedelta(days=3)) >>> for t, x in X.items(): - ... print(rmean.update(x, t=t).get()) + ... rmean.update(x, t=t) + ... print(rmean.get()) 1.0 3.0 5.0 @@ -151,5 +152,3 @@ def update(self, *args, t: dt.datetime, **kwargs): if i > 0: self._timestamps = self._timestamps[i:] self._datum = self._datum[i:] - - return self diff --git a/river/utils/test_rolling.py b/river/utils/test_rolling.py index fef2b764ba..5d484ac7de 100644 --- a/river/utils/test_rolling.py +++ b/river/utils/test_rolling.py @@ -16,7 +16,7 @@ def test_with_counter(): >>> counter = utils.Rolling(collections.Counter(), window_size=3) >>> for i in range(5): - ... counter = counter.update([i]) + ... counter.update([i]) >>> counter Counter({2: 1, 3: 1, 4: 1, 0: 0, 1: 0}) From dd84bacc4987742f85a54dcccf1d585fe34399d2 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 4 Dec 2023 18:17:55 +0100 Subject: [PATCH 207/347] Update emp.py --- river/covariance/emp.py | 31 +++++++++++++++++++------------ 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/river/covariance/emp.py b/river/covariance/emp.py index de0a4b6e4a..5dc5f38045 100644 --- a/river/covariance/emp.py +++ b/river/covariance/emp.py @@ -81,7 +81,7 @@ class EmpiricalCovariance(SymmetricMatrix): >>> cov = covariance.EmpiricalCovariance() >>> for x in X.to_dict(orient="records"): - ... cov = cov.update(x) + ... cov.update(x) >>> cov blue green red blue 0.076 0.020 -0.010 @@ -160,8 +160,6 @@ def update(self, x: dict): var = self[i, i] var.update(xi) - return self - def revert(self, x: dict): """Downdate with a single sample. @@ -178,8 +176,6 @@ def revert(self, x: dict): for i, xi in x.items(): self[i, i].revert(x[i]) - return self - def update_many(self, X: pd.DataFrame): """Update with a dataframe of samples. @@ -198,7 +194,9 @@ def update_many(self, X: pd.DataFrame): mean = dict(zip(X.columns, mean_arr)) cov = { (i, j): cov_arr[r, c] - for (r, i), (c, j) in itertools.combinations_with_replacement(enumerate(X.columns), r=2) + for (r, i), (c, j) in itertools.combinations_with_replacement( + enumerate(X.columns), r=2 + ) } self = self._from_state(n=n, mean=mean, cov=cov, ddof=self.ddof) @@ -249,7 +247,9 @@ def _from_state(cls, n: int, mean: dict, cov: dict, *, ddof=1): new[i, i] except KeyError: new._cov[i, i] = stats.Var(new.ddof) - new._cov[i, i] += stats.Var._from_state(n=n, m=mean[i], sig=cov[i, i], ddof=new.ddof) + new._cov[i, i] += stats.Var._from_state( + n=n, m=mean[i], sig=cov[i, i], ddof=new.ddof + ) return new @@ -281,7 +281,7 @@ class EmpiricalPrecision(SymmetricMatrix): >>> prec = covariance.EmpiricalPrecision() >>> for x in X.to_dict(orient="records"): - ... prec = prec.update(x) + ... prec.update(x) >>> prec 0 1 2 @@ -329,7 +329,10 @@ def update(self, x): # Fortran order is necessary for scipy's linalg.blas.dger inv_cov = np.array( [ - [self._inv_cov.get(min((i, j), (j, i)), 1.0 if i == j else 0.0) for j in x] + [ + self._inv_cov.get(min((i, j), (j, i)), 1.0 if i == j else 0.0) + for j in x + ] for i in x ], order="F", @@ -349,8 +352,6 @@ def update(self, x): for j, fj in enumerate(x): self._inv_cov[min((fi, fj), (fj, fi))] = row[j] - return self - def update_many(self, X: pd.DataFrame): """Update with a dataframe of samples. @@ -366,7 +367,13 @@ def update_many(self, X: pd.DataFrame): loc = np.array([self._loc.get(feature, 0.0) for feature in X]) w = np.array([self._w.get(feature, 0.0) for feature in X]) inv_cov = np.array( - [[self._inv_cov.get(min((i, j), (j, i)), 1.0 if i == j else 0.0) for j in X] for i in X] + [ + [ + self._inv_cov.get(min((i, j), (j, i)), 1.0 if i == j else 0.0) + for j in X + ] + for i in X + ] ) / np.maximum(w, 1) # update formulas From 07fdd365deb2f263d3265c42734ddad4f1c8a59a Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 4 Dec 2023 18:32:29 +0100 Subject: [PATCH 208/347] bump --- docs/releases/0.21.0.md | 4 +++ river/covariance/emp.py | 71 +++++++---------------------------------- 2 files changed, 16 insertions(+), 59 deletions(-) diff --git a/docs/releases/0.21.0.md b/docs/releases/0.21.0.md index fb2a8e4123..cde0682aa9 100644 --- a/docs/releases/0.21.0.md +++ b/docs/releases/0.21.0.md @@ -3,3 +3,7 @@ - The `learn_one` and `learn_many` methods of each estimator don't not return anything anymore. This is to emphasize that the estimators are stateful. - The `update` and `revert` method of classes that have also cease to return anything. - `sample_weight` has been renamed to `w`. + +## covariance + +- Fixed an issue where `update_many` would reset `covariance.EmpiricalCovariance` each time it was called. diff --git a/river/covariance/emp.py b/river/covariance/emp.py index 5dc5f38045..b02b208126 100644 --- a/river/covariance/emp.py +++ b/river/covariance/emp.py @@ -91,7 +91,7 @@ class EmpiricalCovariance(SymmetricMatrix): There is also an `update_many` method to process mini-batches. The results are identical. >>> cov = covariance.EmpiricalCovariance() - >>> cov = cov.update_many(X) + >>> cov.update_many(X) >>> cov blue green red blue 0.076 0.020 -0.010 @@ -108,22 +108,6 @@ class EmpiricalCovariance(SymmetricMatrix): >>> cov["blue", "blue"] Var: 0.076119 - Start from a state: - >>> n = 8 - >>> mean = {'red': 0.416, 'green': 0.387, 'blue': 0.518} - >>> cov_ = {('red', 'red'): 0.079, - ... ('red', 'green'): -0.053, - ... ('red', 'blue'): -0.010, - ... ('green', 'green'): 0.113, - ... ('green', 'blue'): 0.020, - ... ('blue', 'blue'): 0.076} - >>> cov = covariance.EmpiricalCovariance._from_state( - ... n=n, mean=mean, cov=cov_, ddof=1) - >>> cov - blue green red - blue 0.076 0.020 -0.010 - green 0.020 0.113 -0.053 - red -0.010 -0.053 0.079 """ def __init__(self, ddof=1): @@ -149,7 +133,7 @@ def update(self, x: dict): cov = self[i, j] except KeyError: self._cov[i, j] = stats.Cov(self.ddof) - cov = self[i, j] + cov = self._cov[i, j] cov.update(x[i], x[j]) for i, xi in x.items(): @@ -157,7 +141,7 @@ def update(self, x: dict): var = self[i, i] except KeyError: self._cov[i, i] = stats.Var(self.ddof) - var = self[i, i] + var = self._cov[i, i] var.update(xi) def revert(self, x: dict): @@ -199,58 +183,27 @@ def update_many(self, X: pd.DataFrame): ) } - self = self._from_state(n=n, mean=mean, cov=cov, ddof=self.ddof) - - return self - - @classmethod - def _from_state(cls, n: int, mean: dict, cov: dict, *, ddof=1): - """Create a new instance from state information. - - Parameters - ---------- - cls - The class type. - n - The number of data points. - mean - A dictionary of variable means. - cov - A dictionary of covariance or variance values. - ddof - Degrees of freedom for covariance calculation. Defaults to 1. - - Returns - ---------- - cls: A new instance of the class with updated covariance matrix. - - Raises - ---------- - KeyError: If an element in `mean` or `cov` is missing. - """ - new = cls(ddof=ddof) - for i, j in itertools.combinations(mean.keys(), r=2): + for i, j in itertools.combinations(sorted(mean.keys()), r=2): try: - new[i, j] + self[i, j] except KeyError: - new._cov[i, j] = stats.Cov(new.ddof) - new._cov[i, j] += stats.Cov._from_state( + self._cov[i, j] = stats.Cov(self.ddof) + self._cov[i, j] += stats.Cov._from_state( n=n, mean_x=mean[i], mean_y=mean[j], cov=cov.get((i, j), cov.get((j, i))), - ddof=new.ddof, + ddof=self.ddof, ) for i in mean.keys(): try: - new[i, i] + self[i, i] except KeyError: - new._cov[i, i] = stats.Var(new.ddof) - new._cov[i, i] += stats.Var._from_state( - n=n, m=mean[i], sig=cov[i, i], ddof=new.ddof + self._cov[i, i] = stats.Var(self.ddof) + self._cov[i, i] += stats.Var._from_state( + n=n, m=mean[i], sig=cov[i, i], ddof=self.ddof ) - return new class EmpiricalPrecision(SymmetricMatrix): From 8b72d5ad69907ebb0a0bcbc4e173cd19fd4d9a93 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 4 Dec 2023 18:35:16 +0100 Subject: [PATCH 209/347] Update base.py --- river/metrics/multioutput/base.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/river/metrics/multioutput/base.py b/river/metrics/multioutput/base.py index f2935391a8..4768f96ce1 100644 --- a/river/metrics/multioutput/base.py +++ b/river/metrics/multioutput/base.py @@ -38,7 +38,7 @@ def update( y_pred: dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]], w=1.0, - ) -> MultiOutputClassificationMetric: + ): """Update the metric.""" self.cm.update(y_true, y_pred, w) @@ -48,7 +48,7 @@ def revert( y_pred: dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]], w=1.0, - ) -> MultiOutputClassificationMetric: + ): """Revert the metric.""" self.cm.revert(y_true, y_pred, w) @@ -72,7 +72,7 @@ def update( self, y_true: dict[str | int, float | int], y_pred: dict[str | int, float | int], - ) -> MultiOutputRegressionMetric: + ): """Update the metric.""" @abc.abstractmethod @@ -80,7 +80,7 @@ def revert( self, y_true: dict[str | int, float | int], y_pred: dict[str | int, float | int], - ) -> MultiOutputRegressionMetric: + ): """Revert the metric.""" def works_with(self, model) -> bool: From 1d104da49c4561d523b760374b8812f08bd068ac Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 4 Dec 2023 18:36:01 +0100 Subject: [PATCH 210/347] Update no_drift.py --- river/drift/no_drift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/drift/no_drift.py b/river/drift/no_drift.py index 98c2a010a3..f0cad478d4 100644 --- a/river/drift/no_drift.py +++ b/river/drift/no_drift.py @@ -68,7 +68,7 @@ class NoDrift(base.DriftDetector): def __init__(self): super().__init__() - def update(self, x: int | float) -> DriftDetector: + def update(self, x: int | float): ... @property From 876b7c30a80e61ce30d3228d117a9b0cf99d6091 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 4 Dec 2023 18:36:14 +0100 Subject: [PATCH 211/347] Update no_drift.py --- river/drift/no_drift.py | 1 - 1 file changed, 1 deletion(-) diff --git a/river/drift/no_drift.py b/river/drift/no_drift.py index f0cad478d4..56ca6e121e 100644 --- a/river/drift/no_drift.py +++ b/river/drift/no_drift.py @@ -1,7 +1,6 @@ from __future__ import annotations from river import base -from river.base.drift_detector import DriftDetector class NoDrift(base.DriftDetector): From 78ca5d58b6721b707b8f3db08153177d8450e19e Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 4 Dec 2023 19:03:50 +0100 Subject: [PATCH 212/347] fix examples --- docs/examples/batch-to-online.ipynb | 82 +- docs/examples/bike-sharing-forecasting.ipynb | 48 +- .../building-a-simple-nowcasting-model.ipynb | 100 +- docs/examples/content-personalization.ipynb | 216 +- docs/examples/debugging-a-pipeline.ipynb | 26 +- docs/examples/imbalanced-learning.ipynb | 72 +- .../part-1.ipynb | 96 +- .../part-2.ipynb | 132 +- .../quantile-regression-uncertainty.ipynb | 18 +- docs/examples/sentence-classification.ipynb | 112 +- .../examples/the-art-of-using-pipelines.ipynb | 88 +- .../binary-classification.ipynb | 96 +- .../concept-drift-detection.ipynb | 20 +- .../multiclass-classification.ipynb | 72 +- .../getting-started/regression.ipynb | 64 +- docs/recipes/active-learning.ipynb | 34 +- docs/recipes/bandits-101.ipynb | 14118 +--------------- docs/recipes/cloning-and-mutating.ipynb | 56 +- docs/recipes/mini-batching.ipynb | 32 +- docs/recipes/on-hoeffding-trees.ipynb | 126 +- docs/recipes/pipelines.ipynb | 80 +- docs/recipes/reading-data.ipynb | 64 +- docs/recipes/rolling-computations.ipynb | 8 +- 23 files changed, 923 insertions(+), 14837 deletions(-) diff --git a/docs/examples/batch-to-online.ipynb b/docs/examples/batch-to-online.ipynb index fc5f8255db..703a2cbb23 100644 --- a/docs/examples/batch-to-online.ipynb +++ b/docs/examples/batch-to-online.ipynb @@ -25,10 +25,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:26.584039Z", - "iopub.status.busy": "2023-11-09T07:13:26.583739Z", - "iopub.status.idle": "2023-11-09T07:13:27.166882Z", - "shell.execute_reply": "2023-11-09T07:13:27.156212Z" + "iopub.execute_input": "2023-12-04T17:49:59.272045Z", + "iopub.status.busy": "2023-12-04T17:49:59.271371Z", + "iopub.status.idle": "2023-12-04T17:49:59.753872Z", + "shell.execute_reply": "2023-12-04T17:49:59.715242Z" }, "tags": [] }, @@ -102,10 +102,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:27.189863Z", - "iopub.status.busy": "2023-11-09T07:13:27.189347Z", - "iopub.status.idle": "2023-11-09T07:13:27.208208Z", - "shell.execute_reply": "2023-11-09T07:13:27.206462Z" + "iopub.execute_input": "2023-12-04T17:49:59.761043Z", + "iopub.status.busy": "2023-12-04T17:49:59.759833Z", + "iopub.status.idle": "2023-12-04T17:49:59.779917Z", + "shell.execute_reply": "2023-12-04T17:49:59.778435Z" }, "tags": [] }, @@ -128,10 +128,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:27.213628Z", - "iopub.status.busy": "2023-11-09T07:13:27.212467Z", - "iopub.status.idle": "2023-11-09T07:13:27.233529Z", - "shell.execute_reply": "2023-11-09T07:13:27.232804Z" + "iopub.execute_input": "2023-12-04T17:49:59.790039Z", + "iopub.status.busy": "2023-12-04T17:49:59.788985Z", + "iopub.status.idle": "2023-12-04T17:49:59.840386Z", + "shell.execute_reply": "2023-12-04T17:49:59.837617Z" }, "tags": [] }, @@ -167,10 +167,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:27.239697Z", - "iopub.status.busy": "2023-11-09T07:13:27.239252Z", - "iopub.status.idle": "2023-11-09T07:13:27.277655Z", - "shell.execute_reply": "2023-11-09T07:13:27.276221Z" + "iopub.execute_input": "2023-12-04T17:49:59.846611Z", + "iopub.status.busy": "2023-12-04T17:49:59.845976Z", + "iopub.status.idle": "2023-12-04T17:49:59.870158Z", + "shell.execute_reply": "2023-12-04T17:49:59.868690Z" }, "tags": [] }, @@ -235,10 +235,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:27.282609Z", - "iopub.status.busy": "2023-11-09T07:13:27.281556Z", - "iopub.status.idle": "2023-11-09T07:13:27.363041Z", - "shell.execute_reply": "2023-11-09T07:13:27.361561Z" + "iopub.execute_input": "2023-12-04T17:49:59.876894Z", + "iopub.status.busy": "2023-12-04T17:49:59.875900Z", + "iopub.status.idle": "2023-12-04T17:50:00.034657Z", + "shell.execute_reply": "2023-12-04T17:50:00.034212Z" }, "tags": [] }, @@ -284,10 +284,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:27.368371Z", - "iopub.status.busy": "2023-11-09T07:13:27.367591Z", - "iopub.status.idle": "2023-11-09T07:13:27.391267Z", - "shell.execute_reply": "2023-11-09T07:13:27.390641Z" + "iopub.execute_input": "2023-12-04T17:50:00.036591Z", + "iopub.status.busy": "2023-12-04T17:50:00.036482Z", + "iopub.status.idle": "2023-12-04T17:50:00.054486Z", + "shell.execute_reply": "2023-12-04T17:50:00.054213Z" }, "tags": [] }, @@ -327,10 +327,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:27.395942Z", - "iopub.status.busy": "2023-11-09T07:13:27.395217Z", - "iopub.status.idle": "2023-11-09T07:13:27.410988Z", - "shell.execute_reply": "2023-11-09T07:13:27.410729Z" + "iopub.execute_input": "2023-12-04T17:50:00.056049Z", + "iopub.status.busy": "2023-12-04T17:50:00.055945Z", + "iopub.status.idle": "2023-12-04T17:50:00.066262Z", + "shell.execute_reply": "2023-12-04T17:50:00.066001Z" }, "tags": [] }, @@ -366,10 +366,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:27.412488Z", - "iopub.status.busy": "2023-11-09T07:13:27.412405Z", - "iopub.status.idle": "2023-11-09T07:13:27.446197Z", - "shell.execute_reply": "2023-11-09T07:13:27.445827Z" + "iopub.execute_input": "2023-12-04T17:50:00.067722Z", + "iopub.status.busy": "2023-12-04T17:50:00.067629Z", + "iopub.status.idle": "2023-12-04T17:50:00.100568Z", + "shell.execute_reply": "2023-12-04T17:50:00.100266Z" }, "tags": [] }, @@ -395,10 +395,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:27.447825Z", - "iopub.status.busy": "2023-11-09T07:13:27.447750Z", - "iopub.status.idle": "2023-11-09T07:13:27.495443Z", - "shell.execute_reply": "2023-11-09T07:13:27.495189Z" + "iopub.execute_input": "2023-12-04T17:50:00.102058Z", + "iopub.status.busy": "2023-12-04T17:50:00.101985Z", + "iopub.status.idle": "2023-12-04T17:50:00.150440Z", + "shell.execute_reply": "2023-12-04T17:50:00.150140Z" }, "tags": [] }, @@ -452,10 +452,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:27.496931Z", - "iopub.status.busy": "2023-11-09T07:13:27.496855Z", - "iopub.status.idle": "2023-11-09T07:13:27.609803Z", - "shell.execute_reply": "2023-11-09T07:13:27.609550Z" + "iopub.execute_input": "2023-12-04T17:50:00.151961Z", + "iopub.status.busy": "2023-12-04T17:50:00.151859Z", + "iopub.status.idle": "2023-12-04T17:50:00.270128Z", + "shell.execute_reply": "2023-12-04T17:50:00.269887Z" }, "tags": [] }, @@ -539,7 +539,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/bike-sharing-forecasting.ipynb b/docs/examples/bike-sharing-forecasting.ipynb index 470363bdfc..0c5f9d3fcb 100644 --- a/docs/examples/bike-sharing-forecasting.ipynb +++ b/docs/examples/bike-sharing-forecasting.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:28.595456Z", - "iopub.status.busy": "2023-11-09T07:13:28.595182Z", - "iopub.status.idle": "2023-11-09T07:13:29.004623Z", - "shell.execute_reply": "2023-11-09T07:13:29.004315Z" + "iopub.execute_input": "2023-12-04T17:50:01.661427Z", + "iopub.status.busy": "2023-12-04T17:50:01.661176Z", + "iopub.status.idle": "2023-12-04T17:50:02.101176Z", + "shell.execute_reply": "2023-12-04T17:50:02.100862Z" }, "tags": [] }, @@ -67,10 +67,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:29.006292Z", - "iopub.status.busy": "2023-11-09T07:13:29.006158Z", - "iopub.status.idle": "2023-11-09T07:13:34.564864Z", - "shell.execute_reply": "2023-11-09T07:13:34.564546Z" + "iopub.execute_input": "2023-12-04T17:50:02.102857Z", + "iopub.status.busy": "2023-12-04T17:50:02.102717Z", + "iopub.status.idle": "2023-12-04T17:50:07.739676Z", + "shell.execute_reply": "2023-12-04T17:50:07.739367Z" }, "tags": [] }, @@ -131,10 +131,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:34.566502Z", - "iopub.status.busy": "2023-11-09T07:13:34.566385Z", - "iopub.status.idle": "2023-11-09T07:13:43.686695Z", - "shell.execute_reply": "2023-11-09T07:13:43.686371Z" + "iopub.execute_input": "2023-12-04T17:50:07.742038Z", + "iopub.status.busy": "2023-12-04T17:50:07.741936Z", + "iopub.status.idle": "2023-12-04T17:50:16.806178Z", + "shell.execute_reply": "2023-12-04T17:50:16.805895Z" }, "tags": [] }, @@ -201,10 +201,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:43.688298Z", - "iopub.status.busy": "2023-11-09T07:13:43.688204Z", - "iopub.status.idle": "2023-11-09T07:13:43.702197Z", - "shell.execute_reply": "2023-11-09T07:13:43.701978Z" + "iopub.execute_input": "2023-12-04T17:50:16.807839Z", + "iopub.status.busy": "2023-12-04T17:50:16.807737Z", + "iopub.status.idle": "2023-12-04T17:50:16.822188Z", + "shell.execute_reply": "2023-12-04T17:50:16.821945Z" }, "tags": [] }, @@ -412,10 +412,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:43.703680Z", - "iopub.status.busy": "2023-11-09T07:13:43.703602Z", - "iopub.status.idle": "2023-11-09T07:13:44.089579Z", - "shell.execute_reply": "2023-11-09T07:13:44.089307Z" + "iopub.execute_input": "2023-12-04T17:50:16.823702Z", + "iopub.status.busy": "2023-12-04T17:50:16.823613Z", + "iopub.status.idle": "2023-12-04T17:50:17.222219Z", + "shell.execute_reply": "2023-12-04T17:50:17.221915Z" }, "tags": [] }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:44.091270Z", - "iopub.status.busy": "2023-11-09T07:13:44.091166Z", - "iopub.status.idle": "2023-11-09T07:13:53.376511Z", - "shell.execute_reply": "2023-11-09T07:13:53.376206Z" + "iopub.execute_input": "2023-12-04T17:50:17.223831Z", + "iopub.status.busy": "2023-12-04T17:50:17.223728Z", + "iopub.status.idle": "2023-12-04T17:50:26.483009Z", + "shell.execute_reply": "2023-12-04T17:50:26.482763Z" }, "tags": [] }, diff --git a/docs/examples/building-a-simple-nowcasting-model.ipynb b/docs/examples/building-a-simple-nowcasting-model.ipynb index 500ffaa590..80e476d490 100644 --- a/docs/examples/building-a-simple-nowcasting-model.ipynb +++ b/docs/examples/building-a-simple-nowcasting-model.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:54.533808Z", - "iopub.status.busy": "2023-11-09T07:13:54.533370Z", - "iopub.status.idle": "2023-11-09T07:13:54.963329Z", - "shell.execute_reply": "2023-11-09T07:13:54.963037Z" + "iopub.execute_input": "2023-12-04T17:50:27.898909Z", + "iopub.status.busy": "2023-12-04T17:50:27.898538Z", + "iopub.status.idle": "2023-12-04T17:50:28.349546Z", + "shell.execute_reply": "2023-12-04T17:50:28.349217Z" }, "tags": [] }, @@ -59,10 +59,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:54.965002Z", - "iopub.status.busy": "2023-11-09T07:13:54.964864Z", - "iopub.status.idle": "2023-11-09T07:13:54.985089Z", - "shell.execute_reply": "2023-11-09T07:13:54.984832Z" + "iopub.execute_input": "2023-12-04T17:50:28.351650Z", + "iopub.status.busy": "2023-12-04T17:50:28.351468Z", + "iopub.status.idle": "2023-12-04T17:50:28.371869Z", + "shell.execute_reply": "2023-12-04T17:50:28.371482Z" }, "tags": [] }, @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:54.986720Z", - "iopub.status.busy": "2023-11-09T07:13:54.986638Z", - "iopub.status.idle": "2023-11-09T07:13:55.216775Z", - "shell.execute_reply": "2023-11-09T07:13:55.216512Z" + "iopub.execute_input": "2023-12-04T17:50:28.373932Z", + "iopub.status.busy": "2023-12-04T17:50:28.373815Z", + "iopub.status.idle": "2023-12-04T17:50:28.574309Z", + "shell.execute_reply": "2023-12-04T17:50:28.574000Z" }, "tags": [] }, @@ -153,17 +153,17 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:55.218516Z", - "iopub.status.busy": "2023-11-09T07:13:55.218372Z", - "iopub.status.idle": "2023-11-09T07:13:55.341470Z", - "shell.execute_reply": "2023-11-09T07:13:55.341081Z" + "iopub.execute_input": "2023-12-04T17:50:28.576400Z", + "iopub.status.busy": "2023-12-04T17:50:28.576232Z", + "iopub.status.idle": "2023-12-04T17:50:28.872325Z", + "shell.execute_reply": "2023-12-04T17:50:28.871769Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -188,17 +188,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:55.343297Z", - "iopub.status.busy": "2023-11-09T07:13:55.343193Z", - "iopub.status.idle": "2023-11-09T07:13:55.453441Z", - "shell.execute_reply": "2023-11-09T07:13:55.453155Z" + "iopub.execute_input": "2023-12-04T17:50:28.874466Z", + "iopub.status.busy": "2023-12-04T17:50:28.874327Z", + "iopub.status.idle": "2023-12-04T17:50:29.001707Z", + "shell.execute_reply": "2023-12-04T17:50:29.001422Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -234,17 +234,17 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:55.455166Z", - "iopub.status.busy": "2023-11-09T07:13:55.455031Z", - "iopub.status.idle": "2023-11-09T07:13:55.570302Z", - "shell.execute_reply": "2023-11-09T07:13:55.569976Z" + "iopub.execute_input": "2023-12-04T17:50:29.004048Z", + "iopub.status.busy": "2023-12-04T17:50:29.003933Z", + "iopub.status.idle": "2023-12-04T17:50:29.151033Z", + "shell.execute_reply": "2023-12-04T17:50:29.150715Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -290,10 +290,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:55.571899Z", - "iopub.status.busy": "2023-11-09T07:13:55.571799Z", - "iopub.status.idle": "2023-11-09T07:13:55.583309Z", - "shell.execute_reply": "2023-11-09T07:13:55.582997Z" + "iopub.execute_input": "2023-12-04T17:50:29.152764Z", + "iopub.status.busy": "2023-12-04T17:50:29.152637Z", + "iopub.status.idle": "2023-12-04T17:50:29.164461Z", + "shell.execute_reply": "2023-12-04T17:50:29.164031Z" }, "tags": [] }, @@ -339,17 +339,17 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:55.585204Z", - "iopub.status.busy": "2023-11-09T07:13:55.585085Z", - "iopub.status.idle": "2023-11-09T07:13:55.695779Z", - "shell.execute_reply": "2023-11-09T07:13:55.695478Z" + "iopub.execute_input": "2023-12-04T17:50:29.166517Z", + "iopub.status.busy": "2023-12-04T17:50:29.166407Z", + "iopub.status.idle": "2023-12-04T17:50:29.307795Z", + "shell.execute_reply": "2023-12-04T17:50:29.307515Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -396,17 +396,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:55.697342Z", - "iopub.status.busy": "2023-11-09T07:13:55.697247Z", - "iopub.status.idle": "2023-11-09T07:13:55.808959Z", - "shell.execute_reply": "2023-11-09T07:13:55.808709Z" + "iopub.execute_input": "2023-12-04T17:50:29.309825Z", + "iopub.status.busy": "2023-12-04T17:50:29.309706Z", + "iopub.status.idle": "2023-12-04T17:50:29.490676Z", + "shell.execute_reply": "2023-12-04T17:50:29.490112Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -456,17 +456,17 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:55.810582Z", - "iopub.status.busy": "2023-11-09T07:13:55.810475Z", - "iopub.status.idle": "2023-11-09T07:13:55.920717Z", - "shell.execute_reply": "2023-11-09T07:13:55.920475Z" + "iopub.execute_input": "2023-12-04T17:50:29.492524Z", + "iopub.status.busy": "2023-12-04T17:50:29.492402Z", + "iopub.status.idle": "2023-12-04T17:50:29.634856Z", + "shell.execute_reply": "2023-12-04T17:50:29.634576Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -496,10 +496,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:55.922261Z", - "iopub.status.busy": "2023-11-09T07:13:55.922164Z", - "iopub.status.idle": "2023-11-09T07:13:55.935603Z", - "shell.execute_reply": "2023-11-09T07:13:55.935386Z" + "iopub.execute_input": "2023-12-04T17:50:29.636941Z", + "iopub.status.busy": "2023-12-04T17:50:29.636775Z", + "iopub.status.idle": "2023-12-04T17:50:29.650448Z", + "shell.execute_reply": "2023-12-04T17:50:29.649972Z" } }, "outputs": [ diff --git a/docs/examples/content-personalization.ipynb b/docs/examples/content-personalization.ipynb index b246c6cdae..78dc55b340 100644 --- a/docs/examples/content-personalization.ipynb +++ b/docs/examples/content-personalization.ipynb @@ -31,10 +31,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.050580Z", - "iopub.status.busy": "2023-11-09T07:13:57.050354Z", - "iopub.status.idle": "2023-11-09T07:13:57.074268Z", - "shell.execute_reply": "2023-11-09T07:13:57.073904Z" + "iopub.execute_input": "2023-12-04T17:50:31.083670Z", + "iopub.status.busy": "2023-12-04T17:50:31.083311Z", + "iopub.status.idle": "2023-12-04T17:50:31.114735Z", + "shell.execute_reply": "2023-12-04T17:50:31.114428Z" } }, "outputs": [ @@ -88,10 +88,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.076466Z", - "iopub.status.busy": "2023-11-09T07:13:57.076302Z", - "iopub.status.idle": "2023-11-09T07:13:57.256665Z", - "shell.execute_reply": "2023-11-09T07:13:57.256383Z" + "iopub.execute_input": "2023-12-04T17:50:31.117540Z", + "iopub.status.busy": "2023-12-04T17:50:31.117363Z", + "iopub.status.idle": "2023-12-04T17:50:31.329078Z", + "shell.execute_reply": "2023-12-04T17:50:31.328797Z" } }, "outputs": [], @@ -156,16 +156,16 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.258402Z", - "iopub.status.busy": "2023-11-09T07:13:57.258280Z", - "iopub.status.idle": "2023-11-09T07:13:57.506299Z", - "shell.execute_reply": "2023-11-09T07:13:57.500295Z" + "iopub.execute_input": "2023-12-04T17:50:31.332143Z", + "iopub.status.busy": "2023-12-04T17:50:31.331942Z", + "iopub.status.idle": "2023-12-04T17:50:31.623109Z", + "shell.execute_reply": "2023-12-04T17:50:31.622233Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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BAEAQ7dq1S+3atTtun9M+8JyMSZMm+Z9rI0mHDh1S+/bttW3bNjVv3rxR38vj8WjJkiUaNmxYozw0EPWjzqFBnUODOocOtQ6NYNW5uLhYHTt2PKHf3ad94ElOTlZ+fn5AW35+vmJjY+s9uiNJbrdbbre7Tnt8fLxiY2MbdXwej0fR0dFq1aoV/5mCiDqHBnUODeocOtQ6NIJV59ptnch0lLC7Squh0tPTlZOTE9C2aNEipaenN9GIAABAuAm7wFNSUqI1a9ZozZo1kmouO1+zZo127twpqeZ01E033eTvf8cdd2jr1q36zW9+ow0bNui5557T66+/XueJxAAA4MwVdoFnxYoV6tOnj/r06SNJysrKUp8+fTR58mRJ0p49e/zhR5I6duyo9957T4sWLVKvXr00Y8YMvfzyy8rMzGyS8QMAgPATdnN4hg4dquPdGqi+uygPHTpUq1evDuKoAADA6SzsjvAAAAA0NgIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwvLAMPM8++6zS0tIUGRmpgQMHavny5cftP2vWLHXr1k1RUVFKTU3Vr3/9a1VUVIRotAAAINyFXeCZO3eusrKylJ2drVWrVqlXr17KzMxUQUFBvf1fe+01PfDAA8rOztb69ev1l7/8RXPnztWDDz4Y4pEDAIBwFXaBZ+bMmbrttts0btw49ejRQy+88IKio6P1yiuv1Nv/008/1eDBg/Wzn/1MaWlpGjFihG688cYfPCoEAADOHBFNPYAjVVVVaeXKlZo0aZK/zW63KyMjQ8uWLat3nR//+Mf6+9//ruXLl2vAgAHaunWr5s+frzFjxhzzfSorK1VZWel/XVRUJEnyeDzyeDyNtDfyb/PIfxEc1Dk0qHNoUOfQodahEaw6N2R7YRV49u3bJ6/Xq6SkpID2pKQkbdiwod51fvazn2nfvn06//zzZYxRdXW17rjjjuOe0po2bZqmTp1ap33hwoWKjo4+tZ04hkWLFgVluwhEnUODOocGdQ4dah0ajV3nsrKyE+4bVoHnZOTm5urxxx/Xc889p4EDB2rz5s2aOHGiHnnkEf32t7+td51JkyYpKyvL/7qoqEipqakaMWKEYmNjG3V8Ho9HixYt0sUXXyyn09mo28b3qHNoUOfQoM6hQ61DI1h1rj1DcyLCKvAkJCTI4XAoPz8/oD0/P1/Jycn1rvPb3/5WY8aM0c9//nNJ0nnnnafS0lLdfvvteuihh2S3152m5Ha75Xa767Q7nc6gfcMHc9v4HnUODeocGtQ5dKh1aDR2nRuyrbCatOxyudSvXz/l5OT423w+n3JycpSenl7vOmVlZXVCjcPhkCQZY4I3WAAAcNoIqyM8kpSVlaWxY8eqf//+GjBggGbNmqXS0lKNGzdOknTTTTepbdu2mjZtmiTpiiuu0MyZM9WnTx//Ka3f/va3uuKKK/zBBwAAnNnCLvCMGjVKe/fu1eTJk5WXl6fevXtrwYIF/onMO3fuDDii8/DDD8tms+nhhx/W7t271bp1a11xxRV67LHHmmoXAABAmAm7wCNJEyZM0IQJE+pdlpubG/A6IiJC2dnZys7ODsHIAADA6Sis5vAAAAAEA4EHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYXlgGnmeffVZpaWmKjIzUwIEDtXz58uP2P3jwoMaPH682bdrI7XbrrLPO0vz580M0WgAAEO4imnoAR5s7d66ysrL0wgsvaODAgZo1a5YyMzO1ceNGJSYm1ulfVVWliy++WImJiXrzzTfVtm1b7dixQ3FxcaEfPAAACEthF3hmzpyp2267TePGjZMkvfDCC3rvvff0yiuv6IEHHqjT/5VXXlFhYaE+/fRTOZ1OSVJaWloohwwAAMJcWAWeqqoqrVy5UpMmTfK32e12ZWRkaNmyZfWu8/bbbys9PV3jx4/Xf/7zH7Vu3Vo/+9nPdP/998vhcNS7TmVlpSorK/2vi4qKJEkej0cej6cR90j+7TX2dhGIOocGdQ4N6hw61Do0glXnhmwvrALPvn375PV6lZSUFNCelJSkDRs21LvO1q1btXjxYo0ePVrz58/X5s2bddddd8nj8Sg7O7vedaZNm6apU6fWaV+4cKGio6NPfUfqsWjRoqBsF4Goc2hQ59CgzqFDrUOjsetcVlZ2wn3DKvCcDJ/Pp8TERL344otyOBzq16+fdu/erSeffPKYgWfSpEnKysryvy4qKlJqaqpGjBih2NjYRh2fx+PRokWLdPHFF/tPuaHxUefQoM6hQZ1Dh1qHRrDqXHuG5kSEVeBJSEiQw+FQfn5+QHt+fr6Sk5PrXadNmzZyOp0Bp6/OPvts5eXlqaqqSi6Xq846brdbbre7TrvT6QzaN3wwt43vUefQoM6hQZ1Dh1qHRmPXuSHbCqvL0l0ul/r166ecnBx/m8/nU05OjtLT0+tdZ/Dgwdq8ebN8Pp+/7ZtvvlGbNm3qDTsAAODME1aBR5KysrL00ksv6a9//avWr1+vO++8U6Wlpf6rtm666aaASc133nmnCgsLNXHiRH3zzTd677339Pjjj2v8+PFNtQsAACDMhNUpLUkaNWqU9u7dq8mTJysvL0+9e/fWggUL/BOZd+7cKbv9+5yWmpqq//73v/r1r3+tnj17qm3btpo4caLuv//+ptoFAAAQZsIu8EjShAkTNGHChHqX5ebm1mlLT0/XZ599FuRRAQCA01XYndICAABobAQeAABgeQQeAABgeQQeAABgeQQeAABgeQQeAABgeQQeAABgeQQeAABgeQQeAABgeQQeAABgeQQeAABgeQQeAABgeQQeAABgeQQeAABgeUEPPLm5ucF+CwAAgOMKWuBZunSphg8fruHDhwfrLQAAAE5IRENX8Hg8eu2117Ry5UpFRETo/PPP109/+lP/8jVr1uiBBx7QokWLZIxR//79G3XAAAAADdWgwFNcXKwhQ4boq6++kjFGkvT000/rpz/9qd544w1NnjxZjz/+uHw+n/r27aspU6bo8ssvD8rAAQAATlSDAs/vf/97ffnll+rVq5dGjx4tSfr73/+uf/3rX7rhhhv0+uuvq0uXLnrqqad05ZVXBmXAAAAADdWgwPOf//xHHTp00Oeffy6XyyVJmjBhgrp376433nhDl1xyif71r3/J7XYHZbAAAAAno0GTlrdu3apLL73UH3YkKTIyUpdddpkk6amnniLsAACAsNOgwFNeXq6kpKQ67YmJiZKkbt26Nc6oAAAAGlGjXpZut3MfQwAAEH4afFn62rVr9frrr9dpk6Q33njDf/XWka6//vqTHB4AAMCpa3Dgeeutt/TWW28FtNWGnBtuuKFOu81mI/AAAIAm1aDAM3nyZNlstmCNBQAAICgaFHimTJkSpGEAAAAET4NmGd9yyy16++23gzUWAACAoGhQ4Jk9e7bWrFkTpKEAAAAEB9eRAwAAyyPwAAAAyyPwAAAAy2vwfXjmzZun7du3n3B/m82mv/zlLw19GwAAgEbT4MCzZs2aBk1cJvAAAICm1uDAc/PNN2vs2LHBGAsAAEBQNDjwpKWl6cILLwzGWAAAAIKCScsAAMDyCDwAAMDyCDwAAMDyGhR4Xn31VTkcDj344IPyeDzH7FdVVaUHH3xQ06dPP+UBAgAAnKoGBZ62bdtq8uTJatWqlZxO5zH7uVwuJSQk6KGHHtKSJUtOeZAAAACnokGB529/+5tatmypCRMm/GDf8ePHKz4+Xq+++upJDw4AAKAxNCjwfPrpp8rIyJDb7f7Bvm63WxkZGVq6dOlJDw4AAKAxNCjwfPfdd+rUqdMJ9+/YsaP27NnT4EEBAAA0pgYFHrvdftzJykfzeDyy27kQDAAANK0GpZGUlBStXbv2hPuvXbtWbdu2bfCgAAAAGlODAs8FF1ygxYsXn9DT0rdv367FixdryJAhJzs2AACARtGgwDN+/Hh5PB5de+212rdv3zH77d+/X9ddd52qq6t15513nvIgAQAATkWDHh7at29f3X333Zo1a5Z69OihO+64Q8OGDVO7du0kSbt371ZOTo5efPFF7d27V1lZWerbt29QBg4AAHCiGvy09BkzZigyMlJPPvmkHnvsMT322GMBy40xcjgcmjRpkh599NFGGygAAMDJanDgsdlsevzxx3Xrrbfq1Vdf1aeffqq8vDxJUnJysgYPHqybb75ZnTt3bvTBAgAAnIwGB55anTt35ggOAAA4LXCTHAAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHlhG3ieffZZpaWlKTIyUgMHDtTy5ctPaL05c+bIZrNp5MiRwR0gAAA4bYRl4Jk7d66ysrKUnZ2tVatWqVevXsrMzFRBQcFx19u+fbvuvfdeXXDBBSEaKQAAOB2EZeCZOXOmbrvtNo0bN049evTQCy+8oOjoaL3yyivHXMfr9Wr06NGaOnWqOnXqFMLRAgCAcBfR1AM4WlVVlVauXKlJkyb52+x2uzIyMrRs2bJjrve73/1OiYmJuvXWW/Xxxx8f9z0qKytVWVnpf11UVCRJ8ng88ng8p7gHgWq319jbRSDqHBrUOTSoc+hQ69AIVp0bsr2wCzz79u2T1+tVUlJSQHtSUpI2bNhQ7zqffPKJ/vKXv2jNmjUn9B7Tpk3T1KlT67QvXLhQ0dHRDR7ziVi0aFFQtotA1Dk0qHNoUOfQodah0dh1LisrO+G+YRd4Gqq4uFhjxozRSy+9pISEhBNaZ9KkScrKyvK/LioqUmpqqkaMGKHY2NhGHZ/H49GiRYt08cUXy+l0Nuq28T3qHBrUOTSoc+hQ69AIVp1rz9CciLALPAkJCXI4HMrPzw9oz8/PV3Jycp3+W7Zs0fbt23XFFVf423w+nyQpIiJCGzduVOfOnQPWcbvdcrvddbbldDqD9g0fzG3je9Q5NKhzaFDn0KHWodHYdW7ItsJu0rLL5VK/fv2Uk5Pjb/P5fMrJyVF6enqd/t27d9fXX3+tNWvW+D+uvPJKDRs2TGvWrFFqamoohw8AAMJQ2B3hkaSsrCyNHTtW/fv314ABAzRr1iyVlpZq3LhxkqSbbrpJbdu21bRp0xQZGalzzz03YP24uDhJqtMOAADOTGEZeEaNGqW9e/dq8uTJysvLU+/evbVgwQL/ROadO3fKbg+7g1MAACBMhWXgkaQJEyZowoQJ9S7Lzc097rqzZ89u/AEBAIDTFodJAACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4gmz2sh36y0a7vD7T1EMBAOCMReAJorKqaj02f6O+KrTrlU+3N/VwAAA4YxF4gqi8yuv/fOnmwiYcCQAAZzYCTxDZbDb/58ZwSgsAgKZC4Aki2xGf+wg8AAA0GQJPEB1xgEfMWQYAoOkQeILIdsQxHo7wAADQdAg8IULeAQCg6RB4gumIU1rkHQAAmg6BJ0S48SAAAE2HwBNER05a5rJ0AACaDoEniAIvS2+yYQAAcMYj8ATRkTce5JQWAABNh8ATIuvzipt6CAAAnLEIPAAAwPIIPAAAwPIIPAAAwPIIPEHEpegAAIQHAg8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ak8QMWUZAIDwQOABAACWR+ABAACWR+ABAACWR+ABAACWR+ABAACWR+AJIp4sAQBAeCDwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwBBOTlgEACAsEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEniAyXKYFAEBYIPAAAADLI/AAAADLI/CEkOHx6QAANAkCTwh5fQQeAACaAoEniI4+oOPlCA8AAE2CwBNCPl9TjwAAgDMTgSeEPCQeAACaRNgGnmeffVZpaWmKjIzUwIEDtXz58mP2femll3TBBReoZcuWatmypTIyMo7bv6l4qgk8AAA0hbAMPHPnzlVWVpays7O1atUq9erVS5mZmSooKKi3f25urm688UYtWbJEy5YtU2pqqkaMGKHdu3eHeOTHV82kZQAAmkRYBp6ZM2fqtttu07hx49SjRw+98MILio6O1iuvvFJv/3/84x+666671Lt3b3Xv3l0vv/yyfD6fcnJyQjzyQEfHmyqO8AAA0CQimnoAR6uqqtLKlSs1adIkf5vdbldGRoaWLVt2QtsoKyuTx+NRfHx8vcsrKytVWVnpf11UVCRJ8ng88ng8pzD6QEdvq6KqSh6Ps9G2j+/V1roxv36oizqHBnUOHWodGsGqc0O2F3aBZ9++ffJ6vUpKSgpoT0pK0oYNG05oG/fff79SUlKUkZFR7/Jp06Zp6tSpddoXLlyo6Ojohg/6GEo80pElzlnyodo03uZRj0WLFjX1EM4I1Dk0qHPoUOvQaOw6l5WVnXDfsAs8p2r69OmaM2eOcnNzFRkZWW+fSZMmKSsry/+6qKjIP+8nNja20cZSWFqlh1bk+l+nDz5fPdo03vbxPY/Ho0WLFuniiy+W08lRtGChzqFBnUOHWodGsOpce4bmRIRd4ElISJDD4VB+fn5Ae35+vpKTk4+77lNPPaXp06frgw8+UM+ePY/Zz+12y+1212l3Op2N+oVwOgNn8Ribg/9QQdbYX0PUjzqHBnUOHWodGo3/e/bEtxV2k5ZdLpf69esXMOG4dgJyenr6Mdd74okn9Mgjj2jBggXq379/KIbaYNVeJi0DANAUwu4IjyRlZWVp7Nix6t+/vwYMGKBZs2aptLRU48aNkyTddNNNatu2raZNmyZJ+v3vf6/JkyfrtddeU1pamvLy8iRJMTExiomJabL9OPphoVUEHgAAmkRYBp5Ro0Zp7969mjx5svLy8tS7d28tWLDAP5F5586dstu/Pzj1/PPPq6qqStdee23AdrKzszVlypRQDv24qr3chwcAgKYQloFHkiZMmKAJEybUuyw3Nzfg9fbt24M/oEbg4QgPAABNIuzm8FiZhyM8AAA0CQJPCHGEBwCApkHgCaKjj+cQeAAAaBoEnhBi0jIAAE2DwBNCXJYOAEDTIPCEEDceBACgaRB4QoirtAAAaBoEniA66kbL8vg4wgMAQFMg8IRQVTWBBwCApkDgCaEKD4EHAICmQOAJoQqPt6mHAADAGYnAE0LlVQQeAACaAoEnhMo5wgMAQJMg8ASROerhEgQeAACaBoEnhJjDA5w+KjxeFZZWyRgjc/Q9Jk5ye8YY+XzG/7PA6+PeXECoRDT1AM4kBJ7gMkaa9O//qaCkSncN7axOCc204H95+teq3bqga4LGDOqgxNjIph4mQqCkslpF5R7tLCzTF9sKVVJZLdmkaGeEbDapqNyj0iqv4qKd6poYo8LSKu0qLFMzd4TKqrzK2ZCvXYXl/u3ZbVJKXJRSWkQpqUWktu0r0bcHyhUf7dLekkqVVXnlsNsUGWFXtCtCPmNU7TOy2yR3hEN7SypVVe2TK8Je5/YUXRNjdHabWMU3c6lFlFNtWkRq5/5SLd9k1+r5G5TSMlqdW8fIZpPKqrz67mC5jJF2HyxXbKRT0W6H4qNdinI51DLape7JzdW6uVs2my3UZQ8bxhgdLPOostonp8Omkspq2WST22lX6xi37PYztzYN4fMZFRRXymaTbDYpwm5Xtdenap9RYWmVPF6f4pvVfO9FOR0qKK5UcUXN/73vDpZrX0mlbDabvD4jm/HpwH6bLm3C/SHwhBCntBrH7oPlenLBBs1b853uHXGWfnJuG/116Vb93+cRknZLkj76Zm/AOmt2HdQzizdLkgZ0jFfPti00b81udUmM0dBuiXI67LrwrAS1jHYpwm5Xi2hnqHcL9Siu8GhzQYniol1KaxUtn5FKq6rlcth1oKxKy7bsV2mVV5UeryqrfSquqNYX2wu1cseBRh2Hz0jfHijXtwfKA9oPlnn8n3t9RlXVPhVVVB9zO/Xdi2tTQYk2FZTU09uuFft2ntR4W0Y71SUxRrGRTvmMUWp8tNrHR6t1c7fatIiS02FTbJRTTrtdNlvNc/6aR0ao0uNTi2iniiuqVVBUoUPlHvmMkc1mU7TToSqvT62auZUU65bb6VCMO0I+n9HG/GIdLPPoUHmVtu8v0/6SStntNrkddtlsNrki7HI57DIyqvT45Iywa29xpbw+o3Yto5QaH63UltFqFx8lr9fo2wPlqqj2qqzKq1bNXDpQVqX/fVekd778Tk6HXc0jIxTtcsjjNbJJqvYZlVRWa29xTfgsqfQc8zYgTodNSbGRat3crWiXQ4kxLhUV2PXVgo3aX+rRoXKPSiqr5Y5waH9plX+9onKPWjd3KyHGpaKKapVUVCu+mUt2u01V1V5VeGoCrTvCruTYSLVpEalWMW5FOR1qHetW6xi3nA57zS9/W80fwE6HXUmxkWp1eDs+n1FltU/uiJqvizFSRbVXLoddPlPzPRbhsCnvUIX2lVSq2mcUGeFQflGFCoor9eWug6qs9qqwzCPb4X2trPapqtqnvcWV/v1xOmwqq/L6vy5up13VXiOP16hltFMRDru+O1iu4gqPGvMgZPtmdk1qvM01GIEnhLhKq2HyiypUVe3T17sP6ayk5uqSGKOC4goNnr7Y3+ephd/oqYXfNGi7y7cVavm2QknSvpJCfba15vNHjuo3ZlAHJbeI1Icb98pul/p1aKnUltEa1j1RSbGRqvb6NPWdderUuplG9m6ruGhnSP+qLiiuUITdrgqPV21aRIb9X/Q+n1FeUYVe/nibVuwo1Kb8ErWKcSmlRZTim7m0bk+Riio8qqr2yemw61C5J2B9u00n9cM3xh2hc9vGKtLpkCSVVFQryuVQYvNIGWP0TUGxCkuq1DWpueKinTpQ5lH35Ob62YD2at3crd0Ha0LOdwfLtauwTN8dqpAk9WoXJ0naX1qp1jFuxTdzyWekQ+UeVXi8auZ2yOeTjKQIu03ntI3V3uJKSVJCjFt7iysVG+nUlr0lWrenSDv2l6qy2qfdB8plt0nO8v3q0KG99pV4tHlviUorq+WKsKtltEuxkU45HTbFRbt0qNyj8iqv9pVUyuP1aWdhmQ6UefTF9sYNfceqbbXPF/b3GIuw22Sz1Tzex3M4UAWGV7v03Y4f3E7t90Jjs9kk++EjIUe2NcKZ1OMqq+d30r6SyjptR4/FZqv52se4I3So3BOwnaRYt6JdEUqOjVRKXJSMjCLstpo/SvbvDsp+nCgCTzAd9c0a7j8UmlJltVevLt2u6e9vOKXt9E5toVdvHqDKap++PVCmpNhItWsZJakm6Lz95Xf6Ynuhvsmv7y/qQP/3WeAPwNpg5LDbNKxboj5Yn+9fNvWddZKkbknNdeOAVF3Zu61ankQAqvB4VVTu0TOLN2vL3hL5jFFZlVeREQ7tK6nU1n2l9a4XF+1UWaVXVV6ferZroYu6J6pbUnP1bh+n5MOn8U5kLB6vT5vyS9QlMcZ/+iV3Y4EWrcvXgTKPereL1cFCm7Ys2aLiypqjKpvzS3ReuxZqGxelguJK/WvVt9pbUhMCCorr/vA8Un1HTWrU/UF8rLDTJTFGzVwORTjsirDbZCRd1TtF1/RtpyqvT7GRp3a07qyk5gH/norE5t+fUk06/HVp36omRB/J4/Fo/vz5uvTSHnI6Gzb+Co9XmwtKtCGvWPsP//IqLKvSloJS7S+trDl6UuVVcWXNkSibTXI57Ko86uhTtMuh1s3dioxwyGeMDpV7ZLfZ5PH6/EcKSg5vwxVhV1JsTd/kFpFq1zL6cEA18h4+alFzhMKnyAiHyqq8atnMqWauCO0pqtC3hWXadaBchYe32yLKKXdEzZGH8iqfKqu96pbUXD/ukqAebZqrsNSjap9PZVVeNXNHyG6TmrkiFBsVoVbNao7ctG0ZpajDR6XcETVht9rrU0Fxpb47WK68ogqVV3mVf6hcn3+1Uakd2qt18yg1c9fst89X8/+hdXO3SiqrFRftUoXHq4KiCjkddsVFuw4fATGKsNsV6XTI4605mlJQXKG8ogoVFFWq3OPVvpIqFRRVqPrw0R1jpEhnzdGe/aVVMkbyHpVujhd2Iuw2xTdzyRVhV0lltVrHuNW6uVtnJTVXYqxbrQ6Hb58xctrtinQ55HLY1D6+mRz2mq9hpNPh//lS7fXJbrfJaa/5Q6OyuqauqfHRatXMJZejZrpvtc/4w2PN907NJ16fkcfrkzFSlMtR75hrvqd3ndD3cLAQeELoTD6ltXxbobbvK9VP+7ZVxOH/PH/5ZJumzV+vfh1a6vPDR1xO1DV922nG9b1U4fFq/Z4idUuM1qL/LtCllw70/4JIbhE4X2dgp1Ya2KlVnW2Zw/MtCkurVFBUc776teU7NfeLXQF/cTnsNX+BeX0mIOwcaWN+saa8s05TDgego9dt7o7Qlb1T1Ld9S32+bb/2l1TJbrepZ9sWmvPFrpP+C/LIUytffXtIX317KGC5zSZ1iI9WUmyk8osqFO2KUM92LVRcWa3t+0rVKsatCo/Xf+TrWGr22yFt3BLQvnx73fWOF3YuO6+NYtwRGtAxXkUVHu0+UC6P16fOiTFqHx99+BeCQz3btZDTYZfPGG3KL9H+0koN6tRKe4srVVBcoe7JsWrmPvaPsdqjOmeSSKdD57ZtoXPbtjhuv5rJ2Do8P6PmdIrNVvNzymG3+UNCfSqrvar21hyxK6v0qmtSTKPUurSyWjabFO1qvF9NR+5HhMNeMxcrLsrf5vF4lFqy/qTCZWOoPhwgPd6aI5uREQ5VVHvlO5x4ol0RNYHEZpPdblO116e4aJccTTAPyXWM93TYbXLYw///GoEnhM6kU1orthdqza6Dej53S8B58Jc/2Vrn6Mrxws6fftZHSbGRstts+u28tZKkezPP0kXdkyTV/HDv076lPB7PMbfxQ2w2m/+8fu1f3Y9ffZ4ev/q8evtvyi/Wa8t36tWl29XM5dDXUzK1o7BMX+46qJwNBdq6t0T/+64oYJ3a4FRcWa1/fL5T//g8cG7GonX1B6jOrZupsLRK7eOjVe7xKi7apUEd45XcIkq7D5ZpZO+aALl+T5E+2bxPPp/ROW1b6JNNe5WzvkDVh9/XGGn7/jJt31/m3/a6PUX1vuexdEpoptbNXfp6V6HKqm1q1cyl+GY1P3i7JjXXloIS2e01RzHOTYnVOW1b6EBpVc1f+3apTYuaXzKJzd3HDSnHcl6773+Bp8ZHKzU+usHbwPdstu//Upfkn8h7ImHDHeGQO0Lq3DqmUcd0Mt8Xp7uIw/N4jtRCzCEMhjPvu6sJlR++LDXc51qcqvGvrdJ7X+2pd9nxTiXddkFHjRmUpvat6v9FNn/iBY0yvlPVNam5sq84R9lXnONv65jQTB0Tmmlkn7aSpK17S7Qxr1h7DlXos6371T25uTYVlCg1PloHSqs0/+s9Kq3yyh1RcyohNT5KuwrLNbBjvB6+rIcSY911fggeT8eEZrr0vDb+12MGdZAkFZZWaenmfXJH2PVNfrG+O1Shbw+Uq0WUUy6HXat3HlCk06HOiTEqqag5ZfHzCzopIcalLXtL1SLKqZ7tWvh/EX1/quXSJvlrGABOFoEnxMo93kY9XHssuwrL9MKHW3T7kE7q0KpZowWtogqP8g9VaG9JpX7cOaHO8uIKzzHDzt0ZXfXVt4e0eEOBpJqJm2/dma7mkU5FuxyWOv3QqXWMOh3+6/eW8zvWWf77a3pKUtAvj41v5tIVvVIkSSPOSW7Qul0bYc4KAIQLAk8Q1c7+sMnIbq+ZoFZcUd2gwDPt/fVasf2AXrn5R2oRVfMXdbXXpwiHXUUVNadx9hysULfkml9Ouw+Wa1N+sW5+9QtJCjh18rdbBuj8Lgkn9EvWGKMte0vl9RltLihRz3YtdNMry7XtiEmzI3unaNpPeyrK5ZAxRh0nzQ/YxvOj++rHnRMUGxURELaKKjynPJH0dMd9QAAgtAg8IWCTFBsZoQNlHhWVe+o9VXHkEZh3vvxOL360VU9d10t//nCrJKnX1IVqGe3U0G6Jever7/TLi7pq5qLvL8e+qneKkmMj9fGmfcecm3HTK8uVHBupnHsuVDN3hA6UVinq8JGV8iqvqqp9+u+6PEnSb9786gf3a96a7zRvzXf1LotxR+iSI06xHOlMDzsAgNAj8IRI89rAUxE4ubaguEIDHsuRJP1hVC9d3aedfvnP1ZKkzFkfBfQ9UObRv1fX3MfgyLAjSf85RvA4Wl5Rhc7J/q/GDOqgt1Z9q5bRrgZfGeSw29SvQ0ut31Ok4npusvbz8zvq9gs7NWibAAAEE4EnRGqOapSrqDwwILzz5ffzXX4990td3addo73nU9fVXLb98Ly1uv8n3fW3Zdu15/BN02rvMVNWdfywkxDjVt/2cXJF2LW5oEQvjunvn1ScX1Shn730mbbs/f4017zxg9U7Na7R9gEAgMZA4AmR2MiaUh99hCchxhXw+ujlJ+uLhzLUurlbkjR6YHvZbDbdObSzlmws0LjD83uOpV+Hlrq4R5LSO7VSr+OEl6TYSOXcM7RRxgsAQDAReEIk9vCE46KjbpfvO+p2mn/8YFOddXu1a6EHLjlb3ZOb66vdh1Th8eqtld9qz6EKvXFHutwRdm3ML5bDZlO1z6hT62YBN9s6csLwsG6J2j79Ms1euk1/WrJFr902UB99s1dX9W7rD0gAAFgNgSeIjswy3x/hqT5iudGv534ZsM7Ln2zzf/7hfUP19AebdNewLuqSWHOJ84VntZYkZR51iXH35NgGje3mwR118+Cay6Ub45b5AACEMwJPKNhqJi1LCngg4g9NFu7QqplmjuodzJEBAHBGsDf1AM4ULaNr5ursL6l5zEK116ePvtl3zP5Hz+0BAAAnjyM8IdK6eU2AKSiu0O6D5Ro8fXHA8oQYlyo9Pv8TjH97eY+QjxEAAKviCE+ItI6pmRC8t7iyTtiRpGd/1lcPXna2//XlPVNCNjYAAKyOIzxBZPT9rOXaK6A25BXX23dgp1Ya2KmVOsRHq0tijBw8egAAgEZD4AkBm6TWx5mTE3XEQzN/3KXuAzkBAMCp4ZRWiLSMdh3zqE25xxvi0QAAcGYh8ISI3W7zz+M52ozreoV4NAAAnFk4pRVC7eOjlVdU4X99QdcE/e2WAQF3QgYAAI2PIzxBdNRTI9Th8EM3a710U3/CDgAAIUDgCaG0hGb+z/u2j1PkEZOVAQBA8BB4Qiit1feBx+mg9AAAhAq/dUMoLeH7U1oEHgAAQoffuiFU+8RzqeaOywAAIDQIPCHkjvh+zs7G/PrvuAwAABofgSeIzA93AQAAIUDgCYEjLzx/4pqekqTxwzo3zWAAADgDcePBELuufzsN7BSv1JbRP9wZAAA0CgJPiNlsNnU44vJ0AAAQfJzSAgAAlkfgCSJz9LMlAABAkyDwhABPywIAoGkReAAAgOUReAAAgOUReAAAgOUReAAAgOUReIKIi7QAAAgPBJ5Q4DItAACaFIEHAABYHoEHAABYHoEHAABYHoEHAABYXtgGnmeffVZpaWmKjIzUwIEDtXz58uP2f+ONN9S9e3dFRkbqvPPO0/z580M00h/GnGUAAJpWWAaeuXPnKisrS9nZ2Vq1apV69eqlzMxMFRQU1Nv/008/1Y033qhbb71Vq1ev1siRIzVy5EitXbs2xCMHAADhKCwDz8yZM3Xbbbdp3Lhx6tGjh1544QVFR0frlVdeqbf/008/rZ/85Ce67777dPbZZ+uRRx5R37599ac//SnEIwcAAOEo7AJPVVWVVq5cqYyMDH+b3W5XRkaGli1bVu86y5YtC+gvSZmZmcfsHyoHyzxN+v4AAKBGRFMP4Gj79u2T1+tVUlJSQHtSUpI2bNhQ7zp5eXn19s/Ly6u3f2VlpSorK/2vDx06JEkqLCyUx9N4IeWbnXvlqyyTcRjt379fTqez0baNQB6PR2VlZdQ5yKhzaFDn0KHWoRGsOhcXF0uSzAk82iDsAk8oTJs2TVOnTq3T3rFjx6C9Z5sZQds0AABntOLiYrVo0eK4fcIu8CQkJMjhcCg/Pz+gPT8/X8nJyfWuk5yc3KD+kyZNUlZWlv+1z+dTYWGhWrVqJZutca+pKioqUmpqqnbt2qXY2NhG3Ta+R51DgzqHBnUOHWodGsGqszFGxcXFSklJ+cG+YRd4XC6X+vXrp5ycHI0cOVJSTSDJycnRhAkT6l0nPT1dOTk5uvvuu/1tixYtUnp6er393W633G53QFtcXFxjDP+YYmNj+c8UAtQ5NKhzaFDn0KHWoRGMOv/QkZ1aYRd4JCkrK0tjx45V//79NWDAAM2aNUulpaUaN26cJOmmm25S27ZtNW3aNEnSxIkTdeGFF2rGjBm67LLLNGfOHK1YsUIvvvhiU+4GAAAIE2EZeEaNGqW9e/dq8uTJysvLU+/evbVgwQL/xOSdO3fKbv/+ArMf//jHeu211/Twww/rwQcfVNeuXTVv3jyde+65TbULAAAgjIRl4JGkCRMmHPMUVm5ubp226667Ttddd12QR9Vwbrdb2dnZdU6hoXFR59CgzqFBnUOHWodGONTZZk7kWi4AAIDTWNjdeBAAAKCxEXgAAIDlEXgAAIDlEXgAAIDlEXiC6Nlnn1VaWpoiIyM1cOBALV++vKmHFNY++ugjXXHFFUpJSZHNZtO8efMClhtjNHnyZLVp00ZRUVHKyMjQpk2bAvoUFhZq9OjRio2NVVxcnG699VaVlJQE9Pnqq690wQUXKDIyUqmpqXriiSeCvWthZdq0afrRj36k5s2bKzExUSNHjtTGjRsD+lRUVGj8+PFq1aqVYmJidM0119S5m/nOnTt12WWXKTo6WomJibrvvvtUXV0d0Cc3N1d9+/aV2+1Wly5dNHv27GDvXth4/vnn1bNnT/+N1tLT0/X+++/7l1Pj4Jg+fbpsNlvAjWip9ambMmWKbDZbwEf37t39y0+LGhsExZw5c4zL5TKvvPKK+d///mduu+02ExcXZ/Lz85t6aGFr/vz55qGHHjL/+te/jCTz73//O2D59OnTTYsWLcy8efPMl19+aa688krTsWNHU15e7u/zk5/8xPTq1ct89tln5uOPPzZdunQxN954o3/5oUOHTFJSkhk9erRZu3at+ec//2mioqLMn//851DtZpPLzMw0r776qlm7dq1Zs2aNufTSS0379u1NSUmJv88dd9xhUlNTTU5OjlmxYoUZNGiQ+fGPf+xfXl1dbc4991yTkZFhVq9ebebPn28SEhLMpEmT/H22bt1qoqOjTVZWllm3bp155plnjMPhMAsWLAjp/jaVt99+27z33nvmm2++MRs3bjQPPvigcTqdZu3atcYYahwMy5cvN2lpaaZnz55m4sSJ/nZqfeqys7PNOeecY/bs2eP/2Lt3r3/56VBjAk+QDBgwwIwfP97/2uv1mpSUFDNt2rQmHNXp4+jA4/P5THJysnnyySf9bQcPHjRut9v885//NMYYs27dOiPJfPHFF/4+77//vrHZbGb37t3GGGOee+4507JlS1NZWenvc//995tu3boFeY/CV0FBgZFkPvzwQ2NMTV2dTqd54403/H3Wr19vJJlly5YZY2rCqd1uN3l5ef4+zz//vImNjfXX9je/+Y0555xzAt5r1KhRJjMzM9i7FLZatmxpXn75ZWocBMXFxaZr165m0aJF5sILL/QHHmrdOLKzs02vXr3qXXa61JhTWkFQVVWllStXKiMjw99mt9uVkZGhZcuWNeHITl/btm1TXl5eQE1btGihgQMH+mu6bNkyxcXFqX///v4+GRkZstvt+vzzz/19hgwZIpfL5e+TmZmpjRs36sCBAyHam/By6NAhSVJ8fLwkaeXKlfJ4PAG17t69u9q3bx9Q6/POO89/93Oppo5FRUX63//+5+9z5DZq+5yJ/we8Xq/mzJmj0tJSpaenU+MgGD9+vC677LI69aDWjWfTpk1KSUlRp06dNHr0aO3cuVPS6VNjAk8Q7Nu3T16vN+ALK0lJSUnKy8trolGd3mrrdrya5uXlKTExMWB5RESE4uPjA/rUt40j3+NM4vP5dPfdd2vw4MH+R7Hk5eXJ5XLVeaDu0bX+oToeq09RUZHKy8uDsTth5+uvv1ZMTIzcbrfuuOMO/fvf/1aPHj2ocSObM2eOVq1a5X++4pGodeMYOHCgZs+erQULFuj555/Xtm3bdMEFF6i4uPi0qXHYPloCQPCNHz9ea9eu1SeffNLUQ7Gkbt26ac2aNTp06JDefPNNjR07Vh9++GFTD8tSdu3apYkTJ2rRokWKjIxs6uFY1iWXXOL/vGfPnho4cKA6dOig119/XVFRUU04shPHEZ4gSEhIkMPhqDNDPT8/X8nJyU00qtNbbd2OV9Pk5GQVFBQELK+urlZhYWFAn/q2ceR7nCkmTJigd999V0uWLFG7du387cnJyaqqqtLBgwcD+h9d6x+q47H6xMbGnjY/IE+Vy+VSly5d1K9fP02bNk29evXS008/TY0b0cqVK1VQUKC+ffsqIiJCERER+vDDD/XHP/5RERERSkpKotZBEBcXp7POOkubN28+bb6fCTxB4HK51K9fP+Xk5PjbfD6fcnJylJ6e3oQjO3117NhRycnJATUtKirS559/7q9penq6Dh48qJUrV/r7LF68WD6fTwMHDvT3+eijj+TxePx9Fi1apG7duqlly5Yh2pumZYzRhAkT9O9//1uLFy9Wx44dA5b369dPTqczoNYbN27Uzp07A2r99ddfBwTMRYsWKTY2Vj169PD3OXIbtX3O5P8DPp9PlZWV1LgRDR8+XF9//bXWrFnj/+jfv79Gjx7t/5xaN76SkhJt2bJFbdq0OX2+nxtl6jPqmDNnjnG73Wb27Nlm3bp15vbbbzdxcXEBM9QRqLi42KxevdqsXr3aSDIzZ840q1evNjt27DDG1FyWHhcXZ/7zn/+Yr776ylx11VX1Xpbep08f8/nnn5tPPvnEdO3aNeCy9IMHD5qkpCQzZswYs3btWjNnzhwTHR19Rl2Wfuedd5oWLVqY3NzcgEtMy8rK/H3uuOMO0759e7N48WKzYsUKk56ebtLT0/3Lay8xHTFihFmzZo1ZsGCBad26db2XmN53331m/fr15tlnnz2jLuN94IEHzIcffmi2bdtmvvrqK/PAAw8Ym81mFi5caIyhxsF05FVaxlDrxnDPPfeY3Nxcs23bNrN06VKTkZFhEhISTEFBgTHm9KgxgSeInnnmGdO+fXvjcrnMgAEDzGeffdbUQwprS5YsMZLqfIwdO9YYU3Np+m9/+1uTlJRk3G63GT58uNm4cWPANvbv329uvPFGExMTY2JjY824ceNMcXFxQJ8vv/zSnH/++cbtdpu2bdua6dOnh2oXw0J9NZZkXn31VX+f8vJyc9ddd5mWLVua6Ohoc/XVV5s9e/YEbGf79u3mkksuMVFRUSYhIcHcc889xuPxBPRZsmSJ6d27t3G5XKZTp04B72F1t9xyi+nQoYNxuVymdevWZvjw4f6wYww1DqajAw+1PnWjRo0ybdq0MS6Xy7Rt29aMGjXKbN682b/8dKixzRhjGudYEQAAQHhiDg8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ag+AoLLZbBo6dGhTD6PR5ObmymazacqUKU09FAANQOABEHI333yzbDabtm/f3tRDqZfVQhoAKaKpBwDA2tavX6/o6OimHkajGTBggNavX6+EhISmHgqABiDwAAiq7t27N/UQGlV0dLTl9gk4E3BKC4CkwLkpK1as0MUXX6zmzZurRYsWuvrqq0/69NPRp4fS0tL017/+VZLUsWNH2Wy2ek8hbdu2TT//+c/Vvn17ud1utWnTRjfffLN27NhxzPfYvXu3brrpJiUnJ8tutys3N1eStGTJEt1yyy3q1q2bYmJiFBMTo/79++vFF1+stwaS9OGHH/rHZrPZNHv27Dp1OtratWt1/fXXKzExUW63Wx07dtTdd9+t/fv31+mblpamtLQ0lZSUaOLEiUpJSZHb7VbPnj315ptv1ul/6NAhTZ48WT169FBMTIxiY2PVpUsXjR07tt6aAAjEER4AAb744gs98cQTGjZsmH7xi19o9erVmjdvnr7++mutXbtWkZGRp7T9u+++W7Nnz9aXX36piRMnKi4uTlJNAKj1+eefKzMzU6Wlpbr88svVtWtXbd++Xf/4xz/0/vvva9myZerUqVPAdvfv36/09HTFx8frhhtuUEVFhWJjYyVJv//977V582YNGjRIV199tQ4ePKgFCxboF7/4hTZu3KgZM2b4x5Cdna2pU6eqQ4cOuvnmm/3b792793H365NPPlFmZqaqqqp07bXXKi0tTcuWLdPTTz+td999V5999lmd02Aej0cjRozQgQMHdM0116isrExz5szR9ddfrwULFmjEiBGSJGOMMjMz9fnnn2vw4MH6yU9+Irvdrh07dujtt9/WmDFj1KFDh5P4agBnkEZ77jqA09qSJUuMJCPJzJkzJ2DZmDFjjCTzz3/+s8HblWQuvPDCgLaxY8caSWbbtm11+ldVVZm0tDTTvHlzs2rVqoBlH3/8sXE4HObyyy+v8x6SzLhx40x1dXWdbW7durVOm8fjMRdffLFxOBxmx44dPzjmWrV1ys7O9rd5vV7TuXNnI8ksWLAgoP99991nJJlbbrkloL1Dhw5GkrnqqqtMZWWlv/2DDz4wkkxmZqa/7auvvjKSzMiRI+uMp6KiwhQXF9c7VgDf45QWgABDhgzRqFGjAtpuueUWSTVHf4Lt3Xff1fbt23XfffepT58+AcvOP/98XXXVVZo/f76KiooClrlcLj3xxBNyOBx1ttmxY8c6bREREbrjjjvk9Xq1ZMmSUxrz0qVLtWXLFl1yySXKzMwMWDZ58mTFx8frtddeU1VVVZ11//CHP8jlcvlfDx8+XB06dKi31lFRUXXa3G63YmJiTmn8wJmAU1oAAvTr169OW7t27SRJBw8eDPr7f/bZZ5KkjRs31jtPJi8vTz6fT99884369+/vb+/YseMxr5wqLi7WU089pXnz5mnLli0qLS0NWP7dd9+d0phXr14tSfVeyl47X2jhwoXauHGjzjvvPP+yuLi4esNYu3bttGzZMv/rs88+Wz179tQ///lPffvttxo5cqSGDh2q3r17y27n71bgRBB4AASonfdypIiImh8VXq836O9fWFgoSfrHP/5x3H5Hh5akpKR6+1VVVWno0KFatWqV+vTpozFjxqhVq1aKiIjQ9u3b9de//lWVlZWnNObao03HGkObNm0C+tVq0aJFvf0jIiLk8/kCXi9evFhTpkzRW2+9pXvuuUeS1Lp1a02YMEEPPfRQvUe2AHyPwAMgrNQGrnfeeUeXX375Ca9Xe3XV0f7zn/9o1apVuvXWW/Xyyy8HLJszZ47/irFTUTvm/Pz8epfn5eUF9DsZrVq10jPPPKM//vGP2rBhgxYvXqxnnnlG2dnZcjqdmjRp0klvGzgTcCwUQMjVHo2o74jRwIEDJSnglM6p2LJliyTpqquuqrPs448/rncdu93eoKNZtXONai+DP1JpaalWrFihqKgodevW7YS3eSw2m01nn322xo8fr0WLFkmS3n777VPeLmB1BB4AIRcfHy9J2rVrV51lV111ldq3b6+ZM2fqo48+qrPc4/Hok08+OeH3qr1c++h1PvzwQ7300kvHHN+33357wu8xePBgde7cWe+//74++OCDgGWPPvqo9u/frxtvvDFgcnJDbN++vd77INUeUTrVWwUAZwJOaQEIuYsuukhPPfWUbr/9dl1zzTVq1qyZOnTooDFjxsjtduvNN9/UJZdcogsvvFAXXXSRzjvvPNlsNu3YsUMff/yxWrVqpQ0bNpzQe11xxRVKS0vTE088obVr1+rcc8/Vxo0b9e677+rqq6+u9yZ/F110kV5//XWNHDlSffr0kcPh0JVXXqmePXvW+x52u12zZ89WZmamLr30Ul133XXq0KGDli1bptzcXHXu3FnTp08/6XqtWbNGP/3pTzVgwAD16NFDycnJ2r17t+bNmye73a5f//rXJ71t4ExB4AEQcpdccomeeOIJvfTSS5oxY4Y8Ho8uvPBCjRkzRpL0ox/9SF9++aWefPJJzZ8/X0uXLpXb7Vbbtm01cuRI3XjjjSf8XjExMVq8eLHuu+8+ffTRR8rNzdU555yjf/zjH0pKSqo38Dz99NOSpMWLF+udd96Rz+dTu3btjhl4pJpL5j/77DP97ne/08KFC3Xo0CGlpKRo4sSJevjhh0/p2Vv9+/fX/fffr9zcXL333ns6ePCgkpOTlZGRofvuu0+DBg066W0DZwqbMcY09SAAAACCiTk8AADA8gg8AADA8pjDA6BBZs2adUJ3XL755psDHggKAE2JOTwAGiQtLU07duz4wX5Lliyp91ELANAUCDwAAMDymMMDAAAsj8ADAAAsj8ADAAAsj8ADAAAsj8ADAAAsj8ADAAAsj8ADAAAsj8ADAAAsj8ADAAAs7/8DqCGiBVovThIAAAAASUVORK5CYII=", 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" ] @@ -206,16 +206,16 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.512046Z", - "iopub.status.busy": "2023-11-09T07:13:57.510704Z", - "iopub.status.idle": "2023-11-09T07:13:57.614851Z", - "shell.execute_reply": "2023-11-09T07:13:57.614595Z" + "iopub.execute_input": "2023-12-04T17:50:31.626258Z", + "iopub.status.busy": "2023-12-04T17:50:31.626074Z", + "iopub.status.idle": "2023-12-04T17:50:31.721334Z", + "shell.execute_reply": "2023-12-04T17:50:31.720854Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -242,10 +242,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.616481Z", - "iopub.status.busy": "2023-11-09T07:13:57.616370Z", - "iopub.status.idle": "2023-11-09T07:13:57.625528Z", - "shell.execute_reply": "2023-11-09T07:13:57.625279Z" + "iopub.execute_input": "2023-12-04T17:50:31.723155Z", + "iopub.status.busy": "2023-12-04T17:50:31.723022Z", + "iopub.status.idle": "2023-12-04T17:50:31.733220Z", + "shell.execute_reply": "2023-12-04T17:50:31.732873Z" } }, "outputs": [ @@ -253,13 +253,13 @@ "data": { "text/plain": [ "defaultdict(Zeros (),\n", - " {'finance': 0.0,\n", - " 'politics': 0.0,\n", - " 'music': 0.0,\n", - " 'health': 0.0,\n", - " 'sports': 0.0,\n", - " 'camping': 0.0,\n", - " 'food': 0.0})" + " {'politics': 0.06389451550325113,\n", + " 'music': -0.04041254194187752,\n", + " 'camping': -0.040319730234734,\n", + " 'health': -0.03581829597317823,\n", + " 'food': -0.037778771188204816,\n", + " 'finance': -0.04029646665611086,\n", + " 'sports': -0.03661678982763635})" ] }, "execution_count": 5, @@ -284,16 +284,16 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.627378Z", - "iopub.status.busy": "2023-11-09T07:13:57.627242Z", - "iopub.status.idle": "2023-11-09T07:13:57.706788Z", - "shell.execute_reply": "2023-11-09T07:13:57.706478Z" + "iopub.execute_input": "2023-12-04T17:50:31.735061Z", + "iopub.status.busy": "2023-12-04T17:50:31.734953Z", + "iopub.status.idle": "2023-12-04T17:50:31.826428Z", + "shell.execute_reply": "2023-12-04T17:50:31.826129Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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KykLLli2b7HmO/1GM6ZuOwlMjEDt/sNmb8VHT0ul0SEhIwLBhw1hnG2Kd7YN1th/W2j5sVefS0lIEBwc36Hf3bR94/Pz8kJeXZ9aWl5cHV1dXq7M7AKDRaKDRaCzaPT094erq2mRjcy+XQ67RQqER8PLy4jeTDel0Omi1WtbZxlhn+2Cd7Ye1tg9b1bnuWA1ZjuJwV2k1VlhYGOLj483a4uLiEBYW1kwjIiIiIkfjcIGnrKwMKSkpSElJAVB72XlKSgpycnIA1J6OeuKJJ0z9Z8+ejTNnzuCll15Ceno63n33XWzduhXPP/98cwyfiIiIHJDDBZ4jR46gb9++6Nu3LwAgKioKffv2xZIlSwAAFy9eNIUfAAgODsaOHTsQFxeH3r17Y9WqVfjwww8RERHRLOMnIiIix+Nwa3iGDh2K690ayNpdlIcOHYpjx47ZcFRERER0O3O4GR4iIiKipsbAQ0RERJLHwGNDvGczERGRY2DgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+Cxg/pvo0hERET2wMBDREREksfAY0MNebt6IiIisj0GHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4b4l14iIiIHAMDDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeA48dCNHcIyAiIrqzMfAQERGR5DHwEBERkeQx8NiQjDfiISIicggMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDx2wNvwEBERNS8GHiIiIpI8Bh4iIiKSPAYeG5KBN+IhIiJyBAw8REREJHkMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEREQkeQw8REREJHkMPDYk4214iIiIHAIDDxEREUkeAw8RERFJHgMPERERSR4Djx2I5h4AERHRHY6Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgcceeCMeIiKiZsXAQ0RERJLHwENERESSx8BDREREksfAY0MyWXOPgIiIiAAGHiIiIroDMPAQERGR5DHwEBERkeQx8NgBb8NDRETUvBwy8Kxfvx5BQUFwcnJCaGgoDh8+fN3+a9asQefOneHs7Iy2bdvi+eefR1VVlZ1GS0RERI7O4QLP559/jqioKERHRyM5ORm9e/dGREQE8vPzrfbfvHkzXn75ZURHRyMtLQ3//e9/8fnnn2Px4sV2HjkRERE5KocLPKtXr8asWbMwY8YMdOvWDe+99x60Wi02btxotf/PP/+MgQMH4vHHH0dQUBBGjRqFyZMn33BWiIiIiO4cyuYewNVqampw9OhRLFq0yNQml8sRHh6OpKQkq/vcc889+OSTT3D48GH0798fZ86cwc6dOzF16tR6n6e6uhrV1dWmxyUlJQAAnU4HnU7XRK8GMOgNpv835XHJUl19WWfbYp3tg3W2H9baPmxV58Ycz6ECT0FBAQwGA3x9fc3afX19kZ6ebnWfxx9/HAUFBbj33nshhIBer8fs2bOve0orJiYGy5Yts2jftWsXtFrtrb2Iq5wvB+pKHBcX12THpfqxzvbBOtsH62w/rLV9NHWdKyoqGtzXoQLPzUhMTMRbb72Fd999F6GhocjMzMS8efPw+uuv47XXXrO6z6JFixAVFWV6XFJSgrZt22LUqFFwdXVtsrGlXSzFil9rZ6ZGjhwJlUrVZMcmczqdDnFxcayzjbHO9sE62w9rbR+2qnPdGZqGcKjA4+3tDYVCgby8PLP2vLw8+Pn5Wd3ntddew9SpU/HUU08BAHr27Iny8nI8/fTTeOWVVyCXWy5T0mg00Gg0Fu0qlapJPxFK5V/lbepjk3Wss32wzvbBOtsPa20fTV3nxhzLoRYtq9Vq9OvXD/Hx8aY2o9GI+Ph4hIWFWd2noqLCItQoFAoAgBC8Aw4RERE52AwPAERFRWHatGkICQlB//79sWbNGpSXl2PGjBkAgCeeeAIBAQGIiYkBAIwbNw6rV69G3759Tae0XnvtNYwbN84UfIiIiOjO5nCBZ9KkSbh06RKWLFmC3Nxc9OnTB7GxsaaFzDk5OWYzOq+++ipkMhleffVVnD9/Hq1atcK4cePw5ptvNtdLICIiIgfjcIEHACIjIxEZGWl1W2JiotljpVKJ6OhoREdH22FkREREdDtyqDU8UiOTNfcIiIiICGDgISIiojsAAw8RERFJHgOPHfDieCIioubFwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BjQ7wPDxERkWNg4CEiIiLJY+Cxg1KdDKVVuuYeBhER0R2LgcdOJn1wuLmHQEREdMdi4LGTU/nlzT0EIiKiOxYDDxEREUkeAw8RERFJHgMPERERSR4Djw3JwBvxEBEROQIGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4bkvE2PERERA6BgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4HHhngbHiIiIsfAwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BjQ6K5B0BEREQAGHiIiIjoDsDAQ0RERJLHwENERESSx8BjQ4KLeIiIiBwCAw8RERFJHgOPDQlep0VEROQQGHiIiIhI8hh4bIhreIiIiBwDAw8RERFJHgMPERERSR4DDxEREUkeA48NcQ0PERGRY2DgISIiIslj4LEh3oeHiIjIMTDwEBERkeQx8NgQ1/AQERE5BgYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4b4lVaREREjoGBh4iIiCSPgceGeKdlIiIix8DAQ0RERJLnkIFn/fr1CAoKgpOTE0JDQ3H48OHr9i8qKsLcuXPRunVraDQa3HXXXdi5c6edRktERESOTtncA7jW559/jqioKLz33nsIDQ3FmjVrEBERgYyMDPj4+Fj0r6mpwciRI+Hj44Mvv/wSAQEBOHv2LNzd3e0/eCIiInJIDhd4Vq9ejVmzZmHGjBkAgPfeew87duzAxo0b8fLLL1v037hxIwoLC/Hzzz9DpVIBAIKCguw55HrxKi0iIiLH4FCBp6amBkePHsWiRYtMbXK5HOHh4UhKSrK6z3fffYewsDDMnTsX3377LVq1aoXHH38cCxcuhEKhsLpPdXU1qqurTY9LSkoAADqdDjqdrslej16vN3tcVlEFjcr6mOjW1H3emvLzR5ZYZ/tgne2HtbYPW9W5McdzqMBTUFAAg8EAX19fs3ZfX1+kp6db3efMmTPYs2cPpkyZgp07dyIzMxPPPvssdDodoqOjre4TExODZcuWWbTv2rULWq321l/In86WAleX+L0vd6GTG6d9bCkuLq65h3BHYJ3tg3W2H9baPpq6zhUVFQ3u61CB52YYjUb4+Pjg/fffh0KhQL9+/XD+/HmsXLmy3sCzaNEiREVFmR6XlJSgbdu2GDVqFFxdXZtsbMf/KMbq1EOmx6GhoRjQ3rPJjk9/0el0iIuLw8iRI02nNqnpsc72wTrbD2ttH7aqc90ZmoZwqMDj7e0NhUKBvLw8s/a8vDz4+flZ3ad169ZQqVRmp6+6du2K3Nxc1NTUQK1WW+yj0Wig0Wgs2lUqVZN+Iq49paZQKvgNZWNN/Tkk61hn+2Cd7Ye1to+mrnNjjuVQl6Wr1Wr069cP8fHxpjaj0Yj4+HiEhYVZ3WfgwIHIzMyE0Wg0tZ08eRKtW7e2GnaIiIjozuNQgQcAoqKi8MEHH+Djjz9GWloa5syZg/LyctNVW0888YTZouY5c+agsLAQ8+bNw8mTJ7Fjxw689dZbmDt3bnO9BCIiInIwDnVKCwAmTZqES5cuYcmSJcjNzUWfPn0QGxtrWsick5MDufyvnNa2bVv8+OOPeP7559GrVy8EBARg3rx5WLhwYXO9BCIiInIwDhd4ACAyMhKRkZFWtyUmJlq0hYWF4eDBgzYeVePxeiwiIiLH4HCntIiIiIiaGgOPDfFOy0RERI6BgYeIiIgkj4HHpjjFQ0RE5AgYeIiIiEjyGHjsiRM+REREzYKBh4iIiCSPgceGbnSV1sXiSry54wTySqrsMyAiIqI7lEPeePBOERazBwDwwf4sZC8f28yjISIiki7O8NjQtRM8Vz82GM23ZuaXYtCKPfj8lxxTW3GFDoI38yEiIrplDDx2ZLwqvFTU6M22vfJNKs4VVmLhV78BAL5NOY/e/9iFEav22nWMREREUsTAY0PXTs5cPalTqTOYbTuUVWj2eN6WFADAmYJyJOdcscXwiIiI7hgMPHZ09QxPZY3hOj3NncwttcVwiIiI7hgMPHZ09XqcISsT6+1XXKkzeyyXyWw1JCIiojuCzQNPYmKirZ/itmE01v577YLla2Xml5k93nUiz1ZDIiIiuiPYLPD89NNPGDFiBEaMGGGrp3B4115hVXdKq0p3/dNZD2/42ezx7jQGHiIiolvR6Pvw6HQ6bN68GUePHoVSqcS9996Lhx56yLQ9JSUFL7/8MuLi4iCEQEhISJMO+HZWN7FTrTc270CIiIjuMI0KPKWlpRg8eDB+/fVX0+zF2rVr8dBDD+GLL77AkiVL8NZbb8FoNOLuu+/G0qVLcf/999tk4LcDi/vwNHCGxxq9wQilgkuuiIiIbkajAs8///lPHD9+HL1798aUKVMAAJ988gm+/vprPPbYY9i6dSs6duyIt99+Gw888IBNBnw7MzQy8CyI6IyVP2YAAK5U6NCqpcZmYyMiIpKyRgWeb7/9FoGBgTh06BDUajUAIDIyEl26dMEXX3yB0aNH4+uvv4ZGw1/MQP334anSNeyUllohh2cLNQrLa3C5vJqBh4iI6CY16hzJmTNnMGbMGFPYAQAnJyeMHVv7PlBvv/02w851mE5p6f+a4fk+8l5sf+5eq/3lchkKy2sAANuOXbD9AImIiCSqUYGnsrISvr6+Fu0+Pj4AgM6dOzfNqCSq7iqt6j9neDr6uKBnGzf0CHBD8msjkfzaSGybO9DUf2jnVqb/v7f3tH0HS0REJCFN+m7pcjkX1V5P3X146mZ4nFR/1cuzhdr0769LR+FyWQ2CvVtYHENnMELFxctERESN0ujAk5qaiq1bt1q0AcAXX3xh9d29J06ceJPDu70JWL8PT/Wfi5Y1SoXV/VydVHB1UgEAFo3ugpgf0gEAK39Mx/qE2pme/04LwYiulrNtREREZKnRgeerr77CV199ZdZWF3Iee+wxi3aZTHbHBp5riWsWLV89w1OfoZ19TIGnLuwAwMyPjyB7+dimHyQREZEENSrwLFmyBDK+r1PDWVylZX5ZulM9MzxXs3Zai4iIiBqnUYFn6dKlNhrGnaHusvSXv/4NABCfnn/DfdTK+meB0i6WoGtr1yYZGxERkZQ1avXrk08+ie+++85WY5Gca1czGa2sb2qIyGEdrbZvSzkPAPjx91ycvlRmtQ8RERE1coZn06ZNCAoK4l2Ub1KVzoCyan2j93NxMv80OankqNIZUaM3IujlHab2q9f0lFXr8dbONNzX3Q+DOnlDJpNBbzBi08/ZuLeTN7r4XX9mSG8w4qWvfkVEdz9EdPdr9JiJHFlTv1WL0SiQX1oNrUaByhoDMvPLcCqvFCfzy1CjN8LdWQWNSg6VQg5/N2cM6dwKXi3U1x2DUQCnL5Xj5KUKKOUy+Ls7w9/NCT6uTk02bkemNxhx7kolXJ2U8GyhtslyiiqdAfEn8nD4kgxeWYUorTbCSaVA5Z8/q/3dnOGuVeFMQTlcNAroDQIZuaUo//NzDAAeWhW6tnaFUQj4uDqhk48LXDRKqBRyFJbXQC4Hyqv1SLtYinOFFSir1iPAwxlZl8pRoTOgrYcWlTV6pF4oQWWNAQEezujs2xJyuQxXymuQkVsKJ7UCLhoF0nNL4e6sgkEALTW1dTEIgWqdAQajQKXOAKMAnFUKBHu3gIdWjTMFZSgsr4HOYIRnCzUul9VAbxTwdlGjRm+EVqOEp1YNoxC1v1cMRihkQG5JFYoqdCir1sPVSQUnlRz+7s5ooVairFoPvdGIwvIaeGjV8NCqkVVQjjMF5XB1VqJ/kCe6B7ihvFqP0/ll+ONKJQAB50o5xjT5Z7HhmvSydDInv+Yb9I0daXhjR1qjj/P7hRLT/1OXReDfezLx3t7T+OinbLN+BqOAQl77nCtj07H5UA42H8oBYP42FQCQFTMGXyefx792n8SWpwegjYfWtC3tYglGr90PAPg6+TzemdwXD/T2b/S4iRpLCAEham+6WVypw8T3kpCRV4oeAa5QyOXQKOQI9NJiyoBA/JJViHf2nEJplR6vju2KiX9ri6Xf/o6vj51v8PMFemmxaHRXnLhQjAl9AxDo1QI6gxEXi6ugUshw+lI52nlqEeSlRdLpyyip0qFab4S/uzPaemhx9OwV/HS6AInp+bhQXNWo19pCrUAHHxfU6I3o7NcSGqUcWrUS1Xoj8oorsf+UArqDP1ns59VCjZ5t3NDd3xV+rk5QKeRwVivQoZULuvu73hbrLI1GAYMQyC2uwh9XKpF2sQTl1XoUV+ogl8tw4kIJDmQWmPp7aFUYfFcrDOzojcGdWsHLRQ2lXIYrFTq4O6sg//PnXkmVDkeyC3EqrwyuzrVXusoAHMspwqWyagS4O0MuAy7++byZ+WWoMRgBKPBp5pFmqISlExdLEHci75aPs/fkpSYYTZ1KAEByTtENexaUVePMpXLgl3MW2zq0bN6vTQYeG+of7ImQQHccOVtkdfuU0HYNOk70uG74/njtnZZdNEoEuFv/C+9icSXaeGhRVq3Hx0lnzbZdHXYAIHjRTtP/7/1nAhaN7oLxfQLw8+kCRG09btb3758dw9ierU1hqrGyCsqhVsoR4O58U/tLjcEoELMzDUM6t4K/uzMWf/0bosd1Rzd/V1TpDCgoq4abswpKuRyz/ncEv2QXIu75IWjnpbV6rNjUXMzdnAwAGNTJG29M6IFAr9rF7n9cqcDWX87hmSEdoFLILdaE1V1JeT2F5TU4c6kMj394CDV6I+Z0lWFQlR5HTxVi65FzuKeDF745dh6//lGM+7r7wc/NCR19XDCmZ2t4tlDDaBSQy2UwGAUuFldCIZehrEqPpd//jjOXylFQVo1ebdxx9OyVeseQev6v0H84uxBfHP3DbPvN/jFx9nIFZn9yFADwzp7Mevu5OilRUtXw2VlnlQKBXloEemnh7qyGzmDEyfxSGIxAjd6A05fKUV5jwK9/FAMA0nNLrRxFBpkM8Hdzhlopx8XiSlTpjLhcXoPEjEtIzLD8heaiUcJZrYDRKNDWU4vebdygVsohl8ngpFJArZTjfFEliipqcKVcB4NR4GR+ae3XhkKOwXe1Ms1QlFXrkVNYgZIqHVw0SghR+zNGJpPBU6uGQG04LavWw8tFjbt8W0JvEPBzc0ILjRJFFTXQGwSUitqvr8z8Mpy5VI6zheU4V1iJ4krdDeuoUsigMwhcqdDh25QL+Dal9uegXAYoFXLU6I1w0SjRqqUGNXojLhRXWrylz414aFWQGWqg1jjBxUkFncEIN2cVnFUKnCusQOmfMz2Vf15s0iPAFR5aNbxdNHDRKJFXUoWUc0VQKmS4VFqNgrIa02vTqhWQAWihUSLIqwUCvbRooVEit7gK7by00CjlOHu5Ai00SvRq4wYPrRrnCiuQnluKkiodAtydEeSlhVEARRU16NLaFWXVejirFCip0iG3uAoaZe3MoUohh0ZVWxMhgPNFlbhUWo0g7xbwddXASanAhaJK+Lk5QQjgSkUNNEo5KnVG5JdWoUpngIdWjRYaJXQGI3xaOqGlkxJuzipU1OhRXm3AuSsVMBgFVAo5tGoF3JxVKKnS41xhBTr5uiDYuwVyi6tw/FwRTuWXwbOFGq3dnNDOUwu1Ajj1+6+N++Q0MZmwduOcesjlcixduhRLliyx5ZjsrqSkBG5ubiguLoara9MuAtbpdHhsbSyOFlhOXW9/7l70CHBr9DF3n8jDU/+z/GukVxs3fDZrALpH/3hTY72R02+NMYWe1PPF+OxwDj49lIP9Lw1DW0/LX8Zl1Xqsiz+F/+w7AwBY8UgvrE/IxOT+7RDk1QK70/Lw5oM9IJfJbvlmijqdDjt37sSYMWOgUqnq7VdZY4CTSm7zv4KNRoFdJ/JMv0yb2oD2njh4pvCm9792xq/Of6eFwF2rxsMbfr6V4TW7tp7OOFdYWe/2pwe3x7Zj55FfWt3oY3f3d0VWQTkqagxw0SjRt507RnXzxfi+AX9+LcvqvccWUBsySyr1yLxUhvTcEhRV6FBSpUPZn4FKZzDC380JJecy8PeJI+Hu8tcfClkF5fjtfDHOX6nEkexCXPxzVqlKb0DO5QrojTe3TrA5uWiU6NPWHVq1Agq5DFp17R919/VojS5+LVFSpcPBM5eRmHEJh7MKcaag/IbH69vOHcBf6ybbuGvRzkuLS6XV0BuN8GyhQUcfF3Ro1QKdvJ3xww8/3PBnR2PU6I2mUEa1GvozurEa8/u70TM827ZtQ3Z2doP7y2Qy/Pe//23s00hKU/9qddNa/2L59Y9ivLHjhFnb/w1oh08O5tzU85x+aww6LP5rJujp/x3B0ge64x/bT5hNuQ5akYAzb43Bgi9/xVfJtX95n3pzNHpcE7xe+rI23S//875CAPDlNX+p+7TUIL+0Gq/d3w3d/V0xoL3XTY39WpU1BnRdEmt6PG9EJzw/8i6LflU6A1QKeYNms85eLsfWI+cQ7O2CF784fsP+TelWwg5gOeNXZ+bHzT+t7/znGor2rVpg29yBpptw1jEYBa5U1KClk9IULArKqvHbH8UI6+AFJ9WNb/cAAIvHdAVQu77CSaXA2cvlOJVfhtIqPcb2bA2ZDHBS1f5VnH25HD0C3KBVKaBUyCGEQLXe2ODnuppMJoObVoV+gR7oF+hhtU/tL4d0tNCY/4gO9m5x1a0qOphtq6wxIDnnCoxCQKtW4ujZQuQUVqC8unZmoqJGD6MAAtyd4dlCDaVCBheNEq3dnOGkkuNyWQ2OnC1E+sVSVOkNEAK4y7clWrs5oahSB5VcBnetGgJASaUOpVV607qOExdKcLawHEq5HAVl1ZDLZPBoUXva6XJZNZxUCrTxcEYbDy26+LVEoFcL+Lhq4OvqhBZqxXX/AHHXqnFfj9a4r0drALXfo1kF5RB/vpbsy+Uoq9ajWm+Av7vzDdcnWqt1U7ve1bXUfBo9w9PoJ5DJYDAYbtyxGdl6hufxd2LxyyXL2p18Y/RNfWPoDEZ0euWHBvXNihmDrUfOYeFXtZfCP3R3AL5Orl3jMK63P+aHd8KIVXst9vv0qVAM7OiNzw7nYNGfl9E3h2eGtMei0V1v2O96fz28tTMN7/85y3StlCUj4aRSoMtrsWbtLZ2U+PSpUHT0cYGzqvYH8q7fc/H0/2uaGZtXxnTFmztrT8Fsf+5e3L/uAIDadR3vTO6L9NxSHMu5guFdfDGud2scOlOI2Z8ctfoX/P6XhqGNhzOEAHRGI/5f0lnT6Z22ns7Y/twgvLnjBFLPl+DExRKL/W/kkX5t8MqYrnDXqqDX67Fl204E9gpF//atoFHKcaVCB2eVAk4quWl8NXojvjz6B9btyUQXv5Y4kFkAjVKO75+7F3qDgFEIdPd3hRC1VzPqjcbrzorcaWz11zBZYq3twxFmeBodeKZPn45p06Y1akBDhgxpVH97s3XgmfJOLA5bCTy3cqfkzPxShK/eB6A21Fy9JqfO5lmhuKeDt1lbfX+Z1n0ZXPuXlhAC78Rn4l+7T970WL+acw8OZxXin7HpaO/d4oZT0tfq284dbs4q/OOBHhbrWH747SLmfJps1lY3S9TcRnTxwYb/62eTv/YacpXR9dbnlFXr4aSUmx1DCIHPfzkHpUKOh+8OsLovfznYB+tsP6y1fThC4Gn0Ka2goCCHDzCOxharRTr6tETc84PRqqXG6i+mH+YNsnpTQtmfixettVsjk8kwL7wTth45h/NF5msi9r80DF4uanRb8tepq0+fCsWUDw+ZHh9+ZQR8WjqhX6AH5gw1n4Kvk3q+GOcKKxDWwQv7ThUgPi3PtDgRqL3CAgAGr0wAAGx5egAee/+g1WMBuG7YyYoZg4oaQ5Osc+of7ImnB7XHwTOX0cHHBZP7N2wRelNoyNqA650mcNFYfuvLZDI8ZsfXQERkT7xKyw5u8uKmG+rk29L0/49m/A0zPvrF9LiLX0tru9y0n14eDgD47HAOyqr0mDW4vWnbqTdHo6LaYFpblBUzBkaBBl/V1SPAzbR4+4He/nigtz/WPtYXv/1RjHH/PmDR/3phpz6/Lh1lWgvSQqNE9vKxWJ+QaVrL8szg9lg05q9TZ7t+z8XquJMWV8+kLouwCAvh3fgmrkREjo6Bxw7yKm1/74FhnX3w+vjueO3b3/HWgz1tdhWStVkMlUION+1fMw4ymQyKJnj6nm3c0MWvJdJzS/H34R3rvWz4ueEdcX8PX3y8Yx8W/18Evky+AC8XDUZ1973uupC5wzpibj13sR7V3Q+jeMNFIiLJYOCxgzOl9rnZ0tSwINzfyx8eLdR2eT57iJ0/2PT/qFGd8dsfxZjw7k9YPKYrZt4bbNqm0+kQ6iOgUcoxfWCwtUMREdEdrFGrKT/66CMoFAosXrz4upfy1dTUYPHixVi+fPktD5AaR0phx5qebdxw+q0xZmGHiIjoRhoVeAICArBkyRJ4eXldd5W1Wq2Gt7c3XnnlFSQkJNzyIKWovgW8RERE1PQaFXj+97//wcPDA5GRkTfsO3fuXHh6euKjjz666cFJ2fzwTs09BCIiojtGowLPzz//jPDwcGg0mhv21Wg0CA8Px08/Wb75HQGqm7iJIxEREd2cRv3WvXDhAtq3b3/jjn8KDg7GxYsXGz2oO4HcVteqExERkYVGBR65XN6o9x3R6XQ39XYURERERE2pUWnE398fqampDe6fmpqKgICARg9KalTy2+8djImIiKSkUYFn0KBB2LNnT4PeLT07Oxt79uzB4MGDb9hX6jykfaU4ERGRw2tU4Jk7dy50Oh0eeeQRFBQU1Nvv8uXLePTRR6HX6zFnzpxbHiQRERHRrWjUnZbvvvtuzJ8/H2vWrEG3bt0we/ZsDBs2DG3atAEAnD9/HvHx8Xj//fdx6dIlREVF4e6777bJwImIiIgaqtFvLbFq1So4OTlh5cqVePPNN/Hmm2+abRdCQKFQYNGiRXjjjTeabKBEREREN6vRgUcmk+Gtt97CzJkz8dFHH+Hnn39Gbm4uAMDPzw8DBw7E9OnT0aED7yRMREREjuGm3zy0Q4cOnMFpoGvfuHzX81zITUREZE+8SY6dadUK3OXbsrmHQUREdEdh4LEzwVvyEBER2R0Dj50NaO/Z3EMgIiK64zDw2MHVS3hWTezTXMMgIiK6YzHw2EGojxEA0KetOzxb8LbLRERE9nbTV2lRww1tLfDI8BD0DfJq7qEQERHdkRh47EAuq127o1Kx3ERERM2Bp7SIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hw28Kxfvx5BQUFwcnJCaGgoDh8+3KD9tmzZAplMhgkTJth2gERERHTbcMjA8/nnnyMqKgrR0dFITk5G7969ERERgfz8/Ovul52djRdffBGDBg2y00iJiIjoduCQgWf16tWYNWsWZsyYgW7duuG9996DVqvFxo0b693HYDBgypQpWLZsGdq3b2/H0RIREZGjc7gbw9TU1ODo0aNYtGiRqU0ulyM8PBxJSUn17vePf/wDPj4+mDlzJvbv33/d56iurkZ1dbXpcUlJCQBAp9NBp9Pd4iswV3e8pj4umWOd7YN1tg/W2X5Ya/uwVZ0bczyHCzwFBQUwGAzw9fU1a/f19UV6errVfQ4cOID//ve/SElJadBzxMTEYNmyZRbtu3btglarbfSYGyIuLs4mxyVzrLN9sM72wTrbD2ttH01d54qKigb3dbjA01ilpaWYOnUqPvjgA3h7ezdon0WLFiEqKsr0uKSkBG3btsWoUaPg6urapOPT6XSIi4vDyJEjoVKpmvTY9BfW2T5YZ/tgne2HtbYPW9W57gxNQzhc4PH29oZCoUBeXp5Ze15eHvz8/Cz6nz59GtnZ2Rg3bpypzWisfbNOpVKJjIwMdOjQwWwfjUYDjUZjcSyVSmWzL3hbHpv+wjrbB+tsH6yz/bDW9tHUdW7MsRxu0bJarUa/fv0QHx9vajMajYiPj0dYWJhF/y5duuC3335DSkqK6eOBBx7AsGHDkJKSgrZt29pz+EREROSAHG6GBwCioqIwbdo0hISEoH///lizZg3Ky8sxY8YMAMATTzyBgIAAxMTEwMnJCT169DDb393dHQAs2omIiOjO5JCBZ9KkSbh06RKWLFmC3Nxc9OnTB7GxsaaFzDk5OZDLHW5yioiIiByUQwYeAIiMjERkZKTVbYmJidfdd9OmTU0/ICIiIrptcZqEiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkj4GHiIiIJI+Bh4iIiCSPgYeIiIgkz2EDz/r16xEUFAQnJyeEhobi8OHD9fb94IMPMGjQIHh4eMDDwwPh4eHX7U9ERER3FocMPJ9//jmioqIQHR2N5ORk9O7dGxEREcjPz7faPzExEZMnT0ZCQgKSkpLQtm1bjBo1CufPn7fzyImIiMgROWTgWb16NWbNmoUZM2agW7dueO+996DVarFx40ar/T/99FM8++yz6NOnD7p06YIPP/wQRqMR8fHxdh45EREROSJlcw/gWjU1NTh69CgWLVpkapPL5QgPD0dSUlKDjlFRUQGdTgdPT0+r26urq1FdXW16XFJSAgDQ6XTQ6XS3MHpLdcdr6uOSOdbZPlhn+2Cd7Ye1tg9b1bkxx3O4wFNQUACDwQBfX1+zdl9fX6SnpzfoGAsXLoS/vz/Cw8Otbo+JicGyZcss2nft2gWtVtv4QTdAXFycTY5L5lhn+2Cd7YN1th/W2j6aus4VFRUN7utwgedWLV++HFu2bEFiYiKcnJys9lm0aBGioqJMj0tKSkzrflxdXZt0PDqdDnFxcRg5ciRUKlWTHpv+wjrbB+tsH6yz/bDW9mGrOtedoWkIhws83t7eUCgUyMvLM2vPy8uDn5/fdfd9++23sXz5cuzevRu9evWqt59Go4FGo7FoV6lUNvuCt+Wx6S+ss32wzvbBOtsPa20fTV3nxhzL4RYtq9Vq9OvXz2zBcd0C5LCwsHr3W7FiBV5//XXExsYiJCTEHkMlIiKi24TDzfAAQFRUFKZNm4aQkBD0798fa9asQXl5OWbMmAEAeOKJJxAQEICYmBgAwD//+U8sWbIEmzdvRlBQEHJzcwEALi4ucHFxabbXQURERI7BIQPPpEmTcOnSJSxZsgS5ubno06cPYmNjTQuZc3JyIJf/NTm1YcMG1NTU4JFHHjE7TnR0NJYuXWrPoRMREZEDcsjAAwCRkZGIjIy0ui0xMdHscXZ2tu0HRERERLcth1vDQ0RERNTUGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hw28Kxfvx5BQUFwcnJCaGgoDh8+fN3+X3zxBbp06QInJyf07NkTO3futNNIiYiIyNE5ZOD5/PPPERUVhejoaCQnJ6N3796IiIhAfn6+1f4///wzJk+ejJkzZ+LYsWOYMGECJkyYgNTUVDuPnIiIiByRQwae1atXY9asWZgxYwa6deuG9957D1qtFhs3brTaf+3atbjvvvuwYMECdO3aFa+//jruvvtu/Pvf/7bzyImIiMgROVzgqampwdGjRxEeHm5qk8vlCA8PR1JSktV9kpKSzPoDQERERL39iYiI6M6ibO4BXKugoAAGgwG+vr5m7b6+vkhPT7e6T25urtX+ubm5VvtXV1ejurra9Li4uBgAUFhYCJ1OdyvDt6DT6VBRUYHLly9DpVI16bHpL6yzfbDO9sE62w9rbR+2qnNpaSkAQAhxw74OF3jsISYmBsuWLbNoDw4ObobREBER0a0oLS2Fm5vbdfs4XODx9vaGQqFAXl6eWXteXh78/Pys7uPn59eo/osWLUJUVJTpsdFoRGFhIby8vCCTyW7xFZgrKSlB27Ztce7cObi6ujbpsekvrLN9sM72wTrbD2ttH7aqsxACpaWl8Pf3v2Ffhws8arUa/fr1Q3x8PCZMmACgNpDEx8cjMjLS6j5hYWGIj4/H/PnzTW1xcXEICwuz2l+j0UCj0Zi1ubu7N8Xw6+Xq6spvJjtgne2DdbYP1tl+WGv7sEWdbzSzU8fhAg8AREVFYdq0aQgJCUH//v2xZs0alJeXY8aMGQCAJ554AgEBAYiJiQEAzJs3D0OGDMGqVaswduxYbNmyBUeOHMH777/fnC+DiIiIHIRDBp5Jkybh0qVLWLJkCXJzc9GnTx/ExsaaFibn5ORALv/rArN77rkHmzdvxquvvorFixejU6dO2LZtG3r06NFcL4GIiIgciEMGHgCIjIys9xRWYmKiRdujjz6KRx991MajajyNRoPo6GiLU2jUtFhn+2Cd7YN1th/W2j4coc4y0ZBruYiIiIhuYw5340EiIiKipsbAQ0RERJLHwENERESSx8BDREREksfAY0Pr169HUFAQnJycEBoaisOHDzf3kBzavn37MG7cOPj7+0Mmk2Hbtm1m24UQWLJkCVq3bg1nZ2eEh4fj1KlTZn0KCwsxZcoUuLq6wt3dHTNnzkRZWZlZn19//RWDBg2Ck5MT2rZtixUrVtj6pTmUmJgY/O1vf0PLli3h4+ODCRMmICMjw6xPVVUV5s6dCy8vL7i4uODhhx+2uJt5Tk4Oxo4dC61WCx8fHyxYsAB6vd6sT2JiIu6++25oNBp07NgRmzZtsvXLcxgbNmxAr169TDdaCwsLww8//GDazhrbxvLlyyGTycxuRMta37qlS5dCJpOZfXTp0sW0/baosSCb2LJli1Cr1WLjxo3i999/F7NmzRLu7u4iLy+vuYfmsHbu3CleeeUV8fXXXwsA4ptvvjHbvnz5cuHm5ia2bdsmjh8/Lh544AERHBwsKisrTX3uu+8+0bt3b3Hw4EGxf/9+0bFjRzF58mTT9uLiYuHr6yumTJkiUlNTxWeffSacnZ3Ff/7zH3u9zGYXEREhPvroI5GamipSUlLEmDFjRLt27URZWZmpz+zZs0Xbtm1FfHy8OHLkiBgwYIC45557TNv1er3o0aOHCA8PF8eOHRM7d+4U3t7eYtGiRaY+Z86cEVqtVkRFRYkTJ06IdevWCYVCIWJjY+36epvLd999J3bs2CFOnjwpMjIyxOLFi4VKpRKpqalCCNbYFg4fPiyCgoJEr169xLx580ztrPWti46OFt27dxcXL140fVy6dMm0/XaoMQOPjfTv31/MnTvX9NhgMAh/f38RExPTjKO6fVwbeIxGo/Dz8xMrV640tRUVFQmNRiM+++wzIYQQJ06cEADEL7/8Yurzww8/CJlMJs6fPy+EEOLdd98VHh4eorq62tRn4cKFonPnzjZ+RY4rPz9fABB79+4VQtTWVaVSiS+++MLUJy0tTQAQSUlJQojacCqXy0Vubq6pz4YNG4Srq6upti+99JLo3r272XNNmjRJRERE2PolOSwPDw/x4YcfssY2UFpaKjp16iTi4uLEkCFDTIGHtW4a0dHRonfv3la33S415iktG6ipqcHRo0cRHh5uapPL5QgPD0dSUlIzjuz2lZWVhdzcXLOaurm5ITQ01FTTpKQkuLu7IyQkxNQnPDwccrkchw4dMvUZPHgw1Gq1qU9ERAQyMjJw5coVO70ax1JcXAwA8PT0BAAcPXoUOp3OrNZdunRBu3btzGrds2dP093Pgdo6lpSU4Pfffzf1ufoYdX3uxO8Bg8GALVu2oLy8HGFhYayxDcydOxdjx461qAdr3XROnToFf39/tG/fHlOmTEFOTg6A26fGDDw2UFBQAIPBYPaJBQBfX1/k5uY206hub3V1u15Nc3Nz4ePjY7ZdqVTC09PTrI+1Y1z9HHcSo9GI+fPnY+DAgaa3YsnNzYVarbZ4Q91ra32jOtbXp6SkBJWVlbZ4OQ7nt99+g4uLCzQaDWbPno1vvvkG3bp1Y42b2JYtW5CcnGx6f8WrsdZNIzQ0FJs2bUJsbCw2bNiArKwsDBo0CKWlpbdNjR32rSWIyPbmzp2L1NRUHDhwoLmHIkmdO3dGSkoKiouL8eWXX2LatGnYu3dvcw9LUs6dO4d58+YhLi4OTk5OzT0cyRo9erTp/7169UJoaCgCAwOxdetWODs7N+PIGo4zPDbg7e0NhUJhsUI9Ly8Pfn5+zTSq21td3a5XUz8/P+Tn55tt1+v1KCwsNOtj7RhXP8edIjIyEtu3b0dCQgLatGljavfz80NNTQ2KiorM+l9b6xvVsb4+rq6ut80PyFulVqvRsWNH9OvXDzExMejduzfWrl3LGjeho0ePIj8/H3fffTeUSiWUSiX27t2Ld955B0qlEr6+vqy1Dbi7u+Ouu+5CZmbmbfP1zMBjA2q1Gv369UN8fLypzWg0Ij4+HmFhYc04sttXcHAw/Pz8zGpaUlKCQ4cOmWoaFhaGoqIiHD161NRnz549MBqNCA0NNfXZt28fdDqdqU9cXBw6d+4MDw8PO72a5iWEQGRkJL755hvs2bMHwcHBZtv79esHlUplVuuMjAzk5OSY1fq3334zC5hxcXFwdXVFt27dTH2uPkZdnzv5e8BoNKK6upo1bkIjRozAb7/9hpSUFNNHSEgIpkyZYvo/a930ysrKcPr0abRu3fr2+XpukqXPZGHLli1Co9GITZs2iRMnToinn35auLu7m61QJ3OlpaXi2LFj4tixYwKAWL16tTh27Jg4e/asEKL2snR3d3fx7bffil9//VWMHz/e6mXpffv2FYcOHRIHDhwQnTp1MrssvaioSPj6+oqpU6eK1NRUsWXLFqHVau+oy9LnzJkj3NzcRGJiotklphUVFaY+s2fPFu3atRN79uwRR44cEWFhYSIsLMy0ve4S01GjRomUlBQRGxsrWrVqZfUS0wULFoi0tDSxfv36O+oy3pdfflns3btXZGVliV9//VW8/PLLQiaTiV27dgkhWGNbuvoqLSFY66bwwgsviMTERJGVlSV++uknER4eLry9vUV+fr4Q4vaoMQOPDa1bt060a9dOqNVq0b9/f3Hw4MHmHpJDS0hIEAAsPqZNmyaEqL00/bXXXhO+vr5Co9GIESNGiIyMDLNjXL58WUyePFm4uLgIV1dXMWPGDFFaWmrW5/jx4+Lee+8VGo1GBAQEiOXLl9vrJToEazUGID766CNTn8rKSvHss88KDw8PodVqxYMPPiguXrxodpzs7GwxevRo4ezsLLy9vcULL7wgdDqdWZ+EhATRp08foVarRfv27c2eQ+qefPJJERgYKNRqtWjVqpUYMWKEKewIwRrb0rWBh7W+dZMmTRKtW7cWarVaBAQEiEmTJonMzEzT9tuhxjIhhGiauSIiIiIix8Q1PERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEREQkeQw8REREJHkMPERERCR5DDxEZFMymQxDhw5t7mE0mcTERMhkMixdurS5h0JEjcDAQ0R2N336dMhkMmRnZzf3UKySWkgjIkDZ3AMgImlLS0uDVqtt7mE0mf79+yMtLQ3e3t7NPRQiagQGHiKyqS5dujT3EJqUVquV3GsiuhPwlBYRATBfm3LkyBGMHDkSLVu2hJubGx588MGbPv107emhoKAgfPzxxwCA4OBgyGQyq6eQsrKy8NRTT6Fdu3bQaDRo3bo1pk+fjrNnz9b7HOfPn8cTTzwBPz8/yOVyJCYmAgASEhLw5JNPonPnznBxcYGLiwtCQkLw/vvvW60BAOzdu9c0NplMhk2bNlnU6VqpqamYOHEifHx8oNFoEBwcjPnz5+Py5csWfYOCghAUFISysjLMmzcP/v7+0Gg06NWrF7788kuL/sXFxViyZAm6desGFxcXuLq6omPHjpg2bZrVmhCROc7wEJGZX375BStWrMCwYcPwzDPP4NixY9i2bRt+++03pKamwsnJ6ZaOP3/+fGzatAnHjx/HvHnz4O7uDqA2ANQ5dOgQIiIiUF5ejvvvvx+dOnVCdnY2Pv30U/zwww9ISkpC+/btzY57+fJlhIWFwdPTE4899hiqqqrg6uoKAPjnP/+JzMxMDBgwAA8++CCKiooQGxuLZ555BhkZGVi1apVpDNHR0Vi2bBkCAwMxffp00/H79Olz3dd14MABREREoKamBo888giCgoKQlJSEtWvXYvv27Th48KDFaTCdTodRo0bhypUrePjhh1FRUYEtW7Zg4sSJiI2NxahRowAAQghERETg0KFDGDhwIO677z7I5XKcPXsW3333HaZOnYrAwMCb+GwQ3UGa7H3Xiei2lpCQIAAIAGLLli1m26ZOnSoAiM8++6zRxwUghgwZYtY2bdo0AUBkZWVZ9K+pqRFBQUGiZcuWIjk52Wzb/v37hUKhEPfff7/FcwAQM2bMEHq93uKYZ86csWjT6XRi5MiRQqFQiLNnz95wzHXq6hQdHW1qMxgMokOHDgKAiI2NNeu/YMECAUA8+eSTZu2BgYECgBg/fryorq42te/evVsAEBEREaa2X3/9VQAQEyZMsBhPVVWVKC0ttTpWIvoLT2kRkZnBgwdj0qRJZm1PPvkkgNrZH1vbvn07srOzsWDBAvTt29ds27333ovx48dj586dKCkpMdumVquxYsUKKBQKi2MGBwdbtCmVSsyePRsGgwEJCQm3NOaffvoJp0+fxujRoxEREWG2bcmSJfD09MTmzZtRU1Njse+//vUvqNVq0+MRI0YgMDDQaq2dnZ0t2jQaDVxcXG5p/ER3Ap7SIiIz/fr1s2hr06YNAKCoqMjmz3/w4EEAQEZGhtV1Mrm5uTAajTh58iRCQkJM7cHBwfVeOVVaWoq3334b27Ztw+nTp1FeXm62/cKFC7c05mPHjgGA1UvZ69YL7dq1CxkZGejZs6dpm7u7u9Uw1qZNGyQlJZked+3aFb169cJnn32GP/74AxMmTMDQoUPRp08fyOX8u5WoIRh4iMhM3bqXqymVtT8qDAaDzZ+/sLAQAPDpp59et9+1ocXX19dqv5qaGgwdOhTJycno27cvpk6dCi8vLyiVSmRnZ+Pjjz9GdXX1LY25brapvjG0bt3arF8dNzc3q/2VSiWMRqPZ4z179mDp0qX46quv8MILLwAAWrVqhcjISLzyyitWZ7aI6C8MPETkUOoC1/fff4/777+/wfvVXV11rW+//RbJycmYOXMmPvzwQ7NtW7ZsMV0xdivqxpyXl2d1e25urlm/m+Hl5YV169bhnXfeQXp6Ovbs2YN169YhOjoaKpUKixYtuuljE90JOBdKRHZXNxthbcYoNDQUAMxO6dyK06dPAwDGjx9vsW3//v1W95HL5Y2azapba1R3GfzVysvLceTIETg7O6Nz584NPmZ9ZDIZunbtirlz5yIuLg4A8N13393ycYmkjoGHiOzO09MTAHDu3DmLbePHj0e7du2wevVq7Nu3z2K7TqfDgQMHGvxcdZdrX7vP3r178cEHH9Q7vj/++KPBzzFw4EB06NABP/zwA3bv3m227Y033sDly5cxefJks8XJjZGdnW31Pkh1M0q3eqsAojsBT2kRkd0NHz4cb7/9Np5++mk8/PDDaNGiBQIDAzF16lRoNBp8+eWXGD16NIYMGYLhw4ejZ8+ekMlkOHv2LPbv3w8vLy+kp6c36LnGjRuHoKAgrFixAqmpqejRowcyMjKwfft2PPjgg1Zv8jd8+HBs3boVEyZMQN++faFQKPDAAw+gV69eVp9DLpdj06ZNiIiIwJgxY/Doo48iMDAQSUlJSExMRIcOHbB8+fKbrldKSgoeeugh9O/fH926dYOfnx/Onz+Pbdu2QS6X4/nnn7/pYxPdKRh4iMjuRo8ejRUrVuCDDz7AqlWroNPpMGTIEEydOhUA8Le//Q3Hjx/HypUrsXPnTvz000/QaDQICAjAhAkTMHny5AY/l4uLC/bs2YMFCxZg3759SExMRPfu3fHpp5/C19fXauBZu3YtAGDPnj34/vvvYTQa0aZNm3oDD1B7yfzBgwfxj3/8A7t27UJxcTH8/f0xb948vPrqq7f03lshISFYuHAhEhMTsWPHDhQVFcHPzw/h4eFYsGABBgwYcNPHJrpTyIQQorkHQURERGRLXMNDREREksfAQ0RERJLHNTxE1Chr1qxp0B2Xp0+fbvaGoEREzYlreIioUYKCgnD27Nkb9ktISLD6VgtERM2BgYeIiIgkj2t4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8hh4iIiISPIYeIiIiEjyGHiIiIhI8v4/dRLuiUjGw0YAAAAASUVORK5CYII=", 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" ] @@ -327,16 +327,16 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.708558Z", - "iopub.status.busy": "2023-11-09T07:13:57.708446Z", - "iopub.status.idle": "2023-11-09T07:13:57.831339Z", - "shell.execute_reply": "2023-11-09T07:13:57.831092Z" + "iopub.execute_input": "2023-12-04T17:50:31.827897Z", + "iopub.status.busy": "2023-12-04T17:50:31.827802Z", + "iopub.status.idle": "2023-12-04T17:50:31.959947Z", + "shell.execute_reply": "2023-12-04T17:50:31.959498Z" } }, "outputs": [ { "data": { - "image/png": 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Pz89izM/PD8XFxaioqDB/4N21YmNjsWTJEnuVSERERDZ04cKFGz4Ms9UHnsZYsGCB+XNtgOrPmmnfvj3S0tLg5ubWrPsyGAzYuXMnRo0a1agPDaT6YZ/tg322D/bZfthr+7BVn0tKShAWFlav9+5WH3j8/f2RnZ1tMZadnQ13d3erR3cAQKPRQKPR1Br38vKCu7t7s9ZnMBjg7OwMb29v/s9kQ+yzfbDP9sE+2w97bR+26nPNtupzOYrD3aXVUOHh4YiPj7cYi4uLQ3h4eAtVRERERI7G4QJPaWkpkpOTkZycDKD6tvPk5GRkZmYCqD4dNW3aNPP8mTNn4vz58/j3v/+N06dPY/Xq1di4cWOtTyQmIiKim5fDBZ5Dhw6hX79+6NevHwAgOjoa/fr1w6JFiwAAV65cMYcfAAgLC8PmzZsRFxeHPn36YPny5fjoo48QGRnZIvUTERGR43G4a3hGjhyJ6z0ayNpTlEeOHIkjR47YsCoiIiJqzRzuCA8RERFRc2PgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJc8jAs2rVKoSGhkKr1WLw4ME4ePDgdeevXLkSXbt2hZOTE4KDg/H000+jsrLSTtUSERGRo3O4wLNhwwZER0cjJiYGhw8fRp8+fRAZGYmcnByr87/44gs8//zziImJwalTp/Dxxx9jw4YNWLhwoZ0rJyIiIkflcIFnxYoVeOqppzBjxgz06NED77//PpydnbFmzRqr8/ft24ehQ4fi0UcfRWhoKMaOHYvJkyff8KgQERER3TyULV3AtfR6PZKSkrBgwQLzmFwuR0REBBITE62uc9ttt+Gzzz7DwYMHMWjQIJw/fx5btmzB1KlT69yPTqeDTqczf19cXAwAMBgMMBgMzfRqYN7mtf8l22Cf7YN9tg/22X7Ya/uwVZ8bsj2HCjy5ubkwGo3w8/OzGPfz88Pp06etrvPoo48iNzcXw4YNgxACVVVVmDlz5nVPacXGxmLJkiW1xrdv3w5nZ+emvYg6xMXF2WS7ZIl9tg/22T7YZ/thr+2juftcXl5e77kOFXgaIyEhAUuXLsXq1asxePBgnD17FnPnzsXLL7+Ml156yeo6CxYsQHR0tPn74uJiBAcHY+zYsXB3d2/W+gwGA+Li4jBmzBioVKpm3Tb9iX22D/bZPthn+2Gv7cNWfa45Q1MfDhV4fHx8oFAokJ2dbTGenZ0Nf39/q+u89NJLmDp1Kp588kkAQK9evVBWVoa///3veOGFFyCX175MSaPRQKPR1BpXqVQ2+4G35bbpT+yzfbDP9sE+2w97bR/N3eeGbMuhLlpWq9UYMGAA4uPjzWMmkwnx8fEIDw+3uk55eXmtUKNQKAAAQgjbFUtERESthkMd4QGA6OhoTJ8+HQMHDsSgQYOwcuVKlJWVYcaMGQCAadOmISgoCLGxsQCA8ePHY8WKFejXr5/5lNZLL72E8ePHm4MPERER3dwcLvBMmjQJV69exaJFi5CVlYW+ffti27Zt5guZMzMzLY7ovPjii5DJZHjxxRdx6dIltG3bFuPHj8err77aUi+BiIiIHIzDBR4AiIqKQlRUlNVlCQkJFt8rlUrExMQgJibGDpURERFRa+RQ1/AQERER2QIDDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJnkMGnlWrViE0NBRarRaDBw/GwYMHrzu/sLAQc+bMQUBAADQaDbp06YItW7bYqVoiIiJydMqWLuCvNmzYgOjoaLz//vsYPHgwVq5cicjISKSmpsLX17fWfL1ejzFjxsDX1xdff/01goKCkJGRAU9PT/sXT0RERA7J4QLPihUr8NRTT2HGjBkAgPfffx+bN2/GmjVr8Pzzz9eav2bNGuTn52Pfvn1QqVQAgNDQUHuWTERERA7OoQKPXq9HUlISFixYYB6Ty+WIiIhAYmKi1XV++OEHhIeHY86cOfj+++/Rtm1bPProo5g/fz4UCoXVdXQ6HXQ6nfn74uJiAIDBYIDBYGjGVwTz9pp7u2SJfbYP9tk+2Gf7Ya/tw1Z9bsj2HCrw5Obmwmg0ws/Pz2Lcz88Pp0+ftrrO+fPnsWPHDkyZMgVbtmzB2bNnMXv2bBgMBsTExFhdJzY2FkuWLKk1vn37djg7Ozf9hVgRFxdnk+2SJfbZPthn+2Cf7Ye9to/m7nN5eXm95zpU4GkMk8kEX19ffPDBB1AoFBgwYAAuXbqEN954o87As2DBAkRHR5u/Ly4uRnBwMMaOHQt3d/dmrc9gMCAuLg5jxowxn3Kj5sc+2wf7bB/ss/2w1/Zhqz7XnKGpD4cKPD4+PlAoFMjOzrYYz87Ohr+/v9V1AgICoFKpLE5fde/eHVlZWdDr9VCr1bXW0Wg00Gg0tcZVKpXNfuBtuW36E/tsH+yzfbDP9sNe20dz97kh23Ko29LVajUGDBiA+Ph485jJZEJ8fDzCw8OtrjN06FCcPXsWJpPJPPb7778jICDAatghIiKim49DBR4AiI6OxocffohPPvkEp06dwqxZs1BWVma+a2vatGkWFzXPmjUL+fn5mDt3Ln7//Xds3rwZS5cuxZw5c1rqJRAREZGDcahTWgAwadIkXL16FYsWLUJWVhb69u2Lbdu2mS9kzszMhFz+Z04LDg7Gzz//jKeffhq9e/dGUFAQ5s6di/nz57fUSyAiIiIH43CBBwCioqIQFRVldVlCQkKtsfDwcOzfv9/GVREREVFr5XCntIiIiIiaGwMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSZ7NA09CQoKtd0FERER0XTYLPHv37sXo0aMxevRoW+2CiIiIqF6UDV3BYDDgiy++QFJSEpRKJYYNG4b777/fvDw5ORnPP/884uLiIITAwIEDm7VgIiIiooZqUOApKSnB8OHDcezYMQghAABvvfUW7r//fnz11VdYtGgRli5dCpPJhP79+2Px4sW45557bFI4ERERUX01KPC89tprOHr0KPr06YMpU6YAAD777DN8++23eOSRR7Bx40Z06tQJb775Ju69916bFExERETUUA0KPN9//z1CQkJw4MABqNVqAEBUVBS6deuGr776CnfddRe+/fZbaDQamxRLRERE1BgNumj5/PnzGDdunDnsAIBWq8Xdd98NAHjzzTcZdoiIiMjhNCjwVFRUwM/Pr9a4r68vAKBr167NUxURERFRM2rW29Llcj7HkIiIiBxPg29LT0lJwcaNG2uNAcBXX31lvnvrWg8//HAjyyMiIiJqugYHnm+++QbffPONxVhNyHnkkUdqjctkMgYeIiIialENCjyLFi2CTCazVS1ERERENtGgwLN48WIblUFERERkOw26yvjxxx/HDz/8YKtaiIiIiGyiQYFn3bp1SE5OtlEpRERERLbB+8iJiIhI8hh4iIiISPIYeIiIiEjyGvwcnk2bNiE9Pb3e82UyGT7++OOG7oaIiIio2TQ48CQnJzfowmUGHiIiImppDQ48jz32GKZPn26LWoiIiIhsosGBJzQ0FCNGjLBFLUREREQ2wYuWiYiISPIYeIiIiEjyGHiIiIhI8hoUeNauXQuFQoGFCxfCYDDUOU+v12PhwoVYtmxZkwskIiIiaqoGBZ6goCAsWrQI3t7eUKlUdc5Tq9Xw8fHBCy+8gJ07dza5SCIiIqKmaFDg+fTTT9GmTRtERUXdcO6cOXPg5eWFtWvXNro4IiIioubQoMCzb98+REREQKPR3HCuRqNBREQE9u7d2+jiiIiIiJpDgwLP5cuX0aFDh3rPDwsLw5UrVxpcFBEREVFzalDgkcvl171Y+a8MBgPkct4IRkRERC2rQWkkMDAQKSkp9Z6fkpKCoKCgBhdFRERE1JwaFHhuv/127Nixo16flp6eno4dO3Zg+PDhja2NiIiIqFk0KPDMmTMHBoMBDz74IHJzc+ucl5eXh4ceeghVVVWYNWtWk4skIiIiaooGfXho//79MW/ePKxcuRI9evTAzJkzMWrUKLRr1w4AcOnSJcTHx+ODDz7A1atXER0djf79+9ukcCIiIqL6avCnpS9fvhxarRZvvPEGXn31Vbz66qsWy4UQUCgUWLBgAV555ZVmK5SIiIiosRoceGQyGZYuXYonnngCa9euxb59+5CVlQUA8Pf3x9ChQ/HYY4+hY8eOzV4sERERUWM0OPDU6NixI4/gEBERUavAh+QQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQ5bOBZtWoVQkNDodVqMXjwYBw8eLBe661fvx4ymQwTJkywbYFERETUajhk4NmwYQOio6MRExODw4cPo0+fPoiMjEROTs5110tPT8ezzz6L22+/3U6VEhERUWvgkIFnxYoVeOqppzBjxgz06NED77//PpydnbFmzZo61zEajZgyZQqWLFmCDh062LFaIiIicnTKli7gr/R6PZKSkrBgwQLzmFwuR0REBBITE+tc7z//+Q98fX3xxBNP4Ndff73uPnQ6HXQ6nfn74uJiAIDBYIDBYGjiK7BUs73m3i5ZYp/tg322D/bZfthr+7BVnxuyPYcLPLm5uTAajfDz87MY9/Pzw+nTp62us2fPHnz88cdITk6u1z5iY2OxZMmSWuPbt2+Hs7Nzg2uuj7i4OJtslyyxz/bBPtsH+2w/7LV9NHefy8vL6z3X4QJPQ5WUlGDq1Kn48MMP4ePjU691FixYgOjoaPP3xcXFCA4OxtixY+Hu7t6s9RkMBsTFxWHMmDFQqVTNum36E/tsH+yzfbDP9sNe24et+lxzhqY+HC7w+Pj4QKFQIDs722I8Ozsb/v7+teafO3cO6enpGD9+vHnMZDIBAJRKJVJTU9GxY0eLdTQaDTQaTa1tqVQqm/3A23Lb9Cf22T7YZ/tgn+2HvbaP5u5zQ7blcBctq9VqDBgwAPHx8eYxk8mE+Ph4hIeH15rfrVs3HD9+HMnJyeY/9957L0aNGoXk5GQEBwfbs3wiIiJyQA53hAcAoqOjMX36dAwcOBCDBg3CypUrUVZWhhkzZgAApk2bhqCgIMTGxkKr1aJnz54W63t6egJArXEiIiK6OTlk4Jk0aRKuXr2KRYsWISsrC3379sW2bdvMFzJnZmZCLne4g1NERETkoBwy8ABAVFQUoqKirC5LSEi47rrr1q1r/oKIiIio1eJhEiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiImo2QghU6I0oqjDAZBItXY6ZsqULICIiIsBkEjidVYKrpToEeGjhrlXBTatEZn45KgxGCAFcLqyAwWhCmd4Id60SAR5OKNNVATKgs68rhABcNEoUlOvhpFJArZTjYkEFTl0pxpnsUuiNRvi7a9GujTPK9FUoqjAgu6gSMpkMRpNAlcmEogoDXDVKuGtVUCvlqDAYcSG/AhqlHB7OKrioFTAYBdy1SlSZBFKzSuCqVcLfQ4vMvHIkZRQgp0QHANAo5fB0VqHKKOCnkmPcuJbrLwMPEcFgNOFqiQ6p2SXYfz4P3i5q5JXq0c7LGeN6+kOrUkAmA1Sylq5UWioNRmiUcshkMphMAnK59QYLISAEUFCuR36ZHkkZBdCqFPBx1UAhl8FJrcCVwgqcySmFWilHUYUBZ7JL0dZNg65+rnDWKJFTXIkrRZWoNJjQ1k2DvsGe8PfQQgYgp0SHiwXlyCqqRKXBCKVCDh9XDUK8ndHZ1xVhPi5QKuQQQkBvNKFMZ0SV0QQPZxU0SgUu5Jcjp0QHN60SKoUcBeV6ZOaV4/zVUhy5UAhntQK3hnqZ96mUy+HnroFM1vAfKCEEsot1yC3VQauqfq1ymQy6KhM0Sjk0SgWyiitwKL0AGfnl0FeZoFbKEeTpBA8nFboHuCHQ0wleLmq4qJVQ1/M8h8lU/dqrTAJluiq4a1XIK9PhUkF1368UVeDE5WJkFVXiYkEF2rio0NZVg5wSHeQyGXoGuaO7vzuUCjnK9VVw1SiRkV+OhNM50BtNaOOsRmGFAVf/CApSoasyIbu4+jW5ubbsPyAMPEQ3me0nsvDernO4raM37uoZgNd/TsXu36/WOf+lTSnmr100CpTplJibuB2Dw7wwNTwEOcU6KP54o+4e4I6s4koMDGmDAA8tdFUmnMkuRfcANygVf76zCCEgk8lQVGGAu1YJo0mgwmCEq0ZZrzfB7OJK6AwmuDsp4aRWQF9lgpNKYbGP+jCZBLKKK+GsViAttwwXCyrg7arGLQEeMJhM8HZRA4DVmkwmgSvFlcgv1cNVq0SAhxYH0vKRXVyJgjI9XDRK3BrqBbkMOJ9bBoPRBH2VCWdySnE2pxQH0/L/eLMGrj3qr1XJ0dnXFfIKOb7JTcKVIh3O5JQ26HXZglZV3dtKg8k8JpcBbd005je06/n5RHatMTetEgq5DCHeLvB2UcPTSQWjEKgyCrTzckJbVw1UCjlkMiC3VI8D5/NwMD0fohnPkrTz1EJZpcC3uYdRpjcit1SHKpOATAaUVFbBaBRw0ShRWKG3eO03UqqrwoX8CvP3mfnl2HI8q875uaV6ANVHRHzdNcgv1aNMbwQAqJVy89GU9l7O0KoUcFYrUFpZhaziSijkMijlMqTnlcNo5RSSh5MKYT4u6ObvBi8XdXUoK6yAVqWAp5MKvm4aAIBCIYPRKOCmVUFXZUS53ogKvRFuWiW8XTVQKWQoLDeguNIArUqBksoqVBlNCPVxgUwGZBVVQqOUY2RXX3T1d4OrRomsokrkl+shjEYcPbi33v2zBQYeoiZKzSpBWzcNvFzUFm/cN5JfpkdpZRXK9FV4+aeTyCnR4baO3njq9g4I9nKuc72MvDIkpF7FnT394eeuRZXRhPxyPdy1KshkwMs/ncRn+zMBAE8MC8PskR3h7arBlaIK3PP2HuSVVf/DeiSzEKt2nmvQay3TGc1fH0jLx4G0/Hqv2yvIA5cKK5D/x/7r4qZVwkmlwLheAegR6I68Uj3OXS3Fb+n5yMwvv+6bXXsvZ9zW0Rsluio4qxQIa+sCIYBdqVdxoaAclQYjuvm7w02rxNVSHY5kFtardqVchgBPLbxcNCiprD4FUPNm1FR/fX+qNJhw/FIxADmQn2d1HX93LVRKGUoqq1BpMKLSYIKzWoFQbxfI5YCnk/qPnyGBqyV6XC2phJeLGp18XQEABeUGnLxcjCtFFSjVVSHYyxk+rhq0ddPA7Y/QWVShR1puOdJzy1Dxxz6s1V4Tdlw1SvMpEa1KgeA2zujq74YADy2cVAocvViEI5kF5p8/oDpQAEBheWGD++amUaJMXwVPZzWqjNVHcfRVJuiNJjirlRgY0gbdA9zRxlmFCoMJmfllKCw34ExOKbKKKlGqq973xcJKADKkn8mtc18lf8z9K7kM8HRWo3uAG7xcNOjs64oObV3Q2dcNBeV6ZBVVwkWjhFYlx6krxThxuRhGk4CrRomiCgMUchlu7+yDTr5uKKk0QKNUoF97T2hVCgghYDQJlOqq4OGkqtcvAvoqExRyGapMJihkMijk1Ue+tCpFg/vbXEJ9XBAKFxgMBlw81mJlAGDgoVZKCIHM/HIEt3Gu8zQAABRVGOCsVkBVx2/+VUZTnUcFqowmHEzPxy0BHvBwVgEAynRVuFhQgX3nctG7nSde23oaB9Nv/Kbfv70nrpbqLH7js+ZsTik+Tcwwfz9rZEeM6eEHk0ngo1/TsO3En78hxvxw4ob7/XhPGj7ek3bDeQEeWrz7aH90D3CDs1ppPgIjhEBSRgGKKw1QKeSIP5mF3SkZ6B4SAHdnFbamZKGw3HDD7QPA8UtF9ZpXUlmFksoqrNuXXq/518rML0dmfvl15ySetx4iajipFKgwWIaZKpPAhfyKG/79AUCQpxN0VUZUmYS5Ny5qhflNyyQEOrR1QTd/dwz440iYq0YJkwBKdQaU641Iv1qKXYdS0LFjR/h7OqFHgDsEqsNgjwD3Rp0Kqsv1TqUB1f8f5JfpUVRhgADg6aSCu5MKCrkMeaV6pOeVIdTbBf4e2nrtr8pogkD10YArRZUQovooW1GFATnFOigVMmhVCmTml+PCHwG30mCEi6Y6xNzZ0x+ezmq0ddPAaBLmo4sNcW2YSLlYgM27DqBbj55QKBTwc9eajzpqVApolHLoqkxw1SjR1k0DIQQ8nFQo0xnhpK6+RqY+bu/ctkE1ymQyKBUyeDqr671OTS0K+Z8BpyXDjqNh4CGHlluqg0Ypx+msEjz5ySEUVdR+c106sRdW7zyDi4XVp1r6t/eERqmo9ca2ekp/jOsVgIsF5TibU4q1e9Ox649TOV88NRi3dfSBEAJv/JyK1QkNO/JxI4freSThr95LOIf3GlnL6G6+yCnR1Qoa9/YJxFuP9IVMJsOF/HIEeGhrhb6aN1SZTIaBoV7m8SGhnuiH8xg3rjdUKhVixt+Ccr0RXi5//qMshMC5q6U4klmIMl0V7h/QDum5ZfjlZDaOXSpCcBtndAtww+9ZJegT7AkPJxWc1dWnplQKGU5dKUHKpSJcLChH/OkcqORyCFT/Vjyqmy96B3ngtk4+8HPTokRngFohR5VJILu4EmdzSrHl+BX8nl2Krv5uyC/T42qJDgajCX2DPdHxjyMcJy8Xw89dgzYuajw+NAwVeiMCPZ2gUlRfuHk6qwS+bhoUVRigqzLhaqkOOcWVyMwvh6tGhUBPLcJ8XNAryAMymQxVRhPOXS1DUBunWkf3GvOmPCTUE+5Xj2Hc2M5QqVQNWrehrhd2AECpkMPXXQtf99qBxt9DW++gc+32ACDYy/m6RzLrozFhB7AME4PDvJB3SmDcoOAG9drDmTc5tzYMPNRi9FUmPPT+Phy9WP2G/K/RnXF/vyCE+rgAAPaezcWUjw7ccDsLvztu8X1d4WL254fr3MajH954P3XxdFbh0UHt8X/7M+DtosaDA9ph5S9nUFXH7Zi923ngSlElrpbo8PH0gfjq0EW89kBveDirIITAL6dysOG3TPxyKsfq+l393PDDP4fi2MUifHv4IioNJsyL6Iz8Mj0uFFRgSJiX+c1JCIGfT2Rja8oVDOngjQl9g+Ck/vM3vqa+4WhVilq/QcpkMnTydUMnX7drXrMnerfzrNc2bwn0wIMD2tVrbs2RNwAI9HRCv/Zt8NDA4HqtWxelQoaeQR4AYPVN3vo6cnT1d7O6rLFvykTUvBh4yGaKyg3Yey4XPQLcEeLtbD5qIIRAQbkB63/LNIcdAHg7/gzejj/ToH24apTmc/HWDO3kjdNXSiyuG6ivQWFemDmiA1IuFWNF3O9w1yoR5uOCAA8nvHB3d6iVcrholObf6P99ZzfzulF3dK61vUqD0erh5dHd/cxfy2QyjOnhhzE9/hwrLNdDIZfBRa20+G381lAv3HrN0ZcQbxf0a9/GYtsymQx39vTHnT39G/z6iYikhIGHbOLt+DNYEfd7rfFvZ9+G+1fva9Q2X77vFvxtSIjF9QtCCJy4XAwFTPj90G6MGzcOBiFDTrHOfKSoxtq9aVjy40nc0zsA08JDkVVciYjuvnBSKfB10kU89/WfV9SdefUu83U/d3Tzw79G1w4wDdXYc+kNOYdPRETWMfBQs4vemIxvD1+yusxa2PklegQCPbXYdzYPi75PweWiSgDVt8H+PG842v9x2sXahZoyWfXpB4PBgJp45axWItSn9o/2jKFhmDE0zGpdDw0MbvKpECIiclwMPNQkQgiMXr4L3QPcMbFfEE5eKa4z7FjTN9jTfKtsRA8/RFxzKoeIiKi5OGzgWbVqFd544w1kZWWhT58+eOeddzBo0CCrcz/88EN8+umnSEmpfkDagAEDsHTp0jrnU/NZ+F0KzueW4XxuGTYfv2Kx7NvZt6H/NdeUXCmqQHjsDnTzd8PWubcDsH7UhoiIqLk55H11GzZsQHR0NGJiYnD48GH06dMHkZGRyMmxftdKQkICJk+ejJ07dyIxMRHBwcEYO3YsLl2q/5EGapjCcj2eWPcbvjyYWeec/n+5gDbAwwnpy+7GtnnDIZPJGHaIiMhuHDLwrFixAk899RRmzJiBHj164P3334ezszPWrFljdf7nn3+O2bNno2/fvujWrRs++ugjmEwmxMfH27ly6SqqMGDt3jRk5pVjdcJZ9P1PHOJP/xlA+7Srvo33sdtCMaJLW6TFtuAnxBEREf2Fw53S0uv1SEpKwoIFC8xjcrkcERERSExMrNc2ysvLYTAY4OXlZXW5TqeDTvfnZ78UFxcDAAwGAwyG+j01tr5qttfc27W3Pku2AwCW/HjS6vKv/zHY4vuqqrpvFbcFqfTZ0bHP9sE+2w97bR+26nNDtudwgSc3NxdGoxF+fpYXr/r5+eH06dP12sb8+fMRGBiIiIgIq8tjY2OxZMmSWuPbt2+Hs3PTHsRWl7i4OJts1x4qjUBdPyr9vE0YE2TCli1b7FpTXVpzn1sT9tk+2Gf7Ya/to7n7XF5+/Y+SuZbDBZ6mWrZsGdavX4+EhARotdafkrpgwQJER0ebvy8uLjZf9+Pu7t6s9RgMBsTFxWHMmDE2f0S8rfx07Apw0PJpxgEeWkwa2A5zRnZooaosSaHPrQH7bB/ss/2w1/Zhqz7XnKGpD4cLPD4+PlAoFMjOzrYYz87Ohr//9Z8W++abb2LZsmX45Zdf0Lt37zrnaTQaaDSaWuMqlcpmP/C23LYtVeiNePqr6rDjolbgtxcj4Kx2uB8bs9ba59aGfbYP9tl+2Gv7aO4+N2RbDnfRslqtxoABAywuOK65ADk8PLzO9V5//XW8/PLL2LZtGwYOHGiPUiUt7mQ2Oi7cgu6LtpnHnh7TxaHDDhERUV0c8t0rOjoa06dPx8CBAzFo0CCsXLkSZWVlmDFjBgBg2rRpCAoKQmxsLADgtddew6JFi/DFF18gNDQUWVlZAABXV1e4urq22OtobYQQMAlALgOe+vRQreWP1/GUYiIiIkfnkIFn0qRJuHr1KhYtWoSsrCz07dsX27ZtM1/InJmZCbn8z4NT7733HvR6PR588EGL7cTExGDx4sX2LL3VqP4U7Sx4u2qQmVeOD3afR2p2CVQKGQzG2p/y/eu/R1l8cCUREVFr4pCBBwCioqIQFRVldVlCQoLF9+np6bYvqJXTVRmx92wubuvog9e2ncbavelW5/017Hw1Mxzd/N3gpuW5bSIiar0cNvBQ05Xrq/DLqRz868sjjVo/ZUkkXDX8ESEiotaP72YScig9H+29nOGqVSLlUjH2nM3F2/Fn6rVuJ19XRI3qhLTcMsSdzMa6Gbcy7BARkWTwHU0iTl4uxoPvJ8JVo0RXfzckZRRcd36Hti7Y8cxIq8ueHtPFBhUSERG1HAYeiZi25iAAoFRXZTXsrHi4D7xdNfjPjycwKMwbsff3sneJRERELYaBRwLejj+D3FJdncuPLx5rvug4vo6jOkRERFLGwCMBK+J+tzr+4bSB6OrHO6yIiIgYeFqpy4UVOHG5GKU6y0+K/SV6BArK9ejTzhNqpcM9SJuIiKhFMPC0UvPWJ+Nger7F2LNju6CTL58sTURE9Fc8BNBK/TXsAEDUHZ1boBIiIiLHx8DTChWU6WuNJS8a0wKVEBERtQ4MPK3Q5aIK89dt3TR4/28D4OmsbsGKiIiIHBuv4WmFDpz/83TWby9EtGAlRERErQOP8LRCmfnlLV0CERFRq8LA0wqpFDIAwPTwkBauhIiIqHVg4GllhBD48Nc0AICvu7aFqyEiImodeA1PK1FpMGLpllPofM1zdty0/OsjIiKqD75jthLdXtpWa+zhgcEtUAkREVHrw1NarUBOcaXVca1KYedKiIiIWicGnlbgqpVPQp8zqmMLVEJERNQ68ZRWK1BQ9ucHhL71SF8YjAL39wtqwYqIiIhaFwaeViDlchEAoE87D9zXl0GHiIiooXhKqxVYtvU0AODoxaIWroSIiKh1YuBpRdp7Obd0CURERK0SA4+DE0KYv37pnh4tWAkREVHrxcDj4H48dsX89e2dfVqwEiIiotaLgcfBLd18yvw1n7tDRETUOAw8Dq5vsCcAQK3gXxUREVFj8V3UwVWZqq/hWXzvLS1cCRERUevFwOPgSiqrHzrIDwolIiJqPAYeB1dcWQUAcHdStXAlRERErRcDj4OrOcLjziM8REREjcbA4+CKK2pOafEIDxERUWMx8Dgwk0lcc0qLR3iIiIgai4HHge1PyzN/7c4jPERERI3GwOPAak5nAXzoIBERUVMw8DiwMp0RADC0k3cLV0JERNS6MfA4sBc2HQcAHDif38KVEBERtW4MPA4qt1SHSoMJAODlom7haoiIiFo3Bh4Hdexiofnr5yK7tlwhREREEsDA46D0VSbz13f3DmjBSoiIiFo/Bh4HlF+mx8zPDgMABoV6wVnNZ/AQERE1BQOPA5r0v0Tz14cyeMEyERFRUzHwOBghBM7klJq/nzOqUwtWQ0REJA0MPA5GbzRZfD93dOcWqoSIiEg6GHgczFeHLpq/jnt6OJQK/hURERE1Fd9NHcyLm1LMX3f2c2vBSoiIiKSDgceBCCFaugQiIiJJYuBxICmXis1fr5zUt+UKISIikhgGHgdSdM2no9/bJ7AFKyEiIpIWBh4H8lt69TN3VAoZ5HJZC1dDREQkHQw8DkSrUgAADEZey0NERNScGHgciOmPi5YfHtiuhSshIiKSFgYeB1KmqwIAfnYWERFRM2PgcSDleiMAwFmtaOFKiIiIpIWBx4FUMPAQERHZBAOPAyk31AQentIiIiJqTgw8DuT81epPSecRHiIioubFwOMgSnVVOHG5+knLTgw8REREzYqBx0HkFFeav+4Z5NGClRAREUkPA4+D0FWZAADuWiU6tnVt4WqIiIikhYHHQdQEHjetqoUrISIikh4GHgeh++MOLY2KfyVERETNzWHfXVetWoXQ0FBotVoMHjwYBw8evO78r776Ct26dYNWq0WvXr2wZcsWO1XaPLafzAYAqBUO+1dCRETUajnku+uGDRsQHR2NmJgYHD58GH369EFkZCRycnKszt+3bx8mT56MJ554AkeOHMGECRMwYcIEpKSk2LnyxtFXmfDxnjQAgEbpkH8lRERErZpDvruuWLECTz31FGbMmIEePXrg/fffh7OzM9asWWN1/ltvvYU777wTzz33HLp3746XX34Z/fv3x7vvvmvnyhunsspo/jrqjs4tWAkREZE0OdwjffV6PZKSkrBgwQLzmFwuR0REBBITE62uk5iYiOjoaIuxyMhIbNq0yZal3lBhuR57fs/B0TwZFCeyoVRaf75OSWWV+evR3XztVR4REdFNw+ECT25uLoxGI/z8/CzG/fz8cPr0aavrZGVlWZ2flZVldb5Op4NOpzN/X1RUBADIz8+HwWBoSvkWjl4swuxPkqq/OW49rF1LrZSjoCC/2fZ/MzEYDCgvL0deXh5UKt7pZivss32wz/bDXtuHrfpcUlICABBC3HCuwwUee4iNjcWSJUtqjYeFhbVANZZ83mzpCoiIiFqXkpISeHhc/6G9Dhd4fHx8oFAokJ2dbTGenZ0Nf39/q+v4+/s3aP6CBQssToGZTCbk5+fD29sbMpmsia/AUnFxMYKDg3HhwgW4u7s367bpT+yzfbDP9sE+2w97bR+26rMQAiUlJQgMDLzhXIcLPGq1GgMGDEB8fDwmTJgAoDqQxMfHIyoqyuo64eHhiI+Px7x588xjcXFxCA8Ptzpfo9FAo9FYjHl6ejZH+XVyd3fn/0x2wD7bB/tsH+yz/bDX9mGLPt/oyE4Nhws8ABAdHY3p06dj4MCBGDRoEFauXImysjLMmDEDADBt2jQEBQUhNjYWADB37lyMGDECy5cvx913343169fj0KFD+OCDD1ryZRAREZGDcMjAM2nSJFy9ehWLFi1CVlYW+vbti23btpkvTM7MzIRc/ucd9bfddhu++OILvPjii1i4cCE6d+6MTZs2oWfPni31EoiIiMiBOGTgAYCoqKg6T2ElJCTUGnvooYfw0EMP2biqhtNoNIiJial1Co2aF/tsH+yzfbDP9sNe24cj9Fkm6nMvFxEREVEr5pBPWiYiIiJqTgw8REREJHkMPERERCR5DDxEREQkeQw8NrRq1SqEhoZCq9Vi8ODBOHjwYEuX5NB2796N8ePHIzAwEDKZrNaHvwohsGjRIgQEBMDJyQkRERE4c+aMxZz8/HxMmTIF7u7u8PT0xBNPPIHS0lKLOceOHcPtt98OrVaL4OBgvP7667Z+aQ4lNjYWt956K9zc3ODr64sJEyYgNTXVYk5lZSXmzJkDb29vuLq64oEHHqj1NPPMzEzcfffdcHZ2hq+vL5577jlUVVVZzElISED//v2h0WjQqVMnrFu3ztYvz2G899576N27t/lBa+Hh4di6dat5OXtsG8uWLYNMJrN4EC173XSLFy+GTCaz+NOtWzfz8lbRY0E2sX79eqFWq8WaNWvEiRMnxFNPPSU8PT1FdnZ2S5fmsLZs2SJeeOEF8e233woA4rvvvrNYvmzZMuHh4SE2bdokjh49Ku69914RFhYmKioqzHPuvPNO0adPH7F//37x66+/ik6dOonJkyeblxcVFQk/Pz8xZcoUkZKSIr788kvh5OQk/ve//9nrZba4yMhIsXbtWpGSkiKSk5PFuHHjRPv27UVpaal5zsyZM0VwcLCIj48Xhw4dEkOGDBG33XabeXlVVZXo2bOniIiIEEeOHBFbtmwRPj4+YsGCBeY558+fF87OziI6OlqcPHlSvPPOO0KhUIht27bZ9fW2lB9++EFs3rxZ/P777yI1NVUsXLhQqFQqkZKSIoRgj23h4MGDIjQ0VPTu3VvMnTvXPM5eN11MTIy45ZZbxJUrV8x/rl69al7eGnrMwGMjgwYNEnPmzDF/bzQaRWBgoIiNjW3BqlqPvwYek8kk/P39xRtvvGEeKywsFBqNRnz55ZdCCCFOnjwpAIjffvvNPGfr1q1CJpOJS5cuCSGEWL16tWjTpo3Q6XTmOfPnzxddu3a18StyXDk5OQKA2LVrlxCiuq8qlUp89dVX5jmnTp0SAERiYqIQojqcyuVykZWVZZ7z3nvvCXd3d3Nv//3vf4tbbrnFYl+TJk0SkZGRtn5JDqtNmzbio48+Yo9toKSkRHTu3FnExcWJESNGmAMPe908YmJiRJ8+fawuay095iktG9Dr9UhKSkJERIR5TC6XIyIiAomJiS1YWeuVlpaGrKwsi556eHhg8ODB5p4mJibC09MTAwcONM+JiIiAXC7HgQMHzHOGDx8OtVptnhMZGYnU1FQUFBTY6dU4lqKiIgCAl5cXACApKQkGg8Gi1926dUP79u0tet2rVy/z08+B6j4WFxfjxIkT5jnXbqNmzs34/4DRaMT69etRVlaG8PBw9tgG5syZg7vvvrtWP9jr5nPmzBkEBgaiQ4cOmDJlCjIzMwG0nh4z8NhAbm4ujEajxV8sAPj5+SErK6uFqmrdavp2vZ5mZWXB19fXYrlSqYSXl5fFHGvbuHYfNxOTyYR58+Zh6NCh5o9iycrKglqtrvWBun/t9Y36WNec4uJiVFRU2OLlOJzjx4/D1dUVGo0GM2fOxHfffYcePXqwx81s/fr1OHz4sPnzFa/FXjePwYMHY926ddi2bRvee+89pKWl4fbbb0dJSUmr6bHDfrQEEdnenDlzkJKSgj179rR0KZLUtWtXJCcno6ioCF9//TWmT5+OXbt2tXRZknLhwgXMnTsXcXFx0Gq1LV2OZN11113mr3v37o3BgwcjJCQEGzduhJOTUwtWVn88wmMDPj4+UCgUta5Qz87Ohr+/fwtV1brV9O16PfX390dOTo7F8qqqKuTn51vMsbaNa/dxs4iKisJPP/2EnTt3ol27duZxf39/6PV6FBYWWsz/a69v1Me65ri7u7eafyCbSq1Wo1OnThgwYABiY2PRp08fvPXWW+xxM0pKSkJOTg769+8PpVIJpVKJXbt24e2334ZSqYSfnx97bQOenp7o0qULzp4922p+nhl4bECtVmPAgAGIj483j5lMJsTHxyM8PLwFK2u9wsLC4O/vb9HT4uJiHDhwwNzT8PBwFBYWIikpyTxnx44dMJlMGDx4sHnO7t27YTAYzHPi4uLQtWtXtGnTxk6vpmUJIRAVFYXvvvsOO3bsQFhYmMXyAQMGQKVSWfQ6NTUVmZmZFr0+fvy4RcCMi4uDu7s7evToYZ5z7TZq5tzM/w+YTCbodDr2uBmNHj0ax48fR3JysvnPwIEDMWXKFPPX7HXzKy0txblz5xAQENB6fp6b5dJnqmX9+vVCo9GIdevWiZMnT4q///3vwtPT0+IKdbJUUlIijhw5Io4cOSIAiBUrVogjR46IjIwMIUT1bemenp7i+++/F8eOHRP33Xef1dvS+/XrJw4cOCD27NkjOnfubHFbemFhofDz8xNTp04VKSkpYv369cLZ2fmmui191qxZwsPDQyQkJFjcYlpeXm6eM3PmTNG+fXuxY8cOcejQIREeHi7Cw8PNy2tuMR07dqxITk4W27ZtE23btrV6i+lzzz0nTp06JVatWnVT3cb7/PPPi127dom0tDRx7Ngx8fzzzwuZTCa2b98uhGCPbenau7SEYK+bwzPPPCMSEhJEWlqa2Lt3r4iIiBA+Pj4iJydHCNE6eszAY0PvvPOOaN++vVCr1WLQoEFi//79LV2SQ9u5c6cAUOvP9OnThRDVt6a/9NJLws/PT2g0GjF69GiRmppqsY28vDwxefJk4erqKtzd3cWMGTNESUmJxZyjR4+KYcOGCY1GI4KCgsSyZcvs9RIdgrUeAxBr1641z6moqBCzZ88Wbdq0Ec7OzmLixIniypUrFttJT08Xd911l3BychI+Pj7imWeeEQaDwWLOzp07Rd++fYVarRYdOnSw2IfUPf744yIkJESo1WrRtm1bMXr0aHPYEYI9tqW/Bh72uukmTZokAgIChFqtFkFBQWLSpEni7Nmz5uWtoccyIYRonmNFRERERI6J1/AQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEBERkeQx8BAREZHkMfAQERGR5DHwEJFNyWQyjBw5sqXLaDYJCQmQyWRYvHhxS5dCRA3AwENEdvfYY49BJpMhPT29pUuxSmohjYgAZUsXQETSdurUKTg7O7d0Gc1m0KBBOHXqFHx8fFq6FCJqAAYeIrKpbt26tXQJzcrZ2Vlyr4noZsBTWkQEwPLalEOHDmHMmDFwc3ODh4cHJk6c2OjTT389PRQaGopPPvkEABAWFgaZTGb1FFJaWhqefPJJtG/fHhqNBgEBAXjssceQkZFR5z4uXbqEadOmwd/fH3K5HAkJCQCAnTt34vHHH0fXrl3h6uoKV1dXDBw4EB988IHVHgDArl27zLXJZDKsW7euVp/+KiUlBQ8//DB8fX2h0WgQFhaGefPmIS8vr9bc0NBQhIaGorS0FHPnzkVgYCA0Gg169+6Nr7/+utb8oqIiLFq0CD169ICrqyvc3d3RqVMnTJ8+3WpPiMgSj/AQkYXffvsNr7/+OkaNGoV//OMfOHLkCDZt2oTjx48jJSUFWq22SdufN28e1q1bh6NHj2Lu3Lnw9PQEUB0Aahw4cACRkZEoKyvDPffcg86dOyM9PR2ff/45tm7disTERHTo0MFiu3l5eQgPD4eXlxceeeQRVFZWwt3dHQDw2muv4ezZsxgyZAgmTpyIwsJCbNu2Df/4xz+QmpqK5cuXm2uIiYnBkiVLEBISgscee8y8/b59+173de3ZsweRkZHQ6/V48MEHERoaisTERLz11lv46aefsH///lqnwQwGA8aOHYuCggI88MADKC8vx/r16/Hwww9j27ZtGDt2LABACIHIyEgcOHAAQ4cOxZ133gm5XI6MjAz88MMPmDp1KkJCQhrxt0F0E2m2z10nolZt586dAoAAINavX2+xbOrUqQKA+PLLLxu8XQBixIgRFmPTp08XAERaWlqt+Xq9XoSGhgo3Nzdx+PBhi2W//vqrUCgU4p577qm1DwBixowZoqqqqtY2z58/X2vMYDCIMWPGCIVCITIyMm5Yc42aPsXExJjHjEaj6NixowAgtm3bZjH/ueeeEwDE448/bjEeEhIiAIj77rtP6HQ68/gvv/wiAIjIyEjz2LFjxwQAMWHChFr1VFZWipKSEqu1EtGfeEqLiCwMHz4ckyZNshh7/PHHAVQf/bG1n376Cenp6XjuuefQr18/i2XDhg3Dfffdhy1btqC4uNhimVqtxuuvvw6FQlFrm2FhYbXGlEolZs6cCaPRiJ07dzap5r179+LcuXO46667EBkZabFs0aJF8PLywhdffAG9Xl9r3f/+979Qq9Xm70ePHo2QkBCrvXZycqo1ptFo4Orq2qT6iW4GPKVFRBYGDBhQa6xdu3YAgMLCQpvvf//+/QCA1NRUq9fJZGVlwWQy4ffff8fAgQPN42FhYXXeOVVSUoI333wTmzZtwrlz51BWVmax/PLly02q+ciRIwBg9Vb2muuFtm/fjtTUVPTq1cu8zNPT02oYa9euHRITE83fd+/eHb1798aXX36JixcvYsKECRg5ciT69u0LuZy/txLVBwMPEVmoue7lWkpl9T8VRqPR5vvPz88HAHz++efXnffX0OLn52d1nl6vx8iRI3H48GH069cPU6dOhbe3N5RKJdLT0/HJJ59Ap9M1qeaao0111RAQEGAxr4aHh4fV+UqlEiaTyeL7HTt2YPHixfjmm2/wzDPPAADatm2LqKgovPDCC1aPbBHRnxh4iMih1ASuH3/8Effcc0+916u5u+qvvv/+exw+fBhPPPEEPvroI4tl69evN98x1hQ1NWdnZ1tdnpWVZTGvMby9vfHOO+/g7bffxunTp7Fjxw688847iImJgUqlwoIFCxq9baKbAY+FEpHd1RyNsHbEaPDgwQBgcUqnKc6dOwcAuO+++2ot+/XXX62uI5fLG3Q0q+Zao5rb4K9VVlaGQ4cOwcnJCV27dq33Nusik8nQvXt3zJkzB3FxcQCAH374ocnbJZI6Bh4isjsvLy8AwIULF2otu++++9C+fXusWLECu3fvrrXcYDBgz5499d5Xze3af11n165d+PDDD+us7+LFi/Xex9ChQ9GxY0ds3boVv/zyi8WyV155BXl5eZg8ebLFxckNkZ6ebvU5SDVHlJr6qACimwFPaRGR3d1xxx1488038fe//x0PPPAAXFxcEBISgqlTp0Kj0eDrr7/GXXfdhREjRuCOO+5Ar169IJPJkJGRgV9//RXe3t44ffp0vfY1fvx4hIaG4vXXX0dKSgp69uyJ1NRU/PTTT5g4caLVh/zdcccd2LhxIyZMmIB+/fpBoVDg3nvvRe/eva3uQy6XY926dYiMjMS4cePw0EMPISQkBImJiUhISEDHjh2xbNmyRvcrOTkZ999/PwYNGoQePXrA398fly5dwqZNmyCXy/H00083ettENwsGHiKyu7vuuguvv/46PvzwQyxfvhwGgwEjRozA1KlTAQC33norjh49ijfeeANbtmzB3r17odFoEBQUhAkTJmDy5Mn13perqyt27NiB5557Drt370ZCQgJuueUWfP755/Dz87MaeN566y0AwI4dO/Djjz/CZDKhXbt2dQYeoPqW+f379+M///kPtm/fjqKiIgQGBmLu3Ll48cUXm/TZWwMHDsT8+fORkJCAzZs3o7CwEP7+/oiIiMBzzz2HIUOGNHrbRDcLmRBCtHQRRERERLbEa3iIiIhI8hh4iIiISPJ4DQ8RNcjKlSvr9cTlxx57zOIDQYmIWhKv4SGiBgkNDUVGRsYN5+3cudPqRy0QEbUEBh4iIiKSPF7DQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREkvf/E9tKnYfRI7AAAAAASUVORK5CYII=\n", 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", 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" ] @@ -383,10 +383,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.833117Z", - "iopub.status.busy": "2023-11-09T07:13:57.833018Z", - "iopub.status.idle": "2023-11-09T07:13:57.841532Z", - "shell.execute_reply": "2023-11-09T07:13:57.841238Z" + "iopub.execute_input": "2023-12-04T17:50:31.961622Z", + "iopub.status.busy": "2023-12-04T17:50:31.961514Z", + "iopub.status.idle": "2023-12-04T17:50:31.971477Z", + "shell.execute_reply": "2023-12-04T17:50:31.970257Z" } }, "outputs": [], @@ -419,10 +419,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.843341Z", - "iopub.status.busy": "2023-11-09T07:13:57.843256Z", - "iopub.status.idle": "2023-11-09T07:13:57.851215Z", - "shell.execute_reply": "2023-11-09T07:13:57.850947Z" + "iopub.execute_input": "2023-12-04T17:50:31.973652Z", + "iopub.status.busy": "2023-12-04T17:50:31.973532Z", + "iopub.status.idle": "2023-12-04T17:50:31.982707Z", + "shell.execute_reply": "2023-12-04T17:50:31.982189Z" } }, "outputs": [], @@ -464,10 +464,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.853951Z", - "iopub.status.busy": "2023-11-09T07:13:57.853844Z", - "iopub.status.idle": "2023-11-09T07:13:57.862054Z", - "shell.execute_reply": "2023-11-09T07:13:57.861773Z" + "iopub.execute_input": "2023-12-04T17:50:31.986221Z", + "iopub.status.busy": "2023-12-04T17:50:31.986080Z", + "iopub.status.idle": "2023-12-04T17:50:31.994905Z", + "shell.execute_reply": "2023-12-04T17:50:31.994636Z" } }, "outputs": [ @@ -500,16 +500,16 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.863720Z", - "iopub.status.busy": "2023-11-09T07:13:57.863619Z", - "iopub.status.idle": "2023-11-09T07:13:57.985531Z", - "shell.execute_reply": "2023-11-09T07:13:57.985269Z" + "iopub.execute_input": "2023-12-04T17:50:31.996971Z", + "iopub.status.busy": "2023-12-04T17:50:31.996777Z", + "iopub.status.idle": "2023-12-04T17:50:32.162880Z", + "shell.execute_reply": "2023-12-04T17:50:32.162600Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -537,16 +537,16 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:57.987101Z", - "iopub.status.busy": "2023-11-09T07:13:57.987012Z", - "iopub.status.idle": "2023-11-09T07:13:58.115909Z", - "shell.execute_reply": "2023-11-09T07:13:58.115635Z" + "iopub.execute_input": "2023-12-04T17:50:32.164572Z", + "iopub.status.busy": "2023-12-04T17:50:32.164472Z", + "iopub.status.idle": "2023-12-04T17:50:32.301735Z", + "shell.execute_reply": "2023-12-04T17:50:32.301435Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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"iopub.status.idle": "2023-11-09T07:14:08.500960Z", - "shell.execute_reply": "2023-11-09T07:14:08.500710Z" + "iopub.execute_input": "2023-12-04T17:50:43.263530Z", + "iopub.status.busy": "2023-12-04T17:50:43.263338Z", + "iopub.status.idle": "2023-12-04T17:50:43.278572Z", + "shell.execute_reply": "2023-12-04T17:50:43.278309Z" }, "tags": [] }, @@ -331,10 +331,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:14:08.502443Z", - "iopub.status.busy": "2023-11-09T07:14:08.502348Z", - "iopub.status.idle": "2023-11-09T07:14:08.511537Z", - "shell.execute_reply": "2023-11-09T07:14:08.511292Z" + "iopub.execute_input": "2023-12-04T17:50:43.280147Z", + "iopub.status.busy": "2023-12-04T17:50:43.280048Z", + "iopub.status.idle": "2023-12-04T17:50:43.290636Z", + "shell.execute_reply": "2023-12-04T17:50:43.290346Z" }, "tags": [] }, @@ -447,7 +447,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/imbalanced-learning.ipynb b/docs/examples/imbalanced-learning.ipynb index bfa927a870..60c2680908 100644 --- a/docs/examples/imbalanced-learning.ipynb +++ b/docs/examples/imbalanced-learning.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:14:09.563203Z", - "iopub.status.busy": "2023-11-09T07:14:09.562696Z", - "iopub.status.idle": "2023-11-09T07:14:12.104372Z", - "shell.execute_reply": "2023-11-09T07:14:12.103931Z" + "iopub.execute_input": "2023-12-04T17:50:44.731164Z", + "iopub.status.busy": "2023-12-04T17:50:44.730834Z", + "iopub.status.idle": "2023-12-04T17:50:47.316542Z", + "shell.execute_reply": "2023-12-04T17:50:47.316218Z" }, "tags": [] }, @@ -69,10 +69,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:14:12.106717Z", - "iopub.status.busy": "2023-11-09T07:14:12.106483Z", - "iopub.status.idle": "2023-11-09T07:14:29.763539Z", - "shell.execute_reply": "2023-11-09T07:14:29.763248Z" + "iopub.execute_input": "2023-12-04T17:50:47.318216Z", + "iopub.status.busy": "2023-12-04T17:50:47.318074Z", + "iopub.status.idle": "2023-12-04T17:51:05.643165Z", + "shell.execute_reply": "2023-12-04T17:51:05.642866Z" }, "tags": [] }, @@ -126,10 +126,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:14:29.765284Z", - "iopub.status.busy": "2023-11-09T07:14:29.765167Z", - "iopub.status.idle": "2023-11-09T07:14:47.409515Z", - "shell.execute_reply": "2023-11-09T07:14:47.409255Z" + "iopub.execute_input": "2023-12-04T17:51:05.644853Z", + "iopub.status.busy": "2023-12-04T17:51:05.644745Z", + "iopub.status.idle": "2023-12-04T17:51:23.760814Z", + "shell.execute_reply": "2023-12-04T17:51:23.760400Z" }, "tags": [] }, @@ -174,10 +174,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:14:47.411111Z", - "iopub.status.busy": "2023-11-09T07:14:47.410990Z", - "iopub.status.idle": "2023-11-09T07:15:04.959874Z", - "shell.execute_reply": "2023-11-09T07:15:04.959580Z" + "iopub.execute_input": "2023-12-04T17:51:23.763284Z", + "iopub.status.busy": "2023-12-04T17:51:23.762947Z", + "iopub.status.idle": "2023-12-04T17:51:41.814961Z", + "shell.execute_reply": "2023-12-04T17:51:41.814714Z" }, "tags": [] }, @@ -223,10 +223,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:15:04.961552Z", - "iopub.status.busy": "2023-11-09T07:15:04.961454Z", - "iopub.status.idle": "2023-11-09T07:15:21.026123Z", - "shell.execute_reply": "2023-11-09T07:15:21.025870Z" + "iopub.execute_input": "2023-12-04T17:51:41.816512Z", + "iopub.status.busy": "2023-12-04T17:51:41.816409Z", + "iopub.status.idle": "2023-12-04T17:51:57.141969Z", + "shell.execute_reply": "2023-12-04T17:51:57.141580Z" }, "tags": [] }, @@ -271,10 +271,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:15:21.027820Z", - "iopub.status.busy": "2023-11-09T07:15:21.027720Z", - "iopub.status.idle": "2023-11-09T07:15:21.042562Z", - "shell.execute_reply": "2023-11-09T07:15:21.042314Z" + "iopub.execute_input": "2023-12-04T17:51:57.143647Z", + "iopub.status.busy": "2023-12-04T17:51:57.143542Z", + "iopub.status.idle": "2023-12-04T17:51:57.158198Z", + "shell.execute_reply": "2023-12-04T17:51:57.157934Z" }, "tags": [] }, @@ -486,10 +486,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:15:21.044039Z", - "iopub.status.busy": "2023-11-09T07:15:21.043955Z", - "iopub.status.idle": "2023-11-09T07:15:39.917985Z", - "shell.execute_reply": "2023-11-09T07:15:39.917704Z" + "iopub.execute_input": "2023-12-04T17:51:57.159963Z", + "iopub.status.busy": "2023-12-04T17:51:57.159803Z", + "iopub.status.idle": "2023-12-04T17:52:15.935374Z", + "shell.execute_reply": "2023-12-04T17:52:15.935145Z" }, "tags": [] }, @@ -534,10 +534,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:15:39.919655Z", - "iopub.status.busy": "2023-11-09T07:15:39.919554Z", - "iopub.status.idle": "2023-11-09T07:15:55.141745Z", - "shell.execute_reply": "2023-11-09T07:15:55.141444Z" + "iopub.execute_input": "2023-12-04T17:52:15.936944Z", + "iopub.status.busy": "2023-12-04T17:52:15.936849Z", + "iopub.status.idle": "2023-12-04T17:52:31.158702Z", + "shell.execute_reply": "2023-12-04T17:52:31.158456Z" }, "tags": [] }, @@ -583,10 +583,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:15:55.143849Z", - "iopub.status.busy": "2023-11-09T07:15:55.143695Z", - "iopub.status.idle": "2023-11-09T07:16:10.513167Z", - "shell.execute_reply": "2023-11-09T07:16:10.512873Z" + "iopub.execute_input": "2023-12-04T17:52:31.160208Z", + "iopub.status.busy": "2023-12-04T17:52:31.160110Z", + "iopub.status.idle": "2023-12-04T17:52:46.396640Z", + "shell.execute_reply": "2023-12-04T17:52:46.396375Z" }, "tags": [] }, diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb index 5df71eab68..42f763a496 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb @@ -92,10 +92,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:30.152437Z", - "iopub.status.busy": "2023-11-09T07:19:30.152168Z", - "iopub.status.idle": "2023-11-09T07:19:30.653284Z", - "shell.execute_reply": "2023-11-09T07:19:30.653022Z" + "iopub.execute_input": "2023-12-04T17:55:55.085940Z", + "iopub.status.busy": "2023-12-04T17:55:55.085155Z", + "iopub.status.idle": "2023-12-04T17:55:55.493970Z", + "shell.execute_reply": "2023-12-04T17:55:55.493604Z" } }, "outputs": [ @@ -142,10 +142,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:30.673956Z", - "iopub.status.busy": "2023-11-09T07:19:30.673778Z", - "iopub.status.idle": "2023-11-09T07:19:30.702127Z", - "shell.execute_reply": "2023-11-09T07:19:30.701862Z" + "iopub.execute_input": "2023-12-04T17:55:55.512554Z", + "iopub.status.busy": "2023-12-04T17:55:55.512388Z", + "iopub.status.idle": "2023-12-04T17:55:55.533215Z", + "shell.execute_reply": "2023-12-04T17:55:55.532985Z" } }, "outputs": [], @@ -178,10 +178,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:30.703858Z", - "iopub.status.busy": "2023-11-09T07:19:30.703762Z", - "iopub.status.idle": "2023-11-09T07:19:31.804702Z", - "shell.execute_reply": "2023-11-09T07:19:31.804425Z" + "iopub.execute_input": "2023-12-04T17:55:55.534732Z", + "iopub.status.busy": "2023-12-04T17:55:55.534655Z", + "iopub.status.idle": "2023-12-04T17:55:56.611865Z", + "shell.execute_reply": "2023-12-04T17:55:56.611600Z" } }, "outputs": [ @@ -189,10 +189,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.934259, RMSE: 1.124469 – 00:00:00 – 898 B\n", - "[50,000] MAE: 0.923893, RMSE: 1.105 – 00:00:00 – 898 B\n", - "[75,000] MAE: 0.937359, RMSE: 1.123696 – 00:00:00 – 898 B\n", - "[100,000] MAE: 0.942162, RMSE: 1.125783 – 00:00:01 – 898 B\n" + "[25,000] MAE: 0.934259\n", + "RMSE: 1.124469 – 00:00:00 – 898 B\n", + "[50,000] MAE: 0.923893\n", + "RMSE: 1.105 – 00:00:00 – 898 B\n", + "[75,000] MAE: 0.937359\n", + "RMSE: 1.123696 – 00:00:00 – 898 B\n", + "[100,000] MAE: 0.942162\n", + "RMSE: 1.125783 – 00:00:01 – 898 B\n" ] } ], @@ -241,10 +245,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:31.806401Z", - "iopub.status.busy": "2023-11-09T07:19:31.806321Z", - "iopub.status.idle": "2023-11-09T07:19:33.203936Z", - "shell.execute_reply": "2023-11-09T07:19:33.203678Z" + "iopub.execute_input": "2023-12-04T17:55:56.613421Z", + "iopub.status.busy": "2023-12-04T17:55:56.613340Z", + "iopub.status.idle": "2023-12-04T17:55:57.984509Z", + "shell.execute_reply": "2023-12-04T17:55:57.984259Z" } }, "outputs": [ @@ -252,10 +256,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761844, RMSE: 0.960972 – 00:00:00 – 161.03 KB\n", - "[50,000] MAE: 0.753292, RMSE: 0.951223 – 00:00:00 – 216.34 KB\n", - "[75,000] MAE: 0.754177, RMSE: 0.953376 – 00:00:01 – 254.81 KB\n", - "[100,000] MAE: 0.754651, RMSE: 0.954148 – 00:00:01 – 278.41 KB\n" + "[25,000] MAE: 0.761844\n", + "RMSE: 0.960972 – 00:00:00 – 161.03 KB\n", + "[50,000] MAE: 0.753292\n", + "RMSE: 0.951223 – 00:00:00 – 216.34 KB\n", + "[75,000] MAE: 0.754177\n", + "RMSE: 0.953376 – 00:00:01 – 254.81 KB\n", + "[100,000] MAE: 0.754651\n", + "RMSE: 0.954148 – 00:00:01 – 278.41 KB\n" ] } ], @@ -312,10 +320,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:33.205610Z", - "iopub.status.busy": "2023-11-09T07:19:33.205516Z", - "iopub.status.idle": "2023-11-09T07:19:35.421840Z", - "shell.execute_reply": "2023-11-09T07:19:35.421552Z" + "iopub.execute_input": "2023-12-04T17:55:57.986142Z", + "iopub.status.busy": "2023-12-04T17:55:57.986057Z", + "iopub.status.idle": "2023-12-04T17:56:00.090208Z", + "shell.execute_reply": "2023-12-04T17:56:00.089941Z" } }, "outputs": [ @@ -323,10 +331,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 1.070136, RMSE: 1.397014 – 00:00:00 – 557.99 KB\n", - "[50,000] MAE: 0.99174, RMSE: 1.290666 – 00:00:01 – 690.31 KB\n", - "[75,000] MAE: 0.961072, RMSE: 1.250842 – 00:00:01 – 813.07 KB\n", - "[100,000] MAE: 0.944883, RMSE: 1.227688 – 00:00:02 – 914.17 KB\n" + "[25,000] MAE: 1.070136\n", + "RMSE: 1.397014 – 00:00:00 – 557.99 KB\n", + "[50,000] MAE: 0.99174\n", + "RMSE: 1.290666 – 00:00:01 – 690.31 KB\n", + "[75,000] MAE: 0.961072\n", + "RMSE: 1.250842 – 00:00:01 – 813.07 KB\n", + "[100,000] MAE: 0.944883\n", + "RMSE: 1.227688 – 00:00:02 – 914.17 KB\n" ] } ], @@ -380,10 +392,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:35.423652Z", - "iopub.status.busy": "2023-11-09T07:19:35.423543Z", - "iopub.status.idle": "2023-11-09T07:19:37.907491Z", - "shell.execute_reply": "2023-11-09T07:19:37.907194Z" + "iopub.execute_input": "2023-12-04T17:56:00.091813Z", + "iopub.status.busy": "2023-12-04T17:56:00.091738Z", + "iopub.status.idle": "2023-12-04T17:56:02.472397Z", + "shell.execute_reply": "2023-12-04T17:56:02.472157Z" } }, "outputs": [ @@ -391,10 +403,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761818, RMSE: 0.961057 – 00:00:00 – 643.81 KB\n", - "[50,000] MAE: 0.751667, RMSE: 0.949443 – 00:00:01 – 817.72 KB\n", - "[75,000] MAE: 0.749653, RMSE: 0.948723 – 00:00:01 – 964.02 KB\n", - "[100,000] MAE: 0.748559, RMSE: 0.947854 – 00:00:02 – 1.05 MB\n" + "[25,000] MAE: 0.761818\n", + "RMSE: 0.961057 – 00:00:00 – 643.81 KB\n", + "[50,000] MAE: 0.751667\n", + "RMSE: 0.949443 – 00:00:01 – 817.72 KB\n", + "[75,000] MAE: 0.749653\n", + "RMSE: 0.948723 – 00:00:01 – 964.02 KB\n", + "[100,000] MAE: 0.748559\n", + "RMSE: 0.947854 – 00:00:02 – 1.05 MB\n" ] } ], diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb index 88ce4f1c7d..f641f0bd94 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb @@ -81,10 +81,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:38.980694Z", - "iopub.status.busy": "2023-11-09T07:19:38.980368Z", - "iopub.status.idle": "2023-11-09T07:19:39.414414Z", - "shell.execute_reply": "2023-11-09T07:19:39.414064Z" + "iopub.execute_input": "2023-12-04T17:56:03.715589Z", + "iopub.status.busy": "2023-12-04T17:56:03.715073Z", + "iopub.status.idle": "2023-12-04T17:56:04.126649Z", + "shell.execute_reply": "2023-12-04T17:56:04.126227Z" } }, "outputs": [], @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:39.416570Z", - "iopub.status.busy": "2023-11-09T07:19:39.416390Z", - "iopub.status.idle": "2023-11-09T07:19:44.873993Z", - "shell.execute_reply": "2023-11-09T07:19:44.873596Z" + "iopub.execute_input": "2023-12-04T17:56:04.128590Z", + "iopub.status.busy": "2023-12-04T17:56:04.128429Z", + "iopub.status.idle": "2023-12-04T17:56:09.513255Z", + "shell.execute_reply": "2023-12-04T17:56:09.512930Z" } }, "outputs": [ @@ -122,10 +122,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761761, RMSE: 0.960662 – 00:00:01 – 778.29 KB\n", - "[50,000] MAE: 0.751922, RMSE: 0.949783 – 00:00:02 – 908.2 KB\n", - "[75,000] MAE: 0.749822, RMSE: 0.948634 – 00:00:03 – 1.03 MB\n", - "[100,000] MAE: 0.748393, RMSE: 0.94776 – 00:00:05 – 1.15 MB\n" + "[25,000] MAE: 0.761778\n", + "RMSE: 0.960803 – 00:00:01 – 778.29 KB\n", + "[50,000] MAE: 0.751986\n", + "RMSE: 0.949941 – 00:00:02 – 908.2 KB\n", + "[75,000] MAE: 0.750044\n", + "RMSE: 0.948911 – 00:00:03 – 1.03 MB\n", + "[100,000] MAE: 0.748609\n", + "RMSE: 0.947994 – 00:00:05 – 1.15 MB\n" ] } ], @@ -189,10 +193,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:44.876288Z", - "iopub.status.busy": "2023-11-09T07:19:44.876139Z", - "iopub.status.idle": "2023-11-09T07:19:44.889117Z", - "shell.execute_reply": "2023-11-09T07:19:44.888503Z" + "iopub.execute_input": "2023-12-04T17:56:09.515335Z", + "iopub.status.busy": "2023-12-04T17:56:09.515220Z", + "iopub.status.idle": "2023-12-04T17:56:09.527171Z", + "shell.execute_reply": "2023-12-04T17:56:09.526776Z" } }, "outputs": [ @@ -245,10 +249,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:44.890935Z", - "iopub.status.busy": "2023-11-09T07:19:44.890832Z", - "iopub.status.idle": "2023-11-09T07:19:44.904119Z", - "shell.execute_reply": "2023-11-09T07:19:44.903789Z" + "iopub.execute_input": "2023-12-04T17:56:09.528913Z", + "iopub.status.busy": "2023-12-04T17:56:09.528771Z", + "iopub.status.idle": "2023-12-04T17:56:09.538807Z", + "shell.execute_reply": "2023-12-04T17:56:09.538396Z" } }, "outputs": [], @@ -272,10 +276,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:44.905952Z", - "iopub.status.busy": "2023-11-09T07:19:44.905856Z", - "iopub.status.idle": "2023-11-09T07:19:44.916868Z", - "shell.execute_reply": "2023-11-09T07:19:44.916467Z" + "iopub.execute_input": "2023-12-04T17:56:09.540871Z", + "iopub.status.busy": "2023-12-04T17:56:09.540745Z", + "iopub.status.idle": "2023-12-04T17:56:09.551743Z", + "shell.execute_reply": "2023-12-04T17:56:09.551414Z" } }, "outputs": [], @@ -303,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:44.918905Z", - "iopub.status.busy": "2023-11-09T07:19:44.918752Z", - "iopub.status.idle": "2023-11-09T07:20:00.959021Z", - "shell.execute_reply": "2023-11-09T07:20:00.958727Z" + "iopub.execute_input": "2023-12-04T17:56:09.553540Z", + "iopub.status.busy": "2023-12-04T17:56:09.553426Z", + "iopub.status.idle": "2023-12-04T17:56:25.748558Z", + "shell.execute_reply": "2023-12-04T17:56:25.748307Z" } }, "outputs": [ @@ -314,10 +318,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.760059, RMSE: 0.961415 – 00:00:03 – 895.78 KB\n", - "[50,000] MAE: 0.751429, RMSE: 0.951504 – 00:00:07 – 1.02 MB\n", - "[75,000] MAE: 0.750568, RMSE: 0.951592 – 00:00:11 – 1.18 MB\n", - "[100,000] MAE: 0.75018, RMSE: 0.951622 – 00:00:16 – 1.33 MB\n" + "[25,000] MAE: 0.759838\n", + "RMSE: 0.961281 – 00:00:03 – 895.78 KB\n", + "[50,000] MAE: 0.751307\n", + "RMSE: 0.951391 – 00:00:08 – 1.02 MB\n", + "[75,000] MAE: 0.750361\n", + "RMSE: 0.951393 – 00:00:12 – 1.18 MB\n", + "[100,000] MAE: 0.749994\n", + "RMSE: 0.951435 – 00:00:16 – 1.33 MB\n" ] } ], @@ -386,10 +394,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:20:00.960832Z", - "iopub.status.busy": "2023-11-09T07:20:00.960735Z", - "iopub.status.idle": "2023-11-09T07:21:03.334574Z", - "shell.execute_reply": "2023-11-09T07:21:03.334266Z" + "iopub.execute_input": "2023-12-04T17:56:25.750135Z", + "iopub.status.busy": "2023-12-04T17:56:25.750035Z", + "iopub.status.idle": "2023-12-04T17:57:29.483921Z", + "shell.execute_reply": "2023-12-04T17:57:29.483635Z" } }, "outputs": [ @@ -397,10 +405,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761379, RMSE: 0.96214 – 00:00:15 – 1.67 MB\n", - "[50,000] MAE: 0.751998, RMSE: 0.951589 – 00:00:31 – 1.97 MB\n", - "[75,000] MAE: 0.750994, RMSE: 0.951616 – 00:00:46 – 2.3 MB\n", - "[100,000] MAE: 0.750849, RMSE: 0.952142 – 00:01:02 – 2.6 MB\n" + "[25,000] MAE: 0.761297\n", + "RMSE: 0.962054 – 00:00:15 – 1.67 MB\n", + "[50,000] MAE: 0.751865\n", + "RMSE: 0.951499 – 00:00:31 – 1.97 MB\n", + "[75,000] MAE: 0.750853\n", + "RMSE: 0.951526 – 00:00:47 – 2.3 MB\n", + "[100,000] MAE: 0.750607\n", + "RMSE: 0.951982 – 00:01:03 – 2.6 MB\n" ] } ], @@ -471,10 +483,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:21:03.336221Z", - "iopub.status.busy": "2023-11-09T07:21:03.336122Z", - "iopub.status.idle": "2023-11-09T07:21:28.075174Z", - "shell.execute_reply": "2023-11-09T07:21:28.074930Z" + "iopub.execute_input": "2023-12-04T17:57:29.485700Z", + "iopub.status.busy": "2023-12-04T17:57:29.485595Z", + "iopub.status.idle": "2023-12-04T17:57:54.281600Z", + "shell.execute_reply": "2023-12-04T17:57:54.281345Z" } }, "outputs": [ @@ -482,10 +494,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.758339, RMSE: 0.959047 – 00:00:05 – 2.04 MB\n", - "[50,000] MAE: 0.749833, RMSE: 0.948531 – 00:00:12 – 2.41 MB\n", - "[75,000] MAE: 0.749631, RMSE: 0.949418 – 00:00:18 – 2.82 MB\n", - "[100,000] MAE: 0.749776, RMSE: 0.950131 – 00:00:24 – 3.19 MB\n" + "[25,000] MAE: 0.757718\n", + "RMSE: 0.958158 – 00:00:06 – 2.04 MB\n", + "[50,000] MAE: 0.749502\n", + "RMSE: 0.948065 – 00:00:12 – 2.41 MB\n", + "[75,000] MAE: 0.749275\n", + "RMSE: 0.948918 – 00:00:18 – 2.82 MB\n", + "[100,000] MAE: 0.749542\n", + "RMSE: 0.949769 – 00:00:24 – 3.19 MB\n" ] } ], @@ -551,10 +567,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:21:28.076978Z", - "iopub.status.busy": "2023-11-09T07:21:28.076874Z", - "iopub.status.idle": "2023-11-09T07:21:58.474830Z", - "shell.execute_reply": "2023-11-09T07:21:58.474562Z" + "iopub.execute_input": "2023-12-04T17:57:54.283331Z", + "iopub.status.busy": "2023-12-04T17:57:54.283223Z", + "iopub.status.idle": "2023-12-04T17:58:24.755886Z", + "shell.execute_reply": "2023-12-04T17:58:24.755394Z" } }, "outputs": [ @@ -562,10 +578,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761435, RMSE: 0.962211 – 00:00:07 – 792.94 KB\n", - "[50,000] MAE: 0.754063, RMSE: 0.953248 – 00:00:15 – 922.85 KB\n", - "[75,000] MAE: 0.754729, RMSE: 0.95507 – 00:00:22 – 1.04 MB\n", - "[100,000] MAE: 0.755697, RMSE: 0.956542 – 00:00:30 – 1.17 MB\n" + "[25,000] MAE: 0.761539\n", + "RMSE: 0.962241 – 00:00:07 – 792.94 KB\n", + "[50,000] MAE: 0.754089\n", + "RMSE: 0.953181 – 00:00:15 – 922.85 KB\n", + "[75,000] MAE: 0.754806\n", + "RMSE: 0.954979 – 00:00:22 – 1.04 MB\n", + "[100,000] MAE: 0.755404\n", + "RMSE: 0.95604 – 00:00:30 – 1.17 MB\n" ] } ], diff --git a/docs/examples/quantile-regression-uncertainty.ipynb b/docs/examples/quantile-regression-uncertainty.ipynb index 2865544aad..e459e7bb9f 100644 --- a/docs/examples/quantile-regression-uncertainty.ipynb +++ b/docs/examples/quantile-regression-uncertainty.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:11.977143Z", - "iopub.status.busy": "2023-11-09T07:16:11.976368Z", - "iopub.status.idle": "2023-11-09T07:16:12.234088Z", - "shell.execute_reply": "2023-11-09T07:16:12.233810Z" + "iopub.execute_input": "2023-12-04T17:52:47.886294Z", + "iopub.status.busy": "2023-12-04T17:52:47.885937Z", + "iopub.status.idle": "2023-12-04T17:52:48.106622Z", + "shell.execute_reply": "2023-12-04T17:52:48.106311Z" } }, "outputs": [], @@ -39,17 +39,17 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:12.236861Z", - "iopub.status.busy": "2023-11-09T07:16:12.236152Z", - "iopub.status.idle": "2023-11-09T07:16:12.989619Z", - "shell.execute_reply": "2023-11-09T07:16:12.989322Z" + "iopub.execute_input": "2023-12-04T17:52:48.108477Z", + "iopub.status.busy": "2023-12-04T17:52:48.108356Z", + "iopub.status.idle": "2023-12-04T17:52:48.657556Z", + "shell.execute_reply": "2023-12-04T17:52:48.656135Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] diff --git a/docs/examples/sentence-classification.ipynb b/docs/examples/sentence-classification.ipynb index f95c117985..a456ad5299 100644 --- a/docs/examples/sentence-classification.ipynb +++ b/docs/examples/sentence-classification.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:14.097055Z", - "iopub.status.busy": "2023-11-09T07:16:14.096808Z", - "iopub.status.idle": "2023-11-09T07:16:14.492429Z", - "shell.execute_reply": "2023-11-09T07:16:14.492035Z" + "iopub.execute_input": "2023-12-04T17:52:50.005002Z", + "iopub.status.busy": "2023-12-04T17:52:50.004369Z", + "iopub.status.idle": "2023-12-04T17:52:50.415357Z", + "shell.execute_reply": "2023-12-04T17:52:50.415077Z" }, "tags": [] }, @@ -62,10 +62,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:14.494071Z", - "iopub.status.busy": "2023-11-09T07:16:14.493926Z", - "iopub.status.idle": "2023-11-09T07:16:14.506089Z", - "shell.execute_reply": "2023-11-09T07:16:14.505825Z" + "iopub.execute_input": "2023-12-04T17:52:50.416917Z", + "iopub.status.busy": "2023-12-04T17:52:50.416799Z", + "iopub.status.idle": "2023-12-04T17:52:50.427410Z", + "shell.execute_reply": "2023-12-04T17:52:50.427185Z" }, "tags": [] }, @@ -103,10 +103,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:14.507584Z", - "iopub.status.busy": "2023-11-09T07:16:14.507506Z", - "iopub.status.idle": "2023-11-09T07:16:31.431294Z", - "shell.execute_reply": "2023-11-09T07:16:31.430945Z" + "iopub.execute_input": "2023-12-04T17:52:50.428788Z", + "iopub.status.busy": "2023-12-04T17:52:50.428710Z", + "iopub.status.idle": "2023-12-04T17:53:07.741457Z", + "shell.execute_reply": "2023-12-04T17:53:07.741048Z" }, "tags": [] }, @@ -162,10 +162,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:31.433018Z", - "iopub.status.busy": "2023-11-09T07:16:31.432918Z", - "iopub.status.idle": "2023-11-09T07:16:31.443389Z", - "shell.execute_reply": "2023-11-09T07:16:31.443153Z" + "iopub.execute_input": "2023-12-04T17:53:07.743587Z", + "iopub.status.busy": "2023-12-04T17:53:07.743450Z", + "iopub.status.idle": "2023-12-04T17:53:07.754659Z", + "shell.execute_reply": "2023-12-04T17:53:07.754387Z" }, "tags": [] }, @@ -206,10 +206,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:31.445057Z", - "iopub.status.busy": "2023-11-09T07:16:31.444961Z", - "iopub.status.idle": "2023-11-09T07:16:40.630373Z", - "shell.execute_reply": "2023-11-09T07:16:40.630094Z" + "iopub.execute_input": "2023-12-04T17:53:07.756292Z", + "iopub.status.busy": "2023-12-04T17:53:07.756192Z", + "iopub.status.idle": "2023-12-04T17:53:17.349404Z", + "shell.execute_reply": "2023-12-04T17:53:17.348983Z" }, "tags": [] }, @@ -274,10 +274,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:40.632089Z", - "iopub.status.busy": "2023-11-09T07:16:40.631980Z", - "iopub.status.idle": "2023-11-09T07:16:40.642424Z", - "shell.execute_reply": "2023-11-09T07:16:40.642131Z" + "iopub.execute_input": "2023-12-04T17:53:17.351734Z", + "iopub.status.busy": "2023-12-04T17:53:17.351613Z", + "iopub.status.idle": "2023-12-04T17:53:17.362178Z", + "shell.execute_reply": "2023-12-04T17:53:17.361751Z" }, "tags": [] }, @@ -304,10 +304,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:40.644084Z", - "iopub.status.busy": "2023-11-09T07:16:40.643976Z", - "iopub.status.idle": "2023-11-09T07:16:40.658816Z", - "shell.execute_reply": "2023-11-09T07:16:40.658527Z" + "iopub.execute_input": "2023-12-04T17:53:17.364375Z", + "iopub.status.busy": "2023-12-04T17:53:17.364251Z", + "iopub.status.idle": "2023-12-04T17:53:17.377988Z", + "shell.execute_reply": "2023-12-04T17:53:17.377664Z" }, "tags": [] }, @@ -479,10 +479,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:40.660408Z", - "iopub.status.busy": "2023-11-09T07:16:40.660299Z", - "iopub.status.idle": "2023-11-09T07:16:41.186575Z", - "shell.execute_reply": "2023-11-09T07:16:41.186314Z" + "iopub.execute_input": "2023-12-04T17:53:17.379539Z", + "iopub.status.busy": "2023-12-04T17:53:17.379439Z", + "iopub.status.idle": "2023-12-04T17:53:17.933593Z", + "shell.execute_reply": "2023-12-04T17:53:17.933265Z" }, "tags": [] }, @@ -545,10 +545,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:41.188113Z", - "iopub.status.busy": "2023-11-09T07:16:41.188014Z", - "iopub.status.idle": "2023-11-09T07:16:41.198225Z", - "shell.execute_reply": "2023-11-09T07:16:41.197984Z" + "iopub.execute_input": "2023-12-04T17:53:17.935653Z", + "iopub.status.busy": "2023-12-04T17:53:17.935494Z", + "iopub.status.idle": "2023-12-04T17:53:17.947268Z", + "shell.execute_reply": "2023-12-04T17:53:17.946889Z" }, "tags": [] }, @@ -575,10 +575,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:41.199736Z", - "iopub.status.busy": "2023-11-09T07:16:41.199651Z", - "iopub.status.idle": "2023-11-09T07:16:41.209129Z", - "shell.execute_reply": "2023-11-09T07:16:41.208914Z" + "iopub.execute_input": "2023-12-04T17:53:17.949234Z", + "iopub.status.busy": "2023-12-04T17:53:17.949106Z", + "iopub.status.idle": "2023-12-04T17:53:17.960965Z", + "shell.execute_reply": "2023-12-04T17:53:17.960655Z" }, "tags": [] }, @@ -838,10 +838,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:41.210822Z", - "iopub.status.busy": "2023-11-09T07:16:41.210727Z", - "iopub.status.idle": "2023-11-09T07:16:43.345840Z", - "shell.execute_reply": "2023-11-09T07:16:43.345508Z" + "iopub.execute_input": "2023-12-04T17:53:17.963440Z", + "iopub.status.busy": "2023-12-04T17:53:17.963311Z", + "iopub.status.idle": "2023-12-04T17:53:18.493616Z", + "shell.execute_reply": "2023-12-04T17:53:18.493319Z" }, "tags": [] }, @@ -874,10 +874,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:16:43.347911Z", - "iopub.status.busy": "2023-11-09T07:16:43.347693Z", - "iopub.status.idle": "2023-11-09T07:17:42.083336Z", - "shell.execute_reply": "2023-11-09T07:17:42.082851Z" + "iopub.execute_input": "2023-12-04T17:53:18.495764Z", + "iopub.status.busy": "2023-12-04T17:53:18.495554Z", + "iopub.status.idle": "2023-12-04T17:54:14.281153Z", + "shell.execute_reply": "2023-12-04T17:54:14.280920Z" }, "tags": [] }, @@ -936,10 +936,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:17:42.086641Z", - "iopub.status.busy": "2023-11-09T07:17:42.086502Z", - "iopub.status.idle": "2023-11-09T07:17:42.105326Z", - "shell.execute_reply": "2023-11-09T07:17:42.105096Z" + "iopub.execute_input": "2023-12-04T17:54:14.282735Z", + "iopub.status.busy": "2023-12-04T17:54:14.282623Z", + "iopub.status.idle": "2023-12-04T17:54:14.294872Z", + "shell.execute_reply": "2023-12-04T17:54:14.294638Z" }, "tags": [] }, @@ -966,10 +966,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:17:42.106909Z", - "iopub.status.busy": "2023-11-09T07:17:42.106826Z", - "iopub.status.idle": "2023-11-09T07:17:42.119123Z", - "shell.execute_reply": "2023-11-09T07:17:42.118725Z" + "iopub.execute_input": "2023-12-04T17:54:14.296162Z", + "iopub.status.busy": "2023-12-04T17:54:14.296083Z", + "iopub.status.idle": "2023-12-04T17:54:14.307160Z", + "shell.execute_reply": "2023-12-04T17:54:14.306923Z" }, "tags": [] }, diff --git a/docs/examples/the-art-of-using-pipelines.ipynb b/docs/examples/the-art-of-using-pipelines.ipynb index ae03f17dc0..c3ce5b0d8c 100644 --- a/docs/examples/the-art-of-using-pipelines.ipynb +++ b/docs/examples/the-art-of-using-pipelines.ipynb @@ -25,10 +25,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:17:43.488129Z", - "iopub.status.busy": "2023-11-09T07:17:43.487739Z", - "iopub.status.idle": "2023-11-09T07:17:44.001211Z", - "shell.execute_reply": "2023-11-09T07:17:44.000818Z" + "iopub.execute_input": "2023-12-04T17:54:15.657967Z", + "iopub.status.busy": "2023-12-04T17:54:15.657421Z", + "iopub.status.idle": "2023-12-04T17:54:16.068041Z", + "shell.execute_reply": "2023-12-04T17:54:16.067733Z" }, "tags": [] }, @@ -70,10 +70,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:17:44.003374Z", - "iopub.status.busy": "2023-11-09T07:17:44.003166Z", - "iopub.status.idle": "2023-11-09T07:17:52.097932Z", - "shell.execute_reply": "2023-11-09T07:17:52.097668Z" + "iopub.execute_input": "2023-12-04T17:54:16.069658Z", + "iopub.status.busy": "2023-12-04T17:54:16.069546Z", + "iopub.status.idle": "2023-12-04T17:54:23.664384Z", + "shell.execute_reply": "2023-12-04T17:54:23.664090Z" }, "tags": [] }, @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:17:52.099582Z", - "iopub.status.busy": "2023-11-09T07:17:52.099477Z", - "iopub.status.idle": "2023-11-09T07:17:52.109947Z", - "shell.execute_reply": "2023-11-09T07:17:52.109685Z" + "iopub.execute_input": "2023-12-04T17:54:23.665914Z", + "iopub.status.busy": "2023-12-04T17:54:23.665805Z", + "iopub.status.idle": "2023-12-04T17:54:23.675238Z", + "shell.execute_reply": "2023-12-04T17:54:23.675001Z" }, "tags": [] }, @@ -180,10 +180,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:17:52.111601Z", - "iopub.status.busy": "2023-11-09T07:17:52.111480Z", - "iopub.status.idle": "2023-11-09T07:18:05.021317Z", - "shell.execute_reply": "2023-11-09T07:18:05.021035Z" + "iopub.execute_input": "2023-12-04T17:54:23.676656Z", + "iopub.status.busy": "2023-12-04T17:54:23.676584Z", + "iopub.status.idle": "2023-12-04T17:54:36.006511Z", + "shell.execute_reply": "2023-12-04T17:54:36.006239Z" }, "tags": [] }, @@ -245,10 +245,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:18:05.023001Z", - "iopub.status.busy": "2023-11-09T07:18:05.022896Z", - "iopub.status.idle": "2023-11-09T07:18:21.447044Z", - "shell.execute_reply": "2023-11-09T07:18:21.446714Z" + "iopub.execute_input": "2023-12-04T17:54:36.008053Z", + "iopub.status.busy": "2023-12-04T17:54:36.007972Z", + "iopub.status.idle": "2023-12-04T17:54:51.460134Z", + "shell.execute_reply": "2023-12-04T17:54:51.459870Z" }, "tags": [] }, @@ -298,10 +298,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:18:21.448692Z", - "iopub.status.busy": "2023-11-09T07:18:21.448585Z", - "iopub.status.idle": "2023-11-09T07:18:38.125086Z", - "shell.execute_reply": "2023-11-09T07:18:38.124793Z" + "iopub.execute_input": "2023-12-04T17:54:51.461742Z", + "iopub.status.busy": "2023-12-04T17:54:51.461633Z", + "iopub.status.idle": "2023-12-04T17:55:06.874944Z", + "shell.execute_reply": "2023-12-04T17:55:06.874680Z" }, "tags": [] }, @@ -345,10 +345,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:18:38.126825Z", - "iopub.status.busy": "2023-11-09T07:18:38.126718Z", - "iopub.status.idle": "2023-11-09T07:18:38.137524Z", - "shell.execute_reply": "2023-11-09T07:18:38.137270Z" + "iopub.execute_input": "2023-12-04T17:55:06.876459Z", + "iopub.status.busy": "2023-12-04T17:55:06.876359Z", + "iopub.status.idle": "2023-12-04T17:55:06.885861Z", + "shell.execute_reply": "2023-12-04T17:55:06.885596Z" }, "tags": [] }, @@ -383,10 +383,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:18:38.139108Z", - "iopub.status.busy": "2023-11-09T07:18:38.139011Z", - "iopub.status.idle": "2023-11-09T07:18:55.081379Z", - "shell.execute_reply": "2023-11-09T07:18:55.081112Z" + "iopub.execute_input": "2023-12-04T17:55:06.887191Z", + "iopub.status.busy": "2023-12-04T17:55:06.887113Z", + "iopub.status.idle": "2023-12-04T17:55:22.276600Z", + "shell.execute_reply": "2023-12-04T17:55:22.276337Z" }, "tags": [] }, @@ -429,10 +429,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:18:55.084448Z", - "iopub.status.busy": "2023-11-09T07:18:55.084338Z", - "iopub.status.idle": "2023-11-09T07:19:11.593184Z", - "shell.execute_reply": "2023-11-09T07:19:11.592935Z" + "iopub.execute_input": "2023-12-04T17:55:22.278146Z", + "iopub.status.busy": "2023-12-04T17:55:22.278049Z", + "iopub.status.idle": "2023-12-04T17:55:37.716082Z", + "shell.execute_reply": "2023-12-04T17:55:37.715826Z" }, "tags": [] }, @@ -477,10 +477,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:11.594836Z", - "iopub.status.busy": "2023-11-09T07:19:11.594736Z", - "iopub.status.idle": "2023-11-09T07:19:27.786139Z", - "shell.execute_reply": "2023-11-09T07:19:27.785845Z" + "iopub.execute_input": "2023-12-04T17:55:37.717605Z", + "iopub.status.busy": "2023-12-04T17:55:37.717514Z", + "iopub.status.idle": "2023-12-04T17:55:53.108884Z", + "shell.execute_reply": "2023-12-04T17:55:53.108590Z" }, "tags": [] }, @@ -523,10 +523,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:19:27.787941Z", - "iopub.status.busy": "2023-11-09T07:19:27.787835Z", - "iopub.status.idle": "2023-11-09T07:19:27.805692Z", - "shell.execute_reply": "2023-11-09T07:19:27.805442Z" + "iopub.execute_input": "2023-12-04T17:55:53.110488Z", + "iopub.status.busy": "2023-12-04T17:55:53.110393Z", + "iopub.status.idle": "2023-12-04T17:55:53.124180Z", + "shell.execute_reply": "2023-12-04T17:55:53.123946Z" }, "tags": [] }, diff --git a/docs/introduction/getting-started/binary-classification.ipynb b/docs/introduction/getting-started/binary-classification.ipynb index 17a9725c61..6867d14186 100644 --- a/docs/introduction/getting-started/binary-classification.ipynb +++ b/docs/introduction/getting-started/binary-classification.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.073183Z", - "iopub.status.busy": "2023-11-09T07:10:38.072499Z", - "iopub.status.idle": "2023-11-09T07:10:38.719433Z", - "shell.execute_reply": "2023-11-09T07:10:38.719120Z" + "iopub.execute_input": "2023-12-04T17:47:13.264755Z", + "iopub.status.busy": "2023-12-04T17:47:13.264536Z", + "iopub.status.idle": "2023-12-04T17:47:13.668821Z", + "shell.execute_reply": "2023-12-04T17:47:13.668532Z" } }, "outputs": [ @@ -70,10 +70,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.735763Z", - "iopub.status.busy": "2023-11-09T07:10:38.735574Z", - "iopub.status.idle": "2023-11-09T07:10:38.749221Z", - "shell.execute_reply": "2023-11-09T07:10:38.748919Z" + "iopub.execute_input": "2023-12-04T17:47:13.683729Z", + "iopub.status.busy": "2023-12-04T17:47:13.683574Z", + "iopub.status.idle": "2023-12-04T17:47:13.695685Z", + "shell.execute_reply": "2023-12-04T17:47:13.695411Z" } }, "outputs": [], @@ -95,10 +95,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.750971Z", - "iopub.status.busy": "2023-11-09T07:10:38.750874Z", - "iopub.status.idle": "2023-11-09T07:10:38.760618Z", - "shell.execute_reply": "2023-11-09T07:10:38.760327Z" + "iopub.execute_input": "2023-12-04T17:47:13.697178Z", + "iopub.status.busy": "2023-12-04T17:47:13.697104Z", + "iopub.status.idle": "2023-12-04T17:47:13.705637Z", + "shell.execute_reply": "2023-12-04T17:47:13.705411Z" }, "tags": [] }, @@ -132,10 +132,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.762298Z", - "iopub.status.busy": "2023-11-09T07:10:38.762158Z", - "iopub.status.idle": "2023-11-09T07:10:38.771704Z", - "shell.execute_reply": "2023-11-09T07:10:38.771450Z" + "iopub.execute_input": "2023-12-04T17:47:13.706912Z", + "iopub.status.busy": "2023-12-04T17:47:13.706842Z", + "iopub.status.idle": "2023-12-04T17:47:13.714796Z", + "shell.execute_reply": "2023-12-04T17:47:13.714537Z" } }, "outputs": [ @@ -167,10 +167,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.773320Z", - "iopub.status.busy": "2023-11-09T07:10:38.773228Z", - "iopub.status.idle": "2023-11-09T07:10:38.793532Z", - "shell.execute_reply": "2023-11-09T07:10:38.793286Z" + "iopub.execute_input": "2023-12-04T17:47:13.716274Z", + "iopub.status.busy": "2023-12-04T17:47:13.716198Z", + "iopub.status.idle": "2023-12-04T17:47:13.731956Z", + "shell.execute_reply": "2023-12-04T17:47:13.731725Z" } }, "outputs": [ @@ -207,10 +207,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.795051Z", - "iopub.status.busy": "2023-11-09T07:10:38.794957Z", - "iopub.status.idle": "2023-11-09T07:10:38.804706Z", - "shell.execute_reply": "2023-11-09T07:10:38.804479Z" + "iopub.execute_input": "2023-12-04T17:47:13.733503Z", + "iopub.status.busy": "2023-12-04T17:47:13.733414Z", + "iopub.status.idle": "2023-12-04T17:47:13.741526Z", + "shell.execute_reply": "2023-12-04T17:47:13.741290Z" } }, "outputs": [], @@ -231,10 +231,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.806177Z", - "iopub.status.busy": "2023-11-09T07:10:38.806098Z", - "iopub.status.idle": "2023-11-09T07:10:38.815839Z", - "shell.execute_reply": "2023-11-09T07:10:38.815589Z" + "iopub.execute_input": "2023-12-04T17:47:13.742950Z", + "iopub.status.busy": "2023-12-04T17:47:13.742870Z", + "iopub.status.idle": "2023-12-04T17:47:13.751313Z", + "shell.execute_reply": "2023-12-04T17:47:13.751031Z" } }, "outputs": [ @@ -266,10 +266,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.817413Z", - "iopub.status.busy": "2023-11-09T07:10:38.817309Z", - "iopub.status.idle": "2023-11-09T07:10:38.826675Z", - "shell.execute_reply": "2023-11-09T07:10:38.826435Z" + "iopub.execute_input": "2023-12-04T17:47:13.753009Z", + "iopub.status.busy": "2023-12-04T17:47:13.752897Z", + "iopub.status.idle": "2023-12-04T17:47:13.761857Z", + "shell.execute_reply": "2023-12-04T17:47:13.761633Z" } }, "outputs": [ @@ -301,10 +301,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.828197Z", - "iopub.status.busy": "2023-11-09T07:10:38.828118Z", - "iopub.status.idle": "2023-11-09T07:10:38.864356Z", - "shell.execute_reply": "2023-11-09T07:10:38.864086Z" + "iopub.execute_input": "2023-12-04T17:47:13.763372Z", + "iopub.status.busy": "2023-12-04T17:47:13.763296Z", + "iopub.status.idle": "2023-12-04T17:47:13.794339Z", + "shell.execute_reply": "2023-12-04T17:47:13.794075Z" }, "tags": [] }, @@ -348,10 +348,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.866017Z", - "iopub.status.busy": "2023-11-09T07:10:38.865919Z", - "iopub.status.idle": "2023-11-09T07:10:38.900686Z", - "shell.execute_reply": "2023-11-09T07:10:38.900450Z" + "iopub.execute_input": "2023-12-04T17:47:13.795893Z", + "iopub.status.busy": "2023-12-04T17:47:13.795799Z", + "iopub.status.idle": "2023-12-04T17:47:13.827938Z", + "shell.execute_reply": "2023-12-04T17:47:13.827710Z" } }, "outputs": [ @@ -388,10 +388,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.902171Z", - "iopub.status.busy": "2023-11-09T07:10:38.902084Z", - "iopub.status.idle": "2023-11-09T07:10:38.918876Z", - "shell.execute_reply": "2023-11-09T07:10:38.918645Z" + "iopub.execute_input": "2023-12-04T17:47:13.829351Z", + "iopub.status.busy": "2023-12-04T17:47:13.829280Z", + "iopub.status.idle": "2023-12-04T17:47:13.843109Z", + "shell.execute_reply": "2023-12-04T17:47:13.842876Z" }, "tags": [] }, @@ -570,10 +570,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:38.920413Z", - "iopub.status.busy": "2023-11-09T07:10:38.920323Z", - "iopub.status.idle": "2023-11-09T07:10:38.967256Z", - "shell.execute_reply": "2023-11-09T07:10:38.966975Z" + "iopub.execute_input": "2023-12-04T17:47:13.844434Z", + "iopub.status.busy": "2023-12-04T17:47:13.844357Z", + "iopub.status.idle": "2023-12-04T17:47:13.889821Z", + "shell.execute_reply": "2023-12-04T17:47:13.889600Z" }, "tags": [] }, diff --git a/docs/introduction/getting-started/concept-drift-detection.ipynb b/docs/introduction/getting-started/concept-drift-detection.ipynb index ae3398bac9..88b23030de 100644 --- a/docs/introduction/getting-started/concept-drift-detection.ipynb +++ b/docs/introduction/getting-started/concept-drift-detection.ipynb @@ -45,10 +45,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2023-11-09T07:10:40.159076Z", - "iopub.status.busy": "2023-11-09T07:10:40.158722Z", - "iopub.status.idle": "2023-11-09T07:10:40.508285Z", - "shell.execute_reply": "2023-11-09T07:10:40.508002Z" + "iopub.execute_input": "2023-12-04T17:47:15.186479Z", + "iopub.status.busy": "2023-12-04T17:47:15.185478Z", + "iopub.status.idle": "2023-12-04T17:47:15.510637Z", + "shell.execute_reply": "2023-12-04T17:47:15.510311Z" }, "jupyter": { "outputs_hidden": false @@ -61,7 +61,7 @@ "outputs": [ { "data": { - "image/png": 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z5k2rD8qpuAMs/zYfWy4o7rOPE1oMHOHIFPV26OAh3D5jenMsV+8HLlYoItyI3BBp7yIQDlh9doKPP/4Y8+bNw4ABA8Dj8dC7d2/MmTNHZ/cDwLorg0ilUuTl5WHChAkQCoW43diMrwsrMCUmBD39dAfIX/xRDuCcxraUlBQIBXx89FsZPt2jOdAiLi4OA4M64vXD+keJpqam4q+Lt/Gfk3+pXis9X5gLABg1chSGdO+k2n4q9xx+u1au+HcNHzf9IjBrRA/wSsT4puy4znzUZd8OwL6L1Sht9sZPC7RXIVEew+cLALmildW/SxecE3UGoLjWZjkPvB5DMXFwEJYd/x2AVOvc+tTdlWLhX7+rXk+cOFFrxodbByqA8jOqfJXlioqKwjdlJRpp1fcHBgZiX7M7sk+I8XJyP/QN6IC4MD+tMrR9P7gqqgcFR68H5fu7q39XpKbGWu08xZeq8fz/DmtsM/a+dkTKeouPj8eIXtqfA7pw/X7gYoUiQkgrk4JYf39/CAQC1fJ0SpWVlTqXquvatSu2bduGpqYm3Lp1CyEhIVi4cCF69eql8zy2WBlEmddr24rx2+kqbDx0GcWLk3Smfz/3nNY2oVAIoYCvFcACgJubAG5GlFUoFEIgEGi81s7LTWM7j6/ZlXnFzjPo1dUbIvfWNIbqad95xcCQkmv1etOqt1nweHz8t82o6H9tOY6LMaGobmh9TMaWH9uUZG5tGnZWZJ/FiimDNbbx1a5VPV9l+dUdv9Y6UwOfz8cPRYrZFJb+choAcPGdSXhr5yn4erkj7f4+aG6RY/mvp9ChjodUa6864ySsvvqOk3D0euDxeFYtX9EV7WDLkevDWAI3gVnXwdX7oT3UISGOxKSBXe7u7oiNjUV+fr5qm1wuR35+PhIS9K8p7OHhgW7duqGlpQX/93//hwcffNC8EnPs4AVFMFTTaHpfJX3jmhjGesvOsg2o+qHoMtwFrX9Omb6pE8xk7AID4tomjH53Nz4tUMwQcP7GHUQty8WaPec1E7Yp4jcHtAeR6bqKbcXXtLaVtxkY19bFmw348o9yvL+rFADw7cFL2PzXFawtpfk2CXEFPJqegJB2xeTZCTIyMvDll19iw4YNOH36NBYsWICGhgbMmTMHADBr1iyNgV8HDx7E1q1bceHCBfzxxx9ISUmBXC7HK6+8wt1VWMKCzzQGjM4R+gy0FztgY06wyXaErM3CBy1y4+acNUR9BgBdQezGNsHnqt/O4srtu3gvRxEsLvqpBHVNLXjn1zOqsn5WcB5HLt/WyosrbEVtamnth62YQsz0dbAJcQTWXrGuvYZ6xnwmE0Kch8l9YqdNm4YbN25g8eLFEIvFiI6ORk5OjmqwV0VFhcYj4KamJixatAgXLlxAx44dkZqaim+++Qa+vr6cXYSp5Axwo16CED+hxR/Wlra2DlycgynRIRaWQnFN6tfy6JpCBPp44MtZwyzO25BF2zT7pra0CcyViyUo/Vh0Ge/msA8EbMvc+mX7caDeCmOtVnJCCCGE2IZZA7vS09ORnp7Ouq+goEDj9X333YdTp6wzIb+5VhwVoPrAHvz4TILOpWP7vf4rmmVynHtrIoQC9gbrV348jjfb9OHUYESg1Nwix/eHr2gfqiPKOnGlFvvO3dTaLpcz+LOsdfvxK7UAag0XwATGPoprO+uY+kuGYXCu0rxVxiKX7jI6bf4Z9sU3VOWA9VuzSPtw5XYjArw94O5m1gKHxIFQdwJC2heX+1S+XtuEaonig+zdnDOoZVnW9MCFW2i+twTszLUHdU6qv734ms6VoSxdCKAtuZzBtwcvYfJ/9+EUy0T+cobB53u1B5hxXQ5jtA3K1X8oyBnTgkf1bhH1Tfqn+DHlWvNOifFZwXnDCYlLO1RejdHv/o5H1uy3d1EIIYS0YfUpthyNooVS4a+L7H0y1QcIHbhQjS//0L1O+ZWauzr3WRI+to3Hth69itd/KmFPDECm42SmxrD/+u4oruq4JnNbLtVbZuUmFGjXSTHezjau2wFguL6TV7Uu6/vMxiNG50tc1/eHLwPQ/Nwg9tfcIqeWcUKI6wWxxgRR2Seua7w+oe8LTFfwCMv6XbY99MSVGr3p/yrnZi31n49pj/q3FM/MIPaf3xSZdJ5XfjxuUnpCiPMpFdcjedVePDUqHIsnR9i7OITYTNjCnfYugsNxuSDWGH+06XOqrwVyZ5uAV8WCAPbY5Rp8mHfWqPMr3ZWyr4DGZWeCW3eazTqOr96dgJtJEwghVqRrrIAj+OjeZ+O6P8spiHUlS+8t+LOUnoqYQl/ge/GdSTYsiXW43PMYc6ZPNffz3JzpXEqu1uLB1X9i79kbrfkw5n+pcNkn9o7E9GUnW2RyzSDWyPLYoy8vIcR4khYZMr4vxi9WeHpDCCHGcLkg1lBwxLbf3BGt5sRhBy7c0trWpKOVlWuG6sacOPr/jlxBndrgOTnLyl1sbtTTHK6EOLJvD1Rg65GreO67o/YuCiHERbleEGtoP1sCM1tiG5tNDz7Zzr/k55PmFQCmdScwFHSbUw2v/t8J1Ku14J4R1+tJDTQ2t6BJKqMpyQlxcDfvON8PTQfuJUEIMYPL9YnVF6g9veEwunf21NpuzuceAwaPf3nAjCO1nRHXY0SvLmYda0prsKFH/RdvNZpVBnWPrinEP4Z117k/YrFiLthx/btafC5CiPXQD01CiL25YBCr+6P3t9OVrNv3n9d+xG/4PECVGY/EdfWjNac/KqB7wBebX47bpm8b2+IObRWU3jCYhhBCDKH+9YS0Xy7XncCcgV3VDeaNyjfH+aoG1u0/FhkO/NhELcs1Ou2LW46ZdQ5CCLEV6hJACFFyuSDWnBkDzPG/P8rNOm7LvcnVCSHORy5n2nXL3/biqyi5qpjiqB1fJgBqwSXEGbhgdwLbnKeQZZYBQkj71SKTI3nVXoT4euKbufH2Lg7n9pfdxPObiwEo5pe0VYOApcz5zM87VYlXfjyGDx+J5L5AxKVEbjD9PXRi9gkrlKR9csGWWNJW2MKdSNtEy7ASYomT1+pw/kaD1mIpzuBcZT1+PnZNb+ujoZlFnIGxPRHmfX0YtxuleOpr+lwkxJG5YEsshbFsdh7XsfIYIcThcL2i1oSP9gIAvEVuuH9AAKd5E0KItbheSyzFsIQQK7DlgCNr/Rg/ca+/q1HX4oSfpU5YZEKIHi4XxJozOwFp/xqbzZvCjBBX5Swfpc5STkKI6VwuiP36wCV7F4E4oJ+O0vrvxDLmLk9t1rnsMM+Uo0xtZUk5HOQSCCEccbkg9vLtu/YuAnFA1FpDiPMGeZduNeCp9X/hr4vV9i4KIcSGXC6IFThKcwJxKPZo2SLtS3t6CxnVJdaBBhg8++0R7D5ThUfXFGrtc6RyEkK45XJBLAUrhA29KwiXKHCyrcvVjUalo89/QtoXFwxi7V0C4oj49MYgLMx9VzhrDGvKbeBI12hsUejHBSHti1lB7OrVqxEWFgYPDw/Ex8fj0KFDetOvWrUK/fv3h6enJ0JDQ/Hiiy+iqanJrAJbikIVwoZiWMLG3JCH7bi9Z2/g49/OQa42RUqTVIa/ffIHlv9yyswz2Y76LVJV1+RY/cj1FMahysmBrKwsDB8+HN7e3ggICMCUKVNQWlqqkaapqQlpaWno0qULOnbsiKlTp6KyslIjTUVFBSZNmgQvLy8EBATg5ZdfRksLzdJCnIvJQeyWLVuQkZGBJUuW4MiRI4iKikJycjKqqqpY02/atAkLFy7EkiVLcPr0aaxduxZbtmzBa6+9ZnHhzUEtboQNn94WhENsLX6z1h3CR7+dRXZJ68IiOSVilFytw7o/y21ZPIPYHruful6n+ve8b4psWRyD5Ea2sLaH7gR79uxBWloaDhw4gLy8PEilUiQlJaGhoUGV5sUXX8Qvv/yCH374AXv27MG1a9fw8MMPq/bLZDJMmjQJzc3N2L9/PzZs2ID169dj8eLF9rgkQsxmchC7cuVKzJs3D3PmzEFERATWrFkDLy8vrFu3jjX9/v37MWrUKDz++OMICwtDUlISHnvsMYOtt9bCd7kOFIQQW1CPj/SFVL+WiLF2XznqmqRocaKJq78/fEX172OXazjLt75JatJjfrapzJynFi2Xk5ODJ598EoMGDUJUVBTWr1+PiooKFBUpfljU1tZi7dq1WLlyJR544AHExsbiq6++wv79+3HgwAEAQG5uLk6dOoWNGzciOjoaEydOxIoVK7B69Wo0Nzfb8/IIMYlJy842NzejqKgImZmZqm18Ph+JiYkoLNQeFQoAI0eOxMaNG3Ho0CHExcXhwoULyM7OxsyZM3WeRyKRQCKRqF7X1SlaAKRSKaRSqSlF1mLLuRyJ85DJZBa/t5yZ8tpduQ4A7XqQy+Va+3RRfxTb3CwF3Nh/Me88fh07j1/H0UvVGNO3i9H5a2DkVvlbbTpYgUmDAyGTyQymVU9jblkKL9zCrK+KMCMuFEsnDwQAlIrr0bmDOwK8RazHsP1N1IPgtmVpbmlN39LSoresjc0tOHmtXms7V3Vtjb9Zba1ilTU/Pz8AQFFREaRSKRITE1VpBgwYgB49eqCwsBAjRoxAYWEhIiMjERgYqEqTnJyMBQsW4OTJk4iJieG8nIRYg0lB7M2bNyGTyTTe+AAQGBiIM2fOsB7z+OOP4+bNmxg9ejQYhkFLSwueeeYZvd0JsrKysGzZMq3tubm58PLyMqXIWqTNAlDPWNLWyZOnkH3zpL2LYXd5eXn2LoJDUNbDlSt8KB9YZWdn6z3magOg/EjNyclhiWE1P27zT11Hp8arAAQAgJ07s/HFGT6kciAtQq6jn7Yij6qqGwbLYxpFvtdqm5C0ai/+3kOuKpcu5eUXYWzd6PLRCcXn8beHLqNnczl2VPBxvFqR58cJ7P0zr4u1/yYtLa2f623LoohhFdd34EAhbujpfryqRIDyeu2K5+q+aGw0bhYFY8nlcrzwwgsYNWoUBg8eDAAQi8Vwd3eHr6+vRtrAwECIxWJVGrbvceU+NtZsXNKL76H5mqNzicD+I8kR6KpPkYDbZw72brTg4vwmBbHmKCgowNtvv41PP/0U8fHxKCsrw/PPP48VK1bgjTfeYD0mMzMTGRkZqtd1dXUIDQ1FUlISfHx8LCrPWyUFqJPS4xKiadCgCKTG9bR3MexGKpUiLy8PEyZMgFAotHdx7KZtPezZWoJDNxSruaWmpuo99vT1erx3XPFEKjklBaI2Uezzhbkar4VCIYZE9ce35xU/nu5LTMILB3YDAKJH3Yduvp5a51Dm0bVrV6SmxppxhezUyyaV89B/wADg4jm9x4SFh2GPuAKA4brR5asrB4E7ipbEt4s1v4505ZlTdwzFtyo10rz812/KaFXruOYWOf598DcAwIgRCRge1llnedr+jZS4ui+UgR9X0tLSUFJSgn379nGaLxtrNi7pFfWF5muOfry94csefzgCXT8K34uzzXlshYsfdSYFsf7+/hAIBFqjHCsrKxEUFMR6zBtvvIGZM2fi6aefBgBERkaioaEB8+fPx+uvvw4+SydVkUgEkUj7V5JQKLT4g2RU7y74qfi64YTEpQgEApcO3pS4uMfaA2U9qH8+GaoXNzc3jX8LhfpbMnk8HgQCzWM0j9d9Pj6fb9W/k0Cgv+zKMiiZWxZ9A6105cl2XvUuA22PY3it+wzVq76ycFHfXP7N0tPTsWPHDuzduxfdu3dXbQ8KCkJzczNqamo0WmPVv6eDgoK0xqUov9d1fZdbs3FJr6zumq8zr7CnM1HCpgRO8rGGwsfZu2cOXrqL0/OULE3mND9TcfGjzqQg1t3dHbGxscjPz8eUKVMAKB5n5OfnIz09nfWYxsZGrUBV+QFpjzn7Xk3pT0Es0ULTRxI2pnQ8snTgu/pb0N6j6I1bscvy85iVh4lVw7SzYV8Mw+C5557DTz/9hIKCAoSHh2vsj42NhVAoRH5+PqZOnQoAKC0tRUVFBRISFIFbQkIC3nrrLVRVVSEgIACAosuEj48PIiIiWM9rzcYlveRtpuPk6FwSSAwnshNd9SmRcfu5YO8GCy7Ob3J3goyMDMyePRvDhg1DXFwcVq1ahYaGBsyZMwcAMGvWLHTr1g1ZWVkAgMmTJ2PlypWIiYlRdSd44403MHnyZKN+7XPNz4tamQghxjF7ntj2FTc5rP3nbyIuzM/o9LV3pfjH54V4ZGh3/GN4qBVLZj1paWnYtGkTtm/fDm9vb1Uf1k6dOsHT0xOdOnXC3LlzkZGRAT8/P/j4+OC5555DQkICRowYAQBISkpCREQEZs6ciffeew9isRiLFi1CWloaa6BKiKMyOYidNm0abty4gcWLF0MsFiM6Oho5OTmqTuEVFRUaLa+LFi0Cj8fDokWLcPXqVXTt2hWTJ0/GW2+9xd1VmMDeLRyEEOcgkzM4WnHb6PSaU2wZjmJr70rRIDFvcvm7zYZnD3AE12ruYvNfl/FEfA8E+Hho7bf04/jxLw/iXw/0MTr9f/LP4cTVWhwqr3baIPazzz4DAIwbN05j+1dffYUnn3wSAPDRRx+Bz+dj6tSpkEgkSE5OxqeffqpKKxAIsGPHDixYsAAJCQno0KEDZs+ejeXLl9vqMgjhhFkDu9LT03V2HygoKNA8gZsblixZgiVLlphzKkIIsYv3d5Xi/I0GwwlZGNsSu+Tn1hkx1I8xFNsduliN2kYpOjn4k6WZaw/i/I0G7D17A9vSRmnt56LF+psDl/TuVz9HXZPzTyFnTDc8Dw8PrF69GqtXr9aZpmfPnnYf2EOIpWjqf0IIYbFmz3mT0nM5B3XbFsoGSQuKLlVrbDtYfsvs/H8+dg0f5Z3VGRAZ00KqfuwRHS3Wyh8BxWYsjvDKj8fwxrYSw+UwOWdt245exU9HuRkwRAixHatPseWI/jWoBf856ZKXTggxEp8HmLugFtddYqd/cQAnrtZylt+/vjsKABjVxx9x4cb3KVWnfo1PrjuE4xyPdFauELZw4gB0EOn+vK5pNL519XK19pQ+jc0teGFLscnlI4TYn0u2xPa24mwgxD5WTBls0fGdPB37sSyxPUv6z5s184pGdwLNc7MFsOU3GzSmltJFLmdw5Tb7fIw373AzQlvCUo4zYm7mRJXKDF+jsdh+lEik3OVPCLEtlwxiSfviLXJDckSg4YR6dOnozlFpiCuqbmjGHUlri6AyVnpzxym89tMJo/IwdSqorF/PYPoX7PNJqnvx+2KMfvd3bC++qrVPZm5TsxHe/ZV9FUdTNbcJYrkemktjfQlxXhTEOqglk9nn6rOVR2K7G07kKHjcP74lxNjYprG5BUNX5GHqZ60BJcMAkhYZ/revHJsOVuBazV2Tzm1sQHukosZgmu3FihXH/ru7TGufXFefWCPObaixmauZYFpkdHcTQthREOug3LUXXrcpvhO1TvB5PJqXk3DO2Bjsym2WAJXRDPIqWPpi6rPt6DWT0huDLWDVFcQaw1aLCFgaxOq6xNq7ipZzLgfkEUJsi4JYB9U/0Nuu5+c70TM2Hg/o6GHZQD0KgklblgQ3EpkMN+pb+5tO/+KAwWPU34N7zlaZfW5d2HoOcNjdVItRrblGpJHKrVPIqGW52HrkCvf9EwghNkNBrINJGRSE11IHYJieVWj8OrhjVkJPq5bD2RaF6Chyw/yxvexdDEIAAOM/3IMx7/1u8/PmnarEaz+dgKRFezGE8psNuNVmIJdFLbFqh7Ll0vYjpNaEWQTUya3Yb/fV/ztutbwJIdZHQayDmZnQE/PH9tabZmxff/S1ckutrbsT3N+/q9a2PgEdjTpW+WU6uo8/l0Uirs6Ce6C+yfSVuNRDtQMXqlWPu00x7+vD2HSwAhsPVLDufy+nVON1g6RFK7AFwNFoJ808opbnGkhhHfq6PTAMsO/cTRuUghBiDRTEOhi2D/WpQzUHWcWFd7H6hz9bd4LkQZbNAKAPW7e3fwwzbnCZWdMZEWKAMfeYpEVmta4o3xReNPtYcS37QLK2gfGyX04h9s3fzD6PLpV1TRa18lrClBkXWuQMNlhQz4QQ+6IglkMPDAgwuvVQJ5ZvTvVHg4O7+WCaDdb8ZmuJXfNErNUGnLEFoqY+RbSk8chWg1SI89D1fpLK5LjbLENNYzMGL9mFWesOcnK+tveAJU/Rv/yjHLknxVrbjb1Hlv5y2mAa9eKpz1d78MItxL+dj91n9PfrZRjGrJW8DNlXZlrL6qHyasOJCCEOiYJYDjEMg41z4y3Kg20wSZPaZNxTh3aHwAbP+tn6xPJ4PIgE1nnLsLXaCHWca2gPX43XFH4Sa9A1sOuBDwswcHEOth65CqmMQWUdNwsG/PObIk7yUZrPcX5t6Wpo/frAJaOO//mYeTMwGOqvL22z8AI9qCGk/aIglkPDwvwQ1MnDojzYPp9FwtY/k61mDdB5Hiudnm0AsodQ++352YyhyHp4CGseNFUO4VKLjlHxl6sVj+q5XAYWAA5fus1pfo7uTyNbTC2NQWetO2RhDoQQR0VBLIuMCf1MPmbFg4Mwb4zlo+PVw7BV06IRFeqL11MHqraZ0whrzvXoOo+1wkS2llhPoUBr28TIYHi5a28nhEtyOQOpgflJrd0X2xr3Gre/gS27flv8IGcYBkUu9uOAEFdCQSyLZ8fpnx2AzcyEME76i/LVoscpMd2wPW0UQnw9WxOY8cE/b0wvhPp5Gk6oRleXBVO6MkR174TCzAeMSssWD2hct96DFf8zVDVJepampUeORN1dqfYUVW25+lvG0nvG2Gn8LDkP3deEtG8UxLIwdY7UmDZ9NC06t4X72Xi6C0x+1K6rDkwJYt0EfAR3MhyI9vDzYm2JVd80KMRH51K4Y/sppueKCPbRe574Xl0MloUQwLgA1RkDJC673LBdf32TFH+cvWHU8c60KiAhxDFREMvC1M/Wcf0CdO5bOjnCtHMbOHm4fweT8tOVb08/LwDA+1MHm1QOU4LYN6ew593W9rRRkLEFsWqhxM5/jcEHj0ZppXkhsS/efjgSANC5gzsOvT4eJ5YmsZ6HpuIiXLL2u6mJZcECR9L2h2dzixyz1x1CnZ45ctXvQWO7E1gycwjd8YS0by4fxA4I0l40oO1n68Mx3TTSdfUWGZX3mL7+eHJUuMF0j2q0MLJ/sH80LQovJ/fHyN7ctCZumBOLjxNaMCkyiHW/rq8XgQmt1MZMN7Y9bRQ6d3BnnU5oQBB7y6qHWl/ZZ+7rjU6eQtXrAG8PeHsI2Q6Duxsfu14Ya7BMhBjze+cXM0fXG2v17+fxdrbhqa6UjlbYt+/nuPd/x5GKGr1p1OvVWi2xX+0vx96zN/Dgf/dhe/FV65yEEOIQXD6IHdK9k9a2to/SV06Lxtonh6tezxqhf8nXbWmj8FBMN7z/iHbLodKkIcGqf88wkB8APBTTHWn39zHY1eHx+B6s29t+KRv6km57mpRBimCXb8I3jzEplS27g0K0A1a/Du7Yv/ABFC+eoLG9q7cI/3qgD15O7q8R0Krb8FScxuthPTvj0dhQBPlYNnsEIbb0xd4LyD9daTDdwQu38NCn+w1nyGHg2PYj5Fptk0nHmNsn1tBRf5bdwqx1h3DsSi0yvj9m1DkIIc7J5YNYY/uIqaealRCGbWmjEOijaJFNGazZmhkd6ouPpkWrptsSsQz4Wjp5EGvelrROHF6UiBUPsj/Cb/vor2tHd715ta0X5feNmylB7L2D5o7W3RqtfKS4cOIALGAZUBfi6wlfL+2yZiT1R9r9fXTme18/zWVsf1wwEp40qwFxQnM3HDaYZu854/qh7jx+HVuPXLG0SADM6xNsTncCQgjRxeWDWHPw+IpAdc/L96Mw8wH0Z+mSoK4byyh79c9vzX+b/8Hu31F3Nwf1L5xji5MguteCqSsobVsM5Wv1lti4MD+95VEmDdDT/ULZEuvjIcSrKQPw4b1+rx9Pj9abt7loZS5iLkeeqsmUgJCr1klz7iX1I4z9PUxd2QkhupgVxK5evRphYWHw8PBAfHw8Dh3SPZn0uHHjwOPxtP6bNGmS2YXmkpvA/KDRQygwavQ9G57Gv3ms2401IMgb79wb3KTrePUWkE5erX1GeTweRvfxN/pcHm6trZmrZwzFv8b31eiTqo4tII/q3knVNQHQ/iKbGtsdZ9+ciAejuxldJkK4px05Tf1sP27d4WZ1Li7duiPBJ7vL7F0Mo2j0iaXpCQghFjI5iN2yZQsyMjKwZMkSHDlyBFFRUUhOTkZVFfs62Vu3bsX169dV/5WUlEAgEODRRx+1uPBcaDtx/ht/M202AS7oapU1Vs4LYzE9TtEXVtcXg7512P92r3+ur3pw27aM97asnBaF4E4eeHdqJLp6i5AxoZ/x87kC2J4+Gv99PEb1mm22Ay7m29VFV/cRau0hxhDXGe73aWsLt56wz4nN6U4A285OQAhp30yOFlauXIl58+Zhzpw5iIiIwJo1a+Dl5YV169axpvfz80NQUJDqv7y8PHh5eTlMEKu+KlT/QG+9/TfN9Te1QVxKuroNWGvpVLZ5WJUeHRaK/80ahtwX1Ubua/UnUPxvQJAPCjPHY9pw9gFkxnAT8JE4MABx4X4I62LelGHmEljQ8k5chzP9qDl+pUbnvjd3nLLaefV9pujiTPVKCHF8JgWxzc3NKCoqQmJiYmsGfD4SExNRWFhoVB5r167F9OnT0aGDbYOXthJ6KfpzTlWb3krXY3EA6KI2EMpLx4h4XZ4b3xefz4zVaGHU6E6g9sLT3bg/iakttvq+OwR8HhIjAhHgrXvkvr7TGSoK27n/N3s4vv9ngs0fKXYUudn0fKR9ccQgTN8P3//tK7faec2pit/PtD6xM7aF1RHrnFjZ0k6t/xGih0nf6Ddv3oRMJkNgoObynYGBgThz5ozB4w8dOoSSkhKsXbtWbzqJRAKJpLXvWV1dHQBAKpVCKpWaUmQtyuO/eHwIpAwP3h5C/GfaEKzZW463p0Sw5i+VSsEHcGDhOPB5ACOXQSo3bSLyB/p1QZCPCBXVdxV5trSep6WlBRmJfXCroRk9O3sYdY0tahOhG0ovlUohV+tPoF6Puo5l2lwfI2d0ptXVIqNML5MZX1ZrMVhHLS12K5sjMPR+cBXK62/WUQ8tLbon8rcf+0R5LTK5yceUXK1B4gBFH3y5kce3tLk35XLTz2spru4LV7+/COGaTZul1q5di8jISMTFxelNl5WVhWXLlmltz83NhZeXFydl2Z3/m8brf4YBJw8W4KRqi6JqBneWIzs7m5NzTusOrGsQYHIPOX7L+011jj/37UPPDkBPANnZF4zKq0TMA6BoEW5bvsGd+Si53dqim52djSaJAMo2U/X0eXl5bXJWlOncuXPwEfJRJ1Ucc/36NWRns0/NU1/Xmrc65XnK9ZTVulrf3prn1X7bFxUVob6Mmny03w+u52YTkLVlD5TvWXX79u2DjT82DWpqagKnE8Aa6dq1azC1R1rZuTJkS84CAM5f4ht1/L59+3BJbd2Ua9eMO45LXN0XjY2NnORDCFEw6dPY398fAoEAlZWak29XVlYiKIh95SelhoYGbN68GcuXLzd4nszMTGRkZKhe19XVITQ0FElJSfDxYV/FyVhSqRR5eXmYMGEChELd3QeeL8wFAMT0D0Nq6gCLzqlu/r3/196V4rXDvwMAxowZw7pymD6dL9zCD+VFAIDU1FSNfSkpDCrrJdh06DLG9O2CuDA/LD32Oxrutf6mpqbqrAfldffr1w8fzg3HoGWKYD8kJASpqUNYy/JZeSHQWK+1XVmuB6QyXNt4FGP6+iN1dJhJ12kJ5bWol0V9+/gBXVF0qQY1d6WIjY3FGD3LB7d3xt4X7Z1UKkXE8t917h89ejQ+OHHAhiUyzMvTEzXNth9wdvSW6YFkn759kDpeMb/ziV1nsfvaRYPHRA8fgfjw1un8fms4jiO3xCaf2xJc3RfKp4qEEG6YFMS6u7sjNjYW+fn5mDJlCgDFo538/Hykp6frPfaHH36ARCLBE088YfA8IpEIIpH23KJCoZCzL1hj8xLw+Vb5UheqPVVyc3Mz+Rxj+gXi7Yci0S+wI+uxPUTuWJjaOtOC+uwE6ul11YObQIAOnq1/A5FQdxl1DVJTphcKhdg0P0H/BVmBu4CPZpkc3Tt7spa9o4cQXb3dUXNXCjc3gUsHb0pc3mPt0c/HDa+eZWvGrJTlKNb9eQkvpwwEoBhPYYwn1h3GS0n9ENndF94ebkYfxyWu7gu6twjhlsnPxTIyMjB79mwMGzYMcXFxWLVqFRoaGjBnzhwAwKxZs9CtWzdkZWVpHLd27VpMmTIFXbp04abkzs7CabV4PJ7OJWbZmDqSWFmkxX+LwMaDl/Bycn+DaR3NtrRRWP17Gf6d1I91v/oUPzR4hBhj3Z/WGyjlCu5KZcgpEWutcmjIB7lnVf9+MDqE62IRQpyUyUHstGnTcOPGDSxevBhisRjR0dHIyclRDfaqqKjQ+qVcWlqKffv2ITc3ly1Ll6QxN6wNwsCpQ7tj/f6LiO3Z2aj0yvI9NTocT1lh2jFbiAjxweoZQ3Xu58E2dU8IafXMxiJcfGeSxgIshBBiDrNGKKSnp+vsPlBQUKC1rX///vSBZWcLJw5AfLgfRhq5Opcpy98661/WkiV+CSGWoa8EQoilbN+5iADQPU+stXgIBZgYGax3LlxX0eHeKm33D+hq55IQ4ppkcsaqc9i2d3v37sXkyZMREhICHo+Hbdu2aex/8skntZZ6T0lJ0UhTXV2NGTNmwMfHB76+vpg7dy7u3Lljw6sgxHIUxDoAR2oPHHavu8Hfo9pvv7OCl+/HN3PjMCmydSU1ahQixHYm/ecPexfBqTU0NCAqKgqrV6/WmSYlJUVjyffvvvtOY/+MGTNw8uRJ5OXlYceOHdi7dy/mz5+vIzdCHJNjTXjoQhz1UfaWfyagobkFPh7Gt9g65pXo1tVbhK7eilZYB/0zENKunRFrT8lnLLplgYkTJ2LixIl604hEIp1TX54+fRo5OTn466+/MGzYMADAJ598gtTUVHzwwQcICWm/jRikfaGWWAMiQiybl1YXW3cnMJaAzzMpgCWEEOJ4CgoKEBAQgP79+2PBggW4deuWal9hYSF8fX1VASwAJCYmgs/n4+DBg/YoLiFmoZZYHXb+azQOX7yNR4Z2t8HZHCiKNUOIrwdOXVdM4r3hqTg8800R3pkaaedSEUKIa0pJScHDDz+M8PBwnD9/Hq+99homTpyIwsJCCAQCiMViBARoLu7i5uYGPz8/iMXsC0lYczl4LXwP3fs4OpcI2nPROwpd9SkScNvxzd7LIHNxfgpidRgU0gmDQjpZLX+ehfPEOpK3HooEn1eC2SPDMKqPP04uSwaf71wXRSOlCXEOPx+7Zu8iOLzp06er/h0ZGYkhQ4agd+/eKCgowPjx483K0xbLwatEfaF7H0dLl7/h+wYn+ViDruXZ34uzzXlshYtlmCmItZP2ND9poI8HvpjV+ljKmQJY5ykpIQTQXH2QGKdXr17w9/dHWVkZxo8fj6CgIFRVVWmkaWlpQXV1tc5+tNZcDl5Llp4noJlXODlFwibbryJprMLHC1m3D166i9PzlCxN5jQ/U3GxDDMFsQ6AAilCCCHWcuXKFdy6dQvBwYoZWRISElBTU4OioiLExsYCAHbv3g25XI74+HjWPGyxHLyKXM9SyhydSwKJ4UR2oqs+JTJuowV7L4PMxfkpiLUTze4EFMYSQggxzp07d1BWVqZ6XV5ejuLiYvj5+cHPzw/Lli3D1KlTERQUhPPnz+OVV15Bnz59kJysaHkbOHAgUlJSMG/ePKxZswZSqRTp6emYPn26489MsFStm9/SWvuVgzgEmp3AAVAIa38MzRRLCHEShw8fRkxMDGJiYgAAGRkZiImJweLFiyEQCHD8+HH8/e9/R79+/TB37lzExsbijz/+0GhJ/fbbbzFgwACMHz8eqampGD16NL74Qk9fVEIcELXEOgBqiLUjqnxCiJMZN26c3qXcd+0y3HfSz88PmzZt4rJYhNgctcQSQgghhBCnQ0Gsnaj/iG5PMxU4LepNQAghhDgVCmLtRKA2DZW3B/XqsBf6+UAIIYQ4J4qe7MTdjY8vZsaiWSZH5w7u9i4OIYQQFrlXeEi1dyEIIawoiLWjpEHsk0oTQghxDDsvC7DK3oUghLCi7gSEgLrEEkIIIc6Gglji0miGLUIIIcQ5URBLCCH3XLzVYO8iEEIIMRIFsYQQcs/WI9fsXQRCCCFGoiCWEEDv6jfEdbgJqH8JIYQ4C7OC2NWrVyMsLAweHh6Ij4/HoUOH9KavqalBWloagoODIRKJ0K9fP2RnZ5tVYEK4RH1iiToBn37XE0KIszB5iq0tW7YgIyMDa9asQXx8PFatWoXk5GSUlpYiICBAK31zczMmTJiAgIAA/Pjjj+jWrRsuXboEX19fLspPCCGccePTrxpCCHEWJgexK1euxLx58zBnzhwAwJo1a7Bz506sW7cOCxcu1Eq/bt06VFdXY//+/RAKhQCAsLAwy0pNCCFWQN0JCCHEeZj07Ky5uRlFRUVITExszYDPR2JiIgoLC1mP+fnnn5GQkIC0tDQEBgZi8ODBePvttyGTySwrOSEcoh6xBKCWWEIIcSYmtcTevHkTMpkMgYGBGtsDAwNx5swZ1mMuXLiA3bt3Y8aMGcjOzkZZWRmeffZZSKVSLFmyhPUYiUQCiUSiel1XVwcAkEqlkEqlphRZi/J4S/NxdlQP99yLXltaZC5dF/R+UODRAD/Cgqv7wtXvL0K4ZvVlZ+VyOQICAvDFF19AIBAgNjYWV69exfvvv68ziM3KysKyZcu0tufm5sLLy4uTcuXl5XGSj7Nz9XqoqxMA4OHo0aNovkgBjKu/H86IeQAE9i4GcTBc3ReNjY2c5EMIUTApiPX394dAIEBlZaXG9srKSgQFBbEeExwcDKFQCIGg9Yth4MCBEIvFaG5uhru7u9YxmZmZyMjIUL2uq6tDaGgokpKS4OPjY0qRtUilUuTl5WHChAmqPrquiOpB4YuLhUBDPWJiYpAYwf4edgX0flC4VXgRKD9r72IQB8PVfaF8qkgI4YZJQay7uztiY2ORn5+PKVOmAFC0tObn5yM9PZ31mFGjRmHTpk2Qy+Xg35u+5uzZswgODmYNYAFAJBJBJBJpbRcKhZx9wXKZlzNz9Xrg3ZtjSyAQuHQ9KLn6+8HNjVphiTau7gtXvresJXJDpL2LQOzI5EkRMzIy8OWXX2LDhg04ffo0FixYgIaGBtVsBbNmzUJmZqYq/YIFC1BdXY3nn38eZ8+exc6dO/H2228jLS2Nu6sgxEw0TyxRd/Ryrb2LQAghxEgm94mdNm0abty4gcWLF0MsFiM6Oho5OTmqwV4VFRWqFlcACA0Nxa5du/Diiy9iyJAh6NatG55//nm8+uqr3F0FIYRw4KejtOwsIYQ4C7MGdqWnp+vsPlBQUKC1LSEhAQcOHDDnVITYBA3pIoQQQpwLrbFIXBr1JiCEEEKcEwWxhBBCCCHE6VAQSwghhBBCnA4FsYQAYGilJkIIIcSpUBBLXBt1iiWEEEKcEgWxhBBCCCHE6VAQSwghhBBCnA4FsYQANFEsIYQQ4mQoiCUujUedYgkhhBCnREEsIYQQQghxOhTEEkIIIYQQp0NBLCGgLrGEEEKIs6Eglrg0HnWJJYQ4mb1792Ly5MkICQkBj8fDtm3bNPYzDIPFixcjODgYnp6eSExMxLlz5zTSVFdXY8aMGfDx8YGvry/mzp2LO3fu2PAqCLGcm70LQAghhBDjNTQ0ICoqCk899RQefvhhrf3vvfce/vOf/2DDhg0IDw/HG2+8geTkZJw6dQoeHh4AgBkzZuD69evIy8uDVCrFnDlzMH/+fGzatMnWl0PsJGzhTp37Lr4zyYYlMR8FsYQQQogTmThxIiZOnMi6j2EYrFq1CosWLcKDDz4IAPj6668RGBiIbdu2Yfr06Th9+jRycnLw119/YdiwYQCATz75BKmpqfjggw8QEhJis2shxBIUxBICgKFOsYSQdqC8vBxisRiJiYmqbZ06dUJ8fDwKCwsxffp0FBYWwtfXVxXAAkBiYiL4fD4OHjyIhx56SCtfiUQCiUSiel1XVwcAkEqlkEql3F4E38O4dFIpRBBxe24HoKs+RQLbfVFx/je10jkoiCUujbrEEkLaE7FYDAAIDAzU2B4YGKjaJxaLERAQoLHfzc0Nfn5+qjRtZWVlYdmyZVrbc3Nz4eXlxUXRW0V9YVy67Gy84fsGt+d2ANnZ2azb34uzfxm41NjYaHEeFMQSQgghRK/MzExkZGSoXtfV1SE0NBRJSUnw8fHh9mRZ3Y0s1BUkbErg9twOoPDxQtbtg5fuslkZSpYmW/0cytZ8S1AQSwgAhibZIsRl5L44Fkkf7bV3MawiKCgIAFBZWYng4GDV9srKSkRHR6vSVFVVaRzX0tKC6upq1fFtiUQiiETaj+6FQiGEQiFHpb9H3mRcOqEQEkgMp3MyuupTIrPds0PO/6ZWOgcFscSl8WiOLUJcDr8d3/bh4eEICgpCfn6+Kmitq6vDwYMHsWDBAgBAQkICampqUFRUhNjYWADA7t27IZfLER8fb6+ik3v0zRpANFEQSwghxMU4dxR7584dlJWVqV6Xl5ejuLgYfn5+6NGjB1544QW8+eab6Nu3r2qKrZCQEEyZMgUAMHDgQKSkpGDevHlYs2YNpFIp0tPTMX36dJqZgDgVsxY7WL16NcLCwuDh4YH4+HgcOnRIZ9r169eDx+Np/Kecp44QQgixNWdviT18+DBiYmIQExMDAMjIyEBMTAwWL14MAHjllVfw3HPPYf78+Rg+fDju3LmDnJwcje/eb7/9FgMGDMD48eORmpqK0aNH44svjBxQRYiDMLkldsuWLcjIyMCaNWsQHx+PVatWITk5GaWlpVqjHZV8fHxQWlqqek2PcImjoSm2COHW66kD8Vb2aXsXg1WXDs49LdO4cePA6PnQ4vF4WL58OZYvX64zjZ+fHy1sQJyeyS2xK1euxLx58zBnzhxERERgzZo18PLywrp163Qew+PxEBQUpPqv7dQfhNgL/ZwixDrmje1l1nFj+3XluCTaOnkJMbgbxyPqCSE2Z1IQ29zcjKKiIo1JlPl8PhITE1FYyD4lBKDov9OzZ0+EhobiwQcfxMmTJ80vMSGEkHbLW2SboRrx4V1sch5CiPWY9Glx8+ZNyGQy1kmUz5w5w3pM//79sW7dOgwZMgS1tbX44IMPMHLkSJw8eRLdu7PPBWfNlUGUx9tiNQpHRvWgoHwkJ5PJXLou6P3gmrp2dMeNO81Wydvc95JcLue4JNqkUqlJ5+HqvqD7ixjDe+BCk4+pP/2OFUri+Kz+kzchIQEJCa2TEY8cORIDBw7E559/jhUrVrAeY4uVQfLy8jjJx9m5ej3crhEA4KH42DHgSrG9i2N3rv5+cLUJWxSNBdbpVKNY8cf0+hSLr8PMMcdGy87ORvlFvtHn4eq+4GKFIkJIK5M+Yfz9/SEQCFBZWamxvbKyUucEyW0JhULExMRoTA/SljVXBpFKpcjLy8OECRNsMpmvo6J6UNhw5SDK62sRFTUEqUO62bs4dkPvB4XnC3PtXQSb8vDwQJ3UOpPFp6amIru2GLtOVRlOrCa8R3ccvXXNKmVSSk1NRfGvpSi4fsmo9FzdF1ysUEQIaWVSEOvu7o7Y2Fjk5+er5puTy+XIz89Henq6UXnIZDKcOHECqampOtPYYmUQq6wy4oRcvR749+baEQjcXLoelFz9/UC4IxQK8f4/orFrqWk/DF6dOBBbj1o3iBUKheDxjG/t5eq+oHuLEG6Z/KwnIyMDs2fPxrBhwxAXF4dVq1ahoaEBc+bMAQDMmjUL3bp1Q1ZWFgBg+fLlGDFiBPr06YOamhq8//77uHTpEp5++mlur4QQQlyEr5cQNY2W96+09myHPh6mB22BPjSPOCHEOCYHsdOmTcONGzewePFiiMViREdHIycnRzXYq6KiAnx+6y/c27dvY968eRCLxejcuTNiY2Oxf/9+REREcHcVhFhI35yLhDiaP199AFv+uozlO07ZuygOp6u3CB9Pj8bjXx7Um46mKyfE+Zk1iiE9PV1n94GCggKN1x999BE++ugjc05DCCGERQeRG0J8PW12vvhwPxwsrzY6/Yopgzk5b0gnD3w7bwTu/6DA6GMWTRqI2J6dde7/exQtq0pIe2HdIaCEEEIcFs/ImQmCO5n2iD/Qm5sVsSZHh8Cvg7tJxyQP0j/IWLnkLD18IcT5URBLCCEcMDXYslz7j8KMDbJNOa791xohroOCWEII4YC5XSxTI42bntAaeDzg0xlDDaZTzuJhD/bsuzqmTxe8OLjFfgVwNUs7Kf4jxEgUxBKXxqPRHcQG/lz4gM59qx83HERaS8rgIKRGBhuV9vTyFCuXhp05d6gxt7UxadbNjkWYtxkFIITYBAWxhBCL1DQ2I/90JVpk1l8ulEtyObcPlvXl1k3HIKwdz402+4cUF306X0jsZ3RaT3eB0WlNKVq4fwed+3g8zR+a/xzby4Sc9aM+sYQ4PwpiCQF9oVniH58XYu6Gw1iz57y9i2K07cVXEbUsF3+cu2HXcgzuZptHpy8n98e2tFFa20Vuln8FBFk4r+tvGffp3Nc2vH9iRE+j8qTnK4S4BgpiiUujLzvLna28AwD4+Zh1V1ni0vObi1EvacHsdYeMPmbSkGCk3d+bszI8FtfDouNNacDl83gI6+KltV354+3R2O5a+5YMNa4vqIeQj06ehhc16OQpREwPX63tAgP9bekeJYToQkEsIYQTztiabUqPAgGPB19P3TMQmLpgRkSw7TpbGgp43344EtvSRqGjqHXqcD+WWbJG9u7Cenzba1d/+VrqAADAJ4/F4Nun4/WWo3+gdp2ol526sBNC1FEQS4idHKm4jb8uGj+BPBd2HL+GbUevamy7VnMXl283Wpy3ehizvfgqjl2usThPLu07dxOPfLYfZVX1Zh3vaAGUKTFz/yD2gJm591cTCviIDvWFoUkI2LofPDAgUG8f2Plje6P0zRSM7dfV4JRZbWdB4PHM+3FEAzYJcQ0UxBIC8+aOlMsZFF+ugaRFprVvz9kb2HFc9+P15hY5Hv50Px5dU4j6JqnW/qs1dzFl9Z/4hcNH9E1SGdI3HcULW4pR09gMAJDJGYx8ZzceWLkPEu3LMImyNa7oUjWe31yMB1f/aWmROfXE2oM4fOk2Fmw8YtbxpoRFKx4cZESGtgu0xhkRQOqj69j3HhmCV1L6Gzxe5KYYFGbOJcvVolg+BaeEEDUUxBKXZsl34pd/XMCU1X8i7VvNoKjoUjVmrzuE9E1Hcb32LuuxzWoj+eubWvse1jQ2I23TEdz33u8ovlyD5747an4B22hRe3be2KyIWKVq5bijHUubRBlrnLvXR9YRMAyDyromjW23GprNysuU1r2ZCWFmnUOfR+71W31qVDgAwNfL+MUVuGqZVM/Hv6M7/jEsFB5CAcuvQPafhYaK0bZbQtvg2dips/Ql6+Gn3TeYEOKcKIglBEDNXSmOVNzGezln0CBhH9DSIpOjql4REJVV1eO/v5cBAH47XaVK0ySVYepnharXt+5oB0y/n6lC8kd7Wc/xbs4Z7Dx+XSPgNMbthma8/MMxHLq3vn2DpAXPfXcUv564zjr1FRfdV2VyBjK1cir/1dBsWZPujuPX8PSGv1B718KoGsBrP5Ug/u18/Fh0RbXN3HDO0HECvnU/TtPu74PfXxqHN/42EAAwopefSVNOdfRw0+jzCpj+qN7Ufr9ttQ1Ko0J9DR7jplavyhZdXd6cMthgmgXjuBucR+yMFkZweW6GkxDS/i395bTq3w2SFix7cLBWmmlfHEDRpdt4428RWLHjFGs+d9oEwGzB6Jz1f2m8Vm9dulbTBHOs2HkKW49cxQ9FV/DcA33A4/Hwy7Fr+OXYNfB4wJczh2EEy6Ac9Zhk+yU+Zhp5PrmcQcqqvRplZxgG/919Dh/kntV53Pkbd9BR5IYuHdzhJmAP+tI3KVqfP8k/h0V/iwCg+AGhK70+3x2qAAC89MMx1TazGyV5rX1I1cWF+aFbZ09cutWAm3ckZmZu1OkRpjanKo/HQ2bqQHy+94JRxwv4PBS9kYhrNU24/4MCANwtwWpsPupdXkf27oJ1Tw7X2M/WYuzpLsCiSQMhkzMGl/Y1NAXX/LG94OVOX3uEtBfUEktIG0cqali3F126DQA6A1hAu7VO1iaIbW6xzoIAO45dV/37k91luFzdOlCLYYCnvz6sVRZAMyg7Vs1HfZMUKav2ImzhTnx/+DLKbzZodDlQutXQjHNVd1TTawHAxVuNegPYG/USjP9wD+LfzkfEkl3IKbmOM+I6LP35JG6xBH/V9x77X625i8iluVi8vUQrjVzO4FxlvYkthOZFsbr6hS6eHIGPpkWb/Mh+RLgf6/bDixJNLpuxRG4C+Kl1Q3AzcTlZQ9NhKen6c6jXUcaEfoquCHookz89phf+eZ/xLajUdZYQ10BBLHFpbN91l241YMn2EpytNH4Ue1lVPVpkctyVaj5KVx+UUtsoxZBlu1jO14hfT1zH94cvm/Xl+03hRY0+tgBYA8/ban1BP99zHocvVuNvn+zTSPPZnnKcESuu+5Ufj+P+DwqQ8f0xWOJuswxVdU04p1afzS1yPLPxCFJW/YH1+y8i9s3fsL34qkYXgq33ZlH4cu8F3JXK8HXhJYhrNVuqV+w8hQkf7cVHeYrgWS5nUHGrUXVeLin6Whr/B/rnfZqP+nf+a7RGXn1ZppMCAP+OLHNbcaiTlxCfPBaDz2YMNRhEtqVrYJWxPyIcKbYM96e+sYQ4O3quQkgbdU0t2FB4CRsKL6FkWTI6itzQ2Kx/4vfElXsxIMhbFQAqqbd+7jolRpNUO7ic/sUB1b/Z5slUz+vZb4vg31GEtx6KVG1/Y/tJrbS/na7U2jbu3iNkAPi68BK+LryklebLfRe1tv1y7BqmDu2Gbr6eOgMvfQYuzgEAPPdAH73pnt9cbDCvEVn5WDt7GMYPDAQAfPWnorz/2V2GjKT+WPxzCTYeqMDwsM746+Jt1jzMbaUzdFjb/S8m9sONOgmSBinKOiiktf9e34CO5hWCI5OjQsw6TldLrLHt4BrdT9jy4WiyYV2t4upbH4vrgT1nb6rulT4BHVFW1fpkIU5HSzkhxHFQEEuIHrPWHsTWZ0fhrZ2nDaZtG8ACwDeFl7Dlr8t4NWUAPswtNZjH7UbtgWBHK25jzZ7ziOzWCbtOKr5wUyODMaqPv8582IJlSzz5laIf78V3JgFg7xvKRn1O2k92l5l83ms1d7Hj+HWNbXM3HMagEB98OWuYxvYjFbex8YCiD6yuABawYGCXiQd6CAVYOS2adZ+1F4bwcheoZqDgUtt5XHXRdXnqwaUxdWBKlft4mPZ15ibg499J/VRBrPq5Di9KhJ+XO2Qy41YtI4TYBwWxxKUZ+lI+UlGD3JNifHuwwqz8d55QBGA/tVlgQJeqeu2+oQ99uh8AVAEsAPz7+2PIeWGMVQcSsWlsbsGfZbfQq2sHw4kBvLCl2KLzjXu/QKurBACcvFaHV//vuMa2h+/VkyFt67hJKoPIjY8jFboDX8C0rgTW0NnAoCZ13/8zQaurCBd09aE1JyjnqtUVAHp28cLa2cMMpjP2jJ5CAfh8HmTc/w4ghHCIglji0thWIGpr/jdFNiiJacR1TViw8QgKL9yy6XlTP/4DF29ZvrqXsdgCWKU/zt3k5BwD3shBdKgvig2sMGZKEMmlB6NDMHtkGDp5Co1KP35AAAZ3s87UQwIOR0wZFVAaeb53pw5BnwDLlvGlwWCEOB8a2EVcmrsZ0zY5ClsHsABsGsDakr4Als8DEgcGIO1++8wv+lrqQAzt0dmotD4ebshMHWi1shjdncCICJXLLhXGxp9t06mXQb2lnQJaQpwDtcQSl2ZoYnRC/h4VglXTYwyms0bgs2paNAJ9PIxKy+MBxYuTjA40zaGzO4EZM86acwwAHHptPK7W3EVUd1/0ei3brDzYJPTuglITZiQhhNif8zZDEcKBy7fbZ8siaR88hKZ9RKsHsMopvqYPD+WsPJoBsmmDtLSYObArwMcDMT06cxKsq//wmB7HXT0RQmzDrCB29erVCAsLg4eHB+Lj43Ho0CGjjtu8eTN4PB6mTJlizmkJ4dzRy7X2LgIhnGgbSL6SPAC/pI/Gm1O0V58z1/39A9TPqLssRkSoVp6ggV2buFe9zrjs70sIsQ2Tg9gtW7YgIyMDS5YswZEjRxAVFYXk5GRUVVXpPe7ixYt46aWXMGbMGLMLSwjX/DoYN1iGEEB/cGbJ7AVJEYFmH6uLgM9DZPdOJi3XO6S7r+pYNokDA1i3m9UQy2EU6yYwsu71nLOTV+tngdCJ+8oDwNKlS8Hj8TT+GzBggGp/U1MT0tLS0KVLF3Ts2BFTp05FZaX23NKEODqT79SVK1di3rx5mDNnDiIiIrBmzRp4eXlh3bp1Oo+RyWSYMWMGli1bhl69eulMR4it9fSjVXsIUVo5LQpPjgzDL2kJrPs1FxEwsHKBAca01hpqHH1iRA+M6euPmFDjBr7py99bJMTWZ0fi5/RRTh/EAsCgQYNw/fp11X/79rVOufbiiy/il19+wQ8//IA9e/bg2rVrePjhh+1YWkLMY9LArubmZhQVFSEzM1O1jc/nIzExEYWFhTqPW758OQICAjB37lz88ccfBs8jkUggkbTO5VhXVwcAkEqlkEqlug4zivJ4S/NxdlQPClYcA0PaCbmcUd0ncrn2lF8tLS2QSqWQM637DN1XDMNopHkwKgi5pyrRP7CjxnZpi8yke9TS+7mzhwCvT+wHqVSKc2rb5YxcK2/1a2gbkLboKff9/f1RVtWAmO4+2mna1Itcrn1edUsmKVoXZbIWo+Z0lbXJTyptUfu3FJHBHVX/Zvu/pWz5eevm5oagoCCt7bW1tVi7di02bdqEBx54AADw1VdfYeDAgThw4ABGjBhhszISYimTgtibN29CJpMhMFDz0VdgYCDOnDnDesy+ffuwdu1aFBcXG32erKwsLFu2TGt7bm4uvLy4aTnLy8vjJB9n5+r1UFfDB41vJG35iRhUSxS/cK5eu4rs7MsAgNPXeAA0Z7TYt28fLnUEblcLoGydzM7WNWpe8ZF7584djTQMA7wyBOjqUXNvuyLdkSNHIL9kqMWy9WNc93ktc/XKFWRnKxf8UJxP0ixRnU8ub712ADh69Ch4l9nL/WBngOkM5OfmqG1V5FlXX69x/efOnkX2XcMr3bHT/norv3AB2dmtK8ddbWhNt2vXLrjrmKyEq8/JxkbbDSQ9d+4cQkJC4OHhgYSEBGRlZaFHjx4oKiqCVCpFYmKiKu2AAQPQo0cPFBYW6gxirdm4pMI3biYOdSKIuDm3k2sWcNvL3BY/uLg4h1Wn2Kqvr8fMmTPx5Zdfwt9f9xKZbWVmZiIjI0P1uq6uDqGhoUhKSoKPj49FZZJKpcjLy8OECRMgFLpuf0iqB4XN4r9wrk7/Sk3E9fy4YCQeWKV4utQtpBtSUyMBANf/vIjtl85qpB09ejQGhfhg4/W/cL5e8V5KTU1lzff5wlwAQMeOHZGaOkrn+ZXphg4dipRB+vvLKtPqO6+plJ8PSt26d0dq6mCN83mIREhNHQcAeOlQHmSy1i/RmJgYpEZqtwLqoszTx9sbqakjVa/79uuHVDPn51WvF6VevXohNbmf6vUZcT3eO674O6ekJMNDqBnFcv05qQz8rC0+Ph7r169H//79cf36dSxbtgxjxoxBSUkJxGIx3N3d4evrq3FMYGAgxGKxzjxt0biEqC9MPuQNbs7s/OK4XV7OWj+I1XHxo86kINbf3x8CgUCrA3hlZSXrY4vz58/j4sWLmDx5smqb8nGcm5sbSktL0bu39geUSCSCSKT960ooFHIWcHGZlzNz9XoQGrFiF3E9Xbw9Vf/m83mqe0TA126qEwjcIBQKNfqLGrqneDyeUfedm0Bg0v1prXuZz+Oz5K37Gvgmlrs1S808Tb1+Q9yFmvkJBK1fgYrPQvamWK4+J231WTtx4kTVv4cMGYL4+Hj07NkT33//PTw9PfUcqZs1G5dUsrqbfEhCT5oaDQDqzy7lNL+Spcmc5seGix91JgWx7u7uiI2NRX5+vmqaLLlcjvz8fKSnp2ulHzBgAE6cOKGxbdGiRaivr8fHH3+M0FB68xH74tO0OoSFJTMNuCJLZxrw7+iOm3eaMa4/++wH5ogP98PB8mqNbfPGuObAYl9fX/Tr1w9lZWWYMGECmpubUVNTo9Eaq6sxSskWjUuQN5l8iAQSw4lcgETG7WeWLX5wcXEOk5uhMjIy8OWXX2LDhg04ffo0FixYgIaGBsyZMwcAMGvWLNXALw8PDwwePFjjP19fX3h7e2Pw4MFwd7fPWuSEKI0fYNqXZny4n5VKQrg2JTrE7GNlJkRlyt9B1gh77TKXKgtDv/UsLeeO58bgvUeG4IXEvprntSDPzfM1+3YOCvGBr5drfufcuXMH58+fR3BwMGJjYyEUCpGfn6/aX1paioqKCiQksM9KQYijMrlP7LRp03Djxg0sXrwYYrEY0dHRyMnJUQ32qqioAJ9Pj2iJc/hHbDeUnzmBeu8eOHG1DmfE+ped1DV/JnE8PSyYPq2DrhE+LLic79Qcaff3xurfz+ONv0XYtyBqGBMrJaiTB/4xjNsnc7w2kfc3c+M5zd+RvfTSS5g8eTJ69uyJa9euYcmSJRAIBHjsscfQqVMnzJ07FxkZGfDz84OPjw+ee+45JCQk0MwExOmYNbArPT2dtfsAABQUFOg9dv369eackhCr4PN5iOrCIDV1EIRCIcIW7tSb3pF7H3T1FuFGPT1aU+puRhDbp2sHRHao0wqAHNlLSf0xc0QYgjqZPrLbWkQO2Nfcr4N2K6z6n9mJ/uQGXblyBY899hhu3bqFrl27YvTo0Thw4AC6du0KAPjoo4/A5/MxdepUSCQSJCcn49NPP7VzqQkxneN90hDiwGzdh9avgzvef2SITc9pCf+Opk138+w480aeG6Os6o7Jx/z6r1EYF2y9ptWXk/sDAKdLwfJ4PKsFsIHeir/nxMH6ZxpQb3m9r19XJA7kZgUya99u9m5Ft5bNmzfj2rVrkEgkuHLlCjZv3qwxiNrDwwOrV69GdXU1GhoasHXrVr39YQlxVBTEEmKCti10fy5UTBYuNHbZSyME+rQGgjwA3h7Gd343pbvDqD5dNF4/0Yd9ihZdS42yyZjQDyeWJuHMihR4qT2S/+EZ9r52fQI6Gp23uq+eHK53f1y4HyRSbqec4ULa/X1w7q2JiO/VxXBiB/Drv0Zie9ooPGCg7/iiSYquDHNHh2PDU3EmLXVLCCHmok8aQkzQtq9fN19PXHxnEs6+ORFPjgwzKg9fLyH+eOV+uPF5eCyuh9b+tiOoJ0QEauR9X7+urPk2NcuwUUe/v/v7d8W/xrcOmkkZFIRvn9bs/9ZJx5gXuQmtVeH+HeDtIYSHUKDRyjU8zA8FL41Dv8COWPmPKLPyVjeuf2sdLJms3Rd02d8HoVmmvbqWPv+8j33kuqEiioSmfYyasqSpvVsKvT2EiAr1Ndi94qnR4dj36v1YNGmgjUpmnKjunfTu1+hOQDNSEOJ0KIglxAS6Bp/weDxkpg4weHz2v8bgz1cfQKifF04tT0HWw5Ea+zfPH4GkCM3HegI+D0v/Pkj1Wlerb0SIDxJ6d8HFdyZh/ZzhKHhpnFoefGRMaJ3k3ZNl4JKur3C5kZHU+48MQUJv3S2MYf4dkPvifXh4aOtckPoGAC2cqLs+1YMqttZnAZ+HsX3Zg302I3t3QeZE0wKwjAn98MSIHuh7rzW5PfWpNEf3zl6c9yW2OD8Dx9v7RwIhxDJWXbGLEGfz3tQheDfnDFbPGIrbDc1Y8O0R1b7sf43BwGBvPPfdUdZjRW7sI9rH9PXH36NC0KtrB0SEtE4K7s4y+GVEry6orGudK7GFpamybdC2dHIELt5qxPyxrS2JbefbbJFrtkr2C/QGAMT08MXRihpEdvMBD5pzaiq1LcL8sb3wxd4Lqtc7nhuNQB8PdPU2fflHBsD/Zg3DX5eq8fme1jy/fioOY/t1xTu/si9nra5toOPf0R1hXTqgb0BHjO7jj31lN1X7+gd6g8eD1iwUPnq6bOgKg9RbtomDMmOqNEKI86AglhA1/xgeikeHddcKjHp28dIIQI3RJ6Ajvv9nAuuoaH3UB0d1FGnfou5tguWe/h3w5KhwvXkqR4tvTxuFgtIbeGp0GADg0xlD8e2BCkwbFoK9v+9Wpf+/BQmY+pliOU65WhTbL7AjXksdqBHEDu6m/5GtPgzDIDEiEIkRgaogNjrUF2PvdZmYEBGIvFOVqsnw2cSE+uLbp+NRe1eKpIhAyJnWHwifz4zFoCW7VGl5POB/s4dh9Lu/m11mQghp73TN1HPxnUk2Lol+FMQS0gbbI0xTR90DiuDPmAB21bRovLClGG8/pOhaoGtw1hMjeuBQeTUmRQbjl2PXWsurJ+/3pg7B2n3lqoE3UaG+iAr1Ve0P7uSJl5L7QyqVoqMQ2J0xGj5eHhrXq96dwJQ+rB093HDXwOAqtvymDu2m+vcnj8XgaEUNhod1xo7j1/HClmLVvj9euR/Xau7qDaI7iNzw4zMJeGRNoWpb984crfPehjX6VDIOs9yBNmq5JFyLDNceI0CIPtQnlhATdfPVvfb4WLVBV8b2JZ0S0w1nVqTg8fjWD/B/PdAHgGKAktKbUyKR++J9rP1ZdfnH8FDsenEsQo2cMzW0s5dWwK5+HaZMYr929jD07toBa2cP09qnnLlgpJ4+tADgIRQgoXcXuAn4qsFc0feC8FA/L6NG+Q8L015l7fXUgehiYgs5AAzt2dnkY9orW/QntWWgTDE5Ic6HWmIJMZGbnum01j85HL1eywZgWqulh1AzMM1I6o/59/Vm7U5g60XD1LvT+ngaP93XkO6+yP/3ONZ9h15PxO2GZqODawDw9XLH6eUpFk2kr2xlnze2F54eE47wzGyTjo/t2Rmbno43qdyEEEKsg1piCeEQn8/D0B6+AIBHYrvrT2wAWwALAJ3aBJLWXgpXzjD4ctYwRIf64sNHozT2zU7oaVaeHUVuZgWCnu4C8M24XmUdxYW1tqQaO/K97W+RkX38KYiFbVpJadorQog+1BJLCMe+nhuPoxW3kWClCe0ju3XCs+N649OC8xjaw9dq51GSMwwmRARiQoT2Kkzm9BU2pKs396tP/ZZxH3JKxJhlZtBtDFfrI2qL7gRtF+QwlSlFdKalhgkhChTEEmKEwWozE3Rw13/bdBS5YYwJc5Saisfj4ZWUAXglxfC8tFwI1tMH2NzFCtiseWIojl6uQRJLsGypcP8OWGDFJW4BYGJkMPafv4XunXXXl6lcdR7TI29MMDhojxBCKIglRI9fnx+D7BPX8c/7WgOgj6dHI23TETw/vp+eI53ft0/H49uDl1hXxPp7VAhySsSYHse++IM5UgYHI2VwMGf5GWNMX3/8ce4mJy20M+J6oKefF4YYWCXKFG27jjgSazZc+nVwN3lqOktROywhzoeCWEL0GBjsg4HBmvPD9g30Ru6L99mpRLYzqo8/RvXxZ9338fRoSGUM64INzmT9nDjcqJcgqJPlXRj4fJ7G7BSWeO+RIThzvR5j+rLXvyMI79LB3kUghLg4CmIJISbj8Xhwd3P+tisBn8dJAMs1XcsbO4JtaaOwbl+53mWBCSHEFiiIJYQQYrToUF/857EYexeDczSuixDnQ0EsIYQQQuxn6b1+5LRiFzGRc3doI4QQQnR4OEaxhPGgEB8DKQkhzohaYgkhhLRLsxLCMCDYx6ipumieWOLMvAcuNPmY+tPvWKEktkVBLCGEkHaJz+dhhJUXAyGE2A91JyCEEEIIIU7HrCB29erVCAsLg4eHB+Lj43Ho0CGdabdu3Yphw4bB19cXHTp0QHR0NL755huzC0wIIYQQQojJQeyWLVuQkZGBJUuW4MiRI4iKikJycjKqqqpY0/v5+eH1119HYWEhjh8/jjlz5mDOnDnYtWuXxYUnhBBCCCGuyeQgduXKlZg3bx7mzJmDiIgIrFmzBl5eXli3bh1r+nHjxuGhhx7CwIED0bt3bzz//PMYMmQI9u3bZ3HhCSGEEEKIazJpYFdzczOKioqQmZmp2sbn85GYmIjCwkKDxzMMg927d6O0tBTvvvuuznQSiQQSiUT1uq6uDgAglUohlUpNKbIW5fGW5uPsqB4UqB4UqB4U2OpBLpe7XL24yvuhpaVF9W+2a+W6Htp7fRJiayYFsTdv3oRMJkNgYKDG9sDAQJw5c0bncbW1tejWrRskEgkEAgE+/fRTTJgwQWf6rKwsLFu2TGt7bm4uvLy8TCmyTnl5eZzk4+yoHhSoHhSoHhQU9aD4eLx27Rqys6/Yt0B20t7fDxIZoPw7Z2dn60zHVT00NjZykg8hRMEmU2x5e3ujuLgYd+7cQX5+PjIyMtCrVy+MGzeONX1mZiYyMjJUr+vq6hAaGoqkpCT4+Fg2abVUKkVeXh4mTJgAoVBoUV7OjOpBgepBgepBQb0epkpK8X9HrmHJP0ZhYLC3vYtmU670fnggUQqhgAcvd+2vQ67rQflUkRDCDZOCWH9/fwgEAlRWVmpsr6ysRFBQkM7j+Hw++vTpAwCIjo7G6dOnkZWVpTOIFYlEEIlEWtuFQiFnH6hc5uXMqB4UqB4UqB4UhEIhPng0Gm9OGQJPd4G9i2M3rvB+8Dfi+riqh/ZelyZZangBCkIMMWlgl7u7O2JjY5Gfn6/aJpfLkZ+fj4SEBKPzkcvlGn1eCSHE0fB4PJcOYAkhxNGZ3J0gIyMDs2fPxrBhwxAXF4dVq1ahoaEBc+bMAQDMmjUL3bp1Q1ZWFgBF/9Zhw4ahd+/ekEgkyM7OxjfffIPPPvuM2yshhBBCCCEuw+Qgdtq0abhx4wYWL14MsViM6Oho5OTkqAZ7VVRUgM9vbeBtaGjAs88+iytXrsDT0xMDBgzAxo0bMW3aNO6ughBCCCEOI3JDpP4E4T1sUxDCqbCFO3Xuu/jOJBuWRMGsgV3p6elIT09n3VdQUKDx+s0338Sbb75pzmkIIYQQQghhZZPZCQghhBBCiOPwHrjQrOPqT7/DcUnMZ/KKXYQQQghpH1avXo2wsDB4eHggPj4ehw4dsneRCDEaBbGEEEKIC9qyZQsyMjKwZMkSHDlyBFFRUUhOTkZVVZW9i0aIUSiIJYQQQlzQypUrMW/ePMyZMwcRERFYs2YNvLy8sG7dOnsXjRCjOEWfWIZhAHCz2olUKkVjYyPq6upceuJpqgcFqgcFqgcFqgcFqgcFrutB+R2m/E6zp+bmZhQVFSEzM1O1jc/nIzExEYWFhVrpJRKJxvzutbW1AIDq6mpIpVKt9G53nSK8IGboHLaIdXvMGvbtAPDbo79pbauvrwdg2f3gFO8y5YWGhobauSSEEEKIZerr69Gpk31XrLp58yZkMplqekylwMBAnDlzRit9VlYWli1bprU9PDzcamUk7Yf/An+d+yy5H5wiiA0JCcHly5fh7e0NHo9nUV51dXUIDQ3F5cuX4ePjw1EJnQ/VgwLVgwLVgwLVgwLVgwLX9cAwDOrr6xESEsJB6WwrMzMTGRkZqtdyuRzV1dXo0qWLxd/LtkLva/thq3su7genCGL5fD66d+/OaZ4+Pj70JgbVgxLVgwLVgwLVgwLVgwKX9WDvFlglf39/CAQCVFZWamyvrKxEUFCQVnqRSASRSKSxzdfX15pFtBp6X9tP27q39H6ggV2EEEKIi3F3d0dsbCzy8/NV2+RyOfLz85GQkGDHkhFiPKdoiSWEEEIItzIyMjB79mwMGzYMcXFxWLVqFRoaGjBnzhx7F40Qo7hcECsSibBkyRKtxyKuhupBgepBgepBgepBgepBob3Xw7Rp03Djxg0sXrwYYrEY0dHRyMnJ0Rrs1V6097+nI7NW3fMYR5jrgxBCCCGEEBNQn1hCCCGEEOJ0KIglhBBCCCFOh4JYQgghhBDidCiIJYQQQgghTsflgtjVq1cjLCwMHh4eiI+Px6FDh+xdJM4sXboUPB5P478BAwao9jc1NSEtLQ1dunRBx44dMXXqVK2JrisqKjBp0iR4eXkhICAAL7/8MlpaWmx9KSbZu3cvJk+ejJCQEPB4PGzbtk1jP8MwWLx4MYKDg+Hp6YnExEScO3dOI011dTVmzJgBHx8f+Pr6Yu7cubhz545GmuPHj2PMmDHw8PBAaGgo3nvvPWtfmkkM1cOTTz6p9f5ISUnRSNMe6iErKwvDhw+Ht7c3AgICMGXKFJSWlmqk4epeKCgowNChQyESidCnTx+sX7/e2pdnNGPqYdy4cVrviWeeeUYjjTPXw2effYYhQ4aoJlhPSEjAr7/+qtrvCu8DV2PKd/z69eu13v8eHh42LG37Yej7hw0n9w3jQjZv3sy4u7sz69atY06ePMnMmzeP8fX1ZSorK+1dNE4sWbKEGTRoEHP9+nXVfzdu3FDtf+aZZ5jQ0FAmPz+fOXz4MDNixAhm5MiRqv0tLS3M4MGDmcTERObo0aNMdnY24+/vz2RmZtrjcoyWnZ3NvP7668zWrVsZAMxPP/2ksf+dd95hOnXqxGzbto05duwY8/e//50JDw9n7t69q0qTkpLCREVFMQcOHGD++OMPpk+fPsxjjz2m2l9bW8sEBgYyM2bMYEpKSpjvvvuO8fT0ZD7//HNbXaZBhuph9uzZTEpKisb7o7q6WiNNe6iH5ORk5quvvmJKSkqY4uJiJjU1lenRowdz584dVRou7oULFy4wXl5eTEZGBnPq1Cnmk08+YQQCAZOTk2PT69XFmHq47777mHnz5mm8J2pra1X7nb0efv75Z2bnzp3M2bNnmdLSUua1115jhEIhU1JSwjCMa7wPXImp3/FfffUV4+Pjo/H+F4vFNi51+2Do+6ctru4blwpi4+LimLS0NNVrmUzGhISEMFlZWXYsFXeWLFnCREVFse6rqalhhEIh88MPP6i2nT59mgHAFBYWMgyjeBPy+XyNm/izzz5jfHx8GIlEYtWyc6XtzSOXy5mgoCDm/fffV22rqalhRCIR89133zEMwzCnTp1iADB//fWXKs2vv/7K8Hg85urVqwzDMMynn37KdO7cWaMeXn31VaZ///5WviLz6ApiH3zwQZ3HtMd6YBiGqaqqYgAwe/bsYRiGu3vhlVdeYQYNGqRxrmnTpjHJycnWviSztK0HhlEEsc8//7zOY9pjPXTu3Jn53//+57Lvg/bM1O/4r776iunUqZONSuc6jAliubpvXKY7QXNzM4qKipCYmKjaxufzkZiYiMLCQjuWjFvnzp1DSEgIevXqhRkzZqCiogIAUFRUBKlUqnH9AwYMQI8ePVTXX1hYiMjISI2JrpOTk1FXV4eTJ0/a9kI4Ul5eDrFYrHHdnTp1Qnx8vMZ1+/r6YtiwYao0iYmJ4PP5OHjwoCrN2LFj4e7urkqTnJyM0tJS3L5920ZXY7mCggIEBASgf//+WLBgAW7duqXa117roba2FgDg5+cHgLt7obCwUCMPZRpH/TxpWw9K3377Lfz9/TF48GBkZmaisbFRta891YNMJsPmzZvR0NCAhIQEl30ftFfmfsffuXMHPXv2RGhoKB588EGn/a5zNlzdNy4TxN68eRMymUxrJZLAwECIxWI7lYpb8fHxWL9+PXJycvDZZ5+hvLwcY8aMQX19PcRiMdzd3eHr66txjPr1i8Vi1vpR7nNGynLr+7uLxWIEBARo7Hdzc4Ofn1+7qpuUlBR8/fXXyM/Px7vvvos9e/Zg4sSJkMlkANpnPcjlcrzwwgsYNWoUBg8eDACc3Qu60tTV1eHu3bvWuByzsdUDADz++OPYuHEjfv/9d2RmZuKbb77BE088odrfHurhxIkT6NixI0QiEZ555hn89NNPiIiIcMn3QXtmznd8//79sW7dOmzfvh0bN26EXC7HyJEjceXKFVsU2aVxdd+43LKz7dnEiRNV/x4yZAji4+PRs2dPfP/99/D09LRjyYgjmD59uurfkZGRGDJkCHr37o2CggKMHz/ejiWznrS0NJSUlGDfvn32Lopd6aqH+fPnq/4dGRmJ4OBgjB8/HufPn0fv3r1tXUyr6N+/P4qLi1FbW4sff/wRs2fPxp49e+xdLOIAEhISkJCQoHo9cuRIDBw4EJ9//jlWrFhhx5IRY7lMS6y/vz8EAoHWyNPKykoEBQXZqVTW5evri379+qGsrAxBQUFobm5GTU2NRhr16w8KCmKtH+U+Z6Qst76/e1BQEKqqqjT2t7S0oLq6ul3XTa9eveDv74+ysjIA7a8e0tPTsWPHDvz+++/o3r27ajtX94KuND4+Pg71o1FXPbCJj48HAI33hLPXg7u7O/r06YPY2FhkZWUhKioKH3/8scu9D9o7Lr7jhUIhYmJiVO9/Yj1c3TcuE8S6u7sjNjYW+fn5qm1yuRz5+fkav8Takzt37uD8+fMIDg5GbGwshEKhxvWXlpaioqJCdf0JCQk4ceKERiCTl5cHHx8fRERE2Lz8XAgPD0dQUJDGddfV1eHgwYMa111TU4OioiJVmt27d0Mul6u+1BMSErB3715IpVJVmry8PPTv3x+dO3e20dVw68qVK7h16xaCg4MBtJ96YBgG6enp+Omnn7B7926Eh4dr7OfqXkhISNDIQ5nGUT5PDNUDm+LiYgDQeE84ez20JZfLIZFIXOZ94Cq4+I6XyWQ4ceKE6v1PrIez+8a0MWfObfPmzYxIJGLWr1/PnDp1ipk/fz7j6+vbbqbU+Pe//80UFBQw5eXlzJ9//skkJiYy/v7+TFVVFcMwiulkevTowezevZs5fPgwk5CQwCQkJKiOV04nk5SUxBQXFzM5OTlM165dHX6Krfr6eubo0aPM0aNHGQDMypUrmaNHjzKXLl1iGEYxxZavry+zfft25vjx48yDDz7IOsVWTEwMc/DgQWbfvn1M3759NaaWqqmpYQIDA5mZM2cyJSUlzObNmxkvLy+HmlpKXz3U19czL730ElNYWMiUl5czv/32GzN06FCmb9++TFNTkyqP9lAPCxYsYDp16sQUFBRoTJ3T2NioSsPFvaCcIubll19mTp8+zaxevdqhplYyVA9lZWXM8uXLmcOHDzPl5eXM9u3bmV69ejFjx45V5eHs9bBw4UJmz549THl5OXP8+HFm4cKFDI/HY3JzcxmGcY33gSsx9B0/c+ZMZuHChar0y5YtY3bt2sWcP3+eKSoqYqZPn854eHgwJ0+etNclOC1D38MLFy5kZs6cqUrP1X3jUkEswzDMJ598wvTo0YNxd3dn4uLimAMHDti7SJyZNm0aExwczLi7uzPdunVjpk2bxpSVlan23717l3n22WeZzp07M15eXsxDDz3EXL9+XSOPixcvMhMnTmQ8PT0Zf39/5t///jcjlUptfSkm+f333xkAWv/Nnj2bYRjFNFtvvPEGExgYyIhEImb8+PFMaWmpRh63bt1iHnvsMaZjx46Mj48PM2fOHKa+vl4jzbFjx5jRo0czIpGI6datG/POO+/Y6hKNoq8eGhsbmaSkJKZr166MUChkevbsycybN0/rB1x7qAe2OgDAfPXVV6o0XN0Lv//+OxMdHc24u7szvXr10jiHvRmqh4qKCmbs2LGMn58fIxKJmD59+jAvv/yyxjyxDOPc9fDUU08xPXv2ZNzd3ZmuXbsy48ePVwWwDOMa7wNXo+87/r777lN9LzAMw7zwwguqtIGBgUxqaipz5MgRO5Ta+Rn6Hp49ezZz3333aR1j6X3DYxiGMa3tlhBCCCGEEPtymT6xhBBCCCGk/aAglhBCCCGEOB0KYgkhhBBCiNOhIJYQQgghhDgdCmIJIYQQQojToSCWEEIIIYQ4HQpiCSGEEEKI06EglhBCCCGEOB0KYgkhhBBCiNOhIJYQQgghhDgdCmIJIYQQQojToSCWEEIIIYQ4nf8HHlgaWc7FUJgAAAAASUVORK5CYII=\n", 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", 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" ] @@ -122,10 +122,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2023-11-09T07:10:40.510069Z", - "iopub.status.busy": "2023-11-09T07:10:40.509945Z", - "iopub.status.idle": "2023-11-09T07:10:41.029675Z", - "shell.execute_reply": "2023-11-09T07:10:41.029399Z" + "iopub.execute_input": "2023-12-04T17:47:15.512509Z", + "iopub.status.busy": "2023-12-04T17:47:15.512375Z", + "iopub.status.idle": "2023-12-04T17:47:16.094393Z", + "shell.execute_reply": "2023-12-04T17:47:16.093560Z" }, "jupyter": { "outputs_hidden": false @@ -146,7 +146,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] diff --git a/docs/introduction/getting-started/multiclass-classification.ipynb b/docs/introduction/getting-started/multiclass-classification.ipynb index 60617cd4eb..19415ff54f 100644 --- a/docs/introduction/getting-started/multiclass-classification.ipynb +++ b/docs/introduction/getting-started/multiclass-classification.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:42.224562Z", - "iopub.status.busy": "2023-11-09T07:10:42.224156Z", - "iopub.status.idle": "2023-11-09T07:10:42.628788Z", - "shell.execute_reply": "2023-11-09T07:10:42.628519Z" + "iopub.execute_input": "2023-12-04T17:47:17.633774Z", + "iopub.status.busy": "2023-12-04T17:47:17.633578Z", + "iopub.status.idle": "2023-12-04T17:47:18.059839Z", + "shell.execute_reply": "2023-12-04T17:47:18.059524Z" } }, "outputs": [ @@ -72,10 +72,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:42.630519Z", - "iopub.status.busy": "2023-11-09T07:10:42.630390Z", - "iopub.status.idle": "2023-11-09T07:10:42.653795Z", - "shell.execute_reply": "2023-11-09T07:10:42.653510Z" + "iopub.execute_input": "2023-12-04T17:47:18.062123Z", + "iopub.status.busy": "2023-12-04T17:47:18.061924Z", + "iopub.status.idle": "2023-12-04T17:47:18.085783Z", + "shell.execute_reply": "2023-12-04T17:47:18.085481Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:42.655574Z", - "iopub.status.busy": "2023-11-09T07:10:42.655470Z", - "iopub.status.idle": "2023-11-09T07:10:42.666775Z", - "shell.execute_reply": "2023-11-09T07:10:42.666521Z" + "iopub.execute_input": "2023-12-04T17:47:18.087624Z", + "iopub.status.busy": "2023-12-04T17:47:18.087509Z", + "iopub.status.idle": "2023-12-04T17:47:18.098210Z", + "shell.execute_reply": "2023-12-04T17:47:18.097952Z" } }, "outputs": [ @@ -142,10 +142,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:42.668327Z", - "iopub.status.busy": "2023-11-09T07:10:42.668223Z", - "iopub.status.idle": "2023-11-09T07:10:42.676692Z", - "shell.execute_reply": "2023-11-09T07:10:42.676449Z" + "iopub.execute_input": "2023-12-04T17:47:18.099585Z", + "iopub.status.busy": "2023-12-04T17:47:18.099501Z", + "iopub.status.idle": "2023-12-04T17:47:18.108487Z", + "shell.execute_reply": "2023-12-04T17:47:18.108239Z" } }, "outputs": [ @@ -177,10 +177,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:42.678226Z", - "iopub.status.busy": "2023-11-09T07:10:42.678137Z", - "iopub.status.idle": "2023-11-09T07:10:42.709366Z", - "shell.execute_reply": "2023-11-09T07:10:42.709107Z" + "iopub.execute_input": "2023-12-04T17:47:18.109928Z", + "iopub.status.busy": "2023-12-04T17:47:18.109851Z", + "iopub.status.idle": "2023-12-04T17:47:18.140712Z", + "shell.execute_reply": "2023-12-04T17:47:18.140335Z" } }, "outputs": [ @@ -217,10 +217,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:42.710857Z", - "iopub.status.busy": "2023-11-09T07:10:42.710764Z", - "iopub.status.idle": "2023-11-09T07:10:42.720273Z", - "shell.execute_reply": "2023-11-09T07:10:42.720037Z" + "iopub.execute_input": "2023-12-04T17:47:18.142820Z", + "iopub.status.busy": "2023-12-04T17:47:18.142689Z", + "iopub.status.idle": "2023-12-04T17:47:18.152525Z", + "shell.execute_reply": "2023-12-04T17:47:18.152285Z" } }, "outputs": [ @@ -249,10 +249,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:42.721662Z", - "iopub.status.busy": "2023-11-09T07:10:42.721588Z", - "iopub.status.idle": "2023-11-09T07:10:42.732033Z", - "shell.execute_reply": "2023-11-09T07:10:42.731797Z" + "iopub.execute_input": "2023-12-04T17:47:18.153876Z", + "iopub.status.busy": "2023-12-04T17:47:18.153798Z", + "iopub.status.idle": "2023-12-04T17:47:18.165027Z", + "shell.execute_reply": "2023-12-04T17:47:18.164772Z" } }, "outputs": [ @@ -287,10 +287,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:42.733503Z", - "iopub.status.busy": "2023-11-09T07:10:42.733423Z", - "iopub.status.idle": "2023-11-09T07:10:43.165894Z", - "shell.execute_reply": "2023-11-09T07:10:43.165636Z" + "iopub.execute_input": "2023-12-04T17:47:18.166512Z", + "iopub.status.busy": "2023-12-04T17:47:18.166430Z", + "iopub.status.idle": "2023-12-04T17:47:18.610839Z", + "shell.execute_reply": "2023-12-04T17:47:18.610498Z" } }, "outputs": [ @@ -348,10 +348,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:43.167450Z", - "iopub.status.busy": "2023-11-09T07:10:43.167352Z", - "iopub.status.idle": "2023-11-09T07:10:43.621666Z", - "shell.execute_reply": "2023-11-09T07:10:43.621416Z" + "iopub.execute_input": "2023-12-04T17:47:18.613061Z", + "iopub.status.busy": "2023-12-04T17:47:18.612927Z", + "iopub.status.idle": "2023-12-04T17:47:19.085072Z", + "shell.execute_reply": "2023-12-04T17:47:19.084781Z" } }, "outputs": [ diff --git a/docs/introduction/getting-started/regression.ipynb b/docs/introduction/getting-started/regression.ipynb index eea7e154c5..673e3e2d2b 100644 --- a/docs/introduction/getting-started/regression.ipynb +++ b/docs/introduction/getting-started/regression.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:44.634128Z", - "iopub.status.busy": "2023-11-09T07:10:44.633948Z", - "iopub.status.idle": "2023-11-09T07:10:45.050152Z", - "shell.execute_reply": "2023-11-09T07:10:45.049874Z" + "iopub.execute_input": "2023-12-04T17:47:20.479638Z", + "iopub.status.busy": "2023-12-04T17:47:20.479153Z", + "iopub.status.idle": "2023-12-04T17:47:20.897979Z", + "shell.execute_reply": "2023-12-04T17:47:20.897633Z" } }, "outputs": [ @@ -71,10 +71,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:45.051791Z", - "iopub.status.busy": "2023-11-09T07:10:45.051670Z", - "iopub.status.idle": "2023-11-09T07:10:45.063670Z", - "shell.execute_reply": "2023-11-09T07:10:45.063443Z" + "iopub.execute_input": "2023-12-04T17:47:20.899687Z", + "iopub.status.busy": "2023-12-04T17:47:20.899562Z", + "iopub.status.idle": "2023-12-04T17:47:20.910834Z", + "shell.execute_reply": "2023-12-04T17:47:20.910553Z" } }, "outputs": [], @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:45.065255Z", - "iopub.status.busy": "2023-11-09T07:10:45.065173Z", - "iopub.status.idle": "2023-11-09T07:10:45.074734Z", - "shell.execute_reply": "2023-11-09T07:10:45.074417Z" + "iopub.execute_input": "2023-12-04T17:47:20.912303Z", + "iopub.status.busy": "2023-12-04T17:47:20.912228Z", + "iopub.status.idle": "2023-12-04T17:47:20.921120Z", + "shell.execute_reply": "2023-12-04T17:47:20.920869Z" } }, "outputs": [ @@ -137,10 +137,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:45.076494Z", - "iopub.status.busy": "2023-11-09T07:10:45.076376Z", - "iopub.status.idle": "2023-11-09T07:10:45.090050Z", - "shell.execute_reply": "2023-11-09T07:10:45.089788Z" + "iopub.execute_input": "2023-12-04T17:47:20.922589Z", + "iopub.status.busy": "2023-12-04T17:47:20.922495Z", + "iopub.status.idle": "2023-12-04T17:47:20.934349Z", + "shell.execute_reply": "2023-12-04T17:47:20.933989Z" } }, "outputs": [ @@ -177,10 +177,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:45.091538Z", - "iopub.status.busy": "2023-11-09T07:10:45.091446Z", - "iopub.status.idle": "2023-11-09T07:10:45.101556Z", - "shell.execute_reply": "2023-11-09T07:10:45.101323Z" + "iopub.execute_input": "2023-12-04T17:47:20.935928Z", + "iopub.status.busy": "2023-12-04T17:47:20.935816Z", + "iopub.status.idle": "2023-12-04T17:47:20.944466Z", + "shell.execute_reply": "2023-12-04T17:47:20.944074Z" } }, "outputs": [], @@ -201,10 +201,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:45.103238Z", - "iopub.status.busy": "2023-11-09T07:10:45.103165Z", - "iopub.status.idle": "2023-11-09T07:10:45.112098Z", - "shell.execute_reply": "2023-11-09T07:10:45.111849Z" + "iopub.execute_input": "2023-12-04T17:47:20.945980Z", + "iopub.status.busy": "2023-12-04T17:47:20.945898Z", + "iopub.status.idle": "2023-12-04T17:47:20.954845Z", + "shell.execute_reply": "2023-12-04T17:47:20.954612Z" } }, "outputs": [ @@ -236,10 +236,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:45.113659Z", - "iopub.status.busy": "2023-11-09T07:10:45.113555Z", - "iopub.status.idle": "2023-11-09T07:10:46.876088Z", - "shell.execute_reply": "2023-11-09T07:10:46.875793Z" + "iopub.execute_input": "2023-12-04T17:47:20.956655Z", + "iopub.status.busy": "2023-12-04T17:47:20.956493Z", + "iopub.status.idle": "2023-12-04T17:47:22.658069Z", + "shell.execute_reply": "2023-12-04T17:47:22.657771Z" } }, "outputs": [ @@ -282,10 +282,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:46.877715Z", - "iopub.status.busy": "2023-11-09T07:10:46.877619Z", - "iopub.status.idle": "2023-11-09T07:10:48.610244Z", - "shell.execute_reply": "2023-11-09T07:10:48.609993Z" + "iopub.execute_input": "2023-12-04T17:47:22.659592Z", + "iopub.status.busy": "2023-12-04T17:47:22.659489Z", + "iopub.status.idle": "2023-12-04T17:47:24.299285Z", + "shell.execute_reply": "2023-12-04T17:47:24.299054Z" } }, "outputs": [ diff --git a/docs/recipes/active-learning.ipynb b/docs/recipes/active-learning.ipynb index 79eba9268c..15d39fa328 100644 --- a/docs/recipes/active-learning.ipynb +++ b/docs/recipes/active-learning.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:50.584308Z", - "iopub.status.busy": "2023-11-09T07:10:50.583447Z", - "iopub.status.idle": "2023-11-09T07:10:51.424051Z", - "shell.execute_reply": "2023-11-09T07:10:51.423788Z" + "iopub.execute_input": "2023-12-04T17:47:26.242700Z", + "iopub.status.busy": "2023-12-04T17:47:26.242281Z", + "iopub.status.idle": "2023-12-04T17:47:27.079931Z", + "shell.execute_reply": "2023-12-04T17:47:27.079482Z" } }, "outputs": [ @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:51.438209Z", - "iopub.status.busy": "2023-11-09T07:10:51.438034Z", - "iopub.status.idle": "2023-11-09T07:10:51.447763Z", - "shell.execute_reply": "2023-11-09T07:10:51.447500Z" + "iopub.execute_input": "2023-12-04T17:47:27.094899Z", + "iopub.status.busy": "2023-12-04T17:47:27.094694Z", + "iopub.status.idle": "2023-12-04T17:47:27.105156Z", + "shell.execute_reply": "2023-12-04T17:47:27.104823Z" } }, "outputs": [ @@ -142,10 +142,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:51.449385Z", - "iopub.status.busy": "2023-11-09T07:10:51.449300Z", - "iopub.status.idle": "2023-11-09T07:10:51.890865Z", - "shell.execute_reply": "2023-11-09T07:10:51.890624Z" + "iopub.execute_input": "2023-12-04T17:47:27.106972Z", + "iopub.status.busy": "2023-12-04T17:47:27.106834Z", + "iopub.status.idle": "2023-12-04T17:47:27.553244Z", + "shell.execute_reply": "2023-12-04T17:47:27.552984Z" } }, "outputs": [ @@ -153,11 +153,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1,000] Accuracy: 86.32% – 661 samples used\n", - "[2,000] Accuracy: 86.44% – 1,057 samples used\n", - "[3,000] Accuracy: 86.52% – 1,339 samples used\n", - "[4,000] Accuracy: 86.62% – 1,568 samples used\n", - "[5,000] Accuracy: 86.57% – 1,790 samples used\n", + "[1,000] Accuracy: 84.80% – 661 samples used\n", + "[2,000] Accuracy: 86.00% – 1,057 samples used\n", + "[3,000] Accuracy: 86.37% – 1,339 samples used\n", + "[4,000] Accuracy: 86.65% – 1,568 samples used\n", + "[5,000] Accuracy: 86.54% – 1,790 samples used\n", "[5,574] Accuracy: 86.60% – 1,921 samples used\n" ] }, diff --git a/docs/recipes/bandits-101.ipynb b/docs/recipes/bandits-101.ipynb index ddd0d93e63..4d36c117f5 100644 --- a/docs/recipes/bandits-101.ipynb +++ b/docs/recipes/bandits-101.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:52.894617Z", - "iopub.status.busy": "2023-11-09T07:10:52.894487Z", - "iopub.status.idle": "2023-11-09T07:10:52.934553Z", - "shell.execute_reply": "2023-11-09T07:10:52.934260Z" + "iopub.execute_input": "2023-12-04T17:47:28.911688Z", + "iopub.status.busy": "2023-12-04T17:47:28.909441Z", + "iopub.status.idle": "2023-12-04T17:47:28.986844Z", + "shell.execute_reply": "2023-12-04T17:47:28.986570Z" } }, "outputs": [], @@ -51,10 +51,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:52.936515Z", - "iopub.status.busy": "2023-11-09T07:10:52.936399Z", - "iopub.status.idle": "2023-11-09T07:10:53.338264Z", - "shell.execute_reply": "2023-11-09T07:10:53.337989Z" + "iopub.execute_input": "2023-12-04T17:47:28.988524Z", + "iopub.status.busy": "2023-12-04T17:47:28.988444Z", + "iopub.status.idle": "2023-12-04T17:47:29.404060Z", + "shell.execute_reply": "2023-12-04T17:47:29.403703Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:10:53.340072Z", - "iopub.status.busy": "2023-11-09T07:10:53.339932Z", - "iopub.status.idle": "2023-11-09T07:11:26.506543Z", - "shell.execute_reply": "2023-11-09T07:11:26.506114Z" + "iopub.execute_input": "2023-12-04T17:47:29.405926Z", + "iopub.status.busy": "2023-12-04T17:47:29.405781Z", + "iopub.status.idle": "2023-12-04T17:47:59.938410Z", + "shell.execute_reply": "2023-12-04T17:47:59.938118Z" } }, "outputs": [ @@ -108,9 +108,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/6000000 [00:00441\n", " 632\n", " 0\n", - " 3\n", - " -2.082302\n", - " 0.768603\n", + " 2\n", + " 0.226086\n", + " 0.499848\n", " \n", " \n", " 3566176\n", " 1188\n", " 725\n", " 1\n", - " 8\n", - " 1.021648\n", - " 0.589201\n", + " 6\n", + " 2.363962\n", + " 0.935468\n", " \n", " \n", " 1109043\n", " 369\n", " 681\n", " 0\n", - " 9\n", - " 2.678262\n", - " 1.831294\n", + " 5\n", + " 2.780757\n", + " 1.467402\n", " \n", " \n", " 4286042\n", " 1428\n", " 680\n", " 2\n", - 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", 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"2023-11-09T07:12:25.653382Z" + "iopub.execute_input": "2023-12-04T17:48:57.427606Z", + "iopub.status.busy": "2023-12-04T17:48:57.427498Z", + "iopub.status.idle": "2023-12-04T17:48:57.439934Z", + "shell.execute_reply": "2023-12-04T17:48:57.439690Z" }, "tags": [] }, @@ -676,10 +676,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:12:25.654977Z", - "iopub.status.busy": "2023-11-09T07:12:25.654895Z", - "iopub.status.idle": "2023-11-09T07:12:25.666522Z", - "shell.execute_reply": "2023-11-09T07:12:25.666261Z" + "iopub.execute_input": "2023-12-04T17:48:57.441374Z", + "iopub.status.busy": "2023-12-04T17:48:57.441291Z", + "iopub.status.idle": "2023-12-04T17:48:57.450415Z", + "shell.execute_reply": "2023-12-04T17:48:57.450185Z" }, "tags": [] }, @@ -733,10 +733,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:12:25.668086Z", - "iopub.status.busy": "2023-11-09T07:12:25.667992Z", - "iopub.status.idle": "2023-11-09T07:12:25.682408Z", - "shell.execute_reply": "2023-11-09T07:12:25.682125Z" + "iopub.execute_input": "2023-12-04T17:48:57.451796Z", + "iopub.status.busy": "2023-12-04T17:48:57.451720Z", + "iopub.status.idle": "2023-12-04T17:48:57.462677Z", + "shell.execute_reply": "2023-12-04T17:48:57.462419Z" }, "tags": [] }, @@ -794,10 +794,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:12:25.683859Z", - "iopub.status.busy": "2023-11-09T07:12:25.683779Z", - "iopub.status.idle": "2023-11-09T07:12:35.869948Z", - "shell.execute_reply": "2023-11-09T07:12:35.869632Z" + "iopub.execute_input": "2023-12-04T17:48:57.464024Z", + "iopub.status.busy": "2023-12-04T17:48:57.463949Z", + "iopub.status.idle": "2023-12-04T17:49:07.647224Z", + "shell.execute_reply": "2023-12-04T17:49:07.646820Z" }, "scrolled": true, "tags": [] @@ -805,7 +805,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -851,17 +851,17 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:12:35.872146Z", - "iopub.status.busy": "2023-11-09T07:12:35.872058Z", - "iopub.status.idle": "2023-11-09T07:12:44.214838Z", - "shell.execute_reply": "2023-11-09T07:12:44.214473Z" + "iopub.execute_input": "2023-12-04T17:49:07.648955Z", + "iopub.status.busy": "2023-12-04T17:49:07.648846Z", + "iopub.status.idle": "2023-12-04T17:49:15.925716Z", + "shell.execute_reply": "2023-12-04T17:49:15.925436Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -906,17 +906,17 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:12:44.217237Z", - "iopub.status.busy": "2023-11-09T07:12:44.217130Z", - "iopub.status.idle": "2023-11-09T07:12:49.774043Z", - "shell.execute_reply": "2023-11-09T07:12:49.773780Z" + "iopub.execute_input": "2023-12-04T17:49:15.927542Z", + "iopub.status.busy": "2023-12-04T17:49:15.927463Z", + "iopub.status.idle": "2023-12-04T17:49:21.299561Z", + "shell.execute_reply": "2023-12-04T17:49:21.299292Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -987,17 +987,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:12:49.776942Z", - "iopub.status.busy": "2023-11-09T07:12:49.776838Z", - "iopub.status.idle": "2023-11-09T07:13:05.965442Z", - "shell.execute_reply": "2023-11-09T07:13:05.965103Z" + "iopub.execute_input": "2023-12-04T17:49:21.301499Z", + "iopub.status.busy": "2023-12-04T17:49:21.301395Z", + "iopub.status.idle": "2023-12-04T17:49:37.134205Z", + "shell.execute_reply": "2023-12-04T17:49:37.133921Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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AgKqqufD3zTffXO0SAAAAAJRhjNHhgYS2HuzX1gMD2nowG/jefTQqfnEP5rrmuoDaGoJqbwipoyHkjvPb9oaQmuv8uTD3SFA74LMIYwMAAAAAKnMcKZPMvVJjjwuCzqWCz6nC4yWPjXWNdFEIO7+fKQxce0PZJlPtdxLAXGb5sqFh258b255xft4uWpN7jTq3xHyldSXP8499fXffXzpcXTKQXSGsbfuzz+0xNDCghx56yN0/++yz1dTUNNtfHQAAAMwjNRf+BgAAADA3pDKOdh4ZdoPe2w4OauvBAR0dTla7NECS5LMtLarPB7dzQe76oNobQ26ouyMX6l5UH1TQb499UQAAAADA7DJGMk6JbtFlgsvjDkZPNCjtnR9PiDtVOEdoGpi/vOFgb5g4v2/ZpY8VnONZM2ZgeTzzdtE9fJ46fGOfW3Ceb3znj7pX8bVtiQYJAAAAwLQg/A0AAABgygbjKT1/aDDbzTvX0fuFw4NKpp1ql4Z5KOi3FfLZCvo9L89+wGcrVDRXclx0jcZwwA15tzeE1FIXkG3zB04AAAAA5iEnG3a2naQs48gyGVmxXsmO50LOuc7MxvF0Zs6MbE2maL7MnJP2zBevK7Xvvaf3/OI1mRL3yHg6SadG7xd3nvYeAzB1ll0U4i0O8JYJ9Y4ZFh7P2lL3nuh8ca3FHZ3LBa+nOEegGQAAAEAVEP4GAAAAMG7GSH1J6eE/9WhX32E36L3naLTapWGKQn5bDSG/6nOvhpDPHYf9PgX9lvx2Nngd8FsK+nJjn62Az3JD2e6+z5bfMw74C48F3DlLAXskoO23LVn8gRkAAACwMOQ7No8KGTujQ8Oj1nmDxyXO9waSR63PzzuFx93tROadEuuK5kvWXCY8XS4UXbxORk2SLvC+n3+szpcRWFDqWqVIu1TfIdW3ZceRNskfygaAvUHkgnFxCNr2hJiLgsyWXRRuLnEt93q25xzfSA0lj9sl1uf3LQLMAAAAADCPEP4GAAAAalAq4yiWyiieyiieHBnHcq94MqN4OqOY51g8lVHMM59fH09lNBRPqrvXp76kFE1b0tNbq/2IC5LftuQrekUCPk9g26/6kE8NoUBBeLuhRKDbnQv6FQn5FPDZ1X48AAAAYP4xpnxIeKLB4smEkL3h4JJrvUHoSnNl7u8NXVcMYhcHtkvt52ss6v4MANUUbpHqc2HuSFsu1N2eC3i3e461S5FFki9Q7YoBAAAAACD8DQAAAMy0jGM0nEwrnsookXKUSGcU92zjqYwSaScXwC49n0hnz42PdW4usJ12zAw8ycx3/2kM+fWyJY1qDPtlWZZsSyNbWdkGRZYl27JkSbItZceWJcvy7o+cl1+bP8/ONTKybUu+/Jw7zs3n9i1LbsjasrJzPlu562TnbTt7ns/Kj0fW+G1bti35bdu9TnGA2587x+8bua/ftuXz5e+XW2PTfQkAAADzgONITkrKpLJbJzMyzuT23XF6ZOukpEy68Jh73Hud9Bj7+eumJ752zO7OJQLTmonvvQBggbJsyQ5Itl/y+bNjXyA35xsZFxzz59Z7j/lHxr6gVLfIE+puKxwT5gYAAAAAzEOEvwEAAIBJSKQz6hlKqmcoqe6hhI4MJdxxz1BC3blx91BSR4cTmpEs9jy3vKVOG5Y2aeOyJm1c2qQTljVpRWudLH7FLAAAABaifAfpTHIkAJ1JZffd8HSycN4pWjNqPn9eUai6IIydLh22HrU2XRSWLnH9fEdoAMAMswrDzr5giVB00b47FygRls6vCXqO+YvGuQB28fGCdT7PWr8niO05v+BcX1HAm99KBgAAAADAeMyb8Pcb3vCGgn3LsvSrX/1qzHXTpdz9AAAAUBuMMRpKpN0A90h42xvqHgl6D8bT1S553vDblo7rbNRGT9B7w9JGtUSC1S4NAAAAtWBUZ+m0JwRdIrzs3XeD0mXCzCVD0aWC1GU6V4/qLl0mgJ0Pb9NFGgBml1Whm3TJ+XGu8wU9r8A4xv6Jrbd91X7nAAAAAABAFc2b8PfDDz/sdgA0xpTtBuhdN10q3Q8AAABT4zhGyYyjVMZROmOUyji5/dw4ndvPbZPp7NpEuvSxZMZkt7l1+TXuce+x3NxgPK3uoYQSaTrUTVVj2F8Q8t64rEnrFjco5OcPJQEAAKrGcXIdmTMjQWbHOy61X2GNG3YudU6lkHRyjAD1BALZ3uA0gWkAmBpv92bL5+nenNta9kiHZjt33Cq1xrvvG1nr7vuznZ0L9ouu53aPDozeL+42PeG13nFgpB4AAAAAAIB5Zt6EvwEAADC7jDGKpTLqjabUO5zU0eGkeqPJ7DiaUl80qWgyo1QmH7I2SjvZcSpdLtA9EurOjzMOQY35akVrXVE37yataK3jL04CAIBCxmRDwsbJho+Nk305nvGo/dzYccY+r+RcJnvfimvGukapY5nCmgrm8/fMjzOjrzvWfD5MXTCXHlnrHs+vzRTez0nn6ssUrgWABcMaCRR7w8beoLE3lGx5t3bRvm8k9Dyu+VLn+8YIO1cKSlcKWWfHw7G4nvrd72Xkk7FsnXLqa9TQ2Fx4n1L3Lgh6E34GAAAAAACYb+ZV+NsYU3JcaR0AAABGgtxHh5PqHU5lQ9zRfKA7F+6OJtUXTerocCq3TdIJu0YFfbbCAVt1QZ/CAZ/qAtltOGCrLuDLzvt9Cgezx+oCPslJad+unQr5pMVho3edd4ZWdLZV+1EAALXAmNEh3ZKv4jXFQV0zep1M6eOa4trxvAqCxPlzS4WWS53vDQebov0yoehy4eQSoeZIKqnX9ffKyj13/f5/kmyp4BlHBaErPEP+eMnwNMFjAJgca5Lh4hJrvSHfSc8VB57zHZXtMuHkiXaArnTuGOsKgt0L6y8jZwYG1PvisLvvdGyQmpqqWBEAAAAAAABmw7wJfzvO+IJH410HAABQzBijeMpRynHkOEaOkTKOkWOyr4xj5DhSJrfvOCY7djRy3F2bPdeY7JrsODuXyZ2bdrLz+Vd23ymYT3u2Tpk1xesyTrbb9lAiXRDsThLknpMsSwr57Wz42u9TKGBng9cBW6H8fsA3siY3790WHh8Jc2eD3Nl1dcGReZ898T8MHxgY0EPJHe5+U11gOt8GAKiefGDVSXte3g66nrly+5lU0XHPfiZVdF7+nBJzJa+XGgnUKh+SNp6xM45xUYC53Lj4nFGh6OK5Sa4rfom/xD/b/JIWeSeiVSoEALwsn+QLjISKfQHJDki+fMg4kJvzZcfuGu++v8KxcawtF6y2JtIR2qpwnfHML8wQMwAAAABg4TLGyCj7Z+uOHHffMY7bBNYxjjuXP8e71nvOqOP5Nflr5tcXneMeK3PcW1PxPY2MZFSypnwt5Y4X1JRfX3zPEjUZed6bcjUVHx/HfWRKvBee9SXf7xLvTfH7X+rcsu9j8XtS5p+H6fxaTvSfp1LvsWOcgnuY/zL6wMYP6OrNV0///3Gw4M2b8HfeF7/4RUmSZVl63/vepzVr1lS3oAVkx44devLJJ7Vv3z4lk0m1trbq+OOP12mnnaZwOFzt8gAAC4QxRom0o2gyo2gyndtmx7HcOJbMaDh3zJ1LZfeHEyPjmHtu7vxURvwCEUyGz7bUVh9Ue0NIbQ1BdTSE1N4YKphbVB9UJOhzA9354HbQZ8viD/UBzAVuCNobVs6MEVguDkSXepUJTbuh6DKh6fGEpMcMYY8R2nbSdCUGAMxvdkDyBbPhZV9ubPtzc8GRgLPPG3LOz5U6VmptuWOlgtmeY96A9Vih7nwXaAAAAADAuFUKlhYHIMcbmK0UMp1IyDJ/jzFrKAp2uuvLhWNL3KM4nDoqVDqRZ64QcJ2u61d8vlKh4RIh3PGGZMtec5Lvx6ivZ4kw82QCv0Aty/9/B5hu8y78ff3117vhmNNPP53w9yy4++679aUvfUlPP/10yeMNDQ364Ac/qC984Qtqb2+f5eoAYGHKd6jOh5/jqZEQ88g4G2aOJTOKpTJuJ+tsZ+r8DwNUMG/yxzSyJt/VOj9vPNfIn5Ofy5TsgD3SLbuwE7ZGOmebwi7axednPF24E+mMHP7bGLMgHLDV3hDyvEaC3MVzzXUB2ZPopg1gjjOmTDfmVJnuzLlgcaZCeLpccLlkELrCOdPSTbroWoSgAQALidtlulQIOlgiuFy8zlfhWKmQc7lj+fB2LqjtCxbNe0PdAY0KefMXSQEAAIBJqRioLBFilMoEWMuELksFIssFPIeGh7Q/vd89/9mjzyoSj4wrdFnumhPpMOt95kqB1rE6pDrGqXjfgrrG8d4XvNcVjpcNr5YKRBffv8LXpVKt5cLLY4VlK3aMrfDPTtl/zgAAc17+3/XAdJt34W8p+x/QdEeceYlEQpdffrluueWWiuuGhob0jW98Q7feeqvuuOMOnXnmmbNUIQDMX9FkWru6o3qpe1gH+mIaznWtjqUyno7UI+HtaO6YdwwsRD7bUtBnK+i3FfDZCvnzY0tBf7aLdiB3POQfGQd9tgK57cg5I8fCAZ8b6u7IBbzrQ/PyP5WBmWeMZBxP+DjlCRynigLI5Y4Vh6dLhKUnGoaueE7RPQpqKhOkzqQIQwMA4GV7gsu2zzMuse8NO5ftGF2qg3SpMHW57tVl9se8fpAu0wAAAFNQrhtquQ6z3v2JdB41KuxmOioAWiJE6b3fWPeYTEfVSiHU8XQ5LVvfdIRQy4VMJxhCLfn8EwzNlgsPl/rnY0LdZ0uEXif7LPn3Ys56pNoFAACA6ZL/7yBgus3LRAvB75nnOI7e9a536Z577imY9/l8WrVqlZqbm/XSSy+pv7/fPXbkyBGdf/75euCBB/Ta1752tksGgDknkc5oT0824L2rZ1gvdY+8Dg8kql0eMGGWJbXUBdRaH1RrJPtqDPtzwWorG6b22fL7suP8fsBnKZALWwdGHcvO+fP7nusEPNfKB7l9dNfGXOftFF0cKPbuVzrm7hcHk8fan0CH58muz9cFAMCcZUmWPfKyfZ59K9vtueRx73lFayw7GxTOz9s+z3i65j1b25+dd+f8I2ttX9F4AmvdTtdjBLdLzeXfIwAAMCVjdVyVKgdn3f1KQdlxdpktFSwdVzizXLfSCt1VKz1zpbBnuWceqxvqRIOzU+k0W+o9n1Aw1nOO99nHG6qt9L6N6+uWXz+B5wAAAAAwfvw3NGbKvAx/Y+b90z/906jg90c/+lF9/vOf17JlyyRlA+L33HOPrrzySu3Zs0eSFI1G9c53vlPPPvusmpubZ71uAJht6Yyjfb0xvdQzrF3dhQHvA30xOfw3HOYo25JaIkG1RAJaFAnmAt3ZYPeiXLC7eK6pLkD4GlNjTGEHZm/X5zGD0t6gc7kO0kXnlutMnfEGnz2h51Fdo8cTmi7ap1M0ANQAS6PCwAWB4qJwccUQcakgsVUUSPaus0pczxsarhQoLj5W7jo+xZNJvfTSbhlZMpatteuOU7guMrrOckHognuWeuWfxSoMJo/5POXeU1+J+5Z6RgLKAFDLxgrNjgqFVgiolls73mCl9/hEA7OVAq0F9x+rBmMUjUX1fOJ5d33fzj6FwqGKQd8phVQnEIqtdNwbNi33NaoUGi4VMi4ZUK3Qrbdi0LboOmWDsXIIzQIAAAAAKsp//wlMN8LfGKWnp0df/vKXC+a+8pWv6DOf+UzBnG3bevvb365TTz1VZ5xxhnbt2iVJ2rdvn/7lX/5FN9xww2yVDAAzynGMDg7EC8Ld+fGeo1GlSXijAsuSfJYl27Zke8Y+25Lf3WY7WvuK5ov3/bYtu2DfGrXvs235bMlv2woFbDfYnd0G1BoJalF9UE3hgGyC3HOLt2O0Gz7OjA4bG6doLlMijJwqE54e43hBmNq7LqW6ZFyvOXJYlsnIMo4ih/+vZJnRwepRoeyiYwBQi9xuu4GRjrk+z9id90s+b2ddTwden2dN8VrLEyr2BpOVD9eWmh/PessTbK4UdLZKzJUJROffj5LX83ZdLhOgLhk+HsfxfPi3ZEi76H2oGOheGP99lBwY0Iuxh9z9laecrXBTUxUrAjBTynZ49QQcpcqB1nIB0jEDmWU61044wFsiUDpWh9mx7l0pIDyesOyocGn+/sWB1zKB2Ur1eI/PRIB4PF+7SsfHGwAe6/0ttT/Ws2Acnql2AQAAAACw8NiWLUuWLMtS/n+2ZcvK/bzZ3c+vsSzZGjk+6nzPce+8nfsZfPG1vPcsd71S2/w6WSp5v3wd2SWFz1Xpmdxzi+oorq/k/jhrKfuMxefk7lGq3oL3IF/HOL4Gxe+Z9zli0ZieeeYZt5aTTjpJx3YcO0v/JGKhIfyNUb72ta9pcHDQ3T/zzDN1zTXXlF2/fPly3XTTTXrjG9/ozt14443627/9W7W1tc1orQAWrlgyo57hhBJpR6mMo2R65JUo2k9lHCVzc4n8fKboeInzkhlHg/GUdvdElUjzN/Hmg3DAViToV13Ap0jQp0jIr0huXBfMzQX9ua1PdZ5xfr4u6FPQZ8u28uFrybYsd39UiDt3zLaVPe5da8n9hg0T4DiFYeFRXZlLhaLH2i/1KnFOyXsUd4Mu0/HZ29G6ZN2e801m9PXm+N/4DUjq9E4MVakQAHNb2ZCzfyTknH9Zdi7wHBh93BcoWl+8X+r6JYLTlieE7Qa0K9Tk7vvGscYTzLbtar/zAGbYZLvNVgo0lg1GeoKapcKgEwrHlgiejhlenUxwd5y1jBncHWdIuFRH1Upfl3LPOt4grGOccdVa7j3z7ld8T8b4ernrK3WYLfH+AwAAAAAw14wVoqwU1JRGh1tL7pcKcY4VlB0j8FlQR5mQarkgrze0Ouo5xxPyHeMe43k/vNeclhoq3W+Mr4esMULAkrs2/36XfPbJfD08z17qn49KgWDvPckCoNjAwICigai7v6ljk5povIIZQvgbBRzH0c0331wwd/3114/5YXXOOefoda97nR599FFJ0uDgoG677Tb9f//f/zdjtQKoTcYYDcTSOjQQ18H+mA71x3VoID5q2xdNVbvUOSvot7Nh5oBP4XzIOeDLdY3OBpmlfKA5u7WsfFB5JOg8MlbuG53R52TncvuSG4YuG5K288FoeQLSlue80UHr/LxlZcehgD0qrJ0PfPtqvZN1vjN0QUDZ29XZG15OlQkfe7s9e4PLnvXuOSXCz8X3LltP8b7n3qP2izpDE1AAUCsqBZcLOkOXChhXCCkXBKNLdZouDkGXWD9W92k3mF2h/lH3IgCNQlPpJFopMDtWYLVSwLVcSHVSAcvi7rklQptu/WWe13u8oLvreLrjFj//BDrrJpIJ7Yvuy66Xoyd/96T8AX/lwO443ouJ1Dnu920S72vx/Fg1jec4AAAAAABzhRtkHCPYWSr4mj9eKnjpDUWWC4COJ+Dp1jCOLq1uHWN1Vx1HWHU8HWdL1TqetRPp0lvueKlAa6mw6XgCxGMFZsfVRXeKgdlKHXTH8zUGAABTQ/gbBbZs2aIjR464+8cee6zOOuuscZ17+eWXu+FvSbr77rsJfwMo4DhG3cMJHeqP62B/XIcHctvcfj7cHUtlql3qrAn4LK1sjaitIahwYKQDdV0usJ0PN4+Ms52s6zydrOsCCywAPRbHyYaI84HoTNqz7wlKj9pPS5lkYRC6YtC5XJB6PEHp4iD0OI+ZhfP/DQA1yu28XCqA7J+mMLT3uPd6dnbfFxh93D2/VNfqCmHpUuFob3g69wPsiQRgRwVAK3RRrRSYnUiYsnQINSMpI5MxMpmxw5Jln61MHd7g6Fh1TfS9GVVXiaCpTIl7jnGfgjqnuZtscR35sHO598a7X/xs7lyZ97zkdrz/HE40mI35Y1e1CwAAAACAmVMcQBxvqLJcCDW/P1bocsIdZPPnVgrA5jvjjvEs5UKp5cKopQKeE6m3ONxZNiBaFCAd9V5XeL+k0oHZUs9YKQg9kectFVgdb0h51PGierzPNDw47GYtLFk66/VnqbmpuezXEwAAACD8jQI/+9nPCvbPPffccX/zcO655xbsP/zwwxoeHlZ9ff201Qdg+hljlHGM0o5RKuMonTFKOdmtd5zKOEo7RumMo1TGKF00nz837WT3o4mMG+Y+2B/T4YGEDg/ElXYWXgjEtqTlrXU6pr1Bx7RFtKa9XsfkXstb6uT3VbFLpjEjYeNMqigYXSk4nSx/zA1ClxoXB529IevicZnrjFUbXaOBBcvkXk5uK0mOJRlZyv+bIX/MO+9IMpbkyHKPZdfljlv561qetfn7lVhjjdSRXW8V1JW9h+WO3WOWLWP7ZGy/HMuWY/sk2yfH8snYtozlk7F9cqz82M6Oc3PKzTu2lb1W7uXktrIsd+xYlpTbGsuWIyt3vuXOGVmefWtkP7fNzzmWJOXnlbuW5Xmvrdxxk3vekUDuWOHY4nDq6EBsSkbJwnBxxpEypcOwJe85jpCuN6xbHGTOB1zz1yoVgAYAAAAAYL4bd4CzUgfWsYK1pc6dQDfUiYQ4pxwCLhNMnUzn1ZL1Tia0O0ZodjwdWivtj6fOcs85VjfeiT5n/n8lA9Ml7gHMeQEpZIXc3ZAvpKAvWMWCAAAAMNcR/kaB3//+9wX7p5122rjPXbZsmdasWaNdu3ZJkpLJpLZu3arNmzdPY4UAiqUyjvqiKfXHkuqNptQXTak3mlR/btsXS6kvmlRf7lhfNKnBRNoNaqcyBJImx8gnR35lFFBaAaW1vMmvY1qCWt0a1OqWgFY0+7Wiya/OeltBy5Eyw1KmLxtQTqek/Slpbz5IXaILdXFgejKdrMecT1X7jQQkjQRgvQFZxyoM7I4K66pcYLd0IHdkvSfYWxDStdxrZJQNsWaK5h1PXRllQ70ZT32O577eccZToylRo1RYe/Fa7zMWrPWEit33yjNXbq3xzBXse97nwlByYc0qem+99xmp0RNutio/n3vtoudzr21VOnckkF3bjKR07lXmcP4jnV8SAAAAAABzRrngadlganE30DKhR9uyZRyjWDyWvaYsRSIR+X3+SYU7pfLdW8vWWiaoWSk06w1kuvf0hDPznXSLj+cDpaXqH2/Npe4xVsh4XMHY8bzP4wzljhmcLQq9lvpaF/xzU6HL7Fhfv1LvHQAAAAAAmBvmdfj7k5/8pFpbW2flXpZl6Ve/+tWs3Kuatm3bVrC/cePGCZ2/ceNGN/ydvx7hb0ByHKNMrsO2425H5r3HE2nHDWv3Rj3B7ZgnwB1Lqnc4pf5YSkOJMiGwOcfIr4x8chRQOrfNyKeMAlZ263dfjvzeNVZmZK0K1wasbPja3VdGfqXlt/LjkXB2fr0/tybgvY4yCljpEvP5a2Xng7mxTxnZxZ08k5K6cq9Ze1cLA7P5OW9HWakwKOuGJ6VsJ1nPfOE1CsOWpQKX7n2tokCpLM91R4c7R9WlkWDqeJ/Fyf2BQ0GYtej53PXFwdEK68d69uw1C0Ot3oDu6GuMDqwWfk1KBHJlFYVlC595JBibu1Zx2LdU/SXeh9Ff1/E/bz48nb+fN6xbKmRtrMK6Hau4Nv4ACQAAAAAwolygs1yH2bFCl2VDlsXXKtdRtkIos2wgtlxI0xMqLbh/iZBoqWctd7xkqHOs7qjlnncCNYwrUFzi/asU1i2+/lgdY8vdu2KdZa7l/dpU+mepXGC2uNaZNDAwoIceesjdP/vss9XU1DSj9wQAAAAAAED1zdvwtzFGf/jDH2btXgvhb7THYjHt2bOnYG7lypUTukbx+hdeeGHKdQFlGSM5Gck47stx0oqnUoon04onU4olE0qk04onk4qn0kokk0qkUkqmM4qnkkqm0kqkU0qm0kqmU0qlM0qlUkpm0kqlM7m5tNJORul0WhknI8dkZJxspNGYjIxjJCt7f2M8W2W32X+HOLmgcDYaasmRZWXPsz2RWysfm7SM7JE+r7KtbCTTllGbHLVbuWN1RnbdSHTVykc3rdLX9B6zPFFPy8rdxxP9zM5lt96YqKWR842VP18yo+Kkkm25sU/JMhMKwnrDsNLocGt+nLIsJT13LQ4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E4sWLtWbNGnt70KBBGjRokE9jkNd/Q14HAFSW3bt3m9qNGjVS586dLY298847Te19+/a57Lt8+XIdOnTI3k5ISNDIkSPLjeGYj+fPn6+cnBy3YxzvZz/55JOqVq2a2zFt2rTR3XffbW+XlJQ4vTdw5M89AQAuPc2aNVObNm0UFFR5paqcuX9T1fcE36L4G4DX1q9frxMnTtjbTZs2Va9evSyNHTVqlKn9xRdf+HBlAAAEvgsPTJKUmZmpvLw8t2O+/PJLU9sx37py9913q3r16vb25s2bdfjwYZf9lyxZouLiYnu7V69eatq0ablxgoKCnG4cl/cewF97AgDAG5s3b9b06dPt7XfeeUdRUVEVnm/x4sWmttX817t3byUmJtrbR48e1Y8//uiyvz/P9f7aEwAAnpg5c6ap7fiHUl8gr/8PeR0AUBlOnTplajdu3Njy2CZNmpja2dnZLvs65r+RI0eWWxwl/Vb4dt1119nbRUVF+uabb1z2T09P19atW+3tqKgoDR48uNw4knNOdlyzI3/tCQCAysKZ+3+q8p7gWxR/A/DakiVLTO1+/fpZOgye73uhpKQk5ebm+mxtAAAEulq1ajldc/fkjN27d5ueSlK9enV169bNUizHvoZhOOX5Czm+dsMNN1iKIzm/B/j6669d9vXnngAAqKiioiKNGjVKJSUlkn57YuhNN91U4fnOnj2rtWvXmq5ZzbU2m019+/Y1XXOXa/11rvfnngAAsCojI8P0LVsdOnRQ27ZtfRqDvG5GXgcAVIaaNWua2vn5+ZbHOvatU6eOy77+ui/uGKd79+6mB5240717d0VGRtrbu3fv1t69ey3Hqqw9AQBQGThzm1XVPcH3KP4G4LWff/7Z1LZajCVJDRo0UEJCgr1dWFioX375xUcrAwAg8GVkZDhdq127tsv+jnm5S5cuCgkJsRyve/fubudz95on7wE6deqk8PBwe/vw4cOmTxS7i1OZewIAoKJefvll7dixQ5IUExOjGTNmeDXfrl27VFRUZG8nJiaqfv36lsf7K6d7cq73554AALBq2bJl9g9vSb89IcvXyOvOyOsAAF/r0KGDqZ2cnGy5+GjTpk2mdpcuXcrsd+zYMR09etTeDg8PV8eOHS2v0V85PSQkxGkPrmL5c08AAFQGztzOquKe4HsUfwPwWnJysqndpk0bj8Y79necDwCAS9n3339vasfHxyssLMxlf3/l5aKiItPTuD2NFR4ermbNmlmKxXsNAEBV98svv+jFF1+0t1955RWPbsSWxZ/5z1+xyOkAgKpo8+bNpnb79u3t/71t2zY9/PDDat++vWrVqqXIyEglJCSoX79+ev3118v8wHZZyOsVjwMAgFWNGjUyFSsVFBRY+mB2QUGBpk+fbro2atSoMvs65qvmzZu7vV/vyDH/7du3T8XFxZZi+SunV+aeAACoDJy5Kx7H37HgWxR/A/BKfn6+Dh48aLrWuHFjj+Zw7L97926v1wUAwO/FrFmzTO0BAwa47e+YRysrL6ekpJhu4EZERLj9KkxvYvlrTwAAVERpaalGjRqlwsJCSVLPnj01evRor+f1df5LS0vTuXPnnPr581zvrz0BAOAJx+Lvpk2b6uzZsxo1apQ6duyov//979q+fbuys7OVn5+vtLQ0rVy5UhMnTlSLFi00efJk09O4ykJeLz8OeR0A4AuvvPKKgoL+Vwbz9NNP6+OPP3bZPzs7W3fddZepSOnmm2/WzTffXGZ/b/Nf3bp1Va1aNXu7sLBQqamplRLLXzndkz0BAFAZOHOXH6cq7Am+R/E3AK+cPHlShmHY26GhoapXr55HczRs2NDUPn78uE/WBgBAoPvmm2+0du1a07URI0a4HeOYRxs1auRRTMe8fOLECUtxHMdVJJar9wD+2hMAABUxY8YM/fjjj5KksLAwzZw5Uzabzet5vc1/cXFxCgkJsbdLS0uVmZnp1M+f53p/7QkAAE84fqtVUFCQrr32WqcPY5clPz9fL7/8sgYMGKAzZ8647Eded0ZeBwBUhh49eugf//iH/VxeXFysESNGqEuXLpo2bZoWLVqkZcuW6T//+Y/Gjx+vZs2a6euvv7aP79evn+bMmeNyfm/znyQ1aNDA7ZznOd7H9va+eGXldMn6ngAAqAycuZ1VxT3B90LK7wIArp09e9bUjoyM9PiP3NWrV3c7JwAAl6JTp05pzJgxpmu33XabunTp4nacYx51zLPlcexfVFSkgoIChYeH+zROWWNcvQfw154AAPBUamqqpkyZYm8/+eSTatWqlU/m9jb/2Ww2RUREmArRysq1/jzX+2tPAABYVVpa6lS0/fDDD2vbtm2Sfss9N904wXB6AAAsCUlEQVR0kwYMGKBGjRopNzdX27Zt0yeffKLDhw/bx6xcuVIjRozQ559/XmYc8roz8joAoLI89NBDuvzyy/Xwww9r165dkn77pg/Hb/u4UNOmTTVp0iSNHj3a9ORwR/66L56fn6+SkhKvYvkrp3sSCwCAysCZ21lV3BN8jyd/A/CK4/9hX/iVTlZFRES4nRMAgEtNaWmphg0bpvT0dPu1mjVrasaMGeWO9TY3O+blsub0RZyyYlk93FbWngAA8NSDDz6o3NxcSVKrVq00efJkn83tr1wbSDndk1gAAFiRk5NjesKVJG3dulWSVLt2bX333Xf68ssvNXbsWN100026++67NW3aNO3evVtDhw41jVu4cKH+/e9/lxmHvO5dLAAAPHX99ddr8+bNeuyxxxQcHOy2b5MmTfTYY49p6NChbgu/pYuX0ysSi5wOALhUcOaueCzeBwQ2ir8BeOXcuXOmdlhYmMdzOD51Mz8/36s1AQAQ6CZOnKilS5earr333ntq3LhxuWO9zc1lPQ27rNzsz/cA/toTAACe+PDDD7Vy5UpJvz1FY+bMmRXKh674K9cGUk73JBYAAFa4+oNkcHCwlixZop49e5b5elRUlD755BPdcMMNpusvvfSSUzG5RF73NhYAAJ76v//7PzVr1kyvv/660xO0HR08eFDjxo1TQkKCZs2a5bbvxcrpFYlFTgcAXCo4c1c8Fu8DAhvF3wC84viJn8LCQo/nKCgocDsnAACXkhkzZujNN980XZs0aZLuvvtuS+O9zc2OebmsOX0Rp6xYrt4D+GtPAABYdeTIET322GP29gMPPOCyOKyi/JVrAymnexILAAArXOWRBx54QF27dnU7NigoSO+++67pCaG7d+/Wd999V24c8rpnsQAAsKqoqEh33XWXHnroIR05ckSSFBsbq6efflqbNm1SVlaWCgsLdfjwYX355Ze6/fbbZbPZJEmnTp3SqFGjNHHiRJfzX6ycXpFY5HQAwKWCM3fFY/E+ILBR/A3AK1FRUaZ2WZ9CLo/jJ34c5wQA4FIxe/ZsPfLII6ZrI0aM0LRp0yzP4W1uLuuTuGXlZn++B/DXngAAsOrPf/6zsrOzJUn169fXq6++6vMY/sq1gZTTPYkFAIAVrvLI6NGjLY1v2rSp+vbta7pWVvE3ed27WAAAWPXQQw/p888/t7e7dOmiXbt26dlnn1Xnzp0VExOj0NBQXXbZZbr55pu1cOFCffHFF6Yipddff10fffRRmfNfrJxekVjkdADApYIzd8Vj8T4gsFH8DcArjv+HnZeXV+bXWrqTm5vrdk4AAC4FX3/9tYYPH27Ko3fccYc++OAD+5NHrHDMo455tjyO/UNCQsr8dK63ccoaY/VwW1l7AgDAivnz52vRokX29ttvv62YmBifx/E2/xmGUaGbu5V5rvfXngAAsCoiIkLBwcGma9HR0brqqqssz3HdddeZ2lu2bHHqQ153Rl4HAPhaUlKSPvzwQ3u7Xr16+vrrr1W/fn2342655Ra98847pmsTJ0609FCRyrovXtZ7FG/vi1dWTvckFgAAlYEzt7OquCf4HsXfALxSp04dU0FaUVGRjh8/7tEcGRkZpna9evV8sjYAAALFmjVrNGjQIBUXF9uv9evXT3PmzHG6wVsexzyanp7u0XjHvFy3bl1LcRzHVSSWq/cA/toTAABWXPj1zwMHDtTgwYMrJY63+e/YsWOm9xZBQUGqU6eOUz9/nuv9tScAADzhmJ+aN2+uoCDrfz67/PLLTe2y8ih53Rl5HQDgazNmzDC1H3nkEcv3gkeMGKGWLVva25mZmVq4cKFTP2/znyQdPnzY7ZznOa7d2/vilZXTJet7AgCgMnDmdlYV9wTfo/gbgFciIiLUpEkT07WDBw96NIdj/1atWnm9LgAAAsXGjRt1yy23mL5CqVu3blq0aJHCwsI8ns/xj86VlZebNm2qkJAQezs/P18nTpyolFj+2hMAAFZkZ2fb/3vJkiWy2Wzl/vTu3ds0R1pamlOfn3/+2dTH1/kvPj6+zG++8Oe53l97AgDAE61btza1a9So4dF4x/5ZWVlOfcjr5cchrwMAvGEYhlavXm26dvPNN1seHxQUpIEDB5qurV271qmft/nv+PHjpr8FhIWFqWnTpmX29dd9cX/uCQCAysCZu/w4VWFP8D2KvwF4zfH/tH/55RePxicnJ7udDwCA36vt27frxhtv1NmzZ+3XrrrqKn3zzTeqXr16heb0V14ODQ1Vs2bNKhyroKBAKSkplmLxXgMAcCnyZ/7zVyxyOgCgKmrTpo2pXVBQ4NH4C4udJCkyMtKpD3m94nEAALAiKytLOTk5pmuJiYkezeHYv6xvu3TMV/v371dhYaHlGI75r1mzZqaHrLiL5a+cXpl7AgCgMnDmrngcf8eCb1H8DcBrHTp0MLXXr19veeyRI0d04MABezs0NNTpZjsAAL9Hu3fvVr9+/UxPBGvdurWWL1+umjVrVnhex7y8efNm01c6leeHH35wO5+71zx5D/DTTz+Z/qB+2WWXufwKKH/uCQCAqqJt27YKDQ21tw8cOKAjR45YHu+vnO7Jud6fewIAwKqOHTua2seOHfNovOPXIdeuXdupD3ndGXkdAOBLZX14y9M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" ] @@ -1051,17 +1051,17 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:05.968078Z", - "iopub.status.busy": "2023-11-09T07:13:05.967962Z", - "iopub.status.idle": "2023-11-09T07:13:19.811749Z", - "shell.execute_reply": "2023-11-09T07:13:19.811473Z" + "iopub.execute_input": "2023-12-04T17:49:37.136108Z", + "iopub.status.busy": "2023-12-04T17:49:37.136009Z", + "iopub.status.idle": "2023-12-04T17:49:51.322412Z", + "shell.execute_reply": "2023-12-04T17:49:51.322119Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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aPK6n42JWVpZOnjzp1M6b9wO8tScAADypaqJ5cnKyTpw4oZtuukl9+/bV3//+d23evFkFBQUqKSlRVlaWVq5cqQceeECdO3fW9OnTTaeFVYe4X/c8xH0AQGN77rnnZLX+mkrz2GOP6e23366xfUFBga6++mpT0tNll12myy67rNr2DY2N0dHRCgwMdJTLysqUkZHRKHN5K967sycAALyNa/W652kOe0LjItEcgMmRI0dkGIaj7Ofnp7Zt27o1RmxsrKmck5PjkbUBANDSffrpp/rqq69MdVOnTq21T9U4GxcX59acVeN2bm6uS/NU7VefuWp6jeCtPQEA4EmvvPKKvvvuO0mSv7+/Xn/9dVkslgaP29C4GBMTI19fX0fZbrcrLy/PqZ037wd4a08AAHhS1U/5slqtGj58uNMbxqtTUlKiZ599VmPHjtXx48drbEfcd0bcBwB429ChQ/WPf/zDcU1fXl6uqVOnauDAgZo1a5YWL16szz77TP/973915513qlOnTvrkk08c/ceMGaP33nuvxvEbGhslqUOHDrWOeVrVe+MNvdfeWPFecn1PAAB4G9fqzprjntC4fOtuAuBscuLECVM5ODjY7T+Mh4SE1DomAABwdvToUd12222mugkTJmjgwIG19qsaZ6vG4bpUbW+z2VRaWqqAgACPzlNdn5peI3hrTwAAeEpGRoZmzJjhKD/yyCPq1q2bR8ZuaFy0WCwKCgoyJbVVF4O9eT/AW3sCAMBT7Ha7U4L4XXfdpU2bNkk6FZsuvfRSjR07VnFxcSoqKtKmTZv0zjvv6MCBA44+K1eu1NSpU/Xhhx9WOw9x3xlxHwDQFO644w517dpVd911l7Zt2ybp1KebVP2Ek8qSk5P14IMP6pZbbjGdiF6Vt+61l5SUqKKiokFzeSveuzMXAADexrW6s+a4JzQuTjQHYFL1l3Hlj6hyVVBQUK1jAgAAM7vdruuuu0779+931LVu3VqvvPJKnX0bGrurxu3qxvTEPNXN5eqFbWPtCQAAT7n11ltVVFQkSerWrZumT5/usbG9FYPPpFjvzlwAAHhCYWGh6QQuSfrxxx8lSW3atNGXX36pjz76SLfffrsuvfRSXXPNNZo1a5Z27NihKVOmmPotWrRI//nPf6qdh7jfsLkAAPCkCy+8UBs3btT9998vHx+fWtt27NhR999/v6ZMmVJrkrnUdPG+PnMR7wEA4Fq9IXPxGqHlINEcgMnJkydNZX9/f7fHqHpaaElJSYPWBABAS/fAAw9o+fLlprp//etfio+Pr7NvQ2N3dad8Vxe7vfkawVt7AgDAE958802tXLlS0qlTPF5//fV6xcmaeCsGn0mx3p25AADwhJr+iOnj46Nly5Zp2LBh1T4fGhqqd955RxdddJGp/plnnnFKXJeI+w2dCwAAT/p//+//qVOnTnrxxRedTgavau/evZo2bZoSExM1d+7cWts2Vbyvz1zEewAAuFZvyFy8Rmg5SDQHYFL1nUNlZWVuj1FaWlrrmAAA4FevvPKK/va3v5nqHnzwQV1zzTUu9W9o7K4at6sb0xPzVDdXTa8RvLUnAAAa6uDBg7r//vsd5ZtvvrnGRLP68lYMPpNivTtzAQDgCTXFmZtvvlmDBg2qta/VatU///lP0+mmO3bs0JdfflnnPMR99+YCAMATbDabrr76at1xxx06ePCgJCkyMlKPPfaYNmzYoPz8fJWVlenAgQP66KOPdMUVV8hisUiSjh49qptuukkPPPBAjeM3Vbyvz1zEewAAuFZvyFy8Rmg5SDQHYBIaGmoqV/dO57pUfedQ1TEBAMAp8+bN0z333GOqmzp1qmbNmuXyGA2N3dW947e62O3N1wje2hMAAA31xz/+UQUFBZKkdu3a6fnnn/f4HN6KwWdSrHdnLgAAPKGmOHPLLbe41D85OVmjR4821VWXaE7cb9hcAAB4wh133KEPP/zQUR44cKC2bdummTNnasCAAQoPD5efn5/at2+vyy67TIsWLdKSJUtMSU8vvvii3nrrrWrHb6p4X5+5iPcAAHCt3pC5eI3QcpBoDsCk6i/j4uLiaj/CszZFRUW1jgkAAKRPPvlEN9xwgynOXnnllZozZ47j9BNXVI2zVeNwXaq29/X1rfZdwA2dp7o+rl7YNtaeAABoiAULFmjx4sWO8ssvv6zw8HCPz9PQuGgYRr1u+Dbm/QBv7QkAAE8JCgqSj4+PqS4sLEznnnuuy2NccMEFpvL333/v1Ia474y4DwDwptTUVL355puOctu2bfXJJ5+oXbt2tfa7/PLL9eqrr5rqHnjgAZcORWmse+3VvX5p6L32xor37swFAIC3ca3urDnuCY2LRHMAJlFRUabkNpvNppycHLfGyM7ONpXbtm3rkbUBANBSrFmzRhMnTlR5ebmjbsyYMXrvvfecbvzWpWqc3b9/v1v9q8bt6Ohol+ap2q8+c9X0GsFbewIAoCEqfwz2uHHjNGnSpEaZp6Fx8fDhw6bXHFarVVFRUU7tvHk/wFt7AgDAk6rGr5SUFFmtrv+ZrWvXrqZydXGWuO+MuA8A8KZXXnnFVL7nnntcvr88depUdenSxVHOy8vTokWLnNo1NDZK0oEDB2od87Sqa2/ovfbGiveS63sCAMDbuFZ31hz3hMZFojkAk6CgIHXs2NFUt3fvXrfGqNq+W7duDV4XAAAtxfr163X55ZebPhZq8ODBWrx4sfz9/d0er+ofqhsrbicnJ8vX19dRLikpUW5ubqPM5a09AQDQEAUFBY6vly1bJovFUudj5MiRpjGysrKc2vz000+mNp6OiwkJCdV+0oc37wd4a08AAHhS9+7dTeVWrVq51b9q+/z8fKc2xP265yHuAwAai2EYWr16tanusssuc7m/1WrVuHHjTHVfffWVU7uGxsacnBzT3xf8/f2VnJxcbVtv3Wv35p4AAPA2rtXrnqc57AmNi0RzAE6q/kLevn27W/3T0tJqHQ8AgLPV5s2bdckll+jEiROOunPPPVeffvqpQkJC6jWmt+K2n5+fOnXqVO+5SktLlZ6e7tJcvBYBAOBX3oyL3pqLWA8AOBP16NHDVC4tLXWrf+XkKUkKDg52akPcr/88AAA0VH5+vgoLC011SUlJbo1RtX11nwxaNZbt2bNHZWVlLs9RNTZ26tTJdEhMbXN5K9435p4AAPA2rtXrP4+350LjIdEcgJM+ffqYyuvWrXO578GDB5WZmeko+/n5Od2ABwDgbLRjxw6NGTPGdGJZ9+7d9fnnn6t169b1Hrdq3N64caPpY6rq8s0339Q6Xm3PufMa4YcffjD9Eb59+/Y1fqyVN/cEAEBz17NnT/n5+TnKmZmZOnjwoMv9vRXr3bkf4M09AQDgKX379jWVDx8+7Fb/qh8N3aZNG6c2xH1nxH0AgLdU9yYyd5OdK8c8SaqoqHBq065dO7Vr18407w8//ODyHN6K9+Xl5dqwYYNLc3lzTwAAeBvX6s6a457QuEg0B+Dk0ksvNZVXrlwpwzBc6vvFF1+YyiNHjlRoaKjH1gYAwJkoKytLo0ePNv1ROSkpSStWrFB0dHSDxu7WrZvppPGioiKXL86Kior07bffOsoWi8XpdUBlVZ9bsWKFy+us2ra2jxz15p4AAKivpUuXasWKFW49XnzxRdMYMTExTm1SUlJMbcLCwjR8+HBTnasx2DAMrVy50lRXWwz21v0Ab+4JAABPGTdunKzWX/+slpGRoaNHj7rcv2qyVdWPqZaI+1UR9wEA3lTdm8AOHDjg1hhVTzCv6f7/uHHjTOXGutdedZ5169apqKjIpXm++eYbFRcXO8pdunRRly5dXJ6rsfYEAIC3ca1u1lz3hMZFojkAJ4MHD1ZUVJSjnJ6ertTUVJf6vvnmm6by+PHjPbk0AADOOAcPHtSoUaO0f/9+R11sbKxWrVql2NhYj8xx+eWXm8pV43FN5s+frxMnTjjK/fv3V4cOHWpsP3bsWNMJLqmpqUpPT69zHsMw9O9//9tUV9drBG/tCQCA+rrgggs0evRotx79+vUzjREYGOjUprqbpPWNi2vWrFFGRoajHBMTo0GDBtXY3pv3A7y1JwAAPKVt27YaMmSIqW7RokUu9S0vL9fixYtNdSNGjKi2LXH/V8R9AIA3+fv7q3379qa61atXuzXGqlWrTOXKB6pUVjU2vvXWWy4lXO3Zs0dffvmlo+zn56exY8fW2D4+Pl7nnnuuo3zixAl98MEHdc4jNTzeN9aeAABoClyr/6o57wmNh0RzAE6sVqumTp1qqps5c2adF4KrVq3S2rVrHeWwsDBNmjSpMZYIAMAZ4ejRoxozZoz27NnjqIuOjtaKFSuUlJTksXn+8Ic/yGKxOMrvv/++0tLSau1z8uRJzZo1y1R300031donMjJSEyZMcJQNw9ATTzxR5/rmzp1r+kirhIQEjR49utY+3toTAABngsmTJyskJMRR/uqrr+r8Y7dhGJo5c6ap7sYbbzSdxFqVN+8HeGtPAAB40m233WYqv/DCCyotLa2z3xtvvKFDhw45yq1atdLFF19cbVvi/inEfQBAUxg1apSpPHv2bJWXl7vU98svvzR92mZ145128cUXKy4uzlHOzMzUW2+9VeccTzzxhClWX3XVVWrdunWtfareI581a5ZOnjxZa5+0tDTNnz/fUa7udUNV3twTAADexrX6Kc19T2hEBgBUIzc31wgNDTUkOR7PPvtsje33799vJCYmmtrPmDHDiysGAKB5OXbsmDFgwABTbAwPDzc2bdrUKPNdc801prkGDBhgFBYWVtvWbrcbt912m6l9cnKyUVZWVuc827ZtM6xWq6nvvHnzam0fHh5uaj9nzpxmtScAALxlzZo1pliVkJDgct+HHnrI1DcpKcnIzs6usf3TTz9tat+6dWsjLy+vznm8eT/AW3sCAMBTKioqjN/85jemeHTDDTcYFRUVNfb57rvvnGLrww8/XOs8xH3iPgCgaXz22Wem+CPJuOWWW2q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"text/plain": [ "
" ] diff --git a/docs/recipes/pipelines.ipynb b/docs/recipes/pipelines.ipynb index e5b9533737..2ef994ab44 100644 --- a/docs/recipes/pipelines.ipynb +++ b/docs/recipes/pipelines.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.052106Z", - "iopub.status.busy": "2023-11-09T07:13:21.051775Z", - "iopub.status.idle": "2023-11-09T07:13:21.381473Z", - "shell.execute_reply": "2023-11-09T07:13:21.381222Z" + "iopub.execute_input": "2023-12-04T17:49:52.864965Z", + "iopub.status.busy": "2023-12-04T17:49:52.864389Z", + "iopub.status.idle": "2023-12-04T17:49:53.144412Z", + "shell.execute_reply": "2023-12-04T17:49:53.144042Z" }, "tags": [] }, @@ -54,10 +54,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.383218Z", - "iopub.status.busy": "2023-11-09T07:13:21.383116Z", - "iopub.status.idle": "2023-11-09T07:13:21.391122Z", - "shell.execute_reply": "2023-11-09T07:13:21.390898Z" + "iopub.execute_input": "2023-12-04T17:49:53.146332Z", + "iopub.status.busy": "2023-12-04T17:49:53.146215Z", + "iopub.status.idle": "2023-12-04T17:49:53.154883Z", + "shell.execute_reply": "2023-12-04T17:49:53.154651Z" }, "tags": [] }, @@ -82,10 +82,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.392637Z", - "iopub.status.busy": "2023-11-09T07:13:21.392566Z", - "iopub.status.idle": "2023-11-09T07:13:21.399859Z", - "shell.execute_reply": "2023-11-09T07:13:21.399636Z" + "iopub.execute_input": "2023-12-04T17:49:53.156607Z", + "iopub.status.busy": "2023-12-04T17:49:53.156514Z", + "iopub.status.idle": "2023-12-04T17:49:53.165628Z", + "shell.execute_reply": "2023-12-04T17:49:53.165348Z" }, "tags": [] }, @@ -108,10 +108,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.401312Z", - "iopub.status.busy": "2023-11-09T07:13:21.401230Z", - "iopub.status.idle": "2023-11-09T07:13:21.414775Z", - "shell.execute_reply": "2023-11-09T07:13:21.414515Z" + "iopub.execute_input": "2023-12-04T17:49:53.167122Z", + "iopub.status.busy": "2023-12-04T17:49:53.167045Z", + "iopub.status.idle": "2023-12-04T17:49:53.180439Z", + "shell.execute_reply": "2023-12-04T17:49:53.180202Z" }, "tags": [] }, @@ -297,10 +297,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.416182Z", - "iopub.status.busy": "2023-11-09T07:13:21.416092Z", - "iopub.status.idle": "2023-11-09T07:13:21.548154Z", - "shell.execute_reply": "2023-11-09T07:13:21.547921Z" + "iopub.execute_input": "2023-12-04T17:49:53.181786Z", + "iopub.status.busy": "2023-12-04T17:49:53.181717Z", + "iopub.status.idle": "2023-12-04T17:49:53.319962Z", + "shell.execute_reply": "2023-12-04T17:49:53.319706Z" }, "tags": [] }, @@ -342,10 +342,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.549730Z", - "iopub.status.busy": "2023-11-09T07:13:21.549592Z", - "iopub.status.idle": "2023-11-09T07:13:21.559482Z", - "shell.execute_reply": "2023-11-09T07:13:21.559259Z" + "iopub.execute_input": "2023-12-04T17:49:53.321544Z", + "iopub.status.busy": "2023-12-04T17:49:53.321411Z", + "iopub.status.idle": "2023-12-04T17:49:53.331177Z", + "shell.execute_reply": "2023-12-04T17:49:53.330912Z" }, "tags": [] }, @@ -377,10 +377,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.560881Z", - "iopub.status.busy": "2023-11-09T07:13:21.560805Z", - "iopub.status.idle": "2023-11-09T07:13:21.569542Z", - "shell.execute_reply": "2023-11-09T07:13:21.569327Z" + "iopub.execute_input": "2023-12-04T17:49:53.332637Z", + "iopub.status.busy": "2023-12-04T17:49:53.332562Z", + "iopub.status.idle": "2023-12-04T17:49:53.341944Z", + "shell.execute_reply": "2023-12-04T17:49:53.341509Z" }, "tags": [] }, @@ -420,10 +420,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.570977Z", - "iopub.status.busy": "2023-11-09T07:13:21.570908Z", - "iopub.status.idle": "2023-11-09T07:13:21.579655Z", - "shell.execute_reply": "2023-11-09T07:13:21.579439Z" + "iopub.execute_input": "2023-12-04T17:49:53.344203Z", + "iopub.status.busy": "2023-12-04T17:49:53.344052Z", + "iopub.status.idle": "2023-12-04T17:49:53.355449Z", + "shell.execute_reply": "2023-12-04T17:49:53.355075Z" }, "tags": [] }, @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.581059Z", - "iopub.status.busy": "2023-11-09T07:13:21.580992Z", - "iopub.status.idle": "2023-11-09T07:13:21.590688Z", - "shell.execute_reply": "2023-11-09T07:13:21.590482Z" + "iopub.execute_input": "2023-12-04T17:49:53.357462Z", + "iopub.status.busy": "2023-12-04T17:49:53.357331Z", + "iopub.status.idle": "2023-12-04T17:49:53.369477Z", + "shell.execute_reply": "2023-12-04T17:49:53.369159Z" }, "tags": [] }, @@ -686,10 +686,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:21.592485Z", - "iopub.status.busy": "2023-11-09T07:13:21.592401Z", - "iopub.status.idle": "2023-11-09T07:13:21.601115Z", - "shell.execute_reply": "2023-11-09T07:13:21.600876Z" + "iopub.execute_input": "2023-12-04T17:49:53.371204Z", + "iopub.status.busy": "2023-12-04T17:49:53.371072Z", + "iopub.status.idle": "2023-12-04T17:49:53.381553Z", + "shell.execute_reply": "2023-12-04T17:49:53.381303Z" }, "tags": [] }, diff --git a/docs/recipes/reading-data.ipynb b/docs/recipes/reading-data.ipynb index ffe9fabd12..f88da6ee96 100644 --- a/docs/recipes/reading-data.ipynb +++ b/docs/recipes/reading-data.ipynb @@ -25,10 +25,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:22.614405Z", - "iopub.status.busy": "2023-11-09T07:13:22.614052Z", - "iopub.status.idle": "2023-11-09T07:13:23.018338Z", - "shell.execute_reply": "2023-11-09T07:13:23.018013Z" + "iopub.execute_input": "2023-12-04T17:49:54.824345Z", + "iopub.status.busy": "2023-12-04T17:49:54.823858Z", + "iopub.status.idle": "2023-12-04T17:49:55.245273Z", + "shell.execute_reply": "2023-12-04T17:49:55.244942Z" }, "tags": [] }, @@ -83,10 +83,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:23.020193Z", - "iopub.status.busy": "2023-11-09T07:13:23.020070Z", - "iopub.status.idle": "2023-11-09T07:13:23.029968Z", - "shell.execute_reply": "2023-11-09T07:13:23.029739Z" + "iopub.execute_input": "2023-12-04T17:49:55.246985Z", + "iopub.status.busy": "2023-12-04T17:49:55.246871Z", + "iopub.status.idle": "2023-12-04T17:49:55.257529Z", + "shell.execute_reply": "2023-12-04T17:49:55.257231Z" }, "tags": [] }, @@ -126,10 +126,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:23.031463Z", - "iopub.status.busy": "2023-11-09T07:13:23.031373Z", - "iopub.status.idle": "2023-11-09T07:13:23.039934Z", - "shell.execute_reply": "2023-11-09T07:13:23.039687Z" + "iopub.execute_input": "2023-12-04T17:49:55.258972Z", + "iopub.status.busy": "2023-12-04T17:49:55.258882Z", + "iopub.status.idle": "2023-12-04T17:49:55.267622Z", + "shell.execute_reply": "2023-12-04T17:49:55.267371Z" }, "tags": [] }, @@ -172,10 +172,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:23.041472Z", - "iopub.status.busy": "2023-11-09T07:13:23.041400Z", - "iopub.status.idle": "2023-11-09T07:13:23.050253Z", - "shell.execute_reply": "2023-11-09T07:13:23.050041Z" + "iopub.execute_input": "2023-12-04T17:49:55.269019Z", + "iopub.status.busy": "2023-12-04T17:49:55.268947Z", + "iopub.status.idle": "2023-12-04T17:49:55.277375Z", + "shell.execute_reply": "2023-12-04T17:49:55.277124Z" }, "tags": [] }, @@ -220,10 +220,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:23.051634Z", - "iopub.status.busy": "2023-11-09T07:13:23.051564Z", - "iopub.status.idle": "2023-11-09T07:13:23.060533Z", - "shell.execute_reply": "2023-11-09T07:13:23.060305Z" + "iopub.execute_input": "2023-12-04T17:49:55.278741Z", + "iopub.status.busy": "2023-12-04T17:49:55.278667Z", + "iopub.status.idle": "2023-12-04T17:49:55.287392Z", + "shell.execute_reply": "2023-12-04T17:49:55.287176Z" }, "tags": [] }, @@ -279,10 +279,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:23.062000Z", - "iopub.status.busy": "2023-11-09T07:13:23.061924Z", - "iopub.status.idle": "2023-11-09T07:13:23.150089Z", - "shell.execute_reply": "2023-11-09T07:13:23.149850Z" + "iopub.execute_input": "2023-12-04T17:49:55.288856Z", + "iopub.status.busy": "2023-12-04T17:49:55.288786Z", + "iopub.status.idle": "2023-12-04T17:49:55.328888Z", + "shell.execute_reply": "2023-12-04T17:49:55.328647Z" }, "tags": [] }, @@ -333,10 +333,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:23.151543Z", - "iopub.status.busy": "2023-11-09T07:13:23.151448Z", - "iopub.status.idle": "2023-11-09T07:13:23.208472Z", - "shell.execute_reply": "2023-11-09T07:13:23.208213Z" + "iopub.execute_input": "2023-12-04T17:49:55.330316Z", + "iopub.status.busy": "2023-12-04T17:49:55.330224Z", + "iopub.status.idle": "2023-12-04T17:49:55.392538Z", + "shell.execute_reply": "2023-12-04T17:49:55.392188Z" }, "tags": [] }, @@ -365,10 +365,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:23.210222Z", - "iopub.status.busy": "2023-11-09T07:13:23.210088Z", - "iopub.status.idle": "2023-11-09T07:13:23.220180Z", - "shell.execute_reply": "2023-11-09T07:13:23.219956Z" + "iopub.execute_input": "2023-12-04T17:49:55.394374Z", + "iopub.status.busy": "2023-12-04T17:49:55.394235Z", + "iopub.status.idle": "2023-12-04T17:49:55.404823Z", + "shell.execute_reply": "2023-12-04T17:49:55.404450Z" }, "tags": [] }, diff --git a/docs/recipes/rolling-computations.ipynb b/docs/recipes/rolling-computations.ipynb index a0c30b926a..f338c25a7b 100644 --- a/docs/recipes/rolling-computations.ipynb +++ b/docs/recipes/rolling-computations.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-09T07:13:24.199721Z", - "iopub.status.busy": "2023-11-09T07:13:24.199487Z", - "iopub.status.idle": "2023-11-09T07:13:24.675669Z", - "shell.execute_reply": "2023-11-09T07:13:24.675399Z" + "iopub.execute_input": "2023-12-04T17:49:56.754814Z", + "iopub.status.busy": "2023-12-04T17:49:56.754374Z", + "iopub.status.idle": "2023-12-04T17:49:57.224249Z", + "shell.execute_reply": "2023-12-04T17:49:57.223852Z" } }, "outputs": [ From 9d4de29ac5aea19b1606bc6d6fd6ed8405302a34 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 4 Dec 2023 19:04:28 +0100 Subject: [PATCH 213/347] Update __version__.py --- river/__version__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/__version__.py b/river/__version__.py index 6008fcce9d..8873a450be 100644 --- a/river/__version__.py +++ b/river/__version__.py @@ -1,3 +1,3 @@ from __future__ import annotations -__version__ = "0.20.1" +__version__ = "0.21.0" From b19b9cab061c9adc971fdcbba6e1580f39b3e4a8 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 4 Dec 2023 19:17:06 +0100 Subject: [PATCH 214/347] fix adwin --- CONTRIBUTING.md | 41 ++++++----------------------------------- Makefile | 12 ------------ pyproject.toml | 2 +- river/drift/adwin_c.pyx | 2 +- 4 files changed, 8 insertions(+), 49 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9058023527..8de672f6c5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -106,40 +106,10 @@ From the root of the repository, you can then run the `make livedoc` command to All classes and function are automatically picked up and added to the documentation. The only thing you have to do is to add an entry to the relevant file in the [`docs/releases` directory](docs/releases). -## Building Cython extensions - -```sh -make build-cython -``` - -## Building Rust extensions - -Debug settings: - -```sh -make develop -``` - -Release settings: - -```sh -make build-rust -``` - -After building the project by modifying the rust part of the codebase (changing the project architecture, renaming it, etc.), it happens that by importing `river,` the python process is killed. If this happens, we invite you to remove the following things and start a new build: - -```sh -# remove all .so output from rust ie river/stats/_rust_stats.cpython* -rm -rf target -rm -rf river.egg-info -rm Cargo.lock -rm -rf build -``` - ## Build Cython and Rust extensions ```sh -make build_all +poetry install ``` ## Testing @@ -182,10 +152,11 @@ make execute-notebooks 2. Run `make execute-notebooks` just to be safe 3. Run the [benchmarks](benchmarks) 4. Bump the version in `river/__version__.py` -5. Tag and date the `docs/releases/unreleased.md` file -6. Commit and push -7. Wait for CI to [run the unit tests](https://github.com/online-ml/river/actions/workflows/ci.yml) -8. Push the tag: +5. Bump the version in `pyproject.toml` +6. Tag and date the `docs/releases/unreleased.md` file +7. Commit and push +8. Wait for CI to [run the unit tests](https://github.com/online-ml/river/actions/workflows/ci.yml) +9. Push the tag: ```sh RIVER_VERSION=$(python -c "import river; print(river.__version__)") diff --git a/Makefile b/Makefile index 748db226f2..b5eb969486 100644 --- a/Makefile +++ b/Makefile @@ -25,15 +25,3 @@ livedoc: doc rebase: git fetch && git rebase origin/main - -develop: - python ./setup.py develop - -build-cython: - python setup.py build_ext --inplace --force - -build-rust: - python setup.py build_rust --inplace --release - -build: - python setup.py build_rust --inplace --release build_ext --inplace --force diff --git a/pyproject.toml b/pyproject.toml index ebbfe1c8df..560377e64a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "river" -version = "0.20.1" +version = "0.21.0" description = "Online machine learning in Python" readme = "README.md" homepage = "https://riverml.xyz/" diff --git a/river/drift/adwin_c.pyx b/river/drift/adwin_c.pyx index bddebd63a1..322859c780 100644 --- a/river/drift/adwin_c.pyx +++ b/river/drift/adwin_c.pyx @@ -88,7 +88,7 @@ cdef class AdaptiveWindowing: If True then a change is detected. """ - self._update(value) + return self._update(value) cdef bint _update(self, double value): # Increment window with one element From b93a1696da4b0d268a3feff8227855cbf74b09c3 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Wed, 6 Dec 2023 00:10:50 +0700 Subject: [PATCH 215/347] Remove math.sqrt from the distance calculating function within DBSTREAM (resolve partially issue #1468). --- river/cluster/dbstream.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 93a1cea682..e098f0de2e 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -161,7 +161,7 @@ def __init__( @staticmethod def _distance(point_a, point_b): - return math.sqrt(utils.math.minkowski_distance(point_a, point_b, 2)) + return utils.math.minkowski_distance(point_a, point_b, 2) def _find_fixed_radius_nn(self, x): fixed_radius_nn = {} From 4660aeadc2a95f1aeaad4ec728c6b76bb9db5c8f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 5 Dec 2023 20:10:11 +0100 Subject: [PATCH 216/347] Bump jupyter-server from 2.11.1 to 2.11.2 (#1469) --- poetry.lock | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/poetry.lock b/poetry.lock index 20c4bb5322..ec8dbe14ff 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "alabaster" @@ -1544,13 +1544,13 @@ jupyter-server = ">=1.1.2" [[package]] name = "jupyter-server" -version = "2.11.1" +version = "2.11.2" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." optional = false python-versions = ">=3.8" files = [ - {file = "jupyter_server-2.11.1-py3-none-any.whl", hash = "sha256:4b3a16e3ed16fd202588890f10b8ca589bd3e29405d128beb95935f059441373"}, - {file = "jupyter_server-2.11.1.tar.gz", hash = "sha256:fe80bab96493acf5f7d6cd9a1575af8fbd253dc2591aa4d015131a1e03b5799a"}, + {file = "jupyter_server-2.11.2-py3-none-any.whl", hash = "sha256:0c548151b54bcb516ca466ec628f7f021545be137d01b5467877e87f6fff4374"}, + {file = "jupyter_server-2.11.2.tar.gz", hash = "sha256:0c99f9367b0f24141e527544522430176613f9249849be80504c6d2b955004bb"}, ] [package.dependencies] From 3ea1918b2d254c903ed87ec71581e912a75627a8 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Wed, 6 Dec 2023 17:01:52 +0700 Subject: [PATCH 217/347] Modify the distance calculating function in Silhouette coefficient (related to an issue raised in #1468). --- river/metrics/silhouette.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/river/metrics/silhouette.py b/river/metrics/silhouette.py index d3c6d0a27d..3c0bb3d05c 100644 --- a/river/metrics/silhouette.py +++ b/river/metrics/silhouette.py @@ -47,7 +47,7 @@ class Silhouette(metrics.base.ClusteringMetric): ... metric.update(x, y_pred, k_means.centers) >>> metric - Silhouette: 0.568058 + Silhouette: 0.32145 References ---------- @@ -65,18 +65,18 @@ def __init__(self): @staticmethod def _find_distance_second_closest_center(centers, x): - distances = {i: math.sqrt(utils.math.minkowski_distance(centers[i], x, 2)) for i in centers} + distances = {i: utils.math.minkowski_distance(centers[i], x, 2) for i in centers} return sorted(distances.values())[-2] def update(self, x, y_pred, centers, w=1.0): - distance_closest_centroid = math.sqrt(utils.math.minkowski_distance(centers[y_pred], x, 2)) + distance_closest_centroid = utils.math.minkowski_distance(centers[y_pred], x, 2) self._sum_distance_closest_centroid += distance_closest_centroid distance_second_closest_centroid = self._find_distance_second_closest_center(centers, x) self._sum_distance_second_closest_centroid += distance_second_closest_centroid def revert(self, x, y_pred, centers, w=1.0): - distance_closest_centroid = math.sqrt(utils.math.minkowski_distance(centers[y_pred], x, 2)) + distance_closest_centroid = utils.math.minkowski_distance(centers[y_pred], x, 2) self._sum_distance_closest_centroid -= distance_closest_centroid distance_second_closest_centroid = self._find_distance_second_closest_center(centers, x) From 930fe0219a8806d5009fab47340440f185380e94 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Wed, 6 Dec 2023 17:14:56 +0700 Subject: [PATCH 218/347] Modify the distance calculating function in DenStream (relating to an issued mentioned in #1468). --- river/cluster/denstream.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/river/cluster/denstream.py b/river/cluster/denstream.py index 4d060795de..5100b30e2b 100644 --- a/river/cluster/denstream.py +++ b/river/cluster/denstream.py @@ -120,16 +120,16 @@ class DenStream(base.Clusterer): ... denstream.learn_one(x) >>> denstream.predict_one({0: -1, 1: -2}) - 0 + 1 >>> denstream.predict_one({0: 5, 1: 4}) - 1 + 2 >>> denstream.predict_one({0: 1, 1: 1}) 0 >>> denstream.n_clusters - 2 + 3 """ @@ -183,7 +183,7 @@ def centers(self): @staticmethod def _distance(point_a, point_b): - return math.sqrt(utils.math.minkowski_distance(point_a, point_b, 2)) + return utils.math.minkowski_distance(point_a, point_b, 2) def _get_closest_cluster_key(self, point, clusters): min_distance = math.inf From e4ebcd04e87aac678591f84818df326c8fbf2480 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Thu, 7 Dec 2023 00:12:55 +0700 Subject: [PATCH 219/347] Change the inserted value of the shared density when KeyError occurs to 1 instead of 0. --- river/cluster/dbstream.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index e098f0de2e..ce58515578 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -227,10 +227,10 @@ def _update(self, x): self.s_t[i][j] = self._time_stamp except KeyError: try: - self.s[i][j] = 0 + self.s[i][j] = 1 self.s_t[i][j] = self._time_stamp except KeyError: - self.s[i] = {j: 0} + self.s[i] = {j: 1} self.s_t[i] = {j: self._time_stamp} # prevent collapsing clusters From c4a86761c832cee03f0996437ce854cb3e6ebbc2 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Sat, 9 Dec 2023 01:55:58 +0700 Subject: [PATCH 220/347] Removing entries within shared density and associated timestamp when removing a micro-cluster (related to an inquiry from #1468) --- river/cluster/dbstream.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index ce58515578..0b4408271f 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -266,6 +266,15 @@ def _cleanup(self): if micro_cluster_i.weight * value < weight_weak: micro_clusters.pop(i) + self.s.pop(i, None) + self.s_t.pop(i, None) + # Since self.s and self.s_t always have the same keys and are arranged in ascending orders + for j in self.s: + if j < i: + self.s[j].pop(i, None) + self.s_t[j].pop(i, None) + else: + break # Update microclusters self._micro_clusters = micro_clusters From 80757e3924f94e1249881bf82dd4222cf7001dba Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Tue, 12 Dec 2023 16:23:58 +0700 Subject: [PATCH 221/347] Modify the first test within test_dbstream.py file --- river/cluster/test_dbstream.py | 27 ++++++++++++++++++++++----- 1 file changed, 22 insertions(+), 5 deletions(-) diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py index 255c71e7a3..a28c589399 100644 --- a/river/cluster/test_dbstream.py +++ b/river/cluster/test_dbstream.py @@ -5,7 +5,7 @@ from river.cluster import DBSTREAM -def build_dbstream(fading_factor=0.001, intersection_factor=0.05): +def build_dbstream(fading_factor=0.01, intersection_factor=0.05): return DBSTREAM( fading_factor=fading_factor, clustering_threshold=1, @@ -31,27 +31,44 @@ def test_cluster_formation_and_cleanup(): X = [ {1: 1}, + {1: 2}, {1: 3}, {1: 3}, {1: 3}, {1: 5}, {1: 7}, {1: 9}, + {1: 10}, {1: 11}, {1: 11}, + {1: 12}, {1: 13}, {1: 11}, {1: 15}, + {1: 15}, + {1: 16}, + {1: 17}, + {1: 17}, {1: 17}, ] for x in X: dbstream.learn_one(x) - assert len(dbstream._micro_clusters) == 3 - assert_micro_cluster_properties(dbstream.micro_clusters[1], center={1: 3}, last_update=3) - assert_micro_cluster_properties(dbstream.micro_clusters[5], center={1: 11}, last_update=10) - assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 17}, last_update=12) + assert len(dbstream._micro_clusters) == 4 + assert_micro_cluster_properties(dbstream.micro_clusters[2], center={1: 3}, last_update=4) + assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 11}, last_update=13) + assert_micro_cluster_properties(dbstream.micro_clusters[8], center={1: 15}, last_update=15) + assert_micro_cluster_properties(dbstream.micro_clusters[10], center={1: 17}, last_update=19) + + assert dbstream.predict_one({1: 2.0}) == 0 + assert dbstream.predict_one({1: 13.0}) == 1 + assert dbstream.predict_one({1: 13 + 1e-10}) == 2 + assert dbstream.predict_one({1: 16 - 1e-10}) == 2 + assert dbstream.predict_one({1: 18}) == 3 + + assert len(dbstream._clusters) == 4 + assert dbstream.s == dbstream.s_t == {} def test_with_two_micro_clusters(): From 0a5a1c0b71078ebb10247ebc267a1098b87d72ed Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Tue, 12 Dec 2023 17:34:09 +0700 Subject: [PATCH 222/347] Update the second test within test_dbstream.py file --- river/cluster/test_dbstream.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py index a28c589399..1359cc7745 100644 --- a/river/cluster/test_dbstream.py +++ b/river/cluster/test_dbstream.py @@ -76,24 +76,21 @@ def test_with_two_micro_clusters(): add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25) add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25) - # Points in the middle of first and second micro-clusters - for _ in range(5): - dbstream.learn_one({1: 2, 2: 2}) - assert len(dbstream._micro_clusters) == 2 + assert len(dbstream.micro_clusters) == 2 assert_micro_cluster_properties( - dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56 + dbstream.micro_clusters[0], center={1: 2.137623, 2: 2.137623}, last_update=51 ) assert_micro_cluster_properties( - dbstream.micro_clusters[1], center={1: 2.402677, 2: 2.402677}, last_update=56 + dbstream.micro_clusters[1], center={1: 2.914910, 2: 2.914910}, last_update=51 ) - assert dbstream.s == {0: {1: 3.995844478090532}} - assert dbstream.s_t == {0: {1: 56}} + assert dbstream.s == {0: {1: 23.033438964246173}} + assert dbstream.s_t == {0: {1: 51}} dbstream._recluster() assert len(dbstream.clusters) == 1 - assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.003033, 2: 2.003033}) + assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.415239, 2: 2.415239}) def test_density_graph_with_three_micro_clusters(): From 2fd8bc4e44e1c59800e1fe83f9a88811bab4bd6c Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Thu, 14 Dec 2023 16:13:20 +0700 Subject: [PATCH 223/347] Add test for DBSTREAM with synthetic data --- river/cluster/test_dbstream.py | 45 ++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py index 1359cc7745..9d780b7d2b 100644 --- a/river/cluster/test_dbstream.py +++ b/river/cluster/test_dbstream.py @@ -157,3 +157,48 @@ def test_density_graph_with_removed_microcluster(): dbstream._recluster() assert len(dbstream.clusters) == 1 assert_micro_cluster_properties(dbstream.clusters[0], center={1: 3.152231, 2: 3.152231}) + + +def test_dbstream_synthetic_sklearn(): + centers = [(-10, -10), (-5, -5), (0, 0), (5, 5), (10, 10)] + cluster_std = [0.6] * 5 + + # Create a dataset with 15000 data points with 5 centers and cluster SD of 0.6 each + X, y = make_blobs(n_samples=15_000, + cluster_std=cluster_std, + centers=centers, + n_features=2, + random_state=42) + + dbstream = DBSTREAM( + clustering_threshold=2, + fading_factor=0.05, + intersection_factor=0.1, + cleanup_interval=1.0, + minimum_weight=1.0, + ) + + # Use VBeta as the metric to investigate the performance of DBSTREAM + v_beta = metrics.VBeta(beta=1.0) + + for x, y_true in stream.iter_array(X, y): + dbstream.learn_one(x) + y_pred = dbstream.predict_one(x) + v_beta.update(y_true, y_pred) + + assert len(dbstream._micro_clusters) == 12 + assert round(v_beta.get(), 4) == 0.9816 + + assert dbstream.s.keys() == dbstream.s_t.keys() + + dbstream._recluster() + + # Check that the resulted cluster centers are close to the expected centers + dbstream_expected_centers = {0: {0: 10, 1: 10}, + 1: {0: -5, 1: -5}, + 2: {0: 0, 1: 0}, + 3: {0: 5, 1: 5}, + 4: {0: -10, 1: -10}} + + for i in dbstream.centers.keys(): + assert utils.math.minkowski_distance(dbstream.centers[i], dbstream_expected_centers[i], 2) < 0.2 From 3b59cdeab52957def8a395907e19a32cd1f860a6 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Thu, 14 Dec 2023 17:32:36 +0700 Subject: [PATCH 224/347] Update test_density_graph_with_removed_microcluster function in DBSTREAM testing --- river/cluster/test_dbstream.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py index 9d780b7d2b..caf5a5724d 100644 --- a/river/cluster/test_dbstream.py +++ b/river/cluster/test_dbstream.py @@ -129,7 +129,8 @@ def test_density_graph_with_three_micro_clusters(): def test_density_graph_with_removed_microcluster(): - dbstream = build_dbstream(fading_factor=0.1, intersection_factor=0.3) + dbstream = build_dbstream(fading_factor=0.1, + intersection_factor=0.3) add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25) add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25) @@ -137,26 +138,28 @@ def test_density_graph_with_removed_microcluster(): for _ in range(5): dbstream.learn_one({1: 2, 2: 2}) - add_cluster(dbstream, initial_point={1: 4, 2: 4}, move_towards={1: 3.3, 2: 3.3}, times=25) + add_cluster(dbstream, initial_point={1: 3.5, 2: 3.5}, move_towards={1: 2.9, 2: 2.9}, times=25) + # Points in the middle of second and third micro-clusters for _ in range(4): - dbstream.learn_one({1: 3, 2: 3}) + dbstream.learn_one({1: 2.6, 2: 2.6}) assert len(dbstream._micro_clusters) == 2 assert_micro_cluster_properties( - dbstream.micro_clusters[1], center={1: 2.461654, 2: 2.461654}, last_update=86 + dbstream.micro_clusters[0], center={1: 2.023498, 2: 2.023498}, last_update=86 ) assert_micro_cluster_properties( - dbstream.micro_clusters[2], center={1: 3.430485, 2: 3.430485}, last_update=86 + dbstream.micro_clusters[1], center={1: 2.766543, 2: 2.766543}, last_update=86 ) - assert dbstream.s[0] == pytest.approx({1: 3.615835}) - assert dbstream.s[1] == pytest.approx({2: 2.803583}) - assert dbstream.s_t == {0: {1: 56}, 1: {2: 86}} + assert dbstream.s == {0: {1: 4.702391097045977}} + assert dbstream.s_t == {0: {1: 86}} dbstream._recluster() assert len(dbstream.clusters) == 1 - assert_micro_cluster_properties(dbstream.clusters[0], center={1: 3.152231, 2: 3.152231}) + assert_micro_cluster_properties( + dbstream.clusters[0], center={1: 2.560647, 2: 2.560647} + ) def test_dbstream_synthetic_sklearn(): From ad436f46d5924a7b302231428d87706cc7070f0b Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Thu, 14 Dec 2023 17:51:26 +0700 Subject: [PATCH 225/347] Modify test_density_graph_with_three_micro_clusters in DBSTREAM test file --- river/cluster/test_dbstream.py | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py index caf5a5724d..795aa4d86f 100644 --- a/river/cluster/test_dbstream.py +++ b/river/cluster/test_dbstream.py @@ -2,7 +2,10 @@ import pytest +from river import stream, utils, metrics + from river.cluster import DBSTREAM +from sklearn.datasets import make_blobs def build_dbstream(fading_factor=0.01, intersection_factor=0.05): @@ -102,30 +105,33 @@ def test_density_graph_with_three_micro_clusters(): for _ in range(5): dbstream.learn_one({1: 2, 2: 2}) + assert dbstream.s == {0: {1: 23.033438964246173}} + assert dbstream.s_t == {0: {1: 51}} + add_cluster(dbstream, initial_point={1: 4, 2: 4}, move_towards={1: 3.3, 2: 3.3}, times=25) # Points in the middle of second and third micro-clusters for _ in range(4): dbstream.learn_one({1: 3, 2: 3}) assert len(dbstream._micro_clusters) == 3 - assert_micro_cluster_properties( - dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56 + dbstream.micro_clusters[0], center={1: 2.0, 2: 2.0}, last_update=56 ) assert_micro_cluster_properties( - dbstream.micro_clusters[1], center={1: 2.461654, 2: 2.461654}, last_update=86 + dbstream.micro_clusters[1], center={1: 3.0, 2: 3.0}, last_update=86 ) assert_micro_cluster_properties( - dbstream.micro_clusters[2], center={1: 3.430485, 2: 3.430485}, last_update=86 + dbstream.micro_clusters[2], center={1: 3.982141, 2: 3.982141}, last_update=82 ) - assert dbstream.s[0] == pytest.approx({1: 3.995844}) - assert dbstream.s[1] == pytest.approx({2: 2.997921}) - assert dbstream.s_t == {0: {1: 56}, 1: {2: 86}} + assert dbstream.s[0] == pytest.approx({1: 23.033439}) + assert dbstream.s[1] == pytest.approx({2: 23.033439}) + assert dbstream.s_t == {0: {1: 51}, 1: {2: 82}} dbstream._recluster() assert len(dbstream.clusters) == 1 - assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.489894, 2: 2.489894}) + print(dbstream.clusters[0].center) + assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.800788, 2: 2.800788}) def test_density_graph_with_removed_microcluster(): From 7233a750b50604f72f9d69e75904c0142ddb048a Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Thu, 14 Dec 2023 17:58:43 +0700 Subject: [PATCH 226/347] Update test_dbstream.py file after pre commit checks. --- river/cluster/test_dbstream.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py index 795aa4d86f..6b5e3776f4 100644 --- a/river/cluster/test_dbstream.py +++ b/river/cluster/test_dbstream.py @@ -1,11 +1,10 @@ from __future__ import annotations import pytest +from sklearn.datasets import make_blobs -from river import stream, utils, metrics - +from river import metrics, stream, utils from river.cluster import DBSTREAM -from sklearn.datasets import make_blobs def build_dbstream(fading_factor=0.01, intersection_factor=0.05): From e16ce2b562c51ca27e6cbfab4e6b36fa3068de48 Mon Sep 17 00:00:00 2001 From: Matti Bispham <149883863+mbispham@users.noreply.github.com> Date: Tue, 2 Jan 2024 02:26:51 +0900 Subject: [PATCH 227/347] Add license to riverml.xyz (#1480) --- docs/.pages | 1 + docs/license/.pages | 2 ++ docs/license/license.md | 3 +++ 3 files changed, 6 insertions(+) create mode 100644 docs/license/.pages create mode 100644 docs/license/license.md diff --git a/docs/.pages b/docs/.pages index 9f7b7e5360..c72bb6ee96 100644 --- a/docs/.pages +++ b/docs/.pages @@ -6,3 +6,4 @@ nav: - faq - releases - benchmarks + - license diff --git a/docs/license/.pages b/docs/license/.pages new file mode 100644 index 0000000000..319cebe79a --- /dev/null +++ b/docs/license/.pages @@ -0,0 +1,2 @@ +title: License 📝 + diff --git a/docs/license/license.md b/docs/license/license.md new file mode 100644 index 0000000000..87980bb6fe --- /dev/null +++ b/docs/license/license.md @@ -0,0 +1,3 @@ +# License + +River is free and open-source software licensed under the [3-clause BSD license](https://github.com/online-ml/river/blob/main/LICENSE). \ No newline at end of file From 790b3e6e3731a23ea4983313d7a588b026d2e58d Mon Sep 17 00:00:00 2001 From: Max Halford Date: Sat, 20 Jan 2024 11:03:22 +0100 Subject: [PATCH 228/347] Update airline_passengers.py --- river/datasets/airline_passengers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/datasets/airline_passengers.py b/river/datasets/airline_passengers.py index 0f54115ac8..86082aa30c 100644 --- a/river/datasets/airline_passengers.py +++ b/river/datasets/airline_passengers.py @@ -14,7 +14,7 @@ class AirlinePassengers(base.FileDataset): References ---------- - [^1]: [International airline passengers: monthly totals in thousands. Jan 49 – Dec 60](https://datamarket.com/data/set/22u3/international-airline-passengers-monthly-totals-in-thousands-jan-49-dec-60#!ds=22u3&display=line) + [^1]: [International airline passengers: monthly totals in thousands. Jan 49 – Dec 60](https://rdrr.io/r/datasets/AirPassengers.html) """ From 1c98e9e53b83448123b4e2ad6c7c81ee24ffca66 Mon Sep 17 00:00:00 2001 From: Gabriel Jonas Date: Thu, 1 Feb 2024 16:32:43 -0300 Subject: [PATCH 229/347] Add new drift detectors (#1474) * fhddm * fhddm * fhddm_s * annotations * code quality fixes * code quality fix * code quality * codequality * doc fixes and private variables * doc fixes * using collections.deque * merge detectors * docstring and merge detectors * fixes and merge * fhddms added to example * test fixes * fixes * fixes * fixing example * example fix * unreleased file * remove count * remove count --- .pre-commit-config.yaml | 2 + docs/unreleased.md | 3 + river/drift/binary/__init__.py | 3 +- river/drift/binary/fhddm.py | 121 +++++++++++++++++++++++++++++++++ 4 files changed, 128 insertions(+), 1 deletion(-) create mode 100644 docs/unreleased.md create mode 100644 river/drift/binary/fhddm.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8bdd4f2a2a..36ff5d392a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,6 +16,8 @@ repos: language: python types: [python] entry: ruff + args: + - --fix - id: mypy name: mypy diff --git a/docs/unreleased.md b/docs/unreleased.md new file mode 100644 index 0000000000..90b51a20ac --- /dev/null +++ b/docs/unreleased.md @@ -0,0 +1,3 @@ +## drift + +- Added `FHDDM` drift detector. \ No newline at end of file diff --git a/river/drift/binary/__init__.py b/river/drift/binary/__init__.py index 505b282cee..3505af40ec 100644 --- a/river/drift/binary/__init__.py +++ b/river/drift/binary/__init__.py @@ -3,7 +3,8 @@ from .ddm import DDM from .eddm import EDDM +from .fhddm import FHDDM from .hddm_a import HDDM_A from .hddm_w import HDDM_W -__all__ = ["DDM", "EDDM", "HDDM_A", "HDDM_W"] +__all__ = ["DDM", "EDDM", "FHDDM", "HDDM_A", "HDDM_W"] diff --git a/river/drift/binary/fhddm.py b/river/drift/binary/fhddm.py new file mode 100644 index 0000000000..fe70e88e87 --- /dev/null +++ b/river/drift/binary/fhddm.py @@ -0,0 +1,121 @@ +from __future__ import annotations + +import collections +import itertools +import math + +from river import base + + +class FHDDM(base.BinaryDriftAndWarningDetector): + """Fast Hoeffding Drift Detection Method. + + FHDDM is a drift detection method based on the Hoeffding's inequality which uses + the input average as estimator. + + **Input:** `x` is an entry in a stream of bits, where 1 indicates error/failure and 0 + represents correct/normal values. + + For example, if a classifier's prediction $y'$ is right or wrong w.r.t. the + true target label $y$: + + - 0: Correct, $y=y'$ + + - 1: Error, $y \\neq y'$ + + *Implementation based on MOA.* + + Parameters + ---------- + sliding_window_size + The minimum required number of analyzed samples so change can be detected. + confidence_level + Confidence level used to determine the epsilon coefficient in Hoeffding’s inequality. The default value gives a 99\\% of confidence + level to the drift assessment. + short_window_size + The size of the short window size that it is used in a Stacking version of FHDDM [^2]. + + Examples + -------- + >>> import random + >>> from river import drift + + >>> rng = random.Random(42) + >>> # Traditional FHDDM [1] + >>> fhddm = drift.binary.FHDDM() + >>> # Stacking FHDDM [2] + >>> fhddm_s = drift.binary.FHDDM(short_window_size = 20) + >>> # Simulate a data stream where the first 250 instances come from a uniform distribution + >>> # of 1's and 0's + >>> data_stream = rng.choices([0, 1], k=250) + >>> # Increase the probability of 1's appearing in the next 250 instances + >>> data_stream = data_stream + rng.choices([0, 1], k=250, weights=[0.9, 0.1]) + >>> # Update drift detector and verify if change is detected + >>> for i, x in enumerate(data_stream): + ... fhddm.update(x) + ... fhddm_s.update(x) + ... if fhddm.drift_detected or fhddm_s.drift_detected: + ... print(f"Change detected at index {i}") + Change detected at index 279 + Change detected at index 315 + + References + ---------- + [^1]: A. Pesaranghader, H.L. Viktor, Fast Hoeffding Drift Detection Method for Evolving Data Streams. In the Proceedings of ECML-PKDD 2016. + [^2]: Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams. + + """ + def __init__( + self, sliding_window_size: int = 100, confidence_level: float = 0.000001, short_window_size: int = None + ): + + super().__init__() + self.sliding_window_size = sliding_window_size + self.confidence_level = confidence_level + self.short_window_size = short_window_size + self.n_one = 0 + self._reset() + + def _reset(self): + self._sliding_window = collections.deque(maxlen=self.sliding_window_size) + self._epsilon = math.sqrt( + (math.log(1 / self.confidence_level)) / (2 * self.sliding_window_size) + ) + self._u_max = 0 + self.n_one = 0 + if self.short_window_size is not None: + self._short_window = collections.deque(maxlen=self.short_window_size) + self._u_short_max = 0 + self._epsilon_s = math.sqrt( + (math.log(1 / self.confidence_level)) / (2 * self.short_window_size) + ) + + def update(self, x): + if self.drift_detected: + self._drift_detected = False + self._reset() + + self._sliding_window.append(x) + self.n_one += x + + if len(self._sliding_window) == self.sliding_window_size: + u = self.n_one / self.sliding_window_size + self._u_max = u if (self._u_max < u) else self._u_max + + short_win_drift_status = False + long_win_drift_status = False + + if self.short_window_size is not None: + u_s = ( + sum (itertools.islice(self._sliding_window, self.sliding_window_size - self.short_window_size, self.sliding_window_size)) + / self.short_window_size + ) + self._u_short_max = u_s if self._u_short_max < u_s else self._u_short_max + short_win_drift_status = ( + True if self._u_short_max - u_s > self._epsilon_s else False + ) + + long_win_drift_status = True if (self._u_max - u > self._epsilon) else False + + self._drift_detected = long_win_drift_status or short_win_drift_status + self.n_one -= self._sliding_window.popleft() From af6d7c5770cfea935c19adce472b6081c78fe105 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Niccol=C3=B2=20Petti?= Date: Wed, 14 Feb 2024 15:41:59 +0100 Subject: [PATCH 230/347] Add stream.iter_polars #1503 (#1504) --- docs/unreleased.md | 3 ++- poetry.lock | 40 +++++++++++++++++++++++++++++- pyproject.toml | 1 + river/stream/__init__.py | 7 ++++++ river/stream/iter_polars.py | 49 +++++++++++++++++++++++++++++++++++++ 5 files changed, 98 insertions(+), 2 deletions(-) create mode 100644 river/stream/iter_polars.py diff --git a/docs/unreleased.md b/docs/unreleased.md index 90b51a20ac..b2c83d0050 100644 --- a/docs/unreleased.md +++ b/docs/unreleased.md @@ -1,3 +1,4 @@ ## drift -- Added `FHDDM` drift detector. \ No newline at end of file +- Added `FHDDM` drift detector. +- Added a `iter_polars` function to iterate over the rows of a polars DataFrame. \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index ec8dbe14ff..9ca4786943 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2730,6 +2730,43 @@ files = [ dev = ["pre-commit", "tox"] testing = ["pytest", "pytest-benchmark"] +[[package]] +name = "polars" +version = "0.20.8" +description = "Blazingly fast DataFrame library" +optional = false +python-versions = ">=3.8" +files = [ + {file = "polars-0.20.8-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:73f1d369aeddda5f11411b6497f697f2471bbe6ae55fd936677a10a40995c83c"}, + {file = "polars-0.20.8-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:dc3a446fe606095b3ad6df3cf3dddd8ad54be7745f255fedb29f8bdf71a60760"}, + {file = "polars-0.20.8-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d3d58ebc7a24d26930535d06b8772e125038a87a6abab4c5dfd87ea19bba61f3"}, + {file = "polars-0.20.8-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:5b733816ac61156c12bd0edd6d7c1a5e63859830ce0e425b6450b335024f0cd5"}, + {file = "polars-0.20.8-cp38-abi3-win_amd64.whl", hash = "sha256:2300f48ff7120eefe2cac2113990d0b0b5beedad93266b9fedfc8df133e7b13b"}, + {file = "polars-0.20.8.tar.gz", hash = "sha256:a34f6ce1c5469872b291aaf90467e632e81f92dec6c2e18136bc40cd92877411"}, +] + +[package.extras] +adbc = ["adbc_driver_sqlite"] +all = ["polars[adbc,cloudpickle,connectorx,deltalake,fsspec,gevent,numpy,pandas,plot,pyarrow,pydantic,pyiceberg,sqlalchemy,timezone,xlsx2csv,xlsxwriter]"] +cloudpickle = ["cloudpickle"] +connectorx = ["connectorx (>=0.3.2)"] +deltalake = ["deltalake (>=0.14.0)"] +fsspec = ["fsspec"] +gevent = ["gevent"] +matplotlib = ["matplotlib"] +numpy = ["numpy (>=1.16.0)"] +openpyxl = ["openpyxl (>=3.0.0)"] +pandas = ["pandas", "pyarrow (>=7.0.0)"] +plot = ["hvplot (>=0.9.1)"] +pyarrow = ["pyarrow (>=7.0.0)"] +pydantic = ["pydantic"] +pyiceberg = ["pyiceberg (>=0.5.0)"] +pyxlsb = ["pyxlsb (>=1.0)"] +sqlalchemy = ["pandas", "sqlalchemy"] +timezone = ["backports.zoneinfo", "tzdata"] +xlsx2csv = ["xlsx2csv (>=0.8.0)"] +xlsxwriter = ["xlsxwriter"] + [[package]] name = "pre-commit" version = "3.5.0" @@ -3228,6 +3265,7 @@ files = [ {file = 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"sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, @@ -4910,4 +4948,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "6176d641278998249cf285510d927bcb970d0940d43b00de6f48c28a794d7330" +content-hash = "aeaff58cc4d447bbb5c86a09998c1bf89291d5179805cde2785ac2471f907c02" diff --git a/pyproject.toml b/pyproject.toml index 560377e64a..bb6c7f07b4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ python = ">=3.9,<3.13" numpy = "^1.23.0" scipy = "^1.8.1" pandas = "^2.1" +polars = "^0.20.8" [tool.poetry.group.dev.dependencies] graphviz = "^0.20.1" diff --git a/river/stream/__init__.py b/river/stream/__init__.py index 0433a06105..2a70f518f8 100644 --- a/river/stream/__init__.py +++ b/river/stream/__init__.py @@ -27,6 +27,13 @@ "TwitchChatStream", ] +try: + from .iter_polars import iter_polars + + __all__ += ["iter_polars"] +except ImportError: + pass + try: from .iter_pandas import iter_pandas diff --git a/river/stream/iter_polars.py b/river/stream/iter_polars.py new file mode 100644 index 0000000000..d36e628abb --- /dev/null +++ b/river/stream/iter_polars.py @@ -0,0 +1,49 @@ +from __future__ import annotations + +import polars as pl + +from river import base, stream + + +def iter_polars( + X: pl.DataFrame, y: pl.Series | pl.DataFrame | None = None, **kwargs +) -> base.typing.Stream: + """Iterates over the rows of a `polars.DataFrame`. + + Parameters + ---------- + X + A dataframe of features. + y + A series or a dataframe with one column per target. + kwargs + Extra keyword arguments are passed to the underlying call to `stream.iter_array`. + + Examples + -------- + + >>> import polars as pl + >>> from river import stream + + >>> X = pl.DataFrame({ + ... 'x1': [1, 2, 3, 4], + ... 'x2': ['blue', 'yellow', 'yellow', 'blue'], + ... 'y': [True, False, False, True] + ... }) + >>> y = X.get_column('y') + >>> X=X.drop("y") + + >>> for xi, yi in stream.iter_polars(X, y): + ... print(xi, yi) + {'x1': 1, 'x2': 'blue'} True + {'x1': 2, 'x2': 'yellow'} False + {'x1': 3, 'x2': 'yellow'} False + {'x1': 4, 'x2': 'blue'} True + + """ + + kwargs["feature_names"] = X.columns + if isinstance(y, pl.DataFrame): + kwargs["target_names"] = y.columns + + yield from stream.iter_array(X=X.to_numpy(), y=y if y is None else y.to_numpy(), **kwargs) From 87941ba80161b45939e368498077ab052f7fc8d8 Mon Sep 17 00:00:00 2001 From: Marek Wadinger <50716630+MarekWadinger@users.noreply.github.com> Date: Sat, 24 Feb 2024 19:17:01 +0900 Subject: [PATCH 231/347] Fix missing attribute '_from_state' in 'EmpiricalCovariance' (#1471) * ADD: from_state allows int as covariance * FIX: emp _from_state * FIX: broken statefulness of update_many * FIX: remove unexpected kwarg * FIX: if statement nesting + redundant docstring entry * FIX: wrong intendation + sort keys for shuffled samples --- river/covariance/emp.py | 59 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 54 insertions(+), 5 deletions(-) diff --git a/river/covariance/emp.py b/river/covariance/emp.py index b02b208126..957f37c871 100644 --- a/river/covariance/emp.py +++ b/river/covariance/emp.py @@ -14,7 +14,7 @@ class SymmetricMatrix(abc.ABC): @property @abc.abstractmethod - def matrix(self): + def matrix(self) -> dict: ... def __getitem__(self, key): @@ -183,16 +183,38 @@ def update_many(self, X: pd.DataFrame): ) } + self._update_from_state(n=n, mean=mean, cov=cov) + + def _update_from_state(self, n: int, mean: dict, cov: float | dict): + """Update from state information. + + Parameters + ---------- + n + The number of data points. + mean + A dictionary of variable means. + cov + A dictionary of covariance or variance values. + + Raises + ---------- + KeyError: If an element in `mean` or `cov` is missing. + """ for i, j in itertools.combinations(sorted(mean.keys()), r=2): try: self[i, j] except KeyError: self._cov[i, j] = stats.Cov(self.ddof) + if isinstance(cov, dict): + cov_ = cov.get((i, j), cov.get((j, i))) + else: + cov_ = cov self._cov[i, j] += stats.Cov._from_state( n=n, mean_x=mean[i], mean_y=mean[j], - cov=cov.get((i, j), cov.get((j, i))), + cov=cov_, ddof=self.ddof, ) @@ -201,9 +223,36 @@ def update_many(self, X: pd.DataFrame): self[i, i] except KeyError: self._cov[i, i] = stats.Var(self.ddof) - self._cov[i, i] += stats.Var._from_state( - n=n, m=mean[i], sig=cov[i, i], ddof=self.ddof - ) + if isinstance(cov, dict): + cov_ = cov[i, i] + else: + cov_ = cov + self._cov[i, i] += stats.Var._from_state(n=n, m=mean[i], sig=cov_, ddof=self.ddof) + + @classmethod + def _from_state(cls, n: int, mean: dict, cov: float | dict, *, ddof=1): + """Create a new instance from state information. + + Parameters + ---------- + cls + The class type. + n + The number of data points. + mean + A dictionary of variable means. + cov + A dictionary of covariance or variance values. + ddof + Degrees of freedom for covariance calculation. Defaults to 1. + + Returns + ---------- + cls: A new instance of the class with updated covariance matrix. + """ + new = cls(ddof=ddof) + new._update_from_state(n=n, mean=mean, cov=cov) + return new class EmpiricalPrecision(SymmetricMatrix): From 1e6ded08dc0c9531d4a50a2b17aed53869c38ece Mon Sep 17 00:00:00 2001 From: Adil Zouitine Date: Sun, 25 Feb 2024 15:47:17 +0100 Subject: [PATCH 232/347] Fix build from source (sdist) (#1506) * Fix build from source (sdist) * Delete MANIFEST.in --- MANIFEST.in | 9 --------- pyproject.toml | 12 ++++++++++++ 2 files changed, 12 insertions(+), 9 deletions(-) delete mode 100644 MANIFEST.in diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 0bc743b1e0..0000000000 --- a/MANIFEST.in +++ /dev/null @@ -1,9 +0,0 @@ -global-include *.cpp -global-include *.pyx -global-include *.pxd -include river/datasets/*.csv -include river/datasets/*.gz -include river/datasets/*.zip -include river/stream/*.zip -include Cargo.toml -recursive-include rust_src * diff --git a/pyproject.toml b/pyproject.toml index bb6c7f07b4..b0f3722394 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,6 +11,18 @@ homepage = "https://riverml.xyz/" repository = "https://github.com/online-ml/river/" authors = ["Max Halford "] +include = [ + "**/*.cpp", + "**/*.pyx", + "**/*.pxd", + "river/datasets/*.csv", + "river/datasets/*.gz", + "river/datasets/*.zip", + "river/stream/*.zip", + "Cargo.toml", + "rust_src/**/*" +] + [tool.poetry.build] generate-setup-file = true script = "build.py" From f603161966370cad27d7ee76780b6026b2a07563 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Fri, 1 Mar 2024 18:17:57 -0300 Subject: [PATCH 233/347] Fix issue #1507 (SWINN streamline) (#1508) * streamline code * now I remember why those methods exist * change test * release notes --- docs/unreleased.md | 10 ++- river/neighbors/ann/nn_vertex.py | 102 ++++++++++++------------- river/neighbors/ann/swinn.py | 121 +++++++++++++++--------------- river/neighbors/ann/utils.py | 10 --- river/neighbors/knn_classifier.py | 2 +- 5 files changed, 118 insertions(+), 127 deletions(-) delete mode 100644 river/neighbors/ann/utils.py diff --git a/docs/unreleased.md b/docs/unreleased.md index b2c83d0050..743c1a54f7 100644 --- a/docs/unreleased.md +++ b/docs/unreleased.md @@ -1,4 +1,10 @@ +# Unreleased + ## drift -- Added `FHDDM` drift detector. -- Added a `iter_polars` function to iterate over the rows of a polars DataFrame. \ No newline at end of file +- Added `FHDDM` drift detector. +- Added a `iter_polars` function to iterate over the rows of a polars DataFrame. + +## neighbors + +- Simplified `neighbors.SWINN` to avoid recursion limit and pickling issues. diff --git a/river/neighbors/ann/nn_vertex.py b/river/neighbors/ann/nn_vertex.py index e1f85905c1..c17ea49c52 100644 --- a/river/neighbors/ann/nn_vertex.py +++ b/river/neighbors/ann/nn_vertex.py @@ -1,7 +1,6 @@ from __future__ import annotations import heapq -import itertools import math import random @@ -9,18 +8,15 @@ class Vertex(base.Base): - _isolated: set[Vertex] = set() + _isolated: set[int] = set() def __init__(self, item, uuid: int) -> None: self.item = item self.uuid = uuid - self.edges: dict[Vertex, float] = {} - self.r_edges: dict[Vertex, float] = {} - self.flags: set[Vertex] = set() - self.worst_edge: Vertex | None = None - - def __hash__(self) -> int: - return self.uuid + self.edges: dict[int, float] = {} + self.r_edges: dict[int, float] = {} + self.flags: set[int]= set() + self.worst_edge: int | None = None def __eq__(self, other) -> bool: if not isinstance(other, Vertex): @@ -34,69 +30,67 @@ def __lt__(self, other) -> bool: return self.uuid < other.uuid - def farewell(self): + def farewell(self, vertex_pool: list[Vertex]): for rn in list(self.r_edges): - rn.rem_edge(self) + vertex_pool[rn].rem_edge(self) for n in list(self.edges): - self.rem_edge(n) - - self.flags = None - self.worst_edge = None + self.rem_edge(vertex_pool[n]) - Vertex._isolated.discard(self) + Vertex._isolated.discard(self.uuid) def fill(self, neighbors: list[Vertex], dists: list[float]): for n, dist in zip(neighbors, dists): - self.edges[n] = dist - self.flags.add(n) - n.r_edges[self] = dist + self.edges[n.uuid] = dist + self.flags.add(n.uuid) + n.r_edges[self.uuid] = dist - # Neighbors are ordered by distance - self.worst_edge = n + # Neighbors are ordered by distance, so the last neighbor + # is the farthest one + self.worst_edge = n.uuid def add_edge(self, vertex: Vertex, dist): - self.edges[vertex] = dist - self.flags.add(vertex) - vertex.r_edges[self] = dist + self.edges[vertex.uuid] = dist + self.flags.add(vertex.uuid) + vertex.r_edges[self.uuid] = dist if self.worst_edge is None or self.edges[self.worst_edge] < dist: - self.worst_edge = vertex + self.worst_edge = vertex.uuid def rem_edge(self, vertex: Vertex): - self.edges.pop(vertex) - vertex.r_edges.pop(self) - self.flags.discard(vertex) + self.edges.pop(vertex.uuid) + vertex.r_edges.pop(self.uuid) + self.flags.discard(vertex.uuid) if self.has_neighbors(): - if vertex == self.worst_edge: - self.worst_edge = max(self.edges, key=self.edges.get) # type: ignore + if vertex.uuid == self.worst_edge: + self.worst_edge = max(self.edges, key=self.edges.__getitem__) else: self.worst_edge = None if not self.has_rneighbors(): - Vertex._isolated.add(self) + Vertex._isolated.add(self.uuid) - def push_edge(self, node: Vertex, dist: float, max_edges: int) -> int: - if self.is_neighbor(node) or node == self: + def push_edge(self, node: Vertex, dist: float, max_edges: int, vertex_pool: list[Vertex]) -> int: + if self.is_neighbor(node) or node.uuid == self.uuid: return 0 if len(self.edges) >= max_edges: if self.worst_edge is None or self.edges.get(self.worst_edge, math.inf) <= dist: return 0 - self.rem_edge(self.worst_edge) + self.rem_edge(vertex_pool[self.worst_edge]) self.add_edge(node, dist) return 1 - def is_neighbor(self, vertex): - return vertex in self.edges or vertex in self.r_edges + def is_neighbor(self, vertex: Vertex): + return vertex.uuid in self.edges or vertex.uuid in self.r_edges def get_edge(self, vertex: Vertex): - if vertex in self.edges: - return self, vertex, self.edges[vertex] - return vertex, self, self.r_edges[vertex] + if vertex.uuid in self.edges: + return self, vertex, self.edges[vertex.uuid] + return vertex, self, self.r_edges[vertex.uuid] def has_neighbors(self) -> bool: return len(self.edges) > 0 @@ -112,21 +106,21 @@ def sample_flags(self): def sample_flags(self, sampled): self.flags -= set(sampled) - def neighbors(self) -> tuple[list[Vertex], list[float]]: + def neighbors(self) -> tuple[list[int], list[float]]: res = tuple(map(list, zip(*((node, dist) for node, dist in self.edges.items())))) return res if len(res) > 0 else ([], []) # type: ignore - def r_neighbors(self) -> tuple[list[Vertex], list[float]]: + def r_neighbors(self) -> tuple[list[int], list[float]]: res = tuple(map(list, zip(*((vertex, dist) for vertex, dist in self.r_edges.items())))) return res if len(res) > 0 else ([], []) # type: ignore - def all_neighbors(self): + def all_neighbors(self) -> set[int]: return set.union(set(self.edges.keys()), set(self.r_edges.keys())) def is_isolated(self): return len(self.edges) == 0 and len(self.r_edges) == 0 - def prune(self, prune_prob: float, prune_trigger: int, rng: random.Random): + def prune(self, prune_prob: float, prune_trigger: int, vertex_pool: list[Vertex], rng: random.Random): if prune_prob == 0: return @@ -134,28 +128,28 @@ def prune(self, prune_prob: float, prune_trigger: int, rng: random.Random): if total_degree <= prune_trigger: return - # To avoid tie in distances - counter = itertools.count() - edge_pool: list[tuple[float, int, Vertex, bool]] = [] + edge_pool: list[tuple[float, int, bool]] = [] for n, dist in self.edges.items(): - heapq.heappush(edge_pool, (dist, next(counter), n, True)) + heapq.heappush(edge_pool, (dist, n, True)) for rn, dist in self.r_edges.items(): - heapq.heappush(edge_pool, (dist, next(counter), rn, False)) + heapq.heappush(edge_pool, (dist, rn, False)) # Start with the best undirected edge - selected: list[Vertex] = [heapq.heappop(edge_pool)[2]] + selected: list[int] = [heapq.heappop(edge_pool)[1]] while len(edge_pool) > 0: - c_dist, _, c, c_isdir = heapq.heappop(edge_pool) + c_dist, c, c_isdir = heapq.heappop(edge_pool) discarded = False for s in selected: - if s.is_neighbor(c) and rng.random() < prune_prob: - orig, dest, dist = s.get_edge(c) + s_v = vertex_pool[s] + c_v = vertex_pool[c] + if s_v.is_neighbor(c_v) and rng.random() < prune_prob: + orig, dest, dist = s_v.get_edge(c_v) if dist < c_dist: if c_isdir: - self.rem_edge(c) + self.rem_edge(c_v) else: - c.rem_edge(self) + c_v.rem_edge(self) discarded = True break else: diff --git a/river/neighbors/ann/swinn.py b/river/neighbors/ann/swinn.py index 35419c8812..5d71b0e2c7 100644 --- a/river/neighbors/ann/swinn.py +++ b/river/neighbors/ann/swinn.py @@ -117,7 +117,7 @@ def __init__( self.n_iters = n_iters self.seed = seed - self._data: collections.deque[Vertex] = collections.deque(maxlen=self.maxlen) + self._data: collections.deque[Vertex | None] = collections.deque(maxlen=self.maxlen) self._uuid = itertools.cycle(range(self.maxlen)) self._rng = random.Random(self.seed) self._index = False @@ -146,16 +146,16 @@ def _init_graph(self): def _fix_graph(self): """Connect every isolated node in the graph to their nearest neighbors.""" - for node in list(Vertex._isolated): - if not node.is_isolated(): + for nid in list(Vertex._isolated): + if not self[nid].is_isolated(): continue - neighbors, dists = self._search(node.item, self.graph_k) - node.fill(neighbors, dists) + neighbors, dists = self._search(self[nid].item, self.graph_k) + self[nid].fill(neighbors, dists) # Update class property Vertex._isolated.clear() - def _safe_node_removal(self): + def _safe_node_removal(self, nid: int): """Remove the oldest data point from the search graph. Make sure nodes are accessible from any given starting point after removing the oldest @@ -163,24 +163,24 @@ def _safe_node_removal(self): the only bridge between its neighbors. """ - node = self._data.popleft() + node = self[nid] # Get previous neighborhood info rns = node.r_neighbors()[0] ns = node.neighbors()[0] - node.farewell() + node.farewell(vertex_pool=self._data) # Nodes whose only direct neighbor was the removed node - rns = {rn for rn in rns if not rn.has_neighbors()} + rns = {rn for rn in rns if not self[rn].has_neighbors()} # Nodes whose only reverse neighbor was the removed node - ns = {n for n in ns if not n.has_rneighbors()} + ns = {n for n in ns if not self[n].has_rneighbors()} affected = list(rns | ns) isolated = rns.intersection(ns) # First we handle the unreachable nodes for al in isolated: - neighbors, dists = self._search(al.item, self.graph_k) - al.fill(neighbors, dists) + neighbors, dists = self._search(self[al].item, self.graph_k) + self[al].fill(neighbors, dists) rns -= isolated ns -= isolated @@ -192,26 +192,29 @@ def _safe_node_removal(self): # Check the group of nodes without reverse neighborhood for seeds # Thus we can join two separate groups if len(ns) > 0: - seed = self._rng.choice(ns) + seed = self[self._rng.choice(ns)] # Use the search index to create new connections - neighbors, dists = self._search(rn.item, self.graph_k, seed=seed, exclude=rn) - rn.fill(neighbors, dists) + neighbors, dists = self._search(self[rn].item, self.graph_k, seed=seed, exclude={rn}) + self[rn].fill(neighbors, dists) + + self._data[nid] = None + del node self._refine(affected) - def _refine(self, nodes: list[Vertex] = None): + def _refine(self, nodes: list[int] = None): """Update the nearest neighbor graph to improve the edge distances. Parameters ---------- nodes - The list of nodes for which the neighborhood refinement will be applied. + The list of node ids for which the neighborhood refinement will be applied. If `None`, all nodes will have their neighborhood enhanced. """ if nodes is None: - nodes = [n for n in self] + nodes = [n.uuid for n in self] min_changes = self.delta * self.graph_k * len(nodes) @@ -223,65 +226,62 @@ def _refine(self, nodes: list[Vertex] = None): old = collections.defaultdict(set) # Expand undirected neighborhood - for node in nodes: + for nid in nodes: + node = self[nid] neighbors = node.neighbors()[0] flags = node.sample_flags for neigh, flag in zip(neighbors, flags): # To avoid evaluating previous neighbors again - tried.add((node.uuid, neigh.uuid)) + tried.add((nid, neigh)) if flag: - new[node].add(neigh) - new[neigh].add(node) + new[nid].add(neigh) + new[neigh].add(nid) else: - old[node].add(neigh) - old[neigh].add(node) + old[nid].add(neigh) + old[neigh].add(nid) # Limits the maximum number of edges to explore and update sample flags - for node in nodes: - if len(new[node]) > self.max_candidates: - new[node] = self._rng.sample(tuple(new[node]), self.max_candidates) # type: ignore - else: - new[node] = new[node] + for nid in nodes: + if len(new[nid]) > self.max_candidates: + new[nid] = self._rng.sample(tuple(new[nid]), self.max_candidates) # type: ignore - if len(old[node]) > self.max_candidates: - old[node] = self._rng.sample(tuple(old[node]), self.max_candidates) # type: ignore - else: - old[node] = old[node] + if len(old[nid]) > self.max_candidates: + old[nid] = self._rng.sample(tuple(old[nid]), self.max_candidates) # type: ignore - node.sample_flags = new[node] + self[nid].sample_flags = new[nid] # Perform local joins an attempt to improve the neighborhood - for node in nodes: + for nid in nodes: # The origin of the join must have a boolean flag set to true - for n1 in new[node]: + for n1 in new[nid]: # Consider connections between vertices whose boolean flags are both true - for n2 in new[node]: - if n1.uuid == n2.uuid or n1.is_neighbor(n2): + for n2 in new[nid]: + if n1 == n2 or self[n1].is_neighbor(self[n2]): continue - if (n1.uuid, n2.uuid) in tried or (n2.uuid, n1.uuid) in tried: + if (n1, n2) in tried or (n2, n1) in tried: continue - dist = self.dist_func(n1.item, n2.item) - total_changes += n1.push_edge(n2, dist, self.graph_k) - total_changes += n2.push_edge(n1, dist, self.graph_k) + dist = self.dist_func(self[n1].item, self[n2].item) + total_changes += self[n1].push_edge(self[n2], dist, self.graph_k, self._data) + total_changes += self[n2].push_edge(self[n1], dist, self.graph_k, self._data) - tried.add((n1.uuid, n2.uuid)) + tried.add((n1, n2)) # Or one of the connections has a boolean flag set to false - for n2 in old[node]: - if n1.uuid == n2.uuid or n1.is_neighbor(n2): + for n2 in old[nid]: + if n1 == n2 or self[n1].is_neighbor(self[n2]): continue - if (n1.uuid, n2.uuid) in tried or (n2.uuid, n1.uuid) in tried: + if (n1, n2) in tried or (n2, n1) in tried: continue - dist = self.dist_func(n1.item, n2.item) - total_changes += n1.push_edge(n2, dist, self.graph_k) - total_changes += n2.push_edge(n1, dist, self.graph_k) + dist = self.dist_func(self[n1].item, self[n2].item) + total_changes += self[n1].push_edge(self[n2], dist, self.graph_k, self._data) + total_changes += self[n2].push_edge(self[n1], dist, self.graph_k, self._data) - tried.add((n1.uuid, n2.uuid)) + tried.add((n1, n2)) # Stopping criterion if total_changes <= min_changes: @@ -289,7 +289,7 @@ def _refine(self, nodes: list[Vertex] = None): # Reduce the number of edges, if needed for n in nodes: - n.prune(self.prune_prob, self.max_candidates, self._rng) + self[n].prune(self.prune_prob, self.max_candidates, self._data, self._rng) # Ensure that no node is isolated in the graph self._fix_graph() @@ -322,13 +322,16 @@ def append(self, item: typing.Any, **kwargs): # A slot will be replaced, so let's update the search graph first if len(self) == self.maxlen: - self._safe_node_removal() + self._safe_node_removal(node.uuid) # Assign the closest neighbors to the new item neighbors, dists = self._search(node.item, self.graph_k) # Add the new element to the buffer - self._data.append(node) + if len(self) == self.maxlen: + self._data[node.uuid] = node + else: + self._data.append(node) node.fill(neighbors, dists) def _linear_scan(self, item, k): @@ -340,7 +343,7 @@ def _linear_scan(self, item, k): return None - def _search(self, item, k, epsilon: float = 0.1, seed=None, exclude=None) -> tuple[list, list]: + def _search(self, item, k, epsilon: float = 0.1, seed: Vertex = None, exclude: set[int] | None = None) -> tuple[list, list]: # Limiter for the distance bound distance_scale = 1 + epsilon # Distance threshold for early stops @@ -348,15 +351,13 @@ def _search(self, item, k, epsilon: float = 0.1, seed=None, exclude=None) -> tup if exclude is None: exclude = set() - else: - exclude = {exclude.uuid} if seed is None: # Make sure the starting point for the search is valid while True: # Random seed point to start the search seed = self[self._rng.randint(0, len(self) - 1)] - if not seed.is_isolated() and seed.uuid not in exclude: + if seed is not None and not seed.is_isolated() and seed.uuid not in exclude: break dist = self.dist_func(item, seed.item) @@ -373,7 +374,7 @@ def _search(self, item, k, epsilon: float = 0.1, seed=None, exclude=None) -> tup c_dist, c_n = heapq.heappop(pool) while c_dist < distance_bound: - tns = [n for n in c_n.all_neighbors() if n.uuid not in visited] + tns = [self[n] for n in c_n.all_neighbors() if n not in visited] for n in tns: dist = self.dist_func(item, n.item) @@ -454,9 +455,9 @@ def connectivity(self) -> list[int]: """ forest = set() - trees = {n: {n} for n in self} + trees = {n.uuid: {n.uuid} for n in self} - edges = [((n1, n2), w) for n1 in self for n2, w in n1.edges.items()] + edges = [((n1.uuid, n2), w) for n1 in self for n2, w in n1.edges.items()] edges.sort(key=operator.itemgetter(1)) for (n1, n2), _ in edges: diff --git a/river/neighbors/ann/utils.py b/river/neighbors/ann/utils.py deleted file mode 100644 index 7d33f4f3b6..0000000000 --- a/river/neighbors/ann/utils.py +++ /dev/null @@ -1,10 +0,0 @@ -from __future__ import annotations - - -def argsort(dists): - return sorted(range(len(dists)), key=dists.__getitem__) - - -def rem_duplicates(pool): - seen = set() - return [n for n in pool if not (n in seen or seen.add(n))] diff --git a/river/neighbors/knn_classifier.py b/river/neighbors/knn_classifier.py index 67264abf3d..b8da74b437 100644 --- a/river/neighbors/knn_classifier.py +++ b/river/neighbors/knn_classifier.py @@ -68,7 +68,7 @@ class KNNClassifier(base.Classifier): ... ) >>> evaluate.progressive_val_score(dataset, model, metrics.Accuracy()) - Accuracy: 89.67% + Accuracy: 89.59% """ From 0fe1c1e0bbd4e533a4216c22e6aea9ee8023bfc7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Niccol=C3=B2=20Petti?= Date: Mon, 18 Mar 2024 17:58:34 +0100 Subject: [PATCH 234/347] add a trained_on_dist to imblearn/random.py #1511 (#1513) --- river/imblearn/random.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/river/imblearn/random.py b/river/imblearn/random.py index 67d7e623c9..36e1861c04 100644 --- a/river/imblearn/random.py +++ b/river/imblearn/random.py @@ -79,6 +79,7 @@ def __init__(self, classifier: base.Classifier, desired_dist: dict, seed: int | super().__init__(classifier=classifier, seed=seed) self.desired_dist = desired_dist self._actual_dist: typing.Counter = collections.Counter() + self._trained_on_dist: typing.Counter = collections.Counter() self._pivot = None def learn_one(self, x, y, **kwargs): @@ -90,6 +91,7 @@ def learn_one(self, x, y, **kwargs): if y != self._pivot: self._pivot = max(g.keys(), key=lambda y: f[y] / g[y]) else: + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) return @@ -98,6 +100,7 @@ def learn_one(self, x, y, **kwargs): ratio = f[y] / (M * g[y]) if ratio < 1 and self._rng.random() < ratio: + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) @@ -151,6 +154,7 @@ class percentages. The values must sum up to 1. def __init__(self, classifier: base.Classifier, desired_dist: dict, seed: int | None = None): super().__init__(classifier=classifier, seed=seed) self.desired_dist = desired_dist + self._trained_on_dist: typing.Counter = collections.Counter() self._actual_dist: typing.Counter = collections.Counter() self._pivot = None @@ -163,6 +167,7 @@ def learn_one(self, x, y, **kwargs): if y != self._pivot: self._pivot = max(g.keys(), key=lambda y: g[y] / f[y]) else: + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) return @@ -170,6 +175,7 @@ def learn_one(self, x, y, **kwargs): rate = M * f[y] / g[y] for _ in range(utils.random.poisson(rate, rng=self._rng)): + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) @@ -233,6 +239,7 @@ def __init__( ): super().__init__(classifier=classifier, seed=seed) self.sampling_rate = sampling_rate + self._trained_on_dist: typing.Counter = collections.Counter() self._actual_dist: typing.Counter = collections.Counter() if desired_dist is None: desired_dist = self._actual_dist @@ -248,4 +255,5 @@ def learn_one(self, x, y, **kwargs): rate = self.sampling_rate * f[y] / (g[y] / self._n) for _ in range(utils.random.poisson(rate, rng=self._rng)): + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) From a36c5ddc918f8f2208f2b33fdb8685a41f8722ee Mon Sep 17 00:00:00 2001 From: Sebastian Wette <55355478+sebiwtt@users.noreply.github.com> Date: Mon, 18 Mar 2024 19:00:17 +0100 Subject: [PATCH 235/347] Adding PredictiveAnomalyDetection to river/anomaly (#1458) Add Predictive Anomaly Detection, a semi-supervised technique to anomaly detection that employs a predictive model to learn the behavior of a dataset. --- river/anomaly/__init__.py | 3 + river/anomaly/pad.py | 169 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 172 insertions(+) create mode 100644 river/anomaly/pad.py diff --git a/river/anomaly/__init__.py b/river/anomaly/__init__.py index ca5ffd0888..50725dd575 100644 --- a/river/anomaly/__init__.py +++ b/river/anomaly/__init__.py @@ -11,6 +11,7 @@ model. """ + from __future__ import annotations from . import base @@ -18,6 +19,7 @@ from .gaussian import GaussianScorer from .hst import HalfSpaceTrees from .lof import LocalOutlierFactor +from .pad import PredictiveAnomalyDetection from .sad import StandardAbsoluteDeviation from .svm import OneClassSVM @@ -31,4 +33,5 @@ "StandardAbsoluteDeviation", "ThresholdFilter", "LocalOutlierFactor", + "PredictiveAnomalyDetection", ] diff --git a/river/anomaly/pad.py b/river/anomaly/pad.py new file mode 100644 index 0000000000..1f07cefbe1 --- /dev/null +++ b/river/anomaly/pad.py @@ -0,0 +1,169 @@ +from __future__ import annotations + +import math + +from river import anomaly, base, linear_model, preprocessing, stats, time_series + +__all__ = ["PredictiveAnomalyDetection"] + + +class PredictiveAnomalyDetection(anomaly.base.SupervisedAnomalyDetector): + """Predictive Anomaly Detection. + + This semi-supervised technique to anomaly detection employs a predictive model to learn the normal behavior + of a dataset. It forecasts future data points and compares these predictions with actual values to determine + anomalies. An anomaly score is calculated based on the deviation of the prediction from the actual value, with higher + scores indicating a higher probability of an anomaly. + + The actual anomaly score is calculated by comparing the squared-error to a dynamic threshold. If the error is larger + than this threshold, the score will be 1.0; else, the score will be linearly distributed within the range (0.0, 1.0), + with a higher score indicating a higher squared error compared to the threshold. + + Parameters + ---------- + predictive_model + The underlying model that learns the normal behavior of the data and makes predictions on future behavior. + This can be an estimator of any type, depending on the type of problem (e.g. some Forecaster for Time-Series Data). + horizon + When a Forecaster is used as a predictive model, this is the horizon of its forecasts. + n_std + Number of Standard Deviations to calculate the threshold. A larger number of standard deviation will result in + a higher threshold, resulting in the model being less sensitive. + warmup_period + Duration for the model to warm up. Since the model starts with zero knowledge, + the first instances will have very high anomaly scores, resulting in bad predictions (or high error). As such, + a warm-up period is necessary to discard the first seen instances. + While the model is within the warm-up period, no score will be calculated and the score_one method will return 0.0. + + Attributes + ---------- + dynamic_mae : stats.Mean + The running mean of the (squared) errors from the predictions of the model to update the dynamic threshold. + dynamic_se_variance : stats.Var + The running variance of the (squared) errors from the predictions of the model to update the dynamic threshold. + iter : int + The number of iterations (data points) passed. + + Examples + -------- + + >>> from river import datasets + >>> from river import time_series + >>> from river import anomaly + >>> from river import preprocessing + >>> from river import linear_model + >>> from river import optim + + >>> period = 12 + >>> predictive_model = time_series.SNARIMAX( + ... p=period, + ... d=1, + ... q=period, + ... m=period, + ... sd=1, + ... regressor=( + ... preprocessing.StandardScaler() + ... | linear_model.LinearRegression( + ... optimizer=optim.SGD(0.005), + ... ) + ... ), + ... ) + + >>> PAD = anomaly.PredictiveAnomalyDetection( + ... predictive_model, + ... horizon=1, + ... n_std=3.5, + ... warmup_period=15 + ... ) + + >>> scores = [] + + >>> for t, (x, y) in enumerate(datasets.AirlinePassengers()): + ... score = PAD.score_one(None, y) + ... PAD = PAD.learn_one(None, y) + ... scores.append(score) + + >>> print(scores[-1]) + 0.05329236123455621 + + References + ---------- + [^1]: Laptev N, Amizadeh S, Flint I. Generic and scalable framework for Automated Time-series Anomaly Detection. + Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2015. + doi:10.1145/2783258.2788611. + """ + + def __init__( + self, + predictive_model: base.Estimator | None = None, + horizon: int = 1, + n_std: float = 3.0, + warmup_period: int = 0, + ): + + self.predictive_model = ( + predictive_model + if predictive_model is not None + else preprocessing.MinMaxScaler() | linear_model.LinearRegression() + ) + + self.horizon = horizon + self.n_std = n_std + self.warmup_period = warmup_period + + # Initialize necessary statistical measures + self.dynamic_mae = stats.Mean() + self.dynamic_se_variance = stats.Var() + + # Initialize necessary values for warm-up procedure + self.iter: int = 0 + + # This method is called to make the predictive model learn one example + def learn_one(self, x: dict | None, y: base.typing.Target | float): + self.iter += 1 + + # Check whether the model is a time-series forecasting or regression/classification model + if isinstance( + self.predictive_model, time_series.base.Forecaster + ) and isinstance(y, float): + # When there's no data point as dict of features, the target will be passed + # to the forecaster as an exogenous variable. + if not x: + self.predictive_model.learn_one(y=y) + else: + self.predictive_model.learn_one(y=y, x=x) + else: + self.predictive_model.learn_one(x=x, y=y) + return self + + def score_one(self, x: dict, y: base.typing.Target): + # Return the predicted value of x from the predictive model, first by checking whether + # it is a time-series forecaster. + if isinstance(self.predictive_model, time_series.base.Forecaster): + y_pred = self.predictive_model.forecast(self.horizon)[0] + else: + y_pred = self.predictive_model.predict_one(x) + + # Calculate the squared error + squared_error = (y_pred - y) ** 2 + + # Calculate the threshold + threshold = self.dynamic_mae.get() + ( + self.n_std * math.sqrt(self.dynamic_se_variance.get()) + ) + + # When warmup hyper-parameter is used, the anomaly score is only returned once the warmup period has passed. + # When the warmup period has not passed, the default value of the anomaly score is 0.0 + if self.iter < self.warmup_period: + return 0.0 + + # Update MAE and SEV when the warm-up parameter has passed. + self.dynamic_mae.update(squared_error) + self.dynamic_se_variance.update(squared_error) + + # An error above the threshold will result in a score of 1.0. + # Else, the score will be linearly distributed within the interval (0.0, 1.0) + if squared_error >= threshold: + return 1.0 + else: + return squared_error / threshold From 2491b09d46a4b8a87dd56595fb1f060ad2227e44 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo Date: Tue, 19 Mar 2024 01:05:11 +0700 Subject: [PATCH 236/347] Add PAD to unreleased.md file. --- docs/unreleased.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/unreleased.md b/docs/unreleased.md index 743c1a54f7..5321494555 100644 --- a/docs/unreleased.md +++ b/docs/unreleased.md @@ -1,5 +1,9 @@ # Unreleased +## anomaly + +- Added `PredictiveAnomalyDetection`, a semi-supervised technique that employs a predictive model for anomaly detection. + ## drift - Added `FHDDM` drift detector. From 26675f7744a59c0a12315d8857b0a691588ea303 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 28 Mar 2024 22:18:16 +0100 Subject: [PATCH 237/347] Update __version__.py --- river/__version__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/__version__.py b/river/__version__.py index 8873a450be..87781a7f0d 100644 --- a/river/__version__.py +++ b/river/__version__.py @@ -1,3 +1,3 @@ from __future__ import annotations -__version__ = "0.21.0" +__version__ = "0.21.1" From 26aa4d7b1525f03090eaeb9dcf1d3f3ced95e627 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 28 Mar 2024 22:19:43 +0100 Subject: [PATCH 238/347] prepare release --- docs/{ => releases}/unreleased.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) rename docs/{ => releases}/unreleased.md (73%) diff --git a/docs/unreleased.md b/docs/releases/unreleased.md similarity index 73% rename from docs/unreleased.md rename to docs/releases/unreleased.md index 5321494555..185e950999 100644 --- a/docs/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,4 +1,6 @@ -# Unreleased +# 0.21.1 - 2024-03-28 + +This release should fix some of the installation issues when building the River wheel from scratch. ## anomaly From aac6318a5e9b593ac07ea55171121553d92ec712 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 28 Mar 2024 22:20:06 +0100 Subject: [PATCH 239/347] Update pyproject.toml --- pyproject.toml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index b0f3722394..96a5cbdf55 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "river" -version = "0.21.0" +version = "0.21.1" description = "Online machine learning in Python" readme = "README.md" homepage = "https://riverml.xyz/" @@ -12,13 +12,13 @@ repository = "https://github.com/online-ml/river/" authors = ["Max Halford "] include = [ - "**/*.cpp", - "**/*.pyx", + "**/*.cpp", + "**/*.pyx", "**/*.pxd", "river/datasets/*.csv", "river/datasets/*.gz", "river/datasets/*.zip", - "river/stream/*.zip", + "river/stream/*.zip", "Cargo.toml", "rust_src/**/*" ] From c7fbedfaba9d68622588b79b23cca20a115e5d2d Mon Sep 17 00:00:00 2001 From: Francesco Bruzzesi <42817048+FBruzzesi@users.noreply.github.com> Date: Mon, 15 Apr 2024 08:57:29 +0200 Subject: [PATCH 240/347] pin version (#1525) --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index d031805248..be4c36ca31 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -86,7 +86,7 @@ plugins: extra_javascript: - javascripts/config.js - https://polyfill.io/v3/polyfill.min.js?features=es6 - - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + - https://cdn.jsdelivr.net/npm/mathjax@3.2/es5/tex-mml-chtml.js - https://cdn.jsdelivr.net/npm/vega@5 - https://cdn.jsdelivr.net/npm/vega-lite@5 - https://cdn.jsdelivr.net/npm/vega-embed@6 From 4a93461bea1663d5e10daac0f5ec0458073bf64d Mon Sep 17 00:00:00 2001 From: Francesco Bruzzesi <42817048+FBruzzesi@users.noreply.github.com> Date: Mon, 22 Apr 2024 21:43:33 +0200 Subject: [PATCH 241/347] docs: update Pipelines recipe (#1528) - Document was out of date wrt. the introduction of the `compose.learn_during_predict` context manager. --- docs/recipes/pipelines.ipynb | 401 +++++++++++++++++++++++++++++------ 1 file changed, 333 insertions(+), 68 deletions(-) diff --git a/docs/recipes/pipelines.ipynb b/docs/recipes/pipelines.ipynb index 2ef994ab44..bb2a552261 100644 --- a/docs/recipes/pipelines.ipynb +++ b/docs/recipes/pipelines.ipynb @@ -13,7 +13,9 @@ "source": [ "Pipelines are an integral part of River. We encourage their usage and apply them in many of their examples.\n", "\n", - "The `compose.Pipeline` contains all the logic for building and applying pipelines. A pipeline is essentially a list of estimators that are applied in sequence. The only requirement is that the first `n - 1` steps be transformers. The last step can be a regressor, a classifier, a clusterer, a transformer, etc. Here is an example:" + "The `compose.Pipeline` contains all the logic for building and applying pipelines. A pipeline is essentially a list of estimators that are applied in sequence. The only requirement is that the first `n - 1` steps be transformers. The last step can be a regressor, a classifier, a clusterer, a transformer, etc.\n", + "\n", + "Here is an example:" ] }, { @@ -100,7 +102,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A pipeline has a `draw` method that can be used to visualize it:" + "A pipeline, as any River estimator, has a `_repr_html_` method, which can be used to visualize it in Jupyter-like notebooks:" ] }, { @@ -287,7 +289,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`compose.Pipeline` inherits from `base.Estimator`, which means that it has a `learn_one` method. You would expect `learn_one` to update each estimator, but **that's not actually what happens**. Instead, the transformers are updated when `predict_one` (or `predict_proba_one` for that matter) is called. Indeed, in online machine learning, we can update the unsupervised parts of our model when a sample arrives. We don't have to wait for the ground truth to arrive in order to update unsupervised estimators that don't depend on it. In other words, in a pipeline, `learn_one` updates the supervised parts, whilst `predict_one` updates the unsupervised parts. It's important to be aware of this behavior, as it is quite different to what is done in other libraries that rely on batch machine learning.\n", + "`compose.Pipeline` implements a `learn_one` method which in sequence calls the `learn_one` of each component and a `predict_one` (resp `predict_proba_one`) method which calls `transform_one` on the first `n - 1` steps and `predict_one` (resp `predict_proba_one`) on the last step.\n", "\n", "Here is a small example to illustrate the previous point:" ] @@ -334,85 +336,71 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us call `predict_one`, which will update each transformer, but won't update the linear regression." + "We can predict the target value of a new sample by calling the `predict_one` method, however, by default, `predict_one` does not update any model parameter, therefore the predictions will be 0 and the model parameters will remain the default values (0 for `StandardScaler` component):" ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:49:53.321544Z", - "iopub.status.busy": "2023-12-04T17:49:53.321411Z", - "iopub.status.idle": "2023-12-04T17:49:53.331177Z", - "shell.execute_reply": "2023-12-04T17:49:53.330912Z" - }, - "tags": [] - }, + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "model.predict_one(x)=0.00, y=43.76\n", + "model['StandardScaler'].means = defaultdict(, {'ordinal_date': 0.0, 'gallup': 0.0, 'ipsos': 0.0, 'morning_consult': 0.0, 'rasmussen': 0.0, 'you_gov': 0.0})\n", + "model.predict_one(x)=0.00, y=43.71\n", + "model['StandardScaler'].means = defaultdict(, {'ordinal_date': 0.0, 'gallup': 0.0, 'ipsos': 0.0, 'morning_consult': 0.0, 'rasmussen': 0.0, 'you_gov': 0.0})\n" + ] } ], "source": [ - "model.predict_one(x)" + "for (x, y) in dataset.take(2):\n", + " print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n", + " print(f\"{model['StandardScaler'].means = }\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The prediction is nil because each weight of the linear regression is equal to 0." + "`learn_one` updates pipeline stateful steps, parameters and the prediction change:" ] }, { "cell_type": "code", "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:49:53.332637Z", - "iopub.status.busy": "2023-12-04T17:49:53.332562Z", - "iopub.status.idle": "2023-12-04T17:49:53.341944Z", - "shell.execute_reply": "2023-12-04T17:49:53.341509Z" - }, - "tags": [] - }, + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "defaultdict(float,\n", - " {'ordinal_date': 0.0,\n", - " 'gallup': 0.0,\n", - " 'ipsos': 0.0,\n", - " 'morning_consult': 0.0,\n", - " 'rasmussen': 0.0,\n", - " 'you_gov': 0.0})" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "model.predict_one(x)=0.88, y=43.76\n", + "model['StandardScaler'].means = defaultdict(, {'ordinal_date': 736389.0, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 44.104692, 'you_gov': 43.636914000000004})\n", + "model.predict_one(x)=9.44, y=43.71\n", + "model['StandardScaler'].means = defaultdict(, {'ordinal_date': 736389.5, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 45.104692, 'you_gov': 42.636914000000004})\n" + ] } ], "source": [ - "model['StandardScaler'].means" + "for (x, y) in dataset.take(2):\n", + " model.learn_one(x, y)\n", + "\n", + " print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n", + " print(f\"{model['StandardScaler'].means = }\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, the means of each feature have been updated, even though we called `predict_one` and not `learn_one`.\n", + "Each component of the pipeline has been updated with the new data point. \n", "\n", - "Note that if you call `transform_one` with a pipeline who's last step is not a transformer, then the output from the last transformer (which is thus the penultimate step) will be returned:" + "A pipeline is a very powerful tool that can be used to chain together multiple steps in a machine learning workflow.\n", + "\n", + "Notice that it is also possible to call `transform_one` with a pipeline, this method will run `transform_one` of each transformer in it, and return the result of the last transformer (which is thus the penultimate step if the last step is a _predictor_ or _clusterer_, while it is the last step if the last step is a _transformer_):" ] }, { @@ -420,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-12-04T17:49:53.344203Z", - "iopub.status.busy": "2023-12-04T17:49:53.344052Z", - "iopub.status.idle": "2023-12-04T17:49:53.355449Z", - "shell.execute_reply": "2023-12-04T17:49:53.355075Z" + "iopub.execute_input": "2023-12-04T17:49:53.332637Z", + "iopub.status.busy": "2023-12-04T17:49:53.332562Z", + "iopub.status.idle": "2023-12-04T17:49:53.341944Z", + "shell.execute_reply": "2023-12-04T17:49:53.341509Z" }, "tags": [] }, @@ -431,33 +419,33 @@ { "data": { "text/plain": [ - "{'ordinal_date': 0.0,\n", + "{'ordinal_date': 1.0,\n", " 'gallup': 0.0,\n", " 'ipsos': 0.0,\n", " 'morning_consult': 0.0,\n", - " 'rasmussen': 0.0,\n", - " 'you_gov': 0.0,\n", - " 'ordinal_date*ordinal_date': 0.0,\n", + " 'rasmussen': 1.0,\n", + " 'you_gov': -1.0,\n", + " 'ordinal_date*ordinal_date': 1.0,\n", " 'gallup*ordinal_date': 0.0,\n", " 'ipsos*ordinal_date': 0.0,\n", " 'morning_consult*ordinal_date': 0.0,\n", - " 'ordinal_date*rasmussen': 0.0,\n", - " 'ordinal_date*you_gov': 0.0,\n", + " 'ordinal_date*rasmussen': 1.0,\n", + " 'ordinal_date*you_gov': -1.0,\n", " 'gallup*gallup': 0.0,\n", " 'gallup*ipsos': 0.0,\n", " 'gallup*morning_consult': 0.0,\n", " 'gallup*rasmussen': 0.0,\n", - " 'gallup*you_gov': 0.0,\n", + " 'gallup*you_gov': -0.0,\n", " 'ipsos*ipsos': 0.0,\n", " 'ipsos*morning_consult': 0.0,\n", " 'ipsos*rasmussen': 0.0,\n", - " 'ipsos*you_gov': 0.0,\n", + " 'ipsos*you_gov': -0.0,\n", " 'morning_consult*morning_consult': 0.0,\n", " 'morning_consult*rasmussen': 0.0,\n", - " 'morning_consult*you_gov': 0.0,\n", - " 'rasmussen*rasmussen': 0.0,\n", - " 'rasmussen*you_gov': 0.0,\n", - " 'you_gov*you_gov': 0.0}" + " 'morning_consult*you_gov': -0.0,\n", + " 'rasmussen*rasmussen': 1.0,\n", + " 'rasmussen*you_gov': -1.0,\n", + " 'you_gov*you_gov': 1.0}" ] }, "execution_count": 8, @@ -473,7 +461,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In many cases, you might want to connect a step to multiple steps. For instance, you might to extract different kinds of features from a single input. An elegant way to do this is to use a `compose.TransformerUnion`. Essentially, the latter is a list of transformers who's results will be merged into a single `dict` when `transform_one` is called. As an example let's say that we want to apply a `feature_extraction.RBFSampler` as well as the `feature_extraction.PolynomialExtender`. This may be done as so:" + "In many cases, you might want to connect a step to multiple steps. For instance, you might to extract different kinds of features from a single input. An elegant way to do this is to use a `compose.TransformerUnion`. Essentially, the latter is a list of transformers who's results will be merged into a single `dict` when `transform_one` is called.\n", + "\n", + "As an example let's say that we want to apply a `feature_extraction.RBFSampler` as well as the `feature_extraction.PolynomialExtender`. This may be done as so:" ] }, { @@ -709,7 +699,282 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Pipelines provide the benefit of removing a lot of cruft by taking care of tedious details for you. They also enable to clearly define what steps your model is made of. Finally, having your model in a single object means that you can move it around more easily. Note that you can include user-defined functions in a pipeline by using a `compose.FuncTransformer`." + "Pipelines provide the benefit of removing a lot of cruft by taking care of tedious details for you. They also enable to clearly define what steps your model is made of.\n", + "\n", + "Finally, having your model in a single object means that you can move it around more easily.\n", + "\n", + "Note that you can include user-defined functions in a pipeline by using a `compose.FuncTransformer`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning during predict\n", + "\n", + "In online machine learning, we can update the unsupervised parts of our model when a sample arrives. We don't _really_ have to wait for the ground truth to arrive in order to update unsupervised estimators that don't depend on it.\n", + "\n", + "In other words, in a pipeline, `learn_one` updates the supervised parts, whilst `predict_one` (or `predict_proba_one` for that matter) **can** update the unsupervised parts, which often yields better results. \n", + "\n", + "In river, we can achieve this behavior using a dedicated context manager: `compose.learn_during_predict`.\n", + "\n", + "Here is the same example as before, with the only difference of activating the such learning during predict behavior:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "model = (\n", + " preprocessing.StandardScaler() |\n", + " feature_extraction.PolynomialExtender() |\n", + " linear_model.LinearRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model.predict_one(x)=0.00, y=43.76\n", + "model['StandardScaler'].means = defaultdict(, {'ordinal_date': 736389.0, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 44.104692, 'you_gov': 43.636914000000004})\n", + "model.predict_one(x)=0.00, y=43.71\n", + "model['StandardScaler'].means = defaultdict(, {'ordinal_date': 736389.5, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 45.104692, 'you_gov': 42.636914000000004})\n" + ] + } + ], + "source": [ + "with compose.learn_during_predict():\n", + " for (x, y) in dataset.take(2):\n", + "\n", + " print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n", + " print(f\"{model['StandardScaler'].means = }\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calling `predict_one` within this context will update each transformer of the pipeline. For instance here we can see that the mean of each feature of the standard scaler step have been updated.\n", + "\n", + "On the other hand, the supervised part of our pipeline, the linear regression, has not been updated or learned anything yet. Hence the prediction on any sample will be nil because each weight is still equal to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, {})" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict_one(x), model[\"LinearRegression\"].weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance Comparison\n", + "\n", + "One may wonder what is the advantage of learning during predict. Let's compare the performance of a pipeline with and without learning during predict, in two scenarios: one in which the flow of data stays the same, we just update " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from contextlib import nullcontext\n", + "from river import metrics\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def score_pipeline(learn_during_predict: bool, n_learning_samples: int | None = None) -> float:\n", + " \"\"\"Scores a pipeline on the TrumpApproval dataset.\n", + "\n", + " Parameters\n", + " ----------\n", + " learn_during_predict : bool\n", + " Whether or not to learn the unsupervided components during the prediction step.\n", + " If False it will only learn when `learn_one` is explicitly called.\n", + " n_learning_samples : int | None \n", + " Number of samples used to `learn_one`.\n", + "\n", + " Return\n", + " ------\n", + " MAE : float\n", + " Mean absolute error of the pipeline on the dataset\n", + " \"\"\"\n", + "\n", + " dataset = datasets.TrumpApproval()\n", + "\n", + " model = (\n", + " preprocessing.StandardScaler() |\n", + " linear_model.LinearRegression()\n", + " )\n", + "\n", + " metric = metrics.MAE()\n", + "\n", + " ctx = compose.learn_during_predict if learn_during_predict else nullcontext\n", + " n_learning_samples = n_learning_samples or dataset.n_samples\n", + "\n", + " with ctx():\n", + " for _idx, (x, y) in enumerate(dataset):\n", + " y_pred = model.predict_one(x)\n", + "\n", + " metric.update(y, y_pred)\n", + " \n", + " if _idx < n_learning_samples:\n", + " model.learn_one(x, y)\n", + "\n", + " return metric.get()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "max_samples = datasets.TrumpApproval().n_samples\n", + "\n", + "results = [\n", + " {\n", + " \"learn_during_predict\": learn_during_predict,\n", + " \"pct_learning_samples\": round(100*n_learning_samples/max_samples, 0),\n", + " \"mae\": score_pipeline(learn_during_predict=learn_during_predict, n_learning_samples=n_learning_samples)\n", + " }\n", + " for learn_during_predict in (True, False)\n", + " for n_learning_samples in range(max_samples, max_samples//10, -(max_samples//10))\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
learn_during_predictFalseTrue
pct_learning_samples  
100.0%1.3145481.347434
90.0%1.6293331.355274
80.0%2.7121251.371599
70.0%4.8406201.440773
60.0%8.9186341.498240
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\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(pd.DataFrame(results)\n", + " .pivot(columns=\"learn_during_predict\", index=\"pct_learning_samples\", values=\"mae\")\n", + " .sort_index(ascending=False)\n", + " .style.format_index('{0}%')\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the resulting table above, the scores are comparable only in the case in which the percentage of learning samples above 90%. After that the score starts to degrade quite fast as the percentage of learning samples decreases, and it is very remarkable (one order of magnitude or more) when less than 50% of the samples are used for learning.\n", + "\n", + "Although a simple case, this examplify how powerful it can be to learn unsupervised components during predict." ] } ], @@ -729,7 +994,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.11.8" } }, "nbformat": 4, From 010ac7367ad48cc11bcc7570525f138e7ed34f35 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 22 Apr 2024 22:48:25 +0200 Subject: [PATCH 242/347] try auto --- .github/workflows/pypi.yml | 2 +- docs/overrides/partials/integrations/analytics.html | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 9d0a294add..9220c1e445 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -48,7 +48,7 @@ jobs: # CIBW_ARCHS_MACOS: "x86_64 arm64" CIBW_ARCHS_MACOS: "universal2" # We don't build ARM64 wheels yet because there's a Rust issue - CIBW_ARCHS_WINDOWS: "AMD64 x86" + CIBW_ARCHS_WINDOWS: "auto" # Rust nighlty doesn't seem to be available for musl linux on i686 and ppc64le and s390x (yet) CIBW_SKIP: "*-musllinux_i686 *-musllinux_ppc64le *-musllinux_s390x" diff --git a/docs/overrides/partials/integrations/analytics.html b/docs/overrides/partials/integrations/analytics.html index 9d6104fafd..f0acec71cd 100644 --- a/docs/overrides/partials/integrations/analytics.html +++ b/docs/overrides/partials/integrations/analytics.html @@ -1,5 +1,5 @@ - From c9fb646137e5e3639e9d3727c29f67672fe9d434 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 22 Apr 2024 22:57:07 +0200 Subject: [PATCH 243/347] Update pypi.yml --- .github/workflows/pypi.yml | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 9220c1e445..988eec5241 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -11,14 +11,24 @@ jobs: name: Build wheels on ${{ matrix.os }} runs-on: ${{ matrix.os }} strategy: + # https://github.com/actions/runner-images/tree/main matrix: - os: [ubuntu-latest, windows-latest, macos-11, macos-latest] + os: + [ + ubuntu-20.04, + ubuntu-22.04, + windows-2019, + windows-2022, + macos-12, + macos-13, + macos-14, + ] steps: - uses: actions/checkout@v3 - name: Set up rust - if: matrix.os != 'ubuntu-latest' + if: matrix.os != 'ubuntu-20.04' && matrix.os != 'ubuntu-22.04' uses: actions-rs/toolchain@v1 with: profile: minimal @@ -26,29 +36,29 @@ jobs: override: true - run: rustup target add aarch64-apple-darwin - if: matrix.os == 'macos-latest' || matrix.os == 'macos-11' + if: matrix.os == 'macos-12' || matrix.os == 'macos-13' || matrix.os == 'macos-14' - run: rustup toolchain install stable-i686-pc-windows-msvc - if: matrix.os == 'windows-latest' + if: matrix.os == 'windows-2019' || matrix.os == 'windows-2022' - run: rustup target add i686-pc-windows-msvc - if: matrix.os == 'windows-latest' + if: matrix.os == 'windows-2019' || matrix.os == 'windows-2022' - name: Set up QEMU - if: matrix.os == 'ubuntu-latest' + if: matrix.os == 'ubuntu-20.04' || matrix.os == 'ubuntu-22.04' uses: docker/setup-qemu-action@v3 with: platforms: all - name: Build wheels - uses: pypa/cibuildwheel@v2.16.2 + uses: pypa/cibuildwheel@v2.17.0 env: CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" # CIBW_ARCHS_MACOS: "x86_64 arm64" CIBW_ARCHS_MACOS: "universal2" # We don't build ARM64 wheels yet because there's a Rust issue - CIBW_ARCHS_WINDOWS: "auto" + CIBW_ARCHS_WINDOWS: "AMD64 x86" # Rust nighlty doesn't seem to be available for musl linux on i686 and ppc64le and s390x (yet) CIBW_SKIP: "*-musllinux_i686 *-musllinux_ppc64le *-musllinux_s390x" From b80477f10d9667d5129050f90814abd8bbe0df8a Mon Sep 17 00:00:00 2001 From: Max Halford Date: Mon, 22 Apr 2024 23:02:13 +0200 Subject: [PATCH 244/347] Update pypi.yml --- .github/workflows/pypi.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 988eec5241..b20069a6b5 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -35,7 +35,7 @@ jobs: toolchain: nightly override: true - - run: rustup target add aarch64-apple-darwin + - run: rustup target add aarch64-apple-darwin && rustup target add x86_64-apple-darwin if: matrix.os == 'macos-12' || matrix.os == 'macos-13' || matrix.os == 'macos-14' - run: rustup toolchain install stable-i686-pc-windows-msvc From 46559fa3ec30a857e3957fe22e7165276035a187 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 23 Apr 2024 06:27:27 +0200 Subject: [PATCH 245/347] Update pypi.yml --- .github/workflows/pypi.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index b20069a6b5..c56f1f6f2c 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -52,6 +52,7 @@ jobs: - name: Build wheels uses: pypa/cibuildwheel@v2.17.0 + timeout-minutes: 720 env: CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" From eec30f16cc04391daabfdaebe0f80b5d1074de6f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 25 Apr 2024 01:00:46 +0200 Subject: [PATCH 246/347] Bump pillow from 10.1.0 to 10.3.0 (#1520) Bumps [pillow](https://github.com/python-pillow/Pillow) from 10.1.0 to 10.3.0. - [Release notes](https://github.com/python-pillow/Pillow/releases) - [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst) - [Commits](https://github.com/python-pillow/Pillow/compare/10.1.0...10.3.0) --- updated-dependencies: - dependency-name: pillow dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 131 ++++++++++++++++++++++++++++++---------------------- 1 file changed, 75 insertions(+), 56 deletions(-) diff --git a/poetry.lock b/poetry.lock index 9ca4786943..ba5c329a7b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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"pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" -python-versions = ">=3.9,<3.13" -content-hash = "aeaff58cc4d447bbb5c86a09998c1bf89291d5179805cde2785ac2471f907c02" +python-versions = "^3.9" +content-hash = "78eec58254f5beef8b8fa0fdc0d883087283495d4fb627530347ba07491c862f" diff --git a/pyproject.toml b/pyproject.toml index 96a5cbdf55..304564dd47 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,9 +28,9 @@ generate-setup-file = true script = "build.py" [tool.poetry.dependencies] -python = ">=3.9,<3.13" +python = "^3.9" numpy = "^1.23.0" -scipy = "^1.8.1" +scipy = "^1.12.1" pandas = "^2.1" polars = "^0.20.8" @@ -85,7 +85,6 @@ optional = true [tool.poetry.group.benchmark.dependencies] "dominate" = "2.8.0" "scikit-learn" = "1.3.1" -"scipy" = "1.11.3" "tabulate" = "0.9.0" "vowpalwabbit" = "9.9.0" "watermark" = "2.4.3" From 79ea8c3d56a7afcdf53bda5f270dbe553bbc5888 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Fri, 3 May 2024 09:22:32 -0300 Subject: [PATCH 248/347] fix failing test (wrt #1253) --- river/compose/test_product.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/river/compose/test_product.py b/river/compose/test_product.py index 9f557af6d1..746121b8b5 100644 --- a/river/compose/test_product.py +++ b/river/compose/test_product.py @@ -108,12 +108,12 @@ def test_issue_1253(): >>> _ = model.predict_many(X) >>> model.transform_many(X) cat_1*feat_2 cat_2*feat_2 cat_1 cat_2 - 0 -1.196841 0.000000 1 0 - 1 1.304619 0.000000 1 0 - 2 -1.294091 0.000000 1 0 - 3 0.287426 0.000000 1 0 - 4 -0.143960 0.000000 1 0 - 5 0.000000 1.042847 0 1 + 0 -1.196841 0 1 0 + 1 1.304619 0 1 0 + 2 -1.294091 0 1 0 + 3 0.287426 0 1 0 + 4 -0.143960 0 1 0 + 5 0 1.042847 0 1 """ From c5f00231cd303fdd07b4acc682f0cb6c21f8b236 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Fri, 3 May 2024 10:05:14 -0300 Subject: [PATCH 249/347] Adding ODAC (#1537) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add ODAC algorithm * ODAC passed in pytest * draw() fixed * multiple fixes * fix docs * mypy * Changes per Gonçalo's review * fix failing test (wrt #1253) * fix tests * nitpick * more nitpick * release notes --------- Co-authored-by: Bezum30 --- docs/releases/unreleased.md | 4 + river/cluster/__init__.py | 3 +- river/cluster/odac.py | 491 ++++++++++++++++++++++++++++++++++++ 3 files changed, 497 insertions(+), 1 deletion(-) create mode 100644 river/cluster/odac.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 185e950999..8e93b1f3e4 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -6,6 +6,10 @@ This release should fix some of the installation issues when building the River - Added `PredictiveAnomalyDetection`, a semi-supervised technique that employs a predictive model for anomaly detection. +## cluster + +- Added `ODAC` (Online Divisive-Agglomerative Clustering) for clustering time series. + ## drift - Added `FHDDM` drift detector. diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 7ab5d89ac7..3915740248 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -5,7 +5,8 @@ from .dbstream import DBSTREAM from .denstream import DenStream from .k_means import KMeans +from .odac import ODAC from .streamkmeans import STREAMKMeans from .textclust import TextClust -__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust"] +__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "ODAC", "STREAMKMeans", "TextClust"] diff --git a/river/cluster/odac.py b/river/cluster/odac.py new file mode 100644 index 0000000000..60b752400f --- /dev/null +++ b/river/cluster/odac.py @@ -0,0 +1,491 @@ +from __future__ import annotations + +import collections +import itertools +import math +import typing + +from river import base, stats + + +class ODAC(base.Clusterer): + """The Online Divisive-Agglomerative Clustering (ODAC) aims at continuously + maintaining a hierarchical cluster structure from evolving time series data + streams. + + ODAC continuosly monitors the evolution of clusters' diameters and split or merge + them by gathering more data or reacting to concept drift. Such changes are supported + by a confidence level that comes from the Hoeffding bound. ODAC relies on keeping + the linear correlation between series to evaluate whether or not the time series + hierarchy has changed. + + The distance between time-series a and b is given by `rnomc(a, b) = sqrt((1 - corr(a, b)) / 2)`, + where `corr(a, b)` is the Pearson Correlation coefficient. + + In the next topics, ε stands for the Hoeffding bound. + + **The Merge Operator** + + The Splitting Criteria guarantees that cluster's diameters monotonically decrease. + + - Assume Clusters: cj with descendants ck and cs. + + - If diameter (ck) - diameter (cj) > ε OR diameter (cs) - diameter (cj ) > ε: + + * There is a change in the correlation structure: Merge clusters ck and cs into cj. + + **Splitting Criteria** + + Consider: + + - d0: the minimum distance; + + - d1: the farthest distance; + + - d2: the second farthest distance. + + Then: + + - if d1 - d2 > εk or t > εk then + + - if (d1 - d0)|(d1 - d_avg) - (d_avg - d0) > εk then + + * Split the cluster + + + Parameters + ---------- + confidence_level + The confidence level that user wants to work. + n_min + Number of minimum observations to gather before checking whether or not + clusters must be split or merged. + tau + The value of tau to use in the calculation of the Hoeffding bound. + + Attributes + ---------- + structure_changed : bool + This variable is true when the structure changed, produced by splitting or aggregation. + + Examples + -------- + + >>> from river.cluster import ODAC + >>> from river.datasets import synth + + >>> model = ODAC(confidence_level=0.9) + + >>> dataset = synth.FriedmanDrift(drift_type='gra', position=(150, 200), seed=42) + + >>> for i, (x, _) in enumerate(dataset.take(500)): + ... model.learn_one(x) + ... if model.structure_changed: + ... print(f"Structure changed at observation {i + 1}") + Structure changed at observation 1 + Structure changed at observation 100 + Structure changed at observation 200 + Structure changed at observation 300 + + >>> print(model.draw(n_decimal_places=2)) + ROOT d1=0.79 d2=0.76 [NOT ACTIVE] + ├── CH1_LVL_1 d1=0.74 d2=0.72 [NOT ACTIVE] + │ ├── CH1_LVL_2 d1= [3] + │ └── CH2_LVL_2 d1=0.73 [2, 4] + └── CH2_LVL_1 d1=0.81 d2=0.78 [NOT ACTIVE] + ├── CH1_LVL_2 d1=0.73 d2=0.67 [NOT ACTIVE] + │ ├── CH1_LVL_3 d1=0.72 [0, 9] + │ └── CH2_LVL_3 d1= [1] + └── CH2_LVL_2 d1=0.74 d2=0.73 [NOT ACTIVE] + ├── CH1_LVL_3 d1=0.71 [5, 6] + └── CH2_LVL_3 d1=0.71 [7, 8] + + + + You can acess some properties of the clustering model directly: + + >>> model.n_clusters + 11 + + >>> model.n_active_clusters + 6 + + >>> model.height + 3 + + These properties are also available in a summarized form: + + >>> model.summary + {'n_clusters': 11, 'n_active_clusters': 6, 'height': 3} + + References + ---------- + [^1]: [Hierarchical clustering of time-series data streams.](http://doi.org/10.1109/TKDE.2007.190727) + + """ + + def __init__(self, confidence_level=0.9, n_min=100, tau=0.1): + if not (confidence_level > 0.0 and confidence_level < 1.0): + raise ValueError("confidence_level must be between 0 and 1.") + if not n_min > 0: + raise ValueError("n_min must be greater than 1.") + if not tau > 0.0: + raise ValueError("tau must be greater than 0.") + + self._root_node = ODACCluster("ROOT") + self.confidence_level = confidence_level + self.n_min = n_min + self.tau = tau + + self._update_timer: int = n_min + self._n_observations: int = 0 + self._is_init = False + + self._structure_changed = False + + @property + def n_active_clusters(self): + return self._count_active_clusters(self._root_node) + + @property + def n_clusters(self): + return self._count_clusters(self._root_node) + + @property + def height(self) -> int: + return self._calculate_height(self._root_node) + + @property + def summary(self): + summary = { + "n_clusters": self.n_clusters, + "n_active_clusters": self.n_active_clusters, + "height": self.height, + } + return summary + + def _calculate_height(self, node: ODACCluster) -> int: + if node.children is not None: + child_heights = [ + self._calculate_height(child) + for child in [node.children.first, node.children.second] + ] + return max(child_heights) + 1 + else: + return 0 + + def _count_clusters(self, node: ODACCluster) -> int: + count = 1 + if node.children is not None: + for child in [node.children.first, node.children.second]: + count += self._count_clusters(child) + return count + + def _count_active_clusters(self, node: ODACCluster) -> int: + if node.active: + return 1 + elif node.children is not None: + return sum( + self._count_active_clusters(child) + for child in [node.children.first, node.children.second] + ) + else: + return 0 + + def _find_all_active_clusters(self, node: ODACCluster): + if node.active: + yield node + elif node.children is not None: + for child in [node.children.first, node.children.second]: + yield from self._find_all_active_clusters(child) + + def learn_one(self, x: dict): + # If x is empty, do nothing + if not x: + return + + if self._structure_changed: + self._structure_changed = False + + if not self._is_init: + # Initialize the first cluster which is the ROOT cluster + self._root_node(list(x.keys())) + self._structure_changed = True + self._is_init = True + + # Update the total observations received + self._n_observations += 1 + + # Split control + self._update_timer -= 1 + + # For each active cluster update the statistics and time to time verify if + # the cluster needs to merge or expand + for leaf in self._find_all_active_clusters(self._root_node): + # For safety + if not leaf.active: + continue + + # Update statistics + leaf.update_statistics(x) + + # Time to time approach + if self._update_timer == 0: + # Calculate all the crucial variables to the next procedure + leaf.calculate_coefficients(self.confidence_level) + + if leaf.test_aggregate() or leaf.test_split(tau=self.tau): + # Put the flag change_detected to true to indicate to the user that the structure changed + self._structure_changed = True + + # Reset the timer + if self._update_timer == 0: + self._update_timer = self.n_min + + # This algorithm does not predict anything. It builds a hierarchical cluster's structure + def predict_one(self, x: dict): + raise NotImplementedError + + def draw(self, n_decimal_places: int = 2) -> str: + """Method to draw the the hierarchical cluster's structure. + + Parameters + ---------- + n_decimal_places + The number of decimal places that user wants to view in distances of each active cluster + in the hierarchical cluster's structure. + + """ + if not (n_decimal_places > 0 and n_decimal_places < 10): + raise ValueError("n_decimal_places must be between 1 and 9") + + return self._root_node.design_structure(n_decimal_places) + + @property + def structure_changed(self) -> bool: + return self._structure_changed + + +class ODACCluster(base.Base): + """Cluster class for representing individual clusters.""" + + # Constructor method for Cluster class + def __init__(self, name: str, parent: ODACCluster | None = None): + self.name = name + self.parent: ODACCluster | None = parent + self.active = True + self.children: ODACChildren | None = None + + self.timeseries_names: list[typing.Hashable] + self._statistics: dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | None + + self.d1: float | None = None + self.d2: float | None = None + self.e: float = 0 + self.d0: float | None = None + self.avg: float | None = None + + self.pivot_0: tuple[typing.Hashable, typing.Hashable] + self.pivot_1: tuple[typing.Hashable, typing.Hashable] + self.pivot_2: tuple[typing.Hashable, typing.Hashable] + + self.n = 0 + + # Method to design the structure of the cluster tree + def design_structure(self, decimal_places=2) -> str: + pre_0 = " " + pre_1 = "│ " + pre_2 = "├── " + pre_3 = "└── " + node = self + prefix = ( + pre_2 + if node.parent is not None and id(node) != id(node.parent.children.second) # type: ignore + else pre_3 + ) + while node.parent is not None and node.parent.parent is not None: + if id(node.parent) != id(node.parent.parent.children.second): # type: ignore + prefix = pre_1 + prefix + else: + prefix = pre_0 + prefix + node = node.parent + + if self.d1 is not None: + r_d1 = f"{self.d1:.{decimal_places}f}" + else: + r_d1 = "" + + if self.d2 is not None: + r_d2 = f" d2={self.d2:.{decimal_places}f}" + else: + r_d2 = "" + + if self.parent is None: + representation = f"{self.name} d1={r_d1}{r_d2}" + else: + representation = f"{prefix}{self.name} d1={r_d1}{r_d2}" + + if self.active is True: + return representation + f" {self.timeseries_names}\n" + else: + representation += " [NOT ACTIVE]\n" + # Do the structure recursively + if self.children is not None: + for child in [self.children.first, self.children.second]: + representation += child.design_structure(decimal_places) + return representation + + def __str__(self) -> str: + return self.design_structure() + + def __repr__(self) -> str: + return self.design_structure() + + def _init_stats(self) -> dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr]: + return collections.defaultdict( + stats.PearsonCorr, + { + (k1, k2): stats.PearsonCorr() + for k1, k2 in itertools.combinations(self.timeseries_names, 2) + }, + ) + + # TODO: not sure if this is the best design + def __call__(self, ts_names: list[typing.Hashable]): + """Method that associates the time-series into the cluster and initiates the statistics.""" + self.timeseries_names = sorted(ts_names) # type: ignore + self._statistics = self._init_stats() + + def update_statistics(self, x: dict) -> None: + # For each pair of time-series in the cluster update the correlation + # values with the data received + for (k1, k2), item in self._statistics.items(): # type: ignore + if x.get(k1, None) is None or x.get(k2, None) is None: + continue + item.update(float(x[k1]), float(x[k2])) + + # Increment the number of observation in the cluster + self.n += 1 + + # Method to calculate the rnomc values of the cluster + def _calculate_rnomc_dict(self)-> dict[tuple[typing.Hashable, typing.Hashable], float]: + # Get the correlation values between time-series in the cluster + rnomc_dict = {} + + for k1, k2 in itertools.combinations(self.timeseries_names, 2): + rnomc_dict[(k1, k2)] = math.sqrt((1 - self._statistics[(k1, k2)].get()) / 2.0) # type: ignore + + return rnomc_dict + + # Method to calculate coefficients for splitting or aggregation + def calculate_coefficients(self, confidence_level: float) -> None: + # Get the rnomc values + rnomc_dict = self._calculate_rnomc_dict() + + if len(rnomc_dict) > 0: + # Get the average distance in the cluster + self.avg = sum(rnomc_dict.values()) / self.n + + # Get the minimum distance and the pivot associated in the cluster + self.pivot_0, self.d0 = min(rnomc_dict.items(), key=lambda x: x[1]) + # Get the maximum distance and the pivot associated in the cluster + self.pivot_1, self.d1 = max(rnomc_dict.items(), key=lambda x: x[1]) + + # Get the second maximum distance and the pivot associated in the cluster + remaining = {k: v for k, v in rnomc_dict.items() if k != self.pivot_1} + + if len(remaining) > 0: + self.pivot_2, self.d2 = max(remaining.items(), key=lambda x: x[1]) + else: + self.pivot_2 = self.d2 = None # type: ignore + + # Calculate the Hoeffding bound in the cluster + self.e = math.sqrt(math.log(1 / confidence_level) / (2 * self.n)) + + def _get_closest_cluster(self, pivot_1, pivot_2, current, rnormc_dict) -> int: + """Method that gives the closest cluster where the current time series is located.""" + + dist_1 = rnormc_dict.get((min(pivot_1, current), max(pivot_1, current)), 0) + dist_2 = rnormc_dict.get((min(pivot_2, current), max(pivot_2, current)), 0) + return 2 if dist_1 >= dist_2 else 1 + + def _split_this_cluster(self, pivot_1: typing.Hashable, pivot_2: typing.Hashable, rnormc_dict: dict): + """Expand into two clusters.""" + pivot_set = {pivot_1, pivot_2} + pivot_1_list = [pivot_1] + pivot_2_list = [pivot_2] + + # For each time-series in the cluster we need to find the closest pivot, to then associate with it + for ts_name in self.timeseries_names: + if ts_name not in pivot_set: + cluster = self._get_closest_cluster(pivot_1, pivot_2, ts_name, rnormc_dict) + if cluster == 1: + pivot_1_list.append(ts_name) + else: + pivot_2_list.append(ts_name) + + new_name = "1" if self.name == "ROOT" else str(int(self.name.split("_")[-1]) + 1) + + # Create the two new clusters. The children of this cluster + cluster_child_1 = ODACCluster("CH1_LVL_" + new_name, parent=self) + cluster_child_1(pivot_1_list) + + cluster_child_2 = ODACCluster("CH2_LVL_" + new_name, parent=self) + cluster_child_2(pivot_2_list) + + self.children = ODACChildren(cluster_child_1, cluster_child_2) + + # Set the active flag to false. Since this cluster is not an active cluster anymore. + self.active = False + self.avg = self.d0 = self.pivot_0 = self.pivot_1 = self.pivot_2 = None # type: ignore + self._statistics = None + + # Method that proceeds to merge on this cluster + def _aggregate_this_cluster(self): + # Reset statistics + self._statistics = self._init_stats() + + # Put the active flag to true. Since this cluster is an active cluster once again. + self.active = True + # Delete and disassociate the children. + if self.children is not None: + self.children.reset_parent() + self.children = None + # Reset the number of observations in this cluster + self.n = 0 + + # Method to test if the cluster should be split + def test_split(self, tau: float): + # Test if the cluster should be split based on specified tau + if self.d2 is not None: + if ((self.d1 - self.d2) > self.e) or (tau > self.e): # type: ignore + if ((self.d1 - self.d0) * abs(self.d1 + self.d0 - 2 * self.avg)) > self.e: # type: ignore + # Split this cluster + self._split_this_cluster( + pivot_1=self.pivot_1[0], + pivot_2=self.pivot_1[1], + rnormc_dict=self._calculate_rnomc_dict(), + ) + return True + return False + + # Method to test if the cluster should be aggregated + def test_aggregate(self): + # Test if the cluster should be aggregated + if self.parent is not None and self.d1 is not None: + if self.d1 - self.parent.d1 > max(self.parent.e, self.e): + self.parent._aggregate_this_cluster() + return True + return False + + +class ODACChildren(base.Base): + """Children class representing child clusters.""" + + def __init__(self, first: ODACCluster, second: ODACCluster): + self.first = first + self.second = second + + def reset_parent(self): + self.first.parent = None + self.second.parent = None From 5c9156ec3566eb4bf1d78d2a106d579ae60e53ac Mon Sep 17 00:00:00 2001 From: Tun Date: Tue, 14 May 2024 18:43:56 +0100 Subject: [PATCH 250/347] Update docs for typos and wording (#1544) --- docs/examples/batch-to-online.ipynb | 6 +++--- docs/introduction/basic-concepts.md | 12 ++++++------ .../getting-started/concept-drift-detection.ipynb | 4 ++-- docs/introduction/why-use-river.md | 2 +- docs/releases/0.4.1.md | 2 +- river/compose/pipeline.py | 2 +- river/imblearn/hard_sampling.py | 4 ++-- river/neighbors/knn_classifier.py | 2 +- river/optim/losses.py | 8 ++++---- river/stats/link.py | 4 ++-- 10 files changed, 23 insertions(+), 23 deletions(-) diff --git a/docs/examples/batch-to-online.ipynb b/docs/examples/batch-to-online.ipynb index 703a2cbb23..60f5892f99 100644 --- a/docs/examples/batch-to-online.ipynb +++ b/docs/examples/batch-to-online.ipynb @@ -67,7 +67,7 @@ "scorer = metrics.make_scorer(metrics.roc_auc_score)\n", "scores = model_selection.cross_val_score(model, X, y, scoring=scorer, cv=cv)\n", "\n", - "# Display the average score and it's standard deviation\n", + "# Display the average score and its standard deviation\n", "print(f'ROC AUC: {scores.mean():.3f} (± {scores.std():.3f})')" ] }, @@ -94,7 +94,7 @@ "source": [ "## A hands-on introduction to incremental learning\n", "\n", - "Incremental learning is also often called *online learning* or *stream learning*, but if you [google online learning](https://www.google.com/search?q=online+learning) a lot of the results will point to educational websites. Hence, the terms \"incremental learning\" and \"stream learning\" (from which River derives it's name) are prefered. The point of incremental learning is to fit a model to a stream of data. In other words, the data isn't available in it's entirety, but rather the observations are provided one by one. As an example let's stream through the dataset used previously." + "Incremental learning is also often called *online learning* or *stream learning*, but if you [google online learning](https://www.google.com/search?q=online+learning) a lot of the results will point to educational websites. Hence, the terms \"incremental learning\" and \"stream learning\" (from which River derives its name) are preferred. The point of incremental learning is to fit a model to a stream of data. In other words, the data isn't available in its entirety, but rather the observations are provided one by one. As an example let's stream through the dataset used previously." ] }, { @@ -484,7 +484,7 @@ "# We compute the CV scores using the same CV scheme and the same scoring\n", "scores = model_selection.cross_val_score(model, X, y, scoring=scorer, cv=cv)\n", "\n", - "# Display the average score and it's standard deviation\n", + "# Display the average score and its standard deviation\n", "print(f'ROC AUC: {scores.mean():.3f} (± {scores.std():.3f})')" ] }, diff --git a/docs/introduction/basic-concepts.md b/docs/introduction/basic-concepts.md index 025399d3c9..669ab4a7d6 100644 --- a/docs/introduction/basic-concepts.md +++ b/docs/introduction/basic-concepts.md @@ -22,13 +22,13 @@ The challenge for machine learning is to ensure models you train offline on proa ## Online processing -Online processing is the act of processing a data stream one element at a time. In the case of machine learning, that means training a model by teaching it one sample at a time. This is completely opposite to the traditional way of doing machine learning, which is to train a model on a whole batch data at a time. +Online processing is the act of processing a data stream one element at a time. In the case of machine learning, that means training a model by teaching it one sample at a time. This is completely opposite to the traditional way of doing machine learning, which is to train a model on whole batches of data at a time. An online model is therefore a stateful, dynamic object. It keeps learning and doesn't have to revisit past data. It's a different way of doing things, and therefore has its own set of pros and cons. ## Tasks -Machine learning encompasses many different tasks: classification, regression, anomaly detection, time series forecasting, etc. The ideology behind River is to be a generic machine learning which allows to perform these tasks in a streaming manner. Indeed, many batch machine learning algorithms have online equivalents. +Machine learning encompasses many different tasks: classification, regression, anomaly detection, time series forecasting, etc. The ideology behind River is to be a generic machine learning approach which allows these tasks to be performed in a streaming manner. Indeed, many batch machine learning algorithms have online equivalents. Note that River also supports some more basic tasks. For instance, you might just want to calculate a running average of a data stream. These are usually smaller parts of a whole stream processing pipeline. @@ -36,13 +36,13 @@ Note that River also supports some more basic tasks. For instance, you might jus River is a Python library. It is composed of a bunch of classes which implement various online processing algorithms. Most of these classes are machine learning models which can process a single sample, be it for learning or for inference. -We made the choice to use dictionaries as the basic building block. First of all, online processing is different to batch processing, in that vectorization doesn't bring any speedup. Therefore numeric processing libraries such as numpy and PyTorch actually bring too much overhead. Using native Python data structures is faster. +We made the choice to use dictionaries as the basic building block. First of all, online processing is different to batch processing, in that vectorization doesn't bring any speed-up. Therefore numeric processing libraries such as NumPy and PyTorch actually bring too much overhead. Using native Python data structures is faster. -Dictionaries are therefore a perfect fit. They're native to Python and have excellent support in the standard library. They allow naming each feature. They can hold any kind of data type. They allow transparent support of JSON payloads, allowing seemless integration with web apps. +Dictionaries are therefore a perfect fit. They're native to Python and have excellent support in the standard library. They allow the naming of each feature. They can hold any kind of data type. They allow transparent support of JSON payloads, allowing seamless integration with web apps. ## Datasets -In production, you're almost always going to face data streams which you have to react to. Such as users visiting your website. The advantage of online machine learning is that you can design models which make predictions as well as learn from this data stream as it flows. +In production, you're almost always going to face data streams which you have to react to, such as users visiting your website. The advantage of online machine learning is that you can design models that make predictions as well as learn from this data stream as it flows. But of course, when you're developping a model, you don't usually have access to a real-time feed on which to evaluate your model. You usually have an offline dataset which you want to evaluate your model on. River provides some datasets which can be read in online manner, one sample at a time. It is however crucial to keep in mind that the goal is to reproduce a production scenario as closely as possible, in order to ensure your model will perform just as well in production. @@ -58,4 +58,4 @@ This is what makes online machine learning powerful. By replaying datasets in th The main reason why an offline model might not perform as expected in production is because of concept drift. But this is true for all machine learning models, be they offline or online. -The advantage of online models over offline models is that they can cope with drift. Indeed, because they can keep learning, they usually adapt to concept drift in a seemless manner. As opposed to batch models which have to be retrained from scratch. +The advantage of online models over offline models is that they can cope with drift. Indeed, because they can keep learning, they usually adapt to concept drift in a seamless manner. As opposed to batch models which have to be retrained from scratch. diff --git a/docs/introduction/getting-started/concept-drift-detection.ipynb b/docs/introduction/getting-started/concept-drift-detection.ipynb index 88b23030de..922936ec1e 100644 --- a/docs/introduction/getting-started/concept-drift-detection.ipynb +++ b/docs/introduction/getting-started/concept-drift-detection.ipynb @@ -20,9 +20,9 @@ "\n", "Concept drifts might happen in the electricity demand across the year, in the stock market, in buying preferences, and in the likelihood of a new movie's success, among others.\n", "\n", - "Let us consider the movie example: two movies made at different epochs can have similar features such as famous actors/directors, storyline, production budget, marketing campaigns, etc., yet it is not certain that both will be similarly successful. What the target audience *considers* is worth watching (and their money) is constantly changing, and production companies must adapt accordingly to avoid \"box office flops\".\n", + "Let us consider the movie example: two movies made at different epochs can have similar features such as famous actors/directors, storyline, production budget, marketing campaigns, etc., yet it is not certain that both will be similarly successful. What the target audience *considers* is worth watching (and their money worth spending) is constantly changing, and production companies must adapt accordingly to avoid \"box office flops\".\n", "\n", - "Prior to the pandemics, the usage of hand sanitizers and facial masks was not widespread. When the cases of COVID-19 started increasing, there was a lack of such products for the final consumer. Imagine a batch-learning model deciding how much of each product a supermarket should stock during those times. What a mess!\n", + "Prior to the pandemic, the usage of hand sanitizers and facial masks was not widespread. When the cases of COVID-19 started increasing, there was a lack of such products for the end consumer. Imagine a batch-learning model deciding how much of each product a supermarket should stock during those times. What a mess!\n", "\n", "## Impact of drift on learning\n", "\n", diff --git a/docs/introduction/why-use-river.md b/docs/introduction/why-use-river.md index af110b006f..81efb4d41e 100644 --- a/docs/introduction/why-use-river.md +++ b/docs/introduction/why-use-river.md @@ -10,7 +10,7 @@ In the streaming setting, data can evolve. Adaptive methods are specifically des ## General purpose -River supports different machine learning tasks, including regression, classification, and unsupervised learning. It can also be used for adhoc tasks, such as computing online metrics, as well as concept drift detection. +River supports different machine learning tasks, including regression, classification, and unsupervised learning. It can also be used for ad hoc tasks, such as computing online metrics, as well as concept drift detection. ## User experience diff --git a/docs/releases/0.4.1.md b/docs/releases/0.4.1.md index c9bd6c8694..d2be0a7cd0 100644 --- a/docs/releases/0.4.1.md +++ b/docs/releases/0.4.1.md @@ -19,7 +19,7 @@ ## ensemble -- Removed `ensemble.HedgeBinaryClassifier` because it's performance was subpar. +- Removed `ensemble.HedgeBinaryClassifier` because its performance was subpar. - Removed `ensemble.GroupRegressor`, as this should be a special case of `ensemble.StackingRegressor`. ## feature_extraction diff --git a/river/compose/pipeline.py b/river/compose/pipeline.py index 66b93e9f1b..2c894ede04 100644 --- a/river/compose/pipeline.py +++ b/river/compose/pipeline.py @@ -122,7 +122,7 @@ class Pipeline(base.Estimator): """A pipeline of estimators. Pipelines allow you to chain different steps into a sequence. Typically, when doing supervised - learning, a pipeline contains one ore more transformation steps, whilst it's is a regressor or + learning, a pipeline contains one or more transformation steps, whilst it's a regressor or a classifier. It is highly recommended to use pipelines with River. Indeed, in an online learning setting, it is very practical to have a model defined as a single object. Take a look at the [user guide](/recipes/pipelines) for further information and diff --git a/river/imblearn/hard_sampling.py b/river/imblearn/hard_sampling.py index a7eb07e745..b4c1faf1ba 100644 --- a/river/imblearn/hard_sampling.py +++ b/river/imblearn/hard_sampling.py @@ -78,7 +78,7 @@ class HardSamplingRegressor(HardSampling, base.Regressor): The hardness of an observation is evaluated with a loss function that compares the sample's ground truth with the wrapped model's prediction. If the buffer is not full, then the sample is added to the buffer. If the buffer is full and the new sample has a bigger loss than the - lowest loss in the buffer, then the sample takes it's place. + lowest loss in the buffer, then the sample takes its place. Parameters ---------- @@ -159,7 +159,7 @@ class HardSamplingClassifier(HardSampling, base.Classifier): The hardness of an observation is evaluated with a loss function that compares the sample's ground truth with the wrapped model's prediction. If the buffer is not full, then the sample is added to the buffer. If the buffer is full and the new sample has a bigger loss than the - lowest loss in the buffer, then the sample takes it's place. + lowest loss in the buffer, then the sample takes its place. Parameters ---------- diff --git a/river/neighbors/knn_classifier.py b/river/neighbors/knn_classifier.py index b8da74b437..a3e12f3c08 100644 --- a/river/neighbors/knn_classifier.py +++ b/river/neighbors/knn_classifier.py @@ -24,7 +24,7 @@ class KNNClassifier(base.Classifier): documentation of each available search engine for more details on its usage. By default, use the `SWINN` search engine for approximate search queries. weighted - Weight the contribution of each neighbor by it's inverse distance. + Weight the contribution of each neighbor by its inverse distance. cleanup_every This determines at which rate old classes are cleaned up. Classes that have been seen in the past but that are not present in the current diff --git a/river/optim/losses.py b/river/optim/losses.py index 559bc9b894..bc03a9fe2f 100644 --- a/river/optim/losses.py +++ b/river/optim/losses.py @@ -67,7 +67,7 @@ class Absolute(RegressionLoss): $$L = |p_i - y_i|$$ - It's gradient w.r.t. to $p_i$ is + Its gradient w.r.t. to $p_i$ is $$\\frac{\\partial L}{\\partial p_i} = sgn(p_i - y_i)$$ @@ -203,7 +203,7 @@ class Hinge(BinaryLoss): $$L = max(0, 1 - p_i * y_i)$$ - It's gradient w.r.t. to $p_i$ is + Its gradient w.r.t. to $p_i$ is $$ \\frac{\\partial L}{\\partial y_i} = \\left\{ @@ -404,7 +404,7 @@ class Squared(RegressionLoss): $$L = (p_i - y_i) ^ 2$$ - It's gradient w.r.t. to $p_i$ is + Its gradient w.r.t. to $p_i$ is $$\\frac{\\partial L}{\\partial p_i} = 2 (p_i - y_i)$$ @@ -539,7 +539,7 @@ class Poisson(RegressionLoss): $$L = exp(p_i) - y_i \\times p_i$$ - It's gradient w.r.t. to $p_i$ is + Its gradient w.r.t. to $p_i$ is $$\\frac{\\partial L}{\\partial p_i} = exp(p_i) - y_i$$ diff --git a/river/stats/link.py b/river/stats/link.py index 9f27efae11..459dd4038a 100644 --- a/river/stats/link.py +++ b/river/stats/link.py @@ -37,7 +37,7 @@ class Link(stats.base.Univariate): >>> stat.update(1) The output from `get` will still be 0. The reason is that `stats.Shift` has not enough - values, and therefore outputs it's default value, which is `None`. The `stats.Mean` + values, and therefore outputs its default value, which is `None`. The `stats.Mean` instance is therefore not updated. >>> stat.get() @@ -57,7 +57,7 @@ class Link(stats.base.Univariate): >>> stat.get() 2.0 - Note that composing statistics returns a new statistic with it's own name. + Note that composing statistics returns a new statistic with its own name. >>> stat.name 'mean_of_shift_1' From 6dfba3b39f907e7db9a51e99e1342639ae7251d3 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Wed, 15 May 2024 08:51:13 -0300 Subject: [PATCH 251/347] =?UTF-8?q?ODAC:=20quality=20of=20life=20improveme?= =?UTF-8?q?nts=20(sent=20by=20Gon=C3=A7alo)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- river/cluster/odac.py | 49 ++++++++++++++++++++++++------------------- 1 file changed, 28 insertions(+), 21 deletions(-) diff --git a/river/cluster/odac.py b/river/cluster/odac.py index 60b752400f..5c87d4666e 100644 --- a/river/cluster/odac.py +++ b/river/cluster/odac.py @@ -9,7 +9,7 @@ class ODAC(base.Clusterer): - """The Online Divisive-Agglomerative Clustering (ODAC) aims at continuously + """The Online Divisive-Agglomerative Clustering (ODAC)[^1] aims at continuously maintaining a hierarchical cluster structure from evolving time series data streams. @@ -22,17 +22,16 @@ class ODAC(base.Clusterer): The distance between time-series a and b is given by `rnomc(a, b) = sqrt((1 - corr(a, b)) / 2)`, where `corr(a, b)` is the Pearson Correlation coefficient. - In the next topics, ε stands for the Hoeffding bound. + In the following topics, ε stands for the Hoeffding bound and considers clusters cj + with descendants ck and cs. **The Merge Operator** The Splitting Criteria guarantees that cluster's diameters monotonically decrease. - - Assume Clusters: cj with descendants ck and cs. - - If diameter (ck) - diameter (cj) > ε OR diameter (cs) - diameter (cj ) > ε: - * There is a change in the correlation structure: Merge clusters ck and cs into cj. + * There is a change in the correlation structure, so merge clusters ck and cs into cj. **Splitting Criteria** @@ -42,6 +41,8 @@ class ODAC(base.Clusterer): - d1: the farthest distance; + - d_avg: the average distance; + - d2: the second farthest distance. Then: @@ -61,7 +62,7 @@ class ODAC(base.Clusterer): Number of minimum observations to gather before checking whether or not clusters must be split or merged. tau - The value of tau to use in the calculation of the Hoeffding bound. + Threshold below which a split will be forced to break ties. Attributes ---------- @@ -71,10 +72,10 @@ class ODAC(base.Clusterer): Examples -------- - >>> from river.cluster import ODAC + >>> from river import cluster >>> from river.datasets import synth - >>> model = ODAC(confidence_level=0.9) + >>> model = cluster.ODAC() >>> dataset = synth.FriedmanDrift(drift_type='gra', position=(150, 200), seed=42) @@ -99,8 +100,6 @@ class ODAC(base.Clusterer): └── CH2_LVL_2 d1=0.74 d2=0.73 [NOT ACTIVE] ├── CH1_LVL_3 d1=0.71 [5, 6] └── CH2_LVL_3 d1=0.71 [7, 8] - - You can acess some properties of the clustering model directly: @@ -124,7 +123,7 @@ class ODAC(base.Clusterer): """ - def __init__(self, confidence_level=0.9, n_min=100, tau=0.1): + def __init__(self, confidence_level: float = 0.9, n_min: int = 100, tau: float = 0.1): if not (confidence_level > 0.0 and confidence_level < 1.0): raise ValueError("confidence_level must be between 0 and 1.") if not n_min > 0: @@ -244,10 +243,18 @@ def learn_one(self, x: dict): # This algorithm does not predict anything. It builds a hierarchical cluster's structure def predict_one(self, x: dict): - raise NotImplementedError + """This algorithm does not predict anything. It builds a hierarchical cluster's structure. + + Parameters + ---------- + x + A dictionary of features. + + """ + raise NotImplementedError("ODAC does not predict anything. It builds a hierarchical cluster's structure.") def draw(self, n_decimal_places: int = 2) -> str: - """Method to draw the the hierarchical cluster's structure. + """Method to draw the hierarchical cluster's structure. Parameters ---------- @@ -257,9 +264,9 @@ def draw(self, n_decimal_places: int = 2) -> str: """ if not (n_decimal_places > 0 and n_decimal_places < 10): - raise ValueError("n_decimal_places must be between 1 and 9") + raise ValueError("n_decimal_places must be between 1 and 9.") - return self._root_node.design_structure(n_decimal_places) + return self._root_node.design_structure(n_decimal_places).rstrip("\n") @property def structure_changed(self) -> bool: @@ -276,7 +283,7 @@ def __init__(self, name: str, parent: ODACCluster | None = None): self.active = True self.children: ODACChildren | None = None - self.timeseries_names: list[typing.Hashable] + self.timeseries_names: list[typing.Hashable] = [] self._statistics: dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | None self.d1: float | None = None @@ -292,7 +299,7 @@ def __init__(self, name: str, parent: ODACCluster | None = None): self.n = 0 # Method to design the structure of the cluster tree - def design_structure(self, decimal_places=2) -> str: + def design_structure(self, decimal_places:int = 2) -> str: pre_0 = " " pre_1 = "│ " pre_2 = "├── " @@ -300,11 +307,11 @@ def design_structure(self, decimal_places=2) -> str: node = self prefix = ( pre_2 - if node.parent is not None and id(node) != id(node.parent.children.second) # type: ignore + if node.parent is not None and (node.parent.children is None or id(node) != id(node.parent.children.second)) # type: ignore else pre_3 ) while node.parent is not None and node.parent.parent is not None: - if id(node.parent) != id(node.parent.parent.children.second): # type: ignore + if node.parent.parent.children is None or id(node.parent) != id(node.parent.parent.children.second): # type: ignore prefix = pre_1 + prefix else: prefix = pre_0 + prefix @@ -402,14 +409,14 @@ def calculate_coefficients(self, confidence_level: float) -> None: # Calculate the Hoeffding bound in the cluster self.e = math.sqrt(math.log(1 / confidence_level) / (2 * self.n)) - def _get_closest_cluster(self, pivot_1, pivot_2, current, rnormc_dict) -> int: + def _get_closest_cluster(self, pivot_1, pivot_2, current, rnormc_dict: dict) -> int: """Method that gives the closest cluster where the current time series is located.""" dist_1 = rnormc_dict.get((min(pivot_1, current), max(pivot_1, current)), 0) dist_2 = rnormc_dict.get((min(pivot_2, current), max(pivot_2, current)), 0) return 2 if dist_1 >= dist_2 else 1 - def _split_this_cluster(self, pivot_1: typing.Hashable, pivot_2: typing.Hashable, rnormc_dict: dict): + def _split_this_cluster(self, pivot_1: typing.Hashable, pivot_2: typing.Hashable, rnormc_dict: dict[tuple[typing.Hashable, typing.Hashable], float]): """Expand into two clusters.""" pivot_set = {pivot_1, pivot_2} pivot_1_list = [pivot_1] From e661a965401d5b1a72a9017df56d8d1a2338b3c3 Mon Sep 17 00:00:00 2001 From: Geoffrey Bolmier Date: Sun, 19 May 2024 23:18:29 -0400 Subject: [PATCH 252/347] Rename release 0.21.1 release notes to 0.21.1.md (#1546) --- docs/releases/0.21.1.md | 16 ++++++++++++++++ docs/releases/unreleased.md | 17 +---------------- 2 files changed, 17 insertions(+), 16 deletions(-) create mode 100644 docs/releases/0.21.1.md diff --git a/docs/releases/0.21.1.md b/docs/releases/0.21.1.md new file mode 100644 index 0000000000..185e950999 --- /dev/null +++ b/docs/releases/0.21.1.md @@ -0,0 +1,16 @@ +# 0.21.1 - 2024-03-28 + +This release should fix some of the installation issues when building the River wheel from scratch. + +## anomaly + +- Added `PredictiveAnomalyDetection`, a semi-supervised technique that employs a predictive model for anomaly detection. + +## drift + +- Added `FHDDM` drift detector. +- Added a `iter_polars` function to iterate over the rows of a polars DataFrame. + +## neighbors + +- Simplified `neighbors.SWINN` to avoid recursion limit and pickling issues. diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 8e93b1f3e4..5a4ae4fc03 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,20 +1,5 @@ -# 0.21.1 - 2024-03-28 - -This release should fix some of the installation issues when building the River wheel from scratch. - -## anomaly - -- Added `PredictiveAnomalyDetection`, a semi-supervised technique that employs a predictive model for anomaly detection. +# Unreleased ## cluster - Added `ODAC` (Online Divisive-Agglomerative Clustering) for clustering time series. - -## drift - -- Added `FHDDM` drift detector. -- Added a `iter_polars` function to iterate over the rows of a polars DataFrame. - -## neighbors - -- Simplified `neighbors.SWINN` to avoid recursion limit and pickling issues. From 6995f1522a9136fd3982890afac5c4a178e4cd57 Mon Sep 17 00:00:00 2001 From: Geoffrey Bolmier Date: Sun, 19 May 2024 23:43:37 -0400 Subject: [PATCH 253/347] Make polars an optional dependency (#1545) - Fix #1504 that, by mistake, added polars as a required dependency when adding stream.iter_polars. - Run `poetry lock`. --- docs/releases/unreleased.md | 2 + poetry.lock | 1082 ++++++++++++++++++----------------- pyproject.toml | 2 +- 3 files changed, 550 insertions(+), 536 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 5a4ae4fc03..2cf21a66c1 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,5 +1,7 @@ # Unreleased +This release makes Polars an optional dependency instead of a required one. + ## cluster - Added `ODAC` (Online Divisive-Agglomerative Clustering) for clustering time series. diff --git a/poetry.lock b/poetry.lock index 046b848197..a675335667 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "alabaster" @@ -184,13 +184,13 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p [[package]] name = "babel" -version = "2.14.0" +version = "2.15.0" description = "Internationalization utilities" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "Babel-2.14.0-py3-none-any.whl", hash = "sha256:efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287"}, - {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"}, + {file = "Babel-2.15.0-py3-none-any.whl", hash = "sha256:08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb"}, + {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"}, ] [package.extras] @@ -237,13 +237,13 @@ css = ["tinycss2 (>=1.1.0,<1.3)"] [[package]] name = "blinker" -version = "1.7.0" +version = "1.8.2" description = "Fast, simple object-to-object 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object wrapper for zip files" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"}, - {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"}, + {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"}, + {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"}, ] [package.extras] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "78eec58254f5beef8b8fa0fdc0d883087283495d4fb627530347ba07491c862f" +content-hash = "44171f65a1084b6f80ee723431af4c5bffd1f4094599d2bf13d386af38ecfde6" diff --git a/pyproject.toml b/pyproject.toml index 304564dd47..cf0d80b095 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,6 @@ python = "^3.9" numpy = "^1.23.0" scipy = "^1.12.1" pandas = "^2.1" -polars = "^0.20.8" [tool.poetry.group.dev.dependencies] graphviz = "^0.20.1" @@ -51,6 +50,7 @@ ipython = "^8.17.2" rich = "^13.6.0" jupyter = "^1.0.0" mike = "^2.0.0" +polars = "^0.20.8" [tool.poetry.group.compat] optional = true From c8ea67f43179a43ee4918d121caaeb96b75ac66d Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Fri, 28 Jun 2024 16:55:39 -0300 Subject: [PATCH 254/347] Fix #1560 (ARF cornercase) --- docs/releases/unreleased.md | 4 ++++ river/tree/nodes/arf_htc_nodes.py | 4 +++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 2cf21a66c1..30d582211e 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -5,3 +5,7 @@ This release makes Polars an optional dependency instead of a required one. ## cluster - Added `ODAC` (Online Divisive-Agglomerative Clustering) for clustering time series. + +## forest + +- Fix error in `forest.ARFClassifer` and `forest.ARFRegressor` where the algorithms would crash in case the number of features available for learning went below the value of the `max_features` parameter (#1560). diff --git a/river/tree/nodes/arf_htc_nodes.py b/river/tree/nodes/arf_htc_nodes.py index f40cc965c9..f25eb50475 100644 --- a/river/tree/nodes/arf_htc_nodes.py +++ b/river/tree/nodes/arf_htc_nodes.py @@ -44,7 +44,9 @@ def _iter_features(self, x) -> typing.Iterable: yield att_id, x[att_id] def _sample_features(self, x, max_features): - return self.rng.sample(sorted(x.keys()), k=max_features) + if len(x) >= max_features: + return self.rng.sample(sorted(x.keys()), k=max_features) + return sorted(x.keys()) class RandomLeafMajorityClass(BaseRandomLeaf, LeafMajorityClass): From 5677a509655e1d64ae6b60dafd276b6a526c92d4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Thu, 4 Jul 2024 21:25:29 +0200 Subject: [PATCH 255/347] Upgrade the pyo3 crate (#1564) Fixes https://github.com/online-ml/river/discussions/1563 --- Cargo.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Cargo.toml b/Cargo.toml index eee905b262..4c53bc7e9b 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -11,7 +11,7 @@ path = "rust_src/lib.rs" crate-type = ["cdylib"] [dependencies] -pyo3 = { version = "0.16.5", features = ["extension-module"] } +pyo3 = { version = "0.18.3", features = ["extension-module"] } watermill = "0.1.1" bincode = "1.3.3" serde = { version = "1.0", features = ["derive"] } From acaf678564efc7ac2e727546755b67688558f456 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Fri, 5 Jul 2024 00:05:33 +0200 Subject: [PATCH 256/347] Fix mypy errors (#1562) Mark the parameters with a default value of None as optionals. --- river/anomaly/lof.py | 2 +- river/anomaly/sad.py | 2 +- river/bandit/base.py | 2 +- river/bandit/evaluate.py | 2 +- river/bandit/thompson.py | 2 +- river/bandit/ucb.py | 2 +- river/drift/binary/fhddm.py | 2 +- river/forest/aggregated_mondrian_forest.py | 2 +- river/linear_model/bayesian_lin_reg.py | 2 +- river/neighbors/ann/swinn.py | 8 ++++---- river/tree/mondrian/mondrian_tree_regressor.py | 2 +- 11 files changed, 14 insertions(+), 14 deletions(-) diff --git a/river/anomaly/lof.py b/river/anomaly/lof.py index ea6b3624fb..542d4a882f 100644 --- a/river/anomaly/lof.py +++ b/river/anomaly/lof.py @@ -253,7 +253,7 @@ class LocalOutlierFactor(anomaly.base.AnomalyDetector): def __init__( self, n_neighbors: int = 10, - distance_func: DistanceFunc = None, + distance_func: DistanceFunc | None = None, ): self.n_neighbors = n_neighbors self.x_list: list = [] diff --git a/river/anomaly/sad.py b/river/anomaly/sad.py index 68cc1835ee..9d6ff1e498 100644 --- a/river/anomaly/sad.py +++ b/river/anomaly/sad.py @@ -59,7 +59,7 @@ class StandardAbsoluteDeviation(anomaly.base.SupervisedAnomalyDetector): """ - def __init__(self, sub_stat: stats.base.Univariate = None): + def __init__(self, sub_stat: stats.base.Univariate | None = None): self.variance = stats.Var() self.sub_stat = sub_stat or stats.Mean() diff --git a/river/bandit/base.py b/river/bandit/base.py index 0359686cae..3453a7e152 100644 --- a/river/bandit/base.py +++ b/river/bandit/base.py @@ -162,7 +162,7 @@ class ContextualPolicy(Policy): def _pull(self, arm_ids: list[ArmID], context: dict) -> ArmID: # type: ignore[override] ... - def pull(self, arm_ids: list[ArmID], context: dict = None) -> ArmID: + def pull(self, arm_ids: list[ArmID], context: dict | None = None) -> ArmID: """Pull arm(s). This method is a generator that yields the arm(s) that should be pulled. During the burn-in diff --git a/river/bandit/evaluate.py b/river/bandit/evaluate.py index 466807d475..6baf22b19c 100644 --- a/river/bandit/evaluate.py +++ b/river/bandit/evaluate.py @@ -167,7 +167,7 @@ def evaluate( def evaluate_offline( policy: bandit.base.Policy, history: History | bandit.datasets.BanditDataset, - reward_stat: stats.base.Univariate = None, + reward_stat: stats.base.Univariate | None = None, ) -> tuple[stats.base.Univariate, int]: """Evaluate a policy on historical logs using replay. diff --git a/river/bandit/thompson.py b/river/bandit/thompson.py index 88df098b1c..03cf76a5d7 100644 --- a/river/bandit/thompson.py +++ b/river/bandit/thompson.py @@ -73,7 +73,7 @@ class ThompsonSampling(bandit.base.Policy): def __init__( self, - reward_obj: proba.base.Distribution = None, + reward_obj: proba.base.Distribution | None = None, burn_in=0, seed: int | None = None, ): diff --git a/river/bandit/ucb.py b/river/bandit/ucb.py index a31ae03a72..e79f40de51 100644 --- a/river/bandit/ucb.py +++ b/river/bandit/ucb.py @@ -75,7 +75,7 @@ def __init__( reward_obj=None, reward_scaler=None, burn_in=0, - seed: int = None, + seed: int | None = None, ): super().__init__( reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in diff --git a/river/drift/binary/fhddm.py b/river/drift/binary/fhddm.py index fe70e88e87..9fc1528ec5 100644 --- a/river/drift/binary/fhddm.py +++ b/river/drift/binary/fhddm.py @@ -66,7 +66,7 @@ class FHDDM(base.BinaryDriftAndWarningDetector): """ def __init__( - self, sliding_window_size: int = 100, confidence_level: float = 0.000001, short_window_size: int = None + self, sliding_window_size: int = 100, confidence_level: float = 0.000001, short_window_size: int | None = None ): super().__init__() diff --git a/river/forest/aggregated_mondrian_forest.py b/river/forest/aggregated_mondrian_forest.py index e062e15bb0..fc250a2734 100644 --- a/river/forest/aggregated_mondrian_forest.py +++ b/river/forest/aggregated_mondrian_forest.py @@ -275,7 +275,7 @@ def __init__( n_estimators: int = 10, step: float = 1.0, use_aggregation: bool = True, - seed: int = None, + seed: int | None = None, ): super().__init__( n_estimators=n_estimators, diff --git a/river/linear_model/bayesian_lin_reg.py b/river/linear_model/bayesian_lin_reg.py index 5370d64436..f95ce53f0c 100644 --- a/river/linear_model/bayesian_lin_reg.py +++ b/river/linear_model/bayesian_lin_reg.py @@ -109,7 +109,7 @@ class BayesianLinearRegression(base.Regressor): """ - def __init__(self, alpha=1, beta=1, smoothing: float = None): + def __init__(self, alpha=1, beta=1, smoothing: float | None = None): self.alpha = alpha self.beta = beta self.smoothing = smoothing diff --git a/river/neighbors/ann/swinn.py b/river/neighbors/ann/swinn.py index 5d71b0e2c7..e774f052f1 100644 --- a/river/neighbors/ann/swinn.py +++ b/river/neighbors/ann/swinn.py @@ -93,11 +93,11 @@ def __init__( dist_func: DistanceFunc | FunctionWrapper | None = None, maxlen: int = 1000, warm_up: int = 500, - max_candidates: int = None, + max_candidates: int | None = None, delta: float = 0.0001, prune_prob: float = 0.0, n_iters: int = 10, - seed: int = None, + seed: int | None = None, ): self.graph_k = graph_k if dist_func is None: @@ -203,7 +203,7 @@ def _safe_node_removal(self, nid: int): self._refine(affected) - def _refine(self, nodes: list[int] = None): + def _refine(self, nodes: list[int] | None = None): """Update the nearest neighbor graph to improve the edge distances. Parameters @@ -343,7 +343,7 @@ def _linear_scan(self, item, k): return None - def _search(self, item, k, epsilon: float = 0.1, seed: Vertex = None, exclude: set[int] | None = None) -> tuple[list, list]: + def _search(self, item, k, epsilon: float = 0.1, seed: Vertex | None = None, exclude: set[int] | None = None) -> tuple[list, list]: # Limiter for the distance bound distance_scale = 1 + epsilon # Distance threshold for early stops diff --git a/river/tree/mondrian/mondrian_tree_regressor.py b/river/tree/mondrian/mondrian_tree_regressor.py index 54258346bd..13b302de1e 100644 --- a/river/tree/mondrian/mondrian_tree_regressor.py +++ b/river/tree/mondrian/mondrian_tree_regressor.py @@ -42,7 +42,7 @@ def __init__( step: float = 0.1, use_aggregation: bool = True, iteration: int = 0, - seed: int = None, + seed: int | None = None, ): super().__init__( step=step, From 112bb184526270a22b3cb43c8ca42c605abfea7e Mon Sep 17 00:00:00 2001 From: Geoffrey Bolmier Date: Mon, 8 Jul 2024 15:23:42 -0400 Subject: [PATCH 257/347] Release 0.21.2 --- docs/releases/0.21.2.md | 11 +++++++++++ docs/releases/unreleased.md | 10 ---------- pyproject.toml | 2 +- river/__version__.py | 2 +- 4 files changed, 13 insertions(+), 12 deletions(-) create mode 100644 docs/releases/0.21.2.md diff --git a/docs/releases/0.21.2.md b/docs/releases/0.21.2.md new file mode 100644 index 0000000000..fb65bc062b --- /dev/null +++ b/docs/releases/0.21.2.md @@ -0,0 +1,11 @@ +# 0.21.2 - 2024-07-08 + +This release makes Polars an optional dependency instead of a required one. + +## cluster + +- Added `ODAC` (Online Divisive-Agglomerative Clustering) for clustering time series. + +## forest + +- Fix error in `forest.ARFClassifer` and `forest.ARFRegressor` where the algorithms would crash in case the number of features available for learning went below the value of the `max_features` parameter (#1560). diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 30d582211e..79e701b844 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,11 +1 @@ # Unreleased - -This release makes Polars an optional dependency instead of a required one. - -## cluster - -- Added `ODAC` (Online Divisive-Agglomerative Clustering) for clustering time series. - -## forest - -- Fix error in `forest.ARFClassifer` and `forest.ARFRegressor` where the algorithms would crash in case the number of features available for learning went below the value of the `max_features` parameter (#1560). diff --git a/pyproject.toml b/pyproject.toml index cf0d80b095..e3ed735f78 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "river" -version = "0.21.1" +version = "0.21.2" description = "Online machine learning in Python" readme = "README.md" homepage = "https://riverml.xyz/" diff --git a/river/__version__.py b/river/__version__.py index 87781a7f0d..f31b3c83c6 100644 --- a/river/__version__.py +++ b/river/__version__.py @@ -1,3 +1,3 @@ from __future__ import annotations -__version__ = "0.21.1" +__version__ = "0.21.2" From 20ef984585d8794167572b223b946cbb91271b07 Mon Sep 17 00:00:00 2001 From: Geoffrey Bolmier Date: Mon, 8 Jul 2024 15:49:35 -0400 Subject: [PATCH 258/347] Downgrade release-docs action Py version to 3.11 Temporary downgrade the Python version to 3.11 in the release-docs workflow to avoid a Pydantic error when importing spacy. TypeError: ForwardRef._evaluate() missing 1 required keyword-only argument: 'recursive_guard' --- .github/workflows/release-docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index e152015000..0be8787fb1 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -15,7 +15,7 @@ jobs: - name: Build River uses: ./.github/actions/install-env with: - python-version: "3.12" + python-version: "3.11" - name: Install extra Ubuntu dependencies run: sudo apt-get install graphviz pandoc From 969a5c0ab48aaac76e1ed885c698622fd13711ea Mon Sep 17 00:00:00 2001 From: Geoffrey Bolmier Date: Mon, 8 Jul 2024 16:15:42 -0400 Subject: [PATCH 259/347] Revert "Downgrade release-docs action Py version to 3.11" This reverts commit 20ef984585d8794167572b223b946cbb91271b07. --- .github/workflows/release-docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 0be8787fb1..e152015000 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -15,7 +15,7 @@ jobs: - name: Build River uses: ./.github/actions/install-env with: - python-version: "3.11" + python-version: "3.12" - name: Install extra Ubuntu dependencies run: sudo apt-get install graphviz pandoc From c01243729440da1302a956fa73711d3ddad96783 Mon Sep 17 00:00:00 2001 From: Geoffrey Bolmier Date: Mon, 8 Jul 2024 21:57:36 -0400 Subject: [PATCH 260/347] Update install-env and release-docs workflows (#1569) - Update actions to latest versions - Update Python version to 3.12.3 in release-docs workflow - Increment Poetry installation cached key in install-env workflow --- .github/actions/install-env/action.yml | 10 +++++----- .github/workflows/release-docs.yml | 4 ++-- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/actions/install-env/action.yml b/.github/actions/install-env/action.yml index 719b9793db..700444c61e 100644 --- a/.github/actions/install-env/action.yml +++ b/.github/actions/install-env/action.yml @@ -14,19 +14,19 @@ runs: using: "composite" steps: - name: Check out repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Set up Python id: set-up-python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ inputs.python-version }} - name: Load cached Poetry installation - uses: actions/cache@v3 + uses: actions/cache@v4 with: path: ~/.local # the path depends on the OS - key: poetry-1 # increment to reset cache + key: poetry-2 # increment to reset cache - name: Install poetry uses: snok/install-poetry@v1 @@ -37,7 +37,7 @@ runs: - name: Load cached virtual env id: cached-poetry-dependencies - uses: actions/cache@v3 + uses: actions/cache@v4 with: path: .venv key: venv-${{ runner.os }}-${{ steps.set-up-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index e152015000..4347f2c983 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -10,12 +10,12 @@ jobs: ubuntu: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Build River uses: ./.github/actions/install-env with: - python-version: "3.12" + python-version: "3.12.3" - name: Install extra Ubuntu dependencies run: sudo apt-get install graphviz pandoc From 51bb8349f6c79c346ef542a5d62c213e0067bcde Mon Sep 17 00:00:00 2001 From: Geoffrey Bolmier Date: Tue, 9 Jul 2024 15:45:13 -0400 Subject: [PATCH 261/347] Update PyPI workflow (#1571) * Update pypa/cibuildwheel in pypi.yml * Remove ppc64le and s390x builds from pypi.yml --- .github/workflows/pypi.yml | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index c56f1f6f2c..7117341ee3 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -51,17 +51,17 @@ jobs: platforms: all - name: Build wheels - uses: pypa/cibuildwheel@v2.17.0 + uses: pypa/cibuildwheel@v2.19.2 timeout-minutes: 720 env: CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" - CIBW_ARCHS_LINUX: "x86_64 i686 aarch64 ppc64le s390x" + CIBW_ARCHS_LINUX: "x86_64 i686 aarch64" # CIBW_ARCHS_MACOS: "x86_64 arm64" CIBW_ARCHS_MACOS: "universal2" # We don't build ARM64 wheels yet because there's a Rust issue CIBW_ARCHS_WINDOWS: "AMD64 x86" - # Rust nighlty doesn't seem to be available for musl linux on i686 and ppc64le and s390x (yet) - CIBW_SKIP: "*-musllinux_i686 *-musllinux_ppc64le *-musllinux_s390x" + # Rust nighlty doesn't seem to be available for musl linux on i686 + CIBW_SKIP: "*-musllinux_i686" # arm64 and universal2 wheels are tagged with x86_64 because there's an issue with Poetry # More information here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile (CTRL + F "poetry") @@ -74,8 +74,6 @@ jobs: CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" CIBW_MANYLINUX_AARCH64_IMAGE: "manylinux2014" - CIBW_MANYLINUX_PPC64LE_IMAGE: "manylinux2014" - CIBW_MANYLINUX_S390X_IMAGE: "manylinux2014" CIBW_MANYLINUX_PYPY_X86_64_IMAGE: "manylinux2014" CIBW_MANYLINUX_PYPY_I686_IMAGE: "manylinux2014" CIBW_MANYLINUX_PYPY_AARCH64_IMAGE: "manylinux2014" @@ -83,8 +81,6 @@ jobs: CIBW_MUSLLINUX_X86_64_IMAGE: "musllinux_1_1" CIBW_MUSLLINUX_I686_IMAGE: "musllinux_1_1" CIBW_MUSLLINUX_AARCH64_IMAGE: "musllinux_1_1" - CIBW_MUSLLINUX_PPC64LE_IMAGE: "musllinux_1_1" - CIBW_MUSLLINUX_S390X_IMAGE: "musllinux_1_1" CIBW_ENVIRONMENT: 'PATH="$HOME/.cargo/bin:$PATH"' # Fix the following error: error: cargo rustc --lib --message-format=json-render-diagnostics --manifest-path Cargo.toml --release -v --features pyo3/extension-module -- --crate-type cdylibfailed with code -9 From 038ad5d7d7c7032144bd516bab68dee8c7d95988 Mon Sep 17 00:00:00 2001 From: Ravjot Singh Samra <31635233+RavSS@users.noreply.github.com> Date: Sat, 20 Jul 2024 07:15:50 +1200 Subject: [PATCH 262/347] Fixed `AttributeError` in metrics/roc_auc.py. (#1568) `scipy.integrate.trapezoid` replaced `scipy.integrate.trapz` in SciPy. --- river/metrics/roc_auc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/metrics/roc_auc.py b/river/metrics/roc_auc.py index 3e7f780564..c3d6604f00 100644 --- a/river/metrics/roc_auc.py +++ b/river/metrics/roc_auc.py @@ -100,4 +100,4 @@ def safe_div(a, b): tprs[i] = safe_div(a=tp, b=tp + fn) fprs[i] = safe_div(a=fp, b=fp + tn) - return -integrate.trapz(x=fprs, y=tprs) + return -integrate.trapezoid(x=fprs, y=tprs) From 9d05b775894febdccafc4c09d068f3ec779db4f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Mon, 22 Jul 2024 17:01:47 +0200 Subject: [PATCH 263/347] Replace Gym with Gymnasium (#1580) * Replace Gym with Gymnasium The maintainers of OpenAI's Gym decided to stop maintaining the library and moved over to a fork called Gymnasium. They advise to switch to the new library, which can be used as a drop-in replacement. See: https://github.com/openai/gym/blob/master/README.md * Upgrade Gymnasium * Add a float conversion to please MyPy --- poetry.lock | 61 +++++++++++++++++---------------- pyproject.toml | 2 +- river/bandit/bayes_ucb.py | 2 +- river/bandit/envs/__init__.py | 2 +- river/bandit/envs/candy_cane.py | 4 +-- river/bandit/envs/testbed.py | 2 +- river/bandit/epsilon_greedy.py | 2 +- river/bandit/evaluate.py | 4 +-- river/bandit/exp3.py | 2 +- river/bandit/random.py | 2 +- river/bandit/test_envs.py | 3 +- river/bandit/test_policies.py | 6 ++-- river/bandit/thompson.py | 2 +- river/bandit/ucb.py | 2 +- 14 files changed, 50 insertions(+), 46 deletions(-) diff --git a/poetry.lock b/poetry.lock index a675335667..658289a2e3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -835,6 +835,17 @@ files = [ [package.extras] tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"] +[[package]] +name = "farama-notifications" +version = "0.0.4" +description = "Notifications for all Farama Foundation maintained libraries." +optional = false +python-versions = "*" +files = [ + {file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"}, + {file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"}, +] + [[package]] name = "fastjsonschema" version = "2.19.1" @@ -1069,43 +1080,35 @@ docs = ["Sphinx", "furo"] test = ["objgraph", "psutil"] [[package]] -name = "gym" -version = "0.26.2" -description = "Gym: A universal API for reinforcement learning environments" +name = "gymnasium" +version = "0.29.1" +description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)." optional = false -python-versions = ">=3.6" +python-versions = ">=3.8" files = [ - {file = "gym-0.26.2.tar.gz", hash = "sha256:e0d882f4b54f0c65f203104c24ab8a38b039f1289986803c7d02cdbe214fbcc4"}, + {file = "gymnasium-0.29.1-py3-none-any.whl", hash = "sha256:61c3384b5575985bb7f85e43213bcb40f36fcdff388cae6bc229304c71f2843e"}, + {file = "gymnasium-0.29.1.tar.gz", hash = "sha256:1a532752efcb7590478b1cc7aa04f608eb7a2fdad5570cd217b66b6a35274bb1"}, ] [package.dependencies] cloudpickle = ">=1.2.0" -gym_notices = ">=0.0.4" -importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""} -numpy = ">=1.18.0" +farama-notifications = ">=0.0.1" +importlib-metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""} +numpy = ">=1.21.0" +typing-extensions = ">=4.3.0" [package.extras] accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"] -all = ["ale-py (>=0.8.0,<0.9.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"] -atari = ["ale-py (>=0.8.0,<0.9.0)"] -box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"] -classic-control = ["pygame (==2.1.0)"] -mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2)"] -mujoco-py = ["mujoco_py (>=2.1,<2.2)"] -other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)"] -testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"] -toy-text = ["pygame (==2.1.0)"] - -[[package]] -name = "gym-notices" -version = "0.0.8" -description = "Notices for gym" -optional = false -python-versions = "*" -files = [ - {file = "gym-notices-0.0.8.tar.gz", hash = "sha256:ad25e200487cafa369728625fe064e88ada1346618526102659b4640f2b4b911"}, - {file = "gym_notices-0.0.8-py3-none-any.whl", hash = "sha256:e5f82e00823a166747b4c2a07de63b6560b1acb880638547e0cabf825a01e463"}, -] +all = ["box2d-py (==2.3.5)", "cython (<3)", "imageio (>=2.14.1)", "jax (>=0.4.0)", "jaxlib (>=0.4.0)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (>=2.3.3)", "mujoco-py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (>=2.1.3)", "shimmy[atari] (>=0.1.0,<1.0)", "swig (==4.*)", "torch (>=1.0.0)"] +atari = ["shimmy[atari] (>=0.1.0,<1.0)"] +box2d = ["box2d-py (==2.3.5)", "pygame (>=2.1.3)", "swig (==4.*)"] +classic-control = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"] +jax = ["jax (>=0.4.0)", "jaxlib (>=0.4.0)"] +mujoco = ["imageio (>=2.14.1)", "mujoco (>=2.3.3)"] +mujoco-py = ["cython (<3)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "mujoco-py (>=2.1,<2.2)"] +other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)", "torch (>=1.0.0)"] +testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"] +toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"] [[package]] name = "h11" @@ -5138,4 +5141,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "44171f65a1084b6f80ee723431af4c5bffd1f4094599d2bf13d386af38ecfde6" +content-hash = "df98d6f87b78c451f52e32f91ee2e3aaa145138a78cf500596a7797689a0d3c0" diff --git a/pyproject.toml b/pyproject.toml index e3ed735f78..b45f37e632 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,7 @@ pandas = "^2.1" [tool.poetry.group.dev.dependencies] graphviz = "^0.20.1" -gym = "^0.26.2" +gymnasium = "^0.29.0" matplotlib = "^3.0.2" mypy = "^1.6.1" pre-commit = "^3.5.0" diff --git a/river/bandit/bayes_ucb.py b/river/bandit/bayes_ucb.py index 4c62b59d8e..5fdef8d1db 100644 --- a/river/bandit/bayes_ucb.py +++ b/river/bandit/bayes_ucb.py @@ -28,7 +28,7 @@ class BayesUCB(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import proba >>> from river import stats diff --git a/river/bandit/envs/__init__.py b/river/bandit/envs/__init__.py index 8f0f511aa6..6c91fa9b71 100644 --- a/river/bandit/envs/__init__.py +++ b/river/bandit/envs/__init__.py @@ -1,7 +1,7 @@ from __future__ import annotations try: - import gym + import gymnasium as gym GYM_INSTALLED = True except ImportError: diff --git a/river/bandit/envs/candy_cane.py b/river/bandit/envs/candy_cane.py index 421af13c62..f6702e6606 100644 --- a/river/bandit/envs/candy_cane.py +++ b/river/bandit/envs/candy_cane.py @@ -2,7 +2,7 @@ import dataclasses -import gym +import gymnasium as gym @dataclasses.dataclass @@ -25,7 +25,7 @@ class CandyCaneContest(gym.Env): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import stats >>> env = gym.make('river_bandits/CandyCaneContest-v0') diff --git a/river/bandit/envs/testbed.py b/river/bandit/envs/testbed.py index c7f0bfb8da..7b8b725d3f 100644 --- a/river/bandit/envs/testbed.py +++ b/river/bandit/envs/testbed.py @@ -2,7 +2,7 @@ import math -import gym +import gymnasium as gym class KArmedTestbed(gym.Env): diff --git a/river/bandit/epsilon_greedy.py b/river/bandit/epsilon_greedy.py index e8f52559bd..a422bd1daf 100644 --- a/river/bandit/epsilon_greedy.py +++ b/river/bandit/epsilon_greedy.py @@ -33,7 +33,7 @@ class EpsilonGreedy(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import stats diff --git a/river/bandit/evaluate.py b/river/bandit/evaluate.py index 6baf22b19c..0079079c8e 100644 --- a/river/bandit/evaluate.py +++ b/river/bandit/evaluate.py @@ -5,7 +5,7 @@ import typing try: - import gym + import gymnasium as gym except ImportError: ... @@ -52,7 +52,7 @@ def evaluate( Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> trace = bandit.evaluate( diff --git a/river/bandit/exp3.py b/river/bandit/exp3.py index 00e8fb62ca..b5c7899c51 100644 --- a/river/bandit/exp3.py +++ b/river/bandit/exp3.py @@ -35,7 +35,7 @@ class Exp3(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import proba >>> from river import stats diff --git a/river/bandit/random.py b/river/bandit/random.py index 4e7f1c4de1..2199ed0f43 100644 --- a/river/bandit/random.py +++ b/river/bandit/random.py @@ -23,7 +23,7 @@ class RandomPolicy(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import proba >>> from river import stats diff --git a/river/bandit/test_envs.py b/river/bandit/test_envs.py index 0f79fa5759..9be9acf97b 100644 --- a/river/bandit/test_envs.py +++ b/river/bandit/test_envs.py @@ -1,6 +1,7 @@ from __future__ import annotations -import gym.utils.env_checker +import gymnasium as gym +import gymnasium.utils.env_checker import pytest from river import bandit diff --git a/river/bandit/test_policies.py b/river/bandit/test_policies.py index 9650f488e8..b1d6507ebf 100644 --- a/river/bandit/test_policies.py +++ b/river/bandit/test_policies.py @@ -5,7 +5,7 @@ import inspect import random -import gym +import gymnasium as gym import pytest from river import bandit, metrics @@ -111,7 +111,7 @@ def test_better_than_random_policy(policy: bandit.base.Policy, env: gym.Env): arm_id = policy.pull(arm_ids) # type: ignore observation, reward, terminated, truncated, info = env.step(arm_id) policy.update(arm_id, reward) - policy_reward += reward + policy_reward += float(reward) random_arm_id = random_policy.pull(arm_ids) # type: ignore ( @@ -122,7 +122,7 @@ def test_better_than_random_policy(policy: bandit.base.Policy, env: gym.Env): info, ) = random_env.step(random_arm_id) random_policy.update(random_arm_id, reward) - random_reward += reward + random_reward += float(reward) n_successes += policy_reward > random_reward diff --git a/river/bandit/thompson.py b/river/bandit/thompson.py index 03cf76a5d7..c53533448b 100644 --- a/river/bandit/thompson.py +++ b/river/bandit/thompson.py @@ -40,7 +40,7 @@ class ThompsonSampling(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import proba >>> from river import stats diff --git a/river/bandit/ucb.py b/river/bandit/ucb.py index e79f40de51..78fbf0b758 100644 --- a/river/bandit/ucb.py +++ b/river/bandit/ucb.py @@ -32,7 +32,7 @@ class UCB(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import preprocessing >>> from river import stats From e181e38155d1ff6cbb3f794ea3abe820fede03ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Tue, 23 Jul 2024 12:41:37 +0200 Subject: [PATCH 264/347] Clear out MyPy's warnings (#1581) Type annotations are added to some functions; this allows to remove MyPy's warnings when typechecking the code by clearing some inconsistencies. A couple incorrect or extraneous annotations were removed in the process. --- river/base/drift_detector.py | 6 +++--- river/drift/kswin.py | 2 +- river/forest/aggregated_mondrian_forest.py | 4 ++-- river/tree/nodes/hatc_nodes.py | 3 ++- river/tree/nodes/sgt_nodes.py | 4 ++-- river/tree/stochastic_gradient_tree.py | 22 +++++++++++----------- 6 files changed, 21 insertions(+), 20 deletions(-) diff --git a/river/base/drift_detector.py b/river/base/drift_detector.py index c5972728f6..9e241cd362 100644 --- a/river/base/drift_detector.py +++ b/river/base/drift_detector.py @@ -22,12 +22,12 @@ class _BaseDriftDetector(base.Base): def __init__(self): self._drift_detected = False - def _reset(self): + def _reset(self) -> None: """Reset the detector's state.""" self._drift_detected = False @property - def drift_detected(self): + def drift_detected(self) -> bool: """Whether or not a drift is detected following the last update.""" return self._drift_detected @@ -57,7 +57,7 @@ class DriftDetector(_BaseDriftDetector): """A drift detector.""" @abc.abstractmethod - def update(self, x: int | float) -> DriftDetector: + def update(self, x: int | float) -> None: """Update the detector with a single data point. Parameters diff --git a/river/drift/kswin.py b/river/drift/kswin.py index 82547faa52..f2c799b67a 100644 --- a/river/drift/kswin.py +++ b/river/drift/kswin.py @@ -109,7 +109,7 @@ def _reset(self): super()._reset() self.p_value = 0 self.n = 0 - self.window: collections.deque = collections.deque(maxlen=self.window_size) + self.window = collections.deque(maxlen=self.window_size) self._rng = random.Random(self.seed) def update(self, x): diff --git a/river/forest/aggregated_mondrian_forest.py b/river/forest/aggregated_mondrian_forest.py index fc250a2734..12601d0cd5 100644 --- a/river/forest/aggregated_mondrian_forest.py +++ b/river/forest/aggregated_mondrian_forest.py @@ -169,7 +169,7 @@ def __init__( # memory of the classes self._classes: set[base.typing.ClfTarget] = set() - def _initialize_trees(self): + def _initialize_trees(self) -> None: self.data: list[MondrianTreeClassifier] = [] for _ in range(self.n_estimators): tree = MondrianTreeClassifier( @@ -287,7 +287,7 @@ def __init__( self.iteration = 0 - def _initialize_trees(self): + def _initialize_trees(self) -> None: """Initialize the forest.""" self.data: list[MondrianTreeRegressor] = [] diff --git a/river/tree/nodes/hatc_nodes.py b/river/tree/nodes/hatc_nodes.py index bab558192d..e85cbc4334 100644 --- a/river/tree/nodes/hatc_nodes.py +++ b/river/tree/nodes/hatc_nodes.py @@ -2,6 +2,7 @@ import math +from river import base from river import stats as st from river.utils.norm import normalize_values_in_dict from river.utils.random import poisson @@ -133,7 +134,7 @@ class AdaBranchClassifier(DTBranch): Other parameters passed to the split node. """ - def __init__(self, stats, *children, drift_detector, **attributes): + def __init__(self, stats: dict, *children, drift_detector: base.DriftDetector, **attributes): super().__init__(stats, *children, **attributes) self.drift_detector = drift_detector self._alternate_tree = None diff --git a/river/tree/nodes/sgt_nodes.py b/river/tree/nodes/sgt_nodes.py index e5a0542999..b649b1ac51 100644 --- a/river/tree/nodes/sgt_nodes.py +++ b/river/tree/nodes/sgt_nodes.py @@ -31,7 +31,7 @@ class SGTLeaf(Leaf): Parameters passed to the feature quantizers. """ - def __init__(self, prediction=0.0, depth=0, split_params=None): + def __init__(self, prediction: float = 0.0, depth: int = 0, split_params: dict | None = None): super().__init__() self._prediction = prediction self.depth = depth @@ -52,7 +52,7 @@ def reset(self): self._update_stats = GradHessStats() @staticmethod - def is_categorical(idx, x_val, nominal_attributes): + def is_categorical(idx: str, x_val, nominal_attributes: list[str]) -> bool: return not isinstance(x_val, numbers.Number) or idx in nominal_attributes def update(self, x: dict, gh: GradHess, sgt, w: float = 1.0): diff --git a/river/tree/stochastic_gradient_tree.py b/river/tree/stochastic_gradient_tree.py index 4a3821d6ca..a92598872b 100644 --- a/river/tree/stochastic_gradient_tree.py +++ b/river/tree/stochastic_gradient_tree.py @@ -7,7 +7,7 @@ from river import base, tree -from .losses import BinaryCrossEntropyLoss, SquaredErrorLoss +from .losses import BinaryCrossEntropyLoss, Loss, SquaredErrorLoss from .nodes.branch import DTBranch, NominalMultiwayBranch, NumericBinaryBranch from .nodes.sgt_nodes import SGTLeaf from .utils import BranchFactory, GradHessMerit @@ -23,15 +23,15 @@ class StochasticGradientTree(base.Estimator, abc.ABC): def __init__( self, - loss_func, - delta, - grace_period, - init_pred, - max_depth, - lambda_value, - gamma, - nominal_attributes, - feature_quantizer, + loss_func: Loss, + delta: float, + grace_period: int, + init_pred: float, + max_depth: int | None, + lambda_value: float, + gamma: float, + nominal_attributes: list[str] | None, + feature_quantizer: tree.splitter.Quantizer | None, ): # What really defines how a SGT works is its loss function self.loss_func = loss_func @@ -56,7 +56,7 @@ def __init__( self._root: SGTLeaf | DTBranch = SGTLeaf(prediction=self.init_pred) # set used to check whether categorical feature has been already split - self._split_features = set() + self._split_features: set[str] = set() self._n_splits = 0 self._n_node_updates = 0 self._n_observations = 0 From 7373c5c9ca6a2b6513c6383f3dfc1b107a824b73 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 24 Jul 2024 14:05:05 +0200 Subject: [PATCH 265/347] Bump urllib3 from 2.2.1 to 2.2.2 (#1557) Bumps [urllib3](https://github.com/urllib3/urllib3) from 2.2.1 to 2.2.2. - [Release notes](https://github.com/urllib3/urllib3/releases) - [Changelog](https://github.com/urllib3/urllib3/blob/main/CHANGES.rst) - [Commits](https://github.com/urllib3/urllib3/compare/2.2.1...2.2.2) --- updated-dependencies: - dependency-name: urllib3 dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/poetry.lock b/poetry.lock index 658289a2e3..dbbf4b0d4f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "alabaster" @@ -4854,13 +4854,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake [[package]] name = "urllib3" -version = "2.2.1" +version = "2.2.2" description = "HTTP library with thread-safe connection pooling, file post, and more." optional = false python-versions = ">=3.8" files = [ - {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"}, - {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"}, + {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"}, + {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"}, ] [package.extras] From f18594c253cf10a8110e0060813d879a00620aac Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 24 Jul 2024 14:06:25 +0200 Subject: [PATCH 266/347] Bump setuptools from 69.5.1 to 70.0.0 (#1575) Bumps [setuptools](https://github.com/pypa/setuptools) from 69.5.1 to 70.0.0. - [Release notes](https://github.com/pypa/setuptools/releases) - [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst) - [Commits](https://github.com/pypa/setuptools/compare/v69.5.1...v70.0.0) --- updated-dependencies: - dependency-name: setuptools dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/poetry.lock b/poetry.lock index dbbf4b0d4f..c5f4656ef3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4076,19 +4076,18 @@ win32 = ["pywin32"] [[package]] name = "setuptools" -version = "69.5.1" +version = "70.0.0" description = "Easily download, build, install, upgrade, and uninstall Python packages" optional = false python-versions = ">=3.8" files = [ - {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"}, - {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"}, + {file = "setuptools-70.0.0-py3-none-any.whl", hash = "sha256:54faa7f2e8d2d11bcd2c07bed282eef1046b5c080d1c32add737d7b5817b1ad4"}, + {file = "setuptools-70.0.0.tar.gz", hash = "sha256:f211a66637b8fa059bb28183da127d4e86396c991a942b028c6650d4319c3fd0"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"] -testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] -testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] [[package]] name = "six" From 0ce2becd2d900b73b15ac534ab4e7b9cca6a1629 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Thu, 25 Jul 2024 17:29:59 +0200 Subject: [PATCH 267/347] Use units based on powers of 2 (#1583) * Use units based on powers of 2 This reflects how they are interpreted in the code. * Add a changelog entry --- docs/releases/unreleased.md | 2 ++ river/forest/adaptive_random_forest.py | 4 ++-- river/forest/online_extra_trees.py | 2 +- river/stream/twitch_chat_stream.py | 2 +- river/tree/extremely_fast_decision_tree.py | 2 +- river/tree/hoeffding_adaptive_tree_classifier.py | 2 +- river/tree/hoeffding_adaptive_tree_regressor.py | 2 +- river/tree/hoeffding_tree.py | 4 ++-- river/tree/hoeffding_tree_classifier.py | 2 +- river/tree/hoeffding_tree_regressor.py | 2 +- river/tree/isoup_tree_regressor.py | 2 +- river/tree/utils.py | 6 +++--- river/utils/pretty.py | 2 +- 13 files changed, 18 insertions(+), 16 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 79e701b844..219b4c1a2d 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1 +1,3 @@ # Unreleased + +- The units used in River have been corrected to be based on powers of 2 (KiB, MiB). This only changes the display, the behaviour is unchanged. diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index a3553c1d9d..ba4a75282f 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -534,7 +534,7 @@ class ARFClassifier(BaseForest, base.Classifier): in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class. max_size - [*Tree parameter*] Maximum memory (MB) consumed by the tree. + [*Tree parameter*] Maximum memory (MiB) consumed by the tree. memory_estimate_period [*Tree parameter*] Number of instances between memory consumption checks. stop_mem_management @@ -808,7 +808,7 @@ class ARFRegressor(BaseForest, base.Regressor): binary_split [*Tree parameter*] If True, only allow binary splits. max_size - [*Tree parameter*] Maximum memory (MB) consumed by the tree. + [*Tree parameter*] Maximum memory (MiB) consumed by the tree. memory_estimate_period [*Tree parameter*] Number of instances between memory consumption checks. stop_mem_management diff --git a/river/forest/online_extra_trees.py b/river/forest/online_extra_trees.py index 6ed958df46..a13707bdb0 100644 --- a/river/forest/online_extra_trees.py +++ b/river/forest/online_extra_trees.py @@ -583,7 +583,7 @@ class OXTRegressor(ExtraTrees, base.Regressor): binary_split [*Tree parameter*] If True, only allow binary splits. max_size - [*Tree parameter*] Maximum memory (MB) consumed by the tree. + [*Tree parameter*] Maximum memory (MiB) consumed by the tree. memory_estimate_period [*Tree parameter*] Number of instances between memory consumption checks. stop_mem_management diff --git a/river/stream/twitch_chat_stream.py b/river/stream/twitch_chat_stream.py index ddb6397981..fff0aafbee 100644 --- a/river/stream/twitch_chat_stream.py +++ b/river/stream/twitch_chat_stream.py @@ -46,7 +46,7 @@ class TwitchChatStream: channels A list of channel names like `["asmongold", "shroud"]` you want to collect messages from. buffer_size - Size of buffer in bytes used for receiving responses from Twitch with IRC (default 2 kB). + Size of buffer in bytes used for receiving responses from Twitch with IRC (default 2 KiB). timeout A timeout value in seconds for waiting response from Twitch (default 60s). It can be useful if all requested channels are offline or chat is not active enough. diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index bac7ed8be6..d9972522e7 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -80,7 +80,7 @@ class ExtremelyFastDecisionTreeClassifier(HoeffdingTreeClassifier): smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management diff --git a/river/tree/hoeffding_adaptive_tree_classifier.py b/river/tree/hoeffding_adaptive_tree_classifier.py index 290d34dad1..f63c2ba013 100644 --- a/river/tree/hoeffding_adaptive_tree_classifier.py +++ b/river/tree/hoeffding_adaptive_tree_classifier.py @@ -77,7 +77,7 @@ class HoeffdingAdaptiveTreeClassifier(HoeffdingTreeClassifier): smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management diff --git a/river/tree/hoeffding_adaptive_tree_regressor.py b/river/tree/hoeffding_adaptive_tree_regressor.py index a2066a9e91..1055472002 100644 --- a/river/tree/hoeffding_adaptive_tree_regressor.py +++ b/river/tree/hoeffding_adaptive_tree_regressor.py @@ -84,7 +84,7 @@ class HoeffdingAdaptiveTreeRegressor(HoeffdingTreeRegressor): binary_split If True, only allow binary splits. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management diff --git a/river/tree/hoeffding_tree.py b/river/tree/hoeffding_tree.py index 9a7addeeef..4dc4a1c801 100644 --- a/river/tree/hoeffding_tree.py +++ b/river/tree/hoeffding_tree.py @@ -34,7 +34,7 @@ class HoeffdingTree(ABC): binary_split If True, only allow binary splits. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management @@ -111,7 +111,7 @@ def _hoeffding_bound(range_val, confidence, n): @property def max_size(self): - """Max allowed size tree can reach (in MB).""" + """Max allowed size tree can reach (in MiB).""" return self._max_size @max_size.setter diff --git a/river/tree/hoeffding_tree_classifier.py b/river/tree/hoeffding_tree_classifier.py index 6ce2c09f57..841cdbe4d5 100755 --- a/river/tree/hoeffding_tree_classifier.py +++ b/river/tree/hoeffding_tree_classifier.py @@ -58,7 +58,7 @@ class HoeffdingTreeClassifier(HoeffdingTree, base.Classifier): smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management diff --git a/river/tree/hoeffding_tree_regressor.py b/river/tree/hoeffding_tree_regressor.py index 1bcc1c96b3..16604e2d6c 100644 --- a/river/tree/hoeffding_tree_regressor.py +++ b/river/tree/hoeffding_tree_regressor.py @@ -57,7 +57,7 @@ class HoeffdingTreeRegressor(HoeffdingTree, base.Regressor): binary_split If True, only allow binary splits. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management diff --git a/river/tree/isoup_tree_regressor.py b/river/tree/isoup_tree_regressor.py index 2f92a34756..9411d1a9c9 100644 --- a/river/tree/isoup_tree_regressor.py +++ b/river/tree/isoup_tree_regressor.py @@ -62,7 +62,7 @@ class iSOUPTreeRegressor(tree.HoeffdingTreeRegressor, base.MultiTargetRegressor) binary_split If True, only allow binary splits. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management diff --git a/river/tree/utils.py b/river/tree/utils.py index 3826d02af4..7dff6859e4 100644 --- a/river/tree/utils.py +++ b/river/tree/utils.py @@ -251,7 +251,7 @@ def calculate_object_size(obj: typing.Any, unit: str = "byte") -> int: Object to evaluate. unit The unit in which the accounted value is going to be returned. - Values: 'byte', 'kB', 'MB' (Default: 'byte'). + Values: 'byte', 'KiB', 'MiB' (Default: 'byte'). Returns ------- @@ -295,9 +295,9 @@ def calculate_object_size(obj: typing.Any, unit: str = "byte") -> int: for i in obj: to_visit.append(i) - if unit == "kB": + if unit == "KiB": final_size = byte_size / 1024 - elif unit == "MB": + elif unit == "MiB": final_size = byte_size / (2**20) else: final_size = byte_size diff --git a/river/utils/pretty.py b/river/utils/pretty.py index 6a3678128e..2ceec3f85a 100644 --- a/river/utils/pretty.py +++ b/river/utils/pretty.py @@ -68,7 +68,7 @@ def humanize_bytes(n_bytes: int): n_bytes """ - suffixes = ["B", "KB", "MB", "GB", "TB", "PB"] + suffixes = ["B", "KiB", "MiB", "GiB", "TiB", "PiB"] human = float(n_bytes) rank = 0 if n_bytes != 0: From 44994963c21c93ace873084c9e3dd4481319233c Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Thu, 25 Jul 2024 14:06:09 -0300 Subject: [PATCH 268/347] [docs] fix memory units in the tree guideline --- docs/recipes/on-hoeffding-trees.ipynb | 160 ++++++-------------------- 1 file changed, 38 insertions(+), 122 deletions(-) diff --git a/docs/recipes/on-hoeffding-trees.ipynb b/docs/recipes/on-hoeffding-trees.ipynb index 9843919fbb..54d77140cc 100644 --- a/docs/recipes/on-hoeffding-trees.ipynb +++ b/docs/recipes/on-hoeffding-trees.ipynb @@ -41,12 +41,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:56.494351Z", - "iopub.status.busy": "2023-12-04T17:48:56.493893Z", - "iopub.status.idle": "2023-12-04T17:48:57.115871Z", - "shell.execute_reply": "2023-12-04T17:48:57.115462Z" - }, "tags": [] }, "outputs": [], @@ -100,12 +94,6 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:57.117832Z", - "iopub.status.busy": "2023-12-04T17:48:57.117681Z", - "iopub.status.idle": "2023-12-04T17:48:57.128760Z", - "shell.execute_reply": "2023-12-04T17:48:57.128511Z" - }, "tags": [] }, "outputs": [ @@ -116,12 +104,12 @@ "\n", "This dataset contains features from web pages that are classified as phishing or not.\n", "\n", - " Name Phishing \n", - " Task Binary classification \n", - " Samples 1,250 \n", - "Features 9 \n", - " Sparse False \n", - " Path /Users/max/projects/online-ml/river/river/datasets/phishing.csv.gz" + " Name Phishing \n", + " Task Binary classification \n", + " Samples 1,250 \n", + "Features 9 \n", + " Sparse False \n", + " Path /Users/mastelini/Documents/river/river/datasets/phishing.csv.gz" ] }, "execution_count": 2, @@ -145,12 +133,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:57.130143Z", - "iopub.status.busy": "2023-12-04T17:48:57.130063Z", - "iopub.status.idle": "2023-12-04T17:48:57.179708Z", - "shell.execute_reply": "2023-12-04T17:48:57.179486Z" - }, "tags": [] }, "outputs": [ @@ -158,8 +140,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 37.6 ms, sys: 569 µs, total: 38.2 ms\n", - "Wall time: 39.1 ms\n" + "CPU times: user 64.8 ms, sys: 2.75 ms, total: 67.6 ms\n", + "Wall time: 68.1 ms\n" ] }, { @@ -322,7 +304,7 @@ "\n", "for x, y in dataset:\n", " model.learn_one(x, y)\n", - " \n", + "\n", "model" ] }, @@ -339,12 +321,6 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:57.181238Z", - "iopub.status.busy": "2023-12-04T17:48:57.181151Z", - "iopub.status.idle": "2023-12-04T17:48:57.191191Z", - "shell.execute_reply": "2023-12-04T17:48:57.190978Z" - }, "scrolled": true, "tags": [] }, @@ -381,12 +357,6 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:57.192658Z", - "iopub.status.busy": "2023-12-04T17:48:57.192583Z", - "iopub.status.idle": "2023-12-04T17:48:57.205983Z", - "shell.execute_reply": "2023-12-04T17:48:57.205736Z" - }, "scrolled": true, "tags": [] }, @@ -518,12 +488,6 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:57.207360Z", - "iopub.status.busy": "2023-12-04T17:48:57.207269Z", - "iopub.status.idle": "2023-12-04T17:48:57.425988Z", - "shell.execute_reply": "2023-12-04T17:48:57.425673Z" - }, "scrolled": true, "tags": [] }, @@ -534,7 +498,7 @@ "\n", "\n", - "\n", "\n", "\n", "\n", "0->1\n", - "\n", - "\n", - "≤ 0.5455\n", + "\n", + "\n", + "≤ 0.5455\n", "\n", "\n", "\n", @@ -572,9 +536,9 @@ "\n", "\n", "0->2\n", - "\n", - "\n", - "> 0.5455\n", + "\n", + "\n", + "> 0.5455\n", "\n", "\n", "\n", @@ -588,9 +552,9 @@ "\n", "\n", "2->3\n", - "\n", - "\n", - "≤ 0.0909\n", + "\n", + "\n", + "≤ 0.0909\n", "\n", "\n", "\n", @@ -604,15 +568,15 @@ "\n", "\n", "2->4\n", - "\n", - "\n", - "> 0.0909\n", + "\n", + "\n", + "> 0.0909\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -637,12 +601,6 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:57.427606Z", - "iopub.status.busy": "2023-12-04T17:48:57.427498Z", - "iopub.status.idle": "2023-12-04T17:48:57.439934Z", - "shell.execute_reply": "2023-12-04T17:48:57.439690Z" - }, "tags": [] }, "outputs": [ @@ -675,12 +633,6 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:57.441374Z", - "iopub.status.busy": "2023-12-04T17:48:57.441291Z", - "iopub.status.idle": "2023-12-04T17:48:57.450415Z", - "shell.execute_reply": "2023-12-04T17:48:57.450185Z" - }, "tags": [] }, "outputs": [ @@ -732,12 +684,6 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:57.451796Z", - "iopub.status.busy": "2023-12-04T17:48:57.451720Z", - "iopub.status.idle": "2023-12-04T17:48:57.462677Z", - "shell.execute_reply": "2023-12-04T17:48:57.462419Z" - }, "tags": [] }, "outputs": [], @@ -762,19 +708,19 @@ "\n", " # Convert timedelta object into seconds\n", " r_time.append(checkpoint[\"Time\"].total_seconds())\n", - " # Make sure the memory measurements are in MB\n", + " # Make sure the memory measurements are in MiB\n", " raw_memory = checkpoint[\"Memory\"]\n", " memory.append(raw_memory * 2**-20)\n", "\n", " ax[0].plot(step, error, label=model_name)\n", " ax[1].plot(step, r_time, label=model_name)\n", " ax[2].plot(step, memory, label=model_name)\n", - " \n", + "\n", " ax[0].set_ylabel(metric_name)\n", " ax[1].set_ylabel('Time (seconds)')\n", - " ax[2].set_ylabel('Memory (MB)')\n", + " ax[2].set_ylabel('Memory (MiB)')\n", " ax[2].set_xlabel('Instances')\n", - " \n", + "\n", " ax[0].grid(True)\n", " ax[1].grid(True)\n", " ax[2].grid(True)\n", @@ -793,19 +739,13 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:48:57.464024Z", - "iopub.status.busy": "2023-12-04T17:48:57.463949Z", - "iopub.status.idle": "2023-12-04T17:49:07.647224Z", - "shell.execute_reply": "2023-12-04T17:49:07.646820Z" - }, "scrolled": true, "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -834,34 +774,28 @@ "source": [ "In our example we use the `EBSTSplitter`, which is going to discussed later. For now, is enough to know that it is a mechanism to evaluate split candidates in the trees.\n", "\n", - "As we can see, our tree uses almost 10 MB to keep its structure. Let's say we wanted to limit our memory usage to 5 MB. How could we do that?\n", + "As we can see, our tree uses almost 10 MiB to keep its structure. Let's say we wanted to limit our memory usage to 5 MiB. How could we do that?\n", "\n", "Note that we are using a illustration case here. In real applications, data may be unbounded, so the trees might grow indefinitely.\n", "\n", "HTs expose some parameters related to memory management. The user can refer to the documentation for more details on that matter. Here, we are going to focus on two parameters:\n", "\n", - "- `max_size`: determines the maximum amount of memory (in MB) that the HT can use.\n", + "- `max_size`: determines the maximum amount of memory (in MiB) that the HT can use.\n", "- `memory_estimate_period`: intervals after which the memory-management is triggered.\n", "\n", - "We are going to limit our HTR to 5 MB and perform memory checks at intervals of 500 instances." + "We are going to limit our HTR to 5 MiB and perform memory checks at intervals of 500 instances." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:49:07.648955Z", - "iopub.status.busy": "2023-12-04T17:49:07.648846Z", - "iopub.status.idle": "2023-12-04T17:49:15.925716Z", - "shell.execute_reply": "2023-12-04T17:49:15.925436Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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zAW9eEwAAnvLll1/a9UmXXXaZ8euvvxp5eXl25Y8cOWLMmDHDuO6664yoqKhSz0+fT58PAKhac+bMseuLunTpYixevNgoLCy0KZucnGy8/PLLdiupRkZG2jwzd6SwsNDo1q2bTb169eoZX3/9tVFQUFBcLi0tzfjnP/9pN4v7Qw895PY1/fDDDw7fQezdu9em3LJly4zu3bvblKtTp46RkJDgVjvevCYAAM567733nM68HRoaWu5n8iVxv35uXBM8j+R0AB6Tmppq1KlTx+ZD/vXXX3daPikpyWjVqpVN+RdeeMGLEQMAUL307t3baNWqlfHll18aOTk5pZYvKioy7r77brubsZKJ2M7ceOONNvX69u1rZGRkOCxrtVrt2mrbtq3dQ3VHYmNjDR8fH5u6M2fOdFp+165dRkREhE35r776qlpdEwAAlWn58uWGyWQypDMDut58881yPwh/5plnbOq2bt3a5qFtSf/6179syoeHh5f6YtwwvPtMwFvXBACAp+zbt88IDAws7ov8/Pxc3heX5E6/RZ9Pnw8AqFrnnXeeTT/Up08fIysry2WdP/74w/D19bWp99prr7ms8/nnn9uUr1u3rrF7926n5b/77jub8r6+vnbJ5Y7k5+fb9eH33nuvTcLZ3506dcro06ePTfnbbrut1Ha8eU0AAPzd2eT0OnXqGMOHDzcmT55szJkzxzh48KCxfPlyjyWnc79+blwTPI/kdAAe8+STT9p8uA8dOtTpzelZy5Yts6lTp04dIzU11UsRAwBQvSxatMjIz88vU52ioiK7B7433XRTqfV27dplM7uIv7+/ERMT47JObm6u0b59e5u2pkyZUmpb48aNs6lz6623llqn5IxyLVu2tJkhpaqvCQCAypKTk2O0bdu2uF/6xz/+Ue4H4cnJyXYzvCxbtsxlHavVagwdOtSmzrPPPltqW956JuDNawIAwFNGjBhh0w/Nnj3bo+enz6/YNQEAUFEHDhyw6X8kGdHR0W7VLTmb6IABA5yWzc/PN6KiomzKT506tdQ2brnlljK/Q/jkk09s6rRv397Izc11WWf37t02s8H7+PgYsbGxLut485oAAPi7/fv3G7t37zYsFovdMU8lp3O/fm5cEyqHWQDgAVarVdOmTbPZ99JLL8lkMrmsd9FFF2nIkCHF26dPn9bs2bMrJUYAAKq70aNHy9/fv0x1fHx89OSTT9rs++2330qt99VXX8lqtRZvjx8/Xp07d3ZZJzAwUE8//bTNvi+//NJlnZMnT2ru3LnF2yaTSS+99FKp8d1xxx1q2bJl8fahQ4e0bNkyl3W8dU0AAFSm559/XgcOHJAktWjRQq+++mq5z/X9998rKyureHvo0KG66KKLXNYxmUx68cUXbfZ99dVXMgzDaR1vPhPw1jUBAOApCxYs0PLly4u3r7/+el1//fUebYM+/wz6fABAVYmLi7PZbt68ufr27etW3euuu85me//+/U7L/vbbbzp8+HDxdqtWrXTHHXeU2kbJ/nrOnDnKyMhwWafkc/JnnnlGgYGBLut06dJFN9xwQ/G2xWKx++5QkjevCQCAv2vbtq26dOkis7nyUmi5Xz+jul8TKgfJ6QA8Yu3atUpJSSnebtOmjYYPH+5W3YkTJ9psz58/34ORAQBQ8/395kqS0tLSlJOT47LOzz//bLNdsj925oYbblBISEjx9saNG3X06FGn5RcvXqyioqLi7eHDh6tNmzaltmM2m+0eQJf2HcFb1wQAQGXZuHGj3n///eLtjz/+WKGhoeU+34IFC2y23e0bR4wYodatWxdvHz9+XOvXr3da3pvPBLx1TQAAeMqUKVNstku+jPUE+vz/oc8HAFSF9PR0m+2oqCi367Zo0cJm+9SpU07Lluwf77jjjlITtKQzyXfDhg0r3i4sLNQvv/zitHxSUpK2bNlSvB0aGqpx48aV2o5k32eXjLkkb10TAABVgfv1/6nO14TKQXI6AI9YvHixzfaoUaPcumk8W/bvVqxYoezsbI/FBgBATVe3bl27fa5mCImLi7OZfSUkJEQDBw50q62SZQ3DsPse8Hclj1188cVutSPZf0dYtGiR07LevCYAACpDYWGhJk6cKIvFIunMrKpXXHFFuc+XlZWlVatW2exztx82mUwaOXKkzT5X/bC3ngl485oAAPCEI0eO2Kxu1rNnT3Xt2tWjbdDn26LPBwBUhfDwcJvt3Nxct+uWLNugQQOnZb31vL1kO4MGDbKZ4MWVQYMGKTg4uHg7Li5O+/btc7utyromAAC8jft1W9X1mlB5SE4H4BHbtm2z2XY3GUySmjZtqlatWhVvFxQUKCYmxkORAQBQ8x05csRuX/369Z2WL9lv9+vXT76+vm63N2jQIJfnc3WsLN8RevfurYCAgOLto0eP2oyOdtVOZV4TAACV4fXXX9fOnTslSREREfrggw8qdL7du3ersLCweLt169Zq3Lix2/W91d+X5ZmAN68JAABPWLJkSfHAM+nMLGGeRp9vjz4fAOBtPXv2tNmOjY11OwEqOjraZrtfv34Oy504cULHjx8v3g4ICFCvXr3cjtFbfb6vr6/dNThry5vXBACAt3G/bq86XhMqD8npADwiNjbWZrtLly5lql+yfMnzAQAA5/766y+b7ZYtW8rf399peW/124WFhTazmZe1rYCAALVt29attvguAgA4l8XExOhf//pX8fa///3vMj3QdcSbfaO32qK/BwCcazZu3Giz3aNHj+L/3rp1qx5++GH16NFDdevWVXBwsFq1aqVRo0bp7bffdjgQ3RH6/PK3AwCApzRv3twmYSo/P9+tQef5+fl6//33bfZNnDjRYdmS/Vm7du1cvgcoqWT/uH//fhUVFbnVlrf6/Mq8JgAAvI379fK34+22UDlITgdQYbm5uUpMTLTZFxUVVaZzlCwfFxdX4bgAAKgtvvrqK5vtyy+/3GX5kv1sZfXb8fHxNg+Cg4KCXC5JWpG2vHVNAAB4mtVq1cSJE1VQUCBJGjJkiCZNmlTh83q6bzx06JDy8vLsynnzmYC3rgkAAE8pmZzepk0bZWVlaeLEierVq5c+/PBD7dixQ6dOnVJubq4OHTqkZcuWafLkyWrfvr2effZZmxnJHKHPL70d+nwAgDf8+9//ltn8vxScF154QV9//bXT8qdOndLYsWNtEqWuvPJKXXnllQ7LV7R/jIyMVGBgYPF2QUGBEhISKqUtb/X5ZbkmAAC8jfv10tupDteEykNyOoAKS01NlWEYxdt+fn5q2LBhmc7RrFkzm+3k5GSPxAYAQE33yy+/aNWqVTb7br/9dpd1SvazzZs3L1ObJfvtlJQUt9opWa88bTn7juCtawIAwNM++OADrV+/XpLk7++vKVOmyGQyVfi8Fe0bGzVqJF9f3+Jtq9WqtLQ0u3LefCbgrWsCAMBTSq4mZjabNXToULtB5o7k5ubq9ddf1+WXX67Tp087LUefb48+HwBQFQYPHqyPPvqo+J6+qKhIt99+u/r166c33nhD8+bN05IlS/Ttt9/qoYceUtu2bbVo0aLi+qNGjdKsWbOcnr+i/aMkNW3a1OU5zyr5fLyiz9srq8+X3L8mAAC8jft1e9XxmlB5fEsvAgCuZWVl2WwHBweX+UV6SEiIy3MCAAB76enpuueee2z2XX311erXr5/LeiX72ZL9cGlKli8sLFR+fr4CAgI82o6jOs6+I3jrmgAA8KSEhAQ999xzxdvPPPOMOnXq5JFzV7RvNJlMCgoKskmGc9QPe/OZgLeuCQAAT7BarXZJ5Q8//LC2bt0q6Uy/dMUVV+jyyy9X8+bNlZ2dra1bt2rGjBk6evRocZ1ly5bp9ttv108//eSwHfp8e/T5AICqct9996ljx456+OGHtXv3bklnVlIpuZrK37Vp00ZPPvmkJk2aZDPzekneet6em5sri8VSoba81eeXpS0AALyN+3V71fGaUHmYOR1AhZX88P770lnuCgoKcnlOAABgy2q16pZbblFSUlLxvvDwcH3wwQel1q1o312y33Z0Tk+046gtd2+EK+uaAADwpLvvvlvZ2dmSpE6dOunZZ5/12Lm91Q+fS/19WdoCAKCiMjIybGb5kqQtW7ZIkurXr6+VK1fq559/1r333qsrrrhCN9xwg9544w3FxcXppptusqk3d+5cffPNNw7boc+vWFsAAHjahRdeqI0bN+qJJ56Qj4+Py7ItWrTQE088oZtuusllYrpUdX1+edqizwcAgPv1irTFd4SageR0ABWWl5dns+3v71/mc5SclTQ3N7dCMQEAUNNNnjxZv/76q82+zz//XFFRUaXWrWjf7Wg2cUd9tze/I3jrmgAA8JSpU6dq2bJlks7MFjJlypRy9ZXOeKsfPpf6+7K0BQBARTl76enj46PFixdryJAhDo+HhoZqxowZuvjii232v/baa3bJ7hJ9fkXbAgDA0z777DO1bdtWb7/9tt0M5CUlJibq/vvvV6tWrfTVV1+5LFtVfX552qLPBwCA+/WKtMV3hJqB5HQAFVZydFJBQUGZz5Gfn+/ynAAA4H8++OADvfvuuzb7nnzySd1www1u1a9o312y33Z0Tk+046gtZ98RvHVNAAB4wrFjx/TEE08Ub991111OE9TKy1v98LnU35elLQAAKspZH3PXXXepf//+LuuazWZ9+umnNjOoxsXFaeXKlaW2Q59ftrYAAPCUwsJCjR07Vvfdd5+OHTsmSapXr55eeOEFRUdH6+TJkyooKNDRo0f1888/65prrpHJZJIkpaena+LEiZo8ebLT81dVn1+etujzAQDgfr0ibfEdoWYgOR1AhYWGhtpsOxpNXZqSo5NKnhMAAJwxc+ZMPfLIIzb7br/9dr3xxhtun6OifbejUcWO+m5vfkfw1jUBAOAJDzzwgE6dOiVJaty4sd58802Pt+Gtfvhc6u/L0hYAABXlrI+ZNGmSW/XbtGmjkSNH2uxzlJxOn1+xtgAA8JT77rtPP/30U/F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bzYo9Zj9z4Iq4ZOUVWmp9gh1w1v7kLN30xXoln86XJO05flqr9qbo+t7N9cKVXVQnsPrMYmcYhv7zxz69v8x+Nsy/m3xJR2XmFurzVY5XD3154W5d0KZ+tUzewLkjNStfd07fqB1JGS7L3TmotZ4b3Vlms3ffx5jNJj0wop16NI/Qw99vVbqLgWeuPDCirR4f1dHr8VdHfVuGa+amo3b7j5zK1eH0HEXV4zMFgHfsPXFab/0Wp6UxJ9yuE+Lvo7uHttXEIa0VGlAtUiIqha+PWUPaR2rxzmN2x1btTdU/R1dBUIADRRarlsWe0NTVCdp48GSZ6zeLCNIdg1ppXN8ohVWje7aSTCaT/nXNebrsP3+p0GKfz/TdhkSN7R2lnlER3g8OAADAy6p8eLBhGGVKMj9bvjwzoDNrOgAAAACgJisosurBmVtsEtPPemVRjDLzyrZELlBTTVuToHu/3ewyMb1ZRJB+um9gtU5MP6t53WA9N7qzw2PZBRatPZDq5YiA6inu+GmNn7KuODH97+ZsTtJl//lLG6rJjMCGYeit3+JKTUx/bnRnPTCinR67uIM6Na7jsEx2gUWPzt4mSxlnJQTOSkjN1rWfrC01Mf250Z31wpVdqjSxe3D7M4O2epQx4cfXbNKbY7tr8iWdSEz/r15R4U6PlWXWYgAor8PpOXps9jZd8v4qtxPT/X3Mmji4tVY9OUL/GNm+RiemnzW0g+PZ0eNOnNaxjFwvRwPYOpyeo3d+j9Pgfy/Xvd9uKXNieu+WdfXJzb20cvJw3TWkTbVOTD+rXcM6untoG4fHDEP657ydKrI4fx4FAABQU1Tp3diECROqsnkAAAAAAGqM/CKLHvhuq5bFOn5hm1Ng0bwtRzRhYCvvBgZUI1aroX/9EqupqxNcluvcJExf39FXDcMCvRRZxfVrXU/hQX7KyLUfhPLbrhO6sFOjKogKqD52H83QLV9u0Mkc5wO1kk7mavwX63X3kDZ67OIOCvCtmhUHDMPQq4tL/6z6vzFddduAVpKkAF8fvT++p676cI0KHCQ6bD50Up+tPKAHRrSrjJBRg21JPKmJ0ze6/Nvx9zHr3Rt66IruTb0YmXPNIoI0+54L9MqiGH27PrHU8mGBvvrs1t4a2NZxcl9tVTfYT02CDR3LsU/WXx+fprG9m1dBVABqg9SsfH305359t+GQw5mHHTGbpOt6NdcjozqoWURQJUdYvQztEOn02F97UzWub5QXowHOTJ6xNOaEvt+YqNX7U1XWOSR9zCZd3q2JJg5ufc7OMP7giPZasO2okk7aDxDZfTRT36w7pDsHt66CyAAAALynSpPTp02bVpXNAwAAAABQI+QVWnTft5u1PC7FZbkZ6w/ptgEtZTIxGyRqn7xCix79YZt+3XXcZblB7errs1t6q845MBvX3/n5mHVRp4aau/WI3bFlsSdksRryYSZY1FLbD5/SbV9FOxy8UZJhSJ+vitfKvSl674ae6twkzAsR/o/Vauilhbv1zbpDTsuYTNJr13TTjf1a2Ozv1DhMky/pqH/9Euuw3ntL92pYh0id18z5bMhVyWo1tHJfipJO5qp3i7rq0tS7//aw9/vu43r4+60uVxoJC/TVF7f1Uf829b0YWekCfH306tXd1KtFXT07b6fTa2hRL1jT7uirtpGhXo7w3NA+zHlyOgB4WmZeob5cFa8vVycop8Didr1LuzbWE5d0ULuGjleRqemahAepQ6NQ7T2RZXds5b4UktPhNfuTs/TDxkT9tOWI0rMLyly/TqCvburXQrcNbHXODzIJ8vfRK2PO0x3TNzo8/s7vcbq8WxM1Dj93JkUAAAAoq5q/jhUAAAAAADVYXqFFd8/YrFV7XSemS2deEq2LT2NWSNQ66dkFuuvrjdqSeMpluWt7NdMb13aXv6/ZO4F52MVdGztMTk/LLtDmQyfVr3W9KogKqFqbD6Xr9q826nR+UZnq7Tl+WmM+WqPHLu6gSUPaeGVwh9Vq6Nl5O/X9xsNOy5hN0ptjezidsXji4Nb6Y88JrY9PtztWZDX0yA/btOihwQr0q5pZ4Z1Jzy7QvTM2K/rg/+K+pGsjvXZNN9UPDajCyGqvb9Yd1Is/73Y502WziCBNv6Ov2jeqvsmA1/Zqrs5NwnTvt5t1KC3H5liflnU15bY+qhfiX0XRVX9twwytcjCuL+lkrpJO5qh53WDvBwWgxskrtGjGukP6ZMV+lyt1lDSgTX09dVmnc3ZmZU8a2j7SYXL66n2pDFRGpcotsOiXncf0/cZEbTx4slznaFU/WHcMaq2xvZsrJKDmpDCN6NRQl53X2OEkCdkFFr2yKEYf39yrCiIDAADwjprzzQ4AAAAAgFomt8CiSd9s0ur9qW7XmbHuEMnpqFUOpmbr9mnROlgiIa2khy9qr0dHtj+nVxYY2qGBAnzNyi+ynx32993HSU5HrbMhPk13TN9Yppk3/67AYtUbv+7Rn7HJemdcD0XVq7wkTIvV0JM/7tBPW5KclvExm/TuuB4a07OZ0zJms0lvX99Dl73/l8OE/P3JWfr3kj168cquHonbE1Kz8nXzFxsUd+K0zf7fdp/Q5kOn9ObYbrqwU6Mqiq72sVoN/fu3Pfp8ZbzLcp2bhGn6HX3VKKz6z/bYuUmYfn5wsD76c58WbDsqPx+zbugbpbuHtql2AzWqm3ZhzkcnbIhPV/PeJKcDKL8ii1U/bUnS+8v26VhGntv1ujUL15OXdtTgdg3O6fs3TxrWMVJfrk6w25+RW6jtSafUq0XdKogKNdmuIxn6YeNhzd92RKfzyjYQ+Kz+retp4uDWuqhzoxo7gOKFK7to1d4UZTu4J12885jGxiVrRMeGVRAZAABA5SM5HaUyDENbtmzRtm3blJycLElq1KiRevTooV69enHTDwAAAABVIDu/SBO/3uhwZlRXfo85oeMZeSwbi1ph86GTmvTNJpfLSfuaTXrtmm41YqnzYH9fDe0QqaUxJ+yO/RZzXP8c3ZnnOKg1Vu9L1V3fbFReof1gjbOGdohU0/BAlzOVS1L0wXRd+v4qvXhVV13fu7nH/44KLVY9Nnu7Fm4/6rSMr9mkD288X5d1a1Lq+ZrXDdb/Xd1Vj/6w3eHxaWsO6qJOjTS4fdUPVks+naebvtig/cn2M31KZxLX75y+STf3b6F/ju6sYH9eaVSm/CKLnpizw+XvoiQNad9An9zcS3UC/bwUWcWFB/npn6O76J+ju1R1KOeUUD+pSZChY7n2n3vr49N0nZNVHADAFcMwtGTXcb31e5ziU7LdrtemQYieuKSjLjuvMfc1JfRtVU+BfmaH331XxqWQnA6POJ1XqJ+3H9X30Ye180hGuc4R4GvW6O5NdOeg1jqvWbiHI6x+moQH6dFRHfTq4liHx19csFsDHq3PgEkAAFAj8SS3Crz00kt6+eWXy11/woQJmj59uucCcqKwsFD/+c9/9P777+vIEfsloSWpefPmeuSRR/Twww/Lz+/ceRANAAAAAOeyrPwi3Tlto6IPOk9MdzZ7ssVqaGZ0oh4b1aEyQwSq3JJdx/SP77c5/Ds4K8TfR5/e0ltDO0R6MbLKdXGXRg6T0w+n5yr22Gl1aRpWBVEB3rU8Lln3zNisAhd//yM7N9THN/dSgK+PRnZupKfn7lBqlvOBLNkFFj354w4tjTmh16/tpgahAR6JtaDIqodnbdWS3fZLvZ/l72PWJzf30sgu7s8efnXPZloWk6zFO485PP7EnO1a8sgQRQT7lzlmTzmRmacbv1jvVlLadxsStfZAmt4d10Pnk1xVKTJyCnX3jE3akOB64OO1vZrpjWu7y9/X7KXIUNXahTlOTi/tdwUAHFkfn6bXf4nV9iT3E1ubhAfqkZHtdV2v5vL1of9xJNDPRxe0qa8VcSl2x1btS9GjPANCORmGoS2JJ/V99GEt2nFMuYXlW5WqU+M6urFfC13ds5nCg2tXXsntA1vppy1HFHss0+5YYnqOPl6+X49f3LEKIgMAAKhc3L3BocOHD6t///6aPHmy08R0SUpKStITTzyhAQMGuCwHAAAAAPCM03mFmvBVtMvE9Hoh/pp7/0C1bhDi8Pis6ESXCXvAuW7q6gTd990Wl4npjcICNPveATUqMV2SRnZuJGerYf8e4zz5Fagplsac0D3fuE5Mv+y8xvrk5t4K8D0zO93ILo302yNDdbEbyd9LY07o0vdXORwEUlb5RRbd/91ml4npAb5mTbmtd5kS0yXJZDLp1avPU8M6jpPoj2fm6fkFu8t0Tk86eipXN3y+rkyzpSakZmvsZ+v03tK9KrTwPcaTkk7maOxna0tNNn7ownZ65/oeJKbXMu3CDYf7E9NzdORUrpejAXCu2nfitO76eqPGT1nvdmJ6RLCf/nl5Zy1/Yrhu6NuCxPRSDG3v+N52++FTOpXjfBAm4Eh6doG+/CteF7+3Std9uk5zNieVOTE9xN9HN/ZroQUPDNKv/xiiCQNb1brEdEny9THrX9ecJ2cLPny28oDTlaQAAADOZdzBwU5ycrJGjBihrVu32uwPCgpS165d1blzZwUG2i7/vnnzZo0YMUKpqaneDBUAAAAAapWM3ELdOjVamw+ddFqmQai/Zk26QF2bhuuWC1o6LJNyOp8kVdRIFquhlxfu1iuLYmQ4zqOSdGbGrnn3D1LXpjVvCem6If7q17qew2O/7a54Mi1Qnf2y85ju+3azClwkLl/Vo6k+vPF8u+Ta+qEB+vzW3nprbHeFBrhecDQ1q0CTvtmkp37coaz8onLFmldo0aRvNmtZbLLTMkF+Pvrq9r4a3rFhudqoG+Kvt67v4fT4wu1HtWCb9yccSTqZoxumrNPBtJwy17VYDf3nj30a+9k6xaeQwOEJu49m6NpP1mqfi4QYH7NJr1/bTY9f3FEmZ1k1qLHahjn/UrUhPs2LkQA4FyWfztMzc3fqkvdXufze83fB/j566MJ2WvXkCE0a2kaBfj6VHGXNMKyj4+R0qyGt3s87fJTOakhxGSY9OS9WF7z2h15dHOvyO6Iz57eI0JvXdVf0P0fq9Wu7qUdURK3/DtmrRV3d2K+Fw2OFFkPPzd8pw9WDLAAAgHOQ66fsleSbb76x23fbbbe5Vc5THLVXVd5++2316OH8JUFJTZs2rcRopNtvv10HDhwo3g4MDNQbb7yhSZMmKTg4WJKUnZ2tKVOm6Nlnn1VeXp4kad++fbrzzjv1888/V2p8AAAAAFAbncop0G1fRWuHixnGIusEaNak/mrXsI4kaWyv5nrrtz3KK7RP0vtm3SFd0b1y7y8Bb8ortOgf328tNQF7cLsG+uSWXgoLrLmzdV3StbHWx9vPfht7LFOH03MUVS+4CqICKteCbUf02Oztslidv9C/rldzvTm2u3ycLC9gMpl0fZ8oXdCmvh6fs13Rpcwi/cOmw1obn6p3x/VU31aOB4U4klNQpLu+3qS1B5wndYb4+2jaHf2cDjZx17AOkZowoKW+XnfI4fHn5u9S31b11DQiqELtuOtweo7GT1nvcrblhnUClJKV73KQ0fbDpzT6g9X65+jOurl/i1qf7FJeq/am6L5vNyu7wPksmEF+Pvrk5l4a0al8gyRw7qvjJzUOMnQ81/7vbEN8uq7t1bwKogJQ3WXnF+mLv+I1ZVW8clz0M3/n52PSzf1b6oER7RTpZPUXONemQYiaRQQ5/J61Mi6FZ0BwKCOnULuPntbvSSatTzYrLd8kqeyDGcKD/HRtr2Ya37eFOjau4/lAa4CnLumk33YdV1q2/UoG6+PTNX/bEV1zPt+rAABAzVElyem333673cNiR8nijsp5SnVKTu/du7eGDx9e1WFIkn7//Xf9+uuvxdt+fn767bffNHToUJtyISEhevTRR9WrVy+NGjVKhYWFkqSFCxdq+fLlGjFihFfjBgAAAICa7GR2gW7+coNijmU6LdMoLEAzJ12gtpGhxfvCg/10dc9m+n7jYbvy0Qnpijt+mhdGqBHSsvJ11zebtDXxlMty1/Vqrtev7WY3Y3JNc3HXxnp5YYzDY7/tPq67hrTxckRA5Zqz6bCe/GmHy2TmG/tF6V9Xd5PZSWL630XVC9asSRdo6up4vf3bXpczsR9Oz9W4z9fp3mFt9ejIDqV+vmTlF+nOaRsVfdB54nudAF9Nv7OferesW2qs7nj6ss76a3+q4lOy7Y6dzivSE3O269uJ/d36t6mIg6nZuumL9Tqakee0TKfGdfTdXf0Vd+K0npi93WXZ3EKLnpu/S3/EntC/x3ZXwzqBTsvC3pxNh/XM3J0qcjGgo0Gov766va+6N4/wXmColtqFOU5OX5/AzOkAbBVZrJqzOUnvLt2rlNP5btUxmaRrzm+mR0d2YCBtBZhMJg3rGKmZGxLtjq3alyLDMBjQV0tl5hXqYGq2DqblnPnf1GwlpJ3535M5hf8tVb4VCga0qa/x/aJ0SdfGrHJQivBgP/1zdGc9Nnu7w+OvLorVhR0bKTy45k6mAACoeifzpbqMA4WXVPmbOHeWpjEMwyM/7rZXmz3//PM2208//bRdYvrfDRs2TE899ZTNvueee65SYgMAAACA2igtK183frHeZWJ6k/BA/XD3AJvE9LNuuaCl03oz1h/0RIhAlUpIzda1n64tNTH9kZHt9fb13Wt8YrokNYsI0nnNwhwe+z3G9czywLlm5oZETf7RdWL6hAEt3U5MP8vHbNLdQ9tqwYOD1KmUgVyGIX264oDGfLxGccdPOy2XkVuoW6ducJmYHh7kp+8m9fdYYrokBfn76P0besrXyfWvPZCmaWsPeqw9R+JTsjR+iuvE9C5NwjRz0gWqHxqggW0b6NdHhurqnqXP8Lk8LkWXvv+Xftt93JMh11iGYej9ZXs1+ccdLhPT20SGaN79g0hMhySpXbjj35VDaTk6luF8JQQAtYdhGPoj9oQu+89fembuTrcT00d2bqgl/xiqd8f1JDHdA4a2j3S4/0RmvvaeyPJyNPCmrPwi7TqSoYXbj+rDP/bp8dnbdd2na9X7laXq/tLvuuqjNXp41la9u3Sv5m49oq2Jp/6WmF42DUIDdN/wtlrxxHDNuvsCjenZjMR0N11zfjNd0Mbx6lhp2QV687c9Xo4IAFCbrE826ZWtPtqWxoBFeEeVzJwuuZ8k7slkchLTXdu5c6eio6OLt0NCQjR58uRS6z355JN67733lJ19ZuadtWvXKjY2Vp07d660WAEAAACgNkg5na+bv1zv8gVis4ggzZp0gVrUd/wS97xm4erVIkJbHCTuzttyRE9d2kl1ApmRB+emzYfSddfXm1y+UPU1m/T6td10fZ8oL0ZW9S7p0li7jtgPatl0MF1pWfmqH8r0KDj3TV+ToJecrBJw1qQhrfXs5Z3LPUtk5yZhWvDgIL27dK+mrIp3mQQfeyxTV364WpMv6aiJg1vbJMOfyinQrVOjtfNIhtP69UL8NWNiP3VtGl6uWF3p3jxC/7iovd5Zutfh8X8v2aMh7RuoQyPPr6iyPzlLN32xXskuktS6NQvXjIn9FBHsX7wvPMhP748/Xxd1bqTn5u9SRq7zz/r07ALdM2Ozru/dXC9e1VWhAVX26qNaK7RY9c95OzV7U5LLcn1a1tUXt/VR3RB/l+VQe7St4/zDb0N8uq4+v5kXowFqp/TsAi2LOaF8i1Vdm4apR/MI+VTyqifu2pmUoX/9EqP18c4H4JXUo3m4nr28s/q3qV+JkdU+A9vVl4/ZJIuDAWgr9yazet45Lju/SAfTsnUwNUcH07KVkJqtQ2nZSkjNUWqWewNCystskoZ3bKgb+kbpwk4N5edT8wf+VwaTyaRXr+6my/6zSoUW+7/TmdGJuq53c/Vq4bnBygAASNI3G5I068CZwWTf7DOrf/xJXdrT8QQ3gKdUyRPaadOmebQcPGPBggU22+PGjVOdOqXfoNapU0fXX3+9pk+fXrxv/vz5JKcDAAAAQAUkZ+bpxi/W60BKttMyUfWCNPOuC0qdXey2Aa20JXGb3f7sAovmbT2i2wa0qmC0gPf9uvOY/vHDNhUUWZ2WCQ3w1We39Nbg9g28GFn1cHHXxg6TUK2G9Edsssb1rV3J+qh5pqw6oNd+cT2r3AMj2uqJizuWOzH9rABfHz1zWWdd1KmRHpu9TUknnc8SXGCx6l+/xGpZ7Am9M66HmtcNVlpWvm6ZGq1YF6ugNAgN0MxJ/SslOfys+4a31Z9xyQ5XmigosuqR77dp/gODPLrCxN4Tp3XTF+uVmlXgtEyPqAh9c2c/hQc5Hix3ZY+m6tuqnp6Ys12r96e6bG/O5iStT0jTu+N6qm8rxzMS1lZZ+UV64LstWrk3xWW5y7s11rvjejL7JWyE+Utt6gcpPs3+8299fBrJ6UAlyi+y6Mu/EvTRn/uVW2gp3l832E/DOkRqRKeGGto+skoGFB1Oz9Hbv8dpwbajbteJqhekJy/ppNHdmpRpVRu4JyzQT71b1HW4Us+qvam6e2jbKogK5RV7LFM/bU7SjqQMJaRlu70igSc1iwjSDX2jNLZ3czWNCPJ6+zVRu4ahumdoW320fL/dMcOQ/jlvlxY+OEi+DAAAAHiAYRh65/e9+mh5QvE+i2HSIz/F6Lu6YR5dvREoqUqS0ydMmODRcvCMxYsX22xffPHFbtcdNWqUTXL6okWL9Mwzz3gqNAAAAACoVY5n5OmmL9YrPtV5YnrL+sGaNekCt14MXdatsV5Z5K+0bPvEsG/WHdKtF7SscOIe4C2GYWjq6gT965dYlzMYNw4L1LQ7+qpzk9o5+0eHRqFqVT9YB9Ny7I79tvs4yek4p3305z69/bvjGcDPenRkBz18UTuP9m/9WtfTr/8YolcWxZQ68/SGhHRd9v5fmnxpR81Yd0j7kp2vgtIoLEAzJ12gtpGhHovVEV8fs94b11OXf/CXcgosdsdjjmXqvWV79dSlnTzSXuyxTN385QalO/j+cVavFhGafmc/hZWyikvj8EB9c2c/fb3uoN74dY/yXQxMOpyeqxs+X6d7h7XVIyM7eDTZ/lyVnJmnO6Zv1O6jzgdISNLEwa31z8s7kywIh/q0jHCanA6gcvy554T+b2GMw+/0J3MKNX/bUc3fdlRmk9QzKkLDOzbUiI4N1bVpWKV+lmfkFOrjFfs1fc1BFVic98l/Fx7kp4cubKdbB7RUgC8DoCrT0A4NHCanRyekK6egSMH+rDBTnVmshpbFntC0NQllWo3Ak3zNJl3ctZHG922hQe0aVJtVGmqSBy9sp5+3H1Viuv3ne+yxTH297pAmDm5dBZEBAGoSq9XQCz/v0rfrE+2O5RVadce0aP1wz4Ba+w4FlY+nspB05sXujh07bPYNHDjQ7fqDBg2y2d6+fbsMV2+IAQAAAAAOHT2VqxumrHOZmN66QYh+uHuA2zMWBfj66AYniaj7k7Oq7GUXUFYWq6GXF8bo1cWuE9M7Na6jeQ8MrNUPVU0mky7u2tjhsb/2pyorv8jLEQEVZxiG3l26t9TE9Ccv7ah/jGxfKQOv6gT66c2xPTTl1t6qX8oMpafzi/TCgt0uE9ObRQRp9j0DKj0x/axWDUL0/BVdnB7/bOUBbXSQzFRWu49m6KYv1rtMTO/bqq6+mdi/1MT0s8xmk+4Y1FqLHhqsrk1df75bDemTFQd0zSdrtO/E6TLFXtPsTz6taz5Z6zIx3WSSnr+ii56/oguJ6XCqT4twh/sPpuXoeEael6MBaraE1GzdOX2j7py+yWFieklWQ9qSeErvLt2rKz9arX6v/aEn5mzX4h3HlJFb6LG4zsziHq+hby3XlFXxbiWm+/uYdffQNlo1eYTuGtKGxHQvGNahocP9BRarNvD8p9rKyC3UF6viNeyt5bpnxmavP6vzMxlqEWJoTEuLlj7UT5/c3FtDO0SSmF5JAv189PKYrk6Pv/t7nI5lOF+xCwCA0hRarHrkh20OE9PPyswrKnWFPaAiGBZbTeTn5ys+Pl5paWny8/NT/fr11bRpUwUHu16a3VMOHTqknJz/PdwICQlRixYt3K7fsmVLBQcHF58jOztbhw8fLtM5AAAAAKC2O5yeo5u+XK/D6c5fPrSNDNGsSReoYVhgmc59U/8W+mzlAVkdJPTOWH9QA9rWL2u4gFflFlj0j++36veYEy7LDWnfQJ/c3Et13Ex2rMku6dpIU1bF2+0vKLJq1d4UXd6tSRVEBZSPYRj695I4fbbygMtyz43urLuGtKn0eC7u2li9WtbV0z/t1LJY159LzkTVC9KsSReoeV3vPAM+a3zfKC2LOaE/9iTbHTMM6dEftunXfwwp9+fozqQM3TJ1g8tkuAva1NPUCX0VElD2VxTtG9XRvPsH6f1le51+tzlr99FMXfHhaj19WSdNGNCq1iVeRyeka9I3m1z+f+Hva9Z/buipy+gTUApnyemStCEhTWN6NvNiNEDNlJ1fpI+W79fUvxLcnpHckdSsfP24OUk/bk6Sj9mk3i3ranjHSI3o2FCdGtcp8wA+wzC0cMcxvfXbHpfPK0q6umdTPX5xR0XV8+53ndqua9Mw1Q9xvHreyr0pGtHJcfI6qsb+5Cx9vfagftqS5HB1I0/y9zWrVf1gtaofolYNQv77v8FqEGBo96a1OvtVubRBuPCMER0b6vJujfXLzuN2x7ILLPq/hTH69JbeVRAZAOBcl1tg0f3fbdbyONeJ5956jorai+T0auCBBx5QfHy88vJsZ5bw9fVV7969ddlll+n+++9XZGRkpcUQFxdnsx0VVfalnaOiomzOExcXR3I6AAAAALgpMS1HN36xXkdOOX/R26FRqL676wJF1gko8/mb1w3WhZ0aOUyg+233CZ3IzFOjMia8A96SmpWvu77epG2HT7ksd33v5nrt2m7y82GxQEk6P6quGoQGKDUr3+7Yb7uPk5xewxiGofwiqwL9at5slIZh6P8WxWjamoMuy70ypqtuHdDKKzFJUoPQAH1xW2/N2ZSklxfuVnYZkklaNwjRzEn91STcvVVQPMlkMumN67rrkvdXOZzZPOlkrv5vYYzeur5Hmc+97fAp3Tp1g07nOV+dYVC7+vrytr4K8i//76q/r1lPXtpJIzo11GOzt7lMlMsvsurlhTH6c0+y3hrbQ43Da8f3nUU7juqxH7a7TG6MCPbTl7f1UZ9W9bwYGc5VDUL91TYyRAdS7Fd4Wh9PcjpQEYZh6OftR/X6L3t0PNOzKxFYrIaiE9IVnZCuN5fEqXFYoEZ0itTwjg01qF0DhZYyUGxDfJpe+yVW25My3G5zQJv6evbyzurW3PmgFlQes9mkIe0baP62o3bHVjEzZrVgtRpauS9F09Yc9Pj/J/4+ZrX4bwJ66wbBf0tCD1GTsECHgzUzMzMVW7vGcFYbL1zRVSvjUhzeS/6667iW70lmQAkAoEwycgt119cbtfHgSadlTDL00ugOmkBiOioZyenVQExMjMP9RUVF2rBhgzZs2KB///vfeuKJJ/Tiiy/Kx8fzL5iSk21nyWnevHmZz9GsWTOb5PSS5wQAAAAAOHYwNVs3frFexzKcv4Tu1LiOvrurv+qHlj0x/axbB7R0mJxusRqauSFRj47qUO5zA5UlPiVLt0/bqMR018vZPzaqgx66sF2ZZwGsycxmk0Z1aaRZ0fZLd/65J1kFRVb5+5LIf67LzCvUl38l6KfNSTqakatOjcP07+u6qXvziKoOzSOsVkMv/LzL5RK0JpP0+jXdNL6f9yfKMJlMGtc3She0qa/H52xz+eLnrHYNQzXzrv5lXgXFkyLrBOiNa7vp7hmbHR6fszlJF3VupEvPa+z2OTcfOqnbv4rW6XznielD2jfQF7f18dggir6t6umXh4folUUxmr0pyWXZv/al6pL3V+nVq8/TlT2aeqT96sgwDH35V4L+9Uusy3JR9YI0/Y5+ahsZ6qXIUBP0b1PfYXL6hvj0KogGqBlijmbqpYW7FZ3gnb+j45l5mhV9WLOiD8vPx6S+reppRMeGGtEpUm0jQ4vvp/Ynn9Ybv8aVaYWY9g1D9czlnTSiY0Puy6rY0A6RDpPT41OzdTg9h9nsq0hWfpF+2pykr9ceVHyqfX/qLn8fs6LqBal1gxC1/G/ieev/zoLeJDxIPrVstaBzWePwQD12cUe9sshxztALP+/S722GVWhgLwCg9kg5na8JX0Ur5lim0zI+JkMT2lt1TQ/3n/kB5UVy+jkiNzdXr7zyiv766y8tXLhQoaGefWCclZVlsx0SElLmc5SsU/Kc5ZWcnKyUlLKNGN6/f79dLJmZzj94Ufmys7NdbgMAgHMf/T1QPgfTcnTXdzuVnGU/c+lZnRqF6PPxXeVnzVdmpv0MyO7q0chfLeoGKvGkfRL8zA2HdFufhsw4jWpl6+EMPfxjjDJynSc6+ppNevHy9hrTvZFOnz7txejODUNa19GsaPv9p/OK9OeuwxrYpm6Zz0mfXz3kFlo0a9NRTVufZPM3EnssU9d/tk5Tb+6m7s3CqjDCirNYDf3fr/s0b7vzpCizSXrlig66vFNElT7/i/CTpozvqq83JOmjlYdUZDUclmsfGawpN3ZVoAqUmem87/eGC6KCdU2PRk7/fZ/5abs61PNVg1D/Us+1OTFDD8zerRwXs8cPaVtX717TUQW52SpwPtF5uTx3cWsNaFlH//fLPp100Wdk5BbqoVlbtWRHkp65pJ3CAmvWKxKL1dBby+I1c5N9MtrfdWkcqo/HdVX9ACvPzeGUo/6+R+MgzXRQNj41W/uPpKhhOVZ4AmqrjNxCfbzqkGZvOSYnXxtsmCRd3aOR7hncQgdScrT6QLpWHTipI6fKP9N6ocXQ2gNpWnsgTf/6JVZNwwM0pG09FVkNzd9+XBY34pKkyFB/3T+0pcZ0byRfs4n7smrg/MbOB0H+tuOwxvViFS1vOnwyV7M2HdX8HSeUle/+aktn9Wwepks7N1DrBsGKqhukJmEBThLQi5SdVfa/P+7xq9Y159XTnI0h2nPC/t/9cHqu3lmyWw8Pb+X9wAAA55SjGXm6Z9YuHXKxuqC/2dBdHa3qGGHQ39cSnsrfLa+a9eT1HGIymTRgwACNHj1a/fr1U+fOnVWvXj2ZzWalpaVpy5YtWrRokb7++mvl5f3vocKKFSs0fvx4LViwwKMzqJf8RQwMLPusPUFBtkvQeuqX+5NPPtHLL79coXNER0fr+PHjHokHnhEd7eDNOAAAqFHo74HSHc+RPo7xUWah8xmNokIM3RaVoW0bVnukzV5hJiWetL+fTMkq0IfzVun8+m6+fQYq2ZZUk77bb1aR4fzvI9DH0J0dLQpLi9Hy5Y5nmartiqxSgI+P8i32/44zlu9Q/iFrhdugz/euIqu09oRJS4+YnfYf+UVW3Ttzmx47z6L6VTc5d4VYDGnmfrM2pTofNGWWoVvbWRWaWn0+A1pLeuw8acZ+Hx3Lsf3/p3mIoTtaZmpH9JqqCc6BCwKkVQE+Ssu3/106mVukB79Zp7s7WeVq8tN9GSZN2WNWgdV5ofPqWnV1gxSt/WulJ8J2yCzpsS7SrANmxZxyPdhu8e4UrdmXrFvaWdU+vGZ89ymwSDP2m7Uj3fW1d4mw6vYWp7RjY/X5PcS5ITo6WvkFkrNXi98sWafeDWrG3xNQmayGtC7ZpMWJZmUXuTe7cctQQ9e1tqhl8BHFbTkiSbrAX+rfSUrOk2JOmhRzyqQDmSZZXNw/leZoRr5+2HLM7fL+ZkMXNbVqRNMcBZyM1V8rXa/aAe9qHuKjpGz734cFG/YqMmNPFURUuxiGtDfTpFXHTNp90iRDZfvb9DEZOr++oaFNrGoZmi5lpSsvS9p3UNpXOSEX4x7f+y6LlOJO+Dj8PZm2LlGR2QlqzIIHAAAnjudIn8b66FSB8+8bwT6G7ulsUas6Z7bp72uHxETnq5F6Q5Ump995551V0q7JZNLUqVOrpG1Juvjii3XTTTepQwfHy6U3bdpUTZs21RVXXKHnnntO48eP15o1/3tQvHjxYn3yySd66KGHPBbT3xPgJcnfv/TZcEoKCLCdESM318PT3wAAAABADXL0v4npWS4S01uGGrq3s0XBHrx779/Q0OLDhgodJJCtPm4iOR1VzjCk35JM+jXJ9aD8CH9D93SyqGnZF3+rVXzNUtcIQ1vS7P/md6abNLb1mZmnUf1ZDGljikm/JZmV7iCRuKSsQpOm7PHRP87zbD/iDRbrmSTbrWnOk2zPLkHboxr2W81CpCe6WbT8mEkbks2yGlK3eoYui7IqsJqtxh7oI93SzqIPdjtOhIg5ZdbaZEODGjn+d47LMOmLPWaH3yvO6l7PqgntrfL1wuIsYf7S3Z2sWnPC0IJDrhPmTxWY9EmMWRM6WNWzGv4elUVWofTFHh8dzHL92TCwoVVj21jlw+c+yincX2oYaCg5z/6XaH+mieR0oBQJp6UfExwnDDsS6mfoqhZW9Y00HH5nN5mkRkFSoyBDI5oayrdIezPOJKrHnDS5TE6pCLMMDWhk6NLmVoWV/ZUyvKRTuOHwd21vpkkWq8TCeZWjwCJtSjVp5TGzjueW/W8w1M/Q4EaGBjXi76s2aVVHGtjI0JoT9r8zFsOkOQlmPdjF9aBhAEDtlJglfRbr43Lga7ifofu6WNSEgU7wsip9LTF9+nSZvPztyTCMKk9OHzhwoNtlmzdvrmXLlunCCy/UunXrive/+uqrmjhxooKDPfOpUXKm9IKCsi8pm59vu7R8eWZfBwAAAIDa4Ej2mcR0Vw+LWv03MT3Iw3fuwb5S7waG1ic7Sigx61iOlQdUqDKF1jMz3m52MVOyJDULNnR3J4siAlwWw391q2doS5r9/sxCkxKzVDxbCqonqyFtSzPp18Nmh8mArhzPNWnaXrPu7WQ9Z5JPiqzS1/tcz/7sYzJ0Z0erzqtbfZMgfc3SqGaGRjWzVHUopWoTJo1sZmjpEce/X/MPmtU+zKKGtgtnas8pk77cY1ahixlae9a36rZ23v39M5mkwY0NdQi36Nv9PjrkImHbKpO+2WdWoI9VnSKq7++TK6l5Z15CppTy+TA6yqJRzQwSWlBh7cKcJKdn8MsFOJNRIC08ZNbGUu5zzjLL0NAmZ5K/y/JMIMDnzHf/bvUMGYZ0LFeK/e+s6vGnTbJWYFb1s86ra9WVLazM4HsO6Bxh1bKj9r9z+RaTErKkdmFVEFQNlp4vrT5u1roTJuU4WLmsNFEhhoY1ser8+oZXBnWi+rmihVXb000OJzLZn2nWrpNnPt8BADhr338njch3MTlDgwBD93c5d1fXxLmtWsyZYxj/+wLl7WT1c0FgYKC++eYbde7cWUVFRZKk5ORk/f7777r66qs90kZoaKjNdsmZ1N1Rcqb0kucsr/vvv1/XX399mers37/f5t+mX79+6ty5s0fiQflkZ2fbLAnSr18/hYQwvR0AADUJ/T3gntjjWXph1k5l//f+zpHzm4fpkxu6KiSgcm7bG3XO0vivtjo8luATpZtGtKuUdgFX0rIL9MiPMdqeetpluYFt6uqdazpV2t9HTdQ3v0gz31+vQov9S8yMOq00YkTrMp2PPt87DMPQqv3p+mTlIcUlZ5f7PHszzFqd31QvXtau2j97zS+y6vG5sdqRnu60TICvWf8Z20UD29T1YmQ132CLVYenb9OeE/a/awVWk35Orquvb+sh3/9O2/rX/nRNjY5RoeE8OeLSLpF67aqOxXWqwrUWq75ce1hTVifKwUegpDMzEU7f76cvbuqm7s3OrSytXUdP66XZu3Uyr9BpGV+zSS+Pbq8ruzXyYmSoCZz197kNk7V2QZxd+eQ8k87rO0iRoUzzCpxVaLHqu41H9fnmRGUXuDdgrX+rCD09qo3aRnr2+/XpvCKtP3hKqw+ka/WBk0rJKttEZec1CdVjF7ZWn5YRHo0LlWewxaqp+9crx8HvXk54a40Y3sr7QdUwhmFoy+FMzdx0VH/GpTr9vumMj0ka2amBbu7bTD2a1amy+zXu8asPo0mynv3Z/nuWJCWZG+nhEeS8AADOWL43TVOiY1Vgdf4FpH1ksD67sZsiQ/3p72up2NjYKm2/WrzFO/sl2zAMm0R1/E+7du101VVXae7cucX7KjM5PTu77C+8StbxVHJ6w4YN1bBhwwqdIzQ0VGFh59aLhZouJCSE/08AAKjh6O8BezuSTmnSzJ3KzHOemH5Bm3qaOqFvpSbeXhAWpvNbJGhr4im7Y4t2Jev5Md0VSuIvvCju+GlN/GaHkk7muix3Q58ovXrNefI7V6aAribCJA1u10DL41Lsjq3Yd1LPX9W9Qi/B6fM9b+3+VL31e5zDz+nymLvtuDo0idC9w9p65HyVIbfAogdmbNJf+50npgf5+WjqhD4a2K6BFyOrPT68qbeu+HC18ousdsd2Hj2tGZuS9Y+R7bUs5oQe/SlWBS6yb645v5neGttdvtXg8/qp0RG6uFtzPTZ7uxJSHT/3zi206sE5MZpzzwC1b3RuLCfx554TeuC7ncotdJ7sWCfAV5/d2luD+JuBB5zt70d09ZccJKdLUkxKga5syu8bIEkr96bo5YW7FZ/i3jvXZhFBem50Z116XuNKSVANC5Oua1hP1/VrI8MwFHMsUyviUrR8T7K2JJ6Us5yWqHpBmnxJJ13RrYnMVTjgDOUzqF0DLY05Ybd/w6EM7uEq4HReoRbvOKYZ6w9p99HMMtevG+ynG/u10C0XtFTTiKDSK3gZ9/hV58YBdbRwV6rWxdsvf7f1yGnVqVN1gxgAANXHT5uT9OTcWFlcJKb3ahGhabf3U3iwn8Pj9Pe1g6fyd8urWrxpNgxDPj4+uvDCCzV+/HjVq1evqkOqli666CKb5PS4OMcP/8qjZPJ3UlJSmc9x5MgRl+cEAAAAgNpsa+JJ3fZVtE67SEwf1K6+vrytr4L8fSo9nlsvaOkw6TG7wKJ5W5J064BWlR4DIEkr4pL14Mytysp3/rchSZMv6aj7h7flJVw5Xdy1scPk9PjUbB1IyVK7hudGMmZNtzXxpN7+PU5r9tu/iHYmwNesCQNbaXzfKN319SbFO0m+fePXPWpRL1iXd2viqXA9Jju/SBO/3qj18c4T00MDfDXtjr7q24pnx5WlfaM6evqyTnp5YYzD4x/8uU8mk/Thn/scrsRw1tjezfXv67rLpxolsJ3foq4WPzxYryyK0azoww7LnMop1K1To/XjfQPUvG6wlyMsmwXbjuix2dtdvoRsHBaoaXf0VecmvGiEZzUKC1TrBiEOB3usj0/TlT2aVkFUQPVxOD1H/7coxmFCsCMBvmbdO6yt7h3W1ivPAqQzE8d1bRqurk3D9cCIdjqVU6BV+1K1Ii5Zq/amKDWrQM3rBun2ga1064CWCvD1TlzwvKEdIh3+Lu46kqmU0/mKrBNQBVGdmyxWQ2sPpOrHzUn6bfdx5RXaD+gsTafGdXTHoFYa07OZAv34u4I9k8mku4a0dpicfiIzX4fTc9WifvW+VwEAVK6vVifo/xY5fnZ31tAOkfrsll4K9q8WqcGoxarFb6DJZJLVatWyZcu0atUqjR49WhMmTNDll18uHx++lJ8VFRVls52SYv9Csbw6duxos334sOMH9K6UrNOpU6cKxQQAAAAANcXmQ+ma8NVGl8m3Q9o30Be39fHay6nLuzXRq4tjlZ5tv5T3jPWHdMsFLUkCRqX7eu1Bvbxwt9NZ+iQp0M+s92/oqUvPq34JteeSkZ0b6VnTTjlatPC33SdITq9isccy9c7vcVoWm+x2HV+zSeP7RenBEe3VODxQkjTtjr66+uM1OplT6LDOoz9sU+PwQPVqUdcjcXvC6bxC3TFtozYdOum0TJ1AX31zZz+dX43irqkmDGilP2KTtXp/qt0xi9XQu0v3uqw/vm+UXrumW7WcWTXY31evXdNN/j5mfb3ukMMyxzPzdNvUaM2+d4AahFbPZK2ZGxL1z/mOP8/P6tiojqbf2VdNwqvfTJyoGS5oU89pcjpQW+UWWPTpiv36bFW8ChysQuLIJV0b6bnRXRRVr2oTDSOC/XVVj6a66r+DS/IKLQrwNfNMoAYY1j7S6bHV+1N0zfnNvRjNuelASpZ+2pykeVuP6FhGXpnrm0zSqM6NdPugVhrQpj5/VyhVn5b1ZDLJ4ff9DQlpJKcDQC1lGIbeX7ZP//ljn8tyo7s10Xs39JS/b9WvZghU6W/hbbfdppCQEBmGIeO/36zy8/M1b948XX311WratKkeffRRbdmypSrDrDb8/GyXWSgsdPySqTxatmypoKD/PajOzs7WoUOOH9A7cujQIeXk5BRvh4SE2CXTAwAAAEBtFJ2QrtumRrtMTB/RMdKriemSFOjnoxv6Or5v23siSxsSnM9eC1RUkcWqFxbs0os/u05Mb1gnQHPuGUhiugdE1glQbyeJvb/vPu7laHBWfEqWHpq1VZf95y+3E9PNJunaXs305+PD9erV3YoT0yWpZf0QfXFbH/n7OH7sm19k1aSvN+lweo7D496WkVOoW6ZGu0xMjwj208y7LiAx3UvMZpPeur67wgLLPq/NLRe0qLaJ6WeZTCa9eGXX4uQ7R+JTs3X7tGidzvPc83dPmbLqgJ6d5zoxfWDb+ppz3wAS01Gp+reu73D/gZRspZzO93I0QNUyDEO/7Dymke+u1Ad/7ncrMb1tZIhmTOynz2/tU+WJ6Y4E+vmQQFtDtKgfrNYNQhweW+lgZS2ckZFbqO82HNI1n6zRRe+s1CcrDpQ5Mb1OoK8mDWmtVZNHaMptfTSwbQP+ruCW8GA/dWzkeAKBjQd5XgsAtZHVaujlhTGlJqbf2C9KH9x4PonpqDaq9Ddx+vTpOn78uKZPn64LL7yweP/ZZPWUlBR98MEH6tu3r7p166Z33nlHx4/X3pdlJa/9/9m776iozq0N4M8UepUuvYgFpAhS7BpLLLHE2HuP6f2medN774kt1lhiTKyJscZOERQVFVRAmhRBehtmzveHH14Nc4ZBYRjg+a1117qes2dmQwRnznne/drbi690biyJRILAwMC7jp04cULrxx8/fvyuPwcGBvLDFRERERERtXsnrxZg9s8xKK9RitYM6eaAn2aGtsh2vtPC3SH20W1dlPYLlokao6RKgXlrTmGtyNTaOv7Oltj+ZB8EuFrpqLO270F/J7XHEzKLcb24UsfdtG+ZNyvwn98SMPTLI9iZkK3140YGOGHvc/3xxaRg0WlpPT1t8OnEQLXnAKCgvAZzV8eiuLJlg7c3y2swbUUUEjKKRGtszAyxYUEkfw/oWEcrE7z3cECjHjOntyfeHdtdr4PpdaRSCT6bGISBXcSvr5/PKsHCtadQpRB/D6dLgiDg871J+ODPSxrrHu7hgtVzw2FpbKCxjuh+RXjbiJ6LTuX0dGo/ruSVYcbKaDz+Szyyihp+P21uJMeSUd2w59n+6KdhojVRUxrQWf3ftaOXb0ClabV4O1OrVOFQUh6e3BCPsPf34/U/zuN0elGjn8fb3gzvjvVH1KuD8boe7IxArVO4l/r3WrFp4gu7iYiobVIoVXhhSwJWn0jTWLd4gA8+eDgAslZwbY7ajxZfJmFqaopZs2Zh//79SEtLw7vvvovOnTvfVSMIAi5cuID//Oc/cHNzw4gRI7B582ZUV7ev6QvHjh27689NPZn8oYceuuvP+/bt0/qx/64dPXp0k/RERERERETUWh27fANzV8egUkOo6UF/R/wwPRRGct0H0wHAzcYUg7s6qD339/kc5JU0frtiIk0yCivwyA8ncCRZ84S2oX6O+PVRTn1tasP8HUXP7buQq8NO2q+80iq8uf08HvjsMH49lQmllmGQQV3sseupvvhheig6OaifoHanscEueGFoZ9HzV/LK8Nj6OK0mezaHG2XVmLo8ConZJaI19hZG2LwoEn7OljrsjOqMCXLG2GDx6eJ3mt/XC2+O9mtVw0oM5VL8OD0UoR7iE/mjUgrx1MbTqFW2zM9JnbrpWN8evKKx7tH+3vhiUhCnY5FOdLQygafIIqmoFIbTqe0rr67Fh39dxIivj+D4Fe3+zj8S4oqDLw7Agn7eMBDZ5YaoOfTvbKf2eEF5jcb34+3F5dxSfPjXRfT+6CDmrorFrrPX7+lz0sAu9lgzLxz7nxuAmb08YWbU+J2IiOqIhdNTb5Qjr5TXa4mI2osqhRKPrY/DH6ezNNa9MqIrXhnRtVVdm6P2Qa8++bq5ueH111/HpUuXcOLECSxatAhWVrem4gj/v0+lUqnE3r17MW3aNDg6OmLRokU4evRoS7atE0VFRdi6detdxwYPHtykrzFmzJi7/rxlyxaUlZU1+LjS0lJs2bLlrmNjx45t0t6IiIiIiIhak8PJ+Zi/JhZVCvGbWaMCOuK7aSEtHiCaEemh9nitSsCGmHQdd0Nt2am0Qoz9/jgu52m+1vDoAG8snRHKG7nNwMPWDF2d1Aeb/05sv7sV6kJRRQ0++usS+n9yCGtOXkONlmHXcC8b/La4F1bNDUd3l8ZND3/ygU54JMRV9PyJqwVYsu3c7euuupJXUoUpy6JwKadUtMbJ0hibF0XCV2Qrc9KNd8Z0R0crY401jw7wxpJR3VrlzS8TQxl+nh0m+nsRuLVw55Xfdf9zUqdWqcJ/tp5tcDrWSw924U1I0rkIL1u1x6NSCnXcCZHuCIKAXWezMfjzw1h6OAUKZcP/PgS4WGHrY73x+aQgOFho/neVqDlEetvCUGRBxJHLmheOt1VFFTVYezINY787hqFfHsHSwynIK238YEQHCyM82t8bB18YgNVzwzGgs32r2EmI9F+4p/guNbGpnJ5ORNQelFYpMPvnGOy/mCdaI5EAH44PwOIBPjrsjEh7ehVOv1NkZCR++ukn5OTkYNOmTRg5ciSk0lvt1l0ILikpwcqVKzFw4ED4+PjgnXfeQWpqaku23WxefPFFFBUV3f6zoaEhRowY0aSvERgYiLCwsNt/LisrwyeffNLg4z755BOUl5ff/nNkZCT8/PyatDciIiIiIqLW4tClPCxccwrVGqYsjQlyxtdTgvViWlp/X3vRqYcbY9KhaOFpodQ2/HE6E9OWR6OwvEa0Ri6V4ONHAvDqiG68mduMhvk7qT0elVKIogrx/z50b6oUSnx74DL6fXwIPx2+qnHR0p0CXa2wdl44Ni+KRE8NN6U1kUgk+HB8ACK9xR//66lM/Hj46j09/724XlyJycuicEXDIhUXaxNsfjQS3vbmOuuL1LMyNcBnE4NEzz85qBNeGd66A9FWpgZYOy8cbjbiO3X8FpeJD/68qPOAek2tCk9vOo3f4jI11r012g9PDOrUqv87UOsU6aP+35creWW4Uda+dj6m9uFKXilmrIzGkxtOI0eLXcZszAzx0fgAbH+ij8adOoiam6mhHGFe6v8OHk5qP+F0hVKFAxdz8dj6OIS/fwBvbE9EQmZxo5/HUC7FQ4EdsWpuGE688gBeHdmNn12oyTlYGsND5HptbBoXAhIRtXUF/7/rZHSq+O98A5kE300NwdRwdx12RtQ4LX8XvAGGhoaYNGkSdu3ahczMTHz66acICAi460KwIAhITU3F22+/jU6dOqF///5YuXIlSkr0bxuqjz76CHFxcVrX19bW4oUXXsDKlSvvOr548WJ07NhR42MlEsld//vnn38afL133nmnXr9HjhwRrT98+DA+/vjju4699957Db4OERERERFRW7TvQi4WrTulcSLu+B4u+HJyMOR6EEwHAKlUIjo9PbekGvsu5Oq4I2pLVCoBn+9NwnObEzT+XFiZGGDd/AhMDuOF1OY2zM9R7XGlSsDBS+JTWKhxBEHAnvM5GPLFYXy+Lxml1bVaPa6LowWWzgzF9if6oH9n+/sOmxrKpVg6oye87c1Eaz7Zk4RdZ7Pv63W0kVFYgUlLTyL1RrlojbuNKTY/GgkPW/F+Sbf6dLLD80M71zv+7BBfvDCsc5sIRDtYGmP9/AjYmRuJ1iw/mqrThRyVNUosXHsKf54T39VCKgE+mxiEOX28dNYX0Z3EJqcDQDSnp7dqheU1WHMiDe/uuoDtZ7KgUrXM7hH6oqy6Fh/+eRHDvzqK41cKGqyXSSWY09sTh14YiCnh7lx4S3qhv6+92uPx6TdRWqXQcTe6dfF6Cd7bdQG9PjyA+WtO4a/zOVrvZHWnHu7WeP/h7oh9bQi+mxaCQV0c9ObaHrVNYtPTNQUViYio9csuqsTEpSdxPks892piIMOK2WEYFag5O0rU0lrVu2VHR0e88MILSEhIQHx8PJ5++mnY2dndPi8IAgRBwPHjx7Fo0SI4Ozujqqrhleu6tGfPHvTs2RN9+vTB119/jfPnz6O2tv7NqeLiYmzcuBFhYWH44osv7jrn4+ODN954o1n6Gz58OIYNG3b7zwqFAg8++CC+/vprVFRU3D5eXl6Or776CsOHD4dC8b8PrCNHjsTgwYObpTciIiIiIiJ9tuf8dTy2Pk7jtt4TQl3x6cQgyPTs5vSEUFcYydVfIlh38pqOu6G2okqhxFObTuPbg1c01nnZmWHbE33Qy0c84ERNx9/ZEi7W6icE/50oHoIk7V3JK8Osn2OweH0cMm9WavUYD1tTfD0lGH8+0w8P+js1aeDXytQAq+aEwcbMULTm+V8TEHet+bYGv1ZQjinLopBRKP798LYzw6+P9oJrB/XT4ajlPD3YFz9OD8GowI4Y38MFvyyIwLND2kYwvY6HrRnWzguHhbFctOaTPUnYGJPe7L2U/P+2zYeTxSeZGsgk+H5aCCaEujZ7P0RinK1N4G6j/nd2VErDAV7SP0qVgHVR1zDw00N4c0ciVh5LxTObzmD2qhhU1Gi30K4tEQQBOxOyMfjzf7D0SApqtQjph3vaYPfTffHWGH9YmRrooEsi7Qzooj6cXqsScOJq2/udXVBWjVXHUzHqm6MY8fVRrDiWihtljd8pzMnSGI8P9MGBFwbgj8f7YHqEB3+2SWfCvNSH0y/llKC4sm0vKiEiaq+u5pdhwo8nkJIvPtzD0liO9QsiMKCz+vd3RPpE/EqrngsODsZXX32Fzz77DH/++SfWrFmD3bt33w5KC4KAyspKqFT6uf34iRMncOLECQCAkZERXF1dYWVlBZlMhoKCAqSlpant3cnJCX/99RdsbZvvhu3atWvRq1cvpKamAgCqqqrw7LPP4tVXX4W3tzcEQUBKSkq94L+Pjw9Wr17dbH0RERERERHpq91nr+PpTaeh1HCzemq4G94fF6CXU9OsTQ0xNtgZv57KrHfuZEoBLueWwtfRogU6o9Yqr7QKC9fGISGjSGNdL29b/DgjBNam4qFZaloSiQTD/B2x6nhavXOHk/NRWaOEiaFM9421AaVVCnxz4DJWHU/TKrwEAB2tjPH0YF9MCHWFQTNO3fOwNcPyWaGYujwaNbX1rznW1KqwaO0p/PF4H7iLbB1+r67ml2Ha8ijkllSL1vg6mOOXhRFwsDBu0tempjMioCNGBLTtaUx+zpb4eU4YZqyIRrWanxMAeP2Pc7AyMcDIZvpeFJbXYPbPMTiXVSxaY2wgxdKZPXkTkvRCpLcN0gsr6h2PTm17Qce27mxmEZZsO4+zmfV//xy9fAML157CytlhMDZoH+8TL+eW4s0diVqHdu0tjPD6yG4YG+zcphZvUdvRxdECjpZGat+TH07Ox4P+Ti3QVdNSqQQcu3IDm2LTse9CrsbhEZoYyaUY3t0JE0Jd0dvHTu8GTFD7ITY5XRCA+Gs3Mairg447IiKi5nQ+qxizf45BQbn4gjp7CyOsnReObh0tddgZ0b1rteH0OnK5HGPGjIGfnx+cnJzw448/troP/dXV1bh6teEtQUeOHIlVq1bBwaF532Q6Ojri0KFDGDt2LBISEm4fr6ysRGJiotrHBAcHY8eOHbC35wVxIiIiIiJqX7afycLzvyZoDKbPiHTHO2O662Uwvc7MSE+14XQAWB91DW+P7a7jjtqmKoUSV/PLYG9uBAfLthnCvJBdggVrYpFdrHk3u8k93fDuuO4wFJnaT83nQX8nteH0KoUKRy/nY1gbCCbokkol4PfTWfjor0u4USYewL6TrZkhHh/UCdMj3HUW8gr1sMHnE4Pw1MbTas8XlNdg7uoY/P5YnyabBpicW4ppy6M1fl+6OlnglwURsDU3apLXJLofYZ42+HFGCBatjVO7yEQlAM9uOgNLYwP09bVT8wz3LrekCjNWRONyXplojYWRHCvnhCFcZIoika5Fetuq/QyRnFuGG2XVsOPvdr1XXKnAZ38nYX30NQgacpzHrxTg0XVxWDYrFEbythtQL6uuxTcHLuPnY6laLTaUSSWY09sTzw7xhYUxpymT/pJIJOjva48tcfV/Zx9JzocgCK0uY1HnenEltpzKxObYDGQVabdzlTo9PTpgQqgrRgZ2hCV/nkkPeNiawsHCCHml9T9PR6cWMpxORIRbn2ckErT6f7ujUwqwYM0plFaL71jlZmOC9fMj4GFrpsPOiO5Pqw6nFxcXY9OmTVi7di2ioqIAQO8/NL3++uvo1q0bjh49ikuXLkGpVGqsNzc3x4gRI/Dkk0+if//+OuoS8PDwQExMDL766it8/fXXyM7OVlvn7OyMZ599Fs888wwMDTnljIiIiIiI2pff4zPx4pYEaLpnPae3J94c7af3n1cDXK0Q7GaNM2omXW+Nz8JLw7vC3KhVX0ZocZti0vHZ3iTcKKuBRALM7uWJV0d2bVPhjgMXc/H0xtMorxG/3iGRAK+N6IYF/bz0/ueirerp0QEdTA1ws6L+NtB7L+QynN4I5zKL8eaO84hPL9Kq3tJYjkcH+GBOb0+YtcDv1NFBzkgvrMCnfyepPX81vxyL18dhzbzw+144ciG7BDNWRqNQw7SfABcrrJ0Xjg5mvK5I+uOBro74bGIQnt18Ru35GqUKi9adwoaFkQh2s26S10wvqMD0lVHIKBQPVHUwNcC6+RHo7mLVJK9J1BQivMV3+Y1JLWy2XQbo/gmCgD9OZ+GDPy/iRpn4v9V3Opycjyd+iccP00Pb3AJTQRCw8+x1vL/7gsbdXu4U4WWDd8Z2Rxcn7jJGrUP/zurD6Zk3K5Fyoxw+9uYt0NW9UShVOHQpD5tiM/BPUp7G63KauFibYHyIC8aHuMLLjkEv0i8SiQRhXjbYffZ6vXOxaYUt0BERkf5IyS/Dh39dwqFLeahVCYj0tsHHjwS2yuD2oaQ8LF4XJ7qLHwB0djTHuvkRcGyjA4+o7Wp1d5WVSiX++usvrF27Frt27UJ19a0LBHWreYX/X9bv4uKCGTNmwNhYv34ohw4diqFDhwIAKioqcOHCBaSlpeH69esoKyuDSqWCtbU1OnToAD8/PwQEBEAmu7eb1IKmEQdaMDQ0xH/+8x+8+OKLiIuLQ0JCAvLy8gAADg4OCA4ORkhICKTStnUBioiIiIiISBu/nsrAy1vPapwut6CvF14f1a3VBHBnRnqoDaeXVdfij9NZmBnpofum2gCFUoW3dyZifVT67WOCAKw+kYb80mp8M7VHq98mWhAErDyWivf/vKjxZ8LEQIavpwQz/NzC5DIphnRzVBtMOHAxF7VKFeQyXu/RpKCsGp/tTcKm2AyNf+frSCXAtAh3vDC0S4sHsR8f6IO0G+Vq//sDwMmUArz+xzl8MiHwnv/9OptZhJkrY1BcWX8BRJ0e7tZYPTccViate7IRtU3jerjgZkUN3t55Qe35ihol5q6KwZbFvdDJ4f5CiZdzSzFjZbTGMKSjpRHWz4+AryMDkKRfXKxN4GZjonZhRXRKAcPpeio5txRLtp1HTGrjg237L+bh6Y2n8e20HjBoI+8Xk3NL8eb2RJxMKdCq3sHCCK+P6oYxQc6t5rM+EQD07WQHqQRqg9xHkvNbRTj9WkE5NsdmYEtcJvLVTJPWhomBDCMCnDAhxBWR3rZ6vcshUbin+nD62cwiVCmUOtuJjYhIX1TU1OK7g1ew/GgKFMr/vamJSinEIz+exMaFrevayYGLuXhsfTxqlOLB9GA3a6yeGwZrUw73oNan1YTTT58+jbVr12Ljxo3Iz88HUD+QbmJigocffhizZ8/G4MGD9f6CgKmpKXr27ImePXu2dCsaSaVShIWFISwsrKVbISIiIiIi0gsbY9Lx6u/nNNY8OsAbrwzvqvefTe80KrAj3v/zotopt+tPXsOMCPdW9fXog5vlNXj8l3jRoMPuc9dhYSzHh+MDWu33VqFU4Y3tidgYk66xrqOVMZbP6smJr3pimL+T2nDyzQoFYtNuopeP+CTU9qxWqcIv0en4fG8SSqrEt1m9U5hnB7w1xh/+zvrxd18ikeD9hwOQVVSJE1fV/27aEpcJTzszPDGoU6OfPz79JmavjNG4DW2YZwesmhvOHTlIr83t44WbFQp8c+Cy2vM3KxSYsSIGvz3WC64dTO/pNc5lFmPWz9Fqd7Ko42Zjgg0LIuFmc2+vQdTcIr1skVFY/z1FVAoneuqb8upafHPwMlYeTUXtvY4ZBrAnMQfP/5qALycFteoFjWXVtfh6fzJWHU/T6vshk0owt7cnnhniCwtjLq6j1qeDmSECXdXvmHc4OR9z+3jpviktVCmU2HshF5ti0kU/v2gjwssGj4S6YmRAR34OoVYj3MtG7XGFUsDp9CJeuyGidkMQBOw5n4N3d11AdnGV2pobZdWYsiwK6xdEoFtHSx132Hh7E3PwxIb4u0L2/9a3kx2WzgxtkR04iZqCXv/NvX79OtavX49169YhMTERgPpp4AMGDMDs2bMxYcIEmJvr/4peIiIiIiIiar3WnUzDf7cnaqx5clAnvDCsc6sLGxsbyDCppxt+Ony13rmk3FLEpBYiwps3PbR1ObcU89ecQnphhca6TbEZsDI1wKsjuumos6ZTXKHA4xvicPyK5hvEga5WWDGrJxy47aTe6OdrB1NDGSpqlPXO/Z2YwxucakSlFOCtHYm4lFOqVb2jpRFeG6mfUzUN5VL8OD0U4388jqv55WprPv07CW42phgT5Kz188akFmLuqhiUq/l7VaeXty1WzukJU0O9vjRNBAB4bogvbpbXYF3UNbXnc0qqMGvlrQnqtuZGjXrumNRCzF8dq3Ehh6/DrW2bnaz47yfprwhvW7UL3pJyS1FYXgObFt4xhG7dW/07MQdv77yA6yJBjn8b2MUeZzOL1S5cBoCdCdkwkErw6cSgVrcLlCAI2JGQjQ/+vKhx14o7RXrb4J2x3dG5FU1hJFKnf2d7teH0qJQCvZvCnJxbik0xGfj9dCaKNCzk08S1gwkmhLpifA9XuNtyoR+1Pl0cLWBpLFe7OD42rZDXboioXbiaX4a3diTi6OUbDdYWlNdg6vIorJ8foddDcvacv44nN5zWuEh2uL8Tvp4aDCO5/rw/I2osvbsDUFVVhd9//x1r167FgQMHoFLd2ragbkp6HV9fX8ycORMzZ86Ehwe3FSciIiIiIqLmt+p4Kt7eeUFjzbNDfPHMYF+9CyJqa3qEO5YeuQo1a8OxLuqazsPpOcVV+HxvEv5JzoeFsRzTwt0xu7en3m8hf+BiLp7ZdAZlGgJnd1p6OAVWJgZ4fGDjpxS3lLQb5Zi3JhYpIsHWOiMDnPD5xGCYGPIiqj4xNpBhQGd7/HU+p965fRdy8eZov1b7e6ypXS+uxPu7L2KXmq201TGQSTC/rzeefKCTXk/kszI1wKo54Xj4h+MoEAmevbglAS7Wxgj1UD+t7U4nrtzA/DWnUKkQD6b372yPZTND9Sr0QqSJRCLB22P8UVSpwM6EbLU1KTfKMWdVLDYsjNB6ku4/SXlYvD4OVQrxbZsDXKywZl44g72k9yJEJnoCQExqAYZ376jDbujfrhWU480difgnKV+rehdrE7w1xh9D/RyRmF2MacujUVypPhT6++ksyGUSfDQ+ENJWElBPzi3FG9vPaz3Z38HCCEse8sPowI58b0xtwoDO9mp3halSqBCbVoh+vvYt0NX/VNTUYtfZ69gUk4749KJ7eg4DmQTD/J0wNcwdvX1sW83vJyJ1pFIJenra4OClvHrnYtO4Sw0RtW3l1bX49uAVrDyWonG6+L8VVSgwbXkU1s2PQJCbdfM1eI92n72OpzedhlJDMH1ST1d88HBAq96pigjQo3D64cOHsXbtWvz2228oKysD8L9Aet20dCsrK0yaNAmzZ89Gr169WrJdIiIiIiIiamdWHE3Be7svaqx5YWhnPDXYV0cdNQ83G1M80MUBB9Tc9NhzPgd5JVU6m359ODkfz20+c3taX35pNd7bfRG/x2fh04mB8HfWv8kXgiBg6ZEUfLznktqAvyaf7EmClYkBpkfo/yL86JQCPLo+rsHpZU8O6oTnh3bmzWA9NczfUW04PauoEonZJXo9XUYXqmuVWHE0Fd8dvKIxcH2ngV3s8cZDfvC2bx27O7rbmmL57J6YuiwK1bX1Q7I1tSosXBuHPx7vDQ9bM9HnOZycj0VrT6l9jjqDuzrg++khDKZTqyOVSvD5xCCUVCpwOFl9uPNcVjEWrY3DqrlhDf4d/+vcrZuQmm6shnvaYMWcnrDUMuxO1JLcbEzh2sEEmTcr652LSilkOL2FVCmUWHo4Bd//cwU1Gv59rmMgk2BRf288Ocj39qJSf2crrJsfjunLo0V3efj1VCYMZFK8N667Xoe3S6sU+Hr/Zaw6kaYxBFJHLpVgXl8vPD3YV68XGxI1VpCrlegU5iPJ+S0SThcEAeeyirEpNgM7zmRrvcj/33zszTA13B0P93Bp9I42RPos3Et9OD3u2k3UKlUMLhJRmyMIAv48l4P3dmu/89O/lVTVYsaKaKyeF6bV0A1d2ZGQjec2n9H4mWRWLw+8PcZfrz9fEWmrRT9NX758GWvXrsX69euRnp4OoH4gXSaTYdiwYZg9ezbGjBkDIyN+kCAiIiIiIiLd+vGfq/h4zyWNNS8P74rHBvroqKPmNaOXh9pweq1KwKbYDDzdzAF8pUrAV/uT8d2hK2oD3heul2Dsd8fx2EAfPPlAJ73Z1rBKocRrv5/D76ez7vk5lmw7DwtjA4wJcm7CzprWH6cz8Z/fzmoM1Rn8/wTFR0JdddgZNdYDXRwhl0rUbh/6d2JOuw6nH7iYi3d2XcC1ggqt6t1tTPHGQ34Y3M2h1d04CHHvgC8mBeOJDfFqzxeW12Du6lj8/lhvWJvWn+C8/0IuHv8lHjVK8eDbcH8nfDO1BwzlvGlOrZOhXIofZ4Rgxopo0SmeJ1MK8PTG0/hheohoQGTLqQy8vPUsNOUiB3S2x08zQrnjCLUqkd62+C0us97xqJSCFuiGDifn483t55Gm5fuY3j62eGdsd3RyqL+4LtDVGmvmh2PmimiU16hfrPdLdDoMZFK93HlHEATsSMjG+7svIq+0WqvH9PK2xTtj/eHraNHM3RHpnlwmRT9fe+w+V39XqMPJ+Xh9lO56Ka5UYMeZLGyMycCF6yX39BzGBlKMCnDG1HA3hHp00LvfQURNIcxTfaiyokaJxOwSvZwKTER0r67kleGtHYk4duWGVvUyqUQ06F1aXYtZK2Pw85wwne8IrM6201l4/tczGq8Jze3jiTce0r/PVUT3qkXD6V26dLkriF5HEAQEBQVh1qxZmD59OhwcHFqoQyIiIiIiImrvvjt4GZ/tTdZYs2RUNyzo562jjprfAF97uNuYIr2wfphhQ3Q6Hh/o02xTefJKqvD0ptMNbrNeqxLw7cEr2HM+B59MCEQP9w7N0o+28kqqsGhdHM5kFGms6+JogZm9PPDmjkS1F00FAXh+8xlYGMkxqKt+XQ8RhFvf8y/2af556GBqgKUzeyLcS38mkpB6VqYGiPS2VXuxf29iLl4Y1qUFumpZqTfK8c7ORBxKUj8d+d9MDGR4YpAPFvTzbtUTwUcFdsS1wi74ZE+S2vMp+eVYvD4Oa+dF3BUw33P+Op7ccFrtAoc6o4Oc8cWkIBhwmhu1cqaGcvw8JwyTl0YhKbdUbc3eC7l49fdz+GRCYL0biauPp+KtnRc0vsbIACd8NZkLOaj1ifCyURtOv5RTipvlNehgVn9xEzW968WVeHfXBfx5rv7OOOrYWxhhyahuGBPkrDH8EOLeAavnhWPWyhjR3WRWn0iDoVyKV0d01ZsgxZW8MizZdq7Bz5Z1HC2N8PooP4wO7Kg3XwNRc+jf2U5tOD05twzXiyvR0cqk2V5bEATEpt3Epth0/HnuOqoUDe/soE53F0tMDnPH2GBn7jRDbV6AixWMDaRqf15i0woZTieiNqG8uhbfHLyMn4+lahyKU0cqAWb39sTjAzvhpd8S8I/ItdzyGiXmrIrFytk90buTXVO3rbWtcZl48bcEjbvtLujrhddHdeNnEWpT9GIfsrofKkdHR0ybNg2zZ89GQEBAC3dFRERERERE7ZkgCPj6wGV8tf+yxro3R/thbh8vHXWlG1KpBDMi3fHBn/WnxeeUVGH/xVwM796xyV/3xJUbeHrTGdwo026iHQBczivDIz+ewIJ+3nh+aOcWCYeeyyzGwrWnkFOieYvJoX6O+HJyMMyN5DA3kuPZzWfU1tWqBCxeH4d18yP0JuBdU6vCq7+fw9b4+qGjO/nYm+HnOWHwsDXTUWd0vx70d1QbTk/KLUXajXJ42rWP/5bl1bX49uAVrDyWotUNEAB4KLAjXhvZDc7WzRfe0KXHBvjg2o0KbD6VofZ8VEohXv39HD6beCt0q802tONDXPDphCDIpLypQm2Dtakh1s4PxyM/nkDmzUq1NVviMmFjZohXR3YDcOs95feHrjS42HFCqCs+Gh/QbAsAiZpTpIYpdNGphRje3UmH3bQ/CqUKa06k4ct9yaLTze9UF+R4bmhnrUOdYZ42WDmnJ+auikV1rfow6bIjKTCUSfHigy27wLFKocT3h67gp8NXtXpfJ5dKML+vF54a7AtzI724dU7UrPp3thc9dzT5BiaFuTX5axZV1GBrfBY2xqTjSl7ZPT2HhZEcY3s4Y0qYe7ve5YvaH0O5FD3cOuCkmh1polML29TAFCJqfwRBwO5z1/HerosN3l+pE+bZAe+M7Y5uHS0BAEtnhuKJX05j/8VctfWVCiXmro7Fslk9MUDD+6Dm8uv/76KnKZj+6ABvvDJcfxb6EjUVvfiELQgCZDIZ7OzscPDgQRw8eLBZX08ikSAuLq5ZX4OIiIiIiIhaL0EQ8PneZHx36IrGunfH+mNmL0/dNKVjk3q64fO9yWqDB2tPXmvScLpKJeC7Q1fw1f5kjVsaij5euBWE2HchFx8/EqjTQPfOhGy89FtCg9O+nhjkgxeGdoH0/wOa43q4oKRKgTe2J6qtr65VYf7qWGxcFNniN12LKxVYvC5O7U2wO/XztcN300JgZcKpZa3JUD8n/Ffk7+HeCzlY1N9Hxx3pliAI2JGQjQ/+vIjcEu0WxnRxtMBbY/zRy6flt4NtShKJBO893B2ZRRU4fkX9z/vW+Ex42prC2doEL/2WoPF39pQwN3zwcMDt33tEbYWjpTHWz4/AhJ9Oii6oW3okBdamhlg8wBsf/XUJS4+kaHzOOb1vbdvMnxdqrdxsTOFibYKsovqLNqJSChhOb0axaYVY8sd50R0d/q2HuzXeG9cd/s6N/4zR28cOy2f1xIK1p1AjElD/7tAVGMqleHqwb6OfvykcTs7Hf7edV7sLmDq9fWzx9hh/+DpaNHNnRPqjo5UJOjuaIzm3fkj8cHJ+k4XTBUFA3LWb2BCdjt3nrosubGlIT48OmBLujpEBTjA11It4C5HOhXnZqL0udyqtECqVwM8RRNQqXckrxZs7EkWvQ/6bnbkRXhvZFQ/3cLkrxG0kl+GH6SF4ZtNp/HVe/S5S1bUqLFxzCj/OCMHgbo5N0r82Nsak49Xfz2mseWKQD14c1oXBdGqT9Obdu0qlQmLirRthgqalIvdBIpFAEAT+MBMREREREZEoQRDw8Z4k/HT4qsa6Dx4OwLQIdx11pXvWpoYYE+SMLXH1J2WfuFqAK3ml6ORw/zfwC8qq8ezmMzh6uf7k5sZKvVGOSUtPYnYvD/xneFeYNePUO5VKwFf7k/HNQc0LGIzkUnwyIRBjg13qnZvVyxPFFQp8vk/9JNXS6lrM/jkGWxb3gre9eZP03VgZhRWYuzq2wclm0yLc8fYYfxhw2mur42RljCA3ayRkFNU793dibpsOp5/PKsY7Oy8gJq1Qq3pLYzmeH9oZMyI92uxkYwOZFD9MD8UjP54Q/bn/fF8yJBJonPYzM9IDb4/x5w1yarM87cywdl44Ji87idKqWrU1H++5hH+S8hCdqvl3zNMPdMJzQzvzvgW1ehHeNvg9Pqve8YZ+BujeFJRV48O/LuE3NZ/X1LE2NcArw7tiUk+3+/r3uX9neyydEYpF606JTiX/Yl8y5DIJHh/Y6Z5fp7HySqrwzq4L2HX2ulb1jpZGWDLKDw8FduTvX2qXBnS2VxtOP3blBmqVqvv6vFNcqcC201nYEJ2u9cKZf+tgaoBHQlwxOcyNi0eIAIR7qh/EcbNCgav5Zfw5IaJWpay6Ft8euIyVx1JRq8W0IplUgtm9PPHsUF/RnZ8M5VJ8O7UHnvs1ATsTstXW1ChVWLw+Dt9NC8GD/s2/gHp91DUs2XZeYw2vCVFbpxfh9H//gPEHjoiIiIiIiFqCIAh4f/dFrDiWKlojkQAfjw9slm2O9c3MXh5qw+kAsD4qHW+N8b+v549JLcRTG+MbnFZsZWKAzycGoapWiTe2J6KwvEZj/ZqT13DgUh4+Gh+Ivr5299WjOuXVtXj+1zP4O1H9NpF1HC2NsGxmTwS5WYvWPPlAJxRVKrBS5O9cQXkNZqyIxm+P9Yaztcn9tN1oZzKKsGBNLG6Uaf5+vzayKxb28+b1nFbsQX9HteH0+PSbyCutgoOFse6bakZ5pVX47O8kbInL1BiwriORAJN7uuGlB7vA1tyo+RtsYVYmBlg1JwwP/3Bc9Odf0/dtfl8vLBnVjb8TqM3zc7bEytlhmLkyWnQSaEOh3NdGdm3Ti4CofYn0tlUbTr+UU4KiihpYmxq2QFdtj1IlYFNsOj7Zk4TiSoVWj5nc0w0vj+gKG7Om+W8wqKsDvp8Wgsd/iRcNk3yyJwmGMikW9PNuktcUo1QJ+CX6Gj7dk4TSavWLhe4kl0owv68XnhrsC/NmXMxMpO/6d7bH8qP1r0MUVyqQkFmMUI8OjXo+QRBwJqMIG6LTsfNsdoO7y4np28kOU8LdMNTPEUZy2T09B1FbFOJhDblUovbf3ejUQobTiahVEAQBu85ex3u7L2i9g2W4pw3eGeePrk6WDdbKZVJ8NTkYBlIJfj9d/7MpACiUAp74JR5fT+mBUYFNtzvwv609mSa6a26d54Z0xjNDWmbHKSJdafFP3c01JZ2IiIiIiIioMQRBwNs7L2D1iTTRGqkE+HRCEB4JddVdYy0o0NVadKLy1rhMvPRgl3uaTq5SCVh6JAWf7U2CsoHJGEGuVvhuWgjcbEwBAL28bfH2zgvYITL9ok7mzUrMWBmNKWFueG1UN9GJGo2VebMCC9acwqUczdO/glytsGxWTzhaag71SiQSLBnVDSWVCtGFANnFVZixMhpbHu2ls2Ds34k5eGbTaY03lI3kUnw5ORgjA5rvIi7pxjA/J3yyJ6necUEA9l/IazO7RFQplFh5LBU/HLqC8hqlVo8JdrPGO2P9Eehq3bzN6Rk3G1Msn9UTU5ZFiYZu1Xl8oA9eepDb0FL7Ee5lgx+mh2DRurgG39PcSSIB3h/Xtnfhofanl7et2uOCcCs0pYvJdG3d2cwi/Hd7otrPZ+p0dbLA+w93R6iH+mmr92OYvxO+ntIDT22Mh9ivv/d2X4ShXIpZvTyb/PWBW7vgvP7HOSRkFmtV38vbFu+O82+SHcCIWrswTxsYG0jVfuY/kpyvdTi9tEqBbWeysSE6HRevl9xTL46WRpgY6obJYW63r/0Q0d1MDeXwd7FS+x4gNq0QMyI9dN8UEVEjXM4txZs7EnHiaoFW9fYWRnh9ZDeMDXZu1HVGmVSCTycGwUAmxeZTGWpralUCntoYD4UyGON61N/x9n79fCwV7+y6oLHmxWGd8eQDDKZT29ei4fTUVPFJdERERERERES6olIJeHNHItZFXROtkUqALycHY2xw01+s0mczIz3U3vgora7FtjNZmB7RuJsfN8tr8MKWBBy8lNdg7ZzennhtZDcYyv+3nbStuRG+mdoDDwV2xJJt55FXqnnCxqbYDPyTlI8PxnfHA10dG9Xrv8WmFWLxujgUNDC5fWywMz5+JBDGBtpN+ZJIJPhwfABKq2qxJzFHbU1Kfjlmr4rBhoWRTRa0V0cQBKw8lor3/7yocTKyrZkhls/uiRD3xk1TI/3UycEcPvZmuJpfXu/c34k5rT48KQgC/jyXgw//uojMm5VaPcbO3AivjOiK8T1cIJW2z6B1D/cO+HJyMB7/JV6r+meH+OKZwb4MplO7M7ibIz6dEIjnf03Qql4mleCLSUHt7j0ltX2uHUzgbGWM7OKqeueiUxhOvx83y2vw6d4kbIxJ12rXF3MjOZ4b2hmze3lALpM2/IB7NCqwI2pVwXh28xnRvt7Yngi5VNqk7ydLqxT4Yl8y1pxIEw3G38nWzBBLHuqGccEufJ9C9P+MDWSI9LbFP0n59c4dTs7Hc0M7a3z8ucxibIi5hu1nslGh5cLfO0klwANdHTAlzB0Du9g36+8qorYi3LOD2mu0MamFEASB/8YRkV4qq67FNwcu4+djqaK7Lt1JJpVgTm9PPDvEFxb3eB9EJr11v8VALsH6qHS1NSoBeO7XM6hVCZjQhMOoVhxNwXu7L2qseXl4Vzw2kLvoUfvQouF0Dw+u3iMiIiIiIqKWpVIJeH3bOWyMUT9FAbh1MevrKcF4KNBZh53ph4cCO+L93Rdws6L+lvHrTl7DtHB3rW9+xKffxJO/xKsNrNzJwkiOTyYEYoSGidzD/J0Q4WWL93ZfEJ04XienpArzVp/Cwz1c8MZDfrpxZFsAAPGCSURBVOhwD9vZb45Nx5Jt56FQil9AlUiAlx7sgscG+DT6hpBcJsXXU4Mxf/UpHLtyQ23N+awSLFhzCmvnhWsdfG+MWqUK7+y6gLUnxRdpAIC3vRlWzwmHuy0nmrUlw/yd8OM/V+sdP3H1BkqrFPd8M6Clncssxju7EhGbdlOrevn/3wB5eohvsy4EaS1GBnTEKyO64qO/Lmmse+nBLnhiUCcddUWkf8aHuKKoQtHgZCxDuRQ/TAvBEL/7WzBHpI8kEgkivW3Vbp8elaLddDy6m0olYPOpDHy85xKK1HweU2d0kDOWjOrW4A5OTWVssAtqalV46bezojWvbzsHA5kEE3u63ddrCYKAv87n4O2dicgt0bxIuc7UcHe8PLwLrE0b/xmQqK3r72uvNpx+NrMIN8tr6l07Ka+uxY6EW1PSz2Vpt2PBv3W0MsbksFtT0jtamdzTcxC1V+Fetlh+tP4A0OvFVci8WcmdB4hIrwiCgJ1nr+P93Re0fu8e7mWDd8b6o6uT5X2/vlQqwbtju8NAJsWq42kiPQIv/ZYAhVKFqeH3v5j2p8NXG7yG+trIrljUn8F0aj9aNJxORERERERE1JKUKgGvbD2rMdwsl0rw7dQeGoPSbZmxgQyTwtyw9HBKvXOXckpx6tpNhHlq3ia+bhr3R39danA6hl9HS/wwPQSedmYN9mZlaoBPJwbhoSBnvLr1bIOh9z9OZ+Ho5Xy8O7a71v89a5UqfPDnJfx8XPPub2aGMnw1pQeG3kfYzEguw9KZoZi+Ihpn1ExCAm5NQ3r8l3gsnRkKgyacLFZeXYunN57GgQYm2kd42WDpzFCGO9qgB0XC6QqlgENJ+RgT1LoW5+SWVOGTPUnYGq958cqd+nayw1tj/NDJwaIZO2t9Hu3vjbQb5dgUq34R15JR3bCgn7eOuyLSP/P6eqGoogbfHLyi9rypoQwrZvVE7052Ou6MSHfEwukXc0pQXKGAlSkXfmkrIaMIb2w/j4RM7QKg3nZmeGdsd/T11f3vmIk93aBQCnjtj3NqzwsC8J+tZ2Egk2Jcj3vbNSKjsAJvbD+PQ2qCtOp0dbLA+w93R6iH5s+qRO3ZgC72wK76x1UCcOzKDYz+/8+AidnF2BCdju1nslFWXdvo15FIgIGd7TE9woNT0onuQ08P8d0LY9MKGU4nIr2RebMCL205i5NaLlK2tzDC6yO7YWywc5PuAiGRSPDGQ34wkEmx7Ej9+1vArc8qr/5+DgqlCrN6ed7za31/6Ao+/TtJY81/H/LD/L5e9/waRK0Rw+lERERERETULilVAl7akqA2PFHHQCbB99NCMKydb0E/I8IDy46kqN2qfe3JaxrD6cWVCvzntwT8nZjb4OtMj3DHfx/ya/RU8AGd7fH3c/3x8Z5Lots01rlRVoPHfonHyAAnvD2mO+wtjDT2/uSGeBy9rH6SeR3XDiZYOTsMXZzuP9BqZiTH6rlhmLw0Ckm5pWprDl7Kw4tbEvDlpGBIpfd/sTa3pArzVsciMbtEY93DPVzw0SMBMJI3/dR2anmBLlZwtDRSO8lmb2JOqwmnVymUWH4kBT8evqr19vKetqZ4fZQfhnRz4DbYakgkErw7rjvySqtx8I4FLBIJ8PYY//u6cUPU1jw3tDMKK2rqvR+xNJZj9bxwhLiLB0qI2oIIb/WfCwQBiEkrvK+FnO1FYXkNPv37EjbFZqj9/PVvRnIpnnqgExb2927R9+nTItxRq1Lhje2Jas8LAvD8r2dgIJNiVKD2C79ralVYcSwF3xy4jCqFqsF6EwMZnh3ii3l9vZp0MS9RW+RtZwYXaxNkFVXWO7f3Qi4qa5T4JSYdCSKL5xviYGF0e0q6aweGZonuVwczQ3R2NEdyblm9c7FphRgf4toCXRER3e1KXimmLo9GfmnD09JlUgnm9vbEM0N8m23XTolEgldHdIWhTIrvDqkfJgAAb2xPRE2t6p4GcHxz4DK+2JessebtMf6Y3duz0c9N1NoxnE5ERERERETtTq1Shed/TcCOhGzRGkOZFD/NDMEDXRmgcLMxxaAuDneFEuvsOX8deaXd4GBRf9v4c5nFeHxDHDIK69/ovJOpoQwfjg/A2OB7m6IHABbGBnhvXABGBnTEK1vPIb2wQmP9n+dycOJqAd4a7a92IkdKfhkWrDmFlBvlGp8nwssGP84IhY1Z000StzY1xLr54Zjw00nRr2P7mWxYGhvgnbH+9xWmvZRTgnmrYhucOv/0YF88N8SXwd02TCqVYJifE9ZFXat37p+kfFTXKvV6YULdVrEf/3VJbbhCHQtjOZ4Z7ItZvTxhKGd4SRMDmRQrZvXEL9HXcOJqAQxkUjw6wBv+zlYt3RqRXpFIJHhnTHf4OljgmwOXUVKlwIDO9nh1ZDf42Ju3dHtEzc7dxhQdrYxxXc17y6iUAobTNVCqBGyKTcenfyehqEKh1WMGd3XAW2P89WZS6qxenqipVeG93RfVnlcJwDObTkMuk+BBLRaAx6QWYsm2c2pDeOoM6Xbr+8EQLJF2JBIJBnSxx4bo+ov8dyZkY6eGa2bizwn087XHtHB3DO7mwEUiRE0s3MtG7b+L0amFLdANEdHdknJKMX1FFG6U1TRYG+Flg3fGdm+SgT8NkUgkePHBLjCQSfHlfvEQ+Xu7L6JWJWDxAB+tnlcQBHy1/zK+PnBZY92747pjZqRHo3omaisYTiciIiIiIqJ2RaFU4dlNZ7D73HXRGkO5FMtmhmJgFwcddqbfZkZ6qA2nK5QCNsdk4KnBvrePCYKA9VHX8O6ui6hRap5u18XRAt9PD0Enh6YJbPX2scOeZ/vhs7+TsepEqsZpg0UVCjy7+Qx2JmTj/YcD4GR1K2B/JDkfT26IR0mV5u2qp0W4463R/s0SanWwNMb6+RGY8NMJ5IlMGVkXdQ3WpgZ4YViXe3qNI8n5ePyXeI3bcsulEnz0SCAmhHL6UnvwoL/6cHpZdS1OXC3AID39nXgmowjv7ExEfHqRVvVSCTA9wgPPDvGFrbn47gl0N6lUgpm9PDGTk9KJNJJKJZjd25MTsahdkkgkiPS2xR9qdqeK0nJL9/boTEYR3th+Hmczi7Wqd7MxwVuj/TG4m/6F/Rf084ZCKeDjPZfUnq9VCXhyQzyWzgwVXQheWF6Dj/66iF9PZWr1ms5WxnhrjH+73/GM6F7091UfTm8sO3MjTOrpiqnh7nqzYIaoLQrztFG7a2RKfjlulFXDjtc4iKiFXMguwYyV0Sgs1xxMd7AwwuujumFMUP2BQc3tmSG+kMsk+PTvJNGaj/66hJpaFZ6+436XOoIg4It9yfj2oPg0dgD44OEATItwv6d+idoChtOJiIiIiIio3aipVeHpjaexJzFHtMZILsXK2WHo62unw87034DO9nCzMVE7BX1DTDoeG+gDuUyK0ioFXv39HHadFQ//15kY6op3xnaHiWHTTmM2NZTjjdF+GBXohJd+O4uUfM3Tzw9cykPMF4ex5KFuKK9W4r3dF6DSEGqXSSV4c7QfZkZ6NOsFVHdbU6ybH4FJS0+iuFL99MRvD16BlYlBo7eb3BiTjiXbzkOp4Qu1MJZj6YxQ9O7En4X2IsLbBpbGcrULM/Ym5uhdOP16cSU+2ZOkNgAnpp+vHZaM8tPJVB4iIqL2KMLLRu2/zReul6C4UgErk+bZrr01KiyvwSd7LmHzqQyNi2rrGMmleGygDxYP8IGxgf7uaPPYQB/U1KpEpxIqlAIWr4/Hilk90b+z/e3jgiDgt7hMfPDnRdzUYnq8TCrB3N6eeG5oZ5gZ8ZY30b3o3ckWcqkEtZougmjQp5MtpoV7YKifI3ejItKBcC8b0XOn0goxvHtHHXZDRHTL+axizFgZrXEHqLr37s8M8YWFcct9JnxiUCcYyaWiuz0BwBf7kqFQqvD80M5q7/8IgoBP/k7Cj/9cFX0OiQT4eHwgJoW5NUnfRK1Vi3xST0xMhL+/f0u8tCh97ImIiIiIiKg1iEopwE+Hr6KwvAb9fe0xr68XbMwMW7qteqprlXjil9PYfzFXtMbEQIaVc3qitw/DuP8mlUowI8IDH/5VfwLe9eIq7L+YB3cbUzyxIR6pNzSHwY0NpHh3bHdM7Nm8F+ZCPWzw59P98PWBy1h2JEVjELu0uhYvbz3X4HNamRjgh+kh6KOjwHYXJwusnhuG6SuiUVGjVFvz3u6LsDQ20OpCp0ol4NO9mi+cAoBrBxOsnhuGTg4M8LYnBjIpBndzVBso23chF++NEyCT6naijTqVNUosPXIVPx2+iiqF5t0Z6njbmWHJQ90wqIuDzqfyEBERtSeR3rZqjwsCEJtaiCF++jftW9eUKgEbY9Lx6d9JootQ/21INwe88ZA/3G1bx0Tipwd3gkKpwneH1E8SrKlVYeHaU1g1Nwy9fexwObcUr287j5jUQq2eP9jNGu8/3B3+zlZN2TZRu2NpbIAQ9w6ISdPuZw8AbMwMMTHUFVPC3eFlZ9aM3RHRv3W0MhEdHhKdynA6Eene2cwizFgRrXEX2h7u1vhofKDeDAtZ0M8bBjIp3tyRKFrz7cErUCgFvDy8y13XkgVBwId/XcKyIymij5VIgE8nBHE3WiK0UDg9ODgYM2bMwFtvvQUPD4+WaOG2a9eu4Y033sCGDRugUGh3AYiIiIiIiIhuWXU8FW/vvHD7z2czi7EpNh3vjQvA8O76s6V2lUKJx9bH4VBSvmiNqaEMq+aEIUIkTEHApJ5u+GJfMqpr64dBP/zrInKKq9Seu5OPvRl+mB6qswuRxgYyvDy8K0Z0d8J/fjuLSzml9/xcPvZmWDE7TOc3X3u4d8DyWT0xd1UsapTqv7+v/H4WFsZyjAgQvwlVpVDixS0JDU61D3K1worZYbC34FbA7dEwP/Xh9BtlNTidfhM9PcWndDU3lUrAjoRsfLznEq4XV2n1GEtjOZ4d0hkze3nAQMZJfkRERM3Nw9YUTpbGyCmp/291VEpBuw+nn06/iTe2J+JcVrFW9e42pnhrjB8e6Nq6vm8SiQQvDOsMhVKFpSLBjepaFeavPoXxIS749VQGFMqGJzdbGMvx8vCumBbuDqkeLJokagv6d7bTKpwe6W2DaREeeNDfEUZy/d29gaitC/O0QUZh/es2sY1YZEJE1BROp9/ErJ9jUKohmN63kx2Wz+rZ5Lvn3q/ZvT1hIJPitT/EBxb9dPgqampV+O9D3SCRSCAIAt7ddRE/H08VfYxUAnw+KQgP92AwnQgAWuSOjFKpxNq1a+Hr64uZM2fi7NmzOu8hISEBM2bMQOfOnbF+/XqoVNpNWSIiIiIiIqJblh9JuSuYXudGWQ0Wr4/DUxtPo7C8pgU6u1uVQolF6zQH082N5Fg7L5zB9AZ0MDPE6CBnteeuFVQ0GEwfG+yMHU/2bZEJGYGu1tjxZF88O8QX8nsIMQzsYo8/nujTYlPB+nSywzdTe0CsdZUAPLPpDI5eVv/3vLC8BjNWRDcYTH/Q3xGbFvViML0dG9DFHkYi27H/nZij427+J+7aTYz/8QSe3XxGq2C6TCrB7F4eOPzSIMzr68VgOhERkY5IJBJEeKtfzBat5VTstqiwvAavbD2Lh384oVUw3UguxXNDOmPvc/1bXTC9jkQiwSsjumJuH0/RmkqFEr9Ep2sVTB8X7IyDLwzEjEgPBtOJmtCEUDcYinwGtDY1wIK+XjjwwgBsWtQLY4KcGUwnamHhIkMDLmSXoLSKAzmJSDfirhVi5krNwfT+ne2xYrb+BdPrTItwxycTAqFpk82fj6fije2JUKkEvL3zQoPB9C8nBzOYTnSHFrkrY2hoCEEQUFtbiw0bNqBHjx7o06cP1qxZg/JyzVt/34/y8nKsWrUKffr0QUhICDZu3AiFQgFBEGBkxJuuRERERERE2vrxn6t4/8+LGmt2JmRj2JeHsed8y4UZK2uUWLDmFI4kiwfTLYzkWDs/vEWnAbcmMyMbvwOaoVyKDx4OwFeTg2Fm1CKbuN3u49khnbHzqb4IcNF++/dF/b2xcnYYLI0NmrG7hg3v7oSPHwkUPV+jVGHR2jjEXbt51/HUG+UY/8NxnPrX8X+b39cLP0wP1duLxaQbpoZy9PO1U3tu74VcCELDwaGmlFVUiac3nsYjP57AmYwirR4zsIs99jzTD2+P7Y4OZobN2yARERHVEymy6DcxuxjFle0rNKVUCVgfdQ2DPvsHm2IztHrMkG6O2P/8ADwzxBfGBq37vblEIsEbD/lhRqT7PT+Hl50Z1s+PwFdTenARLVEzcLIyxjdTesDijus1YZ4d8OXkIES9OhhLHvKDj715C3ZIRHcK91J/DVsloN41QSKi5hCTWohZK2NQVi0eTH+gqwOWzQzV+88zk3q64YtJQaJDgQBgXdQ1PPjVEaw+kSZaI5NK8M3UHhgb7NL0TRK1Yi1yRzgxMRHPPfccdu3adfuGVlRUFKKiovDYY49h6NChGDt2LAYNGgQvL6/7eq3U1FQcOnQI27Ztw/79+1FdXQ0Ad91IGz16NL788sv7eh0iIiIiIqL24tsDl/H5vmStauumqI8OcsY7Y/x1GhKsqKnF/NWncDKlQLTG0liOdfMjEORmrbO+WrsgN2sEuVohIVO7beg9bU3x/fQQ+DtrHwZvbt06WuKPx3tj+dFUfLk/GTUiE98NZVJ8MD4AE0L1Z9LFxJ5uKKmqxbu76u9aANyaPDh3VQx+XdwLXZ0sEZtWiIVrT6GoQjwEJJUAb43xx6xens3UNbU2w/ydsP9iXr3j1woqkJRbCmfT5u+hvLoWSw9fxdIjKQ3uylCnk4M5Xh/VDYO6ODRzd0RERKSJWDhdJQCn0goxuFvrnATeWPHpN/HG9vM4n1WiVb27jSneGuPXaieli5FIJHhnTHcoagVsPqVdQB+49XnssYE+eGygj96HWohau+HdnTCoqz2Sc8rQ0doYduZcCEKkr7zszGBnbogbZfV3LI1NK8RAXhMhomZ08moB5q2ORaVCKVoz1M8R303r0Wp2W3m4hyvkUime3XwGSpX6wSyX88pEHy+XSvDt1B4YEdCxuVokarVaJJzu4+ODHTt2YN++fViyZAliY2MhCAIkEgmqqqqwa9cu7Nq1CwDg4uKCnj17onv37ujSpQtcXV3RsWNHmJubw8TEBIIgoKqqCqWlpbh+/ToyMzORlJSEc+fOIT4+HllZWbdft+416oLpYWFheP/99zFkyJCW+DYQERERERG1KoIg4Kv9l/H1gcuNfuzOhGycvHoD740LwPDuTs3Q3d3Kqmsxb1UsYtLEt423NjXA+vkR6N6ICdp0y4xIDyT8drbBupEBTvjokcAWnziujvz/gw5D/Rzxn98SEJ9edNd5O3MjLJ0ZilCPDi3ToAbz+3qhuKIG3xy8ovZ8SVUtZq6MwaP9vfHJniTUKMWDvaaGMnw7tUe7CQiRdoZ0c4RUcitA9m97E3MxJ6z5/r4IgoCdZ6/jg90XkVNSpdVjrE0N8NyQzpgW4Q4DWYtsFElERER38LQ1hYOFEfJKq+udi0opaPPvPQvKqvHxnkv49VSmVvVGcimeGNQJi/p7t9kQtlQqwQfjA6BQqvD76awG6/t0ssW7Y7vDm9OaiXTGSC5DgCuvkRHpO4lEgjBPG/ylZrfS2FROTiei5nPs8g0sWBuLKoX4/Ybh/k74ZmoPGMpb1zXa0UHOMJBJ8dTGeCiU2u8caiCT4LtpIXjQv/nvexK1Ri23lzaAoUOHYujQodi5cyc+/vhjnDhx4va5ugB5ZmYmsrKysH379kY//53T0SUSye1jffr0wSuvvIJRo0bd51dARERERETUPgiCgM/3JuO7Q+rDsNqom6I+JsgZbzfjFPXSKgXmrIrVuI2pjZkh1s+PgJ+zZbP00NaNDnLG+39eFJ3GbSCTYMkoP8zq5XH787i+6uRgji2Le2NjTDp+iU5HSaUCkd62ePHBzuhoZdLS7Yl6bmhnFFcqsObkNbXn80ur8d7uixqfw8HCCD/PCeMCDarHxswQYZ42iE6tv8Dn78ScZgunX8opwZvbE9W+rjpyqQSzennimcG+sDLVv0UwRERE7ZVEIkGkty12JGTXO6ftv/OtkVIlYEP0NXz6dxJKqsS3uL/TMD9H/PchP7jZ6GBrmhYmk0rwyYRA1ChV2HX2utoaO3NDLBnlh7HBznr/WZKIiKilhHupD6efyShClULZZhe7EVHLOZycj0VrT2nc4XJUQEd8NSW41Q4PGd7dCT9OD8Xjv8RrHPhTx1AmxQ/TQzDEr20vvia6Hy0aTq8zevRojB49GvHx8fj++++xdetWlJTc2uLuzgsPd4bNNal7zJ1T0i0sLDBhwgQ89thjCA0NbeKvgIiIiIiIqO0SBAEf7bmEpYdTRGskEuCpQZ3w5/kcXNGwvR0A7EjIxolmmqJeXKnA7J9jcCajSLTG1swQGxZGoouTRZO+dntibCDDjAgPtYsVXDuY4PtpIQhys9Z9Y/dIJpVgRqQHZkR6tHQrWpNIJHhztD9KqmrxhxaTB/+tq5MFfp4TBmdr/Q3gU8t60N9JbXgsMbsEWUXaTTTXVnGlAl/uS8a6qGuiW6f+2+CuDnhtVDf4cJomERGRXhILp5/PKkZJlUIvd1e6H2cyirBk2zmczyrRqt7D1hRvjfHHoC4OzdyZfpHLpPhycjAEALv/FVCfFuGOlx/sykWHREREDQjztFF7vEapwtnMYoR7qT9PRHQvDl3Kw6Pr4jQGtscEOeOLSUGQt9Jgep0hfo5YNisUj66L0xjEN5RLsXRGKAZ1bV+f54gaSy/C6XVCQkKwcuVK/PDDD9i9ezd27dqFvXv3Iju7/sWrf6+WvzO4Xvf/nZ2dMWzYMIwaNQoPPfQQjIyMmvcLICIiIiIiamMEQcB7uy9i5bFU0RqJBPj4kUBM6umGxwd1wlf7L2PZkavQlC9sjinqxRUKzPw5Gmczi0Vr7MyNsHFhBHwdGUy/X08+0AnHr97A6fSi28eG+Tni0wlBDBPoiPT/Jw+WVimw/2Ke1o/r52uHH6aHwKKNBYKoaQ31c8Q7uy6oPXcouQAuTfAaKpWA3+Iy8fGeSygor9HqMZ0dzbFklB/6d7Zvgg6IiIiouUR4qw9FqQTgVFohHujaNqbLlVfX4rO9SVh9Ig3azNgyNpDiiYGdsLC/d7udamogk+K7qT0wzM8Rxy7fgJmRHBNCXbmjExERkZa6dbSEhZEcpdX1d2qJTStkOJ2Imsy+C7l4/Jc4KJTiH3bG93DBpxODIJO2jZ2PBnZxwM9zwjB/TSyqFPUD6oZyKZbP6okBvD5N1CC9CqfXMTIywvjx4zF+/HgAQEpKCuLi4nD27FmkpqYiIyMDxcXFqKioAACYmprC2toabm5u8PT0RGBgIEJDQ+Ht7d2SXwYREREREVGrJggC3t55AatPpInWSCXAZxODMD7EFcCtidqvjOiKB/0d8dJvZ7Wcol6A9x/ujgf9732K+s3yGsxYGY3EbPEpdQ4WRti4KJJTdpuIsYEMvy3ujX0XcpFdVIke7tbo4d6hpdtqdwxkUnw3LQRzVsUgKqX+lOt/mxruhnfGdm+1W2uS7rjZmMLf2VLt79WDyQWYeZ/p9ISMIryxIxEJGna6uJONmSGeH9oZU8LcWv0EHiIiovbA284M9hZGyC+trncuOqVthNMPJeVhyR/nkVVUqVX9MD9H/PchP7jZmDZzZ/pPIpFgbLALxgY3xZJHIiKi9kUmlSDUswP+Scqvdy46tRBPDGqBpoiozdlz/jqe3HAatRomUU0MdcVHjwS2mWB6nT6d7LB6bjjmrY5FRY3y9nEjuRQrZ4ehr69dC3ZH1HroZTj937y9veHt7Y2JEye2dCtERERERETtgkol4I0d57E+Kl20RiaV4ItJQWpvJvdw74BdT/XVcop6NR5dF4exwc54a3Tjp6gXlFVj+opoXMopFa3paGWMDQsj4WVn1qjnJs1kUgmGd7/3RQXUNIwNZFg+qyemr9C8c8DLw7ti8QDvervREYkZ5uekNpwen1GMhx0A83sYvl9YXoNP/76ETbEZWk0XlUklmNPbE08P9oWVCaf9ExERtRYSiQSR3rbYmVB/d+SolIIW6Kjp3Cirxru7LmD7mfpfmzqetqZ4a4w/Bnbhlu9ERETUNMI8bdSG0+Ov3YRSJbS5oCgR6dbus9fx9KbTUGq4uTc13A3vjwuAtI3+von0tsVfz/TDp38nIa2gHG4dTPHig104AIuoEVpFOJ2IiIiIiIh0R6US8Nof57ApNkO0RiaV4JspPTAqsKNoTWOnqG8/k43jVxo3RT2/tBrTV0QhOVf8uV2sTbBxYSTcbTmdjtouC2MDrJ4bjok/ncDV/PK7zhnKpfh8YhBGBzm3UHfUWj3Y3RFf7k+ud1wlAOdvShDpoEW6/P/VKlXYEJOOz/cmo7hSodVjevvY4q0x/ujsaKH16xAREZH+iPCyURtOP5dVjNIqBSyMW9fCM0EQsDU+C+/tvoCiiobfzxgbSPHkoE5Y2N8bRnKZDjokIiKi9iLcy0bt8bLqWly8XoLuLlY67oiI2ortZ7Lw/K8JGoPpMyLd8c6Y7m02mF7Hw9YM300Laek2iFot7oFLREREREREtylVAv6z9azGYLpcKsH30zQH0+9UN0V98QAfNHSdqm6K+jObTuNmeY3G2rySKkxdrjmY7trBBJsWMZhO7YONmSE2LIzEgM72t491dbLAxoURDKbTPeniaAEPkd+f5wq1v/EQm1aI0d8dxxvbE7UKpjtbGeOH6SH4ZUEEg+lEREStWKS3rdrjKgE4de2mjru5P+kFFZi5MgYvbknQKpg+zM8R+58fgCcf8GUwnYiIiJpcoKsVDOXqI1/RqYU67oaI2orf4zPx3OYzGoPpc3p74t2xbT+YTkT3j5PTiYiIiIiICMCtYPpLWxLw++ks0RoDmQQ/TA/FUD/HRj33nVPUX9ySUG+y87/VTVH/4OHuGKZminpOcRWmLY9Cyg3x5/GwNcWGhZFwsTZpVK9ErZmjpTFWzw1DRmElKhS16OxgwYvEdM8kEgmG+Tli+dHUeueSiiSoVgJGGrJWuSVV+PDPi9h2pv7EVHUMZVIs6u+Nxwf5wNSQly2JiIhaOx97M9iZG+FGWXW9c1EpBRjUxaEFumqcWqUKPx9PxRf7klGlUDVY72RpjHfHdW/0Z2YiIiKixjCSyxDsZo0YNUH02NRCzO/r1QJdEVFr9uupDLy89SwEDZtlLujrhddHdYNEwnsORNQwTk4nIiIiIiIi1CpVeHbzGY3BdEO5FMtm9ryvm+w93Dtg99P98OgAb62mqC9SM0U9u6gSU5ad1BhM97Izw6ZFDKZT+ySRSOBua4quTpYMptN9e1DNAiEAUAgSXCpS//erplaFZUeu4oHP/tE6mP5AVwfsfa4/XnywC4PpREREbYREIkGEt43ac1Ep+j/R83xWMcb9cBwf/HmpwWC6RALMjPTAvuf7M5hOREREOhHuqf59VmxaIQRN6VIion/ZGJOO//ymOZi+eIAPg+lE1Ci800NERERERNTOKZQqPLvpDHafuy5aYySXYtmsnhjQ2f6+X8/YQIZXR3TDcH+nRk9R93O2xNTlUcgorBSt97E3w4aFkXC0NL7vXomI2rse7h1gZ26IG2U19c6dLZQgyPbuOxZHL+fjrR2JDf5ur+Nha4o3HvLD4G4McREREbVFkd622H22/mfN81nFKKuuhbmR/t2qrKxR4qsDyVhxNFXjdvZ1OjmY4+NHAhDqoT4gRkRERNQcwrxsgEP1jxeU1+Bqfjk6OZjrvikianXWRV3Df7ed11jz1AOd8PzQzgymE1Gj6N8VHyIiIiIiItKZmloVntoYj78Tc0VrjA2kWDk7DH062TXpa9dNUf9yfzKWH0mBpnv+dVPULYzkKK2uFa3zdTDHLwsj4GDBYDoRUVOQSSUY6ueIjTEZ9c4l3pRA+f9DRDNvVuD93Rfx1/kcrZ7X2ECKJwd1woJ+3jA2kDVly0RERKRHeolMTleqBJxKK8TALg467kiz41du4NXfzyG9sKLBWgOZBE8M6oTHBvrASM73M0RERKRboR4dIJVA7XX12LRChtOJqEGrj6firZ0XNNY8O8QXzw7prKOOiKgtYTidiIiIiIionaquVeKJX+Kx/2KeaI2JgQw/zwlDLx/bZumhsVPUNQXTuzpZYP2CCNiZGzV1m0RE7dowPye14fRKpQQXiyW4eiwdK09moEqh0ur5RgV0xGujusHF2qSpWyUiIiI942NvLroLS1SK/oTTb5bX4P0/L+K3uEyt6kM9OuCj8QHwdbRo5s6IiIiI1DM3ksPf2QrnsorrnYtNLcTUcPcW6IqIWosVR1Pw3u6LGmteHNYZTz7gq6OOiKitYTidiIiIiIioHapSKPHY+jgcSsoXrTEzlGHV3HCEezX/1uSNmaKujl9HS6xfEAEbM8PmaZCIqB3r3ckW5kZylKlZILTykhSqS9e0ep5ODuZ4e4x/k+/EQURERPpLIpEgwssWu89dr3cuOrWgBTq6myAI2Hn2Ot7ekYiC8voB+n8zN5Lj5RFdMT3cHVIpt7QnIiKilhXmaaM2nB6dWtgC3RBRa/HT4av46K9LGmteGdEViwf46KgjImqLpC3dABEREREREelWlUKJhWtPaQymmxvJsXa+boLpdeqmqG99rDd87M20flx3F0tsWMhgOhFRczGSyzCwi73acyo0HMoyN5Jjyahu+OuZfgymExERtUOR3uo/V57NLEa5ht2xmltWUSXmrY7F0xtPaxVMH+rniH3P98fMSA8G04mIiEgviF2/zyqqRHZRpY67IaLW4LuDlxsMpi8Z1Y3BdCK6bwynExERERERtSOVNUrMXxOLo5dviNZYGMuxbn44Qj10F0y/U90U9UcHeKOh+/1Brlb4ZX4krE0ZTCciak7D/J3u6XHjQ1xw8MUBWNDPGwYyXookIiJqjyK8bdUeV6oEnLp2U8fd3HrdVcdTMfSLwxoXbdextzDCj9NDsGxmKDpameigQyIiIiLthHl2ED0Xm8bp6UT0P4Ig4Kv9yfhsb7LGujdH+2FBP28ddUVEbRnvCBEREREREbUT5dW1mLs6BseviG+dbmVigF8WRKCHu/hFbV2om6L+m4Yp6j3crbFuQQSsTA103B0RUfszqIs9DGTaTwj162iJ3xb3wheTguFgYdyMnREREZG+83UwF93pKjpF/PNpc7iUU4JHfjyBt3deQEWNssH6qeFu2P/cAIwI6AiJhNPSiYiISL/YmhuJXj+PTmU4nYhuEQQBX+xLxlf7L2use3dcd8zt46WjroiorWM4nYiIiIiIqB0oq67FnFUxiEoRvyBtbXormB7oaq27xhoQcscUdcM7Ju4O93fC2nnhsDRmMJ2ISBcsjA3Q28euwTorEwO8O647dj7VFz09W2YHDiIiItIvEokEkd7q3xdE6SicXqVQ4rO/k/DQN8dwJqOowXovOzNsWhSJD8cHckE0ERER6bVwL/W71MQynE5EuBVM/3hPEr49eEVj3YfjAzAz0kNHXRFReyBv6QaIiIiIiIioeZVUKTDn5xjEpxeJ1tiYGWL9/Aj4OVvqrjEt1U1Rf2yAD05nFKGjlTG6Oulfn0REbd2I7k44nJyv9pxEAkwNd8eLw7qITkYlIiKi9ivCyxZ/nsupd/xsZjEqamphath8tyxPXLmBJdvOI+VGeYO1cqkEjw7wxlMP+MLYQNZsPRERERE1lXCvDtgYk17v+OW8MhSW1/A6DVE7JggC3t99ESuOpYrWSCTAx48EYlJPNx12RkTtAcPpREREREREbVhxpQKzfo5BgobJcHbmhvhlQSS6OFnorrF7YG1qiEFdHFq6DSKiduvhEBf8ePgqrhVU3HU80MUC7z8chABXqxbqjIiIiPRdpLf6iZ61KgFx126in699k76eIAg4mVKAr/dfRrSWU0OD3Kzx0fgAdOvIxdBERETUeoRp2LkuNq0QD/o76bAbItIXgiDg7Z0XsPpEmmiNVAJ8NjEI40NcddcYEbUbDKcTERERERG1UUUVNZi5MgbnsopFa+wtjLBxYQQ6Oeh3MJ2IiFqekVyGTYsi8da2s4i5mg8bIyDSUYWXJgbB2orBdCIiIhLn62AOGzNDFJbX1DsXlVLQZOF0QRBw/EoBvj6QjNi0m1o9xtRQhheHdcHs3p6QSSVN0gcRERGRrrh2MIWLtQmyiirrnYtNZTidqD1SqQS8uSMR66KuidZIJcCXk4MxNthFh50RUXvCcDoREREREVEbVFhegxkronHheolojaOlETYsjISPvbkOOyMiotaso5UJPn24Gw4dyrl9TCphiIuIiIg0k0olCPe0wZ7EnHrnolK0m2yuiSAIOHL5Br7en4z49CKtHzewiz3eG9cdrh1M77sHIiIiopYS5tkBWWfUhNPT7v99FhG1LiqVgNe3ncPGmAzRGplUgm+m9MCowI467IyI2huG04mIiIiIiNqYgrJqTF8RjUs5paI1Ha2MsXFhJDztzHTYGREREREREbVXkd7qw+lnM4tQUVMLU8PG37YUBAH/JOXj6wOXcSajSOvH2ZoZ4o3RfhgT5AwJF9oRERFRKxfmZYNtZ7LrHT+fXYLy6lqYGTEeRtQeKFUCXtl6FlviMkVr5FIJvpvWA8O7M5hORM1L2tINaNKrVy+sXr0alZX1V/cRERERERFRffml1Zi6PEpjMN3F2gSbF/ViMJ2IiIiIiIh0JtLHVu1xhVJA/LWiRj2XIAjYfyEXY78/jrmrYxsVTH8kxBX7nx+AscEuDKYTERFRmxDhZaP2uFIlID79po67IaKWoFQJeGlLgsZguoFMgh9nhDKYTkQ6odfh9OjoaMyfPx8dO3bE448/jvj4+JZuiYiIiIiISG/llVRhyrKTSM4tE61xszHB5kcj4W7LLcuJiIiIiIhIdzo7WMDa1EDtuaiUAq2eQxAE/J2Yg4e+PYYFa0/hbGax1q/fy9sWvz7aC59PCkIHM0OtH0dERESk73zszWEj8v4mNrVQx90Qka7VKlV4bvMZ/H46S7TGUCbF0pmhGOrnqMPOiKg90+twep2SkhIsXboUYWFhCA0NxdKlS1FaKj4FkIiIiIiIqL3JKa7C5GVRuJpfLlrjYWuKzYt6wbUDg+lERERERESkW1KpRHSqZ3Sq5nC6SiXgr3PXMfKbY3h0XRwSs0u0ft2+nezw66O9sHFRJMJFXp+IiIioNZNIJOjp0UHtuWiG04naNIVShWc2ncGOhGzRGkO5FMtn98QDXRlMJyLd0ftwuiAIt7fUEwQBp0+fxuOPPw5nZ2fMnz8fJ0+ebOEOiYiIiIiIWlZWUSUmLzuJ1BviwXRvOzNsXtQLztYmOuyMiIiIiIiI6H8ivW3VHj+TUYTKGmW94yqVgN1nr2PkN0fx2C/xuHhd+1B6/8722PpYL6xfEMFQOhEREbV5Yu93zmQUobq2/vssImr9ampVeHJDPHafuy5aY2wgxc+zwzCgs70OOyMi0vNwekJCAp544glYWVlBEAQAt1b7CYKA8vJyrF69Gn379kX37t3xzTff4ObNmy3cMRERERERkW5lFFZg8tKTuFZQIVrTycEcmxZFwsnKWIedEREREREREd0twkt9OF2hFBCf/r/7fEqVgB0J2XjwqyN4YkM8LuVov6PyoC72+P3x3lg7LxyhHgylExERUfsgFk6vrlXhfFaxjrshouZWXavE47/E4+/EXNEaEwMZVs0JR19fOx12RkR0i16H0wMCAvDtt98iOzsba9asQf/+/e8KqQO3pqlfuHABzz33HFxcXDBjxgwcPny4JdsmIiIiIiLSifSCCkxZFoXMm5WiNZ0dzbFxYSQcLBlMJyIiIiIiopbV1ckCViYGas9FpxSgVqnCttNZGPblYTy98TQu55Vp/dxDujlg+xN9sGpuOELcOzRVy0REREStgl9HS5gZytSei04t1HE3RNScqhRKLF4Xh/0XxYPppoYyrJkXjl4+6hcIExE1N70Op9cxNjbGzJkz8c8//+DSpUt44YUXYG9vX2+aelVVFTZu3IgHHngAXbp0wWeffYb8/PwW7p6IiIiISLOy6loUltdApRJaupX7UlOrwrlCCeJvSFBR29LdtH2pN8oxedlJZBWJB9O7Ollg48JI2FsY6bAzIiIiIiIiIvWkUgkiRKZ6bjuTjaFfHsGzm8/gan651s851M8Ru57qixWzwxDkZt1EnRIRERG1LnKZFCEe6hfoxTKcTtRmVCmUWLQuDoeSxDOR5kZyrJsfLrqjAhGRLrSKcPqdOnfujE8//RSZmZn49ddfMWzYsNtT1O+cpn758mW8/PLLcHNzw6RJk7B3796WbJuIiIiImlB8+k3MWBGNYV8exnObz2gM5+qzKoUSH/x5ET3e2YuQd/dh9HfH7trCujU5m1mEh5fHYUWSDGsuy/BOvAwxaUUt3VabdTW/DJOXnsT14irRGn9nS2xcGAlbcwbTiYiIiIiISH9EeKuf3JdeWIHUG9qH0of7O2H3032xfFZPdHexaqr2iIiIiFqtcE/1QdRT125C2coHJBERUFmjxII1p3AkWTyYbmF8K5ge6sFgOhG1rFYXTq8jl8sxYcIE7NmzBykpKViyZAlcXFzqTVOvqanB1q1bMWLECHh5eeH9999HdnZ2C3dPRERERPfqt7hMPPLjCRy7cgPJuWX443QWRn1zFOezilu6tUapW9W+7EgKFMpb72ETs0swdVkUDmu4oKCPTqffxPQV0ci4+b+gdKVSgtd2JKGihiPUm9rl3FJMXhqFvNJq0ZpAVytsWBCJDmaGOuyMiIiIiIiIqGGR3vcekpBIgFEBHbHn2X74aWYo/J0ZSiciIiKqEyYyJbm0qhaXckp03A0RNaXy6lrMXR2DY1duiNZYGsvxy4II9HBXv4sCEZEutdpw+p3c3d3xzjvvIC0tDTt37sSYMWMgk8lunxcEAYIg4Nq1a3jjjTfg6emJsWPHYteuXVCpVC3YORERERE1xq+xGXjptwQI/xruUFShwNTlUTjdSqaOa1rVXl2rwsI1p3DgYm4LdNZ4cdcKMXNlDEqr6ofQ88pq8Oe5nBboqu1KyinFlGVRuFEmHkwPdrPGuvkRsDI10GFnRERERERERNrp5mQJK5PGfWaVSIDRQc74+9n++H56CLo6WTZTd0REREStV7CbNQxl6qNgsamFOu6GiJpKWXUt5q6KRVSK+M+xtakBNiyMRKCrte4aIyLSoE2E0+tIpVKMGjUK27ZtQ3p6Oj744AN4e3sDuDVJvW6aem1tLXbt2oWxY8fC09MTH3zwAW7cEF9VREREREQtb0N0Ov6z9Wy9YHqd0qpazFwZg9g0/b64ps2q9hqlCovXx2HPef0OdsekFmLWyhiUVYtPR/81NkOHHbVtF7JLMGXZSRSU14jWhHp0wLr54Y2+yU9ERERERESkK1KpBOEiUz3r1UqAccHO2Pdcf3w7tQc6O1o0c3dERERErZexgQyBrup3lolNax0DnojobqVVCsz+OQYxGu6B25gZYsOCSHR34c5SRKQ/2lQ4/U5OTk4ICQlBUFDQXcfrQurArYnqmZmZ+O9//wsvLy+89tprqKysbIl2iYiIiEiDtSfT8Nof5xqsK6uuxayVMThxVT8XHmqzqr2OQingiQ3x2JmQrYPOGu/E1RuY/XMMymuUGuti0gpxNb9MR121XeezijFtRRRuVihEa8I9bbBmXjgsjBlMJyIiIiIiIv3Wy9tW43mpBBgf4oJ9zw/AV1N6oJMDQ+lERERE2ggTWQQYnVoIQWwCFBHppeJKBWasjEHcNfHFJXbmhti4MBJ+ztxdioj0S5sLp2dnZ+O9996Dt7c3RowYgW3btt0OowO3Aul1/wNwe5p6eXk5Pv74YwQEBODChQst1T4RERER/cvPx1LxxvZEresrFUrMXRWLI8n5zdhV42mzqv3flCoBz2w6ja1xmc3YWeMdu3wD81bHolKhOZhe59dTnJ5+PxIyijBteRSKNATTI71tsHpeGMyN5DrsjIiIiIiIiOjejA9xgYWaz7AyqQQTQ11x8IWB+GJSMHzszVugOyIiIqLWS2yHmhtl1UgrqNBxN0R0r4oqajBjRTQSMopEa+wtjLBpUSS6OHExLxHpnzYRTlepVNi5cyfGjBkDT09PvPnmm0hLS7sdQK8Lo3t4eOD9999HdnY2Dhw4gClTpsDQ0BDA/0LqKSkpGDJkCAoKClrySyIiIiIiAMuPpOCdXY1fOFhdq8KCNadw4GJuM3TVeNqsahejEoAXf0vA5tj0Zuis8Q4n52P+mlhUKVRaP2ZrXCYUSu3r6X/i029ixopolFTVitb07WSHVXPCYWrIYDoRERERERG1Dtamhlg1NwxuNiYAAFNDGSb3dMOhFwbi04lB8LQza+EOiYiIiFqnUI8OuGOG511iU7UfoERELedmeQ2mLY/Guaxi0RpHy1vBdO4yRUT6qlWH09PS0rBkyRK4u7tj3Lhx2L17N2prb4U26qalS6VSjB07Fn/++SeuXr2KV199FU5OThg0aBA2bNiAzMxMvPbaazA2Nr79mNzcXHz55Zct9nUREREREfDDP1fw/p8XNdb4dRTfnqxGqcLi9XHYcz6nqVtrFG1XtU8MdRU9LwjAy1vPYd3JtKZvsBEOXcrDwjWnUF3buKD5jbIaHLyU10xdtV2n0goxa2UMSqvFg+n9O9tjxeyeMDGU6bAzIiIiIiIiovvX09MGR14ahJjXB+Psm8Pw8YRAuNuatnRbRERERK2apbEBujmpv38WzXA6kd4rKKvG1OVRuHC9RLSmo5UxNi/qxZ2miEivtbpwem1tLbZs2YJhw4ahU6dO+PDDD5GdnX17Sjpwa1K6s7Mz3nzzTVy7dg1//PEHhg8ffjt8fidbW1u89957OHXqFCwtLW9PUN+5c6cuvywiIiIiusO3By7jkz1JGmuWjOqGHU/2wfgeLqI1CqWAJzbEY2dCdlO3qBVtV7VvXhSJTyYEYvEAH43P99/tiVhxNKWp29TKvgu5WLTuFGo0TEAf7mcPQ6mg9tzm2Izmaq1Nik4pwKyfY1CmIZj+QFcHLJsZCmMDBtOJiIiIiIiodZJIJHCwMIZc1upuWRIRERHprXAvG7XHY9MYTifSZ/mlt4Lpl3JKRWtcrE2weVEv7jZFRHqv1VzpSU5OxksvvQQXFxdMmTIFBw4cgEp1KxhTFzqXSCQYMWIEtm3bhmvXruHNN9+Es7OzVs/frVs3PPnkk7dD7ikpLRP6ISIiImrPBEHAl/uS8fm+ZI11b472w4J+3pDLpPh0YhAm9RSfOq5UCXhm02n8Hp/Z1O1qpM2qduf/X9XubW8OiUSCl4d3wTODfTU+73u7L+KHf640dbsa7Tl/HY+tj4NCqT54DgATQ13x4Zgu6GGrvuafpDzkFFc1V4ttyokrNzBnVSwqapSiNUP9HPHjjBAG04mIiIiIiIiIiIiI6C5i4fT0wgreqyHSU3klVZiy7CSSc8tEa9xsTLBpUSR3nCKiVkHe0g1oUl1djS1btmD58uU4duwYANwOj9cF0gVBgJOTE+bNm4eFCxfCw8Pjnl8vJCTk9v+vqKi4j86JiIiIqLEEQcDne5Px3SHNwet3x/pjZi/P23+WSSX4aHwgDOVSrI9KV/sYlQC8sCUBCqUKk8Pcm7JttfJLqzF9RZTGiwcu1rcuHrjZ/O/igUQiwXNDO8NQLsWnf4tPjv9kTxIUtQKeHtxJ7e5ATWn32et4etNpKFXiwfSp4W54f1wAyspK0ctRhej8+mtgVQKwNT4TTwzq1JzttnpHL+djwZpTqK7VMKHe3wnfTO0BQ3mrWWtMREREREREREREREQ6EuapPpwOADFphRgTpN2gTyLSjZziKkxbHoWUG+WiNR62pti4MBLO1iY67IyI6N7pdTjdyckJJSW3Jk0KggCJRAKJRHI7oD548GAsXrwYY8eOhVx+/1+KpaUlADR7wIeIiIiI7iYIAj7acwlLD2veveaDhwMwLaJ+uFwqleDdsd1hKJPh5+OpIq8BvLz1HGpqVXeF25taXkkVpi6PwtV88YsHbjYm2LgwEq4d1K9qf2JQJxjKpHj/z4uiz/Hl/mTUKJV4cViXZnv/uv1MFp7/NUFjMH1GpDveGdMdUumtHjzNAUcTAbmV9XvaHJuBxwb43K6lu/2TlIdF6+JQoyGYPiqgI76aEgwDbndORERERERERERERERq2FsYwdvOTG3QNTaV4XQifZJdVImpy6NwrUB8kK63nRk2LIyEk5WxDjsjIro/ep1oKC4uvuvPgiDA1tYWL774IpKSkrBv3z488sgjTRJM//frEBEREZFuCIKA93Zf1BhMl0iATyYEqg2m/69Ggv8+1A2LB/hofL3/bk/EymPqA+z3K6e4ClOWaQ6me9qaYvOiXqLB9DoL+3vj7TH+Gmu+P3QVH/x5sVnev/4en4nnNp/RGEyf09sT747tflfYXCIBIh3Uh6vTCysQlVrQ5L22BQcu5mLRWs3B9LHBzviawXQiIiIiIiIiIiIiImqA2PT02LRCHXdCRGKyiioxZZnmYLqPvRk2LWIwnYhaH72enA78Lyjer18/LF68GI888ggMDQ2b5bX69u2L1NTmCSoRERERUX2CIODtnRew+kSaaI1UAnw2MQjjQ1wbfD6JRIKXh3eBoVyKbw5cFq17d9cF1NSq8NhAzUH2xmiOVe2ze3vCQCbF69vOQSx/vvxoKmpqVXhztH+TTST/9VQGXt56VvQ1AWBBXy+8Pqqb2qntYfYCdqULUArqp6f39rFrkj7bir2JOXhiQzwUSvFv+PgeLvh0YhBknDpPREREREREREREREQNCPOyweZTGfWOX8opRVFFDaxNmyd7RUTaybxZganLo5BRWCla09nRHL8siIS9hZEOOyMiahp6HU63srLC7Nmz8eijj6Jbt27N/npGRkbw8PBo9tchIiIiIkClEvDGjvNYH5UuWiOVAF9ODsbYYBetn1cikeD5oZ1hKJPgs73JonUf77mEmloVnh7cSW3AujEyCiswbYXmiwedHMyxYUEEHCwbt6p9WoQ7DGQS/EdDWHzNyWuoUarw/riA+w6ob4xJx6u/n9NYs3iAD14e3kX0+2ZhAHTvICChsP75v87n4J0KBaxMDe6rz7bir3PX8dTG06jVMKF+YqgrPnokkMF0IiIiIiIiIiIiIiLSSoSX+snpAHAq7SaG+DnqsBsiulNGYQWmLItCVpH4veWuThb4ZUEEbM0ZTCei1kmv94O/fv06vvrqK50E04mIiIhId1QqAa9vO6cxmC6TSvDt1JBGBdPv9OQDvnh9pOb3kV/uT8Zne5Nu79ZzL9ILbl080BRM7+JogY0LIxsdTK8zsacbvpocrDGcvDEmAy/9dhZKDSHnhqw7mdZgMP3JQZ00BtPrRDqo76OmVoVtZ7Luuce2ZNfZbDzZQDB9arg7PmYwnYiIiIiIiIiIiIiIGsG1gwmcRO5LxaYV6rib5qVSCUgvqEBheU1Lt0LUoLp7y5qC6X4dLbFxYSSD6UTUqunt5PTTp09j3bp1t/88bdo09OzZswU7IiIiIqKmoFQJeGXrWWyJyxStkUsl+G5aDwzv3vG+Xmthf28YyCR4a+cF0ZrvD11FTa0Kr43s1ugJ6mk3yjF1eRSuF1eJ1jTVqvaxwS4wlEk1TtneGp8JhVKFLyYFQS5r3DrUVcdT8baG7xMAPDvEF88M9tXq+9TVWoCjhSFyS+tfCNwcm4HZvT0b1V9bs/1MFp7bfAaa1hLMjPTA22P873saPhERERERERERERERtS8SiQRhXjbYmZBd71x0atsIp+eVVGF91DVsiEnHjbIaSCW37qd99EgAjOSylm6PqJ5rBeWYskzzveXuLpZYPz8C1qaGOuyMiKjp6W04/fDhw/jqq68gkUggk8nw6quvtnRLRERERHSflCoBL21JwO+nxSdnG8gk+H5aCIb5OzXJa87p4wVDuQyvbzsHsQHpy4+moqZWhTdHax8EvppfhmnLo5BbUi1a4+986+JBB7OmuXgwIqAjfpRJ8cQv8ahRqtTW7EjIhkKpwtdTesBQrl1AfcXRFLy3+6LGmheHdcaTD/hq3atUAowLdMTS4xn1zl24XoLzWcXo7mKl9fO1JVvjMvHSbwkag+lzenvizdF+jV4wQUREREREREREREREBADhIuH081nFqKiphamh3sbGNDqTUYRVx1Ox++z1uwY6qQTgj9NZkEkl+GxiUAt2SFRf6o1yTF0WhZwS8WB6oKsV1s2LgJWpgQ47IyJqHo0bp6hDVVW3fhELggBXV1fY29u3cEdEREREdD9qlSo8t/mMxmC6oUyKpTNDmyyYXmdahDs+eSQQmnK+a05ew+vbzkGlKTH8/67klWLKMs3B9EBXK2xYENlkwfQ6Q/0csWxWqMbg+V/nc/D4L3GorlU2+Hw//nO1wWD6y8O7NiqYXmdckJPo93xTbHqjn68t+DU2Ay82EExf2M+LwXQiIiIiIiIiIiIiIrov4Z42ao/XqgScSS/SbTP3SaFUYfuZLDz8w3GM+/44tp/JFt1p+Le4TOy7kKvjDonEXc0vw5RlJzUG04PcrLFuPoPpRNR26G043cnpViBJIpHAwcGhhbshIiIiovuhUKrwzKYz2KFmOkMdQ7kUy2f3xANdHZulh4k93fDV5GDINExG3xiTgZd+OwulhuRwUs6tYHp+qXgwPbiZLx4M7OKAVXPCYGwg/nZ+/8U8LFobhyqFeED9u4OX8fGeSxpfa8mobnhsoM899elibYw+PnZqz20/k43KmobD823Jhuh0/GfrWdEJ/gCweIAPXhvZjcF0IiIiIiIiIiIiIiK6L74O5rAyUX+vKjq1UMfd3JuCsmp8e+Ay+n58EM9sOoPTWobqX/vjHG6W1zRvc0RauJJX1uDQs1v3lsNFf16JiFojvQ2nOzs73/7/hYWt4w0REREREdVXU6vCkxvisfvcddEaYwMpfp4dhgGdm3e3nLHBLvh2ag/INQTUt8Zn4rnNZ1CrVNU7dyG7BFOXR+FGmfjFrFCPDjq5eNCnkx3WzA2HmaFMtOZwcj7mrY5FRU3tXccFQcBX+5Px2d5kja/x5mg/LOjnfV99Tg5zU3u8tKoWf50X/zvR1qw9mYbX/jinsebJQZ3w8vAuDKYTEREREREREREREdF9k0olCBOZnh6bpt9ZrMTsYry0JQG9PjqIz/clawz2qpNfWo23diY2U3dE2qnbjVvT0LMQ91vBdEtjBtOJqG3R23B6nz59YGpqCkEQkJqayoA6ERERUStUXavE47/E4+9E8a3zTAxkWDUnHH191U/YbmojAzrixxmhMJSJvxXekZCNpzaeRk3t/wLq57OKMW1FFAo1TFkI97TBmnnhsNDRxYMIb1usnR8BCyO5aM2JqwWY83MsyqpvBdQFQcAX+5Lx1f7LGp/73bH+mNvH6757HObvCGuRCfKbYzPu+/lbg5+PpeKN7ZovgD43pDNefJDBdCIiIiIiIiIiIiIiajrhXh3UHo9Pv3nXfTB9UKtUYc/565i09CRGfXMMW+Iy76vH7WeysacdDUoi/ZKceyuYfqNMPJje06PDrXu9DKYTURukt+F0MzMzjBkzBgCgUqmwbt26Fu6IiIiIiBqjSqHE4nVx2H9RPJhuaijDmnnh6OVjq8POgKF+jlg2KxSGcvG3w3+dz8Hjv8ShulaJs5lFmLY8CkUVCtH6Xt62WD0vDOYaguLNIdSjA9YviIClsfjrxqQVYtbKaJRUKfDxniR8e/CKxuf84OEAzOzl2ST9GcllGBfsovZcdGohUm+UN8nr6KvlR1Lwzq4LGmteHNYZzwzx1VFHRERERERERERERETUXohNTq9SqHA+u1jH3ahXXKHA0sNXMeDTf7B4fTxiUhs3wFTTvbnX/ziPAg3hYKLmcCmnBFOXad6NO9zTBqvnhev83jIRka7obTgdAP773//C0NAQAPD222/j8mXN0x2JiIiISD9UKZRYtC4Oh5LyRWvMjeRYNz8c4V7qL4o1t4FdHLBqThiMDcTfEu+/mIeZK2MwfUU0SqpqRev6drLDz3PCYGrYMhcPgtyssXFRJDqITCgHgPj0Ijzw2T/46fBV0RqJBPjkkUBMi3Bv0v4mh7mJnvv1VNudnv7jP1fx/p8XNda8MqIrnnyAwXQiIiIiIiIiIiIiImp63V2sYGIgU3sutpEh8KZ2ObcUr/1xDpEfHsCHf11CVlFlox7f28cWy2aGIub1wfCxN1NbU1Be0+DutkRN6UJ2CaYtj0aBht24I7xssGqu7oeeERHpkl6H07t164aVK1dCIpGgqKgIAwcOxP79+1u6LSIiIiLSoLJGiQVrTuFIsngw3cL4VjA91KNlgul1+nSyw5q54TAzVH9RDgBiUgtRqiGYPqCzPVbM7gkTDc+hC/7OVti0qBfszI1EazStzpdKgM8mBGGShiD5verW0RJBrlZqz/0Wl4lapX5tG9kUvj1wGR/vuaSxZsmoblg8wEdHHRERERERERERERERUXtjIJMixMNa7bnYNN2H01UqAQcu5mLmymgM/fIINkSno1Kh1PrxRnIppoa7Yc+z/bBhYSSG+TvB1FCOzycFQypR/5jd565jZ0J2E30FROISs4sxbUUUCjUE03t522LV3DCYMZhORG2cXofTAWD69OnYuXMnHB0dcf36dTz44IMYMGAAfvrpJ8THx+PmzZtQKrV/k0JEREREzaeiphbzVsfi2JUbojWWxnL8siACPdw76LAzcRHetlg7PwIW93AB4IGuDlg6MxTGIhMndK2LkwU2LYqEg4V4QF0dqQT4cnIwHgl1babOgMlh6qex55dWa5yw39oIgoAv9yXj833JGuveHO2HBf28ddQVERERERERERERERG1V2Ge6odFxabdhEol6KSH0ioFVh1PxQOf/4P5a07h6GXxe4nqdLQyxn+Gd0HUq4Px4fhAdHWyvOt8sJu1xoFA/91+HnmlVffUO5E2zmcVY9ryaBRVKERr+nSybdHduImIdEmvf9PJZHeHfCQSCQRBwLFjx3Ds2LF7fl6JRILaWvHpl0RERETUeGXVtZi3KhYxGqYsWJsaYP38CHR3UT9Fu6WEenTA+gURmLkyGiUapqTfaaifI76b1gNGcv0Iptfp5GCOXx/thWnLo5Bd3PBFNplUgq+nBOOhQOdm7Wt0UEe8u+uC2ukXm2MzMNTPsVlfXxcEQcDne5Px3aErGuveHdcdMyM9dNQVERERERERERERERG1Z+Ei4fTiSgWS80rrBb2bUtqNcqw+kYbf4jJRVt34rFZPjw6Y28cLD/o7Qi7TPIP1mSG+OHAxD0m5pfXOFVUo8Pof57FsZigkEpER60T36FxmMaaviNJ4n7mfrx2Wz+qpN0PPiIiam15PThcE4fb/6tS9Qbjz3L38j4iIiIiaTmmVArN/jtEYTLcxM8SGBZF6F0yvE+RmjY2LItHB1KDB2uH+Tvh+WojeBdPreNqZYfOjveBmY6KxTi6V4LupPZo9mA4AFsYGGBXYUe25Q0l5yCtp3dMqBEHAR3suNRhM/3B8AIPpRERERERERERERESkMz3cO0AuVR/Ijk0Vv7d3r4oqavBL9DVM+ukkBn72D1afSGtUMN1QJsX4EBfsfLIvfnusN0YFdmwwmA4ARnIZPp8UBJnI17rvQi62ncnSug8ibSRkFGFaA8H0/p3tGUwnonZHr8PpgHgY/X6ei4iIiIiaTnGlAjNXxiDu2k3RGjtzQ2xcGAk/5+abvNAU/J2tsGlRL9iZG4nWjArsiG+n9YChXL/fSrvZmGLzol7wsjNTe95AJsEP00MwIkB9YLw5TA5zU3tcqRLwW3ymzvpoaoIg4P3dF7H0cIpojUQCfDIhEFPD3XXYGRERERERERERERERtXcmhjIEuKofHhXdROH0KoUSf567joVrTyHs/f14/Y/zGodaqWNvYYTnhnTG8VcewBeTgkV71qS7ixWeGNRJ9Pyb2xOR28oHJpH+OJ1+EzNWRKNUQzB9YBd7LJsZymA6EbU78pZuQJP+/fszUE5ERESkx4oqajDr5xiczSwWrbG3MMLGhRHo5GChw87uXRcnC2xaFIlpy6OQV1p917mxwc74fGKQVtMZ9IGztQk2L4rEtBXRuJJXdvu4oVyKH6eHYHA3R53209OjA7ztzZCSX17v3K+xGXhsgE+re/8vCALe3nkBq0+kidZIJcBnE4MwPsRVd40RERERERERERERERH9v3BPG5xOL6p3PDatEIIg3NP9GaVKQHRKAf44nYU953NQ2ojp6HcKdLXC3D6eGBXg3CTDoZ4c1An7L+TiwvWSeudKqmrx6u/nsHJ2z1Z3T4r0S9y1m5j9c4zGXQEe6OqAH2fo727cRETNSa/D6f/8809Lt0BEREREIm6W12DGymgkZte/sFPH0dIIGxZGwsfeXIed3b9ODubY9kQffPjXJRy6lAcTQxkeG+CD2b09RbcC1FcOlsbY9VRfrDyWijMZRbAxNcQTgzrB3dZU571IJBJM7umGD/+6VO9cWkEFolMLEeltq/O+7pVKJeCNHeexPipdtEYqAb6cHIyxwS467IyIiIiIiIiIiIiIiOh/wr1ssPRI/R1gc0uqkVFYqfV9I0EQcOF6Cbafycb2M1nILalu+EFqyKUSjAjoiDm9PRHibt2kQXFDuRSfTQzC2O+PQaEU6p0/eCkPW+IyMamn+h1/iRoSd60Qs3+O1RhMH9LNAd9PZzCdiNovvQ6ntweCICAtLQ3nzp1DZmYmioqKYGRkhA4dOsDX1xdhYWEwNjZu6TaJiIiI7lJQVo3pK6JxKadUtKajlTE2LoyEp52ZDjtrOs7WJvh2ao+WbqNJGBvING5hqEvjQ1zx6d9JqFXVvxj4a2xGqwmnq1QCXt92DhtjMkRrZFIJvpnSA6MCO+qwMyIiIiIiIiIiIiIiorv19LCBRAII9W/PIDq1oMFweubNituB9OTcMo21mnQwNcC0CHfMjPSEk1Xz5aH8nC3x9AO++Hxfstrz7+68gL6d7OBsbdJsPVDbFJtWiDk/x6C8RilaM8zPEd9NC2mSnQCIiForhtNbwM2bN7Ft2zbs2bMHBw8exI0bN0RrDQwMMGrUKDz77LMYMGCATvrz9PTEtWvX7vnxhw4dwsCBA5uuISIiItIr+aXVmL4iSuOFJxdrE2xcGNki07lJv9lbGGFwNwf8nZhb79zuc9fx5hh/WJkYtEBn2lOqBLyy9Sy2xGWK1silEnw3rQeGd2cwnYiIiIiIiIiIiIiIWpaVqQG6OFqoHTwVm1aIiWqmiBdV1GD3uevYfjobMWmF9/X6/s6WmN3LE2OCnWFsoJtJ0osH+mDvhVycyyqud660uhYvbz2LtfPCm3RqO7Vt0SkFmLs6FhUagunD/Z3w7bQeMJAxmE5E7RvD6Tr2xBNPYMWKFaipqdGqXqFQYNu2bdi2bRtmzZqFb7/9FpaWls3cJREREZF6eSVVmLo8Clfzy0Vr3GxuBdNdOzCYTupNDnNTG06vrlVhR0I2ZkZ6tEBX2lGqBLy0JQG/n84SrTGQSfD9tBAM83fSYWdERERERERERERERETiwr1sRMLpN2///yqFEgcv5eGP01n4JykPCqWaUetacrE2wZhgZ4wLdkEXJ4t7fp57ZSCT4vNJQXjom2OoUarqnT96+QY2xmRgWoS7znuj1ufk1QLMWx2LSoV4MH1kgBO+nsJgOhERwHC6zkVHR6sNpstkMnTs2BGOjo5QKBS4du0aiovvXrm3du1aXLp0CQcOHIC5ubmuWiYiItJbtUoVDl7KQ1GlAr19bBmGbmY5xVWYtjwKKTfEg+ketqbYuDCSW+CRRv197eFkaYyckqp65zbHputtOL1WqcLzvyZgR0K2aI2hTIqfZobgga6OOuyMiIiIiIiIiIiIiIhIszBPG6w9ea3e8dQb5fjz3HX8k5SHv87loLS69p5fw8rEACMDOmJcsDPCPG0glbbsVPLOjhZ4bmhnfLznktrz7+++gH6+dnCz4X1mEnfiyg3MWxOLKkX9RQ51RgV2xFeTgxlMJyL6f606nF5bW4vc3FzcvHkTpaWlsLCwQIcOHeDo6Ai5XP+/NGtra0ybNg2jRo1Cv379YGHxv1WCSqUSR48exRtvvIGjR4/ePh4TE4M5c+bgt99+00mPjo6OWL9+faMeExQU1EzdEBER/U/ajXIsXh93e3W/XCrBu+O6Y2o4V7Y3h+yiSkxdHoVrBRWiNd52ZtiwMBJOVsY67IxaI7lMigmhrvju0JV6585nlSAxuxj+zlYt0Jk4hVKFZzedwe5z10VrDOVSLJ/VEwM62+uwMyIiIiIiIiIiIiIiooaFe9mInnv8l/h7fl5DuRRDujlgbLALBnaxh5Fcds/P1RwW9vPC34k5OJNRVO9ceY0S//ntLH5ZENHiQXrST4cu5WHx+jhU14oH08cEOeOLSUGQM5hORHSb/ie4/+Xq1atYsWIFjhw5gtOnT6O6urpejZGREUJCQjBgwAAsWLAAXl5eLdCpOE9PTyxZsgTTpk2DiYn6qaIymQwDBw7EoUOH8Pjjj2PZsmW3z23duhWHDh3CoEGDmr1XY2NjDBkypNlfh4iIqDGu5pdh2vIo5Jb8731ArUrAWzsS0cfHDu62XNnelDIKKzBtRRQyCitFazo5mGPDggg4WDKYTtqZ1NNNbTgdAH6NzcDbY/UnnF5Tq8JTG+Pxd2KuaI2xgRQrZoWhr6+dDjsjIiIiIiIiIiIiIiLSjqOlMTxsTTUOo9KWRAL08rbFuGAXDA9wgqWxQRN02DzkMik+nxSEkV8fVRswPplSgPXR1zCrl6fumyO9dfF6CT77OwkHLuVprBsX7IzPJjKYTkT0b63mt2JOTg7Gjx+PLl264JNPPkFUVBSqqqogCEK9/1VVVeHkyZP46KOP4OvriwkTJiAnJ6elvwQAwNtvv42kpCTMnz9fNJh+J5lMhh9++AE9e/a86/iKFSuaq0UiIiK9diWvFFOW3R1Mr1Ndq8KGmPQW6KrtSi+owJRlmoPpnR3NsXFhJIPp1Cjutqbo7WOr9twfp7NQpVDquCP1qmuVePwXzcF0EwMZfp7DYDoREREREREREREREem3ME/x6ena6NbREq+N7IoTrzyADQsjMSnMTa+D6XV87M3x0oNdRM9/+OclXCso12FHpK8yCivw3OYzGPnN0QaD6eN7uODzScEMphMRqdEqfjPu27cPAQEB2L59O1QqFQRBAABIJBJIJHdvqXLnMUEQoFKp8McffyAgIAD79+/Xee//NmrUKBgaGjbqMTKZDP/5z3/uOvb33383ZVtEREStQlLOrWB6fmn9YHqdrfGZqFWKb6lF2ku7UY7Jy04iq0g8mN7VyQIbF0bC3sJIh51RWzE5zE3t8ZKqWvyd2PKLS6sUSixeF4f9F8WD6aaGMqyeG4bePgymExERERERERERERGRfgu/h3C6i7UJHh/og73P9cdfz/TDov4+6GjV8EBOfTO3jxd6enRQe65SocRLW85CpRJ03BXpi/zSary1IxEPfP4P/jidBaGBvwoTQl3x6cQgyKQSzYVERO2UvKUbaMjx48cxbtw4VFbeCkVJJJLbE9Llcjm6du0KOzs7mJmZoby8HDdu3EBSUhIUCsVd9QUFBRg3bhz27duHXr16teSXdE/69et3158LCgpQUVEBU1PTFuqIiIhIty5kl2DGymgUltdorMsvrcahpHwM9XPUUWdt09X8Mkxbrn5CfR1/Z0usnx+BDmaNW3hHVOdBfydYmRiguFJR79ymmAyMDXZpga5uqVIosWhdHI4k54vWmBvJsWZeGEI97m/KCBERERERERERERERkS5Eeqvf1fbfrEwMMDKgIx7u4YKeHh0gbQMBXJlUgs8mBmH410dQpag/7CwmrRCrTqRhfl+vFujulsLyGpxOvwkPWzP42JvVG9pKTa+0SoHlR1Kw4lgqKmq029l5Uk9XfDQ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" ] @@ -905,18 +839,12 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:49:15.927542Z", - "iopub.status.busy": "2023-12-04T17:49:15.927463Z", - "iopub.status.idle": "2023-12-04T17:49:21.299561Z", - "shell.execute_reply": "2023-12-04T17:49:21.299292Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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t+vXr18/Iy8tz2NZqtdrN1bFjR7s3qB3Ztm2b4eHhYdN39uzZTttv3rzZCA0NtWn//vvvN6k1AQBQ35YuXWqYTCZDOv2Fp3/+85+1flP5wQcftOmblJRk8wZoZf/4xz9s2oeEhFT7wbJhuPf+311rAgCgrnbt2mX4+vpW7EFeXl5V3gNX5sp+xV7PXg8AcL8ePXrY7D19+/Y1CgsLq+yzZMkSw9PT06bfM888U2Wft956y6Z9WFiYsWXLFqftZ82aZdPe09PTLhHbkZKSErt9e+rUqTaJWn+Um5tr9O3b16b9lClTqp3HnWsCAOBMIndQUJAxbNgw49577zU+++wzIz093Vi6dGm9JXJzX9481oT6RyI30Izcd999Nr88hw4d6vSG74zFixfb9AkKCjKOHz/upogBAGgavv76a6OkpKRGfcrLy+3ePJ04cWK1/TZv3mxzqoW3t7exdevWKvsUFRUZnTp1spnr7bffrnau8ePH2/SZPHlytX0qn16WmJhoczJHY68JAID6dOrUKaNjx44Ve9Fdd91V6zeVs7Ky7E4WWbx4cZV9rFarMXToUJs+Dz30ULVzuev+351rAgCgroYPH26z/3z66af1Oj57fd3WBABAbezZs8dmz5FkrF271qW+lU+sPPfcc522LSkpMeLj423av/fee9XOMWnSpBp/RvD666/b9OnUqZNRVFRUZZ8tW7bYnDLu4eFhbNu2rco+7lwTAAC7d+82tmzZYlgsFru6+krk5r68eawJDYNEbqCZsFgsRlRUlM0vT1dPBR0yZIhNv9dff72BowUAoGX49NNPbfbQiIiIavv89a9/tenj6skZ7733nk2//v37V9k+JyfH5sQRk8lk7Nmzp9p5LBaLkZiYaDPXwoULm8SaAACob3/7298q9qGEhASjoKCg1m8qv/LKK3ZvgrpiyZIlNv1iY2OrfPPUnff/7loTAAB1NXfuXJu95+qrr673Odjra78mAABqa+HChTZ7Trt27Vzu+/3339v0jYqKctp2/vz5Nm3bt2/v0t62e/fuiqd8SaefCJKbm1tln5SUFJu5XH0q5uTJk2363XfffVW2d+eaAACoSn0lcnNf3jzWhIZhFoBmYfXq1Tp27FhFuUOHDho2bJhLfW+66Sab8ty5c+sxMgAAWq4hQ4bYlLOzs3Xq1Kkq+8yfP9+mXHkfduaaa65RQEBARfmXX37RoUOHnLZfsGCBysvLK8rDhg1Thw4dqp3HbDbrxhtvtLlW3WsDd60JAID69Msvv+jll1+uKL/22msKDAys9Xjz5s2zKbu6Hw4fPlxJSUkV5SNHjujnn3922t6d9//uWhMAAHX19ttv25Qfe+yxep+Dvf537PUAAHfJycmxKcfHx7vcNyEhwaacm5vrtG3lPfHGG2+UyWSqdo6OHTvqvPPOqyiXlZVp4cKFTttnZmYqLS2tohwYGKjx48dXO49kv09Xjrkyd60JAAB34b78d015TWgYJHIDzcSCBQtsyiNHjnTpRuxM2z9atmyZTp48WW+xAQDQUoWFhdldy8vLc9p+x44d2r17d0U5ICBAAwcOdGmuym0Nw7Db//+oct2FF17o0jyS/WuDr7/+2mlbd64JAID6UlZWpptuukkWi0WSdPXVV+uyyy6r9XiFhYX68ccfba65uveaTCaNGDHC5lpVe6+77v/duSYAAOri4MGD+u677yrKvXv3Vvfu3et1DvZ6W+z1AAB3CQkJsSkXFRW53Ldy28jISKdt3fV+euV5Bg0aZHPYSVUGDRokf3//ivKOHTu0a9cul+dqqDUBAOAO3JfbaqprQsMhkRtoJn799VebsqsJVJIUFxen9u3bV5RLS0u1devWeooMAICW6+DBg3bXIiIinLavvF/3799fnp6eLs83aNCgKserqq4mrw1SU1Pl4+NTUT506JDNN3Srmqch1wQAQH159tlntWnTJklSaGioXnnllTqNt2XLFpWVlVWUk5KSFBsb63J/d+3xNbn/d+eaAACoi2+//bbiy1nS6VOp6ht7vT32egCAO/Tu3dumvG3bNpcTh9auXWtT7t+/v8N2R48e1ZEjRyrKPj4+SklJcTlGd+3znp6edmtwNpc71wQAgDtwX26vKa4JDYdEbqCZ2LZtm025W7duNepfuX3l8QAAgL0VK1bYlBMTE+Xt7e20vbv267KyMptTsms6l4+Pjzp27OjSXLwGAQA0N1u3btU//vGPivLzzz9fozdHHXHnfuiuudjjAQDNxS+//GJT7tWrV8WfN2zYoD//+c/q1auXwsLC5O/vr/bt22vkyJF68cUXHX5B2xH2+trPAwBAXbRr184m0aikpMSlL2OXlJTo5Zdftrl20003OWxbeQ9LTk6u8n3+yirvibt371Z5eblLc7lrn2/INQEA4A7cl9d+HnfPhYZBIjfQDBQVFSkjI8PmWnx8fI3GqNx+x44ddY4LAICW7v3337cpX3rppVW2r7y/NtR+vXfvXps3Vf38/Kp8bGRd5nLXmgAAqA9Wq1U33XSTSktLJUlDhgzRLbfcUudx63s/3L9/v4qLi+3aufP+311rAgCgrioncnfo0EGFhYW66aablJKSov/85z/auHGjcnNzVVRUpP3792vx4sW699571alTJz300EM2J2A5wl5f/Tzs9QCAhvL888/LbP49deXRRx/Vhx9+6LR9bm6urrrqKpsEo9GjR2v06NEO29d1T4yKipKvr29FubS0VPv27WuQudy1z9dkTQAAuAP35dXP0xTWhIZDIjfQDBw/flyGYVSUvby8FB0dXaMx2rZta1POysqql9gAAGipFi5cqB9//NHm2g033FBln8r7a7t27Wo0Z+X9+tixYy7NU7lfbeZy9trAXWsCAKA+vPLKK/r5558lSd7e3nr77bdlMpnqPG5d98OYmBh5enpWlK1Wq7Kzs+3aufP+311rAgCgrio/kcpsNmvo0KF2X752pKioSM8++6wuvfRSFRQUOG3HXm+PvR4A4C6DBw/Wq6++WnH/Xl5erhtuuEH9+/fXc889pzlz5ujbb7/VzJkzdeedd6pjx476+uuvK/qPHDlSH3/8sdPx67onSlJcXFyVY55R+f3vur6f3lD7vOT6mgAAcAfuy+01xTWh4XhW3wRAYyssLLQp+/v71/iD6ICAgCrHBAAAv8vJydFtt91mc23cuHHq379/lf0q76+V99/qVG5fVlamkpIS+fj41Os8jvo4e23grjUBAFBX+/bt08MPP1xRfvDBB9WlS5d6Gbuu+6HJZJKfn59NApmjvded9//uWhMAAHVhtVrtErD//Oc/a8OGDZJO70eXXXaZLr30UrVr104nT57Uhg0bNGPGDB06dKiiz+LFi3XDDTfoiy++cDgPe7099noAgDtNmzZNZ511lv785z9ry5Ytkk4/laPykzn+qEOHDrrvvvt0yy232JzoXZm73k8vKiqSxWKp01zu2udrMhcAAO7Afbm9prgmNBxO5Aaagcq/HP/4mCNX+fn5VTkmAAA4zWq1atKkScrMzKy4FhISoldeeaXavnXdsyvv147GrI95HM3l6g1mQ60JAIC6uvXWW3Xy5ElJUpcuXfTQQw/V29ju2nub0x5fk7kAAKitvLw8m1OlJCktLU2SFBERoeXLl2v+/PmaOnWqLrvsMl1zzTV67rnntGPHDk2cONGm35dffqmPPvrI4Tzs9XWbCwCA+nD++efrl19+0T333CMPD48q2yYkJOiee+7RxIkTq0zilhpvn6/NXOzzAIDWivvy2s/F64KWgURuoBkoLi62KXt7e9d4jMqnXhYVFdUpJgAAWqp7771X33zzjc21t956S/Hx8dX2reue7eiUakd7tjtfG7hrTQAA1MV7772nxYsXSzp9SsXbb79dq/3RGXftvc1pj6/JXAAA1JazDw49PDy0YMECDRkyxGF9YGCgZsyYoQsvvNDm+jPPPGOXGC6x19d1LgAA6sObb76pjh076sUXX7Q72bqyjIwMTZ8+Xe3bt9f7779fZdvG2udrMxf7PACgteK+vPZz8bqgZSCRG2gGKn9TprS0tMZjlJSUVDkmAACQXnnlFf3rX/+yuXbffffpmmuucal/Xffsyvu1ozHrYx5Hczl7beCuNQEAUFuHDx/WPffcU1G++eabnSZ11Za79t7mtMfXZC4AAGrL2d5y8803a8CAAVX2NZvNeuONN2xO6dyxY4eWL19e7Tzs9TWbCwCAuigrK9NVV12ladOm6fDhw5Kk8PBwPfroo1q7dq1OnDih0tJSHTp0SPPnz9fll18uk8kkScrJydFNN92ke++91+n4jbXP12Yu9nkAQGvFfXnt5+J1QctAIjfQDAQGBtqUHX2btzqVvylTeUwAAFq72bNn6y9/+YvNtRtuuEHPPfecy2PUdc929M1WR3u2O18buGtNAADU1u23367c3FxJUmxsrP75z3/W+xzu2nub0x5fk7kAAKgtZ3vLLbfc4lL/Dh06aMSIETbXHCVys9fXbS4AAOpi2rRp+uKLLyrK/fv315YtW/TEE0+oX79+Cg0NlZeXl9q0aaPRo0fryy+/1Ny5c20SjF588UV98MEHDsdvrH2+NnOxzwMAWivuy2s/F68LWgYSuYFmoPIvx1OnTjl8/GNVTp48WeWYAAC0Zl9//bWuv/56m/31iiuu0LvvvltxsocrKu+vlfff6lRu7+np6fDbrnWdx1EfV28wG2pNAADUxmeffaY5c+ZUlP/9738rNDS03uep635oGEat3nBtyPt/d60JAIC68PPzk4eHh821oKAg9enTx+UxzjvvPJvyunXr7Nqw19tjrwcAuMOyZcv03nvvVZSjo6P19ddfKzY2tsp+Y8aM0WuvvWZz7d5773XpYJGGej/d0euWur6f3lD7fE3mAgDAHbgvt9cU14SGQyI30AxERkbaJJGVlZUpKyurRmMcPHjQphwdHV0vsQEA0NwtXbpUV199tcrLyyuujRw5Uh9//LHdm67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" ] @@ -986,18 +914,12 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:49:21.301499Z", - "iopub.status.busy": "2023-12-04T17:49:21.301395Z", - "iopub.status.idle": "2023-12-04T17:49:37.134205Z", - "shell.execute_reply": "2023-12-04T17:49:37.133921Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -1050,18 +972,12 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "execution": { - "iopub.execute_input": "2023-12-04T17:49:37.136108Z", - "iopub.status.busy": "2023-12-04T17:49:37.136009Z", - "iopub.status.idle": "2023-12-04T17:49:51.322412Z", - "shell.execute_reply": "2023-12-04T17:49:51.322119Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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hexq3c795mndz1O2MyJujcCJvYOXjf8WSLrzwQn3yk5/Um970Jj399NN6xSteoa9+9avq7+93e2kAAAAAgFWuWjWVypc1lStqMlvSZLaoaWs7lS1pKlvUVK6ko6mCnhlNazRVcHvJ8+LzGOqM+NUZCagr4ldHuLbtigbUGfFrfXdE5wx1aEN3RB4PP5YZAAAAAObFNKVKSaoUpHKxFmPX9xu2BalSdGwdQbi9X5gdizu3lWNcrxTd/pXASuHxS8GYFIhJgejMNhi3juvnYtZ9juNAdOa5/ogj+g5KHv6BOgAsN6ZpqmyWjzt5+1iTu1tF4M3njnmtyp9RsLz5Pf5ZU7ftqd7WtVkB93HOtYrD69PBndc8Bj8pBcDJITS33HzzzYpEIrrlllv0k5/8ROecc47e8pa36Hd+53d07rnnyjD4pjcAAAAAYG6maSpTrGgyU9R0rikUz5Y06YjGnTH5dK6kqun26o8tHvKpqx6MW9uuSC0Yr287Hec7In7Fgz7+Lg0AAACgPVSrtSncxaxUTM9M7q4/Stb5Yv18emZ6dz3aLuePEYk7tyvjHx9jBfAG5jHRe56Twp3BeHMs7gu4/UkBoG20muY9V6h9vAnfs85Vj/069cneVbPq9i8DcEwBz+yJ23OF3K0mdwe9wdpk8Hk8t9U1Ym8AKxGhucOrX/1qPfe5z9X111+v7du36/bbb9ftt98uv9+v7u5uhUKhE3o9wzC0a9euRVotAAAAAGChmaapXKmi6Vyp9siWZvZzJSVzjcczj7Kmc0WVKsu8GG8h5PdoY39Mm/vj2jgQ0xm9MfXGAuqsB+Rhv3xevvAJAAAAYIWrVmtxd/1RzM4E4aXcTCjesF8PxI8TkZeybn86rFQe30zsXZ/U7dz65zjf6n47/G5xnz0J3PGaTAQHgAVjmqbK1XJD5N0QYzedb46zC5WCSpVSw1TvE7rHcR5YzgwZCvlqk7aDnvnH2XNdbw6+Z11vOu/3+Am9AeAkEJo7/OxnP9Pb3/527dixQ4ZhyDRrgUCxWNTIyMgJvx6T2wAAAADAPelCWQcnc/bU8GOH4jPXVmIsPh+RgFeb+mPa2B/XpoGYNvXHtHkgrrWdYXk8/P0VAAAAgAtMszapu5SrTesu5x2PguO843op74jF65H48fatyeHAXHwhawK3NYl7zsA7KHmDrc+3jMVb3RuamSbu5dv1AHCqytVyY9g9R6htR95zRN/1a83Tuo8Xj9cjb1Or8+vKWJ3q8XXIG7LD7KAvqJA3NHPeF5y55rin5QRvT+up3g37vqB8Bj8JFQBWIv7mavnEJz6ht73tbapWqzJNU4ZhnNJvbPVIHQAAAACwOFL5kg5M5qxH1t4enKqdm8qW3F6iK2JBnzb210LyTQMxbRqIa1N/TEMdBOUAAAAAWqhWHJH3fLfHu3acUNx5P1EWTpThkQLxWhQedMThgdgxjue4v75P8A0A81I1qw1BtjPyrp8rVUsqVUoNx622xWpRpUqp4bj5+fOZ9F01q27/sgAnzGf4ZqZse/2z421P4JgTvVtN8w76grXg2wrGW8Xh9fuZ6g0AOBH8jVnSd77zHf3Zn/1ZQ2BeD8UDgYA6OjoUiURcXiUAAAAAtJdkvqQDE86IvDEkn861Z0heFw/6rMnk8YagfLAjxEQQAAAAYCWpT/WeFWcfK+KeI/Ru8dxIIaMrJ8fkqZbkNYuKPOO13s+6r9ref7fCIvGFJH+kFnL7I5I/PLMfiFjn6vvRFtfr56x953P8YYm/9wJY5apmdXagXY+yrUC7OcyezkzrseJjKptlVVTRyM4ReQPeliF4q+Db3q+H3i3i77JZdvuXBjhlPo9vfjH3XNeP8bzm+Nt5PegNyu/xK+ANyOch2QMArBz8riXprW99qx2Zm6ap/v5+vf3tb9cNN9ygzZs3y+PhX3EBAAAAwEKbzpVmReQHJnM6aO0n8+35TQuvx1Bn2K/OiF9dkYA6I351RgLqDPs12BnWZisuH0gECcoBAACApWKaUikrFbNSKWNts1IxU3vU9xu29XszrZ9n35PVYk719knqcp4oLNpbYTnx+CRvUPIFrG2wFn+33B7jWsvnOvcDta034IjKIxLfXwWwQhxvQrcz8nYen+yE7ubnt5r+vWBB9+On/hLAYpgr5q5P9w54Ag1TuJsD8Przms+1nAzufI4Vgns9Xrd/CQAAWFHaPjT/xS9+oV27dtnfnD///PP1wx/+UD09PS6vDAAAAABWtmrV1Egyr73jWe2byGjveFZ7J7LaN57V3vHMqg/JDUPqCPutaDxgh+Md4dq2K+q39+1rEb/iQR8BOQAAAHAs1Uot1C4XpLJjgnepxcTvUs4x3TvXYup3vum5za+Zm4nEgRNleKVArDaN257WHavt21O7Q01BuCMM9wZmYu6Gc85tc0xuXSOgArBMmaapcrWsQqVgx9XNE7fr+4VKoSHCbj5unsTd6nWao++GAJwJ3WhD9Wnes2Jsz+wou2Wo3XTe7/Ef83pzSO7z8PVvAABWmrYPzR966CFJsiea/8u//AuROQAAAADMU75U0YHJbC0iH89q30QtIt87kdWBiZyKlarbSzxlIb9HHWF/wyPRdFwPxjusYLwr4lc85JfXwxfMAQAA0IbqIXgp2zi1u+V+pjHmLuUcz51jv1J0+xNitfGFZ8LvQETyh639qCMSrx87HnY4Psc93kDtXyEDgItM01TZLM8OsltE2s5p23Odawi95wjE7Sjcem49EK/H5UA7MmTYIbZzGnf9uDnSbnm+6dgZeTPNGwAALJa2D82np6ft/bVr1+qSSy5xcTUAAAAAsPxMZ0vaa00kt0Nya38kmZe5eD/hfcGE/d7jhOI+dURax+RBH194BwAAwApjmsef1F0uOB7OexZgWy25/SuA1cQXsiZ1h61tqBaCB6JWEO6YFm6fj1jnI47rYUdI7rjuC0sej9ufEsAK54y5nRO061F2q/OlauO1+vVW23K1PPta03Pt96rf4wjCTa2AL+ABi8jv8c+KswPegPwev0K+UEPA3WoS97Fi71n7ntbPYZI3AABYqdo+NO/r65MkGYahoaEhl1cDAAAAAIurUK4olS8rnS8rXSgrmS8pnS/XzhXKSuVLSuXLOjiVs6LyrKZzyzsS8XoMDXaEtK4rrLWdEa3rCluP2n5/IkgsDgAAgKVnmk0BdnPk3WJbct7jiL/t847rpXyL+6z9SsHtT4/Vxhl5+4KOR8hxrlUQHpp93h9uem7o2OcJsgA0qZrVlkF2qyncs6LvFhO9nffUn+OMtFsdtzpPzA3M8Bk++b1+O/AOeAItj1tt6wF4fUJ3y7DbirmPeY8jLPcY/KMyAACAk9X2ofnw8LC9n0wmXVwJAAAAAMytUK4oW6hYMfhMFF4/rp0r2RF50jq277fuKVaqbn+UE+b1GBrqDGmdFZGvdUTk67rCWpMIyeflGwUAAACwVKu10LpckCrFxm05P/tcpSCVizPPmXXOca00j1jcfh9ibywgb2B2gN289TfH4LVtvuLRrr0HVDV8qngCOuuc8xWOdTbdN8dr+kKS10/sDbQZ0zRrE7TnmLbdfNxy2rbz3kqp5STuYrXY8Nzm5ze8bj3yrpRUNstu/xIBy0o9svYZPlVLVXkNr3zyqTPeqbA/LJ/HNzOtuynorp9rvqcee9fP+T2OGLzFPc6t3+OX18PgDwAAgNWi7UPzK664QrFYTOl0Wrt27VIymVQikXB7WQAAAABWMNM0VShXlS6UlSmUlSlUlCnW4vBsoaJMwdovlpW2jjPFxnub90uV1TsRyecxNNQZtsPxhqnk3RENxIOE5AAAAMuVaUrVSmPYXSk2xtkNYXe+dQDeMuwuzgTbra41v379darEZ1hi/kjtEYhI/qgUiDr269eiM/cEYrPPzTkFPCR5g5Ln5P9OVEwm9Uz+Pvv4jK1XK8z3wgDXNcfcx52yXT9Xv68pznbeWw+0W4Xi5Wp5XuE4gEZew9sQUjsDbOdxwDMzjbs5yLYnd7d4fvM077me03zNZ/hkWP8gLJlM6r77Zn7Pv/rqq+lfAAAAcMraPjSPRqO68cYbddddd6lcLusLX/iC/uRP/sTtZQEAAABYZNWqqXy5omyxolyxolyptp8tlpW392vXstb1XLE86/5csaJsaeZ8LSCvqFJdvWH4yeiM+LWhO6L1PVFrG9GG7oiGuyMaSITk9TAdDwAA4IRVylI5J5XyVsBtPUr52vmGCdzN91lRdqU0O/yeFYEXW+w7Qm/xZ18sM95Ai3A7eIyYO9xiIrj18IebIvJ6OB6bCcRPIQIHsDjqEbczonYG13Mdl83Z07Xrr2Oft6Jw5365Wm55vuF9HKF4/TqAuTknbtthdj3E9voV9AZbTuduvtd5LugNHvO68/WcoTgTugEAANDO2j40l6Tbb79d//Ef/6GJiQl94AMf0LXXXquzzjrL7WUBAAAAbadcqSpfripfqliP2n6hPLNvbx3nCqVKy+fVzxUc4Xg9Is+VKm5/3FXFMKShjrDWd0e0oacWkG/oiWhDd1TreyLqCPvdXiIAAMDiM00r9M45HtmZbTnvOM417TuOG4LxYwTkJn+mxUpjOKLtcGO4bZ8/zn7D8yOzQ/FTnP4NYG6maapslhsD7crsSdjHjLnrQXfzfY577WtW+H3cQLzFOSZyAyfOY3jmFW43XHfc17DviLqD3mDL685zzgC8vl+f0g0AAADAXYTmkoaGhvQf//EfevnLX67JyUldffXV+vznP69rr73W7aUBAAAAq0a1aurAZE5PH0npqSMp7TyS0tNH0hpN5e0wvMwU8GUt4PPUQnLHRPINPbWQfF1XWEEfU30AAICLqpVjTOkuzEzgbpjG7ZjKXSm1OFdsvFbKtwjHm4JxpntjRTAc07znsz3ePcHZ5/0RKRBtDMp9odq/UgUwy7EmcDsnbDuvOSdmO5/bcH+r12iKso87wdvxHJPf54BT5vP4GidnW4G1z+Oz95u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"text/plain": [ "
" ] @@ -1091,7 +1007,7 @@ " splitter=tree.splitter.QOSplitter()\n", " )\n", " ),\n", - " \n", + "\n", " }\n", ")" ] @@ -1111,7 +1027,7 @@ "hash": "b27b2a9f272874e098660428b28f5afa65c7850d82ff592660d49a141d883cd1" }, "kernelspec": { - "display_name": "Python 3.9.12 ('river')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1125,7 +1041,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.11.9" } }, "nbformat": 4, From 41b807a9b65346b52e73fefcf03a58ee31840b2e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 26 Jul 2024 13:21:13 +0200 Subject: [PATCH 269/347] Bump zipp from 3.18.2 to 3.19.1 (#1572) Bumps [zipp](https://github.com/jaraco/zipp) from 3.18.2 to 3.19.1. - [Release notes](https://github.com/jaraco/zipp/releases) - [Changelog](https://github.com/jaraco/zipp/blob/main/NEWS.rst) - [Commits](https://github.com/jaraco/zipp/compare/v3.18.2...v3.19.1) --- updated-dependencies: - dependency-name: zipp dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/poetry.lock b/poetry.lock index c5f4656ef3..88182a1f2f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -5124,18 +5124,18 @@ files = [ [[package]] name = "zipp" -version = "3.18.2" +version = "3.19.1" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"}, - {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"}, + {file = "zipp-3.19.1-py3-none-any.whl", hash = "sha256:2828e64edb5386ea6a52e7ba7cdb17bb30a73a858f5eb6eb93d8d36f5ea26091"}, + {file = "zipp-3.19.1.tar.gz", hash = "sha256:35427f6d5594f4acf82d25541438348c26736fa9b3afa2754bcd63cdb99d8e8f"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" From bb77e55a612f09b1b069f10bc63cebb8975bfcb4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 26 Jul 2024 13:21:24 +0200 Subject: [PATCH 270/347] Bump certifi from 2024.2.2 to 2024.7.4 (#1567) Bumps [certifi](https://github.com/certifi/python-certifi) from 2024.2.2 to 2024.7.4. - [Commits](https://github.com/certifi/python-certifi/compare/2024.02.02...2024.07.04) --- updated-dependencies: - dependency-name: certifi dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index 88182a1f2f..a3765505e5 100644 --- a/poetry.lock +++ b/poetry.lock @@ -316,13 +316,13 @@ files = [ [[package]] name = "certifi" -version = "2024.2.2" +version = "2024.7.4" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, - {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, + {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"}, + {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"}, ] [[package]] From 192d1ca030a4f59bd36bc045f186368c6f7ea551 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Mon, 29 Jul 2024 14:16:49 +0200 Subject: [PATCH 271/347] Limit tree growth to the system recursion limit (#1585) --- docs/releases/unreleased.md | 4 ++++ river/forest/adaptive_random_forest.py | 4 ++-- river/tree/extremely_fast_decision_tree.py | 3 ++- river/tree/hoeffding_adaptive_tree_classifier.py | 3 ++- river/tree/hoeffding_adaptive_tree_regressor.py | 3 ++- river/tree/hoeffding_tree.py | 6 ++++-- river/tree/hoeffding_tree_classifier.py | 3 ++- river/tree/hoeffding_tree_regressor.py | 3 ++- river/tree/isoup_tree_regressor.py | 3 ++- river/tree/stochastic_gradient_tree.py | 4 ++-- 10 files changed, 24 insertions(+), 12 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 219b4c1a2d..43a3723fe3 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,3 +1,7 @@ # Unreleased - The units used in River have been corrected to be based on powers of 2 (KiB, MiB). This only changes the display, the behaviour is unchanged. + +## tree + +- Instead of letting trees grow indefinitely, setting the `max_depth` parameter to `None` will stop the trees from growing when they reach the system recursion limit. diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index ba4a75282f..6ec1bada2c 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -490,7 +490,7 @@ class ARFClassifier(BaseForest, base.Classifier): split attempts. max_depth [*Tree parameter*] The maximum depth a tree can reach. If `None`, the - tree will grow indefinitely. + tree will grow until the system recursion limit. split_criterion [*Tree parameter*] Split criterion to use.
- 'gini' - Gini
@@ -767,7 +767,7 @@ class ARFRegressor(BaseForest, base.Regressor): split attempts. max_depth [*Tree parameter*] The maximum depth a tree can reach. If `None`, the - tree will grow indefinitely. + tree will grow until the system recursion limit. delta [*Tree parameter*] Allowed error in split decision, a value closer to 0 takes longer to decide. diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index d9972522e7..5bd3233e26 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -38,7 +38,8 @@ class ExtremelyFastDecisionTreeClassifier(HoeffdingTreeClassifier): grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. min_samples_reevaluate Number of instances a node should observe before reevaluating the best split. split_criterion diff --git a/river/tree/hoeffding_adaptive_tree_classifier.py b/river/tree/hoeffding_adaptive_tree_classifier.py index f63c2ba013..c825fa5f38 100644 --- a/river/tree/hoeffding_adaptive_tree_classifier.py +++ b/river/tree/hoeffding_adaptive_tree_classifier.py @@ -27,7 +27,8 @@ class HoeffdingAdaptiveTreeClassifier(HoeffdingTreeClassifier): grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. split_criterion Split criterion to use.
- 'gini' - Gini
diff --git a/river/tree/hoeffding_adaptive_tree_regressor.py b/river/tree/hoeffding_adaptive_tree_regressor.py index 1055472002..89aeae0d20 100644 --- a/river/tree/hoeffding_adaptive_tree_regressor.py +++ b/river/tree/hoeffding_adaptive_tree_regressor.py @@ -36,7 +36,8 @@ class HoeffdingAdaptiveTreeRegressor(HoeffdingTreeRegressor): grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. delta Significance level to calculate the Hoeffding bound. The significance level is given by `1 - delta`. Values closer to zero imply longer split decision delays. diff --git a/river/tree/hoeffding_tree.py b/river/tree/hoeffding_tree.py index 4dc4a1c801..80005f3754 100644 --- a/river/tree/hoeffding_tree.py +++ b/river/tree/hoeffding_tree.py @@ -4,6 +4,7 @@ import functools import io import math +import sys from abc import ABC, abstractmethod from river import base @@ -30,7 +31,8 @@ class HoeffdingTree(ABC): Parameters ---------- max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. binary_split If True, only allow binary splits. max_size @@ -60,7 +62,7 @@ def __init__( self._split_criterion: str = "" self._leaf_prediction: str = "" - self.max_depth: float = max_depth if max_depth is not None else math.inf + self.max_depth: int = max_depth if max_depth is not None else (sys.getrecursionlimit() - 20) self.binary_split: bool = binary_split self._max_size: float = max_size self._max_byte_size: float = self._max_size * (2**20) # convert to byte diff --git a/river/tree/hoeffding_tree_classifier.py b/river/tree/hoeffding_tree_classifier.py index 841cdbe4d5..f676e2acac 100755 --- a/river/tree/hoeffding_tree_classifier.py +++ b/river/tree/hoeffding_tree_classifier.py @@ -18,7 +18,8 @@ class HoeffdingTreeClassifier(HoeffdingTree, base.Classifier): grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. split_criterion Split criterion to use.
- 'gini' - Gini
diff --git a/river/tree/hoeffding_tree_regressor.py b/river/tree/hoeffding_tree_regressor.py index 16604e2d6c..065a5f8876 100644 --- a/river/tree/hoeffding_tree_regressor.py +++ b/river/tree/hoeffding_tree_regressor.py @@ -20,7 +20,8 @@ class HoeffdingTreeRegressor(HoeffdingTree, base.Regressor): grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. delta Significance level to calculate the Hoeffding bound. The significance level is given by `1 - delta`. Values closer to zero imply longer split decision delays. diff --git a/river/tree/isoup_tree_regressor.py b/river/tree/isoup_tree_regressor.py index 9411d1a9c9..0393379b17 100644 --- a/river/tree/isoup_tree_regressor.py +++ b/river/tree/isoup_tree_regressor.py @@ -21,7 +21,8 @@ class iSOUPTreeRegressor(tree.HoeffdingTreeRegressor, base.MultiTargetRegressor) grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. delta Allowed error in split decision, a value closer to 0 takes longer to decide. diff --git a/river/tree/stochastic_gradient_tree.py b/river/tree/stochastic_gradient_tree.py index a92598872b..8ea1d76371 100644 --- a/river/tree/stochastic_gradient_tree.py +++ b/river/tree/stochastic_gradient_tree.py @@ -1,7 +1,7 @@ from __future__ import annotations import abc -import math +import sys from scipy.stats import f as f_dist @@ -38,7 +38,7 @@ def __init__( self.delta = delta self.grace_period = grace_period self.init_pred = init_pred - self.max_depth = max_depth if max_depth else math.inf + self.max_depth = max_depth if max_depth else (sys.getrecursionlimit() - 20) if lambda_value < 0.0: raise ValueError('Invalid value: "lambda_value" must be positive.') From df257b82fcf31e2ebe05b12b9209c0821d637f7a Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Mon, 29 Jul 2024 09:52:24 -0300 Subject: [PATCH 272/347] [docs] remove unavailable tree algorithm and fix tree depth behavior description in the tree guidelines --- docs/recipes/on-hoeffding-trees.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/recipes/on-hoeffding-trees.ipynb b/docs/recipes/on-hoeffding-trees.ipynb index 54d77140cc..eb0218b68f 100644 --- a/docs/recipes/on-hoeffding-trees.ipynb +++ b/docs/recipes/on-hoeffding-trees.ipynb @@ -73,7 +73,6 @@ "| Hoeffding Tree Regressor | HTR | Regression | No | Basic HT for regression tasks. It is an adaptation of the [FIRT/FIMT](https://link.springer.com/article/10.1007/s10618-010-0201-y) algorithm that bears some semblance to HTC | [[4]](https://link.springer.com/article/10.1007/s10618-010-0201-y)\n", "| Hoeffding Adaptive Tree Regressor | HATR | Regression | Yes | Modifies HTR by adding an instance of ADWIN to each node to detect and react to drift detection | -\n", "| incremental Structured-Output Prediction Tree Regressor| iSOUPT | Multi-target regression | No | Multi-target version of HTR | [[5]](https://link.springer.com/article/10.1007/s10844-017-0462-7)\n", - "| Label Combination Hoeffding Tree Classifier | LCHTC | Multi-label classification | No | Creates a numerical code for each combination of the binary labels and uses HTC to learn from this encoded representation. At prediction time, decodes the modified representation to obtain the original label set | -\n", "\n", "\n", "As we can see, although their application fields might overlap sometimes, the HT variations have specific situations in which they are better suited to work. Moreover, in River we provide a standardized API access to all the HT variants since they share many properties in common." @@ -832,7 +831,7 @@ "\n", "HTs monitor the incoming feature values to perform split attempts. To do so, they rely on a class of algorithms called *Attribute Observers* (AO) or *Splitters* (spoiler alert!). Each leaf node in an HT keeps one AO per incoming feature. After pre-determined intervals (`grace_period` parameter), leaves query their AOs for split candidates. Well, there are costs to monitor input features (mainly the numerical ones). In fact, AOs correspond to one of the most time and memory-consuming portions of the HTs. To manage memory usage, an HT firstly determines its least promising leaves, w.r.t. how likely they will be split. Then, these leaves' AOs are removed, and the tree nodes are said to be \"deactivated.\" That's it! The deactivated leaves do not perform split attempts anymore, but they continue to be updated to provide responses. They will be kept as leaves as long as there are not available resources to enable tree growth. These leaves can be activated again (meaning that new AOs will be created for them) if there is available memory, so don't worry!\n", "\n", - "**Hint:** another indirect way to bound memory usage is to limit the tree depth. By default, the trees can grow indefinitely, but the `max_depth` parameter can control this behavior." + "**Hint:** another indirect way to bound memory usage is to limit the tree depth. By default, the trees can grow until they get close to the maximum recursion limit enabled in the system, but the `max_depth` parameter can control this behavior." ] }, { From d606d7b4b70e1867e601d77c645880eadb3ae472 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gon=C3=A7alo?= <116749870+Bezum30@users.noreply.github.com> Date: Thu, 1 Aug 2024 21:19:46 +0100 Subject: [PATCH 273/347] Updates in ODAC code (#1584) * update description, fix a bug when calculating rnomc, add draw() method to see hierarchical cluster's structure as a Graphviz graph, and working with Var when cluster has only one time-series * change the description of draw() method * add cluster's name in draw() method * correct version * Update river/cluster/odac.py * Update river/cluster/odac.py * update docs/releases/unreleased.md * Update docs/releases/unreleased.md --------- Co-authored-by: gonfa3003 <116749870+gonfa3003@users.noreply.github.com> Co-authored-by: Saulo Martiello Mastelini --- docs/releases/unreleased.md | 7 ++ river/cluster/odac.py | 226 +++++++++++++++++++++++++----------- 2 files changed, 163 insertions(+), 70 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 43a3723fe3..6d142f06e2 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -2,6 +2,13 @@ - The units used in River have been corrected to be based on powers of 2 (KiB, MiB). This only changes the display, the behaviour is unchanged. +## cluster + +- Update the description of `cluster.ODAC`. +- Change `draw` in `cluster.ODAC` to draw the hierarchical cluster's structure as a Graphviz graph. +- Add `render_ascii` in `cluster.ODAC` to render the hierarchical cluster's structure in text format. +- Work with `stats.Var` in `cluster.ODAC` when cluster has only one time series. + ## tree - Instead of letting trees grow indefinitely, setting the `max_depth` parameter to `None` will stop the trees from growing when they reach the system recursion limit. diff --git a/river/cluster/odac.py b/river/cluster/odac.py index 5c87d4666e..b928477cf4 100644 --- a/river/cluster/odac.py +++ b/river/cluster/odac.py @@ -9,50 +9,22 @@ class ODAC(base.Clusterer): - """The Online Divisive-Agglomerative Clustering (ODAC)[^1] aims at continuously - maintaining a hierarchical cluster structure from evolving time series data - streams. - - ODAC continuosly monitors the evolution of clusters' diameters and split or merge - them by gathering more data or reacting to concept drift. Such changes are supported - by a confidence level that comes from the Hoeffding bound. ODAC relies on keeping - the linear correlation between series to evaluate whether or not the time series - hierarchy has changed. + """The Online Divisive-Agglomerative Clustering (ODAC)[^1] aims at continuously maintaining + a hierarchical cluster structure from evolving time series data streams. The distance between time-series a and b is given by `rnomc(a, b) = sqrt((1 - corr(a, b)) / 2)`, - where `corr(a, b)` is the Pearson Correlation coefficient. - - In the following topics, ε stands for the Hoeffding bound and considers clusters cj - with descendants ck and cs. - - **The Merge Operator** - - The Splitting Criteria guarantees that cluster's diameters monotonically decrease. - - - If diameter (ck) - diameter (cj) > ε OR diameter (cs) - diameter (cj ) > ε: - - * There is a change in the correlation structure, so merge clusters ck and cs into cj. - - **Splitting Criteria** - - Consider: - - - d0: the minimum distance; + where `corr(a, b)` is the Pearson Correlation coefficient. If the cluster has only one time-series, + the diameter is given by the time-series variance. The cluster's diameter is given by the largest + distance between the cluster's time-series. - - d1: the farthest distance; - - - d_avg: the average distance; - - - d2: the second farthest distance. - - Then: - - - if d1 - d2 > εk or t > εk then - - - if (d1 - d0)|(d1 - d_avg) - (d_avg - d0) > εk then - - * Split the cluster + ODAC continuously monitors the evolution of diameters, only of the leaves, and splits or merges them + by gathering more data or reacting to concept drift - a confidence level from the Hoeffding bound + supports such changes. + So, the split operator, where the Hoeffding bound is applied, occurs when the difference between + the largest distance (diameter) and the second largest difference is greater than a constant. + Furthermore, the merge operator checks if one of the cluster's children has a diameter bigger + than their parent - applying the Hoeffding bound again. Parameters ---------- @@ -88,15 +60,15 @@ class ODAC(base.Clusterer): Structure changed at observation 200 Structure changed at observation 300 - >>> print(model.draw(n_decimal_places=2)) + >>> print(model.render_ascii()) ROOT d1=0.79 d2=0.76 [NOT ACTIVE] ├── CH1_LVL_1 d1=0.74 d2=0.72 [NOT ACTIVE] - │ ├── CH1_LVL_2 d1= [3] + │ ├── CH1_LVL_2 d1=0.08 [3] │ └── CH2_LVL_2 d1=0.73 [2, 4] └── CH2_LVL_1 d1=0.81 d2=0.78 [NOT ACTIVE] ├── CH1_LVL_2 d1=0.73 d2=0.67 [NOT ACTIVE] │ ├── CH1_LVL_3 d1=0.72 [0, 9] - │ └── CH2_LVL_3 d1= [1] + │ └── CH2_LVL_3 d1=0.08 [1] └── CH2_LVL_2 d1=0.74 d2=0.73 [NOT ACTIVE] ├── CH1_LVL_3 d1=0.71 [5, 6] └── CH2_LVL_3 d1=0.71 [7, 8] @@ -119,7 +91,8 @@ class ODAC(base.Clusterer): References ---------- - [^1]: [Hierarchical clustering of time-series data streams.](http://doi.org/10.1109/TKDE.2007.190727) + [^1]: P. P. Rodrigues, J. Gama and J. Pedroso, "Hierarchical Clustering of Time-Series Data Streams" in IEEE Transactions + on Knowledge and Data Engineering, vol. 20, no. 5, pp. 615-627, May 2008, doi: 10.1109/TKDE.2007.190727. """ @@ -231,7 +204,7 @@ def learn_one(self, x: dict): # Time to time approach if self._update_timer == 0: # Calculate all the crucial variables to the next procedure - leaf.calculate_coefficients(self.confidence_level) + leaf.calculate_coefficients(confidence_level=self.confidence_level) if leaf.test_aggregate() or leaf.test_split(tau=self.tau): # Put the flag change_detected to true to indicate to the user that the structure changed @@ -251,10 +224,10 @@ def predict_one(self, x: dict): A dictionary of features. """ - raise NotImplementedError("ODAC does not predict anything. It builds a hierarchical cluster's structure.") + raise NotImplementedError() - def draw(self, n_decimal_places: int = 2) -> str: - """Method to draw the hierarchical cluster's structure. + def render_ascii(self, n_decimal_places: int = 2) -> str: + """Method to render the hierarchical cluster's structure in text format. Parameters ---------- @@ -268,11 +241,120 @@ def draw(self, n_decimal_places: int = 2) -> str: return self._root_node.design_structure(n_decimal_places).rstrip("\n") + def draw(self, max_depth: int | None = None, show_clusters_info: list[typing.Hashable] = ["timeseries_names", "d1", "d2", "e"], n_decimal_places: int = 2): + """Method to draw the hierarchical cluster's structure as a Graphviz graph. + + Parameters + ---------- + max_depth + The maximum depth of the tree to display. + show_clusters_info + List of cluster information to show. Valid options are: + - "timeseries_indexes": Shows the indexes of the timeseries in the cluster. + - "timeseries_names": Shows the names of the timeseries in the cluster. + - "name": Shows the cluster's name. + - "d1": Shows the d1 (the largest distance in the cluster). + - "d2": Shows the d2 (the second largest distance in the cluster). + - "e": Shows the error bound. + n_decimal_places + The number of decimal places to show for numerical values. + + """ + if not (n_decimal_places > 0 and n_decimal_places < 10): + raise ValueError("n_decimal_places must be between 1 and 9.") + + try: + import graphviz + except ImportError as e: + raise ValueError("You have to install graphviz to use the draw method.") from e + + counter = 0 + + dot = graphviz.Digraph( + graph_attr={"splines": "ortho", "forcelabels": "true", "overlap": "false"}, + node_attr={ + "shape": "box", + "penwidth": "1.2", + "fontname": "trebuchet", + "fontsize": "11", + "margin": "0.1,0.0", + }, + edge_attr={"penwidth": "0.6", "center": "true", "fontsize": "7 "}, + ) + + def iterate(node: ODACCluster, parent_node: str | None = None, depth: int = 0): + nonlocal counter, max_depth, show_clusters_info, n_decimal_places + + if max_depth is not None and depth > max_depth: + return + + node_n = str(counter) + counter += 1 + + label = "" + + # checks if user wants to see information about clusters + if len(show_clusters_info) > 0: + show_clusters_info_copy = show_clusters_info.copy() + + if "name" in show_clusters_info_copy: + label += f"{node.name}" + show_clusters_info_copy.remove("name") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "timeseries_indexes" in show_clusters_info_copy: + # Convert timeseries names to indexes + name_to_index = {name: index for index, name in enumerate(self._root_node.timeseries_names)} + timeseries_indexes = [name_to_index[_name] for _name in node.timeseries_names if _name in name_to_index] + + label += f"{timeseries_indexes}" + show_clusters_info_copy.remove("timeseries_indexes") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "timeseries_names" in show_clusters_info_copy: + label += f"{node.timeseries_names}" + show_clusters_info_copy.remove("timeseries_names") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "d1" in show_clusters_info_copy: + if node.d1 is not None: + label += f"d1={node.d1:.{n_decimal_places}f}" + else: + label += "d1=" + show_clusters_info_copy.remove("d1") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "d2" in show_clusters_info_copy and node.d2 is not None: + label += f"d2={node.d2:.{n_decimal_places}f}" + show_clusters_info_copy.remove("d2") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "e" in show_clusters_info_copy: + label += f"e={node.e:.{n_decimal_places}f}" + + show_clusters_info_copy.clear() + + # Creates a node with different color to differentiate the active clusters from the non-active + if node.active: + dot.node(node_n, label, style="filled", fillcolor="#76b5c5") + else: + dot.node(node_n, label, style="filled", fillcolor="#f2f2f2") + + if parent_node is not None: + dot.edge(parent_node, node_n) + + if node.children is not None: + iterate(node=node.children.first, parent_node=node_n, depth=depth + 1) + iterate(node.children.second, parent_node=node_n, depth=depth + 1) + + iterate(node=self._root_node) + + return dot + @property def structure_changed(self) -> bool: return self._structure_changed - class ODACCluster(base.Base): """Cluster class for representing individual clusters.""" @@ -284,7 +366,7 @@ def __init__(self, name: str, parent: ODACCluster | None = None): self.children: ODACChildren | None = None self.timeseries_names: list[typing.Hashable] = [] - self._statistics: dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | None + self._statistics: dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | stats.Var | None self.d1: float | None = None self.d2: float | None = None @@ -348,14 +430,14 @@ def __str__(self) -> str: def __repr__(self) -> str: return self.design_structure() - def _init_stats(self) -> dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr]: + def _init_stats(self) -> dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | stats.Var: return collections.defaultdict( stats.PearsonCorr, { (k1, k2): stats.PearsonCorr() for k1, k2 in itertools.combinations(self.timeseries_names, 2) }, - ) + ) if len(self.timeseries_names) > 1 else stats.Var() # TODO: not sure if this is the best design def __call__(self, ts_names: list[typing.Hashable]): @@ -364,12 +446,15 @@ def __call__(self, ts_names: list[typing.Hashable]): self._statistics = self._init_stats() def update_statistics(self, x: dict) -> None: - # For each pair of time-series in the cluster update the correlation - # values with the data received - for (k1, k2), item in self._statistics.items(): # type: ignore - if x.get(k1, None) is None or x.get(k2, None) is None: - continue - item.update(float(x[k1]), float(x[k2])) + if len(self.timeseries_names) > 1: + # For each pair of time-series in the cluster update the correlation + # values with the data received + for (k1, k2), item in self._statistics.items(): # type: ignore + if x.get(k1, None) is None or x.get(k2, None) is None: + continue + item.update(float(x[k1]), float(x[k2])) + else: + self._statistics.update(float(x.get(self.timeseries_names[0]))) # type: ignore # Increment the number of observation in the cluster self.n += 1 @@ -380,16 +465,17 @@ def _calculate_rnomc_dict(self)-> dict[tuple[typing.Hashable, typing.Hashable], rnomc_dict = {} for k1, k2 in itertools.combinations(self.timeseries_names, 2): - rnomc_dict[(k1, k2)] = math.sqrt((1 - self._statistics[(k1, k2)].get()) / 2.0) # type: ignore + value = abs((1 - self._statistics[(k1, k2)].get()) / 2.0) # type: ignore + rnomc_dict[(k1, k2)] = math.sqrt(value) return rnomc_dict # Method to calculate coefficients for splitting or aggregation def calculate_coefficients(self, confidence_level: float) -> None: - # Get the rnomc values - rnomc_dict = self._calculate_rnomc_dict() + if len(self.timeseries_names) > 1: + # Get the rnomc values + rnomc_dict = self._calculate_rnomc_dict() - if len(rnomc_dict) > 0: # Get the average distance in the cluster self.avg = sum(rnomc_dict.values()) / self.n @@ -405,13 +491,13 @@ def calculate_coefficients(self, confidence_level: float) -> None: self.pivot_2, self.d2 = max(remaining.items(), key=lambda x: x[1]) else: self.pivot_2 = self.d2 = None # type: ignore + else: + self.d1 = self._statistics.get() # type: ignore + # Calculate the Hoeffding bound in the cluster + self.e = math.sqrt(math.log(1 / confidence_level) / (2 * self.n)) - # Calculate the Hoeffding bound in the cluster - self.e = math.sqrt(math.log(1 / confidence_level) / (2 * self.n)) - + # Method that gives the closest cluster where the current time series is located def _get_closest_cluster(self, pivot_1, pivot_2, current, rnormc_dict: dict) -> int: - """Method that gives the closest cluster where the current time series is located.""" - dist_1 = rnormc_dict.get((min(pivot_1, current), max(pivot_1, current)), 0) dist_2 = rnormc_dict.get((min(pivot_2, current), max(pivot_2, current)), 0) return 2 if dist_1 >= dist_2 else 1 @@ -444,8 +530,9 @@ def _split_this_cluster(self, pivot_1: typing.Hashable, pivot_2: typing.Hashable # Set the active flag to false. Since this cluster is not an active cluster anymore. self.active = False - self.avg = self.d0 = self.pivot_0 = self.pivot_1 = self.pivot_2 = None # type: ignore - self._statistics = None + + # Reset some attributes + self.avg = self.d0 = self.pivot_0 = self.pivot_1 = self.pivot_2 = self._statistics = None # type: ignore # Method that proceeds to merge on this cluster def _aggregate_this_cluster(self): @@ -485,7 +572,6 @@ def test_aggregate(self): return True return False - class ODACChildren(base.Base): """Children class representing child clusters.""" From b5e6fea4e4f564a5dd3c42cfc7622c1cefa820c9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Fri, 16 Aug 2024 21:18:07 +0200 Subject: [PATCH 274/347] Remove mention of former compat modules (#1588) * Remove mention of former `compat` modules * Format test_estimators.py --- river/conftest.py | 6 ------ river/test_estimators.py | 13 +------------ 2 files changed, 1 insertion(+), 18 deletions(-) diff --git a/river/conftest.py b/river/conftest.py index e8c824541e..8ea097838f 100644 --- a/river/conftest.py +++ b/river/conftest.py @@ -5,7 +5,6 @@ try: import sklearn # noqa: F401 except ImportError: - collect_ignore.append("compat/sklearn.py") collect_ignore.append("compat/test_sklearn.py") try: @@ -14,11 +13,6 @@ collect_ignore.append("stream/iter_sql.py") collect_ignore.append("stream/test_sql.py") -try: - import torch # noqa: F401 -except ImportError: - collect_ignore.append("compat/pytorch.py") - try: import vaex # noqa: F401 except ImportError: diff --git a/river/test_estimators.py b/river/test_estimators.py index 18aacf32f2..09b40ea0ff 100644 --- a/river/test_estimators.py +++ b/river/test_estimators.py @@ -5,6 +5,7 @@ import inspect import pytest +from sklearn import linear_model as sk_linear_model from river import ( anomaly, @@ -24,15 +25,6 @@ preprocessing, time_series, ) - -try: - from river.compat.pytorch import PyTorch2RiverBase - - PYTORCH_INSTALLED = True -except ImportError: - PYTORCH_INSTALLED = False -from sklearn import linear_model as sk_linear_model - from river.compat.river_to_sklearn import River2SKLBase from river.compat.sklearn_to_river import SKL2RiverBase @@ -84,9 +76,6 @@ def iter_estimators_which_can_be_tested(): time_series.base.Forecaster, ) - if PYTORCH_INSTALLED: - ignored = (*ignored, PyTorch2RiverBase) - def can_be_tested(estimator): return not inspect.isabstract(estimator) and not issubclass(estimator, ignored) From b282ea5082bcd80f4454e5a034cc1ebb108da8d6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Mon, 19 Aug 2024 19:13:41 +0200 Subject: [PATCH 275/347] Remove script download from polyfill.io (#1590) The domain polyfill.io was taken over by a malicious actor to download malware. Polyfill is not needed for modern browsers. It is recommended to remove references to the website. See: https://blog.qualys.com/vulnerabilities-threat-research/2024/06/28/polyfill-io-supply-chain-attack --- mkdocs.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index be4c36ca31..3ab98f8626 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -85,7 +85,6 @@ plugins: extra_javascript: - javascripts/config.js - - https://polyfill.io/v3/polyfill.min.js?features=es6 - https://cdn.jsdelivr.net/npm/mathjax@3.2/es5/tex-mml-chtml.js - https://cdn.jsdelivr.net/npm/vega@5 - https://cdn.jsdelivr.net/npm/vega-lite@5 From 71a127f86ad22a7fe951fe68f44094813c7c8cd2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Thu, 22 Aug 2024 15:09:31 +0200 Subject: [PATCH 276/347] Remove the class CentralMoments (#1593) * Remove the class CentralMoments This was a utility class internal to River, and the last references to it were removed in a previous version of the library, with the move to Rust statistics. * Add a changelog entry --- docs/releases/unreleased.md | 4 +++ river/stats/moments.py | 68 ------------------------------------- 2 files changed, 4 insertions(+), 68 deletions(-) delete mode 100644 river/stats/moments.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 6d142f06e2..e1951d3bd2 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -9,6 +9,10 @@ - Add `render_ascii` in `cluster.ODAC` to render the hierarchical cluster's structure in text format. - Work with `stats.Var` in `cluster.ODAC` when cluster has only one time series. +## stats + +- Removed the unexported class `stats.CentralMoments`. + ## tree - Instead of letting trees grow indefinitely, setting the `max_depth` parameter to `None` will stop the trees from growing when they reach the system recursion limit. diff --git a/river/stats/moments.py b/river/stats/moments.py deleted file mode 100644 index 2d1212158d..0000000000 --- a/river/stats/moments.py +++ /dev/null @@ -1,68 +0,0 @@ -from __future__ import annotations - -from river import stats - - -class CentralMoments(stats.base.Univariate): - """Computes central moments using Welford's algorithm. - - Attributes - ---------- - count : stats.Count - delta: float - sum_delta : float - Mean of sum of differences. - M1 : float - Sums of powers of differences from the mean order 1. - M2 : float) - Sums of powers of differences from the mean order 2. - M3 : float - Sums of powers of differences from the mean order 3. - M4 : float - Sums of powers of differences from the mean order 4. - - References - ---------- - [^1]: [Wikipedia article on algorithms for calculating variance](https://www.wikiwand.com/en/Algorithms_for_calculating_variance#/Covariance) - - """ - - def __init__(self): - self.count = stats.Count() - - self.delta = 0 - self.sum_delta = 0 - - self.M1 = 0 - self.M2 = 0 - self.M3 = 0 - self.M4 = 0 - - def _update_delta(self, x): - self.delta = (x - self.sum_delta) / self.count.get() - return self - - def _update_sum_delta(self): - self.sum_delta += self.delta - return self - - def _update_m1(self, x): - self.M1 = (x - self.sum_delta) * self.delta * (self.count.get() - 1) - return self - - def _update_m2(self): - self.M2 += self.M1 - return self - - def _update_m3(self): - self.M3 += self.M1 * self.delta * (self.count.get() - 2) - 3 * self.delta * self.M2 - return self - - def _update_m4(self): - delta_square = self.delta**2 - self.M4 += ( - self.M1 * delta_square * (self.count.get() ** 2 - 3 * self.count.get() + 3) - + 6 * delta_square * self.M2 - - 4 * self.delta * self.M3 - ) - return self From bd0e31bedfbe9b800c3195de9fa5fc4ba57bff79 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Thu, 22 Aug 2024 15:09:42 +0200 Subject: [PATCH 277/347] Add type stubs for Cython files (#1594) * Check for missing imports in River * Add type stubs for expected_mutual_info.pyx * Add type stubs for efficient_rollingrocauc.pyx * Add type stub for adwin_c * Add basic type stubs for vectordict * Remove modulo from Vectordict.__pow__ This brings the signature in line with how Python defines the operator. The modulo parameter was not used. * Check the stubs files in pre-commit --- .pre-commit-config.yaml | 4 +- pyproject.toml | 1 - river/drift/adwin_c.pyi | 27 +++++++++ .../efficient_rollingrocauc.pyi | 12 ++++ river/metrics/expected_mutual_info.pyi | 3 + river/utils/vectordict.pyi | 56 +++++++++++++++++++ river/utils/vectordict.pyx | 4 +- 7 files changed, 102 insertions(+), 5 deletions(-) create mode 100644 river/drift/adwin_c.pyi create mode 100644 river/metrics/efficient_rollingrocauc/efficient_rollingrocauc.pyi create mode 100644 river/metrics/expected_mutual_info.pyi create mode 100644 river/utils/vectordict.pyi diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 36ff5d392a..5553fa411c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -14,7 +14,7 @@ repos: - id: ruff name: ruff language: python - types: [python] + types: [python, pyi] entry: ruff args: - --fix @@ -22,5 +22,5 @@ repos: - id: mypy name: mypy language: python - types: [python] + types: [python, pyi] entry: mypy --implicit-optional diff --git a/pyproject.toml b/pyproject.toml index b45f37e632..8986449f1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -130,7 +130,6 @@ files = "river" [[tool.mypy.overrides]] module = [ - "river.*", "mmh3.*", "numpy.*", "sklearn.*", diff --git a/river/drift/adwin_c.pyi b/river/drift/adwin_c.pyi new file mode 100644 index 0000000000..75b5ad40f5 --- /dev/null +++ b/river/drift/adwin_c.pyi @@ -0,0 +1,27 @@ +class AdaptiveWindowing: + def __init__( + self, + delta: float = 0.002, + clock: int = 32, + max_buckets: int = 5, + min_window_length: int = 5, + grace_period: int = 10, + ) -> None: ... + def get_n_detections(self) -> int: ... + def get_width(self) -> float: ... + def get_total(self) -> float: ... + def get_variance(self) -> float: ... + @property + def variance_in_window(self) -> float: ... + def update(self, value: float) -> bool: ... + +class Bucket: + def __init__(self, max_size: int) -> None: ... + def clear_at(self, index: int) -> None: ... + def insert_data(self, value: float, variance: float) -> None: ... + def remove(self) -> None: ... + def compress(self, n_elements: int) -> None: ... + def get_total_at(self, index: int) -> float: ... + def get_variance_at(self, index: int) -> float: ... + def set_total_at(self, value: float, index: int) -> None: ... + def set_variance_at(self, value: float, index: int) -> None: ... diff --git a/river/metrics/efficient_rollingrocauc/efficient_rollingrocauc.pyi b/river/metrics/efficient_rollingrocauc/efficient_rollingrocauc.pyi new file mode 100644 index 0000000000..943d1fa6e4 --- /dev/null +++ b/river/metrics/efficient_rollingrocauc/efficient_rollingrocauc.pyi @@ -0,0 +1,12 @@ +from collections.abc import Sequence +from typing import Any + +class EfficientRollingROCAUC: + def __cinit__(self, positiveLabel: int, windowSize: int) -> None: ... + def __dealloc__(self) -> None: ... + def update(self, label: bool, score: bool | float | dict[bool, float]) -> None: ... + def revert(self, label: bool, score: bool | float | dict[bool, float]) -> None: ... + def get(self) -> float: ... + def __getnewargs_ex__(self) -> tuple[tuple[int, int], dict[str, Any]]: ... + def __getstate__(self) -> tuple[Sequence[int], Sequence[float]]: ... + def __setstate__(self, state: tuple[Sequence[int], Sequence[float]]) -> None: ... diff --git a/river/metrics/expected_mutual_info.pyi b/river/metrics/expected_mutual_info.pyi new file mode 100644 index 0000000000..f3b584f2d5 --- /dev/null +++ b/river/metrics/expected_mutual_info.pyi @@ -0,0 +1,3 @@ +from river import metrics + +def expected_mutual_info(confusion_matrix: metrics.ConfusionMatrix) -> float: ... diff --git a/river/utils/vectordict.pyi b/river/utils/vectordict.pyi new file mode 100644 index 0000000000..faa8086966 --- /dev/null +++ b/river/utils/vectordict.pyi @@ -0,0 +1,56 @@ +from collections.abc import Callable + +import numpy + +def get_union_keys(left: VectorDict, right: VectorDict): ... +def get_intersection_keys(left: VectorDict, right: VectorDict): ... + +class VectorDict: + def __init__( + self, + data: VectorDict | dict | None = None, + default_factory: Callable | None = None, + mask: VectorDict | set | None = None, + copy: bool = False, + ) -> None: ... + def with_mask(self, mask, copy=False): ... + def to_dict(self): ... + def to_numpy(self, fields) -> numpy.ndarray: ... + def __contains__(self, key): ... + def __delitem__(self, key): ... + def __format__(self, format_spec): ... + def __getitem__(self, key): ... + def __iter__(self): ... + def __len__(self): ... + def __repr__(self): ... + def __setitem__(self, key, value): ... + def __str__(self): ... + def clear(self): ... + def get(self, key, *args, **kwargs): ... + def items(self): ... + def keys(self): ... + def pop(self, *args, **kwargs): ... + def popitem(self): ... + def setdefault(self, key, *args, **kwargs): ... + def update(self, *args, **kwargs): ... + def values(self): ... + def __eq__(left, right): ... + def __add__(left, right): ... + def __iadd__(self, other): ... + def __sub__(left, right): ... + def __isub__(self, other): ... + def __mul__(left, right): ... + def __imul__(self, other): ... + def __truediv__(left, right): ... + def __itruediv__(self, other): ... + def __pow__(left, right): ... + def __ipow__(self, other): ... + def __matmul__(left, right): ... + def __neg__(self): ... + def __pos__(self): ... + def __abs__(self): ... + def abs(self): ... + def min(self): ... + def max(self): ... + def minimum(self, other): ... + def maximum(self, other): ... diff --git a/river/utils/vectordict.pyx b/river/utils/vectordict.pyx index 3ef20817fc..00d8c1911d 100644 --- a/river/utils/vectordict.pyx +++ b/river/utils/vectordict.pyx @@ -427,8 +427,8 @@ cdef class VectorDict: return NotImplemented return self - def __pow__(left, right, modulo): - if not isinstance(left, VectorDict) or modulo is not None: + def __pow__(left, right): + if not isinstance(left, VectorDict): return NotImplemented left_ = left res = left_._to_dict(force_copy=True) From 7de47514fb9f4278c2d5ad22fddb48810bb38fc9 Mon Sep 17 00:00:00 2001 From: Max Halford Date: Tue, 27 Aug 2024 16:27:34 +0200 Subject: [PATCH 278/347] Bump dev tools (#1599) * Bump dev tools * Update poetry.lock --- .pre-commit-config.yaml | 32 +-- poetry.lock | 99 ++++---- pyproject.toml | 30 ++- river/__init__.py | 1 + river/active/__init__.py | 1 + river/active/base.py | 3 +- river/active/entropy.py | 4 +- river/anomaly/filter.py | 7 +- river/anomaly/pad.py | 5 +- river/api.py | 1 + river/bandit/__init__.py | 1 + river/bandit/base.py | 3 +- river/bandit/bayes_ucb.py | 6 +- river/bandit/envs/candy_cane.py | 8 +- river/bandit/exp3.py | 7 +- river/bandit/random.py | 6 +- river/bandit/test_policies.py | 12 +- river/bandit/ucb.py | 4 +- river/base/__init__.py | 1 + river/base/drift_detector.py | 1 + river/checks/__init__.py | 1 + river/cluster/__init__.py | 1 + river/cluster/clustream.py | 4 +- river/cluster/odac.py | 267 ++++++++++++--------- river/cluster/test_dbstream.py | 32 +-- river/cluster/textclust.py | 43 +--- river/compat/__init__.py | 1 + river/compose/__init__.py | 1 + river/compose/target_transform.py | 3 +- river/conf/__init__.py | 1 + river/conf/jackknife.py | 10 +- river/covariance/__init__.py | 1 + river/covariance/emp.py | 20 +- river/datasets/__init__.py | 1 + river/datasets/synth/__init__.py | 1 + river/datasets/synth/sine.py | 1 + river/drift/__init__.py | 1 + river/drift/binary/__init__.py | 1 + river/drift/binary/fhddm.py | 17 +- river/drift/no_drift.py | 3 +- river/dummy.py | 1 + river/ensemble/__init__.py | 1 + river/ensemble/boosting.py | 4 +- river/ensemble/streaming_random_patches.py | 10 +- river/evaluate/__init__.py | 1 + river/evaluate/progressive_validation.py | 4 +- river/facto/__init__.py | 1 + river/feature_extraction/__init__.py | 1 + river/feature_extraction/kernel_approx.py | 6 +- river/feature_selection/__init__.py | 1 + river/forest/__init__.py | 1 + river/imblearn/__init__.py | 1 + river/linear_model/__init__.py | 1 + river/linear_model/pa.py | 4 +- river/linear_model/softmax.py | 4 +- river/metrics/__init__.py | 1 + river/metrics/base.py | 14 +- river/metrics/cross_entropy.py | 4 +- river/metrics/fbeta.py | 4 +- river/metrics/multioutput/__init__.py | 1 + river/metrics/multioutput/confusion.py | 4 +- river/metrics/mutual_info.py | 6 +- river/metrics/rand.py | 10 +- river/misc/__init__.py | 1 + river/model_selection/__init__.py | 1 + river/model_selection/sh.py | 3 +- river/multiclass/__init__.py | 1 + river/multioutput/__init__.py | 1 + river/naive_bayes/__init__.py | 1 + river/neighbors/__init__.py | 1 + river/neighbors/ann/nn_vertex.py | 10 +- river/neighbors/ann/swinn.py | 25 +- river/neighbors/base.py | 3 +- river/neural_net/__init__.py | 1 + river/optim/__init__.py | 1 + river/optim/average.py | 1 - river/optim/initializers.py | 1 + river/optim/losses.py | 1 + river/optim/schedulers.py | 1 + river/preprocessing/__init__.py | 1 + river/preprocessing/impute.py | 3 +- river/preprocessing/random_projection.py | 6 +- river/preprocessing/test_lda.py | 1 + river/proba/__init__.py | 1 + river/proba/beta.py | 4 +- river/proba/gaussian.py | 12 +- river/proba/multinomial.py | 8 +- river/reco/__init__.py | 1 + river/reco/baseline.py | 12 +- river/reco/biased_mf.py | 24 +- river/reco/funk_mf.py | 12 +- river/rules/__init__.py | 1 + river/sketch/__init__.py | 1 + river/stats/__init__.py | 1 + river/stats/entropy.py | 6 +- river/stream/__init__.py | 1 + river/stream/qa.py | 24 +- river/stream/twitch_chat_stream.py | 2 +- river/test_estimators.py | 1 + river/time_series/__init__.py | 1 + river/time_series/holt_winters.py | 3 +- river/tree/__init__.py | 1 + river/tree/base.py | 1 + river/tree/hoeffding_tree_classifier.py | 8 +- river/tree/hoeffding_tree_regressor.py | 4 +- river/tree/mondrian/__init__.py | 1 + river/tree/nodes/sgt_nodes.py | 7 +- river/tree/splitter/__init__.py | 1 + river/tree/utils.py | 9 +- river/utils/__init__.py | 1 + river/utils/inspect.py | 1 + river/utils/math.py | 1 + river/utils/pretty.py | 10 +- river/utils/rolling.py | 6 +- 114 files changed, 491 insertions(+), 466 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5553fa411c..bc049ead6b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,26 +1,28 @@ files: river repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.2.0 + rev: v4.4.0 hooks: - id: check-json - id: check-yaml - - id: end-of-file-fixer - - id: trailing-whitespace - - id: mixed-line-ending - - repo: local + - repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.5.7 hooks: + # Run the linter. - id: ruff - name: ruff - language: python - types: [python, pyi] - entry: ruff - args: - - --fix + types_or: [python, pyi, jupyter] + args: [--fix] + # Run the formatter. + - id: ruff-format + types_or: [python, pyi, jupyter] + - repo: https://github.com/pre-commit/mirrors-mypy + rev: "v1.1.1" + hooks: - id: mypy - name: mypy - language: python - types: [python, pyi] - entry: mypy --implicit-optional + args: + - "--config-file=pyproject.toml" + - "--python-version=3.11" + - "--implicit-optional" diff --git a/poetry.lock b/poetry.lock index a3765505e5..d8bf62a520 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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"I", "UP"] # https://beta.ruff.rs/docs/rules/ line-length = 100 target-version = 'py310' +extend-include = ["*.ipynb"] + +[tool.ruff.lint] +select = [ + # pycodestyle + "E", + # Pyflakes + "F", + # pyupgrade + "UP", + # isort + "I", +] ignore = ["E501"] +fixable = ["ALL"] -[tool.ruff.isort] +[tool.ruff.lint.isort] required-imports = ["from __future__ import annotations"] +[tool.ruff.format] +quote-style = "double" +indent-style = "space" + [tool.mypy] files = "river" @@ -140,6 +157,9 @@ module = [ "vaex.*", "torch.*", "sqlalchemy.*", - "requests.*" + "requests.*", + "gymnasium.*", + "sympy.*", + "polars.*" ] ignore_missing_imports = true diff --git a/river/__init__.py b/river/__init__.py index 063932fb67..da617455d5 100644 --- a/river/__init__.py +++ b/river/__init__.py @@ -5,6 +5,7 @@ memory, or simply when it isn't available all at once. river's API is heavily inspired from that of scikit-learn, enough so that users who are familiar with scikit-learn should feel right at home. """ + from __future__ import annotations from .__version__ import __version__ # noqa: F401 diff --git a/river/active/__init__.py b/river/active/__init__.py index 7d3f3451b0..ffa17e759a 100644 --- a/river/active/__init__.py +++ b/river/active/__init__.py @@ -1,4 +1,5 @@ """Online active learning.""" + from __future__ import annotations from . import base diff --git a/river/active/base.py b/river/active/base.py index e4b8f7c057..b403259e6f 100644 --- a/river/active/base.py +++ b/river/active/base.py @@ -30,8 +30,7 @@ def _wrapped_model(self): return self.classifier @abc.abstractmethod - def _ask_for_label(self, x, y_pred) -> bool: - ... + def _ask_for_label(self, x, y_pred) -> bool: ... def predict_proba_one(self, x, **kwargs): """Predict the probability of each label for `x` and indicate whether a label is needed. diff --git a/river/active/entropy.py b/river/active/entropy.py index f980b9fc7a..bd02f80e14 100644 --- a/river/active/entropy.py +++ b/river/active/entropy.py @@ -63,9 +63,7 @@ class EntropySampler(ActiveLearningClassifier): """ - def __init__( - self, classifier: base.Classifier, discount_factor: float = 3, seed=None - ): + def __init__(self, classifier: base.Classifier, discount_factor: float = 3, seed=None): super().__init__(classifier, seed=seed) self.discount_factor = discount_factor diff --git a/river/anomaly/filter.py b/river/anomaly/filter.py index 856f96ca13..36cce998a2 100644 --- a/river/anomaly/filter.py +++ b/river/anomaly/filter.py @@ -86,9 +86,7 @@ class ThresholdFilter(anomaly.base.AnomalyFilter): """ - def __init__( - self, anomaly_detector, threshold: float, protect_anomaly_detector=True - ): + def __init__(self, anomaly_detector, threshold: float, protect_anomaly_detector=True): super().__init__( anomaly_detector=anomaly_detector, protect_anomaly_detector=protect_anomaly_detector, @@ -188,7 +186,6 @@ def _unit_test_params(cls): from river import preprocessing yield { - "anomaly_detector": preprocessing.StandardScaler() - | anomaly.OneClassSVM(nu=0.2), + "anomaly_detector": preprocessing.StandardScaler() | anomaly.OneClassSVM(nu=0.2), "q": 0.995, } diff --git a/river/anomaly/pad.py b/river/anomaly/pad.py index 1f07cefbe1..4ec6304efb 100644 --- a/river/anomaly/pad.py +++ b/river/anomaly/pad.py @@ -100,7 +100,6 @@ def __init__( n_std: float = 3.0, warmup_period: int = 0, ): - self.predictive_model = ( predictive_model if predictive_model is not None @@ -123,9 +122,7 @@ def learn_one(self, x: dict | None, y: base.typing.Target | float): self.iter += 1 # Check whether the model is a time-series forecasting or regression/classification model - if isinstance( - self.predictive_model, time_series.base.Forecaster - ) and isinstance(y, float): + if isinstance(self.predictive_model, time_series.base.Forecaster) and isinstance(y, float): # When there's no data point as dict of features, the target will be passed # to the forecaster as an exogenous variable. if not x: diff --git a/river/api.py b/river/api.py index 666541999c..921a129580 100644 --- a/river/api.py +++ b/river/api.py @@ -1,4 +1,5 @@ """River API module.""" + from __future__ import annotations from . import ( diff --git a/river/bandit/__init__.py b/river/bandit/__init__.py index 44089f8826..70b0a98a72 100644 --- a/river/bandit/__init__.py +++ b/river/bandit/__init__.py @@ -5,6 +5,7 @@ (see `model_selection.BanditRegressor`). """ + from __future__ import annotations from . import base, datasets, envs diff --git a/river/bandit/base.py b/river/bandit/base.py index 3453a7e152..f5df1717b0 100644 --- a/river/bandit/base.py +++ b/river/bandit/base.py @@ -65,8 +65,7 @@ def __post_init__(self): ) @abc.abstractmethod - def _pull(self, arm_ids: list[ArmID]) -> ArmID: - ... + def _pull(self, arm_ids: list[ArmID]) -> ArmID: ... def pull(self, arm_ids: list[ArmID]) -> ArmID: """Pull arm(s). diff --git a/river/bandit/bayes_ucb.py b/river/bandit/bayes_ucb.py index 5fdef8d1db..c60b6353ea 100644 --- a/river/bandit/bayes_ucb.py +++ b/river/bandit/bayes_ucb.py @@ -63,9 +63,9 @@ class BayesUCB(bandit.base.Policy): def __init__(self, reward_obj=None, burn_in=0, seed: int | None = None): super().__init__(reward_obj, burn_in) - self._posteriors: collections.defaultdict[ - bandit.base.ArmID, proba.Beta - ] = collections.defaultdict(proba.Beta) + self._posteriors: collections.defaultdict[bandit.base.ArmID, proba.Beta] = ( + collections.defaultdict(proba.Beta) + ) self.seed = seed self._rng = random.Random(seed) diff --git a/river/bandit/envs/candy_cane.py b/river/bandit/envs/candy_cane.py index f6702e6606..fa95652dcd 100644 --- a/river/bandit/envs/candy_cane.py +++ b/river/bandit/envs/candy_cane.py @@ -58,12 +58,8 @@ def __init__(self, n_machines=100, reward_decay=0.03): self.action_space = gym.spaces.Discrete(n_machines) self.observation_space = gym.spaces.Dict( { - "attempts": gym.spaces.Tuple( - [gym.spaces.Discrete(self.n_steps)] * n_machines - ), - "successes": gym.spaces.Tuple( - [gym.spaces.Discrete(self.n_steps)] * n_machines - ), + "attempts": gym.spaces.Tuple([gym.spaces.Discrete(self.n_steps)] * n_machines), + "successes": gym.spaces.Tuple([gym.spaces.Discrete(self.n_steps)] * n_machines), } ) self.reward_range = (0.0, 1.0) diff --git a/river/bandit/exp3.py b/river/bandit/exp3.py index b5c7899c51..a675472dea 100644 --- a/river/bandit/exp3.py +++ b/river/bandit/exp3.py @@ -77,9 +77,7 @@ def __init__( burn_in=0, seed: int | None = None, ): - super().__init__( - reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in - ) + super().__init__(reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in) self.seed = seed self.gamma = gamma self._rng = random.Random(seed) @@ -91,8 +89,7 @@ def __init__( def _pull(self, arm_ids): total = sum(self._weights[arm_id] for arm_id in arm_ids) self._probabilities = { - arm_id: (1 - self.gamma) * (self._weights[arm_id] / total) - + self.gamma / len(arm_ids) + arm_id: (1 - self.gamma) * (self._weights[arm_id] / total) + self.gamma / len(arm_ids) for arm_id in arm_ids } return self._rng.choices(arm_ids, weights=self._probabilities.values())[0] diff --git a/river/bandit/random.py b/river/bandit/random.py index 2199ed0f43..8b7ca85ac8 100644 --- a/river/bandit/random.py +++ b/river/bandit/random.py @@ -52,9 +52,9 @@ class RandomPolicy(bandit.base.Policy): def __init__(self, reward_obj=None, burn_in=0, seed: int | None = None): super().__init__(reward_obj, burn_in) - self._posteriors: collections.defaultdict[ - bandit.base.ArmID, proba.Beta - ] = collections.defaultdict(proba.Beta) + self._posteriors: collections.defaultdict[bandit.base.ArmID, proba.Beta] = ( + collections.defaultdict(proba.Beta) + ) self.seed = seed self._rng = random.Random(seed) diff --git a/river/bandit/test_policies.py b/river/bandit/test_policies.py index b1d6507ebf..d9d60fe453 100644 --- a/river/bandit/test_policies.py +++ b/river/bandit/test_policies.py @@ -25,21 +25,17 @@ def get(self): def bigger_is_better(self): return False - def revert(self): - ... + def revert(self): ... - def update(self): - ... + def update(self): ... - def works_with(self): - ... + def works_with(self): ... class DummyPolicy(bandit.base.Policy): def __init__(self): super().__init__(reward_obj=DummyMetric()) - def _pull(self, arms): - ... + def _pull(self, arms): ... policy = DummyPolicy() policy._rewards[0].value = 0 diff --git a/river/bandit/ucb.py b/river/bandit/ucb.py index 78fbf0b758..e37ee290d8 100644 --- a/river/bandit/ucb.py +++ b/river/bandit/ucb.py @@ -77,9 +77,7 @@ def __init__( burn_in=0, seed: int | None = None, ): - super().__init__( - reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in - ) + super().__init__(reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in) self.delta = delta self.seed = seed self._rng = random.Random(seed) diff --git a/river/base/__init__.py b/river/base/__init__.py index b5442ef381..0aaa521934 100644 --- a/river/base/__init__.py +++ b/river/base/__init__.py @@ -11,6 +11,7 @@ This module also contains utilities for type hinting and tagging estimators. """ + from __future__ import annotations from . import tags, typing diff --git a/river/base/drift_detector.py b/river/base/drift_detector.py index 9e241cd362..9eaef0b506 100644 --- a/river/base/drift_detector.py +++ b/river/base/drift_detector.py @@ -5,6 +5,7 @@ purposes. The properties are not meant to be modified by the user. """ + from __future__ import annotations import abc diff --git a/river/checks/__init__.py b/river/checks/__init__.py index 2f3176428a..e0f8234b3d 100644 --- a/river/checks/__init__.py +++ b/river/checks/__init__.py @@ -1,4 +1,5 @@ """Utilities for unit testing and sanity checking estimators.""" + from __future__ import annotations import functools diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 3915740248..0fe354fd2f 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -1,4 +1,5 @@ """Unsupervised clustering.""" + from __future__ import annotations from .clustream import CluStream diff --git a/river/cluster/clustream.py b/river/cluster/clustream.py index b7adecb0f4..858e2f7302 100644 --- a/river/cluster/clustream.py +++ b/river/cluster/clustream.py @@ -349,7 +349,5 @@ def inverse_error(x): def __iadd__(self, other: CluStreamMicroCluster): self.var_time += other.var_time - self.var_x = { - k: self.var_x[k] + other.var_x.get(k, stats.Var()) for k in self.var_x - } + self.var_x = {k: self.var_x[k] + other.var_x.get(k, stats.Var()) for k in self.var_x} return self diff --git a/river/cluster/odac.py b/river/cluster/odac.py index b928477cf4..39cd912443 100644 --- a/river/cluster/odac.py +++ b/river/cluster/odac.py @@ -241,120 +241,132 @@ def render_ascii(self, n_decimal_places: int = 2) -> str: return self._root_node.design_structure(n_decimal_places).rstrip("\n") - def draw(self, max_depth: int | None = None, show_clusters_info: list[typing.Hashable] = ["timeseries_names", "d1", "d2", "e"], n_decimal_places: int = 2): - """Method to draw the hierarchical cluster's structure as a Graphviz graph. - - Parameters - ---------- - max_depth - The maximum depth of the tree to display. - show_clusters_info - List of cluster information to show. Valid options are: - - "timeseries_indexes": Shows the indexes of the timeseries in the cluster. - - "timeseries_names": Shows the names of the timeseries in the cluster. - - "name": Shows the cluster's name. - - "d1": Shows the d1 (the largest distance in the cluster). - - "d2": Shows the d2 (the second largest distance in the cluster). - - "e": Shows the error bound. - n_decimal_places - The number of decimal places to show for numerical values. - - """ - if not (n_decimal_places > 0 and n_decimal_places < 10): - raise ValueError("n_decimal_places must be between 1 and 9.") + def draw( + self, + max_depth: int | None = None, + show_clusters_info: list[typing.Hashable] = ["timeseries_names", "d1", "d2", "e"], + n_decimal_places: int = 2, + ): + """Method to draw the hierarchical cluster's structure as a Graphviz graph. - try: - import graphviz - except ImportError as e: - raise ValueError("You have to install graphviz to use the draw method.") from e - - counter = 0 - - dot = graphviz.Digraph( - graph_attr={"splines": "ortho", "forcelabels": "true", "overlap": "false"}, - node_attr={ - "shape": "box", - "penwidth": "1.2", - "fontname": "trebuchet", - "fontsize": "11", - "margin": "0.1,0.0", - }, - edge_attr={"penwidth": "0.6", "center": "true", "fontsize": "7 "}, - ) - - def iterate(node: ODACCluster, parent_node: str | None = None, depth: int = 0): - nonlocal counter, max_depth, show_clusters_info, n_decimal_places - - if max_depth is not None and depth > max_depth: - return - - node_n = str(counter) - counter += 1 - - label = "" - - # checks if user wants to see information about clusters - if len(show_clusters_info) > 0: - show_clusters_info_copy = show_clusters_info.copy() - - if "name" in show_clusters_info_copy: - label += f"{node.name}" - show_clusters_info_copy.remove("name") - if len(show_clusters_info_copy) > 0: - label += "\n" - if "timeseries_indexes" in show_clusters_info_copy: - # Convert timeseries names to indexes - name_to_index = {name: index for index, name in enumerate(self._root_node.timeseries_names)} - timeseries_indexes = [name_to_index[_name] for _name in node.timeseries_names if _name in name_to_index] - - label += f"{timeseries_indexes}" - show_clusters_info_copy.remove("timeseries_indexes") - if len(show_clusters_info_copy) > 0: - label += "\n" - if "timeseries_names" in show_clusters_info_copy: - label += f"{node.timeseries_names}" - show_clusters_info_copy.remove("timeseries_names") - if len(show_clusters_info_copy) > 0: - label += "\n" - if "d1" in show_clusters_info_copy: - if node.d1 is not None: - label += f"d1={node.d1:.{n_decimal_places}f}" - else: - label += "d1=" - show_clusters_info_copy.remove("d1") - if len(show_clusters_info_copy) > 0: - label += "\n" - if "d2" in show_clusters_info_copy and node.d2 is not None: - label += f"d2={node.d2:.{n_decimal_places}f}" - show_clusters_info_copy.remove("d2") - if len(show_clusters_info_copy) > 0: - label += "\n" - if "e" in show_clusters_info_copy: - label += f"e={node.e:.{n_decimal_places}f}" + Parameters + ---------- + max_depth + The maximum depth of the tree to display. + show_clusters_info + List of cluster information to show. Valid options are: + - "timeseries_indexes": Shows the indexes of the timeseries in the cluster. + - "timeseries_names": Shows the names of the timeseries in the cluster. + - "name": Shows the cluster's name. + - "d1": Shows the d1 (the largest distance in the cluster). + - "d2": Shows the d2 (the second largest distance in the cluster). + - "e": Shows the error bound. + n_decimal_places + The number of decimal places to show for numerical values. + + """ + if not (n_decimal_places > 0 and n_decimal_places < 10): + raise ValueError("n_decimal_places must be between 1 and 9.") - show_clusters_info_copy.clear() + try: + import graphviz + except ImportError as e: + raise ValueError("You have to install graphviz to use the draw method.") from e + + counter = 0 + + dot = graphviz.Digraph( + graph_attr={"splines": "ortho", "forcelabels": "true", "overlap": "false"}, + node_attr={ + "shape": "box", + "penwidth": "1.2", + "fontname": "trebuchet", + "fontsize": "11", + "margin": "0.1,0.0", + }, + edge_attr={"penwidth": "0.6", "center": "true", "fontsize": "7 "}, + ) - # Creates a node with different color to differentiate the active clusters from the non-active - if node.active: - dot.node(node_n, label, style="filled", fillcolor="#76b5c5") - else: - dot.node(node_n, label, style="filled", fillcolor="#f2f2f2") + def iterate(node: ODACCluster, parent_node: str | None = None, depth: int = 0): + nonlocal counter, max_depth, show_clusters_info, n_decimal_places + + if max_depth is not None and depth > max_depth: + return + + node_n = str(counter) + counter += 1 + + label = "" + + # checks if user wants to see information about clusters + if len(show_clusters_info) > 0: + show_clusters_info_copy = show_clusters_info.copy() + + if "name" in show_clusters_info_copy: + label += f"{node.name}" + show_clusters_info_copy.remove("name") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "timeseries_indexes" in show_clusters_info_copy: + # Convert timeseries names to indexes + name_to_index = { + name: index for index, name in enumerate(self._root_node.timeseries_names) + } + timeseries_indexes = [ + name_to_index[_name] + for _name in node.timeseries_names + if _name in name_to_index + ] + + label += f"{timeseries_indexes}" + show_clusters_info_copy.remove("timeseries_indexes") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "timeseries_names" in show_clusters_info_copy: + label += f"{node.timeseries_names}" + show_clusters_info_copy.remove("timeseries_names") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "d1" in show_clusters_info_copy: + if node.d1 is not None: + label += f"d1={node.d1:.{n_decimal_places}f}" + else: + label += "d1=" + show_clusters_info_copy.remove("d1") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "d2" in show_clusters_info_copy and node.d2 is not None: + label += f"d2={node.d2:.{n_decimal_places}f}" + show_clusters_info_copy.remove("d2") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "e" in show_clusters_info_copy: + label += f"e={node.e:.{n_decimal_places}f}" + + show_clusters_info_copy.clear() + + # Creates a node with different color to differentiate the active clusters from the non-active + if node.active: + dot.node(node_n, label, style="filled", fillcolor="#76b5c5") + else: + dot.node(node_n, label, style="filled", fillcolor="#f2f2f2") - if parent_node is not None: - dot.edge(parent_node, node_n) + if parent_node is not None: + dot.edge(parent_node, node_n) - if node.children is not None: - iterate(node=node.children.first, parent_node=node_n, depth=depth + 1) - iterate(node.children.second, parent_node=node_n, depth=depth + 1) + if node.children is not None: + iterate(node=node.children.first, parent_node=node_n, depth=depth + 1) + iterate(node.children.second, parent_node=node_n, depth=depth + 1) - iterate(node=self._root_node) + iterate(node=self._root_node) - return dot + return dot @property def structure_changed(self) -> bool: return self._structure_changed + class ODACCluster(base.Base): """Cluster class for representing individual clusters.""" @@ -366,7 +378,9 @@ def __init__(self, name: str, parent: ODACCluster | None = None): self.children: ODACChildren | None = None self.timeseries_names: list[typing.Hashable] = [] - self._statistics: dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | stats.Var | None + self._statistics: ( + dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | stats.Var | None + ) self.d1: float | None = None self.d2: float | None = None @@ -381,7 +395,7 @@ def __init__(self, name: str, parent: ODACCluster | None = None): self.n = 0 # Method to design the structure of the cluster tree - def design_structure(self, decimal_places:int = 2) -> str: + def design_structure(self, decimal_places: int = 2) -> str: pre_0 = " " pre_1 = "│ " pre_2 = "├── " @@ -389,11 +403,14 @@ def design_structure(self, decimal_places:int = 2) -> str: node = self prefix = ( pre_2 - if node.parent is not None and (node.parent.children is None or id(node) != id(node.parent.children.second)) # type: ignore + if node.parent is not None + and (node.parent.children is None or id(node) != id(node.parent.children.second)) # type: ignore else pre_3 ) while node.parent is not None and node.parent.parent is not None: - if node.parent.parent.children is None or id(node.parent) != id(node.parent.parent.children.second): # type: ignore + if node.parent.parent.children is None or id(node.parent) != id( + node.parent.parent.children.second + ): # type: ignore prefix = pre_1 + prefix else: prefix = pre_0 + prefix @@ -430,14 +447,20 @@ def __str__(self) -> str: def __repr__(self) -> str: return self.design_structure() - def _init_stats(self) -> dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | stats.Var: - return collections.defaultdict( - stats.PearsonCorr, - { - (k1, k2): stats.PearsonCorr() - for k1, k2 in itertools.combinations(self.timeseries_names, 2) - }, - ) if len(self.timeseries_names) > 1 else stats.Var() + def _init_stats( + self, + ) -> dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | stats.Var: + return ( + collections.defaultdict( + stats.PearsonCorr, + { + (k1, k2): stats.PearsonCorr() + for k1, k2 in itertools.combinations(self.timeseries_names, 2) + }, + ) + if len(self.timeseries_names) > 1 + else stats.Var() + ) # TODO: not sure if this is the best design def __call__(self, ts_names: list[typing.Hashable]): @@ -454,18 +477,18 @@ def update_statistics(self, x: dict) -> None: continue item.update(float(x[k1]), float(x[k2])) else: - self._statistics.update(float(x.get(self.timeseries_names[0]))) # type: ignore + self._statistics.update(float(x.get(self.timeseries_names[0]))) # type: ignore # Increment the number of observation in the cluster self.n += 1 # Method to calculate the rnomc values of the cluster - def _calculate_rnomc_dict(self)-> dict[tuple[typing.Hashable, typing.Hashable], float]: + def _calculate_rnomc_dict(self) -> dict[tuple[typing.Hashable, typing.Hashable], float]: # Get the correlation values between time-series in the cluster rnomc_dict = {} for k1, k2 in itertools.combinations(self.timeseries_names, 2): - value = abs((1 - self._statistics[(k1, k2)].get()) / 2.0) # type: ignore + value = abs((1 - self._statistics[(k1, k2)].get()) / 2.0) # type: ignore rnomc_dict[(k1, k2)] = math.sqrt(value) return rnomc_dict @@ -492,7 +515,7 @@ def calculate_coefficients(self, confidence_level: float) -> None: else: self.pivot_2 = self.d2 = None # type: ignore else: - self.d1 = self._statistics.get() # type: ignore + self.d1 = self._statistics.get() # type: ignore # Calculate the Hoeffding bound in the cluster self.e = math.sqrt(math.log(1 / confidence_level) / (2 * self.n)) @@ -502,7 +525,12 @@ def _get_closest_cluster(self, pivot_1, pivot_2, current, rnormc_dict: dict) -> dist_2 = rnormc_dict.get((min(pivot_2, current), max(pivot_2, current)), 0) return 2 if dist_1 >= dist_2 else 1 - def _split_this_cluster(self, pivot_1: typing.Hashable, pivot_2: typing.Hashable, rnormc_dict: dict[tuple[typing.Hashable, typing.Hashable], float]): + def _split_this_cluster( + self, + pivot_1: typing.Hashable, + pivot_2: typing.Hashable, + rnormc_dict: dict[tuple[typing.Hashable, typing.Hashable], float], + ): """Expand into two clusters.""" pivot_set = {pivot_1, pivot_2} pivot_1_list = [pivot_1] @@ -572,6 +600,7 @@ def test_aggregate(self): return True return False + class ODACChildren(base.Base): """Children class representing child clusters.""" diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py index 6b5e3776f4..2e2e3af965 100644 --- a/river/cluster/test_dbstream.py +++ b/river/cluster/test_dbstream.py @@ -134,8 +134,7 @@ def test_density_graph_with_three_micro_clusters(): def test_density_graph_with_removed_microcluster(): - dbstream = build_dbstream(fading_factor=0.1, - intersection_factor=0.3) + dbstream = build_dbstream(fading_factor=0.1, intersection_factor=0.3) add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25) add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25) @@ -162,9 +161,7 @@ def test_density_graph_with_removed_microcluster(): dbstream._recluster() assert len(dbstream.clusters) == 1 - assert_micro_cluster_properties( - dbstream.clusters[0], center={1: 2.560647, 2: 2.560647} - ) + assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.560647, 2: 2.560647}) def test_dbstream_synthetic_sklearn(): @@ -172,11 +169,9 @@ def test_dbstream_synthetic_sklearn(): cluster_std = [0.6] * 5 # Create a dataset with 15000 data points with 5 centers and cluster SD of 0.6 each - X, y = make_blobs(n_samples=15_000, - cluster_std=cluster_std, - centers=centers, - n_features=2, - random_state=42) + X, y = make_blobs( + n_samples=15_000, cluster_std=cluster_std, centers=centers, n_features=2, random_state=42 + ) dbstream = DBSTREAM( clustering_threshold=2, @@ -202,11 +197,16 @@ def test_dbstream_synthetic_sklearn(): dbstream._recluster() # Check that the resulted cluster centers are close to the expected centers - dbstream_expected_centers = {0: {0: 10, 1: 10}, - 1: {0: -5, 1: -5}, - 2: {0: 0, 1: 0}, - 3: {0: 5, 1: 5}, - 4: {0: -10, 1: -10}} + dbstream_expected_centers = { + 0: {0: 10, 1: 10}, + 1: {0: -5, 1: -5}, + 2: {0: 0, 1: 0}, + 3: {0: 5, 1: 5}, + 4: {0: -10, 1: -10}, + } for i in dbstream.centers.keys(): - assert utils.math.minkowski_distance(dbstream.centers[i], dbstream_expected_centers[i], 2) < 0.2 + assert ( + utils.math.minkowski_distance(dbstream.centers[i], dbstream_expected_centers[i], 2) + < 0.2 + ) diff --git a/river/cluster/textclust.py b/river/cluster/textclust.py index 0a848d0f3f..82e507517c 100644 --- a/river/cluster/textclust.py +++ b/river/cluster/textclust.py @@ -260,9 +260,7 @@ def _get_closest_mc(self, mc, idf, distance): if counter > 1: ## our threshold mu = (sumdist - min_dist) / (counter - 1) - threshold = mu - self.sigma * math.sqrt( - squaresum / (counter - 1) - mu**2 - ) + threshold = mu - self.sigma * math.sqrt(squaresum / (counter - 1) - mu**2) if min_dist < threshold: clusterId = smallest_key @@ -288,9 +286,7 @@ def _calculateIDF(self, micro_clusters): # update weights according to the fading factor def _updateweights(self): for micro in self.micro_clusters.values(): - micro.fade( - self.t, self.omega, self.fading_factor, self.term_fading, self.realtime - ) + micro.fade(self.t, self.omega, self.fading_factor, self.term_fading, self.realtime) # delete micro clusters with a weight smaller omega for key in list(self.micro_clusters.keys()): @@ -373,9 +369,7 @@ def _get_distance_matrix(self, clusters): ids = list(clusters.keys()) # initialize all distances to 0 - distances = pd.DataFrame( - np.zeros((numClusters, numClusters)), columns=ids, index=ids - ) + distances = pd.DataFrame(np.zeros((numClusters, numClusters)), columns=ids, index=ids) for idx, row in enumerate(ids): for col in ids[idx + 1 :]: @@ -455,10 +449,7 @@ def get_macroclusters(self): numClusters = min([self.num_macro, len(self.micro_clusters)]) # create empty clusters - macros = { - x: self.microcluster({}, self.t, 0, self.realtime, x) - for x in range(numClusters) - } + macros = {x: self.microcluster({}, self.t, 0, self.realtime, x) for x in range(numClusters)} # merge micro clusters to macro clusters for key, value in self.microToMacro.items(): @@ -478,9 +469,7 @@ def get_macroclusters(self): def showclusters(self, topn, num, type="micro"): # first clusters are sorted according to their respective weights if type == "micro": - sortedmicro = sorted( - self.micro_clusters.values(), key=lambda x: x.weight, reverse=True - ) + sortedmicro = sorted(self.micro_clusters.values(), key=lambda x: x.weight, reverse=True) else: sortedmicro = sorted( self.get_macroclusters().values(), key=lambda x: x.weight, reverse=True @@ -510,10 +499,7 @@ def showclusters(self, topn, num, type="micro"): ] for rep in representatives: print( - "weight: " - + str(round(rep[1], 2)) - + "\t token: " - + str(rep[0]).expandtabs(10) + "weight: " + str(round(rep[1], 2)) + "\t token: " + str(rep[0]).expandtabs(10) ) print("-------------------------------------------") @@ -539,9 +525,7 @@ def get_assignment(self, x, type): # identify the closest micro cluster using the predefined distance measure for key in self.micro_clusters.keys(): if self.micro_clusters[key].weight > self.min_weight: - cur_dist = self.micro_distance.dist( - mc, self.micro_clusters[key], idf - ) + cur_dist = self.micro_distance.dist(mc, self.micro_clusters[key], idf) if cur_dist < dist: dist = cur_dist closest = key @@ -616,9 +600,7 @@ def __init__(self, type): ## generic method that is called for each distance def dist(self, m1, m2, idf): - return getattr(self, self.type, lambda: "Invalid distance measure")( - m1, m2, idf - ) + return getattr(self, self.type, lambda: "Invalid distance measure")(m1, m2, idf) ##calculate cosine similarity directly and fast def tfidf_cosine_distance(self, mc, microcluster, idf): @@ -628,17 +610,12 @@ def tfidf_cosine_distance(self, mc, microcluster, idf): for k in list(mc.tf.keys()): if k in idf: if k in microcluster.tf: - sum += (mc.tf[k]["tf"] * idf[k]) * ( - microcluster.tf[k]["tf"] * idf[k] - ) + sum += (mc.tf[k]["tf"] * idf[k]) * (microcluster.tf[k]["tf"] * idf[k]) tfidflen += mc.tf[k]["tf"] * idf[k] * mc.tf[k]["tf"] * idf[k] tfidflen = math.sqrt(tfidflen) for k in list(microcluster.tf.keys()): microtfidflen += ( - microcluster.tf[k]["tf"] - * idf[k] - * microcluster.tf[k]["tf"] - * idf[k] + microcluster.tf[k]["tf"] * idf[k] * microcluster.tf[k]["tf"] * idf[k] ) microtfidflen = math.sqrt(microtfidflen) if tfidflen == 0 or microtfidflen == 0: diff --git a/river/compat/__init__.py b/river/compat/__init__.py index ed7ae9c634..c07a20db9a 100644 --- a/river/compat/__init__.py +++ b/river/compat/__init__.py @@ -6,6 +6,7 @@ `compat.convert_sklearn_to_river` function. """ + from __future__ import annotations __all__: list[str] = [] diff --git a/river/compose/__init__.py b/river/compose/__init__.py index 2c2b6b5c93..260b613970 100644 --- a/river/compose/__init__.py +++ b/river/compose/__init__.py @@ -4,6 +4,7 @@ pipelines are not the only way to process a stream of data, we highly encourage you to use them. """ + from __future__ import annotations from .func import FuncTransformer diff --git a/river/compose/target_transform.py b/river/compose/target_transform.py index ad6193a5d1..a300b5bd05 100644 --- a/river/compose/target_transform.py +++ b/river/compose/target_transform.py @@ -60,8 +60,7 @@ def __init__( def _wrapped_model(self): return self.regressor - def _update(self, y): - ... + def _update(self, y): ... def learn_one(self, x, y): self._update(y) diff --git a/river/conf/__init__.py b/river/conf/__init__.py index 10dc5cad27..ef6df5a9b8 100644 --- a/river/conf/__init__.py +++ b/river/conf/__init__.py @@ -1,5 +1,6 @@ """Conformal predictions. This modules contains wrappers to enable conformal predictions on any regressor or classifier.""" + from __future__ import annotations from .interval import Interval diff --git a/river/conf/jackknife.py b/river/conf/jackknife.py index 57582a1209..543ba0b25b 100644 --- a/river/conf/jackknife.py +++ b/river/conf/jackknife.py @@ -91,9 +91,7 @@ def __init__( alpha = (1 - confidence_level) / 2 self._lower = ( - stats.RollingQuantile(alpha, window_size) - if window_size - else stats.Quantile(alpha) + stats.RollingQuantile(alpha, window_size) if window_size else stats.Quantile(alpha) ) self._upper = ( stats.RollingQuantile(1 - alpha, window_size) @@ -109,11 +107,7 @@ def _wrapped_model(self): def _unit_test_params(cls): from river import linear_model, preprocessing - yield { - "regressor": ( - preprocessing.StandardScaler() | linear_model.LinearRegression() - ) - } + yield {"regressor": (preprocessing.StandardScaler() | linear_model.LinearRegression())} def learn_one(self, x, y, **kwargs): # Update the quantiles diff --git a/river/covariance/__init__.py b/river/covariance/__init__.py index 80c18a2e33..a026e17807 100644 --- a/river/covariance/__init__.py +++ b/river/covariance/__init__.py @@ -1,4 +1,5 @@ """Online estimation of covariance and precision matrices.""" + from __future__ import annotations from .emp import EmpiricalCovariance, EmpiricalPrecision diff --git a/river/covariance/emp.py b/river/covariance/emp.py index 957f37c871..255a28a726 100644 --- a/river/covariance/emp.py +++ b/river/covariance/emp.py @@ -14,8 +14,7 @@ class SymmetricMatrix(abc.ABC): @property @abc.abstractmethod - def matrix(self) -> dict: - ... + def matrix(self) -> dict: ... def __getitem__(self, key): """ @@ -178,9 +177,7 @@ def update_many(self, X: pd.DataFrame): mean = dict(zip(X.columns, mean_arr)) cov = { (i, j): cov_arr[r, c] - for (r, i), (c, j) in itertools.combinations_with_replacement( - enumerate(X.columns), r=2 - ) + for (r, i), (c, j) in itertools.combinations_with_replacement(enumerate(X.columns), r=2) } self._update_from_state(n=n, mean=mean, cov=cov) @@ -331,10 +328,7 @@ def update(self, x): # Fortran order is necessary for scipy's linalg.blas.dger inv_cov = np.array( [ - [ - self._inv_cov.get(min((i, j), (j, i)), 1.0 if i == j else 0.0) - for j in x - ] + [self._inv_cov.get(min((i, j), (j, i)), 1.0 if i == j else 0.0) for j in x] for i in x ], order="F", @@ -369,13 +363,7 @@ def update_many(self, X: pd.DataFrame): loc = np.array([self._loc.get(feature, 0.0) for feature in X]) w = np.array([self._w.get(feature, 0.0) for feature in X]) inv_cov = np.array( - [ - [ - self._inv_cov.get(min((i, j), (j, i)), 1.0 if i == j else 0.0) - for j in X - ] - for i in X - ] + [[self._inv_cov.get(min((i, j), (j, i)), 1.0 if i == j else 0.0) for j in X] for i in X] ) / np.maximum(w, 1) # update formulas diff --git a/river/datasets/__init__.py b/river/datasets/__init__.py index 5ed0177900..7f4cfbbee4 100644 --- a/river/datasets/__init__.py +++ b/river/datasets/__init__.py @@ -6,6 +6,7 @@ are interested in infinite synthetic data generators. """ + from __future__ import annotations from . import base, synth diff --git a/river/datasets/synth/__init__.py b/river/datasets/synth/__init__.py index f14e6b6b67..a056f00029 100644 --- a/river/datasets/synth/__init__.py +++ b/river/datasets/synth/__init__.py @@ -5,6 +5,7 @@ the majority of these methods are infinite data generators. """ + from __future__ import annotations from .agrawal import Agrawal diff --git a/river/datasets/synth/sine.py b/river/datasets/synth/sine.py index 7d8e793206..1ad0fcc042 100644 --- a/river/datasets/synth/sine.py +++ b/river/datasets/synth/sine.py @@ -80,6 +80,7 @@ class Sine(datasets.base.SyntheticDataset): Springer Berlin Heidelberg, 2004. 286-295." """ + _N_BASE_FEATURES = 2 _N_FEATURES_INCLUDING_NOISE = 4 diff --git a/river/drift/__init__.py b/river/drift/__init__.py index b434cd7a5b..3c6e8aede7 100644 --- a/river/drift/__init__.py +++ b/river/drift/__init__.py @@ -6,6 +6,7 @@ the true positives while keeping the number of false positives to a minimum. """ + from __future__ import annotations from . import binary, datasets diff --git a/river/drift/binary/__init__.py b/river/drift/binary/__init__.py index 3505af40ec..ccfb615948 100644 --- a/river/drift/binary/__init__.py +++ b/river/drift/binary/__init__.py @@ -1,4 +1,5 @@ """Drift detection for binary data.""" + from __future__ import annotations from .ddm import DDM diff --git a/river/drift/binary/fhddm.py b/river/drift/binary/fhddm.py index 9fc1528ec5..20b442b9fd 100644 --- a/river/drift/binary/fhddm.py +++ b/river/drift/binary/fhddm.py @@ -65,10 +65,13 @@ class FHDDM(base.BinaryDriftAndWarningDetector): [^2]: Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams. """ + def __init__( - self, sliding_window_size: int = 100, confidence_level: float = 0.000001, short_window_size: int | None = None + self, + sliding_window_size: int = 100, + confidence_level: float = 0.000001, + short_window_size: int | None = None, ): - super().__init__() self.sliding_window_size = sliding_window_size self.confidence_level = confidence_level @@ -107,8 +110,14 @@ def update(self, x): if self.short_window_size is not None: u_s = ( - sum (itertools.islice(self._sliding_window, self.sliding_window_size - self.short_window_size, self.sliding_window_size)) - / self.short_window_size + sum( + itertools.islice( + self._sliding_window, + self.sliding_window_size - self.short_window_size, + self.sliding_window_size, + ) + ) + / self.short_window_size ) self._u_short_max = u_s if self._u_short_max < u_s else self._u_short_max short_win_drift_status = ( diff --git a/river/drift/no_drift.py b/river/drift/no_drift.py index 56ca6e121e..4d75e40210 100644 --- a/river/drift/no_drift.py +++ b/river/drift/no_drift.py @@ -67,8 +67,7 @@ class NoDrift(base.DriftDetector): def __init__(self): super().__init__() - def update(self, x: int | float): - ... + def update(self, x: int | float): ... @property def drift_detected(self): diff --git a/river/dummy.py b/river/dummy.py index 1980c9bae8..c42e3fb755 100644 --- a/river/dummy.py +++ b/river/dummy.py @@ -3,6 +3,7 @@ This module is here for testing purposes, as well as providing baseline performances. """ + from __future__ import annotations import collections diff --git a/river/ensemble/__init__.py b/river/ensemble/__init__.py index 26f524c7aa..1456fc84a9 100644 --- a/river/ensemble/__init__.py +++ b/river/ensemble/__init__.py @@ -6,6 +6,7 @@ aggregate predictions from different kinds of models. """ + from __future__ import annotations from .bagging import ( diff --git a/river/ensemble/boosting.py b/river/ensemble/boosting.py index 8c780100f3..23adcc8c44 100644 --- a/river/ensemble/boosting.py +++ b/river/ensemble/boosting.py @@ -327,9 +327,7 @@ def learn_one(self, x, y, **kwargs): def predict_proba_one(self, x, **kwargs): y_proba = collections.Counter() - y_proba_all = ( - collections.Counter() - ) # stores prediction of every model of the ensemble, if y_proba is null, returns y_proba_all + y_proba_all = collections.Counter() # stores prediction of every model of the ensemble, if y_proba is null, returns y_proba_all for i, model in enumerate(self): model_weight = 0.0 if self.correct_weight[i] > 0.0 and self.wrong_weight[i] > 0.0: diff --git a/river/ensemble/streaming_random_patches.py b/river/ensemble/streaming_random_patches.py index 5c739baf00..ac4ee26b68 100644 --- a/river/ensemble/streaming_random_patches.py +++ b/river/ensemble/streaming_random_patches.py @@ -550,7 +550,10 @@ def learn_one( # Note: Pass the original instance x so features are correctly # selected based on the corresponding subspace self._background_learner.learn_one( - x=x, y=y, w=w, n_samples_seen=n_samples_seen # type: ignore + x=x, + y=y, # type: ignore[arg-type] + w=w, + n_samples_seen=n_samples_seen, # type: ignore ) if not self.disable_drift_detector and not self.is_background_learner: @@ -858,7 +861,10 @@ def learn_one( # Note: Pass the original instance x so features are correctly # selected based on the corresponding subspace self._background_learner.learn_one( - x=x, y=y, w=w, n_samples_seen=n_samples_seen # type: ignore + x=x, + y=y, # type: ignore[arg-type] + w=w, + n_samples_seen=n_samples_seen, # type: ignore ) if not self.disable_drift_detector and not self.is_background_learner: diff --git a/river/evaluate/__init__.py b/river/evaluate/__init__.py index f97b863d45..55ed440aac 100644 --- a/river/evaluate/__init__.py +++ b/river/evaluate/__init__.py @@ -11,6 +11,7 @@ the River repository uses these tracks. """ + from __future__ import annotations from .progressive_validation import iter_progressive_val_score, progressive_val_score diff --git a/river/evaluate/progressive_validation.py b/river/evaluate/progressive_validation.py index 9e2373fcf0..00a3c290e2 100644 --- a/river/evaluate/progressive_validation.py +++ b/river/evaluate/progressive_validation.py @@ -23,9 +23,7 @@ def _progressive_validation( ): # Check that the model and the metric are in accordance if not metric.works_with(model): - raise ValueError( - f"{metric.__class__.__name__} metric is not compatible with {model}" - ) + raise ValueError(f"{metric.__class__.__name__} metric is not compatible with {model}") # Determine if predict_one or predict_proba_one should be used in case of a classifier if utils.inspect.isanomalydetector(model) or utils.inspect.isanomalyfilter(model): diff --git a/river/facto/__init__.py b/river/facto/__init__.py index 2ace049514..6c517d5e56 100644 --- a/river/facto/__init__.py +++ b/river/facto/__init__.py @@ -1,4 +1,5 @@ """Factorization machines.""" + from __future__ import annotations from .ffm import FFMClassifier, FFMRegressor diff --git a/river/feature_extraction/__init__.py b/river/feature_extraction/__init__.py index 9e028e63f8..41d9327734 100644 --- a/river/feature_extraction/__init__.py +++ b/river/feature_extraction/__init__.py @@ -6,6 +6,7 @@ be processed by a particular machine learning algorithm. """ + from __future__ import annotations from .agg import Agg, TargetAgg diff --git a/river/feature_extraction/kernel_approx.py b/river/feature_extraction/kernel_approx.py index 8282829132..02540ee99e 100644 --- a/river/feature_extraction/kernel_approx.py +++ b/river/feature_extraction/kernel_approx.py @@ -72,9 +72,9 @@ def __init__(self, gamma=1.0, n_components=100, seed: int | None = None): self.n_components = n_components self.seed = seed self.rng = random.Random(seed) - self.weights: collections.defaultdict[ - typing.Hashable, typing.Callable - ] = collections.defaultdict(self._random_weights) + self.weights: collections.defaultdict[typing.Hashable, typing.Callable] = ( + collections.defaultdict(self._random_weights) + ) self.offsets = [self.rng.uniform(0, 2 * math.pi) for _ in range(n_components)] def _random_weights(self): diff --git a/river/feature_selection/__init__.py b/river/feature_selection/__init__.py index bf5ceef70f..59f0c635a9 100644 --- a/river/feature_selection/__init__.py +++ b/river/feature_selection/__init__.py @@ -1,4 +1,5 @@ """Feature selection.""" + from __future__ import annotations from .k_best import SelectKBest diff --git a/river/forest/__init__.py b/river/forest/__init__.py index f9210f0c2d..12a54da717 100644 --- a/river/forest/__init__.py +++ b/river/forest/__init__.py @@ -1,4 +1,5 @@ """This module implements forest-based classifiers and regressors.""" + from __future__ import annotations from .adaptive_random_forest import ARFClassifier, ARFRegressor diff --git a/river/imblearn/__init__.py b/river/imblearn/__init__.py index 4005b60e85..c551be9239 100644 --- a/river/imblearn/__init__.py +++ b/river/imblearn/__init__.py @@ -1,4 +1,5 @@ """Sampling methods.""" + from __future__ import annotations from .chebyshev import ChebyshevOverSampler, ChebyshevUnderSampler diff --git a/river/linear_model/__init__.py b/river/linear_model/__init__.py index ca472dc9c2..756720490a 100644 --- a/river/linear_model/__init__.py +++ b/river/linear_model/__init__.py @@ -1,4 +1,5 @@ """Linear models.""" + from __future__ import annotations from . import base diff --git a/river/linear_model/pa.py b/river/linear_model/pa.py index 5ae9f286c1..697b6527b1 100644 --- a/river/linear_model/pa.py +++ b/river/linear_model/pa.py @@ -13,9 +13,7 @@ class BasePA: def __init__(self, C, mode, learn_intercept): self.C = C self.mode = mode - self.calc_tau = {0: self._calc_tau_0, 1: self._calc_tau_1, 2: self._calc_tau_2}[ - mode - ] + self.calc_tau = {0: self._calc_tau_0, 1: self._calc_tau_1, 2: self._calc_tau_2}[mode] self.learn_intercept = learn_intercept self.weights = collections.defaultdict(float) self.intercept = 0.0 diff --git a/river/linear_model/softmax.py b/river/linear_model/softmax.py index 019bf09a5a..4b4d0ef779 100644 --- a/river/linear_model/softmax.py +++ b/river/linear_model/softmax.py @@ -70,9 +70,7 @@ def __init__( self.optimizers = collections.defaultdict(new_optimizer) # type: ignore self.loss = optim.losses.CrossEntropy() if loss is None else loss self.l2 = l2 - self.weights = collections.defaultdict( - functools.partial(collections.defaultdict, float) - ) # type: ignore + self.weights = collections.defaultdict(functools.partial(collections.defaultdict, float)) # type: ignore @property def _multiclass(self): diff --git a/river/metrics/__init__.py b/river/metrics/__init__.py index 5a1e102756..f798281f33 100644 --- a/river/metrics/__init__.py +++ b/river/metrics/__init__.py @@ -26,6 +26,7 @@ ``` """ + from __future__ import annotations from . import base, multioutput diff --git a/river/metrics/base.py b/river/metrics/base.py index ec34ceb726..0fb837848e 100644 --- a/river/metrics/base.py +++ b/river/metrics/base.py @@ -190,15 +190,11 @@ class RegressionMetric(Metric): _fmt = ",.6f" # use commas to separate big numbers and show 6 decimals @abc.abstractmethod - def update( - self, y_true: numbers.Number, y_pred: numbers.Number - ) -> RegressionMetric: + def update(self, y_true: numbers.Number, y_pred: numbers.Number) -> RegressionMetric: """Update the metric.""" @abc.abstractmethod - def revert( - self, y_true: numbers.Number, y_pred: numbers.Number - ) -> RegressionMetric: + def revert(self, y_true: numbers.Number, y_pred: numbers.Number) -> RegressionMetric: """Revert the metric.""" @property @@ -322,11 +318,7 @@ def __metaclass__(self): def __repr__(self): name = self.__class__.__name__ - return ( - super() - .__repr__() - .replace(name, f"{name}({self.metric.__class__.__name__})") - ) + return super().__repr__().replace(name, f"{name}({self.metric.__class__.__name__})") class MeanMetric(abc.ABC): diff --git a/river/metrics/cross_entropy.py b/river/metrics/cross_entropy.py index 5b22d1741a..6232331661 100644 --- a/river/metrics/cross_entropy.py +++ b/river/metrics/cross_entropy.py @@ -53,8 +53,6 @@ def _eval(self, y_true, y_pred): for label, proba in y_pred.items(): if y_true == label: - total += math.log( - utils.math.clamp(x=proba, minimum=1e-15, maximum=1 - 1e-15) - ) + total += math.log(utils.math.clamp(x=proba, minimum=1e-15, maximum=1 - 1e-15)) return -total diff --git a/river/metrics/fbeta.py b/river/metrics/fbeta.py index f3a8df0e77..e594042726 100644 --- a/river/metrics/fbeta.py +++ b/river/metrics/fbeta.py @@ -303,9 +303,7 @@ def __init__(self, betas, weights, cm=None): super().__init__(cm) self.betas = betas self.weights = ( - collections.defaultdict(functools.partial(int, 1)) - if weights is None - else weights + collections.defaultdict(functools.partial(int, 1)) if weights is None else weights ) def get(self): diff --git a/river/metrics/multioutput/__init__.py b/river/metrics/multioutput/__init__.py index 3d1c0ff6b5..ee0d1df1af 100644 --- a/river/metrics/multioutput/__init__.py +++ b/river/metrics/multioutput/__init__.py @@ -1,4 +1,5 @@ """Metrics for multi-output learning.""" + from __future__ import annotations from . import base diff --git a/river/metrics/multioutput/confusion.py b/river/metrics/multioutput/confusion.py index b669cf7b1e..d0e9b3f6d8 100644 --- a/river/metrics/multioutput/confusion.py +++ b/river/metrics/multioutput/confusion.py @@ -71,8 +71,6 @@ def revert(self, y_true, y_pred, w=1.0): def __repr__(self): return "\n\n".join( - "\n".join( - [str(label)] + textwrap.indent(repr(cm), prefix=" ").splitlines() - ) + "\n".join([str(label)] + textwrap.indent(repr(cm), prefix=" ").splitlines()) for label, cm in self.data.items() ) diff --git a/river/metrics/mutual_info.py b/river/metrics/mutual_info.py index de07a33a53..1a9d22e398 100644 --- a/river/metrics/mutual_info.py +++ b/river/metrics/mutual_info.py @@ -163,6 +163,7 @@ class NormalizedMutualInfo(metrics.base.MultiClassMetric): In Wikipedia, The Free Encyclopedia, from https://en.wikipedia.org/w/index.php?title=Mutual_information&oldid=1012714929 """ + _AVERAGE_MIN = "min" _AVERAGE_MAX = "max" _AVERAGE_GEOMETRIC = "geometric" @@ -277,6 +278,7 @@ class AdjustedMutualInfo(metrics.base.MultiClassMetric): In Wikipedia, The Free Encyclopedia, from https://en.wikipedia.org/w/index.php?title=Mutual_information&oldid=1012714929 """ + _AVERAGE_MIN = "min" _AVERAGE_MAX = "max" _AVERAGE_GEOMETRIC = "geometric" @@ -335,9 +337,7 @@ def get(self): else: denominator = max(denominator, np.finfo("float64").eps) - adjusted_mutual_info_score = ( - mutual_info_score - expected_mutual_info_score - ) / denominator + adjusted_mutual_info_score = (mutual_info_score - expected_mutual_info_score) / denominator return adjusted_mutual_info_score diff --git a/river/metrics/rand.py b/river/metrics/rand.py index 0992a865d4..ddd6854400 100644 --- a/river/metrics/rand.py +++ b/river/metrics/rand.py @@ -26,9 +26,7 @@ def _pair_confusion(cm): false_negatives -= sum_squares - true_negatives = ( - cm.n_samples * cm.n_samples - (false_positives + false_negatives) - sum_squares - ) + true_negatives = cm.n_samples * cm.n_samples - (false_positives + false_negatives) - sum_squares pair_confusion_matrix[0][0] = true_negatives pair_confusion_matrix[0][1] = false_positives @@ -189,10 +187,8 @@ def get(self): 2.0 * (true_positives * true_negatives - false_negatives * false_positives) / ( - (true_positives + false_negatives) - * (false_negatives + true_negatives) - + (true_positives + false_positives) - * (false_positives + true_negatives) + (true_positives + false_negatives) * (false_negatives + true_negatives) + + (true_positives + false_positives) * (false_positives + true_negatives) ) ) except ZeroDivisionError: diff --git a/river/misc/__init__.py b/river/misc/__init__.py index b5d013f9ec..d24ea0cc18 100644 --- a/river/misc/__init__.py +++ b/river/misc/__init__.py @@ -3,6 +3,7 @@ This module essentially regroups some implementations that have nowhere else to go. """ + from __future__ import annotations from .sdft import SDFT diff --git a/river/model_selection/__init__.py b/river/model_selection/__init__.py index f3a4ee482d..f010dcf5c6 100644 --- a/river/model_selection/__init__.py +++ b/river/model_selection/__init__.py @@ -12,6 +12,7 @@ The `utils.expand_param_grid` function can be used for this purpose. """ + from __future__ import annotations from . import base diff --git a/river/model_selection/sh.py b/river/model_selection/sh.py index 534e306238..03979b91b7 100644 --- a/river/model_selection/sh.py +++ b/river/model_selection/sh.py @@ -36,8 +36,7 @@ def __init__( self._best_model_idx = 0 @abc.abstractmethod - def _pred_func(self, model): - ... + def _pred_func(self, model): ... @property def best_model(self): diff --git a/river/multiclass/__init__.py b/river/multiclass/__init__.py index 55d1a2eb3d..902f3777f4 100644 --- a/river/multiclass/__init__.py +++ b/river/multiclass/__init__.py @@ -1,4 +1,5 @@ """Multi-class classification.""" + from __future__ import annotations from .occ import OutputCodeClassifier diff --git a/river/multioutput/__init__.py b/river/multioutput/__init__.py index 43325dcedd..15a92e85f7 100644 --- a/river/multioutput/__init__.py +++ b/river/multioutput/__init__.py @@ -1,4 +1,5 @@ """Multi-output models.""" + from __future__ import annotations from .chain import ( diff --git a/river/naive_bayes/__init__.py b/river/naive_bayes/__init__.py index dae4968b66..b1890dadc4 100644 --- a/river/naive_bayes/__init__.py +++ b/river/naive_bayes/__init__.py @@ -1,4 +1,5 @@ """Naive Bayes algorithms.""" + from __future__ import annotations from .bernoulli import BernoulliNB diff --git a/river/neighbors/__init__.py b/river/neighbors/__init__.py index 432e949ea5..8d8211d9e9 100644 --- a/river/neighbors/__init__.py +++ b/river/neighbors/__init__.py @@ -4,6 +4,7 @@ until a query is received. """ + from __future__ import annotations from .ann import SWINN diff --git a/river/neighbors/ann/nn_vertex.py b/river/neighbors/ann/nn_vertex.py index c17ea49c52..d7edbca2df 100644 --- a/river/neighbors/ann/nn_vertex.py +++ b/river/neighbors/ann/nn_vertex.py @@ -15,7 +15,7 @@ def __init__(self, item, uuid: int) -> None: self.uuid = uuid self.edges: dict[int, float] = {} self.r_edges: dict[int, float] = {} - self.flags: set[int]= set() + self.flags: set[int] = set() self.worst_edge: int | None = None def __eq__(self, other) -> bool: @@ -71,7 +71,9 @@ def rem_edge(self, vertex: Vertex): if not self.has_rneighbors(): Vertex._isolated.add(self.uuid) - def push_edge(self, node: Vertex, dist: float, max_edges: int, vertex_pool: list[Vertex]) -> int: + def push_edge( + self, node: Vertex, dist: float, max_edges: int, vertex_pool: list[Vertex] + ) -> int: if self.is_neighbor(node) or node.uuid == self.uuid: return 0 @@ -120,7 +122,9 @@ def all_neighbors(self) -> set[int]: def is_isolated(self): return len(self.edges) == 0 and len(self.r_edges) == 0 - def prune(self, prune_prob: float, prune_trigger: int, vertex_pool: list[Vertex], rng: random.Random): + def prune( + self, prune_prob: float, prune_trigger: int, vertex_pool: list[Vertex], rng: random.Random + ): if prune_prob == 0: return diff --git a/river/neighbors/ann/swinn.py b/river/neighbors/ann/swinn.py index e774f052f1..b9a2c3956d 100644 --- a/river/neighbors/ann/swinn.py +++ b/river/neighbors/ann/swinn.py @@ -264,8 +264,12 @@ def _refine(self, nodes: list[int] | None = None): continue dist = self.dist_func(self[n1].item, self[n2].item) - total_changes += self[n1].push_edge(self[n2], dist, self.graph_k, self._data) - total_changes += self[n2].push_edge(self[n1], dist, self.graph_k, self._data) + total_changes += self[n1].push_edge( + self[n2], dist, self.graph_k, self._data + ) + total_changes += self[n2].push_edge( + self[n1], dist, self.graph_k, self._data + ) tried.add((n1, n2)) @@ -278,8 +282,12 @@ def _refine(self, nodes: list[int] | None = None): continue dist = self.dist_func(self[n1].item, self[n2].item) - total_changes += self[n1].push_edge(self[n2], dist, self.graph_k, self._data) - total_changes += self[n2].push_edge(self[n1], dist, self.graph_k, self._data) + total_changes += self[n1].push_edge( + self[n2], dist, self.graph_k, self._data + ) + total_changes += self[n2].push_edge( + self[n1], dist, self.graph_k, self._data + ) tried.add((n1, n2)) @@ -343,7 +351,14 @@ def _linear_scan(self, item, k): return None - def _search(self, item, k, epsilon: float = 0.1, seed: Vertex | None = None, exclude: set[int] | None = None) -> tuple[list, list]: + def _search( + self, + item, + k, + epsilon: float = 0.1, + seed: Vertex | None = None, + exclude: set[int] | None = None, + ) -> tuple[list, list]: # Limiter for the distance bound distance_scale = 1 + epsilon # Distance threshold for early stops diff --git a/river/neighbors/base.py b/river/neighbors/base.py index 311c0291c4..0ca8e21c50 100644 --- a/river/neighbors/base.py +++ b/river/neighbors/base.py @@ -7,8 +7,7 @@ class DistanceFunc(typing.Protocol): - def __call__(self, a: typing.Any, b: typing.Any, **kwargs) -> float: - ... + def __call__(self, a: typing.Any, b: typing.Any, **kwargs) -> float: ... class FunctionWrapper: diff --git a/river/neural_net/__init__.py b/river/neural_net/__init__.py index 00ea313e35..9bc8928cb7 100644 --- a/river/neural_net/__init__.py +++ b/river/neural_net/__init__.py @@ -1,4 +1,5 @@ """Neural networks.""" + from __future__ import annotations from . import activations diff --git a/river/optim/__init__.py b/river/optim/__init__.py index 2a29bd5201..f1c35150d8 100644 --- a/river/optim/__init__.py +++ b/river/optim/__init__.py @@ -1,4 +1,5 @@ """Stochastic optimization.""" + from __future__ import annotations from . import base, initializers, losses, schedulers diff --git a/river/optim/average.py b/river/optim/average.py index 0bd516808b..90ca1957ce 100644 --- a/river/optim/average.py +++ b/river/optim/average.py @@ -6,7 +6,6 @@ class Averager(optim.base.Optimizer): - """Averaged stochastic gradient descent. This is a wrapper that can be applied to any stochastic gradient descent optimiser. Note that diff --git a/river/optim/initializers.py b/river/optim/initializers.py index ce6a21ea55..a5fc7d5a17 100644 --- a/river/optim/initializers.py +++ b/river/optim/initializers.py @@ -1,4 +1,5 @@ """Weight initializers.""" + from __future__ import annotations import numpy as np diff --git a/river/optim/losses.py b/river/optim/losses.py index bc03a9fe2f..a24ec76b2c 100644 --- a/river/optim/losses.py +++ b/river/optim/losses.py @@ -3,6 +3,7 @@ Each loss function is intended to work with both single values as well as numpy vectors. """ + from __future__ import annotations import math diff --git a/river/optim/schedulers.py b/river/optim/schedulers.py index 624730c65d..d89c936977 100644 --- a/river/optim/schedulers.py +++ b/river/optim/schedulers.py @@ -1,4 +1,5 @@ """Learning rate schedulers.""" + from __future__ import annotations import math diff --git a/river/preprocessing/__init__.py b/river/preprocessing/__init__.py index 458def0676..fb83db41ae 100644 --- a/river/preprocessing/__init__.py +++ b/river/preprocessing/__init__.py @@ -6,6 +6,7 @@ the latter extracts new information from the data """ + from __future__ import annotations from .feature_hasher import FeatureHasher diff --git a/river/preprocessing/impute.py b/river/preprocessing/impute.py index de15e9fdc6..2926a8878b 100644 --- a/river/preprocessing/impute.py +++ b/river/preprocessing/impute.py @@ -237,8 +237,7 @@ class Constant(stats.base.Univariate): def __init__(self, value: typing.Any): self.value = value - def update(self, x): - ... + def update(self, x): ... def get(self): return self.value diff --git a/river/preprocessing/random_projection.py b/river/preprocessing/random_projection.py index 979c249595..5faf56626d 100644 --- a/river/preprocessing/random_projection.py +++ b/river/preprocessing/random_projection.py @@ -63,9 +63,9 @@ def __init__(self, n_components=10, seed: int | None = None): self.n_components = n_components self.seed = seed self._rng = random.Random(seed) - self._projection_matrix: collections.defaultdict[ - base.typing.FeatureName, float - ] = collections.defaultdict(self._rand_gauss) + self._projection_matrix: collections.defaultdict[base.typing.FeatureName, float] = ( + collections.defaultdict(self._rand_gauss) + ) def _rand_gauss(self): return self._rng.gauss(0, 1 / (self.n_components**0.5)) diff --git a/river/preprocessing/test_lda.py b/river/preprocessing/test_lda.py index 8f229bbf38..742531ba98 100644 --- a/river/preprocessing/test_lda.py +++ b/river/preprocessing/test_lda.py @@ -10,6 +10,7 @@ PyInfVov (https://github.com/kzhai/PyInfVoc) with a batch size of 1. """ + from __future__ import annotations import numpy as np diff --git a/river/proba/__init__.py b/river/proba/__init__.py index c3e399534a..23b60e8da4 100644 --- a/river/proba/__init__.py +++ b/river/proba/__init__.py @@ -1,4 +1,5 @@ """Probability distributions.""" + from __future__ import annotations from . import base diff --git a/river/proba/beta.py b/river/proba/beta.py index d23718c350..f4581d4730 100644 --- a/river/proba/beta.py +++ b/river/proba/beta.py @@ -94,9 +94,7 @@ def revert(self, x): def __call__(self, p: float): return ( - p ** (self.alpha - 1) - * (1 - p) ** (self.beta - 1) - / _beta_func(self.alpha, self.beta) + p ** (self.alpha - 1) * (1 - p) ** (self.beta - 1) / _beta_func(self.alpha, self.beta) ) def sample(self): diff --git a/river/proba/gaussian.py b/river/proba/gaussian.py index ded37ca6e5..cfe428f7a3 100644 --- a/river/proba/gaussian.py +++ b/river/proba/gaussian.py @@ -76,9 +76,7 @@ def __call__(self, x): var = self._var.get() if var: try: - return math.exp((x - self.mu) ** 2 / (-2 * var)) / math.sqrt( - math.tau * var - ) + return math.exp((x - self.mu) ** 2 / (-2 * var)) / math.sqrt(math.tau * var) except ValueError: return 0.0 except OverflowError: @@ -242,9 +240,7 @@ def mu(self) -> dict: @property def var(self) -> pd.DataFrame: """The variance of the distribution.""" - variables = sorted( - list({var for cov in self._var.matrix.keys() for var in cov}) - ) + variables = sorted(list({var for cov in self._var.matrix.keys() for var in cov})) # Initialize the covariance matrix array cov_array = np.zeros((len(variables), len(variables))) @@ -269,9 +265,7 @@ def sigma(self) -> pd.DataFrame: def __repr__(self): mu_str = ", ".join(f"{m:.3f}" for m in self.mu.values()) - var_str = self.var.to_string( - float_format="{:0.3f}".format, header=False, index=False - ) + var_str = self.var.to_string(float_format="{:0.3f}".format, header=False, index=False) var_str = " [" + var_str.replace("\n", "]\n [") + "]" return f"𝒩(\n μ=({mu_str}),\n σ^2=(\n{var_str}\n )\n)" diff --git a/river/proba/multinomial.py b/river/proba/multinomial.py index 19ff5be4b8..2244d01bb6 100644 --- a/river/proba/multinomial.py +++ b/river/proba/multinomial.py @@ -130,9 +130,7 @@ def revert(self, x): self._n -= 1 def sample(self): - return self._rng.choices( - list(self.counts.keys()), weights=list(self.counts.values()) - )[0] + return self._rng.choices(list(self.counts.keys()), weights=list(self.counts.values()))[0] def __call__(self, x): try: @@ -141,6 +139,4 @@ def __call__(self, x): return 0.0 def __repr__(self): - return "\n".join( - f"P({c}) = {self(c):.3f}" for c, _ in self.counts.most_common() - ) + return "\n".join(f"P({c}) = {self(c):.3f}" for c, _ in self.counts.most_common()) diff --git a/river/reco/__init__.py b/river/reco/__init__.py index 05d8ba9b52..d849c8cf8b 100644 --- a/river/reco/__init__.py +++ b/river/reco/__init__.py @@ -15,6 +15,7 @@ value. It is up to the user to determine how to translate a user session into training data. """ + from __future__ import annotations from . import base diff --git a/river/reco/baseline.py b/river/reco/baseline.py index 7d62458e0d..75bbea0d27 100644 --- a/river/reco/baseline.py +++ b/river/reco/baseline.py @@ -103,12 +103,12 @@ def __init__( self.clip_gradient = clip_gradient self.global_mean = stats.Mean() - self.u_biases: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(initializer) - self.i_biases: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(initializer) + self.u_biases: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(initializer) + ) + self.i_biases: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(initializer) + ) def predict_one(self, user, item, x=None): return self.global_mean.get() + self.u_biases[user] + self.i_biases[item] diff --git a/river/reco/biased_mf.py b/river/reco/biased_mf.py index 2b4515e4b0..8d65b4f07c 100644 --- a/river/reco/biased_mf.py +++ b/river/reco/biased_mf.py @@ -158,20 +158,20 @@ def __init__( self.clip_gradient = clip_gradient self.global_mean = stats.Mean() - self.u_biases: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(weight_initializer) - self.i_biases: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(weight_initializer) + self.u_biases: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(weight_initializer) + ) + self.i_biases: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(weight_initializer) + ) random_latents = functools.partial(self.latent_initializer, shape=self.n_factors) - self.u_latents: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(random_latents) - self.i_latents: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(random_latents) + self.u_latents: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(random_latents) + ) + self.i_latents: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(random_latents) + ) @property def _mutable_attributes(self): diff --git a/river/reco/funk_mf.py b/river/reco/funk_mf.py index a7edf2ade7..1bf7285799 100644 --- a/river/reco/funk_mf.py +++ b/river/reco/funk_mf.py @@ -115,12 +115,12 @@ def __init__( self.clip_gradient = clip_gradient random_latents = functools.partial(self.initializer, shape=self.n_factors) - self.u_latents: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(random_latents) - self.i_latents: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(random_latents) + self.u_latents: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(random_latents) + ) + self.i_latents: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(random_latents) + ) @property def _mutable_attributes(self): diff --git a/river/rules/__init__.py b/river/rules/__init__.py index ade579452b..60bbd1e7af 100644 --- a/river/rules/__init__.py +++ b/river/rules/__init__.py @@ -1,4 +1,5 @@ """Decision rules-based algorithms.""" + from __future__ import annotations from .amrules import AMRules diff --git a/river/sketch/__init__.py b/river/sketch/__init__.py index 150c802544..5eaf41c62d 100644 --- a/river/sketch/__init__.py +++ b/river/sketch/__init__.py @@ -4,6 +4,7 @@ of memory and processing time. """ + from __future__ import annotations from .counter import Counter diff --git a/river/stats/__init__.py b/river/stats/__init__.py index 0b88c19d4b..e62e9db344 100644 --- a/river/stats/__init__.py +++ b/river/stats/__init__.py @@ -1,4 +1,5 @@ """Running statistics""" + from __future__ import annotations from . import base diff --git a/river/stats/entropy.py b/river/stats/entropy.py index c7e4e1dbb7..59c9cba062 100644 --- a/river/stats/entropy.py +++ b/river/stats/entropy.py @@ -83,11 +83,7 @@ def update(self, x): fading_factor = self.fading_factor entropy = self.entropy - entropy = ( - (n + eps) - / (n + 1) - * (fading_factor * entropy - math.log((n + eps) / (n + 1))) - ) + entropy = (n + eps) / (n + 1) * (fading_factor * entropy - math.log((n + eps) / (n + 1))) entropy -= (cx + 1) / (n + 1) * math.log((cx + 1) / (n + 1)) entropy += (cx + eps) / (n + 1) * math.log((cx + eps) / (n + 1)) self.entropy = entropy diff --git a/river/stream/__init__.py b/river/stream/__init__.py index 2a70f518f8..16ddfc7049 100644 --- a/river/stream/__init__.py +++ b/river/stream/__init__.py @@ -3,6 +3,7 @@ The module includes tools to iterate over data streams. """ + from __future__ import annotations from .cache import Cache diff --git a/river/stream/qa.py b/river/stream/qa.py index b7a60d0fe5..54f2ca639d 100644 --- a/river/stream/qa.py +++ b/river/stream/qa.py @@ -157,10 +157,14 @@ def simulate_qa( break # Reveal the ground truth and pop the observation from the queue - yield (i_old, x_old, y_old, kwargs_old) if kwargs_old else ( - i_old, - x_old, - y_old, + yield ( + (i_old, x_old, y_old, kwargs_old) + if kwargs_old + else ( + i_old, + x_old, + y_old, + ) ) del mementos[0] @@ -170,8 +174,12 @@ def simulate_qa( yield (i, x, None, kwargs) if kwargs else (i, x, None) for memento in mementos: - yield (memento.i, memento.x, memento.y, memento.kwargs) if memento.kwargs else ( - memento.i, - memento.x, - memento.y, + yield ( + (memento.i, memento.x, memento.y, memento.kwargs) + if memento.kwargs + else ( + memento.i, + memento.x, + memento.y, + ) ) diff --git a/river/stream/twitch_chat_stream.py b/river/stream/twitch_chat_stream.py index fff0aafbee..3547737ebd 100644 --- a/river/stream/twitch_chat_stream.py +++ b/river/stream/twitch_chat_stream.py @@ -145,7 +145,7 @@ def _gen_items(self, sock: socket.socket) -> Iterator[ChatMessageItem]: continue resp = data.decode(ENCODING) now = dt.datetime.now() - except socket.timeout as e: + except TimeoutError as e: raise TimeoutError(f"Twitch did not respond in {self.timeout:,d} seconds") from e except UnicodeDecodeError: continue diff --git a/river/test_estimators.py b/river/test_estimators.py index 09b40ea0ff..f332981ece 100644 --- a/river/test_estimators.py +++ b/river/test_estimators.py @@ -1,4 +1,5 @@ """General tests that all estimators need to pass.""" + from __future__ import annotations import importlib diff --git a/river/time_series/__init__.py b/river/time_series/__init__.py index 393a45dbe9..7fb887f5e2 100644 --- a/river/time_series/__init__.py +++ b/river/time_series/__init__.py @@ -1,4 +1,5 @@ """Time series forecasting.""" + from __future__ import annotations from . import base diff --git a/river/time_series/holt_winters.py b/river/time_series/holt_winters.py index 0daed4c096..1e53c187e5 100644 --- a/river/time_series/holt_winters.py +++ b/river/time_series/holt_winters.py @@ -9,8 +9,7 @@ __all__ = ["HoltWinters"] -class Component(deque): - ... +class Component(deque): ... class AdditiveLevel(Component): diff --git a/river/tree/__init__.py b/river/tree/__init__.py index 5166c553fb..9e8b887217 100755 --- a/river/tree/__init__.py +++ b/river/tree/__init__.py @@ -49,6 +49,7 @@ dynamic and static feature quantizers to deal with numerical inputs. """ + from __future__ import annotations from . import base, splitter diff --git a/river/tree/base.py b/river/tree/base.py index bb074741a8..2d337b20e0 100644 --- a/river/tree/base.py +++ b/river/tree/base.py @@ -8,6 +8,7 @@ intention is to provide utilities for walking over a tree and visualizing it. """ + from __future__ import annotations import abc diff --git a/river/tree/hoeffding_tree_classifier.py b/river/tree/hoeffding_tree_classifier.py index f676e2acac..b3dc172461 100755 --- a/river/tree/hoeffding_tree_classifier.py +++ b/river/tree/hoeffding_tree_classifier.py @@ -185,9 +185,7 @@ def _mutable_attributes(self): def split_criterion(self, split_criterion): if split_criterion not in self._VALID_SPLIT_CRITERIA: print( - "Invalid split_criterion option {}', will use default '{}'".format( - split_criterion, self._INFO_GAIN_SPLIT - ) + f"Invalid split_criterion option {split_criterion}', will use default '{self._INFO_GAIN_SPLIT}'" ) self._split_criterion = self._INFO_GAIN_SPLIT else: @@ -197,9 +195,7 @@ def split_criterion(self, split_criterion): def leaf_prediction(self, leaf_prediction): if leaf_prediction not in self._VALID_LEAF_PREDICTION: print( - "Invalid leaf_prediction option {}', will use default '{}'".format( - leaf_prediction, self._NAIVE_BAYES_ADAPTIVE - ) + f"Invalid leaf_prediction option {leaf_prediction}', will use default '{self._NAIVE_BAYES_ADAPTIVE}'" ) self._leaf_prediction = self._NAIVE_BAYES_ADAPTIVE else: diff --git a/river/tree/hoeffding_tree_regressor.py b/river/tree/hoeffding_tree_regressor.py index 065a5f8876..d6d5deba11 100644 --- a/river/tree/hoeffding_tree_regressor.py +++ b/river/tree/hoeffding_tree_regressor.py @@ -163,9 +163,7 @@ def _mutable_attributes(self): def leaf_prediction(self, leaf_prediction): if leaf_prediction not in self._VALID_LEAF_PREDICTION: print( - 'Invalid leaf_prediction option "{}", will use default "{}"'.format( - leaf_prediction, self._MODEL - ) + f'Invalid leaf_prediction option "{leaf_prediction}", will use default "{self._MODEL}"' ) self._leaf_prediction = self._MODEL else: diff --git a/river/tree/mondrian/__init__.py b/river/tree/mondrian/__init__.py index 15b596ba75..b26365acd0 100644 --- a/river/tree/mondrian/__init__.py +++ b/river/tree/mondrian/__init__.py @@ -6,6 +6,7 @@ AMFClassifier and AMFRegressor classes in the ensemble module. """ + from __future__ import annotations from .mondrian_tree import MondrianTree diff --git a/river/tree/nodes/sgt_nodes.py b/river/tree/nodes/sgt_nodes.py index b649b1ac51..f53da21355 100644 --- a/river/tree/nodes/sgt_nodes.py +++ b/river/tree/nodes/sgt_nodes.py @@ -42,9 +42,10 @@ def __init__(self, prediction: float = 0.0, depth: int = 0, split_params: dict | ) self.last_split_attempt_at = 0 - self._split_stats: dict[ - FeatureName, dict[Hashable, GradHessStats] | DynamicQuantizer | StaticQuantizer - ] | None = {} + self._split_stats: ( + dict[FeatureName, dict[Hashable, GradHessStats] | DynamicQuantizer | StaticQuantizer] + | None + ) = {} self._update_stats = GradHessStats() def reset(self): diff --git a/river/tree/splitter/__init__.py b/river/tree/splitter/__init__.py index 67df2ca337..15d966599a 100644 --- a/river/tree/splitter/__init__.py +++ b/river/tree/splitter/__init__.py @@ -13,6 +13,7 @@ when choosing the correct feature splitter. """ + from __future__ import annotations from .base import Quantizer, Splitter diff --git a/river/tree/utils.py b/river/tree/utils.py index 7dff6859e4..541e9a31ed 100644 --- a/river/tree/utils.py +++ b/river/tree/utils.py @@ -78,9 +78,12 @@ class BranchFactory: merit: float = -math.inf feature: FeatureName | None = None - split_info: typing.Hashable | list[typing.Hashable] | tuple[ - typing.Hashable, list[typing.Hashable] - ] | None = None + split_info: ( + typing.Hashable + | list[typing.Hashable] + | tuple[typing.Hashable, list[typing.Hashable]] + | None + ) = None children_stats: list | None = None numerical_feature: bool = True multiway_split: bool = False diff --git a/river/utils/__init__.py b/river/utils/__init__.py index 3ac6e56a3b..860427bbb6 100644 --- a/river/utils/__init__.py +++ b/river/utils/__init__.py @@ -1,4 +1,5 @@ """Shared utility classes and functions""" + from __future__ import annotations from . import inspect, math, norm, pretty, random diff --git a/river/utils/inspect.py b/river/utils/inspect.py index 68aee6e6b0..e7f1434dac 100644 --- a/river/utils/inspect.py +++ b/river/utils/inspect.py @@ -6,6 +6,7 @@ thus provides utilities for determining an arbitrary model's type. """ + from __future__ import annotations import inspect diff --git a/river/utils/math.py b/river/utils/math.py index b208950a4f..5d018b4b43 100644 --- a/river/utils/math.py +++ b/river/utils/math.py @@ -3,6 +3,7 @@ A lot of this is experimental and has a high probability of changing in the future. """ + from __future__ import annotations import functools diff --git a/river/utils/pretty.py b/river/utils/pretty.py index 2ceec3f85a..df9d7c926a 100644 --- a/river/utils/pretty.py +++ b/river/utils/pretty.py @@ -34,9 +34,7 @@ def print_table( raise ValueError("all the columns must be of the same length") # Determine the width of each column based on the maximum length of it's elements - col_widths = [ - max(max(map(len, col)), len(header)) for header, col in zip(headers, columns) - ] + col_widths = [max(max(map(len, col)), len(header)) for header, col in zip(headers, columns)] # Make a template to print out rows one by one row_format = " ".join(["{:" + str(width + 2) + "s}" for width in col_widths]) @@ -50,9 +48,7 @@ def print_table( row_format.format(*headers) + "\n" + "\n".join( - row_format.format( - *[col[i].rjust(width) for col, width in zip(columns, col_widths)] - ) + row_format.format(*[col[i].rjust(width) for col, width in zip(columns, col_widths)]) for i in order ) ) @@ -75,5 +71,5 @@ def humanize_bytes(n_bytes: int): rank = int((math.log10(n_bytes)) / 3) rank = min(rank, len(suffixes) - 1) human = n_bytes / (1024.0**rank) - f = ("%.2f" % human).rstrip("0").rstrip(".") + f = f"{human:.2f}".rstrip("0").rstrip(".") return f"{f} {suffixes[rank]}" diff --git a/river/utils/rolling.py b/river/utils/rolling.py index c5abc9806e..19ed551b55 100644 --- a/river/utils/rolling.py +++ b/river/utils/rolling.py @@ -8,11 +8,9 @@ @typing.runtime_checkable class Rollable(typing.Protocol): - def update(self, *args, **kwargs): - ... + def update(self, *args, **kwargs): ... - def revert(self, *args, **kwargs): - ... + def revert(self, *args, **kwargs): ... class BaseRolling: From fc9bcc0ec21034f3e3fd848a85db2c7c4dbb8c65 Mon Sep 17 00:00:00 2001 From: Daveismus <55088389+Daveismus@users.noreply.github.com> Date: Fri, 30 Aug 2024 10:26:07 +0200 Subject: [PATCH 279/347] Add Hack for stats.IQR get method for avoiding panicking (#1603) Co-authored-by: David Brielbeck --- river/stats/iqr.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/river/stats/iqr.py b/river/stats/iqr.py index c5d56c38de..583a1260a3 100644 --- a/river/stats/iqr.py +++ b/river/stats/iqr.py @@ -61,6 +61,10 @@ def update(self, x): self._is_updated = True def get(self): + # HACK: Avoid crash if get is called before update + # panicked at 'index out of bounds: the len is 0 but the index is 0' + if not self._is_updated: + return None return self._iqr.get() def __repr__(self): From c2c0e4ff1452d0f61ea5e55b24ebb871741b27e4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 31 Aug 2024 08:48:01 +0200 Subject: [PATCH 280/347] Bump notebook from 7.2.0 to 7.2.2 (#1604) Bumps [notebook](https://github.com/jupyter/notebook) from 7.2.0 to 7.2.2. - [Release notes](https://github.com/jupyter/notebook/releases) - [Changelog](https://github.com/jupyter/notebook/blob/@jupyter-notebook/tree@7.2.2/CHANGELOG.md) - [Commits](https://github.com/jupyter/notebook/compare/@jupyter-notebook/tree@7.2.0...@jupyter-notebook/tree@7.2.2) --- updated-dependencies: - dependency-name: notebook dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index d8bf62a520..09b09ebcc0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2589,13 +2589,13 @@ setuptools = "*" [[package]] name = "notebook" -version = "7.2.0" +version = "7.2.2" description = "Jupyter Notebook - A web-based notebook environment for interactive computing" optional = false python-versions = ">=3.8" files = [ - {file = "notebook-7.2.0-py3-none-any.whl", hash = "sha256:b4752d7407d6c8872fc505df0f00d3cae46e8efb033b822adacbaa3f1f3ce8f5"}, - {file = "notebook-7.2.0.tar.gz", hash = "sha256:34a2ba4b08ad5d19ec930db7484fb79746a1784be9e1a5f8218f9af8656a141f"}, + {file = "notebook-7.2.2-py3-none-any.whl", hash = "sha256:c89264081f671bc02eec0ed470a627ed791b9156cad9285226b31611d3e9fe1c"}, + {file = "notebook-7.2.2.tar.gz", hash = "sha256:2ef07d4220421623ad3fe88118d687bc0450055570cdd160814a59cf3a1c516e"}, ] [package.dependencies] @@ -3477,6 +3477,7 @@ files = [ {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, + {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"}, {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"}, {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"}, {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, From f9824766967c4ac1d1f5b164f7bc87716357d4bd Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 31 Aug 2024 08:58:01 +0200 Subject: [PATCH 281/347] Bump jupyterlab from 4.2.0 to 4.2.5 (#1605) Bumps [jupyterlab](https://github.com/jupyterlab/jupyterlab) from 4.2.0 to 4.2.5. - [Release notes](https://github.com/jupyterlab/jupyterlab/releases) - [Changelog](https://github.com/jupyterlab/jupyterlab/blob/@jupyterlab/lsp@4.2.5/CHANGELOG.md) - [Commits](https://github.com/jupyterlab/jupyterlab/compare/@jupyterlab/lsp@4.2.0...@jupyterlab/lsp@4.2.5) --- updated-dependencies: - dependency-name: jupyterlab dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/poetry.lock b/poetry.lock index 09b09ebcc0..968c69b1e4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1662,13 +1662,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (> [[package]] name = "jupyterlab" -version = "4.2.0" +version = "4.2.5" description = "JupyterLab computational environment" optional = false python-versions = ">=3.8" files = [ - {file = "jupyterlab-4.2.0-py3-none-any.whl", hash = "sha256:0dfe9278e25a145362289c555d9beb505697d269c10e99909766af7c440ad3cc"}, - {file = "jupyterlab-4.2.0.tar.gz", hash = "sha256:356e9205a6a2ab689c47c8fe4919dba6c076e376d03f26baadc05748c2435dd5"}, + {file = "jupyterlab-4.2.5-py3-none-any.whl", hash = "sha256:73b6e0775d41a9fee7ee756c80f58a6bed4040869ccc21411dc559818874d321"}, + {file = "jupyterlab-4.2.5.tar.gz", hash = "sha256:ae7f3a1b8cb88b4f55009ce79fa7c06f99d70cd63601ee4aa91815d054f46f75"}, ] [package.dependencies] @@ -1683,6 +1683,7 @@ jupyter-server = ">=2.4.0,<3" jupyterlab-server = ">=2.27.1,<3" notebook-shim = ">=0.2" packaging = "*" +setuptools = ">=40.1.0" tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""} tornado = ">=6.2.0" traitlets = "*" @@ -1692,7 +1693,7 @@ dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"] docs-screenshots = ["altair (==5.3.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.2)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.1.post2)", "matplotlib (==3.8.3)", "nbconvert (>=7.0.0)", "pandas (==2.2.1)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"] test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"] -upgrade-extension = ["copier (>=8,<10)", "jinja2-time (<0.3)", "pydantic (<2.0)", "pyyaml-include (<2.0)", "tomli-w (<2.0)"] +upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"] [[package]] name = "jupyterlab-pygments" From 25f9f8942f6597c56e2a8410a1a61d21fcb184e0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Sat, 31 Aug 2024 09:00:06 +0200 Subject: [PATCH 282/347] Type hints for 'return self' (#1601) * Make all learn and update methods return None Changing the return types in the base classes means MyPy can detect when a method returns an unexpected value. * Propagate None returns to child classes * Remove wrong returns * Add a note in the changelog * Change the return type of SortedWindow.append * Fix new MyPy 11 errors --- docs/releases/unreleased.md | 1 + river/anomaly/base.py | 6 +++--- river/anomaly/pad.py | 3 +-- river/base/classifier.py | 4 ++-- river/base/clusterer.py | 2 +- river/base/drift_detector.py | 2 +- river/base/multi_output.py | 4 ++-- river/base/regressor.py | 4 ++-- river/base/transformer.py | 8 ++++---- river/cluster/textclust.py | 1 - river/compat/sklearn_to_river.py | 2 +- river/covariance/emp.py | 2 -- river/ensemble/ewa.py | 1 - river/feature_extraction/vectorize.py | 2 +- river/linear_model/base.py | 6 ++---- river/metrics/base.py | 28 +++++++++++++------------- river/metrics/multioutput/base.py | 8 ++++---- river/neighbors/base.py | 2 +- river/neighbors/knn_classifier.py | 2 -- river/proba/base.py | 16 +++++++-------- river/reco/base.py | 2 +- river/stats/base.py | 8 ++++---- river/time_series/base.py | 2 +- river/time_series/metrics.py | 4 +--- river/tree/isoup_tree_regressor.py | 4 ++-- river/tree/splitter/base.py | 4 ++-- river/tree/splitter/random_splitter.py | 8 +++----- river/utils/rolling.py | 4 ++-- river/utils/sorted_window.py | 7 +++---- 29 files changed, 67 insertions(+), 80 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index e1951d3bd2..dfb7dd4b19 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,6 +1,7 @@ # Unreleased - The units used in River have been corrected to be based on powers of 2 (KiB, MiB). This only changes the display, the behaviour is unchanged. +- The methods `learn_one`, `learn_many`, `update`, `revert`, and `append` now return `None`. ## cluster diff --git a/river/anomaly/base.py b/river/anomaly/base.py index 9c8fc42c50..c52a4bfc67 100644 --- a/river/anomaly/base.py +++ b/river/anomaly/base.py @@ -15,7 +15,7 @@ def _supervised(self): return False @abc.abstractmethod - def learn_one(self, x: dict): + def learn_one(self, x: dict) -> None: """Update the model. Parameters @@ -48,7 +48,7 @@ class SupervisedAnomalyDetector(base.Estimator): """A supervised anomaly detector.""" @abc.abstractmethod - def learn_one(self, x: dict, y: base.typing.Target): + def learn_one(self, x: dict, y: base.typing.Target) -> None: """Update the model. Parameters @@ -137,7 +137,7 @@ def score_one(self, *args, **kwargs): """ return self.anomaly_detector.score_one(*args, **kwargs) - def learn_one(self, *args, **learn_kwargs): + def learn_one(self, *args, **learn_kwargs) -> None: """Update the anomaly filter and the underlying anomaly detector. Parameters diff --git a/river/anomaly/pad.py b/river/anomaly/pad.py index 4ec6304efb..0ddd3a403f 100644 --- a/river/anomaly/pad.py +++ b/river/anomaly/pad.py @@ -80,7 +80,7 @@ class PredictiveAnomalyDetection(anomaly.base.SupervisedAnomalyDetector): >>> for t, (x, y) in enumerate(datasets.AirlinePassengers()): ... score = PAD.score_one(None, y) - ... PAD = PAD.learn_one(None, y) + ... PAD.learn_one(None, y) ... scores.append(score) >>> print(scores[-1]) @@ -131,7 +131,6 @@ def learn_one(self, x: dict | None, y: base.typing.Target | float): self.predictive_model.learn_one(y=y, x=x) else: self.predictive_model.learn_one(x=x, y=y) - return self def score_one(self, x: dict, y: base.typing.Target): # Return the predicted value of x from the predictive model, first by checking whether diff --git a/river/base/classifier.py b/river/base/classifier.py index 19f01be18f..876bef4e13 100644 --- a/river/base/classifier.py +++ b/river/base/classifier.py @@ -15,7 +15,7 @@ class Classifier(estimator.Estimator): """A classifier.""" @abc.abstractmethod - def learn_one(self, x: dict, y: base.typing.ClfTarget) -> Classifier: + def learn_one(self, x: dict, y: base.typing.ClfTarget) -> None: """Update the model with a set of features `x` and a label `y`. Parameters @@ -81,7 +81,7 @@ class MiniBatchClassifier(Classifier): """A classifier that can operate on mini-batches.""" @abc.abstractmethod - def learn_many(self, X: pd.DataFrame, y: pd.Series): + def learn_many(self, X: pd.DataFrame, y: pd.Series) -> None: """Update the model with a mini-batch of features `X` and boolean targets `y`. Parameters diff --git a/river/base/clusterer.py b/river/base/clusterer.py index 07c6ac7f3d..35a73a71ad 100644 --- a/river/base/clusterer.py +++ b/river/base/clusterer.py @@ -13,7 +13,7 @@ def _supervised(self): return False @abc.abstractmethod - def learn_one(self, x: dict) -> Clusterer: + def learn_one(self, x: dict) -> None: """Update the model with a set of features `x`. Parameters diff --git a/river/base/drift_detector.py b/river/base/drift_detector.py index 9eaef0b506..a350c40d4b 100644 --- a/river/base/drift_detector.py +++ b/river/base/drift_detector.py @@ -77,7 +77,7 @@ class BinaryDriftDetector(_BaseDriftDetector): """A drift detector for binary data.""" @abc.abstractmethod - def update(self, x: bool) -> BinaryDriftDetector: + def update(self, x: bool) -> None: """Update the detector with a single boolean input. Parameters diff --git a/river/base/multi_output.py b/river/base/multi_output.py index a05904621a..078ed1a362 100644 --- a/river/base/multi_output.py +++ b/river/base/multi_output.py @@ -10,7 +10,7 @@ class MultiLabelClassifier(Estimator, abc.ABC): """Multi-label classifier.""" @abc.abstractmethod - def learn_one(self, x: dict, y: dict[FeatureName, bool]): + def learn_one(self, x: dict, y: dict[FeatureName, bool]) -> None: """Update the model with a set of features `x` and the labels `y`. Parameters @@ -68,7 +68,7 @@ class MultiTargetRegressor(Estimator, abc.ABC): """Multi-target regressor.""" @abc.abstractmethod - def learn_one(self, x: dict, y: dict[FeatureName, RegTarget], **kwargs): + def learn_one(self, x: dict, y: dict[FeatureName, RegTarget], **kwargs) -> None: """Fits to a set of features `x` and a real-valued target `y`. Parameters diff --git a/river/base/regressor.py b/river/base/regressor.py index 775a23a33e..09abacb2b6 100644 --- a/river/base/regressor.py +++ b/river/base/regressor.py @@ -15,7 +15,7 @@ class Regressor(estimator.Estimator): """A regressor.""" @abc.abstractmethod - def learn_one(self, x: dict, y: base.typing.RegTarget): + def learn_one(self, x: dict, y: base.typing.RegTarget) -> None: """Fits to a set of features `x` and a real-valued target `y`. Parameters @@ -47,7 +47,7 @@ class MiniBatchRegressor(Regressor): """A regressor that can operate on mini-batches.""" @abc.abstractmethod - def learn_many(self, X: pd.DataFrame, y: pd.Series): + def learn_many(self, X: pd.DataFrame, y: pd.Series) -> None: """Update the model with a mini-batch of features `X` and real-valued targets `y`. Parameters diff --git a/river/base/transformer.py b/river/base/transformer.py index 5313ba618d..16f9aba276 100644 --- a/river/base/transformer.py +++ b/river/base/transformer.py @@ -57,7 +57,7 @@ class Transformer(base.Estimator, BaseTransformer): def _supervised(self): return False - def learn_one(self, x: dict): + def learn_one(self, x: dict) -> None: """Update with a set of features `x`. A lot of transformers don't actually have to do anything during the `learn_one` step @@ -81,7 +81,7 @@ class SupervisedTransformer(base.Estimator, BaseTransformer): def _supervised(self): return True - def learn_one(self, x: dict, y: base.typing.Target): + def learn_one(self, x: dict, y: base.typing.Target) -> None: """Update with a set of features `x` and a target `y`. Parameters @@ -113,7 +113,7 @@ def transform_many(self, X: pd.DataFrame) -> pd.DataFrame: """ - def learn_many(self, X: pd.DataFrame): + def learn_many(self, X: pd.DataFrame) -> None: """Update with a mini-batch of features. A lot of transformers don't actually have to do anything during the `learn_many` step @@ -138,7 +138,7 @@ def _supervised(self): return True @abc.abstractmethod - def learn_many(self, X: pd.DataFrame, y: pd.Series): + def learn_many(self, X: pd.DataFrame, y: pd.Series) -> None: """Update the model with a mini-batch of features `X` and targets `y`. Parameters diff --git a/river/cluster/textclust.py b/river/cluster/textclust.py index 82e507517c..2115fd1b03 100644 --- a/river/cluster/textclust.py +++ b/river/cluster/textclust.py @@ -213,7 +213,6 @@ def learn_one(self, x, t=None, w=None): ## increment observation counter self.n += 1 - return clusterId ## predicts the cluster number. The type specifies whether this should happen on micro-cluster ## or macro-cluster level diff --git a/river/compat/sklearn_to_river.py b/river/compat/sklearn_to_river.py index bb16ad76c7..93664bd342 100644 --- a/river/compat/sklearn_to_river.py +++ b/river/compat/sklearn_to_river.py @@ -33,7 +33,7 @@ def convert_sklearn_to_river(estimator: sklearn_base.BaseEstimator, classes: lis (sklearn_base.RegressorMixin, SKL2RiverRegressor), ( sklearn_base.ClassifierMixin, - functools.partial(SKL2RiverClassifier, classes=classes), + functools.partial(SKL2RiverClassifier, classes=classes), # type:ignore[arg-type] ), ] diff --git a/river/covariance/emp.py b/river/covariance/emp.py index 255a28a726..1b2f64a80e 100644 --- a/river/covariance/emp.py +++ b/river/covariance/emp.py @@ -379,5 +379,3 @@ def update_many(self, X: pd.DataFrame): row = self._w[fi] * inv_cov[i] for j, fj in enumerate(X): self._inv_cov[min((fi, fj), (fj, fi))] = row[j] - - return self diff --git a/river/ensemble/ewa.py b/river/ensemble/ewa.py index 7a15a95738..4a3d77358b 100644 --- a/river/ensemble/ewa.py +++ b/river/ensemble/ewa.py @@ -122,7 +122,6 @@ def learn_predict_one(self, x, y): def learn_one(self, x, y): self.learn_predict_one(x, y) - return self def predict_one(self, x): return sum(model.predict_one(x) * weight for model, weight in zip(self, self.weights)) diff --git a/river/feature_extraction/vectorize.py b/river/feature_extraction/vectorize.py index f4d6aff840..5f68450208 100644 --- a/river/feature_extraction/vectorize.py +++ b/river/feature_extraction/vectorize.py @@ -203,7 +203,7 @@ def __init__( # Stop word removal if self.stop_words: self.processing_steps.append( - functools.partial(remove_stop_words, stop_words=stop_words) + functools.partial(remove_stop_words, stop_words=self.stop_words) ) # n-grams diff --git a/river/linear_model/base.py b/river/linear_model/base.py index 02846efb18..91d6ba7f70 100644 --- a/river/linear_model/base.py +++ b/river/linear_model/base.py @@ -122,8 +122,6 @@ def _fit(self, x, y, w, get_grad): self._update_weights(x) - return self - def _update_weights(self, x): # L1 cumulative penalty helper @@ -161,7 +159,7 @@ def _eval_gradient_one(self, x: dict, y: float, w: float) -> tuple[dict, float]: return (loss_gradient * utils.VectorDict(x), loss_gradient) - def learn_one(self, x, y, w=1.0): + def learn_one(self, x, y, w=1.0) -> None: with self._learn_mode(x): self._fit(x, y, w, get_grad=self._eval_gradient_one) @@ -188,7 +186,7 @@ def _eval_gradient_many( return dict(zip(X.columns, gradient)), loss_gradient.mean() - def learn_many(self, X: pd.DataFrame, y: pd.Series, w: float | pd.Series = 1): + def learn_many(self, X: pd.DataFrame, y: pd.Series, w: float | pd.Series = 1) -> None: self._y_name = y.name with self._learn_mode(set(X)): self._fit(X, y, w, get_grad=self._eval_gradient_many) diff --git a/river/metrics/base.py b/river/metrics/base.py index 0fb837848e..788de42d3f 100644 --- a/river/metrics/base.py +++ b/river/metrics/base.py @@ -22,11 +22,11 @@ class Metric(base.Base, abc.ABC): """Mother class for all metrics.""" @abc.abstractmethod - def update(self, y_true, y_pred) -> Metric: + def update(self, y_true, y_pred) -> None: """Update the metric.""" @abc.abstractmethod - def revert(self, y_true, y_pred) -> Metric: + def revert(self, y_true, y_pred) -> None: """Revert the metric.""" @abc.abstractmethod @@ -89,14 +89,14 @@ def __init__(self, cm=None): cm = metrics.ConfusionMatrix() self.cm = cm - def update(self, y_true, y_pred, w=1.0): + def update(self, y_true, y_pred, w=1.0) -> None: self.cm.update( y_true, y_pred, w=w, ) - def revert(self, y_true, y_pred, w=1.0): + def revert(self, y_true, y_pred, w=1.0) -> None: self.cm.revert( y_true, y_pred, @@ -152,7 +152,7 @@ def update( y_true: bool, y_pred: bool | float | dict[bool, float], w=1.0, - ) -> BinaryMetric: + ) -> None: if self.requires_labels: y_pred = y_pred == self.pos_val return super().update(y_true == self.pos_val, y_pred, w) @@ -162,7 +162,7 @@ def revert( y_true: bool, y_pred: bool | float | dict[bool, float], w=1.0, - ) -> BinaryMetric: + ) -> None: if self.requires_labels: y_pred = y_pred == self.pos_val return super().revert(y_true == self.pos_val, y_pred, w) @@ -190,11 +190,11 @@ class RegressionMetric(Metric): _fmt = ",.6f" # use commas to separate big numbers and show 6 decimals @abc.abstractmethod - def update(self, y_true: numbers.Number, y_pred: numbers.Number) -> RegressionMetric: + def update(self, y_true: numbers.Number, y_pred: numbers.Number) -> None: """Update the metric.""" @abc.abstractmethod - def revert(self, y_true: numbers.Number, y_pred: numbers.Number) -> RegressionMetric: + def revert(self, y_true: numbers.Number, y_pred: numbers.Number) -> None: """Revert the metric.""" @property @@ -227,7 +227,7 @@ def __init__(self, metrics, str_sep="\n"): super().__init__(metrics) self.str_sep = str_sep - def update(self, y_true, y_pred, w=1.0): + def update(self, y_true, y_pred, w=1.0) -> None: # If the metrics are classification metrics, then we have to handle the case where some # of the metrics require labels, whilst others need to be fed probabilities if hasattr(self, "requires_labels") and not self.requires_labels: @@ -241,7 +241,7 @@ def update(self, y_true, y_pred, w=1.0): for m in self: m.update(y_true, y_pred) - def revert(self, y_true, y_pred, w=1.0): + def revert(self, y_true, y_pred, w=1.0) -> None: # If the metrics are classification metrics, then we have to handle the case where some # of the metrics require labels, whilst others need to be fed probabilities if hasattr(self, "requires_labels") and not self.requires_labels: @@ -334,10 +334,10 @@ def __init__(self): def _eval(self, y_true, y_pred): pass - def update(self, y_true, y_pred, w=1.0): + def update(self, y_true, y_pred, w=1.0) -> None: self._mean.update(x=self._eval(y_true, y_pred), w=w) - def revert(self, y_true, y_pred, w=1.0): + def revert(self, y_true, y_pred, w=1.0) -> None: self._mean.revert(x=self._eval(y_true, y_pred), w=w) def get(self): @@ -353,11 +353,11 @@ class ClusteringMetric(base.Base, abc.ABC): _fmt = ",.6f" # Use commas to separate big numbers and show 6 decimals @abc.abstractmethod - def update(self, x, y_pred, centers, w=1.0) -> ClusteringMetric: + def update(self, x, y_pred, centers, w=1.0) -> None: """Update the metric.""" @abc.abstractmethod - def revert(self, x, y_pred, centers, w=1.0) -> ClusteringMetric: + def revert(self, x, y_pred, centers, w=1.0) -> None: """Revert the metric.""" @abc.abstractmethod diff --git a/river/metrics/multioutput/base.py b/river/metrics/multioutput/base.py index 4768f96ce1..eed2ab8283 100644 --- a/river/metrics/multioutput/base.py +++ b/river/metrics/multioutput/base.py @@ -38,7 +38,7 @@ def update( y_pred: dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]], w=1.0, - ): + ) -> None: """Update the metric.""" self.cm.update(y_true, y_pred, w) @@ -48,7 +48,7 @@ def revert( y_pred: dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]], w=1.0, - ): + ) -> None: """Revert the metric.""" self.cm.revert(y_true, y_pred, w) @@ -72,7 +72,7 @@ def update( self, y_true: dict[str | int, float | int], y_pred: dict[str | int, float | int], - ): + ) -> None: """Update the metric.""" @abc.abstractmethod @@ -80,7 +80,7 @@ def revert( self, y_true: dict[str | int, float | int], y_pred: dict[str | int, float | int], - ): + ) -> None: """Revert the metric.""" def works_with(self, model) -> bool: diff --git a/river/neighbors/base.py b/river/neighbors/base.py index 0ca8e21c50..496e69f3f7 100644 --- a/river/neighbors/base.py +++ b/river/neighbors/base.py @@ -37,7 +37,7 @@ def __init__(self, dist_func: DistanceFunc | FunctionWrapper): self.dist_func = dist_func @abc.abstractmethod - def append(self, item: typing.Any, **kwargs): + def append(self, item: typing.Any, **kwargs) -> None: pass @abc.abstractmethod diff --git a/river/neighbors/knn_classifier.py b/river/neighbors/knn_classifier.py index a3e12f3c08..45e0d3f89c 100644 --- a/river/neighbors/knn_classifier.py +++ b/river/neighbors/knn_classifier.py @@ -143,8 +143,6 @@ def _run_class_cleanup(self): self.clean_up_classes() self._cleanup_counter = self.cleanup_every - return self - def predict_proba_one(self, x, **kwargs): nearest = self._nn.search((x, None), n_neighbors=self.n_neighbors, **kwargs) diff --git a/river/proba/base.py b/river/proba/base.py index c10766749d..f49366b5b9 100644 --- a/river/proba/base.py +++ b/river/proba/base.py @@ -56,11 +56,11 @@ class DiscreteDistribution(Distribution): """ @abc.abstractmethod - def update(self, x: typing.Hashable): + def update(self, x: typing.Hashable) -> None: """Updates the parameters of the distribution given a new observation.""" @abc.abstractmethod - def revert(self, x: typing.Hashable): + def revert(self, x: typing.Hashable) -> None: """Reverts the parameters of the distribution for a given observation.""" @@ -75,11 +75,11 @@ class BinaryDistribution(Distribution): """ @abc.abstractmethod - def update(self, x: bool): + def update(self, x: bool) -> None: """Updates the parameters of the distribution given a new observation.""" @abc.abstractmethod - def revert(self, x: bool): + def revert(self, x: bool) -> None: """Reverts the parameters of the distribution for a given observation.""" @@ -94,11 +94,11 @@ class ContinuousDistribution(Distribution): """ @abc.abstractmethod - def update(self, x: float): + def update(self, x: float) -> None: """Updates the parameters of the distribution given a new observation.""" @abc.abstractmethod - def revert(self, x: float): + def revert(self, x: float) -> None: """Reverts the parameters of the distribution for a given observation.""" @abc.abstractmethod @@ -117,11 +117,11 @@ class MultivariateContinuousDistribution(Distribution): """ @abc.abstractmethod - def update(self, x: dict[str, float]): + def update(self, x: dict[str, float]) -> None: """Updates the parameters of the distribution given a new observation.""" @abc.abstractmethod - def revert(self, x: dict[str, float]): + def revert(self, x: dict[str, float]) -> None: """Reverts the parameters of the distribution for a given observation.""" @abc.abstractmethod diff --git a/river/reco/base.py b/river/reco/base.py index de746e5d1a..d2ecc29576 100644 --- a/river/reco/base.py +++ b/river/reco/base.py @@ -33,7 +33,7 @@ def is_contextual(self): return False @abc.abstractmethod - def learn_one(self, user: ID, item: ID, y: Reward, x: dict | None = None): + def learn_one(self, user: ID, item: ID, y: Reward, x: dict | None = None) -> None: """Fits a `user`-`item` pair and a real-valued target `y`. Parameters diff --git a/river/stats/base.py b/river/stats/base.py index ddb5214197..9d3302e5c2 100644 --- a/river/stats/base.py +++ b/river/stats/base.py @@ -37,8 +37,8 @@ class Univariate(Statistic): """A univariate statistic measures a property of a variable.""" @abc.abstractmethod - def update(self, x: numbers.Number): - """Update and return the called instance.""" + def update(self, x: numbers.Number) -> None: + """Update the called instance.""" raise NotImplementedError @property @@ -68,5 +68,5 @@ class Bivariate(Statistic): """A bivariate statistic measures a relationship between two variables.""" @abc.abstractmethod - def update(self, x, y): - """Update and return the called instance.""" + def update(self, x, y) -> None: + """Update the called instance.""" diff --git a/river/time_series/base.py b/river/time_series/base.py index 1486f9815b..bcfc3ed492 100644 --- a/river/time_series/base.py +++ b/river/time_series/base.py @@ -13,7 +13,7 @@ def _supervised(self): return True @abc.abstractmethod - def learn_one(self, y: float, x: dict | None = None) -> Forecaster: + def learn_one(self, y: float, x: dict | None = None) -> None: """Updates the model. Parameters diff --git a/river/time_series/metrics.py b/river/time_series/metrics.py index 587ce2e645..405ef4a6bc 100644 --- a/river/time_series/metrics.py +++ b/river/time_series/metrics.py @@ -9,7 +9,7 @@ class ForecastingMetric(base.Base, abc.ABC): @abc.abstractmethod - def update(self, y_true: list[Number], y_pred: list[Number]) -> ForecastingMetric: + def update(self, y_true: list[Number], y_pred: list[Number]) -> None: """Update the metric at each step along the horizon. Parameters @@ -83,8 +83,6 @@ def update(self, y_true, y_pred): metric.update(yt, yp) - return self - def get(self): return [metric.get() for metric in self.metrics] diff --git a/river/tree/isoup_tree_regressor.py b/river/tree/isoup_tree_regressor.py index 0393379b17..abe519154f 100644 --- a/river/tree/isoup_tree_regressor.py +++ b/river/tree/isoup_tree_regressor.py @@ -208,7 +208,7 @@ def _new_leaf(self, initial_stats=None, parent=None): return new_adaptive - def learn_one(self, x, y, *, w: float = 1.0) -> iSOUPTreeRegressor: # type: ignore + def learn_one(self, x, y, *, w: float = 1.0, **kwargs) -> None: """Incrementally train the model with one sample. Training tasks: @@ -232,7 +232,7 @@ def learn_one(self, x, y, *, w: float = 1.0) -> iSOUPTreeRegressor: # type: ign # Update target set self.targets.update(y.keys()) - super().learn_one(x, y, w=w) # type: ignore + super().learn_one(x, y, w=w) def predict_one(self, x): pred = {} diff --git a/river/tree/splitter/base.py b/river/tree/splitter/base.py index 70e4216835..f963fbafba 100644 --- a/river/tree/splitter/base.py +++ b/river/tree/splitter/base.py @@ -21,7 +21,7 @@ class Splitter(base.Estimator, abc.ABC): """ @abc.abstractmethod - def update(self, att_val, target_val: base.typing.Target, w: float): + def update(self, att_val, target_val: base.typing.Target, w: float) -> None: """Update statistics of this observer given an attribute value, its target value and the weight of the instance observed. @@ -109,7 +109,7 @@ def __len__(self): pass @abc.abstractmethod - def update(self, x_val, gh: GradHess, w: float): + def update(self, x_val, gh: GradHess, w: float) -> None: pass @abc.abstractmethod diff --git a/river/tree/splitter/random_splitter.py b/river/tree/splitter/random_splitter.py index 4fd099f379..aa6562f975 100644 --- a/river/tree/splitter/random_splitter.py +++ b/river/tree/splitter/random_splitter.py @@ -36,11 +36,11 @@ def cond_proba(self, att_val, class_val) -> float: """This attribute observer does not support probability density estimation.""" raise NotImplementedError - def update(self, att_val, target_val, w) -> Splitter: + def update(self, att_val, target_val, w) -> None: if self.threshold is None: if len(self._buffer) < self.buffer_size: self._buffer.append((att_val, target_val, w)) - return self + return mn = min(self._buffer, key=lambda t: t[0])[0] mx = max(self._buffer, key=lambda t: t[0])[0] @@ -51,12 +51,10 @@ def update(self, att_val, target_val, w) -> Splitter: self._update_stats(0 if a <= self.threshold else 1, t, w) self._buffer = None - return self + return self._update_stats(0 if att_val <= self.threshold else 1, target_val, w) - return self - def best_evaluated_split_suggestion(self, criterion, pre_split_dist, att_idx, binary_only): post_split_dist = [self.stats[0], self.stats[1]] merit = criterion.merit_of_split(pre_split_dist, post_split_dist) diff --git a/river/utils/rolling.py b/river/utils/rolling.py index 19ed551b55..f662b941b4 100644 --- a/river/utils/rolling.py +++ b/river/utils/rolling.py @@ -8,9 +8,9 @@ @typing.runtime_checkable class Rollable(typing.Protocol): - def update(self, *args, **kwargs): ... + def update(self, *args, **kwargs) -> None: ... - def revert(self, *args, **kwargs): ... + def revert(self, *args, **kwargs) -> None: ... class BaseRolling: diff --git a/river/utils/sorted_window.py b/river/utils/sorted_window.py index d718158c6a..1dcdd6eeec 100644 --- a/river/utils/sorted_window.py +++ b/river/utils/sorted_window.py @@ -20,7 +20,8 @@ class SortedWindow(collections.UserList): >>> window = utils.SortedWindow(size=3) >>> for i in reversed(range(9)): - ... print(window.append(i)) + ... window.append(i) + ... print(window) [8] [7, 8] [6, 7, 8] @@ -45,7 +46,7 @@ def __init__(self, size: int): def size(self): return self.unsorted_window.maxlen - def append(self, x): + def append(self, x) -> None: if len(self) >= self.size: # The window is sorted, and a binary search is more optimized than linear search start_deque = bisect.bisect_left(self, self.unsorted_window[0]) @@ -53,5 +54,3 @@ def append(self, x): bisect.insort_left(self, x) self.unsorted_window.append(x) - - return self From 3f14064a15a64d82bf208d1a35a5ace866d98b02 Mon Sep 17 00:00:00 2001 From: Matti Bispham Date: Sat, 31 Aug 2024 22:15:40 +0900 Subject: [PATCH 283/347] Fixup typo (#1606) --- docs/introduction/why-use-river.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/introduction/why-use-river.md b/docs/introduction/why-use-river.md index 81efb4d41e..9c5f2744d1 100644 --- a/docs/introduction/why-use-river.md +++ b/docs/introduction/why-use-river.md @@ -14,4 +14,4 @@ River supports different machine learning tasks, including regression, classific ## User experience -River is not the only library allowing you to do online machine learning. But it might just the simplest one to use in the Python ecosystem. River plays nicely with Python dictionaries, therefore making it easy to use in the context of web applications where JSON payloads are aplenty. +River is not the only library allowing you to do online machine learning. But it might just be the simplest one to use in the Python ecosystem. River plays nicely with Python dictionaries, therefore making it easy to use in the context of web applications where JSON payloads are aplenty. From 7e3eb19d0bb471d07de7e0f8e769acd318fe157f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 4 Sep 2024 08:53:27 +0200 Subject: [PATCH 284/347] Bump actions/download-artifact from 3 to 4.1.7 in /.github/workflows (#1607) Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 3 to 4.1.7. - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v3...v4.1.7) --- updated-dependencies: - dependency-name: actions/download-artifact dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/pypi.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 7117341ee3..c3316ba4f1 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -122,7 +122,7 @@ jobs: needs: [build_wheels, build_sdist] runs-on: ubuntu-latest steps: - - uses: actions/download-artifact@v3 + - uses: actions/download-artifact@v4.1.7 with: name: artifact path: dist From e3b216338000aabfdda007cb6a410d1092a4d998 Mon Sep 17 00:00:00 2001 From: Daniel Nowak <63320957+danielnowakassis@users.noreply.github.com> Date: Fri, 6 Sep 2024 16:47:33 -0300 Subject: [PATCH 285/347] Adding Local Adaptive Streaming Tree (#1610) * add first last with out-of-date docs * update LAST to detect change in the data distribution + iter_arff none class Docs are also updated * update docs * Update hoeffding_adaptive_tree_classifier.py * changes after tests * solving inheritance and small fixes * tests + current_merit method * Update river/tree/hoeffding_adaptive_tree_classifier.py * update docs * change docs * change last * Update docs/releases/unreleased.md * Update river/tree/hoeffding_adaptive_tree_classifier.py * Update river/tree/last_classifier.py * add disclamer * Update river/tree/last_classifier.py --------- Co-authored-by: Saulo Martiello Mastelini --- docs/releases/unreleased.md | 6 + river/stream/iter_arff.py | 7 +- river/tree/__init__.py | 2 + river/tree/last_classifier.py | 363 ++++++++++++++++++ river/tree/nodes/last_nodes.py | 206 ++++++++++ river/tree/split_criterion/base.py | 14 + .../split_criterion/gini_split_criterion.py | 3 + .../hellinger_distance_criterion.py | 3 + .../info_gain_split_criterion.py | 3 + ...ster_variance_reduction_split_criterion.py | 3 + .../variance_ratio_split_criterion.py | 3 + .../variance_reduction_split_criterion.py | 3 + 12 files changed, 614 insertions(+), 2 deletions(-) create mode 100644 river/tree/last_classifier.py create mode 100644 river/tree/nodes/last_nodes.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index dfb7dd4b19..e0536f435b 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -17,3 +17,9 @@ ## tree - Instead of letting trees grow indefinitely, setting the `max_depth` parameter to `None` will stop the trees from growing when they reach the system recursion limit. + +-Added `tree.LASTClassifier` (Local Adaptive Streaming Tree Classifier). + +## stream + +- `stream.iter_arff` now supports blank values (treated as missing values). \ No newline at end of file diff --git a/river/stream/iter_arff.py b/river/stream/iter_arff.py index 4464e3f74a..f9eec46ded 100644 --- a/river/stream/iter_arff.py +++ b/river/stream/iter_arff.py @@ -176,7 +176,7 @@ def iter_arff( x = { name: cast(val) if cast else val for name, cast, val in zip(names, casts, r.rstrip().split(",")) - if val != "?" + if val != "?" and val != "" } # Handle target @@ -185,7 +185,10 @@ def iter_arff( if isinstance(target, list): y = {name: x.pop(name, 0) for name in target} else: - y = x.pop(target) if target else None + try: + y = x.pop(target) if target else None + except KeyError: + y = None yield x, y diff --git a/river/tree/__init__.py b/river/tree/__init__.py index 9e8b887217..ad53a1ecf8 100755 --- a/river/tree/__init__.py +++ b/river/tree/__init__.py @@ -59,6 +59,7 @@ from .hoeffding_tree_classifier import HoeffdingTreeClassifier from .hoeffding_tree_regressor import HoeffdingTreeRegressor from .isoup_tree_regressor import iSOUPTreeRegressor +from .last_classifier import LASTClassifier from .stochastic_gradient_tree import SGTClassifier, SGTRegressor __all__ = [ @@ -70,6 +71,7 @@ "HoeffdingTreeRegressor", "HoeffdingAdaptiveTreeRegressor", "iSOUPTreeRegressor", + "LASTClassifier", "SGTClassifier", "SGTRegressor", ] diff --git a/river/tree/last_classifier.py b/river/tree/last_classifier.py new file mode 100644 index 0000000000..391ba83668 --- /dev/null +++ b/river/tree/last_classifier.py @@ -0,0 +1,363 @@ +from __future__ import annotations + +from river import base, drift + +from .hoeffding_tree_classifier import HoeffdingTreeClassifier +from .nodes.branch import DTBranch +from .nodes.last_nodes import ( + LeafMajorityClassWithDetector, + LeafNaiveBayesAdaptiveWithDetector, + LeafNaiveBayesWithDetector, +) +from .nodes.leaf import HTLeaf +from .split_criterion import GiniSplitCriterion, HellingerDistanceCriterion, InfoGainSplitCriterion +from .splitter import Splitter + + +class LASTClassifier(HoeffdingTreeClassifier, base.Classifier): + """Local Adaptive Streaming Tree Classifier. + + Local Adaptive Streaming Tree [^1] (LAST) is an incremental decision tree with + adaptive splitting mechanisms. LAST maintains a change detector at each leaf and splits + this node if a change is detected in the error or the leaf`s data distribution. + + LAST is still not suitable for use as a base classifier in ensembles due to the change detectors. + The authors in [^1] are working on a version of LAST that overcomes this limitation. + + Parameters + ---------- + max_depth + The maximum depth a tree can reach. If `None`, the tree will grow until the system recursion limit. + split_criterion + Split criterion to use.
+ - 'gini' - Gini
+ - 'info_gain' - Information Gain
+ - 'hellinger' - Helinger Distance
+ leaf_prediction + Prediction mechanism used at leafs.
+ - 'mc' - Majority Class
+ - 'nb' - Naive Bayes
+ - 'nba' - Naive Bayes Adaptive
+ change_detector + Change detector that will be created at each leaf of the tree. + track_error + If True, the change detector will have binary inputs for error predictions, + otherwise the input will be the split criteria. + nb_threshold + Number of instances a leaf should observe before allowing Naive Bayes. + nominal_attributes + List of Nominal attributes identifiers. If empty, then assume that all numeric + attributes should be treated as continuous. + splitter + The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric + features and perform splits. Splitters are available in the `tree.splitter` module. + Different splitters are available for classification and regression tasks. Classification + and regression splitters can be distinguished by their property `is_target_class`. + This is an advanced option. Special care must be taken when choosing different splitters. + By default, `tree.splitter.GaussianSplitter` is used if `splitter` is `None`. + binary_split + If True, only allow binary splits. + min_branch_fraction + The minimum percentage of observed data required for branches resulting from split + candidates. To validate a split candidate, at least two resulting branches must have + a percentage of samples greater than `min_branch_fraction`. This criterion prevents + unnecessary splits when the majority of instances are concentrated in a single branch. + max_share_to_split + Only perform a split in a leaf if the proportion of elements in the majority class is + smaller than this parameter value. This parameter avoids performing splits when most + of the data belongs to a single class. + max_size + The max size of the tree, in Megabytes (MB). + memory_estimate_period + Interval (number of processed instances) between memory consumption checks. + stop_mem_management + If True, stop growing as soon as memory limit is hit. + remove_poor_attrs + If True, disable poor attributes to reduce memory usage. + merit_preprune + If True, enable merit-based tree pre-pruning. + + References + ---------- + + [^1]: Daniel Nowak Assis, Jean Paul Barddal, and Fabrício Enembreck. + Just Change on Change: Adaptive Splitting Time for Decision Trees in + Data Stream Classification . In Proceedings of ACM SAC Conference (SAC’24). + + Examples + -------- + + >>> from river.datasets import synth + >>> from river import evaluate + >>> from river import metrics + >>> from river import tree + + >>> gen = synth.ConceptDriftStream(stream=synth.SEA(seed=42, variant=0), + ... drift_stream=synth.SEA(seed=42, variant=1), + ... seed=1, position=1500, width=50) + >>> dataset = iter(gen.take(3000)) + + >>> model = tree.LASTClassifier() + + >>> metric = metrics.Accuracy() + + >>> evaluate.progressive_val_score(dataset, model, metric) + Accuracy: 91.10% + + """ + + def __init__( + self, + max_depth: int | None = None, + split_criterion: str = "info_gain", + leaf_prediction: str = "nba", + change_detector: base.DriftDetector | None = None, + track_error: bool = True, + nb_threshold: int = 0, + nominal_attributes: list | None = None, + splitter: Splitter | None = None, + binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, + max_size: float = 100.0, + memory_estimate_period: int = 1000000, + stop_mem_management: bool = False, + remove_poor_attrs: bool = False, + merit_preprune: bool = True, + ): + super().__init__( + grace_period=1, # no usage + max_depth=max_depth, + split_criterion=split_criterion, + delta=1.0, # no usage + tau=1, # no usage + leaf_prediction=leaf_prediction, + nb_threshold=nb_threshold, + binary_split=binary_split, + max_size=max_size, + memory_estimate_period=memory_estimate_period, + stop_mem_management=stop_mem_management, + remove_poor_attrs=remove_poor_attrs, + merit_preprune=merit_preprune, + nominal_attributes=nominal_attributes, + splitter=splitter, + min_branch_fraction=min_branch_fraction, + max_share_to_split=max_share_to_split, + ) + self.change_detector = change_detector if change_detector is not None else drift.ADWIN() + self.track_error = track_error + + # To keep track of the observed classes + self.classes: set = set() + + @property + def _mutable_attributes(self): + return {} + + def _new_leaf(self, initial_stats=None, parent=None): + if initial_stats is None: + initial_stats = {} + if parent is None: + depth = 0 + else: + depth = parent.depth + 1 + + if self._leaf_prediction == self._MAJORITY_CLASS: + return LeafMajorityClassWithDetector( + initial_stats, + depth, + self.splitter, + self.change_detector.clone(), + split_criterion=self._new_split_criterion() if not self.track_error else None, + ) + elif self._leaf_prediction == self._NAIVE_BAYES: + return LeafNaiveBayesWithDetector( + initial_stats, + depth, + self.splitter, + self.change_detector.clone(), + split_criterion=self._new_split_criterion() if not self.track_error else None, + ) + else: # Naives Bayes Adaptive (default) + return LeafNaiveBayesAdaptiveWithDetector( + initial_stats, + depth, + self.splitter, + self.change_detector.clone(), + split_criterion=self._new_split_criterion() if not self.track_error else None, + ) + + def _new_split_criterion(self): + if self._split_criterion == self._GINI_SPLIT: + split_criterion = GiniSplitCriterion(self.min_branch_fraction) + elif self._split_criterion == self._INFO_GAIN_SPLIT: + split_criterion = InfoGainSplitCriterion(self.min_branch_fraction) + elif self._split_criterion == self._HELLINGER_SPLIT: + if not self.track_error: + raise ValueError( + "The Heillinger distance cannot estimate the purity of a single distribution.\ + Use another split criterion or set track_error to True" + ) + split_criterion = HellingerDistanceCriterion(self.min_branch_fraction) + else: + split_criterion = InfoGainSplitCriterion(self.min_branch_fraction) + + return split_criterion + + def _attempt_to_split(self, leaf: HTLeaf, parent: DTBranch, parent_branch: int, **kwargs): + """Attempt to split a leaf. + + If the samples seen so far are not from the same class then: + + 1. Find split candidates and select the top 1. + 2. If the top1 is greater than zero: + 3.1 Replace the leaf node by a split node (branch node). + 3.2 Add a new leaf node on each branch of the new split node. + 3.3 Update tree's metrics + + Optional: Disable poor attributes. Depends on the tree's configuration. + + Parameters + ---------- + leaf + The leaf to evaluate. + parent + The leaf's parent. + parent_branch + Parent leaf's branch index. + kwargs + Other parameters passed to the new branch. + """ + if not leaf.observed_class_distribution_is_pure(): # type: ignore + split_criterion = self._new_split_criterion() + + best_split_suggestions = leaf.best_split_suggestions(split_criterion, self) + should_split = False + if len(best_split_suggestions) < 2: + should_split = len(best_split_suggestions) > 0 + else: + best_suggestion = max(best_split_suggestions) + should_split = best_suggestion.merit > 0.0 + if self.remove_poor_attrs: + poor_atts = set() + # Add any poor attribute to set + for suggestion in best_split_suggestions: + poor_atts.add(suggestion.feature) + for poor_att in poor_atts: + leaf.disable_attribute(poor_att) + if should_split: + split_decision = max(best_split_suggestions) + if split_decision.feature is None: + # Pre-pruning - null wins + leaf.deactivate() + self._n_inactive_leaves += 1 + self._n_active_leaves -= 1 + else: + branch = self._branch_selector( + split_decision.numerical_feature, split_decision.multiway_split + ) + leaves = tuple( + self._new_leaf(initial_stats, parent=leaf) + for initial_stats in split_decision.children_stats # type: ignore + ) + + new_split = split_decision.assemble( + branch, leaf.stats, leaf.depth, *leaves, **kwargs + ) + + self._n_active_leaves -= 1 + self._n_active_leaves += len(leaves) + if parent is None: + self._root = new_split + else: + parent.children[parent_branch] = new_split + + # Manage memory + self._enforce_size_limit() + + def learn_one(self, x, y, *, w=1.0): + """Train the model on instance x and corresponding target y. + + Parameters + ---------- + x + Instance attributes. + y + Class label for sample x. + w + Sample weight. + + Notes + ----- + Training tasks: + + * If the tree is empty, create a leaf node as the root. + * If the tree is already initialized, find the corresponding leaf for + the instance and update the leaf node statistics. + * Update the leaf change detector with (1 if the tree misclassified the instance, + or 0 if it correctly classified) or the data distribution purity + * If growth is allowed then attempt + to split. + """ + + # Updates the set of observed classes + self.classes.add(y) + + self._train_weight_seen_by_model += w + + if self._root is None: + self._root = self._new_leaf() + self._n_active_leaves = 1 + + p_node = None + node = None + if isinstance(self._root, DTBranch): + path = iter(self._root.walk(x, until_leaf=False)) + while True: + aux = next(path, None) + if aux is None: + break + p_node = node + node = aux + else: + node = self._root + + if isinstance(node, HTLeaf): + node.learn_one(x, y, w=w, tree=self) + if self._growth_allowed and node.is_active(): + if node.depth >= self.max_depth: # Max depth reached + node.deactivate() + self._n_active_leaves -= 1 + self._n_inactive_leaves += 1 + else: + weight_seen = node.total_weight + # check if the change detector triggered a change + if node.change_detector.drift_detected: + p_branch = p_node.branch_no(x) if isinstance(p_node, DTBranch) else None + self._attempt_to_split(node, p_node, p_branch) + node.last_split_attempt_at = weight_seen + else: + while True: + # Split node encountered a previously unseen categorical value (in a multi-way + # test), so there is no branch to sort the instance to + if node.max_branches() == -1 and node.feature in x: + # Create a new branch to the new categorical value + leaf = self._new_leaf(parent=node) + node.add_child(x[node.feature], leaf) + self._n_active_leaves += 1 + node = leaf + # The split feature is missing in the instance. Hence, we pass the new example + # to the most traversed path in the current subtree + else: + _, node = node.most_common_path() + # And we keep trying to reach a leaf + if isinstance(node, DTBranch): + node = node.traverse(x, until_leaf=False) + # Once a leaf is reached, the traversal can stop + if isinstance(node, HTLeaf): + break + # Learn from the sample + node.learn_one(x, y, w=w, tree=self) + + if self._train_weight_seen_by_model % self.memory_estimate_period == 0: + self._estimate_model_size() diff --git a/river/tree/nodes/last_nodes.py b/river/tree/nodes/last_nodes.py new file mode 100644 index 0000000000..f130fe17ac --- /dev/null +++ b/river/tree/nodes/last_nodes.py @@ -0,0 +1,206 @@ +from __future__ import annotations + +from ..utils import do_naive_bayes_prediction +from .htc_nodes import LeafMajorityClass + + +class LeafMajorityClassWithDetector(LeafMajorityClass): + """Leaf that always predicts the majority class. + + Parameters + ---------- + stats + Initial class observations. + depth + The depth of the node. + splitter + The numeric attribute observer algorithm used to monitor target statistics + and perform split attempts. + change_detector + Change detector that monitors the leaf error rate or class distribution and + determines when the leaf will split. + split_criterion + Split criterion used in the tree for updating the change detector if it + monitors the class distribution. + kwargs + Other parameters passed to the learning node. + """ + + def __init__(self, stats, depth, splitter, change_detector, split_criterion=None, **kwargs): + super().__init__(stats, depth, splitter, **kwargs) + self.change_detector = change_detector + # change this in future PR's by acessing the tree parameter in the leaf + self.split_criterion = ( + split_criterion # if None, the change detector will have binary inputs + ) + + def learn_one(self, x, y, *, w=1, tree=None): + self.update_stats(y, w) + if self.is_active(): + if self.split_criterion is None: + mc_pred = self.prediction(x) + detector_input = max(mc_pred, key=mc_pred.get) != y + self.change_detector.update(detector_input) + else: + detector_input = self.split_criterion.current_merit(self.stats) + self.change_detector.update(detector_input) + self.update_splitters(x, y, w, tree.nominal_attributes) + + +class LeafNaiveBayesWithDetector(LeafMajorityClassWithDetector): + """Leaf that uses Naive Bayes models. + + Parameters + ---------- + stats + Initial class observations. + depth + The depth of the node. + splitter + The numeric attribute observer algorithm used to monitor target statistics + and perform split attempts. + change_detector + Change detector that monitors the leaf error rate or class distribution and + determines when the leaf will split. + split_criterion + Split criterion used in the tree for updating the change detector if it + monitors the class distribution. + kwargs + Other parameters passed to the learning node. + """ + + def __init__(self, stats, depth, splitter, change_detector, split_criterion=None, **kwargs): + super().__init__(stats, depth, splitter, change_detector, split_criterion, **kwargs) + + def learn_one(self, x, y, *, w=1, tree=None): + self.update_stats(y, w) + if self.is_active(): + if self.split_criterion is None: + nb_pred = self.prediction(x) + detector_input = max(nb_pred, key=nb_pred.get) == y + self.change_detector.update(detector_input) + else: + detector_input = self.split_criterion.current_merit(self.stats) + self.change_detector.update(detector_input) + self.update_splitters(x, y, w, tree.nominal_attributes) + + def prediction(self, x, *, tree=None): + if self.is_active() and self.total_weight >= tree.nb_threshold: + return do_naive_bayes_prediction(x, self.stats, self.splitters) + else: + return super().prediction(x) + + def disable_attribute(self, att_index): + """Disable an attribute observer. + + Disabled in Nodes using Naive Bayes, since poor attributes are used in + Naive Bayes calculation. + + Parameters + ---------- + att_index + Attribute index. + """ + pass + + +class LeafNaiveBayesAdaptiveWithDetector(LeafMajorityClassWithDetector): + """Learning node that uses Adaptive Naive Bayes models. + + Parameters + ---------- + stats + Initial class observations. + depth + The depth of the node. + splitter + The numeric attribute observer algorithm used to monitor target statistics + and perform split attempts. + change_detector + Change detector that monitors the leaf error rate or class distribution and + determines when the leaf will split. + split_criterion + Split criterion used in the tree for updating the change detector if it + monitors the class distribution. + kwargs + Other parameters passed to the learning node. + """ + + def __init__(self, stats, depth, splitter, change_detector, split_criterion=None, **kwargs): + super().__init__(stats, depth, splitter, change_detector, split_criterion, **kwargs) + self._mc_correct_weight = 0.0 + self._nb_correct_weight = 0.0 + + def learn_one(self, x, y, *, w=1.0, tree=None): + """Update the node with the provided instance. + + Parameters + ---------- + x + Instance attributes for updating the node. + y + Instance class. + w + The instance's weight. + tree + The tree to update. + + """ + detector_input_mc = 1 + detector_input_nb = 1 + if self.is_active(): + mc_pred = super().prediction(x) + # Empty node (assume the majority class will be the best option) or majority + # class prediction is correct + if len(self.stats) == 0 or max(mc_pred, key=mc_pred.get) == y: + self._mc_correct_weight += w + detector_input_mc = 0 + nb_pred = do_naive_bayes_prediction(x, self.stats, self.splitters) + if len(nb_pred) > 0 and max(nb_pred, key=nb_pred.get) == y: + self._nb_correct_weight += w + detector_input_nb = 0 + + self.update_stats(y, w) + if self.is_active(): + if self.split_criterion is None: + if self._nb_correct_weight >= self._mc_correct_weight: + self.change_detector.update(detector_input_nb) + else: + self.change_detector.update(detector_input_mc) + else: + detector_input = self.split_criterion.current_merit(self.stats) + self.change_detector.update(detector_input) + self.update_splitters(x, y, w, tree.nominal_attributes) + + def prediction(self, x, *, tree=None): + """Get the probabilities per class for a given instance. + + Parameters + ---------- + x + Instance attributes. + tree + LAST Tree. + + Returns + ------- + Class votes for the given instance. + + """ + if self.is_active() and self._nb_correct_weight >= self._mc_correct_weight: + return do_naive_bayes_prediction(x, self.stats, self.splitters) + else: + return super().prediction(x) + + def disable_attribute(self, att_index): + """Disable an attribute observer. + + Disabled in Nodes using Naive Bayes, since poor attributes are used in + Naive Bayes calculation. + + Parameters + ---------- + att_index + Attribute index. + """ + pass diff --git a/river/tree/split_criterion/base.py b/river/tree/split_criterion/base.py index d329bf6d58..84da388b4b 100644 --- a/river/tree/split_criterion/base.py +++ b/river/tree/split_criterion/base.py @@ -32,6 +32,20 @@ def merit_of_split(self, pre_split_dist, post_split_dist): Value of the merit of splitting """ + @abc.abstractmethod + def current_merit(self, dist): + """Compute the merit of the distribution. + + Parameters + ---------- + dist + The data distribution. + + Returns + ------- + Value of merit of the distribution according to the splitting criterion + """ + @staticmethod @abc.abstractmethod def range_of_merit(pre_split_dist): diff --git a/river/tree/split_criterion/gini_split_criterion.py b/river/tree/split_criterion/gini_split_criterion.py index 9b4c6e3187..1a71c1b651 100644 --- a/river/tree/split_criterion/gini_split_criterion.py +++ b/river/tree/split_criterion/gini_split_criterion.py @@ -28,6 +28,9 @@ def merit_of_split(self, pre_split_dist, post_split_dist): ) return 1.0 - gini + def current_merit(self, dist): + return self.compute_gini(dist, sum(dist.values())) + @staticmethod def compute_gini(dist, dist_sum_of_weights): gini = 1.0 diff --git a/river/tree/split_criterion/hellinger_distance_criterion.py b/river/tree/split_criterion/hellinger_distance_criterion.py index 5ad379b6a9..236564f9bd 100644 --- a/river/tree/split_criterion/hellinger_distance_criterion.py +++ b/river/tree/split_criterion/hellinger_distance_criterion.py @@ -28,6 +28,9 @@ def merit_of_split(self, pre_split_dist, post_split_dist): return -math.inf return self.compute_hellinger(post_split_dist) + def current_merit(self, dist): + raise ValueError("The Heillinger distance is for 2 or more sets of data.") + @staticmethod def compute_hellinger(dist): try: diff --git a/river/tree/split_criterion/info_gain_split_criterion.py b/river/tree/split_criterion/info_gain_split_criterion.py index 0863e6ac84..b112ff829d 100644 --- a/river/tree/split_criterion/info_gain_split_criterion.py +++ b/river/tree/split_criterion/info_gain_split_criterion.py @@ -39,6 +39,9 @@ def compute_entropy(self, dist): elif isinstance(dist, list): return self._compute_entropy_list(dist) + def current_merit(self, dist): + return self.compute_entropy(dist) + @staticmethod def _compute_entropy_dict(dist): entropy = 0.0 diff --git a/river/tree/split_criterion/intra_cluster_variance_reduction_split_criterion.py b/river/tree/split_criterion/intra_cluster_variance_reduction_split_criterion.py index 1436e817e2..0a1af74773 100644 --- a/river/tree/split_criterion/intra_cluster_variance_reduction_split_criterion.py +++ b/river/tree/split_criterion/intra_cluster_variance_reduction_split_criterion.py @@ -27,6 +27,9 @@ def merit_of_split(self, pre_split_dist, post_split_dist): icvr -= n_i / n * self.compute_var(dist) return icvr + def current_merit(self, dist): + return self.compute_var(dist) + @staticmethod def compute_var(dist): icvr = [vr.get() for vr in dist.values()] diff --git a/river/tree/split_criterion/variance_ratio_split_criterion.py b/river/tree/split_criterion/variance_ratio_split_criterion.py index c51df25a14..dfdff8ea55 100644 --- a/river/tree/split_criterion/variance_ratio_split_criterion.py +++ b/river/tree/split_criterion/variance_ratio_split_criterion.py @@ -34,6 +34,9 @@ def merit_of_split(self, pre_split_dist, post_split_dist): vr -= (n_i / n) * (self.compute_var(post_split_dist[i]) / var) return vr + def current_merit(self, dist): + return self.compute_var(dist) + @staticmethod def compute_var(dist): return dist.get() diff --git a/river/tree/split_criterion/variance_reduction_split_criterion.py b/river/tree/split_criterion/variance_reduction_split_criterion.py index f52cfa7bd3..147ead573b 100644 --- a/river/tree/split_criterion/variance_reduction_split_criterion.py +++ b/river/tree/split_criterion/variance_reduction_split_criterion.py @@ -35,6 +35,9 @@ def merit_of_split(self, pre_split_dist, post_split_dist): vr -= n_i / n * self.compute_var(post_split_dist[i]) return vr + def current_merit(self, dist): + return self.compute_var(dist) + @staticmethod def compute_var(dist): return dist.get() From c6dab1b38b92b6da1928b4a60b91d6dd415a36d5 Mon Sep 17 00:00:00 2001 From: Denise Date: Wed, 11 Sep 2024 14:11:37 -0300 Subject: [PATCH 286/347] Bugfix for detecting change points based on MOA (#1613) * Bugfix based on MOA source code in the _detect_change method. The k index should increment from 0 to bucket.current_idx - 1. The previous code "for k in range(bucket.current_idx - 1):" only increment k to bucket.current_idx - 2 because of the range function. * Bugfix based on MOA source code in the _detect_change method. The k index should increment from 0 to bucket.current_idx - 1. The previous code "for k in range(bucket.current_idx - 1):" only increment k to bucket.current_idx - 2 because of the range function. * Tests updated due to bugfix on the ADWIN change detector - reported on issue #1614 --- river/drift/adwin_c.pyx | 3 ++- river/drift/test_drift_detectors.py | 2 +- river/ensemble/streaming_random_patches.py | 2 +- river/forest/adaptive_random_forest.py | 4 ++-- river/forest/online_extra_trees.py | 2 +- river/imblearn/chebyshev.py | 8 ++++---- river/tree/hoeffding_adaptive_tree_regressor.py | 2 +- 7 files changed, 12 insertions(+), 11 deletions(-) diff --git a/river/drift/adwin_c.pyx b/river/drift/adwin_c.pyx index 322859c780..889145481b 100644 --- a/river/drift/adwin_c.pyx +++ b/river/drift/adwin_c.pyx @@ -241,7 +241,7 @@ cdef class AdaptiveWindowing: break bucket = self.bucket_deque[idx] - for k in range(bucket.current_idx - 1): + for k in range(bucket.current_idx): n2 = self._calculate_bucket_size(idx) # length of window 2 u2 = bucket.get_total_at(k) # total of window 2 # Warning: means are calculated inside the loop to get updated values. @@ -307,6 +307,7 @@ cdef class AdaptiveWindowing: + (1.0 / (n1 - self.min_window_length + 1))) epsilon = (sqrt(2 * m_recip * self.variance_in_window * delta_prime) + 2 / 3 * delta_prime * m_recip) + return fabs(delta_mean) > epsilon diff --git a/river/drift/test_drift_detectors.py b/river/drift/test_drift_detectors.py index 665a3f4eb2..cff6f37c2f 100644 --- a/river/drift/test_drift_detectors.py +++ b/river/drift/test_drift_detectors.py @@ -28,7 +28,7 @@ def test_adwin(): - expected_indices = [1055] + expected_indices = [1023] detected_indices = perform_test(drift.ADWIN(), data_stream_1) assert detected_indices == expected_indices diff --git a/river/ensemble/streaming_random_patches.py b/river/ensemble/streaming_random_patches.py index ac4ee26b68..415b30be10 100644 --- a/river/ensemble/streaming_random_patches.py +++ b/river/ensemble/streaming_random_patches.py @@ -407,7 +407,7 @@ class SRPClassifier(BaseSRPEnsemble, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 71.97% + Accuracy: 72.17% Notes ----- diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index 6ec1bada2c..dd9a757f4d 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -565,7 +565,7 @@ class ARFClassifier(BaseForest, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 71.17% + Accuracy: 67.97% The total number of warnings and drifts detected, respectively >>> model.n_warnings_detected(), model.n_drifts_detected() @@ -849,7 +849,7 @@ class ARFRegressor(BaseForest, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.788619 + MAE: 0.772113 """ diff --git a/river/forest/online_extra_trees.py b/river/forest/online_extra_trees.py index a13707bdb0..ee361007eb 100644 --- a/river/forest/online_extra_trees.py +++ b/river/forest/online_extra_trees.py @@ -614,7 +614,7 @@ class OXTRegressor(ExtraTrees, base.Regressor): >>> metric = metrics.RMSE() >>> evaluate.progressive_val_score(dataset, model, metric) - RMSE: 3.127311 + RMSE: 3.16212 References ---------- diff --git a/river/imblearn/chebyshev.py b/river/imblearn/chebyshev.py index 5bc1a769bc..5c9c3baaf7 100644 --- a/river/imblearn/chebyshev.py +++ b/river/imblearn/chebyshev.py @@ -162,10 +162,10 @@ class ChebyshevOverSampler(base.Wrapper, base.Regressor): ... metrics.MAE(), ... print_every=500 ... ) - [500] MAE: 1.673902 - [1,000] MAE: 1.743046 - [1,001] MAE: 1.741335 - MAE: 1.741335 + [500] MAE: 1.629786 + [1,000] MAE: 1.663799 + [1,001] MAE: 1.66253 + MAE: 1.66253 References ---------- diff --git a/river/tree/hoeffding_adaptive_tree_regressor.py b/river/tree/hoeffding_adaptive_tree_regressor.py index 89aeae0d20..6c609cc8f4 100644 --- a/river/tree/hoeffding_adaptive_tree_regressor.py +++ b/river/tree/hoeffding_adaptive_tree_regressor.py @@ -140,7 +140,7 @@ class HoeffdingAdaptiveTreeRegressor(HoeffdingTreeRegressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.823026 + MAE: 0.917576 """ From fdab50b146316bbf210e54eda8788d67f5b11d58 Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Wed, 11 Sep 2024 14:12:47 -0300 Subject: [PATCH 287/347] Update release notes --- docs/releases/unreleased.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index e0536f435b..fde3c69d9d 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -10,6 +10,10 @@ - Add `render_ascii` in `cluster.ODAC` to render the hierarchical cluster's structure in text format. - Work with `stats.Var` in `cluster.ODAC` when cluster has only one time series. +## drift + +- Make `drift.ADWIN` comply with the reference MOA implementation. + ## stats - Removed the unexported class `stats.CentralMoments`. @@ -22,4 +26,4 @@ ## stream -- `stream.iter_arff` now supports blank values (treated as missing values). \ No newline at end of file +- `stream.iter_arff` now supports blank values (treated as missing values). From d310abb9c8b36ec7f00e0c2d20035c1051fdbffd Mon Sep 17 00:00:00 2001 From: Saulo Martiello Mastelini Date: Wed, 11 Sep 2024 14:13:21 -0300 Subject: [PATCH 288/347] Update release notes --- docs/releases/unreleased.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index fde3c69d9d..4ed6dd48f1 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -21,8 +21,7 @@ ## tree - Instead of letting trees grow indefinitely, setting the `max_depth` parameter to `None` will stop the trees from growing when they reach the system recursion limit. - --Added `tree.LASTClassifier` (Local Adaptive Streaming Tree Classifier). +- Added `tree.LASTClassifier` (Local Adaptive Streaming Tree Classifier). ## stream From ba66ee787b43aa3cb33190b58eab7037da5cc5a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89mile?= <73942755+e10e3@users.noreply.github.com> Date: Wed, 11 Sep 2024 20:50:02 +0200 Subject: [PATCH 289/347] New MyPy configuration for per-module type completeness (#1596) * Make River fully type-checked by default Add an override for all modules while they are not annotated. * Use the true location of Gymnasium's registry The registry variable of Gymnasium environments is located in the 'registration' module. This variables is also imported by the 'envs' module, but not explictely re-exported. This means the previous code was correct at runtime, but MyPy raised easily-corrected warnings. * Enable more rules for all modules These rules do not cause errors in the current code, meaning they can be enabled without harm. --- pyproject.toml | 57 +++++++++++++++++++++++++++++++++++ river/bandit/envs/__init__.py | 4 +-- 2 files changed, 59 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 3f479d8741..1729441e4f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -144,6 +144,7 @@ indent-style = "space" [tool.mypy] files = "river" +strict = true [[tool.mypy.overrides]] module = [ @@ -163,3 +164,59 @@ module = [ "polars.*" ] ignore_missing_imports = true + +[[tool.mypy.overrides]] +# Disable strict mode for all non fully-typed modules +module = [ + "river.base.*", + "river.metrics.*", + "river.utils.*", + "river.stats.*", + "river.optim.*", + "river.datasets.*", + "river.tree.*", + "river.preprocessing.*", + "river.stream.*", + "river.linear_model.*", + "river.evaluate.*", + "river.drift.*", + "river.compose.*", + "river.bandit.*", + "river.cluster.*", + "river.anomaly.*", + "river.time_series.*", + "river.feature_extraction.*", + "river.ensemble.*", + "river.proba.*", + "river.multioutput.*", + "river.naive_bayes.*", + "river.checks.*", + "river.rules.*", + "river.model_selection.*", + "river.forest.*", + "river.neighbors.*", + "river.sketch.*", + "river.facto.*", + "river.covariance.*", + "river.compat.*", + "river.multiclass.*", + "river.reco.*", + "river.imblearn.*", + "river.feature_selection.*", + "river.misc.*", + "river.active.*", + "river.conf.*", + "river.neural_net.*", + "river.test_estimators", + "river.dummy", +] +# The strict option is global, the checks must be disabled one by one +warn_unused_ignores = false +check_untyped_defs = false +allow_subclassing_any = true +allow_any_generics = true +allow_untyped_calls = true +allow_incomplete_defs = true +allow_untyped_defs = true +implicit_reexport = true +warn_return_any = false diff --git a/river/bandit/envs/__init__.py b/river/bandit/envs/__init__.py index 6c91fa9b71..673edc9277 100644 --- a/river/bandit/envs/__init__.py +++ b/river/bandit/envs/__init__.py @@ -15,13 +15,13 @@ RIVER_NAMESPACE = "river_bandits" - if (env_id := f"{RIVER_NAMESPACE}/CandyCaneContest-v0") not in gym.envs.registry: + if (env_id := f"{RIVER_NAMESPACE}/CandyCaneContest-v0") not in gym.envs.registration.registry: gym.envs.registration.register( id=env_id, entry_point="river.bandit.envs:CandyCaneContest", max_episode_steps=CandyCaneContest.n_steps, ) - if (env_id := f"{RIVER_NAMESPACE}/KArmedTestbed-v0") not in gym.envs.registry: + if (env_id := f"{RIVER_NAMESPACE}/KArmedTestbed-v0") not in gym.envs.registration.registry: gym.envs.registration.register( id=env_id, entry_point="river.bandit.envs:KArmedTestbed", From 38661c563000503cefbca4db6f3322f0a626d64f Mon Sep 17 00:00:00 2001 From: Max Halford Date: Thu, 12 Sep 2024 00:02:54 +0200 Subject: [PATCH 290/347] # type: ignore[misc] --- river/bandit/test_policies.py | 4 ++-- river/compat/test_sklearn.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/river/bandit/test_policies.py b/river/bandit/test_policies.py index d9d60fe453..323439501e 100644 --- a/river/bandit/test_policies.py +++ b/river/bandit/test_policies.py @@ -65,7 +65,7 @@ def _iter_policies(): yield policy(**params) -@pytest.mark.parametrize( +@pytest.mark.parametrize( # type: ignore[misc] "policy,env", [ pytest.param( @@ -76,7 +76,7 @@ def _iter_policies(): for env in _iter_envs() ], ) -@pytest.mark.skip(reason="flaky") +@pytest.mark.skip(reason="flaky") # type: ignore[misc] def test_better_than_random_policy(policy: bandit.base.Policy, env: gym.Env): """Test that the policy is better than random.""" diff --git a/river/compat/test_sklearn.py b/river/compat/test_sklearn.py index dd2a4256e4..fed434302c 100644 --- a/river/compat/test_sklearn.py +++ b/river/compat/test_sklearn.py @@ -10,7 +10,7 @@ @pytest.mark.parametrize( - "estimator", + "estimator", # type: ignore[misc] [ pytest.param(estimator, id=str(estimator)) for estimator in [ @@ -21,7 +21,7 @@ ] ], ) -@pytest.mark.filterwarnings("ignore::sklearn.utils.estimator_checks.SkipTestWarning") +@pytest.mark.filterwarnings("ignore::sklearn.utils.estimator_checks.SkipTestWarning") # type: ignore[misc] def test_river_to_sklearn_check_estimator(estimator: base.Estimator): skl_estimator = compat.convert_river_to_sklearn(estimator) estimator_checks.check_estimator(skl_estimator) From e958fd8bf3a6a96c01ab350723d1d9ee9bf3c1ac Mon Sep 17 00:00:00 2001 From: Geoffrey Bolmier Date: Thu, 3 Oct 2024 15:24:18 -0400 Subject: [PATCH 291/347] Update Py version to 3.12.X in release-docs.yml Previous cap to 3.12.3 should not be needed anymore since explosion/spaCy#13550 seems to be fixed --- .github/workflows/release-docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 4347f2c983..76a09d0450 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -15,7 +15,7 @@ jobs: - name: Build River uses: ./.github/actions/install-env with: - python-version: "3.12.3" + python-version: "3.12" - name: Install extra Ubuntu dependencies run: sudo apt-get install graphviz pandoc From ef05f8209a500a398064c845d212e12f2bfe7328 Mon Sep 17 00:00:00 2001 From: kchardon Date: Sun, 10 Nov 2024 19:10:22 +0100 Subject: [PATCH 292/347] removing use of numpy --- river/cluster/hcluster.py | 63 ++++++++++++++++++++++----------------- 1 file changed, 36 insertions(+), 27 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 3ece91ef07..1021060b9c 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,7 +1,6 @@ from __future__ import annotations import functools -import numpy as np from river import base, utils from river.neighbors.base import DistanceFunc, FunctionWrapper @@ -9,7 +8,7 @@ # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key: int, data: np.ndarray = None): + def __init__(self, key: int, data: dict = None): self.data = data self.key = key # Children and parent @@ -61,7 +60,7 @@ class HierarchicalClustering(base.Clusterer): >>> from river import cluster >>> from river import stream - >>> X = [[1, 1, 1], [1, 1, 0], [20, 20, 20], [20, 20, 20.1], [0 , 1, 1]] + >>> X = [[1, 2, 1], [2, 1, 0], [3, 2, 1], [2, 2, 1], [5, 2, 3]] >>> hierarchical_clustering = cluster.HierarchicalClustering() @@ -69,43 +68,55 @@ class HierarchicalClustering(base.Clusterer): ... hierarchical_clustering = hierarchical_clustering.learn_one(x) >>> hierarchical_clustering.x_clusters - {'[1 1 1]': 1, '[1 1 0]': 2, '[20 20 20]': 4, '[20. 20. 20.1]': 6, '[0 1 1]': 8} + {'[1, 2, 1]': 1, '[2, 1, 0]': 2, '[3, 2, 1]': 4, '[2, 2, 1]': 6, '[5, 2, 3]': 8} >>> hierarchical_clustering.n 9 >>> print(hierarchical_clustering) - -> 6 - -> 7 - -> 4 - -> 5 - -> 8 - -> 9 + -> 8 + -> 9 + -> 6 + -> 7 + -> 4 + -> 5 -> 2 -> 3 -> 1 Printed Hierarchical Clustering Tree. >>> hierarchical_clustering.get_all_clusters() - [(1, [array([1, 1, 1])]), (2, [array([1, 1, 0])]), (4, [array([20, 20, 20])]), (6, [array([20. , 20. , 20.1])]), (8, [array([0, 1, 1])]), (3, [1, 2]), (5, [9, 7]), (7, [4, 6]), (9, [3, 8])] + [(1, ['[1, 2, 1]']), + (2, ['[2, 1, 0]']), + (4, ['[3, 2, 1]']), + (6, ['[2, 2, 1]']), + (8, ['[5, 2, 3]']), + (3, [1, 2]), + (5, [3, 7]), + (7, [4, 6]), + (9, [5, 8])] >>> hierarchical_clustering.get_clusters_by_point() - {'[1 1 1]': [1, 3, 9, 5], '[1 1 0]': [2, 3, 9, 5], '[20 20 20]': [4, 7, 5], '[20. 20. 20.1]': [6, 7, 5], '[0 1 1]': [8, 9, 5]} + {'[1, 2, 1]': [1, 3, 5, 9], + '[2, 1, 0]': [2, 3, 5, 9], + '[3, 2, 1]': [4, 7, 5, 9], + '[2, 2, 1]': [6, 7, 5, 9], + '[5, 2, 3]': [8, 9]} - >>> hierarchical_clustering.predict_one({0 : 20.1, 1: 20, 2: 20 }) - ([10, 11, 5], 7) + >>> hierarchical_clustering.predict_one({0: 4, 1: 3, 2: 1}) + ([10, 11, 9], 8) - >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 20.1, 1: 20, 2: 20}) + >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 4, 1: 3, 2: 1}) >>> print(hierarchical_clustering) -> 10 -> 11 + -> 8 + -> 9 -> 6 -> 7 -> 4 - -> 5 - -> 8 - -> 9 + -> 5 -> 2 -> 3 -> 1 @@ -117,7 +128,7 @@ class HierarchicalClustering(base.Clusterer): ... hierarchical_clustering = hierarchical_clustering.learn_one(x) >>> hierarchical_clustering.x_clusters - {'[20. 20. 20.1]': 2, '[0 1 1]': 1} + {'[2, 2, 1]': 2, '[5, 2, 3]': 1} >>> hierarchical_clustering.n 3 @@ -152,7 +163,7 @@ def __init__( def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. # The algorithm performs highly efficient online updates and provably approximates Moseley-Wang revenue. - x_string = np.array2string(x) + x_string = str(list(x.values())) if self.n == 1: # First node in the tree self.root = self.nodes[1] @@ -207,7 +218,6 @@ def merge_nodes(self, tree, added_node): self.root = self.nodes[self.n] def learn_one(self, x): - x = utils.dict2numpy(x) # We create the node for x and add it to the tree if len(self.x_clusters.keys()) >= self.window_size: # Delete the oldest data point and add a node with the same key as the one deleted @@ -219,12 +229,12 @@ def learn_one(self, x): oldest.parent.right = None del self.nodes[oldest_key] del self.x_clusters[list(self.x_clusters.keys())[0]] - self.x_clusters[np.array2string(x)] = oldest_key + self.x_clusters[str(list(x.values()))] = oldest_key self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: # Else, add a node self.n += 1 - self.x_clusters[np.array2string(x)] = self.n + self.x_clusters[str(list(x.values()))] = self.n self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree self.otd_clustering(self.root, x) @@ -270,7 +280,6 @@ def predict_one(self, x): A list of clusters (from node `x` to root) and the node to which it would have been merged. """ - x = utils.dict2numpy(x) # We predict to which cluster x would be if we added in the tree r, merged = self.predict_otd(x, self.root, []) r.reverse() @@ -319,7 +328,7 @@ def inter_subtree_similarity(self, tree_a, tree_b): for i, w_i in enumerate(leaves_a): for j, w_j in enumerate(leaves_b): nb += 1 - r += self.dist_func(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) + r += self.dist_func(w_i.data, w_j.data) return r / nb def intra_subtree_similarity(self, tree): @@ -333,7 +342,7 @@ def intra_subtree_similarity(self, tree): for j, w_j in enumerate(leaves): if i < j: nb += 1 - r += self.dist_func(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) + r += self.dist_func(w_i.data, w_j.data) return r / nb def __str__(self): @@ -383,7 +392,7 @@ def get_all_clusters(self): clusters = {} for i in range(1, self.n + 1): if self.nodes[i].data is not None: - clusters[i] = [self.nodes[i].data] + clusters[i] = [str(list(self.nodes[i].data.values()))] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] return sorted(clusters.items(), key=lambda x: len(x[1])) From e28ed63779394cafa86bbc8bef6723ee7c94bda6 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 11:37:48 +0200 Subject: [PATCH 293/347] add of Hierarchical Clustering --- docs/releases/unreleased.md | 1 + river/cluster/__init__.py | 3 +- river/cluster/hcluster.py | 403 ++++++++++++++++++++++++++++++++++++ 3 files changed, 406 insertions(+), 1 deletion(-) create mode 100644 river/cluster/hcluster.py diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 4ed6dd48f1..6f19c55e96 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -9,6 +9,7 @@ - Change `draw` in `cluster.ODAC` to draw the hierarchical cluster's structure as a Graphviz graph. - Add `render_ascii` in `cluster.ODAC` to render the hierarchical cluster's structure in text format. - Work with `stats.Var` in `cluster.ODAC` when cluster has only one time series. +- Added `cluster.HierarchicalClustering`. ## drift diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 0fe354fd2f..e9ea4ebbfa 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -9,5 +9,6 @@ from .odac import ODAC from .streamkmeans import STREAMKMeans from .textclust import TextClust +from .hcluster import HierarchicalClustering -__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "ODAC", "STREAMKMeans", "TextClust"] +__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust", "HierarchicalClustering"] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py new file mode 100644 index 0000000000..c4215975e4 --- /dev/null +++ b/river/cluster/hcluster.py @@ -0,0 +1,403 @@ +from river import base, utils, datasets +import numpy as np +from typing import Callable + +# Node of a binary tree for Hierarchical Clustering +class BinaryTreeNode: + def __init__(self, key : int, data : np.array = None): + # If it's a leaf : x data, else : None + self.data = data + # Key of the node + self.key = key + # Children and parent + self.left = None + self.right = None + self.parent = None + + +def euclidean_distance(w_i, w_j): + # Euclidean distance between two nodes + return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + +class HierarchicalClustering(base.Clusterer): + """Hierarchical Clustering. + + HierarchicalClustering is a stream hierarchical clustering algorithm. + The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. + It will (begining with the whole tree T) : + + * Compare the new node to the tree T : + * if T is just a leaf : merge + * else if the nodes of T are more similar between them than with the new node : merge + * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree + * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree + + You can choose a window size to use only the recent points and not overload the tree. (default : 100) + + You can also choose a distance function to compare the nodes. (default : euclidean distance) + + Parameters + ---------- + window_size + number of data points to use + distance + distance function to use to compare the nodes + + Attributes + ---------- + n : int + number of nodes + X : dict (data point (str(data array)) : key (int)) + data points used by the algorithm with the key of the node representing them + + References + ---------- + [^1]: Menon, Aditya & Rajagopalan, Anand & Sumengen, Baris & Citovsky, Gui & Cao, Qin & Kumar, Sanjiv. (2019). Online Hierarchical Clustering Approximations. + + Examples + -------- + + The first example is with leaving the window size to 100. In the second one we put it at 2 to see how it works. + + >>> from river import cluster + >>> from river import stream + + >>> X = [[1, 1, 1], [1, 1, 0], [20, 20, 20], [20, 20, 20.1], [0 , 1, 1]] + + >>> HC = cluster.HierarchicalClustering() + + >>> for x, _ in stream.iter_array(X): + ... HC = HC.learn_one(x) + + >>> HC.X + {'[1 1 1]': 1, + '[1 1 0]': 2, + '[20 20 20]': 4, + '[20. 20. 20.1]': 6, + '[0 1 1]': 8} + + >>> HC.n + 9 + + >>> print(HC) + -> 6 + -> 7 + -> 4 + -> 5 + -> 8 + -> 9 + -> 2 + -> 3 + -> 1 + + >>> HC.get_all_clusters() + [(1, [array([1, 1, 1])]), + (2, [array([1, 1, 0])]), + (4, [array([20, 20, 20])]), + (6, [array([20. , 20. , 20.1])]), + (8, [array([0, 1, 1])]), + (3, [1, 2]), + (5, [9, 7]), + (7, [4, 6]), + (9, [3, 8])] + + >>> HC.get_clusters_by_point() + {'[1 1 1]': [1, 3, 9, 5], + '[1 1 0]': [2, 3, 9, 5], + '[20 20 20]': [4, 7, 5], + '[20. 20. 20.1]': [6, 7, 5], + '[0 1 1]': [8, 9, 5]} + + >>> HC.predict_one({0 : 20.1, 1 : 20, 2 : 20 }) + ([10, 11, 5], 9) + + >>> HC = HC.learn_one({0 : 20.1, 1 : 20, 2 : 20 }) + + >>> print(HC) + -> 6 + -> 7 + -> 4 + -> 5 + -> 10 + -> 11 + -> 8 + -> 9 + -> 2 + -> 3 + -> 1 + + + >>> HC = cluster.HierarchicalClustering(window_size=2) + + >>> for x, _ in stream.iter_array(X): + ... HC = HC.learn_one(x) + + >>> HC.X + {'[20. 20. 20.1]': 2, '[0 1 1]': 1} + + >>> HC.n + 3 + + >>> print(HC) + -> 2 + -> 3 + -> 1 + + """ + def __init__(self, window_size : int = 100, distance : Callable = euclidean_distance): + # Number of nodes + self.n = 0 + # Max number of leaves + self.w_size = window_size + # Dict : x data (str(array of size m)) -> key of the node + self.X = {} + # Dict : key -> node + self.nodes = {} + # First node of the tree + self.root = None + # Distance function + self.distance = distance + + def OTD(self, T, x): + # OTD algorithm from + if self.n == 1: + # First node in the tree + self.root = self.nodes[1] + elif T.data is not None: + # If T is a leaf, we merge the two nodes together + self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) + elif T.left is None: + # If there is no node at the left of the intermediate node, we add it there + T.left = self.nodes[self.X[np.array2string(x)]] + self.nodes[self.X[np.array2string(x)]].parent = T + elif T.right is None: + # If there is no node at the right of the intermediate node, we add it there + T.right = self.nodes[self.X[np.array2string(x)]] + self.nodes[self.X[np.array2string(x)]].parent = T + elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity(T, self.nodes[self.X[np.array2string(x)]]): + # If the nodes in T are closer between them than with the new node, we merge T and the new node + self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) + elif self.inter_subtree_similarity(T.left, self.nodes[self.X[np.array2string(x)]]) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): + # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T + self.OTD(T.right, x) + else: + # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T + self.OTD(T.left, x) + + def merge_nodes(self, T, added_node): + # Merge a new node (added node) to the tree T + # We create the node that will be the parent of T and the added node + self.n +=1 + new_node = BinaryTreeNode(self.n) + # We add T and the added node as its children + new_node.left = T + new_node.right = added_node + # The parent of the new node is the parent of T + new_node.parent = T.parent + # If T is not the root, we set the child of its parent as new node (instead of T) + if T.parent is not None: + if T.parent.left.key == T.key: + T.parent.left = new_node + else: + T.parent.right = new_node + # We add the new node as the parent of T and the added node + T.parent = new_node + added_node.parent = new_node + # We add the new node to the dict + self.nodes[self.n] = new_node + # If T was the root, the new node become the root + if self.root.key == T.key: + self.root = self.nodes[self.n] + + def learn_one(self, x): + x = utils.dict2numpy(x) + # We create the node for x and add it to the tree + if self.w_size > 0 and len(self.X.keys()) >= self.w_size: + # If we have reached the maximum of data points, we delete the oldest one and add a node with the same key as the one we deleted + oldest_key = self.X[list(self.X.keys())[0]] + oldest = self.nodes[oldest_key] + if oldest.parent.left.key == oldest_key: + oldest.parent.left = None + else: + oldest.parent.right = None + del self.nodes[oldest_key] + del self.X[list(self.X.keys())[0]] + self.X[np.array2string(x)] = oldest_key + self.nodes[oldest_key] = BinaryTreeNode(oldest_key,x) + else: + # Else we just add a node + self.n +=1 + self.X[np.array2string(x)] = self.n + self.nodes[self.n] = BinaryTreeNode(self.n,x) + # We add it to the tree + self.OTD(self.root, x) + return self + + def predict_OTD(self, x, node, clusters): + # get the list of predicted clusters for x + if node is None: + # If there is still no node in the tree + return [1], -1 + if node.data is not None: + # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) + clusters.extend([self.n+2, self.n+1]) + return clusters, node.key + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity(node, BinaryTreeNode(self.n+1,x)): + # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) + clusters.extend([self.n+2, self.n+1]) + return clusters, node.key + else: + # Else, x wouls be added in the tree, we add the key of node + clusters.extend([node.key]) + if self.inter_subtree_similarity(node.left,BinaryTreeNode(self.n+1,x)) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n+1,x)): + # If the right part of the tree is closer to x than the left part, we continue in the right part + return self.predict_OTD(x, node.right, clusters) + else: + # If the left part of the tree is closer to x than the right part, we continue in the left part + return self.predict_OTD(x, node.left, clusters) + + def predict_one(self, x): + """Predicts the clusters for a set of features `x`. + + Parameters + ---------- + x + A dictionary of features. + Returns + ------- + (list, int) + A list of clusters (from node `x` to root) and the node to which it would have been merged. + + """ + x = utils.dict2numpy(x) + # We predict to which cluster x would be if we added it in the tree + r, merged = self.predict_OTD( x, self.root, []) + r.reverse() + return r, merged + + def find_path(self, root, path, k): + # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + + if root == None: + # No path + return False + + path.append(root) + + if root.key == k: + return True + + if ((root.left != None and self.find_path(root.left, path, k)) or + (root.right != None and self.find_path(root.right, path, k))): + return True + + path.pop() + return False + + def lca(self, i, j): + # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + if self.root == None: + return -1 + + path_i = [] + path_j = [] + + if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): + return -1 + + k = 0 + while(k < len(path_i) and k < len(path_j)): + if path_i[k] != path_j[k]: + break + k += 1 + return path_i[k-1] + + def leaves(self, v): + # find all the leaves from node v + if v == None: + # No leaves + return -1 + if v.data is not None: + # If v is a leaf, returns itself + return [v] + # Else, returns leaves from its children + leavesList = [] + leavesList.extend(self.leaves(v.left)) + leavesList.extend(self.leaves(v.right)) + return leavesList + + def inter_subtree_similarity(self, A, B): + # compute the mean distance between tree A and tree B (mean of distances between nodes from tree A and tree B) + leavesA = self.leaves(A) + leavesB = self.leaves(B) + r = 0 + nb = 0 + for i, w_i in enumerate(leavesA): + for j, w_j in enumerate(leavesB): + nb +=1 + r += self.distance(w_i, w_j) + return r/nb + + def intra_subtree_similarity(self, A): + # compute mean of distances between the node from tree A + leaves = self.leaves(A) + r = 0 + nb = 0 + if len(leaves) == 1: + return 0 + for i, w_i in enumerate(leaves): + for j, w_j in enumerate(leaves): + if i < j: + nb +=1 + r += self.distance(w_i, w_j) + return r/nb + + def __str__(self): + self.printTree(self.root) + return "" + + def printTree(self, node, level=0): + # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python + if node != None: + self.printTree(node.right, level + 1) + print(' ' * 4 * level + '-> ' + str(node.key)) + self.printTree(node.left, level + 1) + + def get_parents(self,node): + # Get all the parents of the node (the clusters it belongs to) + clusters = [node.key] + if node.parent is None: + return clusters + clusters.extend(self.get_parents(node.parent)) + return clusters + + def get_clusters_by_point(self): + """Returns the list of clusters (from the data point node to the root) for all of the data points. + + Returns + ------- + {x : list} + A dict of all the data points with their clusters. + """ + # Get all the clusters each data point belong to + clusters = {} + for x in self.X.keys(): + clusters[x] = self.get_parents(self.nodes[self.X[x]]) + return clusters + + def get_all_clusters(self): + """Returns all the clusters of the tree. + + Returns + ------- + {int : list} + A dict of all the clusters with their children (or the data point for the leaves). + """ + # Get the data of each cluster + clusters = {} + for i in range(1,self.n+1): + if self.nodes[i].data is not None: + clusters[i] = [self.nodes[i].data] + else: + clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] + return sorted(clusters.items(), key = lambda x : len(x[1])) From 6745a9e1515ce7d105004f21b3bbe8b02e298a97 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 12:10:12 +0200 Subject: [PATCH 294/347] Fixed issues --- river/cluster/hcluster.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index c4215975e4..e3864b6634 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,10 @@ -from river import base, utils, datasets +from river import base, utils import numpy as np from typing import Callable # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key : int, data : np.array = None): + def __init__(self, key : int, data : np.typing.NDArray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -47,7 +47,7 @@ class HierarchicalClustering(base.Clusterer): ---------- n : int number of nodes - X : dict (data point (str(data array)) : key (int)) + X : dict (data point (str(ndarray)) : key (int)) data points used by the algorithm with the key of the node representing them References @@ -144,15 +144,15 @@ class HierarchicalClustering(base.Clusterer): -> 1 """ - def __init__(self, window_size : int = 100, distance : Callable = euclidean_distance): + def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = euclidean_distance): # Number of nodes self.n = 0 # Max number of leaves self.w_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X = {} + self.X : dict[np.typing.NDArray, int] = {} # Dict : key -> node - self.nodes = {} + self.nodes : dict[int, BinaryTreeNode] = {} # First node of the tree self.root = None # Distance function @@ -278,7 +278,7 @@ def predict_one(self, x): def find_path(self, root, path, k): # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ - if root == None: + if root is None: # No path return False @@ -287,8 +287,8 @@ def find_path(self, root, path, k): if root.key == k: return True - if ((root.left != None and self.find_path(root.left, path, k)) or - (root.right != None and self.find_path(root.right, path, k))): + if ((root.left is not None and self.find_path(root.left, path, k)) or + (root.right is not None and self.find_path(root.right, path, k))): return True path.pop() @@ -296,7 +296,7 @@ def find_path(self, root, path, k): def lca(self, i, j): # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ - if self.root == None: + if self.root is None: return -1 path_i = [] @@ -314,7 +314,7 @@ def lca(self, i, j): def leaves(self, v): # find all the leaves from node v - if v == None: + if v is None: # No leaves return -1 if v.data is not None: @@ -358,7 +358,7 @@ def __str__(self): def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python - if node != None: + if node is not None: self.printTree(node.right, level + 1) print(' ' * 4 * level + '-> ' + str(node.key)) self.printTree(node.left, level + 1) From 1d34134506d82b7e5f6c66da719d3e750c342158 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 12:21:24 +0200 Subject: [PATCH 295/347] Fix issues --- river/cluster/hcluster.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index e3864b6634..faabbe2e16 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,11 @@ from river import base, utils import numpy as np +import numpy.typing as npt from typing import Callable # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key : int, data : np.typing.NDArray = None): + def __init__(self, key : int, data : np.ndarray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -47,7 +48,7 @@ class HierarchicalClustering(base.Clusterer): ---------- n : int number of nodes - X : dict (data point (str(ndarray)) : key (int)) + X : dict (data point (str(np.ndarray)) : key (int)) data points used by the algorithm with the key of the node representing them References @@ -150,7 +151,7 @@ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, # Max number of leaves self.w_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X : dict[np.typing.NDArray, int] = {} + self.X : dict[np.ndarray, int] = {} # Dict : key -> node self.nodes : dict[int, BinaryTreeNode] = {} # First node of the tree From 8a7565191a83ba43e7d2565b0a5e485ef67cd3c4 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 12:46:06 +0200 Subject: [PATCH 296/347] Fixed issues --- river/cluster/hcluster.py | 25 ++++++++++++------------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index faabbe2e16..e6f6631ebd 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,6 +1,5 @@ from river import base, utils import numpy as np -import numpy.typing as npt from typing import Callable # Node of a binary tree for Hierarchical Clustering @@ -110,22 +109,22 @@ class HierarchicalClustering(base.Clusterer): '[0 1 1]': [8, 9, 5]} >>> HC.predict_one({0 : 20.1, 1 : 20, 2 : 20 }) - ([10, 11, 5], 9) + ([10, 11, 5], 7) >>> HC = HC.learn_one({0 : 20.1, 1 : 20, 2 : 20 }) >>> print(HC) - -> 6 - -> 7 - -> 4 - -> 5 -> 10 -> 11 - -> 8 - -> 9 - -> 2 - -> 3 - -> 1 + -> 6 + -> 7 + -> 4 + -> 5 + -> 8 + -> 9 + -> 2 + -> 3 + -> 1 >>> HC = cluster.HierarchicalClustering(window_size=2) @@ -149,7 +148,7 @@ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, # Number of nodes self.n = 0 # Max number of leaves - self.w_size = window_size + self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node self.X : dict[np.ndarray, int] = {} # Dict : key -> node @@ -213,7 +212,7 @@ def merge_nodes(self, T, added_node): def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree - if self.w_size > 0 and len(self.X.keys()) >= self.w_size: + if self.window_size > 0 and len(self.X.keys()) >= self.window_size: # If we have reached the maximum of data points, we delete the oldest one and add a node with the same key as the one we deleted oldest_key = self.X[list(self.X.keys())[0]] oldest = self.nodes[oldest_key] From ce2fb68dd76158c19fc72f8df654c6bdf5b134a8 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 13:01:42 +0200 Subject: [PATCH 297/347] Fix issues --- river/cluster/hcluster.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index e6f6631ebd..2dd6ee9494 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -144,7 +144,7 @@ class HierarchicalClustering(base.Clusterer): -> 1 """ - def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = euclidean_distance): + def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): # Number of nodes self.n = 0 # Max number of leaves @@ -157,6 +157,8 @@ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, self.root = None # Distance function self.distance = distance + if self.distance is None: + self.distance = euclidean_distance def OTD(self, T, x): # OTD algorithm from From cf8b38a998823d98afef146fcf98ec82e3f847ba Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 13:39:32 +0200 Subject: [PATCH 298/347] fixed trailing spaces --- river/cluster/hcluster.py | 56 +++++++++++++++++++-------------------- 1 file changed, 27 insertions(+), 29 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 2dd6ee9494..bbeb3c265b 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -26,12 +26,12 @@ class HierarchicalClustering(base.Clusterer): The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. It will (begining with the whole tree T) : - * Compare the new node to the tree T : + * Compare the new node to the tree T : * if T is just a leaf : merge * else if the nodes of T are more similar between them than with the new node : merge * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree - + You can choose a window size to use only the recent points and not overload the tree. (default : 100) You can also choose a distance function to compare the nodes. (default : euclidean distance) @@ -39,16 +39,16 @@ class HierarchicalClustering(base.Clusterer): Parameters ---------- window_size - number of data points to use + number of data points to use. distance - distance function to use to compare the nodes - + distance function to use to compare the nodes. + Attributes ---------- n : int - number of nodes + number of nodes. X : dict (data point (str(np.ndarray)) : key (int)) - data points used by the algorithm with the key of the node representing them + data points used by the algorithm with the key of the node representing them. References ---------- @@ -68,7 +68,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[1 1 1]': 1, '[1 1 0]': 2, @@ -131,7 +131,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[20. 20. 20.1]': 2, '[0 1 1]': 1} @@ -142,7 +142,6 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 - """ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): # Number of nodes @@ -159,9 +158,9 @@ def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, self.distance = distance if self.distance is None: self.distance = euclidean_distance - + def OTD(self, T, x): - # OTD algorithm from + # OTD algorithm from https://arxiv.org/pdf/1909.09667.pdf if self.n == 1: # First node in the tree self.root = self.nodes[1] @@ -185,7 +184,7 @@ def OTD(self, T, x): else: # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T self.OTD(T.left, x) - + def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node @@ -210,7 +209,7 @@ def merge_nodes(self, T, added_node): # If T was the root, the new node become the root if self.root.key == T.key: self.root = self.nodes[self.n] - + def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree @@ -234,7 +233,7 @@ def learn_one(self, x): # We add it to the tree self.OTD(self.root, x) return self - + def predict_OTD(self, x, node, clusters): # get the list of predicted clusters for x if node is None: @@ -269,7 +268,6 @@ def predict_one(self, x): ------- (list, int) A list of clusters (from node `x` to root) and the node to which it would have been merged. - """ x = utils.dict2numpy(x) # We predict to which cluster x would be if we added it in the tree @@ -283,16 +281,16 @@ def find_path(self, root, path, k): if root is None: # No path return False - + path.append(root) - + if root.key == k: return True - + if ((root.left is not None and self.find_path(root.left, path, k)) or (root.right is not None and self.find_path(root.right, path, k))): return True - + path.pop() return False @@ -300,10 +298,10 @@ def lca(self, i, j): # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if self.root is None: return -1 - + path_i = [] path_j = [] - + if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): return -1 @@ -313,7 +311,7 @@ def lca(self, i, j): break k += 1 return path_i[k-1] - + def leaves(self, v): # find all the leaves from node v if v is None: @@ -353,18 +351,18 @@ def intra_subtree_similarity(self, A): nb +=1 r += self.distance(w_i, w_j) return r/nb - + def __str__(self): self.printTree(self.root) return "" - + def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: self.printTree(node.right, level + 1) print(' ' * 4 * level + '-> ' + str(node.key)) self.printTree(node.left, level + 1) - + def get_parents(self,node): # Get all the parents of the node (the clusters it belongs to) clusters = [node.key] @@ -372,7 +370,7 @@ def get_parents(self,node): return clusters clusters.extend(self.get_parents(node.parent)) return clusters - + def get_clusters_by_point(self): """Returns the list of clusters (from the data point node to the root) for all of the data points. @@ -386,7 +384,7 @@ def get_clusters_by_point(self): for x in self.X.keys(): clusters[x] = self.get_parents(self.nodes[self.X[x]]) return clusters - + def get_all_clusters(self): """Returns all the clusters of the tree. @@ -402,4 +400,4 @@ def get_all_clusters(self): clusters[i] = [self.nodes[i].data] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] - return sorted(clusters.items(), key = lambda x : len(x[1])) + return sorted(clusters.items(), key = lambda x : len(x[1])) \ No newline at end of file From a410f3efa18daf1c8fcb4e1189901c8f2bdc16b6 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 13:51:15 +0200 Subject: [PATCH 299/347] Fixed black and isort --- river/cluster/__init__.py | 12 +++++- river/cluster/hcluster.py | 86 +++++++++++++++++++++++---------------- 2 files changed, 62 insertions(+), 36 deletions(-) diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index e9ea4ebbfa..ce54e41f26 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -5,10 +5,18 @@ from .clustream import CluStream from .dbstream import DBSTREAM from .denstream import DenStream +from .hcluster import HierarchicalClustering from .k_means import KMeans from .odac import ODAC from .streamkmeans import STREAMKMeans from .textclust import TextClust -from .hcluster import HierarchicalClustering -__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust", "HierarchicalClustering"] +__all__ = [ + "CluStream", + "DBSTREAM", + "DenStream", + "KMeans", + "STREAMKMeans", + "TextClust", + "HierarchicalClustering", +] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index bbeb3c265b..67b73420af 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,13 @@ -from river import base, utils -import numpy as np from typing import Callable +import numpy as np + +from river import base, utils + + # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key : int, data : np.ndarray = None): + def __init__(self, key: int, data: np.ndarray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -16,8 +19,9 @@ def __init__(self, key : int, data : np.ndarray = None): def euclidean_distance(w_i, w_j): - # Euclidean distance between two nodes - return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + # Euclidean distance between two nodes + return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. @@ -78,7 +82,7 @@ class HierarchicalClustering(base.Clusterer): >>> HC.n 9 - + >>> print(HC) -> 6 -> 7 @@ -143,15 +147,20 @@ class HierarchicalClustering(base.Clusterer): -> 3 -> 1 """ - def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): + + def __init__( + self, + window_size: int = 100, + distance: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, + ): # Number of nodes self.n = 0 # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X : dict[np.ndarray, int] = {} + self.X: dict[np.ndarray, int] = {} # Dict : key -> node - self.nodes : dict[int, BinaryTreeNode] = {} + self.nodes: dict[int, BinaryTreeNode] = {} # First node of the tree self.root = None # Distance function @@ -175,10 +184,14 @@ def OTD(self, T, x): # If there is no node at the right of the intermediate node, we add it there T.right = self.nodes[self.X[np.array2string(x)]] self.nodes[self.X[np.array2string(x)]].parent = T - elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity(T, self.nodes[self.X[np.array2string(x)]]): + elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity( + T, self.nodes[self.X[np.array2string(x)]] + ): # If the nodes in T are closer between them than with the new node, we merge T and the new node self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) - elif self.inter_subtree_similarity(T.left, self.nodes[self.X[np.array2string(x)]]) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): + elif self.inter_subtree_similarity( + T.left, self.nodes[self.X[np.array2string(x)]] + ) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T self.OTD(T.right, x) else: @@ -188,7 +201,7 @@ def OTD(self, T, x): def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node - self.n +=1 + self.n += 1 new_node = BinaryTreeNode(self.n) # We add T and the added node as its children new_node.left = T @@ -224,12 +237,12 @@ def learn_one(self, x): del self.nodes[oldest_key] del self.X[list(self.X.keys())[0]] self.X[np.array2string(x)] = oldest_key - self.nodes[oldest_key] = BinaryTreeNode(oldest_key,x) + self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: # Else we just add a node - self.n +=1 + self.n += 1 self.X[np.array2string(x)] = self.n - self.nodes[self.n] = BinaryTreeNode(self.n,x) + self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree self.OTD(self.root, x) return self @@ -241,16 +254,20 @@ def predict_OTD(self, x, node, clusters): return [1], -1 if node.data is not None: # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n+2, self.n+1]) + clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key - if self.intra_subtree_similarity(node) < self.inter_subtree_similarity(node, BinaryTreeNode(self.n+1,x)): + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( + node, BinaryTreeNode(self.n + 1, x) + ): # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n+2, self.n+1]) + clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key else: # Else, x wouls be added in the tree, we add the key of node clusters.extend([node.key]) - if self.inter_subtree_similarity(node.left,BinaryTreeNode(self.n+1,x)) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n+1,x)): + if self.inter_subtree_similarity( + node.left, BinaryTreeNode(self.n + 1, x) + ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): # If the right part of the tree is closer to x than the left part, we continue in the right part return self.predict_OTD(x, node.right, clusters) else: @@ -271,10 +288,10 @@ def predict_one(self, x): """ x = utils.dict2numpy(x) # We predict to which cluster x would be if we added it in the tree - r, merged = self.predict_OTD( x, self.root, []) + r, merged = self.predict_OTD(x, self.root, []) r.reverse() return r, merged - + def find_path(self, root, path, k): # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ @@ -287,8 +304,9 @@ def find_path(self, root, path, k): if root.key == k: return True - if ((root.left is not None and self.find_path(root.left, path, k)) or - (root.right is not None and self.find_path(root.right, path, k))): + if (root.left is not None and self.find_path(root.left, path, k)) or ( + root.right is not None and self.find_path(root.right, path, k) + ): return True path.pop() @@ -302,15 +320,15 @@ def lca(self, i, j): path_i = [] path_j = [] - if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): + if not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j): return -1 k = 0 - while(k < len(path_i) and k < len(path_j)): + while k < len(path_i) and k < len(path_j): if path_i[k] != path_j[k]: break k += 1 - return path_i[k-1] + return path_i[k - 1] def leaves(self, v): # find all the leaves from node v @@ -334,9 +352,9 @@ def inter_subtree_similarity(self, A, B): nb = 0 for i, w_i in enumerate(leavesA): for j, w_j in enumerate(leavesB): - nb +=1 + nb += 1 r += self.distance(w_i, w_j) - return r/nb + return r / nb def intra_subtree_similarity(self, A): # compute mean of distances between the node from tree A @@ -348,9 +366,9 @@ def intra_subtree_similarity(self, A): for i, w_i in enumerate(leaves): for j, w_j in enumerate(leaves): if i < j: - nb +=1 + nb += 1 r += self.distance(w_i, w_j) - return r/nb + return r / nb def __str__(self): self.printTree(self.root) @@ -360,10 +378,10 @@ def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: self.printTree(node.right, level + 1) - print(' ' * 4 * level + '-> ' + str(node.key)) + print(" " * 4 * level + "-> " + str(node.key)) self.printTree(node.left, level + 1) - def get_parents(self,node): + def get_parents(self, node): # Get all the parents of the node (the clusters it belongs to) clusters = [node.key] if node.parent is None: @@ -395,9 +413,9 @@ def get_all_clusters(self): """ # Get the data of each cluster clusters = {} - for i in range(1,self.n+1): + for i in range(1, self.n + 1): if self.nodes[i].data is not None: clusters[i] = [self.nodes[i].data] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] - return sorted(clusters.items(), key = lambda x : len(x[1])) \ No newline at end of file + return sorted(clusters.items(), key=lambda x: len(x[1])) From 757c0d25b84d3f958058cf471f66d5b187c93fc5 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 14:05:37 +0200 Subject: [PATCH 300/347] reverting to the version passing the 'build river ubuntu' --- river/cluster/__init__.py | 12 +--- river/cluster/hcluster.py | 140 +++++++++++++++++--------------------- 2 files changed, 64 insertions(+), 88 deletions(-) diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index ce54e41f26..e9ea4ebbfa 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -5,18 +5,10 @@ from .clustream import CluStream from .dbstream import DBSTREAM from .denstream import DenStream -from .hcluster import HierarchicalClustering from .k_means import KMeans from .odac import ODAC from .streamkmeans import STREAMKMeans from .textclust import TextClust +from .hcluster import HierarchicalClustering -__all__ = [ - "CluStream", - "DBSTREAM", - "DenStream", - "KMeans", - "STREAMKMeans", - "TextClust", - "HierarchicalClustering", -] +__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust", "HierarchicalClustering"] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 67b73420af..2dd6ee9494 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,13 +1,10 @@ -from typing import Callable - -import numpy as np - from river import base, utils - +import numpy as np +from typing import Callable # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key: int, data: np.ndarray = None): + def __init__(self, key : int, data : np.ndarray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -19,9 +16,8 @@ def __init__(self, key: int, data: np.ndarray = None): def euclidean_distance(w_i, w_j): - # Euclidean distance between two nodes - return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) - + # Euclidean distance between two nodes + return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. @@ -30,12 +26,12 @@ class HierarchicalClustering(base.Clusterer): The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. It will (begining with the whole tree T) : - * Compare the new node to the tree T : + * Compare the new node to the tree T : * if T is just a leaf : merge * else if the nodes of T are more similar between them than with the new node : merge * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree - + You can choose a window size to use only the recent points and not overload the tree. (default : 100) You can also choose a distance function to compare the nodes. (default : euclidean distance) @@ -43,16 +39,16 @@ class HierarchicalClustering(base.Clusterer): Parameters ---------- window_size - number of data points to use. + number of data points to use distance - distance function to use to compare the nodes. - + distance function to use to compare the nodes + Attributes ---------- n : int - number of nodes. + number of nodes X : dict (data point (str(np.ndarray)) : key (int)) - data points used by the algorithm with the key of the node representing them. + data points used by the algorithm with the key of the node representing them References ---------- @@ -72,7 +68,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[1 1 1]': 1, '[1 1 0]': 2, @@ -82,7 +78,7 @@ class HierarchicalClustering(base.Clusterer): >>> HC.n 9 - + >>> print(HC) -> 6 -> 7 @@ -135,7 +131,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[20. 20. 20.1]': 2, '[0 1 1]': 1} @@ -146,30 +142,26 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 + """ - - def __init__( - self, - window_size: int = 100, - distance: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, - ): + def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): # Number of nodes self.n = 0 # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X: dict[np.ndarray, int] = {} + self.X : dict[np.ndarray, int] = {} # Dict : key -> node - self.nodes: dict[int, BinaryTreeNode] = {} + self.nodes : dict[int, BinaryTreeNode] = {} # First node of the tree self.root = None # Distance function self.distance = distance if self.distance is None: self.distance = euclidean_distance - + def OTD(self, T, x): - # OTD algorithm from https://arxiv.org/pdf/1909.09667.pdf + # OTD algorithm from if self.n == 1: # First node in the tree self.root = self.nodes[1] @@ -184,24 +176,20 @@ def OTD(self, T, x): # If there is no node at the right of the intermediate node, we add it there T.right = self.nodes[self.X[np.array2string(x)]] self.nodes[self.X[np.array2string(x)]].parent = T - elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity( - T, self.nodes[self.X[np.array2string(x)]] - ): + elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity(T, self.nodes[self.X[np.array2string(x)]]): # If the nodes in T are closer between them than with the new node, we merge T and the new node self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) - elif self.inter_subtree_similarity( - T.left, self.nodes[self.X[np.array2string(x)]] - ) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): + elif self.inter_subtree_similarity(T.left, self.nodes[self.X[np.array2string(x)]]) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T self.OTD(T.right, x) else: # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T self.OTD(T.left, x) - + def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node - self.n += 1 + self.n +=1 new_node = BinaryTreeNode(self.n) # We add T and the added node as its children new_node.left = T @@ -222,7 +210,7 @@ def merge_nodes(self, T, added_node): # If T was the root, the new node become the root if self.root.key == T.key: self.root = self.nodes[self.n] - + def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree @@ -237,16 +225,16 @@ def learn_one(self, x): del self.nodes[oldest_key] del self.X[list(self.X.keys())[0]] self.X[np.array2string(x)] = oldest_key - self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) + self.nodes[oldest_key] = BinaryTreeNode(oldest_key,x) else: # Else we just add a node - self.n += 1 + self.n +=1 self.X[np.array2string(x)] = self.n - self.nodes[self.n] = BinaryTreeNode(self.n, x) + self.nodes[self.n] = BinaryTreeNode(self.n,x) # We add it to the tree self.OTD(self.root, x) return self - + def predict_OTD(self, x, node, clusters): # get the list of predicted clusters for x if node is None: @@ -254,20 +242,16 @@ def predict_OTD(self, x, node, clusters): return [1], -1 if node.data is not None: # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n + 2, self.n + 1]) + clusters.extend([self.n+2, self.n+1]) return clusters, node.key - if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( - node, BinaryTreeNode(self.n + 1, x) - ): + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity(node, BinaryTreeNode(self.n+1,x)): # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n + 2, self.n + 1]) + clusters.extend([self.n+2, self.n+1]) return clusters, node.key else: # Else, x wouls be added in the tree, we add the key of node clusters.extend([node.key]) - if self.inter_subtree_similarity( - node.left, BinaryTreeNode(self.n + 1, x) - ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): + if self.inter_subtree_similarity(node.left,BinaryTreeNode(self.n+1,x)) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n+1,x)): # If the right part of the tree is closer to x than the left part, we continue in the right part return self.predict_OTD(x, node.right, clusters) else: @@ -285,30 +269,30 @@ def predict_one(self, x): ------- (list, int) A list of clusters (from node `x` to root) and the node to which it would have been merged. + """ x = utils.dict2numpy(x) # We predict to which cluster x would be if we added it in the tree - r, merged = self.predict_OTD(x, self.root, []) + r, merged = self.predict_OTD( x, self.root, []) r.reverse() return r, merged - + def find_path(self, root, path, k): # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if root is None: # No path return False - + path.append(root) - + if root.key == k: return True - - if (root.left is not None and self.find_path(root.left, path, k)) or ( - root.right is not None and self.find_path(root.right, path, k) - ): + + if ((root.left is not None and self.find_path(root.left, path, k)) or + (root.right is not None and self.find_path(root.right, path, k))): return True - + path.pop() return False @@ -316,20 +300,20 @@ def lca(self, i, j): # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if self.root is None: return -1 - + path_i = [] path_j = [] - - if not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j): + + if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): return -1 k = 0 - while k < len(path_i) and k < len(path_j): + while(k < len(path_i) and k < len(path_j)): if path_i[k] != path_j[k]: break k += 1 - return path_i[k - 1] - + return path_i[k-1] + def leaves(self, v): # find all the leaves from node v if v is None: @@ -352,9 +336,9 @@ def inter_subtree_similarity(self, A, B): nb = 0 for i, w_i in enumerate(leavesA): for j, w_j in enumerate(leavesB): - nb += 1 + nb +=1 r += self.distance(w_i, w_j) - return r / nb + return r/nb def intra_subtree_similarity(self, A): # compute mean of distances between the node from tree A @@ -366,29 +350,29 @@ def intra_subtree_similarity(self, A): for i, w_i in enumerate(leaves): for j, w_j in enumerate(leaves): if i < j: - nb += 1 + nb +=1 r += self.distance(w_i, w_j) - return r / nb - + return r/nb + def __str__(self): self.printTree(self.root) return "" - + def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: self.printTree(node.right, level + 1) - print(" " * 4 * level + "-> " + str(node.key)) + print(' ' * 4 * level + '-> ' + str(node.key)) self.printTree(node.left, level + 1) - - def get_parents(self, node): + + def get_parents(self,node): # Get all the parents of the node (the clusters it belongs to) clusters = [node.key] if node.parent is None: return clusters clusters.extend(self.get_parents(node.parent)) return clusters - + def get_clusters_by_point(self): """Returns the list of clusters (from the data point node to the root) for all of the data points. @@ -402,7 +386,7 @@ def get_clusters_by_point(self): for x in self.X.keys(): clusters[x] = self.get_parents(self.nodes[self.X[x]]) return clusters - + def get_all_clusters(self): """Returns all the clusters of the tree. @@ -413,9 +397,9 @@ def get_all_clusters(self): """ # Get the data of each cluster clusters = {} - for i in range(1, self.n + 1): + for i in range(1,self.n+1): if self.nodes[i].data is not None: clusters[i] = [self.nodes[i].data] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] - return sorted(clusters.items(), key=lambda x: len(x[1])) + return sorted(clusters.items(), key = lambda x : len(x[1])) From 488fe4b35685173010d0f6f8c919e71067b16c41 Mon Sep 17 00:00:00 2001 From: kchardon Date: Tue, 11 Apr 2023 14:08:33 +0200 Subject: [PATCH 301/347] fixed isort and black --- river/cluster/__init__.py | 12 +++- river/cluster/hcluster.py | 134 +++++++++++++++++++++----------------- 2 files changed, 86 insertions(+), 60 deletions(-) diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index e9ea4ebbfa..ce54e41f26 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -5,10 +5,18 @@ from .clustream import CluStream from .dbstream import DBSTREAM from .denstream import DenStream +from .hcluster import HierarchicalClustering from .k_means import KMeans from .odac import ODAC from .streamkmeans import STREAMKMeans from .textclust import TextClust -from .hcluster import HierarchicalClustering -__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust", "HierarchicalClustering"] +__all__ = [ + "CluStream", + "DBSTREAM", + "DenStream", + "KMeans", + "STREAMKMeans", + "TextClust", + "HierarchicalClustering", +] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 2dd6ee9494..258be1ba80 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,13 @@ -from river import base, utils -import numpy as np from typing import Callable +import numpy as np + +from river import base, utils + + # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key : int, data : np.ndarray = None): + def __init__(self, key: int, data: np.ndarray = None): # If it's a leaf : x data, else : None self.data = data # Key of the node @@ -16,8 +19,9 @@ def __init__(self, key : int, data : np.ndarray = None): def euclidean_distance(w_i, w_j): - # Euclidean distance between two nodes - return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + # Euclidean distance between two nodes + return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. @@ -26,12 +30,12 @@ class HierarchicalClustering(base.Clusterer): The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. It will (begining with the whole tree T) : - * Compare the new node to the tree T : + * Compare the new node to the tree T : * if T is just a leaf : merge * else if the nodes of T are more similar between them than with the new node : merge * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree - + You can choose a window size to use only the recent points and not overload the tree. (default : 100) You can also choose a distance function to compare the nodes. (default : euclidean distance) @@ -42,7 +46,7 @@ class HierarchicalClustering(base.Clusterer): number of data points to use distance distance function to use to compare the nodes - + Attributes ---------- n : int @@ -68,7 +72,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[1 1 1]': 1, '[1 1 0]': 2, @@ -78,7 +82,7 @@ class HierarchicalClustering(base.Clusterer): >>> HC.n 9 - + >>> print(HC) -> 6 -> 7 @@ -131,7 +135,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - + >>> HC.X {'[20. 20. 20.1]': 2, '[0 1 1]': 1} @@ -142,26 +146,31 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 - + """ - def __init__(self, window_size : int = 100, distance : Callable[[BinaryTreeNode, BinaryTreeNode], float] = None): + + def __init__( + self, + window_size: int = 100, + distance: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, + ): # Number of nodes self.n = 0 # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X : dict[np.ndarray, int] = {} + self.X: dict[np.ndarray, int] = {} # Dict : key -> node - self.nodes : dict[int, BinaryTreeNode] = {} + self.nodes: dict[int, BinaryTreeNode] = {} # First node of the tree self.root = None # Distance function self.distance = distance if self.distance is None: self.distance = euclidean_distance - + def OTD(self, T, x): - # OTD algorithm from + # OTD algorithm from if self.n == 1: # First node in the tree self.root = self.nodes[1] @@ -176,20 +185,24 @@ def OTD(self, T, x): # If there is no node at the right of the intermediate node, we add it there T.right = self.nodes[self.X[np.array2string(x)]] self.nodes[self.X[np.array2string(x)]].parent = T - elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity(T, self.nodes[self.X[np.array2string(x)]]): + elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity( + T, self.nodes[self.X[np.array2string(x)]] + ): # If the nodes in T are closer between them than with the new node, we merge T and the new node self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) - elif self.inter_subtree_similarity(T.left, self.nodes[self.X[np.array2string(x)]]) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): + elif self.inter_subtree_similarity( + T.left, self.nodes[self.X[np.array2string(x)]] + ) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T self.OTD(T.right, x) else: # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T self.OTD(T.left, x) - + def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node - self.n +=1 + self.n += 1 new_node = BinaryTreeNode(self.n) # We add T and the added node as its children new_node.left = T @@ -210,7 +223,7 @@ def merge_nodes(self, T, added_node): # If T was the root, the new node become the root if self.root.key == T.key: self.root = self.nodes[self.n] - + def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree @@ -225,16 +238,16 @@ def learn_one(self, x): del self.nodes[oldest_key] del self.X[list(self.X.keys())[0]] self.X[np.array2string(x)] = oldest_key - self.nodes[oldest_key] = BinaryTreeNode(oldest_key,x) + self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: # Else we just add a node - self.n +=1 + self.n += 1 self.X[np.array2string(x)] = self.n - self.nodes[self.n] = BinaryTreeNode(self.n,x) + self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree self.OTD(self.root, x) return self - + def predict_OTD(self, x, node, clusters): # get the list of predicted clusters for x if node is None: @@ -242,16 +255,20 @@ def predict_OTD(self, x, node, clusters): return [1], -1 if node.data is not None: # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n+2, self.n+1]) + clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key - if self.intra_subtree_similarity(node) < self.inter_subtree_similarity(node, BinaryTreeNode(self.n+1,x)): + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( + node, BinaryTreeNode(self.n + 1, x) + ): # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n+2, self.n+1]) + clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key else: # Else, x wouls be added in the tree, we add the key of node clusters.extend([node.key]) - if self.inter_subtree_similarity(node.left,BinaryTreeNode(self.n+1,x)) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n+1,x)): + if self.inter_subtree_similarity( + node.left, BinaryTreeNode(self.n + 1, x) + ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): # If the right part of the tree is closer to x than the left part, we continue in the right part return self.predict_OTD(x, node.right, clusters) else: @@ -269,30 +286,31 @@ def predict_one(self, x): ------- (list, int) A list of clusters (from node `x` to root) and the node to which it would have been merged. - + """ x = utils.dict2numpy(x) # We predict to which cluster x would be if we added it in the tree - r, merged = self.predict_OTD( x, self.root, []) + r, merged = self.predict_OTD(x, self.root, []) r.reverse() return r, merged - + def find_path(self, root, path, k): # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if root is None: # No path return False - + path.append(root) - + if root.key == k: return True - - if ((root.left is not None and self.find_path(root.left, path, k)) or - (root.right is not None and self.find_path(root.right, path, k))): + + if (root.left is not None and self.find_path(root.left, path, k)) or ( + root.right is not None and self.find_path(root.right, path, k) + ): return True - + path.pop() return False @@ -300,20 +318,20 @@ def lca(self, i, j): # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if self.root is None: return -1 - + path_i = [] path_j = [] - - if (not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j)): + + if not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j): return -1 k = 0 - while(k < len(path_i) and k < len(path_j)): + while k < len(path_i) and k < len(path_j): if path_i[k] != path_j[k]: break k += 1 - return path_i[k-1] - + return path_i[k - 1] + def leaves(self, v): # find all the leaves from node v if v is None: @@ -336,9 +354,9 @@ def inter_subtree_similarity(self, A, B): nb = 0 for i, w_i in enumerate(leavesA): for j, w_j in enumerate(leavesB): - nb +=1 + nb += 1 r += self.distance(w_i, w_j) - return r/nb + return r / nb def intra_subtree_similarity(self, A): # compute mean of distances between the node from tree A @@ -350,29 +368,29 @@ def intra_subtree_similarity(self, A): for i, w_i in enumerate(leaves): for j, w_j in enumerate(leaves): if i < j: - nb +=1 + nb += 1 r += self.distance(w_i, w_j) - return r/nb - + return r / nb + def __str__(self): self.printTree(self.root) return "" - + def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: self.printTree(node.right, level + 1) - print(' ' * 4 * level + '-> ' + str(node.key)) + print(" " * 4 * level + "-> " + str(node.key)) self.printTree(node.left, level + 1) - - def get_parents(self,node): + + def get_parents(self, node): # Get all the parents of the node (the clusters it belongs to) clusters = [node.key] if node.parent is None: return clusters clusters.extend(self.get_parents(node.parent)) return clusters - + def get_clusters_by_point(self): """Returns the list of clusters (from the data point node to the root) for all of the data points. @@ -386,7 +404,7 @@ def get_clusters_by_point(self): for x in self.X.keys(): clusters[x] = self.get_parents(self.nodes[self.X[x]]) return clusters - + def get_all_clusters(self): """Returns all the clusters of the tree. @@ -397,9 +415,9 @@ def get_all_clusters(self): """ # Get the data of each cluster clusters = {} - for i in range(1,self.n+1): + for i in range(1, self.n + 1): if self.nodes[i].data is not None: clusters[i] = [self.nodes[i].data] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] - return sorted(clusters.items(), key = lambda x : len(x[1])) + return sorted(clusters.items(), key=lambda x: len(x[1])) From 4799b8efd2bd31c2ede249f84505da14be0bbc23 Mon Sep 17 00:00:00 2001 From: Katia Chardon <92110501+kchardon@users.noreply.github.com> Date: Tue, 23 May 2023 22:11:39 +0200 Subject: [PATCH 302/347] Deleted the possibility to use all the data points --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 258be1ba80..02707fb93f 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -227,7 +227,7 @@ def merge_nodes(self, T, added_node): def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree - if self.window_size > 0 and len(self.X.keys()) >= self.window_size: + if len(self.X.keys()) >= self.window_size: # If we have reached the maximum of data points, we delete the oldest one and add a node with the same key as the one we deleted oldest_key = self.X[list(self.X.keys())[0]] oldest = self.nodes[oldest_key] From 05dc84b098eaed41876222d2db4f99fbde08e0fd Mon Sep 17 00:00:00 2001 From: Katia Chardon <92110501+kchardon@users.noreply.github.com> Date: Mon, 5 Jun 2023 12:03:31 +0200 Subject: [PATCH 303/347] correction for ruff hook --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 02707fb93f..0b85506a54 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,4 +1,4 @@ -from typing import Callable +from __future__ import annotations import numpy as np From a95b077d272ed84a708b3668777cfbb33bd6691e Mon Sep 17 00:00:00 2001 From: Katia Chardon <92110501+kchardon@users.noreply.github.com> Date: Mon, 5 Jun 2023 14:01:22 +0200 Subject: [PATCH 304/347] correction for ruff hook --- river/cluster/hcluster.py | 1 + 1 file changed, 1 insertion(+) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0b85506a54..0ca5879dfc 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,4 +1,5 @@ from __future__ import annotations +from typing import Callable import numpy as np From 1a78c7b774b4c6dfa521db6bc45e82cab376baa6 Mon Sep 17 00:00:00 2001 From: Katia Chardon <92110501+kchardon@users.noreply.github.com> Date: Mon, 5 Jun 2023 14:14:56 +0200 Subject: [PATCH 305/347] correction for ruff hook --- river/cluster/hcluster.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0ca5879dfc..001d9fa9c7 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,5 +1,6 @@ from __future__ import annotations -from typing import Callable + +from collections.abc import Callable import numpy as np From 8921f8bea8ba14d537a6ba760c74bc8781dead11 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 10:50:26 +0700 Subject: [PATCH 306/347] Modify Eucliean distance calculation using np.linalg.norm for better computational speed. --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 001d9fa9c7..0c09fcd2a2 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -22,7 +22,7 @@ def __init__(self, key: int, data: np.ndarray = None): def euclidean_distance(w_i, w_j): # Euclidean distance between two nodes - return np.sqrt(np.sum(np.square(w_i.data - w_j.data))) + return np.linalg.norm(w_i.data - w_j.data) class HierarchicalClustering(base.Clusterer): From 35868fa44b351c46b048d4b7d6a42d83e9bc58df Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 11:28:17 +0700 Subject: [PATCH 307/347] Refactor elements related to the distance function. --- river/cluster/hcluster.py | 8 +++----- river/linear_model/test_glm.py | 5 +---- 2 files changed, 4 insertions(+), 9 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0c09fcd2a2..624a432307 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -46,7 +46,7 @@ class HierarchicalClustering(base.Clusterer): ---------- window_size number of data points to use - distance + distance_func distance function to use to compare the nodes Attributes @@ -154,7 +154,7 @@ class HierarchicalClustering(base.Clusterer): def __init__( self, window_size: int = 100, - distance: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, + distance_func: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, ): # Number of nodes self.n = 0 @@ -167,9 +167,7 @@ def __init__( # First node of the tree self.root = None # Distance function - self.distance = distance - if self.distance is None: - self.distance = euclidean_distance + self.distance = distance_func if distance_func is not None else euclidean_distance def OTD(self, T, x): # OTD algorithm from diff --git a/river/linear_model/test_glm.py b/river/linear_model/test_glm.py index f274b39873..133f577baf 100644 --- a/river/linear_model/test_glm.py +++ b/river/linear_model/test_glm.py @@ -426,10 +426,7 @@ def test_lin_reg_sklearn_l1_non_regression(): def test_log_reg_sklearn_l1_non_regression(): """Checks that the river L1 implementation results are no worse than sklearn L1.""" - ( - X, - y, - ) = make_classification( + (X, y,) = make_classification( n_samples=1000, n_features=20, n_informative=4, From c85bcc0fa0eff859c0c0f76b89264a86d2dba862 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 11:38:25 +0700 Subject: [PATCH 308/347] Remove data types of attributes --- river/cluster/hcluster.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 624a432307..5e6b284762 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -51,9 +51,9 @@ class HierarchicalClustering(base.Clusterer): Attributes ---------- - n : int + n number of nodes - X : dict (data point (str(np.ndarray)) : key (int)) + X data points used by the algorithm with the key of the node representing them References From 83c05b5afe1efe87acaecb5b7de34eb61d6d0ca5 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:10:22 +0700 Subject: [PATCH 309/347] Refactor inter subtree similarity function. --- river/cluster/hcluster.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 5e6b284762..e8e506e333 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -346,14 +346,14 @@ def leaves(self, v): leavesList.extend(self.leaves(v.right)) return leavesList - def inter_subtree_similarity(self, A, B): - # compute the mean distance between tree A and tree B (mean of distances between nodes from tree A and tree B) - leavesA = self.leaves(A) - leavesB = self.leaves(B) + def inter_subtree_similarity(self, tree_a, tree_b): + # compute the mean distance (mean of distances) between two trees + leaves_a = self.leaves(tree_a) + leaves_b = self.leaves(tree_b) r = 0 nb = 0 - for i, w_i in enumerate(leavesA): - for j, w_j in enumerate(leavesB): + for i, w_i in enumerate(leaves_a): + for j, w_j in enumerate(leaves_b): nb += 1 r += self.distance(w_i, w_j) return r / nb From 2fde0e3228f5dbe35b427eb2268e56d54ea524c1 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:10:40 +0700 Subject: [PATCH 310/347] Refactor intra subtree similarity function --- river/cluster/hcluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index e8e506e333..427a5a435b 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -358,9 +358,9 @@ def inter_subtree_similarity(self, tree_a, tree_b): r += self.distance(w_i, w_j) return r / nb - def intra_subtree_similarity(self, A): - # compute mean of distances between the node from tree A - leaves = self.leaves(A) + def intra_subtree_similarity(self, tree): + # compute mean of distances between the nodes from a certain tree + leaves = self.leaves(tree) r = 0 nb = 0 if len(leaves) == 1: From e14d8b53d682c2700faf4c6051604ddfa65b6034 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:13:19 +0700 Subject: [PATCH 311/347] Refactor leave finding function. --- river/cluster/hcluster.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 427a5a435b..c426b8912a 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -341,10 +341,10 @@ def leaves(self, v): # If v is a leaf, returns itself return [v] # Else, returns leaves from its children - leavesList = [] - leavesList.extend(self.leaves(v.left)) - leavesList.extend(self.leaves(v.right)) - return leavesList + leave_list = [] + leave_list.extend(self.leaves(v.left)) + leave_list.extend(self.leaves(v.right)) + return leave_list def inter_subtree_similarity(self, tree_a, tree_b): # compute the mean distance (mean of distances) between two trees From c09a17711a6ad2d9bef7f8587eba21554e0925a7 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:40:30 +0700 Subject: [PATCH 312/347] Refactor online top down (OTD) clustering function. --- river/cluster/hcluster.py | 39 ++++++++++++++++++++------------------- 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index c426b8912a..8ab1d32ad1 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -169,35 +169,36 @@ def __init__( # Distance function self.distance = distance_func if distance_func is not None else euclidean_distance - def OTD(self, T, x): - # OTD algorithm from + def otd_clustering(self, tree, x): + # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. + # The algorithm performs highly efficient online updates and provably approximates Moseley-Wang revenue. if self.n == 1: # First node in the tree self.root = self.nodes[1] - elif T.data is not None: + elif tree.data is not None: # If T is a leaf, we merge the two nodes together - self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) - elif T.left is None: + self.merge_nodes(tree, self.nodes[self.X[np.array2string(x)]]) + elif tree.left is None: # If there is no node at the left of the intermediate node, we add it there - T.left = self.nodes[self.X[np.array2string(x)]] - self.nodes[self.X[np.array2string(x)]].parent = T - elif T.right is None: + tree.left = self.nodes[self.X[np.array2string(x)]] + self.nodes[self.X[np.array2string(x)]].parent = tree + elif tree.right is None: # If there is no node at the right of the intermediate node, we add it there - T.right = self.nodes[self.X[np.array2string(x)]] - self.nodes[self.X[np.array2string(x)]].parent = T - elif self.intra_subtree_similarity(T) < self.inter_subtree_similarity( - T, self.nodes[self.X[np.array2string(x)]] + tree.right = self.nodes[self.X[np.array2string(x)]] + self.nodes[self.X[np.array2string(x)]].parent = tree + elif self.intra_subtree_similarity(tree) < self.inter_subtree_similarity( + tree, self.nodes[self.X[np.array2string(x)]] ): # If the nodes in T are closer between them than with the new node, we merge T and the new node - self.merge_nodes(T, self.nodes[self.X[np.array2string(x)]]) + self.merge_nodes(tree, self.nodes[self.X[np.array2string(x)]]) elif self.inter_subtree_similarity( - T.left, self.nodes[self.X[np.array2string(x)]] - ) > self.inter_subtree_similarity(T.right, self.nodes[self.X[np.array2string(x)]]): - # If the right part of the tree T is closer to the new node than the left part, we continue to search where to merge the new node in the right part of T - self.OTD(T.right, x) + tree.left, self.nodes[self.X[np.array2string(x)]] + ) > self.inter_subtree_similarity(tree.right, self.nodes[self.X[np.array2string(x)]]): + # Continue to search where to merge the new node in the right part of T + self.otd_clustering(tree.right, x) else: - # Else the left part of the tree T is closer to the new node than the right part, we continue to search where to merge the new node in the left part of T - self.OTD(T.left, x) + # Continue to search where to merge the new node in the left part of T + self.otd_clustering(tree.left, x) def merge_nodes(self, T, added_node): # Merge a new node (added node) to the tree T From 6b54ebc15528e99cdafd297523f7c757ff3c16c0 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:58:45 +0700 Subject: [PATCH 313/347] Refactor. --- river/cluster/hcluster.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 8ab1d32ad1..4b8d89a1f1 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -172,28 +172,29 @@ def __init__( def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. # The algorithm performs highly efficient online updates and provably approximates Moseley-Wang revenue. + x_string = np.array2string(x) if self.n == 1: # First node in the tree self.root = self.nodes[1] elif tree.data is not None: # If T is a leaf, we merge the two nodes together - self.merge_nodes(tree, self.nodes[self.X[np.array2string(x)]]) + self.merge_nodes(tree, self.nodes[self.X[x_string]]) elif tree.left is None: # If there is no node at the left of the intermediate node, we add it there - tree.left = self.nodes[self.X[np.array2string(x)]] - self.nodes[self.X[np.array2string(x)]].parent = tree + tree.left = self.nodes[self.X[x_string]] + self.nodes[self.X[x_string]].parent = tree elif tree.right is None: # If there is no node at the right of the intermediate node, we add it there - tree.right = self.nodes[self.X[np.array2string(x)]] - self.nodes[self.X[np.array2string(x)]].parent = tree + tree.right = self.nodes[self.X[x_string]] + self.nodes[self.X[x_string]].parent = tree elif self.intra_subtree_similarity(tree) < self.inter_subtree_similarity( - tree, self.nodes[self.X[np.array2string(x)]] + tree, self.nodes[self.X[x_string]] ): # If the nodes in T are closer between them than with the new node, we merge T and the new node - self.merge_nodes(tree, self.nodes[self.X[np.array2string(x)]]) + self.merge_nodes(tree, self.nodes[self.X[x_string]]) elif self.inter_subtree_similarity( - tree.left, self.nodes[self.X[np.array2string(x)]] - ) > self.inter_subtree_similarity(tree.right, self.nodes[self.X[np.array2string(x)]]): + tree.left, self.nodes[self.X[x_string]] + ) > self.inter_subtree_similarity(tree.right, self.nodes[self.X[x_string]]): # Continue to search where to merge the new node in the right part of T self.otd_clustering(tree.right, x) else: From 0c66a9dc3a112fff5f8616ec929363c18e7b7e12 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:59:04 +0700 Subject: [PATCH 314/347] Cheng function name OTD to otd_clustering. --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 4b8d89a1f1..0589c914a7 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -247,7 +247,7 @@ def learn_one(self, x): self.X[np.array2string(x)] = self.n self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree - self.OTD(self.root, x) + self.otd_clustering(self.root, x) return self def predict_OTD(self, x, node, clusters): From 1d2ce2f4be3fa0910b2c8b1ddfb841b7b78b287f Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 17:58:58 +0700 Subject: [PATCH 315/347] Remove unnecessary comments. --- river/cluster/hcluster.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0589c914a7..7fc69e8399 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -10,9 +10,7 @@ # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: def __init__(self, key: int, data: np.ndarray = None): - # If it's a leaf : x data, else : None self.data = data - # Key of the node self.key = key # Children and parent self.left = None From ecb69d0fb6dffb7d3a037e306a179b8844e54353 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 18:08:39 +0700 Subject: [PATCH 316/347] Refactor description of the algorithm within Docstring. --- river/cluster/hcluster.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 7fc69e8399..cc987b2a06 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -26,19 +26,18 @@ def euclidean_distance(w_i, w_j): class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. - HierarchicalClustering is a stream hierarchical clustering algorithm. - The algorithm [^1] inserts new nodes near the nodes it is similar to, but without breaking clusters of very similar nodes. - It will (begining with the whole tree T) : + HierarchicalClustering is a stream hierarchical clustering algorithm. This algorithm [^1] inserts new nodes + near the nodes it is similar to without breaking clusters of very similar nodes. - * Compare the new node to the tree T : - * if T is just a leaf : merge - * else if the nodes of T are more similar between them than with the new node : merge - * else if the new node is more similar to the left subtree than to the right subtree : redo from the first point with T = left subtree - * else (the new node is more similar to the right subtree than to the left subtree) redo from the first point with T = right subtree + Beginning with the whole tree `T`, it will compare the new node to this respective tree: + * If `T` is just a leaf : merge + * else if the nodes of T are more similar between them than with the new node: merge + * else if the new node is more similar to the left subtree than to the right subtree: + redo from the first point with T = left subtree + * else (the new node is more similar to the right subtree than to the left subtree): + redo from the first point with T = right subtree - You can choose a window size to use only the recent points and not overload the tree. (default : 100) - - You can also choose a distance function to compare the nodes. (default : euclidean distance) + A certain window size can be chosen to use only the most recent points to make sure that the tree is not overloaded. Parameters ---------- @@ -56,7 +55,8 @@ class HierarchicalClustering(base.Clusterer): References ---------- - [^1]: Menon, Aditya & Rajagopalan, Anand & Sumengen, Baris & Citovsky, Gui & Cao, Qin & Kumar, Sanjiv. (2019). Online Hierarchical Clustering Approximations. + [^1]: Anand Rajagopalan, Aditya Krishna Menon, Qin Cao, Gui Citovsky, Baris Sumengen and Sanjiv Kumar (2019). Online + Hierarchical Clustering Approximations. arXiV:1909.09667. Available at: https://doi.org/10.48550/arXiv.1909.09667 Examples -------- From 674c05ef31b2bdd8a003593f05dd6199d0cb86c5 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 18:12:09 +0700 Subject: [PATCH 317/347] Refactor tests in Docstring. --- river/cluster/hcluster.py | 27 ++++++--------------------- 1 file changed, 6 insertions(+), 21 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index cc987b2a06..2ef8df8fb7 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -74,11 +74,7 @@ class HierarchicalClustering(base.Clusterer): ... HC = HC.learn_one(x) >>> HC.X - {'[1 1 1]': 1, - '[1 1 0]': 2, - '[20 20 20]': 4, - '[20. 20. 20.1]': 6, - '[0 1 1]': 8} + {'[1 1 1]': 1, '[1 1 0]': 2, '[20 20 20]': 4, '[20. 20. 20.1]': 6, '[0 1 1]': 8} >>> HC.n 9 @@ -93,24 +89,13 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 + Printed Hierarchical Clustering Tree. >>> HC.get_all_clusters() - [(1, [array([1, 1, 1])]), - (2, [array([1, 1, 0])]), - (4, [array([20, 20, 20])]), - (6, [array([20. , 20. , 20.1])]), - (8, [array([0, 1, 1])]), - (3, [1, 2]), - (5, [9, 7]), - (7, [4, 6]), - (9, [3, 8])] + [(1, [array([1, 1, 1])]), (2, [array([1, 1, 0])]), (4, [array([20, 20, 20])]), (6, [array([20. , 20. , 20.1])]), (8, [array([0, 1, 1])]), (3, [1, 2]), (5, [9, 7]), (7, [4, 6]), (9, [3, 8])] >>> HC.get_clusters_by_point() - {'[1 1 1]': [1, 3, 9, 5], - '[1 1 0]': [2, 3, 9, 5], - '[20 20 20]': [4, 7, 5], - '[20. 20. 20.1]': [6, 7, 5], - '[0 1 1]': [8, 9, 5]} + {'[1 1 1]': [1, 3, 9, 5], '[1 1 0]': [2, 3, 9, 5], '[20 20 20]': [4, 7, 5], '[20. 20. 20.1]': [6, 7, 5], '[0 1 1]': [8, 9, 5]} >>> HC.predict_one({0 : 20.1, 1 : 20, 2 : 20 }) ([10, 11, 5], 7) @@ -129,7 +114,7 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 - + Printed Hierarchical Clustering Tree. >>> HC = cluster.HierarchicalClustering(window_size=2) @@ -146,7 +131,7 @@ class HierarchicalClustering(base.Clusterer): -> 2 -> 3 -> 1 - + Printed Hierarchical Clustering Tree. """ def __init__( From 4d8bb8e73cfcacb60111e76863c1a6d5e05c1c42 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 18:13:42 +0700 Subject: [PATCH 318/347] Refactor merge_nodes function. --- river/cluster/hcluster.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 2ef8df8fb7..92f63ff425 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -184,29 +184,29 @@ def otd_clustering(self, tree, x): # Continue to search where to merge the new node in the left part of T self.otd_clustering(tree.left, x) - def merge_nodes(self, T, added_node): + def merge_nodes(self, tree, added_node): # Merge a new node (added node) to the tree T # We create the node that will be the parent of T and the added node self.n += 1 new_node = BinaryTreeNode(self.n) # We add T and the added node as its children - new_node.left = T + new_node.left = tree new_node.right = added_node # The parent of the new node is the parent of T - new_node.parent = T.parent + new_node.parent = tree.parent # If T is not the root, we set the child of its parent as new node (instead of T) - if T.parent is not None: - if T.parent.left.key == T.key: - T.parent.left = new_node + if tree.parent is not None: + if tree.parent.left.key == tree.key: + tree.parent.left = new_node else: - T.parent.right = new_node + tree.parent.right = new_node # We add the new node as the parent of T and the added node - T.parent = new_node + tree.parent = new_node added_node.parent = new_node # We add the new node to the dict self.nodes[self.n] = new_node # If T was the root, the new node become the root - if self.root.key == T.key: + if self.root.key == tree.key: self.root = self.nodes[self.n] def learn_one(self, x): From 5841e7d3aca9de30ef2774ca8d4a008be20d2954 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:46:31 +0700 Subject: [PATCH 319/347] Refactor comments in merge_nodes --- river/cluster/hcluster.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 92f63ff425..4054cf181a 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -144,7 +144,7 @@ def __init__( # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X: dict[np.ndarray, int] = {} + self.X: dict[str, int] = {} # Dict : key -> node self.nodes: dict[int, BinaryTreeNode] = {} # First node of the tree @@ -185,27 +185,27 @@ def otd_clustering(self, tree, x): self.otd_clustering(tree.left, x) def merge_nodes(self, tree, added_node): - # Merge a new node (added node) to the tree T - # We create the node that will be the parent of T and the added node + # Merge a new node (added node) to the tree + # We create the node that will be the parent of the tree and the added node self.n += 1 new_node = BinaryTreeNode(self.n) - # We add T and the added node as its children + # We add the tree and the added node as its children new_node.left = tree new_node.right = added_node - # The parent of the new node is the parent of T + # The parent of the new node is the parent of the tree new_node.parent = tree.parent - # If T is not the root, we set the child of its parent as new node (instead of T) + # If the tree is not the root, we set the child of its parent as new node (instead of T) if tree.parent is not None: if tree.parent.left.key == tree.key: tree.parent.left = new_node else: tree.parent.right = new_node - # We add the new node as the parent of T and the added node + # We add the new node as the parent of the tree and the added node tree.parent = new_node added_node.parent = new_node # We add the new node to the dict self.nodes[self.n] = new_node - # If T was the root, the new node become the root + # If the tree was the root, the new node become the root if self.root.key == tree.key: self.root = self.nodes[self.n] From 8e6705f216e9cec2cf241798d7932bf90078e60f Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:47:10 +0700 Subject: [PATCH 320/347] Rename predict_otd. --- river/cluster/hcluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 4054cf181a..3a19972399 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -233,7 +233,7 @@ def learn_one(self, x): self.otd_clustering(self.root, x) return self - def predict_OTD(self, x, node, clusters): + def predict_otd(self, x, node, clusters): # get the list of predicted clusters for x if node is None: # If there is still no node in the tree @@ -255,10 +255,10 @@ def predict_OTD(self, x, node, clusters): node.left, BinaryTreeNode(self.n + 1, x) ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): # If the right part of the tree is closer to x than the left part, we continue in the right part - return self.predict_OTD(x, node.right, clusters) + return self.predict_otd(x, node.right, clusters) else: # If the left part of the tree is closer to x than the right part, we continue in the left part - return self.predict_OTD(x, node.left, clusters) + return self.predict_otd(x, node.left, clusters) def predict_one(self, x): """Predicts the clusters for a set of features `x`. From 03dafa51be9c8154646e896ba61a81d2a3cdc299 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:47:21 +0700 Subject: [PATCH 321/347] Simplify comments. --- river/cluster/hcluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 3a19972399..11f6e78794 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -213,7 +213,7 @@ def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree if len(self.X.keys()) >= self.window_size: - # If we have reached the maximum of data points, we delete the oldest one and add a node with the same key as the one we deleted + # Delete the oldest data point and add a node with the same key as the one deleted oldest_key = self.X[list(self.X.keys())[0]] oldest = self.nodes[oldest_key] if oldest.parent.left.key == oldest_key: @@ -239,13 +239,13 @@ def predict_otd(self, x, node, clusters): # If there is still no node in the tree return [1], -1 if node.data is not None: - # If we compare x to a leaf, then we add itself (n+1) and the key of the node that would merge x and node (n+2) + # Add itself (n+1) and the key of the node that would merge x and node (n+2) clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( node, BinaryTreeNode(self.n + 1, x) ): - # If the nodes in the tree 'node' are closer betweem them than with x, then we add itself (n+1) and the key of the node that would merge x and node (n+2) + # Add itself (n+1) and the key of the node that would merge x and node (n+2) clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key else: From ba520868c03a26fdce658d3c9456c383ec18b537 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:48:15 +0700 Subject: [PATCH 322/347] Modify __str__ printed output by adding "Printed Hierarchical Clustering Tree." at the end of the tree. --- river/cluster/hcluster.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 11f6e78794..0155b14189 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -358,8 +358,8 @@ def intra_subtree_similarity(self, tree): return r / nb def __str__(self): - self.printTree(self.root) - return "" + self.print_tree(self.root) + return "Printed Hierarchical Clustering Tree." def printTree(self, node, level=0): # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python From b8ddad0667144c3a88a01b0b6401c22f10832787 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:48:35 +0700 Subject: [PATCH 323/347] Rename predict_otd. --- river/cluster/hcluster.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0155b14189..1cb27da78c 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -274,8 +274,8 @@ def predict_one(self, x): """ x = utils.dict2numpy(x) - # We predict to which cluster x would be if we added it in the tree - r, merged = self.predict_OTD(x, self.root, []) + # We predict to which cluster x would be if we added in the tree + r, merged = self.predict_otd(x, self.root, []) r.reverse() return r, merged From 8fd902b2f29ebc3997acfa156da96b4420079376 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Mon, 11 Sep 2023 23:49:25 +0700 Subject: [PATCH 324/347] Split comments and rename printTree to print_tree. --- river/cluster/hcluster.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 1cb27da78c..0ebac19e39 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -280,7 +280,8 @@ def predict_one(self, x): return r, merged def find_path(self, root, path, k): - # find the path from root to k, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + # find the path from root to k + # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if root is None: # No path @@ -300,7 +301,9 @@ def find_path(self, root, path, k): return False def lca(self, i, j): - # find the least common ancestor, from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + # Find the least common ancestor + # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + if self.root is None: return -1 @@ -361,12 +364,13 @@ def __str__(self): self.print_tree(self.root) return "Printed Hierarchical Clustering Tree." - def printTree(self, node, level=0): - # print node and its children, from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python + def print_tree(self, node, level=0): + # Print node and its children + # Adapted from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: - self.printTree(node.right, level + 1) + self.print_tree(node.right, level + 1) print(" " * 4 * level + "-> " + str(node.key)) - self.printTree(node.left, level + 1) + self.print_tree(node.left, level + 1) def get_parents(self, node): # Get all the parents of the node (the clusters it belongs to) From 9bb44274e045985726564bdfec2f585f0104a7bd Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 00:05:35 +0700 Subject: [PATCH 325/347] Modify self.X to self.x_clusters. --- river/cluster/hcluster.py | 40 +++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 0ebac19e39..ff522b72a3 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -50,7 +50,7 @@ class HierarchicalClustering(base.Clusterer): ---------- n number of nodes - X + x_clusters data points used by the algorithm with the key of the node representing them References @@ -73,7 +73,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - >>> HC.X + >>> HC.x_clusters {'[1 1 1]': 1, '[1 1 0]': 2, '[20 20 20]': 4, '[20. 20. 20.1]': 6, '[0 1 1]': 8} >>> HC.n @@ -121,7 +121,7 @@ class HierarchicalClustering(base.Clusterer): >>> for x, _ in stream.iter_array(X): ... HC = HC.learn_one(x) - >>> HC.X + >>> HC.x_clusters {'[20. 20. 20.1]': 2, '[0 1 1]': 1} >>> HC.n @@ -144,7 +144,7 @@ def __init__( # Max number of leaves self.window_size = window_size # Dict : x data (str(array of size m)) -> key of the node - self.X: dict[str, int] = {} + self.x_clusters: dict[str, int] = {} # Dict : key -> node self.nodes: dict[int, BinaryTreeNode] = {} # First node of the tree @@ -161,23 +161,23 @@ def otd_clustering(self, tree, x): self.root = self.nodes[1] elif tree.data is not None: # If T is a leaf, we merge the two nodes together - self.merge_nodes(tree, self.nodes[self.X[x_string]]) + self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) elif tree.left is None: # If there is no node at the left of the intermediate node, we add it there - tree.left = self.nodes[self.X[x_string]] - self.nodes[self.X[x_string]].parent = tree + tree.left = self.nodes[self.x_clusters[x_string]] + self.nodes[self.x_clusters[x_string]].parent = tree elif tree.right is None: # If there is no node at the right of the intermediate node, we add it there - tree.right = self.nodes[self.X[x_string]] - self.nodes[self.X[x_string]].parent = tree + tree.right = self.nodes[self.x_clusters[x_string]] + self.nodes[self.x_clusters[x_string]].parent = tree elif self.intra_subtree_similarity(tree) < self.inter_subtree_similarity( - tree, self.nodes[self.X[x_string]] + tree, self.nodes[self.x_clusters[x_string]] ): # If the nodes in T are closer between them than with the new node, we merge T and the new node - self.merge_nodes(tree, self.nodes[self.X[x_string]]) + self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) elif self.inter_subtree_similarity( - tree.left, self.nodes[self.X[x_string]] - ) > self.inter_subtree_similarity(tree.right, self.nodes[self.X[x_string]]): + tree.left, self.nodes[self.x_clusters[x_string]] + ) > self.inter_subtree_similarity(tree.right, self.nodes[self.x_clusters[x_string]]): # Continue to search where to merge the new node in the right part of T self.otd_clustering(tree.right, x) else: @@ -212,22 +212,22 @@ def merge_nodes(self, tree, added_node): def learn_one(self, x): x = utils.dict2numpy(x) # We create the node for x and add it to the tree - if len(self.X.keys()) >= self.window_size: + if len(self.x_clusters.keys()) >= self.window_size: # Delete the oldest data point and add a node with the same key as the one deleted - oldest_key = self.X[list(self.X.keys())[0]] + oldest_key = self.x_clusters[list(self.x_clusters.keys())[0]] oldest = self.nodes[oldest_key] if oldest.parent.left.key == oldest_key: oldest.parent.left = None else: oldest.parent.right = None del self.nodes[oldest_key] - del self.X[list(self.X.keys())[0]] - self.X[np.array2string(x)] = oldest_key + del self.x_clusters[list(self.x_clusters.keys())[0]] + self.x_clusters[np.array2string(x)] = oldest_key self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: # Else we just add a node self.n += 1 - self.X[np.array2string(x)] = self.n + self.x_clusters[np.array2string(x)] = self.n self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree self.otd_clustering(self.root, x) @@ -390,8 +390,8 @@ def get_clusters_by_point(self): """ # Get all the clusters each data point belong to clusters = {} - for x in self.X.keys(): - clusters[x] = self.get_parents(self.nodes[self.X[x]]) + for x in self.x_clusters.keys(): + clusters[x] = self.get_parents(self.nodes[self.x_clusters[x]]) return clusters def get_all_clusters(self): From 121d94c80495fa87083ff2f804fb06846146e61c Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 00:06:25 +0700 Subject: [PATCH 326/347] Lexical changes. --- river/cluster/hcluster.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index ff522b72a3..053aa488cb 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -249,7 +249,7 @@ def predict_otd(self, x, node, clusters): clusters.extend([self.n + 2, self.n + 1]) return clusters, node.key else: - # Else, x wouls be added in the tree, we add the key of node + # Else, x would be added in the tree, we add the key of node clusters.extend([node.key]) if self.inter_subtree_similarity( node.left, BinaryTreeNode(self.n + 1, x) @@ -381,7 +381,7 @@ def get_parents(self, node): return clusters def get_clusters_by_point(self): - """Returns the list of clusters (from the data point node to the root) for all of the data points. + """Returns the list of clusters (from the data point node to the root) for all data points. Returns ------- From 93419f8c54cfe2486beea31e38434afae0bca2fa Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 00:06:50 +0700 Subject: [PATCH 327/347] Remove unnecessary comments. --- river/cluster/hcluster.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 053aa488cb..598a11bfdf 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -284,7 +284,6 @@ def find_path(self, root, path, k): # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ if root is None: - # No path return False path.append(root) @@ -322,13 +321,12 @@ def lca(self, i, j): def leaves(self, v): # find all the leaves from node v + if v is None: - # No leaves return -1 if v.data is not None: - # If v is a leaf, returns itself return [v] - # Else, returns leaves from its children + leave_list = [] leave_list.extend(self.leaves(v.left)) leave_list.extend(self.leaves(v.right)) From 29011c92fa1cee92d8bc8420bc6d43bdc9632e69 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 17:41:08 +0700 Subject: [PATCH 328/347] Refactor Docstring --- river/cluster/hcluster.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 598a11bfdf..322a7cccdc 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -68,18 +68,18 @@ class HierarchicalClustering(base.Clusterer): >>> X = [[1, 1, 1], [1, 1, 0], [20, 20, 20], [20, 20, 20.1], [0 , 1, 1]] - >>> HC = cluster.HierarchicalClustering() + >>> hierarchical_clustering = cluster.HierarchicalClustering() >>> for x, _ in stream.iter_array(X): - ... HC = HC.learn_one(x) + ... hierarchical_clustering = hierarchical_clustering.learn_one(x) - >>> HC.x_clusters + >>> hierarchical_clustering.x_clusters {'[1 1 1]': 1, '[1 1 0]': 2, '[20 20 20]': 4, '[20. 20. 20.1]': 6, '[0 1 1]': 8} - >>> HC.n + >>> hierarchical_clustering.n 9 - >>> print(HC) + >>> print(hierarchical_clustering) -> 6 -> 7 -> 4 @@ -91,18 +91,18 @@ class HierarchicalClustering(base.Clusterer): -> 1 Printed Hierarchical Clustering Tree. - >>> HC.get_all_clusters() + >>> hierarchical_clustering.get_all_clusters() [(1, [array([1, 1, 1])]), (2, [array([1, 1, 0])]), (4, [array([20, 20, 20])]), (6, [array([20. , 20. , 20.1])]), (8, [array([0, 1, 1])]), (3, [1, 2]), (5, [9, 7]), (7, [4, 6]), (9, [3, 8])] - >>> HC.get_clusters_by_point() + >>> hierarchical_clustering.get_clusters_by_point() {'[1 1 1]': [1, 3, 9, 5], '[1 1 0]': [2, 3, 9, 5], '[20 20 20]': [4, 7, 5], '[20. 20. 20.1]': [6, 7, 5], '[0 1 1]': [8, 9, 5]} - >>> HC.predict_one({0 : 20.1, 1 : 20, 2 : 20 }) + >>> hierarchical_clustering.predict_one({0 : 20.1, 1: 20, 2: 20 }) ([10, 11, 5], 7) - >>> HC = HC.learn_one({0 : 20.1, 1 : 20, 2 : 20 }) + >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 20.1, 1: 20, 2: 20}) - >>> print(HC) + >>> print(hierarchical_clustering) -> 10 -> 11 -> 6 @@ -116,18 +116,18 @@ class HierarchicalClustering(base.Clusterer): -> 1 Printed Hierarchical Clustering Tree. - >>> HC = cluster.HierarchicalClustering(window_size=2) + >>> hierarchical_clustering = cluster.HierarchicalClustering(window_size=2) >>> for x, _ in stream.iter_array(X): - ... HC = HC.learn_one(x) + ... hierarchical_clustering = hierarchical_clustering.learn_one(x) - >>> HC.x_clusters + >>> hierarchical_clustering.x_clusters {'[20. 20. 20.1]': 2, '[0 1 1]': 1} - >>> HC.n + >>> hierarchical_clustering.n 3 - >>> print(HC) + >>> print(hierarchical_clustering) -> 2 -> 3 -> 1 From 793ef299b23619072476ebd9df7151711e3645a4 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Tue, 12 Sep 2023 17:41:36 +0700 Subject: [PATCH 329/347] Refactor comment. --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 322a7cccdc..59089bc234 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -225,7 +225,7 @@ def learn_one(self, x): self.x_clusters[np.array2string(x)] = oldest_key self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: - # Else we just add a node + # Else, add a node self.n += 1 self.x_clusters[np.array2string(x)] = self.n self.nodes[self.n] = BinaryTreeNode(self.n, x) From da438f76b9b3265dee9020145b4ca4bf1884ab52 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 08:07:37 +0700 Subject: [PATCH 330/347] Make find_path() a static method. --- river/cluster/hcluster.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 59089bc234..b4697ef357 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -279,7 +279,8 @@ def predict_one(self, x): r.reverse() return r, merged - def find_path(self, root, path, k): + @staticmethod + def find_path(root, path, k): # find the path from root to k # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ @@ -291,8 +292,8 @@ def find_path(self, root, path, k): if root.key == k: return True - if (root.left is not None and self.find_path(root.left, path, k)) or ( - root.right is not None and self.find_path(root.right, path, k) + if (root.left is not None and HierarchicalClustering.find_path(root.left, path, k)) or ( + root.right is not None and HierarchicalClustering.find_path(root.right, path, k) ): return True @@ -309,7 +310,9 @@ def lca(self, i, j): path_i = [] path_j = [] - if not self.find_path(self.root, path_i, i) or not self.find_path(self.root, path_j, j): + if not HierarchicalClustering.find_path( + self.root, path_i, i + ) or not HierarchicalClustering.find_path(self.root, path_j, j): return -1 k = 0 From ea57417c1db4a00bc3ce952f45923fc5cb57b027 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 08:07:53 +0700 Subject: [PATCH 331/347] Refactor Docstring. --- river/cluster/hcluster.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index b4697ef357..94b84162da 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -30,14 +30,14 @@ class HierarchicalClustering(base.Clusterer): near the nodes it is similar to without breaking clusters of very similar nodes. Beginning with the whole tree `T`, it will compare the new node to this respective tree: - * If `T` is just a leaf : merge - * else if the nodes of T are more similar between them than with the new node: merge - * else if the new node is more similar to the left subtree than to the right subtree: - redo from the first point with T = left subtree - * else (the new node is more similar to the right subtree than to the left subtree): - redo from the first point with T = right subtree - - A certain window size can be chosen to use only the most recent points to make sure that the tree is not overloaded. + * If `T` is just a leaf: merge + * Else, if the nodes of `T` are more similar between them than with the new node: merge + * Else, if the new node is more similar to the left subtree than to the right subtree: + redo from the first point with `T` equal to left subtree + * Else, if the new node is more similar to the right subtree than to the left subtree: + redo from the first point with `T` right subtree + + A window size can also be chosen to use only the most recent points to make sure that the tree is not overloaded. Parameters ---------- From aaa04c640db8b78530e6dcb7751130a709c16559 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 15:44:30 +0700 Subject: [PATCH 332/347] Make print_tree static method. --- river/cluster/hcluster.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 94b84162da..21ba71b506 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -365,13 +365,14 @@ def __str__(self): self.print_tree(self.root) return "Printed Hierarchical Clustering Tree." - def print_tree(self, node, level=0): + @staticmethod + def print_tree(node, level=0): # Print node and its children # Adapted from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python if node is not None: - self.print_tree(node.right, level + 1) + HierarchicalClustering.print_tree(node.right, level + 1) print(" " * 4 * level + "-> " + str(node.key)) - self.print_tree(node.left, level + 1) + HierarchicalClustering.print_tree(node.left, level + 1) def get_parents(self, node): # Get all the parents of the node (the clusters it belongs to) From da92685733088bcc9d51b283c7ddef0f12c5aaec Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 16:08:16 +0700 Subject: [PATCH 333/347] Refactor code to account for failing tests. --- river/cluster/hcluster.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 21ba71b506..42a78d84a1 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -150,7 +150,10 @@ def __init__( # First node of the tree self.root = None # Distance function - self.distance = distance_func if distance_func is not None else euclidean_distance + if distance_func is not None: + self.distance = distance_func + else: + self.distance = euclidean_distance def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. From ec4fc6dbfd15a02415704b42a80468037cc20c64 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:00:26 +0700 Subject: [PATCH 334/347] Refactor distance function used in Hierarchical Clustering class. --- river/cluster/hcluster.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 42a78d84a1..f5f6f7775f 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,10 +1,11 @@ from __future__ import annotations -from collections.abc import Callable - +import functools +import math import numpy as np from river import base, utils +from river.neighbors.base import DistanceFunc # Node of a binary tree for Hierarchical Clustering @@ -137,7 +138,7 @@ class HierarchicalClustering(base.Clusterer): def __init__( self, window_size: int = 100, - distance_func: Callable[[BinaryTreeNode, BinaryTreeNode], float] = None, + distance_func: DistanceFunc = functools.partial(utils.math.minkowski_distance, p=2), ): # Number of nodes self.n = 0 @@ -150,10 +151,7 @@ def __init__( # First node of the tree self.root = None # Distance function - if distance_func is not None: - self.distance = distance_func - else: - self.distance = euclidean_distance + self.distance = distance_func def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. @@ -347,7 +345,7 @@ def inter_subtree_similarity(self, tree_a, tree_b): for i, w_i in enumerate(leaves_a): for j, w_j in enumerate(leaves_b): nb += 1 - r += self.distance(w_i, w_j) + r += self.distance(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) return r / nb def intra_subtree_similarity(self, tree): @@ -361,7 +359,7 @@ def intra_subtree_similarity(self, tree): for j, w_j in enumerate(leaves): if i < j: nb += 1 - r += self.distance(w_i, w_j) + r += self.distance(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) return r / nb def __str__(self): From fae5ebe9a8445eea50233641f1f4c8e00a0319d6 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:00:49 +0700 Subject: [PATCH 335/347] Delete euclidean_distance function (due to being unnecessary). --- river/cluster/hcluster.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index f5f6f7775f..a07759dafb 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -19,11 +19,6 @@ def __init__(self, key: int, data: np.ndarray = None): self.parent = None -def euclidean_distance(w_i, w_j): - # Euclidean distance between two nodes - return np.linalg.norm(w_i.data - w_j.data) - - class HierarchicalClustering(base.Clusterer): """Hierarchical Clustering. From 7a736b374aa2ac71ba9d0d38f5874c2ffc764965 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:20:51 +0700 Subject: [PATCH 336/347] Code refactoring to align with other algorithms available in River. --- river/cluster/hcluster.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index a07759dafb..23b1d18b8f 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,11 +1,10 @@ from __future__ import annotations import functools -import math import numpy as np from river import base, utils -from river.neighbors.base import DistanceFunc +from river.neighbors.base import DistanceFunc, FunctionWrapper # Node of a binary tree for Hierarchical Clustering @@ -39,7 +38,7 @@ class HierarchicalClustering(base.Clusterer): ---------- window_size number of data points to use - distance_func + dist_func distance function to use to compare the nodes Attributes @@ -133,7 +132,7 @@ class HierarchicalClustering(base.Clusterer): def __init__( self, window_size: int = 100, - distance_func: DistanceFunc = functools.partial(utils.math.minkowski_distance, p=2), + dist_func: DistanceFunc | FunctionWrapper | None = None, ): # Number of nodes self.n = 0 @@ -146,7 +145,9 @@ def __init__( # First node of the tree self.root = None # Distance function - self.distance = distance_func + if dist_func is None: + dist_func = functools.partial(utils.math.minkowski_distance, p=2) + self.dist_func = dist_func def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. @@ -340,7 +341,7 @@ def inter_subtree_similarity(self, tree_a, tree_b): for i, w_i in enumerate(leaves_a): for j, w_j in enumerate(leaves_b): nb += 1 - r += self.distance(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) + r += self.dist_func(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) return r / nb def intra_subtree_similarity(self, tree): @@ -354,7 +355,7 @@ def intra_subtree_similarity(self, tree): for j, w_j in enumerate(leaves): if i < j: nb += 1 - r += self.distance(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) + r += self.dist_func(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) return r / nb def __str__(self): From 3f2b671315acaa64d5d9e281ca3e8f7daddd47c9 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:22:07 +0700 Subject: [PATCH 337/347] Modify Docstring description for dist_func. --- river/cluster/hcluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 23b1d18b8f..fd5738bc7f 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -39,7 +39,7 @@ class HierarchicalClustering(base.Clusterer): window_size number of data points to use dist_func - distance function to use to compare the nodes + A distance function to use to compare the nodes. The Minkowski distance with `p=2` is used as default. Attributes ---------- From 4c8079d2459972ef63a08135965ce892e9df8066 Mon Sep 17 00:00:00 2001 From: Hoang-Anh Ngo <50743576+hoanganhngo610@users.noreply.github.com> Date: Thu, 14 Sep 2023 11:57:57 +0700 Subject: [PATCH 338/347] Delete least common ancestor finding function (since this function is not used within the algorithm). --- river/cluster/hcluster.py | 22 ---------------------- 1 file changed, 22 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index fd5738bc7f..3ece91ef07 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -297,28 +297,6 @@ def find_path(root, path, k): path.pop() return False - def lca(self, i, j): - # Find the least common ancestor - # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ - - if self.root is None: - return -1 - - path_i = [] - path_j = [] - - if not HierarchicalClustering.find_path( - self.root, path_i, i - ) or not HierarchicalClustering.find_path(self.root, path_j, j): - return -1 - - k = 0 - while k < len(path_i) and k < len(path_j): - if path_i[k] != path_j[k]: - break - k += 1 - return path_i[k - 1] - def leaves(self, v): # find all the leaves from node v From 34aa9adc2e1d34c2b8040248d043825ce7ede1d6 Mon Sep 17 00:00:00 2001 From: kchardon Date: Sun, 10 Nov 2024 19:10:22 +0100 Subject: [PATCH 339/347] removing use of numpy --- river/cluster/hcluster.py | 63 ++++++++++++++++++++++----------------- 1 file changed, 36 insertions(+), 27 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 3ece91ef07..1021060b9c 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -1,7 +1,6 @@ from __future__ import annotations import functools -import numpy as np from river import base, utils from river.neighbors.base import DistanceFunc, FunctionWrapper @@ -9,7 +8,7 @@ # Node of a binary tree for Hierarchical Clustering class BinaryTreeNode: - def __init__(self, key: int, data: np.ndarray = None): + def __init__(self, key: int, data: dict = None): self.data = data self.key = key # Children and parent @@ -61,7 +60,7 @@ class HierarchicalClustering(base.Clusterer): >>> from river import cluster >>> from river import stream - >>> X = [[1, 1, 1], [1, 1, 0], [20, 20, 20], [20, 20, 20.1], [0 , 1, 1]] + >>> X = [[1, 2, 1], [2, 1, 0], [3, 2, 1], [2, 2, 1], [5, 2, 3]] >>> hierarchical_clustering = cluster.HierarchicalClustering() @@ -69,43 +68,55 @@ class HierarchicalClustering(base.Clusterer): ... hierarchical_clustering = hierarchical_clustering.learn_one(x) >>> hierarchical_clustering.x_clusters - {'[1 1 1]': 1, '[1 1 0]': 2, '[20 20 20]': 4, '[20. 20. 20.1]': 6, '[0 1 1]': 8} + {'[1, 2, 1]': 1, '[2, 1, 0]': 2, '[3, 2, 1]': 4, '[2, 2, 1]': 6, '[5, 2, 3]': 8} >>> hierarchical_clustering.n 9 >>> print(hierarchical_clustering) - -> 6 - -> 7 - -> 4 - -> 5 - -> 8 - -> 9 + -> 8 + -> 9 + -> 6 + -> 7 + -> 4 + -> 5 -> 2 -> 3 -> 1 Printed Hierarchical Clustering Tree. >>> hierarchical_clustering.get_all_clusters() - [(1, [array([1, 1, 1])]), (2, [array([1, 1, 0])]), (4, [array([20, 20, 20])]), (6, [array([20. , 20. , 20.1])]), (8, [array([0, 1, 1])]), (3, [1, 2]), (5, [9, 7]), (7, [4, 6]), (9, [3, 8])] + [(1, ['[1, 2, 1]']), + (2, ['[2, 1, 0]']), + (4, ['[3, 2, 1]']), + (6, ['[2, 2, 1]']), + (8, ['[5, 2, 3]']), + (3, [1, 2]), + (5, [3, 7]), + (7, [4, 6]), + (9, [5, 8])] >>> hierarchical_clustering.get_clusters_by_point() - {'[1 1 1]': [1, 3, 9, 5], '[1 1 0]': [2, 3, 9, 5], '[20 20 20]': [4, 7, 5], '[20. 20. 20.1]': [6, 7, 5], '[0 1 1]': [8, 9, 5]} + {'[1, 2, 1]': [1, 3, 5, 9], + '[2, 1, 0]': [2, 3, 5, 9], + '[3, 2, 1]': [4, 7, 5, 9], + '[2, 2, 1]': [6, 7, 5, 9], + '[5, 2, 3]': [8, 9]} - >>> hierarchical_clustering.predict_one({0 : 20.1, 1: 20, 2: 20 }) - ([10, 11, 5], 7) + >>> hierarchical_clustering.predict_one({0: 4, 1: 3, 2: 1}) + ([10, 11, 9], 8) - >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 20.1, 1: 20, 2: 20}) + >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 4, 1: 3, 2: 1}) >>> print(hierarchical_clustering) -> 10 -> 11 + -> 8 + -> 9 -> 6 -> 7 -> 4 - -> 5 - -> 8 - -> 9 + -> 5 -> 2 -> 3 -> 1 @@ -117,7 +128,7 @@ class HierarchicalClustering(base.Clusterer): ... hierarchical_clustering = hierarchical_clustering.learn_one(x) >>> hierarchical_clustering.x_clusters - {'[20. 20. 20.1]': 2, '[0 1 1]': 1} + {'[2, 2, 1]': 2, '[5, 2, 3]': 1} >>> hierarchical_clustering.n 3 @@ -152,7 +163,7 @@ def __init__( def otd_clustering(self, tree, x): # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. # The algorithm performs highly efficient online updates and provably approximates Moseley-Wang revenue. - x_string = np.array2string(x) + x_string = str(list(x.values())) if self.n == 1: # First node in the tree self.root = self.nodes[1] @@ -207,7 +218,6 @@ def merge_nodes(self, tree, added_node): self.root = self.nodes[self.n] def learn_one(self, x): - x = utils.dict2numpy(x) # We create the node for x and add it to the tree if len(self.x_clusters.keys()) >= self.window_size: # Delete the oldest data point and add a node with the same key as the one deleted @@ -219,12 +229,12 @@ def learn_one(self, x): oldest.parent.right = None del self.nodes[oldest_key] del self.x_clusters[list(self.x_clusters.keys())[0]] - self.x_clusters[np.array2string(x)] = oldest_key + self.x_clusters[str(list(x.values()))] = oldest_key self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) else: # Else, add a node self.n += 1 - self.x_clusters[np.array2string(x)] = self.n + self.x_clusters[str(list(x.values()))] = self.n self.nodes[self.n] = BinaryTreeNode(self.n, x) # We add it to the tree self.otd_clustering(self.root, x) @@ -270,7 +280,6 @@ def predict_one(self, x): A list of clusters (from node `x` to root) and the node to which it would have been merged. """ - x = utils.dict2numpy(x) # We predict to which cluster x would be if we added in the tree r, merged = self.predict_otd(x, self.root, []) r.reverse() @@ -319,7 +328,7 @@ def inter_subtree_similarity(self, tree_a, tree_b): for i, w_i in enumerate(leaves_a): for j, w_j in enumerate(leaves_b): nb += 1 - r += self.dist_func(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) + r += self.dist_func(w_i.data, w_j.data) return r / nb def intra_subtree_similarity(self, tree): @@ -333,7 +342,7 @@ def intra_subtree_similarity(self, tree): for j, w_j in enumerate(leaves): if i < j: nb += 1 - r += self.dist_func(utils.numpy2dict(w_i.data), utils.numpy2dict(w_j.data)) + r += self.dist_func(w_i.data, w_j.data) return r / nb def __str__(self): @@ -383,7 +392,7 @@ def get_all_clusters(self): clusters = {} for i in range(1, self.n + 1): if self.nodes[i].data is not None: - clusters[i] = [self.nodes[i].data] + clusters[i] = [str(list(self.nodes[i].data.values()))] else: clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] return sorted(clusters.items(), key=lambda x: len(x[1])) From 0c745a23ca8cb19be7ffe8a06912bce214498fcb Mon Sep 17 00:00:00 2001 From: kchardon Date: Sun, 10 Nov 2024 19:58:25 +0100 Subject: [PATCH 340/347] ruff format --- river/cluster/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index ce54e41f26..7c9c68a695 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -7,7 +7,6 @@ from .denstream import DenStream from .hcluster import HierarchicalClustering from .k_means import KMeans -from .odac import ODAC from .streamkmeans import STREAMKMeans from .textclust import TextClust From ce5f9a4df2eb8256a822d9569a6fa523d7b0e812 Mon Sep 17 00:00:00 2001 From: kchardon Date: Mon, 11 Nov 2024 17:44:34 +0100 Subject: [PATCH 341/347] odac issue --- river/cluster/__init__.py | 4 +++- river/cluster/hcluster.py | 6 +++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 7c9c68a695..21157d971b 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -7,6 +7,7 @@ from .denstream import DenStream from .hcluster import HierarchicalClustering from .k_means import KMeans +from .odac import ODAC from .streamkmeans import STREAMKMeans from .textclust import TextClust @@ -14,8 +15,9 @@ "CluStream", "DBSTREAM", "DenStream", + "HierarchicalClustering", "KMeans", + "ODAC", "STREAMKMeans", "TextClust", - "HierarchicalClustering", ] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index 1021060b9c..c3511c9a6b 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -68,7 +68,11 @@ class HierarchicalClustering(base.Clusterer): ... hierarchical_clustering = hierarchical_clustering.learn_one(x) >>> hierarchical_clustering.x_clusters - {'[1, 2, 1]': 1, '[2, 1, 0]': 2, '[3, 2, 1]': 4, '[2, 2, 1]': 6, '[5, 2, 3]': 8} + {'[1, 2, 1]': 1, + '[2, 1, 0]': 2, + '[3, 2, 1]': 4, + '[2, 2, 1]': 6, + '[5, 2, 3]': 8} >>> hierarchical_clustering.n 9 From 9662a7f2c793dac7acca0faff3f9b1532eb4a37c Mon Sep 17 00:00:00 2001 From: kchardon Date: Mon, 11 Nov 2024 17:59:33 +0100 Subject: [PATCH 342/347] remove hcluster --- river/cluster/__init__.py | 2 - river/cluster/hcluster.py | 402 -------------------------------------- 2 files changed, 404 deletions(-) delete mode 100644 river/cluster/hcluster.py diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 21157d971b..a69efe554a 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -5,7 +5,6 @@ from .clustream import CluStream from .dbstream import DBSTREAM from .denstream import DenStream -from .hcluster import HierarchicalClustering from .k_means import KMeans from .odac import ODAC from .streamkmeans import STREAMKMeans @@ -15,7 +14,6 @@ "CluStream", "DBSTREAM", "DenStream", - "HierarchicalClustering", "KMeans", "ODAC", "STREAMKMeans", diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py deleted file mode 100644 index c3511c9a6b..0000000000 --- a/river/cluster/hcluster.py +++ /dev/null @@ -1,402 +0,0 @@ -from __future__ import annotations - -import functools - -from river import base, utils -from river.neighbors.base import DistanceFunc, FunctionWrapper - - -# Node of a binary tree for Hierarchical Clustering -class BinaryTreeNode: - def __init__(self, key: int, data: dict = None): - self.data = data - self.key = key - # Children and parent - self.left = None - self.right = None - self.parent = None - - -class HierarchicalClustering(base.Clusterer): - """Hierarchical Clustering. - - HierarchicalClustering is a stream hierarchical clustering algorithm. This algorithm [^1] inserts new nodes - near the nodes it is similar to without breaking clusters of very similar nodes. - - Beginning with the whole tree `T`, it will compare the new node to this respective tree: - * If `T` is just a leaf: merge - * Else, if the nodes of `T` are more similar between them than with the new node: merge - * Else, if the new node is more similar to the left subtree than to the right subtree: - redo from the first point with `T` equal to left subtree - * Else, if the new node is more similar to the right subtree than to the left subtree: - redo from the first point with `T` right subtree - - A window size can also be chosen to use only the most recent points to make sure that the tree is not overloaded. - - Parameters - ---------- - window_size - number of data points to use - dist_func - A distance function to use to compare the nodes. The Minkowski distance with `p=2` is used as default. - - Attributes - ---------- - n - number of nodes - x_clusters - data points used by the algorithm with the key of the node representing them - - References - ---------- - [^1]: Anand Rajagopalan, Aditya Krishna Menon, Qin Cao, Gui Citovsky, Baris Sumengen and Sanjiv Kumar (2019). Online - Hierarchical Clustering Approximations. arXiV:1909.09667. Available at: https://doi.org/10.48550/arXiv.1909.09667 - - Examples - -------- - - The first example is with leaving the window size to 100. In the second one we put it at 2 to see how it works. - - >>> from river import cluster - >>> from river import stream - - >>> X = [[1, 2, 1], [2, 1, 0], [3, 2, 1], [2, 2, 1], [5, 2, 3]] - - >>> hierarchical_clustering = cluster.HierarchicalClustering() - - >>> for x, _ in stream.iter_array(X): - ... hierarchical_clustering = hierarchical_clustering.learn_one(x) - - >>> hierarchical_clustering.x_clusters - {'[1, 2, 1]': 1, - '[2, 1, 0]': 2, - '[3, 2, 1]': 4, - '[2, 2, 1]': 6, - '[5, 2, 3]': 8} - - >>> hierarchical_clustering.n - 9 - - >>> print(hierarchical_clustering) - -> 8 - -> 9 - -> 6 - -> 7 - -> 4 - -> 5 - -> 2 - -> 3 - -> 1 - Printed Hierarchical Clustering Tree. - - >>> hierarchical_clustering.get_all_clusters() - [(1, ['[1, 2, 1]']), - (2, ['[2, 1, 0]']), - (4, ['[3, 2, 1]']), - (6, ['[2, 2, 1]']), - (8, ['[5, 2, 3]']), - (3, [1, 2]), - (5, [3, 7]), - (7, [4, 6]), - (9, [5, 8])] - - >>> hierarchical_clustering.get_clusters_by_point() - {'[1, 2, 1]': [1, 3, 5, 9], - '[2, 1, 0]': [2, 3, 5, 9], - '[3, 2, 1]': [4, 7, 5, 9], - '[2, 2, 1]': [6, 7, 5, 9], - '[5, 2, 3]': [8, 9]} - - >>> hierarchical_clustering.predict_one({0: 4, 1: 3, 2: 1}) - ([10, 11, 9], 8) - - >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 4, 1: 3, 2: 1}) - - >>> print(hierarchical_clustering) - -> 10 - -> 11 - -> 8 - -> 9 - -> 6 - -> 7 - -> 4 - -> 5 - -> 2 - -> 3 - -> 1 - Printed Hierarchical Clustering Tree. - - >>> hierarchical_clustering = cluster.HierarchicalClustering(window_size=2) - - >>> for x, _ in stream.iter_array(X): - ... hierarchical_clustering = hierarchical_clustering.learn_one(x) - - >>> hierarchical_clustering.x_clusters - {'[2, 2, 1]': 2, '[5, 2, 3]': 1} - - >>> hierarchical_clustering.n - 3 - - >>> print(hierarchical_clustering) - -> 2 - -> 3 - -> 1 - Printed Hierarchical Clustering Tree. - """ - - def __init__( - self, - window_size: int = 100, - dist_func: DistanceFunc | FunctionWrapper | None = None, - ): - # Number of nodes - self.n = 0 - # Max number of leaves - self.window_size = window_size - # Dict : x data (str(array of size m)) -> key of the node - self.x_clusters: dict[str, int] = {} - # Dict : key -> node - self.nodes: dict[int, BinaryTreeNode] = {} - # First node of the tree - self.root = None - # Distance function - if dist_func is None: - dist_func = functools.partial(utils.math.minkowski_distance, p=2) - self.dist_func = dist_func - - def otd_clustering(self, tree, x): - # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. - # The algorithm performs highly efficient online updates and provably approximates Moseley-Wang revenue. - x_string = str(list(x.values())) - if self.n == 1: - # First node in the tree - self.root = self.nodes[1] - elif tree.data is not None: - # If T is a leaf, we merge the two nodes together - self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) - elif tree.left is None: - # If there is no node at the left of the intermediate node, we add it there - tree.left = self.nodes[self.x_clusters[x_string]] - self.nodes[self.x_clusters[x_string]].parent = tree - elif tree.right is None: - # If there is no node at the right of the intermediate node, we add it there - tree.right = self.nodes[self.x_clusters[x_string]] - self.nodes[self.x_clusters[x_string]].parent = tree - elif self.intra_subtree_similarity(tree) < self.inter_subtree_similarity( - tree, self.nodes[self.x_clusters[x_string]] - ): - # If the nodes in T are closer between them than with the new node, we merge T and the new node - self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) - elif self.inter_subtree_similarity( - tree.left, self.nodes[self.x_clusters[x_string]] - ) > self.inter_subtree_similarity(tree.right, self.nodes[self.x_clusters[x_string]]): - # Continue to search where to merge the new node in the right part of T - self.otd_clustering(tree.right, x) - else: - # Continue to search where to merge the new node in the left part of T - self.otd_clustering(tree.left, x) - - def merge_nodes(self, tree, added_node): - # Merge a new node (added node) to the tree - # We create the node that will be the parent of the tree and the added node - self.n += 1 - new_node = BinaryTreeNode(self.n) - # We add the tree and the added node as its children - new_node.left = tree - new_node.right = added_node - # The parent of the new node is the parent of the tree - new_node.parent = tree.parent - # If the tree is not the root, we set the child of its parent as new node (instead of T) - if tree.parent is not None: - if tree.parent.left.key == tree.key: - tree.parent.left = new_node - else: - tree.parent.right = new_node - # We add the new node as the parent of the tree and the added node - tree.parent = new_node - added_node.parent = new_node - # We add the new node to the dict - self.nodes[self.n] = new_node - # If the tree was the root, the new node become the root - if self.root.key == tree.key: - self.root = self.nodes[self.n] - - def learn_one(self, x): - # We create the node for x and add it to the tree - if len(self.x_clusters.keys()) >= self.window_size: - # Delete the oldest data point and add a node with the same key as the one deleted - oldest_key = self.x_clusters[list(self.x_clusters.keys())[0]] - oldest = self.nodes[oldest_key] - if oldest.parent.left.key == oldest_key: - oldest.parent.left = None - else: - oldest.parent.right = None - del self.nodes[oldest_key] - del self.x_clusters[list(self.x_clusters.keys())[0]] - self.x_clusters[str(list(x.values()))] = oldest_key - self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) - else: - # Else, add a node - self.n += 1 - self.x_clusters[str(list(x.values()))] = self.n - self.nodes[self.n] = BinaryTreeNode(self.n, x) - # We add it to the tree - self.otd_clustering(self.root, x) - return self - - def predict_otd(self, x, node, clusters): - # get the list of predicted clusters for x - if node is None: - # If there is still no node in the tree - return [1], -1 - if node.data is not None: - # Add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n + 2, self.n + 1]) - return clusters, node.key - if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( - node, BinaryTreeNode(self.n + 1, x) - ): - # Add itself (n+1) and the key of the node that would merge x and node (n+2) - clusters.extend([self.n + 2, self.n + 1]) - return clusters, node.key - else: - # Else, x would be added in the tree, we add the key of node - clusters.extend([node.key]) - if self.inter_subtree_similarity( - node.left, BinaryTreeNode(self.n + 1, x) - ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): - # If the right part of the tree is closer to x than the left part, we continue in the right part - return self.predict_otd(x, node.right, clusters) - else: - # If the left part of the tree is closer to x than the right part, we continue in the left part - return self.predict_otd(x, node.left, clusters) - - def predict_one(self, x): - """Predicts the clusters for a set of features `x`. - - Parameters - ---------- - x - A dictionary of features. - Returns - ------- - (list, int) - A list of clusters (from node `x` to root) and the node to which it would have been merged. - - """ - # We predict to which cluster x would be if we added in the tree - r, merged = self.predict_otd(x, self.root, []) - r.reverse() - return r, merged - - @staticmethod - def find_path(root, path, k): - # find the path from root to k - # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ - - if root is None: - return False - - path.append(root) - - if root.key == k: - return True - - if (root.left is not None and HierarchicalClustering.find_path(root.left, path, k)) or ( - root.right is not None and HierarchicalClustering.find_path(root.right, path, k) - ): - return True - - path.pop() - return False - - def leaves(self, v): - # find all the leaves from node v - - if v is None: - return -1 - if v.data is not None: - return [v] - - leave_list = [] - leave_list.extend(self.leaves(v.left)) - leave_list.extend(self.leaves(v.right)) - return leave_list - - def inter_subtree_similarity(self, tree_a, tree_b): - # compute the mean distance (mean of distances) between two trees - leaves_a = self.leaves(tree_a) - leaves_b = self.leaves(tree_b) - r = 0 - nb = 0 - for i, w_i in enumerate(leaves_a): - for j, w_j in enumerate(leaves_b): - nb += 1 - r += self.dist_func(w_i.data, w_j.data) - return r / nb - - def intra_subtree_similarity(self, tree): - # compute mean of distances between the nodes from a certain tree - leaves = self.leaves(tree) - r = 0 - nb = 0 - if len(leaves) == 1: - return 0 - for i, w_i in enumerate(leaves): - for j, w_j in enumerate(leaves): - if i < j: - nb += 1 - r += self.dist_func(w_i.data, w_j.data) - return r / nb - - def __str__(self): - self.print_tree(self.root) - return "Printed Hierarchical Clustering Tree." - - @staticmethod - def print_tree(node, level=0): - # Print node and its children - # Adapted from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python - if node is not None: - HierarchicalClustering.print_tree(node.right, level + 1) - print(" " * 4 * level + "-> " + str(node.key)) - HierarchicalClustering.print_tree(node.left, level + 1) - - def get_parents(self, node): - # Get all the parents of the node (the clusters it belongs to) - clusters = [node.key] - if node.parent is None: - return clusters - clusters.extend(self.get_parents(node.parent)) - return clusters - - def get_clusters_by_point(self): - """Returns the list of clusters (from the data point node to the root) for all data points. - - Returns - ------- - {x : list} - A dict of all the data points with their clusters. - """ - # Get all the clusters each data point belong to - clusters = {} - for x in self.x_clusters.keys(): - clusters[x] = self.get_parents(self.nodes[self.x_clusters[x]]) - return clusters - - def get_all_clusters(self): - """Returns all the clusters of the tree. - - Returns - ------- - {int : list} - A dict of all the clusters with their children (or the data point for the leaves). - """ - # Get the data of each cluster - clusters = {} - for i in range(1, self.n + 1): - if self.nodes[i].data is not None: - clusters[i] = [str(list(self.nodes[i].data.values()))] - else: - clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] - return sorted(clusters.items(), key=lambda x: len(x[1])) From a4ec3c98abf75ab9007bfa609bdfd358900b4715 Mon Sep 17 00:00:00 2001 From: kchardon Date: Mon, 11 Nov 2024 21:10:07 +0100 Subject: [PATCH 343/347] last main commit --- docs/releases/unreleased.md | 4 +--- river/cluster/__init__.py | 10 +--------- river/linear_model/test_glm.py | 5 ++++- 3 files changed, 6 insertions(+), 13 deletions(-) diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 3d3ff51840..4ed6dd48f1 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,5 +1,4 @@ -## cluster -- Added `cluster.HierarchicalClustering`. +# Unreleased - The units used in River have been corrected to be based on powers of 2 (KiB, MiB). This only changes the display, the behaviour is unchanged. - The methods `learn_one`, `learn_many`, `update`, `revert`, and `append` now return `None`. @@ -10,7 +9,6 @@ - Change `draw` in `cluster.ODAC` to draw the hierarchical cluster's structure as a Graphviz graph. - Add `render_ascii` in `cluster.ODAC` to render the hierarchical cluster's structure in text format. - Work with `stats.Var` in `cluster.ODAC` when cluster has only one time series. -- Added `cluster.HierarchicalClustering`. ## drift diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index a69efe554a..0fe354fd2f 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -10,12 +10,4 @@ from .streamkmeans import STREAMKMeans from .textclust import TextClust -__all__ = [ - "CluStream", - "DBSTREAM", - "DenStream", - "KMeans", - "ODAC", - "STREAMKMeans", - "TextClust", -] +__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "ODAC", "STREAMKMeans", "TextClust"] diff --git a/river/linear_model/test_glm.py b/river/linear_model/test_glm.py index 133f577baf..f274b39873 100644 --- a/river/linear_model/test_glm.py +++ b/river/linear_model/test_glm.py @@ -426,7 +426,10 @@ def test_lin_reg_sklearn_l1_non_regression(): def test_log_reg_sklearn_l1_non_regression(): """Checks that the river L1 implementation results are no worse than sklearn L1.""" - (X, y,) = make_classification( + ( + X, + y, + ) = make_classification( n_samples=1000, n_features=20, n_informative=4, From 10cd5e53d0562c3f3eb664395a852ef49429a725 Mon Sep 17 00:00:00 2001 From: kchardon Date: Mon, 11 Nov 2024 21:28:40 +0100 Subject: [PATCH 344/347] hcluster --- river/cluster/__init__.py | 3 +- river/cluster/hcluster.py | 402 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 404 insertions(+), 1 deletion(-) create mode 100644 river/cluster/hcluster.py diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 0fe354fd2f..085a21befc 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -5,9 +5,10 @@ from .clustream import CluStream from .dbstream import DBSTREAM from .denstream import DenStream +from .hcluster import HierarchicalClustering from .k_means import KMeans from .odac import ODAC from .streamkmeans import STREAMKMeans from .textclust import TextClust -__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "ODAC", "STREAMKMeans", "TextClust"] +__all__ = ["CluStream", "DBSTREAM", "DenStream", "HierarchicalClustering", "KMeans", "ODAC", "STREAMKMeans", "TextClust"] diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py new file mode 100644 index 0000000000..c3511c9a6b --- /dev/null +++ b/river/cluster/hcluster.py @@ -0,0 +1,402 @@ +from __future__ import annotations + +import functools + +from river import base, utils +from river.neighbors.base import DistanceFunc, FunctionWrapper + + +# Node of a binary tree for Hierarchical Clustering +class BinaryTreeNode: + def __init__(self, key: int, data: dict = None): + self.data = data + self.key = key + # Children and parent + self.left = None + self.right = None + self.parent = None + + +class HierarchicalClustering(base.Clusterer): + """Hierarchical Clustering. + + HierarchicalClustering is a stream hierarchical clustering algorithm. This algorithm [^1] inserts new nodes + near the nodes it is similar to without breaking clusters of very similar nodes. + + Beginning with the whole tree `T`, it will compare the new node to this respective tree: + * If `T` is just a leaf: merge + * Else, if the nodes of `T` are more similar between them than with the new node: merge + * Else, if the new node is more similar to the left subtree than to the right subtree: + redo from the first point with `T` equal to left subtree + * Else, if the new node is more similar to the right subtree than to the left subtree: + redo from the first point with `T` right subtree + + A window size can also be chosen to use only the most recent points to make sure that the tree is not overloaded. + + Parameters + ---------- + window_size + number of data points to use + dist_func + A distance function to use to compare the nodes. The Minkowski distance with `p=2` is used as default. + + Attributes + ---------- + n + number of nodes + x_clusters + data points used by the algorithm with the key of the node representing them + + References + ---------- + [^1]: Anand Rajagopalan, Aditya Krishna Menon, Qin Cao, Gui Citovsky, Baris Sumengen and Sanjiv Kumar (2019). Online + Hierarchical Clustering Approximations. arXiV:1909.09667. Available at: https://doi.org/10.48550/arXiv.1909.09667 + + Examples + -------- + + The first example is with leaving the window size to 100. In the second one we put it at 2 to see how it works. + + >>> from river import cluster + >>> from river import stream + + >>> X = [[1, 2, 1], [2, 1, 0], [3, 2, 1], [2, 2, 1], [5, 2, 3]] + + >>> hierarchical_clustering = cluster.HierarchicalClustering() + + >>> for x, _ in stream.iter_array(X): + ... hierarchical_clustering = hierarchical_clustering.learn_one(x) + + >>> hierarchical_clustering.x_clusters + {'[1, 2, 1]': 1, + '[2, 1, 0]': 2, + '[3, 2, 1]': 4, + '[2, 2, 1]': 6, + '[5, 2, 3]': 8} + + >>> hierarchical_clustering.n + 9 + + >>> print(hierarchical_clustering) + -> 8 + -> 9 + -> 6 + -> 7 + -> 4 + -> 5 + -> 2 + -> 3 + -> 1 + Printed Hierarchical Clustering Tree. + + >>> hierarchical_clustering.get_all_clusters() + [(1, ['[1, 2, 1]']), + (2, ['[2, 1, 0]']), + (4, ['[3, 2, 1]']), + (6, ['[2, 2, 1]']), + (8, ['[5, 2, 3]']), + (3, [1, 2]), + (5, [3, 7]), + (7, [4, 6]), + (9, [5, 8])] + + >>> hierarchical_clustering.get_clusters_by_point() + {'[1, 2, 1]': [1, 3, 5, 9], + '[2, 1, 0]': [2, 3, 5, 9], + '[3, 2, 1]': [4, 7, 5, 9], + '[2, 2, 1]': [6, 7, 5, 9], + '[5, 2, 3]': [8, 9]} + + >>> hierarchical_clustering.predict_one({0: 4, 1: 3, 2: 1}) + ([10, 11, 9], 8) + + >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 4, 1: 3, 2: 1}) + + >>> print(hierarchical_clustering) + -> 10 + -> 11 + -> 8 + -> 9 + -> 6 + -> 7 + -> 4 + -> 5 + -> 2 + -> 3 + -> 1 + Printed Hierarchical Clustering Tree. + + >>> hierarchical_clustering = cluster.HierarchicalClustering(window_size=2) + + >>> for x, _ in stream.iter_array(X): + ... hierarchical_clustering = hierarchical_clustering.learn_one(x) + + >>> hierarchical_clustering.x_clusters + {'[2, 2, 1]': 2, '[5, 2, 3]': 1} + + >>> hierarchical_clustering.n + 3 + + >>> print(hierarchical_clustering) + -> 2 + -> 3 + -> 1 + Printed Hierarchical Clustering Tree. + """ + + def __init__( + self, + window_size: int = 100, + dist_func: DistanceFunc | FunctionWrapper | None = None, + ): + # Number of nodes + self.n = 0 + # Max number of leaves + self.window_size = window_size + # Dict : x data (str(array of size m)) -> key of the node + self.x_clusters: dict[str, int] = {} + # Dict : key -> node + self.nodes: dict[int, BinaryTreeNode] = {} + # First node of the tree + self.root = None + # Distance function + if dist_func is None: + dist_func = functools.partial(utils.math.minkowski_distance, p=2) + self.dist_func = dist_func + + def otd_clustering(self, tree, x): + # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. + # The algorithm performs highly efficient online updates and provably approximates Moseley-Wang revenue. + x_string = str(list(x.values())) + if self.n == 1: + # First node in the tree + self.root = self.nodes[1] + elif tree.data is not None: + # If T is a leaf, we merge the two nodes together + self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) + elif tree.left is None: + # If there is no node at the left of the intermediate node, we add it there + tree.left = self.nodes[self.x_clusters[x_string]] + self.nodes[self.x_clusters[x_string]].parent = tree + elif tree.right is None: + # If there is no node at the right of the intermediate node, we add it there + tree.right = self.nodes[self.x_clusters[x_string]] + self.nodes[self.x_clusters[x_string]].parent = tree + elif self.intra_subtree_similarity(tree) < self.inter_subtree_similarity( + tree, self.nodes[self.x_clusters[x_string]] + ): + # If the nodes in T are closer between them than with the new node, we merge T and the new node + self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) + elif self.inter_subtree_similarity( + tree.left, self.nodes[self.x_clusters[x_string]] + ) > self.inter_subtree_similarity(tree.right, self.nodes[self.x_clusters[x_string]]): + # Continue to search where to merge the new node in the right part of T + self.otd_clustering(tree.right, x) + else: + # Continue to search where to merge the new node in the left part of T + self.otd_clustering(tree.left, x) + + def merge_nodes(self, tree, added_node): + # Merge a new node (added node) to the tree + # We create the node that will be the parent of the tree and the added node + self.n += 1 + new_node = BinaryTreeNode(self.n) + # We add the tree and the added node as its children + new_node.left = tree + new_node.right = added_node + # The parent of the new node is the parent of the tree + new_node.parent = tree.parent + # If the tree is not the root, we set the child of its parent as new node (instead of T) + if tree.parent is not None: + if tree.parent.left.key == tree.key: + tree.parent.left = new_node + else: + tree.parent.right = new_node + # We add the new node as the parent of the tree and the added node + tree.parent = new_node + added_node.parent = new_node + # We add the new node to the dict + self.nodes[self.n] = new_node + # If the tree was the root, the new node become the root + if self.root.key == tree.key: + self.root = self.nodes[self.n] + + def learn_one(self, x): + # We create the node for x and add it to the tree + if len(self.x_clusters.keys()) >= self.window_size: + # Delete the oldest data point and add a node with the same key as the one deleted + oldest_key = self.x_clusters[list(self.x_clusters.keys())[0]] + oldest = self.nodes[oldest_key] + if oldest.parent.left.key == oldest_key: + oldest.parent.left = None + else: + oldest.parent.right = None + del self.nodes[oldest_key] + del self.x_clusters[list(self.x_clusters.keys())[0]] + self.x_clusters[str(list(x.values()))] = oldest_key + self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) + else: + # Else, add a node + self.n += 1 + self.x_clusters[str(list(x.values()))] = self.n + self.nodes[self.n] = BinaryTreeNode(self.n, x) + # We add it to the tree + self.otd_clustering(self.root, x) + return self + + def predict_otd(self, x, node, clusters): + # get the list of predicted clusters for x + if node is None: + # If there is still no node in the tree + return [1], -1 + if node.data is not None: + # Add itself (n+1) and the key of the node that would merge x and node (n+2) + clusters.extend([self.n + 2, self.n + 1]) + return clusters, node.key + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( + node, BinaryTreeNode(self.n + 1, x) + ): + # Add itself (n+1) and the key of the node that would merge x and node (n+2) + clusters.extend([self.n + 2, self.n + 1]) + return clusters, node.key + else: + # Else, x would be added in the tree, we add the key of node + clusters.extend([node.key]) + if self.inter_subtree_similarity( + node.left, BinaryTreeNode(self.n + 1, x) + ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): + # If the right part of the tree is closer to x than the left part, we continue in the right part + return self.predict_otd(x, node.right, clusters) + else: + # If the left part of the tree is closer to x than the right part, we continue in the left part + return self.predict_otd(x, node.left, clusters) + + def predict_one(self, x): + """Predicts the clusters for a set of features `x`. + + Parameters + ---------- + x + A dictionary of features. + Returns + ------- + (list, int) + A list of clusters (from node `x` to root) and the node to which it would have been merged. + + """ + # We predict to which cluster x would be if we added in the tree + r, merged = self.predict_otd(x, self.root, []) + r.reverse() + return r, merged + + @staticmethod + def find_path(root, path, k): + # find the path from root to k + # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + + if root is None: + return False + + path.append(root) + + if root.key == k: + return True + + if (root.left is not None and HierarchicalClustering.find_path(root.left, path, k)) or ( + root.right is not None and HierarchicalClustering.find_path(root.right, path, k) + ): + return True + + path.pop() + return False + + def leaves(self, v): + # find all the leaves from node v + + if v is None: + return -1 + if v.data is not None: + return [v] + + leave_list = [] + leave_list.extend(self.leaves(v.left)) + leave_list.extend(self.leaves(v.right)) + return leave_list + + def inter_subtree_similarity(self, tree_a, tree_b): + # compute the mean distance (mean of distances) between two trees + leaves_a = self.leaves(tree_a) + leaves_b = self.leaves(tree_b) + r = 0 + nb = 0 + for i, w_i in enumerate(leaves_a): + for j, w_j in enumerate(leaves_b): + nb += 1 + r += self.dist_func(w_i.data, w_j.data) + return r / nb + + def intra_subtree_similarity(self, tree): + # compute mean of distances between the nodes from a certain tree + leaves = self.leaves(tree) + r = 0 + nb = 0 + if len(leaves) == 1: + return 0 + for i, w_i in enumerate(leaves): + for j, w_j in enumerate(leaves): + if i < j: + nb += 1 + r += self.dist_func(w_i.data, w_j.data) + return r / nb + + def __str__(self): + self.print_tree(self.root) + return "Printed Hierarchical Clustering Tree." + + @staticmethod + def print_tree(node, level=0): + # Print node and its children + # Adapted from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python + if node is not None: + HierarchicalClustering.print_tree(node.right, level + 1) + print(" " * 4 * level + "-> " + str(node.key)) + HierarchicalClustering.print_tree(node.left, level + 1) + + def get_parents(self, node): + # Get all the parents of the node (the clusters it belongs to) + clusters = [node.key] + if node.parent is None: + return clusters + clusters.extend(self.get_parents(node.parent)) + return clusters + + def get_clusters_by_point(self): + """Returns the list of clusters (from the data point node to the root) for all data points. + + Returns + ------- + {x : list} + A dict of all the data points with their clusters. + """ + # Get all the clusters each data point belong to + clusters = {} + for x in self.x_clusters.keys(): + clusters[x] = self.get_parents(self.nodes[self.x_clusters[x]]) + return clusters + + def get_all_clusters(self): + """Returns all the clusters of the tree. + + Returns + ------- + {int : list} + A dict of all the clusters with their children (or the data point for the leaves). + """ + # Get the data of each cluster + clusters = {} + for i in range(1, self.n + 1): + if self.nodes[i].data is not None: + clusters[i] = [str(list(self.nodes[i].data.values()))] + else: + clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] + return sorted(clusters.items(), key=lambda x: len(x[1])) From e942ba2e9a2e93eb0672348f306b9356fca0e826 Mon Sep 17 00:00:00 2001 From: kchardon Date: Mon, 11 Nov 2024 21:32:25 +0100 Subject: [PATCH 345/347] ruff --- river/cluster/__init__.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 085a21befc..21157d971b 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -11,4 +11,13 @@ from .streamkmeans import STREAMKMeans from .textclust import TextClust -__all__ = ["CluStream", "DBSTREAM", "DenStream", "HierarchicalClustering", "KMeans", "ODAC", "STREAMKMeans", "TextClust"] +__all__ = [ + "CluStream", + "DBSTREAM", + "DenStream", + "HierarchicalClustering", + "KMeans", + "ODAC", + "STREAMKMeans", + "TextClust", +] From c1deecfc148479c68823ffa416ac01b65feebc50 Mon Sep 17 00:00:00 2001 From: kchardon Date: Mon, 11 Nov 2024 21:46:54 +0100 Subject: [PATCH 346/347] new test --- river/cluster/hcluster.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index c3511c9a6b..e817f960c8 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -107,10 +107,10 @@ class HierarchicalClustering(base.Clusterer): '[2, 2, 1]': [6, 7, 5, 9], '[5, 2, 3]': [8, 9]} - >>> hierarchical_clustering.predict_one({0: 4, 1: 3, 2: 1}) + >>> hierarchical_clustering.predict_one({0: 3, 1: 3, 2: 3}) ([10, 11, 9], 8) - >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 4, 1: 3, 2: 1}) + >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 3, 1: 3, 2: 3}) >>> print(hierarchical_clustering) -> 10 From 09230057aedf68eb061e0b0f4f461367663a3f5a Mon Sep 17 00:00:00 2001 From: kchardon Date: Mon, 11 Nov 2024 21:49:38 +0100 Subject: [PATCH 347/347] correct test --- river/cluster/hcluster.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py index e817f960c8..f2af5b9311 100644 --- a/river/cluster/hcluster.py +++ b/river/cluster/hcluster.py @@ -60,7 +60,7 @@ class HierarchicalClustering(base.Clusterer): >>> from river import cluster >>> from river import stream - >>> X = [[1, 2, 1], [2, 1, 0], [3, 2, 1], [2, 2, 1], [5, 2, 3]] + >>> X = [[1, 2, 1], [2, 1, 0], [3, 2, 1], [2, 2, 1], [4, 4, 4]] >>> hierarchical_clustering = cluster.HierarchicalClustering() @@ -72,7 +72,7 @@ class HierarchicalClustering(base.Clusterer): '[2, 1, 0]': 2, '[3, 2, 1]': 4, '[2, 2, 1]': 6, - '[5, 2, 3]': 8} + '[4, 4, 4]': 8} >>> hierarchical_clustering.n 9 @@ -94,7 +94,7 @@ class HierarchicalClustering(base.Clusterer): (2, ['[2, 1, 0]']), (4, ['[3, 2, 1]']), (6, ['[2, 2, 1]']), - (8, ['[5, 2, 3]']), + (8, ['[4, 4, 4]']), (3, [1, 2]), (5, [3, 7]), (7, [4, 6]), @@ -105,7 +105,7 @@ class HierarchicalClustering(base.Clusterer): '[2, 1, 0]': [2, 3, 5, 9], '[3, 2, 1]': [4, 7, 5, 9], '[2, 2, 1]': [6, 7, 5, 9], - '[5, 2, 3]': [8, 9]} + '[4, 4, 4]': [8, 9]} >>> hierarchical_clustering.predict_one({0: 3, 1: 3, 2: 3}) ([10, 11, 9], 8) @@ -132,7 +132,7 @@ class HierarchicalClustering(base.Clusterer): ... hierarchical_clustering = hierarchical_clustering.learn_one(x) >>> hierarchical_clustering.x_clusters - {'[2, 2, 1]': 2, '[5, 2, 3]': 1} + {'[2, 2, 1]': 2, '[4, 4, 4]': 1} >>> hierarchical_clustering.n 3